diff --git a/.gitignore b/.gitignore index e6c76f7..2c92e3c 100644 --- a/.gitignore +++ b/.gitignore @@ -217,6 +217,7 @@ __marimo__/ # Benchmark output images examples/images/ +examples/outputs/ # Local training outputs and checkpoints runs/ diff --git a/README.md b/README.md index fc9cb7d..7d88194 100644 --- a/README.md +++ b/README.md @@ -213,6 +213,45 @@ Useful flags: - `--train-size 12000 --val-size 3000` - `--max-seq-len 192` +## Self-Supervised Example + +`examples/language_selfsupervised.py` uses the same streaming Wikipedia data loader, +trains a self-supervised token model, exports hidden states to a memmap, and can +optionally plot UMAP projections from those exports. + +Generated artifacts are written under `examples/outputs/language_selfsupervised/` +by default. + +Install the example dependencies: + +```bash +uv sync --group examples +``` + +Train, export, and plot with the default CPU UMAP backend: + +```bash +uv run python examples/language_selfsupervised.py --plot-umap +``` + +To use the CUDA UMAP backend in the project environment: + +```bash +uv sync --group examples --group cuda-umap +uv run python examples/language_selfsupervised.py --device cuda --plot-umap --umap-backend cuda +``` + +Useful flags: +- `--horizon single|multi` +- `--export-mode all|final` +- `--plot-umap` +- `--umap-backend auto|cpu|cuda` +- `--plot-time-indices 1,20,100,-5,-1,0` where `0` means `H_T`, the hidden state after consuming `EOS` and aligned with the first `PAD` target; negative indices are relative to that point, and positive indices are one-indexed absolute timesteps +- `--n-per-lang 4000` + +The CUDA UMAP path is optional. `--umap-backend auto` falls back to CPU UMAP if the +CUDA packages are unavailable. + ## Benchmarking To compare recurrent and parallel throughput across sequence lengths and hidden dimensions: @@ -237,7 +276,7 @@ uv sync --dev - Python ≥ 3.11 - PyTorch ≥ 2.8.0 -- NumPy ≥ 2.4.1 +- NumPy ≥ 2 ## License diff --git a/examples/images/language_selfsupervised_hidden_umap.png b/examples/images/language_selfsupervised_hidden_umap.png new file mode 100644 index 0000000..30a42bd Binary files /dev/null and b/examples/images/language_selfsupervised_hidden_umap.png differ diff --git a/examples/language_selfsupervised.py b/examples/language_selfsupervised.py index 21271eb..abc6b72 100644 --- a/examples/language_selfsupervised.py +++ b/examples/language_selfsupervised.py @@ -1,9 +1,6 @@ """ -Train a stacked SLiCE language model and export hidden states. - -Flags: - --horizon {single,multi} Prediction horizon(s) - --export-mode {all,final} Export H_{T-1} only or all H_t +Train a self-supervised stacked SLiCE language model, export hidden states, +and optionally plot UMAP projections from the exported embeddings. Outputs: .dat : numpy memmap of hidden states @@ -13,6 +10,7 @@ from __future__ import annotations import argparse +import math import random import re from collections import Counter @@ -25,34 +23,36 @@ from slices import StackedSLiCE -# --------------------------------------------------------------------- -# Constants -# --------------------------------------------------------------------- - SPECIAL_TOKENS = ("", "", "", "") MAX_CHAR_VOCAB = 256 MULTI_HORIZONS = (1, 2, 4) - - -# --------------------------------------------------------------------- -# Argument parsing -# --------------------------------------------------------------------- +LANGUAGE_LABELS = ( + (0, "English", "tab:blue"), + (1, "French", "tab:orange"), +) +DEFAULT_TIME_INDICES = "1,20,100,-5,-1,0" +EXAMPLE_DIR = Path(__file__).resolve().parent +DEFAULT_OUTPUT_DIR = EXAMPLE_DIR / "outputs" / "language_selfsupervised" +DEFAULT_EXPORT_PATH = DEFAULT_OUTPUT_DIR / "slice_embeddings" +DEFAULT_IMAGE_DIR = EXAMPLE_DIR / "images" +DEFAULT_PLOT_PATH = DEFAULT_IMAGE_DIR / "language_selfsupervised_hidden_umap.png" def parse_args() -> argparse.Namespace: - parser = argparse.ArgumentParser(description="Train SLiCE and export hidden states") + parser = argparse.ArgumentParser( + description=( + "Train a self-supervised SLiCE language model on streaming " + "Wikipedia text, export hidden states, and optionally plot UMAPs." + ) + ) - # Data parser.add_argument("--n-per-lang", type=int, default=8000) parser.add_argument("--max-seq-len", type=int, default=192) parser.add_argument("--batch-size", type=int, default=256) - parser.add_argument("--train-steps", type=int, default=500) + parser.add_argument("--train-steps", type=int, default=1000) parser.add_argument("--lr", type=float, default=3e-4) parser.add_argument("--seed", type=int, default=0) - - parser.add_argument("--cache-dir", type=str, default="/tmp/wiki_cache") - parser.add_argument("--processed-cache", type=str, default="processed_dataset.pt") - + parser.add_argument("--cache-dir", type=str, default=None) parser.add_argument( "--device", type=str, @@ -60,28 +60,41 @@ def parse_args() -> argparse.Namespace: choices=["cpu", "cuda"], ) - # Model parser.add_argument("--hidden-dim", type=int, default=32) parser.add_argument("--num-layers", type=int, default=4) parser.add_argument("--block-size", type=int, default=4) parser.add_argument("--dropout", type=float, default=0.05) parser.add_argument("--chunk-size", type=int, default=192) - # Experimental flags parser.add_argument("--horizon", choices=["single", "multi"], default="single") parser.add_argument("--export-mode", choices=["all", "final"], default="all") + parser.add_argument("--export-path", type=str, default=str(DEFAULT_EXPORT_PATH)) - # Output - parser.add_argument("--export-path", type=str, default="slice_embeddings") + parser.add_argument("--plot-umap", action="store_true") + parser.add_argument( + "--umap-backend", + choices=["auto", "cpu", "cuda"], + default="auto", + help="Use CPU UMAP by default, or CUDA UMAP if RAPIDS is installed.", + ) + parser.add_argument( + "--plot-time-indices", + type=str, + default=DEFAULT_TIME_INDICES, + help=( + "Comma-separated indices: 0 means the first pad step after the last " + "valid hidden state, negative values are relative to that point, and " + "positive values are one-indexed absolute timesteps." + ), + ) + parser.add_argument("--plot-max-points", type=int, default=4000) + parser.add_argument("--plot-path", type=str, default=str(DEFAULT_PLOT_PATH)) + parser.add_argument("--umap-neighbors", type=int, default=80) + parser.add_argument("--umap-min-dist", type=float, default=0.1) return parser.parse_args() -# --------------------------------------------------------------------- -# Utilities -# --------------------------------------------------------------------- - - def set_seed(seed: int) -> None: random.seed(seed) torch.manual_seed(seed) @@ -93,56 +106,115 @@ def normalise(text: str) -> str: return re.sub(r"\s+", " ", text.lower().strip()) -# --------------------------------------------------------------------- -# Dataset helpers -# --------------------------------------------------------------------- +def parse_time_indices(raw: str) -> list[int]: + indices = [item.strip() for item in raw.split(",") if item.strip()] + if not indices: + raise ValueError("plot-time-indices must contain at least one integer.") + return [int(item) for item in indices] + + +def final_state_indices(x: torch.Tensor, pad_id: int) -> torch.Tensor: + """Index of the last hidden state used for next-token prediction.""" + targets = x[:, 1:] + return targets.ne(pad_id).sum(dim=1).clamp_min(1) - 1 + + +def resolve_time_positions( + time_index: int, + final_indices: np.ndarray, + seq_len: int, +) -> tuple[np.ndarray, np.ndarray]: + """Map a plot index to per-sequence hidden-state positions.""" + if time_index <= 0: + positions = final_indices + time_index + 1 + valid = (positions >= 0) & (positions < seq_len) + else: + positions = np.full_like(final_indices, time_index - 1) + valid = positions <= final_indices + return positions, valid def collect_wikipedia_texts( - config: str, *, needed: int, seed: int, cache_dir: str + config_name: str, + *, + needed: int, + seed: int, + cache_dir: str | None, ) -> list[str]: from datasets import load_dataset - ds = load_dataset( + stream = load_dataset( "wikimedia/wikipedia", - config, + config_name, split="train", + streaming=True, cache_dir=cache_dir, - ).shuffle(seed=seed) + ).shuffle(seed=seed, buffer_size=10_000) texts: list[str] = [] - for row in ds: - t = normalise(row.get("text", "")) - if len(t) >= 8: - texts.append(t) + for row in stream: + text = normalise(row.get("text", "")) + if len(text) < 8: + continue + texts.append(text) if len(texts) >= needed: break + if len(texts) < needed: + raise RuntimeError( + f"Not enough rows for {config_name}. Needed {needed}, got {len(texts)}" + ) return texts +def load_wikipedia_en_fr( + n_per_lang: int, + *, + seed: int, + cache_dir: str | None, +) -> tuple[list[str], torch.Tensor]: + en_texts = collect_wikipedia_texts( + "20231101.en", + needed=n_per_lang, + seed=seed, + cache_dir=cache_dir, + ) + fr_texts = collect_wikipedia_texts( + "20231101.fr", + needed=n_per_lang, + seed=seed + 1, + cache_dir=cache_dir, + ) + + pairs = [(text, 0) for text in en_texts] + [(text, 1) for text in fr_texts] + random.Random(seed).shuffle(pairs) + + texts = [text for text, _ in pairs] + labels = torch.tensor([label for _, label in pairs], dtype=torch.long) + return texts, labels + + def build_vocab(texts: list[str]) -> dict[str, int]: counter = Counter("".join(texts)) vocab = list(SPECIAL_TOKENS) - remaining = MAX_CHAR_VOCAB - len(vocab) + remaining_slots = MAX_CHAR_VOCAB - len(vocab) - common = sorted(counter.items(), key=lambda kv: (-kv[1], kv[0]))[:remaining] - vocab.extend(ch for ch, _ in common) - - return {tok: i for i, tok in enumerate(vocab)} + common_chars = sorted(counter.items(), key=lambda kv: (-kv[1], kv[0]))[ + :remaining_slots + ] + vocab.extend(char for char, _ in common_chars) + return {token: idx for idx, token in enumerate(vocab)} def encode_texts(texts: list[str], vocab: dict[str, int], max_len: int) -> torch.Tensor: - pad, bos, eos, unk = ( - vocab[""], - vocab[""], - vocab[""], - vocab[""], - ) + pad = vocab[""] + bos = vocab[""] + eos = vocab[""] + unk = vocab[""] - rows = [] + rows: list[list[int]] = [] for text in texts: - ids = [bos] + [vocab.get(c, unk) for c in text] + [eos] + ids = [bos] + [vocab.get(char, unk) for char in text] + [eos] if len(ids) > max_len: ids = ids[:max_len] ids[-1] = eos @@ -153,15 +225,8 @@ def encode_texts(texts: list[str], vocab: dict[str, int], max_len: int) -> torch return torch.tensor(rows, dtype=torch.long) -# --------------------------------------------------------------------- -# Model wrapper -# --------------------------------------------------------------------- - - class SelfSupervisedSLiCE(nn.Module): - """ - Thin wrapper around StackedSLiCE providing a horizon-aware loss. - """ + """Thin wrapper around StackedSLiCE providing a horizon-aware loss.""" def __init__(self, vocab_size: int, args: argparse.Namespace, pad_id: int): super().__init__() @@ -170,7 +235,6 @@ def __init__(self, vocab_size: int, args: argparse.Namespace, pad_id: int): if args.horizon == "single": label_dim = vocab_size else: - # Predict x[t+1], x[t+2], x[t+4] label_dim = (vocab_size, vocab_size, vocab_size) self.model = StackedSLiCE( @@ -185,13 +249,11 @@ def __init__(self, vocab_size: int, args: argparse.Namespace, pad_id: int): ) def forward(self, x: torch.Tensor): - """ - Returns: - - Tensor[B, T, V] (single horizon) - - list[Tensor[B, T, V]] (multi-horizon) - """ return self.model(x) + def hidden_states(self, x: torch.Tensor) -> torch.Tensor: + return self.model.hidden(x) + def loss(self, x: torch.Tensor) -> torch.Tensor: outputs = self.forward(x) targets = x[:, 1:] @@ -203,36 +265,25 @@ def loss(self, x: torch.Tensor) -> torch.Tensor: horizons = MULTI_HORIZONS total = 0.0 - for out, k in zip(outputs, horizons, strict=True): - # Align predictions and targets - readout = out[:, :-k] - tgt = targets[:, k - 1 :] - mask = tgt.ne(self.pad_id) + for out, horizon in zip(outputs, horizons, strict=True): + readout = out[:, :-horizon] + target = targets[:, horizon - 1 :] + mask = target.ne(self.pad_id) loss = F.cross_entropy( readout.reshape(-1, readout.size(-1)), - tgt.reshape(-1), + target.reshape(-1), reduction="none", - ).view_as(tgt) + ).view_as(target) total += (loss * mask).sum() / mask.sum().clamp_min(1) return total / len(outputs) -# --------------------------------------------------------------------- -# Export helpers -# --------------------------------------------------------------------- - - def extract_final(states: torch.Tensor, x: torch.Tensor, pad_id: int) -> torch.Tensor: - """ - Extract the last valid hidden state H_{T-1} for each sequence. - """ - targets = x[:, 1:] - valid = targets.ne(pad_id) - idx = valid.long().sum(dim=1).clamp_min(1) - 1 - + """Extract the final hidden state used for next-token prediction.""" + idx = final_state_indices(x, pad_id) gather = idx.view(-1, 1, 1).expand(-1, 1, states.size(-1)) return states.gather(1, gather).squeeze(1) @@ -250,57 +301,48 @@ def export_states_memmap( batch_size: int = 128, dtype: str = "float32", ) -> None: - """ - Memory-safe export of hidden states H_t using numpy memmap. - """ + """Memory-safe export of hidden states using a numpy memmap.""" assert export_mode in {"final", "all"} assert dtype in {"float32", "float16"} - N, T = x_all.shape - D = hidden_dim - - shape = (N, D) if export_mode == "final" else (N, T, D) + total_examples, seq_len = x_all.shape + shape = ( + (total_examples, hidden_dim) + if export_mode == "final" + else (total_examples, seq_len, hidden_dim) + ) + final_indices = final_state_indices(x_all, pad_id).cpu().numpy() mmap_path = export_path + ".dat" meta_path = export_path + ".npz" + torch_dtype = torch.float16 if dtype == "float16" else torch.float32 print(f"Creating memmap {mmap_path} with shape {shape}") - - mmap = np.memmap( - mmap_path, - dtype=dtype, - mode="w+", - shape=shape, - ) + mmap = np.memmap(mmap_path, dtype=dtype, mode="w+", shape=shape) write_idx = 0 model.eval() - torch_dtype = torch.float16 if dtype == "float16" else torch.float32 - with torch.inference_mode(): - for start in range(0, N, batch_size): + for start in range(0, total_examples, batch_size): xb = x_all[start : start + batch_size].to(device) + hidden = model.hidden_states(xb) - h = model.model.hidden(xb) # (B, T, H) + out = ( + extract_final(hidden, xb, pad_id) if export_mode == "final" else hidden + ) + out_np = out.to(dtype=torch_dtype).cpu().numpy() + batch = out_np.shape[0] - if export_mode == "final": - out = extract_final(h, xb, pad_id) - else: - out = h - - out = out.to(dtype=torch_dtype).cpu().numpy() - B = out.shape[0] - - mmap[write_idx : write_idx + B] = out - write_idx += B - - del xb, h, out - torch.cuda.empty_cache() + mmap[write_idx : write_idx + batch] = out_np + write_idx += batch + if device.type == "cuda": + torch.cuda.empty_cache() if write_idx % (batch_size * 10) == 0: - print(f" wrote {write_idx}/{N}") + print(f" wrote {write_idx}/{total_examples}") mmap.flush() + del mmap np.savez( meta_path, @@ -308,7 +350,8 @@ def export_states_memmap( dtype=dtype, labels=labels.numpy(), export_mode=export_mode, - hidden_dim=D, + hidden_dim=hidden_dim, + final_indices=final_indices, ) print("Export complete:") @@ -316,55 +359,201 @@ def export_states_memmap( print(f" meta: {meta_path}") -# --------------------------------------------------------------------- -# Main -# --------------------------------------------------------------------- +def project_umap( + embeddings: np.ndarray, + *, + backend: str, + n_neighbors: int, + min_dist: float, + seed: int, +) -> tuple[np.ndarray, str]: + umap_kwargs = dict( + n_components=2, + n_neighbors=n_neighbors, + min_dist=min_dist, + metric="cosine", + random_state=seed, + ) + if backend in {"auto", "cuda"}: + try: + import cupy as cp + from cuml.manifold import UMAP as CudaUMAP + except ImportError: + if backend == "cuda": + raise RuntimeError( + "CUDA UMAP requires `cupy` and `cuml`. Install the " + "optional CUDA example dependencies with " + "`uv sync --group examples --group cuda-umap`, then rerun " + "with `--umap-backend cuda`." + ) from None + else: + projection = CudaUMAP(**umap_kwargs).fit_transform(cp.asarray(embeddings)) + return cp.asnumpy(projection), "cuda" -def main() -> None: - args = parse_args() - set_seed(args.seed) - device = torch.device(args.device) + try: + from umap import UMAP as CpuUMAP + except ImportError as exc: + raise RuntimeError( + "UMAP plotting requires `umap-learn`. Install example dependencies " + "with `uv sync --group examples`." + ) from exc - cache_dir = Path(args.cache_dir) - cache_dir.mkdir(parents=True, exist_ok=True) - processed_cache = Path(args.processed_cache) + projection = CpuUMAP(**umap_kwargs, n_jobs=1).fit_transform(embeddings) + return projection, "cpu" + + +def plot_umap_from_export( + *, + export_path: str, + plot_path: str, + time_indices: list[int], + backend: str, + max_points: int, + n_neighbors: int, + min_dist: float, + seed: int, +) -> None: + try: + import matplotlib.pyplot as plt + except ImportError as exc: + raise RuntimeError( + "Plotting requires `matplotlib`. Install example dependencies with " + "`uv sync --group examples`." + ) from exc - if processed_cache.exists(): - data = torch.load(processed_cache) - x_all = data["x"] - labels = data["labels"] - vocab = data["vocab"] + meta_path = export_path + ".npz" + data_path = export_path + ".dat" + meta = np.load(meta_path, allow_pickle=True) + shape = tuple(int(v) for v in meta["shape"]) + dtype = np.dtype(meta["dtype"].item()) + labels = np.asarray(meta["labels"]) + export_mode = str(meta["export_mode"].item()) + final_indices = ( + np.asarray(meta["final_indices"], dtype=np.int64) + if "final_indices" in meta.files + else None + ) + + embeddings = np.memmap(data_path, dtype=dtype, mode="r", shape=shape) + if max_points > 0 and len(labels) > max_points: + rng = np.random.default_rng(seed) + sample_idx = np.sort(rng.choice(len(labels), size=max_points, replace=False)) else: - en = collect_wikipedia_texts( - "20231101.en", - needed=args.n_per_lang, - seed=args.seed, - cache_dir=str(cache_dir), - ) - fr = collect_wikipedia_texts( - "20231101.fr", - needed=args.n_per_lang, - seed=args.seed + 1, - cache_dir=str(cache_dir), + sample_idx = np.arange(len(labels)) + + sampled_labels = labels[sample_idx] + if embeddings.ndim == 2: + panels = [ + ( + r"SLiCE $H_{T-1}$", + np.asarray(embeddings[sample_idx]), + sampled_labels, + ) + ] + else: + if final_indices is None: + raise RuntimeError( + "This export is missing per-sequence final-state metadata. " + "Re-run the script to regenerate the embeddings before plotting." + ) + panels = [] + sampled_final_indices = final_indices[sample_idx] + for time_index in time_indices: + positions, valid = resolve_time_positions( + time_index, + sampled_final_indices, + embeddings.shape[1], + ) + if not np.any(valid): + raise IndexError( + f"time index {time_index} is out of range for all sampled sequences" + ) + panels.append( + ( + ( + r"SLiCE $H_{T}$" + if time_index == 0 + else ( + rf"SLiCE $H_{{T{time_index}}}$" + if time_index < 0 + else rf"SLiCE $H_{{t={time_index}}}$" + ) + ), + np.asarray(embeddings[sample_idx[valid], positions[valid], :]), + sampled_labels[valid], + ) + ) + + n_panels = len(panels) + ncols = min(3, n_panels) + nrows = math.ceil(n_panels / ncols) + fig, axs = plt.subplots(nrows, ncols, figsize=(5 * ncols, 4.5 * nrows)) + axs_flat = np.atleast_1d(axs).ravel() + + backend_used = None + for ax, (title, states, panel_labels) in zip(axs_flat, panels, strict=True): + projection, backend_used = project_umap( + states, + backend=backend, + n_neighbors=n_neighbors, + min_dist=min_dist, + seed=seed, ) - texts = en + fr - labels = torch.tensor([0] * len(en) + [1] * len(fr)) - perm = torch.randperm(len(texts)) - texts = [texts[i] for i in perm.tolist()] - labels = labels[perm] + for label, name, colour in LANGUAGE_LABELS: + mask = panel_labels == label + ax.scatter( + projection[mask, 0], + projection[mask, 1], + s=4, + alpha=0.4, + c=colour, + label=name, + linewidths=0, + ) + + ax.set_title(title) + ax.set_xlabel("UMAP-1") + ax.set_ylabel("UMAP-2") + ax.legend(markerscale=2) + + for ax in axs_flat[n_panels:]: + ax.axis("off") + + fig.suptitle( + f"Temporal evolution of SLiCE hidden states ({backend_used.upper()} UMAP)" + if export_mode == "all" + else f"SLiCE hidden state projection ({backend_used.upper()} UMAP)", + fontsize=16, + ) + fig.tight_layout(rect=[0, 0.03, 1, 0.95]) + fig.savefig(plot_path, dpi=200) + print(f"Saved figure to {plot_path}") - vocab = build_vocab(texts) - x_all = encode_texts(texts, vocab, args.max_seq_len) - torch.save( - {"x": x_all, "labels": labels, "vocab": vocab}, - processed_cache, - ) +def main() -> None: + args = parse_args() + set_seed(args.seed) + device = torch.device(args.device) + cache_dir = None if args.cache_dir is None else str(Path(args.cache_dir)) + if cache_dir is not None: + Path(cache_dir).mkdir(parents=True, exist_ok=True) + Path(args.export_path).parent.mkdir(parents=True, exist_ok=True) + if args.plot_umap: + Path(args.plot_path).parent.mkdir(parents=True, exist_ok=True) + + texts, labels = load_wikipedia_en_fr( + args.n_per_lang, + seed=args.seed, + cache_dir=cache_dir, + ) + vocab = build_vocab(texts) + x_all = encode_texts(texts, vocab, args.max_seq_len) - pad_id = vocab[""] + print(f"Loaded {x_all.size(0)} sequences with vocabulary size {len(vocab)}") + pad_id = vocab[""] model = SelfSupervisedSLiCE(len(vocab), args, pad_id).to(device) optimiser = torch.optim.AdamW(model.parameters(), lr=args.lr) @@ -374,7 +563,6 @@ def main() -> None: xb = x_all[idx].to(device) loss = model.loss(xb) - optimiser.zero_grad(set_to_none=True) loss.backward() torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0) @@ -394,6 +582,18 @@ def main() -> None: device=device, ) + if args.plot_umap: + plot_umap_from_export( + export_path=args.export_path, + plot_path=args.plot_path, + time_indices=parse_time_indices(args.plot_time_indices), + backend=args.umap_backend, + max_points=args.plot_max_points, + n_neighbors=args.umap_neighbors, + min_dist=args.umap_min_dist, + seed=args.seed, + ) + if __name__ == "__main__": main() diff --git a/examples/language_selfsupervised_plotting.py b/examples/language_selfsupervised_plotting.py deleted file mode 100644 index 859fb7f..0000000 --- a/examples/language_selfsupervised_plotting.py +++ /dev/null @@ -1,134 +0,0 @@ -""" -UMAP visualisation of SLiCE hidden states across time. - -Assumes: - - embeddings stored as a numpy memmap: (N, T, D) - - labels are binary (e.g. English / French) - - time index is relative to the *end* of the sequence -""" - -import cupy as cp -import matplotlib.pyplot as plt -import numpy as np -from cuml.manifold import UMAP - -# --------------------------------------------------------------------- -# Load embeddings + metadata -# --------------------------------------------------------------------- - -META_PATH = "slice_embeddings.npz" -DATA_PATH = "slice_embeddings.dat" - -meta = np.load(META_PATH, allow_pickle=True) -shape = tuple(meta["shape"]) -dtype = meta["dtype"].item() -labels = meta["labels"] - -embeddings = np.memmap( - DATA_PATH, - dtype=dtype, - mode="r", - shape=shape, -) - -assert embeddings.ndim == 3, "Expected embeddings of shape (N, T, D)" -N, T, D = embeddings.shape - -print(f"Loaded embeddings: N={N}, T={T}, D={D}, dtype={dtype}") - - -# --------------------------------------------------------------------- -# Plot configuration -# --------------------------------------------------------------------- - -# Time indices relative to the end of the sequence -# -1 = H_{T-1}, -2 = H_{T-2}, etc. -TIME_INDICES = [0, 20, 100, -5, -2, -1] - -LABELS = [ - (0, "English", "tab:blue"), - (1, "French", "tab:orange"), -] - -UMAP_KWARGS = dict( - n_components=2, - n_neighbors=80, - min_dist=0.1, - metric="cosine", - random_state=0, -) - -FIGSIZE = (15, 10) -POINT_SIZE = 4 -ALPHA = 0.4 - - -# --------------------------------------------------------------------- -# Create figure -# --------------------------------------------------------------------- - -fig, axs = plt.subplots(2, 3, figsize=FIGSIZE) -axs = axs.flatten() - - -# --------------------------------------------------------------------- -# Main loop -# --------------------------------------------------------------------- - -for ax, t in zip(axs, TIME_INDICES, strict=True): - if not (-T <= t < T): - raise IndexError(f"time index {t} out of range for T={T}") - - # (N, D) slice at time t - H = embeddings[:, t, :] - - # --------------------------- - # Move to GPU - # --------------------------- - H_gpu = cp.asarray(H) - - # --------------------------- - # UMAP (GPU) - # --------------------------- - umap = UMAP(**UMAP_KWARGS) - Z_gpu = umap.fit_transform(H_gpu) - - # Back to CPU for plotting - Z = cp.asnumpy(Z_gpu) - - # --------------------------- - # Plot by label - # --------------------------- - for lab, name, colour in LABELS: - m = labels == lab - ax.scatter( - Z[m, 0], - Z[m, 1], - s=POINT_SIZE, - alpha=ALPHA, - c=colour, - label=name, - linewidths=0, - ) - - # --------------------------- - # Axis cosmetics - # --------------------------- - ax.set_title(rf"cuML UMAP of SLiCE $H_{{t={t}}}$") - ax.set_xlabel("UMAP-1") - ax.set_ylabel("UMAP-2") - ax.legend(markerscale=2) - - -# --------------------------------------------------------------------- -# Finalise and save -# --------------------------------------------------------------------- - -fig.suptitle("Temporal evolution of SLiCE hidden states", fontsize=16) -fig.tight_layout(rect=[0, 0.03, 1, 0.95]) - -OUT_PATH = "slice_hidden_umap.png" -fig.savefig(OUT_PATH, dpi=200) - -print(f"Saved figure to {OUT_PATH}") -plt.show() diff --git a/pyproject.toml b/pyproject.toml index a7f5df1..84a5f9e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -26,7 +26,7 @@ classifiers = [ dependencies = [ "torch>=2.8.0", - "numpy>=2.4.1", + "numpy>=2", ] [dependency-groups] @@ -38,6 +38,12 @@ dev = [ ] examples = [ "datasets>=2.20.0", + "matplotlib>=3.8.0", + "umap-learn>=0.5.7", +] +cuda-umap = [ + "cupy-cuda12x>=14.0.1; sys_platform == 'linux' and platform_machine == 'x86_64'", + "cuml-cu12>=26.2.0; sys_platform == 'linux' and platform_machine == 'x86_64'", ] [project.urls] diff --git a/src/slices/slices.py b/src/slices/slices.py index e851e65..485783b 100644 --- a/src/slices/slices.py +++ b/src/slices/slices.py @@ -660,7 +660,8 @@ class StackedSLiCE(nn.Module): Shape: - Input: (batch_size, seq_len) if the input is tokens or (batch_size, seq_len, data_dim) if the input is time-series values. - - Output: (batch_size, seq_len, label_dim) + - Output: (batch_size, seq_len, label_dim) for scalar label_dim, or + list[(batch_size, seq_len, d)] for tuple label_dim. """ def __init__( @@ -759,7 +760,7 @@ def hidden(self, X: torch.Tensor) -> torch.Tensor: return X - def forward(self, X: torch.Tensor) -> torch.Tensor: + def forward(self, X: torch.Tensor) -> torch.Tensor | list[torch.Tensor]: """ Forward pass of the stacked model: 1. Embed input X @@ -771,7 +772,8 @@ def forward(self, X: torch.Tensor) -> torch.Tensor: If time-series, shape (batch_size, seq_len, data_dim) Returns: - torch.Tensor: shape (batch_size, seq_len, label_dim) + torch.Tensor | list[torch.Tensor]: tensor output for scalar label_dim, + or one tensor per head when label_dim is a tuple. """ X = self.hidden(X) diff --git a/tests/test_slices.py b/tests/test_slices.py index 1542241..5d42c7a 100644 --- a/tests/test_slices.py +++ b/tests/test_slices.py @@ -413,6 +413,77 @@ def test_stacked_slice_propagates_second_norm_toggle(): _assert_no_nan(y) +def test_stacked_slice_hidden_matches_forward_pre_projection(): + batch, seq = 2, 5 + vocab = 11 + hidden = 8 + label = 3 + + x = torch.randint(low=0, high=vocab, size=(batch, seq)) + + m = StackedSLiCE( + num_layers=2, + data_dim=vocab, + hidden_dim=hidden, + label_dim=label, + tokens=True, + block_size=4, + diagonal_dense=False, + use_parallel=False, + dropout_rate=0.0, + ) + m.eval() + + hidden_states = m.hidden(x) + y = m(x) + + assert hidden_states.shape == (batch, seq, hidden) + torch.testing.assert_close(y, m.linear(hidden_states)) + + +def test_stacked_slice_multihead_outputs_match_hidden_projection(): + batch, seq = 2, 4 + data_dim = 6 + hidden = 8 + label_dims = (3, 5, 7) + + x = _rand_x(batch=batch, seq=seq, dim=data_dim, seed=15) + + m = StackedSLiCE( + num_layers=2, + data_dim=data_dim, + hidden_dim=hidden, + label_dim=label_dims, + tokens=False, + block_size=4, + diagonal_dense=True, + use_parallel=False, + dropout_rate=0.0, + ) + m.eval() + + hidden_states = m.hidden(x) + outputs = m(x) + + assert isinstance(outputs, list) + assert len(outputs) == len(label_dims) + for out, head, label_dim in zip(outputs, m.linear, label_dims, strict=True): + assert out.shape == (batch, seq, label_dim) + torch.testing.assert_close(out, head(hidden_states)) + + +def test_stacked_slice_rejects_invalid_label_dim_type(): + with pytest.raises(TypeError, match="label_dim must be int or tuple of int"): + StackedSLiCE( + num_layers=1, + data_dim=4, + hidden_dim=8, + label_dim=[3, 5], + tokens=True, + use_parallel=False, + ) + + def test_slice_increments_mode_preserves_direct_input_behaviour(): """ Hand calculated example with input_dim=2 so augmented inp has 3 channels: diff --git a/uv.lock b/uv.lock index 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