diff --git a/.cursor/rules/dependencies.mdc b/.cursor/rules/dependencies.mdc index 4137d1fa..5e7447ec 100644 --- a/.cursor/rules/dependencies.mdc +++ b/.cursor/rules/dependencies.mdc @@ -6,7 +6,7 @@ alwaysApply: true # Dependencies (Concise) - Use `uv` only. Pin exact versions in `pyproject.toml`; run `uv lock` and `uv sync --all-extras`. Do not edit `uv.lock`. -- Python 3.9–3.11. +- Python 3.12+. - Lint/format: Ruff — `uv run ruff check .`, `uv run ruff format .`. - Defaults: full type hints; `logging.getLogger(__name__)` (no `print`); PEP 8, 100 cols; asyncio with timeouts; seed RNG; never log secrets; use dotenv; HTTPS. - Compatibility: keep Torch/Transformers/Tokenizers aligned; align `bittensor` with CLI/network. diff --git a/.cursor/rules/project_context.mdc b/.cursor/rules/project_context.mdc index 5abe6682..716d8bab 100644 --- a/.cursor/rules/project_context.mdc +++ b/.cursor/rules/project_context.mdc @@ -18,7 +18,7 @@ GRAIL (Guaranteed Rollout Authenticity via Inference Ledger) enables cryptograph - Typer CLI: `grail mine`, `grail validate`, `grail train` **Non-Functional**: -- Python 3.10 or later; seed `random`, `numpy`, `torch` for determinism +- Python 3.12 or later; seed `random`, `numpy`, `torch` for determinism - Reproducibility: manage deps with `uv` (pin exact versions); use `safetensors` - Reliability: timeouts on all network calls; avoid blocking event loop; handle transient failures - Security: never log secrets; load config via environment; HTTPS with cert verification diff --git a/.env.example b/.env.example index 811875de..add18d83 100644 --- a/.env.example +++ b/.env.example @@ -183,27 +183,34 @@ KERNEL_EVAL_BACKEND=persistent KERNEL_EVAL_TIMEOUT=60 # ============================================================================ -# PIPELINED MINING CONFIGURATION (Recommended for 2+ GPUs) +# PIPELINED MINING CONFIGURATION (mandatory; requires 2+ GPUs) # ============================================================================ -# Pipeline mode is the recommended way to run the miner with multiple GPUs. -# It overlaps proof computation (GPU 1) with kernel eval after generation (GPU 0), -# significantly improving per-window throughput. +# The pipelined mining engine is the only generation path: the legacy +# single-GPU fallback was removed in the Stage 1 refactor and the +# GRAIL_PIPELINE_ENABLED flag was deleted (setting it has no effect). +# +# Pipeline mode overlaps proof computation (GPU 1) with kernel eval and +# generation (GPU 0), significantly improving per-window throughput. # # GPU layout: -# GPU 0 = vLLM/SGLang generation server +# GPU 0 = SGLang/vLLM generation server # GPU 1 = HuggingFace model for proof-of-work (logprobs + commitments) # GPU 2 = Triton kernel evaluation (set via KERNEL_EVAL_GPU_IDS above) # -# Requires at least 3 GPUs. On a 4-GPU machine, GPU 3 can be used for -# the validator or as a second kernel eval GPU (KERNEL_EVAL_GPU_IDS=2,3). +# Requires at least 2 GPUs (gen + proof). On a 4-GPU machine, GPU 3 can be +# used for a second kernel eval GPU (KERNEL_EVAL_GPU_IDS=2,3). -# Enable the 3-GPU pipelined mining engine -GRAIL_PIPELINE_ENABLED=true +# Generation backend: sglang (recommended, default) or vllm +# SGLang is the validated production path: it uses the native /generate +# endpoint with input_ids, avoiding text re-tokenization issues entirely. +GRAIL_PIPELINE_BACKEND=sglang -# Generation backend: vllm or sglang -GRAIL_PIPELINE_BACKEND=vllm +# Tensor-parallel size for the generation server (1 = single GPU, 4 = TP=4 +# across consecutive GPUs starting at GRAIL_PIPELINE_VLLM_GPU). Increase +# only if you have spare GPUs and the model fits comfortably with TP=1. +GRAIL_PIPELINE_VLLM_TP=1 -# GPU index for the vLLM/SGLang generation server (relative to CUDA_VISIBLE_DEVICES) +# GPU index for the SGLang/vLLM generation server (relative to CUDA_VISIBLE_DEVICES) GRAIL_PIPELINE_VLLM_GPU=0 # GPU index for the HuggingFace proof-of-work model (relative to CUDA_VISIBLE_DEVICES) @@ -214,7 +221,7 @@ GRAIL_PIPELINE_PROOF_GPU=1 # Setting this to true causes sketch divergence and proof failures. GRAIL_PIPELINE_PROOF_FLASH_ATTN=false -# vLLM/SGLang server tuning (sensible defaults for A100 80GB) +# Generation server tuning (sensible defaults for A100 80GB) GRAIL_PIPELINE_GPU_MEM_UTIL=0.90 GRAIL_PIPELINE_MAX_MODEL_LEN=12288 GRAIL_PIPELINE_MAX_NUM_SEQS=64 @@ -224,6 +231,7 @@ GRAIL_PIPELINE_SERVER_TIMEOUT=300 # Symlink directory for vLLM weight reload (empty = auto, uses checkpoint parent dir) # Set to an ephemeral/tmpfs path if main disk is small: # GRAIL_PIPELINE_SYMLINK_DIR=/dev/shm/grail +# Only used by the vLLM backend; harmless when GRAIL_PIPELINE_BACKEND=sglang. GRAIL_PIPELINE_SYMLINK_DIR= # ============================================================================ @@ -363,6 +371,73 @@ GRAIL_CHECKPOINT_MILESTONE_INTERVAL=100 # Options: sparse_codec_v2 (default), sparse_codec_v3, sparse_codec_v3.1 GRAIL_DELTA_FORMAT=sparse_codec_v2 +# ============================================================================ +# DOCKER COMPOSE - VALIDATOR DEPLOYMENT +# ============================================================================ +# These variables are consumed by docker/docker-compose.validator.yml. They +# only matter when you run the validator via `docker compose` (the bare-metal +# `grail validate` command ignores them). Operators running on bare metal can +# leave them blank. + +# Override the validator image. Defaults to the public ghcr.io tag published +# by .github/workflows/docker-publish.yml. Use a locally-built tag for +# pre-release testing, or a pinned semver/sha256 tag in production: +# GRAIL_VALIDATOR_IMAGE=ghcr.io/one-covenant/grail:v0.5.10 +# GRAIL_VALIDATOR_IMAGE=grail:base # local build +GRAIL_VALIDATOR_IMAGE= + +# Set to "true" to start the validator with --test-mode (it only validates +# its own files and does NOT publish weights on-chain). Required for local +# test deployments; MUST be unset/false in production. +GRAIL_VALIDATOR_TEST_MODE=false + +# Comma-separated list of physical GPU indices to pin the validator to, +# matching docker compose's deploy.resources.reservations.devices.device_ids +# contract. Default "0" assigns the validator to GPU 0 alone (sufficient for +# the validator path when GRAIL_GPU_EVAL=false). Bump to e.g. "0,1" if you +# enable on-GPU kernel evaluation, or "2" to keep the validator off GPUs +# 0+1 when a co-located miner uses them. +GRAIL_VALIDATOR_GPU_IDS=0 + +# Shared-memory size for the validator container. HF dataloaders and +# multiprocessing IPC need more than the 64 MB Docker default. +GRAIL_VALIDATOR_SHM_SIZE=2g + +# ───────────────────────────────────────────────────────────────────────── +# Trainer-side protocol configuration (set on the TRAINER host) +# ───────────────────────────────────────────────────────────────────────── +# These values are baked into every published checkpoint's metadata. The +# miner and validator override their own GRAIL_THINKING_MODE / env_id / +# generation params from the loaded checkpoint at runtime, so the trainer +# is the single source of truth for everything that affects prompt rendering +# or generation. You only need to set these on the TRAINER host; the miner +# and validator pick them up via R2 metadata. They are also passed through +# the validator docker compose so any pre-checkpoint code path (boot +# rendering, parser regex compile) sees the right value. +# +# GRAIL_THINKING_MODE controls the system prompt and chat template: +# "instructed" (DEFAULT) — custom / +# for thinking + / for the +# answer, injected via system prompt + a custom +# ChatML template (mode grail builds itself) +# "native" — model's built-in / tokens +# (e.g. Qwen3) + for the answer +# Miner and validator MUST agree, so the trainer publishes this and the +# consumer sides override their local env var from the checkpoint. +GRAIL_THINKING_MODE=instructed + +# Environment selection (trainer-side; passed via checkpoint metadata) +GRAIL_ENV_ID=mbpp +GRAIL_ENV_SPLIT=train + +# Generation parameters (trainer-side; passed via checkpoint metadata) +# Defaults match grail/trainer/checkpoint_publisher.py::get_default_generation_params +GRAIL_GEN_MAX_TOKENS=2048 +GRAIL_GEN_TEMPERATURE=0.7 +GRAIL_GEN_TOP_P=0.9 +GRAIL_GEN_TOP_K=50 +GRAIL_GEN_REPETITION_PENALTY=1.0 + # Host storage path for Docker volume mount (Docker Compose only) # If your machine has a large secondary disk (e.g., NVMe ephemeral storage), # set this to its mount point so checkpoints and caches are stored there. diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index e4f6b253..f9a733d4 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -20,7 +20,7 @@ jobs: cache-dependency-glob: "uv.lock" - name: Set up Python - run: uv python install 3.11 + run: uv python install 3.12 - name: Install dependencies run: uv sync --extra dev @@ -44,7 +44,7 @@ jobs: cache-dependency-glob: "uv.lock" - name: Set up Python - run: uv python install 3.11 + run: uv python install 3.12 - name: Install dependencies run: uv sync --extra dev @@ -57,7 +57,7 @@ jobs: runs-on: ubuntu-latest strategy: matrix: - python-version: ["3.10", "3.11"] + python-version: ["3.12"] steps: - uses: actions/checkout@v4 @@ -73,7 +73,10 @@ jobs: - name: Install dependencies run: uv sync --extra dev - - name: Run tests with pytest - run: uv run pytest tests/ -v --tb=short + - name: Run tests (parallel) + run: uv run pytest tests/ -n auto --dist loadfile -m "not serial" -v --tb=short + + - name: Run serial tests + run: uv run pytest tests/ -m "serial" -v --tb=short env: PYTHONPATH: ${{ github.workspace }} diff --git a/.github/workflows/docker-publish.yml b/.github/workflows/docker-publish.yml index 4c8a2609..3355fe2f 100644 --- a/.github/workflows/docker-publish.yml +++ b/.github/workflows/docker-publish.yml @@ -38,7 +38,7 @@ jobs: cache-dependency-glob: "uv.lock" - name: Set up Python - run: uv python install 3.11 + run: uv python install 3.12 - name: Install dependencies run: uv sync --extra dev @@ -130,6 +130,17 @@ jobs: - name: Set up Docker Buildx uses: docker/setup-buildx-action@v3 + # Publishes the VALIDATOR-ONLY base image (docker/Dockerfile). The + # miner image (docker/Dockerfile.miner) adds SGLang on top of this + # base and is intentionally NOT published from the standard GitHub + # Actions runner: SGLang's full dependency closure (~3 GB extra + + # transformers downgrade) blows the runner's ~14 GB free disk + # budget. Miners build the miner image locally on their GPU host + # via: + # docker build -f docker/Dockerfile.miner \ + # --build-arg GRAIL_BASE_IMAGE=ghcr.io//grail:latest \ + # -t grail:miner . + # See docs/miner.md for the full operator workflow. - name: Build and push Docker image id: push uses: docker/build-push-action@v6 diff --git a/.gitignore b/.gitignore index a515cb28..74968cb9 100644 --- a/.gitignore +++ b/.gitignore @@ -204,3 +204,6 @@ config.local.* # monitoring files wandb/ + +# RFC drafts (tracked separately) +docs/rfcs/ diff --git a/.python-version b/.python-version index 2c073331..e4fba218 100644 --- a/.python-version +++ b/.python-version @@ -1 +1 @@ -3.11 +3.12 diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index ac4cbe91..d0b78339 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -17,7 +17,7 @@ Thanks for helping make this project better. Please keep changes focused, well-t - General bug fixes ### Getting started -- **Environment**: Python 3.9–3.11. We use `uv` for dependency management. +- **Environment**: Python 3.12+. We use `uv` for dependency management. - Install deps: `uv sync --all-extras` - Add deps: `uv add package==X.Y.Z` (pin exact versions) and then `uv lock` - Run tasks: `uv run ` (e.g., `uv run pytest -q`) diff --git a/docker/Dockerfile b/docker/Dockerfile index 7cedbb96..89e7a8a7 100644 --- a/docker/Dockerfile +++ b/docker/Dockerfile @@ -20,11 +20,11 @@ RUN apt-get update && apt-get install -y --no-install-recommends \ git lfs install && \ rm -rf /var/lib/apt/lists/* -# Install uv, then let uv install Python 3.11 (Ubuntu 22.04 only ships 3.10). +# Install uv, then let uv install Python 3.12 (Ubuntu 22.04 only ships 3.10). # This matches the CI workflow and avoids needing the deadsnakes PPA. RUN curl -LsSf https://astral.sh/uv/install.sh | sh && /root/.local/bin/uv --version ENV PATH="/root/.local/bin:${PATH}" -RUN uv python install 3.11 +RUN uv python install 3.12 WORKDIR /app @@ -54,5 +54,47 @@ COPY README.md /app/README.md RUN uv sync --no-editable && \ uv cache clean -# Entrypoint delegates to uv -ENTRYPOINT ["uv", "run", "grail"] +# Flash Attention 2: required for consistent proof verification across +# miner and validator. ATTN_IMPLEMENTATION is pinned to "flash_attention_2" +# in grail/protocol/constants.py and provider.py raises at startup if the +# import fails. Install the prebuilt cu12+torch2.9+cp312+cxx11abiTRUE wheel +# from Dao-AILab releases (~254 MB, ~10 s install) instead of building from +# source (~30-60 min per CI run). +# +# Compute-capability coverage of this single wheel (Dao-AILab cu12 standard): +# sm_80 (A100, A10G), sm_86 (A40, RTX 30xx), sm_89 (L40, L40S, RTX 40xx), +# sm_90 (H100, H200), sm_100 (B200 - empirically verified on +# basilica-grail-trainer at compute capability 10.0). +# Pre-Ampere GPUs (V100/sm_70, T4/sm_75, RTX 20xx/sm_75) are NOT covered; +# miners on those cards must build flash-attn from source. +# +# The cxx11abi must match torch's; PyTorch >= 2.5 Linux wheels are +# cxx11abi=TRUE by default. Pinned by SHA256 for supply-chain safety. Bump +# FLASH_ATTN_VERSION (and the wheel filename + SHA256) when upgrading +# torch, since the wheel name encodes the matching torch minor. +ARG FLASH_ATTN_VERSION=2.8.3 +ARG FLASH_ATTN_WHEEL=flash_attn-2.8.3+cu12torch2.9cxx11abiTRUE-cp312-cp312-linux_x86_64.whl +ARG FLASH_ATTN_SHA256=4e2f9e39313266b1544b68138b15b91ee6221eccf14f7902b7c6620351340810 +RUN mkdir -p /tmp/wheels && \ + curl -LsSf -o "/tmp/wheels/${FLASH_ATTN_WHEEL}" \ + "https://github.com/Dao-AILab/flash-attention/releases/download/v${FLASH_ATTN_VERSION}/${FLASH_ATTN_WHEEL}" && \ + echo "${FLASH_ATTN_SHA256} /tmp/wheels/${FLASH_ATTN_WHEEL}" | sha256sum -c - && \ + uv pip install "/tmp/wheels/${FLASH_ATTN_WHEEL}" && \ + rm -rf /tmp/wheels && \ + uv cache clean + +# Verify grail's import surface in the base image. This catches any +# silent dep downgrade introduced by future flash-attn/torch pins. +RUN /app/.venv/bin/python -c "\ +import torch, transformers, grail; \ +assert torch.__version__.startswith('2.9.1'), f'torch={torch.__version__}'; \ +assert transformers.__version__ == '4.57.1', f'transformers={transformers.__version__}'; \ +print('grail base image OK: torch=', torch.__version__, 'transformers=', transformers.__version__)" + +# Validator-only base image. The pipelined miner needs SGLang too; +# build the miner image on top of this one via docker/Dockerfile.miner. +# +# Entrypoint: --no-sync so uv does NOT re-resolve the venv on every +# container start (which would silently undo any post-uv-sync installs +# such as the flash-attn wheel and the future SGLang layer). +ENTRYPOINT ["uv", "run", "--no-sync", "grail"] diff --git a/docker/Dockerfile.miner b/docker/Dockerfile.miner new file mode 100644 index 00000000..c1b0546c --- /dev/null +++ b/docker/Dockerfile.miner @@ -0,0 +1,48 @@ +ARG GRAIL_BASE_IMAGE=grail:base +FROM ${GRAIL_BASE_IMAGE} AS miner + +# ───────────────────────────────────────────────────────────────────────────── +# Pipelined miner image. Builds on the validator-only base +# (docker/Dockerfile) and adds SGLang for the pipeline generation server. +# +# Why a separate image: +# - SGLang is large (~3 GB extra over the base) and pulls a long +# dependency chain (sgl-kernel, flashinfer, xgrammar, ...). Validators +# do NOT need any of it; bundling it would balloon the public ghcr.io +# image and break the GitHub Actions runner disk budget. +# - SGLang's pip metadata hard-pins transformers==5.3.0, which conflicts +# with grail's transformers==4.57.1. We work around it with a two-pass +# uv pip install: install sglang first, then force-reinstall the +# conflicting deps back to grail's pinned versions. This is the same +# install pattern that works on basilica-grail-tester and is verified +# end-to-end against the live validator. +# +# This image is intended to be built locally on a GPU host (or by a +# self-hosted GitHub Actions runner with extra disk). The base image +# stays on the standard ghcr.io publish path. +# ───────────────────────────────────────────────────────────────────────────── + +ARG SGLANG_VERSION=0.5.10 +# Pinned to 0.5.10: matches the version validated end-to-end on +# basilica-grail-tester with torch 2.9.1 / transformers 4.57.1. Older +# SGLang releases (0.4.x) silently downgrade torch and transformers, +# which breaks grail's imports. Bump only after a working e2e on a +# new SGLang minor + the corresponding grail torch pin. +RUN uv pip install "sglang[all]==${SGLANG_VERSION}" httpx --prerelease=allow && \ + uv pip install --reinstall \ + "transformers==4.57.1" \ + "tokenizers==0.22.1" \ + "torch==2.9.1" && \ + uv cache clean + +# Verify grail's import surface still works after the sglang install. Fail +# the build loudly if a transitive dep silently downgraded torch or +# transformers (the historical failure mode for SGLang in this image). +RUN /app/.venv/bin/python -c "\ +import torch, transformers, sglang, grail; \ +assert torch.__version__.startswith('2.9.1'), f'torch={torch.__version__}'; \ +assert transformers.__version__ == '4.57.1', f'transformers={transformers.__version__}'; \ +assert sglang.__version__ == '${SGLANG_VERSION}', f'sglang={sglang.__version__}'; \ +print('grail miner image OK: torch=', torch.__version__, 'transformers=', transformers.__version__, 'sglang=', sglang.__version__)" + +# Inherits ENTRYPOINT ["uv", "run", "--no-sync", "grail"] from the base. diff --git a/docker/docker-compose.validator.yml b/docker/docker-compose.validator.yml index f66ed931..923855d5 100644 --- a/docker/docker-compose.validator.yml +++ b/docker/docker-compose.validator.yml @@ -1,15 +1,31 @@ services: validator: - image: ghcr.io/one-covenant/grail:latest + # Image is overrideable so operators can pin a specific tag (semver or + # sha256). Defaults to the public ghcr.io tag published by + # .github/workflows/docker-publish.yml. For local builds set + # GRAIL_VALIDATOR_IMAGE=grail:base in the environment or .env. + image: ${GRAIL_VALIDATOR_IMAGE:-ghcr.io/one-covenant/grail:latest} container_name: grail-validator restart: unless-stopped tty: true stdin_open: true entrypoint: ["/bin/sh", "-c"] - command: ["mkdir -p /var/log/grail && chmod 755 /var/log/grail && if [ \"$$GRAIL_DEBUG\" = \"true\" ]; then exec uv run grail -vv validate; else exec uv run grail validate; fi"] + # GRAIL_VALIDATOR_TEST_MODE=true appends --test-mode (validator only + # validates its own files; no on-chain weight setting). Required for + # local test deployments; MUST be unset/false in production. + command: + - >- + mkdir -p /var/log/grail && chmod 755 /var/log/grail && + if [ "$$GRAIL_DEBUG" = "true" ]; then VERB="-vv"; else VERB=""; fi; + if [ "$$GRAIL_VALIDATOR_TEST_MODE" = "true" ]; then ARGS="--test-mode"; else ARGS=""; fi; + exec uv run --no-sync grail $$VERB validate $$ARGS environment: # Debug configuration - GRAIL_DEBUG=${GRAIL_DEBUG:-false} + # Set to "true" to start the validator with --test-mode (it only + # validates its own files and does NOT publish weights on-chain). + # MUST be unset/false in production. + - GRAIL_VALIDATOR_TEST_MODE=${GRAIL_VALIDATOR_TEST_MODE:-false} # Core Bittensor configuration - BT_WALLET_COLD=${BT_WALLET_COLD} @@ -62,8 +78,32 @@ services: # Environment label for observability - GRAIL_ENV=${GRAIL_ENV:-prod} - # Triton kernel evaluation (requires 2 GPUs: GPU 0 = model, GPU 1 = kernel eval) - - GRAIL_GPU_EVAL=${GRAIL_GPU_EVAL:-true} + # Protocol-affecting configuration. The validator overrides + # GRAIL_THINKING_MODE from the loaded checkpoint metadata at runtime + # (see grail/validation/miner_validator.py), so prompt rendering stays + # in sync with the miner regardless of what's set here. This + # passthrough is belt-and-suspenders for the boot path: any module + # that calls ``get_thinking_config()`` BEFORE the first checkpoint is + # loaded (parser regex compile, debug helpers, etc.) needs a sensible + # value, and ``instructed`` matches the current trainer default. + # + # NOTE: GRAIL_ENV_ID / GRAIL_ENV_SPLIT / GRAIL_GEN_* are NOT passed + # through. They are only consumed on the TRAINER side by + # ``get_default_env_config`` / ``get_default_generation_params`` when + # publishing a checkpoint. The validator reads these values from + # ``checkpoint_metadata.{env_id,env_params,generation_params}`` at + # runtime; passing them to the validator container would imply the + # validator reads them, which would be a footgun. + - GRAIL_THINKING_MODE=${GRAIL_THINKING_MODE:-instructed} + + # Triton kernel evaluation. Only relevant for env_id=triton_kernel + # (evaluates generated kernels on a spare GPU). For MBPP/coding/math + # environments this is a no-op. Default is `false` because most + # validator deployments don't have a spare GPU and `true` would + # silently increase the GPU footprint of the validator container. + # When using triton_kernel, set GRAIL_GPU_EVAL=true on the host .env + # AND bump GRAIL_VALIDATOR_GPU_IDS to include the eval GPU. + - GRAIL_GPU_EVAL=${GRAIL_GPU_EVAL:-false} - KERNEL_EVAL_GPU_IDS=${KERNEL_EVAL_GPU_IDS:-1} - KERNEL_EVAL_BACKEND=${KERNEL_EVAL_BACKEND:-persistent} - KERNEL_EVAL_TIMEOUT=${KERNEL_EVAL_TIMEOUT:-60} @@ -74,6 +114,14 @@ services: - GRAIL_METRIC_BUFFER_SIZE=${GRAIL_METRIC_BUFFER_SIZE} - GRAIL_METRIC_FLUSH_INTERVAL=${GRAIL_METRIC_FLUSH_INTERVAL} + # NVIDIA visibility is controlled by the deploy.resources block below + # via ``device_ids``. We deliberately do NOT set NVIDIA_VISIBLE_DEVICES + # here: when both are present, Docker Compose's ``device_ids`` is the + # contract that actually pins the container to specific physical GPUs, + # while ``NVIDIA_VISIBLE_DEVICES=all`` would override it and expose + # every GPU. Setting capabilities here is still required. + - NVIDIA_DRIVER_CAPABILITIES=compute,utility + # Network timeout and retry configuration - BT_CALL_TIMEOUT=${BT_CALL_TIMEOUT:-15.0} - BT_CALL_RETRIES=${BT_CALL_RETRIES:-3} @@ -96,14 +144,40 @@ services: # Defaults to ~/grail-storage if unset. - ${GRAIL_HOST_STORAGE_PATH:-${HOME}/grail-storage}:${GRAIL_HOST_STORAGE_PATH:-${HOME}/grail-storage} + # HF dataloaders and multiprocessing IPC need more than the 64 MB default. + shm_size: ${GRAIL_VALIDATOR_SHM_SIZE:-2g} + + # Share the host PID namespace. The validator's checkpoint cache + # cleanup uses ``os.kill(pid, 0)`` to detect orphaned ``*.partial`` + # download dirs left behind by crashed downloaders. Inside the + # container's default PID namespace that probe ALWAYS fails for host + # PIDs (ESRCH), so a co-located bare-metal miner sharing the same + # ``GRAIL_HOST_STORAGE_PATH`` cache will see its in-flight partials + # deleted from under it. Sharing the host PID namespace lets the + # validator see the real owners and skip live partials. Production + # validators with their own dedicated cache directory can drop this. + pid: host + deploy: resources: reservations: devices: - driver: nvidia - # 2 GPUs required: GPU 0 = model inference / proof verification, - # GPU 1 = Triton kernel evaluation - count: 2 + # Pin the validator to a single physical GPU via + # GRAIL_VALIDATOR_GPU_IDS (e.g. "0", "2", or "7"). This is the + # right contract on multi-GPU hosts: ``count: N`` lets docker + # pick any N GPUs, which collides with co-located miners and + # trainers. Default to GPU 0 alone (validators only need one + # GPU for model + proof verification when GRAIL_GPU_EVAL=false). + # + # NOTE: Compose interpolates ``["${VAR}"]`` as ONE list element, + # so a comma-separated value like "0,1" would be parsed as the + # literal device id "0,1" (broken). Multi-GPU validators (e.g. + # GPU 0 for model + GPU 1 for kernel eval when + # GRAIL_GPU_EVAL=true) MUST hand-edit this block to a YAML list + # of strings: ``device_ids: ["0", "1"]``. There is no clean way + # to source a YAML list from a single env var. + device_ids: ["${GRAIL_VALIDATOR_GPU_IDS:-0}"] capabilities: [gpu] networks: diff --git a/docs/incentive-mechanism.md b/docs/incentive-mechanism.md index 77e57f66..639b8be4 100644 --- a/docs/incentive-mechanism.md +++ b/docs/incentive-mechanism.md @@ -18,7 +18,7 @@ The subnet switches between two scoring modes depending on network conditions. T ### Capped Mode (current default) -Each miner's unique rollouts are capped at `UNIQUE_ROLLOUTS_CAP` (2,500) per window, with a period cap of 2,500 x 12 = 30,000 over the rolling submission interval. +Each miner's unique rollouts are capped at `UNIQUE_ROLLOUTS_CAP` (5,000) per window, with a period cap of 5,000 x 12 = 60,000 over the rolling submission interval. Scoring: @@ -29,7 +29,7 @@ weight = score / (period_cap ^ SUPERLINEAR_EXPONENT) Producing more than the cap gives no additional weight. Producing less than the cap gives proportionally less, and the "missing" weight goes to burn rather than being redistributed to other miners. -The burn mechanism works as follows. 80% of total emissions always go to the burn UID (UID 0). The remaining 20% is split among miners proportionally to their cap achievement. If miners collectively produce below the cap, the unallocated portion of that 20% also goes to burn. For example, a miner at 50% of cap gets `0.20 x 0.50 = 0.10` (10%) of emissions, with the remaining 90% going to burn. +The burn mechanism works as follows. 90% of total emissions always go to the burn UID (UID 0). The remaining 10% is split among miners proportionally to their cap achievement. If miners collectively produce below the cap, the unallocated portion of that 10% also goes to burn. For example, a miner at 50% of cap gets `0.10 x 0.50 = 0.05` (5%) of emissions, with the remaining 95% going to burn. ### Uncapped Mode @@ -40,11 +40,11 @@ score = unique_rollouts ^ SUPERLINEAR_EXPONENT weight = score / sum(all_scores) ``` -Producing more rollouts yields proportionally more weight. Burn is fixed at 80% regardless of production level, miners always split the remaining 20%. +Producing more rollouts yields proportionally more weight. Burn is fixed at 90% regardless of production level, miners always split the remaining 10%. ### What This Means for Miners -- **Capped mode**: Target 2,500 unique rollouts per window. Going above yields nothing, going below costs you proportionally. Optimize for consistency across windows, not peak throughput. +- **Capped mode**: Target 5,000 unique rollouts per window. Going above yields nothing, going below costs you proportionally. Optimize for consistency across windows, not peak throughput. - **Uncapped mode**: Maximize throughput. More unique rollouts = more weight, no ceiling. - **Both modes**: The superlinear exponent (4.0) heavily amplifies differences. A miner with 2x the unique rollouts of another gets 2^4 = 16x the weight. This also makes sybil splitting unprofitable: splitting into k identities yields k^(1-4) of the total reward. @@ -92,19 +92,19 @@ How it works: 1. The validator picks 32 random positions (`CHALLENGE_K = 32`) in the completion. 2. At each position, the model's hidden activations at the last layer are extracted. -3. The top 32 activations (`PROOF_TOPK = 32`) are selected and mapped into logarithmic buckets (8 buckets per sign, 16 total). +3. The top 16 activations (`PROOF_TOPK = 16`) are selected and mapped into logarithmic buckets (8 buckets per sign, 16 total). 4. A random linear projection (sketch) is computed over the bucketed values modulo a Mersenne prime (2^31 - 1). 5. The validator recomputes this independently and compares against the miner's commitment. Tolerance is position-dependent to account for floating-point drift in causal attention: ``` -tolerance(pos) = 30 + 3.0 * sqrt(pos) +tolerance(pos) = 6000 + 5.0 * sqrt(pos) ``` Early positions have tight tolerance, later positions allow more drift. The bucketing (coarse quantization) makes the proof robust across different backends (vLLM, HuggingFace, SGLang) and CUDA versions while remaining cryptographically binding. -Both miner and validator use SDPA (Scaled Dot-Product Attention) by default, not Flash Attention, to ensure consistent floating-point accumulation. +Both miner and validator use Flash Attention 2 (`ATTN_IMPLEMENTATION = "flash_attention_2"`) to ensure consistent, padding-invariant floating-point accumulation across batch sizes. --- @@ -139,17 +139,17 @@ This means a single bad window can zero your weight for ~1.4 hours. Consistent, |----------|-------|-------------| | `WINDOW_LENGTH` | 30 blocks | ~6 minutes per window | | `ROLLOUTS_PER_PROBLEM` | 16 | GRPO group size | -| `MAX_NEW_TOKENS` | 8192 | Maximum completion length | -| `UNIQUE_ROLLOUTS_CAP` | 2,500 | Per-window cap (capped mode) | +| `MAX_NEW_TOKENS_PROTOCOL_CAP` | 8192 | Hard upper bound on completion length (immutable). Trainer's per-checkpoint `max_tokens` is the actual operating limit. | +| `UNIQUE_ROLLOUTS_CAP` | 5,000 | Per-window cap (capped mode) | | `SUPERLINEAR_EXPONENT` | 4.0 | Weight amplification exponent | -| `GRAIL_BURN_PERCENTAGE` | 80% | Base emissions to burn UID | +| `GRAIL_BURN_PERCENTAGE` | 90% | Base emissions to burn UID | | `GRAIL_BURN_UID` | 0 | Burn destination | | `WEIGHT_ROLLING_WINDOWS` | 12 | Windows per submission interval | | `CHALLENGE_K` | 32 | Proof challenge positions | -| `PROOF_TOPK` | 32 | Activations per position | +| `PROOF_TOPK` | 16 | Activations per position | | `PROOF_NUM_BUCKETS` | 8 | Log-magnitude buckets per sign | -| `PROOF_SKETCH_TOLERANCE_BASE` | 30 | Base FP tolerance | -| `PROOF_SKETCH_TOLERANCE_GROWTH` | 3.0 | Sqrt-growth tolerance factor | +| `PROOF_SKETCH_TOLERANCE_BASE` | 6000 | Base FP tolerance | +| `PROOF_SKETCH_TOLERANCE_GROWTH` | 5.0 | Sqrt-growth tolerance factor | | `SAMPLE_RATE` | 10% | Rollout spot-check fraction | | `MAX_SAMPLES_PER_MINER` | 64 | Max rollouts checked per miner per window | | `MINER_SAMPLE_RATE` | 25% | Fraction of miners checked per window | diff --git a/docs/miner-debugging.md b/docs/miner-debugging.md index 2cc33fe9..f67fdd8c 100644 --- a/docs/miner-debugging.md +++ b/docs/miner-debugging.md @@ -95,20 +95,23 @@ Run trainer + validator + miner locally for end-to-end testing. CUDA_VISIBLE_DEVICES=2 grail -vv validate --test-mode > validate.log 2>&1 & ``` - For **Triton Kernel environment** (miner needs 2+ GPUs for pipeline mode): + The pipelined miner is mandatory and needs at least 2 GPUs (one for SGLang + generation, one for HF proofs): ```bash - # Miner: GPU 0 decoding, GPU 1 proofs (pipeline), GPU 2 kernel eval (physical index) - CUDA_VISIBLE_DEVICES=0,1 GRAIL_GPU_EVAL=true KERNEL_EVAL_GPU_IDS=2 \ - GRAIL_PIPELINE_ENABLED=true GRAIL_PIPELINE_BACKEND=vllm \ + # Miner: GPU 0 SGLang, GPU 1 proofs + CUDA_VISIBLE_DEVICES=0,1 \ + GRAIL_PIPELINE_BACKEND=sglang \ GRAIL_PIPELINE_VLLM_GPU=0 GRAIL_PIPELINE_PROOF_GPU=1 \ grail -vv mine > mine.log 2>&1 & - # Validator on a separate GPU, with its own kernel eval GPU - CUDA_VISIBLE_DEVICES=3 KERNEL_EVAL_GPU_IDS=4 \ + # Validator on a separate GPU + CUDA_VISIBLE_DEVICES=2 \ grail -vv validate --test-mode > validate.log 2>&1 & ``` - > **Note:** `KERNEL_EVAL_GPU_IDS` uses **physical** GPU indices (as shown by `nvidia-smi`). - > `GRAIL_PIPELINE_VLLM_GPU` and `GRAIL_PIPELINE_PROOF_GPU` are **relative** to `CUDA_VISIBLE_DEVICES`. + > **Note:** `GRAIL_PIPELINE_VLLM_GPU` and `GRAIL_PIPELINE_PROOF_GPU` are + > **relative** to `CUDA_VISIBLE_DEVICES`. The miner exits with a clear + > `ProtocolViolationError` at startup if either index exceeds the visible + > device count. **Why this is harder:** - Requires multiple GPUs diff --git a/docs/miner.md b/docs/miner.md index 02652e62..a59215cc 100644 --- a/docs/miner.md +++ b/docs/miner.md @@ -1,6 +1,8 @@ # Miner Setup -This guide explains how to run a Grail miner. Miners generate GRPO rollouts with GRAIL proofs across multiple environments (Triton Kernel, SAT, GSM8K, MATH, MBPP, HumanEval, and more), upload them to object storage, and participate in decentralized scoring via validators. +This guide explains how to run a Grail miner. Miners generate GRPO rollouts with GRAIL proofs for the network's active environment, upload them to object storage, and participate in decentralized scoring via validators. + +For this release the network focuses on **coding** (MBPP, HumanEval) and **math** (GSM8K, MATH) environments. The active environment is published per checkpoint by the trainer and the miner picks it up automatically. ## Table of Contents @@ -22,12 +24,14 @@ This guide explains how to run a Grail miner. Miners generate GRPO rollouts with Grail miners: - Connect to a Bittensor subnet and follow scoring windows. -- Derive per-window randomness from drand and the window’s block hash. -- Generate multiple GRPO rollouts per problem using a HF model across various environments. -- Produce GRAIL proofs (Prover) binding tokens to the model and seed. +- Derive per-window randomness from drand and the window's block hash. +- Run an SGLang generation server (default backend) to produce GRPO rollouts in parallel. +- Compute GRAIL proofs on a dedicated proof GPU (HuggingFace model). - Upload signed rollouts to object storage for validators to verify and score. -The active environment is set network-wide (currently **Triton Kernel**). The environment determines what problems are generated and how rewards are computed. See [Supported Environments](#supported-environments) below. +The active environment, sampling policy (`max_tokens`, `temperature`, `top_p`, `top_k`, `repetition_penalty`), and model are all published per-checkpoint by the trainer. The miner reads them from each checkpoint's metadata and drives the generation server with exactly those values, so the validator's hard checks (`termination_valid`, `logprobs_valid`, `proof_valid`) line up automatically. + +The pipelined mining engine is the **only** generation path. The legacy single-GPU fallback was removed; you need at least two visible GPUs (one for SGLang, one for HF proofs). --- @@ -42,13 +46,11 @@ The active environment is set network-wide (currently **Triton Kernel**). The en For detailed hardware specifications, see [`compute.min.yaml`](../compute.min.yaml). -Hardware requirements: -- **Text-only environments** (SAT, GSM8K, MATH, MBPP, HumanEval): 1 GPU with 24GB+ VRAM -- **Triton Kernel environment** (current default): 2-3 GPUs recommended with pipeline mode: - - **GPU 0** — Model inference / decoding (vLLM/SGLang backend) - - **GPU 1** — Proof computation / logprob verification (HuggingFace model) - - **Separate physical GPU** — Kernel evaluation (Triton JIT compilation and GPU correctness checks). Set via `KERNEL_EVAL_GPU_IDS` using **physical** GPU index. - - The kernel evaluation GPU should be **A100 80GB, H100, or equivalent** to support Triton JIT compilation +Hardware requirements (pipeline mode is mandatory): +- **At least 2 visible GPUs** with 40GB+ VRAM each (A100 / H100 / B200 class): + - **GPU 0** — SGLang generation server (decoding) + - **GPU 1** — HuggingFace model for proof computation (logprobs + GRAIL commitments) +- Both GPUs must be in `CUDA_VISIBLE_DEVICES`. The miner exits with a clear `ProtocolViolationError` at startup if the configured `GRAIL_PIPELINE_VLLM_GPU` / `GRAIL_PIPELINE_PROOF_GPU` indices exceed the visible device count. - At least 64GB RAM recommended - Network bandwidth needs are modest; uploads are Parquet rollout files @@ -93,20 +95,21 @@ Set these in `.env` (see `.env.example` for full list and guidance): - Wallets - `BT_WALLET_COLD` (coldkey name) - `BT_WALLET_HOT` (hotkey name) -- Model & generation (dynamically loaded from R2 checkpoints) - - The model is loaded automatically from R2 checkpoints and evolves through training - - Miners automatically load the latest checkpoint from the previous window - - Maximum new tokens is fixed at 8192 (hardcoded constant `MAX_NEW_TOKENS`) - - Rollouts per problem is fixed at 16 (hardcoded constant `ROLLOUTS_PER_PROBLEM`) - - Models are shared via R2 storage and updated by the trainer after each window - - No manual model configuration required - checkpoints are loaded automatically +- Model & generation (driven by trainer-published checkpoints) + - The model and the active environment are loaded automatically from the trainer's most recent R2 checkpoint. + - Sampling policy (`max_tokens`, `temperature`, `top_p`, `top_k`, `repetition_penalty`) is published per-checkpoint and consumed by the miner via `GenerationParams.from_checkpoint_metadata`. The miner drives the SGLang/vLLM backend with exactly those values, so the validator's `termination_valid` check (`min(metadata.max_tokens, MAX_NEW_TOKENS_PROTOCOL_CAP)`) lines up by construction. + - `MAX_NEW_TOKENS_PROTOCOL_CAP = 8192` (in `grail/protocol/constants.py`) is the protocol cap; the trainer's per-checkpoint `max_tokens` is the actual limit and is typically much smaller. + - Rollouts per problem is fixed at 16 (`ROLLOUTS_PER_PROBLEM`). + - No manual model or sampling configuration required. +- Pipeline backend + - `GRAIL_PIPELINE_BACKEND` (sglang|vllm, default: **sglang**): Generation server backend. SGLang is the production-validated path; it uses the native `/generate` endpoint with `input_ids` so there is no text re-tokenization. + - `GRAIL_PIPELINE_VLLM_GPU` (default: 0): GPU index for the generation server, relative to `CUDA_VISIBLE_DEVICES`. + - `GRAIL_PIPELINE_PROOF_GPU` (default: 1): GPU index for the HuggingFace proof model, relative to `CUDA_VISIBLE_DEVICES`. Must differ from `VLLM_GPU`. + - `GRAIL_PIPELINE_VLLM_TP` (default: 1): Tensor-parallel size for the generation server. Set to 4 to spread the model across consecutive GPUs `[VLLM_GPU, VLLM_GPU+VLLM_TP)`. + - `GRAIL_PIPELINE_MAX_MODEL_LEN` (default: 12288): Server context length. The miner clamps `prompt_len + max_new_tokens` to this value before issuing requests. + - `GRAIL_PIPELINE_PROOF_FLASH_ATTN` (default: false): MUST be left at `false` so the proof path matches the validator's SDPA implementation. Setting it to `true` causes proof divergence. - Performance tuning - - `GRAIL_GENERATION_BATCH_SIZE` (default: 1): Number of rollouts to generate in parallel per batch. Higher values increase throughput but require more VRAM. Must be ≤ 16 and must divide evenly into `ROLLOUTS_PER_PROBLEM`. Valid options: 1, 2, 4, 8, 16. Start with 1 and gradually increase while monitoring GPU memory with `nvidia-smi`. Example: `export GRAIL_GENERATION_BATCH_SIZE=4` for ~3-4x throughput on A100. -- Kernel evaluation (Triton Kernel environment only) - - `GRAIL_GPU_EVAL` (true|false, default: false): Enable GPU-based kernel correctness evaluation. Must be `true` for the `triton_kernel` environment to verify generated kernels on-GPU. - - `KERNEL_EVAL_GPU_IDS` (comma-separated, e.g. `2` or `2,3`): **Physical** GPU device indices for kernel evaluation (as shown by `nvidia-smi`, not relative to `CUDA_VISIBLE_DEVICES`). These GPUs must be separate from decoding and proof GPUs. - - `KERNEL_EVAL_BACKEND` (persistent|subprocess|basilica, default: persistent): Evaluation backend. `persistent` uses a long-lived worker per GPU that reuses the CUDA context (~40x faster than subprocess, auto-recovers from CUDA sticky errors). `subprocess` runs each kernel in an isolated subprocess with its own CUDA context. `basilica` uses Basilica cloud GPU workers (no local GPU needed; not yet implemented). - - `KERNEL_EVAL_TIMEOUT` (default: 60): Per-kernel evaluation timeout in seconds. + - `GRAIL_GENERATION_BATCH_SIZE` (default: 1): Number of rollouts to generate in parallel per batch. Higher values increase throughput but require more VRAM. Must be ≤ 16 and must divide evenly into `ROLLOUTS_PER_PROBLEM`. Valid options: 1, 2, 4, 8, 16. - Object storage (R2/S3) - `R2_BUCKET_ID`, `R2_ACCOUNT_ID` - Dual credentials (recommended): @@ -150,53 +153,46 @@ Public dashboards: ## Supported Environments -The active environment is configured network-wide and determines the problem type and reward structure. Miners automatically use the current environment. +For this release the network is focused on **coding** and **math** environments. The trainer publishes the active environment per checkpoint; the miner reads it from `CheckpointMetadata.env_id` and runs that environment automatically. + +| Environment | ID | Description | +|---|---|---| +| **MBPP** | `mbpp` | Python code generation (Mostly Basic Python Problems) | +| **HumanEval** | `humaneval` | Function-level code generation (OpenAI HumanEval) | +| **GSM8K** | `gsm8k` | Math word problems with step-by-step reasoning | +| **MATH** | `math` | Competition-level mathematics (Hendrycks MATH) | -| Environment | ID | Description | GPU Requirement | -|---|---|---|---| -| **Triton Kernel** | `triton_kernel` | Generate GPU kernels in Triton; evaluated for correctness on-GPU | 3 GPUs (decoding + proof + kernel eval) | -| **3-SAT** | `sat` | Deterministic 3-SAT constraint satisfaction problems | 1 GPU | -| **GSM8K** | `gsm8k` | Math word problems with step-by-step reasoning | 1 GPU | -| **MATH** | `math` | Competition-level mathematics (Hendrycks MATH) | 1 GPU | -| **MBPP** | `mbpp` | Python code generation (Mostly Basic Python Problems) | 1 GPU | -| **HumanEval** | `humaneval` | Function-level code generation (OpenAI HumanEval) | 1 GPU | -| **Affine Trace** | `affine_trace` | Affine type system trace reasoning | 1 GPU | -| **Affine Logic** | `affine_logic` | Affine type system logic reasoning | 1 GPU | +Other environments (`sat`, `affine_*`, `triton_kernel`, etc.) still exist in the codebase but are not the focus of this release. -The current default environment is **Triton Kernel**. Pipeline mode is the **recommended** way to run miners with 2+ GPUs. The miner pipeline uses GPUs in parallel: +### Pipeline architecture -1. **GPU 0 — Decoding**: vLLM/SGLang generates Triton kernel code from problem prompts. -2. **GPU 1 — Proof computation**: A HuggingFace model computes logprobs and GRAIL commitments for verification. -3. **GPU 2 — Kernel evaluation**: Each generated kernel runs in an isolated subprocess with its own CUDA context, checking correctness against a reference implementation. +The pipelined mining engine is mandatory and lays out work across at least two GPUs: -Proof computation (GPU 1) and kernel evaluation (GPU 2) run **in parallel** after decoding completes, since proofs only need token IDs and do not depend on evaluation results. +1. **GPU 0 — Generation server** (SGLang by default): Receives `input_ids`, generates completions in parallel, returns `output_ids` directly. No text re-tokenization. +2. **GPU 1 — Proof worker**: A HuggingFace model on a dedicated GPU computes the GRAIL commitments and chosen-token logprobs (GPU `log_softmax` + gather; only the chosen-token logprobs cross PCIe). -**Weight sync between windows:** When a new checkpoint is available, the pipeline reloads vLLM weights using the sleep/wake/reload API (~3-30 seconds) instead of a full server restart (~5 minutes). This is automatic and requires no configuration. If the fast path fails, it falls back to a full restart. +Generation and proof computation overlap across batches, so the pipeline produces several hundred rollouts per window on a 2-GPU setup. -If checkpoints live on a different volume (e.g. ephemeral/tmpfs), set `GRAIL_PIPELINE_SYMLINK_DIR` to a writable directory on the same filesystem or any accessible path — the symlink used for weight reload will be placed there instead of next to the checkpoint. +**Weight sync between windows:** When the trainer publishes a new checkpoint, the miner reloads the SGLang/vLLM server weights using the sleep/wake/reload API (~3-30 seconds) instead of a full server restart. This is automatic and requires no configuration. -### Triton Kernel GPU Setup Example +If checkpoints live on a different volume (e.g. ephemeral/tmpfs), set `GRAIL_PIPELINE_SYMLINK_DIR` to a writable directory on the same filesystem — the symlink used for weight reload will be placed there. (vLLM backend only; harmless when `GRAIL_PIPELINE_BACKEND=sglang`.) + +### Two-GPU setup example ```bash -# .env configuration for triton_kernel mining with pipeline mode -GRAIL_GPU_EVAL=true -KERNEL_EVAL_GPU_IDS=2 # Physical GPU index for kernel eval (not relative to CUDA_VISIBLE_DEVICES) -KERNEL_EVAL_BACKEND=persistent # Long-lived worker, ~40x faster (default) -KERNEL_EVAL_TIMEOUT=60 # Seconds per kernel (default) - -# Pipeline mode (recommended for 2+ GPUs) -GRAIL_PIPELINE_ENABLED=true -GRAIL_PIPELINE_BACKEND=vllm -GRAIL_PIPELINE_VLLM_GPU=0 # Relative to CUDA_VISIBLE_DEVICES -GRAIL_PIPELINE_PROOF_GPU=1 # Relative to CUDA_VISIBLE_DEVICES - -# Run with GPU 0 for decoding, GPU 1 for proofs, GPU 2 for kernel eval +# .env configuration for the default coding/math envs with pipeline mode +GRAIL_PIPELINE_BACKEND=sglang # Default and recommended +GRAIL_PIPELINE_VLLM_GPU=0 # Relative to CUDA_VISIBLE_DEVICES +GRAIL_PIPELINE_PROOF_GPU=1 # Relative to CUDA_VISIBLE_DEVICES +GRAIL_PIPELINE_VLLM_TP=1 # Bump only if you have spare GPUs +GRAIL_PIPELINE_MAX_MODEL_LEN=12288 +GRAIL_PIPELINE_PROOF_FLASH_ATTN=false # MUST stay false to match validator SDPA + +# Run with GPU 0 for SGLang, GPU 1 for proofs CUDA_VISIBLE_DEVICES=0,1 grail -vv mine ``` -> **Note on GPU indices:** `GRAIL_PIPELINE_VLLM_GPU` and `GRAIL_PIPELINE_PROOF_GPU` are **relative** to `CUDA_VISIBLE_DEVICES`. `KERNEL_EVAL_GPU_IDS` is a **physical** GPU index (the eval subprocess overrides `CUDA_VISIBLE_DEVICES` internally). - -For miners without 3 GPUs or without A100/H100-class hardware, set `GRAIL_GPU_EVAL=false`. This disables on-GPU kernel evaluation (max reward capped at 0.35 based on compilation checks only). +> **Note on GPU indices:** `GRAIL_PIPELINE_VLLM_GPU` and `GRAIL_PIPELINE_PROOF_GPU` are **relative** to `CUDA_VISIBLE_DEVICES`. The miner validates them at startup and exits with a clear `ProtocolViolationError` if they exceed the visible device count. --- @@ -215,17 +211,20 @@ Flags are minimal; most behavior is configured via `.env`. Increase verbosity wi ## Operations -High-level loop (see `grail/cli/mine.py`): +High-level loop (see `grail/cli/mine.py` and `grail/neurons/miner.py`): 1. Load R2 credentials and initialize `GrailChainManager`; commit read credentials on-chain. 2. Connect to subtensor; compute `window_start = (current_block // WINDOW_LENGTH) * WINDOW_LENGTH`. -3. **Load checkpoint**: Download the model checkpoint from the previous window (`window_start - WINDOW_LENGTH`) from R2. -4. For the window: +3. **Load checkpoint**: Download the latest checkpoint from R2 (fast path if a delta on top of a cached checkpoint exists, full reconstruction otherwise). +4. **Initialize the pipeline (first window only)**: Start the SGLang generation server on `GRAIL_PIPELINE_VLLM_GPU` and load the proof model on `GRAIL_PIPELINE_PROOF_GPU`. Init failures `SystemExit(1)` so the supervisor restarts the process. +5. **Read sampling policy**: Pull `generation_params` from `CheckpointMetadata` and convert via `GenerationParams.from_checkpoint_metadata`. Malformed metadata raises `ProtocolViolationError`, which the outer loop catches and skips the window cleanly (no 10s retry storm). +6. For each problem in the window: - Derive randomness: `sha256(block_hash + drand.randomness)` (or block hash only). - - Generate problems from the active environment and create GRPO batches using the loaded checkpoint. - - Use `Prover` to commit/open GRAIL proofs and package signed rollouts. -5. Upload the window's rollouts to R2/S3 with write credentials. -6. Repeat on the next window (loading new checkpoint if available). + - Drive the generation server with the trainer's sampling policy to produce a GRPO group. + - Compute proofs on the proof GPU; drop the entire group if any proof fails (so the validator's `group_size` check never sees a partial group). + - Package signed rollouts. +7. Upload the window's rollouts to R2/S3 with write credentials. +8. Repeat on the next window. New checkpoints are detected automatically. Artifacts uploaded per window (as a Parquet file) include per-rollout: - GRAIL commit (`tokens`, `s_vals`, signature, beacon) @@ -264,13 +263,13 @@ Key points: ## Best Practices -- **GPU-intensive environments** (Triton Kernel): Use 3 GPUs with A100/H100 class for kernel eval. Ensure `GRAIL_GPU_EVAL=true` and `KERNEL_EVAL_GPU_IDS` are set correctly. -- **Text-only environments** (SAT, GSM8K, MATH, etc.): 1 GPU is sufficient; any CUDA-capable accelerator works. -- Models evolve through training: the initial base model is loaded from R2 and automatically updated with new checkpoints each window. Fixed at 8192 max new tokens and 16 rollouts per problem. -- Reserve the final 2 blocks of each window for uploads; the miner does this automatically but avoid heavy generation near the end. +- **Two GPUs are mandatory** for the pipelined miner. SGLang on GPU 0, HF proof model on GPU 1. Both must be in `CUDA_VISIBLE_DEVICES`. +- The trainer publishes the active environment, model, and sampling policy per checkpoint. **Do not override `max_tokens`, `temperature`, etc. locally** — let the miner read them from `CheckpointMetadata.generation_params` so the validator's hard checks pass by construction. +- `GRAIL_PIPELINE_PROOF_FLASH_ATTN` MUST stay `false`. The validator uses SDPA; switching the proof path to FA2 changes FP accumulation and causes sketch divergence. +- Reserve the final 2 blocks of each window for uploads; the miner does this automatically via its EMA-based time-budget gate. - Use `--use-drand` (default) for robust challenge derivation; fall back with `--no-drand` only if needed. - Ensure R2 dual-credential setup: write locally, read credentials are committed on-chain by the miner. -- Monitor GPU memory; the miner periodically empties cache, but size your rollouts to avoid OOM. +- Monitor GPU memory; the miner periodically empties cache, but size your rollouts (`GRAIL_GENERATION_BATCH_SIZE`) to avoid OOM. - Increase verbosity with `-vv` when diagnosing sampling, group sizes, or upload issues. diff --git a/docs/validator.md b/docs/validator.md index 0078203b..b2e06a9b 100644 --- a/docs/validator.md +++ b/docs/validator.md @@ -116,12 +116,11 @@ Set these in `.env` (see `.env.example`). This file will be used with Docker Com - `BT_WALLET_COLD` (coldkey name) - `BT_WALLET_HOT` (hotkey name) - Model (dynamically loaded from R2 checkpoints) - - The model is loaded automatically from R2 checkpoints and evolves through training - - Validators automatically load the appropriate checkpoint for each validation window - - Maximum new tokens is fixed at 8192 (hardcoded constant `MAX_NEW_TOKENS`) - - Rollouts per problem is fixed at 16 (hardcoded constant `ROLLOUTS_PER_PROBLEM`) - - Models are shared via R2 storage and updated by the trainer after each window - - No manual model configuration required - checkpoints are loaded automatically + - The model and the active environment are loaded automatically from each window's R2 checkpoint. + - The trainer publishes the per-checkpoint sampling policy (`max_tokens`, `temperature`, `top_p`, `top_k`, `repetition_penalty`) in `CheckpointMetadata.generation_params`. The validator reads `max_tokens` from there and caps the expected completion length at `min(metadata.max_tokens, MAX_NEW_TOKENS_PROTOCOL_CAP)`; rollouts longer than that fail `termination_valid`. + - `MAX_NEW_TOKENS_PROTOCOL_CAP = 8192` (in `grail/protocol/constants.py`) is the protocol cap; the trainer's per-checkpoint `max_tokens` is the actual limit. + - Rollouts per problem is fixed at 16 (`ROLLOUTS_PER_PROBLEM`). + - No manual model or sampling configuration required. - Object storage (R2/S3) - `R2_BUCKET_ID`, `R2_ACCOUNT_ID` - Dual credentials (recommended): @@ -291,7 +290,7 @@ Apply superlinear curve (`SUPERLINEAR_EXPONENT = 4.0`): score = estimated_unique ** SUPERLINEAR_EXPONENT ``` -Normalize to weights across miners; set on-chain with `set_weights`. An emission burn mechanism (`GRAIL_BURN_PERCENTAGE = 80%`) redirects a portion of emissions to the burn UID. +Normalize to weights across miners; set on-chain with `set_weights`. An emission burn mechanism (`GRAIL_BURN_PERCENTAGE = 90%`) redirects a portion of emissions to the burn UID. ### Publishing @@ -315,5 +314,32 @@ Normalize to weights across miners; set on-chain with `set_weights`. An emission - Verify wallet path: Ensure `~/.bittensor` is accessible - Check hardware support: Ensure your platform's floating point precision is within tolerance thresholds +**Validator exits with `torch.cuda.is_available() is False` / GPU not visible inside the container:** + +The validator refuses to load the model on CPU by design: an 8B model on CPU in FP32 saturates every core and exhausts host RAM (we have seen this freeze a 180 GB host). If you see a `RuntimeError: get_model() called with device=None but torch.cuda.is_available() is False` in `docker logs grail-validator`, the container is running without GPU passthrough. Diagnose in this order: + +1. Confirm the host sees the GPUs: + ```bash + nvidia-smi + ``` + If this fails on the host, reinstall the NVIDIA drivers before touching Docker. + +2. Confirm Docker can pass GPUs into a container (the preflight check): + ```bash + docker run --rm --gpus all nvidia/cuda:12.4.1-base-ubuntu22.04 nvidia-smi + ``` + If this fails but `nvidia-smi` works on the host, `nvidia-container-toolkit` is either not installed or not wired into the Docker daemon. Install or reconfigure it, then restart Docker: + ```bash + sudo nvidia-ctk runtime configure --runtime=docker + sudo systemctl restart docker + ``` + Re-run the preflight until it prints your GPUs, then `docker compose -f docker/docker-compose.validator.yml up -d`. + +3. Confirm the validator container itself sees the GPUs: + ```bash + docker exec grail-validator nvidia-smi + ``` + If the host preflight passes but this fails, check that `CUDA_VISIBLE_DEVICES` is not being set to an empty string anywhere in `.env`, and that `NVIDIA_VISIBLE_DEVICES=all` is still present in the validator service env block of `docker/docker-compose.validator.yml`. + **Watchtower Not Updating:** - Check registry access: ` diff --git a/grail/cli/__init__.py b/grail/cli/__init__.py index aaff3ae3..68051255 100644 --- a/grail/cli/__init__.py +++ b/grail/cli/__init__.py @@ -24,7 +24,7 @@ from ..logging_utils import ExecutionSandboxNoiseFilter, MinerPrefixFilter from ..monitoring import initialize_monitoring from ..monitoring.config import MonitoringConfig -from ..shared.constants import NETUID, NETWORK +from ..shared.config import NETUID, NETWORK from ..shared.logging import ContextFilter # Load environment variables once for the whole CLI at import time so that diff --git a/grail/cli/mine.py b/grail/cli/mine.py index b242ce0d..ebfcf6fc 100644 --- a/grail/cli/mine.py +++ b/grail/cli/mine.py @@ -4,7 +4,6 @@ # --------------------------------------------------------------------------- # import asyncio import hashlib -import json import logging import math import os @@ -17,18 +16,18 @@ import typer from transformers import AutoModelForCausalLM, AutoTokenizer -from ..environments.episode import AgentEnvLoop from ..environments.factory import create_env from ..grail import derive_env_seed from ..infrastructure.comms import sink_window_inferences from ..infrastructure.drand import get_drand_beacon -from ..shared.constants import ( +from ..protocol.constants import ( BLOCK_TIME_SECONDS, CHALLENGE_K, LAYER_INDEX, ROLLOUTS_PER_PROBLEM, WINDOW_LENGTH, ) +from ..protocol.errors import ProtocolViolationError from . import console # --------------------------------------------------------------------------- # @@ -513,18 +512,31 @@ async def _pipelined_generation_loop( checkpoint_window: int | None, env_id: str | None = None, env_params: dict[str, Any] | None = None, + generation_params: dict[str, Any] | None = None, ) -> list[dict]: - """Generate rollouts using the 3-GPU pipeline engine. - - Uses PipelinedMiningEngine for overlapped generation (GPU 0), - proof computation (GPU 1), and kernel eval (GPU 2). + """Generate rollouts using the multi-GPU pipeline engine. - Mirrors the time-budgeting and packaging logic of - ``generate_rollouts_for_window`` but delegates generation to the pipeline. + Reads ``generation_params`` from the trainer-published checkpoint metadata + and forwards them to the engine for every group, so that the SGLang/vLLM + backend's effective ``max_new_tokens`` matches what the validator expects + (otherwise rollouts hard-fail ``termination_valid``). """ - from ..environments.factory import create_env - from ..grail import derive_env_seed - from ..shared.constants import CHALLENGE_K, ROLLOUTS_PER_PROBLEM + if generation_params is None: + raise ProtocolViolationError( + "Pipeline generation requires generation_params from checkpoint metadata; got None." + ) + from ..environments.backends import GenerationParams + + gen_params = GenerationParams.from_checkpoint_metadata(generation_params) + logger.info( + "Pipeline gen params: max_new_tokens=%d temperature=%.2f top_p=%.3f top_k=%s " + "repetition_penalty=%s", + gen_params.max_new_tokens, + gen_params.temperature, + gen_params.top_p, + gen_params.top_k, + gen_params.repetition_penalty, + ) inferences: list[dict] = [] start_time = time.time() @@ -565,6 +577,7 @@ def _env_factory() -> Any: combined_randomness, wallet, seed=seed_int, + gen_params=gen_params, ) if grpo_rollouts: @@ -618,6 +631,14 @@ def _env_factory() -> Any: timers.update_gen_time_ema(min(gen_duration, max_gen_s)) await asyncio.sleep(0.01) + except ProtocolViolationError: + # Window-scoped protocol violations (e.g. malformed checkpoint + # metadata mid-window) must propagate to the miner outer loop's + # ProtocolViolationError branch so the whole window is skipped + # cleanly. ProtocolViolationError is a RuntimeError subclass, so + # this except MUST come before the RuntimeError branch below or + # it would be unreachable. + raise except RuntimeError as e: if "CUDA" in str(e): logger.error("Pipeline CUDA error: %s", e) @@ -625,8 +646,11 @@ def _env_factory() -> Any: torch.cuda.empty_cache() continue raise - except Exception as e: - logger.warning("Pipeline generation failed: %s", e) + except Exception: + # Per-problem failure: log with traceback and try the next problem. + # Use logger.exception so operators can diagnose the failure from + # logs alone, instead of seeing only the exception message. + logger.exception("Pipeline group failed, continuing to next problem") continue elapsed_time = time.time() - start_time @@ -639,358 +663,6 @@ def _env_factory() -> Any: return inferences -async def generate_rollouts_for_window( - wallet: bt.wallet, - model: AutoModelForCausalLM, - tokenizer: AutoTokenizer, - subtensor: bt.subtensor, - window_start: int, - window_block_hash: str, - combined_randomness: str, - timers: MiningTimers, - monitor: Any | None, - use_drand: bool, - checkpoint_window: int | None, - env_id: str | None = None, - env_params: dict[str, Any] | None = None, - generation_params: dict[str, Any] | None = None, -) -> list[dict]: - """Generate as many GRPO rollouts as safely possible within a window. - - Core loop responsibilities: - - Respect time budget using EMAs (stop before window end) - - Periodically clear CUDA cache to reduce fragmentation - - Track and log per-window metrics - - Package each rollout with commit-binding signatures and proofs - - Args: - wallet: Miner wallet for signing and authentication. - model: Loaded model instance. - tokenizer: Loaded tokenizer instance. - subtensor: Bittensor client for chain reads. - window_start: Start block of the current window. - window_block_hash: Block hash at window start. - combined_randomness: Per-window randomness for challenges. - timers: EMA-based timing estimates for safety. - monitor: Optional monitoring client for metrics. - use_drand: Whether drand was used in randomness generation. - checkpoint_window: The checkpoint window used for this generation - env_id: Environment identifier from checkpoint metadata - env_params: Environment parameters from checkpoint metadata - generation_params: Generation parameters from checkpoint metadata - - Returns: - List of signed rollout data ready for upload. - """ - # Window generation state and metrics - inferences: list[dict] = [] - start_time = time.time() - inference_count = 0 # Total number of problems attempted in this window - successful_rollouts = 0 - failed_rollouts = 0 - total_reward = 0.0 - # Avoid flooding logs in debug mode - text_logs_emitted = 0 # Running count of emitted debug texts - problem_count = 0 - - device = model.device # type: ignore[attr-defined] - # Batch size for parallel rollout generation (tune per node for memory/throughput) - batch_size = int(os.getenv("GRAIL_GENERATION_BATCH_SIZE", "2")) - if batch_size > ROLLOUTS_PER_PROBLEM: - logger.warning( - "GRAIL_GENERATION_BATCH_SIZE=%d exceeds ROLLOUTS_PER_PROBLEM=%d; capping at %d", - batch_size, - ROLLOUTS_PER_PROBLEM, - ROLLOUTS_PER_PROBLEM, - ) - batch_size = ROLLOUTS_PER_PROBLEM - # Create AgentEnvLoop with generation parameters from checkpoint metadata (if available) - # Validate and clamp generation params to safe ranges - generation_params = generation_params or {} - - def clamp(value: float | int, min_val: float | int, max_val: float | int) -> float | int: - """Clamp value to [min_val, max_val] range.""" - return max(min_val, min(value, max_val)) - - max_tokens = clamp(generation_params["max_tokens"], 1, 16384) - temperature = clamp(generation_params.get("temperature", 0.7), 0.01, 2.0) - top_p = clamp(generation_params.get("top_p", 0.95), 0.0, 1.0) - top_k = clamp(generation_params.get("top_k", 50), 0, 1000) - repetition_penalty = clamp(generation_params.get("repetition_penalty", 1.1), 1.0, 2.0) - - loop = AgentEnvLoop( - model, - tokenizer, - device, - max_new_tokens=int(max_tokens), - temperature=float(temperature), - top_p=float(top_p), - top_k=int(top_k) if top_k > 0 else None, - repetition_penalty=float(repetition_penalty), - ) - if batch_size > 1: - logger.info("Using batch_size=%d for parallel rollout generation", batch_size) - - while True: - current_block = await subtensor.get_current_block() # type: ignore[await-not-coroutine] - timers.update_block_time_ema(current_block) - current_window = calculate_window_start(current_block) - if current_window > window_start: - logger.info("Window %s has ended, moving to next window", window_start) - break - - blocks_remaining = (window_start + WINDOW_LENGTH) - current_block - needed_blocks = timers.blocks_needed_for_next_gen() - if blocks_remaining <= needed_blocks: - logger.info( - ( - "Stopping generation: %s blocks remain, need %s " - "(gen≈%.1fs, upload≈%.1fs, block≈%.2fs)" - ), - blocks_remaining, - needed_blocks, - (timers.gen_time_ema_s or 0.0), - (timers.upload_time_ema_s or 0.0), - timers.block_time_ema_s, - ) - break - - try: - gen_start = time.time() - problem_count += 1 - inference_count += 1 - - logger.info( - "⚡ Generating GRPO rollouts for problem %s (block %s/%s)...", - problem_count, - current_block, - window_start + WINDOW_LENGTH - 1, - ) - - # Periodically reclaim free memory — helpful for long runs - if inference_count % 10 == 0 and torch.cuda.is_available(): - torch.cuda.empty_cache() - logger.debug( - "GPU memory allocated: %s MB", - f"{torch.cuda.memory_allocated() / 1024**2:.2f}", - ) - - # Deterministically derive environment seed from miner+window+index - problem_index = max(0, problem_count - 1) - seed_int = derive_env_seed(wallet.hotkey.ss58_address, window_block_hash, problem_index) - # Use deterministic problem index as rollout_group identifier - base_nonce = problem_index - logger.debug( - ("MINER SEED DERIVATION: hotkey=%s window_hash=%s problem_index=%d -> seed=%d"), - wallet.hotkey.ss58_address[:12], - window_block_hash[:12], - problem_index, - seed_int, - ) - - # Generate GRPO rollouts using AgentEnvLoop - # Factory uses cached task source automatically (no manual instantiation needed) - # Use environment configuration from checkpoint metadata if available - def _env_factory(): - return create_env(env_id=env_id, env_params=env_params) - - # Time the rollout generation for both logging and monitoring - rollout_gen_start = time.time() - if monitor: - with monitor.timer("profiling/rollout_generation"): - grpo_rollouts = await asyncio.to_thread( - loop.run_grpo_group, - _env_factory, - ROLLOUTS_PER_PROBLEM, - combined_randomness, - wallet, - batch_size=batch_size, - seed=seed_int, - ) - else: - grpo_rollouts = await asyncio.to_thread( - loop.run_grpo_group, - _env_factory, - ROLLOUTS_PER_PROBLEM, - combined_randomness, - wallet, - batch_size=batch_size, - seed=seed_int, - ) - rollout_gen_duration = time.time() - rollout_gen_start - - if grpo_rollouts: - text_logs_emitted = await maybe_log_debug_sample( - tokenizer, - grpo_rollouts[0], - window_start, - base_nonce, - monitor, - text_logs_emitted, - DEBUG_TEXT_LOG_LIMIT_PER_WINDOW, - ) - - successful_count = sum(1 for r in grpo_rollouts if r.success) - mean_reward = ( - sum(r.reward for r in grpo_rollouts) / len(grpo_rollouts) if grpo_rollouts else 0 - ) - logger.info( - "GRPO batch: %s/%s successful, mean reward: %.3f, generation time: %.2fs", - successful_count, - len(grpo_rollouts), - mean_reward, - rollout_gen_duration, - ) - - # Check generation timing and log metrics - await log_generation_timing( - subtensor, timers, window_start, rollout_gen_duration, len(grpo_rollouts), monitor - ) - - if problem_count % 2 == 0: - elapsed = time.time() - start_time - rollouts_per_sec = (len(inferences) / elapsed) if elapsed > 0 else 0 - logger.info( - ("📊 Progress: %s rollouts from %s problems in %.1fs (%.1f rollouts/sec)"), - len(inferences), - problem_count, - elapsed, - rollouts_per_sec, - ) - if monitor: - await monitor.log_gauge("mining/rollouts_generated", len(inferences)) - await monitor.log_gauge("mining/problems_processed", problem_count) - await monitor.log_gauge("mining/rollouts_per_second", rollouts_per_sec) - if successful_rollouts + failed_rollouts > 0: - success_rate = successful_rollouts / (successful_rollouts + failed_rollouts) - await monitor.log_gauge("mining/success_rate", success_rate) - - # ───────────────────────────────────────────────────────────────────── - # COMPLETION LENGTH GATE: Drop entire group if any rollout is too short - # ───────────────────────────────────────────────────────────────────── - # Validators require at least CHALLENGE_K tokens in the completion region - # to perform cryptographic verification (sketch checks at k=32 positions). - # If any rollout in the group has completion_length < CHALLENGE_K, the - # validator will reject that rollout. Rather than waste bandwidth uploading - # a partially valid group, we drop the entire group preemptively. - short_rollouts = [ - (i, r.completion_length) - for i, r in enumerate(grpo_rollouts) - if r.completion_length < CHALLENGE_K - ] - if short_rollouts: - short_details = ", ".join(f"idx={i}:len={length}" for i, length in short_rollouts) - logger.warning( - "Dropping group %d: %d/%d rollouts have completion < %d tokens (%s)", - base_nonce, - len(short_rollouts), - len(grpo_rollouts), - CHALLENGE_K, - short_details, - ) - # Skip packaging this group entirely; continue to next problem - timers.update_gen_time_ema(time.time() - gen_start) - continue - - # Package each rollout with signatures and proofs for validation - for rollout_idx, rollout in enumerate(grpo_rollouts): - rollout_data = package_rollout_data( - model, - wallet, - rollout, - base_nonce, - rollout_idx, - len(grpo_rollouts), - window_start, - current_block, - window_block_hash, - combined_randomness, - use_drand, - checkpoint_window, - ) - inferences.append(rollout_data) - - if rollout.success: - successful_rollouts += 1 - total_reward += rollout.reward - if monitor: - await monitor.log_counter("mining/successful_rollouts") - await monitor.log_histogram("mining/reward_distribution", rollout.reward) - else: - failed_rollouts += 1 - if monitor: - await monitor.log_counter("mining/failed_rollouts") - - timers.update_gen_time_ema(time.time() - gen_start) - await asyncio.sleep(0.01) - - except RuntimeError as e: - if "CUDA" in str(e): - logger.error("CUDA error at inference %s: %s", inference_count, e) - if torch.cuda.is_available(): - torch.cuda.empty_cache() - continue - raise - except Exception as e: - logger.warning("Failed to generate inference %s: %s", inference_count, e) - continue - - elapsed_time = time.time() - start_time - avg_gen_time = timers.gen_time_ema_s or 0.0 - - logger.info( - "🎯 Generated %s rollouts in %.1fs for window %s (avg gen time: %.2fs/problem)", - len(inferences), - elapsed_time, - window_start, - avg_gen_time, - ) - if monitor: - await monitor.log_counter("mining/windows_completed") - await monitor.log_gauge( - "profiling/window_duration", - elapsed_time, - ) - await monitor.log_gauge("mining/total_rollouts_in_window", len(inferences)) - await monitor.log_gauge( - "profiling/average_generation_time", - avg_gen_time, - ) - if successful_rollouts + failed_rollouts > 0: - final_success_rate = successful_rollouts / (successful_rollouts + failed_rollouts) - await monitor.log_gauge("mining/final_success_rate", final_success_rate) - if successful_rollouts > 0: - avg_reward = total_reward / successful_rollouts - await monitor.log_gauge("mining/average_reward", avg_reward) - - # Emit structured window summary log - try: - total_attempts = successful_rollouts + failed_rollouts - window_entry = { - "event": "window_summary", - "window": window_start, - "total_rollouts": len(inferences), - "successful_rollouts": successful_rollouts, - "failed_rollouts": failed_rollouts, - "success_rate": round(successful_rollouts / total_attempts * 100, 1) - if total_attempts - else 0.0, - "avg_reward": round(total_reward / successful_rollouts, 4) - if successful_rollouts - else 0.0, - "elapsed_sec": round(elapsed_time, 1), - "avg_gen_sec": round(avg_gen_time, 2), - "rollouts_per_min": round(len(inferences) / (elapsed_time / 60), 1) - if elapsed_time > 0 - else 0.0, - } - window_logger.info(json.dumps(window_entry)) - except Exception: - logger.debug("Failed to emit window summary log", exc_info=True) - - return inferences - - # --------------------------------------------------------------------------- # # CLI # # --------------------------------------------------------------------------- # diff --git a/grail/cli/validate.py b/grail/cli/validate.py index 3c3e1bc2..4645a9b6 100644 --- a/grail/cli/validate.py +++ b/grail/cli/validate.py @@ -72,6 +72,45 @@ def _excepthook( pass +# --------------------------------------------------------------------------- # +# Validator Startup GPU Probe # +# --------------------------------------------------------------------------- # + + +def _probe_validator_gpus() -> None: + """Verify a CUDA device is visible before any other validator setup runs. + + Validators load a multi-billion-parameter checkpoint and run proof + verification forward passes. A silent CPU fallback pins every vCPU at + 100% and exhausts host RAM, which has frozen production hosts. + + This probe is intentionally scoped to the validator CLI entry only. + Miners and other grail entry points (mine, train) stay device-agnostic + and are free to run on CPU, MLX, MacBook M4, etc. + """ + import torch + + if not torch.cuda.is_available(): + raise RuntimeError( + "Validator startup GPU probe failed: torch.cuda.is_available() is False. " + "The validator requires a visible CUDA device. " + "If running via Docker, run the preflight " + "`docker run --rm --gpus all nvidia/cuda:12.4.1-base-ubuntu22.04 nvidia-smi` " + "on the host. If that fails, reinstall or reconfigure " + "nvidia-container-toolkit (`sudo nvidia-ctk runtime configure " + "--runtime=docker && sudo systemctl restart docker`). " + "See docs/validator.md troubleshooting for the full diagnostic flow." + ) + + device_count = torch.cuda.device_count() + device_names = [torch.cuda.get_device_name(i) for i in range(device_count)] + logger.info( + "Validator startup GPU probe: count=%d names=%s", + device_count, + device_names, + ) + + # --------------------------------------------------------------------------- # # CLI # # --------------------------------------------------------------------------- # @@ -112,6 +151,12 @@ def validate( """ # Install crash diagnostics early to catch silent failures _install_crash_diagnostics() + + # Fail loud and early if no CUDA device is visible to this process. + # Runs before any subtensor, R2, pool, or checkpoint work so a broken + # GPU setup is diagnosed at second 0 instead of ~2 minutes in. + _probe_validator_gpus() + from ..neurons import ValidatorNeuron asyncio.run(ValidatorNeuron(use_drand=use_drand, test_mode=test_mode).main()) diff --git a/grail/environments/backends/base.py b/grail/environments/backends/base.py index 95d8e45c..bb8e0b66 100644 --- a/grail/environments/backends/base.py +++ b/grail/environments/backends/base.py @@ -7,7 +7,7 @@ from dataclasses import dataclass from typing import Any, cast -from ...shared.constants import MAX_NEW_TOKENS +from ...protocol.constants import MAX_NEW_TOKENS_PROTOCOL_CAP logger = logging.getLogger(__name__) @@ -59,7 +59,7 @@ class GenerationParams: implementation allows it (HF via torch.Generator list; vLLM via seed field). """ - max_new_tokens: int = MAX_NEW_TOKENS + max_new_tokens: int = MAX_NEW_TOKENS_PROTOCOL_CAP temperature: float = 0.6 do_sample: bool = True top_p: float = 0.95 @@ -67,6 +67,137 @@ class GenerationParams: repetition_penalty: float | None = 1.1 trim_right_padding: bool = False + @classmethod + def from_checkpoint_metadata(cls, generation_params: dict[str, Any]) -> GenerationParams: + """Build a GenerationParams from trainer-published checkpoint metadata. + + The trainer commits the per-checkpoint sampling policy via + ``generation_params``. The validator's ``TerminationValidator`` caps + completion length at ``min(generation_params["max_tokens"], + MAX_NEW_TOKENS_PROTOCOL_CAP)``, so the miner MUST drive its backend with the same + value or rollouts hard-fail ``termination_valid``. + + Fail loud at the trust boundary if required fields are missing or + invalid: silently falling back to ``MAX_NEW_TOKENS_PROTOCOL_CAP`` was the source + of the ``Exceeds max tokens: 8192 > 2048`` rejections in production. + + Args: + generation_params: Raw dict from + ``CheckpointMetadata.generation_params``. + + Returns: + Typed ``GenerationParams`` ready to hand to a backend. + + ``top_k=None`` and ``repetition_penalty=None`` are intentionally + sentinel values meaning "do not constrain"; backends omit the + corresponding sampling field so the server's own default applies. + ``top_k=0`` from the trainer is normalised to ``None`` for the + same reason (sampling APIs use ``0`` as "disabled"). + + Raises: + ProtocolViolationError: any field is missing, malformed, or out + of range. The miner outer loop catches this and skips the + window cleanly. + """ + from ...protocol.errors import ProtocolViolationError + + if not isinstance(generation_params, dict) or "max_tokens" not in generation_params: + raise ProtocolViolationError( + "Checkpoint metadata is missing 'max_tokens' in generation_params; " + "cannot drive miner without it." + ) + + try: + raw_max = int(generation_params["max_tokens"]) + except (TypeError, ValueError) as exc: + raise ProtocolViolationError( + f"Checkpoint metadata generation_params.max_tokens must be an int; " + f"got {generation_params['max_tokens']!r}" + ) from exc + if raw_max <= 0: + raise ProtocolViolationError( + f"Checkpoint metadata generation_params.max_tokens must be > 0; got {raw_max}" + ) + max_new = min(raw_max, MAX_NEW_TOKENS_PROTOCOL_CAP) + + # All sampling fields are part of the same trust boundary as + # max_tokens. Each field is REQUIRED — silent defaults would let + # a publisher bug ship a half-specified policy that the validator + # would interpret differently. Each range matches the trainer + # publisher in grail/trainer/checkpoint_publisher.py exactly. + for required in ("temperature", "top_p", "top_k", "repetition_penalty"): + if required not in generation_params: + raise ProtocolViolationError( + f"Checkpoint metadata generation_params is missing " + f"required field {required!r}; trainer publisher must " + f"emit all of {{max_tokens, temperature, top_p, top_k, " + f"repetition_penalty}}." + ) + + try: + temperature = float(generation_params["temperature"]) + except (TypeError, ValueError) as exc: + raise ProtocolViolationError( + f"Checkpoint metadata generation_params.temperature must be a " + f"float; got {generation_params['temperature']!r}" + ) from exc + if not (0.01 <= temperature <= 2.0): + raise ProtocolViolationError( + f"Checkpoint metadata generation_params.temperature must be in " + f"[0.01, 2.0]; got {temperature}" + ) + + try: + top_p = float(generation_params["top_p"]) + except (TypeError, ValueError) as exc: + raise ProtocolViolationError( + f"Checkpoint metadata generation_params.top_p must be a float; " + f"got {generation_params['top_p']!r}" + ) from exc + if not (0.0 <= top_p <= 1.0): + raise ProtocolViolationError( + f"Checkpoint metadata generation_params.top_p must be in [0.0, 1.0]; got {top_p}" + ) + + top_k_raw = generation_params["top_k"] + try: + top_k_int = int(top_k_raw) + except (TypeError, ValueError) as exc: + raise ProtocolViolationError( + f"Checkpoint metadata generation_params.top_k must be an int; got {top_k_raw!r}" + ) from exc + if top_k_int < 0 or top_k_int > 1000: + raise ProtocolViolationError( + f"Checkpoint metadata generation_params.top_k must be in [0, 1000]; got {top_k_int}" + ) + # top_k=0 is the sampling-API "disabled" sentinel; backends omit + # the field entirely when top_k is None, which makes the server + # use its full vocabulary (no top-k filter). + top_k: int | None = top_k_int if top_k_int > 0 else None + + repetition_penalty_raw = generation_params["repetition_penalty"] + try: + repetition_penalty_val = float(repetition_penalty_raw) + except (TypeError, ValueError) as exc: + raise ProtocolViolationError( + f"Checkpoint metadata generation_params.repetition_penalty must " + f"be a float; got {repetition_penalty_raw!r}" + ) from exc + if not (1.0 <= repetition_penalty_val <= 2.0): + raise ProtocolViolationError( + f"Checkpoint metadata generation_params.repetition_penalty must " + f"be in [1.0, 2.0]; got {repetition_penalty_val}" + ) + + return cls( + max_new_tokens=max_new, + temperature=temperature, + do_sample=True, + top_p=top_p, + top_k=top_k, + repetition_penalty=repetition_penalty_val, + ) + class TextGenBackend(ABC): """Abstract interface for batched text generation backends. diff --git a/grail/environments/backends/sglang.py b/grail/environments/backends/sglang.py index 5b5b629f..bc206b2d 100644 --- a/grail/environments/backends/sglang.py +++ b/grail/environments/backends/sglang.py @@ -1,4 +1,11 @@ -"""SGLang server (OpenAI-compatible) generation backend.""" +"""SGLang server backend using native /generate endpoint. + +Uses httpx.AsyncClient for concurrent, non-blocking requests to a running +SGLang server. Passes input_ids directly and receives output_ids, avoiding +all text re-tokenization issues (BPE mismatches, stop token stripping). + +Reference: https://docs.sglang.io/basic_usage/native_api.html +""" from __future__ import annotations @@ -6,18 +13,18 @@ import time from typing import Any -from .base import GenerationParams, TextGenBackend, _decode_prompts, _tokenize_completion +from .base import GenerationParams, TextGenBackend logger = logging.getLogger(__name__) class SGLangServerBackend(TextGenBackend): - """sgLang server (OpenAI-compatible) backend over HTTP with async API. + """SGLang server backend via native /generate endpoint. - Uses AsyncOpenAI client for concurrent, non-blocking requests to a running - SGLang server. Runs in a separate subprocess, avoiding Gloo socket corruption. + Sends prompt token IDs directly and receives exact output token IDs + (including stop tokens). No text round-trip, no re-tokenization. - Reference: https://docs.sglang.ai/basic_usage/openai_api.html + Reference: https://docs.sglang.io/basic_usage/native_api.html """ def __init__( @@ -29,6 +36,7 @@ def __init__( timeout: float = 300.0, max_concurrent_requests: int = 4, return_chosen_logprobs: bool = False, + max_model_len: int | None = None, ) -> None: self._base_url = base_url.rstrip("/") self._model_name = model_name @@ -36,21 +44,43 @@ def __init__( self._timeout = float(timeout) self._max_concurrent_requests = max_concurrent_requests self._return_chosen_logprobs = bool(return_chosen_logprobs) + self._max_model_len = max_model_len # Lazy client creation -- defer to first use within the running event loop self._client: Any | None = None def _get_client(self) -> Any: if self._client is None: - from openai import AsyncOpenAI + import httpx - self._client = AsyncOpenAI( - base_url=f"{self._base_url}/v1", - api_key="EMPTY", - timeout=self._timeout, + self._client = httpx.AsyncClient( + base_url=self._base_url, + timeout=httpx.Timeout(self._timeout), ) return self._client + def _cap_max_tokens(self, prompt_len: int, max_new_tokens: int) -> int: + """Decide whether ``prompt_len + max_new_tokens`` fits the model context. + + Returns ``max_new_tokens`` unchanged if it fits, ``0`` if it does not. + + We deliberately do NOT return a partially clamped value: the + validator's ``TerminationValidator`` requires + ``completion_length == min(metadata.max_tokens, protocol_cap)`` or + EOS, so a completion that stops at a clamped intermediate length + (e.g. 1288 instead of 2048) hard-fails ``termination_valid``. + Returning 0 makes the caller skip the prompt entirely; the + per-group ``CHALLENGE_K`` gate in + ``cli/mine.py::_pipelined_generation_loop`` then drops the whole + group, which is the right "loop iteration skip" crash domain. + """ + if self._max_model_len is None: + return int(max_new_tokens) + room = int(self._max_model_len) - int(prompt_len) + if room <= 0 or int(max_new_tokens) > room: + return 0 + return int(max_new_tokens) + async def generate( self, prompt_ids_batch: list[list[int]], @@ -58,9 +88,10 @@ async def generate( params: GenerationParams, seeds: list[int] | None = None, ) -> list[tuple[list[int], list[float] | None]]: - """Generate using async OpenAI API to SGLang server. + """Generate using SGLang native /generate endpoint. - Now properly async - called directly from async context without nested event loops. + Passes input_ids directly and extracts output_ids from the response, + avoiding text re-tokenization entirely. """ return await self._async_generate_batch(prompt_ids_batch, params, seeds) @@ -77,108 +108,119 @@ async def _async_generate_batch( batch_size = len(prompt_ids_batch) logger.info("SGLangServer: Starting ASYNC batch of %d prompts", batch_size) - prompts = _decode_prompts(self._tokenizer, prompt_ids_batch) + # Pre-compute effective max_new_tokens per prompt so the closure stays clean. + effective_max_per_prompt = [ + self._cap_max_tokens(len(p_ids), params.max_new_tokens) for p_ids in prompt_ids_batch + ] - # Use configurable semaphore to control client-side concurrency sem = asyncio.Semaphore(self._max_concurrent_requests) - async def _call_one_async( - idx: int, prompt: str, random_seed: int | None - ) -> tuple[int, str]: - """Make async OpenAI API request with retries and backoff.""" - max_retries: int = 3 - base_backoff: float = 1.0 - - async with sem: # Limit concurrency - for attempt in range(max_retries): + async def _call_one( + idx: int, + prompt_ids: list[int], + max_new_tokens: int, + random_seed: int | None, + ) -> tuple[int, list[int]]: + """POST to /generate with retries and backoff.""" + if max_new_tokens <= 0: + logger.warning( + " Request %d/%d: skipping, prompt (%d tokens) exceeds " + "max_model_len (%s). Will produce 0 tokens.", + idx + 1, + batch_size, + len(prompt_ids), + self._max_model_len, + ) + return (idx, []) + + async with sem: + for attempt in range(3): req_start = time.time() try: - # Build completion kwargs - completion_kwargs: dict[str, Any] = { - "model": self._model_name, - "prompt": prompt, - "max_tokens": int(params.max_new_tokens), + sampling_params: dict[str, Any] = { + "max_new_tokens": max_new_tokens, "temperature": float(params.temperature), "top_p": float(params.top_p), - # Ensure single completion per request - "n": 1, } - # Provide vendor extensions via extra_body for SGLang - extra_body: dict[str, Any] = {} if params.top_k is not None: - extra_body["top_k"] = int(params.top_k) + sampling_params["top_k"] = int(params.top_k) if params.repetition_penalty is not None: - extra_body["repetition_penalty"] = float(params.repetition_penalty) + sampling_params["repetition_penalty"] = float(params.repetition_penalty) + if random_seed is not None: + sampling_params["sampling_seed"] = int(random_seed) - if extra_body: - completion_kwargs["extra_body"] = extra_body + body: dict[str, Any] = { + "input_ids": prompt_ids, + "sampling_params": sampling_params, + "return_token_ids": True, + } - # Add seed (SGLang supports this parameter) - if random_seed is not None: - completion_kwargs["seed"] = int(random_seed) + resp = await self._get_client().post("/generate", json=body) + resp.raise_for_status() + data = resp.json() - # Async call to server - response = await self._get_client().completions.create(**completion_kwargs) + output_ids = data.get("output_ids", []) + if not isinstance(output_ids, list): + logger.warning( + " Request %d/%d: output_ids is not a list, using empty", + idx + 1, + batch_size, + ) + output_ids = [] - req_time = time.time() - req_start - text = response.choices[0].text if response.choices else "" logger.debug( - " Request %d/%d took %.2fs, output_len=%d", + " Request %d/%d took %.2fs, output_tokens=%d", idx + 1, batch_size, - req_time, - len(text), + time.time() - req_start, + len(output_ids), ) - return (idx, text) + return (idx, output_ids) except Exception as e: - req_time = time.time() - req_start - if attempt < max_retries - 1: - backoff = base_backoff * (2**attempt) + if attempt < 2: + backoff = 1.0 * (2**attempt) logger.warning( - " Request %d/%d failed (attempt %d/%d), retrying in %.1fs: %s", + " Request %d/%d failed (attempt %d/3), retrying in %.1fs: %s", idx + 1, batch_size, attempt + 1, - max_retries, backoff, type(e).__name__, ) await asyncio.sleep(backoff) else: logger.warning( - " Request %d/%d failed after %d attempts (%.2fs): %s", + " Request %d/%d failed after 3 attempts (%.2fs): %s", idx + 1, batch_size, - max_retries, - req_time, + time.time() - req_start, type(e).__name__, ) - return (idx, "") + return (idx, []) - return (idx, "") + return (idx, []) # unreachable, satisfies type checker - # Execute all requests concurrently with gather - tasks = [] - for idx, prompt in enumerate(prompts): - seed_val = seeds[idx] if seeds and idx < len(seeds) else None - tasks.append(_call_one_async(idx, prompt, seed_val)) + tasks = [ + _call_one( + idx, + p_ids, + effective_max_per_prompt[idx], + seeds[idx] if seeds and idx < len(seeds) else None, + ) + for idx, p_ids in enumerate(prompt_ids_batch) + ] - # Wait for all completions results_tuples = await asyncio.gather(*tasks, return_exceptions=False) - # Build completion dict from results - completions: dict[int, str] = {} - for idx, text in results_tuples: - completions[idx] = text + completion_ids_map: dict[int, list[int]] = {} + for idx, output_ids in results_tuples: + completion_ids_map[idx] = output_ids - # Reconstruct results in original order results: list[tuple[list[int], list[float] | None]] = [] for idx, p_ids in enumerate(prompt_ids_batch): - completion_text = completions.get(idx, "") - comp_ids = _tokenize_completion(self._tokenizer, completion_text, []) + comp_ids = completion_ids_map.get(idx, []) all_ids = p_ids + comp_ids - # SGLang backend doesn't currently support logprobs extraction results.append((all_ids, None)) batch_time = time.time() - batch_start diff --git a/grail/environments/episode.py b/grail/environments/episode.py index 88eb4e6f..a1714aa2 100644 --- a/grail/environments/episode.py +++ b/grail/environments/episode.py @@ -8,8 +8,8 @@ from dataclasses import replace from typing import Any +from ..protocol.constants import MAX_NEW_TOKENS_PROTOCOL_CAP from ..shared.chat_templates import apply_chat_template as _apply_chat_template -from ..shared.constants import MAX_NEW_TOKENS from ..shared.hf_compat import resolve_hidden_size from .advantages import compute_advantages from .backends.base import GenerationParams, TextGenBackend @@ -34,7 +34,7 @@ def __init__( model: Any, tokenizer: Any, device: str = "cuda", - max_new_tokens: int = MAX_NEW_TOKENS, + max_new_tokens: int = MAX_NEW_TOKENS_PROTOCOL_CAP, temperature: float = 0.6, *, do_sample: bool = True, diff --git a/grail/environments/execution.py b/grail/environments/execution.py index 7afb0631..377f055d 100644 --- a/grail/environments/execution.py +++ b/grail/environments/execution.py @@ -1056,17 +1056,12 @@ def start(self) -> None: os.environ["CUDA_VISIBLE_DEVICES"] = "" os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" - # max_tasks_per_child requires Python 3.11+ - executor_kwargs: dict[str, Any] = { - "max_workers": self.num_workers, - "mp_context": ctx, - "initializer": _pool_worker_init, # Apply security sandbox - } - # Only add max_tasks_per_child on Python 3.11+ - if sys.version_info >= (3, 11): - executor_kwargs["max_tasks_per_child"] = self.max_tasks_per_child - - self._executor = concurrent.futures.ProcessPoolExecutor(**executor_kwargs) + self._executor = concurrent.futures.ProcessPoolExecutor( + max_workers=self.num_workers, + mp_context=ctx, + initializer=_pool_worker_init, # Apply security sandbox + max_tasks_per_child=self.max_tasks_per_child, + ) # Warm up workers - force spawn and apply security now # Timeout increased to 45s to handle high-load scenarios diff --git a/grail/environments/factory.py b/grail/environments/factory.py index dfd2d1f7..3094c679 100644 --- a/grail/environments/factory.py +++ b/grail/environments/factory.py @@ -5,7 +5,7 @@ import logging from typing import Any -from ..shared.constants import CURRENT_ENV_ID +from ..protocol.constants import CURRENT_ENV_ID from .core import MultiTurnEnv logger = logging.getLogger(__name__) diff --git a/grail/environments/proofs.py b/grail/environments/proofs.py index bf3bf8cc..3f90f1fc 100644 --- a/grail/environments/proofs.py +++ b/grail/environments/proofs.py @@ -12,7 +12,7 @@ import torch from ..model.forward import forward_single_layer -from ..shared.constants import GRAIL_PROOF_VERSION, LAYER_INDEX +from ..protocol.constants import GRAIL_PROOF_VERSION, LAYER_INDEX logger = logging.getLogger(__name__) @@ -21,6 +21,8 @@ def _batched_forward_pass( model: Any, device: str, all_token_ids_batch: list[list[int]], + *, + keep_logits_on_gpu: bool = False, ) -> tuple[list[torch.Tensor], list[torch.Tensor]]: """Run sub-batched forward passes with right-padding. @@ -35,13 +37,18 @@ def _batched_forward_pass( model: HuggingFace causal LM device: Device string (e.g. ``"cuda:1"``) all_token_ids_batch: Variable-length token sequences + keep_logits_on_gpu: When True, return logits on GPU (no H2D copy). + Caller is responsible for freeing them after use. Saves + ~1.2 GB/seq H2D transfer when used with GPU log_softmax in + ``compute_proofs(..., gpu_logprobs=True)``. Returns: ``(per_seq_hidden, per_seq_logits)`` -- lists of tensors, one per sequence. hidden: ``[seq_len, hidden_dim]`` on *device*. - logits: ``[seq_len, vocab]`` on CPU. + logits: ``[seq_len, vocab]`` on CPU (default) or *device* (if + ``keep_logits_on_gpu=True``). """ - from ..shared.constants import PROOF_BATCH_SIZE + from ..protocol.constants import PROOF_BATCH_SIZE batch_size = len(all_token_ids_batch) seq_lens = [len(seq) for seq in all_token_ids_batch] @@ -78,7 +85,10 @@ def _batched_forward_pass( # .clone() decouples from the batched tensor so del frees GPU memory. for i, slen in enumerate(sub_lens): per_seq_hidden.append(h_layer[i, :slen, :].clone()) - per_seq_logits.append(logits[i, :slen, :].detach().to("cpu")) + if keep_logits_on_gpu: + per_seq_logits.append(logits[i, :slen, :].clone()) + else: + per_seq_logits.append(logits[i, :slen, :].detach().to("cpu")) del h_layer, logits, input_ids, attn_mask torch.cuda.empty_cache() @@ -129,16 +139,27 @@ def compute_proofs( prompt_lens: list[int], randomness_hex: str, wallet: Any, + *, + gpu_logprobs: bool = True, ) -> list[tuple[list[dict], list[float], bytes, dict, str]]: - """Compute GRAIL commitments and logprobs. Used by AgentEnvLoop and ProofWorker. + """Compute GRAIL commitments and logprobs for the miner pipeline. Two-phase design: Phase 1 -- Forward passes via ``_batched_forward_pass`` (which internally calls ``forward_single_layer``, the same function the validator uses). Sub-batch size is the constant PROOF_BATCH_SIZE (16). Falls back to sequential single-sequence passes on OOM. - Phase 2 -- Per-sequence commitment + logprob extraction from cached - hidden states and logits. + Phase 2 -- Per-sequence commitment + GPU log_softmax + gather. Only the + chosen-token logprobs (~16 KB/seq) are transferred to CPU; full + ``[seq_len, vocab]`` logits never leave the GPU. The validator + recomputes the same fp32 ``log_softmax`` so the divergence stays + well below ``LOGPROB_IS_EPS=0.10``. + + The ``gpu_logprobs=False`` branch is retained ONLY as the OOM fallback + for ``_batched_forward_pass``: when sub-batch=1 still OOMs, the function + falls back to sequential CPU forwards which return CPU logits. In that + rare case the per-sequence loop transparently uses the CPU log_softmax + branch. Steady-state mining always runs the GPU path. Args: model: The loaded PyTorch model @@ -148,6 +169,8 @@ def compute_proofs( prompt_lens: List of prompt lengths corresponding to each sequence randomness_hex: Hex string for randomness beacon wallet: Bittensor wallet for signing commitments + gpu_logprobs: Default True. Set to False only by tests that need + bit-identical CPU output. Returns: List of tuples: (commitments, logprobs, signature, beacon, proof_version) @@ -168,7 +191,9 @@ def compute_proofs( per_seq_logits: list[torch.Tensor | None] = [None] * batch_size try: - hidden_list, logits_list = _batched_forward_pass(model, device, all_token_ids_batch) + hidden_list, logits_list = _batched_forward_pass( + model, device, all_token_ids_batch, keep_logits_on_gpu=gpu_logprobs + ) for i in range(batch_size): per_seq_hidden[i] = hidden_list[i] per_seq_logits[i] = logits_list[i] @@ -199,7 +224,13 @@ def compute_proofs( if use_batched: h_layer = per_seq_hidden[idx] logits = per_seq_logits[idx] - assert h_layer is not None and logits is not None + if h_layer is None or logits is None: + from ..protocol.errors import ProtocolViolationError + + raise ProtocolViolationError( + f"Proof batched forward returned None for sequence {idx} " + f"(batch_size={batch_size}); cannot continue" + ) else: # Sequential fallback: one sequence at a time using the same # forward_single_layer path for numerical consistency. @@ -262,18 +293,40 @@ def compute_proofs( skip_front = valid_start - start_logit n_valid = valid_end - valid_start valid_token_ids = completion_ids[skip_front : skip_front + n_valid] - token_tensor = torch.tensor(valid_token_ids, dtype=torch.long) - - # Chunked log_softmax to cap CPU memory (~600MB per chunk) - LOGPROB_CHUNK = 512 - chunk_logprobs: list[float] = [] - for c_start in range(0, n_valid, LOGPROB_CHUNK): - c_end = min(c_start + LOGPROB_CHUNK, n_valid) - logit_slice = logits[valid_start + c_start : valid_start + c_end] - log_probs_chunk = torch.log_softmax(logit_slice.float(), dim=-1) - tok_slice = token_tensor[c_start:c_end] - selected = log_probs_chunk[torch.arange(c_end - c_start), tok_slice] - chunk_logprobs.extend(selected.tolist()) + + if gpu_logprobs and logits.is_cuda: + # GPU path: chunked log_softmax on the device, gather only + # the chosen-token logprobs, then tiny H2D for the result. + # Caps GPU peak memory to LOGPROB_CHUNK * vocab * 4 B + # (~312 MB for chunk=512, vocab=152k). + token_tensor_gpu = torch.tensor( + valid_token_ids, dtype=torch.long, device=logits.device + ) + LOGPROB_CHUNK = 512 + chunk_logprobs: list[float] = [] + for c_start in range(0, n_valid, LOGPROB_CHUNK): + c_end = min(c_start + LOGPROB_CHUNK, n_valid) + logit_slice = logits[valid_start + c_start : valid_start + c_end] + log_probs_chunk = torch.log_softmax(logit_slice.float(), dim=-1) + tok_slice = token_tensor_gpu[c_start:c_end] + selected = log_probs_chunk[ + torch.arange(c_end - c_start, device=logits.device), tok_slice + ] + chunk_logprobs.extend(selected.tolist()) + del log_probs_chunk + del token_tensor_gpu + else: + # Legacy CPU path (bit-identical to historical behavior). + token_tensor = torch.tensor(valid_token_ids, dtype=torch.long) + LOGPROB_CHUNK = 512 + chunk_logprobs: list[float] = [] + for c_start in range(0, n_valid, LOGPROB_CHUNK): + c_end = min(c_start + LOGPROB_CHUNK, n_valid) + logit_slice = logits[valid_start + c_start : valid_start + c_end] + log_probs_chunk = torch.log_softmax(logit_slice.float(), dim=-1) + tok_slice = token_tensor[c_start:c_end] + selected = log_probs_chunk[torch.arange(c_end - c_start), tok_slice] + chunk_logprobs.extend(selected.tolist()) logprobs = ( [float("-inf")] * skip_front @@ -289,6 +342,10 @@ def compute_proofs( logits.size(0), ) + # Free this sequence's logits eagerly when they're on GPU to bound peak memory. + if gpu_logprobs and use_batched and per_seq_logits[idx] is not None: + per_seq_logits[idx] = None + commitment_data = json.dumps(commitments, sort_keys=True) commitment_hash = hashlib.sha256(commitment_data.encode()).digest() if wallet is None: diff --git a/grail/infrastructure/chain.py b/grail/infrastructure/chain.py index de4c2e27..bc842e84 100644 --- a/grail/infrastructure/chain.py +++ b/grail/infrastructure/chain.py @@ -251,7 +251,7 @@ def get_bucket_for_hotkey(self, hotkey: str) -> Bucket | None: @retry( stop=stop_after_attempt(SUBSTRATE_QUERY_RETRIES), wait=wait_exponential(multiplier=1, min=1, max=4), - retry=retry_if_exception_type(asyncio.TimeoutError), + retry=retry_if_exception_type(TimeoutError), reraise=True, ) async def _query_timestamp_pallet(self, substrate: Any, block_hash: str) -> float | None: @@ -265,7 +265,7 @@ async def _query_timestamp_pallet(self, substrate: Any, block_hash: str) -> floa Timestamp in seconds if found, None otherwise Raises: - asyncio.TimeoutError: If query times out after retries + TimeoutError: If query times out after retries """ res = await asyncio.wait_for( substrate.query( @@ -284,7 +284,7 @@ async def _query_timestamp_pallet(self, substrate: Any, block_hash: str) -> floa @retry( stop=stop_after_attempt(SUBSTRATE_QUERY_RETRIES), wait=wait_exponential(multiplier=1, min=1, max=4), - retry=retry_if_exception_type(asyncio.TimeoutError), + retry=retry_if_exception_type(TimeoutError), reraise=True, ) async def _query_timestamp_extrinsics(self, substrate: Any, block_hash: str) -> float | None: @@ -298,7 +298,7 @@ async def _query_timestamp_extrinsics(self, substrate: Any, block_hash: str) -> Timestamp in seconds if found, None otherwise Raises: - asyncio.TimeoutError: If query times out after retries + TimeoutError: If query times out after retries """ block = await asyncio.wait_for( substrate.get_block(block_hash=block_hash), @@ -356,7 +356,7 @@ async def get_block_timestamp(self, block_number: int) -> float | None: if timestamp is not None: logger.debug("Got timestamp for block %d: %.2f", block_number, timestamp) return timestamp - except asyncio.TimeoutError: + except TimeoutError: logger.warning( "Timeout querying Timestamp.Now for block %d after %d attempts", block_number, @@ -373,7 +373,7 @@ async def get_block_timestamp(self, block_number: int) -> float | None: "Got timestamp from extrinsics for block %d: %.2f", block_number, timestamp ) return timestamp - except asyncio.TimeoutError: + except TimeoutError: logger.warning( "Timeout parsing extrinsics for block %d after %d attempts", block_number, diff --git a/grail/infrastructure/checkpoint_consumer.py b/grail/infrastructure/checkpoint_consumer.py index f117f9e4..b0a85230 100644 --- a/grail/infrastructure/checkpoint_consumer.py +++ b/grail/infrastructure/checkpoint_consumer.py @@ -26,6 +26,12 @@ Note: Remote checkpoint publishing and deletion are handled by grail.trainer.checkpoint_publisher module (producer role). This module should never perform write operations to R2. + +SYNC: The eval service has a standalone reimplementation of delta chain +reconstruction in grail.trainer.eval_service.r2_download (bittensor-free, +aiobotocore-based). If the delta format, metadata schema, sparse codec dispatch, +or chain walk logic changes here, update that module to match. A shared contract +test in tests/unit/eval_service/test_delta_contract.py verifies parity. """ from __future__ import annotations @@ -50,6 +56,11 @@ ) from grail.shared.safetensors_utils import load_model_state_dict +from ..protocol.constants import ( + CHECKPOINT_PREFIX, + CHECKPOINT_TYPE_DELTA, + CHECKPOINT_TYPE_FULL, +) from ..shared.checkpoint_paths import ( checkpoint_delta_metadata_key, checkpoint_delta_prefix, @@ -58,13 +69,7 @@ checkpoint_window_prefix, parse_window_from_prefix, ) -from ..shared.constants import ( - BASE_CHECKPOINT_RETENTION_LIMIT, - CHECKPOINT_PREFIX, - CHECKPOINT_TYPE_DELTA, - CHECKPOINT_TYPE_FULL, - GRAIL_CHECKPOINT_MOD10, -) +from ..shared.config import BASE_CHECKPOINT_RETENTION_LIMIT, GRAIL_CHECKPOINT_MOD10 from . import comms from .delta_checkpoint import apply_sparse_delta, compute_weights_hash @@ -92,7 +97,7 @@ class CheckpointMetadata: checkpoint_type: str = CHECKPOINT_TYPE_FULL # "FULL" or "DELTA" prev_window: int | None = None # For DELTA: immediate predecessor checkpoint (chained) anchor_window: int | None = None # For DELTA: nearest FULL checkpoint for recovery - weights_hash: str | None = None # SHA256 of final weights for verification + weights_hash: str | None = None # xxh3-128 hex digest (32 chars) of final weights # Environment and generation configuration env_id: str | None = None # Environment identifier (e.g., "mbpp", "gsm8k") @@ -100,6 +105,14 @@ class CheckpointMetadata: generation_params: dict[str, Any] = field( default_factory=dict ) # Generation parameters (max_tokens, temperature, etc.) + # Thinking mode is protocol-affecting: it controls the system prompt and the + # chat template applied to every prompt. Miner and validator must agree, so + # the trainer is the single source of truth and publishes it here. Allowed + # values: "native" (model's built-in thinking, e.g. Qwen3 ...) + # or "instructed" (custom / via system + # prompt + ChatML template). NEVER read GRAIL_THINKING_MODE on the miner or + # validator path — always read this field. + thinking_mode: str | None = None def validate_metadata(self) -> list[str]: """Validate that required fields for mining/validation are present. @@ -109,10 +122,23 @@ def validate_metadata(self) -> list[str]: missing = [] if not self.env_id: missing.append("env_id") + if not self.thinking_mode: + missing.append("thinking_mode") + elif self.thinking_mode not in ("native", "instructed"): + missing.append(f"thinking_mode (invalid value '{self.thinking_mode}')") if not self.generation_params: missing.append("generation_params") - elif "max_tokens" not in self.generation_params: - missing.append("generation_params.max_tokens") + else: + required_gen_params = ( + "max_tokens", + "temperature", + "top_p", + "top_k", + "repetition_penalty", + ) + for key in required_gen_params: + if key not in self.generation_params: + missing.append(f"generation_params.{key}") return missing def remote_prefix(self) -> str: @@ -820,37 +846,65 @@ async def _fetch_metadata(self, window: int) -> CheckpointMetadata | None: return metadata return await self._fetch_full_metadata(window) - async def _fetch_delta_metadata(self, window: int) -> CheckpointMetadata | None: - """Fetch DELTA checkpoint metadata from DELTA subdir.""" - cache_key = (window, CHECKPOINT_TYPE_DELTA) - if cache_key in self._metadata_cache: - return self._metadata_cache[cache_key] - - remote_key = checkpoint_delta_metadata_key(window) - payload = await comms.get_file(remote_key, credentials=self.credentials, use_write=False) - if not payload: - return None - - metadata = CheckpointMetadata( + def _build_metadata_from_payload( + self, payload: dict[str, Any], window: int, default_type: str + ) -> CheckpointMetadata: + """Construct CheckpointMetadata from a raw R2 payload dict.""" + return CheckpointMetadata( window=payload.get("window", window), file_manifest=payload.get("file_manifest", {}), training_config=payload.get("training_config", {}), git_commit=payload.get("git_commit", "unknown"), created_at=float(payload.get("created_at", 0.0)), model_name=payload.get("model_name", "no_name"), - checkpoint_type=payload.get("checkpoint_type", CHECKPOINT_TYPE_DELTA), + checkpoint_type=payload.get("checkpoint_type", default_type), prev_window=payload.get("prev_window"), anchor_window=payload.get("anchor_window"), weights_hash=payload.get("weights_hash"), env_id=payload.get("env_id"), env_params=payload.get("env_params", {}), generation_params=payload.get("generation_params", {}), + thinking_mode=payload.get("thinking_mode"), ) + + async def _fetch_delta_metadata(self, window: int) -> CheckpointMetadata | None: + """Fetch DELTA checkpoint metadata from DELTA subdir. + + Cache contract: only metadata that PASSES ``validate_metadata()`` is + cached. An invalid payload (legacy schema, missing thinking_mode, + out-of-range field, etc.) is returned to the caller for inspection + but never written to ``_metadata_cache`` — otherwise a one-time bad + publish would poison the cache for the entire process lifetime, and + a subsequent trainer republish at the same window could not be + observed without restarting the validator. + """ + cache_key = (window, CHECKPOINT_TYPE_DELTA) + if cache_key in self._metadata_cache: + return self._metadata_cache[cache_key] + + remote_key = checkpoint_delta_metadata_key(window) + payload = await comms.get_file(remote_key, credentials=self.credentials, use_write=False) + if not payload: + return None + + metadata = self._build_metadata_from_payload(payload, window, CHECKPOINT_TYPE_DELTA) + missing = metadata.validate_metadata() + if missing: + logger.debug( + "Skipping cache write for DELTA metadata at window %s: missing %s", + window, + ", ".join(missing), + ) + return metadata self._metadata_cache[cache_key] = metadata return metadata async def _fetch_full_metadata(self, window: int) -> CheckpointMetadata | None: - """Fetch FULL checkpoint metadata from FULL subdir.""" + """Fetch FULL checkpoint metadata from FULL subdir. + + Cache contract: only metadata that PASSES ``validate_metadata()`` is + cached. See ``_fetch_delta_metadata`` for the rationale. + """ cache_key = (window, CHECKPOINT_TYPE_FULL) if cache_key in self._metadata_cache: return self._metadata_cache[cache_key] @@ -863,21 +917,15 @@ async def _fetch_full_metadata(self, window: int) -> CheckpointMetadata | None: return None logger.debug("Found FULL metadata for window %s", window) - metadata = CheckpointMetadata( - window=payload.get("window", window), - file_manifest=payload.get("file_manifest", {}), - training_config=payload.get("training_config", {}), - git_commit=payload.get("git_commit", "unknown"), - created_at=float(payload.get("created_at", 0.0)), - model_name=payload.get("model_name", "no_name"), - checkpoint_type=payload.get("checkpoint_type", CHECKPOINT_TYPE_FULL), - prev_window=payload.get("prev_window"), - anchor_window=payload.get("anchor_window"), - weights_hash=payload.get("weights_hash"), - env_id=payload.get("env_id"), - env_params=payload.get("env_params", {}), - generation_params=payload.get("generation_params", {}), - ) + metadata = self._build_metadata_from_payload(payload, window, CHECKPOINT_TYPE_FULL) + missing = metadata.validate_metadata() + if missing: + logger.debug( + "Skipping cache write for FULL metadata at window %s: missing %s", + window, + ", ".join(missing), + ) + return metadata self._metadata_cache[cache_key] = metadata return metadata diff --git a/grail/infrastructure/comms.py b/grail/infrastructure/comms.py index 4f4691ad..a159c759 100644 --- a/grail/infrastructure/comms.py +++ b/grail/infrastructure/comms.py @@ -21,7 +21,8 @@ from tqdm import tqdm # type: ignore[import-untyped] from transformers import AutoModelForCausalLM -from ..shared.constants import UPLOAD_TIMEOUT, WINDOW_LENGTH +from ..protocol.constants import WINDOW_LENGTH +from ..shared.config import UPLOAD_TIMEOUT from ..shared.schemas import Bucket, BucketCredentials logger = logging.getLogger(__name__) @@ -530,7 +531,7 @@ async def upload_file_chunked( ) return True - except asyncio.TimeoutError as e: + except TimeoutError as e: logger.error(f"❌ Upload timeout for {key} after {upload_timeout}s: {e}") if client is not None and upload_id is not None: try: @@ -615,7 +616,7 @@ async def _upload_single_chunk( ) progress.update(len(data)) return True - except asyncio.TimeoutError: + except TimeoutError: if attempt < max_retries - 1: wait_time = await _exponential_backoff(attempt, max_retries) logger.warning( @@ -678,7 +679,7 @@ async def _upload_chunk_with_semaphore( ) progress.update(len(data)) return {"PartNumber": part_number, "ETag": response["ETag"]} - except asyncio.TimeoutError: + except TimeoutError: if attempt < max_retries - 1: wait_time = await _exponential_backoff(attempt, max_retries) logger.warning( @@ -1646,7 +1647,7 @@ async def upload_model_to_huggingface( Returns: True if upload succeeded, False otherwise """ - from grail.shared.constants import HF_TOKEN, HF_USERNAME + from grail.shared.config import HF_TOKEN, HF_USERNAME if not HF_TOKEN: logger.error("HF_TOKEN not set. Cannot upload model to HuggingFace.") diff --git a/grail/infrastructure/delta_checkpoint.py b/grail/infrastructure/delta_checkpoint.py index 9481b2e9..741bd52e 100644 --- a/grail/infrastructure/delta_checkpoint.py +++ b/grail/infrastructure/delta_checkpoint.py @@ -14,12 +14,12 @@ from __future__ import annotations -import hashlib import logging import math from typing import Any import torch +import xxhash logger = logging.getLogger(__name__) @@ -173,82 +173,58 @@ def apply_sparse_delta( def compute_weights_hash(state_dict: dict[str, torch.Tensor]) -> str: - """Compute deterministic hash of all weights for verification. - - Uses sorted keys and raw bytes for reproducibility. - The hash covers parameter names and their byte representations. + """Deterministic xxh3-128 hash of all weights for verification. + + NOT a cryptographic boundary. This hash is purely for detecting + download / reconstruction / load corruption between trainer publish and + miner/validator consumption. Trust in the checkpoint contents is + established by R2 access control, not by this digest. + + Determinism guarantees: + - xxh3-128 digest format frozen since xxhash v0.8.0 (2020) — same input + bytes produce the same digest across CPU architectures and across + ``xxhash`` Python package versions >= 3.0. + - Sorted parameter ordering is deterministic across Python dict insertion + orders. + - GIL is released for the inner ``update()`` call so future parallelization + with ``concurrent.futures.ThreadPoolExecutor`` is trivial if profiling + shows ``.cpu().contiguous()`` is no longer the bottleneck. Args: - state_dict: Model state dict to hash + state_dict: Model state dict to hash. Returns: - SHA256 hex digest of all weights + 32-character hex digest (128 bits). """ - hasher = hashlib.sha256() - - # Log input state info for debugging - sample_dtypes: dict[str, int] = {} - total_bytes = 0 - + hasher = xxhash.xxh3_128() for name in sorted(state_dict.keys()): - tensor = state_dict[name] - # Convert to contiguous CPU bytes in a deterministic way. - # - # Note: torch.bfloat16 tensors cannot be converted to numpy directly. - # We instead reinterpret the underlying storage as uint8 bytes. - tensor_cpu = tensor.detach().cpu().contiguous() - tensor_bytes = tensor_cpu.view(torch.uint8).numpy().tobytes() - - # Track dtype distribution for debugging - dtype_str = str(tensor_cpu.dtype) - sample_dtypes[dtype_str] = sample_dtypes.get(dtype_str, 0) + 1 - total_bytes += len(tensor_bytes) - - # Hash both name and tensor bytes - hasher.update(name.encode("utf-8")) - hasher.update(str(tensor_cpu.dtype).encode("utf-8")) - hasher.update(str(tuple(tensor_cpu.shape)).encode("utf-8")) - hasher.update(tensor_bytes) - - result_hash = hasher.hexdigest() - - logger.debug( - "[compute_weights_hash] Computed hash: %s... | params=%d | bytes=%d | dtypes=%s", - result_hash[:16], - len(state_dict), - total_bytes, - sample_dtypes, - ) - - return result_hash + tensor = state_dict[name].detach().cpu().contiguous() + # Zero-copy: memoryview over the numpy buffer, no .tobytes() materialization. + hasher.update(memoryview(tensor.view(torch.uint8).numpy())) + return hasher.hexdigest() def verify_weights_hash( state_dict: dict[str, torch.Tensor], expected_hash: str, ) -> bool: - """Verify that state dict matches expected hash. + """Verify that state dict matches expected xxh3-128 hash. Args: - state_dict: Model state dict to verify - expected_hash: Expected SHA256 hex digest + state_dict: Model state dict to verify. + expected_hash: Expected xxh3-128 hex digest (32 chars). Returns: - True if hash matches, False otherwise + True if hash matches, False otherwise. """ actual_hash = compute_weights_hash(state_dict) matches = actual_hash == expected_hash if not matches: - # Collect diagnostic info about the state - dtypes = {} - for name, tensor in list(state_dict.items())[:5]: # Sample first 5 - dtypes[name] = str(tensor.dtype) - + dtypes = {name: str(tensor.dtype) for name, tensor in list(state_dict.items())[:5]} logger.error( - "[verify_weights_hash] HASH MISMATCH: expected=%s, got=%s | " - "params=%d | sample_dtypes=%s | " - "This usually indicates floating-point precision differences during reconstruction", + "[verify_weights_hash] HASH MISMATCH: expected=%s, got=%s | params=%d | " + "sample_dtypes=%s | check delta-apply correctness or storage corruption", expected_hash, actual_hash, len(state_dict), @@ -256,8 +232,8 @@ def verify_weights_hash( ) else: logger.debug( - "[verify_weights_hash] Hash verified: %s... | params=%d", - actual_hash[:16], + "[verify_weights_hash] Hash verified: %s | params=%d", + actual_hash, len(state_dict), ) diff --git a/grail/infrastructure/network.py b/grail/infrastructure/network.py index d1108355..9712392d 100644 --- a/grail/infrastructure/network.py +++ b/grail/infrastructure/network.py @@ -117,7 +117,7 @@ async def _close_subtensor_safe(self, subtensor: Any, timeout: float = CLOSE_TIM await asyncio.wait_for(subtensor.close(), timeout=timeout) else: subtensor.close() - except asyncio.TimeoutError: + except TimeoutError: logger.warning("⏱️ Timeout closing subtensor after %.0fs", timeout) except asyncio.CancelledError: raise @@ -135,7 +135,7 @@ async def _restart_subtensor(self) -> None: new_subtensor = bt.async_subtensor(network=network) try: await asyncio.wait_for(new_subtensor.initialize(), timeout=INIT_TIMEOUT) - except (asyncio.TimeoutError, asyncio.CancelledError): + except (TimeoutError, asyncio.CancelledError): logger.error("❌ Subtensor init failed, cleaning up") await self._close_subtensor_safe(new_subtensor, timeout=5.0) raise @@ -266,7 +266,7 @@ async def _call_with_retry( result = await self._attempt_call(method, args, kwargs, timeout) self._handle_success(method_name, args, result, retry) return result - except asyncio.TimeoutError: + except TimeoutError: await self._handle_retry_backoff( "Timeout", method_name, @@ -407,7 +407,7 @@ async def create_subtensor(*, resilient: bool = True) -> bt.subtensor | Resilien ), timeout=INIT_TIMEOUT, ) - except asyncio.TimeoutError: + except TimeoutError: logger.error("❌ Subtensor init timed out after %.0fs", INIT_TIMEOUT) if hasattr(subtensor, "close"): try: diff --git a/grail/mining/config.py b/grail/mining/config.py index 1eaa5042..5f120e8e 100644 --- a/grail/mining/config.py +++ b/grail/mining/config.py @@ -19,8 +19,7 @@ class PipelineConfig: ``GRAIL_PIPELINE_``. """ - enabled: bool = False # GRAIL_PIPELINE_ENABLED - backend: str = "vllm" # GRAIL_PIPELINE_BACKEND (vllm|sglang) + backend: str = "sglang" # GRAIL_PIPELINE_BACKEND (sglang|vllm) vllm_gpu: int = 0 # GRAIL_PIPELINE_VLLM_GPU (first GPU for vLLM) vllm_tp: int = ( 1 # GRAIL_PIPELINE_VLLM_TP (tensor parallel size, uses consecutive GPUs from vllm_gpu) @@ -62,7 +61,6 @@ def _str(key: str, default: str) -> str: return val if val else default return cls( - enabled=_bool("GRAIL_PIPELINE_ENABLED", cls.enabled), backend=_str("GRAIL_PIPELINE_BACKEND", cls.backend), vllm_gpu=_int("GRAIL_PIPELINE_VLLM_GPU", cls.vllm_gpu), vllm_tp=_int("GRAIL_PIPELINE_VLLM_TP", cls.vllm_tp), diff --git a/grail/mining/engine.py b/grail/mining/engine.py index 4a70b9ef..b91cde62 100644 --- a/grail/mining/engine.py +++ b/grail/mining/engine.py @@ -65,6 +65,7 @@ async def generate_grpo_group( *, batch_size: int | None = None, seed: int | None = None, + gen_params: GenerationParams | None = None, ) -> list[GRPORollout]: """Generate a full GRPO group with parallel proof + kernel eval. @@ -83,6 +84,10 @@ async def generate_grpo_group( wallet: Bittensor wallet for signing batch_size: If None, uses ``count`` (vLLM handles parallelism) seed: Optional seed for environment reset + gen_params: Per-call override for generation parameters. Defaults + to the engine's ``_gen_params`` (set at construction). Pass + an explicit ``GenerationParams`` here to enforce a checkpoint's + ``max_tokens`` / temperature without rebuilding the engine. Returns: List of GRPORollout with advantages computed @@ -92,6 +97,7 @@ async def generate_grpo_group( backend = self._weight_sync.get_backend() tokenizer = self._proof_worker.tokenizer + active_gen_params = gen_params if gen_params is not None else self._gen_params all_batch_data: list[tuple[list[int], int, float, dict]] = [] all_proof_results: list[tuple[list[dict], list[float], bytes, dict, str]] = [] @@ -136,7 +142,7 @@ async def generate_grpo_group( # 3. Generate via backend (GPU 0) gen_start = time.time() - gen_results = await backend.generate(prompt_ids_batch, params=self._gen_params) + gen_results = await backend.generate(prompt_ids_batch, params=active_gen_params) gen_time = time.time() - gen_start total_gen_time += gen_time @@ -237,9 +243,11 @@ def _eval_single( ) all_proof_results.extend(proof_results) except Exception: - logger.exception("Proof computation failed") - # Remove the batch data for failed proofs - all_batch_data = all_batch_data[: -len(batch_data)] + logger.exception( + "Proof computation failed; discarding ENTIRE group " + "(partial groups would hard-fail validator group_size check)" + ) + return [] # Check for infrastructure eval errors — if ANY rollout in the group # has an infra error, discard the ENTIRE group. Reducing group size diff --git a/grail/mining/proof_worker.py b/grail/mining/proof_worker.py index b868742e..dfde7087 100644 --- a/grail/mining/proof_worker.py +++ b/grail/mining/proof_worker.py @@ -1,7 +1,8 @@ """Proof computation worker for pipelined mining. -Manages an HF model on a dedicated proof GPU (GPU 1) and computes -GRAIL commitments + logprobs for completed rollouts. +Manages an HF model on a dedicated proof GPU and computes GRAIL commitments ++ chosen-token logprobs for completed rollouts. Always runs the GPU +log_softmax path (no env-var toggle). """ from __future__ import annotations @@ -20,8 +21,9 @@ class ProofWorker: """Manages the HF model on a dedicated proof GPU. Loads the model to ``cuda:{config.proof_gpu}`` and exposes a method - that mirrors ``AgentEnvLoop._batch_compute_commitments_and_logprobs`` - but delegates to the shared ``compute_proofs()`` function. + that delegates to the shared ``compute_proofs()`` function with + ``gpu_logprobs=True`` so log_softmax + gather run on the proof GPU + and only chosen-token logprobs cross PCIe. """ def __init__(self, config: PipelineConfig) -> None: @@ -59,10 +61,15 @@ def load_model(self, checkpoint_path: str) -> None: str(checkpoint_path), device=self._device, eval_mode=True, - use_flash_attention=self._config.proof_flash_attention, ) self._tokenizer = get_tokenizer(str(checkpoint_path)) - assert self._model is not None, "get_model() returned None" + if self._model is None: + from ..protocol.errors import ProtocolViolationError + + raise ProtocolViolationError( + f"get_model({checkpoint_path}) returned None; " + "ProofWorker cannot start without a loaded HF model" + ) self._hidden_dim = resolve_hidden_size(self._model) logger.info( "ProofWorker: model loaded (hidden_dim=%d, device=%s)", @@ -134,6 +141,7 @@ def compute_commitments_and_logprobs( prompt_lens, randomness_hex, wallet, + gpu_logprobs=True, ) def shutdown(self) -> None: diff --git a/grail/mining/weight_sync.py b/grail/mining/weight_sync.py index e4e574bf..9966010d 100644 --- a/grail/mining/weight_sync.py +++ b/grail/mining/weight_sync.py @@ -82,10 +82,17 @@ async def start(self, checkpoint_path: str) -> None: stream_server_logs=True, ) + # Inherit the parent env so SGLang's child processes can find system + # binaries (gcc, nvidia-smi, etc.). Triton's lazy JIT during CUDA-graph + # capture calls shutil.which("gcc"), which returns None if PATH is + # stripped, surfacing as a misleading "Capture cuda graph failed" + # error. Override only CUDA_VISIBLE_DEVICES to pin the GPU. + sglang_env = os.environ.copy() + sglang_env["CUDA_VISIBLE_DEVICES"] = str(self._config.vllm_gpu) server_config = ServerConfig( model_path=checkpoint_path, timeout_s=self._config.server_timeout, - env={"CUDA_VISIBLE_DEVICES": str(self._config.vllm_gpu)}, + env=sglang_env, ) self._manager = SGLangServerManager(config=server_config, eval_config=eval_config) @@ -98,6 +105,7 @@ async def start(self, checkpoint_path: str) -> None: tokenizer=self._tokenizer, timeout=self._config.server_timeout, max_concurrent_requests=self._config.max_concurrent_requests, + max_model_len=self._config.max_model_len, ) logger.info("SGLang generation server started at %s", self._manager.base_url) @@ -235,17 +243,20 @@ async def start(self, checkpoint_path: str) -> None: tp = self._config.vllm_tp vllm_gpu_ids = ",".join(str(self._config.vllm_gpu + i) for i in range(tp)) + # Inherit the parent env so vLLM's child processes can find system + # binaries (gcc, nvidia-smi, etc.). See the SGLang branch above for the + # full rationale — same Triton/JIT failure mode applies. + vllm_env = os.environ.copy() + vllm_env["CUDA_VISIBLE_DEVICES"] = vllm_gpu_ids + vllm_env["PYTORCH_CUDA_ALLOC_CONF"] = "" + vllm_env["VLLM_SERVER_DEV_MODE"] = "1" server_config = ServerConfig( model_path=self._symlink_path, timeout_s=self._config.server_timeout, tokenizer_name=tokenizer_name, enable_sleep_mode=True, tensor_parallel_size=tp, - env={ - "CUDA_VISIBLE_DEVICES": vllm_gpu_ids, - "PYTORCH_CUDA_ALLOC_CONF": "", - "VLLM_SERVER_DEV_MODE": "1", - }, + env=vllm_env, ) self._manager = VLLMServerManager( diff --git a/grail/model/fa4_attention.py b/grail/model/fa4_attention.py new file mode 100644 index 00000000..4da3ac8a --- /dev/null +++ b/grail/model/fa4_attention.py @@ -0,0 +1,207 @@ +"""FlashAttention 4 integration via HF Transformers AttentionInterface. + +Registers FA4 as a native attention backend ('flash_attention_4') that HF's +Qwen3 attention module dispatches to directly, bypassing the FA2 wrapper code. + +FA4 (flash-attn-4) provides Blackwell-native (SM100) attention kernels using +CuTeDSL, UMMA instructions, and TMEM for up to 71% GPU utilization on B200. +""" + +from __future__ import annotations + +import logging +from typing import Any + +import torch + +logger = logging.getLogger(__name__) + +_fa4_dispatch_logged = False + + +def register_fa4_attention() -> None: + """Register FlashAttention 4 as 'flash_attention_4' in HF's attention dispatch. + + Must be called BEFORE model loading so that the model's attention layers + dispatch to our handler at forward time. + + Tensors arrive as ``[B, H, S, D]`` from HF and are transposed to + ``[B, S, H, D]`` for FA4. The output stays ``[B, S, H, D]`` (no transpose + back), matching HF's convention where the caller reshapes via + ``attn_output.reshape(*input_shape, -1)`` with ``input_shape = [B, S]``. + """ + from flash_attn.cute import flash_attn_func as fa4_func # type: ignore[import-not-found] + from flash_attn.cute import ( # type: ignore[import-not-found] + flash_attn_varlen_func as fa4_varlen, + ) + from transformers.modeling_flash_attention_utils import ( + prepare_fa_kwargs_from_position_ids, + ) + from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS + + def _fa4_forward( + module: torch.nn.Module, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + attention_mask: torch.Tensor | None, + dropout: float = 0.0, + scaling: float | None = None, + sliding_window: int | None = None, + softcap: float | None = None, + **kwargs: Any, + ) -> tuple[torch.Tensor, None]: + query = query.transpose(1, 2) + key = key.transpose(1, 2) + value = value.transpose(1, 2) + + fa4_kwargs: dict[str, Any] = {"causal": True, "softmax_scale": scaling} + if softcap is not None and softcap > 0.0: + fa4_kwargs["softcap"] = softcap + + cu_seqlens_q, cu_seqlens_k = _detect_packing( + kwargs, query.shape[0], prepare_fa_kwargs_from_position_ids + ) + + _log_first_dispatch(query, attention_mask, cu_seqlens_q, kwargs) + + if cu_seqlens_q is not None: + out = _varlen_path( + query, key, value, cu_seqlens_q, cu_seqlens_k, fa4_varlen, fa4_kwargs + ) + elif attention_mask is not None: + out = _padded_path(query, key, value, attention_mask, fa4_varlen, fa4_kwargs) + else: + out, _ = fa4_func(query, key, value, **fa4_kwargs) + + return out, None + + ALL_ATTENTION_FUNCTIONS.register("flash_attention_4", _fa4_forward) + logger.info("Registered flash_attention_4 via AttentionInterface") + + +def _detect_packing( + kwargs: dict[str, Any], + batch_size: int, + prepare_fn: Any, +) -> tuple[torch.Tensor | None, torch.Tensor | None]: + """Detect packed sequences from kwargs, returning cu_seqlens or (None, None).""" + cu_seqlens_q = kwargs.get("cu_seq_lens_q") + cu_seqlens_k = kwargs.get("cu_seq_lens_k") + + if cu_seqlens_q is not None: + return cu_seqlens_q, cu_seqlens_k + + position_ids = kwargs.get("position_ids") + if position_ids is not None and batch_size == 1: + try: + (cu_seqlens_q, cu_seqlens_k), _ = prepare_fn(position_ids) + return cu_seqlens_q, cu_seqlens_k + except Exception: # noqa: BLE001 + logger.debug("Failed to detect packing from position_ids, using dense path") + + return None, None + + +def _log_first_dispatch( + query: torch.Tensor, + attention_mask: torch.Tensor | None, + cu_seqlens_q: torch.Tensor | None, + kwargs: dict[str, Any], +) -> None: + """Log the first FA4 dispatch for debugging (once per process).""" + global _fa4_dispatch_logged # noqa: PLW0603 + if _fa4_dispatch_logged: + return + _fa4_dispatch_logged = True + + position_ids = kwargs.get("position_ids") + logger.info( + "FA4 dispatch: query=%s mask=%s cu_seqlens=%s pos_ids=%s", + list(query.shape), + list(attention_mask.shape) if attention_mask is not None else None, + cu_seqlens_q is not None, + list(position_ids.shape) if position_ids is not None else None, + ) + + +def _varlen_path( + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + cu_seqlens_q: torch.Tensor, + cu_seqlens_k: torch.Tensor | None, + fa4_varlen: Any, + fa4_kwargs: dict[str, Any], +) -> torch.Tensor: + """Packed/variable-length attention via flash_attn_varlen_func.""" + B, S, H, D = query.shape + out, _ = fa4_varlen( + query.reshape(-1, H, D), + key.reshape(-1, key.shape[2], D), + value.reshape(-1, value.shape[2], D), + cu_seqlens_q=cu_seqlens_q, + cu_seqlens_k=cu_seqlens_k, + **fa4_kwargs, + ) + return out.view(B, S, H, D) + + +def _padded_path( + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + attention_mask: torch.Tensor, + fa4_varlen: Any, + fa4_kwargs: dict[str, Any], +) -> torch.Tensor: + """Padded attention: unpad real tokens, run varlen, repad.""" + from flash_attn.bert_padding import pad_input # type: ignore[import-not-found] + + batch_size = attention_mask.shape[0] + query_length = query.shape[1] + + q, k, v, indices_q, cu_q, cu_k = _unpad_qkv( + query, key, value, attention_mask, query_length, batch_size + ) + out_unpad, _ = fa4_varlen(q, k, v, cu_seqlens_q=cu_q, cu_seqlens_k=cu_k, **fa4_kwargs) + return pad_input(out_unpad, indices_q, batch_size, query_length) + + +def _unpad_qkv( + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + attention_mask: torch.Tensor, + query_length: int, + batch_size: int, +) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + """Remove padding from Q/K/V using attention_mask, return varlen-ready tensors.""" + indices_k = attention_mask.flatten().nonzero(as_tuple=False).flatten() + cu_seqlens_k = torch.nn.functional.pad( + attention_mask.sum(dim=1, dtype=torch.int32).cumsum(0, dtype=torch.int32), + (1, 0), + ) + seq_len = attention_mask.shape[1] + + if query_length == seq_len: + indices_q = indices_k + cu_seqlens_q = cu_seqlens_k + q = query.reshape(-1, query.shape[-2], query.shape[-1])[indices_k] + elif query_length == 1: + cu_seqlens_q = torch.arange(batch_size + 1, dtype=torch.int32, device=query.device) + indices_q = torch.arange(batch_size, dtype=torch.long, device=query.device) + q = query.squeeze(1) + else: + attention_mask_q = attention_mask[:, -query_length:] + indices_q = attention_mask_q.flatten().nonzero(as_tuple=False).flatten() + cu_seqlens_q = torch.nn.functional.pad( + attention_mask_q.sum(dim=1, dtype=torch.int32).cumsum(0, dtype=torch.int32), + (1, 0), + ) + q = query.reshape(-1, query.shape[-2], query.shape[-1])[indices_q] + + k = key.reshape(-1, key.shape[-2], key.shape[-1])[indices_k] + v = value.reshape(-1, value.shape[-2], value.shape[-1])[indices_k] + + return q, k, v, indices_q, cu_seqlens_q, cu_seqlens_k diff --git a/grail/model/provider.py b/grail/model/provider.py index 851f1dfa..26981c35 100644 --- a/grail/model/provider.py +++ b/grail/model/provider.py @@ -9,6 +9,7 @@ import gc import json import logging +import os from pathlib import Path from typing import Any @@ -58,7 +59,6 @@ def get_model( device: str | None = None, use_safetensors: bool = True, eval_mode: bool = True, - use_flash_attention: bool = False, checkpoint_window: int | None = None, ) -> Any: """Load model with consistent configuration. @@ -68,8 +68,6 @@ def get_model( device: Target device (e.g., "cuda", "cuda:0", "cpu", or None for auto-detect) use_safetensors: Whether to prefer safetensors format eval_mode: Whether to set model to eval() mode - use_flash_attention: Whether to use Flash Attention 2 (requires flash-attn package). - Only enabled for training, not for evaluation/inference. checkpoint_window: Optional checkpoint window number. If not provided, will be extracted from metadata.json or parsed from the path. @@ -78,9 +76,33 @@ def get_model( """ logger.debug(f"Loading model: {model_name}") - # Auto-detect device if not specified + # Auto-detect device if not specified. + # + # We intentionally refuse to fall back to CPU when the caller passes + # device=None and CUDA is not available. Silently loading a multi-billion + # parameter model on CPU saturates every core (PyTorch defaults to all + # visible threads), explodes RAM with FP32 activations, and blocks the + # asyncio event loop. That path froze a validator host in production + # (60 vCPUs pinned, ~150 GB RAM) when nvidia-container-toolkit was not + # wired into Docker. Fail loud instead, so the watchdog restarts us on a + # healthy host. + # + # Escape hatch: tests and dev scripts that genuinely want CPU must pass + # device="cpu" explicitly. if device is None: - device = "cuda" if torch.cuda.is_available() else "cpu" + if not torch.cuda.is_available(): + raise RuntimeError( + "get_model() called with device=None but torch.cuda.is_available() " + "is False. Refusing to silently fall back to CPU: loading a " + "multi-billion-parameter model on CPU will saturate every core and " + "exhaust host RAM. " + "Check that nvidia-container-toolkit is installed and wired to " + "Docker (`docker run --rm --gpus all nvidia/cuda:12.4.1-base-ubuntu22.04 " + "nvidia-smi`), that CUDA_VISIBLE_DEVICES is not empty, and that " + "`nvidia-smi` works on the host. " + 'If CPU is genuinely intended (tests, dev), pass device="cpu" explicitly.' + ) + device = "cuda" logger.debug(f"Auto-detected device: {device}") device_is_cuda = str(device).startswith("cuda") @@ -113,19 +135,63 @@ def get_model( except (ValueError, IndexError): pass - # Configure attention implementation + # Configure attention implementation from protocol constant. + # On CUDA: use ATTN_IMPLEMENTATION (FA2), fail loudly if flash-attn is missing. + # On CPU (tests, dev): use default SDPA (no proof computation happens on CPU). + # GRAIL_TRAINER_ATTN_IMPL overrides for training only (e.g., "sdpa" for torch.compile). + from ..protocol.constants import ATTN_IMPLEMENTATION + attn_implementation = None - if use_flash_attention and device_is_cuda: + trainer_attn_override = os.getenv("GRAIL_TRAINER_ATTN_IMPL") + fa4_registered = False + if trainer_attn_override: + if trainer_attn_override == "flash_attention_4": + # Load with FA2 (HF's import validation requires it), then swap the + # attention handler to FA4 via AttentionInterface after model creation. + attn_implementation = "flash_attention_2" + try: + from .fa4_attention import register_fa4_attention + + register_fa4_attention() + fa4_registered = True + except ImportError: + logger.warning("flash-attn-4 not installed, falling back to FA2") + else: + attn_implementation = trainer_attn_override + logger.info("Using attention implementation: %s (trainer override)", trainer_attn_override) + elif device_is_cuda and ATTN_IMPLEMENTATION: + attn_implementation = ATTN_IMPLEMENTATION + if ATTN_IMPLEMENTATION == "flash_attention_2": + try: + import flash_attn # type: ignore[import-not-found] # noqa: F401 + except ImportError as err: + raise RuntimeError( + "flash-attn is required but not installed. " + "GRAIL requires Flash Attention 2 for consistent proof verification. " + "Install with: uv pip install flash-attn --no-build-isolation" + ) from err + logger.info("Using attention implementation: %s", attn_implementation) + + # Apply Liger kernel optimizations (fused RMSNorm, RoPE, SwiGLU, CrossEntropy) + # Must be called BEFORE model loading as it monkey-patches the model class. + if os.getenv("GRAIL_TRAINER_USE_LIGER_KERNEL", "0") == "1": try: - import flash_attn # type: ignore[import-not-found] # noqa: F401 + from liger_kernel.transformers import ( # type: ignore[import-not-found] + apply_liger_kernel_to_qwen3, + ) - attn_implementation = "flash_attention_2" - logger.info("Using Flash Attention 2 for model loading") - except ImportError: - logger.warning( - "flash-attn not installed; falling back to default attention. " - "Install with: uv pip install flash-attn" + apply_liger_kernel_to_qwen3( + rope=True, + rms_norm=True, + swiglu=True, + cross_entropy=False, + fused_linear_cross_entropy=False, ) + logger.info("Liger kernel applied to Qwen3 (rope, rms_norm, swiglu)") + except ImportError: + logger.warning("GRAIL_TRAINER_USE_LIGER_KERNEL=1 but liger-kernel not installed") + except Exception as exc: # noqa: BLE001 + logger.warning("Failed to apply Liger kernel: %s", exc) # Load model with optimized attention if available model = AutoModelForCausalLM.from_pretrained( @@ -135,6 +201,15 @@ def get_model( torch_dtype=torch.bfloat16 if device_is_cuda else torch.float32, ) + # Swap attention dispatch to FA4 after loading (must be after from_pretrained + # because HF validates attn_implementation during loading). + if fa4_registered: + try: + model.config._attn_implementation = "flash_attention_4" # type: ignore[reportPrivateUsage] + logger.info("Model attention dispatch set to flash_attention_4 (FA4 native)") + except Exception as exc: # noqa: BLE001 + logger.warning("Failed to set FA4 attention dispatch: %s", exc) + # Preserve original model name for GRAIL proof validation model.name_or_path = original_model_name # type: ignore[attr-defined] diff --git a/grail/model/train_loading.py b/grail/model/train_loading.py index e7fcee12..9e2b1a68 100644 --- a/grail/model/train_loading.py +++ b/grail/model/train_loading.py @@ -20,7 +20,6 @@ from grail.model.provider import get_model, get_tokenizer from grail.shared.chat_templates import configure_tokenizer -from grail.shared.constants import TRAINER_USE_FLASH_ATTENTION ModelLoadMode = Literal["latest", "hf", "window"] @@ -155,22 +154,18 @@ async def load_training_artifacts( """Load (train_model, ref_model, tokenizer) per provided specs. - Trainer tokenizer is configured based on thinking mode. - - Train model loads with eval_mode=False and Flash Attention enabled; Ref model with eval_mode=True. - - Flash Attention 2 is enabled for training to optimize performance. + - Train model loads with eval_mode=False; Ref model with eval_mode=True. + - Both use the attention backend set by ATTN_IMPLEMENTATION constant (FA2 on CUDA). - Reference model loading can be skipped by setting load_ref_model=False (e.g., when KL is disabled). """ - # Resolve train source - enable Flash Attention for training if configured + # Resolve train source if train_spec.mode == "hf": train_model_id = train_spec.hf_id or "" - train_model = get_model( - train_model_id, eval_mode=False, use_flash_attention=TRAINER_USE_FLASH_ATTENTION - ) + train_model = get_model(train_model_id, eval_mode=False) tokenizer = get_tokenizer(train_model_id) else: train_ckpt = await _resolve_checkpoint(train_spec, checkpoint_manager) - train_model = get_model( - str(train_ckpt), eval_mode=False, use_flash_attention=TRAINER_USE_FLASH_ATTENTION - ) + train_model = get_model(str(train_ckpt), eval_mode=False) tokenizer = get_tokenizer(str(train_ckpt)) # Configure tokenizer template based on thinking mode @@ -178,7 +173,7 @@ async def load_training_artifacts( ref_model: Any | None = None if load_ref_model: - # Resolve ref source (independent) - no Flash Attention for reference model + # Resolve ref source (independent) if ref_spec.mode == "hf": ref_model_id = ref_spec.hf_id or "" ref_model = get_model(ref_model_id, eval_mode=True) diff --git a/grail/monitoring/backends/wandb_backend.py b/grail/monitoring/backends/wandb_backend.py index e99392ec..e87cd0e4 100644 --- a/grail/monitoring/backends/wandb_backend.py +++ b/grail/monitoring/backends/wandb_backend.py @@ -103,7 +103,7 @@ def _run_async(self, func: Any, *args: Any) -> Any: def _run_executor(self, func: Any, *args: Any) -> Any: """Run synchronous function in executor.""" - return asyncio.get_event_loop().run_in_executor(None, func, *args) + return asyncio.get_running_loop().run_in_executor(None, func, *args) def initialize(self, config: dict[str, Any]) -> None: """Initialize wandb backend synchronously (no network calls). diff --git a/grail/monitoring/manager.py b/grail/monitoring/manager.py index d7429255..d0cfc005 100644 --- a/grail/monitoring/manager.py +++ b/grail/monitoring/manager.py @@ -95,7 +95,7 @@ async def _periodic_flush(self) -> None: await asyncio.wait_for(self._shutdown_event.wait(), timeout=self._flush_interval) # If we get here, shutdown was requested break - except asyncio.TimeoutError: + except TimeoutError: # Timeout is expected - time to flush await self.flush_metrics() except Exception as e: diff --git a/grail/neurons/base.py b/grail/neurons/base.py index ec1f808e..dfdcbbba 100644 --- a/grail/neurons/base.py +++ b/grail/neurons/base.py @@ -25,7 +25,7 @@ from ..infrastructure.network import create_subtensor from ..logging_utils import dump_asyncio_stacks -from ..shared.constants import WINDOW_LENGTH +from ..protocol.constants import WINDOW_LENGTH from ..shared.subnet import get_own_uid_on_subnet logger = logging.getLogger(__name__) diff --git a/grail/neurons/miner.py b/grail/neurons/miner.py index cd6cbde2..df00c331 100644 --- a/grail/neurons/miner.py +++ b/grail/neurons/miner.py @@ -4,6 +4,7 @@ import contextlib import json import logging +import os import time as _time_mod import traceback from types import SimpleNamespace @@ -14,7 +15,6 @@ from grail.cli.mine import ( MiningTimers, _pipelined_generation_loop, - generate_rollouts_for_window, get_conf, get_window_randomness, has_time_for_next_generation, @@ -34,7 +34,8 @@ from grail.model.provider import clear_model_and_tokenizer, get_model, get_tokenizer from grail.monitoring import get_monitoring_manager from grail.monitoring.config import MonitoringConfig -from grail.shared.constants import TRAINER_UID, WINDOW_LENGTH +from grail.protocol.constants import TRAINER_UID, WINDOW_LENGTH +from grail.protocol.errors import ProtocolViolationError from grail.shared.window_utils import ( WindowWaitTracker, calculate_next_window, @@ -156,19 +157,35 @@ def _cleanup_execution_pool() -> None: keep_limit=2, # Keep only current + previous window ) - # Pipeline state (initialized on first checkpoint load if enabled) + # Pipeline state (initialized on first checkpoint load) pipeline_config = PipelineConfig.from_env() pipeline_engine = None weight_sync = None proof_worker = None - if pipeline_config.enabled: - logger.info( - "Pipeline mode ENABLED: backend=%s, gen_gpu=%d, proof_gpu=%d", - pipeline_config.backend, - pipeline_config.vllm_gpu, - pipeline_config.proof_gpu, - ) + # Validate the pipeline GPU layout against visible CUDA devices + # before we attempt any model load. The pipeline is mandatory and + # needs distinct GPUs for generation and proof; surfacing the + # mismatch loudly here is much clearer than the cryptic + # "invalid device ordinal" we'd otherwise get from get_model(). + if torch.cuda.is_available(): + n_devs = torch.cuda.device_count() + if pipeline_config.proof_gpu >= n_devs or pipeline_config.vllm_gpu >= n_devs: + raise ProtocolViolationError( + f"Pipeline GPU layout is invalid: vllm_gpu=" + f"{pipeline_config.vllm_gpu}, proof_gpu=" + f"{pipeline_config.proof_gpu}, but only {n_devs} CUDA " + "device(s) visible. Set GRAIL_PIPELINE_VLLM_GPU and " + "GRAIL_PIPELINE_PROOF_GPU to valid indices, or expose " + "more devices via CUDA_VISIBLE_DEVICES." + ) + + logger.info( + "Pipeline: backend=%s, gen_gpu=%d, proof_gpu=%d", + pipeline_config.backend, + pipeline_config.vllm_gpu, + pipeline_config.proof_gpu, + ) # Initialize monitoring for mining operations monitor = get_monitoring_manager() @@ -289,17 +306,13 @@ def _cleanup_execution_pool() -> None: tokenizer = proof_worker.tokenizer else: model, tokenizer = clear_model_and_tokenizer(model, tokenizer) - # Pipeline: load model to proof GPU to keep gen GPU free - model_device = ( - f"cuda:{pipeline_config.proof_gpu}" - if pipeline_config.enabled - else None - ) + # Pipeline is mandatory: model lives on the proof GPU + # so the proof worker can take ownership without a + # second copy and the gen GPU(s) stay free for SGLang. model = get_model( str(checkpoint_path), - device=model_device, + device=f"cuda:{pipeline_config.proof_gpu}", eval_mode=True, - use_flash_attention=False, ) tokenizer = get_tokenizer(str(checkpoint_path)) @@ -340,20 +353,24 @@ def _cleanup_execution_pool() -> None: last_window_start = window_start # Prevent infinite loop continue - # Initialize pipeline on first successful checkpoint load - if ( - pipeline_config.enabled - and pipeline_engine is None - and checkpoint_path is not None - ): - try: - from grail.mining.engine import PipelinedMiningEngine - from grail.mining.proof_worker import ProofWorker - from grail.mining.weight_sync import ( - SGLangWeightSync, - VLLMWeightSync, - ) + # Initialize pipeline on first successful checkpoint load. + # The pipeline is the only generation path. Init is a one-shot + # setup phase: a failure here means the SGLang/vLLM backend + # can't come up (missing dep, port collision, GPU OOM at + # startup). Retrying in the outer ``except Exception`` would + # silently spin forever while heartbeats keep firing — the + # watchdog would never see it. Convert init failures to + # ``SystemExit`` so they bypass the outer catch-all and let + # the supervisor (docker / systemd) restart the process. + if pipeline_engine is None and checkpoint_path is not None: + from grail.mining.engine import PipelinedMiningEngine + from grail.mining.proof_worker import ProofWorker + from grail.mining.weight_sync import ( + SGLangWeightSync, + VLLMWeightSync, + ) + try: proof_worker = ProofWorker(pipeline_config) proof_worker.set_model(model, tokenizer) @@ -361,87 +378,137 @@ def _cleanup_execution_pool() -> None: weight_sync = SGLangWeightSync( pipeline_config, proof_worker.tokenizer ) - else: + elif pipeline_config.backend == "vllm": weight_sync = VLLMWeightSync( pipeline_config, proof_worker.tokenizer ) + else: + raise ValueError( + f"Unknown GRAIL_PIPELINE_BACKEND=" + f"{pipeline_config.backend!r}; " + "must be 'sglang' or 'vllm'" + ) await weight_sync.start(str(checkpoint_path)) pipeline_engine = PipelinedMiningEngine( pipeline_config, weight_sync, proof_worker ) - logger.info("Pipeline engine initialized successfully") - - # Register cleanup callbacks - async def _shutdown_pipeline( - _ws=weight_sync, - _pe=pipeline_engine, - ) -> None: - if _ws is not None: - await _ws.shutdown() - if _pe is not None: - _pe.shutdown() - - def _schedule_pipeline_shutdown() -> None: - asyncio.ensure_future(_shutdown_pipeline()) - - self.register_shutdown_callback(_schedule_pipeline_shutdown) - except Exception as pipe_exc: - logger.error( - "Failed to initialize pipeline, falling back to " - "single-GPU mode: %s", - pipe_exc, - exc_info=True, + except Exception as init_exc: + logger.exception( + "Pipeline init failed (backend=%s); exiting so " + "supervisor restarts the miner", + pipeline_config.backend, ) - pipeline_engine = None - weight_sync = None - proof_worker = None + raise SystemExit(1) from init_exc + logger.info("Pipeline engine initialized successfully") + + # Register cleanup callbacks + async def _shutdown_pipeline( + _ws=weight_sync, + _pe=pipeline_engine, + ) -> None: + if _ws is not None: + await _ws.shutdown() + if _pe is not None: + _pe.shutdown() + + def _schedule_pipeline_shutdown() -> None: + asyncio.ensure_future(_shutdown_pipeline()) + + self.register_shutdown_callback(_schedule_pipeline_shutdown) + + # Fetch checkpoint metadata for environment and generation + # configuration. The trainer is the SINGLE source of truth + # for env_id, env_params, generation_params, and + # thinking_mode. Any failure to resolve these (R2 down, + # legacy schema, trainer misconfigured) MUST skip the + # entire window — falling through with defaults would + # silently let the miner generate against an unverifiable + # protocol baseline (the same class of bug the validator + # fix closes on its side). + if current_checkpoint_window is None: + logger.error( + "current_checkpoint_window is None — cannot resolve trainer " + "env config; skipping window %s", + window_start, + ) + last_window_start = window_start + continue - # Fetch checkpoint metadata for environment and generation configuration - env_id = None - env_params = {} - generation_params = {} + try: + checkpoint_metadata = await checkpoint_manager.get_checkpoint_metadata( + current_checkpoint_window + ) + except Exception as e: + logger.error( + "Failed to fetch checkpoint metadata for window %s: %s. " + "Skipping window — trainer/R2 may be unhealthy.", + current_checkpoint_window, + e, + exc_info=True, + ) + last_window_start = window_start + continue - if current_checkpoint_window is not None: - try: - checkpoint_metadata = await checkpoint_manager.get_checkpoint_metadata( - current_checkpoint_window - ) - if checkpoint_metadata: - missing = checkpoint_metadata.validate_metadata() - if missing: - logger.error( - "Checkpoint %s missing required metadata: %s. " - "Skipping window — trainer may be misconfigured.", - current_checkpoint_window, - ", ".join(missing), - ) - last_window_start = window_start - continue - - env_id = checkpoint_metadata.env_id - env_params = checkpoint_metadata.env_params or {} - generation_params = checkpoint_metadata.generation_params or {} - logger.info( - f"Using checkpoint config: env_id={env_id}, " - f"generation_params={generation_params}" - ) - else: - logger.warning( - f"No metadata found for checkpoint window {current_checkpoint_window}, " - f"using defaults" - ) - except Exception as e: - logger.warning( - f"Failed to fetch checkpoint metadata (window={current_checkpoint_window}): {e}", - exc_info=True, - ) - # Continue with defaults on error - else: - logger.warning( - "current_checkpoint_window is None, using default env config" + if checkpoint_metadata is None: + logger.error( + "No metadata found for checkpoint window %s. Skipping window.", + current_checkpoint_window, + ) + last_window_start = window_start + continue + + missing = checkpoint_metadata.validate_metadata() + if missing: + logger.error( + "Checkpoint %s missing required metadata: %s. " + "Skipping window — trainer may be misconfigured.", + current_checkpoint_window, + ", ".join(missing), + ) + last_window_start = window_start + continue + + env_id = checkpoint_metadata.env_id + env_params = checkpoint_metadata.env_params or {} + generation_params = checkpoint_metadata.generation_params or {} + thinking_mode = checkpoint_metadata.thinking_mode + if not thinking_mode: + # validate_metadata() guarantees this is non-None and + # one of {"native", "instructed"}, so this branch is a + # belt-and-suspenders against future schema drift. + # Use an explicit raise (not bare assert: -O strips it). + raise ProtocolViolationError( + f"Checkpoint {current_checkpoint_window} validate_metadata() " + f"passed but thinking_mode is empty: {thinking_mode!r}" ) + # Override the process-wide thinking mode from checkpoint + # metadata so all subsequent ``apply_chat_template`` / + # ``get_thinking_config`` calls (engine.py prompt + # rendering, parsers, etc.) see the trainer-published + # value, not whatever happened to be in the local shell + # ``GRAIL_THINKING_MODE``. This is the trust boundary that + # keeps miner and validator in sync when their hosts + # diverge (e.g. validator running in a docker container + # that didn't inherit the var). Must run BEFORE + # ``_pipelined_generation_loop`` renders any prompt. + if os.environ.get("GRAIL_THINKING_MODE") != thinking_mode: + logger.info( + "Overriding GRAIL_THINKING_MODE from checkpoint metadata: " + "prev=%s, new=%s", + os.environ.get("GRAIL_THINKING_MODE", ""), + thinking_mode, + ) + os.environ["GRAIL_THINKING_MODE"] = thinking_mode + logger.info( + "Using checkpoint config: env_id=%s, thinking_mode=%s, " + "generation_params=%s", + env_id, + thinking_mode, + generation_params, + ) + logger.info( f"🔥 Starting inference generation for window " f"{window_start}-{window_start + WINDOW_LENGTH - 1}" @@ -458,40 +525,29 @@ def _schedule_pipeline_shutdown() -> None: self.use_drand, ) - if pipeline_engine is not None: - inferences = await _pipelined_generation_loop( - pipeline_engine, - wallet, - model, - tokenizer, - subtensor, - window_start, - window_block_hash, - combined_randomness, - timers, - monitor, - self.use_drand, - current_checkpoint_window, - env_id=env_id, - env_params=env_params, - ) - else: - inferences = await generate_rollouts_for_window( - wallet, - model, - tokenizer, - subtensor, - window_start, - window_block_hash, - combined_randomness, - timers, - monitor, - self.use_drand, - current_checkpoint_window, - env_id=env_id, - env_params=env_params, - generation_params=generation_params, + # Pipeline is the only generation path; init must have + # succeeded above or the loop body would have raised. + if pipeline_engine is None: + raise RuntimeError( + "Pipeline engine not initialized; cannot generate rollouts." ) + inferences = await _pipelined_generation_loop( + pipeline_engine, + wallet, + model, + tokenizer, + subtensor, + window_start, + window_block_hash, + combined_randomness, + timers, + monitor, + self.use_drand, + current_checkpoint_window, + env_id=env_id, + env_params=env_params, + generation_params=generation_params, + ) if inferences: logger.info( @@ -526,6 +582,18 @@ def _schedule_pipeline_shutdown() -> None: await checkpoint_manager.cleanup_local(window_start) except asyncio.CancelledError: break + except ProtocolViolationError as proto_exc: + # Window-scoped data error: bad checkpoint metadata, missing + # required field, etc. Skip this window cleanly so the next + # one can try a fresh checkpoint. No subtensor reconnect, + # no 10s sleep — the next window will arrive soon enough. + logger.warning( + "Skipping window %s due to protocol violation: %s", + window_start, + proto_exc, + ) + last_window_start = window_start + continue except Exception as e: traceback.print_exc() logger.error(f"Error in miner loop: {e}. Continuing ...") diff --git a/grail/neurons/trainer.py b/grail/neurons/trainer.py index 83ad483b..76e2b1c1 100644 --- a/grail/neurons/trainer.py +++ b/grail/neurons/trainer.py @@ -21,13 +21,11 @@ from grail.infrastructure.checkpoint_consumer import default_checkpoint_cache_root from grail.model.provider import get_model, get_tokenizer from grail.model.train_loading import ModelLoadSpec -from grail.shared.constants import ( - CURRENT_ENV_ID, +from grail.protocol.constants import CURRENT_ENV_ID, WINDOW_LENGTH +from grail.shared.config import ( NETUID, SNAPSHOT_POLL_INTERVAL_SECONDS, - TRAINER_USE_FLASH_ATTENTION, TRAINING_HEARTBEAT_TIMEOUT_SECONDS, - WINDOW_LENGTH, ) from grail.trainer.checkpoint_publisher import CheckpointPublisher from grail.trainer.config import EvalConfig, TrainingConfig @@ -193,7 +191,17 @@ async def run(self) -> None: # ──────────────────────────────────────────────────────────────────────────── def _start_training_process(self) -> None: - """Spawn training process for continuous training.""" + """Spawn training process(es) for continuous training. + + Checks ``GRAIL_DIST_NPROC`` env var. If > 1, spawns N distributed + training processes via ``run_distributed_training_process``. + Otherwise, spawns a single process via ``run_training_process``. + """ + nproc = int(os.getenv("GRAIL_DIST_NPROC", "1")) + if nproc > 1: + self._start_distributed_training(nproc) + return + wallet_args = self._serialize_wallet() monitor_config = self._prepare_monitor_config(subprocess_label="training_process") @@ -215,6 +223,55 @@ def _start_training_process(self) -> None: self._training_process.start() logger.info("Training process started (PID=%d)", self._training_process.pid) + def _start_distributed_training(self, nproc: int) -> None: + """Spawn N distributed training processes via multiprocessing.Process. + + Each process receives the same ``IPCChannels`` object. Only rank 0 + interacts with IPC (heartbeat, stop, snapshots). Other ranks follow + rank 0 via NCCL broadcast. + + Evaluation is disabled in distributed mode (remote eval handles it). + + Args: + nproc: Number of training processes (one per GPU). + """ + from grail.trainer.distributed.config import DistributedConfig + from grail.trainer.distributed.launcher import run_distributed_training_process + + # Disable local evaluation in distributed mode + self._eval_cfg.enabled = False + logger.info("Distributed training: nproc=%d, local evaluation disabled", nproc) + + wallet_args = self._serialize_wallet() + monitor_config = self._prepare_monitor_config(subprocess_label="distributed_training") + dist_cfg = DistributedConfig.from_env() + + self._distributed_processes: list[multiprocessing.Process] = [] + + for rank in range(nproc): + p = multiprocessing.Process( + target=run_distributed_training_process, + args=( + rank, + nproc, + self._ipc, + self._snapshot_manager, + self._train_cfg, + dist_cfg, + self._context.credentials, + wallet_args, + monitor_config, + self._context.verbosity, + self._context.test_mode, + ), + ) + p.start() + logger.info("Distributed rank %d started (PID=%d)", rank, p.pid) + self._distributed_processes.append(p) + + # Point _training_process to rank 0 for health checks + self._training_process = self._distributed_processes[0] + def _start_upload_worker(self) -> None: """Spawn upload worker process for async uploads.""" wallet_args = self._serialize_wallet() @@ -241,7 +298,13 @@ async def _shutdown_processes(self) -> None: self._ipc.stop.set() - await self._shutdown_process(self._training_process, "Training") + # Shutdown distributed training processes (if any) + if hasattr(self, "_distributed_processes"): + for i, p in enumerate(self._distributed_processes): + await self._shutdown_process(p, f"Rank-{i}") + else: + await self._shutdown_process(self._training_process, "Training") + await self._shutdown_process(self._upload_process, "Upload") logger.info("Child processes shut down") @@ -353,7 +416,15 @@ async def _get_current_window(self) -> int: async def _check_process_health(self) -> None: """Monitor training and upload process health.""" - if self._training_process and not self._training_process.is_alive(): + if hasattr(self, "_distributed_processes"): + for i, p in enumerate(self._distributed_processes): + if not p.is_alive(): + logger.error( + "Distributed rank %d died (PID=%s) - system should restart", + i, + p.pid, + ) + elif self._training_process and not self._training_process.is_alive(): logger.error("Training process died - system should restart") if self._upload_process and not self._upload_process.is_alive(): @@ -456,7 +527,7 @@ async def _wait_for_training_pause(self) -> bool: timeout = PAUSE_CONFIRMATION_TIMEOUT_SECONDS # Use Event.wait() with timeout in thread pool to avoid blocking event loop - loop = asyncio.get_event_loop() + loop = asyncio.get_running_loop() while True: # Check for shutdown request @@ -724,7 +795,6 @@ def _load_evaluation_resources( str(snapshot_path), device="cuda", eval_mode=True, - use_flash_attention=TRAINER_USE_FLASH_ATTENTION, ) logger.info(f" ✓ Model loaded in {time.time() - model_start:.2f}s") diff --git a/grail/neurons/validator.py b/grail/neurons/validator.py index 9e3e8fdc..5bce893a 100644 --- a/grail/neurons/validator.py +++ b/grail/neurons/validator.py @@ -19,15 +19,15 @@ from grail.logging_utils import flush_all_logs from grail.monitoring import get_monitoring_manager from grail.monitoring.config import MonitoringConfig -from grail.scoring import WeightComputer -from grail.shared.constants import ( +from grail.protocol.constants import ( GRAIL_BURN_PERCENTAGE, GRAIL_BURN_UID, - NETUID, SUPERLINEAR_EXPONENT, UNIQUE_ROLLOUTS_CAP_ENABLED, WINDOW_LENGTH, ) +from grail.scoring import WeightComputer +from grail.shared.config import NETUID from grail.validation import create_env_validation_pipeline from grail.validation.service import ( WEIGHT_ROLLING_WINDOWS, diff --git a/grail/protocol/constants.py b/grail/protocol/constants.py new file mode 100644 index 00000000..88ffcb4e --- /dev/null +++ b/grail/protocol/constants.py @@ -0,0 +1,214 @@ +"""GRAIL Protocol Constants. + +Immutable values that all network participants must agree on. Miner-validator +proof verification, reward computation, and incentive scoring depend on these. + +Rules: +- No os.getenv() overrides. These are compile-time constants. +- Changes require bumping GRAIL_PROOF_VERSION and coordinated deployment. +- Each constant has a comment explaining what breaks if you change it. +""" + +# ──────────────── GRAIL PROOF VERSION ──────────────── + +# Protocol version tag embedded in every rollout. Validators reject mismatched versions. +GRAIL_PROOF_VERSION = "v5" + +# ──────────────── CRYPTOGRAPHIC CONSTANTS ──────────────── + +# Mersenne prime for modular sketch arithmetic. +# Changing breaks: all proof verification (sketch mod changes). +PRIME_Q = 2_147_483_647 + +# Number of random challenge positions per completion. +# Changing breaks: proof generation/verification position count mismatch. +CHALLENGE_K = 32 + +# PRF domain labels for different randomness derivations. +# Changing breaks: index derivation, sketch computation. +RNG_LABEL = {"sketch": b"sketch", "open": b"open", "sat": b"sat"} + +# Transformer layer index for hidden state extraction (-1 = last layer). +# Changing breaks: proof hidden state extraction layer mismatch. +LAYER_INDEX = -1 + +# Batch size for proof computation (log-softmax / GRAIL commitments). +# Fixed constant: changing causes subtle numerical divergence between +# miner and validator due to padding-induced floating-point differences. +PROOF_BATCH_SIZE = 16 + +# Top-K activation selection for sketch computation. +# Changing breaks: sketch bucket population differs between miner/validator. +PROOF_TOPK = 16 + +# Logarithmic bucketing: buckets per sign (16 total = 8 positive + 8 negative). +# Changing breaks: sketch vector dimensionality mismatch. +PROOF_NUM_BUCKETS = 8 + +# Bounded coefficient range for sketch robustness: r in [-127, 127]. +# Changing breaks: r_vec generation, sketch magnitude. +PROOF_COEFF_RANGE = 127 + +# Sketch tolerance at position 0. Covers cross-GPU drift across 6 models +# (Qwen2.5, Qwen3, Llama-3.2) and 3 GPU architectures (B200, A100, L40). +# Empirical max diff across ~300M positions = 3979. Base of 6000 gives ~50% +# headroom (matching TOPLOC's empirical safety margin) while staying below +# the LSH "two-bucket-shift" knee (~8000) to preserve distillation resistance. +# Forgery prob at base: 10^-167. Changing breaks valid proofs. +PROOF_SKETCH_TOLERANCE_BASE = 6000 + +# Sketch tolerance sqrt growth factor per position. +# FP divergence in causal attention grows as O(sqrt(P)) from different +# reduction orders across SDPA/FA2 implementations, torch versions, and GPUs. +# tolerance(P) = base + growth * sqrt(P). At pos 8192: tol=6452, forgery=10^-166. +# Changing breaks: tolerance envelope too tight (false rejections) or too loose (false accepts). +PROOF_SKETCH_TOLERANCE_GROWTH = 5.0 + +# Attention implementation forced across all model loading paths. +# FA2 is padding-invariant (uses flash_attn_varlen_func with unpadding), preventing +# sketch divergence when batch sizes vary between miner and validator. +# Changing breaks: proof verification (different FP accumulation order). +ATTN_IMPLEMENTATION = "flash_attention_2" + +# ──────────────── TIMING (CONSENSUS) ──────────────── + +# Blocks per window. All roles use this to determine window boundaries. +# Changing breaks: miner/validator window alignment. +WINDOW_LENGTH = 30 + +# Bittensor block time target average (seconds). +# Changing breaks: timing budget calculations across all roles. +BLOCK_TIME_SECONDS = 12 + +# Typical variance in block production time (seconds). +# Changing breaks: upload deadline grace period calculation. +BLOCK_TIME_VARIANCE = 3 + +# Network latency allowance for file uploads (seconds). +# Changing breaks: upload deadline enforcement. +NETWORK_UPLOAD_LATENCY = 30 + +# Grace period = block variance + upload latency. +# Used by validators to allow late uploads within tolerance. +UPLOAD_GRACE_PERIOD = BLOCK_TIME_VARIANCE + NETWORK_UPLOAD_LATENCY + +# Buffer for future drand beacon (seconds after deadline, prevents gaming). +# Changing breaks: drand-based randomness derivation timing. +DRAND_FUTURE_BUFFER = 30 + +# ──────────────── ROLLOUT GENERATION ──────────────── + +# GRPO group size: rollouts generated per problem. +# Changing breaks: advantage computation, group-level validation. +ROLLOUTS_PER_PROBLEM = 16 + +# Network-wide PROTOCOL CAP on completion length: validators reject any +# rollout whose completion exceeds this. The trainer's per-checkpoint +# ``generation_params.max_tokens`` is the actual operating limit and must +# be <= this value; the miner drives its backend with +# ``min(metadata.max_tokens, MAX_NEW_TOKENS_PROTOCOL_CAP)``. +# This is NOT a default; it is an immutable upper bound that miner and +# validator agree on at code level. Changing it would require a +# coordinated network upgrade and a GRAIL_PROOF_VERSION bump. +MAX_NEW_TOKENS_PROTOCOL_CAP = 8192 + +# Minimum EOS probability for valid termination. +# Changing breaks: termination validation (TerminationValidator). +MIN_EOS_PROBABILITY = 0.02 + +# Max acceptable drift between miner/validator logprobs. +# Changing breaks: logprob sanity check threshold. +SANITY_CHECK_DRIFT_THRESHOLD = 0.1 + +# ──────────────── REWARD PARAMETERS ──────────────── + +# Sigmoid formulation: R = sigmoid(1{correct} + min(speedup, clip) - delta) +# Changing breaks: reward computation mismatch between miner and validator. +SIGMOID_DELTA = 1.8 +SPEEDUP_CLIP = 3.0 +SIGMOID_KERNEL_WEIGHT = 0.80 + +# Reward comparison tolerances (used by RewardValidator). +# Changing breaks: valid rewards rejected or invalid rewards accepted. +REWARD_REL_TOL = 0.02 +REWARD_ABS_TOL = 1e-6 + +# ──────────────── ECONOMIC / INCENTIVE ──────────────── + +# Superlinear weighting exponent for sybil resistance. +# w_i proportional to s_i^p. Splitting into k identities yields k^(1-p) * s^p < s^p. +# Changing breaks: weight computation, incentive economics. +SUPERLINEAR_EXPONENT = 4.0 + +# Maximum unique rollouts per miner per window that count toward weight allocation. +# The effective period cap = this value x rolling_windows. +# Changing breaks: weight normalization, cap enforcement. +UNIQUE_ROLLOUTS_CAP = 5000 +UNIQUE_ROLLOUTS_CAP_ENABLED = True + +# Emission burn: percentage burned to UID 0, remainder distributed to miners. +# Changing breaks: weight distribution economics. +GRAIL_BURN_UID = 0 +GRAIL_BURN_PERCENTAGE = 90.0 + +# Trainer identity UID for checkpoint publication and data filtering. +# Changing breaks: checkpoint discovery, trust list filtering. +TRAINER_UID = 0 + +# ──────────────── VALIDATION RULES ──────────────── + +# Miner sampling parameters (validator-local policy, but all validators must agree). +# Changing breaks: sampling fairness, validation coverage consistency. +MINER_SAMPLING_ENABLED = True +MINER_SAMPLE_RATE = 0.25 +MINER_SAMPLE_MIN = 2 +MINER_SAMPLE_MAX = 35 + +# Failure lookback for exclusion from sampling. +# Changing breaks: failure gating duration. +FAILURE_LOOKBACK_WINDOWS = 14 + +# File size bounds for valid rollout window files. +# Changing breaks: file acceptance/rejection thresholds. +MIN_ROLLOUT_FILE_SIZE_BYTES = 200 +MAX_ROLLOUT_FILE_SIZE_BYTES = 350 * 1024 * 1024 # 350 MB + +# ──────────────── TOKEN SAMPLING DISTRIBUTION CHECK ──────────────── + +# Heuristic parameters for the DistributionValidator (soft check). +# Changing breaks: distribution shape detection thresholds. +SAMPLING_MIN_STEPS = 30 +SAMPLING_LOW_P = 0.10 +SAMPLING_HIGH_P = 0.90 +SAMPLING_LOW_FRAC_MIN = 0.20 +SAMPLING_HIGH_FRAC_MIN = 0.50 +SAMPLING_MID_FRAC_MAX = 0.40 +SAMPLING_MEDIAN_LOW_MAX = 0.30 +SAMPLING_LOW_Q10_MAX = 0.025 +SAMPLING_MIN_TOKEN_PROB = 1e-5 +SAMPLING_INITIAL_WINDOW_STEPS = 50 + +# ──────────────── ENVIRONMENT ──────────────── + +# Current environment ID. Validators use this constant, never trust miner data. +# Changing breaks: environment selection mismatch between roles. +CURRENT_ENV_ID = "triton_kernel" + +# ──────────────── STORAGE PATHS ──────────────── + +# R2 bucket prefix for all checkpoints. Must match across trainer/miner/validator. +CHECKPOINT_PREFIX = "grail/checkpoints/" + +# Subdirectory names for checkpoint types. +CHECKPOINT_SUBDIR_DELTA = "DELTA" +CHECKPOINT_SUBDIR_FULL = "FULL" + +# Checkpoint type identifiers (used in metadata.json). +CHECKPOINT_TYPE_DELTA = "DELTA" +CHECKPOINT_TYPE_FULL = "FULL" + +# ──────────────── TRUST LIST ──────────────── + +# Key prefix for trust list files in R2. +TRUST_LIST_KEY_PREFIX = "grail/trust/trust_list_" +TRUST_LIST_VERSION = 1 diff --git a/grail/protocol/crypto.py b/grail/protocol/crypto.py index acdf5280..c0afccd4 100644 --- a/grail/protocol/crypto.py +++ b/grail/protocol/crypto.py @@ -26,7 +26,7 @@ except ImportError: torch = None # type: ignore -from ..shared.constants import CHALLENGE_K, PRIME_Q, RNG_LABEL +from .constants import CHALLENGE_K, PRIME_Q, RNG_LABEL logger = logging.getLogger(__name__) diff --git a/grail/protocol/errors.py b/grail/protocol/errors.py new file mode 100644 index 00000000..df92da2b --- /dev/null +++ b/grail/protocol/errors.py @@ -0,0 +1,21 @@ +"""Protocol-level error types. + +Raise these (instead of bare ``assert``) when an invariant the protocol +relies on is violated. Python ``-O`` strips ``assert`` silently; explicit +exceptions stay loud and carry structured context that surfaces in logs. +""" + +from __future__ import annotations + + +class ProtocolViolationError(RuntimeError): + """A protocol invariant was violated. + + Used by the miner, validator, and trainer when a contract that is + required for miner-validator agreement is broken — for example, a + missing checkpoint metadata field, an unexpected token shape, or a + sketch dimension mismatch. + + The exception message should always include the offending values so + that the failure is debuggable from logs alone. + """ diff --git a/grail/protocol/grail_verifier.py b/grail/protocol/grail_verifier.py index 1c7cca3d..d479039a 100644 --- a/grail/protocol/grail_verifier.py +++ b/grail/protocol/grail_verifier.py @@ -8,7 +8,7 @@ 2. Logarithmic bucketing: Coarse quantization reduces sensitivity 3. Sketch verification: Random linear projection for cryptographic binding -Security: ~10^-234 forgery probability across k=32 positions with sketch-only verification. +Security: ~10^-167 forgery probability across K=32 challenged positions with sketch-only verification. """ from __future__ import annotations @@ -18,7 +18,7 @@ import torch -from ..shared.constants import ( +from .constants import ( PRIME_Q, PROOF_COEFF_RANGE, PROOF_NUM_BUCKETS, @@ -140,9 +140,10 @@ def adaptive_sketch_tolerance( The tolerance follows the same sqrt model so it tracks the actual drift: tolerance = base + growth * sqrt(position) - Security is not affected: even at tolerance=300 (pos=8000), a cheater - with wrong hidden states (expected sketch diff ~1600) has < 10^-23 - probability of passing all 32 challenged positions. + Security: at tolerance=6452 (pos=8192), forgery probability is 10^-166 + per challenged position set (K=32). Cross-GPU empirical max diff across + 6 models (Qwen2.5, Qwen3, Llama-3.2) and 3 GPUs (B200, A100, L40) was + 3979, well within the base tolerance of 6000 (50% headroom). Args: position: Token position in sequence @@ -158,7 +159,7 @@ class GRAILVerifier: """Sketch-based verifier for framework-agnostic hidden state proofs. Uses a single sketch check (random linear projection of bucketed top-k activations) - which provides sufficient security (~10^-234 forgery probability) while being + which provides sufficient security (~10^-167 forgery probability) while being robust to floating-point variations across GPUs and frameworks. """ @@ -231,6 +232,11 @@ def create_commitment(self, hidden_state: torch.Tensor, r_vec: torch.Tensor) -> abs_hidden = torch.abs(hidden_state) topk_result = torch.topk(abs_hidden, k=self.topk) indices = topk_result.indices # [topk] + + # Sort by index position for order-invariant sketch (v3). + # Rank-ordering caused batch-size sensitivity: near-tied activations + # swap ranks under different GEMM tiling, producing sketch drift. + indices, _ = torch.sort(indices) values = hidden_state[indices] # [topk] with signs preserved # Step 2: Logarithmic bucketing @@ -264,18 +270,21 @@ def create_commitments_batch(self, h_layer: torch.Tensor, r_vec: torch.Tensor) - # Step 1: Batched top-k selection abs_h = h_layer.abs() # [seq_len, hidden_dim] _, topk_indices = torch.topk(abs_h, k=self.topk, dim=1) # [seq_len, topk] - signed_values = torch.gather(h_layer, dim=1, index=topk_indices) # [seq_len, topk] del abs_h + # Sort by index position for order-invariant sketch (v3) + topk_indices, _ = torch.sort(topk_indices, dim=1) + signed_values = torch.gather(h_layer, dim=1, index=topk_indices) # [seq_len, topk] + # Step 2: Vectorized log-magnitude bucketing (float64 for precision match) buckets = log_magnitude_bucket_vectorized(signed_values, self.num_buckets) # [seq_len, topk] int64 del signed_values # Step 3: Batched sketch via matrix-vector product - # Max |dot| = topk * 7 * 127 = 28,448 — fits in int32, but CUDA + # Max |dot| = topk * 7 * 127 = 14,224 (topk=16) — fits in int32, but CUDA # doesn't support int matmul ("addmv_impl_cuda" not implemented for 'Int'). - # Use float32 — exact for integers up to 2^24 (our max is 28,448). + # Use float32 — exact for integers up to 2^24 (our max is 14,224). buckets_f = buckets.to(torch.float32) r_vec_f = r_vec.to(torch.float32).to(buckets_f.device) sketches = (buckets_f @ r_vec_f).to(torch.int64) # [seq_len] @@ -318,7 +327,8 @@ def verify_commitment( # Independently compute top-k on validator's own hidden state abs_hidden = torch.abs(validator_hidden) topk_result = torch.topk(abs_hidden, k=self.topk) - validator_values = validator_hidden[topk_result.indices] + indices, _ = torch.sort(topk_result.indices) + validator_values = validator_hidden[indices] # Compute validator's buckets validator_buckets = torch.tensor( diff --git a/grail/protocol/signatures.py b/grail/protocol/signatures.py index bef74296..b2e09404 100644 --- a/grail/protocol/signatures.py +++ b/grail/protocol/signatures.py @@ -139,7 +139,7 @@ def verify_commit_signature(commit: dict, wallet_address: str) -> bool: sig = bytes.fromhex(commit["signature"]) proof_version = commit.get("proof_version") - if not proof_version or proof_version not in ("v1", "v2"): + if not proof_version or proof_version not in ("v4", "v5"): logger.debug(f"Invalid proof version: {proof_version}") return False diff --git a/grail/schemas/rollout.py b/grail/schemas/rollout.py index c482cdb0..133ff60c 100644 --- a/grail/schemas/rollout.py +++ b/grail/schemas/rollout.py @@ -37,7 +37,7 @@ class RolloutMetadata(BaseModel): # Required (validated) prompt_length: int = Field(ge=0) - completion_length: int = Field(gt=0, le=8192) # MAX_NEW_TOKENS + completion_length: int = Field(gt=0, le=8192) # MAX_NEW_TOKENS_PROTOCOL_CAP success: bool total_reward: float advantage: float diff --git a/grail/scoring/weights.py b/grail/scoring/weights.py index 725cdb5a..6e4ed6ff 100644 --- a/grail/scoring/weights.py +++ b/grail/scoring/weights.py @@ -6,7 +6,7 @@ import math from collections import defaultdict -from ..shared.constants import UNIQUE_ROLLOUTS_CAP +from ..protocol.constants import UNIQUE_ROLLOUTS_CAP logger = logging.getLogger(__name__) diff --git a/grail/shared/checkpoint_paths.py b/grail/shared/checkpoint_paths.py index 80e35f85..b764971c 100644 --- a/grail/shared/checkpoint_paths.py +++ b/grail/shared/checkpoint_paths.py @@ -22,7 +22,7 @@ from __future__ import annotations -from grail.shared.constants import ( +from grail.protocol.constants import ( CHECKPOINT_PREFIX, CHECKPOINT_SUBDIR_DELTA, CHECKPOINT_SUBDIR_FULL, diff --git a/grail/shared/config.py b/grail/shared/config.py new file mode 100644 index 00000000..bf90267d --- /dev/null +++ b/grail/shared/config.py @@ -0,0 +1,227 @@ +"""GRAIL Deployment Configuration. + +Environment-configurable parameters that vary per deployment. Each value has a +sensible default and can be overridden via environment variable. + +For immutable protocol constants (values that all network participants must agree on), +see grail.protocol.constants. +""" + +import os + +# ──────────────── NETWORK & IDENTITY ──────────────── + +NETWORK = os.getenv("BT_NETWORK", "finney") +NETUID = int(os.getenv("NETUID", 81)) + +# ──────────────── UPLOAD & TIMING ──────────────── + +# Upload timeout for object storage operations (seconds) +UPLOAD_TIMEOUT = float(os.getenv("UPLOAD_TIMEOUT", "90")) + +# Trainer checkpoint upload buffer (blocks before window ends to force publish) +TRAINER_UPLOAD_BUFFER_BLOCKS = 10 # ~60 seconds at 12s/block + +# Time needed to upload the READY marker file to object storage +READY_MARKER_UPLOAD_BLOCKS = 1 # ~12 seconds at 12s/block + +# ──────────────── TRAINER HYPERPARAMETERS ──────────────── + +TRAINER_LR = float(os.getenv("GRAIL_TRAINER_LR", "1e-6")) +TRAINER_EPOCHS = int(os.getenv("GRAIL_TRAINER_EPOCHS", "1")) +TRAINER_MICRO_BATCH_SIZE = int(os.getenv("GRAIL_TRAINER_MICRO_BATCH_SIZE", "16")) + +# Token-budget dynamic batching: when set (>0), replaces micro_batch_size. +# Each micro-batch gets a variable number of sequences targeting this token count. +# Mutually exclusive with micro_batch_size (follows veRL/OpenRLHF pattern). +TRAINER_MAX_TOKENS_PER_MICRO_BATCH = int(os.getenv("GRAIL_TRAINER_MAX_TOKENS_PER_MICRO_BATCH", "0")) +TRAINER_MAX_LENGTH = int(os.getenv("GRAIL_TRAINER_MAX_LENGTH", "12288")) +TRAINER_GRAD_CLIP = float(os.getenv("GRAIL_TRAINER_GRAD_CLIP", "0.5")) +TRAINER_WARMUP_STEPS = int(os.getenv("GRAIL_TRAINER_WARMUP_STEPS", "10")) +TRAINER_KL_COEF = float(os.getenv("GRAIL_TRAINER_KL_COEF", "0.02")) +TRAINER_ENTROPY_COEF = float(os.getenv("GRAIL_TRAINER_ENTROPY_COEF", "0.001")) +TRAINER_ADV_CLIP_PERCENTILE = float(os.getenv("GRAIL_TRAINER_ADV_CLIP_PERCENTILE", "99.0")) +TRAINER_GROUP_ADV_SUM_TOL = float(os.getenv("GRAIL_TRAINER_GROUP_ADV_SUM_TOL", "0.01")) +TRAINER_GRAD_ACCUM_STEPS = int(os.getenv("GRAIL_TRAINER_GRAD_ACCUM_STEPS", "8")) + +# Effective batch size: number of rollouts per optimizer step. Used with token-budget +# batching to compute grad_accum_steps dynamically. When 0, uses grad_accum_steps directly. +TRAINER_EFFECTIVE_BATCH_SIZE = int(os.getenv("GRAIL_TRAINER_EFFECTIVE_BATCH_SIZE", "0")) + +# ──────────────── IMPORTANCE SAMPLING & PPO CLIPPING ──────────────── + +TRAINER_USE_IS = os.getenv("GRAIL_TRAINER_USE_IS", "1") == "1" +TRAINER_PPO_CLIP_EPS = float(os.getenv("GRAIL_TRAINER_PPO_CLIP_EPS", "0.2")) + +# Asymmetric upper bound for PPO clipping (DAPO-style). If unset, defaults to 0.28. +TRAINER_PPO_CLIP_EPS_UPPER = float(os.getenv("GRAIL_TRAINER_PPO_CLIP_EPS_UPPER", "0.28")) + +# Hard ceiling for importance sampling ratios to prevent instability (DCPO-style safety) +TRAINER_IS_RATIO_MAX = float(os.getenv("GRAIL_TRAINER_IS_RATIO_MAX", "10.0")) + +# Log-ratio clamp for numerical stability when exponentiating to ratios +TRAINER_LOGRATIO_CLAMP = float(os.getenv("GRAIL_TRAINER_LOGRATIO_CLAMP", "5.0")) + +# ──────────────── ADAPTIVE KL ──────────────── + +TRAINER_ADAPTIVE_KL = os.getenv("GRAIL_TRAINER_ADAPTIVE_KL", "1") == "1" +TRAINER_KL_TARGET = float(os.getenv("GRAIL_TRAINER_KL_TARGET", "0.04")) +TRAINER_KL_ADAPT_RATE = float(os.getenv("GRAIL_TRAINER_KL_ADAPT_RATE", "1.5")) +TRAINER_KL_MIN = float(os.getenv("GRAIL_TRAINER_KL_MIN", "0.001")) +TRAINER_KL_MAX = float(os.getenv("GRAIL_TRAINER_KL_MAX", "0.2")) + +# ──────────────── TRAINING MEMORY & PERFORMANCE ──────────────── + +# Gradient checkpointing: reduces activation memory by recomputing on backward pass. +# ~20-30% memory reduction, ~10-15% slower training. +TRAINER_USE_GRADIENT_CHECKPOINTING = ( + os.getenv("GRAIL_TRAINER_USE_GRADIENT_CHECKPOINTING", "1") == "1" +) + +# Selective gradient checkpointing: only checkpoint every Nth layer. +# N=1 = full checkpointing (default). N=2 = every other layer. N=0 = same as N=1. +TRAINER_GC_EVERY_N_LAYERS = int(os.getenv("GRAIL_TRAINER_GC_EVERY_N_LAYERS", "1")) + +# Sequence packing: concatenate micro-batch sequences into a single [1, total_tokens] +# tensor with position_ids resets at boundaries. Requires FA2. +TRAINER_USE_SEQUENCE_PACKING = os.getenv("GRAIL_TRAINER_USE_SEQUENCE_PACKING", "0") == "1" + +# torch.compile for training model (fuses ops, reduces kernel launch overhead). +TRAINER_USE_TORCH_COMPILE = os.getenv("GRAIL_TRAINER_USE_TORCH_COMPILE", "0") == "1" + +# Chunked logit computation: apply lm_head in sequence-dimension chunks to avoid +# materializing the full [batch, seq_len, vocab_size] logits tensor (~5.4 GB for +# Qwen3-8B at seq_len=10K). Saves ~10 GB peak memory on single-GPU training. +TRAINER_CHUNKED_LOGITS = os.getenv("GRAIL_TRAINER_CHUNKED_LOGITS", "1") == "1" +TRAINER_LOGIT_CHUNK_SIZE = int(os.getenv("GRAIL_TRAINER_LOGIT_CHUNK_SIZE", "256")) + +# Trainer miner trust filtering (incentive-based via Yuma Consensus) +TRAINER_MIN_TRUSTED_MINERS = int(os.getenv("GRAIL_TRAINER_MIN_TRUSTED_MINERS", "1")) + +# ──────────────── GRPO DATA FILTERING ──────────────── + +# Stage 1 (fast structural/cheap filters) happens before Stage 2. +GRPO_MAX_GROUPS = int(os.getenv("GRAIL_GRPO_MAX_GROUPS", "32")) +GRPO_MAX_COMPLETION_TOKENS = int(os.getenv("GRAIL_GRPO_MAX_COMPLETION_TOKENS", "8192")) + +# GRPO loss aggregation variant. +# Options: 'grpo' (per-sequence), 'bnpo' (global token avg), +# 'dapo' (distributed token norm), 'dr_grpo' (fixed denominator) +GRPO_VARIANT = os.getenv("GRAIL_GRPO_VARIANT", "dapo") + +# Advantage estimation strategy (orthogonal to GRPO_VARIANT loss aggregation). +# Options: 'grpo' (zero-mean, variance-normalized), 'dr_grpo' (zero-mean only), +# 'dapo' (rank-based) +ADV_ESTIMATOR = os.getenv("GRAIL_ADV_ESTIMATOR", "grpo") + +# Importance sampling level for policy gradient computation. +# Options: 'sequence' (one ratio per sequence), 'token' (per-token ratios) +IMPORTANCE_SAMPLING_LEVEL = os.getenv("GRAIL_IMPORTANCE_SAMPLING_LEVEL", "sequence") + +# Stage 2 (refinement): quality/efficiency-oriented filters. +GRPO_MIN_SUCCESS_FRACTION = float(os.getenv("GRAIL_GRPO_MIN_SUCCESS_FRACTION", "0.0")) +GRPO_MIN_REWARD_PER_TOKEN = float(os.getenv("GRAIL_GRPO_MIN_REWARD_PER_TOKEN", "0.0")) +GRPO_REWARD_PER_TOKEN_DROP_QUANTILE = float( + os.getenv("GRAIL_GRPO_REWARD_PER_TOKEN_DROP_QUANTILE", "0.0") +) + +# Group ranking: combined efficiency score weights (must sum to 1.0). +GRPO_RANKING_REWARD_WEIGHT = float(os.getenv("GRAIL_GRPO_RANKING_REWARD_WEIGHT", "0.7")) +GRPO_RANKING_VARIANCE_WEIGHT = float(os.getenv("GRAIL_GRPO_RANKING_VARIANCE_WEIGHT", "0.3")) + +# ──────────────── CHECKPOINT RETENTION ──────────────── + +CHECKPOINT_MILESTONE_INTERVAL = int(os.getenv("GRAIL_CHECKPOINT_MILESTONE_INTERVAL", "0")) + +# R2 retention limits (used by checkpoint_publisher for trainer uploads). +# BASE: complete model weights (~14GB). DELTA: sparse diffs that depend on a BASE. +BASE_CHECKPOINT_RETENTION_LIMIT = int(os.getenv("GRAIL_BASE_CHECKPOINT_RETENTION_LIMIT", "3")) +DELTA_CHECKPOINT_RETENTION_LIMIT = int(os.getenv("GRAIL_DELTA_CHECKPOINT_RETENTION_LIMIT", "15")) + +# Cleanup frequency: run cleanup every N uploads instead of every upload. +CLEANUP_INTERVAL_UPLOADS = int(os.getenv("GRAIL_CLEANUP_INTERVAL_UPLOADS", "10")) + +# ──────────────── DELTA CHECKPOINT ──────────────── + +# Upload FULL checkpoint every N windows (deltas for intermediate windows). +# Set to 1 to disable delta uploads (all FULL checkpoints). +DELTA_BASE_INTERVAL = int(os.getenv("GRAIL_DELTA_BASE_INTERVAL", "10")) + +# Threshold for sparse delta (0 = keep all non-zero deltas). +DELTA_THRESHOLD = float(os.getenv("GRAIL_DELTA_THRESHOLD", "0.0")) + +# Enable/disable delta checkpoint uploads (fallback to full if disabled). +DELTA_CHECKPOINT_ENABLED = os.getenv("GRAIL_DELTA_CHECKPOINT_ENABLED", "1") == "1" + +# Delta codec format for trainer uploads. +DELTA_CODEC_FORMAT = os.getenv("GRAIL_DELTA_FORMAT", "sparse_codec_v3.1") + +# ──────────────── INVARIANT VALIDATION ──────────────── +# Ensure retained deltas always have their base checkpoint available. +if DELTA_CHECKPOINT_RETENTION_LIMIT < DELTA_BASE_INTERVAL: + raise ValueError( + f"DELTA_CHECKPOINT_RETENTION_LIMIT ({DELTA_CHECKPOINT_RETENTION_LIMIT}) must be >= " + f"DELTA_BASE_INTERVAL ({DELTA_BASE_INTERVAL}) to prevent orphaned delta checkpoints. " + f"Deltas are created every DELTA_BASE_INTERVAL windows, so we must keep at least that " + f"many to ensure validators can reconstruct recent checkpoints." + ) + +# ──────────────── INFERENCE BACKEND ──────────────── + +# Default generation backend for both mining pipeline and trainer evaluation. +INFERENCE_BACKEND = os.getenv("GRAIL_INFERENCE_BACKEND", "sglang") + +# ──────────────── TRUST LIST ──────────────── + +TRUST_LIST_MAX_STALENESS_WINDOWS = int(os.getenv("GRAIL_TRUST_LIST_MAX_STALENESS_WINDOWS", "5")) + +# ──────────────── ASYNC TRAINING ──────────────── + +# Upload worker poll interval (seconds between snapshot checks). +SNAPSHOT_POLL_INTERVAL_SECONDS = int(os.getenv("GRAIL_SNAPSHOT_POLL_INTERVAL", "30")) + +# Training heartbeat timeout for liveness monitoring. +TRAINING_HEARTBEAT_TIMEOUT_SECONDS = int(os.getenv("GRAIL_TRAINING_HEARTBEAT_TIMEOUT", "3600")) + +# Upload retry configuration. +UPLOAD_RETRY_MAX_ATTEMPTS = int(os.getenv("GRAIL_UPLOAD_RETRY_MAX_ATTEMPTS", "3")) +UPLOAD_RETRY_BACKOFF_BASE = int(os.getenv("GRAIL_UPLOAD_RETRY_BACKOFF_BASE", "60")) + +# ──────────────── PARAMETER CHANGE TRACKING ──────────────── + +# Measure parameter changes every N optimizer steps (0 disables tracking). +PARAM_CHANGE_MEASURE_INTERVAL = int(os.getenv("GRAIL_PARAM_CHANGE_MEASURE_INTERVAL", "0")) +PARAM_CHANGE_THRESHOLD = float(os.getenv("GRAIL_PARAM_CHANGE_THRESHOLD", "0.0")) +PARAM_CHANGE_TRACK_PER_LAYER = os.getenv("GRAIL_PARAM_CHANGE_TRACK_PER_LAYER", "1") == "1" +PARAM_CHANGE_TRACK_COMPONENTS = os.getenv("GRAIL_PARAM_CHANGE_TRACK_COMPONENTS", "1") == "1" +PARAM_CHANGE_TRACK_SIGN_FLIPS = os.getenv("GRAIL_PARAM_CHANGE_TRACK_SIGN_FLIPS", "1") == "1" +PARAM_CHANGE_RELATIVE_EPS = float(os.getenv("GRAIL_PARAM_CHANGE_RELATIVE_EPS", "1e-10")) + +# ──────────────── SPARSE QUALITY ANALYSIS ──────────────── + +SPARSE_QUALITY_ENABLED = os.getenv("GRAIL_SPARSE_QUALITY_ENABLED", "0") == "1" + +# ──────────────── TRAINING COMPLETION ──────────────── + +# Total training windows before completing training. +TOTAL_TRAINING_WINDOWS = int(os.getenv("GRAIL_TRAINER_TOTAL_WINDOWS", "100")) + +# Hugging Face credentials for model upload after training completion. +HF_TOKEN = os.getenv("HF_TOKEN", "") +HF_USERNAME = os.getenv("HF_USERNAME", "") + +# ──────────────── TESTING / TEMPORARY ──────────────── + +# Only for testing purposes; going to be removed later on. +GRAIL_CHECKPOINT_MOD10 = False + +# ──────────────── HELPER FUNCTIONS ──────────────── + + +def is_kl_enabled() -> bool: + """Check if KL divergence is enabled based on TRAINER_KL_COEF. + + Returns: + True if KL coefficient is greater than zero, False otherwise. + """ + return float(TRAINER_KL_COEF) > 0.0 diff --git a/grail/shared/constants.py b/grail/shared/constants.py deleted file mode 100644 index 57978dba..00000000 --- a/grail/shared/constants.py +++ /dev/null @@ -1,387 +0,0 @@ -#!/usr/bin/env python3 -""" -GRAIL Shared Constants - -Centralized configuration constants used across the GRAIL codebase. -""" - -import os - -# ──────────────── NETWORK & BLOCKCHAIN ──────────────── - -NETWORK = os.getenv("BT_NETWORK", "finney") -NETUID = int(os.getenv("NETUID", 81)) -WINDOW_LENGTH = 30 - -# ──────────────── TIMING & VALIDATION ──────────────── - -# Bittensor block time (target average) -BLOCK_TIME_SECONDS = 12 - -# Typical variance in block production time (±seconds) -BLOCK_TIME_VARIANCE = 3 - -# Network latency allowance for file uploads (seconds) -NETWORK_UPLOAD_LATENCY = 30 - -# Upload timeout for object storage operations (seconds) -UPLOAD_TIMEOUT = float(os.getenv("UPLOAD_TIMEOUT", "90")) - -# Grace period for upload deadline validation -# = block variance + upload latency -UPLOAD_GRACE_PERIOD = BLOCK_TIME_VARIANCE + NETWORK_UPLOAD_LATENCY - -# Buffer for future drand beacon (prevents gaming) -# Validators use drand from this many seconds AFTER upload deadline -DRAND_FUTURE_BUFFER = 30 - -# Trainer checkpoint upload buffer (blocks before window ends to force publish) -TRAINER_UPLOAD_BUFFER_BLOCKS = 10 # ~60 seconds at 12s/block - -# Time needed to upload the READY marker file to object storage -READY_MARKER_UPLOAD_BLOCKS = 1 # ~12 seconds at 12s/block - -# ──────────────── Trainer MODEL CONFIGURATION ──────────────── - -# Trainer hyperparameters (env configurable) -TRAINER_LR = float(os.getenv("GRAIL_TRAINER_LR", "1e-6")) -TRAINER_EPOCHS = int(os.getenv("GRAIL_TRAINER_EPOCHS", "1")) -TRAINER_MICRO_BATCH_SIZE = int(os.getenv("GRAIL_TRAINER_MICRO_BATCH_SIZE", "16")) -TRAINER_MAX_LENGTH = int(os.getenv("GRAIL_TRAINER_MAX_LENGTH", "12288")) -TRAINER_GRAD_CLIP = float(os.getenv("GRAIL_TRAINER_GRAD_CLIP", "0.5")) -TRAINER_WARMUP_STEPS = int(os.getenv("GRAIL_TRAINER_WARMUP_STEPS", "10")) -TRAINER_KL_COEF = float(os.getenv("GRAIL_TRAINER_KL_COEF", "0.02")) -TRAINER_ENTROPY_COEF = float(os.getenv("GRAIL_TRAINER_ENTROPY_COEF", "0.001")) -TRAINER_ADV_CLIP_PERCENTILE = float(os.getenv("GRAIL_TRAINER_ADV_CLIP_PERCENTILE", "99.0")) -TRAINER_GROUP_ADV_SUM_TOL = float(os.getenv("GRAIL_TRAINER_GROUP_ADV_SUM_TOL", "0.01")) -TRAINER_GRAD_ACCUM_STEPS = int(os.getenv("GRAIL_TRAINER_GRAD_ACCUM_STEPS", "8")) - -# Importance sampling and PPO-style clipping -TRAINER_USE_IS = os.getenv("GRAIL_TRAINER_USE_IS", "1") == "1" -TRAINER_PPO_CLIP_EPS = float(os.getenv("GRAIL_TRAINER_PPO_CLIP_EPS", "0.2")) -# Asymmetric upper bound for PPO clipping (DAPO-style). If unset, defaults to 0.28. -TRAINER_PPO_CLIP_EPS_UPPER = float(os.getenv("GRAIL_TRAINER_PPO_CLIP_EPS_UPPER", "0.28")) -# Hard ceiling for importance sampling ratios to prevent instability (DCPO-style safety) -TRAINER_IS_RATIO_MAX = float(os.getenv("GRAIL_TRAINER_IS_RATIO_MAX", "10.0")) - -# Log-ratio clamp for numerical stability when exponentiating to ratios -TRAINER_LOGRATIO_CLAMP = float(os.getenv("GRAIL_TRAINER_LOGRATIO_CLAMP", "5.0")) - -# Adaptive KL settings -TRAINER_ADAPTIVE_KL = os.getenv("GRAIL_TRAINER_ADAPTIVE_KL", "1") == "1" -TRAINER_KL_TARGET = float(os.getenv("GRAIL_TRAINER_KL_TARGET", "0.04")) -TRAINER_KL_ADAPT_RATE = float(os.getenv("GRAIL_TRAINER_KL_ADAPT_RATE", "1.5")) -TRAINER_KL_MIN = float(os.getenv("GRAIL_TRAINER_KL_MIN", "0.001")) -TRAINER_KL_MAX = float(os.getenv("GRAIL_TRAINER_KL_MAX", "0.2")) - -# Flash Attention for training optimization -TRAINER_USE_FLASH_ATTENTION = os.getenv("GRAIL_TRAINER_USE_FLASH_ATTENTION", "1") == "1" - -# Gradient checkpointing for training memory efficiency -# Reduces activation memory by recomputing on backward pass (~20-30% memory reduction) -# Trade-off: ~10-15% slower training (recomputation cost), but enables larger batches/longer sequences -TRAINER_USE_GRADIENT_CHECKPOINTING = ( - os.getenv("GRAIL_TRAINER_USE_GRADIENT_CHECKPOINTING", "1") == "1" -) - -# Chunked logit computation: apply lm_head in sequence-dimension chunks to avoid -# materializing the full [batch, seq_len, vocab_size] logits tensor (~5.4 GB for -# Qwen3-8B at seq_len=10K). Saves ~10 GB peak memory on single-GPU training. -TRAINER_CHUNKED_LOGITS = os.getenv("GRAIL_TRAINER_CHUNKED_LOGITS", "1") == "1" -TRAINER_LOGIT_CHUNK_SIZE = int(os.getenv("GRAIL_TRAINER_LOGIT_CHUNK_SIZE", "256")) - -# Trainer miner trust filtering (incentive-based via Yuma Consensus) -TRAINER_MIN_TRUSTED_MINERS = int(os.getenv("GRAIL_TRAINER_MIN_TRUSTED_MINERS", "1")) - -# ──────────────── GRPO DATA FILTERING (TWO-STAGE) ──────────────── -# Stage 1 (fast structural/cheap filters) happens before Stage 2. -# Defaults are conservative so behavior is unchanged unless configured. -GRPO_MAX_GROUPS = int(os.getenv("GRAIL_GRPO_MAX_GROUPS", "32")) -GRPO_MAX_COMPLETION_TOKENS = int(os.getenv("GRAIL_GRPO_MAX_COMPLETION_TOKENS", "8192")) - -# GRPO loss aggregation variant -# Options: 'grpo' (per-sequence), 'bnpo' (global token avg), -# 'dapo' (distributed token norm), 'dr_grpo' (fixed denominator) -GRPO_VARIANT = os.getenv("GRAIL_GRPO_VARIANT", "dapo") - -# Importance sampling level for policy gradient computation -# Options: 'sequence' (one ratio per sequence), 'token' (per-token ratios) -# 'token': More fine-grained clipping, inspired by HuggingFace TRL GRPO -IMPORTANCE_SAMPLING_LEVEL = os.getenv("GRAIL_IMPORTANCE_SAMPLING_LEVEL", "sequence") - -# Stage 2 (refinement): quality/efficiency-oriented filters -# Require at least this fraction of successes within a group (0 disables) -GRPO_MIN_SUCCESS_FRACTION = float(os.getenv("GRAIL_GRPO_MIN_SUCCESS_FRACTION", "0.0")) -# Minimum mean reward/token across a group's rollouts (<=0 disables) -GRPO_MIN_REWARD_PER_TOKEN = float(os.getenv("GRAIL_GRPO_MIN_REWARD_PER_TOKEN", "0.0")) -# Drop lowest X quantile of groups by reward/token (0 disables) -GRPO_REWARD_PER_TOKEN_DROP_QUANTILE = float( - os.getenv("GRAIL_GRPO_REWARD_PER_TOKEN_DROP_QUANTILE", "0.0") -) - -# Group ranking: combined efficiency score weights (must sum to 1.0) -# Higher reward_per_token weight prioritizes token efficiency (GFPO) -# Higher advantage_variance weight prioritizes learning signal strength -GRPO_RANKING_REWARD_WEIGHT = float(os.getenv("GRAIL_GRPO_RANKING_REWARD_WEIGHT", "0.7")) -GRPO_RANKING_VARIANCE_WEIGHT = float(os.getenv("GRAIL_GRPO_RANKING_VARIANCE_WEIGHT", "0.3")) - -# Checkpoint retention controls -CHECKPOINT_MILESTONE_INTERVAL = int(os.getenv("GRAIL_CHECKPOINT_MILESTONE_INTERVAL", "0")) - -# R2 retention limits (used by checkpoint_publisher for trainer uploads) -# BASE: complete model weights (~14GB); DELTA: sparse diffs that depend on a BASE -# DELTA_RETENTION should be > DELTA_BASE_INTERVAL to prevent orphaned deltas -BASE_CHECKPOINT_RETENTION_LIMIT = int(os.getenv("GRAIL_BASE_CHECKPOINT_RETENTION_LIMIT", "3")) -DELTA_CHECKPOINT_RETENTION_LIMIT = int(os.getenv("GRAIL_DELTA_CHECKPOINT_RETENTION_LIMIT", "15")) - -# Cleanup frequency control: run cleanup every N uploads instead of every upload -# Set to 1 to run on every upload (old behavior), higher values reduce overhead -CLEANUP_INTERVAL_UPLOADS = int(os.getenv("GRAIL_CLEANUP_INTERVAL_UPLOADS", "10")) - -# Trainer identity used for checkpoint publication -TRAINER_UID = 0 - -# ──────────────── LOGGING ──────────────── - -TRACE = 5 - -# ──────────────── GRAIL CRYPTOGRAPHIC CONSTANTS ──────────────── - -PRIME_Q = 2_147_483_647 -CHALLENGE_K = 32 -RNG_LABEL = {"sketch": b"sketch", "open": b"open", "sat": b"sat"} -LAYER_INDEX = -1 - -# Batch size for proof computation (log-softmax / GRAIL commitments). -# Fixed constant: changing this can cause subtle numerical divergence between -# miner and validator due to padding-induced floating-point differences. -PROOF_BATCH_SIZE = 16 - -# ──────────────── TERMINATION VALIDATION HPs ──────────────── - -MAX_NEW_TOKENS = 8192 - -# Must match rollout generator default -MIN_EOS_PROBABILITY = 0.02 # Minimum probability for valid EOS termination - -# Max acceptable drift between miner/validator -SANITY_CHECK_DRIFT_THRESHOLD = 0.1 - -# ──────────────── TOKEN SAMPLING DIST CHECK HPs ──────────────── - -SAMPLING_MIN_STEPS = 30 -SAMPLING_LOW_P = 0.10 -SAMPLING_HIGH_P = 0.90 -SAMPLING_LOW_FRAC_MIN = 0.20 -SAMPLING_HIGH_FRAC_MIN = 0.50 -SAMPLING_MID_FRAC_MAX = 0.40 - -# Minimal sampling shape hyperparameters (median gate for unimodal-low) -SAMPLING_MEDIAN_LOW_MAX = 0.30 - -# NOTE: this parameter so far has been a good indicator of bimodality -SAMPLING_LOW_Q10_MAX = 0.025 - -# Extra sanity gates for sampling shape checks -SAMPLING_MIN_TOKEN_PROB = 1e-5 -SAMPLING_INITIAL_WINDOW_STEPS = 50 - -# ──────────────── VALIDATOR-SPECIFIC CONSTANTS ──────────────── - -# Superlinear weighting exponent: -# For p > 1, w_i ∝ s_i^p amplifies differences and penalizes sybil splitting: -# splitting into k identities yields k^(1-p) * s^p < s^p. -SUPERLINEAR_EXPONENT = 4.0 - -# Reward comparison tolerances (used by RewardValidator) -REWARD_REL_TOL = 0.02 -REWARD_ABS_TOL = 1e-6 - -# ──────────────── ROLLOUTS PER PROBLEM ──────────────── - -ROLLOUTS_PER_PROBLEM = 16 - -# ──────────────── ENVIRONMENT CONFIGURATION ──────────────── - -# Current environment ID (validators use this constant, never trust miner data) -CURRENT_ENV_ID = "triton_kernel" - -# ──────────────── EMISSION BURN MECHANISM ──────────────── - -GRAIL_BURN_UID = 0 -GRAIL_BURN_PERCENTAGE = 80.0 - -# ──────────────── UNIQUE ROLLOUTS CAP ──────────────── - -# Maximum unique rollouts per miner PER WINDOW that count toward weight allocation. -# The effective period cap = this value × rolling_windows (currently 12). -# Miners are rewarded proportionally to how close they are to the period cap. -UNIQUE_ROLLOUTS_CAP = 1500 -UNIQUE_ROLLOUTS_CAP_ENABLED = True - -# ──────────────── MINER SAMPLING (VALIDATION COST CONTROL) ──────────────── - -# Enable/disable miner-level subsampling per window. -MINER_SAMPLING_ENABLED = True - -# Fraction of active miners (those with a window file) to validate per window. -# Applied after MINER_SAMPLE_MIN and before MINER_SAMPLE_MAX. -MINER_SAMPLE_RATE = 0.25 - -# Minimum number of active miners to validate each window (floor). -MINER_SAMPLE_MIN = 2 - -# Optional cap on miners validated per window. Set to None to disable. -MINER_SAMPLE_MAX = 35 - -# Number of windows to look back for failure-based exclusion from sampling. -# Miners with failures in the last N windows are excluded from selection. -FAILURE_LOOKBACK_WINDOWS = 14 - -# Minimum file size for valid rollout window files (bytes). -# Empty window files are ~170 bytes (just JSON structure with empty array). -# Files below this threshold are considered to have 0 rollouts and are filtered. -MIN_ROLLOUT_FILE_SIZE_BYTES = 200 - -# Maximum file size for valid rollout window files (bytes). -# Files above this threshold are rejected before download to prevent -# resource exhaustion from oversized submissions. -MAX_ROLLOUT_FILE_SIZE_BYTES = 350 * 1024 * 1024 # 350 MB - -# ──────────────── TRUST LIST (VALIDATOR → TRAINER) ──────────────── -TRUST_LIST_KEY_PREFIX = "grail/trust/trust_list_" -TRUST_LIST_VERSION = 1 -TRUST_LIST_MAX_STALENESS_WINDOWS = int(os.getenv("GRAIL_TRUST_LIST_MAX_STALENESS_WINDOWS", "5")) - -# ──────────────── GRAIL PROOF VERIFICATION ──────────────── - -# Top-K activation selection (focus on stable, important features) -# UPDATED: Reduced from 256 to 32 for higher sensitivity to training changes -PROOF_TOPK = 32 - -# Logarithmic bucketing parameters -PROOF_NUM_BUCKETS = 8 # Buckets per sign - -# Small bounded coefficients for sketch robustness -PROOF_COEFF_RANGE = 127 # r ∈ [-127, 127] - -# Sketch tolerance: base tolerance at position 0 (no FP drift expected) -PROOF_SKETCH_TOLERANCE_BASE = 30 - -# Sketch tolerance: sqrt growth factor per position. -# FP divergence in causal attention grows as O(sqrt(P)) from different -# reduction orders across SDPA implementations, torch versions, and GPUs. -# tolerance(P) = base + growth * sqrt(P) -PROOF_SKETCH_TOLERANCE_GROWTH = 3.0 - -# GRAIL proof version -GRAIL_PROOF_VERSION = "v2" - -# ──────────────── PARAMETER CHANGE TRACKING ──────────────── - -# Measure parameter changes every N optimizer steps (0 disables tracking) -PARAM_CHANGE_MEASURE_INTERVAL = int(os.getenv("GRAIL_PARAM_CHANGE_MEASURE_INTERVAL", "0")) - -# Primary threshold for classifying a parameter as "changed" -# With small LR (1e-6), weight updates can be very small (1e-9 to 1e-6) -# Use 1e-10 as floor to catch all meaningful changes -PARAM_CHANGE_THRESHOLD = float(os.getenv("GRAIL_PARAM_CHANGE_THRESHOLD", "0.0")) - -# Enable per-layer sparsity breakdown -PARAM_CHANGE_TRACK_PER_LAYER = os.getenv("GRAIL_PARAM_CHANGE_TRACK_PER_LAYER", "1") == "1" - -# Enable per-component breakdown (q_proj, v_proj, gate_proj, etc.) -PARAM_CHANGE_TRACK_COMPONENTS = os.getenv("GRAIL_PARAM_CHANGE_TRACK_COMPONENTS", "1") == "1" - -# Enable sign flip tracking (detects oscillation/instability) -PARAM_CHANGE_TRACK_SIGN_FLIPS = os.getenv("GRAIL_PARAM_CHANGE_TRACK_SIGN_FLIPS", "1") == "1" - -# Epsilon for relative delta computation to avoid division by zero -PARAM_CHANGE_RELATIVE_EPS = float(os.getenv("GRAIL_PARAM_CHANGE_RELATIVE_EPS", "1e-10")) - -# ──────────────── SPARSE QUALITY ANALYSIS ──────────────── - -# Enable/disable sparse quality analysis (runs at same interval as param tracking) -SPARSE_QUALITY_ENABLED = os.getenv("GRAIL_SPARSE_QUALITY_ENABLED", "0") == "1" - -# ──────────────── CHECKPOINT PATH CONFIGURATION ──────────────── - -# R2 bucket prefix for all checkpoints -CHECKPOINT_PREFIX = "grail/checkpoints/" - -# Subdirectory names for checkpoint types -# At anchor windows, both DELTA and FULL coexist under: -# checkpoint-{window}/DELTA/ (sparse delta for caught-up consumers) -# checkpoint-{window}/FULL/ (full weights for new joiners) -CHECKPOINT_SUBDIR_DELTA = "DELTA" -CHECKPOINT_SUBDIR_FULL = "FULL" - -# Checkpoint type identifiers (used in metadata.json) -CHECKPOINT_TYPE_DELTA = "DELTA" -CHECKPOINT_TYPE_FULL = "FULL" - - -# ──────────────── DELTA CHECKPOINT CONFIGURATION ──────────────── - -# Upload FULL checkpoint every N windows (deltas for intermediate windows) -# Set to 1 to disable delta uploads (all FULL checkpoints) -DELTA_BASE_INTERVAL = int(os.getenv("GRAIL_DELTA_BASE_INTERVAL", "10")) - -# Threshold for sparse delta (0 = keep all non-zero deltas) -DELTA_THRESHOLD = float(os.getenv("GRAIL_DELTA_THRESHOLD", "0.0")) - -# Enable/disable delta checkpoint uploads (fallback to full if disabled) -DELTA_CHECKPOINT_ENABLED = os.getenv("GRAIL_DELTA_CHECKPOINT_ENABLED", "1") == "1" - -# Delta codec format for trainer uploads ("sparse_codec_v2", "sparse_codec_v3", or "sparse_codec_v3.1") -DELTA_CODEC_FORMAT = os.getenv("GRAIL_DELTA_FORMAT", "sparse_codec_v3.1") - -# ──────────────── INVARIANT VALIDATION ──────────────────────────────────────── -# Ensure DELTA_CHECKPOINT_RETENTION_LIMIT >= DELTA_BASE_INTERVAL -# This guarantees that retained deltas always have their base checkpoint available. -# If violated, deltas can become orphaned when their base is deleted. -assert DELTA_CHECKPOINT_RETENTION_LIMIT >= DELTA_BASE_INTERVAL, ( - f"DELTA_CHECKPOINT_RETENTION_LIMIT ({DELTA_CHECKPOINT_RETENTION_LIMIT}) must be >= " - f"DELTA_BASE_INTERVAL ({DELTA_BASE_INTERVAL}) to prevent orphaned delta checkpoints. " - f"Deltas are created every DELTA_BASE_INTERVAL windows, so we must keep at least that many " - f"to ensure validators can reconstruct recent checkpoints." -) - -# ──────────────── ASYNC TRAINING CONFIGURATION ──────────────── - -# Upload worker poll interval (seconds between snapshot checks) -SNAPSHOT_POLL_INTERVAL_SECONDS = int(os.getenv("GRAIL_SNAPSHOT_POLL_INTERVAL", "30")) - -# Training heartbeat timeout for liveness monitoring (60 minutes) -TRAINING_HEARTBEAT_TIMEOUT_SECONDS = int(os.getenv("GRAIL_TRAINING_HEARTBEAT_TIMEOUT", "3600")) - -# Upload retry configuration -UPLOAD_RETRY_MAX_ATTEMPTS = int(os.getenv("GRAIL_UPLOAD_RETRY_MAX_ATTEMPTS", "3")) -UPLOAD_RETRY_BACKOFF_BASE = int(os.getenv("GRAIL_UPLOAD_RETRY_BACKOFF_BASE", "60")) - -# ──────────────── HELPER FUNCTIONS ──────────────── - - -def is_kl_enabled() -> bool: - """Check if KL divergence is enabled based on TRAINER_KL_COEF. - - Returns: - True if KL coefficient is greater than zero, False otherwise. - """ - return float(TRAINER_KL_COEF) > 0.0 - - -# ──────────────── CHECKPOINT MOD10 ──────────────── - -# Only for testing purposes; going to be removed later on -GRAIL_CHECKPOINT_MOD10 = False - - -# ──────────────── TRAINING COMPLETION ──────────────── - -# Total training windows before completing training -TOTAL_TRAINING_WINDOWS = int(os.getenv("GRAIL_TRAINER_TOTAL_WINDOWS", "100")) - -# Hugging Face credentials for model upload after training completion -HF_TOKEN = os.getenv("HF_TOKEN", "") -HF_USERNAME = os.getenv("HF_USERNAME", "") diff --git a/grail/shared/retention_utils.py b/grail/shared/retention_utils.py index bf9b9064..116d000c 100644 --- a/grail/shared/retention_utils.py +++ b/grail/shared/retention_utils.py @@ -8,10 +8,10 @@ from __future__ import annotations -from grail.shared.constants import ( +from grail.protocol.constants import WINDOW_LENGTH +from grail.shared.config import ( CHECKPOINT_MILESTONE_INTERVAL, DELTA_BASE_INTERVAL, - WINDOW_LENGTH, ) SAFETY_MARGIN_WINDOWS = 5 @@ -104,3 +104,42 @@ def is_anchor_window(window: int) -> bool: True if this window is an anchor window """ return window % _anchor_stride() == 0 + + +def compute_chain_windows( + retained_deltas: set[int] | list[int], + chain_map: dict[int, int], + anchor_map: dict[int, int], +) -> set[int]: + """Walk delta chains and return all intermediate windows needed for reconstruction. + + For each retained delta, follows prev_window links back to anchor_window, + collecting every intermediate window. This ensures the retention policy never + breaks a chain by deleting an intermediate link. + + Args: + retained_deltas: Delta windows that are being retained. + chain_map: {delta_window: prev_window} from metadata. + anchor_map: {delta_window: anchor_window} from metadata. + + Returns: + Set of all windows needed to keep chains intact (intermediates + anchors). + """ + keep: set[int] = set() + for delta in retained_deltas: + anchor = anchor_map.get(delta) + if anchor is None: + continue + keep.add(anchor) + + current = delta + visited: set[int] = {delta} + while current in chain_map: + prev = chain_map[current] + if prev in visited or prev == anchor: + keep.add(prev) + break + visited.add(prev) + keep.add(prev) + current = prev + return keep diff --git a/grail/shared/thinking.py b/grail/shared/thinking.py index db3658dd..d12fd1b9 100644 --- a/grail/shared/thinking.py +++ b/grail/shared/thinking.py @@ -90,9 +90,23 @@ def solution_close(self) -> str: def get_thinking_config(mode: ThinkingMode | str | None = None) -> ThinkingConfig: - """Return config for given mode. Reads GRAIL_THINKING_MODE env var if None.""" + """Return config for given mode. + + Reads ``GRAIL_THINKING_MODE`` if ``mode`` is ``None``. Defaults to + ``"instructed"`` because that mode uses tags grail builds itself + (````/```` for thinking, + ````/```` for the answer) and is portable across + base models that don't ship a thinking-aware chat template. + + NOTE: On the miner and validator hot path the env var is overridden at + runtime from the trainer-published ``CheckpointMetadata.thinking_mode``, + so this default only affects boot-time prompt-rendering before the first + checkpoint arrives. The trainer is the single source of truth for the + *protocol* mode; this fallback exists for non-protocol callers (parsers, + debug helpers, tests). + """ if mode is None: - mode = os.getenv("GRAIL_THINKING_MODE", "native") + mode = os.getenv("GRAIL_THINKING_MODE", "instructed") if isinstance(mode, str): mode = ThinkingMode(mode.lower()) return { diff --git a/grail/trainer/advantages.py b/grail/trainer/advantages.py new file mode 100644 index 00000000..7d8dc7e5 --- /dev/null +++ b/grail/trainer/advantages.py @@ -0,0 +1,82 @@ +"""Advantage estimation strategies for GRPO training. + +Computes per-rollout advantages from raw rewards within a group. Orthogonal to +the loss aggregation variant (GRPO_VARIANT), which controls how per-token losses +are normalized. +""" + +from __future__ import annotations + + +def compute_advantages(rewards: list[float], estimator: str = "grpo") -> list[float]: + """Compute advantages from rewards using the selected estimation strategy. + + Args: + rewards: Raw rewards for each rollout in a group. + estimator: Strategy name. One of: "grpo", "dr_grpo", "dapo". + + Returns: + List of advantage values (same length as rewards). + + Raises: + ValueError: If estimator name is unknown. + """ + if not rewards: + return [] + + _dispatch = { + "grpo": _grpo_advantages, + "dr_grpo": _dr_grpo_advantages, + "dapo": _dapo_advantages, + } + fn = _dispatch.get(estimator) + if fn is None: + raise ValueError( + f"Unknown advantage estimator '{estimator}'. Must be one of {sorted(_dispatch.keys())}" + ) + return fn(rewards) + + +def _grpo_advantages(rewards: list[float]) -> list[float]: + """GRPO: zero-mean, variance-normalized advantages.""" + n = len(rewards) + mean_reward = sum(rewards) / n + centered = [r - mean_reward for r in rewards] + std = (sum(a * a for a in centered) / n) ** 0.5 + denom = max(std, 1e-8) + return [a / denom for a in centered] + + +def _dr_grpo_advantages(rewards: list[float]) -> list[float]: + """DR-GRPO: zero-mean, no variance normalization. + + Preserves reward magnitude signal that standard GRPO discards. + """ + n = len(rewards) + mean_reward = sum(rewards) / n + return [r - mean_reward for r in rewards] + + +def _dapo_advantages(rewards: list[float]) -> list[float]: + """DAPO: rank-based advantages. + + Converts rewards to fractional ranks (average rank for ties), then + normalizes to [-1, 1] range. Robust to outlier rewards since only + relative ordering matters. + """ + n = len(rewards) + # Compute fractional ranks (average rank for ties) + sorted_indices = sorted(range(n), key=lambda i: rewards[i]) + ranks = [0.0] * n + i = 0 + while i < n: + j = i + while j < n and rewards[sorted_indices[j]] == rewards[sorted_indices[i]]: + j += 1 + avg_rank = (i + j - 1) / 2.0 + for k in range(i, j): + ranks[sorted_indices[k]] = avg_rank + i = j + # Normalize to [-1, 1] range + max_rank = n - 1 if n > 1 else 1 + return [2.0 * r / max_rank - 1.0 for r in ranks] diff --git a/grail/trainer/algorithms/grpo.py b/grail/trainer/algorithms/grpo.py index 96bb69ab..4201dbb5 100644 --- a/grail/trainer/algorithms/grpo.py +++ b/grail/trainer/algorithms/grpo.py @@ -10,7 +10,6 @@ from __future__ import annotations import logging -import math from collections import defaultdict from dataclasses import dataclass from typing import TYPE_CHECKING, Any @@ -20,13 +19,14 @@ import torch.nn.functional as F from accelerate import Accelerator -from grail.shared.constants import ( +from grail.protocol.constants import ROLLOUTS_PER_PROBLEM +from grail.shared.config import ( GRPO_RANKING_REWARD_WEIGHT, GRPO_RANKING_VARIANCE_WEIGHT, IMPORTANCE_SAMPLING_LEVEL, - ROLLOUTS_PER_PROBLEM, is_kl_enabled, ) +from grail.trainer.advantages import compute_advantages as compute_group_advantages from grail.trainer.dashboard_logger import data_logger, step_logger from grail.trainer.metrics import ( KMetricsAggregator, @@ -271,26 +271,229 @@ def is_valid( return abs(advantage_sum) < advantage_tolerance -def _has_advantage_variance(group: GRPOGroup) -> bool: - """Check if a GRPO group has variance in advantage values. +def _chunked_lm_head_logprobs( + lm_head: torch.nn.Module, + shift_hidden: torch.Tensor, + shift_labels: torch.Tensor, + chunk_size: int, + return_entropy: bool, +) -> tuple[torch.Tensor, torch.Tensor | None]: + """Apply LM head in chunks and extract per-token logprobs (and optionally entropy). + + Processes the vocabulary projection in sequence-dimension chunks to avoid + materializing the full [batch, seq_len, vocab_size] logits tensor. Uses + flash-attn's Triton cross-entropy kernel when entropy is not needed (4.5x + faster), falling back to fp32 log_softmax + gather otherwise. + + Args: + lm_head: The model's language model head (linear projection to vocab). + shift_hidden: Hidden states shifted for next-token prediction [B, S-1, D]. + shift_labels: Token IDs shifted for next-token prediction [B, S-1]. + chunk_size: Number of sequence positions per LM head chunk. + return_entropy: Whether to compute per-token entropy alongside logprobs. + + Returns: + token_log_probs: [B, S-1] per-token log-probabilities. + entropy_per_token: [B, S-1] per-token entropy, or None if not requested. + """ + batch_size, shifted_seq_len = shift_labels.shape + device = shift_labels.device + + token_log_probs = torch.zeros(batch_size, shifted_seq_len, device=device) + entropy_per_token: torch.Tensor | None = None + if return_entropy: + entropy_per_token = torch.zeros(batch_size, shifted_seq_len, device=device) + + _triton_ce = None + try: + from flash_attn.ops.triton.cross_entropy import ( # type: ignore[import-not-found] + cross_entropy_loss as _triton_ce, + ) + except ImportError: + pass + + for chunk_start in range(0, shifted_seq_len, chunk_size): + chunk_end = min(chunk_start + chunk_size, shifted_seq_len) + + chunk_hidden = shift_hidden[:, chunk_start:chunk_end, :] + chunk_logits = lm_head(chunk_hidden) + chunk_labels = shift_labels[:, chunk_start:chunk_end] + + if _triton_ce is not None and not return_entropy: + # Fast path: Triton fused CE extracts logprobs without materializing + # the full [B, chunk, V] log_softmax tensor (~4.5x faster). + # Only available when entropy is not needed. + B_c, T_c, V_c = chunk_logits.shape + ce_loss, _ = _triton_ce( + chunk_logits.reshape(B_c * T_c, V_c), + chunk_labels.reshape(B_c * T_c), + ) + token_log_probs[:, chunk_start:chunk_end] = (-ce_loss).reshape(B_c, T_c) + del ce_loss + else: + # Standard path: log_softmax reused for both logprobs and entropy + # in a single pass over the [B, chunk, V] tensor. + chunk_log_probs = F.log_softmax(chunk_logits.float(), dim=-1) + token_log_probs[:, chunk_start:chunk_end] = chunk_log_probs.gather( + 2, chunk_labels.unsqueeze(-1) + ).squeeze(-1) + if return_entropy and entropy_per_token is not None: + entropy_per_token[:, chunk_start:chunk_end] = -( + chunk_log_probs.exp() * chunk_log_probs + ).sum(dim=-1) + del chunk_log_probs + + del chunk_logits + + return token_log_probs, entropy_per_token + + +def _partition_by_token_budget( + rollouts: list[tuple], + max_tokens: int, + max_length: int, +) -> list[list[tuple]]: + """Partition rollouts into micro-batches targeting a token budget per batch. - Groups with zero advantage variance (all rollouts have identical advantage) - are filtered out as they provide no learning signal. + Uses Greedy LPT (Longest Processing Time first): sort rollouts by length + descending, assign each to the micro-batch with the fewest total tokens. + This achieves <0.1% imbalance across micro-batches. + + When a rollout would cause a micro-batch to exceed max_tokens, a new + micro-batch is created instead (bin-packing constraint). + + Args: + rollouts: List of (GRPORollout, group_id) tuples. + max_tokens: Token budget per micro-batch. + max_length: Maximum sequence length (for truncation accounting). + + Returns: + List of micro-batches, each a list of (rollout, group_id) tuples. + """ + import heapq + + if not rollouts: + return [] + + # Sort longest-first for Greedy LPT + indexed = [(min(len(r[0].tokens), max_length), i, r) for i, r in enumerate(rollouts)] + indexed.sort(key=lambda x: x[0], reverse=True) + + # Min-heap: (total_tokens, batch_index, rollout_list) + batches: list[tuple[int, int, list[tuple]]] = [] + batch_counter = 0 + + for seq_len, _, rollout in indexed: + # Try to fit in the lightest existing batch + if batches and batches[0][0] + seq_len <= max_tokens: + total, idx, batch_list = heapq.heappop(batches) + batch_list.append(rollout) + heapq.heappush(batches, (total + seq_len, idx, batch_list)) + else: + # Start a new batch + heapq.heappush(batches, (seq_len, batch_counter, [rollout])) + batch_counter += 1 + + # Return in creation order for deterministic training + return [batch_list for _, _, batch_list in sorted(batches, key=lambda x: x[1])] + + +@dataclass +class _ModelArchInfo: + """Cached model architecture info for FLOPs computation.""" + + num_params: int + num_layers: int + num_heads: int + head_dim: int + linear_flops_per_token: float # 6 * P (forward + backward) + + @classmethod + def from_model(cls, model: Any) -> _ModelArchInfo | None: + """Extract architecture info from an HF model. Returns None on failure.""" + try: + cfg = model.config + num_params = sum(p.numel() for p in model.parameters()) + return cls( + num_params=num_params, + num_layers=cfg.num_hidden_layers, + num_heads=cfg.num_attention_heads, + head_dim=cfg.hidden_size // cfg.num_attention_heads, + linear_flops_per_token=6.0 * num_params, + ) + except Exception: # noqa: BLE001 + return None + + def compute_mfu( + self, + total_tokens: int, + avg_seq_len: float, + step_time_sec: float, + gpu_peak_tflops: float, + ) -> dict[str, float]: + """Compute MFU for one optimizer step. + + Uses the PaLM/Megatron convention: 6P per token for linear layers + (2P forward + 4P backward) plus 12 * H * D * L * seq_len per token + for attention (4*H*D*L forward, times 3 for fwd+bwd). GC recomputation + is excluded because MFU measures model efficiency, not implementation. + + Args: + total_tokens: All tokens processed through the model (prompt + completion). + avg_seq_len: Average sequence length (for attention FLOPs). + step_time_sec: Wall-clock time for this optimizer step. + gpu_peak_tflops: GPU peak in TFLOPS (e.g., 2250 for B200 BF16). + """ + if step_time_sec <= 0 or total_tokens <= 0: + return {} + + linear_flops = self.linear_flops_per_token * total_tokens + attn_flops = ( + 12.0 * self.num_heads * self.head_dim * avg_seq_len * self.num_layers * total_tokens + ) + total_flops = linear_flops + attn_flops + tflops = total_flops / step_time_sec / 1e12 + mfu = tflops / gpu_peak_tflops if gpu_peak_tflops > 0 else 0.0 + + return { + "mfu": round(mfu, 4), + "tflops": round(tflops, 1), + "tokens_per_sec": round(total_tokens / step_time_sec, 1), + } + + +def _has_reward_variance(group: GRPOGroup) -> bool: + """Check if a GRPO group has variance in reward values. + + Groups with identical rewards across all rollouts produce zero-variance + advantages under any estimation strategy and provide no learning signal. Args: group: The GRPO group to check Returns: - True if the group has advantage variance, False if all advantages are identical + True if the group has reward variance, False if all rewards are identical """ if not group.rollouts: return False - advantages = [r.advantage for r in group.rollouts] - first_advantage = advantages[0] + rewards = [r.reward for r in group.rollouts] + first_reward = rewards[0] + return any(rew != first_reward for rew in rewards) + - # Check if all advantages are the same - return any(adv != first_advantage for adv in advantages) +def _apply_advantages_to_groups(groups: list[GRPOGroup], estimator: str) -> None: + """Compute and assign advantages from rewards for each group. + + Args: + groups: List of GRPO groups with rewards already populated. + estimator: Advantage estimation strategy name. + """ + for group in groups: + rewards = [r.reward for r in group.rollouts] + advantages = compute_group_advantages(rewards, estimator=estimator) + for rollout, adv in zip(group.rollouts, advantages, strict=True): + rollout.advantage = adv def _is_valid_logprobs(logprobs: list[float] | None) -> bool: @@ -638,7 +841,7 @@ def _filter_valid_groups( # Stage 2: DAPO filtering - remove uninformative groups (zero advantage variance) # Groups where all rollouts have identical rewards provide no learning signal groups_with_variance: list[GRPOGroup] = [ - group for group in valid_groups if _has_advantage_variance(group) + group for group in valid_groups if _has_reward_variance(group) ] zero_variance_count: int = len(valid_groups) - len(groups_with_variance) if zero_variance_count > 0: @@ -879,6 +1082,10 @@ async def load_grpo_groups( GRPOGroup(group_id, rollouts) for group_id, rollouts in grouped.items() ] + # Compute advantages from raw rewards (trainer-side, ignoring miner-provided values) + adv_estimator = config.adv_estimator if config else "grpo" + _apply_advantages_to_groups(groups, adv_estimator) + # Compute training-set metrics BEFORE filtering invalid groups rollouts_per_problem = config.rollouts_per_problem if config else 5 prefilter_metrics = _compute_training_metrics( @@ -933,6 +1140,17 @@ def _unwrap_for_chunked_logits( except ImportError: pass + # Reject FSDP2 (fully_shard) — wrapped modules become FSDPModule subclasses. + # Calling model.model directly would bypass root-level all-gather/reduce-scatter + # hooks, desynchronizing NCCL collectives across ranks. + try: + from torch.distributed.fsdp import FSDPModule # type: ignore[attr-defined] + + if isinstance(model, FSDPModule): + return None + except ImportError: + pass + # Standard HuggingFace CausalLM: model.model (base) + model.lm_head base_model_prefix = getattr(model, "base_model_prefix", "") base = getattr(model, base_model_prefix, None) if base_model_prefix else None @@ -982,49 +1200,28 @@ def _compute_logprobs_chunked( ) hidden_states = base_out.last_hidden_state # [batch, seq_len, hidden_dim] - # Shift for next-token prediction: logits[t] predicts token[t+1] - shift_hidden = hidden_states[:, :-1, :] - shift_labels = input_ids[:, 1:] + # Skip prompt tokens for LM head: we only need logprobs for completion tokens. + # Due to the shift-by-1 (logits[t] predicts token[t+1]), we need hidden states + # starting at position (min_prompt_len - 1) to predict the first completion token. + # This avoids running the expensive LM head ([hidden_dim, vocab_size] matmul) + # on prompt tokens whose logprobs are never used. + min_prompt_len = min(prompt_lengths) + keep_from = max(0, min_prompt_len - 1) - batch_size, shifted_seq_len = shift_labels.shape - device = shift_labels.device + shift_hidden = hidden_states[:, keep_from:-1, :] + shift_labels = input_ids[:, keep_from + 1 :] - # Pre-allocate output tensors (no vocab dimension — small) - token_log_probs = torch.zeros(batch_size, shifted_seq_len, device=device) - entropy_per_token: torch.Tensor | None = None - if return_entropy: - entropy_per_token = torch.zeros(batch_size, shifted_seq_len, device=device) - - # Process lm_head in chunks to avoid materializing full vocab-sized tensors - for chunk_start in range(0, shifted_seq_len, chunk_size): - chunk_end = min(chunk_start + chunk_size, shifted_seq_len) - - chunk_hidden = shift_hidden[:, chunk_start:chunk_end, :] - chunk_logits = lm_head(chunk_hidden) # [batch, chunk_len, vocab_size] - - # fp32 log_softmax for numerical precision (same as non-chunked path) - chunk_log_probs = F.log_softmax(chunk_logits.float(), dim=-1) - - # Gather logprobs for the actual target tokens - chunk_labels = shift_labels[:, chunk_start:chunk_end] - token_log_probs[:, chunk_start:chunk_end] = chunk_log_probs.gather( - 2, chunk_labels.unsqueeze(-1) - ).squeeze(-1) - - # Entropy: -sum(p * log(p)) per position - if return_entropy and entropy_per_token is not None: - entropy_per_token[:, chunk_start:chunk_end] = -( - chunk_log_probs.exp() * chunk_log_probs - ).sum(dim=-1) - - # Free the chunk's vocab-sized tensors immediately - del chunk_logits, chunk_log_probs + token_log_probs, entropy_per_token = _chunked_lm_head_logprobs( + lm_head, shift_hidden, shift_labels, chunk_size, return_entropy + ) - # --- Extract completion-token results (identical logic to non-chunked path) --- + # Extract completion-token results. Adjust prompt_lengths to account for + # the keep_from offset applied above. + adjusted_prompt_lengths = [p - keep_from for p in prompt_lengths] return _extract_completion_logprobs( token_log_probs, entropy_per_token, - prompt_lengths, + adjusted_prompt_lengths, completion_lengths, return_per_token, return_entropy, @@ -1100,6 +1297,223 @@ def _extract_completion_logprobs( return seq_log_probs_tensor, per_token_padded +def _extract_completion_logprobs_packed( + token_log_probs: torch.Tensor, + entropy_per_token: torch.Tensor | None, + seq_lengths: list[int], + prompt_lengths: list[int], + completion_lengths: list[int], + return_per_token: bool, + return_entropy: bool, +) -> ( + torch.Tensor + | tuple[torch.Tensor, torch.Tensor] + | tuple[torch.Tensor, torch.Tensor, torch.Tensor] +): + """Extract per-sequence completion logprobs from packed (flat) token_log_probs. + + The input tensor is [1, total_tokens-1] from a packed forward pass where all + sequences are concatenated. This function uses seq_lengths offsets to locate + each sequence's completion tokens and re-pads to [B, max_comp_len] for + compatibility with all downstream loss computation code. + + The shift-by-1 indexing: logits[t] predicts token[t+1]. For sequence i's + k-th completion token at absolute position (offset + prompt_len + k), + the logprob is at position (offset + prompt_len + k - 1) in the shifted tensor. + + Cross-boundary positions (last token of seq_i predicting first token of seq_{i+1}) + are never accessed because extraction starts at offset + prompt_len - 1, which is + within the sequence. + """ + device = token_log_probs.device + total_shifted = token_log_probs.shape[1] + num_seqs = len(seq_lengths) + + if not return_per_token: + seq_log_probs: list[torch.Tensor] = [] + offset = 0 + for idx in range(num_seqs): + start_idx = offset + max(0, prompt_lengths[idx] - 1) + end_idx = min(offset + seq_lengths[idx] - 1, start_idx + completion_lengths[idx]) + end_idx = min(end_idx, total_shifted) + if end_idx > start_idx: + seq_log_probs.append(token_log_probs[0, start_idx:end_idx].sum()) + else: + seq_log_probs.append(torch.tensor(0.0, device=device)) + offset += seq_lengths[idx] + return torch.stack(seq_log_probs) + + max_comp_len = max(completion_lengths) if completion_lengths else 1 + per_token_padded = torch.zeros(num_seqs, max_comp_len, device=device) + seq_log_probs_tensor = torch.zeros(num_seqs, device=device) + entropies_list: list[torch.Tensor] = [] + + offset = 0 + for idx in range(num_seqs): + start_idx = offset + max(0, prompt_lengths[idx] - 1) + end_idx = min(offset + seq_lengths[idx] - 1, start_idx + completion_lengths[idx]) + end_idx = min(end_idx, total_shifted) + if end_idx > start_idx: + comp_logps = token_log_probs[0, start_idx:end_idx] + per_token_padded[idx, : len(comp_logps)] = comp_logps + seq_log_probs_tensor[idx] = comp_logps.sum() + if return_entropy and entropy_per_token is not None: + entropies_list.append(entropy_per_token[0, start_idx:end_idx].mean()) + else: + if return_entropy and entropy_per_token is not None: + entropies_list.append(torch.tensor(0.0, device=device)) + offset += seq_lengths[idx] + + if return_entropy and entropy_per_token is not None: + mean_entropies = torch.stack(entropies_list) + return seq_log_probs_tensor, per_token_padded, mean_entropies + + return seq_log_probs_tensor, per_token_padded + + +def _compute_logprobs_chunked_packed( + base_model: Any, + lm_head: torch.nn.Module, + input_ids: torch.Tensor, + position_ids: torch.Tensor, + seq_lengths: list[int], + prompt_lengths: list[int], + completion_lengths: list[int], + chunk_size: int, + return_per_token: bool = False, + return_entropy: bool = False, +) -> ( + torch.Tensor + | tuple[torch.Tensor, torch.Tensor] + | tuple[torch.Tensor, torch.Tensor, torch.Tensor] +): + """Compute logprobs from packed sequences using chunked LM head. + + Like _compute_logprobs_chunked but for packed [1, total_tokens] inputs. + Passes position_ids (with resets) and attention_mask=None so HF auto-detects + packing and uses flash_attn_varlen_func. + """ + # Model forward on packed input (attention_mask=None triggers packing detection) + base_out = base_model( + input_ids, + position_ids=position_ids, + attention_mask=None, + use_cache=False, + ) + hidden_states = base_out.last_hidden_state # [1, total_tokens, hidden_dim] + + # Shift for next-token prediction + shift_hidden = hidden_states[:, :-1, :] + shift_labels = input_ids[:, 1:] + + token_log_probs, entropy_per_token = _chunked_lm_head_logprobs( + lm_head, shift_hidden, shift_labels, chunk_size, return_entropy + ) + + return _extract_completion_logprobs_packed( + token_log_probs, + entropy_per_token, + seq_lengths, + prompt_lengths, + completion_lengths, + return_per_token, + return_entropy, + ) + + +def compute_logprobs_packed( + model: Any, + input_ids: torch.Tensor, + position_ids: torch.Tensor, + seq_lengths: list[int], + prompt_lengths: list[int], + completion_lengths: list[int], + return_per_token: bool = False, + return_entropy: bool = False, + *, + chunked: bool = False, + chunk_size: int = 256, +) -> ( + torch.Tensor + | tuple[torch.Tensor, torch.Tensor] + | tuple[torch.Tensor, torch.Tensor, torch.Tensor] +): + """Compute logprobs from packed [1, total_tokens] inputs. + + Mirrors compute_logprobs() but for packed sequences. Dispatches to chunked + or standard path. Output format is identical: [B, max_comp_len] padded tensors. + """ + if chunked: + parts = _unwrap_for_chunked_logits(model) + if parts is not None: + base_model, lm_head = parts + return _compute_logprobs_chunked_packed( + base_model, + lm_head, + input_ids, + position_ids, + seq_lengths, + prompt_lengths, + completion_lengths, + chunk_size, + return_per_token=return_per_token, + return_entropy=return_entropy, + ) + logger.warning( + "Chunked logits requested but model structure incompatible; " + "falling back to standard packed forward" + ) + + # Standard (non-chunked) packed path + outputs = model( + input_ids=input_ids, + position_ids=position_ids, + attention_mask=None, + ) + logits = outputs.logits # [1, total_tokens, vocab_size] + + shift_logits = logits[:, :-1, :].contiguous() + shift_labels = input_ids[:, 1:].contiguous() + + # Try Triton fused CE (avoids materializing full fp32 log_softmax tensor, + # saves ~18 GB for 48K tokens and is ~4.5x faster). Falls back to standard + # path when entropy is needed or Triton is unavailable. + _triton_ce_fn = None + if not return_entropy: + try: + from flash_attn.ops.triton.cross_entropy import ( # type: ignore[import-not-found] + cross_entropy_loss as _triton_ce_fn, + ) + except ImportError: + pass + + entropy_per_token = None + if _triton_ce_fn is not None: + B_s, T_s, V_s = shift_logits.shape + ce_loss, _ = _triton_ce_fn( + shift_logits.reshape(B_s * T_s, V_s), + shift_labels.reshape(B_s * T_s), + ) + token_log_probs = (-ce_loss).reshape(B_s, T_s) + del ce_loss + else: + log_probs = F.log_softmax(shift_logits.float(), dim=-1) + token_log_probs = log_probs.gather(2, shift_labels.unsqueeze(-1)).squeeze(-1) + if return_entropy: + entropy_per_token = -(log_probs.exp() * log_probs).sum(dim=-1) + del log_probs + + return _extract_completion_logprobs_packed( + token_log_probs, + entropy_per_token, + seq_lengths, + prompt_lengths, + completion_lengths, + return_per_token, + return_entropy, + ) + + def compute_logprobs( model: Any, input_ids: torch.Tensor, @@ -1183,22 +1597,33 @@ def compute_logprobs( shift_logits = logits[:, :-1, :].contiguous() shift_labels = input_ids[:, 1:].contiguous() - # Cast to float32 for precise log_softmax (even if model is bfloat16) - log_probs = F.log_softmax(shift_logits.float(), dim=-1) - token_log_probs = log_probs.gather( - 2, - shift_labels.unsqueeze(-1), - ).squeeze(-1) + # Try Triton fused CE (avoids materializing full fp32 log_softmax tensor, + # saves ~18 GB for long sequences and is ~4.5x faster). Falls back to + # standard path when entropy is needed or Triton is unavailable. + _triton_ce_fn = None + if not return_entropy: + try: + from flash_attn.ops.triton.cross_entropy import ( # type: ignore[import-not-found] + cross_entropy_loss as _triton_ce_fn, + ) + except ImportError: + pass - # Derive entropy from the same log_probs — no extra vocab-sized allocation. - # entropy = -sum(p * log(p)) = -sum(exp(log_p) * log_p) entropy_per_token: torch.Tensor | None = None - if return_entropy: - entropy_per_token = -(log_probs.exp() * log_probs).sum(dim=-1) - - # Free the large log_probs tensor now that we've extracted what we need. - # token_log_probs and entropy_per_token are [batch, seq_len-1] (no vocab dim). - del log_probs + if _triton_ce_fn is not None: + B_s, T_s, V_s = shift_logits.shape + ce_loss, _ = _triton_ce_fn( + shift_logits.reshape(B_s * T_s, V_s), + shift_labels.reshape(B_s * T_s), + ) + token_log_probs = (-ce_loss).reshape(B_s, T_s) + del ce_loss + else: + log_probs = F.log_softmax(shift_logits.float(), dim=-1) + token_log_probs = log_probs.gather(2, shift_labels.unsqueeze(-1)).squeeze(-1) + if return_entropy: + entropy_per_token = -(log_probs.exp() * log_probs).sum(dim=-1) + del log_probs return _extract_completion_logprobs( token_log_probs, @@ -1308,6 +1733,16 @@ def __init__( self.importance_sampling_level: str = raw_is_level logger.info(f"Using {self.importance_sampling_level}-level importance sampling") + # Validate advantage estimation strategy + self.adv_estimator: str = self.config.adv_estimator + valid_estimators = {"grpo", "dr_grpo", "dapo"} + if self.adv_estimator not in valid_estimators: + raise ValueError( + f"Invalid adv_estimator '{self.adv_estimator}'. " + f"Must be one of {sorted(valid_estimators)}" + ) + logger.info("Using %s advantage estimator", self.adv_estimator.upper()) + def _prepare_batch_tensors( self, batch_rollouts: list[GRPORollout], @@ -1401,7 +1836,14 @@ def _prepare_batch_tensors( }, ) - max_len = max(len(tokens) for tokens in batch_tokens) + # Pad to fixed max_length so tensor shapes are constant across micro-batches. + # This enables torch.compile to reuse compiled graphs and avoids recompilation + # per unique seq_len. The attention mask ensures padding tokens are ignored. + max_len = ( + self.config.max_length + if self.config.use_torch_compile + else max(len(tokens) for tokens in batch_tokens) + ) # Ensure pad_token_id is set (fallback to eos_token_id if needed) pad_id = tokenizer.pad_token_id if pad_id is None: @@ -1436,6 +1878,90 @@ def _prepare_batch_tensors( batch_rewards, ) + def _prepare_packed_batch_tensors( + self, + batch_rollouts: list[GRPORollout], + tokenizer: Any, + accelerator: Accelerator, + ) -> tuple[ + torch.Tensor, # input_ids [1, total_tokens] + torch.Tensor, # position_ids [1, total_tokens] + list[int], # prompt_lengths + list[int], # completion_lengths + list[float], # advantages + list[list[float]], # behavior_per_token_logprobs + list[float], # rewards + list[int], # seq_lengths (per-sequence token counts for unpacking) + ]: + """Pack batch sequences into a single flat tensor for flash_attn_varlen_func. + + Instead of padding all sequences to the longest, concatenates them into + [1, total_tokens] with position_ids that reset at each boundary. HF + Transformers auto-detects this and uses flash_attn_varlen_func, which + processes each sequence independently without cross-boundary attention. + + Output metadata (prompt_lengths, completion_lengths, advantages, etc.) + is identical to _prepare_batch_tensors. The seq_lengths list enables + downstream code to locate each sequence within the flat tensor. + """ + all_tokens: list[int] = [] + all_position_ids: list[int] = [] + seq_lengths: list[int] = [] + batch_prompt_lens: list[int] = [] + batch_comp_lens: list[int] = [] + batch_advantages: list[float] = [] + batch_behavior_per_token_logprobs: list[list[float]] = [] + batch_rewards: list[float] = [] + + for rollout in batch_rollouts: + tokens = rollout.tokens[: self.config.max_length] + seq_len = len(tokens) + + # Recalculate prompt/completion after truncation (same as padded path) + actual_prompt_len = min(rollout.prompt_length, self.config.max_length) + actual_comp_len = max( + 0, + min( + rollout.completion_length, + self.config.max_length - rollout.prompt_length, + ), + ) + + # Concatenate tokens and position_ids + all_tokens.extend(tokens) + all_position_ids.extend(range(seq_len)) + seq_lengths.append(seq_len) + + batch_prompt_lens.append(actual_prompt_len) + batch_comp_lens.append(actual_comp_len) + batch_advantages.append(rollout.advantage) + batch_rewards.append(rollout.reward) + + # Extract completion behavior logprobs (same logic as padded path) + tlp: list[float] = list((rollout.token_logprobs or [])[: self.config.max_length]) + expected_len = actual_prompt_len + actual_comp_len + if len(tlp) >= expected_len: + completion_logprobs = tlp[actual_prompt_len : actual_prompt_len + actual_comp_len] + else: + completion_logprobs = tlp[:actual_comp_len] + batch_behavior_per_token_logprobs.append(completion_logprobs) + + input_ids_tensor = torch.tensor([all_tokens], dtype=torch.long, device=accelerator.device) + position_ids_tensor = torch.tensor( + [all_position_ids], dtype=torch.long, device=accelerator.device + ) + + return ( + input_ids_tensor, + position_ids_tensor, + batch_prompt_lens, + batch_comp_lens, + batch_advantages, + batch_behavior_per_token_logprobs, + batch_rewards, + seq_lengths, + ) + def _normalize_advantages(self, advantages_tensor: torch.Tensor) -> torch.Tensor: """Normalize advantages with clipping and standardization. @@ -1509,11 +2035,14 @@ def _compute_policy_gradient_loss( ratios_pre_ceiling = torch.exp(log_ratio_seq_clamped) elif self.importance_sampling_level == "sequence": - # Sequence-level: sum logprobs first, then compute ratio + # Sequence-level: length-normalized log-ratio (GSPO-style geometric mean). + # Without normalization, summing log_ratios across thousands of tokens + # produces huge values that saturate PPO clipping on every sequence. # Shape: [batch_size] logprobs_current_sum = (logprobs_current_per_token * completion_mask).sum(dim=1) logprobs_old_sum = (logprobs_old_per_token * completion_mask).sum(dim=1) - log_ratio = logprobs_current_sum - logprobs_old_sum + seq_lengths = completion_mask.sum(dim=1).clamp(min=1.0) + log_ratio = (logprobs_current_sum - logprobs_old_sum) / seq_lengths if not _is_finite_tensor(log_ratio): logger.debug("Non-finite log-ratio before clamp; applying clamp") @@ -1895,8 +2424,68 @@ async def train_epoch( all_rollouts.sort(key=lambda item: (item[1], item[0].nonce)) - # Use micro batch size parameter - num_micro_batches = math.ceil(len(all_rollouts) / micro_batch_size) + # Split rollouts into mini-batches (one optimizer step each), then each + # mini-batch into micro-batches (forward/backward passes). + # + # With token-budget mode (max_tokens_per_micro_batch > 0): + # - Mini-batch size = grad_accum_steps * avg_seqs_per_micro_batch ≈ effective batch + # - Micro-batches are variable-sized, targeting a token budget + # - Loss scaled by len(micro) / len(mini_batch) (veRL pattern) + # + # With fixed mode: + # - Mini-batch size = micro_batch_size * grad_accum_steps + # - Micro-batches are fixed-sized + # - Loss scaled by 1 / grad_accum_steps (equivalent) + total_rollouts_count = len(all_rollouts) + use_token_budget = config.max_tokens_per_micro_batch > 0 + + if use_token_budget: + # Token-budget: partition ALL rollouts by token budget first, + # then group micro-batches into mini-batches of grad_accum_steps each. + all_micro_partitions = _partition_by_token_budget( + all_rollouts, + max_tokens=config.max_tokens_per_micro_batch, + max_length=config.max_length, + ) + num_micro_batches = len(all_micro_partitions) + + # Group micro-batches into mini-batches (each gets one optimizer step). + # With token-budget batching, compute from effective_batch_size (rollouts + # per optimizer step) rather than fixed grad_accum_steps, since the number + # of micro-batches varies per epoch. + if config.effective_batch_size > 0: + target_steps = max(1, total_rollouts_count // config.effective_batch_size) + else: + target_steps = max(1, num_micro_batches // grad_accum_steps) + # Ceiling division ensures all micro-batches fit in exactly target_steps + # groups, preventing tiny remainders with noisy gradients. + grad_accum_steps = max(1, -(-num_micro_batches // target_steps)) + + mini_batches: list[list[list[tuple]]] = [] + for i in range(0, num_micro_batches, grad_accum_steps): + mini_batches.append(all_micro_partitions[i : i + grad_accum_steps]) + + logger.info( + "Token-budget batching: %d micro-batches in %d mini-batches " + "(budget=%d tokens, accum=%d, %d rollouts)", + num_micro_batches, + len(mini_batches), + config.max_tokens_per_micro_batch, + grad_accum_steps, + total_rollouts_count, + ) + else: + # Fixed mode: chunk rollouts into micro-batches of micro_batch_size, + # then group into mini-batches of grad_accum_steps each. + all_micro_partitions = [] + for i in range(0, total_rollouts_count, micro_batch_size): + chunk = all_rollouts[i : i + micro_batch_size] + all_micro_partitions.append(chunk) + num_micro_batches = len(all_micro_partitions) + + mini_batches = [] + for i in range(0, num_micro_batches, grad_accum_steps): + mini_batches.append(all_micro_partitions[i : i + grad_accum_steps]) epoch_metrics: dict[str, list[float]] = defaultdict(list) grad_accum_counter = 0 @@ -1917,9 +2506,21 @@ async def train_epoch( "kl_sum": 0.0, "ratio_clip_sum": 0.0, "ratio_ceiling_sum": 0.0, + "total_model_tokens": 0, + "avg_seq_len_sum": 0.0, } + # MFU computation: extract model architecture once, track step timing + import os + import time as _time + + _arch_info = _ModelArchInfo.from_model(model) + _gpu_peak_tflops = float(os.getenv("GRAIL_GPU_PEAK_TFLOPS", "2250")) + _step_start_time = _time.monotonic() + def reset_actual_batch_tracker() -> None: + nonlocal _step_start_time + _step_start_time = _time.monotonic() actual_batch_tracker.update( { "micro_batches": 0, @@ -1938,6 +2539,8 @@ def reset_actual_batch_tracker() -> None: "kl_sum": 0.0, "ratio_clip_sum": 0.0, "ratio_ceiling_sum": 0.0, + "total_model_tokens": 0, + "avg_seq_len_sum": 0.0, } ) @@ -1953,23 +2556,105 @@ def reset_actual_batch_tracker() -> None: input_ids_tensor: torch.Tensor | None = None attention_mask_tensor: torch.Tensor | None = None + # Flatten mini-batches back to a flat micro-batch list for the training loop, + # but track which mini-batch each micro-batch belongs to for loss scaling. + flat_micro_batches: list[list[tuple]] = [] + micro_to_mini_rollout_count: list[int] = [] + for mini_batch_micros in mini_batches: + mini_rollout_count = sum(len(mb) for mb in mini_batch_micros) + for micro in mini_batch_micros: + flat_micro_batches.append(micro) + micro_to_mini_rollout_count.append(mini_rollout_count) + num_micro_batches = len(flat_micro_batches) + _local_real_micro_count = num_micro_batches + + # ── FSDP2 collective sync ── + # Token-budget packing can produce different micro-batch counts per DP + # rank. FSDP2 requires all ranks to execute the same number of + # forward/backward passes (each triggers all-gather + reduce-scatter). + # Synchronize counts and pad the shorter rank with dummy passes. + import torch.distributed as _dist + + if _dist.is_initialized() and _dist.get_world_size() > 1: + _sync_dev = accelerator.device + _sync_rank = _dist.get_rank() + # Sync grad_accum_steps so optimizer steps happen at the same indices + _gas_t = torch.tensor([grad_accum_steps], dtype=torch.int64, device=_sync_dev) + _dist.all_reduce(_gas_t, op=_dist.ReduceOp.MAX) + grad_accum_steps = int(_gas_t.item()) + + # Sync micro-batch count, round up to multiple of grad_accum_steps + _cnt_t = torch.tensor([num_micro_batches], dtype=torch.int64, device=_sync_dev) + _dist.all_reduce(_cnt_t, op=_dist.ReduceOp.MAX) + _global_max = int(_cnt_t.item()) + _padded = -(-_global_max // grad_accum_steps) * grad_accum_steps + _num_dummy = _padded - num_micro_batches + print( + f"[Rank {_sync_rank}] FSDP2 sync: local={num_micro_batches}, " + f"global_max={_global_max}, padded={_padded}, dummy={_num_dummy}, " + f"accum={grad_accum_steps}", + flush=True, + ) + num_micro_batches = _padded + for micro_idx in range(num_micro_batches): - start_idx = micro_idx * micro_batch_size - end_idx = min(start_idx + micro_batch_size, len(all_rollouts)) - if start_idx >= len(all_rollouts): - break - batch_rollouts = [all_rollouts[i][0] for i in range(start_idx, end_idx)] - - # Step 1: Prepare batch tensors from rollouts - ( - input_ids_tensor, - attention_mask_tensor, - batch_prompt_lens, - batch_comp_lens, - batch_advantages, - batch_behavior_per_token_logprobs, - batch_rewards, - ) = self._prepare_batch_tensors(batch_rollouts, tokenizer, accelerator) + # ── Dummy micro-batch for FSDP2 collective sync ── + # Must mirror ALL NCCL collectives from the real path (FSDP2 forward/backward + # + DAPO gather + any other distributed ops) to keep ranks in lockstep. + if micro_idx >= _local_real_micro_count: + with accelerator.autocast(): + _dummy_ids = torch.zeros(1, 2, dtype=torch.long, device=accelerator.device) + _dummy_out = model( + input_ids=_dummy_ids, + attention_mask=torch.ones_like(_dummy_ids), + ) + _dummy_loss = _dummy_out.logits.sum() * 0.0 + accelerator.backward(_dummy_loss) + del _dummy_ids, _dummy_out, _dummy_loss + # Mirror DAPO gather collective (Step 6 in real path) + if self.grpo_variant == "dapo": + _zero_tok = torch.zeros(1, device=accelerator.device) + accelerator.gather(_zero_tok) + grad_accum_counter += 1 + if grad_accum_counter >= grad_accum_steps: + # Must mirror clip_grad_norm_ (has FSDP2 all_reduce for total norm) + torch.nn.utils.clip_grad_norm_(model.parameters(), self.config.grad_clip) + optimizer.step() + self.optimizer_step_count += 1 + optimizer.zero_grad(set_to_none=True) + grad_accum_counter = 0 + continue + + micro_partition = flat_micro_batches[micro_idx] + batch_rollouts = [r[0] for r in micro_partition] + + # Step 1: Prepare batch tensors (packed or padded) + _use_packing = config.use_sequence_packing + seq_lengths: list[int] = [] + position_ids_tensor: torch.Tensor | None = None + attention_mask_tensor: torch.Tensor | None = None + + if _use_packing: + ( + input_ids_tensor, + position_ids_tensor, + batch_prompt_lens, + batch_comp_lens, + batch_advantages, + batch_behavior_per_token_logprobs, + batch_rewards, + seq_lengths, + ) = self._prepare_packed_batch_tensors(batch_rollouts, tokenizer, accelerator) + else: + ( + input_ids_tensor, + attention_mask_tensor, + batch_prompt_lens, + batch_comp_lens, + batch_advantages, + batch_behavior_per_token_logprobs, + batch_rewards, + ) = self._prepare_batch_tensors(batch_rollouts, tokenizer, accelerator) advantages_tensor = torch.tensor( batch_advantages, @@ -1978,21 +2663,45 @@ def reset_actual_batch_tracker() -> None: ) # Step 2: Compute current policy logprobs and entropy in a single forward pass. - # Fusing entropy here avoids a redundant forward that would double peak - # GPU memory from vocab-sized tensors (~6 GB each at seq_len=10K). - _want_entropy = self.config.entropy_coef > 0.0 + # Under FSDP2 (non-chunked path), skip entropy to avoid materializing the + # full fp32 log_softmax tensor (~27 GB for 48K tokens). The Triton fused + # CE fast path is only available when entropy is not needed. + _is_fsdp2 = False + try: + from torch.distributed.fsdp import FSDPModule # type: ignore[attr-defined] + + _is_fsdp2 = isinstance(model, FSDPModule) + except ImportError: + pass + _want_entropy = not _is_fsdp2 with accelerator.autocast(): - _logprob_result = compute_logprobs( - model, - input_ids_tensor, - attention_mask_tensor, - batch_prompt_lens, - batch_comp_lens, - return_per_token=True, - return_entropy=_want_entropy, - chunked=config.chunked_logits, - chunk_size=config.logit_chunk_size, - ) + if _use_packing: + assert position_ids_tensor is not None + _logprob_result = compute_logprobs_packed( + model, + input_ids_tensor, + position_ids_tensor, + seq_lengths, + batch_prompt_lens, + batch_comp_lens, + return_per_token=True, + return_entropy=_want_entropy, + chunked=config.chunked_logits, + chunk_size=config.logit_chunk_size, + ) + else: + assert attention_mask_tensor is not None + _logprob_result = compute_logprobs( + model, + input_ids_tensor, + attention_mask_tensor, + batch_prompt_lens, + batch_comp_lens, + return_per_token=True, + return_entropy=_want_entropy, + chunked=config.chunked_logits, + chunk_size=config.logit_chunk_size, + ) if _want_entropy: assert isinstance(_logprob_result, tuple) and len(_logprob_result) == 3 logprobs_current_sum, logprobs_current_per_token, entropies = _logprob_result @@ -2002,22 +2711,33 @@ def reset_actual_batch_tracker() -> None: entropies = None if torch.cuda.is_available(): + _log_batch = len(batch_rollouts) + _log_seqlen = ( + sum(seq_lengths) // _log_batch if _use_packing else input_ids_tensor.shape[1] + ) logger.debug( - "MEMORY after_logprobs alloc_gb=%.2f reserved_gb=%.2f seq_len=%d batch=%d", + "MEMORY after_logprobs alloc_gb=%.2f reserved_gb=%.2f seq_len=%d micro_batch=%d packed=%s", torch.cuda.memory_allocated() / (1024**3), torch.cuda.memory_reserved() / (1024**3), - input_ids_tensor.shape[1], - input_ids_tensor.shape[0], + _log_seqlen, + _log_batch, + _use_packing, ) + # FSDP2 safety: after the model forward, NEVER skip backward. + # FSDP2 reduce-scatter hooks fire during backward; skipping would + # desynchronize NCCL collectives across ranks. Instead, flag the + # micro-batch as "skip" and run a zero-loss backward at the end. + _fsdp2_skip_micro_batch = False + if not _is_finite_tensor(logprobs_current_sum): cur_min = torch.nan_to_num(logprobs_current_sum).min().item() cur_max = torch.nan_to_num(logprobs_current_sum).max().item() logger.warning( - "Non-finite current logprobs; skipping batch", + "Non-finite current logprobs; skipping micro-batch", extra={"min": float(cur_min), "max": float(cur_max)}, ) - continue + _fsdp2_skip_micro_batch = True # Step 3: Prepare behavior policy logprobs and completion mask for importance sampling # Convert per-token behavior logprobs to padded tensor matching completion shape @@ -2041,42 +2761,55 @@ def reset_actual_batch_tracker() -> None: # Validate behavior logprobs logprobs_old_sum = (logprobs_old_per_token * completion_mask).sum(dim=1) - if not _is_finite_tensor(logprobs_old_sum): + if not _fsdp2_skip_micro_batch and not _is_finite_tensor(logprobs_old_sum): old_min = torch.nan_to_num(logprobs_old_sum).min().item() old_max = torch.nan_to_num(logprobs_old_sum).max().item() logger.warning( - "Non-finite old/behavior logprobs; skipping batch", + "Non-finite old/behavior logprobs; skipping micro-batch", extra={"min": float(old_min), "max": float(old_max)}, ) - continue + _fsdp2_skip_micro_batch = True # Step 4: Compute reference model logprobs for KL divergence penalty (only if enabled) logprobs_ref_per_token = None if kl_enabled: if ref_model is not None: with torch.no_grad(), accelerator.autocast(): - _ref_result = compute_logprobs( - ref_model, - input_ids_tensor, - attention_mask_tensor, - batch_prompt_lens, - batch_comp_lens, - return_per_token=True, - chunked=config.chunked_logits, - chunk_size=config.logit_chunk_size, - ) + if _use_packing: + assert position_ids_tensor is not None + _ref_result = compute_logprobs_packed( + ref_model, + input_ids_tensor, + position_ids_tensor, + seq_lengths, + batch_prompt_lens, + batch_comp_lens, + return_per_token=True, + chunked=config.chunked_logits, + chunk_size=config.logit_chunk_size, + ) + else: + assert attention_mask_tensor is not None + _ref_result = compute_logprobs( + ref_model, + input_ids_tensor, + attention_mask_tensor, + batch_prompt_lens, + batch_comp_lens, + return_per_token=True, + chunked=config.chunked_logits, + chunk_size=config.logit_chunk_size, + ) assert isinstance(_ref_result, tuple) and len(_ref_result) == 2 logprobs_ref_sum, logprobs_ref_per_token = _ref_result - if not _is_finite_tensor(logprobs_ref_sum): + if not _fsdp2_skip_micro_batch and not _is_finite_tensor(logprobs_ref_sum): ref_min = torch.nan_to_num(logprobs_ref_sum).min().item() ref_max = torch.nan_to_num(logprobs_ref_sum).max().item() logger.warning( - "Non-finite reference logprobs; skipping batch", + "Non-finite reference logprobs; skipping micro-batch", extra={"min": float(ref_min), "max": float(ref_max)}, ) - # Free tensors before continuing - del logprobs_ref_sum, logprobs_ref_per_token - continue + _fsdp2_skip_micro_batch = True # Delete reference sum to free memory (only need per-token) del logprobs_ref_sum else: @@ -2125,65 +2858,85 @@ def reset_actual_batch_tracker() -> None: # Step 9: Entropy regularization (already computed in Step 2's forward pass) if entropies is not None: - if not _is_finite_tensor(entropies): - logger.warning("Non-finite entropies; skipping batch") - continue - loss_entropy = -self.config.entropy_coef * entropies.mean() + if not _fsdp2_skip_micro_batch and not _is_finite_tensor(entropies): + logger.warning("Non-finite entropies; skipping micro-batch") + _fsdp2_skip_micro_batch = True + loss_entropy = ( + -self.config.entropy_coef * entropies.mean() + if not _fsdp2_skip_micro_batch + else torch.tensor(0.0, device=accelerator.device) + ) else: entropies = torch.zeros(len(batch_rollouts), device=accelerator.device) loss_entropy = torch.tensor(0.0, device=accelerator.device) # Step 10: Aggregate total loss - loss_total = loss_pg + loss_kl + loss_entropy + # Zero-loss fallback flows through the model graph so backward() + # still touches every parameter (required for FSDP2 collectives). + _zero_loss = logprobs_current_sum.sum() * 0.0 + if _fsdp2_skip_micro_batch: + loss_total = _zero_loss + else: + loss_total = loss_pg + loss_kl + loss_entropy + if not torch.isfinite(loss_total): + logger.warning( + "Non-finite total loss; skipping micro-batch", + extra={ + "loss_pg": float(torch.nan_to_num(loss_pg).item()), + "loss_kl": float(torch.nan_to_num(loss_kl).item()), + "loss_entropy": float(torch.nan_to_num(loss_entropy).item()), + }, + ) + _fsdp2_skip_micro_batch = True + loss_total = _zero_loss + + if not _fsdp2_skip_micro_batch: + micro_sequence_count = len(batch_rollouts) + micro_token_count = float(completion_mask.sum().item()) + reward_sum = float(sum(batch_rewards)) if batch_rewards else 0.0 + + # Track total tokens through the model (prompt+completion) and avg seq_len for MFU + micro_total_tokens = input_ids_tensor.numel() # B*S or 1*total_packed + actual_batch_tracker["total_model_tokens"] += micro_total_tokens + if _use_packing and seq_lengths: + actual_batch_tracker["avg_seq_len_sum"] += sum(seq_lengths) + else: + actual_batch_tracker["avg_seq_len_sum"] += float( + input_ids_tensor.shape[1] * micro_sequence_count + ) - # Skip backward on non-finite loss to avoid corrupting optimizer state - if not torch.isfinite(loss_total): - loss_pg_v = torch.nan_to_num(loss_pg).item() - loss_kl_v = torch.nan_to_num(loss_kl).item() - loss_ent_v = torch.nan_to_num(loss_entropy).item() - logger.warning( - "Non-finite total loss; skipping batch", - extra={ - "loss_pg": float(loss_pg_v), - "loss_kl": float(loss_kl_v), - "loss_entropy": float(loss_ent_v), - }, + actual_batch_tracker["micro_batches"] += 1 + actual_batch_tracker["sequence_count"] += float(micro_sequence_count) + actual_batch_tracker["token_count"] += micro_token_count + actual_batch_tracker["loss_total_sum"] += float(loss_total.item()) + actual_batch_tracker["loss_pg_sum"] += float(loss_pg.item()) + actual_batch_tracker["loss_kl_sum"] += float(loss_kl.item()) + actual_batch_tracker["loss_entropy_sum"] += float(loss_entropy.item()) + actual_batch_tracker["adv_sum"] += float(advantages_tensor.sum().item()) + actual_batch_tracker["adv_count"] += float(advantages_tensor.numel()) + actual_batch_tracker["reward_sum"] += reward_sum + actual_batch_tracker["reward_count"] += float(len(batch_rewards)) + actual_batch_tracker["entropy_sum"] += float(entropies.sum().item()) + actual_batch_tracker["entropy_count"] += float(entropies.numel()) + actual_batch_tracker["kl_sum"] += float(kl_value) + ratio_weight = ( + micro_token_count + if self.importance_sampling_level == "token" + else float(micro_sequence_count) ) - continue - - micro_sequence_count = len(batch_rollouts) - micro_token_count = float(completion_mask.sum().item()) - reward_sum = float(sum(batch_rewards)) if batch_rewards else 0.0 - - actual_batch_tracker["micro_batches"] += 1 - actual_batch_tracker["sequence_count"] += float(micro_sequence_count) - actual_batch_tracker["token_count"] += micro_token_count - actual_batch_tracker["loss_total_sum"] += float(loss_total.item()) - actual_batch_tracker["loss_pg_sum"] += float(loss_pg.item()) - actual_batch_tracker["loss_kl_sum"] += float(loss_kl.item()) - actual_batch_tracker["loss_entropy_sum"] += float(loss_entropy.item()) - actual_batch_tracker["adv_sum"] += float(advantages_tensor.sum().item()) - actual_batch_tracker["adv_count"] += float(advantages_tensor.numel()) - actual_batch_tracker["reward_sum"] += reward_sum - actual_batch_tracker["reward_count"] += float(len(batch_rewards)) - actual_batch_tracker["entropy_sum"] += float(entropies.sum().item()) - actual_batch_tracker["entropy_count"] += float(entropies.numel()) - actual_batch_tracker["kl_sum"] += float(kl_value) - ratio_weight = ( - micro_token_count - if self.importance_sampling_level == "token" - else float(micro_sequence_count) - ) - actual_batch_tracker["ratio_clip_sum"] += ratio_clip_frac_val * ratio_weight - actual_batch_tracker["ratio_ceiling_sum"] += ratio_ceiling_frac_val * ratio_weight - - # Step 11: Perform gradient accumulation and optimization - # Zero gradients before backward pass (only at start of accumulation) + actual_batch_tracker["ratio_clip_sum"] += ratio_clip_frac_val * ratio_weight + actual_batch_tracker["ratio_ceiling_sum"] += ratio_ceiling_frac_val * ratio_weight + + # Step 11: Gradient accumulation with veRL-style loss scaling. + # Each micro-batch's loss is scaled by its rollout count relative to + # the mini-batch total. This ensures each sequence contributes equally + # regardless of micro-batch sizes (critical for token-budget batching). + # With fixed batching this is equivalent to 1/grad_accum_steps. if grad_accum_counter == 0: optimizer.zero_grad(set_to_none=True) - - # Scale loss by accumulation steps to keep effective LR stable - scaled_loss = loss_total / float(grad_accum_steps) + mini_rollout_count = micro_to_mini_rollout_count[micro_idx] + loss_scale = len(batch_rollouts) / max(1, mini_rollout_count) + scaled_loss = loss_total * loss_scale accelerator.backward(scaled_loss) grad_accum_counter += 1 @@ -2195,7 +2948,7 @@ def reset_actual_batch_tracker() -> None: ) grad_norm_scalar = None - # Only step optimizer and clip gradients every N accumulation steps + # Step optimizer at mini-batch boundaries (every grad_accum_steps micro-batches) if grad_accum_counter >= grad_accum_steps: # Clip gradients in fp32 (no mixed precision) grad_norm = torch.nn.utils.clip_grad_norm_( @@ -2215,7 +2968,7 @@ def reset_actual_batch_tracker() -> None: adv_mean = advantages_tensor.mean().item() adv_std = advantages_tensor.std().item() logger.warning( - "NaN/Inf gradient norm detected; skipping batch", + "NaN/Inf gradient norm detected; skipping optimizer step", extra={ "loss_total": float(loss_tot_v), "log_ratio_mean": float(lr_mean), @@ -2325,11 +3078,32 @@ def reset_actual_batch_tracker() -> None: except (IndexError, KeyError): pass step_payload["kl_coef"] = current_kl_coef + + # Compute and log MFU (Model FLOPs Utilization) + if _arch_info is not None: + step_time = _time.monotonic() - _step_start_time + total_model_toks = int(actual_batch_tracker["total_model_tokens"]) + seq_count = actual_batch_tracker["sequence_count"] + avg_seq = ( + actual_batch_tracker["avg_seq_len_sum"] / max(1.0, seq_count) + if seq_count > 0 + else 0.0 + ) + mfu_metrics = _arch_info.compute_mfu( + total_tokens=total_model_toks, + avg_seq_len=avg_seq, + step_time_sec=step_time, + gpu_peak_tflops=_gpu_peak_tflops, + ) + step_payload.update(mfu_metrics) + step_logger.emit(step_payload) reset_actual_batch_tracker() - # Step 12: Collect batch metrics for epoch aggregation + # Step 12: Collect batch metrics for epoch aggregation (skip for dropped micro-batches) + if _fsdp2_skip_micro_batch: + continue self._collect_batch_metrics( epoch_metrics, loss_total, diff --git a/grail/trainer/checkpoint_publisher.py b/grail/trainer/checkpoint_publisher.py index 566d4848..b48e5f52 100644 --- a/grail/trainer/checkpoint_publisher.py +++ b/grail/trainer/checkpoint_publisher.py @@ -46,19 +46,22 @@ encode_sparse_delta_v3, encode_sparse_delta_v3_1, ) +from grail.protocol.constants import ( + CHECKPOINT_PREFIX, + CHECKPOINT_TYPE_DELTA, + CHECKPOINT_TYPE_FULL, + CURRENT_ENV_ID, + WINDOW_LENGTH, +) from grail.shared.checkpoint_paths import ( checkpoint_delta_prefix, checkpoint_full_prefix, checkpoint_ready_marker_key, checkpoint_window_prefix, ) -from grail.shared.constants import ( +from grail.shared.config import ( BASE_CHECKPOINT_RETENTION_LIMIT, - CHECKPOINT_PREFIX, - CHECKPOINT_TYPE_DELTA, - CHECKPOINT_TYPE_FULL, CLEANUP_INTERVAL_UPLOADS, - CURRENT_ENV_ID, DELTA_CHECKPOINT_RETENTION_LIMIT, DELTA_CODEC_FORMAT, DELTA_THRESHOLD, @@ -71,7 +74,6 @@ TRAINER_MICRO_BATCH_SIZE, TRAINER_WARMUP_STEPS, UPLOAD_TIMEOUT, - WINDOW_LENGTH, ) from grail.shared.safetensors_utils import load_model_state_dict @@ -209,6 +211,34 @@ def _parse_float_param( return float(default) +def get_default_thinking_mode() -> str: + """Get the trainer-side thinking mode. + + Reads ``GRAIL_THINKING_MODE`` once on the trainer host and validates it. + The trainer is the single source of truth: it bakes the value into every + published checkpoint's metadata so the miner and validator can read it + from there instead of their own (potentially diverging) env var. + + Allowed values: + - "instructed" (DEFAULT): custom ````/```` + for thinking and ````/```` for the final answer, + injected via a system prompt + a custom ChatML template. This is the + mode grail builds entirely itself, so it's portable across base + models that don't ship a thinking-aware chat template. + - "native": model's built-in thinking tokens (e.g. Qwen3 ````) + via the model's own chat template; ```` for the answer. + + Invalid values raise ``ValueError`` so the trainer fails loudly at + startup instead of silently publishing an unverifiable mode. + """ + raw = os.getenv("GRAIL_THINKING_MODE", "instructed").strip().lower() + if raw not in ("native", "instructed"): + raise ValueError( + f"GRAIL_THINKING_MODE='{raw}' is invalid; must be 'native' or 'instructed'" + ) + return raw + + def get_default_generation_params() -> dict[str, Any]: """Get default generation parameters from environment variables. @@ -218,8 +248,21 @@ def get_default_generation_params() -> dict[str, Any]: Returns: Dictionary of validated generation parameters """ + # max_tokens default is 2048: matches the production policy on + # basilica-grail-tester and avoids the historical 8192-vs-trainer + # mismatch that caused validator hard-rejects. The protocol cap + # (MAX_NEW_TOKENS_PROTOCOL_CAP=8192 in grail/protocol/constants.py) + # still acts as the immutable upper bound; this is just the default + # the trainer publishes per checkpoint when GRAIL_GEN_MAX_TOKENS is + # unset. + # + # Each range here MUST match the validation in + # grail/environments/backends/base.py::GenerationParams.from_checkpoint_metadata + # exactly. If they drift, the trainer will publish a value the miner + # rejects with ProtocolViolationError, and the miner skips the + # affected window. Keep them in sync. return { - "max_tokens": _parse_int_param("GRAIL_GEN_MAX_TOKENS", "8192", min_val=1, max_val=16384), + "max_tokens": _parse_int_param("GRAIL_GEN_MAX_TOKENS", "2048", min_val=1, max_val=16384), "temperature": _parse_float_param( "GRAIL_GEN_TEMPERATURE", "0.7", min_val=0.01, max_val=2.0 ), @@ -367,38 +410,34 @@ def _parse_checkpoint_inventory(keys: list[str]) -> tuple[list[int], list[int], return all_windows_sorted, full_windows_sorted, delta_windows_sorted -async def _fetch_delta_anchor_window( +async def _fetch_delta_chain_info( delta_window: int, credentials: Any, - anchor_cache: dict[int, int] | None = None, -) -> int | None: - """Fetch the anchor_window a delta checkpoint depends on for recovery. + chain_cache: dict[int, tuple[int | None, int | None]] | None = None, +) -> tuple[int | None, int | None]: + """Fetch (anchor_window, prev_window) for a delta checkpoint. - For chained deltas, each delta has: + Each delta has: - prev_window: immediate predecessor (for computing the delta) - anchor_window: nearest FULL checkpoint (for recovery/reconstruction) - This function retrieves the anchor_window, which is the FULL checkpoint - that must be retained for this delta to be usable. + Both are needed for chain-aware retention: anchor_window identifies the FULL + checkpoint to keep, prev_window enables walking the chain to find all + intermediate deltas that must also be kept. Args: - delta_window: The delta checkpoint window - credentials: R2 credentials - anchor_cache: Optional cache of {delta_window: anchor_window} mappings. - If provided and delta_window is in cache, returns cached value - without network call. If not in cache, fetches and caches the result. + delta_window: The delta checkpoint window. + credentials: R2 credentials. + chain_cache: Optional cache of {delta_window: (anchor, prev)} tuples. + Reused across cleanup calls to avoid redundant metadata downloads. Returns: - The anchor_window from metadata, or None if unavailable + (anchor_window, prev_window), either may be None if unavailable. """ - # Check cache first to avoid redundant downloads - if anchor_cache is not None and delta_window in anchor_cache: - return anchor_cache[delta_window] + if chain_cache is not None and delta_window in chain_cache: + return chain_cache[delta_window] from grail.infrastructure.comms import download_file_chunked - - # Delta checkpoints store their metadata under the DELTA sub-prefix so they can - # coexist with a FULL checkpoint at the same window. from grail.shared.checkpoint_paths import checkpoint_delta_metadata_key metadata_key = checkpoint_delta_metadata_key(delta_window) @@ -411,39 +450,42 @@ async def _fetch_delta_anchor_window( if metadata_bytes: metadata = json.loads(metadata_bytes.decode("utf-8")) anchor = metadata.get("anchor_window") - # Cache the result if cache is provided - if anchor_cache is not None and anchor is not None: - anchor_cache[delta_window] = anchor - return anchor + prev = metadata.get("prev_window") + result = (anchor, prev) + if chain_cache is not None: + chain_cache[delta_window] = result + return result except Exception as exc: - logger.warning("Failed to read metadata for delta %d: %s", delta_window, exc) - return None + logger.warning("Failed to read chain info for delta %d: %s", delta_window, exc) + return (None, None) async def _compute_keep_windows( inventory: tuple[list[int], list[int], list[int]], credentials: Any, - anchor_cache: dict[int, int] | None = None, + chain_cache: dict[int, tuple[int | None, int | None]] | None = None, ) -> set[int]: - """Calculate which checkpoint windows to retain with DELTA dependency tracking. + """Calculate which checkpoint windows to retain with chain-aware dependency tracking. - For chained deltas, we need to keep entire chains from anchor (FULL) to tip. - This function combines two strategies: + Combines three retention strategies: - 1. Chain-based retention: Keep current anchor + chain, previous anchor + chain - (from shared retention_utils) - 2. Dependency-based retention: Ensure any retained DELTA has its anchor FULL + 1. Arithmetic retention: Keep windows near computed anchor positions + (from shared retention_utils, serves as baseline). + 2. Recency retention: Keep latest N FULL and M DELTA checkpoints. + 3. Chain-walk retention: For each retained DELTA, walk its prev_window chain + back to its anchor, keeping every intermediate delta. This prevents + breaking chains when the trainer skips anchor windows. Args: - inventory: Parsed checkpoint inventory (all_windows, full_windows, delta_windows) - credentials: R2 credentials for querying checkpoint metadata - anchor_cache: Optional cache of {delta_window: anchor_window} mappings. + inventory: Parsed checkpoint inventory (all_windows, full_windows, delta_windows). + credentials: R2 credentials for querying checkpoint metadata. + chain_cache: Optional cache of {delta_window: (anchor, prev)} tuples. Reused across cleanup calls to avoid redundant metadata downloads. Returns: - Set of window numbers to retain + Set of window numbers to retain. """ - from grail.shared.retention_utils import compute_retention_windows + from grail.shared.retention_utils import compute_chain_windows, compute_retention_windows all_windows, full_windows, delta_windows = inventory @@ -459,53 +501,52 @@ async def _compute_keep_windows( # Get current window (newest in inventory) current_window = max(all_windows) if all_windows else 0 - # Start with chain-based retention (keeps entire chains for reconstruction) + # Strategy 1: Arithmetic retention (baseline) keep = compute_retention_windows(current_window) - # Also keep latest N FULL checkpoints (anchors) as additional safety margin + # Strategy 2: Recency retention keep.update(full_windows[:BASE_CHECKPOINT_RETENTION_LIMIT]) - - # Also keep latest M DELTA checkpoints deltas_to_keep = delta_windows[:DELTA_CHECKPOINT_RETENTION_LIMIT] keep.update(deltas_to_keep) - # Ensure any retained DELTA has its anchor FULL kept - # Use cache to avoid redundant metadata downloads - anchor_windows = await asyncio.gather( - *( - _fetch_delta_anchor_window(delta_window, credentials, anchor_cache) - for delta_window in deltas_to_keep - ) + # Strategy 3: Chain-walk retention + # Fetch chain info (anchor_window, prev_window) for all deltas in inventory + # so we can walk complete chains even for intermediates outside deltas_to_keep. + chain_infos = await asyncio.gather( + *(_fetch_delta_chain_info(dw, credentials, chain_cache) for dw in delta_windows) ) - anchors_kept: set[int] = set() - fallback_anchors_kept: set[int] = set() - for delta_window, anchor_window in zip(deltas_to_keep, anchor_windows, strict=False): - if anchor_window is not None: - keep.add(int(anchor_window)) - anchors_kept.add(int(anchor_window)) - continue - # Fallback: keep nearest FULL checkpoint before this delta - for full_window in full_windows: - if full_window < delta_window: - keep.add(full_window) - fallback_anchors_kept.add(full_window) + chain_map: dict[int, int] = {} + anchor_map: dict[int, int] = {} + for dw, (anchor, prev) in zip(delta_windows, chain_infos, strict=False): + if anchor is not None: + anchor_map[dw] = int(anchor) + if prev is not None: + chain_map[dw] = int(prev) + + # Walk chains for ALL retained deltas (recency + arithmetic), not just + # deltas_to_keep, to prevent breaking chains for arithmetically-retained deltas. + delta_set = set(delta_windows) + all_retained_deltas = keep & delta_set | set(deltas_to_keep) + chain_windows = compute_chain_windows(all_retained_deltas, chain_map, anchor_map) + keep.update(chain_windows) + + # Fallback: if chain info unavailable, keep nearest FULL before the delta + full_set = set(full_windows) + for dw in deltas_to_keep: + if dw in anchor_map: + continue + for fw in full_windows: + if fw < dw: + keep.add(fw) break - # Log anchor retention summary once (instead of per-delta) - if anchors_kept or fallback_anchors_kept: - logger.debug( - "Anchor retention: %d anchors kept for %d deltas (fallback: %d)", - len(anchors_kept), - len(deltas_to_keep), - len(fallback_anchors_kept), - ) - logger.info( - "Retention: keeping %d checkpoints (%d FULL, %d DELTA)", + "Retention: keeping %d checkpoints (%d FULL, %d DELTA, %d chain intermediates)", len(keep), - sum(1 for w in keep if w in full_windows), - sum(1 for w in keep if w in delta_windows), + sum(1 for w in keep if w in full_set), + sum(1 for w in keep if w in set(delta_windows)), + len(chain_windows - set(deltas_to_keep) - full_set), ) return keep @@ -535,10 +576,10 @@ def __init__(self, *, credentials: Any, wallet: bt.wallet) -> None: self.credentials = credentials self.wallet = wallet - # Optimization: Cache {delta_window: anchor_window} mappings to avoid - # redundant metadata downloads during cleanup. Anchor windows never change - # after a delta is uploaded. - self._anchor_cache: dict[int, int] = {} + # Optimization: Cache {delta_window: (anchor_window, prev_window)} mappings + # to avoid redundant metadata downloads during cleanup. These values never + # change after a delta is uploaded. + self._chain_cache: dict[int, tuple[int | None, int | None]] = {} # Optimization: Track upload count to run cleanup less frequently. # Cleanup runs every CLEANUP_INTERVAL_UPLOADS uploads instead of every upload. @@ -614,29 +655,22 @@ async def cleanup_old_checkpoints( # Prune cache: remove entries for deltas that no longer exist in R2 # This prevents unbounded cache growth delta_set = set(delta_windows) - stale_keys = [k for k in self._anchor_cache if k not in delta_set] + stale_keys = [k for k in self._chain_cache if k not in delta_set] for k in stale_keys: - del self._anchor_cache[k] + del self._chain_cache[k] if stale_keys: - logger.debug("Pruned %d stale entries from anchor cache", len(stale_keys)) + logger.debug("Pruned %d stale entries from chain cache", len(stale_keys)) - # Calculate windows to keep with proper dependency tracking - # Pass anchor cache to avoid redundant metadata downloads - # Count cache hits: how many deltas_to_keep are already in cache - deltas_to_keep = delta_windows[:DELTA_CHECKPOINT_RETENTION_LIMIT] - cache_hits = sum(1 for d in deltas_to_keep if d in self._anchor_cache) - cache_size_before = len(self._anchor_cache) + # Calculate windows to keep with chain-aware dependency tracking + cache_size_before = len(self._chain_cache) - keep_windows = await _compute_keep_windows(inventory, self.credentials, self._anchor_cache) + keep_windows = await _compute_keep_windows(inventory, self.credentials, self._chain_cache) - # Cache misses = new entries added (may undercount if some fetches failed) - cache_misses = len(self._anchor_cache) - cache_size_before + cache_misses = len(self._chain_cache) - cache_size_before logger.debug( - "Anchor cache: %d hits, %d misses, %d queried (total cached: %d)", - cache_hits, + "Chain cache: %d total, %d new fetches", + len(self._chain_cache), cache_misses, - len(deltas_to_keep), - len(self._anchor_cache), ) if not keep_windows: @@ -740,6 +774,11 @@ async def publish_checkpoint( rel_path = str(file_path.relative_to(temp_dir)) file_manifest[rel_path] = hashlib.sha256(file_path.read_bytes()).hexdigest() + # Compute end-to-end weights hash from the live model state. The + # state_dict is a view into the trained model's parameters, so this + # is exactly the bytes consumers will reconstruct after download. + weights_hash = compute_weights_hash(model.state_dict()) + training_config = { "lr": TRAINER_LR, "epochs": TRAINER_EPOCHS, @@ -761,6 +800,7 @@ async def publish_checkpoint( # Get environment and generation configuration env_id, env_params = get_default_env_config() generation_params = get_default_generation_params() + thinking_mode = get_default_thinking_mode() metadata = CheckpointMetadata( window=target_window, @@ -771,9 +811,11 @@ async def publish_checkpoint( created_at=time.time(), model_name=model_name, checkpoint_type=CHECKPOINT_TYPE_FULL, + weights_hash=weights_hash, env_id=env_id, env_params=env_params, generation_params=generation_params, + thinking_mode=thinking_mode, ) metadata_dict = {**metadata.__dict__, "config_hash": config_hash} @@ -812,7 +854,7 @@ async def upload_file(path: Path) -> bool: use_write=True, upload_timeout=upload_timeout, ) - except asyncio.TimeoutError: + except TimeoutError: logger.error( "Upload TIMEOUT for %s (exceeds %s seconds)", rel_path, @@ -926,6 +968,14 @@ async def upload_from_staging( rel_path = str(file_path.relative_to(staging_path)) file_manifest[rel_path] = hashlib.sha256(file_path.read_bytes()).hexdigest() + # Compute end-to-end weights hash from the staged safetensors. With + # xxh3-128 the load+hash is ~1-2 s for a 7B model — affordable on + # the synchronous publish path. + staged_state = load_model_state_dict(staging_path) + if staged_state is None: + raise UploadError(f"No model weights found in staging path: {staging_path}") + weights_hash = compute_weights_hash(staged_state) + # Read training config from snapshot metadata or use defaults training_config = snapshot_metadata.get( "training_config", @@ -951,6 +1001,7 @@ async def upload_from_staging( # Get environment and generation configuration env_id, env_params = get_default_env_config() generation_params = get_default_generation_params() + thinking_mode = get_default_thinking_mode() metadata = CheckpointMetadata( window=target_window, @@ -961,9 +1012,11 @@ async def upload_from_staging( created_at=snapshot_metadata.get("timestamp", time.time()), model_name="async_trainer_snapshot", checkpoint_type=CHECKPOINT_TYPE_FULL, + weights_hash=weights_hash, env_id=env_id, env_params=env_params, generation_params=generation_params, + thinking_mode=thinking_mode, ) config_hash = hashlib.sha256( @@ -1012,7 +1065,7 @@ async def upload_file(path: Path) -> bool: use_write=True, upload_timeout=upload_timeout, ) - except asyncio.TimeoutError: + except TimeoutError: logger.error( "Upload TIMEOUT for %s (exceeds %s seconds)", rel_path, @@ -1233,6 +1286,7 @@ async def upload_delta( # Get environment and generation configuration env_id, env_params = get_default_env_config() generation_params = get_default_generation_params() + thinking_mode = get_default_thinking_mode() metadata = CheckpointMetadata( window=target_window, @@ -1249,6 +1303,7 @@ async def upload_delta( env_id=env_id, env_params=env_params, generation_params=generation_params, + thinking_mode=thinking_mode, ) config_hash = hashlib.sha256( @@ -1303,7 +1358,7 @@ async def upload_file(path: Path) -> bool: use_write=True, upload_timeout=upload_timeout, ) - except asyncio.TimeoutError: + except TimeoutError: logger.error( "Delta upload TIMEOUT for %s (exceeds %s seconds)", rel_path, @@ -1414,6 +1469,22 @@ async def upload_full_background( rel_path = str(file_path.relative_to(staging_path)) file_manifest[rel_path] = hashlib.sha256(file_path.read_bytes()).hexdigest() + # Compute end-to-end weights hash from the staged safetensors. This + # is the background FULL upload path (anchor windows), so we don't + # raise on a load failure — we log and ship the FULL with no hash, + # and the consumer's verify-on-download will catch it on read. + staged_state = load_model_state_dict(staging_path) + if staged_state is None: + logger.warning( + "[upload_full_background] No model weights in staging %s; " + "publishing FULL anchor without weights_hash for window %s", + staging_path, + target_window, + ) + weights_hash = None + else: + weights_hash = compute_weights_hash(staged_state) + # Read snapshot metadata snapshot_metadata_path = staging_path / "snapshot_metadata.json" if snapshot_metadata_path.exists(): @@ -1444,6 +1515,7 @@ async def upload_full_background( # Get environment and generation configuration env_id, env_params = get_default_env_config() generation_params = get_default_generation_params() + thinking_mode = get_default_thinking_mode() metadata = CheckpointMetadata( window=target_window, @@ -1454,9 +1526,11 @@ async def upload_full_background( created_at=snapshot_metadata.get("timestamp", time.time()), model_name="async_trainer_snapshot", checkpoint_type=CHECKPOINT_TYPE_FULL, + weights_hash=weights_hash, env_id=env_id, env_params=env_params, generation_params=generation_params, + thinking_mode=thinking_mode, ) config_hash = hashlib.sha256( diff --git a/grail/trainer/config.py b/grail/trainer/config.py index d405f942..3648360c 100644 --- a/grail/trainer/config.py +++ b/grail/trainer/config.py @@ -8,20 +8,22 @@ import os from dataclasses import dataclass, field -from grail.shared import constants +from grail.protocol import constants as protocol +from grail.shared import config as constants @dataclass class TrainingConfig: """Hyperparameters and settings for training. - Values default from `grail.shared.constants` to avoid duplication. + Values default from `grail.shared.config` to avoid duplication. """ # Basic training parameters lr: float = constants.TRAINER_LR epochs: int = constants.TRAINER_EPOCHS micro_batch_size: int = constants.TRAINER_MICRO_BATCH_SIZE + max_tokens_per_micro_batch: int = constants.TRAINER_MAX_TOKENS_PER_MICRO_BATCH max_length: int = constants.TRAINER_MAX_LENGTH grad_clip: float = constants.TRAINER_GRAD_CLIP warmup_steps: int = constants.TRAINER_WARMUP_STEPS @@ -36,6 +38,7 @@ class TrainingConfig: # Gradient accumulation and clipping grad_accum_steps: int = constants.TRAINER_GRAD_ACCUM_STEPS + effective_batch_size: int = constants.TRAINER_EFFECTIVE_BATCH_SIZE # Advantage normalization and PPO clipping adv_clip_percentile: float = constants.TRAINER_ADV_CLIP_PERCENTILE @@ -50,15 +53,24 @@ class TrainingConfig: # GRPO variant selection grpo_variant: str = constants.GRPO_VARIANT + # Advantage estimation strategy (orthogonal to grpo_variant) + adv_estimator: str = constants.ADV_ESTIMATOR + # Gradient checkpointing for memory efficiency use_gradient_checkpointing: bool = constants.TRAINER_USE_GRADIENT_CHECKPOINTING + # Sequence packing (eliminates padding waste, requires FA2) + use_sequence_packing: bool = constants.TRAINER_USE_SEQUENCE_PACKING + + # torch.compile for training (fuses ops, reduces kernel launch overhead) + use_torch_compile: bool = constants.TRAINER_USE_TORCH_COMPILE + # Chunked logit computation (avoids materializing full vocab-sized tensors) chunked_logits: bool = constants.TRAINER_CHUNKED_LOGITS logit_chunk_size: int = constants.TRAINER_LOGIT_CHUNK_SIZE # Data loading - rollouts_per_problem: int = constants.ROLLOUTS_PER_PROBLEM + rollouts_per_problem: int = protocol.ROLLOUTS_PER_PROBLEM # Miner/data quality filters group_adv_sum_tolerance: float = constants.TRAINER_GROUP_ADV_SUM_TOL @@ -97,7 +109,7 @@ class EvalConfig: Defaults chosen to be safe and reasonably fast for initial integration. """ - enabled: bool = True + enabled: bool = os.getenv("GRAIL_EVAL_ENABLED", "1").lower() in ("1", "true", "yes") window_interval: int = 20 env_id: str | None = None # If None, uses CURRENT_ENV_ID from constants split: str = "val" # dataset-backed envs (e.g., GSM8K, MBPP) @@ -111,7 +123,7 @@ class EvalConfig: top_p: float = 0.95 do_sample: bool = True # Backend control: "hf" | "vllm" | "sglang" - backend: str = "vllm" # Server mode with async API avoids Gloo socket issues + backend: str = constants.INFERENCE_BACKEND # sgLang server options (used when backend == "sglang") server_host: str = "127.0.0.1" server_port: int = 30000 @@ -143,11 +155,14 @@ class EvalConfig: 48 # 12288 max_model_len × 64 seqs = 786K KV tokens, fits in ~115GB KV cache at 0.95 util ) vllm_max_concurrent_requests: int = 32 # 75% of max_num_seqs=64 - # SGLang server memory and concurrency tuning - sglang_mem_fraction_static: float = 0.75 # Fraction of GPU memory for SGLang - sglang_context_length: int = 1024 # Maximum sequence length - sglang_max_running_requests: int = 4 # Server-side: max concurrent requests - sglang_max_concurrent_requests: int = 4 # Client-side: max parallel HTTP requests + # SGLang server memory and concurrency tuning (optimized for single A100) + sglang_python_executable: str = field( + default_factory=lambda: os.getenv("GRAIL_SGLANG_PYTHON", "") + ) + sglang_mem_fraction_static: float = 0.90 # Fraction of GPU memory for SGLang + sglang_context_length: int = 12288 # Maximum sequence length + sglang_max_running_requests: int = 48 # Server-side: max concurrent requests + sglang_max_concurrent_requests: int = 32 # Client-side: max parallel HTTP requests use_num_return_sequences: bool = False # HF-only optimization # Metrics aggregation: which k to report (subset of 1..replicates) report_ks: tuple[int, ...] = (1, 5, 10) diff --git a/grail/trainer/dashboard_logger.py b/grail/trainer/dashboard_logger.py index f054206d..43623eb2 100644 --- a/grail/trainer/dashboard_logger.py +++ b/grail/trainer/dashboard_logger.py @@ -45,11 +45,15 @@ def _sanitize_key(key: str) -> str: def _sanitize_value(v: Any) -> Any: - """Ensure value is JSON-serializable and finite.""" + """Ensure value is JSON-serializable and finite. + + Uses 6 significant digits (not 6 decimal places) so small values like + LR=2e-8 are preserved instead of rounding to 0.0. + """ if isinstance(v, float): if math.isnan(v) or math.isinf(v): return None - return round(v, 6) + return float(f"{v:.6g}") if isinstance(v, dict): return {_sanitize_key(k): _sanitize_value(val) for k, val in v.items()} if isinstance(v, (list, tuple)): diff --git a/grail/trainer/distributed/__init__.py b/grail/trainer/distributed/__init__.py new file mode 100644 index 00000000..36a5a9c6 --- /dev/null +++ b/grail/trainer/distributed/__init__.py @@ -0,0 +1,55 @@ +"""Distributed training primitives for multi-strategy parallelism (FSDP2, DDP, DILOCO).""" + +from grail.trainer.distributed.checkpoint import ( + async_save_sharded_checkpoint, + create_checkpoint_stager, + load_hf_into_distributed, + load_sharded_checkpoint, + save_ddp_checkpoint, + save_full_checkpoint, + save_sharded_checkpoint, +) +from grail.trainer.distributed.compat import DistributedContext +from grail.trainer.distributed.config import DistributedConfig, StrategyType +from grail.trainer.distributed.grad_utils import check_grad_nan_across_ranks, clip_grad_norm_ +from grail.trainer.distributed.logprobs import ( + compute_logprobs_distributed, + tp_chunked_logprobs, +) +from grail.trainer.distributed.parallelism import ( + apply_ddp, + apply_fsdp2, + apply_gradient_checkpointing, + apply_tp_sp, + create_device_mesh, + replace_rmsnorm_for_sp, + setup_ref_model, + setup_training_model, +) +from grail.trainer.distributed.training_service import DistributedTrainingService + +__all__ = [ + "DistributedConfig", + "StrategyType", + "DistributedContext", + "DistributedTrainingService", + "check_grad_nan_across_ranks", + "clip_grad_norm_", + "apply_ddp", + "apply_fsdp2", + "apply_gradient_checkpointing", + "apply_tp_sp", + "async_save_sharded_checkpoint", + "compute_logprobs_distributed", + "create_checkpoint_stager", + "create_device_mesh", + "load_hf_into_distributed", + "load_sharded_checkpoint", + "replace_rmsnorm_for_sp", + "save_ddp_checkpoint", + "save_full_checkpoint", + "save_sharded_checkpoint", + "setup_ref_model", + "setup_training_model", + "tp_chunked_logprobs", +] diff --git a/grail/trainer/distributed/checkpoint.py b/grail/trainer/distributed/checkpoint.py new file mode 100644 index 00000000..39d4cfb5 --- /dev/null +++ b/grail/trainer/distributed/checkpoint.py @@ -0,0 +1,472 @@ +"""Distributed checkpointing for FSDP2 + TP training. + +Provides three checkpoint formats: + +1. **Full HF checkpoint** (``save_full_checkpoint``): Gathers the complete state dict + across all ranks, writes HuggingFace-compatible safetensors on rank 0. Used for + publishing weights to miners/validators. + +2. **Sharded DCP checkpoint** (``async_save_sharded_checkpoint``, ``save_sharded_checkpoint``): + Each rank saves its local shard via ``torch.distributed.checkpoint``. Supports async + writes (pinned + shared memory staging) for overlapping I/O with training. Used for + fast training resume. + +3. **HF-to-distributed loader** (``load_hf_into_distributed``): Reads a full HF + safetensors checkpoint on rank 0 and broadcasts into an already-sharded FSDP2+TP + model, including non-parameter buffers (RoPE embeddings, etc.). +""" + +from __future__ import annotations + +import logging +from pathlib import Path +from typing import Any + +import torch +import torch.distributed as dist +import torch.nn as nn + +logger = logging.getLogger(__name__) + +# --------------------------------------------------------------------------- +# Lazy imports for DCP APIs that may not exist in older PyTorch versions. +# Each function guards its own imports so callers get a clear error message +# rather than an ImportError at module load time. +# --------------------------------------------------------------------------- + +_DCP_AVAILABLE: bool | None = None + + +def _check_dcp_available() -> bool: + """Return True if torch.distributed.checkpoint is usable.""" + global _DCP_AVAILABLE # noqa: PLW0603 + if _DCP_AVAILABLE is not None: + return _DCP_AVAILABLE + try: + import torch.distributed.checkpoint # noqa: F401 + from torch.distributed.checkpoint.state_dict import ( # noqa: F401 + get_model_state_dict, + set_model_state_dict, + ) + + _DCP_AVAILABLE = True + except ImportError: + _DCP_AVAILABLE = False + return _DCP_AVAILABLE + + +# --------------------------------------------------------------------------- +# 1. Full HF checkpoint (collective gather + rank-0 write) +# --------------------------------------------------------------------------- + + +def save_full_checkpoint( + model: nn.Module, + tokenizer: Any, + path: str | Path, + rank: int, +) -> None: + """Gather full state dict on all ranks, save HF safetensors on rank 0. + + This is a **collective** operation: every rank must call this function + because ``get_model_state_dict`` with ``full_state_dict=True`` performs + all-gathers internally. + + Args: + model: FSDP2-wrapped model. + tokenizer: HuggingFace tokenizer (must support ``save_pretrained``). + path: Directory where the checkpoint will be written (rank 0 only). + rank: Current process rank. + """ + from torch.distributed.checkpoint.state_dict import ( + StateDictOptions, + get_model_state_dict, + ) + + path = Path(path) + + # Collective: all ranks participate in the gather. + logger.info("Gathering full state dict (rank=%d)...", rank) + full_sd = get_model_state_dict( + model, + options=StateDictOptions(full_state_dict=True, cpu_offload=True), + ) + + if rank == 0: + path.mkdir(parents=True, exist_ok=True) + + # Pass the gathered state_dict directly to save_pretrained. + # Do NOT use load_state_dict(assign=True) as that would mutate + # the live training model, replacing CUDA DTensor params with CPU + # tensors and breaking FSDP2 invariants. + model.save_pretrained( # type: ignore[attr-defined] + str(path), + state_dict=full_sd, + safe_serialization=True, + ) + tokenizer.save_pretrained(str(path)) + + logger.info("Full HF checkpoint saved to %s", path) + + dist.barrier() + + +# --------------------------------------------------------------------------- +# 1b. DDP checkpoint (rank-0 only, no collective gather) +# --------------------------------------------------------------------------- + + +def save_ddp_checkpoint( + model: nn.Module, + tokenizer: Any, + path: str | Path, + rank: int, +) -> None: + """Save checkpoint from a DDP-wrapped or bare model. Only rank 0 writes. + + Unlike ``save_full_checkpoint``, this is NOT a collective operation. + Only rank 0 writes to disk. Callers are responsible for synchronization + (e.g., placing a ``dist.barrier()`` after this call if needed). + + Args: + model: DDP-wrapped or bare model. + tokenizer: HuggingFace tokenizer (must support ``save_pretrained``). + path: Directory where the checkpoint will be written (rank 0 only). + rank: Current process rank. + """ + if rank != 0: + return + + path = Path(path) + path.mkdir(parents=True, exist_ok=True) + + # DDP wraps the model; .module is the original model. Bare models + # (e.g., DILOCO) don't have .module. + unwrapped = model.module if hasattr(model, "module") else model + unwrapped.save_pretrained( # type: ignore[attr-defined] + str(path), + safe_serialization=True, + ) + tokenizer.save_pretrained(str(path)) + logger.info("DDP/DILOCO checkpoint saved to %s", path) + + +# --------------------------------------------------------------------------- +# 2a. Async sharded DCP save +# --------------------------------------------------------------------------- + + +def create_checkpoint_stager() -> Any: + """Create a ``DefaultStager`` configured for async DCP checkpointing. + + Uses pinned memory, shared memory, and async staging for maximum + overlap with training computation. + + Returns: + A ``DefaultStager`` instance. + + Raises: + ImportError: If staging APIs are not available. + """ + from torch.distributed.checkpoint.staging import DefaultStager, StagingOptions + + return DefaultStager( + StagingOptions( + use_pinned_memory=True, + use_shared_memory=True, + use_async_staging=True, + ) + ) + + +def async_save_sharded_checkpoint( + model: nn.Module, + optimizer: torch.optim.Optimizer, + path: str | Path, + stager: Any | None = None, +) -> Any: + """Async DCP save for training resume. Each rank saves its own shard. + + The caller is responsible for tracking the returned ``AsyncSaveResponse`` + (call ``.result()`` to block until the background write completes, or + poll ``.done()``). + + Args: + model: FSDP2-wrapped model. + optimizer: Optimizer whose state to checkpoint. + path: Directory for the sharded checkpoint. + stager: Optional ``DefaultStager``. Created via + :func:`create_checkpoint_stager` if not provided. + + Returns: + An ``AsyncSaveResponse`` (or similar future) from ``dcp.async_save``. + + Raises: + ImportError: If async DCP APIs are not available. + """ + import torch.distributed.checkpoint as dcp + from torch.distributed.checkpoint.state_dict import ( + StateDictOptions, + get_model_state_dict, + get_optimizer_state_dict, + ) + + path = Path(path) + path.mkdir(parents=True, exist_ok=True) + + opts = StateDictOptions(full_state_dict=False, cpu_offload=True) + state_dict: dict[str, Any] = { + "model": get_model_state_dict(model, options=opts), + "optimizer": get_optimizer_state_dict(model, optimizer, options=opts), + } + + if stager is None: + stager = create_checkpoint_stager() + + try: + from torch.distributed.checkpoint.async_checkpoint import ( # type: ignore[import-not-found] + AsyncCheckpointerType, + ) + + _async_save: Any = dcp.async_save # type: ignore[attr-defined] + _writer: Any = dcp.FileSystemWriter # type: ignore[attr-defined] + response = _async_save( + state_dict, + storage_writer=_writer(str(path)), + async_checkpointer_type=AsyncCheckpointerType.PROCESS, + async_stager=stager, + ) + except ImportError: + # Older PyTorch without AsyncCheckpointerType: fall back to the + # simpler async_save signature. + _async_save = dcp.async_save # type: ignore[attr-defined] + _writer = dcp.FileSystemWriter # type: ignore[attr-defined] + response = _async_save( + state_dict, + storage_writer=_writer(str(path)), + ) + + logger.info("Async sharded DCP save initiated to %s", path) + return response + + +# --------------------------------------------------------------------------- +# 2b. Synchronous sharded DCP save (fallback) +# --------------------------------------------------------------------------- + + +def save_sharded_checkpoint( + model: nn.Module, + optimizer: torch.optim.Optimizer, + path: str | Path, +) -> None: + """Synchronous DCP save for training resume. + + Each rank saves its own shard. Use this as a fallback when async + checkpointing is not available or not desired. + + Args: + model: FSDP2-wrapped model. + optimizer: Optimizer whose state to checkpoint. + path: Directory for the sharded checkpoint. + """ + import torch.distributed.checkpoint as dcp + from torch.distributed.checkpoint.state_dict import ( + StateDictOptions, + get_model_state_dict, + get_optimizer_state_dict, + ) + + path = Path(path) + path.mkdir(parents=True, exist_ok=True) + + opts = StateDictOptions(full_state_dict=False, cpu_offload=True) + state_dict: dict[str, Any] = { + "model": get_model_state_dict(model, options=opts), + "optimizer": get_optimizer_state_dict(model, optimizer, options=opts), + } + + _save = dcp.save # type: ignore[attr-defined] + _writer = dcp.FileSystemWriter # type: ignore[attr-defined] + _save(state_dict, storage_writer=_writer(str(path))) + + logger.info("Sharded DCP checkpoint saved to %s", path) + + +# --------------------------------------------------------------------------- +# 3. Sharded DCP load (resume) +# --------------------------------------------------------------------------- + + +def load_sharded_checkpoint( + model: nn.Module, + optimizer: torch.optim.Optimizer, + path: str | Path, +) -> None: + """Load a sharded DCP checkpoint for training resume. + + Each rank loads only its own shard. DCP handles automatic resharding if + the topology (number of ranks, TP/DP degrees) changed between the save + and load. + + Args: + model: FSDP2-wrapped model (must match architecture). + optimizer: Optimizer to restore state into. + path: Directory containing the sharded checkpoint. + + Raises: + FileNotFoundError: If the checkpoint directory does not exist. + """ + import torch.distributed.checkpoint as dcp + from torch.distributed.checkpoint.state_dict import ( + StateDictOptions, + get_model_state_dict, + get_optimizer_state_dict, + set_model_state_dict, + set_optimizer_state_dict, + ) + + path = Path(path) + if not path.exists(): + raise FileNotFoundError(f"Checkpoint directory not found: {path}") + + # cpu_offload=False: keep state on GPU during resume (no unnecessary roundtrip) + opts = StateDictOptions(full_state_dict=False, cpu_offload=False) + + # Build empty state dicts with the correct structure for DCP to fill. + model_sd = get_model_state_dict(model, options=opts) + optim_sd = get_optimizer_state_dict(model, optimizer, options=opts) + + state_dict: dict[str, Any] = { + "model": model_sd, + "optimizer": optim_sd, + } + + _load = dcp.load # type: ignore[attr-defined] + _reader = dcp.FileSystemReader # type: ignore[attr-defined] + _load(state_dict, storage_reader=_reader(str(path))) + + # Apply the loaded state back into the live model and optimizer. + set_model_state_dict( + model, + state_dict["model"], + options=opts, + ) + set_optimizer_state_dict( + model, + optimizer, + state_dict["optimizer"], + options=opts, + ) + + logger.info("Sharded DCP checkpoint loaded from %s", path) + + +# --------------------------------------------------------------------------- +# 4. Load HF safetensors into a distributed model +# --------------------------------------------------------------------------- + + +def load_hf_into_distributed( + model: nn.Module, + checkpoint_path: str | Path, + rank: int, +) -> None: + """Load an HF safetensors checkpoint into an already-sharded FSDP2+TP model. + + Rank 0 reads the full state dict from disk. ``set_model_state_dict`` with + ``broadcast_from_rank0=True`` distributes the weights to all ranks and + reshards them according to each rank's FSDP2/TP configuration. + + After parameters are set, non-parameter buffers (e.g. RoPE ``inv_freq``) + are explicitly broadcast from rank 0 so that all ranks share identical + values. + + Args: + model: FSDP2+TP wrapped model (architecture must match checkpoint). + checkpoint_path: Path to a directory containing HF-format safetensors + (``model.safetensors`` or sharded ``model-*.safetensors``). + rank: Current process rank. + + Raises: + FileNotFoundError: If no model weights are found at ``checkpoint_path``. + """ + from torch.distributed.checkpoint.state_dict import ( + StateDictOptions, + set_model_state_dict, + ) + + checkpoint_path = Path(checkpoint_path) + + # Resolve HuggingFace model IDs (e.g. "Qwen/Qwen3-8B") to local cache paths. + # Only rank 0 needs the resolved path; others pass an empty state dict. + if not checkpoint_path.exists() and "/" in str(checkpoint_path): + try: + from huggingface_hub import snapshot_download + + resolved = snapshot_download(str(checkpoint_path)) + checkpoint_path = Path(resolved) + logger.info("Resolved HF model ID to cache: %s", checkpoint_path) + except Exception as exc: + logger.warning("Failed to resolve HF model ID %s: %s", checkpoint_path, exc) + + # Rank 0 loads the full state dict; other ranks pass an empty dict. + if rank == 0: + from grail.shared.safetensors_utils import load_model_state_dict + + full_sd = load_model_state_dict(checkpoint_path) + if full_sd is None: + raise FileNotFoundError( + f"No model weights found at {checkpoint_path}. " + "Expected model.safetensors or model.safetensors.index.json." + ) + logger.info( + "Rank 0 loaded %d parameter tensors from %s", + len(full_sd), + checkpoint_path, + ) + else: + full_sd = {} + + # Distribute weights: DCP broadcasts from rank 0 and reshards automatically. + set_model_state_dict( + model, + full_sd, # type: ignore[arg-type] + options=StateDictOptions( + full_state_dict=True, + broadcast_from_rank0=True, + cpu_offload=True, + ), + ) + logger.info("Model parameters set from HF checkpoint (rank=%d)", rank) + + # Broadcast non-parameter buffers (RoPE inv_freq, layer norms, etc.). + # set_model_state_dict handles parameters but buffers need manual sync. + _broadcast_buffers(model, src_rank=0) + + dist.barrier() + logger.info("HF checkpoint loaded into distributed model (rank=%d)", rank) + + +def _broadcast_buffers(model: nn.Module, src_rank: int = 0) -> None: + """Broadcast all named buffers from ``src_rank`` to every other rank. + + This ensures non-parameter state like RoPE inverse frequency tensors are + identical across all ranks after loading from a single-rank checkpoint. + """ + if not dist.is_initialized(): + return + + for name, buf in model.named_buffers(): + if buf is None: + continue + try: + dist.broadcast(buf, src=src_rank) + except RuntimeError: + # Buffer may be on CPU or have an unsupported dtype for NCCL. + # Move to GPU, broadcast, then update in-place. + device = torch.device("cuda", dist.get_rank() % torch.cuda.device_count()) + buf_gpu = buf.to(device) + dist.broadcast(buf_gpu, src=src_rank) + buf.copy_(buf_gpu.to(buf.device)) + logger.debug("Buffer %s broadcast via GPU fallback", name) diff --git a/grail/trainer/distributed/compat.py b/grail/trainer/distributed/compat.py new file mode 100644 index 00000000..272edf78 --- /dev/null +++ b/grail/trainer/distributed/compat.py @@ -0,0 +1,97 @@ +"""Drop-in replacement for HuggingFace Accelerate's ``Accelerator``. + +``DistributedContext`` exposes the minimal surface used by +``GRPOAlgorithm.train_epoch()`` (device, autocast, backward, gather, +unwrap_model) without pulling in Accelerate as a dependency. When running +under FSDP2, mixed-precision is handled by ``MixedPrecisionPolicy`` rather +than autocast, so :meth:`autocast` returns a no-op context manager. + +Supports all distributed strategies (FSDP2, DDP, DILOCO) with minimal +behavioral differences (only ``unwrap_model`` varies). +""" + +from __future__ import annotations + +import contextlib +from typing import TYPE_CHECKING, Any + +import torch +import torch.distributed as dist + +if TYPE_CHECKING: + from grail.trainer.distributed.config import StrategyType + + +class DistributedContext: + """Lightweight Accelerator-compatible context for distributed training.""" + + def __init__( + self, + device: torch.device | None = None, + rank: int = 0, + world_size: int = 1, + dp_group: dist.ProcessGroup | None = None, + dp_size: int = 1, + strategy: StrategyType = "fsdp2", + ) -> None: + if device is not None: + self.device = device + elif torch.cuda.is_available(): + local_rank = rank % torch.cuda.device_count() + self.device = torch.device("cuda", local_rank) + else: + self.device = torch.device("cpu") + + self.rank = rank + self.world_size = world_size + self._dp_group = dp_group + self._dp_size = dp_size + self._strategy = strategy + + # ------------------------------------------------------------------ + # Accelerator-compatible API + # ------------------------------------------------------------------ + + def autocast(self) -> contextlib.AbstractContextManager[None]: + """Return a bf16 autocast context manager. + + FSDP2 applies mixed precision to sharded parameters via + ``MixedPrecisionPolicy``, but the non-chunked logprob path runs + lm_head as part of the full model forward and needs autocast for + consistent bf16 compute (e.g., embedding output, log_softmax inputs). + """ + return torch.autocast("cuda", dtype=torch.bfloat16) # type: ignore[return-value] + + def backward(self, loss: torch.Tensor) -> None: + """Run the backward pass. + + Under FSDP2, gradient synchronization is handled by the sharded + module hooks, so a plain ``.backward()`` is sufficient. + """ + loss.backward() + + def gather(self, tensor: torch.Tensor) -> torch.Tensor: + """All-gather ``tensor`` across the DP process group. + + Uses the DP group (not the full world group) so that TP-replicated + tensors are not double-counted. Falls back to returning ``tensor`` + unchanged when not distributed. + """ + gather_size = self._dp_size if self._dp_group is not None else self.world_size + if gather_size <= 1 or not dist.is_initialized(): + return tensor + + gathered = [torch.zeros_like(tensor) for _ in range(gather_size)] + dist.all_gather(gathered, tensor, group=self._dp_group) + return torch.cat(gathered, dim=0) + + def unwrap_model(self, model: Any) -> Any: + """Return the underlying model, unwrapping any distributed wrapper. + + DDP wraps the model in ``DistributedDataParallel``, so the original + model lives at ``model.module``. FSDP2 and DILOCO do not require + unwrapping. + """ + if self._strategy == "ddp" and hasattr(model, "module"): + return model.module + return model diff --git a/grail/trainer/distributed/config.py b/grail/trainer/distributed/config.py new file mode 100644 index 00000000..60efb7cd --- /dev/null +++ b/grail/trainer/distributed/config.py @@ -0,0 +1,155 @@ +"""Distributed training configuration for multi-strategy parallelism. + +Reads from environment variables prefixed with ``GRAIL_DIST_`` and provides +auto-detection heuristics for tensor/data parallelism degrees. + +Supported strategies: + - ``fsdp2``: Fully Sharded Data Parallelism v2 (default). Shards model + parameters, gradients, and optimizer states across GPUs. + - ``ddp``: DistributedDataParallel. Replicates the full model on each GPU, + all-reduces gradients during backward. Simpler and faster when the model + fits in a single GPU's memory. + - ``diloco``: Distributed Low-Communication training. Each GPU runs + independent inner optimization (AdamW) for H steps, then synchronizes + via pseudo-gradient all-reduce and an outer Nesterov SGD step. +""" + +from __future__ import annotations + +import os +from dataclasses import dataclass +from typing import Literal + +StrategyType = Literal["fsdp2", "ddp", "diloco"] + +_VALID_STRATEGIES: frozenset[str] = frozenset({"fsdp2", "ddp", "diloco"}) + + +@dataclass +class DistributedConfig: + """Parameters governing distributed training. + + Defaults are conservative: FSDP2 strategy, auto-detect parallelism, + offload the reference model to CPU, and use asynchronous checkpointing. + """ + + # Strategy selection + strategy: StrategyType = "fsdp2" + + # FSDP2/TP settings (ignored for ddp strategy) + tp_degree: int = 0 # 0 = auto-detect from world_size and model size + ref_model_offload: bool = True # CPU offload for ref model when beta > 0 + reshard_after_forward: bool = False # ZeRO-2 style (faster, +8GB VRAM) + gc_skip_last_n: int = 0 # Skip gradient checkpointing on last N layers + async_checkpoint: bool = True # Non-blocking checkpoint writes + + # DILOCO settings (ignored unless strategy == "diloco") + diloco_inner_steps: int = 10 # H: number of inner optimizer steps between outer syncs + diloco_outer_lr: float = 0.7 # Outer Nesterov SGD learning rate + diloco_outer_momentum: float = 0.9 # Outer Nesterov momentum coefficient + + # PULSE-DiLoCo: BF16-gated sparse communication with residual buffer + pulse_diloco: bool = False # Enable PULSE-DiLoCo sparse outer step + + @classmethod + def from_env(cls) -> DistributedConfig: + """Construct a ``DistributedConfig`` from ``GRAIL_DIST_*`` env vars. + + Unset variables fall back to dataclass defaults. + """ + + def _bool(key: str, default: bool) -> bool: + raw = os.getenv(key) + if raw is None: + return default + return raw.lower() in ("1", "true", "yes") + + def _int(key: str, default: int) -> int: + raw = os.getenv(key) + if raw is None: + return default + return int(raw) + + def _float(key: str, default: float) -> float: + raw = os.getenv(key) + if raw is None: + return default + return float(raw) + + strategy_raw = os.getenv("GRAIL_DIST_STRATEGY", "fsdp2").strip().lower() + if strategy_raw not in _VALID_STRATEGIES: + raise ValueError( + f"Invalid GRAIL_DIST_STRATEGY={strategy_raw!r}. " + f"Must be one of: {sorted(_VALID_STRATEGIES)}" + ) + + return cls( + strategy=strategy_raw, # type: ignore[arg-type] + tp_degree=_int("GRAIL_DIST_TP_DEGREE", 0), + ref_model_offload=_bool("GRAIL_DIST_REF_MODEL_OFFLOAD", True), + reshard_after_forward=_bool("GRAIL_DIST_RESHARD_AFTER_FORWARD", False), + gc_skip_last_n=_int("GRAIL_DIST_GC_SKIP_LAST_N", 0), + async_checkpoint=_bool("GRAIL_DIST_ASYNC_CHECKPOINT", True), + diloco_inner_steps=_int("GRAIL_DIST_DILOCO_INNER_STEPS", 10), + diloco_outer_lr=_float("GRAIL_DIST_DILOCO_OUTER_LR", 0.7), + diloco_outer_momentum=_float("GRAIL_DIST_DILOCO_OUTER_MOMENTUM", 0.9), + pulse_diloco=_bool("GRAIL_DIST_PULSE_DILOCO", False), + ) + + def validate(self, world_size: int) -> None: + """Validate config against the runtime environment. + + Args: + world_size: Total number of distributed ranks. + + Raises: + ValueError: If the config is invalid for the given world_size. + """ + if self.strategy == "ddp" and self.tp_degree > 1: + raise ValueError("DDP does not support tensor parallelism (tp_degree must be 0 or 1)") + + if self.strategy == "diloco" and world_size < 2: + raise ValueError("DILOCO requires at least 2 workers (GRAIL_DIST_NPROC >= 2)") + + if self.strategy == "diloco" and self.diloco_inner_steps < 1: + raise ValueError(f"diloco_inner_steps must be >= 1, got {self.diloco_inner_steps}") + + @staticmethod + def auto_detect_parallelism( + world_size: int, + model_params_b: float, + ) -> tuple[int, int]: + """Return ``(tp_degree, dp_degree)`` for a given cluster and model size. + + Heuristics (tuned for Qwen-family models with 8 KV heads): + + * 4 GPUs: TP=2, DP=2 + * 8 GPUs, <=14B params: TP=2, DP=4 + * 8 GPUs, >14B params: TP=4, DP=2 + + Raises: + ValueError: If the resulting degrees are inconsistent with + ``world_size`` or if TP does not evenly divide the 8 KV heads + assumed for Qwen models. + """ + if world_size == 4: + tp, dp = 2, 2 + elif world_size == 8 and model_params_b <= 14.0: + tp, dp = 2, 4 + elif world_size == 8 and model_params_b > 14.0: + tp, dp = 4, 2 + else: + # Fallback: no tensor parallelism, pure data parallelism. + tp, dp = 1, world_size + + if tp * dp != world_size: + raise ValueError( + f"Parallelism mismatch: tp_degree={tp} * dp_degree={dp} = {tp * dp} " + f"!= world_size={world_size}" + ) + + num_kv_heads = 8 # Qwen family default + if num_kv_heads % tp != 0: + raise ValueError(f"tp_degree={tp} does not evenly divide num_kv_heads={num_kv_heads}") + + return tp, dp diff --git a/grail/trainer/distributed/grad_utils.py b/grail/trainer/distributed/grad_utils.py new file mode 100644 index 00000000..b41ec984 --- /dev/null +++ b/grail/trainer/distributed/grad_utils.py @@ -0,0 +1,223 @@ +"""DTensor-safe gradient clipping for FSDP2 parameters. + +Works around two upstream PyTorch bugs in ``torch.nn.utils.clip_grad_norm_`` +when used with FSDP2 / DTensor parameters: + +1. **Numeric epsilon bug** (`pytorch/pytorch#149768`): the stock implementation + computes ``total_norm + 1e-6`` where ``total_norm`` is a DTensor. Each rank + adds epsilon to its local shard independently, inflating the denominator by + ``world_size * 1e-6`` instead of a single ``1e-6``. + +2. **O(n^2) complexity bug** (`pytorch/pytorch#169445`): ``torch.stack`` on a + list of DTensors has quadratic cost in the number of tensors, making gradient + clipping prohibitively slow for models with many parameters. + +The fix separates DTensor and regular-tensor gradients, computes partial norms +locally, manually all-reduces, and then clips uniformly. + +Adapted from ``pytorch/torchtitan`` (``torchtitan/distributed/utils.py``). +""" + +from __future__ import annotations + +import logging +import math +from collections.abc import Iterable + +import torch +import torch.distributed as dist +from torch.distributed.tensor import DTensor + +logger = logging.getLogger(__name__) + + +# --------------------------------------------------------------------------- +# Internal helpers +# --------------------------------------------------------------------------- + + +def _local_norm( + tensors: list[torch.Tensor], + norm_type: float, + device: torch.device | None = None, +) -> torch.Tensor: + """Compute the (partial) p-norm of a flat list of local tensors. + + For ``norm_type == inf`` this returns the element-wise max. Otherwise + it returns ``(sum |t|^p)`` **without** the final ``^(1/p)`` root so that + partial results can be summed across ranks before taking the root once. + """ + if len(tensors) == 0: + return torch.tensor(0.0, device=device, dtype=torch.float32) + + device = tensors[0].device + dtype = torch.float32 # always accumulate in fp32 + + if math.isinf(norm_type): + # max-norm: local max, to be all-reduced with MAX later + return torch.stack([t.detach().abs().max().to(dtype) for t in tensors]).max().to(device) + + # p-norm: accumulate sum of |t|^p in a scalar + partial = torch.tensor(0.0, device=device, dtype=dtype) + for t in tensors: + local = t.detach().to(dtype) + partial += torch.norm(local, norm_type).pow(norm_type) + return partial + + +def _get_fsdp_process_group(dtensor: DTensor) -> dist.ProcessGroup | None: + """Extract the FSDP process group from a DTensor's device mesh. + + Returns ``None`` when the mesh does not contain an FSDP dimension (e.g. the + tensor is only replicated), in which case no all-reduce is needed. + """ + mesh = dtensor.device_mesh + if mesh is None: + return None + + mesh_dim_names = mesh.mesh_dim_names + if mesh_dim_names is None: + # Single-dim mesh: treat as the FSDP group + return mesh.get_group(0) + + # Look for common FSDP mesh dimension names used by PyTorch FSDP2 + for name in ("dp", "dp_shard", "dp_replicate_and_shard", "fsdp"): + if name in mesh_dim_names: + idx = list(mesh_dim_names).index(name) + return mesh.get_group(idx) + + # Fallback: flatten the entire mesh into one group + return mesh.get_group() + + +# --------------------------------------------------------------------------- +# Public API +# --------------------------------------------------------------------------- + + +@torch.no_grad() +def clip_grad_norm_( + parameters: torch.Tensor | Iterable[torch.Tensor], + max_norm: float, + norm_type: float = 2.0, + foreach: bool | None = None, +) -> torch.Tensor: + """DTensor-safe gradient clipping that avoids upstream PyTorch bugs. + + Separates DTensor gradients (sharded by FSDP2) from plain tensor gradients, + computes local partial norms, all-reduces across the FSDP process group, + and clips all gradients uniformly. + + Args: + parameters: Model parameters (or a single tensor) whose ``.grad`` will + be clipped in-place. + max_norm: Maximum allowed gradient norm. + norm_type: Type of p-norm (default 2.0). Use ``float('inf')`` for the + max-norm. + foreach: Use the faster foreach-based norm for regular tensors. When + ``None``, auto-detects based on device type. + + Returns: + The total gradient norm as a plain (non-DTensor) tensor. + """ + if isinstance(parameters, torch.Tensor): + parameters = [parameters] + else: + parameters = list(parameters) + + max_norm = float(max_norm) + norm_type = float(norm_type) + + # Partition gradients by type + dtensor_grads: list[torch.Tensor] = [] # local data of DTensor .grad + regular_grads: list[torch.Tensor] = [] + fsdp_pg: dist.ProcessGroup | None = None + + for p in parameters: + if p.grad is None: + continue + if isinstance(p.grad, DTensor): + # Extract local shard to avoid the O(n^2) torch.stack bug + dtensor_grads.append(p.grad._local_tensor) + if fsdp_pg is None: + fsdp_pg = _get_fsdp_process_group(p.grad) + else: + regular_grads.append(p.grad) + + # ------------------------------------------------------------------ + # Compute norms for each group + # ------------------------------------------------------------------ + + # Determine a common device for zero-tensors (empty partition edge case) + _dev: torch.device | None = None + if dtensor_grads: + _dev = dtensor_grads[0].device + elif regular_grads: + _dev = regular_grads[0].device + + # DTensor grads: compute local partial norm, then all-reduce + dtensor_partial = _local_norm(dtensor_grads, norm_type, device=_dev) + + if fsdp_pg is not None and len(dtensor_grads) > 0: + if math.isinf(norm_type): + dist.all_reduce(dtensor_partial, op=dist.ReduceOp.MAX, group=fsdp_pg) + else: + dist.all_reduce(dtensor_partial, op=dist.ReduceOp.SUM, group=fsdp_pg) + + # Regular grads: standard local norm (no all-reduce needed) + regular_partial = _local_norm(regular_grads, norm_type, device=_dev) + + # ------------------------------------------------------------------ + # Combine into total norm + # ------------------------------------------------------------------ + + if math.isinf(norm_type): + total_norm = torch.maximum(dtensor_partial, regular_partial) + else: + total_norm = (dtensor_partial + regular_partial).pow(1.0 / norm_type) + + # ------------------------------------------------------------------ + # Clip all grads uniformly + # ------------------------------------------------------------------ + + clip_coef = max_norm / (total_norm + 1e-6) + # Clamped to [0, 1] so we never scale gradients *up* + clip_coef_clamped = torch.clamp(clip_coef, max=1.0) + + if foreach is None: + foreach = all(g.device.type in ("cuda", "cpu") for g in (*dtensor_grads, *regular_grads)) + + all_grads = dtensor_grads + regular_grads + if len(all_grads) > 0: + if foreach: + torch._foreach_mul_(all_grads, clip_coef_clamped) + else: + for g in all_grads: + g.mul_(clip_coef_clamped) + + return total_norm + + +def check_grad_nan_across_ranks(grad_norm: torch.Tensor) -> bool: + """All-reduce NaN/Inf flag so all ranks agree on skip/step. + + Returns ``True`` if *any* rank observes a NaN or Inf gradient norm, + ensuring all ranks make the same decision about whether to skip the + optimizer step. When ``torch.distributed`` is not initialized (single-GPU + training), falls back to a local check. + + Args: + grad_norm: Scalar gradient norm tensor (output of :func:`clip_grad_norm_`). + + Returns: + ``True`` if the gradient norm is NaN or Inf on any rank. + """ + has_bad = torch.isnan(grad_norm) | torch.isinf(grad_norm) + + if not dist.is_initialized(): + return bool(has_bad.item()) + + # Convert to int for all_reduce (boolean reduce is not portable across backends) + flag = has_bad.int().to(grad_norm.device) + dist.all_reduce(flag, op=dist.ReduceOp.MAX) + return bool(flag.item() > 0) diff --git a/grail/trainer/distributed/launcher.py b/grail/trainer/distributed/launcher.py new file mode 100644 index 00000000..4801b007 --- /dev/null +++ b/grail/trainer/distributed/launcher.py @@ -0,0 +1,798 @@ +"""Distributed GRPO training launcher. + +Two entry points: + +1. ``torchrun --nproc_per_node=N -m grail.trainer.distributed.launcher`` + Standalone mode: ``main()`` handles process group init, model loading, and + the full training loop. IPC is unavailable (torchrun uses subprocess.Popen). + +2. ``run_distributed_training_process(rank, world_size, ...)`` + Orchestrated mode: called as ``multiprocessing.Process`` target from + ``TrainerNeuron._start_distributed_training()``. Receives ``IPCChannels`` + and ``SnapshotManager`` for heartbeat, stop signal, and snapshot upload. + +Both paths call the same ``_run_training()`` loop. When ``ipc`` is provided, +rank 0 updates the heartbeat, checks the stop signal, saves an initial +checkpoint, and queues snapshots for the upload worker. +""" + +from __future__ import annotations + +import asyncio +import logging +import multiprocessing +import os +import pickle +import re +import subprocess +import sys +import time +from multiprocessing.synchronize import Event +from pathlib import Path +from typing import Any + +import bittensor as bt +import torch +import torch.distributed as dist + +from grail.infrastructure.chain import GrailChainManager +from grail.infrastructure.credentials import load_r2_credentials +from grail.infrastructure.network import create_subtensor +from grail.protocol.constants import WINDOW_LENGTH +from grail.shared.config import NETUID +from grail.trainer.algorithms.grpo import GRPOGroup, load_grpo_groups +from grail.trainer.config import TrainingConfig +from grail.trainer.distributed.config import DistributedConfig +from grail.trainer.distributed.parallelism import create_device_mesh +from grail.trainer.distributed.training_service import DistributedTrainingService +from grail.trainer.ipc import IPCChannels +from grail.trainer.replay_buffer import create_replay_buffer +from grail.trainer.snapshot_manager import SnapshotManager + +logger = logging.getLogger(__name__) + +# Training loop constants +NO_DATA_SLEEP_SECONDS = 60 +LOOP_ERROR_SLEEP_SECONDS = 30 + + +# --------------------------------------------------------------------------- +# Helpers +# --------------------------------------------------------------------------- + + +def _estimate_model_params_b(model_id: str) -> float: + """Extract approximate model size in billions from a HuggingFace model ID.""" + match = re.search(r"(\d+(?:\.\d+)?)[bB]", model_id) + if match: + return float(match.group(1)) + return 7.0 + + +def _resolve_tp_degree(dist_cfg: DistributedConfig, world_size: int) -> int: + """Determine TP degree based on strategy and config. + + DDP and DILOCO do not support tensor parallelism (each GPU holds a full + model replica), so tp_degree is forced to 1. For FSDP2, use the configured + or auto-detected value. + """ + if dist_cfg.strategy in ("ddp", "diloco"): + return 1 + + if dist_cfg.tp_degree > 0: + return dist_cfg.tp_degree + + model_id = os.getenv("GRAIL_TRAIN_MODEL_ID", "") + params_b = _estimate_model_params_b(model_id) if model_id else 7.0 + tp_degree, _ = DistributedConfig.auto_detect_parallelism(world_size, params_b) + return tp_degree + + +def _configure_logging(rank: int, verbosity: int = 1) -> None: + """Configure logging for the distributed worker process.""" + if rank == 0: + level = logging.DEBUG if verbosity >= 2 else logging.INFO + else: + level = logging.WARNING + logging.basicConfig( + format=f"[rank {rank}] %(asctime)s %(name)s %(levelname)s %(message)s", + level=level, + force=True, + ) + + +def _broadcast_groups( + groups: list[GRPOGroup] | None, + rank: int, + device: torch.device, +) -> list[GRPOGroup]: + """Broadcast GRPO groups from rank 0 to all ranks via pickle + NCCL.""" + if rank == 0: + buf = pickle.dumps(groups or []) + size_tensor = torch.tensor([len(buf)], dtype=torch.int64, device=device) + else: + size_tensor = torch.tensor([0], dtype=torch.int64, device=device) + + dist.broadcast(size_tensor, src=0) + nbytes = int(size_tensor.item()) + + if nbytes == 0: + return [] + + if rank == 0: + data_tensor = torch.frombuffer(bytearray(buf), dtype=torch.uint8).to(device) # type: ignore[arg-type] + else: + data_tensor = torch.empty(nbytes, dtype=torch.uint8, device=device) + + dist.broadcast(data_tensor, src=0) + + if rank != 0: + return pickle.loads(data_tensor.cpu().numpy().tobytes()) # type: ignore[return-value] + + assert groups is not None + return groups + + +# --------------------------------------------------------------------------- +# Production training loop +# --------------------------------------------------------------------------- + + +async def _run_training( + service: DistributedTrainingService, + rank: int, + device: torch.device, + train_config: TrainingConfig, + snapshot_manager: SnapshotManager | None, + wallet: bt.wallet | None, + credentials: Any | None, + stop_event: Event, + test_mode: bool = False, + ipc: IPCChannels | None = None, + monitor: Any | None = None, +) -> None: + """Main distributed training loop. + + Rank 0: chain queries, R2 data loading, replay buffer, snapshots. + All ranks: FSDP2 training (forward/backward/optimizer step). + + When *ipc* is provided (orchestrated mode), rank 0 also: + - Updates the heartbeat each iteration + - Checks ``ipc.stop`` for graceful shutdown + - Saves an initial checkpoint before the first epoch + - Queues snapshots for the upload worker via ``ipc.queue_snapshot()`` + - Logs training metrics to W&B via *monitor* + """ + subtensor = None + chain_manager = None + replay_buffer = None + last_loaded_window = -1 + epoch_counter = 0 + current_block = 0 + current_window = 0 + + if rank == 0: + from types import SimpleNamespace + + subtensor = await create_subtensor(resilient=True) + metagraph = await subtensor.metagraph(NETUID) # type: ignore[misc] + chain_config = SimpleNamespace(netuid=NETUID) + assert wallet is not None + assert credentials is not None + chain_manager = GrailChainManager( + chain_config, + wallet, + metagraph, + subtensor, # type: ignore[arg-type] # ResilientSubtensor compat + credentials, # type: ignore[arg-type] + ) + await chain_manager.initialize() + replay_buffer = create_replay_buffer( + buffer_type="recency_weighted", + max_windows=train_config.replay_buffer_max_windows, + recent_window_fraction=train_config.replay_buffer_recent_fraction, + decay_factor=train_config.replay_buffer_decay_factor, + ) + logger.info("Rank 0: chain + replay buffer initialized") + + # ── Initial checkpoint bootstrap (orchestrated mode only) ── + is_fsdp2 = service.dist_config.strategy == "fsdp2" + if ipc is not None: + try: + if is_fsdp2: + # FSDP2: collective save (all ranks must participate) + from grail.trainer.distributed.checkpoint import save_full_checkpoint + + if rank == 0 and snapshot_manager is not None: + initial_dir = ( + snapshot_manager.cache_root / "snapshots" / f"initial.tmp.{os.getpid()}" + ) + save_full_checkpoint(service.train_model, service.tokenizer, initial_dir, rank) + else: + save_full_checkpoint( + service.train_model, service.tokenizer, Path("/tmp/unused"), rank + ) + else: + # DDP/DILOCO: rank-0-only save (no collective, no barrier) + initial_dir = None + if rank == 0 and snapshot_manager is not None: + from grail.trainer.distributed.checkpoint import save_ddp_checkpoint + + initial_dir = ( + snapshot_manager.cache_root / "snapshots" / f"initial.tmp.{os.getpid()}" + ) + save_ddp_checkpoint(service.train_model, service.tokenizer, initial_dir, rank) + + # Rank 0: adopt and queue snapshot for upload + if rank == 0 and snapshot_manager is not None: + initial_dir_path = ( + snapshot_manager.cache_root / "snapshots" / f"initial.tmp.{os.getpid()}" + ) + snapshot_metadata = { + "epoch": 0, + "timestamp": time.time(), + "status": "initial_upload", + "window": current_window, + } + snapshot_manager.adopt_snapshot_atomic(initial_dir_path, snapshot_metadata) + snapshot_path = snapshot_manager.get_latest_snapshot_path() + if snapshot_path: + ipc.queue_snapshot(str(snapshot_path), snapshot_metadata, current_window) + logger.info("Initial checkpoint saved and queued for upload") + except Exception as exc: + if rank == 0: + logger.error("Initial checkpoint failed: %s", exc) + + def _should_stop() -> bool: + if stop_event.is_set(): + return True + if ipc is not None and ipc.stop.is_set(): + return True + return False + + while not _should_stop(): + try: + # ── Rank 0: heartbeat + stop broadcast ── + if rank == 0 and ipc is not None: + ipc.update_heartbeat() + + # Broadcast stop signal from rank 0 to all ranks + stop_flag = torch.zeros(1, dtype=torch.int32, device=device) + if rank == 0 and _should_stop(): + stop_flag.fill_(1) + dist.broadcast(stop_flag, src=0) + if stop_flag.item() == 1: + logger.info("Stop signal received (rank=%d)", rank) + break + + # ── Rank 0: fetch data ── + groups: list[GRPOGroup] | None = None + if rank == 0: + assert subtensor is not None + assert replay_buffer is not None + assert chain_manager is not None + + try: + current_block = await subtensor.get_current_block() # type: ignore[misc] + current_window = (current_block // WINDOW_LENGTH) * WINDOW_LENGTH + except Exception as exc: + logger.warning("Failed to get current block: %s", exc) + + if monitor is not None: + monitor.set_block_context(current_block, current_window) + + target_data_window = current_window - WINDOW_LENGTH + + # Load new data when window changes + if target_data_window != last_loaded_window and target_data_window >= 0: + try: + # Refresh metagraph for trust computation + metagraph = await subtensor.metagraph(NETUID) # type: ignore[misc] + + # Get trusted miners + from grail.trainer.trust import ( + get_trust_list_from_validator, + get_trusted_miner_hotkeys, + ) + + trusted: set[str] | None = None + try: + trusted = await get_trust_list_from_validator( + metagraph, chain_manager, target_data_window + ) + except Exception: + pass + + if not trusted: + trusted = await get_trusted_miner_hotkeys( + metagraph, + min_trusted_miners=train_config.min_trusted_miners, + timeout=5.0, + ) + + if trusted: + new_groups = await load_grpo_groups( + window=target_data_window, + advantage_tolerance=train_config.group_adv_sum_tolerance, + trusted_miner_hotkeys=trusted, + credentials=credentials, + chain_manager=chain_manager, + config=train_config, + monitor=monitor, + eos_token_id=service.tokenizer.eos_token_id, + ) + if new_groups: + replay_buffer.add_window(target_data_window, new_groups) + logger.info( + "Loaded %d groups from window %d", + len(new_groups), + target_data_window, + ) + last_loaded_window = target_data_window + except Exception as exc: + logger.exception("Data loading failed: %s", exc) + + # Sample from replay buffer + seed = current_window + epoch_counter + groups = replay_buffer.sample_groups( + train_config.replay_buffer_max_groups_per_epoch, seed + ) + if not groups: + logger.warning("No training data, sleeping %ds", NO_DATA_SLEEP_SECONDS) + + # ── Broadcast groups to all ranks ── + groups = _broadcast_groups(groups, rank, device) + + if not groups: + await asyncio.sleep(NO_DATA_SLEEP_SECONDS) + continue + + if rank == 0: + total_rollouts = sum(len(g.rollouts) for g in groups) + logger.info( + "Epoch %d: %d groups, %d rollouts", + epoch_counter + 1, + len(groups), + total_rollouts, + ) + + # ── All ranks: train ── + t0 = time.time() + metrics = await service._train_epoch( # noqa: SLF001 + groups, + monitor=monitor if rank == 0 else None, + window=current_window, + ) + elapsed = time.time() - t0 + + # ── Save checkpoint ── + try: + strategy = service.dist_config.strategy + + # DILOCO: only save model weights and update snapshots/latest + # after outer sync, when local params == global consensus. + # Between outer syncs each GPU has diverged params; overwriting + # snapshots/latest would expose non-consensus weights to the + # upload worker and eval resource loader. + diloco_synced = service._diloco_sync_happened # noqa: SLF001 + should_save_weights = strategy != "diloco" or diloco_synced + + temp_ckpt: Path | None = None + if should_save_weights: + if rank == 0 and snapshot_manager is not None: + temp_ckpt = ( + snapshot_manager.cache_root / "snapshots" / f"epoch.tmp.{epoch_counter}" + ) + + if strategy == "fsdp2": + # FSDP2: collective gather (all ranks must participate) + from grail.trainer.distributed.checkpoint import save_full_checkpoint + + save_full_checkpoint( + service.train_model, + service.tokenizer, + temp_ckpt or Path("/tmp/fsdp2_unused"), + rank, + ) + elif rank == 0 and temp_ckpt is not None: + # DDP/DILOCO: rank 0 saves directly, no collective + from grail.trainer.distributed.checkpoint import save_ddp_checkpoint + + save_ddp_checkpoint( + service.train_model, + service.tokenizer, + temp_ckpt, + rank, + ) + + # Rank 0: adopt snapshot and queue for upload (only when + # weights were saved, i.e. not a DILOCO non-sync epoch) + if rank == 0 and snapshot_manager is not None and temp_ckpt is not None: + snapshot_metadata = { + "epoch": epoch_counter + 1, + "timestamp": time.time(), + "window": current_window, + "metrics": { + "loss_total": metrics.get("loss_total", 0.0), + }, + } + snapshot_manager.adopt_snapshot_atomic(temp_ckpt, snapshot_metadata) + + if ipc is not None: + snapshot_path = snapshot_manager.get_latest_snapshot_path() + if snapshot_path: + ipc.queue_snapshot( + str(snapshot_path), + snapshot_metadata, + current_window, + ) + + upload_status = "uploaded" + else: + upload_status = "skipped (awaiting DILOCO sync)" + + # Always save resume state for crash recovery (rank 0 only). + # This is cheap (optimizer + scheduler + DILOCO state, no model + # weights) and ensures we can restart from the last sync point. + if rank == 0: + try: + service._save_resume_checkpoint() # noqa: SLF001 + except Exception as resume_exc: + logger.warning("Resume checkpoint save failed: %s", resume_exc) + + if rank == 0: + logger.info( + "Epoch %d complete in %.1fs: loss=%.4f (%s)", + epoch_counter + 1, + elapsed, + metrics.get("loss_total", 0.0), + upload_status, + ) + + except Exception as exc: + if rank == 0: + logger.error("Checkpoint save failed: %s", exc) + + # ── Rank 0: log epoch-level metrics to W&B ── + if rank == 0 and monitor is not None and metrics: + try: + await monitor.log_gauge("training/epoch_duration", elapsed) + await monitor.log_gauge("training/epoch_loss", metrics.get("loss_total", 0.0)) + lr = ( + service.scheduler.get_last_lr()[0] if service.scheduler else train_config.lr + ) + await monitor.log_gauge("training/lr", lr) + await monitor.log_gauge("training/epoch", epoch_counter + 1) + await monitor.flush_metrics() + except Exception as exc: + logger.debug("Failed to log epoch metrics to W&B: %s", exc) + + if service.scheduler is not None: + service.scheduler.step() + epoch_counter += 1 + service.epoch_counter = epoch_counter + + dist.barrier() + + except asyncio.CancelledError: + logger.info("Training loop cancelled (rank=%d)", rank) + break + except Exception as exc: + logger.exception("Training loop error (rank=%d): %s", rank, exc) + # All ranks must hit the same collectives, so broadcast a "skip" signal + try: + _broadcast_groups(None if rank == 0 else None, rank, device) + except Exception: + pass + await asyncio.sleep(LOOP_ERROR_SLEEP_SECONDS) + + +# --------------------------------------------------------------------------- +# Main entry point +# --------------------------------------------------------------------------- + + +def main() -> None: + """Entry point for ``torchrun --nproc_per_node=N -m grail.trainer.distributed.launcher``.""" + if "PYTORCH_CUDA_ALLOC_CONF" not in os.environ: + os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "expandable_segments:True" + + # Force glibc to use mmap for allocations >= 64KB. Without this, after + # a 32GB tensor is freed via munmap, glibc raises its mmap threshold and + # routes subsequent medium allocations (1-100MB) through the heap arena. + # Arena frees never return memory to the OS, causing RSS to grow + # monotonically across PULSE outer steps until the OOM killer fires. + if "MALLOC_MMAP_THRESHOLD_" not in os.environ: + os.environ["MALLOC_MMAP_THRESHOLD_"] = "65536" + if "MALLOC_TRIM_THRESHOLD_" not in os.environ: + os.environ["MALLOC_TRIM_THRESHOLD_"] = "65536" + + dist.init_process_group(backend="cuda:nccl,cpu:gloo") + + try: + local_rank = int(os.environ["LOCAL_RANK"]) + torch.cuda.set_device(local_rank) + rank = dist.get_rank() + world_size = dist.get_world_size() + + verbosity = int(os.getenv("GRAIL_VERBOSITY", "2")) + _configure_logging(rank, verbosity) + + train_cfg = TrainingConfig() + dist_cfg = DistributedConfig.from_env() + dist_cfg.validate(world_size) + + tp_degree = _resolve_tp_degree(dist_cfg, world_size) + mesh = create_device_mesh(world_size, tp_degree) + device = torch.device("cuda", local_rank) + + if rank == 0: + logger.info( + "Distributed launcher: strategy=%s, rank=%d, world=%d, tp=%d, dp=%d", + dist_cfg.strategy, + rank, + world_size, + tp_degree, + world_size // tp_degree, + ) + + # Safety: override DAPO variant (its inner-loop gather causes NCCL mismatch) + train_cfg.grpo_variant = "grpo" + + # Build service + model_id = os.getenv("GRAIL_TRAIN_MODEL_ID", "Qwen/Qwen3-8B") + service = DistributedTrainingService( + train_config=train_cfg, + dist_config=dist_cfg, + rank=rank, + world_size=world_size, + local_rank=local_rank, + mesh=mesh, + model_name_or_path=model_id, + ) + + logger.info("Initializing resources (rank=%d)...", rank) + service._initialize_resources() # noqa: SLF001 + + # Rank 0 only: wallet, credentials, snapshot manager + wallet = None + credentials = None + snapshot_manager = None + + if rank == 0: + coldkey = os.getenv("BT_WALLET_COLD", "default") + hotkey = os.getenv("BT_WALLET_HOT", "default") + wallet = bt.wallet(name=coldkey, hotkey=hotkey) + credentials = load_r2_credentials() + + snapshot_dir = Path( + os.getenv( + "GRAIL_SNAPSHOT_DIR", + os.path.expanduser("~/grail_cache/checkpoints/async_trainer/snapshots"), + ) + ) + snapshot_manager = SnapshotManager(snapshot_dir) + logger.info("Rank 0: wallet=%s/%s, snapshots=%s", coldkey, hotkey, snapshot_dir) + + stop_event = multiprocessing.Event() + dist.barrier() + + test_mode = os.getenv("GRAIL_TEST_MODE", "0") == "1" + asyncio.run( + _run_training( + service, + rank, + device, + train_cfg, + snapshot_manager, + wallet, + credentials, + stop_event, + test_mode, + ) + ) + + except KeyboardInterrupt: + if dist.is_initialized(): + logger.info("Interrupted (rank=%d)", dist.get_rank()) + except Exception as exc: + r = dist.get_rank() if dist.is_initialized() else -1 + logger.exception("Launcher failed (rank=%d): %s", r, exc) + finally: + if dist.is_initialized(): + dist.destroy_process_group() + + +# --------------------------------------------------------------------------- +# Orchestrated entry point (called from TrainerNeuron via mp.Process) +# --------------------------------------------------------------------------- + + +def run_distributed_training_process( + rank: int, + world_size: int, + ipc: IPCChannels, + snapshot_manager: SnapshotManager, + train_cfg: TrainingConfig, + dist_cfg: DistributedConfig, + credentials: Any, + wallet_args: dict[str, str], + monitor_config: dict[str, Any], + verbosity: int = 1, + test_mode: bool = False, + master_addr: str = "127.0.0.1", + master_port: int = 29500, +) -> None: + """Entry point for distributed training via ``multiprocessing.Process``. + + Called by ``TrainerNeuron._start_distributed_training()``. Unlike + ``main()`` (torchrun), this receives ``IPCChannels`` and + ``SnapshotManager`` directly so the upload worker can consume + snapshots and the orchestrator can monitor heartbeat/stop. + + Args: + rank: Process rank (0-based). + world_size: Total number of training processes. + ipc: IPC channels shared with the orchestrator and upload worker. + snapshot_manager: Snapshot storage manager (rank 0 writes, upload worker reads). + train_cfg: Training hyperparameters. + dist_cfg: Distributed parallelism config (tp_degree, etc.). + credentials: R2 credentials (rank 0 only, for chain manager). + wallet_args: Serialized wallet (name, hotkey, path). + monitor_config: Monitoring/W&B configuration. + verbosity: Logging verbosity (0=silent, 1=INFO, >=2=DEBUG). + test_mode: If True, train only on TRAINER_UID data. + master_addr: NCCL rendezvous address. + master_port: NCCL rendezvous port. + """ + # ── Environment setup (replaces torchrun's env injection) ── + os.environ["RANK"] = str(rank) + os.environ["LOCAL_RANK"] = str(rank) + os.environ["WORLD_SIZE"] = str(world_size) + os.environ["MASTER_ADDR"] = master_addr + os.environ["MASTER_PORT"] = str(master_port) + os.environ["NCCL_TIMEOUT"] = "120" + + if "PYTORCH_CUDA_ALLOC_CONF" not in os.environ: + os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "expandable_segments:True" + if "MALLOC_MMAP_THRESHOLD_" not in os.environ: + os.environ["MALLOC_MMAP_THRESHOLD_"] = "65536" + if "MALLOC_TRIM_THRESHOLD_" not in os.environ: + os.environ["MALLOC_TRIM_THRESHOLD_"] = "65536" + + _configure_logging(rank, verbosity) + + try: + dist.init_process_group(backend="cuda:nccl,cpu:gloo") + torch.cuda.set_device(rank) + device = torch.device("cuda", rank) + + logger.info( + "Distributed worker started: rank=%d, world=%d, master=%s:%d", + rank, + world_size, + master_addr, + master_port, + ) + + dist_cfg.validate(world_size) + tp_degree = _resolve_tp_degree(dist_cfg, world_size) + mesh = create_device_mesh(world_size, tp_degree) + + # Safety: override DAPO variant (its inner-loop gather causes NCCL mismatch) + train_cfg.grpo_variant = "grpo" + + model_id = os.getenv("GRAIL_TRAIN_MODEL_ID", "Qwen/Qwen3-8B") + service = DistributedTrainingService( + train_config=train_cfg, + dist_config=dist_cfg, + rank=rank, + world_size=world_size, + local_rank=rank, + mesh=mesh, + model_name_or_path=model_id, + ) + + logger.info("Initializing resources (rank=%d)...", rank) + service._initialize_resources() # noqa: SLF001 + + # Rank 0: wallet and credentials for chain data loading + wallet = None + creds = None + sm = None + + if rank == 0: + wallet = bt.wallet( + name=wallet_args["name"], + hotkey=wallet_args["hotkey"], + path=wallet_args.get("path", "~/.bittensor/wallets"), + ) + creds = credentials + sm = snapshot_manager + + dist.barrier() + + async def _run_with_monitor() -> None: + # Rank 0: init W&B monitoring (shared mode, attaches to orchestrator's run) + rank0_monitor = None + if rank == 0 and monitor_config: + from grail.monitoring import initialize_subprocess_monitoring + + rank0_monitor = await initialize_subprocess_monitoring( + monitor_config, + "distributed_rank0", + test_connection=True, + ) + if rank0_monitor: + logger.info("Rank 0: W&B monitoring initialized") + else: + logger.warning("Rank 0: W&B monitoring init failed, metrics will not be logged") + + try: + await _run_training( + service, + rank, + device, + train_cfg, + sm, + wallet, + creds, + ipc.stop, + test_mode, + ipc, + monitor=rank0_monitor, + ) + finally: + if rank0_monitor is not None: + try: + await rank0_monitor.shutdown() + except Exception as exc: + logger.debug("Monitor shutdown error: %s", exc) + + asyncio.run(_run_with_monitor()) + + except KeyboardInterrupt: + if dist.is_initialized(): + logger.info("Interrupted (rank=%d)", dist.get_rank()) + except Exception as exc: + r = dist.get_rank() if dist.is_initialized() else rank + logger.exception("Distributed worker failed (rank=%d): %s", r, exc) + finally: + if dist.is_initialized(): + dist.destroy_process_group() + + +# --------------------------------------------------------------------------- +# Subprocess launcher (kept for standalone torchrun usage) +# --------------------------------------------------------------------------- + + +def launch_distributed( + nproc: int, + env: dict[str, str] | None = None, +) -> subprocess.Popen[bytes]: + """Launch distributed training as a subprocess via torchrun.""" + cmd = [ + sys.executable, + "-m", + "torch.distributed.run", + f"--nproc_per_node={nproc}", + "--master_addr=127.0.0.1", + "--master_port=29500", + "-m", + "grail.trainer.distributed.launcher", + ] + + merged_env = os.environ.copy() + if env is not None: + merged_env.update(env) + + if "PYTORCH_CUDA_ALLOC_CONF" not in merged_env: + merged_env["PYTORCH_CUDA_ALLOC_CONF"] = "expandable_segments:True" + + logger.info("Launching distributed training: nproc=%d", nproc) + return subprocess.Popen(cmd, env=merged_env) + + +if __name__ == "__main__": + main() diff --git a/grail/trainer/distributed/logprobs.py b/grail/trainer/distributed/logprobs.py new file mode 100644 index 00000000..dc03da8d --- /dev/null +++ b/grail/trainer/distributed/logprobs.py @@ -0,0 +1,412 @@ +"""TP-aware chunked log-probability computation for GRPO training. + +When the lm_head is tensor-parallel (ColwiseParallel with Shard(-1) output), +each TP rank holds ``vocab_size / tp_size`` logits. This module computes +per-token log-probabilities without all-gathering the full vocab logits by +using a distributed logsumexp reduction (the Megatron pattern): + +Communication per chunk: 3 all-reduces of shape ``[B, chunk]`` (tiny), +instead of a 1.86 GB vocab all-gather. + +Uses ``torch.distributed.nn.functional.all_reduce`` (the differentiable +version) so that gradients flow correctly through the softmax normalisation +during the current-policy forward pass. +""" + +from __future__ import annotations + +import logging +from typing import TYPE_CHECKING, Any + +import torch +import torch.distributed as dist + +if TYPE_CHECKING: + pass + + +def _differentiable_all_reduce( + tensor: torch.Tensor, + op: dist.ReduceOp.RedOpType, # type: ignore[assignment] + group: dist.ProcessGroup, +) -> torch.Tensor: + """Differentiable all-reduce that participates in autograd. + + Uses ``torch.distributed.nn.functional.all_reduce`` which registers + autograd hooks, ensuring correct gradient flow through softmax + normalisation in the current-policy forward pass. + """ + import torch.distributed.nn.functional as dist_fn + + result = dist_fn.all_reduce(tensor, op=op, group=group) + # dist_fn.all_reduce returns a tuple; extract the tensor + if isinstance(result, tuple): + return result[0] + return result # type: ignore[return-value] + + +logger = logging.getLogger(__name__) + + +# --------------------------------------------------------------------------- +# Core: TP-aware chunked logprobs via distributed logsumexp +# --------------------------------------------------------------------------- + + +def tp_chunked_logprobs( + lm_head: torch.nn.Module, + hidden_states: torch.Tensor, + labels: torch.Tensor, + chunk_size: int, + tp_group: dist.ProcessGroup, + return_entropy: bool = False, + prompt_lengths: list[int] | None = None, +) -> tuple[torch.Tensor, torch.Tensor | None]: + """Compute per-token log-probs when lm_head output is TP-sharded. + + Each TP rank holds a shard of the vocabulary logits (``V / tp_size`` + columns). Instead of all-gathering the full ``[B, chunk, V]`` tensor we + perform three lightweight all-reduces per chunk (max, sum-exp, target + logit) to reconstruct exact log-probabilities. + + Args: + lm_head: Language model head (linear projection to vocab). Under TP + its output is ``[B, S, V_local]`` where ``V_local = V / tp_size`` + (possibly with a remainder on the last rank). + hidden_states: ``[B, S, H]`` hidden states (already shifted if the + caller handled next-token alignment). + labels: ``[B, S]`` global token IDs aligned with *hidden_states*. + chunk_size: Number of sequence positions per LM-head chunk. + tp_group: The process group spanning the TP ranks. + return_entropy: Whether to also compute per-token entropy. + prompt_lengths: If provided, prompt positions are zeroed out in the + returned tensors (optional convenience; the caller can also mask + afterwards). + + Returns: + token_log_probs: ``[B, S]`` per-token log-probabilities. + entropy_per_token: ``[B, S]`` per-token entropy, or ``None``. + """ + batch_size, seq_len = labels.shape + device = hidden_states.device + + tp_rank = dist.get_rank(tp_group) + tp_size = dist.get_world_size(tp_group) + + token_log_probs = torch.zeros(batch_size, seq_len, device=device) + entropy_per_token: torch.Tensor | None = None + if return_entropy: + entropy_per_token = torch.zeros(batch_size, seq_len, device=device) + + for chunk_start in range(0, seq_len, chunk_size): + chunk_end = min(chunk_start + chunk_size, seq_len) + + chunk_hidden = hidden_states[:, chunk_start:chunk_end, :] + chunk_labels = labels[:, chunk_start:chunk_end] # [B, C] global IDs + + # (a) Project through lm_head -> local logits [B, C, V_local] + chunk_logits = lm_head(chunk_hidden) + + # If the output is a DTensor (torch.distributed.tensor), unwrap. + # Use the public to_local() API when available, fall back to _local_tensor. + if hasattr(chunk_logits, "to_local"): + chunk_logits = chunk_logits.to_local() + elif hasattr(chunk_logits, "_local_tensor"): + chunk_logits = chunk_logits._local_tensor + + vocab_local = chunk_logits.shape[-1] + + # (b) Cast to fp32 for numerically stable logsumexp + chunk_logits = chunk_logits.float() # [B, C, V_local] + + # (c) Local max, then differentiable all-reduce MAX across TP group. + # We use torch.distributed.nn.functional.all_reduce which participates + # in autograd, ensuring correct gradients through the softmax + # normalisation for the current policy forward pass. + local_max, _ = chunk_logits.max(dim=-1) # [B, C] + global_max = _differentiable_all_reduce(local_max, op=dist.ReduceOp.MAX, group=tp_group) + + # (d) Shift logits, compute local exp sum, differentiable all-reduce SUM + shifted_logits = chunk_logits - global_max.unsqueeze(-1) # [B, C, V_local] + local_exp_sum = shifted_logits.exp().sum(dim=-1) # [B, C] + global_exp_sum = _differentiable_all_reduce( + local_exp_sum, op=dist.ReduceOp.SUM, group=tp_group + ) + + # (e) Global logsumexp + global_logsumexp = global_max + global_exp_sum.log() # [B, C] + + # (f) Extract target token's logit. + # Labels are global vocab indices. DTensor Shard(-1) may distribute + # unevenly: the first (full_vocab % tp_size) ranks get ceil(V/tp), + # the rest get floor(V/tp). Compute correct partition boundaries. + # We cannot assume all ranks have the same vocab_local. Compute + # this rank's partition start by summing sizes of preceding ranks. + # Since DTensor Shard distributes as: first (V%N) ranks get (V//N)+1, + # rest get V//N, and vocab_local on this rank is already correct, + # we use a simple offset formula. + # For rank r with tp_size N: partition_start = sum_{i=0}^{r-1} vocab_local_i + # With DTensor Shard(0): rank i gets (V//N + (1 if i < V%N else 0)) + # We don't know full_vocab here, but we can reconstruct it: + # Gather vocab_local from all ranks. + local_vocab_tensor = torch.tensor([vocab_local], device=device, dtype=torch.long) + all_vocab_sizes = [torch.zeros(1, device=device, dtype=torch.long) for _ in range(tp_size)] + dist.all_gather(all_vocab_sizes, local_vocab_tensor, group=tp_group) + partition_start = sum(v.item() for v in all_vocab_sizes[:tp_rank]) + partition_end = partition_start + vocab_local + + # Map global label to local index; out-of-range labels get index 0 + # (the gathered value will be zeroed out below). + local_label = chunk_labels - partition_start # [B, C] + in_partition = (chunk_labels >= partition_start) & (chunk_labels < partition_end) + # Clamp to valid range so gather doesn't fail + local_label = local_label.clamp(0, vocab_local - 1) + + # Gather the logit at the local index + target_logit = chunk_logits.gather(2, local_label.unsqueeze(-1)).squeeze(-1) # [B, C] + + # Zero out contributions from ranks that don't own this label + target_logit = target_logit * in_partition.float() + + # (g) Differentiable all-reduce SUM (exactly one rank has the real value) + target_logit = _differentiable_all_reduce( + target_logit, op=dist.ReduceOp.SUM, group=tp_group + ) + + # (h) logprob = target_logit - global_logsumexp + token_log_probs[:, chunk_start:chunk_end] = target_logit - global_logsumexp + + # (i) Entropy: -sum(softmax * logits) across full vocab. + # Each rank computes its local piece, then all-reduce SUM. + if return_entropy and entropy_per_token is not None: + softmax_local = shifted_logits.exp() / global_exp_sum.unsqueeze(-1) # [B, C, V_local] + log_probs_local = shifted_logits - global_exp_sum.log().unsqueeze(-1) + local_entropy = -(softmax_local * log_probs_local).sum(dim=-1) # [B, C] + local_entropy = _differentiable_all_reduce( + local_entropy, op=dist.ReduceOp.SUM, group=tp_group + ) + entropy_per_token[:, chunk_start:chunk_end] = local_entropy + del softmax_local, log_probs_local, local_entropy + + del chunk_logits, shifted_logits, local_exp_sum, target_logit + del local_max, global_max, global_exp_sum, global_logsumexp + + # Optionally zero out prompt positions + if prompt_lengths is not None: + for idx, plen in enumerate(prompt_lengths): + if plen > 0: + token_log_probs[idx, :plen] = 0.0 + if entropy_per_token is not None: + entropy_per_token[idx, :plen] = 0.0 + + return token_log_probs, entropy_per_token + + +# --------------------------------------------------------------------------- +# Wrapper: dispatch between TP and non-TP paths +# --------------------------------------------------------------------------- + + +def _unwrap_for_chunked_logits( + model: Any, +) -> tuple[Any, Any] | None: + """Extract ``(base_model, lm_head)`` from a causal LM. + + Returns ``None`` if the model structure is incompatible (e.g. DDP/FSDP + wrappers or non-standard architectures). + """ + try: + from torch.nn.parallel import DistributedDataParallel + + if isinstance(model, DistributedDataParallel): + return None + except ImportError: + pass + try: + from torch.distributed.fsdp import FullyShardedDataParallel + + if isinstance(model, FullyShardedDataParallel): + return None + except ImportError: + pass + + base_model_prefix = getattr(model, "base_model_prefix", "") + base = getattr(model, base_model_prefix, None) if base_model_prefix else None + lm_head = getattr(model, "lm_head", None) + + if base is not None and lm_head is not None: + return base, lm_head + + return None + + +def compute_logprobs_distributed( + model: torch.nn.Module, + input_ids: torch.Tensor, + attention_mask: torch.Tensor | None, + prompt_lengths: list[int], + completion_lengths: list[int], + *, + chunked: bool = True, + chunk_size: int = 256, + tp_group: dist.ProcessGroup | None = None, + return_per_token: bool = True, + return_entropy: bool = False, +) -> tuple[torch.Tensor, torch.Tensor | None, torch.Tensor | None]: + """Compute completion-token log-probs, dispatching to TP or standard path. + + When *tp_group* is provided, uses :func:`tp_chunked_logprobs` to avoid + the costly all-gather of full-vocab logits. Otherwise, falls back to the + standard chunked implementation from ``grpo.py``. + + Returns: + A 3-tuple ``(logprobs_sum, logprobs_per_token, entropies)`` matching + the interface expected by ``GRPOAlgorithm``: + + * ``logprobs_sum`` -- ``[B]`` sum of log-probs per sequence. + * ``logprobs_per_token`` -- ``[B, max_comp_len]`` padded per-token + log-probs if *return_per_token* is ``True``, else ``None``. + * ``entropies`` -- ``[B]`` mean entropy per sequence if + *return_entropy* is ``True``, else ``None``. + """ + # ---- TP path: distributed logsumexp without full-vocab all-gather ---- + if tp_group is not None: + parts = _unwrap_for_chunked_logits(model) + if parts is None: + raise RuntimeError( + "Cannot extract (base_model, lm_head) from model for TP logprobs. " + "Ensure the model is a standard HuggingFace CausalLM (not DDP/FSDP wrapped)." + ) + base_model, lm_head = parts + + # Forward through the base model to get hidden states + base_out = base_model( + input_ids, + attention_mask=attention_mask, + use_cache=False, + ) + hidden_states = base_out.last_hidden_state # [B, S, H] + + # Apply the same prompt-skip optimisation as the non-TP path: + # shift for next-token prediction, skipping prompt-only positions. + min_prompt_len = min(prompt_lengths) + keep_from = max(0, min_prompt_len - 1) + + shift_hidden = hidden_states[:, keep_from:-1, :] + shift_labels = input_ids[:, keep_from + 1 :] + + token_log_probs, entropy_per_token = tp_chunked_logprobs( + lm_head, + shift_hidden, + shift_labels, + chunk_size, + tp_group, + return_entropy=return_entropy, + ) + + # Extract completion-token results, adjusting for the keep_from offset + adjusted_prompt_lengths = [p - keep_from for p in prompt_lengths] + return _extract_completion_logprobs_tp( + token_log_probs, + entropy_per_token, + adjusted_prompt_lengths, + completion_lengths, + return_per_token, + return_entropy, + ) + + # ---- Non-TP path: delegate to existing implementation ---- + from grail.trainer.algorithms.grpo import compute_logprobs + + result = compute_logprobs( + model, + input_ids, + attention_mask if attention_mask is not None else torch.ones_like(input_ids), + prompt_lengths, + completion_lengths, + return_per_token=return_per_token, + return_entropy=return_entropy, + chunked=chunked, + chunk_size=chunk_size, + ) + + # Normalise return format to a consistent 3-tuple + if return_per_token and return_entropy: + if isinstance(result, tuple) and len(result) == 3: + return result[0], result[1], result[2] + if return_per_token and not return_entropy: + if isinstance(result, tuple) and len(result) == 2: + return result[0], result[1], None + # return_per_token=False (sum only) + if isinstance(result, torch.Tensor): + return result, None, None + # Fallback for unexpected shapes + if isinstance(result, tuple): + logprobs_sum = result[0] + logprobs_pt = result[1] if len(result) > 1 else None + entropies = result[2] if len(result) > 2 else None + return logprobs_sum, logprobs_pt, entropies + + return result, None, None # type: ignore[return-value] + + +# --------------------------------------------------------------------------- +# Completion extraction (mirrors grpo._extract_completion_logprobs) +# --------------------------------------------------------------------------- + + +def _extract_completion_logprobs_tp( + token_log_probs: torch.Tensor, + entropy_per_token: torch.Tensor | None, + prompt_lengths: list[int], + completion_lengths: list[int], + return_per_token: bool, + return_entropy: bool, +) -> tuple[torch.Tensor, torch.Tensor | None, torch.Tensor | None]: + """Extract completion-token logprobs/entropy and return a consistent 3-tuple. + + This mirrors ``grpo._extract_completion_logprobs`` but always returns a + 3-tuple ``(logprobs_sum, logprobs_per_token | None, entropies | None)`` + for a uniform interface. + + Args: + token_log_probs: ``[B, shifted_seq_len]`` per-token log-probs. + entropy_per_token: ``[B, shifted_seq_len]`` per-token entropy (or ``None``). + prompt_lengths: Per-sample prompt lengths (already adjusted for any + ``keep_from`` offset). + completion_lengths: Per-sample completion lengths. + return_per_token: Whether to return padded per-token logprobs. + return_entropy: Whether to return mean entropy per sequence. + """ + seq_len_minus_1 = token_log_probs.shape[1] + device = token_log_probs.device + batch_size = len(prompt_lengths) + + max_comp_len = max(completion_lengths) if completion_lengths else 1 + seq_log_probs = torch.zeros(batch_size, device=device) + per_token_padded: torch.Tensor | None = None + if return_per_token: + per_token_padded = torch.zeros(batch_size, max_comp_len, device=device) + + entropies_list: list[torch.Tensor] = [] + + for idx, prompt_len in enumerate(prompt_lengths): + completion_len = completion_lengths[idx] + start_idx = max(0, prompt_len - 1) + end_idx = min(seq_len_minus_1, start_idx + completion_len) + if end_idx > start_idx: + comp_logps = token_log_probs[idx, start_idx:end_idx] + seq_log_probs[idx] = comp_logps.sum() + if per_token_padded is not None: + per_token_padded[idx, : len(comp_logps)] = comp_logps + if return_entropy and entropy_per_token is not None: + entropies_list.append(entropy_per_token[idx, start_idx:end_idx].mean()) + else: + if return_entropy and entropy_per_token is not None: + entropies_list.append(torch.tensor(0.0, device=device)) + + entropies: torch.Tensor | None = None + if return_entropy and entropy_per_token is not None: + entropies = torch.stack(entropies_list) if entropies_list else torch.zeros(0, device=device) + + return seq_log_probs, per_token_padded, entropies diff --git a/grail/trainer/distributed/parallelism.py b/grail/trainer/distributed/parallelism.py new file mode 100644 index 00000000..aec9b91f --- /dev/null +++ b/grail/trainer/distributed/parallelism.py @@ -0,0 +1,461 @@ +"""FSDP2 + Tensor Parallelism setup for HuggingFace transformer models. + +Handles DeviceMesh creation, TP+SP (Sequence Parallelism) application, +per-layer FSDP2 wrapping, gradient checkpointing, and RMSNorm replacement. +Supports Qwen2, Qwen3, and Llama model families. +""" + +from __future__ import annotations + +import logging +from typing import Any, cast + +import torch +import torch.nn as nn +from torch.distributed.device_mesh import DeviceMesh, init_device_mesh +from torch.distributed.fsdp import CPUOffloadPolicy, MixedPrecisionPolicy, fully_shard +from torch.distributed.tensor.parallel import ( + ColwiseParallel, + PrepareModuleInput, + RowwiseParallel, + SequenceParallel, + parallelize_module, +) +from torch.utils.checkpoint import checkpoint + +from grail.trainer.distributed.config import DistributedConfig + +logger = logging.getLogger(__name__) + +# HuggingFace RMSNorm class names that need replacement for SP compatibility. +# torch.nn.RMSNorm supports DTensor sharding; the HF variants do not. +_HF_RMSNORM_NAMES = frozenset({"Qwen2RMSNorm", "Qwen3RMSNorm", "LlamaRMSNorm"}) + + +def _get_base_model(model: nn.Module) -> nn.Module: + """Return the inner transformer model (model.model for HF CausalLM wrappers).""" + if hasattr(model, "model"): + return cast(nn.Module, model.model) # type: ignore[union-attr] + return model + + +def _get_layers(base_model: nn.Module) -> nn.ModuleList: + """Return the decoder layer list from the base transformer model. + + Raises: + AttributeError: If the model has no .layers attribute. + """ + if not hasattr(base_model, "layers"): + raise AttributeError( + f"{type(base_model).__name__} has no .layers attribute. " + "Expected a HuggingFace Qwen2/Qwen3/Llama model." + ) + return cast(nn.ModuleList, base_model.layers) # type: ignore[union-attr] + + +def create_device_mesh(world_size: int, tp_degree: int) -> DeviceMesh: + """Create a 2D DeviceMesh with (dp, tp) dimensions. + + Args: + world_size: Total number of ranks in the process group. + tp_degree: Tensor parallelism degree. Must evenly divide world_size. + + Returns: + A 2D DeviceMesh with dimension names ("dp", "tp"). + + Raises: + ValueError: If tp_degree does not evenly divide world_size or is < 1. + """ + if tp_degree < 1: + raise ValueError(f"tp_degree must be >= 1, got {tp_degree}") + if world_size % tp_degree != 0: + raise ValueError(f"tp_degree={tp_degree} does not evenly divide world_size={world_size}") + + dp_degree = world_size // tp_degree + logger.info( + "Creating 2D device mesh: world_size=%d, dp=%d, tp=%d", + world_size, + dp_degree, + tp_degree, + ) + + mesh = init_device_mesh( + "cuda", + (dp_degree, tp_degree), + mesh_dim_names=("dp", "tp"), + ) + return mesh + + +def replace_rmsnorm_for_sp(model: nn.Module) -> None: + """Replace HuggingFace RMSNorm modules with torch.nn.RMSNorm. + + HuggingFace's Qwen2RMSNorm, Qwen3RMSNorm, and LlamaRMSNorm do not support + DTensor inputs required by SequenceParallel. torch.nn.RMSNorm (added in + PyTorch 2.4) handles sharded tensors natively. This function walks the + module tree, finds matching RMSNorm submodules by class name, copies their + weights and eps, and replaces them on the parent module. + + Args: + model: The HuggingFace model to modify in-place. + """ + replacements: list[tuple[str, nn.Module]] = [] + + for name, module in model.named_modules(): + class_name = type(module).__name__ + if class_name not in _HF_RMSNORM_NAMES: + continue + + # Extract parameters from the HF RMSNorm + weight = cast(nn.Parameter, module.weight) # type: ignore[union-attr] + eps: float = getattr(module, "variance_epsilon", getattr(module, "eps", 1e-6)) + normalized_shape: int = weight.shape[0] + + # Create the torch.nn.RMSNorm replacement + new_norm = torch.nn.RMSNorm(normalized_shape, eps=eps, device=weight.device) + new_norm.weight = weight # Share the same parameter (no copy) + + replacements.append((name, new_norm)) + + # Apply replacements by walking parent -> child relationships + replaced = 0 + for name, new_module in replacements: + parts = name.rsplit(".", 1) + if len(parts) == 2: + parent_name, attr_name = parts + parent = model.get_submodule(parent_name) + else: + attr_name = parts[0] + parent = model + setattr(parent, attr_name, new_module) + replaced += 1 + + if replaced > 0: + logger.info( + "Replaced %d HF RMSNorm modules with torch.nn.RMSNorm for SP compatibility", + replaced, + ) + + +def apply_tp_sp(model: nn.Module, tp_mesh: DeviceMesh) -> None: + """Apply Tensor Parallelism + Sequence Parallelism to a HuggingFace model. + + Parallelization plan (matching Qwen2/Qwen3/Llama architectures): + + Top-level: + - model.embed_tokens: RowwiseParallel (output sharded on seq dim for SP) + - lm_head: ColwiseParallel (input sharded on seq dim, output sharded on vocab) + - model.norm: SequenceParallel + + Per decoder layer: + - input_layernorm, post_attention_layernorm: SequenceParallel + - self_attn: PrepareModuleInput (Shard(1) -> Replicate for attention) + - self_attn.{q,k,v}_proj: ColwiseParallel + - self_attn.o_proj: RowwiseParallel (output Shard(1) for residual) + - mlp: PrepareModuleInput (Shard(1) -> Replicate for MLP) + - mlp.{gate,up}_proj: ColwiseParallel + - mlp.down_proj: RowwiseParallel (output Shard(1) for residual) + + Notes: + - o_proj and down_proj use output_layouts=Shard(1) so their outputs match + the sharded residual connection (both Shard(1) on the sequence dimension). + - self_attn uses input_kwarg_layouts because HuggingFace attention modules + receive hidden_states as a keyword argument. + - No num_heads patching is needed; HF uses -1 in reshape. + + Args: + model: HuggingFace CausalLM model (e.g., Qwen2ForCausalLM). + tp_mesh: The TP sub-mesh from the 2D device mesh. + """ + from torch.distributed.tensor import Replicate, Shard + + # Resolve the base transformer (model.model for HF CausalLM wrappers) + base_model = _get_base_model(model) + + # --- Top-level parallelization --- + parallelize_module( + base_model, + tp_mesh, + { + "embed_tokens": RowwiseParallel( + input_layouts=Replicate(), + output_layouts=Shard(1), + ), + "norm": SequenceParallel(), + }, + ) + + # lm_head lives on the outer CausalLM wrapper + parallelize_module( + model, + tp_mesh, + { + "lm_head": ColwiseParallel( + input_layouts=Shard(1), + output_layouts=Shard(-1), + use_local_output=False, + ), + }, + ) + + # --- Per-layer parallelization --- + layers = _get_layers(base_model) + for layer in layers: + plan: dict[str, Any] = { + # Layer norms operate on sharded sequences + "input_layernorm": SequenceParallel(), + "post_attention_layernorm": SequenceParallel(), + # Attention: gather sharded hidden_states before QKV projections. + # HF passes hidden_states as a kwarg, so use input_kwarg_layouts. + "self_attn": PrepareModuleInput( + input_kwarg_layouts={"hidden_states": Shard(1)}, + desired_input_kwarg_layouts={"hidden_states": Replicate()}, + ), + "self_attn.q_proj": ColwiseParallel(), + "self_attn.k_proj": ColwiseParallel(), + "self_attn.v_proj": ColwiseParallel(), + "self_attn.o_proj": RowwiseParallel(output_layouts=Shard(1)), + # MLP: gather sharded hidden_states before gate/up projections. + # HF MLP forward() takes positional args, so use input_layouts. + "mlp": PrepareModuleInput( + input_layouts=(Shard(1),), + desired_input_layouts=(Replicate(),), + ), + "mlp.gate_proj": ColwiseParallel(), + "mlp.up_proj": ColwiseParallel(), + "mlp.down_proj": RowwiseParallel(output_layouts=Shard(1)), + } + + parallelize_module(layer, tp_mesh, plan) + + logger.info( + "Applied TP+SP to %d transformer layers (tp_degree=%d)", + len(layers), + tp_mesh.size(), + ) + + +def apply_gradient_checkpointing( + model: nn.Module, + gc_every_n: int, + skip_last_n: int, +) -> None: + """Apply per-layer gradient checkpointing with selective layer skipping. + + Wraps each eligible decoder layer's forward method with + ``torch.utils.checkpoint.checkpoint(use_reentrant=False)``. Layers are + checkpointed every ``gc_every_n`` layers, and the last ``skip_last_n`` + layers are always skipped (their activations are kept in memory for faster + backward). + + Args: + model: HuggingFace CausalLM model. + gc_every_n: Checkpoint every Nth layer (1 = all eligible layers). + skip_last_n: Number of final layers to skip checkpointing. + """ + base_model = _get_base_model(model) + if not hasattr(base_model, "layers"): + logger.warning("Model has no .layers attribute, skipping gradient checkpointing") + return + + layers = _get_layers(base_model) + num_layers = len(layers) + eligible_end = num_layers - skip_last_n + + if eligible_end <= 0: + logger.warning( + "skip_last_n=%d >= num_layers=%d, no layers will be checkpointed", + skip_last_n, + num_layers, + ) + return + + checkpointed = 0 + for i in range(eligible_end): + if i % gc_every_n != 0: + continue + + layer = layers[i] + original_forward = layer.forward + + # Use a factory to capture the correct original_forward per layer + def _make_ckpt_forward( + orig_fn: Any, + ) -> Any: + def ckpt_forward(*args: Any, **kwargs: Any) -> Any: + return checkpoint(orig_fn, *args, use_reentrant=False, **kwargs) + + return ckpt_forward + + layer.forward = _make_ckpt_forward(original_forward) # type: ignore[method-assign] + checkpointed += 1 + + logger.info( + "Gradient checkpointing: %d/%d layers (every %d, skipping last %d)", + checkpointed, + num_layers, + gc_every_n, + skip_last_n, + ) + + +def apply_fsdp2( + model: nn.Module, + dp_mesh: DeviceMesh, + mp_policy: MixedPrecisionPolicy, + reshard_after_forward: bool, +) -> None: + """Apply FSDP2 wrapping: per-layer sharding then root model sharding. + + Each decoder layer is individually wrapped with ``fully_shard`` to enable + communication/computation overlap and fine-grained memory management. The + root model is then wrapped to handle any remaining parameters (embeddings, + final norm, lm_head). + + Args: + model: HuggingFace CausalLM model (already TP-parallelized if applicable). + dp_mesh: The DP sub-mesh from the 2D device mesh. + mp_policy: Mixed precision policy (e.g., param bf16, reduce fp32). + reshard_after_forward: If True, reshard parameters after forward (ZeRO-3). + If False, keep unsharded (ZeRO-2 style, faster but uses more memory). + """ + base_model = _get_base_model(model) + + # Per-layer FSDP wrapping + if hasattr(base_model, "layers"): + layer_list = _get_layers(base_model) + for layer in layer_list: + fully_shard( + layer, + mesh=dp_mesh, + mp_policy=mp_policy, + reshard_after_forward=reshard_after_forward, + ) + logger.debug("FSDP2: wrapped %d decoder layers", len(layer_list)) + + # Root model wrapping (covers embed_tokens, norm, lm_head, etc.) + fully_shard( + model, + mesh=dp_mesh, + mp_policy=mp_policy, + reshard_after_forward=reshard_after_forward, + ) + + logger.info( + "FSDP2 applied: reshard_after_forward=%s, mp_policy=%s", + reshard_after_forward, + mp_policy, + ) + + +def apply_ddp( + model: nn.Module, + local_rank: int, +) -> nn.Module: + """Wrap model with DistributedDataParallel. + + DDP replicates the full model on each GPU and all-reduces gradients during + backward, overlapped with computation. Suitable when the model + optimizer + states fit in a single GPU's memory. + + Args: + model: Model already moved to the correct CUDA device. + local_rank: Local GPU index for this process. + + Returns: + The DDP-wrapped model. + """ + from torch.nn.parallel import DistributedDataParallel as DDP + + wrapped = DDP( + model, + device_ids=[local_rank], + static_graph=False, + gradient_as_bucket_view=True, + ) + logger.info("DDP applied: device_ids=[%d], static_graph=False", local_rank) + return wrapped + + +def setup_ref_model(model: nn.Module) -> None: + """Freeze and FSDP2-wrap the reference model with CPU offloading. + + The reference model is used for KL divergence computation during GRPO + training. It does not need TP (replicated across all ranks) and its + parameters are offloaded to CPU with pinned memory to minimize GPU + memory usage. + + Args: + model: HuggingFace CausalLM model to use as the frozen reference. + """ + # Freeze all parameters + for param in model.parameters(): + param.requires_grad = False + + # Apply FSDP2 per-layer with CPU offload. + # Match the training model's bf16 precision for consistent KL computation. + base_model = _get_base_model(model) + offload_policy = CPUOffloadPolicy(pin_memory=True) + mp_policy = MixedPrecisionPolicy(param_dtype=torch.bfloat16) + + if hasattr(base_model, "layers"): + layer_list = _get_layers(base_model) + for layer in layer_list: + fully_shard(layer, offload_policy=offload_policy, mp_policy=mp_policy) + + # Root wrapping + fully_shard(model, offload_policy=offload_policy, mp_policy=mp_policy) + + logger.info("Reference model frozen and wrapped with FSDP2 + CPU offload (pin_memory=True)") + + +def setup_training_model( + model: nn.Module, + mesh: DeviceMesh, + config: DistributedConfig, +) -> None: + """Full distributed setup: RMSNorm replacement, TP+SP, GC, and FSDP2. + + Orchestrates the complete parallelization pipeline for the training model: + 1. Replace HF RMSNorm with torch.nn.RMSNorm (SP compatibility) + 2. Apply Tensor Parallelism + Sequence Parallelism + 3. Apply selective gradient checkpointing + 4. Apply FSDP2 data-parallel sharding + + Args: + model: HuggingFace CausalLM model (on meta device or CPU). + mesh: 2D DeviceMesh with ("dp", "tp") dimensions. + config: Distributed training configuration. + """ + tp_mesh: DeviceMesh = mesh["tp"] + dp_mesh: DeviceMesh = mesh["dp"] + + # Step 1–2: TP + SP (skip when tp_degree == 1 to avoid DTensor conflicts with FSDP2) + if tp_mesh.size() > 1: + replace_rmsnorm_for_sp(model) + apply_tp_sp(model, tp_mesh) + else: + logger.info("Skipping TP+SP (tp_degree=1, pure data parallelism)") + + # Step 3: Gradient checkpointing via HF's built-in support. + # The custom per-layer checkpoint wrapper (apply_gradient_checkpointing) is + # incompatible with FSDP2: tensor metadata changes between forward and + # recomputation cause CheckpointError. HF's implementation handles this + # correctly by integrating with the model's own forward hooks. + if hasattr(model, "gradient_checkpointing_enable"): + model.gradient_checkpointing_enable(gradient_checkpointing_kwargs={"use_reentrant": False}) # type: ignore[operator] # HF dynamic method + logger.info("Gradient checkpointing enabled (HF built-in, use_reentrant=False)") + else: + gc_every_n = 1 + apply_gradient_checkpointing( + model, gc_every_n=gc_every_n, skip_last_n=config.gc_skip_last_n + ) + + # Step 4: FSDP2 wrapping with mixed precision + mp_policy = MixedPrecisionPolicy( + param_dtype=torch.bfloat16, + reduce_dtype=torch.float32, + ) + apply_fsdp2(model, dp_mesh, mp_policy, reshard_after_forward=config.reshard_after_forward) + + logger.info("Training model distributed setup complete") diff --git a/grail/trainer/distributed/training_service.py b/grail/trainer/distributed/training_service.py new file mode 100644 index 00000000..f8e7300b --- /dev/null +++ b/grail/trainer/distributed/training_service.py @@ -0,0 +1,1286 @@ +"""Distributed training service for FSDP2 + Tensor Parallelism GRPO training. + +Replaces ``TrainingService`` from ``grail.trainer.training_process`` for +multi-GPU training across data-parallel and tensor-parallel dimensions. +Coordinates model setup (TP+SP, FSDP2, gradient checkpointing), training +loop orchestration, and distributed checkpointing. +""" + +from __future__ import annotations + +import asyncio +import gc +import json +import logging +import os +import time +from multiprocessing.synchronize import Event +from pathlib import Path +from typing import Any + +import torch +import torch.distributed as dist +from torch.distributed.device_mesh import DeviceMesh +from torch.optim.lr_scheduler import CosineAnnealingLR, LambdaLR, SequentialLR + +from grail.model.provider import get_model, get_tokenizer +from grail.shared.config import TOTAL_TRAINING_WINDOWS, is_kl_enabled +from grail.trainer.algorithms import GRPOAlgorithm +from grail.trainer.algorithms.grpo import GRPOGroup +from grail.trainer.config import TrainingConfig +from grail.trainer.distributed.checkpoint import ( + load_hf_into_distributed, + save_ddp_checkpoint, + save_full_checkpoint, +) +from grail.trainer.distributed.compat import DistributedContext +from grail.trainer.distributed.config import DistributedConfig +from grail.trainer.distributed.parallelism import ( + apply_ddp, + setup_ref_model, + setup_training_model, +) + +logger = logging.getLogger(__name__) + +# --------------------------------------------------------------------------- +# Optimizer hyperparameters (mirrored from training_process.py) +# --------------------------------------------------------------------------- + +OPTIMIZER_BETAS = (0.9, 0.95) +OPTIMIZER_WEIGHT_DECAY = float(os.getenv("GRAIL_OPTIMIZER_WEIGHT_DECAY", "0.1")) + +# Scheduler hyperparameters +WARMUP_FRACTION = float(os.getenv("GRAIL_WARMUP_FRACTION", "0.05")) +SCHEDULER_ETA_MIN = float(os.getenv("GRAIL_SCHEDULER_ETA_MIN", "1e-7")) +LR_SCHEDULER_TYPE = os.getenv("GRAIL_LR_SCHEDULER_TYPE", "constant").strip().lower() + +# Training loop timing constants +NO_DATA_SLEEP_SECONDS = 60 +EPOCH_FAILURE_SLEEP_SECONDS = 30 +LOOP_ERROR_SLEEP_SECONDS = 30 +PAUSE_CHECK_INTERVAL_SECONDS = 5 + +# Pause marker filename (rank 0 checks for this file) +PAUSE_MARKER_FILENAME = "PAUSE_TRAINING" + + +class DistributedTrainingService: + """FSDP2 + TP distributed training service for GRPO. + + Manages the full lifecycle of distributed training: model initialization + with tensor and data parallelism, training loop with gradient accumulation, + checkpoint saving (both full HF and sharded DCP), and pause/resume + coordination across ranks. + """ + + def __init__( + self, + train_config: TrainingConfig, + dist_config: DistributedConfig, + rank: int, + world_size: int, + local_rank: int, + mesh: DeviceMesh, + *, + snapshot_dir: str | Path | None = None, + credentials: Any | None = None, + model_name_or_path: str | None = None, + ref_model_name_or_path: str | None = None, + ) -> None: + self.train_config = train_config + self.dist_config = dist_config + self.rank = rank + self.world_size = world_size + self.local_rank = local_rank + self.mesh = mesh + + self.snapshot_dir = Path(snapshot_dir) if snapshot_dir else Path("snapshots") + self.credentials = credentials + self.model_name_or_path = model_name_or_path or os.getenv("GRAIL_TRAIN_MODEL_ID", "") + self.ref_model_name_or_path = ref_model_name_or_path or os.getenv("GRAIL_REF_MODEL_ID", "") + + # DP/TP sub-mesh references + self.dp_mesh: DeviceMesh = mesh["dp"] + self.tp_mesh: DeviceMesh = mesh["tp"] + self.dp_rank: int = self.dp_mesh.get_local_rank() + self.dp_size: int = self.dp_mesh.size() + self.tp_size: int = self.tp_mesh.size() + + # TP process group for distributed logprobs (None when tp_size == 1) + self._tp_group: dist.ProcessGroup | None = None + if self.tp_size > 1: + self._tp_group = self.tp_mesh.get_group() + + # Models and training state (initialized in _initialize_resources) + self.train_model: Any = None + self.ref_model: Any = None + self.tokenizer: Any = None + self.optimizer: torch.optim.Optimizer | None = None + self.scheduler: torch.optim.lr_scheduler.LRScheduler | None = None + self.algorithm: GRPOAlgorithm | None = None + self.context: DistributedContext | None = None + + # Counters + self.epoch_counter: int = 0 + + self._is_rank0 = rank == 0 + + # DILOCO state (initialized in _initialize_resources when strategy == "diloco") + self._diloco_global_params: list[torch.Tensor] | None = None + self._diloco_outer_optimizer: torch.optim.Optimizer | None = None + self._diloco_inner_step_counter: int = 0 + self._diloco_sync_happened: bool = False + + # ------------------------------------------------------------------ + # Public entry point + # ------------------------------------------------------------------ + + async def run(self, stop_event: Event) -> None: + """Main entry point: initialize resources, then run the training loop. + + All ranks must call this method. Collective operations (FSDP2 gather, + all-reduce, barrier) require participation from every rank. + + Args: + stop_event: Multiprocessing event signalling shutdown. + """ + logger.info( + "DistributedTrainingService starting (rank=%d, world=%d, dp=%d, tp=%d)", + self.rank, + self.world_size, + self.dp_size, + self.tp_size, + ) + + self._initialize_resources() + + # Attempt to load resume checkpoint (rank 0 restores optimizer/DILOCO state) + if self._load_resume_checkpoint(): + logger.info( + "Resumed from checkpoint (rank=%d, epoch=%d)", self.rank, self.epoch_counter + ) + + await self._training_loop(stop_event) + + logger.info( + "DistributedTrainingService exiting (rank=%d, epochs=%d)", + self.rank, + self.epoch_counter, + ) + + # ------------------------------------------------------------------ + # Resource initialization + # ------------------------------------------------------------------ + + def _initialize_resources(self) -> None: + """Load models, apply parallelism, create optimizer and algorithm. + + Strategy dispatch: + - fsdp2: Load on CPU, apply TP+SP+FSDP2, load weights via DCP broadcast. + - ddp: Load on CPU, move to device, wrap with DDP. + - diloco: Same as ddp (each GPU holds a full model), plus initialize + CPU-offloaded global params copy and outer Nesterov optimizer. + """ + strategy = self.dist_config.strategy + device = torch.device("cuda", self.local_rank) + + logger.info("Initializing resources: strategy=%s, rank=%d", strategy, self.rank) + + # Step 1: Load training model and tokenizer + logger.info("Loading training model: %s", self.model_name_or_path) + self.train_model = get_model( + self.model_name_or_path, + device="cpu", + eval_mode=False, + ) + self.tokenizer = get_tokenizer(self.model_name_or_path) + + # Step 2: Apply strategy-specific parallelism + if strategy == "fsdp2": + self._init_fsdp2(device) + elif strategy == "ddp": + self._init_ddp(device) + elif strategy == "diloco": + self._init_diloco_model(device) + else: + raise ValueError(f"Unknown strategy: {strategy}") + + # Step 3: Reference model (if KL is enabled) + kl_enabled = is_kl_enabled() + if kl_enabled and self.ref_model_name_or_path: + logger.info("Loading reference model: %s", self.ref_model_name_or_path) + self.ref_model = get_model( + self.ref_model_name_or_path, + device="cpu", + eval_mode=True, + ) + if strategy == "fsdp2": + setup_ref_model(self.ref_model) + load_hf_into_distributed(self.ref_model, self.ref_model_name_or_path, self.rank) + else: + # DDP/DILOCO: load weights normally, move to device + self.ref_model.to(device) + self.ref_model.eval() + else: + self.ref_model = None + + # Step 4: Optimizer (after parallelism wrapping) + params = self.train_model.parameters() + self.optimizer = torch.optim.AdamW( + params, + lr=self.train_config.lr, + betas=OPTIMIZER_BETAS, + weight_decay=OPTIMIZER_WEIGHT_DECAY, + foreach=True, + ) + logger.info("Optimizer created (AdamW, lr=%.2e, foreach=True)", self.train_config.lr) + + # Step 5: Scheduler + self.scheduler = self._create_scheduler() + + # Step 6: Algorithm + self.algorithm = GRPOAlgorithm(config=self.train_config) + + # Step 7: DistributedContext (Accelerator shim) + self.context = DistributedContext( + device=device, + rank=self.rank, + world_size=self.world_size, + dp_group=self.dp_mesh.get_group(), + dp_size=self.dp_size, + strategy=strategy, + ) + + # Step 8: DILOCO outer optimization state + if strategy == "diloco": + self._init_diloco() + + dist.barrier() + logger.info("Resource initialization complete (strategy=%s, rank=%d)", strategy, self.rank) + + def _init_fsdp2(self, device: torch.device) -> None: + """Initialize model with FSDP2 parallelism.""" + logger.info("Applying FSDP2 parallelism (rank=%d)", self.rank) + setup_training_model(self.train_model, self.mesh, self.dist_config) + load_hf_into_distributed(self.train_model, self.model_name_or_path, self.rank) + + def _init_ddp(self, device: torch.device) -> None: + """Initialize model with DDP wrapping.""" + logger.info("Applying DDP parallelism (rank=%d)", self.rank) + self.train_model.to(device) + + # Enable gradient checkpointing for memory savings + if hasattr(self.train_model, "gradient_checkpointing_enable"): + self.train_model.gradient_checkpointing_enable( # type: ignore[operator] + gradient_checkpointing_kwargs={"use_reentrant": False} + ) + logger.info("Gradient checkpointing enabled (DDP path)") + + self.train_model = apply_ddp(self.train_model, self.local_rank) + + def _init_diloco_model(self, device: torch.device) -> None: + """Initialize model for DILOCO: bare model, no DDP wrapping. + + DILOCO requires each GPU to train independently (no gradient sync) + for H inner steps. DDP wraps the model and all-reduces gradients on + every backward call, which would defeat DILOCO's purpose. Instead, + we just move the model to the device and enable gradient checkpointing. + """ + logger.info("Initializing bare model for DILOCO (rank=%d, no DDP)", self.rank) + self.train_model.to(device) + + if hasattr(self.train_model, "gradient_checkpointing_enable"): + self.train_model.gradient_checkpointing_enable( # type: ignore[operator] + gradient_checkpointing_kwargs={"use_reentrant": False} + ) + logger.info("Gradient checkpointing enabled (DILOCO path)") + + def _init_diloco(self) -> None: + """Initialize DILOCO outer optimization state. + + Creates: + - CPU copy of global parameters (theta_global) + - Nesterov SGD outer optimizer operating on the global params + - Step counter for tracking inner steps between syncs + """ + assert self.train_model is not None + + # Snapshot global params to CPU. These are the "consensus" parameters + # that the outer optimizer updates every H inner steps. + unwrapped = ( + self.train_model.module if hasattr(self.train_model, "module") else self.train_model + ) + self._diloco_global_params = [ + p.data.detach().to(device="cpu", dtype=torch.float32).clone() + for p in unwrapped.parameters() + ] + + # Verify precision: outer optimizer MUST operate in FP32 for correct + # PULSE BF16 gating. Log model and global param dtypes for debugging. + model_dtype = next(unwrapped.parameters()).dtype + global_dtype = self._diloco_global_params[0].dtype + logger.info( + "DILOCO precision: model_params=%s, global_params=%s, residual=float32", + model_dtype, + global_dtype, + ) + if global_dtype != torch.float32: + raise RuntimeError( + f"DILOCO global params must be FP32, got {global_dtype}. " + "The outer optimizer and PULSE BF16 gate require FP32 precision." + ) + + # Outer optimizer: Nesterov SGD on the FP32 global params. + # The .grad field will be filled with pseudo-gradients before each step. + for p in self._diloco_global_params: + p.requires_grad = True + + outer_momentum = self.dist_config.diloco_outer_momentum + self._diloco_outer_optimizer = torch.optim.SGD( + self._diloco_global_params, + lr=self.dist_config.diloco_outer_lr, + momentum=outer_momentum, + nesterov=outer_momentum > 0, # PyTorch requires nesterov=False when momentum=0 + ) + + self._diloco_inner_step_counter = 0 + + # PULSE-DiLoCo: residual buffer for BF16-gated sparse communication + self._pulse_residual_buffer: torch.Tensor | None = None + if self.dist_config.pulse_diloco: + total_numel = sum(p.numel() for p in self._diloco_global_params) + self._pulse_residual_buffer = torch.zeros(total_numel, dtype=torch.float32) + logger.info( + "PULSE-DiLoCo residual buffer initialized: %d params, %.1f MB", + total_numel, + total_numel * 4 / 1e6, + ) + + logger.info( + "DILOCO initialized: H=%d, outer_lr=%.3f, outer_momentum=%.2f, " + "pulse=%s, global_params on CPU (%d tensors)", + self.dist_config.diloco_inner_steps, + self.dist_config.diloco_outer_lr, + self.dist_config.diloco_outer_momentum, + self.dist_config.pulse_diloco, + len(self._diloco_global_params), + ) + + def _create_scheduler(self) -> torch.optim.lr_scheduler.LRScheduler: + """Create LR scheduler with warmup, matching training_process.py.""" + assert self.optimizer is not None + warmup_steps = max(1, int(WARMUP_FRACTION * TOTAL_TRAINING_WINDOWS)) + + def lr_lambda_warmup(current_step: int) -> float: + if current_step < warmup_steps: + return float(current_step) / float(max(1, warmup_steps)) + return 1.0 + + warmup_scheduler = LambdaLR(self.optimizer, lr_lambda_warmup) + + if LR_SCHEDULER_TYPE == "cosine": + cosine_scheduler = CosineAnnealingLR( + self.optimizer, + T_max=TOTAL_TRAINING_WINDOWS - warmup_steps, + eta_min=SCHEDULER_ETA_MIN, + ) + scheduler: torch.optim.lr_scheduler.LRScheduler = SequentialLR( + self.optimizer, + schedulers=[warmup_scheduler, cosine_scheduler], + milestones=[warmup_steps], + ) + logger.info( + "Scheduler: warmup (%d steps) + cosine (T_max=%d, eta_min=%.2e)", + warmup_steps, + TOTAL_TRAINING_WINDOWS - warmup_steps, + SCHEDULER_ETA_MIN, + ) + else: + scheduler = warmup_scheduler + logger.info("Scheduler: warmup (%d steps) + constant", warmup_steps) + + return scheduler + + # ------------------------------------------------------------------ + # Training loop + # ------------------------------------------------------------------ + + async def _training_loop(self, stop_event: Event) -> None: + """Main loop: fetch groups, train epochs, save checkpoints. + + The loop structure mirrors ``TrainingService._training_loop`` but + replaces Accelerator with DistributedContext and adds rank-aware + data splitting and checkpoint coordination. + + Args: + stop_event: Multiprocessing event signalling shutdown. + """ + logger.info("Training loop starting (rank=%d)", self.rank) + + while not stop_event.is_set(): + try: + # Check for pause (rank 0 checks filesystem, broadcasts to others) + if await self._handle_pause(stop_event): + if stop_event.is_set(): + break + continue + + # Fetch training groups. In a full deployment this would query + # the replay buffer or load from R2. For the initial distributed + # implementation, callers supply groups via _run_epoch_with_groups. + # The loop skeleton is kept for structural parity with the + # single-GPU service. + groups = await self._fetch_groups() + if not groups: + if self._is_rank0: + logger.warning( + "No training groups available, sleeping %ds", + NO_DATA_SLEEP_SECONDS, + ) + await asyncio.sleep(NO_DATA_SLEEP_SECONDS) + continue + + # Train one epoch + metrics = await self._train_epoch(groups) + + # Save checkpoint + self._save_snapshot(metrics) + + # Step scheduler + if self.scheduler is not None: + self.scheduler.step() + + self.epoch_counter += 1 + if self._is_rank0: + logger.info( + "Epoch %d complete: loss=%.4f", + self.epoch_counter, + metrics.get("loss_total", 0.0), + ) + + except asyncio.CancelledError: + logger.info("Training loop cancelled (rank=%d)", self.rank) + break + except Exception as exc: + logger.exception("Training loop error (rank=%d): %s", self.rank, exc) + await asyncio.sleep(LOOP_ERROR_SLEEP_SECONDS) + + async def _fetch_groups(self) -> list[GRPOGroup]: + """Fetch GRPO groups for the current training window. + + Placeholder for the full data pipeline integration. Override or extend + this method to wire up the replay buffer and R2 data loading. + + Returns: + List of GRPO groups (empty if no data available). + """ + # In the full integration, this would call load_grpo_groups and + # replay buffer sampling, similar to TrainingService._load_grpo_groups + # and the replay buffer logic in _training_loop. + return [] + + async def run_epoch_with_groups( + self, + groups: list[GRPOGroup], + stop_event: Event | None = None, + ) -> dict[str, float]: + """Train a single epoch with pre-supplied groups. + + Convenience method for callers that manage data loading externally + (e.g., research scripts, integration tests). Handles the TP logprob + monkey-patch, trains, and saves a checkpoint. + + Args: + groups: Pre-loaded GRPO groups. + stop_event: Optional stop event (unused in single-epoch mode). + + Returns: + Training metrics dictionary. + """ + metrics = await self._train_epoch(groups) + self._save_snapshot(metrics) + if self.scheduler is not None: + self.scheduler.step() + self.epoch_counter += 1 + return metrics + + # ------------------------------------------------------------------ + # Core training + # ------------------------------------------------------------------ + + async def _train_epoch( + self, + groups: list[GRPOGroup], + monitor: Any | None = None, + window: int = 0, + ) -> dict[str, float]: + """Train one epoch, splitting data across DP ranks. + + 1. Split groups across data-parallel ranks. + 2. Monkey-patch the logprob functions in grpo module when TP is active + so that compute_logprobs and compute_logprobs_packed use the + distributed logsumexp path instead of materializing full vocab logits. + 3. Call GRPOAlgorithm.train_epoch with the DistributedContext shim. + 4. Restore original logprob functions. + + Args: + groups: Full set of GRPO groups (before DP splitting). + monitor: Optional monitoring manager for W&B metric logging (rank 0 only). + window: Current training window number for metric context. + + Returns: + Training metrics dictionary. + """ + assert self.algorithm is not None + assert self.optimizer is not None + assert self.context is not None + assert self.train_model is not None + + # Split groups across DP ranks (round-robin) + local_groups = groups[self.dp_rank :: self.dp_size] + if not local_groups: + logger.warning( + "DP rank %d received 0 groups (total=%d, dp_size=%d)", + self.dp_rank, + len(groups), + self.dp_size, + ) + + # Check if ANY rank has 0 groups. If so, all ranks must skip this epoch + # to avoid deadlock (FSDP2 collectives require all ranks to participate). + has_groups = torch.tensor( + [1 if len(local_groups) > 0 else 0], + device=self.context.device, + dtype=torch.int32, + ) + dist.all_reduce(has_groups, op=dist.ReduceOp.MIN) + if has_groups.item() == 0: + if self._is_rank0: + logger.warning("Skipping epoch: at least one DP rank has 0 groups") + return {} + + if self._is_rank0: + logger.info( + "Training epoch %d: %d total groups, %d local (dp_rank=%d/%d)", + self.epoch_counter + 1, + len(groups), + len(local_groups), + self.dp_rank, + self.dp_size, + ) + + epoch_start = time.time() + + # Track optimizer steps for DILOCO (H counts optimizer steps, not epochs) + steps_before = self.algorithm.optimizer_step_count + + # Monkey-patch logprob functions for TP-aware computation + patched = self._patch_logprobs_for_tp() + + try: + metrics = await self.algorithm.train_epoch( + self.train_model, + self.ref_model, + self.tokenizer, + local_groups, + self.optimizer, + self.context, # type: ignore[arg-type] # duck-type Accelerator shim + monitor, + window, + self.train_config, + ) + finally: + self._restore_logprobs(patched) + + epoch_duration = time.time() - epoch_start + + # DILOCO: track inner optimizer steps and trigger outer sync when H is reached. + # H counts optimizer steps (not epochs) to match the DiLoCo/PULSE literature + # and the TRL reference implementation. + if self.dist_config.strategy == "diloco": + self._diloco_sync_happened = False + steps_this_epoch = self.algorithm.optimizer_step_count - steps_before + self._diloco_inner_step_counter += max(1, steps_this_epoch) + diloco_metrics = self._diloco_maybe_outer_step() + if diloco_metrics: + metrics.update(diloco_metrics) + + if self._is_rank0: + logger.info( + "Epoch %d trained in %.1fs (loss=%.4f, reward_mean=%.4f)", + self.epoch_counter + 1, + epoch_duration, + metrics.get("loss_total", 0.0), + metrics.get("reward_mean", 0.0), + ) + + return metrics + + # ------------------------------------------------------------------ + # DILOCO outer optimization + # ------------------------------------------------------------------ + + def _diloco_maybe_outer_step(self) -> dict[str, float]: + """Check if H inner optimizer steps have elapsed and perform outer sync if so. + + H counts optimizer steps (not epochs). The counter may increment by + more than 1 per epoch if GRPO runs multiple optimizer steps per epoch. + We use >= (not modulo) to handle non-unit increments correctly. + + Returns: + Dictionary of DILOCO-specific metrics (empty if no sync happened). + """ + H = self.dist_config.diloco_inner_steps + if self._diloco_inner_step_counter < H: + return {} + + # Reset counter, carrying over excess steps beyond H + self._diloco_inner_step_counter -= H + + if self.dist_config.pulse_diloco: + return self._pulse_diloco_outer_step() + return self._diloco_outer_step() + + def _diloco_outer_step(self) -> dict[str, float]: + """Perform the DILOCO outer optimization step. + + Algorithm: + 1. Compute pseudo-gradients: delta_i = theta_global - theta_local_i + 2. All-reduce pseudo-gradients across all workers (average) + 3. Apply outer Nesterov SGD update to global params + 4. Overwrite local model params with the updated global params + + Returns: + Dictionary with DILOCO metrics (pseudo_grad_norm, etc.) + """ + assert self._diloco_global_params is not None + assert self._diloco_outer_optimizer is not None + assert self.train_model is not None + assert self.context is not None + + device = self.context.device + dp_group = self.dp_mesh.get_group() + dp_size = self.dp_size + + # DILOCO uses a bare model (no DDP wrapper), so no unwrapping needed + local_params = list(self.train_model.parameters()) + + pseudo_grad_norm_sq = 0.0 + sync_start = time.time() + + # Step 1-2: Compute pseudo-gradients and all-reduce + for global_p, local_p in zip(self._diloco_global_params, local_params, strict=True): + # pseudo_gradient = theta_global - theta_local (on GPU for NCCL) + delta = global_p.to(device) - local_p.data + # All-reduce: average across DP workers (not TP ranks) + dist.all_reduce(delta, op=dist.ReduceOp.SUM, group=dp_group) + delta.div_(dp_size) + # Store as .grad on the CPU global param for the outer optimizer + grad = delta.cpu() + global_p.grad = grad + pseudo_grad_norm_sq += grad.norm().item() ** 2 + + # Step 3: Outer Nesterov SGD step + self._diloco_outer_optimizer.step() + self._diloco_outer_optimizer.zero_grad() + + # Step 4: Overwrite local model params with updated global params (consensus) + with torch.no_grad(): + for global_p, local_p in zip(self._diloco_global_params, local_params, strict=True): + local_p.data.copy_(global_p.to(device)) + + self._diloco_sync_happened = True + + sync_duration = time.time() - sync_start + pseudo_grad_norm = pseudo_grad_norm_sq**0.5 + + if self._is_rank0: + logger.info( + "DILOCO outer step %d: pseudo_grad_norm=%.4f, sync_time=%.2fs", + self._diloco_inner_step_counter // self.dist_config.diloco_inner_steps, + pseudo_grad_norm, + sync_duration, + ) + + return { + "diloco/pseudo_grad_norm": pseudo_grad_norm, + "diloco/sync_duration": sync_duration, + "diloco/outer_step": float( + self._diloco_inner_step_counter // self.dist_config.diloco_inner_steps + ), + } + + def _pulse_diloco_outer_step(self) -> dict[str, float]: + """PULSE-DiLoCo outer step: BF16-gated sparse communication with residual buffer. + + Instead of dense all-reduce, computes BF16-visible pseudo-gradients, + communicates only non-zero entries via sparse all-gather, and accumulates + invisible residuals for future rounds. + + Algorithm: + 1. Compute residual-corrected BF16-visible pseudo-gradients + 2. Sparse all-gather (exchange only non-zero entries) + 3. Outer Nesterov SGD step on averaged pseudo-gradients + 4. Hard reset: overwrite local params with consensus + + Returns: + Dictionary with PULSE-DiLoCo metrics. + """ + assert self._diloco_global_params is not None + assert self._diloco_outer_optimizer is not None + assert self.train_model is not None + assert self.context is not None + assert self._pulse_residual_buffer is not None + + # Free accumulated garbage before allocating ~32GB for pseudo-gradients. + # Training epochs accumulate unreferenced tensors (logprobs, advantages, + # rollout data) that Python GC hasn't collected yet. + gc.collect() + torch.cuda.empty_cache() + + device = self.context.device + dp_group = self.dp_mesh.get_group() + dp_size = self.dp_size + local_params = list(self.train_model.parameters()) + + total_numel = self._pulse_residual_buffer.numel() + sync_start = time.time() + + # ── Step 1: BF16-gated FP32 pseudo-gradient (v6 algorithm) ── + # v6 uses the BF16 gate only for sparsity selection but transmits + # raw FP32 s values instead of BF16-quantized delta_hat. This + # eliminates overshoot oscillations caused by BF16 quantization + # error in the residual buffer. + flat_pseudo_grad = torch.zeros(total_numel, dtype=torch.float32) + offset = 0 + for global_p, local_p in zip(self._diloco_global_params, local_params, strict=True): + numel = global_p.numel() + param_cpu = local_p.data.to("cpu").float() + snapshot = global_p.data.view(-1) # theta^(t-1), flat FP32 on CPU + + delta = snapshot - param_cpu.view(-1) # exact FP32 pseudo-grad + s = self._pulse_residual_buffer[offset : offset + numel] + delta + w_tilde = snapshot - s # virtual corrected model + + # BF16 gate: determines which entries to transmit (sparsity mask) + b_theta = snapshot.bfloat16().float() + b_w_tilde = w_tilde.bfloat16().float() + gate_mask = b_theta != b_w_tilde + + # v6: transmit raw FP32 s at fired indices (not BF16-quantized delta_hat) + transmitted = torch.zeros_like(s) + transmitted[gate_mask] = s[gate_mask] + flat_pseudo_grad[offset : offset + numel] = transmitted + + # v6 residual: fired entries fully consumed (0), unfired accumulate + self._pulse_residual_buffer[offset : offset + numel] = s + self._pulse_residual_buffer[offset : offset + numel][gate_mask] = 0 + offset += numel + + # Free loop temporaries explicitly to avoid heap fragmentation + del param_cpu, delta, s, w_tilde, b_theta, b_w_tilde, gate_mask, transmitted + + # ── Step 2: Sparse all-gather ── + # Extract sparse indices/values, then delete flat_pseudo_grad + # to free ~32GB before allocating GPU tensors for all-gather. + nonzero_mask = flat_pseudo_grad != 0 + indices = nonzero_mask.nonzero(as_tuple=True)[0] # int64 + values = flat_pseudo_grad[nonzero_mask] # FP32 + nnz = indices.numel() + del nonzero_mask # ~8GB bool mask no longer needed + + # Exchange counts across workers + nnz_tensor = torch.tensor([nnz], dtype=torch.int64, device=device) + nnz_list = [torch.zeros(1, dtype=torch.int64, device=device) for _ in range(dp_size)] + dist.all_gather(nnz_list, nnz_tensor, group=dp_group) + max_nnz = max(int(t.item()) for t in nnz_list) + + comm_sparsity = 1.0 - nnz / total_numel if total_numel > 0 else 0.0 + + del flat_pseudo_grad + + if max_nnz > 0: + # Pad and gather on GPU (NCCL requires equal-size tensors) + idx_pad = torch.zeros(max_nnz, dtype=torch.int64, device=device) + val_pad = torch.zeros(max_nnz, dtype=torch.float32, device=device) + if nnz > 0: + idx_pad[:nnz] = indices.to(device) + val_pad[:nnz] = values.to(device) + + all_idx = [torch.zeros_like(idx_pad) for _ in range(dp_size)] + all_val = [torch.zeros_like(val_pad) for _ in range(dp_size)] + dist.all_gather(all_idx, idx_pad, group=dp_group) + dist.all_gather(all_val, val_pad, group=dp_group) + + # Reconstruct averaged pseudo-gradient on CPU via scatter-add + avg_pseudo_grad = torch.zeros(total_numel, dtype=torch.float32) + for r in range(dp_size): + n = int(nnz_list[r].item()) + if n > 0: + avg_pseudo_grad.index_add_(0, all_idx[r][:n].cpu().long(), all_val[r][:n].cpu()) + avg_pseudo_grad.div_(dp_size) + else: + avg_pseudo_grad = torch.zeros(total_numel, dtype=torch.float32) + + # ── Step 3: Outer optimizer ── + offset = 0 + for global_p in self._diloco_global_params: + numel = global_p.numel() + global_p.grad = avg_pseudo_grad[offset : offset + numel].view(global_p.shape) + offset += numel + + self._diloco_outer_optimizer.step() + self._diloco_outer_optimizer.zero_grad() + del avg_pseudo_grad + + # ── Step 4: Hard reset ── + with torch.no_grad(): + for global_p, local_p in zip(self._diloco_global_params, local_params, strict=True): + local_p.data.copy_(global_p.to(device)) + + self._diloco_sync_happened = True + + # ── Metrics ── + sync_duration = time.time() - sync_start + outer_step_num = self._diloco_inner_step_counter // self.dist_config.diloco_inner_steps + + if self._is_rank0: + logger.info( + "PULSE-DiLoCo outer step %d: comm_sparsity=%.2f%%, nnz=%d/%d, sync_time=%.2fs", + outer_step_num, + comm_sparsity * 100, + nnz, + total_numel, + sync_duration, + ) + + self._trim_heap() + + return { + "diloco/sync_duration": sync_duration, + "diloco/outer_step": float(outer_step_num), + "pulse/comm_sparsity": comm_sparsity, + "pulse/nnz": float(nnz), + "pulse/total_numel": float(total_numel), + } + + @staticmethod + def _trim_heap() -> None: + """Return freed glibc heap pages to the OS via malloc_trim. + + Without this, glibc's arena allocator retains freed memory, causing + RSS to grow monotonically across PULSE outer steps and checkpoint + saves. Each call reclaims ~10-50 GB depending on fragmentation. + """ + import ctypes + + try: + ctypes.CDLL("libc.so.6").malloc_trim(0) + except OSError: + pass # Non-Linux or missing libc + + # ------------------------------------------------------------------ + # TP logprob monkey-patching + # ------------------------------------------------------------------ + + def _patch_logprobs_for_tp(self) -> dict[str, Any] | None: + """Replace module-level logprob functions with TP-aware wrappers. + + When TP is active (tp_size > 1), the lm_head output is sharded across + TP ranks. The standard compute_logprobs path would see partial vocab + logits and produce incorrect results. We temporarily replace the + module-level functions with wrappers that route through + ``compute_logprobs_distributed`` which uses distributed logsumexp. + + Returns: + A dict of original functions to restore, or None if no patching + was needed (tp_size == 1). + """ + if self._tp_group is None: + return None + + import grail.trainer.algorithms.grpo as grpo_module + from grail.trainer.distributed.logprobs import compute_logprobs_distributed + + originals: dict[str, Any] = { + "compute_logprobs": grpo_module.compute_logprobs, + "compute_logprobs_packed": grpo_module.compute_logprobs_packed, + } + + tp_group = self._tp_group + chunk_size = self.train_config.logit_chunk_size + + # Wrapper for the padded (non-packed) path + def _tp_compute_logprobs( + model: Any, + input_ids: torch.Tensor, + attention_mask: torch.Tensor, + prompt_lengths: list[int], + completion_lengths: list[int], + return_per_token: bool = False, + return_entropy: bool = False, + *, + chunked: bool = False, + chunk_size: int = chunk_size, + ) -> ( + torch.Tensor + | tuple[torch.Tensor, torch.Tensor] + | tuple[torch.Tensor, torch.Tensor, torch.Tensor] + ): + logprobs_sum, logprobs_pt, entropies = compute_logprobs_distributed( + model, + input_ids, + attention_mask, + prompt_lengths, + completion_lengths, + chunked=chunked, + chunk_size=chunk_size, + tp_group=tp_group, + return_per_token=return_per_token, + return_entropy=return_entropy, + ) + + # Match the return convention of the original function + if return_per_token and return_entropy and logprobs_pt is not None: + assert entropies is not None + return logprobs_sum, logprobs_pt, entropies + if return_per_token and logprobs_pt is not None: + return logprobs_sum, logprobs_pt + return logprobs_sum + + # Wrapper for the packed path. The packed path does not use TP-aware + # logprobs (sequence packing is incompatible with TP sequence sharding + # in the current implementation). Fall back to the original function, + # which will work correctly when TP is 1 on this dimension, or raise + # an informative error. + def _tp_compute_logprobs_packed( + model: Any, + input_ids: torch.Tensor, + position_ids: torch.Tensor, + seq_lengths: list[int], + prompt_lengths: list[int], + completion_lengths: list[int], + return_per_token: bool = False, + return_entropy: bool = False, + *, + chunked: bool = False, + chunk_size: int = chunk_size, + ) -> ( + torch.Tensor + | tuple[torch.Tensor, torch.Tensor] + | tuple[torch.Tensor, torch.Tensor, torch.Tensor] + ): + # Packing + TP is not yet supported. Log a warning on first call + # and delegate to the original implementation. This is safe when + # use_sequence_packing is disabled (the default for distributed). + logger.warning( + "compute_logprobs_packed called under TP; " + "falling back to standard packed path (TP logprobs not applied)" + ) + return originals["compute_logprobs_packed"]( + model, + input_ids, + position_ids, + seq_lengths, + prompt_lengths, + completion_lengths, + return_per_token=return_per_token, + return_entropy=return_entropy, + chunked=chunked, + chunk_size=chunk_size, + ) + + grpo_module.compute_logprobs = _tp_compute_logprobs # type: ignore[assignment] + grpo_module.compute_logprobs_packed = _tp_compute_logprobs_packed # type: ignore[assignment] + + logger.debug("Logprob functions patched for TP (tp_size=%d)", self.tp_size) + return originals + + def _restore_logprobs(self, originals: dict[str, Any] | None) -> None: + """Restore original logprob functions after training epoch.""" + if originals is None: + return + + import grail.trainer.algorithms.grpo as grpo_module + + grpo_module.compute_logprobs = originals["compute_logprobs"] # type: ignore[assignment] + grpo_module.compute_logprobs_packed = originals["compute_logprobs_packed"] # type: ignore[assignment] + logger.debug("Logprob functions restored to originals") + + # ------------------------------------------------------------------ + # Checkpoint saving + # ------------------------------------------------------------------ + + def _save_snapshot(self, metrics: dict[str, Any]) -> None: + """Save checkpoint after an epoch. + + Dispatches to the correct checkpoint function based on strategy: + - fsdp2: ``save_full_checkpoint`` (collective gather + rank-0 write) + - ddp/diloco: ``save_ddp_checkpoint`` (rank-0 only, no collective) + + Args: + metrics: Training metrics for metadata. + """ + if self.train_model is None or self.tokenizer is None: + return + + checkpoint_dir = self.snapshot_dir / f"checkpoint-{self.epoch_counter}" + + try: + if self.dist_config.strategy == "fsdp2": + save_full_checkpoint( + self.train_model, + self.tokenizer, + checkpoint_dir, + self.rank, + ) + else: + save_ddp_checkpoint( + self.train_model, + self.tokenizer, + checkpoint_dir, + self.rank, + ) + + # Rank 0 writes metadata alongside the checkpoint + if self._is_rank0: + metadata = { + "epoch": self.epoch_counter, + "timestamp": time.time(), + "metrics": {k: float(v) for k, v in metrics.items()}, + "lr": ( + self.scheduler.get_last_lr()[0] if self.scheduler else self.train_config.lr + ), + "world_size": self.world_size, + "dp_size": self.dp_size, + "tp_size": self.tp_size, + } + metadata_path = checkpoint_dir / "snapshot_metadata.json" + metadata_path.parent.mkdir(parents=True, exist_ok=True) + with open(metadata_path, "w", encoding="utf-8") as f: + json.dump(metadata, f, indent=2) + + logger.info( + "Checkpoint saved: %s (epoch=%d)", + checkpoint_dir, + self.epoch_counter, + ) + + # Save resume state (rank 0 only, includes optimizer + DILOCO state) + if self._is_rank0: + try: + self._save_resume_checkpoint() + except Exception as resume_exc: + logger.warning("Resume checkpoint save failed: %s", resume_exc) + + except Exception as exc: + logger.exception("Checkpoint save failed (rank=%d): %s", self.rank, exc) + + # ------------------------------------------------------------------ + # Resume checkpoint (separate from weight sync) + # ------------------------------------------------------------------ + + def _save_resume_checkpoint(self) -> None: + """Save full training state for crash recovery (rank 0 only). + + This is separate from the weight sync checkpoint (HF safetensors uploaded + to R2). The resume checkpoint contains everything needed to restart + training from the exact point where it left off, including optimizer + states, scheduler, DILOCO outer optimizer, and PULSE residual buffer. + + Saved as a single .pt file to ``snapshot_dir/resume_state.pt``. + """ + if not self._is_rank0: + return + + state: dict[str, Any] = { + "epoch_counter": self.epoch_counter, + } + + # Inner optimizer (AdamW) state + if self.optimizer is not None: + state["optimizer_state_dict"] = self.optimizer.state_dict() + + # LR scheduler state + if self.scheduler is not None: + state["scheduler_state_dict"] = self.scheduler.state_dict() + + # DILOCO-specific state + if self.dist_config.strategy == "diloco": + state["diloco_inner_step_counter"] = self._diloco_inner_step_counter + + # Use direct references (no .clone()) for CPU tensors. These are + # not modified during torch.save serialization, and eliminating + # the clones avoids ~91 GB of transient CPU allocations that + # cause glibc heap fragmentation and OOM on tight-RAM systems. + if self._diloco_global_params is not None: + state["diloco_global_params"] = [p.data for p in self._diloco_global_params] + + if self._diloco_outer_optimizer is not None: + state["diloco_outer_optimizer_state_dict"] = ( + self._diloco_outer_optimizer.state_dict() + ) + + # PULSE residual buffer (CPU, not modified during save) + if self._pulse_residual_buffer is not None: + state["pulse_residual_buffer"] = self._pulse_residual_buffer + + resume_path = self.snapshot_dir / "resume_state.pt" + resume_path.parent.mkdir(parents=True, exist_ok=True) + torch.save(state, resume_path) + del state + logger.info("Resume checkpoint saved: %s (epoch=%d)", resume_path, self.epoch_counter) + self._trim_heap() + + def _load_resume_checkpoint(self) -> bool: + """Load training state from a resume checkpoint if available. + + Must be called AFTER ``_initialize_resources()`` so that optimizer, + scheduler, and DILOCO state exist and can receive the loaded state. + + Returns: + True if a resume checkpoint was loaded, False otherwise. + """ + resume_path = self.snapshot_dir / "resume_state.pt" + if not resume_path.exists(): + return False + + if not self._is_rank0: + # Only rank 0 loads; state will be synchronized via the next + # outer step (DILOCO) or is already identical (DDP/FSDP2). + return False + + try: + state = torch.load(resume_path, map_location="cpu", weights_only=False) + + self.epoch_counter = state.get("epoch_counter", 0) + + if "optimizer_state_dict" in state and self.optimizer is not None: + self.optimizer.load_state_dict(state["optimizer_state_dict"]) + + if "scheduler_state_dict" in state and self.scheduler is not None: + self.scheduler.load_state_dict(state["scheduler_state_dict"]) + + # DILOCO state + if self.dist_config.strategy == "diloco": + self._diloco_inner_step_counter = state.get("diloco_inner_step_counter", 0) + + if "diloco_global_params" in state and self._diloco_global_params is not None: + for stored, current in zip( + state["diloco_global_params"], + self._diloco_global_params, + strict=True, + ): + current.data.copy_(stored) + + if ( + "diloco_outer_optimizer_state_dict" in state + and self._diloco_outer_optimizer is not None + ): + self._diloco_outer_optimizer.load_state_dict( + state["diloco_outer_optimizer_state_dict"] + ) + + if "pulse_residual_buffer" in state and self._pulse_residual_buffer is not None: + self._pulse_residual_buffer.copy_(state["pulse_residual_buffer"]) + + logger.info( + "Resume checkpoint loaded: epoch=%d, inner_step=%d", + self.epoch_counter, + self._diloco_inner_step_counter, + ) + return True + + except Exception as exc: + logger.warning("Failed to load resume checkpoint: %s", exc) + return False + + # ------------------------------------------------------------------ + # Pause/resume coordination + # ------------------------------------------------------------------ + + async def _handle_pause(self, stop_event: Event) -> bool: + """Check for pause marker and coordinate across ranks. + + Rank 0 polls the filesystem for a pause marker file. When detected, + it broadcasts a flag to all ranks. All ranks then participate in a + checkpoint save before entering a wait loop. Rank 0 polls for the + marker to be removed (resume signal). + + Args: + stop_event: Shutdown signal. + + Returns: + True if a pause was handled (caller should ``continue`` the loop). + False if no pause was requested. + """ + # Rank 0 checks for the pause marker + pause_flag = torch.zeros(1, dtype=torch.int32, device=f"cuda:{self.local_rank}") + if self._is_rank0: + marker = self.snapshot_dir / PAUSE_MARKER_FILENAME + if marker.exists(): + pause_flag.fill_(1) + + # Broadcast decision to all ranks + dist.broadcast(pause_flag, src=0) + + if pause_flag.item() == 0: + return False + + logger.info("Pause requested (rank=%d), saving checkpoint before pause", self.rank) + + # All ranks participate in checkpoint save + self._save_snapshot({"status": "paused"}) + + # Synchronize before entering wait loop + dist.barrier() + + if self._is_rank0: + logger.info("All ranks paused, waiting for resume signal") + + # Wait for resume (rank 0 polls, broadcasts to others) + while not stop_event.is_set(): + resume_flag = torch.zeros(1, dtype=torch.int32, device=f"cuda:{self.local_rank}") + if self._is_rank0: + marker = self.snapshot_dir / PAUSE_MARKER_FILENAME + if not marker.exists(): + resume_flag.fill_(1) + + dist.broadcast(resume_flag, src=0) + if resume_flag.item() == 1: + break + + await asyncio.sleep(PAUSE_CHECK_INTERVAL_SECONDS) + + if self._is_rank0: + logger.info("Resume signal received, continuing training") + + dist.barrier() + return True + + # ------------------------------------------------------------------ + # Cleanup + # ------------------------------------------------------------------ + + def cleanup(self) -> None: + """Release GPU memory and destroy process group. + + Should be called in a finally block after ``run()`` completes. + """ + del self.train_model + del self.ref_model + del self.optimizer + self.train_model = None + self.ref_model = None + self.optimizer = None + + # DILOCO cleanup + self._diloco_global_params = None + self._diloco_outer_optimizer = None + + gc.collect() + if torch.cuda.is_available(): + torch.cuda.empty_cache() + + logger.info("Cleanup complete (rank=%d)", self.rank) diff --git a/grail/trainer/evaluator.py b/grail/trainer/evaluator.py index 8147677c..78418ee2 100644 --- a/grail/trainer/evaluator.py +++ b/grail/trainer/evaluator.py @@ -13,7 +13,7 @@ import time from collections.abc import Callable from dataclasses import asdict, dataclass, field -from datetime import datetime, timezone +from datetime import UTC, datetime from typing import Any import torch @@ -50,7 +50,7 @@ class EvalSample: success: bool reward_components: dict[str, float] | None = None window: int | None = None - timestamp: str = field(default_factory=lambda: datetime.now(timezone.utc).isoformat()) + timestamp: str = field(default_factory=lambda: datetime.now(UTC).isoformat()) class EvaluatorService: @@ -664,7 +664,7 @@ def _write_eval_samples(self) -> None: window_suffix = ( f"_w{self._current_window_number}" if self._current_window_number else "" ) - timestamp = datetime.now(timezone.utc).strftime("%Y%m%d_%H%M%S") + timestamp = datetime.now(UTC).strftime("%Y%m%d_%H%M%S") filename = f"eval_samples{window_suffix}_{timestamp}.jsonl" filepath = os.path.join(self._cfg.store_predictions_path, filename) diff --git a/grail/trainer/inference_server.py b/grail/trainer/inference_server.py index a33e5cf2..cb820919 100644 --- a/grail/trainer/inference_server.py +++ b/grail/trainer/inference_server.py @@ -421,14 +421,13 @@ async def _start_server(self) -> None: stderr_target = ( subprocess.STDOUT if self._eval_config.stream_server_logs else subprocess.DEVNULL ) - # Compose environment for subprocess: inherit and then apply overrides - popen_env = os.environ.copy() - if isinstance(self._config.env, dict) and self._config.env: - popen_env.update({str(k): str(v) for k, v in self._config.env.items()}) - # Log CUDA device mapping if present (avoid logging other env content) - cuda_env = self._config.env.get("CUDA_VISIBLE_DEVICES") - if cuda_env is not None: - logger.info("Launching vLLM with CUDA_VISIBLE_DEVICES=%s", cuda_env) + # Use config.env as the base if provided (allows callers to pass + # a clean env without the parent's CUDA_VISIBLE_DEVICES to avoid + # NCCL IPC failures from nested device remapping). + popen_env = dict(self._config.env) if self._config.env else os.environ.copy() + cuda_env = popen_env.get("CUDA_VISIBLE_DEVICES") + if cuda_env is not None: + logger.info("Launching vLLM with CUDA_VISIBLE_DEVICES=%s", cuda_env) self._process = subprocess.Popen( cmd, stdout=stdout_target, @@ -645,9 +644,11 @@ def __init__( *, config: ServerConfig, eval_config: EvalConfig, + python_executable: str = "", ) -> None: super().__init__(config=config) self._eval_config = eval_config + self._python_executable = python_executable async def _start_server(self) -> None: """Launch SGLang server with optimized memory settings.""" @@ -671,19 +672,21 @@ async def _start_server(self) -> None: subprocess.STDOUT if self._eval_config.stream_server_logs else subprocess.DEVNULL ) - # Prepare environment for subprocess - popen_env = os.environ.copy() - if self._config.env: - popen_env.update(self._config.env) - cuda_env = self._config.env.get("CUDA_VISIBLE_DEVICES") - if cuda_env is not None: - logger.info("Launching SGLang with CUDA_VISIBLE_DEVICES=%s", cuda_env) + # Prepare environment for subprocess. + # Use config.env as the base if provided (allows callers to pass + # a clean env without the parent's CUDA_VISIBLE_DEVICES to avoid + # NCCL custom_all_reduce IPC failures from nested device remapping). + popen_env = dict(self._config.env) if self._config.env else os.environ.copy() + cuda_env = popen_env.get("CUDA_VISIBLE_DEVICES") + if cuda_env is not None: + logger.info("Launching SGLang with CUDA_VISIBLE_DEVICES=%s", cuda_env) # Ensure the Python executable's directory is on PATH so that # tools installed in the same venv (e.g. ninja for flashinfer JIT) # are discoverable by SGLang subprocesses. import sys - python_bin_dir = os.path.dirname(os.path.abspath(sys.executable)) + python_exe = self._python_executable or sys.executable + python_bin_dir = os.path.dirname(os.path.abspath(python_exe)) existing_path = popen_env.get("PATH", "") if python_bin_dir not in existing_path.split(os.pathsep): popen_env["PATH"] = f"{python_bin_dir}{os.pathsep}{existing_path}" @@ -757,12 +760,50 @@ async def _reload_models(self) -> None: """No-op for SGLang backend (models remain in external process).""" pass + async def reload_with_new_checkpoint(self, new_checkpoint_path: str) -> None: + """Reload SGLang server with updated model checkpoint. + + Stops the current server, updates the model path, and restarts with + new weights. Waits for GPU memory cleanup between stop and start. + """ + if not os.path.exists(new_checkpoint_path): + raise FileNotFoundError(f"Checkpoint not found: {new_checkpoint_path}") + + logger.info( + "Reloading SGLang server with new checkpoint", + extra={"path": new_checkpoint_path}, + ) + + try: + await self._stop_server() + self._config.model_path = new_checkpoint_path + await self._start_server() + + if self._process is None or self._bound_port is None: + raise RuntimeError("Failed to restart SGLang server after reload") + + logger.info( + "SGLang server reloaded successfully", + extra={ + "checkpoint": new_checkpoint_path, + "url": self.base_url, + "model": self.model_name, + }, + ) + except Exception as exc: + logger.error( + "Failed to reload SGLang server", + extra={"checkpoint": new_checkpoint_path, "error": str(exc)}, + ) + raise RuntimeError(f"SGLang server reload failed: {exc}") from exc + def _build_command(self) -> list[str]: """Build SGLang server launch command with optimized parameters.""" import sys + python_exe = self._python_executable or sys.executable cmd = [ - sys.executable, + python_exe, "-m", "sglang.launch_server", "--model-path", @@ -777,20 +818,27 @@ def _build_command(self) -> list[str]: self._config.dtype, "--tp-size", str(self._config.tensor_parallel_size), - # Memory optimizations from EvalConfig to prevent OOM during KV cache allocation "--mem-fraction-static", str(self._eval_config.sglang_mem_fraction_static), - "--max-running-requests", - str(self._eval_config.sglang_max_running_requests), - "--schedule-policy", - "fcfs", # Fair scheduling "--context-length", str(self._eval_config.sglang_context_length), + "--trust-remote-code", + "--load-balance-method", + "total_tokens", + # Disable custom all-reduce to avoid NCCL IPC handle conflicts when + # the parent process already has a CUDA context (e.g. miner's proof + # model). Negligible perf impact on NVLink-connected GPUs. + "--disable-custom-all-reduce", + # Disable piecewise CUDA graph capture. SGLang 0.5.10 hits an + # illegal memory access during piecewise graph capture for Qwen3 + # on B200 (and likely other Hopper/Blackwell SKUs); the SGLang + # error message itself recommends this flag as the workaround. + # Reproduced on basilica-grail-trainer (1xB200) 2026-04-08; without + # this flag the SGLang server fails to start with `cudaErrorIllegalAddress` + # during CUDA graph warmup, and the miner falls back to slow HF generation. + "--disable-piecewise-cuda-graph", ] - if self._config.trust_remote_code: - cmd.append("--trust-remote-code") - # Explicitly provide chat template to ensure SGLang uses the correct formatting # with system prompt and reasoning tags during generation if self._config.chat_template_path: @@ -844,7 +892,11 @@ def create_inference_server( module_entrypoint=eval_config.vllm_module_entrypoint, ) if backend_lower == "sglang": - return SGLangServerManager(config=config, eval_config=eval_config) + return SGLangServerManager( + config=config, + eval_config=eval_config, + python_executable=eval_config.sglang_python_executable, + ) msg = f"Unsupported backend: {backend} (choose 'hf', 'vllm', or 'sglang')" raise ValueError(msg) diff --git a/grail/trainer/metrics.py b/grail/trainer/metrics.py index 9c74d916..f66b51e9 100644 --- a/grail/trainer/metrics.py +++ b/grail/trainer/metrics.py @@ -109,10 +109,10 @@ def summarize(self) -> dict[str, float]: return {} # Task-averaged accumulators - pass_unbiased_at_k: dict[int, float] = dict.fromkeys(self._report_ks, 0.0) # type: ignore - pass_ordered_at_k: dict[int, float] = dict.fromkeys(self._report_ks, 0.0) # type: ignore - mean_at_k: dict[int, float] = dict.fromkeys(self._report_ks, 0.0) # type: ignore - best_at_k: dict[int, float] = dict.fromkeys(self._report_ks, 0.0) # type: ignore + pass_unbiased_at_k: dict[int, float] = dict.fromkeys(self._report_ks, 0.0) + pass_ordered_at_k: dict[int, float] = dict.fromkeys(self._report_ks, 0.0) + mean_at_k: dict[int, float] = dict.fromkeys(self._report_ks, 0.0) + best_at_k: dict[int, float] = dict.fromkeys(self._report_ks, 0.0) num_tasks = len(self._by_task) diff --git a/grail/trainer/snapshot_manager.py b/grail/trainer/snapshot_manager.py index cf7e9714..b6f74a73 100644 --- a/grail/trainer/snapshot_manager.py +++ b/grail/trainer/snapshot_manager.py @@ -136,6 +136,67 @@ def save_snapshot_atomic( logger.debug("Cleanup failed: %s", cleanup_exc) raise + def adopt_snapshot_atomic( + self, + source_dir: Path | str, + metadata: dict[str, Any], + ) -> None: + """Atomically adopt an already-written checkpoint directory as the latest snapshot. + + Unlike ``save_snapshot_atomic``, this does NOT call ``model.save_pretrained()``. + It writes only metadata into *source_dir*, then performs the same atomic rename + dance so that ``snapshots/latest/`` always points to a complete snapshot. + + Use this when FSDP2's ``save_full_checkpoint()`` has already written model + weights to *source_dir* and we just need the snapshot manager to adopt it. + + Args: + source_dir: Directory containing model weights (already written). + metadata: Snapshot metadata (epoch, timestamp, metrics, etc.). + """ + source_dir = Path(source_dir) + target_dir = self.snapshot_dir / "latest" + + if not source_dir.exists(): + raise FileNotFoundError(f"Source directory does not exist: {source_dir}") + + try: + # Write metadata into the source directory + metadata_path = source_dir / "snapshot_metadata.json" + with open(metadata_path, "w", encoding="utf-8") as f: + json.dump(metadata, f, indent=2) + + # Fsync directory + try: + fd = os.open(source_dir, os.O_RDONLY) + os.fsync(fd) + os.close(fd) + except (OSError, AttributeError): + pass + + # Atomic rename chain (same protocol as save_snapshot_atomic) + backup_dir = self.snapshot_dir / f"latest.backup.{uuid.uuid4().hex[:8]}" + + if target_dir.exists(): + target_dir.rename(backup_dir) + + source_dir.rename(target_dir) + + if backup_dir.exists(): + try: + shutil.rmtree(backup_dir) + except Exception as cleanup_exc: + logger.warning("Failed to cleanup backup snapshot: %s", cleanup_exc) + + # Set SNAPSHOT_READY marker + self._snapshot_ready_marker.touch() + + logger.info("Snapshot adopted atomically from %s to %s", source_dir, target_dir) + + except Exception as exc: + logger.error("Failed to adopt snapshot: %s", exc) + raise + def check_snapshot_ready(self) -> bool: """Check if new snapshot is available for upload. diff --git a/grail/trainer/training_process.py b/grail/trainer/training_process.py index 67eacc43..b1061ad5 100644 --- a/grail/trainer/training_process.py +++ b/grail/trainer/training_process.py @@ -40,7 +40,8 @@ from grail.infrastructure.network import create_subtensor from grail.model.train_loading import ModelLoadSpec, load_training_artifacts from grail.monitoring import initialize_subprocess_monitoring -from grail.shared.constants import NETUID, TOTAL_TRAINING_WINDOWS, WINDOW_LENGTH, is_kl_enabled +from grail.protocol.constants import WINDOW_LENGTH +from grail.shared.config import NETUID, TOTAL_TRAINING_WINDOWS, is_kl_enabled from grail.trainer.algorithms import GRPOAlgorithm, TrainingAlgorithm from grail.trainer.algorithms.grpo import load_grpo_groups from grail.trainer.config import TrainingConfig @@ -276,7 +277,36 @@ async def _initialize_resources(self) -> None: # Enable gradient checkpointing on the raw model before accelerator wrapping (if configured) if self.config.use_gradient_checkpointing: - if hasattr(self.train_model, "gradient_checkpointing_enable"): + gc_freq = int(os.getenv("GRAIL_TRAINER_GC_EVERY_N_LAYERS", "1")) + if ( + gc_freq > 1 + and hasattr(self.train_model, "model") + and hasattr(self.train_model.model, "layers") + ): + # Selective checkpointing: only wrap every Nth layer + from torch.utils.checkpoint import checkpoint + + num_layers = len(self.train_model.model.layers) + checkpointed = 0 + for i, layer in enumerate(self.train_model.model.layers): + if i % gc_freq == 0: + original_forward = layer.forward + + def make_ckpt_forward(orig_fn): + def ckpt_forward(*args, **kwargs): + return checkpoint(orig_fn, *args, use_reentrant=False, **kwargs) + + return ckpt_forward + + layer.forward = make_ckpt_forward(original_forward) + checkpointed += 1 + logger.info( + "Selective gradient checkpointing: %d/%d layers checkpointed (every %d)", + checkpointed, + num_layers, + gc_freq, + ) + elif hasattr(self.train_model, "gradient_checkpointing_enable"): try: self.train_model.gradient_checkpointing_enable() logger.info( @@ -290,6 +320,28 @@ async def _initialize_resources(self) -> None: logger.info( "Gradient checkpointing disabled (via GRAIL_TRAINER_USE_GRADIENT_CHECKPOINTING=0)" ) + + # Apply torch.compile to the base transformer (not the full CausalLM) so both + # chunked-logits (calls base_model directly) and non-chunked paths benefit. + if self.config.use_torch_compile: + prefix = getattr(self.train_model, "base_model_prefix", "") + base = getattr(self.train_model, prefix, None) if prefix else None + if base is not None: + try: + compiled = torch.compile(base, mode="default") + setattr(self.train_model, prefix, compiled) + logger.info( + "torch.compile applied to %s.%s (mode=default, fixed shapes via max_length padding)", + type(self.train_model).__name__, + prefix, + ) + except Exception as exc: # noqa: BLE001 + logger.warning("Failed to apply torch.compile: %s", exc) + else: + logger.warning( + "torch.compile requested but base model not found (prefix=%r)", prefix + ) + logger.info( "Training artifacts loaded (train=%s, ref=%s, ref_enabled=%s)", getattr(self.train_model, "name_or_path", "unknown"), @@ -351,14 +403,32 @@ async def get_block_context() -> tuple[int, int]: def _create_optimizer(self) -> torch.optim.Optimizer: """Create AdamW optimizer with configured hyperparameters. + Uses fused implementation when available (PyTorch 2.x CUDA) for + single-kernel-per-parameter updates instead of multiple kernels. + Returns: Configured optimizer instance """ + use_fused = False + if torch.cuda.is_available(): + try: + _probe = torch.optim.AdamW([torch.zeros(1, device="cuda")], fused=True) + del _probe + use_fused = True + except (RuntimeError, TypeError): + pass + + if use_fused: + logger.info("Using fused AdamW optimizer") + else: + logger.info("Using standard AdamW optimizer (fused not available)") + return torch.optim.AdamW( self.train_model.parameters(), lr=self.config.lr, betas=OPTIMIZER_BETAS, weight_decay=OPTIMIZER_WEIGHT_DECAY, + fused=use_fused, ) def _create_scheduler(self) -> torch.optim.lr_scheduler.LRScheduler: @@ -837,7 +907,7 @@ async def _get_trusted_miners(self, target_window: int) -> set[str]: # Test mode: only use TRAINER_UID if self.test_mode: - from grail.shared.constants import TRAINER_UID + from grail.protocol.constants import TRAINER_UID if TRAINER_UID < len(metagraph.hotkeys): trainer_hotkey = metagraph.hotkeys[TRAINER_UID] @@ -1257,7 +1327,7 @@ def _start_background_huggingface_upload(self, checkpoint_path: Path) -> None: Args: checkpoint_path: Path to the saved checkpoint directory """ - from grail.shared.constants import HF_TOKEN, HF_USERNAME + from grail.shared.config import HF_TOKEN, HF_USERNAME if not HF_TOKEN or not HF_USERNAME: logger.warning( diff --git a/grail/trainer/trust.py b/grail/trainer/trust.py index f4aa7a3c..5a458017 100644 --- a/grail/trainer/trust.py +++ b/grail/trainer/trust.py @@ -74,13 +74,13 @@ import numpy as np from ..infrastructure.comms import get_file -from ..shared.constants import ( +from ..protocol.constants import ( GRAIL_BURN_UID, TRUST_LIST_KEY_PREFIX, - TRUST_LIST_MAX_STALENESS_WINDOWS, TRUST_LIST_VERSION, WINDOW_LENGTH, ) +from ..shared.config import TRUST_LIST_MAX_STALENESS_WINDOWS logger = logging.getLogger(__name__) @@ -110,7 +110,7 @@ async def get_trusted_miner_hotkeys( _select_top_miners_by_incentive(metagraph, min_trusted_miners), timeout=timeout, ) - except asyncio.TimeoutError: + except TimeoutError: logger.error( "Timeout selecting trusted miners after %.1fs; skipping training", timeout, diff --git a/grail/trainer/upload_worker.py b/grail/trainer/upload_worker.py index 751faade..667309a5 100644 --- a/grail/trainer/upload_worker.py +++ b/grail/trainer/upload_worker.py @@ -25,12 +25,12 @@ import torch from grail.infrastructure.network import create_subtensor -from grail.shared.constants import ( +from grail.protocol.constants import WINDOW_LENGTH +from grail.shared.config import ( DELTA_BASE_INTERVAL, DELTA_CHECKPOINT_ENABLED, UPLOAD_RETRY_BACKOFF_BASE, UPLOAD_RETRY_MAX_ATTEMPTS, - WINDOW_LENGTH, ) from grail.shared.safetensors_utils import load_model_state_dict from grail.trainer.checkpoint_publisher import ( @@ -58,13 +58,11 @@ async def _wait_for_snapshot( Returns: Snapshot message dict if available, None if timeout """ - loop = asyncio.get_event_loop() - try: - # Use run_in_executor to avoid blocking the event loop - msg = await loop.run_in_executor( - None, - lambda: ipc.snapshot_queue.get(timeout=poll_interval), + # Use to_thread to avoid blocking the event loop + msg = await asyncio.to_thread( + ipc.snapshot_queue.get, + timeout=poll_interval, ) if msg and msg.get("type") == "snapshot_ready": logger.debug("Received snapshot message from queue: window=%s", msg.get("window")) @@ -180,9 +178,11 @@ async def upload_worker_loop( # Track for chained deltas: # - prev_window/prev_state: immediate predecessor (for delta computation) # - anchor_window: nearest FULL checkpoint (for recovery metadata) + # - delta_chain_length: actual count of deltas since last FULL (not arithmetic distance) prev_window: int | None = None prev_state: dict[str, torch.Tensor] | None = None anchor_window: int | None = None + delta_chain_length: int = 0 while not ipc.stop.is_set(): try: @@ -240,14 +240,26 @@ async def upload_worker_loop( # Decide upload strategy: # - FULL-only: When delta disabled or no prev_state (initial/restart) - # - DELTA (+ FULL background at anchors): Normal operation + # - DELTA + background FULL: At stride-aligned anchors, or when chain + # has grown too long (trainer skipped anchor windows). Both DELTA and + # FULL are uploaded so caught-up validators use the small delta while + # validators that need to catch up download the FULL. is_anchor_window = checkpoint_window % base_stride_blocks == 0 + chain_exceeds_limit = delta_chain_length >= delta_base_interval_windows + if chain_exceeds_limit and DELTA_CHECKPOINT_ENABLED and prev_window is not None: + logger.info( + "Chain length %d >= limit %d, will upload DELTA + background FULL for checkpoint-%s (last anchor=%s)", + delta_chain_length, + delta_base_interval_windows, + checkpoint_window, + anchor_window, + ) should_upload_full_only = ( not DELTA_CHECKPOINT_ENABLED or prev_window is None or prev_state is None ) should_upload_full_background = ( - is_anchor_window and not should_upload_full_only # Only if we're doing DELTA - ) + is_anchor_window or chain_exceeds_limit + ) and not should_upload_full_only # Attempt upload with retry logic upload_result: UploadResult | None = None @@ -295,6 +307,10 @@ async def upload_worker_loop( # Start background FULL upload at anchor windows (non-blocking) if should_upload_full_background: + # Reset chain counter immediately to prevent re-triggering + # while the bg FULL is in flight. anchor_window advances + # only when the bg FULL completes (in _on_bg_done). + delta_chain_length = 0 logger.info( "Starting background FULL upload for anchor checkpoint-%s", checkpoint_window, @@ -318,18 +334,27 @@ def _on_bg_done( *, _checkpoint_window: int = checkpoint_window_for_task, ) -> None: - nonlocal anchor_window + nonlocal anchor_window, delta_chain_length try: ok = task.result() except Exception: # noqa: BLE001 ok = False if ok: - # Only advance the anchor after we know the FULL upload succeeded. - anchor_window = _checkpoint_window - logger.info( - "✅ Background FULL anchor confirmed for checkpoint-%s", - _checkpoint_window, - ) + # Only advance forward: guard against an older bg FULL + # completing after a newer one (would regress the anchor). + if anchor_window is None or _checkpoint_window > anchor_window: + anchor_window = _checkpoint_window + delta_chain_length = 0 + logger.info( + "✅ Background FULL anchor confirmed for checkpoint-%s", + _checkpoint_window, + ) + else: + logger.info( + "Background FULL for checkpoint-%s completed but anchor already at %s, skipping", + _checkpoint_window, + anchor_window, + ) bg_task.add_done_callback(_on_bg_done) @@ -360,17 +385,28 @@ def _on_bg_done( if loaded_state is not None: prev_window = checkpoint_window prev_state = loaded_state - # Update anchor on FULL uploads (including background FULL at anchors) + # Update anchor and chain counter is_new_anchor = should_upload_full_only or did_fallback_to_full if is_new_anchor: + # Synchronous FULL: advance anchor immediately anchor_window = checkpoint_window + delta_chain_length = 0 logger.info( "Cached checkpoint-%s as new anchor (%d tensors, %d params)", anchor_window, len(prev_state), sum(t.numel() for t in prev_state.values()), ) + elif should_upload_full_background: + # Delta with bg FULL in flight: chain counter was already + # reset when bg FULL was triggered, don't increment. + logger.info( + "Cached checkpoint-%s as prev (bg FULL in flight, chain reset)", + prev_window, + ) else: + # Pure delta: increment chain counter + delta_chain_length += 1 logger.info( "Cached checkpoint-%s as prev for chained delta (%d tensors)", prev_window, diff --git a/grail/validation/miner_validator.py b/grail/validation/miner_validator.py index 22852c01..1774964e 100644 --- a/grail/validation/miner_validator.py +++ b/grail/validation/miner_validator.py @@ -10,6 +10,7 @@ import json import logging import math +import os import random import time from collections import Counter, defaultdict @@ -22,7 +23,7 @@ from ..infrastructure.chain import GrailChainManager from ..infrastructure.comms import get_parquet_file from ..model.forward import forward_single_layer -from ..shared.constants import ( +from ..protocol.constants import ( FAILURE_LOOKBACK_WINDOWS, MAX_ROLLOUT_FILE_SIZE_BYTES, MIN_ROLLOUT_FILE_SIZE_BYTES, @@ -46,6 +47,35 @@ FAILURE_FLAG_KEY = "had_failure" +class MissingCheckpointMetadataError(RuntimeError): + """Raised when the validator cannot resolve the trainer-published metadata + required to validate a window (env_id, env_params, generation_params, + thinking_mode). + + This is treated as a *window-level* failure, not a per-miner failure: the + issue is on the trainer/validator side, not in any miner's submission. + The window processor catches this and skips the entire window so the + validator never silently scores miners against an unverifiable baseline. + """ + + +@dataclass(frozen=True, slots=True) +class WindowEnvConfig: + """Per-window protocol configuration resolved from trainer's checkpoint. + + All four fields are protocol-affecting and must come from the same + ``CheckpointMetadata``. Frozen so the resolved value cannot accidentally + drift mid-window. Constructed only by + ``MinerValidator.resolve_window_env_config``; raise + ``MissingCheckpointMetadataError`` if any field is missing or malformed. + """ + + env_id: str + env_params: dict[str, Any] + generation_params: dict[str, Any] + thinking_mode: str # "native" or "instructed" + + @dataclass class ValidationMetrics: """Metrics from validating a miner's window.""" @@ -127,6 +157,103 @@ def __init__( f"and {len(self._soft_check_keys)} soft checks (thresholds={self._soft_thresholds})" ) + async def resolve_window_env_config( + self, + model: AutoModelForCausalLM, + ) -> WindowEnvConfig: + """Resolve the per-window protocol config from the trainer's checkpoint. + + Reads ``model.grail_checkpoint_window`` (set by ``get_model``), fetches + the matching ``CheckpointMetadata`` from R2, runs ``validate_metadata``, + and overrides the process-wide ``GRAIL_THINKING_MODE`` env var so all + subsequent ``apply_chat_template``/``get_thinking_config`` calls in this + window agree with what the trainer published. + + Raises ``MissingCheckpointMetadataError`` if any required field is + missing or unfetchable. Callers MUST treat this as a window-level + skip — falling through with defaults would silently let the validator + score every miner against the wrong baseline. + """ + if self._checkpoint_manager is None: + raise MissingCheckpointMetadataError( + "Validator has no checkpoint_manager; cannot resolve window env config" + ) + + validator_checkpoint_window = getattr(model, "grail_checkpoint_window", None) + if validator_checkpoint_window is None: + raise MissingCheckpointMetadataError( + "Validator model has no grail_checkpoint_window; cannot resolve window env config" + ) + + try: + checkpoint_metadata = await self._checkpoint_manager.get_checkpoint_metadata( + validator_checkpoint_window + ) + except Exception as e: + raise MissingCheckpointMetadataError( + f"Failed to fetch checkpoint metadata for window {validator_checkpoint_window}: {e}" + ) from e + + if checkpoint_metadata is None: + raise MissingCheckpointMetadataError( + f"No checkpoint metadata found for window {validator_checkpoint_window}" + ) + + missing = checkpoint_metadata.validate_metadata() + if missing: + raise MissingCheckpointMetadataError( + f"Checkpoint {validator_checkpoint_window} missing required " + f"trainer-published fields: {', '.join(missing)}. " + f"This indicates a trainer misconfiguration; skipping window." + ) + + # validate_metadata() guarantees env_id and thinking_mode are non-None + # / well-formed; raise explicitly (NOT bare assert: -O strips it) if + # that invariant ever drifts so the failure surfaces at the trust + # boundary instead of producing a silently malformed dataclass. + if checkpoint_metadata.env_id is None: + raise MissingCheckpointMetadataError( + f"Checkpoint {validator_checkpoint_window} has env_id=None after " + "validate_metadata() passed — schema invariant violated" + ) + if checkpoint_metadata.thinking_mode is None: + raise MissingCheckpointMetadataError( + f"Checkpoint {validator_checkpoint_window} has thinking_mode=None " + "after validate_metadata() passed — schema invariant violated" + ) + config = WindowEnvConfig( + env_id=checkpoint_metadata.env_id, + env_params=checkpoint_metadata.env_params or {}, + generation_params=checkpoint_metadata.generation_params or {}, + thinking_mode=checkpoint_metadata.thinking_mode, + ) + + # Override the process-wide thinking mode from the checkpoint so all + # subsequent ``get_thinking_config()`` callers (registry adapters, + # parsers, debug helpers) see the trainer-published value, never the + # validator host's local ``GRAIL_THINKING_MODE``. This is the trust + # boundary that keeps the validator in sync with the miner when their + # hosts diverge — e.g. validator running in a docker container that + # didn't inherit ``GRAIL_THINKING_MODE`` from the host ``.env``. + if os.environ.get("GRAIL_THINKING_MODE") != config.thinking_mode: + logger.info( + "Overriding GRAIL_THINKING_MODE from checkpoint metadata: prev=%s, new=%s", + os.environ.get("GRAIL_THINKING_MODE", ""), + config.thinking_mode, + ) + os.environ["GRAIL_THINKING_MODE"] = config.thinking_mode + + logger.info( + "Resolved window env config from checkpoint %s: env_id=%s " + "thinking_mode=%s max_tokens=%s", + validator_checkpoint_window, + config.env_id, + config.thinking_mode, + config.generation_params.get("max_tokens"), + ) + + return config + async def validate_miner( self, miner_hotkey: str, @@ -141,6 +268,7 @@ async def validate_miner( monitor: Any | None, uid_by_hotkey: dict[str, int], text_logs_emitted: dict[str, int], + env_config: WindowEnvConfig, heartbeat_callback: Any = None, deadline_ts: float | None = None, download_times: list[float] | None = None, @@ -160,6 +288,11 @@ async def validate_miner( monitor: Optional monitoring client uid_by_hotkey: Mapping of hotkey to UID text_logs_emitted: Counter of text logs per miner + env_config: Per-window protocol config resolved once via + ``resolve_window_env_config``. Carries env_id, env_params, + generation_params, and thinking_mode from the trainer's + published ``CheckpointMetadata``. Never read from local env + vars. heartbeat_callback: Optional callback to update watchdog deadline_ts: Upload deadline timestamp (unix seconds) download_times: Optional list to collect download durations @@ -212,53 +345,13 @@ async def validate_miner( inferences, miner_hotkey, window_rand, validator_wallet, total_inferences ) - # Extract validator's checkpoint window from model attribute (set by get_model()) + # Extract validator's checkpoint window from model attribute (set by get_model()). + # The trainer-published env config (env_id, env_params, generation_params, + # thinking_mode) is resolved ONCE per window by + # ``resolve_window_env_config`` and passed in as parameters above. We + # do not refetch it here. validator_checkpoint_window = getattr(model, "grail_checkpoint_window", None) - # Fetch environment config from checkpoint metadata if checkpoint_manager is available - env_id = None - env_params = {} - generation_params: dict[str, Any] = {} - if self._checkpoint_manager is not None and validator_checkpoint_window is not None: - try: - checkpoint_metadata = await self._checkpoint_manager.get_checkpoint_metadata( - validator_checkpoint_window - ) - if checkpoint_metadata is not None: - missing = checkpoint_metadata.validate_metadata() - if missing: - logger.error( - "Checkpoint %s missing required metadata: %s. " - "Skipping validation — trainer may be misconfigured.", - validator_checkpoint_window, - ", ".join(missing), - ) - return self._create_not_found_result(miner_hotkey, uid) - - env_id = checkpoint_metadata.env_id - env_params = checkpoint_metadata.env_params or {} - generation_params = checkpoint_metadata.generation_params or {} - logger.debug( - "Loaded checkpoint config from %s: env_id=%s max_tokens=%s", - validator_checkpoint_window, - env_id, - generation_params.get("max_tokens"), - ) - else: - logger.warning( - f"No metadata found for validator checkpoint window {validator_checkpoint_window}" - ) - if monitor: - monitor.log_counter("validation/metadata_missing") - except Exception as e: - logger.warning( - f"Failed to fetch checkpoint metadata for env config " - f"(window={validator_checkpoint_window}): {e}", - exc_info=True, - ) - if monitor: - monitor.log_counter("validation/metadata_fetch_errors") - # Step 4: Validate selected rollouts validation_state = await self._validate_rollouts( inferences=inferences, @@ -275,9 +368,9 @@ async def validate_miner( text_logs_emitted=text_logs_emitted, heartbeat_callback=heartbeat_callback, validator_checkpoint_window=validator_checkpoint_window, - env_id=env_id, - env_params=env_params, - generation_params=generation_params, + env_id=env_config.env_id, + env_params=env_config.env_params, + generation_params=env_config.generation_params, ) # Step 5: Check for early failures @@ -562,7 +655,7 @@ def _precompute_batched_forward( import torch - from ..shared.constants import LAYER_INDEX, PROOF_BATCH_SIZE + from ..protocol.constants import LAYER_INDEX, PROOF_BATCH_SIZE # Collect valid token sequences token_seqs: list[list[int]] = [] @@ -698,8 +791,8 @@ def _precompute_challenged_logprobs( """ import torch + from ..protocol.constants import CHALLENGE_K from ..protocol.crypto import indices_from_root_in_range - from ..shared.constants import CHALLENGE_K result: dict[int, dict[int, float]] = {} @@ -925,8 +1018,24 @@ async def _validate_rollouts( rollout_group = str(rollout_group_raw) state["rollout_groups"][rollout_group].append(inference) - # Derive file-order group index for deterministic seed derivation - group_index = int(group_index_by_id.get(rollout_group, 0)) + # Use the rollout_group integer DIRECTLY as the seed material — + # this is the same value the miner used (`problem_index = + # problem_count - 1`). The previous file-encounter index + # (``group_index_by_id``) was a latent bug: if the miner ever + # dropped a group (proof failure, short rollouts, etc.), the + # file would have non-contiguous rollout_group values, the + # validator would assign 0..N-1 in encounter order, and every + # rollout's seed would differ from the miner's by one slot. + # See ``grail/cli/mine.py::_pipelined_generation_loop`` for + # the matching seed derivation on the miner side. + try: + group_index = int(rollout_group_raw) + except (TypeError, ValueError): + state["hard_failure"] = True + logger.warning( + "rollout_group=%r is not an integer; invalidating uid", rollout_group_raw + ) + break # Inject canonical env field (validator-derived, never trust miner) seed_int = derive_env_seed(miner_hotkey, window_hash, group_index) diff --git a/grail/validation/sampling.py b/grail/validation/sampling.py index a077736d..be115c21 100644 --- a/grail/validation/sampling.py +++ b/grail/validation/sampling.py @@ -13,7 +13,7 @@ from collections import deque from typing import Any -from ..shared.constants import MIN_ROLLOUT_FILE_SIZE_BYTES +from ..protocol.constants import MIN_ROLLOUT_FILE_SIZE_BYTES logger = logging.getLogger(__name__) @@ -150,7 +150,7 @@ async def _check(hotkey: str) -> tuple[str, bool]: return hotkey, False return hotkey, bool(exists) - except asyncio.TimeoutError: + except TimeoutError: elapsed = time.time() - start_time logger.debug( "Window file check TIMEOUT for %s window=%s after %.2fs", diff --git a/grail/validation/service.py b/grail/validation/service.py index 0f40852c..f9c021d1 100644 --- a/grail/validation/service.py +++ b/grail/validation/service.py @@ -13,7 +13,7 @@ import logging import time from collections import defaultdict, deque -from datetime import datetime, timezone +from datetime import UTC, datetime from types import SimpleNamespace from typing import Any @@ -34,8 +34,7 @@ get_model, get_tokenizer, ) -from ..scoring.weights import WeightComputer -from ..shared.constants import ( +from ..protocol.constants import ( FAILURE_LOOKBACK_WINDOWS, MINER_SAMPLE_MAX, MINER_SAMPLE_MIN, @@ -47,6 +46,7 @@ UNIQUE_ROLLOUTS_CAP, WINDOW_LENGTH, ) +from ..scoring.weights import WeightComputer from ..shared.window_utils import ( WindowWaitTracker, calculate_next_window, @@ -56,7 +56,7 @@ # Imports retained only where used from .copycat_service import COPYCAT_SERVICE -from .miner_validator import MinerValidator +from .miner_validator import MinerValidator, MissingCheckpointMetadataError from .pipeline import ValidationPipeline from .sampling import MinerSampler from .window_processor import WindowProcessor @@ -465,7 +465,10 @@ async def _load_checkpoint_for_window(self, target_window: int) -> bool: gc.collect() if torch.cuda.is_available(): torch.cuda.empty_cache() - self._model = get_model(str(checkpoint_path), device=None, eval_mode=True) + # Pin to CUDA explicitly. Matches the miner/trainer pattern and + # prevents the silent CPU fallback that previously froze a + # validator host. The provider will raise if CUDA is missing. + self._model = get_model(str(checkpoint_path), device="cuda", eval_mode=True) self._tokenizer = get_tokenizer(str(checkpoint_path)) self._current_checkpoint_id = str(checkpoint_path) self._current_checkpoint_window = result.window @@ -513,6 +516,36 @@ async def _process_window( test_mode: Test mode flag heartbeat_callback: Optional heartbeat callback """ + # Resolve the trainer-published per-window protocol config BEFORE any + # rolling-history mutation. If the trainer has not published the + # required metadata (env_id, env_params, generation_params, + # thinking_mode), the validator MUST abort this window without + # touching availability_counts/selection_counts/inference_counts so + # that downstream weight + sampling state remains untouched. Falling + # through with defaults — or even returning empty WindowResults + # AFTER advancing the rolling history — would silently distort + # weights and sampling. + if self._window_processor is None: + raise RuntimeError("WindowProcessor not initialized") + if self._model is None: + raise RuntimeError("Model must be loaded") + try: + env_config = await self._window_processor.resolve_window_env_config(self._model) + except MissingCheckpointMetadataError as exc: + logger.error( + "Window %s: cannot resolve trainer-published env config: %s. " + "Aborting window before any rolling-history update — no miner " + "will be scored, no rolling counters will advance.", + target_window, + exc, + ) + if self._monitor: + try: + await self._monitor.log_counter("validation/window_skipped_missing_metadata") + except Exception: + pass + return + # Get window block hash and randomness if self._subtensor is None: raise RuntimeError("Subtensor not initialized") @@ -540,7 +573,7 @@ async def _process_window( if deadline_ts is not None: deadline_ts += DEADLINE_SLACK_SECONDS readable_ts: str = ( - datetime.fromtimestamp(deadline_ts, tz=timezone.utc).isoformat() + datetime.fromtimestamp(deadline_ts, tz=UTC).isoformat() if deadline_ts is not None else "None" ) @@ -653,6 +686,7 @@ async def _process_window( monitor=self._monitor, uid_by_hotkey=uid_by_hotkey, subtensor=self._subtensor, + env_config=env_config, heartbeat_callback=heartbeat_callback, deadline_ts=deadline_ts, ) diff --git a/grail/validation/validators/distribution.py b/grail/validation/validators/distribution.py index 564275c4..ebf07a19 100644 --- a/grail/validation/validators/distribution.py +++ b/grail/validation/validators/distribution.py @@ -3,10 +3,17 @@ from __future__ import annotations import logging +from typing import Any, cast import torch +from torch import FloatTensor, LongTensor +from transformers import ( + LogitsProcessorList, + RepetitionPenaltyLogitsProcessor, + TemperatureLogitsWarper, +) -from ...shared.constants import ( +from ...protocol.constants import ( SAMPLING_HIGH_P, SAMPLING_INITIAL_WINDOW_STEPS, SAMPLING_LOW_P, @@ -67,7 +74,13 @@ def validate(self, ctx: ValidationContext) -> bool: return not suspicious def _collect_probs(self, ctx: ValidationContext) -> list[float] | None: - """Collect chosen token probabilities from cached logits.""" + """Collect chosen token probabilities from cached logits. + + Applies the drift-safe subset of HF's sample-mode pipeline + (repetition_penalty, temperature) before reading the chosen-token + probability. ``top_k`` and ``top_p`` are intentionally NOT applied + here; see ``_build_hf_processors`` for the rationale. + """ try: tokens = ctx.commit.get("tokens", []) rollout = ctx.commit.get("rollout", {}) @@ -84,13 +97,38 @@ def _collect_probs(self, ctx: ValidationContext) -> list[float] | None: return None logits = ctx.cached_logits + device = ctx.model.device + + # Build HF logits processors from trusted generation_params. + # generation_params is enforced upstream by + # CheckpointMetadata.validate_metadata(); the required keys are + # guaranteed to be present here. + processors = self._build_hf_processors(ctx.generation_params) + + # Materialise the full token sequence on the model device once so + # processors can slice prefix views without per-step host->device + # transfers. + full_input_ids = torch.tensor(tokens, dtype=torch.long, device=device).unsqueeze(0) - # Collect probabilities for completion tokens - probs = [] + probs: list[float] = [] for t in range(prompt_len, min(prompt_len + completion_len, len(tokens))): - if t > 0 and t - 1 < logits.size(0): - step_probs = torch.softmax(logits[t - 1], dim=-1) - probs.append(float(step_probs[tokens[t]].item())) + if t == 0 or t - 1 >= logits.size(0): + continue + + # Logits the model produced when predicting token tokens[t]. + # HF processors mutate scores in place; pass a clone so the + # cached_logits buffer is never touched. An empty processor + # list is a no-op identity, so we always call through. + step_logits = processors( + cast(LongTensor, full_input_ids[:, :t]), + cast( + FloatTensor, + logits[t - 1].to(device=device, dtype=torch.float32).unsqueeze(0).clone(), + ), + ) + + step_probs = torch.softmax(step_logits, dim=-1).squeeze(0) + probs.append(float(step_probs[tokens[t]].item())) return probs if probs else None @@ -98,6 +136,51 @@ def _collect_probs(self, ctx: ValidationContext) -> list[float] | None: logger.debug(f"Failed to collect token probs: {e}") return None + @staticmethod + def _build_hf_processors(generation_params: dict[str, Any]) -> LogitsProcessorList: + """Construct the drift-safe subset of HF's sample-mode pipeline. + + Applies (in HF order): + + 1. RepetitionPenaltyLogitsProcessor (when rep_penalty != 1.0) + 2. TemperatureLogitsWarper (when temperature != 1.0) + + ``top_k`` and ``top_p`` are deliberately NOT applied. They are hard + masks that set logits to ``-inf``, and the cutoff boundary is sensitive + to bf16 prefill-vs-decode numerical drift between the miner's + incremental decode and the validator's full prefill. In practice + ~30-75% of legitimately-sampled tokens flip from "kept" to "masked" + across the two runs and end up with ``softmax`` prob exactly ``0.0``, + which trips the ``min_low`` rule on honest miners. ``temperature`` and + ``repetition_penalty`` are smooth, monotonic, drift-tolerant + operations and do not have this failure mode. + + TODO(distribution-validator): the consequence of dropping top_k/top_p + is that the original false-positive case this validator was being + rewritten to fix (high-T sampling makes raw chosen probs cluster + below ``SAMPLING_MEDIAN_LOW_MAX``) is only partially addressed. The + proper fix is per-position ratio normalization + ``r_t = P_raw(chosen) / E_p_raw[t]`` (expected raw prob under the + sampling distribution), which is drift-tolerant because it's a + weighted sum, not a hard mask. Tracked in the original plan at + ``~/.claude/plans/federated-sparking-flute.md``. + + ``generation_params`` is enforced upstream by + ``CheckpointMetadata.validate_metadata()``; missing keys raise + ``KeyError`` here so regressions are loud. + """ + processors: list[Any] = [] + + rep = float(generation_params["repetition_penalty"]) + if rep != 1.0: + processors.append(RepetitionPenaltyLogitsProcessor(rep)) + + temperature = float(generation_params["temperature"]) + if temperature != 1.0: + processors.append(TemperatureLogitsWarper(temperature)) + + return LogitsProcessorList(processors) + def _compute_metrics(self, probs: list[float]) -> dict: """Compute distribution statistics (quantiles and fractions).""" try: diff --git a/grail/validation/validators/environment.py b/grail/validation/validators/environment.py index 7aee7852..2015bb2f 100644 --- a/grail/validation/validators/environment.py +++ b/grail/validation/validators/environment.py @@ -4,18 +4,19 @@ import logging import math +from statistics import median import torch from ...environments.registry import get_adapter -from ...protocol.crypto import indices_from_root_in_range -from ...protocol.signatures import derive_env_seed -from ...shared.constants import ( +from ...protocol.constants import ( CHALLENGE_K, CURRENT_ENV_ID, REWARD_ABS_TOL, REWARD_REL_TOL, ) +from ...protocol.crypto import indices_from_root_in_range +from ...protocol.signatures import derive_env_seed from ..base import Validator from ..context import ValidationContext @@ -253,11 +254,26 @@ def validate(self, ctx: ValidationContext) -> bool: class LogprobValidator(Validator): + """Validate miner-claimed logprobs via a robust median IS-ratio check. + + Over the K=32 cryptographically chosen completion positions, computes the + per-position importance-sampling deviation + dev_i = exp(|model_lp_i - miner_lp_i|) - 1 + and rejects the rollout iff ``median(dev_i) > LOGPROB_IS_EPS``. + + The median is robust to a handful of bf16-noise outliers while still + rejecting miners whose distribution diverges meaningfully from the + canonical model. Empirically (430k honest cross-GPU/cross-attn/cross-batch + trials, 0% FP) ``LOGPROB_IS_EPS=0.10`` leaves ~50% headroom over the worst + observed honest pair (max median dev = 0.066). + """ + check_name = "logprobs_valid" severity = "hard" - LOGPROB_ABS_TOL = 1e-3 - LOGPROB_REL_TOL = 0.02 + # Maximum allowed median importance-sampling deviation per K-sample. + # Equivalent PPO ratio bound is ``1 + LOGPROB_IS_EPS``. + LOGPROB_IS_EPS = 0.10 def validate(self, ctx: ValidationContext) -> bool: try: @@ -267,16 +283,15 @@ def validate(self, ctx: ValidationContext) -> bool: ctx.checks[self.check_name] = False return False - # Use cached logits from proof validator - logits = ctx.cached_logits # shape [seq_len, vocab] tokens = ctx.commit.get("tokens", []) prompt_len = int(rollout.get("prompt_length", 0)) completion_len = int(rollout.get("completion_length", 0)) - # Enforce minimum completion length + # Enforce minimum completion length so the K=32 challenge fits if completion_len < CHALLENGE_K: logger.debug( - "[logprobs_valid] Completion too short | required>=%d got=%d | prompt_len=%d seq_len=%d", + "[logprobs_valid] Completion too short | required>=%d got=%d " + "| prompt_len=%d seq_len=%d", CHALLENGE_K, completion_len, prompt_len, @@ -285,23 +300,23 @@ def validate(self, ctx: ValidationContext) -> bool: ctx.checks[self.check_name] = False return False - if logits is None: + # Miner emits token_logprobs as [0.0] * prompt_len + completion_lps + if len(claimed) != len(tokens): logger.debug( - "[logprobs_valid] No cached logits available so we set the checks to False for now. (Investigate why this is happening.)" + "[logprobs_valid] Length mismatch: expected %d, got %d", + len(tokens), + len(claimed), ) ctx.checks[self.check_name] = False return False - # Miner expect to generate token_logprobs as: [0.0] * prompt_len + logprobs - # So len(claimed) should equal len(tokens) = prompt_len + completion_len - if len(claimed) != len(tokens): - logger.debug( - "Logprob length mismatch: expected %d, got %d", len(tokens), len(claimed) - ) + logits = ctx.cached_logits # [seq_len, vocab] on CPU; may be None + precomputed = ctx.precomputed_logprobs + if logits is None and precomputed is None: + logger.debug("[logprobs_valid] No cached logits or precomputed logprobs available") ctx.checks[self.check_name] = False return False - # Choose deterministic challenge indices restricted to completion slice challenged_idxs = indices_from_root_in_range( tokens, ctx.challenge_randomness, @@ -310,63 +325,47 @@ def validate(self, ctx: ValidationContext) -> bool: CHALLENGE_K, ) - # Debug: challenge selection overview - logger.debug( - ("VALIDATOR LOGPROB CHALLENGE: completion_len=%d selected=%d"), - completion_len, - len(challenged_idxs), + devs = self._compute_devs( + challenged_idxs=challenged_idxs, + tokens=tokens, + claimed=claimed, + logits=logits, + precomputed=precomputed, ) - if challenged_idxs: - preview = challenged_idxs[: min(5, len(challenged_idxs))] - logger.debug("Challenged completion indices (abs): %s", preview) - - # logits correspond to next-token scores; align per-generation index - mismatches = 0 - total = len(challenged_idxs) - first_mismatch_details = None - precomputed = ctx.precomputed_logprobs - for abs_idx in challenged_idxs: - if precomputed is not None and abs_idx in precomputed: - model_lp = precomputed[abs_idx] - else: - # Fallback: compute per-position log_softmax - pos = abs_idx - 1 - if pos < 0 or pos >= logits.size(0): - continue - dist = torch.log_softmax(logits[pos].float(), dim=-1) - model_lp = float(dist[tokens[abs_idx]].item()) - - miner_lp = float(claimed[abs_idx]) - if not self._close_lp(model_lp, miner_lp): - mismatches += 1 - if first_mismatch_details is None: - first_mismatch_details = { - "abs_index": abs_idx, - "rel_index": abs_idx - prompt_len, - "token_id": tokens[abs_idx], - "model_lp": model_lp, - "miner_lp": miner_lp, - "diff": abs(model_lp - miner_lp), - } - - # Soft check: allow some noise - ok = mismatches <= max(1, total // 10) - ctx.metadata["logprob_mismatches"] = mismatches - ctx.metadata["logprob_total"] = total - - if not ok and first_mismatch_details: + # Fail closed if any challenged position could not be resolved. + if devs is None or len(devs) != CHALLENGE_K: + resolved = -1 if devs is None else len(devs) logger.debug( - "Logprob validation failed: %d/%d mismatches. First mismatch at abs %d (rel %d): " - "token=%d, model_lp=%.6f, miner_lp=%.6f, diff=%.6f", - mismatches, - total, - first_mismatch_details["abs_index"], - first_mismatch_details["rel_index"], - first_mismatch_details["token_id"], - first_mismatch_details["model_lp"], - first_mismatch_details["miner_lp"], - first_mismatch_details["diff"], + "[logprobs_valid] Could not resolve all challenged positions " + "| resolved=%d expected=%d", + resolved, + CHALLENGE_K, + ) + ctx.checks[self.check_name] = False + return False + + median_dev = median(devs) + max_dev = max(devs) + ok = median_dev <= self.LOGPROB_IS_EPS + + ctx.metadata["logprob_median_dev"] = median_dev + ctx.metadata["logprob_max_dev"] = max_dev + ctx.metadata["logprob_total"] = len(devs) + + if not ok: + worst_idx = max(range(len(devs)), key=devs.__getitem__) + worst_abs = challenged_idxs[worst_idx] + logger.debug( + "[logprobs_valid] FAIL median_dev=%.6f max_dev=%.6f eps=%.4f " + "| worst pos=%d (rel=%d) token=%d miner_lp=%.6f", + median_dev, + max_dev, + self.LOGPROB_IS_EPS, + worst_abs, + worst_abs - prompt_len, + tokens[worst_abs], + float(claimed[worst_abs]), ) ctx.checks[self.check_name] = ok @@ -376,8 +375,29 @@ def validate(self, ctx: ValidationContext) -> bool: ctx.checks[self.check_name] = False return False - def _close_lp(self, a: float, b: float) -> bool: - if abs(a - b) <= self.LOGPROB_ABS_TOL: - return True - denom = max(1e-6, abs(a)) - return abs(a - b) / denom <= self.LOGPROB_REL_TOL + @staticmethod + def _compute_devs( + *, + challenged_idxs: list[int], + tokens: list[int], + claimed: list[float], + logits: torch.Tensor | None, + precomputed: dict[int, float] | None, + ) -> list[float] | None: + """Return per-position IS deviation ``exp(|Δlp|) - 1`` or ``None`` on failure.""" + devs: list[float] = [] + for abs_idx in challenged_idxs: + if precomputed is not None and abs_idx in precomputed: + model_lp = precomputed[abs_idx] + else: + if logits is None: + return None + pos = abs_idx - 1 + if pos < 0 or pos >= logits.size(0): + return None + dist = torch.log_softmax(logits[pos].float(), dim=-1) + model_lp = float(dist[tokens[abs_idx]].item()) + + miner_lp = float(claimed[abs_idx]) + devs.append(math.exp(abs(model_lp - miner_lp)) - 1.0) + return devs diff --git a/grail/validation/validators/proof.py b/grail/validation/validators/proof.py index 6eea34a9..6c3ff778 100644 --- a/grail/validation/validators/proof.py +++ b/grail/validation/validators/proof.py @@ -14,10 +14,10 @@ import torch from ...model.forward import forward_single_layer +from ...protocol.constants import CHALLENGE_K, LAYER_INDEX from ...protocol.crypto import indices_from_root_in_range from ...protocol.grail_verifier import GRAILVerifier from ...protocol.signatures import verify_commit_signature -from ...shared.constants import CHALLENGE_K, LAYER_INDEX from ...shared.hf_compat import resolve_hidden_size, resolve_vocab_size from ..base import Validator from ..context import ValidationContext @@ -34,7 +34,7 @@ class GRAILProofValidator(Validator): 3. Sketch verification at challenged indices 4. Caches logits for downstream validators (termination check) - Security: ~10^-234 forgery probability with sketch check at k=32 positions. + Security: ~10^-167 forgery probability with sketch check at K=32 challenged positions. """ check_name = "proof_valid" @@ -50,10 +50,10 @@ def validate(self, ctx: ValidationContext) -> bool: # Check proof version proof_version = ctx.commit.get("proof_version") - if not proof_version or proof_version not in ("v1", "v2"): + if not proof_version or proof_version not in ("v4", "v5"): logger.debug( f"[proof_valid] Proof version validation failed | " - f"Expected: v1 or v2 | " + f"Expected: v4 or v5 | " f"Got: {proof_version}" ) ctx.checks[self.check_name] = False diff --git a/grail/validation/validators/schema.py b/grail/validation/validators/schema.py index 1622246e..39bc1877 100644 --- a/grail/validation/validators/schema.py +++ b/grail/validation/validators/schema.py @@ -23,7 +23,7 @@ class SchemaValidator(Validator): This validator runs FIRST (before GPU validators) to filter out: - Missing required fields - Wrong types (str instead of int, etc.) - - Out-of-range values (completion_length > MAX_NEW_TOKENS) + - Out-of-range values (completion_length > MAX_NEW_TOKENS_PROTOCOL_CAP) - Cross-field inconsistencies (commitments length != tokens length) Uses Pydantic for automatic validation with clear error messages. diff --git a/grail/validation/validators/termination.py b/grail/validation/validators/termination.py index bbc93369..7c5b8233 100644 --- a/grail/validation/validators/termination.py +++ b/grail/validation/validators/termination.py @@ -6,7 +6,7 @@ import torch -from ...shared.constants import MAX_NEW_TOKENS, MIN_EOS_PROBABILITY +from ...protocol.constants import MAX_NEW_TOKENS_PROTOCOL_CAP, MIN_EOS_PROBABILITY from ..base import Validator from ..context import ValidationContext @@ -38,13 +38,13 @@ def validate(self, ctx: ValidationContext) -> bool: # Prefer checkpoint-provided generation max_tokens when available. # This avoids false failures when miners/validators intentionally use # shorter completions (e.g., max_tokens=512 for code tasks). - expected_max_new_tokens = MAX_NEW_TOKENS + expected_max_new_tokens = MAX_NEW_TOKENS_PROTOCOL_CAP max_tokens_raw = ctx.generation_params.get("max_tokens") if max_tokens_raw is not None: try: max_tokens_int = int(max_tokens_raw) if max_tokens_int > 0: - expected_max_new_tokens = min(max_tokens_int, MAX_NEW_TOKENS) + expected_max_new_tokens = min(max_tokens_int, MAX_NEW_TOKENS_PROTOCOL_CAP) except (TypeError, ValueError): pass diff --git a/grail/validation/window_processor.py b/grail/validation/window_processor.py index 743c680f..4c39b263 100644 --- a/grail/validation/window_processor.py +++ b/grail/validation/window_processor.py @@ -19,7 +19,11 @@ from ..infrastructure.chain import GrailChainManager from ..logging_utils import miner_log_context from .copycat_service import CopycatService -from .miner_validator import FAILURE_FLAG_KEY, MinerValidator +from .miner_validator import ( + FAILURE_FLAG_KEY, + MinerValidator, + WindowEnvConfig, +) from .types import MinerResults, WindowResults logger = logging.getLogger(__name__) @@ -57,6 +61,15 @@ def __init__( self._miner_validator = miner_validator self._copycat_service = copycat_service + async def resolve_window_env_config(self, model: AutoModelForCausalLM) -> WindowEnvConfig: + """Public delegate to ``MinerValidator.resolve_window_env_config``. + + Exposed so that ``ValidationService._process_window`` can resolve the + per-window protocol config BEFORE it touches any rolling history, + without having to reach into ``WindowProcessor``'s internals. + """ + return await self._miner_validator.resolve_window_env_config(model) + async def process_window( self, window: int, @@ -71,6 +84,7 @@ async def process_window( monitor: Any | None, uid_by_hotkey: dict[str, int], subtensor: Any, # bt.subtensor + env_config: WindowEnvConfig, heartbeat_callback: Any = None, deadline_ts: float | None = None, ) -> WindowResults: @@ -89,6 +103,11 @@ async def process_window( monitor: Optional monitoring client uid_by_hotkey: Mapping of hotkey to UID subtensor: Subtensor instance for block queries + env_config: Per-window protocol config resolved by + ``ValidationService._process_window`` BEFORE any rolling-history + mutation. Carries the trainer-published env_id, env_params, + generation_params, and thinking_mode. If the resolve failed, + the service aborts the window and never reaches this method. heartbeat_callback: Optional watchdog heartbeat callback deadline_ts: Upload deadline timestamp (unix seconds) @@ -152,6 +171,7 @@ async def process_window( monitor=monitor, uid_by_hotkey=uid_by_hotkey, text_logs_emitted=text_logs_emitted, + env_config=env_config, heartbeat_callback=heartbeat_callback, deadline_ts=deadline_ts, download_times=download_times, diff --git a/pyproject.toml b/pyproject.toml index 5825d58a..ac0fa6b2 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -22,11 +22,10 @@ classifiers = [ "Intended Audience :: Science/Research", "License :: OSI Approved :: MIT License", "Programming Language :: Python :: 3", - "Programming Language :: Python :: 3.10", - "Programming Language :: Python :: 3.11", + "Programming Language :: Python :: 3.12", "Topic :: Scientific/Engineering :: Artificial Intelligence", ] -requires-python = ">=3.10,<3.12" +requires-python = ">=3.12" dependencies = [ "torch>=2.9.1", "numpy>=1.20.0", @@ -54,12 +53,14 @@ dependencies = [ "seaborn>=0.13.2", "pyarrow>=14.0.0", "zstandard>=0.21.0", + "xxhash>=3.5,<4", ] [project.optional-dependencies] dev = [ "pytest>=7.0.0", "pytest-asyncio>=0.21.0", + "pytest-xdist>=3.5.0", "black>=22.0.0", "flake8>=5.0.0", "mypy>=1.0.0", @@ -98,7 +99,7 @@ path = "grail/__init__.py" [tool.black] line-length = 100 -target-version = ['py310', 'py311'] +target-version = ['py312'] include = '\.pyi?$' extend-exclude = ''' /( @@ -117,7 +118,7 @@ extend-exclude = ''' ''' [tool.mypy] -python_version = "3.10" +python_version = "3.12" warn_return_any = true warn_unused_configs = true disallow_untyped_defs = true @@ -180,15 +181,17 @@ markers = [ "gpu_real_data: marks tests as GPU real-data tests (long-running, requires GPU) - include with: pytest -m gpu_real_data", "long: marks tests as long-running (typically 10-15min, opt-in) - include with: pytest -m long", "benchmark: marks tests as performance benchmarks (requires GPU) - include with: pytest -m benchmark", + "serial: marks tests that must not run in parallel (global singletons, shared state)", ] # Test suite selection: -# - pytest = Run all tests EXCEPT GPU, long, and benchmark (default, ~2 min) +# - pytest = Run all tests EXCEPT GPU, long, and benchmark (default, ~5 min) +# - pytest -n auto = Run default tests in parallel (one worker per core) +# - pytest -n auto -m "not serial" = Parallel, skipping tests that need serial execution # - pytest -m long = Run ONLY long-running tests -# - pytest -m gpu_real_data = Run ONLY GPU real-data tests -# - pytest -m benchmark = Run ONLY benchmark tests -# - pytest -m "gpu_real_data or long" = Run both GPU and long-running tests -# - pytest --override-ini="addopts=" tests/ = Run ALL tests including GPU and long-running -# - pytest -m "not gpu_real_data and not long" tests/ = Explicitly exclude both +# - pytest -m gpu_real_data = Run ONLY GPU real-data tests +# - pytest -m benchmark = Run ONLY benchmark tests +# - pytest -m "gpu_real_data or long" = Run both GPU and long-running tests +# - pytest --override-ini="addopts=" tests/ = Run ALL tests including GPU and long-running [tool.coverage.run] source = ["grail"] @@ -228,8 +231,8 @@ dev = [ [tool.ruff] line-length = 100 -target-version = "py310" -extend-exclude = ["scripts", "research", "results", "vendor", "examples", "benchmark_results", "data"] +target-version = "py312" +extend-exclude = ["scripts", "research", "results", "vendor", "examples", "benchmark_results", "data", ".claude"] [tool.ruff.lint] select = [ @@ -256,9 +259,9 @@ line-ending = "auto" [tool.pyright] include = ["grail"] -exclude = ["**/__pycache__", ".venv", "build", "dist"] +exclude = ["**/__pycache__", ".venv", "build", "dist", "grail/environments/compliance_env.py", "grail/environments/error_injection", "grail/environments/world_model_env.py", "grail/environments/experiment_design_env.py"] typeCheckingMode = "basic" -pythonVersion = "3.10" +pythonVersion = "3.12" reportMissingImports = true reportMissingTypeStubs = false reportUnusedImport = "warning" diff --git a/research/infrastructure/QUICKSTART.md b/research/infrastructure/QUICKSTART.md index a8e4747b..57d79636 100644 --- a/research/infrastructure/QUICKSTART.md +++ b/research/infrastructure/QUICKSTART.md @@ -4,7 +4,7 @@ Get up and running with Lium infrastructure in 5 minutes. ## Prerequisites -- Python 3.11+ +- Python 3.12+ - SSH key for authentication - Lium account and API key - WandB account (optional, for tracking) diff --git a/research/infrastructure/SETUP_GUIDE.md b/research/infrastructure/SETUP_GUIDE.md index bfbab2a9..4d89fcde 100644 --- a/research/infrastructure/SETUP_GUIDE.md +++ b/research/infrastructure/SETUP_GUIDE.md @@ -112,7 +112,7 @@ CUDA_VISIBLE_DEVICES=1 python research/trl/train_trl_grpo.py \ ### Prerequisites -1. **Python 3.11+** +1. **Python 3.12+** 2. **Lium SDK and AsyncSSH** ```bash uv pip install lium-sdk asyncssh diff --git a/research/infrastructure/VALIDATION.md b/research/infrastructure/VALIDATION.md index 29d83930..6aac9da3 100644 --- a/research/infrastructure/VALIDATION.md +++ b/research/infrastructure/VALIDATION.md @@ -185,4 +185,4 @@ The system is ready for distributed training experiments on Lium infrastructure. --- **Validated by**: Claude Code -**Environment**: Ubuntu 22.04, Python 3.11, lium-sdk 0.2.13 +**Environment**: Ubuntu 22.04, Python 3.12, lium-sdk 0.2.13 diff --git a/research/infrastructure/basilica_manager.py b/research/infrastructure/basilica_manager.py index 4447ebd2..175292b6 100644 --- a/research/infrastructure/basilica_manager.py +++ b/research/infrastructure/basilica_manager.py @@ -320,7 +320,7 @@ def wait_for_ssh(host: str, port: int, timeout: int = SSH_CONNECT_TIMEOUT) -> bo try: with socket.create_connection((host, port), timeout=5): return True - except (socket.error, OSError): + except OSError: time.sleep(5) return False @@ -390,7 +390,7 @@ def _load_state(self) -> None: data = json.loads(backup.read_text()) for name, inst_data in data.get("instances", {}).items(): self._instances[name] = Instance.from_dict(inst_data) - logger.info(f"Recovered state from backup") + logger.info("Recovered state from backup") except Exception as e2: logger.error(f"Backup also corrupted: {e2}") logger.warning("Starting with empty state - may have orphaned instances!") @@ -504,7 +504,7 @@ def provision(self, config: GpuConfig, max_retries: int = 3) -> Instance | None: if not instance: print(f" ❌ Rental {rental_id[:8]}... failed to become ready") # Optionally terminate the failed rental - print(f" 🗑️ Terminating failed rental...") + print(" 🗑️ Terminating failed rental...") BasilicaCli.stop_rental(rental_id) return instance @@ -525,7 +525,7 @@ def _wait_for_rental_ready(self, rental_id: str, config: GpuConfig) -> Instance # Wait for SSH info (polls until provisioning complete) ssh_info = self._wait_for_ssh_info(rental_id) if not ssh_info: - print(f" ❌ SSH info not available after provisioning") + print(" ❌ SSH info not available after provisioning") return None host, port = ssh_info @@ -533,12 +533,12 @@ def _wait_for_rental_ready(self, rental_id: str, config: GpuConfig) -> Instance # Wait for SSH connectivity (port open) print(f" ⏳ Waiting for SSH port ({host}:{port})...") if not wait_for_ssh(host, port): - print(f" ❌ SSH port connection timeout") + print(" ❌ SSH port connection timeout") return None # Verify SSH login works (retry for up to 20 minutes - system boot can take time) # Basilica instances often show "System is booting up" even after port is open - print(f" 🔍 Verifying SSH login (may take 10-20 min for system boot)...") + print(" 🔍 Verifying SSH login (may take 10-20 min for system boot)...") ssh_user = None max_ssh_verify_attempts = 80 # 80 * 15s = 20 minutes for attempt in range(max_ssh_verify_attempts): @@ -589,7 +589,7 @@ def _wait_for_ssh_info( # Check status - it's a string like "running", "provisioning", "failed" rental_status = status.get("status", "") if rental_status == "failed": - print(f" ❌ Rental failed") + print(" ❌ Rental failed") return None # Extract SSH info from ip_address field @@ -693,10 +693,10 @@ def verify_health(self, configs: list[GpuConfig], max_retries: int = 3) -> dict[ # Verify existing instance print(f"🔍 Verifying: {config.name} ({instance.ssh.host}:{instance.ssh.port})") if verify_ssh(instance.ssh.host, instance.ssh.port, user=instance.ssh.user): - print(f" ✅ Healthy") + print(" ✅ Healthy") healthy[config.name] = instance else: - print(f" ❌ Unhealthy - replacing...") + print(" ❌ Unhealthy - replacing...") new_instance = self.provision(config, max_retries=max_retries) if new_instance: healthy[config.name] = new_instance diff --git a/research/infrastructure/create_qwen_pods.py b/research/infrastructure/create_qwen_pods.py index 6d6ee37c..4a11284d 100644 --- a/research/infrastructure/create_qwen_pods.py +++ b/research/infrastructure/create_qwen_pods.py @@ -1,8 +1,8 @@ #!/usr/bin/env python3 -import os -import sys from pathlib import Path + from dotenv import load_dotenv + from lium_manager import LiumInfra, PodSpec load_dotenv(Path("/root/grail/.env")) @@ -25,6 +25,7 @@ print(f" ❌ {name}: Failed to create", flush=True) import json + with open("qwen_pods_ssh.json", "w") as f: json.dump(created, f, indent=2) print("\nSaved pod info to qwen_pods_ssh.json", flush=True) diff --git a/research/infrastructure/debug_executors.py b/research/infrastructure/debug_executors.py index 556b2c17..e4a62c91 100755 --- a/research/infrastructure/debug_executors.py +++ b/research/infrastructure/debug_executors.py @@ -2,7 +2,8 @@ """Debug script to inspect available Lium executors.""" import os -from lium import Lium, Config + +from lium import Config, Lium # Initialize Lium api_key = os.getenv("LIUM_API_KEY") diff --git a/research/infrastructure/deploy.py b/research/infrastructure/deploy.py index 0408abd3..f9764cf7 100755 --- a/research/infrastructure/deploy.py +++ b/research/infrastructure/deploy.py @@ -167,7 +167,7 @@ def main(): if args.inspect_executors: print(f"\n=== Executor Specs ({args.gpu_type}) ===\n") infra.inspect_executor_specs(gpu_type=args.gpu_type) - print(f"\n=== Executors with Bandwidth Info ===\n") + print("\n=== Executors with Bandwidth Info ===\n") infra.list_executors_with_bandwidth( gpu_type=args.gpu_type, min_upload=0, @@ -195,7 +195,7 @@ def main(): print(f"Error: {e}") return 1 - print(f"Configuration loaded:") + print("Configuration loaded:") print(f" - Pods: {len(pod_specs)}") print(f" - Total experiments: {sum(len(exps) for exps in pod_experiments.values())}") @@ -205,7 +205,7 @@ def main(): print("🚀 DEPLOYING PODS") print(f"{'='*80}\n") infra.apply(pod_specs) - print(f"\n✅ Pod deployment complete\n") + print("\n✅ Pod deployment complete\n") if args.deploy_only: print("Pod deployment complete (--deploy-only specified, skipping experiments)") diff --git a/research/infrastructure/deploy_parallel.py b/research/infrastructure/deploy_parallel.py index d9a56496..2cd46ebb 100755 --- a/research/infrastructure/deploy_parallel.py +++ b/research/infrastructure/deploy_parallel.py @@ -23,7 +23,6 @@ import os import sys from pathlib import Path -from typing import Optional from dotenv import load_dotenv diff --git a/research/infrastructure/deploy_parallel_basilica.py b/research/infrastructure/deploy_parallel_basilica.py index f284dff3..c4b5bf5a 100755 --- a/research/infrastructure/deploy_parallel_basilica.py +++ b/research/infrastructure/deploy_parallel_basilica.py @@ -503,7 +503,7 @@ async def deploy_and_run( logger.error(f"No instances defined in config '{config_name}'") return False - logger.info(f"Configurations:") + logger.info("Configurations:") missing_model_configs = [] for c in configs: logger.info(f" - {c.name}: {c.gpu_count}x {c.gpu_type}") diff --git a/research/infrastructure/deploy_qwen_only.py b/research/infrastructure/deploy_qwen_only.py index d49178f6..0ae73a7c 100644 --- a/research/infrastructure/deploy_qwen_only.py +++ b/research/infrastructure/deploy_qwen_only.py @@ -2,8 +2,8 @@ """Deploy only the Qwen models.""" import asyncio -import os from pathlib import Path + from dotenv import load_dotenv from lium_manager import LiumInfra, PodSpec diff --git a/research/infrastructure/example_simple.py b/research/infrastructure/example_simple.py index f570e8ed..49738b08 100755 --- a/research/infrastructure/example_simple.py +++ b/research/infrastructure/example_simple.py @@ -60,7 +60,7 @@ def main(): print("Error: Failed to deploy pod") return 1 - print(f"\nPod deployed successfully:") + print("\nPod deployed successfully:") print(f" SSH: {pod_info['ssh']['host']}:{pod_info['ssh']['port']}") print(f" Bandwidth: ↑{pod_info['bandwidth']['upload']:.0f} ↓{pod_info['bandwidth']['download']:.0f} Mbps") @@ -88,8 +88,8 @@ def main(): infra.destroy() print("✅ Pod destroyed") else: - print(f"⚠️ Pod still running. To destroy later, run:") - print(f" python deploy.py --destroy --state-file .lium_state_example.json") + print("⚠️ Pod still running. To destroy later, run:") + print(" python deploy.py --destroy --state-file .lium_state_example.json") print("\n" + "=" * 80) print("EXAMPLE COMPLETE") diff --git a/research/infrastructure/experiment_runner.py b/research/infrastructure/experiment_runner.py index a1c35c04..18addc19 100644 --- a/research/infrastructure/experiment_runner.py +++ b/research/infrastructure/experiment_runner.py @@ -11,7 +11,7 @@ import time from dataclasses import dataclass, field from pathlib import Path -from typing import Any, Optional +from typing import Any import asyncssh @@ -102,7 +102,7 @@ def __init__( ssh_host: str, ssh_port: int, ssh_user: str = "root", - ssh_key_path: Optional[str] = None, + ssh_key_path: str | None = None, ): """Initialize experiment runner. @@ -116,7 +116,7 @@ def __init__( self.ssh_port = ssh_port self.ssh_user = ssh_user self.ssh_key_path = ssh_key_path - self._conn: Optional[asyncssh.SSHClientConnection] = None + self._conn: asyncssh.SSHClientConnection | None = None async def connect(self): """Establish SSH connection to the pod.""" @@ -145,8 +145,8 @@ async def disconnect(self): async def run_command( self, command: str, - env: Optional[dict[str, str]] = None, - timeout: Optional[int] = None, + env: dict[str, str] | None = None, + timeout: int | None = None, ) -> tuple[str, str, int]: """Run a command on the remote pod. @@ -174,7 +174,7 @@ async def run_command( async def setup_code( self, - local_path: Optional[Path] = None, + local_path: Path | None = None, git_repo: str = "https://github.com/your-org/grail.git", git_branch: str = "main", use_git: bool = True, @@ -237,7 +237,7 @@ async def setup_code( ) if result.returncode == 0: - print(f"✅ Code synced successfully") + print("✅ Code synced successfully") else: print(f"❌ Sync failed: {result.stderr}") raise RuntimeError(f"Code sync failed: {result.stderr}") @@ -328,7 +328,7 @@ async def run_experiment( self, config: ExperimentConfig, remote_path: str = "~/grail", - log_file: Optional[str] = None, + log_file: str | None = None, background: bool = False, ) -> int: """Run a training experiment with the given configuration. @@ -474,9 +474,9 @@ async def cleanup_experiment(self, config: ExperimentConfig): async def run_experiments_on_pod( pod_info: dict, experiments: list[ExperimentConfig], - local_code_path: Optional[Path] = None, - local_env_path: Optional[Path] = None, - git_repo: Optional[str] = None, + local_code_path: Path | None = None, + local_env_path: Path | None = None, + git_repo: str | None = None, git_branch: str = "main", use_git: bool = True, setup_env: bool = True, @@ -553,9 +553,9 @@ async def run_experiments_on_pod( async def run_experiments_parallel( pod_experiments: dict[str, tuple[dict, list[ExperimentConfig]]], - local_code_path: Optional[Path] = None, - local_env_path: Optional[Path] = None, - git_repo: Optional[str] = None, + local_code_path: Path | None = None, + local_env_path: Path | None = None, + git_repo: str | None = None, git_branch: str = "main", use_git: bool = True, setup_env: bool = True, diff --git a/research/infrastructure/lium_manager.py b/research/infrastructure/lium_manager.py index 0f0c1d8a..a4b832bb 100644 --- a/research/infrastructure/lium_manager.py +++ b/research/infrastructure/lium_manager.py @@ -17,9 +17,8 @@ import subprocess import time from dataclasses import dataclass -from datetime import datetime, timedelta, timezone +from datetime import UTC, datetime, timedelta from pathlib import Path -from typing import Optional from lium import Config, ExecutorInfo, Lium @@ -52,7 +51,7 @@ def _wait_for_ssh(host: str, port: int, timeout: int = 120, interval: int = 5) - sock.close() if result == 0: return True - except (socket.error, OSError): + except OSError: pass time.sleep(interval) return False @@ -108,12 +107,12 @@ class PodSpec: name: str gpu_type: str = "A100" gpu_count: int = 8 - country: Optional[str] = None - volume_id: Optional[str] = None - ttl_hours: Optional[int] = None + country: str | None = None + volume_id: str | None = None + ttl_hours: int | None = None # Bandwidth requirements (in Mbps) - min_upload_mbps: Optional[float] = None - min_download_mbps: Optional[float] = None + min_upload_mbps: float | None = None + min_download_mbps: float | None = None class LiumInfra: @@ -199,7 +198,7 @@ def _find_executor(self, spec: PodSpec) -> ExecutorInfo | None: # if e.status not in ["available", "unknown"]: # reasons.append(f"status={e.status}") if spec.country and e.location.get("country") != spec.country: - reasons.append(f"country mismatch") + reasons.append("country mismatch") # Bandwidth filters upload, download = self._get_bandwidth(e) @@ -215,7 +214,7 @@ def _find_executor(self, spec: PodSpec) -> ExecutorInfo | None: candidates.append((e, upload, download)) if not candidates: - print(f" ❌ No matching executors. Rejection reasons:") + print(" ❌ No matching executors. Rejection reasons:") for e, reasons in rejected[:5]: # Show first 5 print(f" Executor {e.id[:8]}... ({e.gpu_count}x{e.gpu_type}): {', '.join(reasons)}") return None @@ -318,7 +317,7 @@ def apply(self, pods: list[PodSpec]) -> dict: executor = self._find_executor(spec) if not executor: - print(f" ❌ No executor found matching requirements") + print(" ❌ No executor found matching requirements") continue upload, download = self._get_bandwidth(executor) @@ -332,7 +331,7 @@ def apply(self, pods: list[PodSpec]) -> dict: pod = self.lium.wait_ready(result, timeout=300) if not pod: - print(f" ❌ Pod creation failed or timed out") + print(" ❌ Pod creation failed or timed out") continue # Get SSH connection info - try multiple sources @@ -344,18 +343,18 @@ def apply(self, pods: list[PodSpec]) -> dict: ssh_host = executor.ip if hasattr(executor, "ip") else None if not ssh_host: - print(f" ❌ Could not determine SSH host for pod") + print(" ❌ Could not determine SSH host for pod") continue ssh_port = pod.ssh_port if not ssh_port: - print(f" ❌ Could not determine SSH port for pod") + print(" ❌ Could not determine SSH port for pod") continue # Wait for SSH port to be accessible (crucial - API reports ready before SSH) print(f" ⏳ Waiting for SSH ({ssh_host}:{ssh_port})...") if not _wait_for_ssh(ssh_host, ssh_port, timeout=180, interval=5): - print(f" ⚠️ SSH not accessible after 180s, continuing anyway...") + print(" ⚠️ SSH not accessible after 180s, continuing anyway...") print(f" ✅ Ready: {ssh_host}:{ssh_port}") self.state["pods"][name] = { @@ -367,7 +366,7 @@ def apply(self, pods: list[PodSpec]) -> dict: } if spec.ttl_hours: - term_time = datetime.now(timezone.utc) + timedelta(hours=spec.ttl_hours) + term_time = datetime.now(UTC) + timedelta(hours=spec.ttl_hours) self.lium.schedule_termination(pod, termination_time=term_time.isoformat()) print(f" ⏰ Auto-terminate in {spec.ttl_hours}h") @@ -469,7 +468,7 @@ def replace_pod( pod = self.lium.wait_ready(result, timeout=300) if not pod: - print(f" ❌ Pod creation timed out") + print(" ❌ Pod creation timed out") exclude_ids.add(executor.id) continue @@ -478,7 +477,7 @@ def replace_pod( ssh_port = pod.ssh_port if not ssh_host or not ssh_port: - print(f" ❌ Could not get SSH info") + print(" ❌ Could not get SSH info") self._cleanup_failed_pod(pod.id, spec.name) exclude_ids.add(executor.id) continue @@ -492,9 +491,9 @@ def replace_pod( continue # Verify SSH actually works - print(f" 🔍 Verifying SSH connection...") + print(" 🔍 Verifying SSH connection...") if not _verify_ssh_connection(ssh_host, ssh_port): - print(f" ❌ SSH verification failed") + print(" ❌ SSH verification failed") self._cleanup_failed_pod(pod.id, spec.name) exclude_ids.add(executor.id) continue @@ -510,7 +509,7 @@ def replace_pod( } if spec.ttl_hours: - term_time = datetime.now(timezone.utc) + timedelta(hours=spec.ttl_hours) + term_time = datetime.now(UTC) + timedelta(hours=spec.ttl_hours) self.lium.schedule_termination(pod, termination_time=term_time.isoformat()) print(f" ⏰ Auto-terminate in {spec.ttl_hours}h") @@ -580,7 +579,7 @@ def _cleanup_failed_pod(self, pod_id: str, name: str) -> None: current_pods = {p.id: p for p in self.lium.ps()} if pod_id in current_pods: self.lium.down(current_pods[pod_id]) - print(f" 🧹 Cleaned up failed pod") + print(" 🧹 Cleaned up failed pod") except Exception as e: logger.debug(f"Failed to cleanup pod {name}: {e}") @@ -622,10 +621,10 @@ def verify_and_replace_unhealthy( print(f"🔍 Verifying pod: {spec.name} ({ssh_host}:{ssh_port})") if _verify_ssh_connection(ssh_host, ssh_port): - print(f" ✅ Healthy") + print(" ✅ Healthy") healthy_pods[spec.name] = pod_info else: - print(f" ❌ Unhealthy - replacing...") + print(" ❌ Unhealthy - replacing...") exclude_ids = [pod_info.get("executor_id")] if pod_info.get("executor_id") else [] new_info = self.replace_pod(spec, exclude_executor_ids=exclude_ids, max_retries=max_retries) if new_info: diff --git a/research/infrastructure/nohup_experiment_runner.py b/research/infrastructure/nohup_experiment_runner.py index 2f6ee340..11ad7aa0 100644 --- a/research/infrastructure/nohup_experiment_runner.py +++ b/research/infrastructure/nohup_experiment_runner.py @@ -13,7 +13,6 @@ import subprocess from dataclasses import dataclass from pathlib import Path -from typing import Optional import asyncssh @@ -89,7 +88,7 @@ def __init__( ssh_port: int, r2_config: dict[str, str], ssh_user: str = "root", - ssh_key_path: Optional[str] = None, + ssh_key_path: str | None = None, remote_path: str = "~/grail", ): """Initialize experiment runner. @@ -108,7 +107,7 @@ def __init__( self.ssh_key_path = ssh_key_path self.remote_path = remote_path self.r2_config = r2_config - self._conn: Optional[asyncssh.SSHClientConnection] = None + self._conn: asyncssh.SSHClientConnection | None = None async def connect(self): """Establish SSH connection to the pod with keepalive.""" @@ -141,7 +140,7 @@ async def disconnect(self): async def run_command( self, command: str, - timeout: Optional[int] = None, + timeout: int | None = None, check: bool = True, ) -> tuple[str, str, int]: """Run a command on the remote pod. @@ -610,7 +609,7 @@ async def download_artifacts(self, local_download_dir: Path, output_base: str = rsync_cmd = [ "rsync", "-avz", - f"-e", f"ssh -p {self.ssh_port} -o StrictHostKeyChecking=no", + "-e", f"ssh -p {self.ssh_port} -o StrictHostKeyChecking=no", f"{self.ssh_user}@{self.ssh_host}:{remote_dir}/", f"{local_dir}/", ] @@ -722,10 +721,10 @@ async def run_experiment( self, config: ExperimentConfig, local_code_path: Path, - local_env_path: Optional[Path] = None, + local_env_path: Path | None = None, sync_code: bool = True, setup_env: bool = True, - download_dir: Optional[Path] = None, + download_dir: Path | None = None, upload_to_r2: bool = True, cleanup_local: bool = False, remote_upload: bool = True, diff --git a/research/infrastructure/pyproject.toml b/research/infrastructure/pyproject.toml index b865f78c..096deb75 100644 --- a/research/infrastructure/pyproject.toml +++ b/research/infrastructure/pyproject.toml @@ -3,7 +3,7 @@ name = "grail-infrastructure" version = "0.1.0" description = "Infrastructure automation for distributed GRAIL training on Lium" readme = "README.md" -requires-python = ">=3.10" +requires-python = ">=3.12" dependencies = [ # Lium SDK for pod management "lium.io", @@ -38,7 +38,7 @@ dev = [ [tool.ruff] line-length = 100 -target-version = "py310" +target-version = "py312" [tool.ruff.lint] select = [ @@ -60,7 +60,7 @@ ignore = [ "__init__.py" = ["F401"] # Allow unused imports in __init__.py [tool.mypy] -python_version = "3.10" +python_version = "3.12" warn_return_any = true warn_unused_configs = true disallow_untyped_defs = false diff --git a/research/infrastructure/r2_uploader.py b/research/infrastructure/r2_uploader.py index 64bf0260..e7564ef0 100644 --- a/research/infrastructure/r2_uploader.py +++ b/research/infrastructure/r2_uploader.py @@ -10,7 +10,7 @@ import logging import time from pathlib import Path -from typing import Any, Optional +from typing import Any import boto3 from boto3.exceptions import Boto3Error @@ -165,7 +165,7 @@ def upload_file( except (ClientError, BotoCoreError, Boto3Error) as e: error_code = type(e).__name__ - error_message: Optional[str] = None + error_message: str | None = None if isinstance(e, ClientError): err = e.response.get("Error", {}) error_code = err.get("Code", "Unknown") @@ -300,7 +300,7 @@ def verify_bucket_access(self, bucket_name: str) -> bool: return True except (ClientError, BotoCoreError, Boto3Error) as e: error_code = type(e).__name__ - error_message: Optional[str] = None + error_message: str | None = None if isinstance(e, ClientError): err = e.response.get("Error", {}) error_code = err.get("Code", "Unknown") diff --git a/research/infrastructure/requirements.txt b/research/infrastructure/requirements.txt index 52b181d6..fed5e4eb 100644 --- a/research/infrastructure/requirements.txt +++ b/research/infrastructure/requirements.txt @@ -7,7 +7,7 @@ lium-sdk>=0.1.0 # AsyncSSH for remote execution asyncssh>=2.14.0 -# Standard library alternatives (already in Python 3.11+) +# Standard library alternatives (already in Python 3.12+) # These are typically included but listed for completeness # - asyncio (built-in) # - dataclasses (built-in) diff --git a/research/infrastructure/test_lium_api.py b/research/infrastructure/test_lium_api.py index 50abc8f4..014ed918 100644 --- a/research/infrastructure/test_lium_api.py +++ b/research/infrastructure/test_lium_api.py @@ -3,9 +3,8 @@ import inspect import json -from typing import Any -from lium import Lium, Config +from lium import Config, Lium def test_lium_methods() -> None: @@ -43,7 +42,7 @@ def test_executor_fields() -> None: if executors: executor = executors[0] print(f"\nSample Executor: {executor.huid}") - print(f"Available attributes:") + print("Available attributes:") for attr in dir(executor): if not attr.startswith("_"): try: @@ -53,7 +52,7 @@ def test_executor_fields() -> None: except Exception: pass - print(f"\nExecutor specs structure:") + print("\nExecutor specs structure:") print(json.dumps(executor.specs, indent=2, default=str)) except Exception as e: @@ -74,7 +73,7 @@ def test_pod_info_fields() -> None: if pods: pod = pods[0] print(f"\nSample Pod: {pod.name}") - print(f"Available attributes:") + print("Available attributes:") for attr in dir(pod): if not attr.startswith("_"): try: diff --git a/research/infrastructure/test_lium_manager.py b/research/infrastructure/test_lium_manager.py index 91d14e57..431bb328 100644 --- a/research/infrastructure/test_lium_manager.py +++ b/research/infrastructure/test_lium_manager.py @@ -6,7 +6,7 @@ from dotenv import load_dotenv -from lium_manager import LiumInfra, PodSpec +from lium_manager import LiumInfra # Setup logging logging.basicConfig( diff --git a/research/infrastructure/uv.lock b/research/infrastructure/uv.lock index 45eee516..c485ea2d 100644 --- a/research/infrastructure/uv.lock +++ b/research/infrastructure/uv.lock @@ -1,10 +1,6 @@ version = 1 revision = 3 -requires-python = ">=3.10" -resolution-markers = [ - "python_full_version >= '3.11'", - "python_full_version < '3.11'", -] +requires-python = ">=3.12" [[package]] name = "aiohappyeyeballs" @@ -22,7 +18,6 @@ source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "aiohappyeyeballs" }, { name = "aiosignal" }, - 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"tags": ["offline_trainer", "grpo", str(cfg.data.environment)], + "project": str(wandb_cfg.get("project", "grail-offline")), + "entity": str(wandb_cfg.get("entity", "tplr")), + "tags": tags, "hyperparameters": { "model": str(cfg.model.train_id), "environment": str(cfg.data.environment), @@ -57,6 +61,12 @@ def _initialize_monitoring(cfg: DictConfig) -> Any | None: }, } ) + run_name = wandb_cfg.get("run_name", None) + if run_name: + monitoring_config["run_name"] = str(run_name) + notes = wandb_cfg.get("notes", None) + if notes: + monitoring_config["notes"] = str(notes) backend_type = monitoring_config.pop("backend_type", "wandb") initialize_monitoring(backend_type=backend_type, **monitoring_config) return get_monitoring_manager() diff --git a/research/offline_trainer/conf/offline_grpo.yaml b/research/offline_trainer/conf/offline_grpo.yaml index 70e0cd89..0ef1baf4 100644 --- a/research/offline_trainer/conf/offline_grpo.yaml +++ b/research/offline_trainer/conf/offline_grpo.yaml @@ -8,7 +8,7 @@ seed: 42 model: train_id: "Qwen/Qwen2.5-1.5B-Instruct" ref_id: "Qwen/Qwen2.5-1.5B-Instruct" - device: "auto" # auto|cuda|cpu|cuda:0|cuda:1 + # Note: device is derived from gpu.strategy and gpu.training_gpu, not set here # ============================================================================ # MULTI-GPU CONFIGURATION @@ -16,29 +16,31 @@ model: gpu: # GPU allocation strategy: "single" or "multi" # - single: Run everything on one GPU (good for small models, limited memory) - # - multi: Use separate GPUs for vLLM server and training (recommended for production) - strategy: "multi" # single|multi + # - multi: Use separate GPUs for server and training (recommended for production) + strategy: "single" # single|multi # GPU assignments (only used when strategy="multi") - vllm_gpu: 1 # GPU ID for vLLM inference server + vllm_gpu: 1 # GPU ID for inference server training_gpu: 0 # GPU ID for training model - # vLLM memory settings (adjusted based on strategy) - # For multi-GPU: can use more memory since vLLM has dedicated GPU + # Server memory settings (adjusted based on strategy) + # For multi-GPU: can use more memory since server has dedicated GPU # For single-GPU: must leave room for training model vllm_gpu_memory_utilization: 0.85 # For multi-GPU vllm_gpu_memory_utilization_single: 0.25 # For single-GPU + sglang_mem_fraction_static: 0.85 # SGLang memory fraction (multi-GPU) + sglang_mem_fraction_static_single: 0.30 # SGLang memory fraction (single-GPU, shared with training) # ============================================================================ # GENERATION CONFIGURATION (for rollout generation) # ============================================================================ generation: - backend: "vllm_server" # vllm_server|sglang_server (for offline_rollouts.py) - port: 30001 # vLLM server port (managed by pipeline) - max_model_len: 2048 # vLLM max sequence length - max_num_seqs: 64 # vLLM concurrent sequences + backend: "sglang_server" # vllm_server|sglang_server (for offline_rollouts.py) + port: 30001 # Inference server port (managed by pipeline) + max_model_len: 4096 # Server max sequence length (prompt + completion) + max_num_seqs: 64 # Server concurrent sequences batch_size: 16 # Match GRAIL_GENERATION_BATCH_SIZE - max_new_tokens: 2048 # Match MAX_NEW_TOKENS from constants + max_new_tokens: 512 # Completion tokens (prompt ~250 tokens, so total < max_model_len) temperature: 0.7 top_p: 0.95 top_k: 50 @@ -97,6 +99,7 @@ data: rollouts_per_problem: 16 # ROLLOUTS_PER_PROBLEM from constants train_seed_start: 1000 # Starting seed for training problems num_train_seeds: 320 # Larger pool to reduce seed reuse (5 iter × 8 prob = 40 unique calls) + generation_batch_size: 4 # Number of problem seeds per generation batch # ============================================================================ # EVALUATION CONFIGURATION @@ -104,7 +107,7 @@ data: eval: enabled: true interval: 1 # Evaluate every N iterations - backend: "vllm" # vllm|sglang|hf (evaluator.py expects these names) + backend: "sglang" # vllm|sglang|hf (evaluator.py expects these names) batch_size: 16 do_sample: true max_new_tokens: 2048 @@ -133,4 +136,5 @@ logging: # ============================================================================ checkpoint: save_interval: 1 # Save checkpoint every N iterations - keep_last_k: 3 # Keep only the last K checkpoints + keep_last_k: 3 # Keep only the last K training checkpoints + keep_last_vllm_k: 3 # Keep only the last K vLLM/SGLang server checkpoints diff --git a/research/offline_trainer/pyproject.toml b/research/offline_trainer/pyproject.toml index ae570093..ea19b648 100644 --- a/research/offline_trainer/pyproject.toml +++ b/research/offline_trainer/pyproject.toml @@ -5,7 +5,7 @@ name = "grail-offline-trainer" version = "0.1.0" description = "Offline GRPO trainer and evaluator runner for GRAIL (no bittensor)." -requires-python = ">=3.10,<3.12" +requires-python = ">=3.12" dependencies = [ # Core ML/Training "torch>=2.4.1", diff --git a/research/offline_trainer/run_offline_grpo.py b/research/offline_trainer/run_offline_grpo.py index 48dcb591..1f74a476 100644 --- a/research/offline_trainer/run_offline_grpo.py +++ b/research/offline_trainer/run_offline_grpo.py @@ -66,10 +66,14 @@ def _initialize_monitoring(cfg: DictConfig) -> Any | None: return None logger.info("Initializing monitoring system (WandB enabled)") + wandb_cfg = cfg.logging.wandb + tags = list(wandb_cfg.get("tags", [])) or ["offline_trainer", "grpo", str(cfg.data.environment)] monitoring_config = MonitoringConfig.for_training() monitoring_config.update( { - "tags": ["offline_trainer", "grpo", str(cfg.data.environment)], + "project": str(wandb_cfg.get("project", "grail-offline")), + "entity": str(wandb_cfg.get("entity", "tplr")), + "tags": tags, "hyperparameters": { "model": str(cfg.model.train_id), "environment": str(cfg.data.environment), @@ -83,6 +87,12 @@ def _initialize_monitoring(cfg: DictConfig) -> Any | None: }, } ) + run_name = wandb_cfg.get("run_name", None) + if run_name: + monitoring_config["run_name"] = str(run_name) + notes = wandb_cfg.get("notes", None) + if notes: + monitoring_config["notes"] = str(notes) backend_type = monitoring_config.pop("backend_type", "wandb") initialize_monitoring(backend_type=backend_type, **monitoring_config) return get_monitoring_manager() diff --git a/research/offline_trainer/src/grail_offline/data/offline_rollouts.py b/research/offline_trainer/src/grail_offline/data/offline_rollouts.py index 71bbbef9..132e516c 100644 --- a/research/offline_trainer/src/grail_offline/data/offline_rollouts.py +++ b/research/offline_trainer/src/grail_offline/data/offline_rollouts.py @@ -9,12 +9,13 @@ import numpy as np import torch -from grail.environments.core import ChatMessage, MultiTurnEnv -from grail.environments.gsm8k_env import GSM8KEnv from grail.environments.backends import SGLangServerBackend, VLLMServerBackend +from grail.environments.core import ChatMessage, MultiTurnEnv from grail.environments.episode import AgentEnvLoop +from grail.environments.gsm8k_env import GSM8KEnv from grail.environments.sat_env import SATEnv -from grail.shared.constants import ROLLOUTS_PER_PROBLEM, TRAINER_MAX_LENGTH +from grail.protocol.constants import ROLLOUTS_PER_PROBLEM +from grail.shared.config import TRAINER_MAX_LENGTH from grail.trainer.algorithms.grpo import GRPOGroup, GRPORollout if TYPE_CHECKING: @@ -208,12 +209,13 @@ def _compute_hf_logprobs( # Prepare input tensor on model's device input_ids = torch.tensor([token_ids], dtype=torch.long, device=model.device) - # Forward pass without gradients for efficiency + # Forward pass without gradients for efficiency while preserving the + # caller's training/eval state after logprob recomputation. was_training = model.training model.eval() try: - with torch.inference_mode(): - outputs = model(input_ids) + with torch.no_grad(): + outputs = model(input_ids, use_cache=False) except RuntimeError as exc: logger.error( "Failed to compute HF logprobs due to model forward error", diff --git a/research/offline_trainer/src/grail_offline/pipelines/offline_grpo.py b/research/offline_trainer/src/grail_offline/pipelines/offline_grpo.py index 077615a0..a632aeba 100644 --- a/research/offline_trainer/src/grail_offline/pipelines/offline_grpo.py +++ b/research/offline_trainer/src/grail_offline/pipelines/offline_grpo.py @@ -33,7 +33,11 @@ from grail.trainer.config import EvalConfig, TrainingConfig from grail.trainer.eval_planner import EvaluationPlan from grail.trainer.evaluator import EvaluatorService -from grail.trainer.inference_server import ServerConfig, VLLMServerManager +from grail.trainer.inference_server import ( + ServerConfig, + SGLangServerManager, + VLLMServerManager, +) from grail_offline.data.offline_rollouts import OfflineRolloutGenerator, RolloutGenConfig logger = logging.getLogger(__name__) @@ -256,12 +260,12 @@ async def _run_evaluation( def _sync_kl_constant(kl_coef: float) -> None: """Patch TRAINER_KL_COEF constant to match config. - is_kl_enabled() in grail.shared.constants reads the module-level + is_kl_enabled() in grail.shared.config reads the module-level TRAINER_KL_COEF constant, which is evaluated once at import time from the env var. Setting the env var at runtime has no effect on the already-loaded constant. We must patch the constant directly. """ - import grail.shared.constants as _constants + import grail.shared.config as _constants _constants.TRAINER_KL_COEF = kl_coef os.environ["GRAIL_TRAINER_KL_COEF"] = str(kl_coef) @@ -306,10 +310,28 @@ async def run_training(cfg: DictConfig, workdir: Path, monitor: Any | None = Non """ _set_global_seed(int(cfg.seed)) - # Patch the module-level TRAINER_KL_COEF constant so that is_kl_enabled() - # (called inside GRPOAlgorithm.train_epoch) returns the correct value. + # Patch module-level constants that may have been set by the main .env file + # (designed for online miner/validator). The offline trainer uses its own + # Hydra config for these values. + import grail.shared.config as _constants + kl_coef = float(cfg.train.kl_coef) - _sync_kl_constant(kl_coef) + _constants.TRAINER_KL_COEF = kl_coef + os.environ["GRAIL_TRAINER_KL_COEF"] = str(kl_coef) + _constants.GRPO_VARIANT = str(cfg.train.get("grpo_variant", "grpo")) + _constants.ADV_ESTIMATOR = str(cfg.train.get("grpo_variant", "grpo")) + _constants.TRAINER_USE_SEQUENCE_PACKING = False + _constants.TRAINER_CHUNKED_LOGITS = bool(cfg.train.get("chunked_logits", False)) + logger.info( + "Patched shared constants for offline training", + extra={ + "grpo_variant": _constants.GRPO_VARIANT, + "adv_estimator": _constants.ADV_ESTIMATOR, + "use_sequence_packing": _constants.TRAINER_USE_SEQUENCE_PACKING, + "chunked_logits": _constants.TRAINER_CHUNKED_LOGITS, + "kl_coef": _constants.TRAINER_KL_COEF, + }, + ) # Setup directories metrics_dir = workdir / "metrics" @@ -355,8 +377,8 @@ async def run_training(cfg: DictConfig, workdir: Path, monitor: Any | None = Non else: logger.info("Loaded train model only (KL disabled)") - # Save initial model and tokenizer for vLLM server - logger.info("Saving initial model checkpoint for vLLM server...") + # Save initial model and tokenizer for inference server + logger.info("Saving initial model checkpoint for inference server...") server_model_dir = _save_checkpoint_for_vllm( model=train_model, tokenizer=tokenizer, @@ -364,23 +386,30 @@ async def run_training(cfg: DictConfig, workdir: Path, monitor: Any | None = Non iteration=-1, # -1 indicates initial checkpoint ) - # Start vLLM server using the saved checkpoint - logger.info("Starting vLLM inference server...") + # Determine generation backend (vllm or sglang) + gen_backend_name = str(cfg.generation.backend).lower() + use_sglang = "sglang" in gen_backend_name + + # Start inference server using the saved checkpoint + logger.info( + "Starting inference server...", + extra={"backend": "sglang" if use_sglang else "vllm"}, + ) # Configure server environment (pin GPU for multi-GPU) server_env: dict[str, str] | None = None if gpu_strategy == "multi": - try: - server_env = {"CUDA_VISIBLE_DEVICES": str(int(cfg.gpu.vllm_gpu))} - except Exception: - server_env = None + server_env = {**os.environ, "CUDA_VISIBLE_DEVICES": str(int(cfg.gpu.vllm_gpu))} + # Don't pass chat_template_path to SGLang - it causes the /v1/completions + # endpoint to reject requests. The tokenizer in the checkpoint already + # includes the chat template for proper prompt formatting. server_config = ServerConfig( host="127.0.0.1", port=int(cfg.generation.get("port", 30001)), timeout_s=180.0, trust_remote_code=True, model_path=str(server_model_dir), # Use saved checkpoint - chat_template_path=str(server_model_dir / "chat_template.jinja"), + chat_template_path=None if use_sglang else str(server_model_dir / "chat_template.jinja"), env=server_env, ) # Pick memory utilization based on GPU strategy @@ -389,25 +418,40 @@ async def run_training(cfg: DictConfig, workdir: Path, monitor: Any | None = Non if gpu_strategy == "single" else float(cfg.gpu.get("vllm_gpu_memory_utilization", 0.75)) ) + # SGLang memory fraction: use single-GPU setting when sharing GPU with training + sglang_mem = ( + float(cfg.gpu.get("sglang_mem_fraction_static_single", 0.25)) + if gpu_strategy == "single" + else float(cfg.gpu.get("sglang_mem_fraction_static", 0.85)) + ) eval_config = EvalConfig( vllm_gpu_memory_utilization=mem_util, vllm_max_model_len=int(cfg.generation.get("max_model_len", 2048)), vllm_max_num_seqs=int(cfg.generation.get("max_num_seqs", 64)), + sglang_mem_fraction_static=sglang_mem, + sglang_context_length=int(cfg.generation.get("max_model_len", 2048)), stream_server_logs=True, ) - server_manager = VLLMServerManager( - config=server_config, - eval_config=eval_config, - python_executable=sys.executable, - ) + if use_sglang: + server_manager = SGLangServerManager( + config=server_config, + eval_config=eval_config, + python_executable=sys.executable, + ) + else: + server_manager = VLLMServerManager( + config=server_config, + eval_config=eval_config, + python_executable=sys.executable, + ) # Use async context manager for server lifecycle async with server_manager: await server_manager.start_server() base_url = server_manager.base_url model_name = server_manager.model_name - logger.info(f"✓ vLLM server ready at {base_url} serving {model_name}") + logger.info(f"Inference server ready at {base_url} serving {model_name}") # Create optimizer and accelerator (match main trainer betas) optimizer = torch.optim.AdamW( @@ -436,8 +480,8 @@ async def run_training(cfg: DictConfig, workdir: Path, monitor: Any | None = Non rollouts_per_problem=int(cfg.data.rollouts_per_problem), environment=str(cfg.data.environment), ) - # Pass HF model for computing ground-truth logprobs - # This ensures behavior policy logprobs match training logprobs exactly + # Pass HF model for computing ground-truth logprobs. + # This ensures behavior policy logprobs match training logprobs exactly. generator = OfflineRolloutGenerator( tokenizer=tokenizer, config=gen_cfg, hf_model=train_model ) @@ -451,7 +495,7 @@ async def run_training(cfg: DictConfig, workdir: Path, monitor: Any | None = Non train_cfg = TrainingConfig( # Basic training parameters lr=float(cfg.train.lr), - batch_size=int(cfg.train.batch_size), + micro_batch_size=int(cfg.train.batch_size), grad_clip=float(cfg.train.grad_clip), warmup_steps=int(cfg.train.warmup_steps), max_length=int(cfg.train.max_length), @@ -493,7 +537,10 @@ async def run_training(cfg: DictConfig, workdir: Path, monitor: Any | None = Non scheduler = _create_lr_scheduler(optimizer, warmup_steps=warmup_iters) logger.info( "LR scheduler created (warmup + constant)", - extra={"warmup_iterations": warmup_iters, "total_iterations": int(cfg.train.iterations)}, + extra={ + "warmup_iterations": warmup_iters, + "total_iterations": int(cfg.train.iterations), + }, ) # Initialize GRPO algorithm with config @@ -545,27 +592,52 @@ async def run_training(cfg: DictConfig, workdir: Path, monitor: Any | None = Non # Generate rollouts logger.info("Generating rollouts", extra={"num_seeds": len(iter_seeds)}) - gen_batch_size = ( - int(cfg.data.generation_batch_size) - if hasattr(cfg.data, "generation_batch_size") - else 4 - ) + gen_batch_size = int(cfg.data.get("generation_batch_size", 4)) groups = await generator.generate_groups( iter_seeds, batch_size=gen_batch_size, ) + # Log detailed rollout stats for debugging + all_r = [r for g in groups for r in g.rollouts] + comp_lens = [r.completion_length for r in all_r] + prompt_lens = [r.prompt_length for r in all_r] + tok_lens = [len(r.tokens) for r in all_r] + has_lps = sum(1 for r in all_r if r.token_logprobs is not None) logger.info( - "Rollouts generated", - extra={ - "num_groups": len(groups), - "total_rollouts": sum(len(g.rollouts) for g in groups), - }, + "Rollouts generated: groups=%d rollouts=%d " + "comp_lens=[min=%d max=%d] prompt_lens=[min=%d max=%d] " + "tok_lens=[min=%d max=%d] has_logprobs=%d/%d", + len(groups), + len(all_r), + min(comp_lens) if comp_lens else 0, + max(comp_lens) if comp_lens else 0, + min(prompt_lens) if prompt_lens else 0, + max(prompt_lens) if prompt_lens else 0, + min(tok_lens) if tok_lens else 0, + max(tok_lens) if tok_lens else 0, + has_lps, + len(all_r), ) await _log_rollout_stats(monitor, groups) + # Restore training-mode settings after rollout generation, which + # temporarily switches the shared train model to eval() for HF + # logprob computation. + train_model.train() + if use_grad_ckpt and hasattr(train_model, "gradient_checkpointing_enable"): + train_model.gradient_checkpointing_enable() + # Train one epoch - logger.info("Starting training epoch") + _p = next(train_model.parameters()) + logger.info( + "Starting training epoch", + extra={ + "model_training": train_model.training, + "params_require_grad": _p.requires_grad, + "grad_checkpointing": getattr(train_model, "is_gradient_checkpointing", False), + }, + ) metrics = await algorithm.train_epoch( model=train_model, ref_model=ref_model, diff --git a/research/offline_trainer/tests/smoke_test.py b/research/offline_trainer/tests/smoke_test.py index bb675835..ee5c17ce 100644 --- a/research/offline_trainer/tests/smoke_test.py +++ b/research/offline_trainer/tests/smoke_test.py @@ -111,7 +111,7 @@ async def _train_epoch_smoke() -> dict[str, float]: optimizer = torch.optim.AdamW(model.parameters(), lr=1e-3) algo = GRPOAlgorithm() - train_cfg = TrainingConfig(lr=1e-3, batch_size=4) + train_cfg = TrainingConfig(lr=1e-3, micro_batch_size=4) metrics = await algo.train_epoch( model=model, diff --git a/research/offline_trainer/tests/test_gpu_integration.py b/research/offline_trainer/tests/test_gpu_integration.py index 458b853e..d7c6d320 100644 --- a/research/offline_trainer/tests/test_gpu_integration.py +++ b/research/offline_trainer/tests/test_gpu_integration.py @@ -233,7 +233,7 @@ async def test_vllm_server_training_epoch() -> None: optimizer = torch.optim.AdamW(train_model.parameters(), lr=1e-4) algo = GRPOAlgorithm() - train_cfg = TrainingConfig(lr=1e-4, batch_size=8) + train_cfg = TrainingConfig(lr=1e-4, micro_batch_size=8) metrics = await algo.train_epoch( model=train_model, @@ -354,7 +354,7 @@ async def test_end_to_end_iteration() -> None: optimizer = torch.optim.AdamW(train_model.parameters(), lr=1e-4) algo = GRPOAlgorithm() - train_cfg = TrainingConfig(lr=1e-4, batch_size=8) + train_cfg = TrainingConfig(lr=1e-4, micro_batch_size=8) train_metrics = await algo.train_epoch( model=train_model, diff --git a/research/offline_trainer/tests/test_offline_trainer.py b/research/offline_trainer/tests/test_offline_trainer.py index d78a03b8..d5f4ac64 100644 --- a/research/offline_trainer/tests/test_offline_trainer.py +++ b/research/offline_trainer/tests/test_offline_trainer.py @@ -132,7 +132,7 @@ async def test_training_epoch() -> None: optimizer = torch.optim.AdamW(model.parameters(), lr=1e-3) algo = GRPOAlgorithm() - train_cfg = TrainingConfig(lr=1e-3, batch_size=4) + train_cfg = TrainingConfig(lr=1e-3, micro_batch_size=4) metrics = await algo.train_epoch( model=model, @@ -214,7 +214,7 @@ async def test_training_with_gpu() -> None: optimizer = torch.optim.AdamW(model.parameters(), lr=1e-3) algo = GRPOAlgorithm() - train_cfg = TrainingConfig(lr=1e-3, batch_size=8) + train_cfg = TrainingConfig(lr=1e-3, micro_batch_size=8) metrics = await algo.train_epoch( model=model, diff --git a/research/offline_trainer/uv.lock b/research/offline_trainer/uv.lock index f1f6ae0e..cdbc6182 100644 --- a/research/offline_trainer/uv.lock +++ b/research/offline_trainer/uv.lock @@ -1,11 +1,9 @@ version = 1 revision = 3 -requires-python = ">=3.10, <3.12" +requires-python = ">=3.12" resolution-markers = [ - "python_full_version >= '3.11' and sys_platform == 'linux'", - "python_full_version >= '3.11' and sys_platform != 'linux'", - "python_full_version < '3.11' and sys_platform == 'linux'", - "python_full_version < '3.11' and sys_platform != 'linux'", + "sys_platform == 'linux'", + "sys_platform != 'linux'", ] [[package]] @@ -42,7 +40,6 @@ source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "aiohappyeyeballs" }, { name = "aiosignal" }, - { name = "async-timeout", marker = "python_full_version < '3.11'" }, { name = "attrs" }, { name = "frozenlist" }, { name = "multidict" }, @@ -51,40 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d79106a4..ab13e32d 100644 --- a/research/trl/analysis_integration_example.py +++ b/research/trl/analysis_integration_example.py @@ -21,10 +21,9 @@ import logging from typing import TYPE_CHECKING, Any -from transformers import TrainerCallback - # Import the analysis framework from grail.trainer.analysis import AnalysisConfig, ModelAnalysisManager +from transformers import TrainerCallback if TYPE_CHECKING: from transformers import TrainingArguments diff --git a/research/trl/delta_checkpoint_callback.py b/research/trl/delta_checkpoint_callback.py index 5da73689..5ef0bcfa 100644 --- a/research/trl/delta_checkpoint_callback.py +++ b/research/trl/delta_checkpoint_callback.py @@ -425,7 +425,7 @@ def _update_metadata(self, checkpoint_meta: dict, step: int, path: Path) -> None # Load existing metadata if metadata_path.exists(): - with open(metadata_path, "r") as f: + with open(metadata_path) as f: metadata = json.load(f) else: metadata = { diff --git a/research/trl/pyproject.toml b/research/trl/pyproject.toml index 745ed53c..41255b92 100644 --- a/research/trl/pyproject.toml +++ b/research/trl/pyproject.toml @@ -2,7 +2,7 @@ name = "grail-vllm-server" version = "0.1.0" description = "Isolated vLLM server environment for GRAIL evaluation" -requires-python = ">=3.10,<3.12" +requires-python = ">=3.12" dependencies = [ # Latest stable vLLM with CUDA support "vllm==0.10.2", @@ -18,7 +18,7 @@ dependencies = [ [tool.ruff] line-length = 100 -target-version = "py310" +target-version = "py312" [tool.ruff.lint] select = ["E", "W", "F", "I", "B", "C4", "UP"] diff --git a/research/trl/run_parallel_training.py b/research/trl/run_parallel_training.py index 14bdf21b..4de1e2e3 100755 --- a/research/trl/run_parallel_training.py +++ b/research/trl/run_parallel_training.py @@ -435,7 +435,7 @@ def run(self) -> None: if i < len(self.gpu_pairs) - 1: # Don't sleep after the last one # Longer delay (45s) to ensure init_communicator completes fully # before next instance starts (vLLM uses fire_and_forget async RPC) - print(f" ⏳ Waiting 45s for NCCL init before next instance...") + print(" ⏳ Waiting 45s for NCCL init before next instance...") time.sleep(45) print("\n✅ All training processes started!") diff --git a/research/trl/train_trl_grpo.py b/research/trl/train_trl_grpo.py index 16014589..df4fa978 100644 --- a/research/trl/train_trl_grpo.py +++ b/research/trl/train_trl_grpo.py @@ -1859,6 +1859,7 @@ def main() -> None: # Set random seeds for reproducibility import random + import numpy as np random.seed(args.seed) np.random.seed(args.seed) @@ -1968,7 +1969,7 @@ def main() -> None: with profiler.track("model_loading"): if cfg.dtype == "float32": # Flash Attention 2 doesn't support fp32; use SDPA directly - logger.info(f" Full FP32 mode: using SDPA attention (Flash Attention 2 requires bf16/fp16)") + logger.info(" Full FP32 mode: using SDPA attention (Flash Attention 2 requires bf16/fp16)") model = AutoModelForCausalLM.from_pretrained( cfg.model_id, torch_dtype=torch.float32, @@ -2177,7 +2178,7 @@ def main() -> None: if cfg.delta_checkpoint_enabled: logger.info(f" ✓ Delta checkpointing enabled (threshold=0.0, exact sparsity, dtype={cfg.delta_checkpoint_dtype})") else: - logger.info(f" ✗ Delta checkpointing disabled (set cfg.delta_checkpoint_enabled=True to enable)") + logger.info(" ✗ Delta checkpointing disabled (set cfg.delta_checkpoint_enabled=True to enable)") # Initialize evaluation callback vllm_eval_callback = VLLMEvalCallback( diff --git a/research/trl/train_trl_sft.py b/research/trl/train_trl_sft.py index e747985f..4b5bb971 100644 --- a/research/trl/train_trl_sft.py +++ b/research/trl/train_trl_sft.py @@ -64,6 +64,13 @@ ModelAnalysisManager, ) +# Import shared callbacks +from callbacks import ( # noqa: E402 + DeltaCheckpointCallback, + SparsityCallback, + get_profiler, +) + # Import from train_trl_grpo.py for shared utilities from train_trl_grpo import ( # noqa: E402 QWEN_CHAT_TEMPLATE, @@ -79,13 +86,6 @@ 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dotenv import load_dotenv + load_dotenv(os.path.join(_PROJECT_ROOT, ".env"), override=False) sys.path.insert(0, _SCRIPT_DIR) diff --git a/scripts/benchmark_parallel_varied_kernels.py b/scripts/benchmark_parallel_varied_kernels.py index 21207220..7c18955d 100644 --- a/scripts/benchmark_parallel_varied_kernels.py +++ b/scripts/benchmark_parallel_varied_kernels.py @@ -23,7 +23,7 @@ tag = f"{cuda_version}-{flavor}-{operating_sys}" image = ( - modal.Image.from_registry(f"nvidia/cuda:{tag}", add_python="3.11") + modal.Image.from_registry(f"nvidia/cuda:{tag}", add_python="3.12") .apt_install("git", "gcc-10", "g++-10", "clang") .pip_install("torch", "triton", "numpy") ) diff --git a/tests/conftest.py b/tests/conftest.py index 607341ed..c5e0540a 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -7,6 +7,7 @@ from __future__ import annotations import hashlib +import os import pathlib import sys from collections import Counter @@ -19,8 +20,6 @@ if TYPE_CHECKING: from accelerate import Accelerator - from grail.validation.copycat_service import CopycatTracker - from .proof_test_utils import generate_realistic_sat_prompt # ============================================================================ @@ -29,10 +28,25 @@ TEST_MODEL_ID = "Qwen/Qwen2.5-1.5B-Instruct" +# Skip test files for features not yet merged or requiring Linux sandbox +collect_ignore_glob = [ + "unit/environments/test_basilica_backend.py", + "unit/infrastructure/test_io_pipeline.py", + "unit/environments/test_execution_unit.py", +] + # ============================================================================ # Test Environment Setup # ============================================================================ +# grail/__init__.py calls load_dotenv(override=True) on import, which can set +# HF_HOME to a server-specific path (e.g. /ephemeral/huggingface). Remove it +# at module level so session-scoped fixtures (which run before monkeypatch) +# use the default local HuggingFace cache. +_SERVER_ENV_VARS = ("HF_HOME", "HF_HUB_CACHE", "TRANSFORMERS_CACHE") +for _var in _SERVER_ENV_VARS: + os.environ.pop(_var, None) + @pytest.fixture(autouse=True) def _set_test_env(monkeypatch: pytest.MonkeyPatch) -> None: @@ -44,6 +58,9 @@ def _set_test_env(monkeypatch: pytest.MonkeyPatch) -> None: monkeypatch.setenv("GRAIL_MONITORING_BACKEND", "null") monkeypatch.setenv("BT_NETWORK", "test") monkeypatch.setenv("NETUID", "1") + # Ensure server-specific HF paths from .env don't leak into tests + for var in _SERVER_ENV_VARS: + monkeypatch.delenv(var, raising=False) # Ensure project root is on path when running from different CWDs root = str(pathlib.Path(__file__).resolve().parents[1]) if root not in sys.path: @@ -188,16 +205,20 @@ def sample_size_cases(request: pytest.FixtureRequest) -> tuple[int, float, int, @pytest.fixture(scope="session") -def sat_prompts() -> list[str]: +def sat_prompts(tmp_path_factory: pytest.TempPathFactory) -> list[str]: """Pregenerated realistic SAT prompts for proof tests. Uses deterministic seeds and production chat templates to ensure - prompts match actual mining scenarios. + prompts match actual mining scenarios. Filelock prevents concurrent + HuggingFace cache corruption under pytest-xdist. """ + from filelock import FileLock from transformers import AutoTokenizer - # Load tokenizer for chat template application - tokenizer = AutoTokenizer.from_pretrained(TEST_MODEL_ID) + # Serialize model downloads across xdist workers + lock_path = tmp_path_factory.getbasetemp().parent / "hf_download.lock" + with FileLock(str(lock_path)): + tokenizer = AutoTokenizer.from_pretrained(TEST_MODEL_ID) seeds = [ "test_seed_easy_01", @@ -222,14 +243,6 @@ def sat_prompt_tokens(sat_prompts: list[str]) -> list[int]: return [tokenizer.encode(prompt, add_special_tokens=False) for prompt in sat_prompts] -@pytest.fixture(scope="session") -def tracker() -> CopycatTracker: - """CopycatTracker instance for testing.""" - from grail.validation.copycat_service import CopycatTracker - - return CopycatTracker() - - # ============================================================================ # TRAINER-SPECIFIC FIXTURES # ============================================================================ @@ -271,37 +284,54 @@ def seeded_torch_env(monkeypatch: pytest.MonkeyPatch) -> None: torch.backends.cudnn.deterministic = True -@pytest.fixture -def tiny_qwen_model_and_tokenizer() -> tuple[Any, Any]: - """Load Qwen 1.5B model and tokenizer, ensure pad_token_id is set. - - Uses a small model suitable for fast CPU-based testing. Lazy-loads - to avoid slow imports in unrelated tests. +@pytest.fixture(scope="session") +def _cached_qwen_model_and_tokenizer( + tmp_path_factory: pytest.TempPathFactory, +) -> tuple[Any, Any]: + """Session-scoped: load Qwen 1.5B model and tokenizer once per worker. - Returns: - Tuple of (model, tokenizer) + Uses a filelock to prevent concurrent HuggingFace cache corruption + when running under pytest-xdist (multiple workers downloading simultaneously). """ + from filelock import FileLock from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "Qwen/Qwen2.5-1.5B" - # Load tokenizer - tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True) - if tokenizer.pad_token_id is None: - tokenizer.pad_token_id = tokenizer.eos_token_id - - # Load model on CPU - model = AutoModelForCausalLM.from_pretrained( - model_name, - trust_remote_code=True, - torch_dtype="auto", - device_map="cpu", - ) + # Serialize model downloads across xdist workers + lock_path = tmp_path_factory.getbasetemp().parent / "hf_download.lock" + with FileLock(str(lock_path)): + tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True) + if tokenizer.pad_token_id is None: + tokenizer.pad_token_id = tokenizer.eos_token_id + + model = AutoModelForCausalLM.from_pretrained( + model_name, + trust_remote_code=True, + torch_dtype="auto", + device_map="cpu", + ) + model.eval() return model, tokenizer +@pytest.fixture +def tiny_qwen_model_and_tokenizer( + _cached_qwen_model_and_tokenizer: tuple[Any, Any], +) -> tuple[Any, Any]: + """Per-test: return a deep copy of the cached model so training tests can't leak state. + + The tokenizer is immutable so it's shared directly. The model is deepcopied + (~0.5s) instead of reloaded from disk (~15s). + """ + import copy + + model, tokenizer = _cached_qwen_model_and_tokenizer + return copy.deepcopy(model), tokenizer + + @pytest.fixture def monkeypatch_trainer_constants(monkeypatch: pytest.MonkeyPatch) -> None: """Override trainer constants for fast tests. @@ -309,12 +339,13 @@ def monkeypatch_trainer_constants(monkeypatch: pytest.MonkeyPatch) -> None: Shrinks batch size, max length, and gradient accumulation steps to speed up test execution while maintaining correctness. """ - import grail.shared.constants as constants + import grail.protocol.constants as proto_constants + import grail.shared.config as config_constants - monkeypatch.setattr(constants, "TRAINER_MAX_LENGTH", 256) - monkeypatch.setattr(constants, "TRAINER_MICRO_BATCH_SIZE", 4) - monkeypatch.setattr(constants, "TRAINER_GRAD_ACCUM_STEPS", 2) - monkeypatch.setattr(constants, "ROLLOUTS_PER_PROBLEM", 4) + monkeypatch.setattr(config_constants, "TRAINER_MAX_LENGTH", 256) + monkeypatch.setattr(config_constants, "TRAINER_MICRO_BATCH_SIZE", 4) + monkeypatch.setattr(config_constants, "TRAINER_GRAD_ACCUM_STEPS", 2) + monkeypatch.setattr(proto_constants, "ROLLOUTS_PER_PROBLEM", 4) @pytest.fixture diff --git a/tests/integration/infrastructure/test_block_timestamp.py b/tests/integration/infrastructure/test_block_timestamp.py index 22784b10..4a05a867 100644 --- a/tests/integration/infrastructure/test_block_timestamp.py +++ b/tests/integration/infrastructure/test_block_timestamp.py @@ -4,7 +4,7 @@ block timestamps using the Substrate Timestamp pallet. """ -from datetime import datetime, timedelta, timezone +from datetime import UTC, datetime, timedelta from types import SimpleNamespace import pytest @@ -95,8 +95,8 @@ async def test_get_block_timestamp_current_block(chain_manager: GrailChainManage ) # Convert to datetime and verify it's recent - block_datetime = datetime.fromtimestamp(timestamp, tz=timezone.utc) - now = datetime.now(timezone.utc) + block_datetime = datetime.fromtimestamp(timestamp, tz=UTC) + now = datetime.now(UTC) age = now - block_datetime # Block should be from within the last hour (Bittensor blocks are ~12s) @@ -178,11 +178,9 @@ async def test_estimate_block_timestamp_accuracy(chain_manager: GrailChainManage ) print(f"✅ Block {test_block} estimation:") + print(f" Actual: {actual_timestamp} ({datetime.fromtimestamp(actual_timestamp, tz=UTC)})") print( - f" Actual: {actual_timestamp} ({datetime.fromtimestamp(actual_timestamp, tz=timezone.utc)})" - ) - print( - f" Estimated: {estimated_timestamp} ({datetime.fromtimestamp(estimated_timestamp, tz=timezone.utc)})" + f" Estimated: {estimated_timestamp} ({datetime.fromtimestamp(estimated_timestamp, tz=UTC)})" ) print(f" Error: {error_seconds:.2f}s ({error_percent:.3f}%)") print(f"✅ Future block {future_block} estimate: {future_estimate}") @@ -204,8 +202,8 @@ async def test_block_timestamp_edge_cases(chain_manager: GrailChainManager) -> N old_timestamp = await chain_manager.get_block_timestamp(1) if old_timestamp is not None: # If we got it, verify it's plausible (should be years ago) - old_datetime = datetime.fromtimestamp(old_timestamp, tz=timezone.utc) - now = datetime.now(timezone.utc) + old_datetime = datetime.fromtimestamp(old_timestamp, tz=UTC) + now = datetime.now(UTC) age = now - old_datetime assert age > timedelta(days=30), "Genesis block should be at least months old" print(f"✅ Genesis block (1) timestamp: {old_datetime} UTC") @@ -224,8 +222,8 @@ async def test_block_timestamp_edge_cases(chain_manager: GrailChainManager) -> N future_estimate = await chain_manager.estimate_block_timestamp(future_block) if future_estimate is not None: # If we got an estimate, it should be in the future - future_datetime = datetime.fromtimestamp(future_estimate, tz=timezone.utc) - now = datetime.now(timezone.utc) + future_datetime = datetime.fromtimestamp(future_estimate, tz=UTC) + now = datetime.now(UTC) assert future_datetime > now, "Estimated future timestamp should be in the future" print(f"✅ Far future block estimate: {future_datetime} UTC") diff --git a/tests/integration/infrastructure/test_delta_checkpoint_flow.py b/tests/integration/infrastructure/test_delta_checkpoint_flow.py index eaced270..c71dd30d 100644 --- a/tests/integration/infrastructure/test_delta_checkpoint_flow.py +++ b/tests/integration/infrastructure/test_delta_checkpoint_flow.py @@ -333,7 +333,8 @@ class TestRetentionPolicy: def test_compute_keep_windows_includes_base(self, temp_cache: Path) -> None: """Test that _compute_keep_windows includes base checkpoints.""" - from grail.shared.constants import DELTA_BASE_INTERVAL, WINDOW_LENGTH + from grail.protocol.constants import WINDOW_LENGTH + from grail.shared.config import DELTA_BASE_INTERVAL manager = CheckpointManager( cache_root=temp_cache, diff --git a/tests/integration/protocol/test_proof_cross_framework.py b/tests/integration/protocol/test_proof_cross_framework.py index d5a1ca4b..c99927c2 100644 --- a/tests/integration/protocol/test_proof_cross_framework.py +++ b/tests/integration/protocol/test_proof_cross_framework.py @@ -12,7 +12,7 @@ from transformers.modeling_utils import PreTrainedModel from transformers.tokenization_utils_base import PreTrainedTokenizerBase -from grail.shared.constants import ( +from grail.protocol.constants import ( CHALLENGE_K, GRAIL_PROOF_VERSION, ) diff --git a/tests/integration/test_dynamic_env_config.py b/tests/integration/test_dynamic_env_config.py index 71efb254..33eb3273 100644 --- a/tests/integration/test_dynamic_env_config.py +++ b/tests/integration/test_dynamic_env_config.py @@ -128,14 +128,14 @@ def test_generation_params_validation(): ): params = get_default_generation_params() # Should fall back to defaults - assert params["max_tokens"] == 8192 # Default from parse_int_param + assert params["max_tokens"] == 2048 # Default from parse_int_param assert params["temperature"] == 0.7 # Default from parse_float_param assert params["top_p"] == 0.9 # Default from parse_float_param # Invalid types should fallback gracefully with patch.dict("os.environ", {"GRAIL_GEN_MAX_TOKENS": "not_a_number"}): params = get_default_generation_params() - assert params["max_tokens"] == 8192 # Falls back to default + assert params["max_tokens"] == 2048 # Falls back to default def test_environment_factory_respects_runtime_params(): diff --git a/tests/integration/test_gen_params_pipeline.py b/tests/integration/test_gen_params_pipeline.py new file mode 100644 index 00000000..cb34cb16 --- /dev/null +++ b/tests/integration/test_gen_params_pipeline.py @@ -0,0 +1,269 @@ +"""Integration tests for the trainer→miner→validator generation_params flow. + +These tests cover the bug fixed in Stage 1: the pipeline path was driving +the SGLang backend with ``GenerationParams.max_new_tokens = MAX_NEW_TOKENS_PROTOCOL_CAP`` +(8192) regardless of the trainer-published checkpoint metadata, while the +validator's ``TerminationValidator`` caps at ``min(metadata.max_tokens, +MAX_NEW_TOKENS_PROTOCOL_CAP)``. The result was hard-rejection on every long completion. + +The tests here exercise the full conversion chain end-to-end: + + trainer payload dict + → CheckpointMetadata.generation_params + → GenerationParams.from_checkpoint_metadata + → environments.backends.GenerationParams + → fed to TerminationValidator.validate via the same pathway + +and verify that the value the miner drives the backend with is identical to +the value the validator's ``TerminationValidator`` will accept (no drift). + +Wiring tests for ``SGLangServerBackend`` live in +``tests/unit/mining/test_weight_sync.py`` to keep the backend constructor +contract in one place. +""" + +from __future__ import annotations + +from unittest.mock import MagicMock + +import pytest +import torch + +from grail.environments.backends import GenerationParams +from grail.infrastructure.checkpoint_consumer import CheckpointMetadata +from grail.protocol.constants import MAX_NEW_TOKENS_PROTOCOL_CAP +from grail.protocol.errors import ProtocolViolationError +from grail.validation.context import ValidationContext +from grail.validation.validators.termination import TerminationValidator + +# --------------------------------------------------------------------------- # +# GenerationParams.from_checkpoint_metadata trust-boundary tests # +# --------------------------------------------------------------------------- # + + +def _full_payload(**overrides: object) -> dict[str, object]: + """Build a complete trainer-style payload with optional field overrides. + + The trust boundary requires every sampling field to be present; + callers vary individual fields for failure tests by overriding here. + Defaults match grail/trainer/checkpoint_publisher.py exactly. + """ + payload: dict[str, object] = { + "max_tokens": 2048, + "temperature": 0.7, + "top_p": 0.9, + "top_k": 50, + "repetition_penalty": 1.0, + } + payload.update(overrides) + return payload + + +def test_from_checkpoint_metadata_happy_path() -> None: + """All trainer fields propagate to GenerationParams unchanged.""" + gp = GenerationParams.from_checkpoint_metadata( + _full_payload(temperature=0.7, top_p=0.9, top_k=20, repetition_penalty=1.05) + ) + assert isinstance(gp, GenerationParams) + assert gp.max_new_tokens == 2048 + assert gp.temperature == pytest.approx(0.7) + assert gp.top_p == pytest.approx(0.9) + assert gp.top_k == 20 + assert gp.repetition_penalty == pytest.approx(1.05) + assert gp.do_sample is True + + +def test_from_checkpoint_metadata_caps_at_protocol_max() -> None: + """A trainer publishing more than MAX_NEW_TOKENS_PROTOCOL_CAP is capped, never raised.""" + gp = GenerationParams.from_checkpoint_metadata( + _full_payload(max_tokens=MAX_NEW_TOKENS_PROTOCOL_CAP * 4) + ) + assert gp.max_new_tokens == MAX_NEW_TOKENS_PROTOCOL_CAP + + +@pytest.mark.parametrize( + "missing_field", + ["max_tokens", "temperature", "top_p", "top_k", "repetition_penalty"], +) +def test_from_checkpoint_metadata_missing_required_field_raises(missing_field: str) -> None: + """Every sampling field is required at the trust boundary. + + Silent defaults would let a publisher bug ship a half-specified + policy that the validator interprets differently. Each missing field + must produce a ProtocolViolationError naming the field. + """ + payload = _full_payload() + payload.pop(missing_field) + with pytest.raises(ProtocolViolationError, match=missing_field): + GenerationParams.from_checkpoint_metadata(payload) + + +def test_from_checkpoint_metadata_zero_max_tokens_raises() -> None: + """Non-positive max_tokens fails loud (catch publisher bugs early).""" + with pytest.raises(ProtocolViolationError, match="must be > 0"): + GenerationParams.from_checkpoint_metadata(_full_payload(max_tokens=0)) + + +def test_from_checkpoint_metadata_top_k_zero_disables() -> None: + """top_k=0 (sampling-API "disabled") becomes None, not 0.""" + gp = GenerationParams.from_checkpoint_metadata(_full_payload(top_k=0)) + assert gp.top_k is None + + +def test_from_checkpoint_metadata_non_dict_raises() -> None: + """Non-dict generation_params payload is a protocol violation.""" + with pytest.raises(ProtocolViolationError, match="max_tokens"): + GenerationParams.from_checkpoint_metadata([]) # type: ignore[arg-type] + + +@pytest.mark.parametrize("bad_value", [-1, -2048, "abc", "2048.5"]) +def test_from_checkpoint_metadata_rejects_negative_or_malformed(bad_value: object) -> None: + """Negative ints and non-int strings hit the trust boundary loudly. + + Trainer publisher bugs (typo, env-var parsing slip) must not corrupt + the miner's sampling policy silently. + """ + with pytest.raises(ProtocolViolationError): + GenerationParams.from_checkpoint_metadata(_full_payload(max_tokens=bad_value)) + + +@pytest.mark.parametrize( + "field,bad_value", + [ + # Type errors + ("temperature", "abc"), + ("top_p", "xyz"), + ("top_k", "abc"), + ("top_k", "1.5"), + ("repetition_penalty", "abc"), + # Range violations: temperature must be in [0.01, 2.0] + ("temperature", 0.0), + ("temperature", -0.5), + ("temperature", 3.0), + # Range violations: top_p must be in [0.0, 1.0] + ("top_p", -0.1), + ("top_p", 1.5), + # Range violations: top_k must be in [0, 1000] + ("top_k", -1), + ("top_k", 1001), + # Range violations: repetition_penalty must be in [1.0, 2.0] + # (matches trainer publisher's range exactly to prevent contract drift) + ("repetition_penalty", 0.0), + ("repetition_penalty", -1.0), + ("repetition_penalty", 0.5), + ("repetition_penalty", 2.5), + ], +) +def test_from_checkpoint_metadata_rejects_malformed_secondary_fields( + field: str, bad_value: object +) -> None: + """Non-max_tokens fields also fail loud with ProtocolViolationError. + + Without this hardening, bad values would raise raw ValueError/TypeError + and bypass the miner's ProtocolViolationError skip-window branch, falling + into the generic 10s retry loop instead. The accepted ranges match + grail/trainer/checkpoint_publisher.py exactly so trainer/miner stay + in lock-step. + """ + payload = _full_payload(**{field: bad_value}) + with pytest.raises(ProtocolViolationError, match=field): + GenerationParams.from_checkpoint_metadata(payload) + + +# --------------------------------------------------------------------------- # +# Trainer-payload → CheckpointMetadata → GenerationParams round-trip # +# --------------------------------------------------------------------------- # + + +def test_explicit_payload_round_trip() -> None: + """An explicit non-default payload survives the metadata → builder chain. + + Uses an explicit dict (NOT ``get_default_generation_params``) so the + test is hermetic against env-var defaults like ``GRAIL_GEN_MAX_TOKENS``, + and so the regression for the 8192/2048 bug fails deterministically if + the miner ever reverts to hardcoded ``MAX_NEW_TOKENS_PROTOCOL_CAP``. + """ + payload = _full_payload( + max_tokens=2048, temperature=0.7, top_p=0.9, top_k=20, repetition_penalty=1.05 + ) + metadata = CheckpointMetadata(window=1000, file_manifest={}, generation_params=payload) + gp = GenerationParams.from_checkpoint_metadata(metadata.generation_params) + + assert gp.max_new_tokens == 2048 # would FAIL if miner hardcodes MAX_NEW_TOKENS_PROTOCOL_CAP + assert gp.temperature == pytest.approx(0.7) + assert gp.top_p == pytest.approx(0.9) + assert gp.top_k == 20 + assert gp.repetition_penalty == pytest.approx(1.05) + + +# --------------------------------------------------------------------------- # +# Miner ↔ TerminationValidator parity # +# --------------------------------------------------------------------------- # + + +def _make_termination_ctx(*, completion_length: int, max_tokens: int) -> ValidationContext: + """Build the minimal ValidationContext that TerminationValidator needs. + + The max-length termination path returns True/False before touching + tokenizer/model, so MagicMocks are sufficient for those resources. + Tokens list is non-empty (the validator only checks ``not tokens`` + at the entrance, then compares ``completion_length`` for the cap path). + """ + return ValidationContext( + commit={ + "tokens": [1, 2, 3], + "rollout": {"completion_length": completion_length}, + }, + prover_address="hotkey", + challenge_randomness="00", + model=MagicMock(), + tokenizer=MagicMock(), + device=torch.device("cpu"), + generation_params={"max_tokens": max_tokens}, + ) + + +@pytest.mark.parametrize( + "trainer_max_tokens", [256, 2048, MAX_NEW_TOKENS_PROTOCOL_CAP, MAX_NEW_TOKENS_PROTOCOL_CAP * 2] +) +def test_miner_max_new_tokens_accepted_by_termination_validator( + trainer_max_tokens: int, +) -> None: + """Miner-driven max_new_tokens must be accepted by TerminationValidator. + + Drives the REAL ``TerminationValidator.validate`` (not a re-implementation + of its clamp) with a completion of length equal to the miner's chosen + cap. Catches BOTH miner drift AND validator drift, since either side + diverging from the ``min(metadata.max_tokens, MAX_NEW_TOKENS_PROTOCOL_CAP)`` rule + fails the assertion. + """ + metadata = CheckpointMetadata( + window=1000, + file_manifest={}, + generation_params=_full_payload(max_tokens=trainer_max_tokens), + ) + gp = GenerationParams.from_checkpoint_metadata(metadata.generation_params) + + ctx = _make_termination_ctx( + completion_length=gp.max_new_tokens, + max_tokens=trainer_max_tokens, + ) + assert TerminationValidator().validate(ctx) is True + assert ctx.checks["termination_valid"] is True + + +@pytest.mark.parametrize("trainer_max_tokens", [256, 2048]) +def test_termination_validator_rejects_completion_above_cap( + trainer_max_tokens: int, +) -> None: + """One-token-over the cap must hard-fail termination_valid. + + This is the exact failure mode the Stage 1 bug produced (validator log + line: ``Exceeds max tokens: 8192 > 2048``). + """ + ctx = _make_termination_ctx( + completion_length=trainer_max_tokens + 1, + max_tokens=trainer_max_tokens, + ) + assert TerminationValidator().validate(ctx) is False + assert ctx.checks["termination_valid"] is False diff --git a/tests/integration/test_miner_validator_proofs.py b/tests/integration/test_miner_validator_proofs.py index e39b2103..8a388d5c 100644 --- a/tests/integration/test_miner_validator_proofs.py +++ b/tests/integration/test_miner_validator_proofs.py @@ -69,8 +69,8 @@ def test_miner_proofs_pass_validator_verification( """ from grail.environments.episode import AgentEnvLoop from grail.environments.factory import create_env + from grail.protocol.constants import ROLLOUTS_PER_PROBLEM from grail.protocol.signatures import sign_commit_binding - from grail.shared.constants import ROLLOUTS_PER_PROBLEM from grail.validation.context import ValidationContext from grail.validation.validators.proof import GRAILProofValidator diff --git a/tests/integration/validation/test_pipeline_golden.py b/tests/integration/validation/test_pipeline_golden.py index c031342e..ae1b695b 100644 --- a/tests/integration/validation/test_pipeline_golden.py +++ b/tests/integration/validation/test_pipeline_golden.py @@ -15,7 +15,7 @@ import pytest import torch -from grail.shared.constants import CHALLENGE_K, CURRENT_ENV_ID +from grail.protocol.constants import CHALLENGE_K, CURRENT_ENV_ID from grail.validation import create_env_validation_pipeline from grail.validation.context import ValidationContext diff --git a/tests/proof_test_utils.py b/tests/proof_test_utils.py index 1784866c..40ef2407 100644 --- a/tests/proof_test_utils.py +++ b/tests/proof_test_utils.py @@ -14,14 +14,14 @@ from transformers.tokenization_utils_base import PreTrainedTokenizerBase from grail.environments.sat_env import create_sat_prompt, generate_sat_problem -from grail.protocol.crypto import indices_from_root -from grail.protocol.grail_verifier import GRAILVerifier -from grail.shared.constants import ( +from grail.protocol.constants import ( CHALLENGE_K, GRAIL_PROOF_VERSION, LAYER_INDEX, PROOF_TOPK, ) +from grail.protocol.crypto import indices_from_root +from grail.protocol.grail_verifier import GRAILVerifier from grail.shared.hf_compat import resolve_hidden_size diff --git a/tests/trainer/test_grpo_gpu_real_data.py b/tests/trainer/test_grpo_gpu_real_data.py index 28c9c8ea..5c03b12e 100644 --- a/tests/trainer/test_grpo_gpu_real_data.py +++ b/tests/trainer/test_grpo_gpu_real_data.py @@ -875,7 +875,7 @@ async def test_grpo_real_gsm8k_with_learning_curve( # Load configuration load_dotenv(override=True) - import grail.shared.constants as C + import grail.shared.config as C C = importlib.reload(C) @@ -943,7 +943,7 @@ async def test_grpo_real_gsm8k_with_learning_curve( model, tokenizer, device=str(accelerator.device), - max_new_tokens=int(C.MAX_NEW_TOKENS), + max_new_tokens=int(C.MAX_NEW_TOKENS_PROTOCOL_CAP), temperature=TEST_CONFIG.temperature, ) diff --git a/tests/trainer/test_grpo_integration.py b/tests/trainer/test_grpo_integration.py index 9e2a5f7d..1348faa4 100644 --- a/tests/trainer/test_grpo_integration.py +++ b/tests/trainer/test_grpo_integration.py @@ -260,7 +260,8 @@ def test_valid_groups_in_synthetic_data( self, synthetic_grpo_groups: list[GRPOGroup], monkeypatch_trainer_constants: None ) -> None: """Test synthetic groups are valid.""" - from grail.shared.constants import ROLLOUTS_PER_PROBLEM, TRAINER_GROUP_ADV_SUM_TOL + from grail.protocol.constants import ROLLOUTS_PER_PROBLEM + from grail.shared.config import TRAINER_GROUP_ADV_SUM_TOL for group in synthetic_grpo_groups: assert group.is_valid( @@ -303,7 +304,7 @@ async def test_long_sequence_truncated_and_trained( run_grpo_epoch: Any, ) -> None: """Test training with sequences exceeding TRAINER_MAX_LENGTH.""" - from grail.shared.constants import TRAINER_MAX_LENGTH + from grail.shared.config import TRAINER_MAX_LENGTH model, tokenizer = tiny_qwen_model_and_tokenizer ref_model = model diff --git a/tests/trainer/test_grpo_unit.py b/tests/trainer/test_grpo_unit.py index 0cabf310..6759e802 100644 --- a/tests/trainer/test_grpo_unit.py +++ b/tests/trainer/test_grpo_unit.py @@ -309,7 +309,7 @@ class TestGRPOGroupValidation: def test_valid_group(self, monkeypatch_trainer_constants: None) -> None: """Test valid group with correct count and zero-sum advantages.""" - from grail.shared.constants import ROLLOUTS_PER_PROBLEM + from grail.protocol.constants import ROLLOUTS_PER_PROBLEM rollouts = [ GRPORollout( diff --git a/tests/unit/environments/test_refactored_modules.py b/tests/unit/environments/test_refactored_modules.py index 307e4441..f82b85d6 100644 --- a/tests/unit/environments/test_refactored_modules.py +++ b/tests/unit/environments/test_refactored_modules.py @@ -25,10 +25,10 @@ def test_generation_params_defaults(): """Verify dataclass defaults match expected constants.""" - from grail.shared.constants import MAX_NEW_TOKENS + from grail.protocol.constants import MAX_NEW_TOKENS_PROTOCOL_CAP p = GenerationParams() - assert p.max_new_tokens == MAX_NEW_TOKENS + assert p.max_new_tokens == MAX_NEW_TOKENS_PROTOCOL_CAP assert p.temperature == 0.6 assert p.do_sample is True assert p.top_p == 0.95 diff --git a/tests/unit/environments/test_sglang_backend.py b/tests/unit/environments/test_sglang_backend.py new file mode 100644 index 00000000..9c7d18b1 --- /dev/null +++ b/tests/unit/environments/test_sglang_backend.py @@ -0,0 +1,93 @@ +"""Unit tests for SGLangServerBackend's context-length safeguard. + +The ``_cap_max_tokens`` helper decides whether a (prompt, max_new_tokens) +pair fits the configured model context length. The contract is binary: +either fit (return ``max_new_tokens`` unchanged) or skip (return ``0``). + +We deliberately do NOT return a partially clamped value: the validator's +``TerminationValidator`` requires ``completion_length == min(metadata.max_tokens, +protocol_cap)`` or EOS, so a clamped intermediate completion would +hard-fail ``termination_valid``. Returning ``0`` lets the per-group +``CHALLENGE_K`` gate drop the whole group (skip-iteration crash domain). +""" + +from __future__ import annotations + +import pytest + +from grail.environments.backends import SGLangServerBackend + + +def _backend(max_model_len: int | None = 12288) -> SGLangServerBackend: + return SGLangServerBackend( + base_url="http://localhost:30000", + model_name="Qwen/Qwen2.5-0.5B-Instruct", + tokenizer=None, + max_model_len=max_model_len, + ) + + +def test_cap_max_tokens_passthrough_when_fits() -> None: + """Prompt + max_new fits comfortably: return max_new unchanged.""" + backend = _backend(max_model_len=12288) + assert backend._cap_max_tokens(prompt_len=1000, max_new_tokens=2048) == 2048 # noqa: SLF001 + assert backend._cap_max_tokens(prompt_len=10239, max_new_tokens=2048) == 2048 # noqa: SLF001 + + +def test_cap_max_tokens_returns_zero_at_exact_boundary_overflow() -> None: + """Exactly one token over: return 0 so the caller skips the prompt. + + Regression for the validator-rejection bug: previously this returned + a partially clamped value (e.g. 2047), the SGLang server stopped at + that intermediate length, and the validator hard-rejected the rollout + because completion_length != min(metadata.max_tokens, protocol_cap). + """ + backend = _backend(max_model_len=12288) + # 12288 - 10241 = 2047 room < requested 2048 → skip + assert backend._cap_max_tokens(prompt_len=10241, max_new_tokens=2048) == 0 # noqa: SLF001 + + +def test_cap_max_tokens_returns_zero_when_prompt_dominates() -> None: + """Prompt eats most of the context: return 0, do not generate a stub.""" + backend = _backend(max_model_len=12288) + assert backend._cap_max_tokens(prompt_len=11000, max_new_tokens=2048) == 0 # noqa: SLF001 + + +def test_cap_max_tokens_returns_zero_when_prompt_already_too_long() -> None: + """Prompt alone exceeds max_model_len: zero room means skip.""" + backend = _backend(max_model_len=12288) + assert backend._cap_max_tokens(prompt_len=12500, max_new_tokens=2048) == 0 # noqa: SLF001 + assert backend._cap_max_tokens(prompt_len=12288, max_new_tokens=1) == 0 # noqa: SLF001 + + +def test_cap_max_tokens_unset_max_model_len_is_passthrough() -> None: + """Without max_model_len configured, the helper trusts the caller. + + This is the legacy behaviour for tests that construct a backend + without going through the mining-path wiring; the live mining path + always provides max_model_len from PipelineConfig. + """ + backend = _backend(max_model_len=None) + assert backend._cap_max_tokens(prompt_len=99999, max_new_tokens=2048) == 2048 # noqa: SLF001 + + +@pytest.mark.parametrize( + "prompt_len,max_new_tokens,max_model_len,expected", + [ + # Comfortable fit + (500, 2048, 12288, 2048), + # Exactly fits (no headroom) + (10240, 2048, 12288, 2048), + # One token over + (10241, 2048, 12288, 0), + # Larger model context + (10000, 4096, 16384, 4096), + (12289, 4096, 16384, 0), + ], +) +def test_cap_max_tokens_parametrized( + prompt_len: int, max_new_tokens: int, max_model_len: int, expected: int +) -> None: + """Decision boundary across realistic (prompt, max_new) combinations.""" + backend = _backend(max_model_len=max_model_len) + assert backend._cap_max_tokens(prompt_len, max_new_tokens) == expected # noqa: SLF001 diff --git a/tests/unit/infrastructure/test_checkpoint_cleanup.py b/tests/unit/infrastructure/test_checkpoint_cleanup.py index 83df6b52..652a474e 100644 --- a/tests/unit/infrastructure/test_checkpoint_cleanup.py +++ b/tests/unit/infrastructure/test_checkpoint_cleanup.py @@ -13,7 +13,7 @@ import pytest from grail.infrastructure.checkpoint_consumer import CheckpointMetadata -from grail.shared.retention_utils import compute_retention_windows +from grail.shared.retention_utils import compute_chain_windows, compute_retention_windows # ============================================================================ # Tests for _compute_keep_windows @@ -28,7 +28,14 @@ def _make_metadata(self, **overrides: object) -> CheckpointMetadata: "window": 100, "file_manifest": {}, "env_id": "triton_kernel", - "generation_params": {"max_tokens": 8192, "temperature": 0.7}, + "thinking_mode": "instructed", + "generation_params": { + "max_tokens": 8192, + "temperature": 0.7, + "top_p": 0.9, + "top_k": 50, + "repetition_penalty": 1.0, + }, } defaults.update(overrides) return CheckpointMetadata(**defaults) # type: ignore[arg-type] @@ -53,17 +60,108 @@ def test_missing_generation_params(self) -> None: assert "generation_params" in missing def test_missing_max_tokens(self) -> None: - meta = self._make_metadata(generation_params={"temperature": 0.7}) + meta = self._make_metadata( + generation_params={ + "temperature": 0.7, + "top_p": 0.9, + "top_k": 50, + "repetition_penalty": 1.0, + } + ) missing = meta.validate_metadata() assert "generation_params.max_tokens" in missing + def test_missing_temperature(self) -> None: + meta = self._make_metadata( + generation_params={ + "max_tokens": 8192, + "top_p": 0.9, + "top_k": 50, + "repetition_penalty": 1.0, + } + ) + missing = meta.validate_metadata() + assert "generation_params.temperature" in missing + + def test_missing_top_p(self) -> None: + meta = self._make_metadata( + generation_params={ + "max_tokens": 8192, + "temperature": 0.7, + "top_k": 50, + "repetition_penalty": 1.0, + } + ) + missing = meta.validate_metadata() + assert "generation_params.top_p" in missing + + def test_missing_top_k(self) -> None: + meta = self._make_metadata( + generation_params={ + "max_tokens": 8192, + "temperature": 0.7, + "top_p": 0.9, + "repetition_penalty": 1.0, + } + ) + missing = meta.validate_metadata() + assert "generation_params.top_k" in missing + + def test_missing_repetition_penalty(self) -> None: + meta = self._make_metadata( + generation_params={ + "max_tokens": 8192, + "temperature": 0.7, + "top_p": 0.9, + "top_k": 50, + } + ) + missing = meta.validate_metadata() + assert "generation_params.repetition_penalty" in missing + + def test_all_sampling_params_missing_when_only_max_tokens(self) -> None: + meta = self._make_metadata(generation_params={"max_tokens": 8192}) + missing = meta.validate_metadata() + assert "generation_params.temperature" in missing + assert "generation_params.top_p" in missing + assert "generation_params.top_k" in missing + assert "generation_params.repetition_penalty" in missing + assert "generation_params.max_tokens" not in missing + def test_multiple_missing_fields(self) -> None: meta = self._make_metadata(env_id=None, generation_params={}) missing = meta.validate_metadata() assert "env_id" in missing assert "generation_params" in missing + assert "thinking_mode" not in missing # still set in defaults assert len(missing) == 2 + def test_missing_thinking_mode(self) -> None: + meta = self._make_metadata(thinking_mode=None) + missing = meta.validate_metadata() + assert "thinking_mode" in missing + + def test_empty_thinking_mode(self) -> None: + meta = self._make_metadata(thinking_mode="") + missing = meta.validate_metadata() + assert "thinking_mode" in missing + + def test_invalid_thinking_mode_value(self) -> None: + meta = self._make_metadata(thinking_mode="bogus") + missing = meta.validate_metadata() + # validate_metadata reports the bad value in the field name so + # operators see exactly what's wrong in the verdict log. + assert any("thinking_mode" in m for m in missing) + assert any("bogus" in m for m in missing) + + def test_native_thinking_mode_is_valid(self) -> None: + meta = self._make_metadata(thinking_mode="native") + assert meta.validate_metadata() == [] + + def test_instructed_thinking_mode_is_valid(self) -> None: + meta = self._make_metadata(thinking_mode="instructed") + assert meta.validate_metadata() == [] + class TestComputeKeepWindows: """Tests for the retention window calculation function (chained deltas). @@ -326,7 +424,7 @@ async def test_cleanup_deletes_old_checkpoints(self) -> None: mock_list = AsyncMock(return_value=mock_keys) mock_delete = AsyncMock() - mock_fetch_anchor = AsyncMock(return_value=60) # All deltas depend on anchor 60 + mock_fetch_chain = AsyncMock(return_value=(60, None)) # All deltas: anchor=60 with patch("grail.shared.retention_utils.DELTA_BASE_INTERVAL", 2): with patch("grail.shared.retention_utils.WINDOW_LENGTH", 30): @@ -339,8 +437,8 @@ async def test_cleanup_deletes_old_checkpoints(self) -> None: mock_delete, ): with patch( - "grail.trainer.checkpoint_publisher._fetch_delta_anchor_window", - mock_fetch_anchor, + "grail.trainer.checkpoint_publisher._fetch_delta_chain_info", + mock_fetch_chain, ): from grail.trainer.checkpoint_publisher import CheckpointPublisher @@ -365,7 +463,7 @@ async def test_cleanup_keeps_retention_limit_checkpoints(self) -> None: mock_list = AsyncMock(return_value=mock_keys) mock_delete = AsyncMock() - mock_fetch_anchor = AsyncMock(return_value=7055500) + mock_fetch_chain = AsyncMock(return_value=(7055500, None)) with patch("grail.shared.retention_utils.DELTA_BASE_INTERVAL", 10): with patch("grail.shared.retention_utils.WINDOW_LENGTH", 30): @@ -378,8 +476,8 @@ async def test_cleanup_keeps_retention_limit_checkpoints(self) -> None: mock_delete, ): with patch( - "grail.trainer.checkpoint_publisher._fetch_delta_anchor_window", - mock_fetch_anchor, + "grail.trainer.checkpoint_publisher._fetch_delta_chain_info", + mock_fetch_chain, ): from grail.trainer.checkpoint_publisher import CheckpointPublisher @@ -431,7 +529,7 @@ async def test_cleanup_handles_delete_error(self) -> None: mock_list = AsyncMock(return_value=mock_keys) mock_delete = AsyncMock() - mock_fetch_anchor = AsyncMock(return_value=0) + mock_fetch_chain = AsyncMock(return_value=(0, None)) with patch("grail.shared.retention_utils.DELTA_BASE_INTERVAL", 1): with patch("grail.shared.retention_utils.WINDOW_LENGTH", 30): @@ -444,8 +542,8 @@ async def test_cleanup_handles_delete_error(self) -> None: mock_delete, ): with patch( - "grail.trainer.checkpoint_publisher._fetch_delta_anchor_window", - mock_fetch_anchor, + "grail.trainer.checkpoint_publisher._fetch_delta_chain_info", + mock_fetch_chain, ): from grail.trainer.checkpoint_publisher import CheckpointPublisher @@ -477,7 +575,7 @@ async def test_cleanup_parses_window_from_path_correctly(self) -> None: mock_list = AsyncMock(return_value=mock_keys) mock_delete = AsyncMock() - mock_fetch_anchor = AsyncMock(return_value=400) + mock_fetch_chain = AsyncMock(return_value=(400, None)) with patch("grail.shared.retention_utils.DELTA_BASE_INTERVAL", 1): with patch("grail.shared.retention_utils.WINDOW_LENGTH", 100): @@ -491,8 +589,8 @@ async def test_cleanup_parses_window_from_path_correctly(self) -> None: mock_delete, ): with patch( - "grail.trainer.checkpoint_publisher._fetch_delta_anchor_window", - mock_fetch_anchor, + "grail.trainer.checkpoint_publisher._fetch_delta_chain_info", + mock_fetch_chain, ): from grail.trainer.checkpoint_publisher import CheckpointPublisher @@ -505,3 +603,72 @@ async def test_cleanup_parses_window_from_path_correctly(self) -> None: # With WINDOW_LENGTH=100, bootstrap_windows=10 keeps 0, 100, 200, 300, 400, 500, 600, 700, 800, 900 # So nothing is deleted assert mock_delete.call_count == 0 + + +# ============================================================================ +# Tests for compute_chain_windows (chain-aware retention) +# ============================================================================ + + +class TestComputeChainWindows: + """Tests for the chain-walking retention utility.""" + + def test_single_chain(self) -> None: + """Walk a simple chain: anchor=0 -> 30 -> 60 -> 90.""" + chain_map = {90: 60, 60: 30, 30: 0} + anchor_map = {90: 0} + result = compute_chain_windows({90}, chain_map, anchor_map) + assert result == {0, 30, 60} + + def test_keeps_all_intermediates(self) -> None: + """Retained delta at tip should keep every link back to anchor.""" + chain_map = {120: 90, 90: 60, 60: 30, 30: 0} + anchor_map = {120: 0} + result = compute_chain_windows({120}, chain_map, anchor_map) + assert 0 in result + assert 30 in result + assert 60 in result + assert 90 in result + + def test_shared_chain_prefix(self) -> None: + """Two retained deltas sharing a chain prefix don't duplicate work.""" + chain_map = {90: 60, 60: 30, 30: 0, 120: 90} + anchor_map = {90: 0, 120: 0} + result = compute_chain_windows({90, 120}, chain_map, anchor_map) + assert result == {0, 30, 60, 90} + + def test_missing_anchor_skips_delta(self) -> None: + """Delta without anchor info is skipped (no crash).""" + chain_map = {90: 60} + anchor_map: dict[int, int] = {} + result = compute_chain_windows({90}, chain_map, anchor_map) + assert result == set() + + def test_broken_chain_stops_gracefully(self) -> None: + """Chain with a missing link stops walking without error.""" + chain_map = {90: 60} # 60 -> ? is missing + anchor_map = {90: 0} + result = compute_chain_windows({90}, chain_map, anchor_map) + # Keeps anchor and the known link (60), stops there + assert 0 in result + assert 60 in result + + def test_cycle_protection(self) -> None: + """Cycle in chain_map does not cause infinite loop.""" + chain_map = {90: 60, 60: 90} # cycle + anchor_map = {90: 0} + result = compute_chain_windows({90}, chain_map, anchor_map) + assert 0 in result + # Should terminate without hanging + + def test_empty_inputs(self) -> None: + """No retained deltas returns empty set.""" + result = compute_chain_windows(set(), {}, {}) + assert result == set() + + def test_delta_is_direct_successor_of_anchor(self) -> None: + """Delta whose prev_window is the anchor needs no intermediates.""" + chain_map = {30: 0} + anchor_map = {30: 0} + result = compute_chain_windows({30}, chain_map, anchor_map) + assert result == {0} diff --git a/tests/unit/infrastructure/test_delta_checkpoint.py b/tests/unit/infrastructure/test_delta_checkpoint.py index ab8a6bb5..bdbb3bf9 100644 --- a/tests/unit/infrastructure/test_delta_checkpoint.py +++ b/tests/unit/infrastructure/test_delta_checkpoint.py @@ -283,7 +283,7 @@ def test_bfloat16_hash_supported(self) -> None: digest = compute_weights_hash(state) assert isinstance(digest, str) - assert len(digest) == 64 + assert len(digest) == 32 # xxh3-128 hex digest def test_different_states_different_hash(self) -> None: """Test that different states produce different hashes.""" @@ -312,14 +312,38 @@ def test_order_independent_keys(self) -> None: assert hash1 == hash2 def test_hash_format(self) -> None: - """Test that hash is a valid hex string.""" + """Test that hash is a valid xxh3-128 hex string.""" state = {"layer": torch.tensor([1.0])} hash_value = compute_weights_hash(state) assert isinstance(hash_value, str) - assert len(hash_value) == 64 # SHA256 hex digest + assert len(hash_value) == 32 # xxh3-128 hex digest assert all(c in "0123456789abcdef" for c in hash_value) + def test_dtype_change_changes_hash(self) -> None: + """Tensors with same byte pattern but different dtype hash differently. + + Because the hash digests raw bytes, a tensor's dtype is reflected + implicitly via its byte width: a float32 tensor and a float16 tensor + with the same numeric values produce different byte streams (4 bytes + vs 2 bytes per element) and therefore different hashes. + """ + state_fp32 = {"layer": torch.tensor([1.0, 2.0, 3.0], dtype=torch.float32)} + state_bf16 = {"layer": torch.tensor([1.0, 2.0, 3.0], dtype=torch.bfloat16)} + + assert compute_weights_hash(state_fp32) != compute_weights_hash(state_bf16) + + def test_large_state_deterministic(self) -> None: + """Hashing a multi-MB synthetic state is stable across two calls.""" + torch.manual_seed(42) + state = {f"layer_{i}.weight": torch.randn(256, 256, dtype=torch.bfloat16) for i in range(8)} + + hash1 = compute_weights_hash(state) + hash2 = compute_weights_hash(state) + + assert hash1 == hash2 + assert len(hash1) == 32 + class TestVerifyWeightsHash: """Tests for verify_weights_hash function.""" @@ -334,7 +358,7 @@ def test_valid_hash_verification(self) -> None: def test_invalid_hash_verification(self) -> None: """Test that incorrect hash fails verification.""" state = {"layer": torch.tensor([1.0, 2.0, 3.0])} - wrong_hash = "0" * 64 + wrong_hash = "0" * 32 assert verify_weights_hash(state, wrong_hash) is False diff --git a/tests/unit/infrastructure/test_sparse_codec.py b/tests/unit/infrastructure/test_sparse_codec.py index fe5e31f8..b9a119aa 100644 --- a/tests/unit/infrastructure/test_sparse_codec.py +++ b/tests/unit/infrastructure/test_sparse_codec.py @@ -570,11 +570,13 @@ def test_compression_ratio_multiple_tensors(self): ) +@pytest.mark.serial class TestPerformance: """Tests for encoding/decoding throughput. Note: Encoding includes O(n log n) lexsort which dominates for large tensors. Decoding is faster as it only needs segment-wise cumsum. + Must run serial: throughput thresholds assume uncontested CPU. """ @pytest.mark.slow diff --git a/tests/unit/mining/test_config.py b/tests/unit/mining/test_config.py index 5b053398..2e6309f4 100644 --- a/tests/unit/mining/test_config.py +++ b/tests/unit/mining/test_config.py @@ -9,29 +9,24 @@ def test_defaults() -> None: - """Default config has pipeline disabled.""" + """Default config: SGLang on GPU 0, HF proof on GPU 1. + + SGLang is the production-validated mining backend (Stage 1 e2e); the + default flipped from vllm to sglang along with the legacy-path removal. + """ cfg = PipelineConfig() - assert cfg.enabled is False - assert cfg.backend == "vllm" + assert cfg.backend == "sglang" assert cfg.vllm_gpu == 0 assert cfg.proof_gpu == 1 assert cfg.gpu_memory_utilization == 0.90 -def test_from_env_enabled() -> None: - """GRAIL_PIPELINE_ENABLED=1 enables pipeline.""" - env = {"GRAIL_PIPELINE_ENABLED": "1"} - with patch.dict(os.environ, env, clear=False): - cfg = PipelineConfig.from_env() - assert cfg.enabled is True - - -def test_from_env_backend_sglang() -> None: - """GRAIL_PIPELINE_BACKEND=sglang selects SGLang backend.""" - env = {"GRAIL_PIPELINE_BACKEND": "sglang"} +def test_from_env_backend_vllm() -> None: + """GRAIL_PIPELINE_BACKEND=vllm overrides the sglang default.""" + env = {"GRAIL_PIPELINE_BACKEND": "vllm"} with patch.dict(os.environ, env, clear=False): cfg = PipelineConfig.from_env() - assert cfg.backend == "sglang" + assert cfg.backend == "vllm" def test_from_env_gpu_ids() -> None: @@ -61,25 +56,8 @@ def test_from_env_float_params() -> None: def test_from_env_empty_values_use_defaults() -> None: """Empty env values fall back to class defaults.""" env = { - "GRAIL_PIPELINE_ENABLED": "", "GRAIL_PIPELINE_BACKEND": "", } with patch.dict(os.environ, env, clear=False): cfg = PipelineConfig.from_env() - assert cfg.enabled is False - assert cfg.backend == "vllm" - - -def test_from_env_bool_variants() -> None: - """Various true/false string representations.""" - for true_val in ("1", "true", "True", "TRUE", "yes", "YES"): - env = {"GRAIL_PIPELINE_ENABLED": true_val} - with patch.dict(os.environ, env, clear=False): - cfg = PipelineConfig.from_env() - assert cfg.enabled is True, f"Failed for {true_val!r}" - - for false_val in ("0", "false", "no", "nope"): - env = {"GRAIL_PIPELINE_ENABLED": false_val} - with patch.dict(os.environ, env, clear=False): - cfg = PipelineConfig.from_env() - assert cfg.enabled is False, f"Failed for {false_val!r}" + assert cfg.backend == "sglang" diff --git a/tests/unit/mining/test_engine.py b/tests/unit/mining/test_engine.py index 6c4712ac..c3e43e56 100644 --- a/tests/unit/mining/test_engine.py +++ b/tests/unit/mining/test_engine.py @@ -1,4 +1,4 @@ -"""Tests for PipelinedMiningEngine pipeline overlap, drain, and exceptions.""" +"""Tests for PipelinedMiningEngine lifecycle and proof submission.""" from __future__ import annotations @@ -6,13 +6,14 @@ import pytest +from grail.environments.backends import GenerationParams from grail.mining.config import PipelineConfig from grail.mining.engine import PipelinedMiningEngine @pytest.fixture def config() -> PipelineConfig: - return PipelineConfig(enabled=True) + return PipelineConfig() @pytest.fixture @@ -32,33 +33,64 @@ def mock_proof_worker() -> MagicMock: return pw +def _make_engine( + config: PipelineConfig, + mock_weight_sync: MagicMock, + mock_proof_worker: MagicMock, + gen_params: GenerationParams | None = None, +) -> PipelinedMiningEngine: + return PipelinedMiningEngine(config, mock_weight_sync, mock_proof_worker, gen_params=gen_params) + + class TestPipelinedMiningEngine: - def test_init( + """Engine lifecycle and proof dispatch.""" + + def test_default_gen_params( self, config: PipelineConfig, mock_weight_sync: MagicMock, mock_proof_worker: MagicMock, ) -> None: - engine = PipelinedMiningEngine(config, mock_weight_sync, mock_proof_worker) - assert engine._config is config + """Engine uses default GenerationParams when none provided.""" + engine = _make_engine(config, mock_weight_sync, mock_proof_worker) + assert isinstance(engine._gen_params, GenerationParams) + engine.shutdown() - def test_shutdown( + def test_custom_gen_params_propagated( self, config: PipelineConfig, mock_weight_sync: MagicMock, mock_proof_worker: MagicMock, ) -> None: - engine = PipelinedMiningEngine(config, mock_weight_sync, mock_proof_worker) + """Custom gen_params override the default.""" + params = GenerationParams(temperature=0.5, max_new_tokens=128) + engine = _make_engine(config, mock_weight_sync, mock_proof_worker, gen_params=params) + assert engine._gen_params.temperature == 0.5 + assert engine._gen_params.max_new_tokens == 128 + engine.shutdown() + + def test_shutdown_stops_proof_worker( + self, + config: PipelineConfig, + mock_weight_sync: MagicMock, + mock_proof_worker: MagicMock, + ) -> None: + """Shutdown delegates to proof worker and is idempotent.""" + engine = _make_engine(config, mock_weight_sync, mock_proof_worker) engine.shutdown() mock_proof_worker.shutdown.assert_called_once() - def test_submit_proofs_returns_future( + # Second shutdown should not raise + engine.shutdown() + + def test_submit_proofs_dispatches_to_worker( self, config: PipelineConfig, mock_weight_sync: MagicMock, mock_proof_worker: MagicMock, ) -> None: - engine = PipelinedMiningEngine(config, mock_weight_sync, mock_proof_worker) + """submit_proofs dispatches to proof_worker and returns valid results.""" + engine = _make_engine(config, mock_weight_sync, mock_proof_worker) batch_data = [([1, 2, 3, 4, 5], 2, 1.0, {"success": True})] mock_proof_worker.compute_commitments_and_logprobs.return_value = [ ([{"sketch_hash": "abc"}], [0.1, 0.2, 0.3], b"sig", {"randomness": "hex"}, "v1") @@ -68,5 +100,8 @@ def test_submit_proofs_returns_future( result = future.result(timeout=5) assert len(result) == 1 - assert result[0][0] == [{"sketch_hash": "abc"}] + commitments, logprobs, _sig, _beacon, version = result[0] + assert commitments == [{"sketch_hash": "abc"}] + assert logprobs == [0.1, 0.2, 0.3] + assert version == "v1" engine.shutdown() diff --git a/tests/unit/mining/test_weight_sync.py b/tests/unit/mining/test_weight_sync.py index da6ac9c4..d72409ec 100644 --- a/tests/unit/mining/test_weight_sync.py +++ b/tests/unit/mining/test_weight_sync.py @@ -14,7 +14,6 @@ @pytest.fixture def config() -> PipelineConfig: return PipelineConfig( - enabled=True, backend="vllm", vllm_gpu=0, proof_gpu=1, @@ -62,6 +61,50 @@ async def test_shutdown_no_op_when_not_started( await sync.shutdown() assert sync._manager is None + @pytest.mark.asyncio + async def test_start_passes_max_model_len_to_backend( + self, config: PipelineConfig, mock_tokenizer: MagicMock + ) -> None: + """Regression: SGLangServerBackend must receive PipelineConfig.max_model_len. + + Without it, the backend's _cap_max_tokens clamp is a no-op and overlong + prompts hit the server, surface as failed retries, and produce empty + completions instead of clean clamping. Was a real bug in the Stage 1 + refactor before being caught in code review. + """ + from unittest.mock import AsyncMock, patch + + cfg = PipelineConfig( + backend="sglang", + vllm_gpu=0, + proof_gpu=1, + max_model_len=12288, + server_timeout=10.0, + max_concurrent_requests=4, + ) + sync = SGLangWeightSync(cfg, mock_tokenizer) + + fake_manager = MagicMock() + fake_manager.__aenter__ = AsyncMock(return_value=fake_manager) + fake_manager.start_server = AsyncMock() + fake_manager.base_url = "http://localhost:30000" + fake_manager.model_name = "Qwen/Qwen2.5-0.5B-Instruct" + + with ( + patch( + "grail.trainer.inference_server.SGLangServerManager", + return_value=fake_manager, + ), + patch("grail.environments.backends.SGLangServerBackend") as fake_backend_cls, + ): + await sync.start("/some/checkpoint/path") + + fake_backend_cls.assert_called_once() + kwargs = fake_backend_cls.call_args.kwargs + assert kwargs.get("max_model_len") == 12288, ( + f"SGLangServerBackend was constructed without max_model_len: kwargs={kwargs}" + ) + class TestVLLMWeightSync: """Test VLLMWeightSync with mocked server manager.""" diff --git a/tests/unit/protocol/test_proof_unit.py b/tests/unit/protocol/test_proof_unit.py index 4de47895..9f34b915 100644 --- a/tests/unit/protocol/test_proof_unit.py +++ b/tests/unit/protocol/test_proof_unit.py @@ -6,16 +6,16 @@ import pytest import torch +from grail.protocol.constants import ( + PROOF_NUM_BUCKETS, + PROOF_SKETCH_TOLERANCE_BASE, +) from grail.protocol.grail_verifier import ( GRAILVerifier, adaptive_sketch_tolerance, log_magnitude_bucket, log_magnitude_bucket_vectorized, ) -from grail.shared.constants import ( - PROOF_NUM_BUCKETS, - PROOF_SKETCH_TOLERANCE_BASE, -) class TestLogMagnitudeBucket: @@ -168,11 +168,17 @@ def test_robustness_to_small_drift(self, verifier: GRAILVerifier, randomness: st assert diagnostics["sketch_diff"] < PROOF_SKETCH_TOLERANCE_BASE def test_rejects_different_hidden_state(self, verifier: GRAILVerifier, randomness: str) -> None: - """Completely different hidden state should be rejected.""" + """Completely different hidden state should be rejected. + + Uses log-normal scaled activations to produce realistic bucket diversity + (spanning multiple magnitude buckets), unlike randn which maps everything + to bucket +/-1. + """ torch.manual_seed(7) r_vec = verifier.generate_r_vec(randomness) - hidden1 = torch.randn(4096) - hidden2 = torch.randn(4096) # Completely different + # Log-normal scaling gives realistic activation magnitude diversity + hidden1 = torch.randn(4096) * torch.randn(4096).abs().clamp(min=0.01) * 10 + hidden2 = torch.randn(4096) * torch.randn(4096).abs().clamp(min=0.01) * 10 commitment = verifier.create_commitment(hidden1, r_vec) @@ -180,7 +186,9 @@ def test_rejects_different_hidden_state(self, verifier: GRAILVerifier, randomnes hidden2, commitment, r_vec, sequence_length=100, position=0 ) - assert not is_valid, "Different hidden state should be rejected" + assert not is_valid, ( + f"Different hidden state should be rejected, sketch_diff={diagnostics['sketch_diff']}" + ) class TestVectorizedBucketing: @@ -236,7 +244,7 @@ class TestCreateCommitmentsBatch: @pytest.fixture def verifier(self) -> GRAILVerifier: - return GRAILVerifier(hidden_dim=3584, topk=32) + return GRAILVerifier(hidden_dim=3584, topk=16) @pytest.fixture def randomness(self) -> str: diff --git a/tests/unit/shared/test_basics.py b/tests/unit/shared/test_basics.py index 21668cc7..72e991a8 100644 --- a/tests/unit/shared/test_basics.py +++ b/tests/unit/shared/test_basics.py @@ -1,7 +1,9 @@ +"""Sanity checks for protocol constants and test environment defaults.""" + from __future__ import annotations from grail.monitoring.config import MonitoringConfig -from grail.shared.constants import ( +from grail.protocol.constants import ( ROLLOUTS_PER_PROBLEM, SUPERLINEAR_EXPONENT, WINDOW_LENGTH, @@ -9,7 +11,7 @@ def test_constants_are_reasonable() -> None: - # Basic sanity checks on shared constants used broadly across the system + """Protocol constants have valid types and ranges (catches accidental edits).""" assert isinstance(WINDOW_LENGTH, int) and WINDOW_LENGTH > 0 assert isinstance(ROLLOUTS_PER_PROBLEM, int) and ROLLOUTS_PER_PROBLEM >= 1 assert isinstance(SUPERLINEAR_EXPONENT, float) @@ -17,9 +19,8 @@ def test_constants_are_reasonable() -> None: def test_monitoring_config_disabled_env() -> None: - # With defaults from conftest, monitoring should be effectively disabled + """Conftest autouse fixture disables monitoring; verify it takes effect.""" assert MonitoringConfig.is_monitoring_enabled() is False cfg = MonitoringConfig.from_environment() - # Ensure the parsed configuration reflects disabled/NULL backend assert cfg["backend_type"] == "null" assert cfg["mode"] in {"disabled", "online", "offline"} diff --git a/tests/unit/test_qwen_batch_equivalence.py b/tests/unit/test_qwen_batch_equivalence.py index 7f8d34dc..355d531d 100644 --- a/tests/unit/test_qwen_batch_equivalence.py +++ b/tests/unit/test_qwen_batch_equivalence.py @@ -1,21 +1,9 @@ -"""Unit test proving Qwen models don't support left-padded batch equivalence. +"""Qwen left-padded batching produces divergent hidden states. -This test suite demonstrates why GRAIL proof computation cannot use batched forward -passes with left-padding on Qwen models, even with explicit position_ids and -attention_mask parameters. - -The primary test shows that hidden states diverge by ~0.3-0.5 (300-500x the tolerance), -which was causing all proof verifications to fail in production. - -Additional exploratory tests check if alternative configurations can achieve -numerical equivalence: -- Eager attention (standard PyTorch instead of Flash Attention) -- BFloat16 precision (better numerical stability than float16) -- Float32 precision (full precision) -- Combinations of attention implementations and dtypes - -These tests help determine if there's any viable way to use batched inference -for proof computation, or if unbatched forward passes remain necessary. +Regression test proving why GRAIL proof computation must use unbatched forward +passes. Float16/bfloat16 accumulation order differs between batched and unbatched +paths, causing hidden state divergence of ~0.3-0.5 (300-500x tolerance). +Only float32+eager achieves equivalence, which is too slow for production. """ import os @@ -32,762 +20,99 @@ ), ] +MODEL_NAME = os.getenv("QWEN_EQ_TEST_MODEL", "Qwen/Qwen2.5-0.5B-Instruct") -@torch.inference_mode() -def _run_unbatched(model, tokens, device): - """Run a single sequence without padding and return hidden states.""" - input_ids = torch.tensor(tokens, dtype=torch.long, device=device).unsqueeze(0) - outputs = model(input_ids, output_hidden_states=True) - return outputs.hidden_states[-1][0].detach().to("cpu") +# Sequences with different lengths to force left-padding. +PROMPT_SHORT = "You are given a math problem. Show work." +PROMPT_LONG = "You are given a different math problem. Think carefully and produce the final tags." -@torch.inference_mode() -def _run_unbatched_with_logits(model, tokens, device): - """Run a single sequence without padding and return hidden states + logits.""" - input_ids = torch.tensor(tokens, dtype=torch.long, device=device).unsqueeze(0) - outputs = model(input_ids, output_hidden_states=True) - return (outputs.hidden_states[-1][0].detach().to("cpu"), outputs.logits[0].detach().to("cpu")) +@pytest.fixture(scope="module") +def tokenizer(): + """Shared tokenizer for the module.""" + return AutoTokenizer.from_pretrained(MODEL_NAME) @torch.inference_mode() -def _run_left_padded_batch(model, batch_tokens, device, pad_id): - """Run a left-padded batch with explicit attention mask + position IDs.""" - max_len = max(len(seq) for seq in batch_tokens) - padded_inputs = [] - attention_masks = [] - position_ids = [] - left_pads = [] - for seq in batch_tokens: - pad_len = max_len - len(seq) - left_pads.append(pad_len) - padded_inputs.append([pad_id] * pad_len + seq) - attention_masks.append([0] * pad_len + [1] * len(seq)) - position_ids.append([0] * pad_len + list(range(len(seq)))) +def _hidden_divergence(model, tokenizer, device): + """Measure max hidden-state divergence between unbatched and left-padded batched forward.""" + seq_a = tokenizer(PROMPT_SHORT, return_tensors="pt")["input_ids"][0].tolist() + seq_b = tokenizer(PROMPT_LONG, return_tensors="pt")["input_ids"][0].tolist() + assert len(seq_b) > len(seq_a) - input_ids = torch.tensor(padded_inputs, dtype=torch.long, device=device) - attention_mask = torch.tensor(attention_masks, dtype=torch.long, device=device) - pos_ids = torch.tensor(position_ids, dtype=torch.long, device=device) + pad_id = tokenizer.pad_token_id or tokenizer.eos_token_id - outputs = model( - input_ids=input_ids, - attention_mask=attention_mask, - position_ids=pos_ids, - output_hidden_states=True, + # Unbatched + ids_single = torch.tensor(seq_a, dtype=torch.long, device=device).unsqueeze(0) + single_hidden = model(ids_single, output_hidden_states=True).hidden_states[-1][0] + + # Left-padded batch + pad_len = len(seq_b) - len(seq_a) + input_ids = torch.tensor([[pad_id] * pad_len + seq_a, seq_b], dtype=torch.long, device=device) + attention_mask = torch.tensor( + [[0] * pad_len + [1] * len(seq_a), [1] * len(seq_b)], + dtype=torch.long, + device=device, + ) + position_ids = torch.tensor( + [[0] * pad_len + list(range(len(seq_a))), list(range(len(seq_b)))], + dtype=torch.long, + device=device, ) - hidden_states = outputs.hidden_states[-1].detach().to("cpu") - return hidden_states, left_pads - - -@torch.inference_mode() -def _run_left_padded_batch_with_logits(model, batch_tokens, device, pad_id): - """Run a left-padded batch with explicit attention mask + position IDs, return hidden states + logits.""" - max_len = max(len(seq) for seq in batch_tokens) - padded_inputs = [] - attention_masks = [] - position_ids = [] - left_pads = [] - for seq in batch_tokens: - pad_len = max_len - len(seq) - left_pads.append(pad_len) - padded_inputs.append([pad_id] * pad_len + seq) - attention_masks.append([0] * pad_len + [1] * len(seq)) - position_ids.append([0] * pad_len + list(range(len(seq)))) - - input_ids = torch.tensor(padded_inputs, dtype=torch.long, device=device) - attention_mask = torch.tensor(attention_masks, dtype=torch.long, device=device) - pos_ids = torch.tensor(position_ids, dtype=torch.long, device=device) - outputs = model( + batch_hidden = model( input_ids=input_ids, attention_mask=attention_mask, - position_ids=pos_ids, + position_ids=position_ids, output_hidden_states=True, - ) - hidden_states = outputs.hidden_states[-1].detach().to("cpu") - logits = outputs.logits.detach().to("cpu") - return hidden_states, logits, left_pads + ).hidden_states[-1][0, pad_len : pad_len + len(seq_a)] + return torch.max(torch.abs(single_hidden - batch_hidden)).item() -def test_left_padded_batch_differs_from_unbatched(): - """Regression test: Left-padded batching produces different hidden states on Qwen. - This test proves why the miner must use unbatched forward passes for proofs. - Even with explicit position_ids and attention_mask, Qwen's fused attention - kernels produce different hidden states when using left-padding vs no padding. - - Typical divergence: ~0.3-0.5 (300-500x larger than tolerance) - This is why proofs were failing in production before the unbatching fix. - """ - model_name = os.getenv("QWEN_EQ_TEST_MODEL", "Qwen/Qwen2.5-0.5B-Instruct") - tokenizer = AutoTokenizer.from_pretrained(model_name) - - # Try to use default attention (usually Flash Attention 2 for Qwen) +def test_left_padded_batch_diverges_from_unbatched(tokenizer): + """Regression: default float16 batching diverges, proving unbatched proofs are required.""" model = AutoModelForCausalLM.from_pretrained( - model_name, - torch_dtype=torch.float16, - device_map="cuda", + MODEL_NAME, torch_dtype=torch.float16, device_map="cuda" ).eval() - # Two sequences with different lengths to force left padding. - seq_a = tokenizer("You are given a math problem. Show work.", return_tensors="pt")["input_ids"][ - 0 - ].tolist() - seq_b = tokenizer( - "You are given a different math problem. Think carefully and produce the final tags.", - return_tensors="pt", - )["input_ids"][0].tolist() - - # Sanity: ensure seq_b is longer to trigger non-zero left pad for seq_a. - assert len(seq_b) > len(seq_a) - - device = "cuda" - pad_id = tokenizer.pad_token_id or tokenizer.eos_token_id - - single_hidden = _run_unbatched(model, seq_a, device) - batch_hidden, left_pads = _run_left_padded_batch( - model, - [seq_a, seq_b], - device, - pad_id, - ) - - left_pad_a = left_pads[0] - batched_slice = batch_hidden[0, left_pad_a : left_pad_a + len(seq_a)] - - diff = torch.max(torch.abs(single_hidden - batched_slice)).item() - - # Empirically, Qwen diverges by >1e-3 even with explicit masks/position ids. - print(f"\n📊 Hidden state divergence: {diff:.6f} (threshold: 1e-3)") - print(f" Left padding: {left_pad_a} tokens") - print(f" Sequence length: {len(seq_a)} tokens") - + diff = _hidden_divergence(model, tokenizer, "cuda") assert diff > 1e-3, ( - f"Expected meaningful divergence (>1e-3), got max hidden-state diff {diff:.6f}" - ) - - -def test_eager_attention_batch_equivalence(): - """Test if eager attention implementation achieves better numerical equivalence. - - This test checks whether switching from Flash Attention to eager (standard PyTorch) - attention reduces or eliminates the hidden state divergence seen with batched - left-padding. - - Flash Attention uses fused kernels that may have different numerical behavior - with padding masks. Eager attention uses standard PyTorch operations which may - be more numerically stable across different batching strategies. - """ - model_name = os.getenv("QWEN_EQ_TEST_MODEL", "Qwen/Qwen2.5-0.5B-Instruct") - tokenizer = AutoTokenizer.from_pretrained(model_name) - - # Use eager attention implementation for better numerical stability - model = AutoModelForCausalLM.from_pretrained( - model_name, - torch_dtype=torch.float16, - device_map="cuda", - attn_implementation="eager", # Force standard PyTorch attention - ).eval() - - # Two sequences with different lengths to force left padding. - seq_a = tokenizer("You are given a math problem. Show work.", return_tensors="pt")["input_ids"][ - 0 - ].tolist() - seq_b = tokenizer( - "You are given a different math problem. Think carefully and produce the final tags.", - return_tensors="pt", - )["input_ids"][0].tolist() - - # Sanity: ensure seq_b is longer to trigger non-zero left pad for seq_a. - assert len(seq_b) > len(seq_a) - - device = "cuda" - pad_id = tokenizer.pad_token_id or tokenizer.eos_token_id - - single_hidden = _run_unbatched(model, seq_a, device) - batch_hidden, left_pads = _run_left_padded_batch( - model, - [seq_a, seq_b], - device, - pad_id, - ) - - left_pad_a = left_pads[0] - batched_slice = batch_hidden[0, left_pad_a : left_pad_a + len(seq_a)] - - diff = torch.max(torch.abs(single_hidden - batched_slice)).item() - - print(f"\n📊 [Eager Attention] Hidden state divergence: {diff:.6f}") - print(f" Left padding: {left_pad_a} tokens") - print(f" Sequence length: {len(seq_a)} tokens") - - if diff < 1e-3: - print(" ✅ SUCCESS: Eager attention achieves numerical equivalence!") - else: - print(f" ⚠️ Still diverges by {diff:.6f} (threshold: 1e-3)") - - # This is an exploratory test - we don't assert pass/fail - # Just report the results for investigation - - -def test_float32_batch_equivalence(): - """Test if float32 precision achieves better numerical equivalence. - - This test checks whether using full float32 precision instead of float16 - reduces or eliminates the hidden state divergence with batched left-padding. - - Higher precision may reduce numerical errors that accumulate differently - between batched and unbatched forward passes. - """ - model_name = os.getenv("QWEN_EQ_TEST_MODEL", "Qwen/Qwen2.5-0.5B-Instruct") - tokenizer = AutoTokenizer.from_pretrained(model_name) - - # Use float32 for maximum numerical precision - model = AutoModelForCausalLM.from_pretrained( - model_name, - torch_dtype=torch.float32, - device_map="cuda", - ).eval() - - # Two sequences with different lengths to force left padding. - seq_a = tokenizer("You are given a math problem. Show work.", return_tensors="pt")["input_ids"][ - 0 - ].tolist() - seq_b = tokenizer( - "You are given a different math problem. Think carefully and produce the final tags.", - return_tensors="pt", - )["input_ids"][0].tolist() - - # Sanity: ensure seq_b is longer to trigger non-zero left pad for seq_a. - assert len(seq_b) > len(seq_a) - - device = "cuda" - pad_id = tokenizer.pad_token_id or tokenizer.eos_token_id - - single_hidden = _run_unbatched(model, seq_a, device) - batch_hidden, left_pads = _run_left_padded_batch( - model, - [seq_a, seq_b], - device, - pad_id, + f"Expected meaningful divergence (>1e-3), got {diff:.6f}. " + "If this passes, batched proofs may now be viable." ) - left_pad_a = left_pads[0] - batched_slice = batch_hidden[0, left_pad_a : left_pad_a + len(seq_a)] - - diff = torch.max(torch.abs(single_hidden - batched_slice)).item() - print(f"\n📊 [Float32] Hidden state divergence: {diff:.6f}") - print(f" Left padding: {left_pad_a} tokens") - print(f" Sequence length: {len(seq_a)} tokens") - - if diff < 1e-3: - print(" ✅ SUCCESS: Float32 precision achieves numerical equivalence!") - else: - print(f" ⚠️ Still diverges by {diff:.6f} (threshold: 1e-3)") - - # This is an exploratory test - we don't assert pass/fail - # Just report the results for investigation - - -def test_bfloat16_batch_equivalence(): - """Test if bfloat16 precision achieves better numerical equivalence. - - This test checks whether using bfloat16 instead of float16 reduces or - eliminates the hidden state divergence with batched left-padding. - - BFloat16 has the same dynamic range as float32 (8 exponent bits) but - reduced mantissa precision (7 bits vs 10 for float16). This can provide - better numerical stability than float16 in some operations while being - faster than float32. +@pytest.mark.parametrize( + "dtype,attn_impl", + [ + (torch.float16, "eager"), + (torch.bfloat16, None), + (torch.bfloat16, "eager"), + (torch.float32, None), + (torch.float32, "eager"), + ], + ids=["fp16-eager", "bf16-default", "bf16-eager", "fp32-default", "fp32-eager"], +) +def test_batch_equivalence_across_configs(tokenizer, dtype, attn_impl): + """Exploratory: which (dtype, attention) combos achieve batching equivalence? + + Known results: only float32 configs achieve < 1e-3 divergence. + This test documents the behavior without asserting pass/fail, since + the outcome depends on hardware and driver versions. """ - model_name = os.getenv("QWEN_EQ_TEST_MODEL", "Qwen/Qwen2.5-0.5B-Instruct") - tokenizer = AutoTokenizer.from_pretrained(model_name) - - # Use bfloat16 for better numerical stability than float16 - model = AutoModelForCausalLM.from_pretrained( - model_name, - torch_dtype=torch.bfloat16, - device_map="cuda", - ).eval() - - # Two sequences with different lengths to force left padding. - seq_a = tokenizer("You are given a math problem. Show work.", return_tensors="pt")["input_ids"][ - 0 - ].tolist() - seq_b = tokenizer( - "You are given a different math problem. Think carefully and produce the final tags.", - return_tensors="pt", - )["input_ids"][0].tolist() - - # Sanity: ensure seq_b is longer to trigger non-zero left pad for seq_a. - assert len(seq_b) > len(seq_a) - - device = "cuda" - pad_id = tokenizer.pad_token_id or tokenizer.eos_token_id - - single_hidden = _run_unbatched(model, seq_a, device) - batch_hidden, left_pads = _run_left_padded_batch( - model, - [seq_a, seq_b], - device, - pad_id, - ) - - left_pad_a = left_pads[0] - batched_slice = batch_hidden[0, left_pad_a : left_pad_a + len(seq_a)] - - diff = torch.max(torch.abs(single_hidden - batched_slice)).item() - - print(f"\n📊 [BFloat16] Hidden state divergence: {diff:.6f}") - print(f" Left padding: {left_pad_a} tokens") - print(f" Sequence length: {len(seq_a)} tokens") - - if diff < 1e-3: - print(" ✅ SUCCESS: BFloat16 precision achieves numerical equivalence!") - else: - print(f" ⚠️ Still diverges by {diff:.6f} (threshold: 1e-3)") - - # This is an exploratory test - we don't assert pass/fail - # Just report the results for investigation - - -def test_eager_bfloat16_batch_equivalence(): - """Test if combining eager attention + bfloat16 achieves numerical equivalence. - - This test combines eager attention (standard PyTorch) with bfloat16 precision. - This may offer a good balance between numerical stability and performance. - """ - model_name = os.getenv("QWEN_EQ_TEST_MODEL", "Qwen/Qwen2.5-0.5B-Instruct") - tokenizer = AutoTokenizer.from_pretrained(model_name) - - # Use eager attention with bfloat16 - model = AutoModelForCausalLM.from_pretrained( - model_name, - torch_dtype=torch.bfloat16, - device_map="cuda", - attn_implementation="eager", - ).eval() - - # Two sequences with different lengths to force left padding. - seq_a = tokenizer("You are given a math problem. Show work.", return_tensors="pt")["input_ids"][ - 0 - ].tolist() - seq_b = tokenizer( - "You are given a different math problem. Think carefully and produce the final tags.", - return_tensors="pt", - )["input_ids"][0].tolist() - - # Sanity: ensure seq_b is longer to trigger non-zero left pad for seq_a. - assert len(seq_b) > len(seq_a) - - device = "cuda" - pad_id = tokenizer.pad_token_id or tokenizer.eos_token_id - - single_hidden = _run_unbatched(model, seq_a, device) - batch_hidden, left_pads = _run_left_padded_batch( - model, - [seq_a, seq_b], - device, - pad_id, - ) + kwargs = {"torch_dtype": dtype, "device_map": "cuda"} + if attn_impl: + kwargs["attn_implementation"] = attn_impl - left_pad_a = left_pads[0] - batched_slice = batch_hidden[0, left_pad_a : left_pad_a + len(seq_a)] + model = AutoModelForCausalLM.from_pretrained(MODEL_NAME, **kwargs).eval() + diff = _hidden_divergence(model, tokenizer, "cuda") - diff = torch.max(torch.abs(single_hidden - batched_slice)).item() - - print(f"\n📊 [Eager + BFloat16] Hidden state divergence: {diff:.6f}") - print(f" Left padding: {left_pad_a} tokens") - print(f" Sequence length: {len(seq_a)} tokens") - - if diff < 1e-3: - print(" ✅ SUCCESS: Eager+BFloat16 achieves numerical equivalence!") - else: - print(f" ⚠️ Still diverges by {diff:.6f} (threshold: 1e-3)") - - # This is an exploratory test - we don't assert pass/fail - # Just report the results for investigation - - -def test_eager_float32_batch_equivalence(): - """Test if combining eager attention + float32 achieves numerical equivalence. - - This test combines both strategies: eager attention (no Flash Attention kernels) - and full float32 precision to see if this eliminates the batching divergence. - - This is the most numerically stable configuration but also the slowest. - """ - model_name = os.getenv("QWEN_EQ_TEST_MODEL", "Qwen/Qwen2.5-0.5B-Instruct") - tokenizer = AutoTokenizer.from_pretrained(model_name) - - # Use both eager attention and float32 for maximum stability - model = AutoModelForCausalLM.from_pretrained( - model_name, - torch_dtype=torch.float32, - device_map="cuda", - attn_implementation="eager", - ).eval() - - # Two sequences with different lengths to force left padding. - seq_a = tokenizer("You are given a math problem. Show work.", return_tensors="pt")["input_ids"][ - 0 - ].tolist() - seq_b = tokenizer( - "You are given a different math problem. Think carefully and produce the final tags.", - return_tensors="pt", - )["input_ids"][0].tolist() - - # Sanity: ensure seq_b is longer to trigger non-zero left pad for seq_a. - assert len(seq_b) > len(seq_a) - - device = "cuda" - pad_id = tokenizer.pad_token_id or tokenizer.eos_token_id - - single_hidden = _run_unbatched(model, seq_a, device) - batch_hidden, left_pads = _run_left_padded_batch( - model, - [seq_a, seq_b], - device, - pad_id, - ) - - left_pad_a = left_pads[0] - batched_slice = batch_hidden[0, left_pad_a : left_pad_a + len(seq_a)] - - diff = torch.max(torch.abs(single_hidden - batched_slice)).item() - - print(f"\n📊 [Eager + Float32] Hidden state divergence: {diff:.6f}") - print(f" Left padding: {left_pad_a} tokens") - print(f" Sequence length: {len(seq_a)} tokens") + del model + torch.cuda.empty_cache() + label = f"{dtype} + {attn_impl or 'default'}" if diff < 1e-3: - print(" ✅ SUCCESS: Eager+Float32 achieves numerical equivalence!") + print(f"\n {label}: divergence {diff:.6f} -- equivalence achieved") else: - print(f" ⚠️ Still diverges by {diff:.6f} (threshold: 1e-3)") - - # This is an exploratory test - we don't assert pass/fail - # Just report the results for investigation - - -def test_diagnose_padding_issue(): - """Diagnostic test to understand why position_ids don't achieve equivalence. - - This test examines layer-by-layer hidden states to identify where the - divergence occurs and helps understand the root cause. - """ - model_name = os.getenv("QWEN_EQ_TEST_MODEL", "Qwen/Qwen2.5-0.5B-Instruct") - tokenizer = AutoTokenizer.from_pretrained(model_name) - - # Use eager attention and float16 to isolate the issue - model = AutoModelForCausalLM.from_pretrained( - model_name, - torch_dtype=torch.float16, - device_map="cuda", - attn_implementation="eager", - ).eval() - - # Simple short sequence for easier debugging - seq_a = tokenizer("Hello world", return_tensors="pt")["input_ids"][0].tolist() - seq_b = tokenizer("Hello world this is longer", return_tensors="pt")["input_ids"][0].tolist() - - assert len(seq_b) > len(seq_a) - - device = "cuda" - pad_id = tokenizer.pad_token_id or tokenizer.eos_token_id - - # Unbatched run - input_ids_single = torch.tensor(seq_a, dtype=torch.long, device=device).unsqueeze(0) - outputs_single = model(input_ids_single, output_hidden_states=True) - - # Batched run - max_len = max(len(seq_a), len(seq_b)) - pad_len = max_len - len(seq_a) - - padded_a = [pad_id] * pad_len + seq_a - padded_b = seq_b - attention_mask_a = [0] * pad_len + [1] * len(seq_a) - attention_mask_b = [1] * len(seq_b) - position_ids_a = [0] * pad_len + list(range(len(seq_a))) - position_ids_b = list(range(len(seq_b))) - - input_ids_batch = torch.tensor([padded_a, padded_b], dtype=torch.long, device=device) - attention_mask_batch = torch.tensor( - [attention_mask_a, attention_mask_b], dtype=torch.long, device=device - ) - position_ids_batch = torch.tensor( - [position_ids_a, position_ids_b], dtype=torch.long, device=device - ) - - print("\n🔍 Diagnostic Information:") - print(f" Sequence A length: {len(seq_a)}") - print(f" Left padding: {pad_len} tokens") - print(f" Position IDs (padded): {position_ids_a}") - print(f" Attention mask: {attention_mask_a}") - - outputs_batch = model( - input_ids=input_ids_batch, - attention_mask=attention_mask_batch, - position_ids=position_ids_batch, - output_hidden_states=True, - ) - - # Compare layer by layer - print("\n📊 Layer-by-layer divergence:") - num_layers = len(outputs_single.hidden_states) - - for layer_idx in range(num_layers): - single_hidden = outputs_single.hidden_states[layer_idx][0].detach().to("cpu") - batch_hidden = outputs_batch.hidden_states[layer_idx][0, pad_len:].detach().to("cpu") - - diff = torch.max(torch.abs(single_hidden - batch_hidden)).item() - - layer_name = f"Layer {layer_idx}" if layer_idx > 0 else "Embedding" - print(f" {layer_name:12s}: {diff:.6f}") - - if layer_idx == 1 and diff > 0.01: - print(" ⚠️ Divergence starts early (layer 1)!") - break - - print("\n💡 Key insight: Divergence starts at Layer 1 (first transformer layer)") - print(" This means RoPE/position embeddings work correctly,") - print(" but something in attention or feedforward causes divergence.") - - -def test_understand_root_cause(): - """Deep dive: What causes the divergence even with position_ids? - - Theory: Float16 accumulation order differs between batched and unbatched. - Even though attention_mask prevents attending to padding, the actual - computation order/grouping differs, causing float16 rounding errors to - accumulate differently. - - This test validates that the issue is fundamentally about floating-point - arithmetic, not missing parameters. - """ - model_name = os.getenv("QWEN_EQ_TEST_MODEL", "Qwen/Qwen2.5-0.5B-Instruct") - tokenizer = AutoTokenizer.from_pretrained(model_name) - - print("\n" + "=" * 70) - print("ROOT CAUSE ANALYSIS") - print("=" * 70) - - # Test 1: Eager attention with float16 - print("\n📊 Test 1: Eager Attention + Float16") - model_f16 = AutoModelForCausalLM.from_pretrained( - model_name, - torch_dtype=torch.float16, - device_map="cuda", - attn_implementation="eager", - ).eval() - - seq_a = tokenizer("Test", return_tensors="pt")["input_ids"][0].tolist() - seq_b = tokenizer("Test sequence", return_tensors="pt")["input_ids"][0].tolist() - device = "cuda" - pad_id = tokenizer.pad_token_id or tokenizer.eos_token_id - - single_hidden_f16 = _run_unbatched(model_f16, seq_a, device) - batch_hidden_f16, left_pads_f16 = _run_left_padded_batch( - model_f16, [seq_a, seq_b], device, pad_id - ) - diff_f16 = torch.max( - torch.abs( - single_hidden_f16 - - batch_hidden_f16[0, left_pads_f16[0] : left_pads_f16[0] + len(seq_a)] - ) - ).item() - print(f" Divergence: {diff_f16:.6f}") - - del model_f16 - torch.cuda.empty_cache() - - # Test 2: Eager attention with float32 - print("\n📊 Test 2: Eager Attention + Float32") - model_f32 = AutoModelForCausalLM.from_pretrained( - model_name, - torch_dtype=torch.float32, - device_map="cuda", - attn_implementation="eager", - ).eval() - - single_hidden_f32 = _run_unbatched(model_f32, seq_a, device) - batch_hidden_f32, left_pads_f32 = _run_left_padded_batch( - model_f32, [seq_a, seq_b], device, pad_id - ) - diff_f32 = torch.max( - torch.abs( - single_hidden_f32 - - batch_hidden_f32[0, left_pads_f32[0] : left_pads_f32[0] + len(seq_a)] - ) - ).item() - print(f" Divergence: {diff_f32:.6f}") - - del model_f32 - torch.cuda.empty_cache() - - print("\n" + "=" * 70) - print("CONCLUSION") - print("=" * 70) - print(f"Float16 divergence: {diff_f16:.6f} ({'FAIL' if diff_f16 > 1e-3 else 'PASS'})") - print(f"Float32 divergence: {diff_f32:.6f} ({'FAIL' if diff_f32 > 1e-3 else 'PASS'})") - print() - print("🎯 ROOT CAUSE: Floating-point accumulation order") - print() - print("In batched processing, PyTorch groups operations differently:") - print("- Batched: Operations vectorized across batch dimension") - print(" - Matrix multiplications grouped: [batch, seq, hidden] x [hidden, hidden]") - print(" - Reductions and norms computed with batch dimension present") - print() - print("- Unbatched: Operations on single sequence") - print(" - Matrix multiplications: [1, seq, hidden] x [hidden, hidden]") - print(" - Different memory layout and computation grouping") - print() - print("Float16 has only ~3 decimal digits of precision. These tiny") - print("differences in computation order cause rounding errors that") - print("accumulate across 24 transformer layers, resulting in 0.3-0.5") - print("divergence.") - print() - print("Float32 has ~7 decimal digits, so the accumulation errors stay") - print("below 1e-3 threshold.") - print() - print("⚠️ This is NOT a bug - it's inherent to float16 arithmetic!") - print(" Even with perfect position_ids and attention_mask, you cannot") - print(" achieve exact numerical equivalence with float16 batching.") - print() - - -def test_compare_logits_divergence(): - """Compare how hidden state divergence affects final logits. - - This test measures divergence in both hidden states and logits to understand - whether the hidden state differences actually impact the final predictions. - - Key metrics: - - Max absolute logit difference - - Top-1 token agreement - - Top-5 token overlap - """ - model_name = os.getenv("QWEN_EQ_TEST_MODEL", "Qwen/Qwen2.5-0.5B-Instruct") - tokenizer = AutoTokenizer.from_pretrained(model_name) - - print("\n" + "=" * 70) - print("HIDDEN STATES vs LOGITS DIVERGENCE ANALYSIS") - print("=" * 70) - - device = "cuda" - pad_id = tokenizer.pad_token_id or tokenizer.eos_token_id - - # Use a meaningful sequence for better analysis - seq_a = tokenizer("You are given a math problem.", return_tensors="pt")["input_ids"][0].tolist() - seq_b = tokenizer( - "You are given a different math problem with more tokens.", return_tensors="pt" - )["input_ids"][0].tolist() - - assert len(seq_b) > len(seq_a) - - # Test different configurations - configs = [ - ("Float16 + Flash Attention", torch.float16, None), - ("Float16 + Eager Attention", torch.float16, "eager"), - ("BFloat16 + Flash Attention", torch.bfloat16, None), - ("Float32 + Flash Attention", torch.float32, None), - ("Float32 + Eager Attention", torch.float32, "eager"), - ] - - results = [] - - for config_name, dtype, attn_impl in configs: - print(f"\n📊 Testing: {config_name}") - print("-" * 70) - - kwargs = {"torch_dtype": dtype, "device_map": "cuda"} - if attn_impl: - kwargs["attn_implementation"] = attn_impl - - model = AutoModelForCausalLM.from_pretrained(model_name, **kwargs).eval() - - # Run unbatched - single_hidden, single_logits = _run_unbatched_with_logits(model, seq_a, device) - - # Run batched - batch_hidden, batch_logits, left_pads = _run_left_padded_batch_with_logits( - model, [seq_a, seq_b], device, pad_id - ) - - left_pad_a = left_pads[0] - seq_len = len(seq_a) - - # Extract the relevant slice from batch - batched_hidden_slice = batch_hidden[0, left_pad_a : left_pad_a + seq_len] - batched_logits_slice = batch_logits[0, left_pad_a : left_pad_a + seq_len] - - # Calculate hidden state divergence - hidden_diff = torch.max(torch.abs(single_hidden - batched_hidden_slice)).item() - - # Calculate logit divergence - logit_diff = torch.max(torch.abs(single_logits - batched_logits_slice)).item() - - # Check top-1 token agreement (most important for generation) - single_top1 = torch.argmax(single_logits, dim=-1) - batched_top1 = torch.argmax(batched_logits_slice, dim=-1) - top1_agreement = (single_top1 == batched_top1).float().mean().item() - - # Check top-5 overlap - single_top5 = torch.topk(single_logits, k=5, dim=-1).indices - batched_top5 = torch.topk(batched_logits_slice, k=5, dim=-1).indices - - # Calculate average top-5 overlap per position - top5_overlaps = [] - for pos in range(seq_len): - overlap = len(set(single_top5[pos].tolist()) & set(batched_top5[pos].tolist())) - top5_overlaps.append(overlap / 5.0) - avg_top5_overlap = sum(top5_overlaps) / len(top5_overlaps) - - print(f" Hidden state max diff: {hidden_diff:.6f}") - print(f" Logit max diff: {logit_diff:.6f}") - print(f" Top-1 agreement: {top1_agreement * 100:.1f}%") - print(f" Avg Top-5 overlap: {avg_top5_overlap * 100:.1f}%") - - results.append( - { - "config": config_name, - "hidden_diff": hidden_diff, - "logit_diff": logit_diff, - "top1_agreement": top1_agreement, - "top5_overlap": avg_top5_overlap, - } - ) - - del model - torch.cuda.empty_cache() - - # Summary - print("\n" + "=" * 70) - print("SUMMARY: Impact on Predictions") - print("=" * 70) - - for r in results: - status = ( - "✅ PASS" - if r["top1_agreement"] == 1.0 - else f"⚠️ {r['top1_agreement'] * 100:.1f}% agree" - ) - print(f"\n{r['config']}:") - print(f" Hidden diff: {r['hidden_diff']:.4f} → Logit diff: {r['logit_diff']:.4f}") - print(f" Top-1 tokens: {status}") - - print("\n" + "=" * 70) - print("💡 KEY INSIGHTS:") - print("=" * 70) - print() - print("1. Hidden state divergence amplifies through the LM head:") - print(" - Hidden states differ by ~0.5-6.0") - print(" - Logits can differ by 10-100x more!") - print() - print("2. Float16/BFloat16 may produce DIFFERENT TOP-1 TOKENS") - print(" - This directly affects generation quality") - print(" - Critical for proof verification where exact tokens matter") - print() - print("3. Float32 achieves both numerical and token equivalence") - print(" - Small hidden state differences don't propagate") - print(" - Top-1 tokens remain identical") - print() + print(f"\n {label}: divergence {diff:.6f} -- still diverges") diff --git a/tests/unit/test_weight_computer.py b/tests/unit/test_weight_computer.py index 5734a5cd..348a3e17 100644 --- a/tests/unit/test_weight_computer.py +++ b/tests/unit/test_weight_computer.py @@ -7,8 +7,8 @@ import pytest +from grail.protocol.constants import UNIQUE_ROLLOUTS_CAP from grail.scoring.weights import WeightComputer -from grail.shared.constants import UNIQUE_ROLLOUTS_CAP # --------------------------------------------------------------------------- # Helpers diff --git a/tests/unit/trainer/test_advantages.py b/tests/unit/trainer/test_advantages.py new file mode 100644 index 00000000..79a1304d --- /dev/null +++ b/tests/unit/trainer/test_advantages.py @@ -0,0 +1,96 @@ +"""Tests for trainer-side advantage estimation strategies.""" + +from __future__ import annotations + +import pytest + +from grail.trainer.advantages import compute_advantages + +# ── GRPO (zero-mean, variance-normalized) ──────────────────────────── + + +def test_grpo_zero_mean() -> None: + """GRPO advantages are zero-mean and default estimator is 'grpo'.""" + advs = compute_advantages([1.0, 2.0, 3.0, 4.0, 5.0], estimator="grpo") + assert len(advs) == 5 + assert abs(sum(advs)) < 1e-6 + # Default estimator should be grpo + assert compute_advantages([1.0, 2.0, 3.0]) == compute_advantages( + [1.0, 2.0, 3.0], estimator="grpo" + ) + + +def test_grpo_variance_normalized() -> None: + """GRPO normalizes to unit variance.""" + advs = compute_advantages([1.0, 2.0, 3.0, 4.0], estimator="grpo") + variance = sum(a * a for a in advs) / len(advs) + assert abs(variance - 1.0) < 1e-6 + + +def test_grpo_uniform_rewards() -> None: + """Uniform rewards produce zero advantages (no signal).""" + advs = compute_advantages([3.0, 3.0, 3.0], estimator="grpo") + assert all(abs(a) < 1e-6 for a in advs) + + +def test_grpo_empty() -> None: + """Empty reward list returns empty advantages.""" + assert compute_advantages([], estimator="grpo") == [] + + +# ── DR-GRPO (zero-mean, no variance normalization) ────────────────── + + +def test_dr_grpo_preserves_magnitude() -> None: + """DR-GRPO subtracts mean but does not normalize by std.""" + advs = compute_advantages([0.0, 10.0], estimator="dr_grpo") + # mean = 5.0, so advantages should be [-5.0, 5.0] (not normalized by std) + assert abs(advs[0] - (-5.0)) < 1e-6 + assert abs(advs[1] - 5.0) < 1e-6 + + +def test_dr_grpo_uniform() -> None: + """Uniform rewards produce zero advantages under DR-GRPO.""" + advs = compute_advantages([2.0, 2.0, 2.0], estimator="dr_grpo") + assert all(abs(a) < 1e-6 for a in advs) + + +# ── DAPO (rank-based) ─────────────────────────────────────────────── + + +def test_dapo_rank_ordering() -> None: + """DAPO advantages are strictly ordered by reward rank.""" + advs = compute_advantages([10.0, 20.0, 30.0, 40.0], estimator="dapo") + for i in range(len(advs) - 1): + assert advs[i] < advs[i + 1] + + +def test_dapo_ties() -> None: + """Tied rewards get equal advantages under DAPO.""" + advs = compute_advantages([1.0, 2.0, 2.0, 3.0], estimator="dapo") + assert abs(advs[1] - advs[2]) < 1e-6 + + +def test_dapo_range() -> None: + """DAPO maps lowest rank to -1.0 and highest to +1.0.""" + advs = compute_advantages([1.0, 2.0, 3.0], estimator="dapo") + # Lowest rank maps to -1.0, highest to 1.0 + assert abs(advs[0] - (-1.0)) < 1e-6 + assert abs(advs[2] - 1.0) < 1e-6 + + +# ── Dispatch / cross-cutting ──────────────────────────────────────── + + +def test_unknown_estimator_raises() -> None: + """Invalid estimator name is rejected with a clear error.""" + with pytest.raises(ValueError, match="Unknown advantage estimator"): + compute_advantages([1.0, 2.0], estimator="ppo") + + +def test_all_estimators_produce_zero_mean() -> None: + """Cross-cutting invariant: all estimators produce zero-mean advantages.""" + rewards = [0.1, 0.5, 0.3, 0.9, 0.2] + for est in ("grpo", "dr_grpo", "dapo"): + advs = compute_advantages(rewards, estimator=est) + assert abs(sum(advs)) < 1e-6, f"{est} advantages don't sum to 0" diff --git a/tests/unit/trainer/test_distributed_integration.py b/tests/unit/trainer/test_distributed_integration.py new file mode 100644 index 00000000..ac0cc7b5 --- /dev/null +++ b/tests/unit/trainer/test_distributed_integration.py @@ -0,0 +1,519 @@ +"""Tests for distributed training integration into TrainerNeuron. + +Verifies the Phase 1 integration: mp.Process spawn, IPC wiring, +snapshot adopt, and orchestrator lifecycle. All tests run on CPU +without requiring NCCL or multiple GPUs. +""" + +from __future__ import annotations + +import json +import multiprocessing +import os +import tempfile +import time +from pathlib import Path +from typing import TYPE_CHECKING +from unittest.mock import Mock, patch + +import pytest + +from grail.trainer.ipc import IPCChannels, create_ipc_channels +from grail.trainer.snapshot_manager import SnapshotManager + +if TYPE_CHECKING: + from grail.neurons.trainer import TrainerNeuron + +# IPC cross-process tests need a consistent mp context. On Linux the default +# is "fork"; on macOS 3.12+ it's "spawn". Production code calls +# set_start_method("spawn") early. We pick the safest option per platform: +# "fork" on Linux (avoids the SemLock fork/spawn mismatch), "spawn" elsewhere. +_MP_CTX = multiprocessing.get_context("fork" if os.name != "nt" else "spawn") + + +# --------------------------------------------------------------------------- +# adopt_snapshot_atomic +# --------------------------------------------------------------------------- + + +class TestAdoptSnapshotAtomic: + """Test SnapshotManager.adopt_snapshot_atomic() method.""" + + def test_adopt_creates_latest_from_source(self) -> None: + """adopt_snapshot_atomic moves source_dir to snapshots/latest.""" + with tempfile.TemporaryDirectory() as tmpdir: + manager = SnapshotManager(Path(tmpdir)) + + # Simulate FSDP2 save_full_checkpoint writing to a temp dir + source = manager.snapshot_dir / "epoch.tmp.0" + source.mkdir(parents=True) + (source / "model.safetensors").write_bytes(b"fake-weights") + (source / "config.json").write_text('{"model_type": "qwen2"}') + + metadata = {"epoch": 1, "timestamp": time.time(), "window": 100} + manager.adopt_snapshot_atomic(source, metadata) + + # Source should be renamed (no longer exists at original path) + assert not source.exists() + + # latest/ should exist with the files + latest = manager.snapshot_dir / "latest" + assert latest.exists() + assert (latest / "model.safetensors").read_bytes() == b"fake-weights" + assert (latest / "config.json").exists() + + # Metadata should be written + meta_path = latest / "snapshot_metadata.json" + assert meta_path.exists() + saved_meta = json.loads(meta_path.read_text()) + assert saved_meta["epoch"] == 1 + assert saved_meta["window"] == 100 + + def test_adopt_sets_snapshot_ready_marker(self) -> None: + """adopt_snapshot_atomic touches the SNAPSHOT_READY marker.""" + with tempfile.TemporaryDirectory() as tmpdir: + manager = SnapshotManager(Path(tmpdir)) + + source = manager.snapshot_dir / "initial.tmp.123" + source.mkdir(parents=True) + (source / "model.safetensors").write_bytes(b"weights") + + assert not manager.check_snapshot_ready() + + manager.adopt_snapshot_atomic(source, {"epoch": 0}) + + assert manager.check_snapshot_ready() + + def test_adopt_replaces_existing_latest(self) -> None: + """adopt_snapshot_atomic atomically replaces an existing latest/.""" + with tempfile.TemporaryDirectory() as tmpdir: + manager = SnapshotManager(Path(tmpdir)) + + # Create initial latest/ + mock_model = Mock() + mock_model.save_pretrained = Mock() + mock_tokenizer = Mock() + mock_tokenizer.save_pretrained = Mock() + manager.save_snapshot_atomic( + mock_model, mock_tokenizer, {"epoch": 0, "timestamp": time.time()} + ) + + # Now adopt a new checkpoint + source = manager.snapshot_dir / "epoch.tmp.1" + source.mkdir(parents=True) + (source / "model.safetensors").write_bytes(b"new-weights") + + manager.adopt_snapshot_atomic(source, {"epoch": 1}) + + latest = manager.snapshot_dir / "latest" + assert latest.exists() + assert (latest / "model.safetensors").read_bytes() == b"new-weights" + + meta = json.loads((latest / "snapshot_metadata.json").read_text()) + assert meta["epoch"] == 1 + + def test_adopt_raises_on_missing_source(self) -> None: + """adopt_snapshot_atomic raises FileNotFoundError for missing source.""" + with tempfile.TemporaryDirectory() as tmpdir: + manager = SnapshotManager(Path(tmpdir)) + + with pytest.raises(FileNotFoundError): + manager.adopt_snapshot_atomic(Path(tmpdir) / "nonexistent", {"epoch": 0}) + + def test_adopt_then_copy_to_staging(self) -> None: + """Full pipeline: adopt -> check_ready -> copy_to_staging -> cleanup.""" + with tempfile.TemporaryDirectory() as tmpdir: + manager = SnapshotManager(Path(tmpdir)) + + # Simulate FSDP2 checkpoint write + source = manager.snapshot_dir / "epoch.tmp.42" + source.mkdir(parents=True) + (source / "model.safetensors").write_bytes(b"checkpoint-data") + (source / "tokenizer.json").write_text('{"type": "test"}') + + # Adopt + manager.adopt_snapshot_atomic(source, {"epoch": 42, "timestamp": time.time()}) + assert manager.check_snapshot_ready() + + # Upload worker: copy to staging + staging = manager.copy_snapshot_to_staging() + assert staging.exists() + assert (staging / "model.safetensors").read_bytes() == b"checkpoint-data" + assert (staging / "snapshot_metadata.json").exists() + assert not manager.check_snapshot_ready() + + # Upload worker: cleanup + manager.cleanup_staging() + assert not staging.exists() + + +# --------------------------------------------------------------------------- +# IPC across processes (verifies ForkingPickler works with spawn) +# --------------------------------------------------------------------------- + + +def _child_heartbeat_worker(ipc: IPCChannels, ready: multiprocessing.Event) -> None: # type: ignore[type-arg] + """Child process that updates heartbeat and signals ready.""" + ipc.update_heartbeat() + ready.set() # type: ignore[union-attr] + + +def _child_stop_worker(ipc: IPCChannels, result_queue: multiprocessing.Queue) -> None: # type: ignore[type-arg] + """Child process that checks stop signal.""" + # Wait for stop signal (with timeout) + for _ in range(50): + if ipc.stop.is_set(): + result_queue.put("stopped") + return + time.sleep(0.01) + result_queue.put("timeout") + + +def _child_snapshot_worker( + ipc: IPCChannels, + snapshot_manager: SnapshotManager, +) -> None: + """Child process that saves a snapshot and queues it via IPC.""" + # Simulate writing a checkpoint + source = snapshot_manager.snapshot_dir / "child.tmp" + source.mkdir(parents=True, exist_ok=True) + (source / "model.safetensors").write_bytes(b"child-weights") + + metadata = {"epoch": 1, "timestamp": time.time(), "window": 200} + snapshot_manager.adopt_snapshot_atomic(source, metadata) + + path = snapshot_manager.get_latest_snapshot_path() + if path: + ipc.queue_snapshot(str(path), metadata, 200) + + +class TestIPCAcrossProcesses: + """Test IPC primitives work across multiprocessing.Process (spawn method).""" + + def test_heartbeat_crosses_process_boundary(self) -> None: + """Heartbeat updated in child process is visible in parent.""" + ctx = _MP_CTX + ipc = create_ipc_channels() + ready = ctx.Event() + + # Initially no heartbeat + assert ipc.get_heartbeat_age() == float("inf") + + p = ctx.Process(target=_child_heartbeat_worker, args=(ipc, ready)) + p.start() + ready.wait(timeout=10) + p.join(timeout=10) + + # Heartbeat should be recent + age = ipc.get_heartbeat_age() + assert age < 5.0, f"Heartbeat age {age}s is too old" + + def test_stop_signal_crosses_process_boundary(self) -> None: + """Stop event set in parent is visible in child process.""" + ctx = _MP_CTX + ipc = create_ipc_channels() + result_queue = ctx.Queue() + + p = ctx.Process(target=_child_stop_worker, args=(ipc, result_queue)) + p.start() + + # Set stop from parent + time.sleep(0.05) + ipc.stop.set() + + p.join(timeout=10) + result = result_queue.get(timeout=5) + assert result == "stopped" + + def test_snapshot_queue_crosses_process_boundary(self) -> None: + """Snapshot queued in child process is consumable in parent.""" + ctx = _MP_CTX + ipc = create_ipc_channels() + + with tempfile.TemporaryDirectory() as tmpdir: + sm = SnapshotManager(Path(tmpdir)) + + p = ctx.Process(target=_child_snapshot_worker, args=(ipc, sm)) + p.start() + p.join(timeout=10) + + # Parent should see the snapshot message + msg = ipc.snapshot_queue.get(timeout=5) + assert msg["type"] == "snapshot_ready" + assert msg["window"] == 200 + assert "epoch" in msg["metadata"] + + # Snapshot should be at latest/ + latest = sm.get_latest_snapshot_path() + assert latest is not None + assert (latest / "model.safetensors").read_bytes() == b"child-weights" + + +# --------------------------------------------------------------------------- +# TrainerNeuron distributed spawning logic +# --------------------------------------------------------------------------- + + +class TestTrainerDistributedSpawning: + """Test TrainerNeuron._start_distributed_training() logic.""" + + def test_nproc_1_uses_single_gpu_path(self) -> None: + """GRAIL_DIST_NPROC=1 or unset uses the single-GPU training process.""" + + with patch.dict(os.environ, {"GRAIL_DIST_NPROC": "1"}, clear=False): + neuron = self._make_neuron() + + with patch.object(neuron, "_start_distributed_training") as mock_dist: + with patch("grail.neurons.trainer.run_training_process"): + with patch("multiprocessing.Process") as mock_proc: + mock_proc.return_value.start = Mock() + mock_proc.return_value.pid = 12345 + neuron._start_training_process() + + mock_dist.assert_not_called() + + def test_nproc_2_uses_distributed_path(self) -> None: + """GRAIL_DIST_NPROC=2 dispatches to _start_distributed_training.""" + + with patch.dict(os.environ, {"GRAIL_DIST_NPROC": "2"}, clear=False): + neuron = self._make_neuron() + + with patch.object(neuron, "_start_distributed_training") as mock_dist: + neuron._start_training_process() + mock_dist.assert_called_once_with(2) + + def test_distributed_disables_eval(self) -> None: + """_start_distributed_training disables local evaluation.""" + + neuron = self._make_neuron() + # Force eval enabled (server .env may have it disabled) + neuron._eval_cfg.enabled = True + assert neuron._eval_cfg.enabled + + with patch("grail.trainer.distributed.launcher.run_distributed_training_process"): + with patch("multiprocessing.Process") as mock_proc: + mock_proc.return_value.start = Mock() + mock_proc.return_value.pid = 99999 + neuron._start_distributed_training(2) + + assert not neuron._eval_cfg.enabled + + def test_distributed_spawns_n_processes(self) -> None: + """_start_distributed_training spawns exactly N processes.""" + + neuron = self._make_neuron() + + with patch("grail.trainer.distributed.launcher.run_distributed_training_process"): + with patch("multiprocessing.Process") as mock_proc: + mock_instance = Mock() + mock_instance.start = Mock() + mock_instance.pid = 10000 + mock_proc.return_value = mock_instance + neuron._start_distributed_training(3) + + assert mock_proc.call_count == 3 + + # Verify rank 0 is used as _training_process for health checks + assert neuron._training_process is not None + + @pytest.mark.asyncio + async def test_shutdown_terminates_all_distributed_processes(self) -> None: + """_shutdown_processes handles all distributed processes.""" + + neuron = self._make_neuron() + + # Simulate distributed processes + procs = [] + for _ in range(3): + p = Mock() + p.pid = 10000 + p.is_alive.return_value = False + p.join = Mock() + procs.append(p) + + neuron._distributed_processes = procs + neuron._upload_process = Mock() + neuron._upload_process.pid = 20000 + neuron._upload_process.is_alive.return_value = False + neuron._upload_process.join = Mock() + + await neuron._shutdown_processes() + + # All processes should have been joined + for p in procs: + p.join.assert_called() + + @pytest.mark.asyncio + async def test_health_check_detects_dead_distributed_rank(self) -> None: + """_check_process_health logs error when a distributed rank dies.""" + + neuron = self._make_neuron() + + # Rank 1 is dead + procs = [] + for i in range(2): + p = Mock() + p.pid = 10000 + i + p.is_alive.return_value = i == 0 # rank 0 alive, rank 1 dead + procs.append(p) + + neuron._distributed_processes = procs + neuron._upload_process = Mock() + neuron._upload_process.is_alive.return_value = True + + with patch("grail.neurons.trainer.logger") as mock_logger: + await neuron._check_process_health() + # Should log error for dead rank + mock_logger.error.assert_called() + error_msg = mock_logger.error.call_args[0][0] + assert "rank" in error_msg.lower() or "Distributed" in error_msg + + def _make_neuron(self) -> TrainerNeuron: + """Create a TrainerNeuron with all dependencies mocked.""" + from grail.model.train_loading import ModelLoadSpec + from grail.neurons.trainer import TrainerContext, TrainerNeuron + + context = TrainerContext( + wallet=Mock(), + credentials=Mock(), + checkpoint_publisher=None, + monitor=None, + train_spec=ModelLoadSpec(mode="hf", hf_id="test/model"), + ref_spec=ModelLoadSpec(mode="hf", hf_id="test/model"), + ) + + tmpdir = Path(tempfile.mkdtemp()) + + with ( + patch("multiprocessing.set_start_method"), + patch.dict(os.environ, {"GRAIL_CACHE_DIR": str(tmpdir)}, clear=False), + ): + neuron = TrainerNeuron(context) + + return neuron + + +# --------------------------------------------------------------------------- +# run_distributed_training_process argument validation +# --------------------------------------------------------------------------- + + +class TestRunDistributedTrainingProcess: + """Test the run_distributed_training_process entry point.""" + + def test_function_signature_accepts_required_args(self) -> None: + """Verify the function has the expected signature.""" + import inspect + + from grail.trainer.distributed.launcher import run_distributed_training_process + + sig = inspect.signature(run_distributed_training_process) + params = list(sig.parameters.keys()) + + # Core params that must exist + assert "rank" in params + assert "world_size" in params + assert "ipc" in params + assert "snapshot_manager" in params + assert "train_cfg" in params + assert "dist_cfg" in params + assert "credentials" in params + assert "wallet_args" in params + + def test_ipc_channels_work_across_spawn_process(self) -> None: + """IPCChannels work across process boundary (spawn method). + + Uses the module-level _child_heartbeat_worker which is picklable. + """ + ctx = _MP_CTX + ipc = create_ipc_channels() + ready = ctx.Event() + + p = ctx.Process(target=_child_heartbeat_worker, args=(ipc, ready)) + p.start() + ready.wait(timeout=10) + p.join(timeout=10) + + assert ipc.get_heartbeat_age() < 5.0 + + def test_snapshot_manager_is_picklable(self) -> None: + """SnapshotManager can be pickled (required for spawn start method).""" + import pickle + + with tempfile.TemporaryDirectory() as tmpdir: + sm = SnapshotManager(Path(tmpdir)) + + data = pickle.dumps(sm) + restored = pickle.loads(data) + + assert restored.cache_root == sm.cache_root + + def test_training_config_is_picklable(self) -> None: + """TrainingConfig can be pickled (required for spawn start method).""" + import pickle + + from grail.trainer.config import TrainingConfig + + cfg = TrainingConfig() + cfg.grpo_variant = "grpo" + + data = pickle.dumps(cfg) + restored = pickle.loads(data) + + assert restored.grpo_variant == "grpo" + + def test_distributed_config_is_picklable(self) -> None: + """DistributedConfig can be pickled (required for spawn start method).""" + import pickle + + from grail.trainer.distributed.config import DistributedConfig + + cfg = DistributedConfig(tp_degree=1) + + data = pickle.dumps(cfg) + restored = pickle.loads(data) + + assert restored.tp_degree == 1 + + +# --------------------------------------------------------------------------- +# _run_training IPC integration (mocked, no NCCL) +# --------------------------------------------------------------------------- + + +class TestRunTrainingIPCIntegration: + """Test that _run_training() properly uses IPC when provided.""" + + @pytest.mark.asyncio + async def test_stop_flag_breaks_loop(self) -> None: + """Training loop exits when ipc.stop is set before first iteration. + + Uses rank=1 (non-zero) to skip rank 0's chain initialization which + requires a real subtensor connection. + """ + from grail.trainer.distributed.launcher import _run_training + + ipc = create_ipc_channels() + ipc.stop.set() # Pre-set stop + + service = Mock() + device = Mock() + + # rank=1 skips chain init. _should_stop() returns True immediately. + with patch("grail.trainer.distributed.launcher.dist") as mock_dist: + mock_dist.broadcast = Mock() + await _run_training( + service=service, + rank=1, + device=device, + train_config=Mock(), + snapshot_manager=None, + wallet=None, + credentials=None, + stop_event=ipc.stop, + test_mode=False, + ipc=ipc, + ) + + # Should not have called _train_epoch (loop never entered) + service._train_epoch.assert_not_called() diff --git a/tests/unit/trainer/test_distributed_strategies.py b/tests/unit/trainer/test_distributed_strategies.py new file mode 100644 index 00000000..cd2cac6a --- /dev/null +++ b/tests/unit/trainer/test_distributed_strategies.py @@ -0,0 +1,915 @@ +"""Tests for DDP and DILOCO distributed training strategies. + +Validates strategy configuration, context behavior, checkpoint dispatch, +and DILOCO outer optimization math. All tests run on CPU without +requiring NCCL or multiple GPUs. +""" + +from __future__ import annotations + +from pathlib import Path +from unittest.mock import MagicMock, patch + +import pytest +import torch +import torch.nn as nn + +from grail.trainer.distributed.compat import DistributedContext +from grail.trainer.distributed.config import DistributedConfig + +# ============================================================================ +# DistributedConfig tests +# ============================================================================ + + +class TestDistributedConfig: + """Test strategy configuration parsing and validation.""" + + def test_default_strategy_is_fsdp2(self) -> None: + config = DistributedConfig() + assert config.strategy == "fsdp2" + + def test_from_env_reads_strategy(self, monkeypatch: pytest.MonkeyPatch) -> None: + monkeypatch.setenv("GRAIL_DIST_STRATEGY", "ddp") + config = DistributedConfig.from_env() + assert config.strategy == "ddp" + + def test_from_env_reads_diloco_strategy(self, monkeypatch: pytest.MonkeyPatch) -> None: + monkeypatch.setenv("GRAIL_DIST_STRATEGY", "diloco") + config = DistributedConfig.from_env() + assert config.strategy == "diloco" + + def test_from_env_invalid_strategy_raises(self, monkeypatch: pytest.MonkeyPatch) -> None: + monkeypatch.setenv("GRAIL_DIST_STRATEGY", "horovod") + with pytest.raises(ValueError, match="Invalid GRAIL_DIST_STRATEGY"): + DistributedConfig.from_env() + + def test_from_env_reads_diloco_hyperparams(self, monkeypatch: pytest.MonkeyPatch) -> None: + monkeypatch.setenv("GRAIL_DIST_STRATEGY", "diloco") + monkeypatch.setenv("GRAIL_DIST_DILOCO_INNER_STEPS", "50") + monkeypatch.setenv("GRAIL_DIST_DILOCO_OUTER_LR", "0.5") + monkeypatch.setenv("GRAIL_DIST_DILOCO_OUTER_MOMENTUM", "0.8") + config = DistributedConfig.from_env() + assert config.diloco_inner_steps == 50 + assert config.diloco_outer_lr == 0.5 + assert config.diloco_outer_momentum == 0.8 + + def test_default_diloco_hyperparams(self) -> None: + config = DistributedConfig(strategy="diloco") + assert config.diloco_inner_steps == 10 + assert config.diloco_outer_lr == 0.7 + assert config.diloco_outer_momentum == 0.9 + + def test_validate_ddp_rejects_tp(self) -> None: + config = DistributedConfig(strategy="ddp", tp_degree=2) + with pytest.raises(ValueError, match="DDP does not support tensor parallelism"): + config.validate(world_size=4) + + def test_validate_diloco_requires_multi_gpu(self) -> None: + config = DistributedConfig(strategy="diloco") + with pytest.raises(ValueError, match="DILOCO requires at least 2 workers"): + config.validate(world_size=1) + + def test_validate_diloco_inner_steps_positive(self) -> None: + config = DistributedConfig(strategy="diloco", diloco_inner_steps=0) + with pytest.raises(ValueError, match="diloco_inner_steps must be >= 1"): + config.validate(world_size=2) + + def test_validate_fsdp2_passes(self) -> None: + config = DistributedConfig(strategy="fsdp2", tp_degree=2) + config.validate(world_size=4) # Should not raise + + def test_validate_ddp_tp0_passes(self) -> None: + config = DistributedConfig(strategy="ddp", tp_degree=0) + config.validate(world_size=2) # Should not raise + + def test_validate_ddp_tp1_passes(self) -> None: + config = DistributedConfig(strategy="ddp", tp_degree=1) + config.validate(world_size=2) # Should not raise + + def test_strategy_case_insensitive(self, monkeypatch: pytest.MonkeyPatch) -> None: + monkeypatch.setenv("GRAIL_DIST_STRATEGY", "DDP") + config = DistributedConfig.from_env() + assert config.strategy == "ddp" + + +# ============================================================================ +# DistributedContext tests +# ============================================================================ + + +class TestDistributedContext: + """Test context behavior across strategies.""" + + def test_unwrap_model_fsdp2_returns_as_is(self) -> None: + ctx = DistributedContext(strategy="fsdp2") + model = nn.Linear(10, 10) + assert ctx.unwrap_model(model) is model + + def test_unwrap_model_ddp_returns_module(self) -> None: + ctx = DistributedContext(strategy="ddp") + inner = nn.Linear(10, 10) + wrapper = MagicMock() + wrapper.module = inner + assert ctx.unwrap_model(wrapper) is inner + + def test_unwrap_model_ddp_no_module_returns_as_is(self) -> None: + ctx = DistributedContext(strategy="ddp") + model = nn.Linear(10, 10) + # nn.Linear doesn't have .module attribute + assert ctx.unwrap_model(model) is model + + def test_unwrap_model_diloco_returns_as_is(self) -> None: + """DILOCO uses DDP wrapping but context.unwrap_model should still work.""" + ctx = DistributedContext(strategy="diloco") + model = nn.Linear(10, 10) + assert ctx.unwrap_model(model) is model + + def test_backward_calls_loss_backward(self) -> None: + ctx = DistributedContext(strategy="ddp") + loss = MagicMock() + ctx.backward(loss) + loss.backward.assert_called_once() + + def test_strategy_stored(self) -> None: + for strategy in ("fsdp2", "ddp", "diloco"): + ctx = DistributedContext(strategy=strategy) # type: ignore[arg-type] + assert ctx._strategy == strategy + + def test_default_strategy_is_fsdp2(self) -> None: + ctx = DistributedContext() + assert ctx._strategy == "fsdp2" + + +# ============================================================================ +# DILOCO outer optimization math tests +# ============================================================================ + + +class TestDilocoOuterStep: + """Test DILOCO pseudo-gradient computation and Nesterov update. + + These tests validate the mathematical correctness of the outer optimization + step without needing distributed communication (single-process simulation). + """ + + @staticmethod + def _make_simple_model() -> nn.Module: + """Create a tiny model for testing.""" + model = nn.Sequential(nn.Linear(4, 8), nn.ReLU(), nn.Linear(8, 2)) + return model + + def test_pseudo_gradient_computation(self) -> None: + """Verify pseudo-gradient = theta_global - theta_local.""" + model = self._make_simple_model() + + # Snapshot global params + global_params = [p.detach().clone() for p in model.parameters()] + + # Simulate inner training: modify model params + with torch.no_grad(): + for p in model.parameters(): + p.add_(torch.randn_like(p) * 0.01) + + # Compute pseudo-gradients + for global_p, local_p in zip(global_params, model.parameters(), strict=False): + delta = global_p - local_p.data + # delta should be the direction to move global params toward local + assert delta.shape == global_p.shape + # After adding random noise to local, delta should be non-zero + assert delta.abs().sum() > 0 + + def test_nesterov_outer_update(self) -> None: + """Verify the outer Nesterov SGD update produces correct results.""" + model = self._make_simple_model() + + # Snapshot global params on CPU + global_params = [p.detach().clone().cpu() for p in model.parameters()] + for gp in global_params: + gp.requires_grad = True + + outer_lr = 0.7 + outer_momentum = 0.9 + outer_optimizer = torch.optim.SGD( + global_params, lr=outer_lr, momentum=outer_momentum, nesterov=True + ) + + # Record initial global params + initial_global = [gp.detach().clone() for gp in global_params] + + # Simulate inner training + with torch.no_grad(): + for p in model.parameters(): + p.add_(torch.randn_like(p) * 0.1) + + # Compute pseudo-gradients and set as .grad + for global_p, local_p in zip(global_params, model.parameters(), strict=False): + delta = global_p.data - local_p.data.cpu() + global_p.grad = delta + + # Apply outer optimizer step + outer_optimizer.step() + outer_optimizer.zero_grad() + + # Global params should have changed + for gp, initial in zip(global_params, initial_global, strict=False): + assert not torch.allclose(gp, initial), "Global params should change after outer step" + + def test_diloco_converges_toward_local(self) -> None: + """After outer step with positive pseudo-gradients, global moves toward local.""" + model = self._make_simple_model() + + # Global params on CPU + global_params = [p.detach().clone().cpu() for p in model.parameters()] + for gp in global_params: + gp.requires_grad = True + + outer_optimizer = torch.optim.SGD(global_params, lr=0.7, momentum=0.9, nesterov=True) + + # Simulate inner training: add a known perturbation + perturbation = 0.5 + with torch.no_grad(): + for p in model.parameters(): + p.add_(perturbation) + + local_params = [p.detach().clone().cpu() for p in model.parameters()] + + # Compute pseudo-gradients: delta = global - local + for global_p, local_p in zip(global_params, local_params, strict=False): + global_p.grad = global_p.data - local_p + + # Record distance before outer step + dist_before = sum( + (gp.data - lp).norm().item() + for gp, lp in zip(global_params, local_params, strict=False) + ) + + outer_optimizer.step() + outer_optimizer.zero_grad() + + # Distance after should be smaller (converging) + dist_after = sum( + (gp.data - lp).norm().item() + for gp, lp in zip(global_params, local_params, strict=False) + ) + assert dist_after < dist_before, ( + f"Global params should move toward local: " + f"dist_before={dist_before:.4f}, dist_after={dist_after:.4f}" + ) + + def test_step_counter_triggers_at_H(self) -> None: + """Verify outer step triggers exactly every H inner steps.""" + H = 3 + triggered_at: list[int] = [] + + for step in range(1, 10): + if step % H == 0: + triggered_at.append(step) + + assert triggered_at == [3, 6, 9] + + def test_multiple_outer_steps_accumulate_momentum(self) -> None: + """Nesterov momentum should accumulate across outer steps.""" + model = self._make_simple_model() + global_params = [p.detach().clone().cpu() for p in model.parameters()] + for gp in global_params: + gp.requires_grad = True + + outer_optimizer = torch.optim.SGD(global_params, lr=0.7, momentum=0.9, nesterov=True) + + deltas: list[float] = [] + + for _step in range(5): + # Apply same perturbation each time + with torch.no_grad(): + for p in model.parameters(): + p.add_(0.01) + + for global_p, local_p in zip(global_params, model.parameters(), strict=False): + global_p.grad = global_p.data - local_p.data.cpu() + + # Record param state before step + before = global_params[0].data.clone() + outer_optimizer.step() + outer_optimizer.zero_grad() + + # Copy global back to local + with torch.no_grad(): + for global_p, local_p in zip(global_params, model.parameters(), strict=False): + local_p.data.copy_(global_p.data) + + delta = (global_params[0].data - before).abs().mean().item() + deltas.append(delta) + + # With momentum, later steps should have larger updates (momentum builds up) + # First step has no momentum, subsequent steps should be larger + assert deltas[1] > deltas[0], "Momentum should increase update magnitude" + + +# ============================================================================ +# Strategy dispatch tests (mock-based) +# ============================================================================ + + +class TestStrategyDispatch: + """Test that the correct parallelism and checkpoint functions are called.""" + + def test_resolve_tp_degree_ddp_forces_1(self) -> None: + from grail.trainer.distributed.launcher import _resolve_tp_degree + + config = DistributedConfig(strategy="ddp", tp_degree=4) + assert _resolve_tp_degree(config, world_size=4) == 1 + + def test_resolve_tp_degree_diloco_forces_1(self) -> None: + from grail.trainer.distributed.launcher import _resolve_tp_degree + + config = DistributedConfig(strategy="diloco", tp_degree=2) + assert _resolve_tp_degree(config, world_size=4) == 1 + + def test_resolve_tp_degree_fsdp2_uses_config(self) -> None: + from grail.trainer.distributed.launcher import _resolve_tp_degree + + config = DistributedConfig(strategy="fsdp2", tp_degree=2) + assert _resolve_tp_degree(config, world_size=4) == 2 + + def test_resolve_tp_degree_fsdp2_auto_detect(self) -> None: + from grail.trainer.distributed.launcher import _resolve_tp_degree + + config = DistributedConfig(strategy="fsdp2", tp_degree=0) + tp = _resolve_tp_degree(config, world_size=4) + assert tp >= 1 # Auto-detected + + +# ============================================================================ +# DDP checkpoint tests +# ============================================================================ + + +class TestDDPCheckpoint: + """Test DDP-specific checkpoint saving.""" + + def test_save_ddp_checkpoint_rank0_creates_files(self, tmp_path: Path) -> None: + """Rank 0 should write model files.""" + from grail.trainer.distributed.checkpoint import save_ddp_checkpoint + + model = MagicMock() + model.module = MagicMock() + tokenizer = MagicMock() + + # Mock dist.barrier to be a no-op + with patch("grail.trainer.distributed.checkpoint.dist") as mock_dist: + mock_dist.barrier = MagicMock() + save_ddp_checkpoint(model, tokenizer, tmp_path / "ckpt", rank=0) + + model.module.save_pretrained.assert_called_once() + tokenizer.save_pretrained.assert_called_once() + + def test_save_ddp_checkpoint_rank1_skips(self, tmp_path: Path) -> None: + """Non-rank-0 should not write anything.""" + from grail.trainer.distributed.checkpoint import save_ddp_checkpoint + + model = MagicMock() + tokenizer = MagicMock() + + with patch("grail.trainer.distributed.checkpoint.dist") as mock_dist: + mock_dist.barrier = MagicMock() + save_ddp_checkpoint(model, tokenizer, tmp_path / "ckpt", rank=1) + + model.save_pretrained.assert_not_called() + tokenizer.save_pretrained.assert_not_called() + + +# ============================================================================ +# apply_ddp tests +# ============================================================================ + + +class TestApplyDDP: + """Test DDP wrapping function.""" + + def test_apply_ddp_returns_ddp_module(self) -> None: + """apply_ddp should wrap model with DistributedDataParallel.""" + from grail.trainer.distributed.parallelism import apply_ddp + + model = nn.Linear(4, 4) + + # DDP requires process group, so we mock the import target + with patch("torch.nn.parallel.DistributedDataParallel") as MockDDP: + mock_wrapped = MagicMock() + MockDDP.return_value = mock_wrapped + + result = apply_ddp(model, local_rank=0) + + MockDDP.assert_called_once_with( + model, + device_ids=[0], + static_graph=False, + gradient_as_bucket_view=True, + ) + assert result is mock_wrapped + + +# ============================================================================ +# PULSE-DiLoCo tests +# ============================================================================ + + +class TestPulseDilocoConfig: + """Test PULSE-DiLoCo configuration.""" + + def test_pulse_default_disabled(self) -> None: + config = DistributedConfig(strategy="diloco") + assert config.pulse_diloco is False + + def test_pulse_from_env(self, monkeypatch: pytest.MonkeyPatch) -> None: + monkeypatch.setenv("GRAIL_DIST_STRATEGY", "diloco") + monkeypatch.setenv("GRAIL_DIST_PULSE_DILOCO", "true") + config = DistributedConfig.from_env() + assert config.pulse_diloco is True + + def test_nesterov_disabled_when_momentum_zero(self) -> None: + """PyTorch requires nesterov=False when momentum=0.""" + config = DistributedConfig(strategy="diloco", diloco_outer_momentum=0.0, pulse_diloco=True) + # Verify the optimizer can be created without error + params = [torch.zeros(4, requires_grad=True)] + opt = torch.optim.SGD( + params, + lr=config.diloco_outer_lr, + momentum=config.diloco_outer_momentum, + nesterov=config.diloco_outer_momentum > 0, + ) + assert opt is not None + + +class TestBF16Gating: + """Test BF16 gating math for PULSE-DiLoCo. + + Uses BF16-exact values for deterministic results (pitfall #2 from guide). + """ + + def test_bf16_roundtrip_exact(self) -> None: + """BF16-exact values survive roundtrip.""" + # 0.15234375 is exactly representable in BF16 + val = torch.tensor([0.15234375], dtype=torch.float32) + roundtripped = val.bfloat16().float() + assert torch.equal(val, roundtripped) + + def test_bf16_gating_zeros_small_changes(self) -> None: + """Changes smaller than BF16 ULP produce zero gated pseudo-grad.""" + # Start with a BF16-exact value + theta = torch.tensor([0.15234375], dtype=torch.float32) + # Add a perturbation smaller than BF16 ULP (~1e-4 for this value) + theta_local = theta + 1e-7 + + b_theta = theta.bfloat16().float() + b_local = theta_local.bfloat16().float() + + # Both round to the same BF16 value: gate would not fire + assert torch.equal(b_theta, b_local) + bf16_diff = b_theta - b_local + assert bf16_diff.item() == 0.0 + + def test_bf16_gating_nonzero_for_large_changes(self) -> None: + """Changes larger than BF16 ULP produce non-zero gated pseudo-grad.""" + theta = torch.tensor([1.0], dtype=torch.float32) + theta_local = theta + 0.01 # Large enough to cross BF16 boundary + + b_theta = theta.bfloat16().float() + b_local = theta_local.bfloat16().float() + + # Should be different at BF16: gate would fire + assert not torch.equal(b_theta, b_local) + bf16_diff = b_theta - b_local + assert bf16_diff.item() != 0.0 + + def test_residual_conservation(self) -> None: + """Residual + transmitted = total signal (conservation invariant).""" + # Simulate one round of PULSE gating + n = 100 + snapshot = torch.randn(n, dtype=torch.float32) + # Make snapshot BF16-exact + snapshot = snapshot.bfloat16().float() + + local_params = snapshot + torch.randn(n) * 0.001 # Small perturbation + residual = torch.zeros(n, dtype=torch.float32) + + delta = snapshot - local_params # Exact pseudo-grad + s = residual + delta + w_tilde = snapshot - s + + # v6: BF16 gate as binary mask, transmit raw FP32 s + b_theta = snapshot.bfloat16().float() + b_w_tilde = w_tilde.bfloat16().float() + gate_mask = b_theta != b_w_tilde + + transmitted = torch.zeros_like(s) + transmitted[gate_mask] = s[gate_mask] + + new_residual = s.clone() + new_residual[gate_mask] = 0 # Fired entries fully consumed + + # v6 conservation: transmitted + residual = total signal at all indices + reconstructed = transmitted + new_residual + assert torch.allclose(reconstructed, s, atol=1e-7), ( + f"Conservation violated: max diff = {(reconstructed - s).abs().max().item()}" + ) + + def test_residual_accumulates_across_rounds(self) -> None: + """After multiple rounds, residuals eventually push entries across BF16 boundary.""" + n = 50 + snapshot = torch.ones(n, dtype=torch.float32) # BF16-exact + residual = torch.zeros(n, dtype=torch.float32) + total_transmitted = torch.zeros(n, dtype=torch.float32) + + # Apply the same tiny perturbation many rounds + perturbation = 1e-5 # Much smaller than BF16 ULP for 1.0 + + for _round in range(200): + local_params = snapshot - perturbation # theta_local slightly less than snapshot + delta = snapshot - local_params # = perturbation (positive) + s = residual + delta + w_tilde = snapshot - s + + # v6: BF16 gate as mask, transmit raw FP32 s + b_theta = snapshot.bfloat16().float() + b_w_tilde = w_tilde.bfloat16().float() + gate_mask = b_theta != b_w_tilde + + transmitted = torch.zeros_like(s) + transmitted[gate_mask] = s[gate_mask] + total_transmitted += transmitted + + # v6 residual: fired=0, unfired=s + residual = s.clone() + residual[gate_mask] = 0 + + # After 200 rounds of perturbation=1e-5, total signal = 200 * 1e-5 = 2e-3 + # At least some entries should have been transmitted + assert total_transmitted.abs().sum() > 0, "Residuals should eventually transmit" + + def test_sparsity_high_for_small_perturbations(self) -> None: + """With small perturbations, most entries should be gated to zero.""" + n = 1000 + snapshot = torch.randn(n, dtype=torch.float32).bfloat16().float() + local_params = snapshot + torch.randn(n) * 1e-7 # Tiny perturbation + residual = torch.zeros(n, dtype=torch.float32) + + delta = snapshot - local_params + s = residual + delta + w_tilde = snapshot - s + + # v6: gate mask determines sparsity + b_theta = snapshot.bfloat16().float() + b_w_tilde = w_tilde.bfloat16().float() + gate_mask = b_theta != b_w_tilde + + nnz = gate_mask.sum().item() + sparsity = 1.0 - nnz / n + # With 1e-7 perturbation, almost everything should be sparse + assert sparsity > 0.9, f"Expected high sparsity, got {sparsity:.2%}" + + def test_all_entries_fire(self) -> None: + """When all entries cross BF16 boundary, residual is zero everywhere.""" + n = 100 + snapshot = torch.ones(n, dtype=torch.float32) # BF16-exact + local_params = snapshot + 0.1 # Large perturbation, all entries fire + residual = torch.zeros(n, dtype=torch.float32) + + delta = snapshot - local_params + s = residual + delta + w_tilde = snapshot - s + gate_mask = snapshot.bfloat16().float() != w_tilde.bfloat16().float() + + assert gate_mask.all(), "All entries should fire with large perturbation" + transmitted = torch.zeros_like(s) + transmitted[gate_mask] = s[gate_mask] + new_residual = s.clone() + new_residual[gate_mask] = 0 + + assert torch.equal(transmitted, s), "All-fire: transmitted must equal s" + assert (new_residual == 0).all(), "All-fire: residual must be all zeros" + + def test_no_entries_fire(self) -> None: + """When no entries cross BF16 boundary, transmitted is zero and residual = s.""" + n = 100 + snapshot = torch.ones(n, dtype=torch.float32) # BF16-exact + # Zero perturbation: local == snapshot + local_params = snapshot.clone() + residual = torch.zeros(n, dtype=torch.float32) + + delta = snapshot - local_params # all zeros + s = residual + delta # all zeros + w_tilde = snapshot - s + gate_mask = snapshot.bfloat16().float() != w_tilde.bfloat16().float() + + assert not gate_mask.any(), "No entries should fire with zero perturbation" + transmitted = torch.zeros_like(s) + new_residual = s.clone() + + assert (transmitted == 0).all(), "No-fire: transmitted must be all zeros" + assert torch.equal(new_residual, s), "No-fire: residual must equal s" + + def test_deterministic_residual_crossover(self) -> None: + """Known residual pushes a specific entry across BF16 boundary next round.""" + # Empirically: at snapshot=2.0, the gate fires when s >= 0.004 + # (w_tilde=1.996 rounds to 2.0, w_tilde=1.9960 rounds to 1.992188) + snapshot = torch.tensor([2.0], dtype=torch.float32) + + # Round 1: s = 0.003, below firing threshold -> doesn't fire + residual = torch.tensor([0.001], dtype=torch.float32) + delta1 = torch.tensor([0.002]) + s1 = residual + delta1 # 0.003 + w_tilde1 = snapshot - s1 # 1.997 + gate1 = snapshot.bfloat16().float() != w_tilde1.bfloat16().float() + assert not gate1.item(), "Round 1: should NOT fire (s=0.003 below threshold)" + residual = s1.clone() # carry forward + + # Round 2: s = 0.003 + 0.002 = 0.005, above firing threshold -> fires + delta2 = torch.tensor([0.002]) + s2 = residual + delta2 # 0.005 >= 0.004 + w_tilde2 = snapshot - s2 # 1.995 + gate2 = snapshot.bfloat16().float() != w_tilde2.bfloat16().float() + assert gate2.item(), "Round 2: MUST fire (residual accumulated past threshold)" + + def test_full_pulse_round_single_worker(self) -> None: + """End-to-end single-worker v6 PULSE step: gate, transmit, outer step, reset.""" + model = nn.Sequential(nn.Linear(4, 8), nn.ReLU(), nn.Linear(8, 2)) + + # Snapshot global params + global_params = [p.detach().clone().cpu() for p in model.parameters()] + for gp in global_params: + gp.requires_grad = True + + total_numel = sum(p.numel() for p in global_params) + residual = torch.zeros(total_numel, dtype=torch.float32) + + # Simulate inner training + with torch.no_grad(): + for p in model.parameters(): + p.add_(torch.randn_like(p) * 0.1) + + local_params = list(model.parameters()) + + # Step 1: v6 BF16-gated FP32 pseudo-gradient + flat_pseudo_grad = torch.zeros(total_numel, dtype=torch.float32) + offset = 0 + for global_p, local_p in zip(global_params, local_params, strict=True): + numel = global_p.numel() + param_cpu = local_p.data.cpu().float() + snapshot = global_p.data.view(-1) + + delta = snapshot - param_cpu.view(-1) + s = residual[offset : offset + numel] + delta + w_tilde = snapshot - s + + b_theta = snapshot.bfloat16().float() + b_w_tilde = w_tilde.bfloat16().float() + gate_mask = b_theta != b_w_tilde + + transmitted = torch.zeros_like(s) + transmitted[gate_mask] = s[gate_mask] + flat_pseudo_grad[offset : offset + numel] = transmitted + + residual[offset : offset + numel] = s + residual[offset : offset + numel][gate_mask] = 0 + offset += numel + + # Step 2: Single worker, so avg_pseudo_grad = flat_pseudo_grad + avg_pseudo_grad = flat_pseudo_grad + + # Step 3: Outer optimizer + outer_opt = torch.optim.SGD(global_params, lr=0.7, momentum=0.9, nesterov=True) + offset = 0 + for global_p in global_params: + numel = global_p.numel() + global_p.grad = avg_pseudo_grad[offset : offset + numel].view(global_p.shape) + offset += numel + + before = [gp.data.clone() for gp in global_params] + outer_opt.step() + outer_opt.zero_grad() + + # Step 4: Hard reset + with torch.no_grad(): + for global_p, local_p in zip(global_params, local_params, strict=True): + local_p.data.copy_(global_p) + + # Verify: global params changed (at least for non-zero pseudo-grads) + any_changed = any( + not torch.allclose(gp, b) for gp, b in zip(global_params, before, strict=True) + ) + assert any_changed, "Global params should change after outer step" + + # Verify: local params match global params after reset + for global_p, local_p in zip(global_params, local_params, strict=True): + assert torch.equal(local_p.data.cpu(), global_p.data) + + +# ============================================================================ +# Resume checkpoint tests +# ============================================================================ + + +class TestResumeCheckpoint: + """Test DILOCO resume checkpoint save/load round-trip.""" + + def test_diloco_state_roundtrip(self, tmp_path: Path) -> None: + """Save and load DILOCO state, verify exact restoration.""" + # Simulate DILOCO state + global_params = [torch.randn(4, 4) for _ in range(3)] + for gp in global_params: + gp.requires_grad = True + + outer_opt = torch.optim.SGD(global_params, lr=0.7, momentum=0.9, nesterov=True) + + # Run a fake outer step to populate momentum buffers + for gp in global_params: + gp.grad = torch.randn_like(gp) + outer_opt.step() + outer_opt.zero_grad() + + inner_step = 7 + epoch = 42 + + # Save + state = { + "epoch_counter": epoch, + "diloco_inner_step_counter": inner_step, + "diloco_global_params": [p.data.clone() for p in global_params], + "diloco_outer_optimizer_state_dict": outer_opt.state_dict(), + } + torch.save(state, tmp_path / "resume_state.pt") + + # Load into fresh state + loaded = torch.load(tmp_path / "resume_state.pt", weights_only=False) + + assert loaded["epoch_counter"] == epoch + assert loaded["diloco_inner_step_counter"] == inner_step + for saved, original in zip(loaded["diloco_global_params"], global_params, strict=True): + assert torch.equal(saved, original.data) + + # Restore optimizer into a new optimizer with same params + new_params = [torch.randn(4, 4) for _ in range(3)] + for np_p in new_params: + np_p.requires_grad = True + new_opt = torch.optim.SGD(new_params, lr=0.7, momentum=0.9, nesterov=True) + new_opt.load_state_dict(loaded["diloco_outer_optimizer_state_dict"]) + + # Momentum buffers should match + for (_, orig_state), (_, new_state) in zip( + outer_opt.state.items(), new_opt.state.items(), strict=True + ): + assert torch.equal(orig_state["momentum_buffer"], new_state["momentum_buffer"]) + + def test_pulse_residual_roundtrip(self, tmp_path: Path) -> None: + """PULSE residual buffer survives save/load.""" + residual = torch.randn(1000, dtype=torch.float32) + + state = {"pulse_residual_buffer": residual.clone()} + torch.save(state, tmp_path / "resume_state.pt") + + loaded = torch.load(tmp_path / "resume_state.pt", weights_only=False) + assert torch.equal(loaded["pulse_residual_buffer"], residual) + + def test_resume_without_checkpoint_returns_false(self, tmp_path: Path) -> None: + """No resume file means no loading.""" + # The file doesn't exist, so the path check should fail + resume_path = tmp_path / "resume_state.pt" + assert not resume_path.exists() + + +# ============================================================================ +# P1 fix: DILOCO snapshot adopt gating +# ============================================================================ + + +class TestDilocoSnapshotGating: + """Verify that snapshots/latest is NOT overwritten between DILOCO outer syncs. + + The launcher must only call adopt_snapshot_atomic() when a DILOCO outer sync + has happened, to prevent exposing non-consensus (diverged) weights to the + upload worker or evaluation resource loader. + """ + + def test_diloco_non_sync_skips_weight_save(self) -> None: + """When _diloco_sync_happened is False, no model weights should be saved.""" + service = MagicMock() + service.dist_config.strategy = "diloco" + service._diloco_sync_happened = False + + # should_save_weights logic from launcher + strategy = service.dist_config.strategy + diloco_synced = service._diloco_sync_happened + should_save_weights = strategy != "diloco" or diloco_synced + + assert not should_save_weights, ( + "DILOCO non-sync epoch must not save weights (would overwrite snapshots/latest)" + ) + + def test_diloco_sync_epoch_saves_weights(self) -> None: + """When _diloco_sync_happened is True, weights should be saved and adopted.""" + service = MagicMock() + service.dist_config.strategy = "diloco" + service._diloco_sync_happened = True + + strategy = service.dist_config.strategy + diloco_synced = service._diloco_sync_happened + should_save_weights = strategy != "diloco" or diloco_synced + + assert should_save_weights, "DILOCO sync epoch must save weights" + + def test_ddp_always_saves_weights(self) -> None: + """DDP always saves weights regardless of sync flag.""" + service = MagicMock() + service.dist_config.strategy = "ddp" + + strategy = service.dist_config.strategy + should_save_weights = strategy != "diloco" + + assert should_save_weights, "DDP must always save weights" + + def test_fsdp2_always_saves_weights(self) -> None: + """FSDP2 always saves weights regardless of sync flag.""" + service = MagicMock() + service.dist_config.strategy = "fsdp2" + + strategy = service.dist_config.strategy + should_save_weights = strategy != "diloco" + + assert should_save_weights, "FSDP2 must always save weights" + + def test_resume_checkpoint_saved_on_non_sync_epoch(self) -> None: + """Resume state is saved even when weight checkpoint is skipped.""" + service = MagicMock() + service.dist_config.strategy = "diloco" + service._diloco_sync_happened = False + + # The launcher always calls _save_resume_checkpoint on rank 0, + # regardless of should_save_weights. Verify the call would happen. + rank = 0 + if rank == 0: + service._save_resume_checkpoint() + + service._save_resume_checkpoint.assert_called_once() + + +# ============================================================================ +# Sparse averaging correctness +# ============================================================================ + + +class TestSparseAveraging: + """Verify sparse scatter-add averaging matches dense averaging.""" + + def test_sparse_average_matches_dense(self) -> None: + """Sparse index_add_ averaging produces same result as dense sum.""" + torch.manual_seed(42) + # Simulate: two workers with different pseudo-grads + n = 1000 + pseudo_grad_worker0 = torch.randn(n, dtype=torch.float32) + pseudo_grad_worker1 = torch.randn(n, dtype=torch.float32) + + # Sparse path: extract non-zeros, average + nz0 = pseudo_grad_worker0.nonzero(as_tuple=True)[0] + nz1 = pseudo_grad_worker1.nonzero(as_tuple=True)[0] + vals0 = pseudo_grad_worker0[nz0] + vals1 = pseudo_grad_worker1[nz1] + + sparse_avg = torch.zeros(n) + sparse_avg.index_add_(0, nz0, vals0) + sparse_avg.index_add_(0, nz1, vals1) + sparse_avg.div_(2) + + # Dense path: just average the full tensors + dense_avg = (pseudo_grad_worker0 + pseudo_grad_worker1) / 2 + + assert torch.allclose(sparse_avg, dense_avg, atol=1e-6), ( + "Dense and sparse averaging must produce the same result" + ) + + def test_residual_invariant_independent_of_comms(self) -> None: + """v6 conservation invariant: transmitted + residual = s at all indices.""" + torch.manual_seed(123) + n = 100 + # Simulate step 1 of v6 PULSE + global_params_flat = torch.randn(n, dtype=torch.float32) + local_params_flat = global_params_flat + torch.randn(n) * 0.001 # small perturbation + residual_before = torch.randn(n, dtype=torch.float32) * 0.0001 + + # Step 1: v6 BF16-gated FP32 pseudo-gradient + delta = global_params_flat - local_params_flat + s = residual_before + delta + w_tilde = global_params_flat - s + b_theta = global_params_flat.bfloat16().float() + b_w_tilde = w_tilde.bfloat16().float() + gate_mask = b_theta != b_w_tilde + + transmitted = torch.zeros_like(s) + transmitted[gate_mask] = s[gate_mask] + residual_after = s.clone() + residual_after[gate_mask] = 0 + + # v6 conservation: transmitted + residual = corrected signal + assert torch.allclose(transmitted + residual_after, s, atol=1e-6), ( + "Conservation invariant violated: transmitted + residual != total signal" + ) diff --git a/tests/unit/trainer/test_trust.py b/tests/unit/trainer/test_trust.py index b3016a40..3ab5c065 100644 --- a/tests/unit/trainer/test_trust.py +++ b/tests/unit/trainer/test_trust.py @@ -13,7 +13,7 @@ import numpy as np import pytest -from grail.shared.constants import ( +from grail.protocol.constants import ( TRUST_LIST_KEY_PREFIX, TRUST_LIST_VERSION, WINDOW_LENGTH, diff --git a/tests/unit/validation/test_copycat_service.py b/tests/unit/validation/test_copycat_service.py index 74f444f8..13957e4b 100644 --- a/tests/unit/validation/test_copycat_service.py +++ b/tests/unit/validation/test_copycat_service.py @@ -14,6 +14,8 @@ - Clear test names """ +import pytest + from grail.validation.copycat_service import COPYCAT_SERVICE, COPYCAT_TRACKER, CopycatService @@ -136,6 +138,7 @@ def test_preserves_rollout_structure(self) -> None: assert filtered[0]["list"] == [1, 2, 3] +@pytest.mark.serial class TestServiceIntegration: """Test service integration with global tracker.""" diff --git a/tests/unit/validation/test_grail_sampling_shape_check.py b/tests/unit/validation/test_grail_sampling_shape_check.py deleted file mode 100644 index bc9e64a5..00000000 --- a/tests/unit/validation/test_grail_sampling_shape_check.py +++ /dev/null @@ -1,97 +0,0 @@ -# #!/usr/bin/env python3 -# """ -# Unit test: exploit passes sampling shape check with realistic tokens. - -# Uses real model/tokenizer from constants.py to show boundary manipulation -# exploit with properly spaced SAT solution tokens. -# """ - -# from typing import Any - -# import pytest -# import torch - -# from grail.grail import Verifier -# from grail.mining.rollout_generator import SOLUTION_END, SOLUTION_START -# from grail.shared.constants import MODEL_NAME - - -# @pytest.mark.skipif(not torch.cuda.is_available(), reason="GPU required for real model inference") -# def test_solution_tokens_pass_sampling_check_via_boundaries() -> None: -# """Exploit: include 1 1 1 1 1 manipulating boundaries. - -# Strategy: Set prompt_length to exclude solution tokens from sampling -# analysis, leaving only high-probability tokens in the region. -# """ - -# # Use real model and tokenizer from constants -# verifier = Verifier(model_name=MODEL_NAME) - -# # Create realistic SAT solution text with proper spacing -# solution_bits = "1 1 1 1 1" # 5-variable SAT solution with spaces -# solution_text = f"{SOLUTION_START}{solution_bits}{SOLUTION_END}" - -# # Add high-probability suffix tokens (newline + space) -# suffix_text = "\n " - -# # Build full text: minimal prompt + solution + high-prob suffix -# prompt_text = "Answer: " -# full_text = prompt_text + solution_text + suffix_text - -# # Tokenize with real tokenizer -# prompt_tokens = verifier.tokenizer.encode(prompt_text, add_special_tokens=False) -# full_tokens = verifier.tokenizer.encode(full_text, add_special_tokens=False) -# solution_tokens = verifier.tokenizer.encode(solution_text, add_special_tokens=False) -# suffix_tokens = verifier.tokenizer.encode(suffix_text, add_special_tokens=False) - -# print(f"Prompt tokens ({len(prompt_tokens)}): {prompt_tokens}") -# print(f"Solution tokens ({len(solution_tokens)}): {solution_tokens}") -# print(f"Suffix tokens ({len(suffix_tokens)}): {suffix_tokens}") -# print(f"Full sequence ({len(full_tokens)}): {full_tokens}") - -# # Key exploit: manipulate boundaries to exclude solution from analysis -# commit: dict[str, Any] = { -# "tokens": full_tokens, -# "rollout": { -# # Set prompt_length to include solution tokens as "prompt" -# # Only analyze the high-probability suffix tokens -# "prompt_length": len(full_tokens) - len(suffix_tokens), -# "completion_length": len(suffix_tokens), # Only suffix analyzed -# }, -# } - -# # Test the sampling shape check directly -# passes, metrics = verifier._token_sampling_shape_check(commit) - -# print(f"Sampling check result: {'PASS' if passes else 'FAIL'}") -# print(f"Metrics: {metrics}") - -# # Collect probabilities to see what was actually analyzed -# probs = verifier._collect_chosen_token_probs(commit) -# if probs: -# prob_strs = [f"{p:.4f}" for p in probs] -# print(f"Analyzed {len(probs)} token probabilities: {prob_strs}") - -# # The exploit should pass because: -# # 1. Only suffix tokens analyzed - these have high probability -# # 2. Solution tokens excluded via boundary manipulation -# assert passes is True - -# # Verify the exploit worked as expected -# # Should trigger "insufficient steps" or high-prob bypass -# assert len(suffix_tokens) <= 8 - -# # Verify solution tokens are present in the sequence -# assert len(full_tokens) > len(prompt_tokens) + len(suffix_tokens) -# print("Full sequence contains solution tokens") - -# # Verify the key exploit: analysis region only covers suffix tokens -# analysis_start = commit["rollout"]["prompt_length"] -# analysis_end = analysis_start + commit["rollout"]["completion_length"] - -# # The analysis region should be just the suffix (last 2 tokens) -# expected_analysis_start = len(full_tokens) - len(suffix_tokens) -# assert analysis_start == expected_analysis_start -# assert analysis_end == len(full_tokens) - -# print(f"Analysis region: tokens[{analysis_start}:{analysis_end}] = suffix") diff --git a/tests/unit/validation/test_grail_termination_check.py b/tests/unit/validation/test_grail_termination_check.py deleted file mode 100644 index 8e7d996a..00000000 --- a/tests/unit/validation/test_grail_termination_check.py +++ /dev/null @@ -1,130 +0,0 @@ -# #!/usr/bin/env python3 -# """ -# Unit test: show the exploit passes by fudging rollout metadata. - -# Notes: -# - Keep test fast by monkeypatching heavy model calls. -# - Use shared constants where applicable. -# - Focus on termination/sampling checks not bound to the signature. -# """ - -# from typing import Any, cast - -# import grail.grail as grail_mod -# from grail.environments import generate_sat_problem -# from grail.grail import Verifier -# from grail.shared.constants import CHALLENGE_K, MAX_NEW_TOKENS - - -# def test_exploit_passes_with_fudged_rollout_metadata( -# monkeypatch: Any, -# ) -> None: -# """Exploit: claim max-length termination and minimize sampling region. - -# We stub out crypto/model-heavy checks to isolate vulnerable paths: -# - Signature/model proof forced to pass. -# - Token validity uses generous limits. -# - Sampling collection returns insufficient steps. -# """ - -# # 1) Make Verifier lightweight and deterministic -# def fake_init( -# self: Verifier, -# model_name: str = grail_mod.MODEL_NAME, -# ) -> None: -# self.device = cast(Any, None) -# self.tokenizer = type( -# "Tok", -# (), -# {"eos_token_id": 0, "eos_token": ""}, -# )() -# # Minimal stub for model/config fields accessed by verifier utilities -# self.model = type( -# "ModelStub", -# (), -# {"config": object(), "name_or_path": model_name}, -# )() -# self._last_tokens_hash = None -# self._last_step_logits = None -# self._current_wallet = None - -# monkeypatch.setattr(grail_mod.Verifier, "__init__", fake_init) - -# # 2) Force GRAIL proof to succeed without running the model -# monkeypatch.setattr( -# grail_mod.Verifier, -# "verify", -# lambda self, *a, **k: True, -# ) - -# # 3) Loosen token validity constraints to avoid loading real configs/models -# monkeypatch.setattr( -# grail_mod, -# "resolve_vocab_size", -# lambda _cfg: 1_000_000, -# ) -# monkeypatch.setattr( -# grail_mod, -# "resolve_max_context_length", -# lambda _cfg: 10_000, -# ) - -# # 4) Starve the sampling-shape check so it passes via "insufficient" path -# monkeypatch.setattr( -# grail_mod.Verifier, -# "_collect_chosen_token_probs", -# lambda self, commit: [], -# ) - -# # 5) Build minimal tokens and a SAT problem matching regeneration -# tokens = [999, 123, 456] -# seed = "unit_test_seed" -# difficulty = 0.5 -# problem = generate_sat_problem(seed, difficulty) -# sat_data: dict[str, Any] = { -# "seed": seed, -# "difficulty": difficulty, -# "num_vars": problem.num_vars, -# "clauses": problem.clauses, -# } - -# # Fudged rollout metadata: declare max-length termination -# # and tiny completion slice -# commit: dict[str, Any] = { -# "tokens": tokens, -# "model": { -# "name": "ignored", -# "layer_index": grail_mod.LAYER_INDEX, -# }, -# "sat_problem": sat_data, -# "rollout": { -# # completion is 1 token -# "prompt_length": len(tokens) - 1, -# # trigger max-length path -# "completion_length": MAX_NEW_TOKENS, -# # skip solution check -# "success": False, -# }, -# } -# proof_pkg: dict[str, Any] = {"indices": []} - -# v = Verifier(model_name="stub") - -# ok, checks = v.verify_rollout( -# commit, -# proof_pkg, -# prover_address=("5FictitiousAddressForUnitTest"), -# challenge_randomness="deadbeef", -# min_k=CHALLENGE_K, -# ) - -# # The exploit should (incorrectly) pass under current implementation -# assert ok is True -# assert checks["tokens_valid"] is True -# assert checks["proof_valid"] is True -# assert checks["sat_problem_valid"] is True -# assert checks["termination_valid"] is True -# # Passes due to insufficient steps -# assert checks["token_distribution_valid"] is True -# # Vacuously true when success == False -# assert checks["solution_valid"] is True diff --git a/tests/unit/validation/test_logprob_parallelization.py b/tests/unit/validation/test_logprob_parallelization.py index 5801d87a..a96e0e24 100644 --- a/tests/unit/validation/test_logprob_parallelization.py +++ b/tests/unit/validation/test_logprob_parallelization.py @@ -2,14 +2,15 @@ from __future__ import annotations +import math from types import SimpleNamespace from typing import Any import pytest import torch +from grail.protocol.constants import CHALLENGE_K from grail.protocol.crypto import indices_from_root_in_range -from grail.shared.constants import CHALLENGE_K from grail.validation.context import ValidationContext from grail.validation.miner_validator import MinerValidator from grail.validation.validators.environment import LogprobValidator @@ -235,7 +236,73 @@ def test_logprob_validator_precomputed_matches_fallback_path() -> None: assert fallback_ok is True assert precomputed_ok is True assert fallback_ctx.metadata["logprob_total"] == precomputed_ctx.metadata["logprob_total"] - assert ( - fallback_ctx.metadata["logprob_mismatches"] - == precomputed_ctx.metadata["logprob_mismatches"] + assert fallback_ctx.metadata["logprob_median_dev"] == pytest.approx( + precomputed_ctx.metadata["logprob_median_dev"], abs=1e-9 ) + assert fallback_ctx.metadata["logprob_max_dev"] == pytest.approx( + precomputed_ctx.metadata["logprob_max_dev"], abs=1e-9 + ) + + +def test_median_is_robust_to_outlier_positions() -> None: + """A handful of grossly perturbed positions must NOT trip the median check.""" + tokens, logits, prompt_len, completion_len = _make_rollout() + challenged_logprobs = _compute_challenged_logprobs( + tokens=tokens, + logits=logits, + prompt_len=prompt_len, + completion_len=completion_len, + ) + + # Perturb floor((K-1)/2) positions by a huge amount. + # K=32 → median = avg(sorted[15], sorted[16]); both stay in the unperturbed + # half iff n_perturbed <= 15. + n_perturbed = (CHALLENGE_K - 1) // 2 + claimed = [0.0 for _ in tokens] + for abs_idx, lp in challenged_logprobs.items(): + claimed[abs_idx] = lp + for abs_idx in list(challenged_logprobs.keys())[:n_perturbed]: + claimed[abs_idx] = challenged_logprobs[abs_idx] - 5.0 # exp(5)-1 ≈ 147 deviation + + ctx = _make_context( + tokens=tokens, + logits=logits, + prompt_len=prompt_len, + completion_len=completion_len, + claimed_logprobs=claimed, + precomputed_logprobs=challenged_logprobs, + ) + + assert LogprobValidator().validate(ctx) is True + assert ctx.metadata["logprob_median_dev"] < LogprobValidator.LOGPROB_IS_EPS + assert ctx.metadata["logprob_max_dev"] > 100.0 # the outliers were huge + + +def test_median_rejects_systematic_distribution_drift() -> None: + """A small but uniform shift across all challenged positions must trip the median.""" + tokens, logits, prompt_len, completion_len = _make_rollout() + challenged_logprobs = _compute_challenged_logprobs( + tokens=tokens, + logits=logits, + prompt_len=prompt_len, + completion_len=completion_len, + ) + + # log(1.2) ≈ 0.182 → exp(0.182) - 1 = 0.2 dev for every position; median = 0.2 > 0.10. + drift = math.log(1.2) + claimed = [0.0 for _ in tokens] + for abs_idx, lp in challenged_logprobs.items(): + claimed[abs_idx] = lp - drift + + ctx = _make_context( + tokens=tokens, + logits=logits, + prompt_len=prompt_len, + completion_len=completion_len, + claimed_logprobs=claimed, + precomputed_logprobs=challenged_logprobs, + ) + + assert LogprobValidator().validate(ctx) is False + assert ctx.metadata["logprob_median_dev"] > LogprobValidator.LOGPROB_IS_EPS + assert ctx.metadata["logprob_median_dev"] == pytest.approx(0.2, abs=1e-6) diff --git a/tests/unit/validation/test_trust_list_publish.py b/tests/unit/validation/test_trust_list_publish.py index 1301b799..f78d0ff5 100644 --- a/tests/unit/validation/test_trust_list_publish.py +++ b/tests/unit/validation/test_trust_list_publish.py @@ -13,7 +13,7 @@ import pytest -from grail.shared.constants import TRUST_LIST_KEY_PREFIX, TRUST_LIST_VERSION +from grail.protocol.constants import TRUST_LIST_KEY_PREFIX, TRUST_LIST_VERSION def _make_service(): diff --git a/tests/unit/validation/test_window_env_config.py b/tests/unit/validation/test_window_env_config.py new file mode 100644 index 00000000..1dc0cb01 --- /dev/null +++ b/tests/unit/validation/test_window_env_config.py @@ -0,0 +1,319 @@ +"""Unit tests for WindowEnvConfig + resolve_window_env_config. + +The trainer is the single source of truth for env_id, env_params, +generation_params, and thinking_mode. The validator resolves all four ONCE +per window from the matching CheckpointMetadata, raising +MissingCheckpointMetadataError on any failure so the WHOLE window is skipped +(never silently scoring miners against an unverifiable baseline). + +These tests pin that contract. +""" + +from __future__ import annotations + +import os +from typing import Any +from unittest.mock import AsyncMock, MagicMock + +import pytest + +from grail.infrastructure.checkpoint_consumer import CheckpointMetadata +from grail.validation.miner_validator import ( + MinerValidator, + MissingCheckpointMetadataError, + WindowEnvConfig, +) +from grail.validation.pipeline import create_env_validation_pipeline + +# --------------------------------------------------------------------------- # +# fixtures # +# --------------------------------------------------------------------------- # + + +def _full_metadata(**overrides: Any) -> CheckpointMetadata: + """Return a CheckpointMetadata with all required fields populated.""" + payload: dict[str, Any] = { + "window": 1234, + "file_manifest": {}, + "env_id": "mbpp", + "env_params": {"split": "train"}, + "generation_params": { + "max_tokens": 2048, + "temperature": 0.7, + "top_p": 0.9, + "top_k": 50, + "repetition_penalty": 1.0, + }, + "thinking_mode": "instructed", + } + payload.update(overrides) + return CheckpointMetadata(**payload) + + +def _make_validator( + metadata: CheckpointMetadata | None, raises: Exception | None = None +) -> MinerValidator: + """Build a MinerValidator with a stubbed checkpoint manager.""" + pipeline = create_env_validation_pipeline() + cm = MagicMock() + if raises is not None: + cm.get_checkpoint_metadata = AsyncMock(side_effect=raises) + else: + cm.get_checkpoint_metadata = AsyncMock(return_value=metadata) + return MinerValidator(pipeline, checkpoint_manager=cm) + + +def _make_model(checkpoint_window: int | None = 1234) -> MagicMock: + """Stub of the validator-loaded model with a grail_checkpoint_window attr.""" + model = MagicMock() + if checkpoint_window is None: + # Use spec to make sure the attribute lookup truly returns None. + del model.grail_checkpoint_window + return model + model.grail_checkpoint_window = checkpoint_window + return model + + +# --------------------------------------------------------------------------- # +# happy path: full round-trip # +# --------------------------------------------------------------------------- # + + +@pytest.mark.asyncio +async def test_resolve_returns_window_env_config_with_all_fields() -> None: + """A complete CheckpointMetadata round-trips into WindowEnvConfig.""" + metadata = _full_metadata() + validator = _make_validator(metadata) + model = _make_model() + + config = await validator.resolve_window_env_config(model) + + assert isinstance(config, WindowEnvConfig) + assert config.env_id == "mbpp" + assert config.env_params == {"split": "train"} + assert config.generation_params["max_tokens"] == 2048 + assert config.thinking_mode == "instructed" + + +@pytest.mark.asyncio +async def test_resolve_overrides_grail_thinking_mode_env_var( + monkeypatch: pytest.MonkeyPatch, +) -> None: + """resolve_window_env_config sets os.environ['GRAIL_THINKING_MODE']. + + This is the trust boundary that protects the validator from local-host + drift: even if the docker container booted with the wrong default, the + checkpoint-derived value wins for all subsequent prompt rendering. + """ + monkeypatch.setenv("GRAIL_THINKING_MODE", "native") # wrong/stale value + metadata = _full_metadata(thinking_mode="instructed") + validator = _make_validator(metadata) + model = _make_model() + + await validator.resolve_window_env_config(model) + + assert os.environ["GRAIL_THINKING_MODE"] == "instructed" + + +@pytest.mark.asyncio +async def test_resolve_does_not_touch_env_var_when_already_matching( + monkeypatch: pytest.MonkeyPatch, +) -> None: + """No spurious env-var write when the host already matches the checkpoint.""" + monkeypatch.setenv("GRAIL_THINKING_MODE", "instructed") + metadata = _full_metadata(thinking_mode="instructed") + validator = _make_validator(metadata) + model = _make_model() + + await validator.resolve_window_env_config(model) + + assert os.environ["GRAIL_THINKING_MODE"] == "instructed" + + +# --------------------------------------------------------------------------- # +# failure modes: every branch must raise # +# --------------------------------------------------------------------------- # + + +@pytest.mark.asyncio +async def test_resolve_raises_when_checkpoint_manager_is_none() -> None: + pipeline = create_env_validation_pipeline() + validator = MinerValidator(pipeline, checkpoint_manager=None) + model = _make_model() + + with pytest.raises(MissingCheckpointMetadataError, match="checkpoint_manager"): + await validator.resolve_window_env_config(model) + + +@pytest.mark.asyncio +async def test_resolve_raises_when_model_has_no_checkpoint_window() -> None: + validator = _make_validator(_full_metadata()) + model = MagicMock(spec=[]) # no grail_checkpoint_window attr + + with pytest.raises(MissingCheckpointMetadataError, match="grail_checkpoint_window"): + await validator.resolve_window_env_config(model) + + +@pytest.mark.asyncio +async def test_resolve_raises_when_metadata_fetch_throws() -> None: + validator = _make_validator(None, raises=RuntimeError("R2 down")) + model = _make_model() + + with pytest.raises(MissingCheckpointMetadataError, match="Failed to fetch"): + await validator.resolve_window_env_config(model) + + +@pytest.mark.asyncio +async def test_resolve_raises_when_metadata_is_none() -> None: + validator = _make_validator(None) + model = _make_model() + + with pytest.raises(MissingCheckpointMetadataError, match="No checkpoint metadata"): + await validator.resolve_window_env_config(model) + + +@pytest.mark.asyncio +async def test_resolve_raises_when_thinking_mode_missing() -> None: + metadata = _full_metadata(thinking_mode=None) + validator = _make_validator(metadata) + model = _make_model() + + with pytest.raises(MissingCheckpointMetadataError, match="thinking_mode"): + await validator.resolve_window_env_config(model) + + +@pytest.mark.asyncio +async def test_resolve_raises_when_thinking_mode_is_invalid_value() -> None: + metadata = _full_metadata(thinking_mode="bogus") + validator = _make_validator(metadata) + model = _make_model() + + with pytest.raises(MissingCheckpointMetadataError, match="thinking_mode"): + await validator.resolve_window_env_config(model) + + +@pytest.mark.asyncio +async def test_resolve_raises_when_env_id_missing() -> None: + metadata = _full_metadata(env_id=None) + validator = _make_validator(metadata) + model = _make_model() + + with pytest.raises(MissingCheckpointMetadataError, match="env_id"): + await validator.resolve_window_env_config(model) + + +@pytest.mark.asyncio +async def test_resolve_raises_when_generation_params_missing() -> None: + metadata = _full_metadata(generation_params={}) + validator = _make_validator(metadata) + model = _make_model() + + with pytest.raises(MissingCheckpointMetadataError, match="generation_params"): + await validator.resolve_window_env_config(model) + + +# --------------------------------------------------------------------------- # +# WindowEnvConfig is frozen # +# --------------------------------------------------------------------------- # + + +def test_window_env_config_is_frozen() -> None: + """Resolved values cannot drift mid-window.""" + cfg = WindowEnvConfig( + env_id="mbpp", + env_params={"split": "train"}, + generation_params={"max_tokens": 2048}, + thinking_mode="instructed", + ) + with pytest.raises(AttributeError): + cfg.thinking_mode = "native" # type: ignore[misc] + + +@pytest.mark.asyncio +async def test_resolve_does_not_mutate_env_var_when_metadata_invalid( + monkeypatch: pytest.MonkeyPatch, +) -> None: + """A failing resolve must NOT touch GRAIL_THINKING_MODE. + + The env-var override only fires after validation succeeds. A test that + pins this contract prevents a future refactor from accidentally writing + a half-validated value into process state. + """ + monkeypatch.setenv("GRAIL_THINKING_MODE", "native") + metadata = _full_metadata(thinking_mode=None) # invalid + validator = _make_validator(metadata) + model = _make_model() + + with pytest.raises(MissingCheckpointMetadataError): + await validator.resolve_window_env_config(model) + + # Env var unchanged. + assert os.environ["GRAIL_THINKING_MODE"] == "native" + + +# --------------------------------------------------------------------------- # +# Cache poisoning regression (Claude H4) # +# --------------------------------------------------------------------------- # + + +@pytest.mark.asyncio +async def test_invalid_metadata_is_not_cached() -> None: + """A legacy/invalid R2 payload must NOT poison the metadata cache. + + Otherwise a one-time bad publish would force a process restart to + recover. The fix in checkpoint_consumer.py builds the metadata, runs + validate_metadata(), and refuses to cache anything that fails. + """ + from unittest.mock import AsyncMock + + from grail.infrastructure.checkpoint_consumer import ( + CHECKPOINT_TYPE_FULL, + CheckpointManager, + ) + + # Build a manager with mocked R2 fetch + manager = CheckpointManager.__new__(CheckpointManager) + manager._metadata_cache = {} # type: ignore[attr-defined] + manager.credentials = MagicMock() # type: ignore[attr-defined] + + # First fetch: legacy payload missing thinking_mode → returned but NOT cached + legacy_payload = { + "window": 7926000, + "file_manifest": {}, + "env_id": "mbpp", + "env_params": {"split": "train"}, + "generation_params": { + "max_tokens": 2048, + "temperature": 0.7, + "top_p": 0.9, + "top_k": 50, + "repetition_penalty": 1.0, + }, + # NO thinking_mode + } + fixed_payload = {**legacy_payload, "thinking_mode": "instructed"} + + # Patch comms.get_file to return legacy first, then fixed + fetch_results = [legacy_payload, fixed_payload] + + async def fake_get_file(*args: object, **kwargs: object) -> dict[str, object]: + return fetch_results.pop(0) + + import grail.infrastructure.checkpoint_consumer as cc_module + + original = cc_module.comms.get_file + cc_module.comms.get_file = AsyncMock(side_effect=fake_get_file) + try: + # First fetch: legacy payload returned, NOT cached (validate fails) + m1 = await manager._fetch_full_metadata(7926000) + assert m1 is not None + assert m1.thinking_mode is None # legacy + assert (7926000, CHECKPOINT_TYPE_FULL) not in manager._metadata_cache # type: ignore[attr-defined] + + # Second fetch: trainer republished with the fix → cached + m2 = await manager._fetch_full_metadata(7926000) + assert m2 is not None + assert m2.thinking_mode == "instructed" + assert (7926000, CHECKPOINT_TYPE_FULL) in manager._metadata_cache # type: ignore[attr-defined] + finally: + cc_module.comms.get_file = original diff --git a/tools/vllm-server/pyproject.toml b/tools/vllm-server/pyproject.toml index a013a0fb..24998de5 100644 --- a/tools/vllm-server/pyproject.toml +++ b/tools/vllm-server/pyproject.toml @@ -2,7 +2,7 @@ name = "grail-vllm-server" version = "0.1.0" description = "Isolated vLLM server environment for GRAIL evaluation" -requires-python = ">=3.10,<3.12" +requires-python = ">=3.12" dependencies = [ # Latest stable vLLM with CUDA support "vllm>=0.15.1", @@ -20,7 +20,7 @@ dependencies = [ [tool.ruff] line-length = 100 -target-version = "py310" +target-version = "py312" [tool.ruff.lint] select = ["E", "W", "F", "I", "B", "C4", "UP"] diff --git a/uv.lock b/uv.lock index c6dda44a..208be944 100644 --- a/uv.lock +++ b/uv.lock @@ -1,11 +1,11 @@ version = 1 revision = 3 -requires-python = ">=3.10, <3.12" +requires-python = ">=3.12" resolution-markers = [ - "python_full_version >= '3.11' and sys_platform == 'linux'", - "python_full_version >= '3.11' and sys_platform != 'linux'", - "python_full_version < '3.11' and sys_platform == 'linux'", - 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