Changes to TRT-LLM download tool for multigpu distributed case

examples/distributed_inference/tensor_parallel_initialize_dist.py

@@ -14,32 +14,13 @@
 import tensorrt as trt
 import torch
 import torch.distributed as dist
-from torch.distributed._tensor.device_mesh import init_device_mesh
+from torch.distributed._tensor.device_mesh import DeviceMesh, init_device_mesh
 
+logger = logging.getLogger(__name__)
 
-def find_repo_root(max_depth=10):
-    dir_path = os.path.dirname(os.path.realpath(__file__))
-    for i in range(max_depth):
-        files = os.listdir(dir_path)
-        if "MODULE.bazel" in files:
-            return dir_path
-        else:
-            dir_path = os.path.dirname(dir_path)
 
-    raise RuntimeError("Could not find repo root")
-
-
-def initialize_logger(rank, logger_file_name):
-    logger = logging.getLogger()
-    logger.setLevel(logging.INFO)
-    fh = logging.FileHandler(logger_file_name + f"_{rank}.log", mode="w")
-    fh.setLevel(logging.INFO)
-    logger.addHandler(fh)
-    return logger
-
-
-# This is required for env initialization since we use mpirun
-def initialize_distributed_env(logger_file_name, rank=0, world_size=1, port=29500):
+# this is kept at the application level, when mpirun is used to run the application
+def initialize_distributed_env(rank=0, world_size=1, port=29500):
     local_rank = int(
         os.environ.get("OMPI_COMM_WORLD_LOCAL_RANK", rank % torch.cuda.device_count())
     )
@@ -50,9 +31,6 @@ def initialize_distributed_env(logger_file_name, rank=0, world_size=1, port=2950
     os.environ["WORLD_SIZE"] = str(world_size)
     os.environ["MASTER_ADDR"] = "127.0.0.1"
     os.environ["MASTER_PORT"] = str(port)
-    os.environ["TRTLLM_PLUGINS_PATH"] = (
-        find_repo_root() + "/lib/libnvinfer_plugin_tensorrt_llm.so"
-    )
 
     # Necessary to assign a device to each rank.
     torch.cuda.set_device(local_rank)
@@ -66,16 +44,49 @@ def initialize_distributed_env(logger_file_name, rank=0, world_size=1, port=2950
     device_mesh = init_device_mesh(device_type="cuda", mesh_shape=(world_size,))
     rank = device_mesh.get_rank()
     assert rank == local_rank
-    logger = initialize_logger(rank, logger_file_name)
     device_id = (
         rank % torch.cuda.device_count()
     )  # Ensure each rank gets a unique device
     torch.cuda.set_device(device_id)
 
-    return device_mesh, world_size, rank, logger
+    return device_mesh, world_size, rank
 
 
 def cleanup_distributed_env():
     """Clean up distributed process group to prevent resource leaks."""
     if dist.is_initialized():
         dist.destroy_process_group()
+
+
+def check_tensor_parallel_device_number(world_size: int) -> None:
+    if world_size % 2 != 0:
+        raise ValueError(
+            f"TP examples require even number of GPUs, but got {world_size} gpus"
+        )
+
+
+def get_tensor_parallel_device_mesh(
+    rank: int = 0, world_size: int = 1
+) -> tuple[DeviceMesh, int, int]:
+    local_rank = int(
+        os.environ.get("OMPI_COMM_WORLD_LOCAL_RANK", rank % torch.cuda.device_count())
+    )
+    world_size = int(os.environ.get("OMPI_COMM_WORLD_SIZE", world_size))
+    device_mesh = init_device_mesh(device_type="cuda", mesh_shape=(world_size,))
+    rank = device_mesh.get_rank()
+    assert rank == local_rank
+    device_id = (
+        rank % torch.cuda.device_count()
+    )  # Ensure each rank gets a unique device
+    torch.cuda.set_device(device_id)
+
+    return device_mesh, world_size, rank
+
+
+def initialize_distributed_logger(rank: int, logger_file_name: str) -> logging.Logger:
+    logger = logging.getLogger()
+    logger.setLevel(logging.INFO)
+    fh = logging.FileHandler(logger_file_name + f"_{rank}.log", mode="w")
+    fh.setLevel(logging.INFO)
+    logger.addHandler(fh)
+    return logger

examples/distributed_inference/tensor_parallel_rotary_embedding.py

@@ -9,26 +9,31 @@
 
 """
 
-import logging
-import os
 import time
 
 import torch
-import torch_tensorrt
-from rotary_embedding import RotaryAttention, parallel_rotary_block
+import torch.distributed as dist
 from tensor_parallel_initialize_dist import (
     cleanup_distributed_env,
+    get_tensor_parallel_device_mesh,
     initialize_distributed_env,
+    initialize_distributed_logger,
 )
 
-device_mesh, _world_size, _rank, logger = initialize_distributed_env(
-    "./tensor_parallel_rotary_embedding"
-)
+if not dist.is_initialized():
+    initialize_distributed_env()
 
+import torch_tensorrt
+
+device_mesh, _world_size, _rank = get_tensor_parallel_device_mesh()
+logger = initialize_distributed_logger(_rank, "tensor_parallel_rotary_embedding")
+
+from rotary_embedding import RotaryAttention, parallel_rotary_block
 
 """
 This example covers the rotary embedding in Llama3 model and is derived from https://lightning.ai/lightning-ai/studios/tensor-parallelism-supercharging-large-model-training-with-pytorch-lightning
-Command to run with single GPU: mpirun -n 1 --allow-run-as-root python tensor_parallel_rotary_embedding.py
+Command to run with single GPU: USE_TRTLLM_PLUGINS=1 mpirun -n 1 --allow-run-as-root python tensor_parallel_rotary_embedding.py
+Command to run with 2 GPUs: USE_TRTLLM_PLUGINS=1 mpirun -n 2 --allow-run-as-root python tensor_parallel_rotary_embedding.py
 """
 
 BATCH = 2

examples/distributed_inference/tensor_parallel_simple_example.py

@@ -16,7 +16,7 @@
 -----
 .. code-block:: bash
 
-    mpirun -n 2 --allow-run-as-root python tensor_parallel_simple_example.py
+    USE_TRTLLM_PLUGINS=1 mpirun -n 2 --allow-run-as-root python tensor_parallel_simple_example.py
 """
 
 import time
@@ -25,22 +25,31 @@
 import torch
 import torch.distributed as dist
 import torch.nn as nn
-import torch_tensorrt
 from tensor_parallel_initialize_dist import (
     cleanup_distributed_env,
+    get_tensor_parallel_device_mesh,
     initialize_distributed_env,
+    initialize_distributed_logger,
 )
+
+if not dist.is_initialized():
+    initialize_distributed_env()
+import torch_tensorrt
 from torch.distributed._tensor import Shard
 from torch.distributed.tensor.parallel import (
     ColwiseParallel,
     RowwiseParallel,
     parallelize_module,
 )
-
-device_mesh, _world_size, _rank, logger = initialize_distributed_env(
-    "./tensor_parallel_simple_example"
+from torch_tensorrt.dynamo.distributed.utils import (
+    get_tensor_parallel_device_mesh,
+    initialize_distributed_logger,
 )
 
+device_mesh, _world_size, _rank = get_tensor_parallel_device_mesh()
+logger = initialize_distributed_logger(_rank, "tensor_parallel_simple_example")
+
+
 """
 This example takes some code from https://github.com/pytorch/examples/blob/main/distributed/tensor_parallelism/tensor_parallel_example.py
 """

py/torch_tensorrt/_utils.py

@@ -5,6 +5,7 @@
 import platform
 import sys
 import tempfile
+import time
 import urllib.request
 from pathlib import Path
 from typing import Any, Optional
@@ -143,13 +144,65 @@ def _extracted_dir_trtllm(platform_system: str, platform_machine: str) -> Path:
     )
 
 
+def extract_wheel_file(wheel_path: Path, extract_dir: Path) -> None:
+    """
+    Safely extract a wheel file to a directory with a lock to prevent concurrent extraction.
+    """
+    rank = int(os.environ.get("OMPI_COMM_WORLD_RANK", 0))  # MPI rank from OpenMPI
+    torch.cuda.set_device(rank)
+    lock_file = extract_dir / ".extracting"
+
+    # Rank 0 performs extraction
+    if rank == 0:
+        logger.debug(
+            f"[Rank {rank}] Starting extraction of {wheel_path} to {extract_dir}"
+        )
+        try:
+            import zipfile
+        except ImportError as e:
+            raise ImportError(
+                "zipfile module is required but not found. Please install zipfile"
+            )
+        # Create lock file to signal extraction in progress
+        extract_dir.mkdir(parents=True, exist_ok=False)
+        lock_file.touch(exist_ok=False)
+        try:
+            with zipfile.ZipFile(wheel_path) as zip_ref:
+                zip_ref.extractall(extract_dir)
+            logger.debug(f"[Rank {rank}] Extraction complete: {extract_dir}")
+        except FileNotFoundError as e:
+            logger.error(f"[Rank {rank}] Wheel file not found at {wheel_path}: {e}")
+            raise RuntimeError(
+                f"Failed to find downloaded wheel file at {wheel_path}"
+            ) from e
+        except zipfile.BadZipFile as e:
+            logger.error(f"[Rank {rank}] Invalid or corrupted wheel file: {e}")
+            raise RuntimeError(
+                "Downloaded wheel file is corrupted or not a valid zip archive"
+            ) from e
+        except Exception as e:
+            logger.error(f"[Rank {rank}] Unexpected error while extracting wheel: {e}")
+            raise RuntimeError(
+                "Unexpected error during extraction of TensorRT-LLM wheel"
+            ) from e
+        finally:
+            # Remove lock file to signal completion
+            lock_file.unlink(missing_ok=True)
+
+    else:
+        # Other ranks wait for extraction to complete
+        while lock_file.exists():
+            logger.debug(
+                f"[Rank {rank}] Waiting for extraction to finish at {extract_dir}..."
+            )
+            time.sleep(0.5)
+
+
 def download_and_get_plugin_lib_path() -> Optional[str]:
     """
     Returns the path to the TensorRT‑LLM shared library, downloading and extracting if necessary.
-
     Args:
         platform (str): Platform identifier (e.g., 'linux_x86_64')
-
     Returns:
         Optional[str]: Path to shared library or None if operation fails.
     """
@@ -174,7 +227,6 @@ def download_and_get_plugin_lib_path() -> Optional[str]:
         return str(plugin_lib_path)
 
     wheel_path.parent.mkdir(parents=True, exist_ok=True)
-    extract_dir.mkdir(parents=True, exist_ok=True)
 
     if not wheel_path.exists():
         base_url = "https://pypi.nvidia.com/tensorrt-llm/"
@@ -194,32 +246,7 @@ def download_and_get_plugin_lib_path() -> Optional[str]:
         except OSError as e:
             logger.error(f"Local file write error: {e}")
 
-    try:
-        import zipfile
-    except ImportError as e:
-        raise ImportError(
-            "zipfile module is required but not found. Please install zipfile"
-        )
-    try:
-        with zipfile.ZipFile(wheel_path) as zip_ref:
-            zip_ref.extractall(extract_dir)
-            logger.debug(f"Extracted wheel to {extract_dir}")
-    except FileNotFoundError as e:
-        # This should capture the errors in the download failure above
-        logger.error(f"Wheel file not found at {wheel_path}: {e}")
-        raise RuntimeError(
-            f"Failed to find downloaded wheel file at {wheel_path}"
-        ) from e
-    except zipfile.BadZipFile as e:
-        logger.error(f"Invalid or corrupted wheel file: {e}")
-        raise RuntimeError(
-            "Downloaded wheel file is corrupted or not a valid zip archive"
-        ) from e
-    except Exception as e:
-        logger.error(f"Unexpected error while extracting wheel: {e}")
-        raise RuntimeError(
-            "Unexpected error during extraction of TensorRT-LLM wheel"
-        ) from e
+    extract_wheel_file(wheel_path, extract_dir)
 
     try:
         wheel_path.unlink(missing_ok=True)
@@ -238,10 +265,8 @@ def download_and_get_plugin_lib_path() -> Optional[str]:
 def load_and_initialize_trtllm_plugin(plugin_lib_path: str) -> bool:
     """
     Loads and initializes the TensorRT-LLM plugin from the given shared library path.
-
     Args:
         plugin_lib_path (str): Path to the shared TensorRT-LLM plugin library.
-
     Returns:
         bool: True if successful, False otherwise.
     """
@@ -293,7 +318,6 @@ def load_tensorrt_llm_for_nccl() -> bool:
     Attempts to load the TensorRT-LLM plugin and initialize it.
     Either the env variable TRTLLM_PLUGINS_PATH can specify the path
     Or the user can specify USE_TRTLLM_PLUGINS as either of (1, true, yes, on) to download the TRT-LLM distribution and load it
-
     Returns:
         bool: True if the plugin was successfully loaded and initialized, False otherwise.
     """

tests/py/dynamo/distributed/distributed_utils.py

@@ -1,5 +1,6 @@
 import logging
 import os
+import random
 
 import numpy as np
 import tensorrt as trt
@@ -8,24 +9,21 @@
 from torch.distributed._tensor.device_mesh import init_device_mesh
 
 
-def set_environment_variables_pytest():
+# the below two functions are used to set the environment variables for the pytest single and multi process
+# this is for the github CI where we use pytest
+def set_environment_variables_pytest_single_process():
+    port = 29500 + random.randint(1, 1000)
     os.environ["WORLD_SIZE"] = str(1)
     os.environ["RANK"] = str(0)
     os.environ["MASTER_ADDR"] = "127.0.0.1"
-    os.environ["MASTER_PORT"] = str(29500)
-
-
-def initialize_logger(rank, logger_file_name):
-    logger = logging.getLogger()
-    logger.setLevel(logging.INFO)
-    fh = logging.FileHandler(logger_file_name + f"_{rank}.log", mode="w")
-    fh.setLevel(logging.INFO)
-    logger.addHandler(fh)
-    return logger
+    os.environ["MASTER_PORT"] = str(port)
 
 
-# This is required for env initialization since we use mpirun
-def initialize_distributed_env(logger_file_name, rank=0, world_size=1, port=29500):
+def set_environment_variables_pytest_multi_process(
+    rank: int = 0, world_size: int = 1
+) -> None:
+    port = 29500 + random.randint(1, 1000)
+    # these variables are set by mpirun -n 2
     local_rank = int(
         os.environ.get("OMPI_COMM_WORLD_LOCAL_RANK", rank % torch.cuda.device_count())
     )
@@ -36,7 +34,6 @@ def initialize_distributed_env(logger_file_name, rank=0, world_size=1, port=2950
     os.environ["WORLD_SIZE"] = str(world_size)
     os.environ["MASTER_ADDR"] = "127.0.0.1"
     os.environ["MASTER_PORT"] = str(port)
-    os.environ["TRTLLM_PLUGINS_PATH"] = "./tmp/lib/libnvinfer_plugin_tensorrt_llm.so"
 
     # Necessary to assign a device to each rank.
     torch.cuda.set_device(local_rank)
@@ -46,14 +43,3 @@ def initialize_distributed_env(logger_file_name, rank=0, world_size=1, port=2950
 
     # set a manual seed for reproducibility
     torch.manual_seed(1111)
-
-    device_mesh = init_device_mesh(device_type="cuda", mesh_shape=(world_size,))
-    rank = device_mesh.get_rank()
-    assert rank == local_rank
-    logger = initialize_logger(rank, logger_file_name)
-    device_id = (
-        rank % torch.cuda.device_count()
-    )  # Ensure each rank gets a unique device
-    torch.cuda.set_device(device_id)
-
-    return device_mesh, world_size, rank, logger