mirror of
https://github.com/rasbt/LLMs-from-scratch.git
synced 2026-04-10 12:33:42 +00:00
226 lines
7.8 KiB
Python
226 lines
7.8 KiB
Python
# Copyright (c) Sebastian Raschka under Apache License 2.0 (see LICENSE.txt).
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# Source for "Build a Large Language Model From Scratch"
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# - https://www.manning.com/books/build-a-large-language-model-from-scratch
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# Code: https://github.com/rasbt/LLMs-from-scratch
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import importlib
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from pathlib import Path
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import torch
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from llms_from_scratch.utils import import_definitions_from_notebook
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try:
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from transformers import Cohere2Config, Cohere2ForCausalLM
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except ImportError:
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Cohere2Config = None
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Cohere2ForCausalLM = None
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def tiny_debug_config():
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return {
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"vocab_size": 257,
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"context_length": 8,
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"emb_dim": 32,
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"n_heads": 4,
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"n_layers": 2,
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"hidden_dim": 64,
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"head_dim": 8,
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"n_kv_heads": 2,
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"sliding_window": 4,
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"layer_types": ["sliding_attention", "full_attention"],
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"dtype": torch.float32,
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"attention_bias": False,
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"attention_dropout": 0.0,
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"layer_norm_eps": 1e-5,
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"rope_base": 10_000.0,
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"logit_scale": 1.0,
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"tie_word_embeddings": False,
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}
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def _hf_config_from_dict(cfg):
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if Cohere2Config is None:
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raise ImportError("transformers is required for the Tiny Aya debugger.")
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return Cohere2Config(
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vocab_size=cfg["vocab_size"],
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max_position_embeddings=cfg["context_length"],
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hidden_size=cfg["emb_dim"],
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num_attention_heads=cfg["n_heads"],
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num_hidden_layers=cfg["n_layers"],
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intermediate_size=cfg["hidden_dim"],
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num_key_value_heads=cfg["n_kv_heads"],
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attention_bias=cfg["attention_bias"],
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attention_dropout=cfg["attention_dropout"],
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layer_norm_eps=cfg["layer_norm_eps"],
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sliding_window=cfg["sliding_window"],
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layer_types=cfg["layer_types"],
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logit_scale=cfg["logit_scale"],
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tie_word_embeddings=cfg.get("tie_word_embeddings", False),
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rope_parameters={"rope_type": "default", "rope_theta": cfg["rope_base"]},
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torch_dtype=cfg.get("dtype", torch.float32),
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)
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def load_notebook_defs(nb_name="standalone-tiny-aya.ipynb"):
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nb_dir = Path(__file__).resolve().parents[1]
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return import_definitions_from_notebook(nb_dir, nb_name)
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def build_tiny_aya_pair(import_notebook_defs, cfg, hf_checkpoint=None):
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if Cohere2ForCausalLM is None:
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raise ImportError("transformers is required for the Tiny Aya debugger.")
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ours = import_notebook_defs.TinyAyaModel(cfg)
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hf_cfg = _hf_config_from_dict(cfg)
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if hf_checkpoint:
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hf_model = Cohere2ForCausalLM.from_pretrained(
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hf_checkpoint,
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torch_dtype=cfg.get("dtype", torch.float32),
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attn_implementation="eager",
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)
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else:
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hf_model = Cohere2ForCausalLM(hf_cfg)
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import_notebook_defs.load_weights_into_tiny_aya(ours, cfg, hf_model.state_dict())
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ours.eval()
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hf_model.eval()
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return ours, hf_model
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def _attach_debug_hooks(model, is_hf):
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traces = {}
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handles = []
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def hook(name):
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def _record(_, __, output):
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traces[name] = output.detach().to(torch.float32).cpu()
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return _record
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if is_hf:
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core = model.model
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handles.append(core.embed_tokens.register_forward_hook(hook("embedding")))
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for idx, layer in enumerate(core.layers):
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handles.append(layer.register_forward_hook(hook(f"block_{idx}")))
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handles.append(core.norm.register_forward_hook(hook("final_norm")))
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handles.append(model.lm_head.register_forward_hook(hook("logits")))
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else:
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handles.append(model.tok_emb.register_forward_hook(hook("embedding")))
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blocks = getattr(model, "trf_blocks", None)
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if blocks is None:
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blocks = getattr(model, "blocks", None)
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if blocks is None:
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raise AttributeError("Could not locate Tiny Aya blocks on the local model.")
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for idx, block in enumerate(blocks):
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handles.append(block.register_forward_hook(hook(f"block_{idx}")))
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handles.append(model.final_norm.register_forward_hook(hook("final_norm")))
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handles.append(model.out_head.register_forward_hook(hook("logits")))
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return traces, handles
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def _layer_sort_key(name):
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if name == "embedding":
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return (0, 0)
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if name.startswith("block_"):
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idx = int(name.split("_")[1])
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return (1, idx)
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if name == "final_norm":
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return (2, 0)
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if name == "logits":
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return (3, 0)
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return (4, name)
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def layerwise_differences(ours, hf_model, input_ids, rtol=1e-5, atol=1e-5):
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ours_traces, ours_handles = _attach_debug_hooks(ours, is_hf=False)
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hf_traces, hf_handles = _attach_debug_hooks(hf_model, is_hf=True)
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try:
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with torch.inference_mode():
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ours(input_ids)
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hf_model(input_ids)
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finally:
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for h in ours_handles + hf_handles:
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h.remove()
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layer_names = sorted(set(ours_traces) | set(hf_traces), key=_layer_sort_key)
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results = []
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for name in layer_names:
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ours_tensor = ours_traces.get(name)
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hf_tensor = hf_traces.get(name)
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if ours_tensor is None or hf_tensor is None:
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results.append(
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{
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"name": name,
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"status": "missing",
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"ours_shape": None if ours_tensor is None else tuple(ours_tensor.shape),
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"hf_shape": None if hf_tensor is None else tuple(hf_tensor.shape),
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"max_diff": None,
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"mean_abs_diff": None,
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}
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)
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continue
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if ours_tensor.shape != hf_tensor.shape:
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results.append(
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{
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"name": name,
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"status": "shape_mismatch",
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"ours_shape": tuple(ours_tensor.shape),
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"hf_shape": tuple(hf_tensor.shape),
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"max_diff": None,
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"mean_abs_diff": None,
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}
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)
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continue
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diff = (ours_tensor - hf_tensor).abs()
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max_diff = float(diff.max().item())
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mean_diff = float(diff.mean().item())
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allclose = torch.allclose(ours_tensor, hf_tensor, rtol=rtol, atol=atol)
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results.append(
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{
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"name": name,
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"status": "ok" if allclose else "mismatch",
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"ours_shape": tuple(ours_tensor.shape),
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"hf_shape": tuple(hf_tensor.shape),
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"max_diff": max_diff,
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"mean_abs_diff": mean_diff,
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}
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)
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return results
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def format_report(differences):
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lines = []
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for diff in sorted(differences, key=lambda d: _layer_sort_key(d["name"])):
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if diff["status"] == "ok":
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lines.append(f"[OK] {diff['name']}: max={diff['max_diff']:.2e}, mean={diff['mean_abs_diff']:.2e}")
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elif diff["status"] == "mismatch":
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lines.append(f"[DIFF] {diff['name']}: max={diff['max_diff']:.2e}, mean={diff['mean_abs_diff']:.2e}")
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elif diff["status"] == "shape_mismatch":
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lines.append(f"[SHAPE] {diff['name']}: ours={diff['ours_shape']}, hf={diff['hf_shape']}")
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else:
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lines.append(f"[MISSING] {diff['name']}: ours={diff['ours_shape']}, hf={diff['hf_shape']}")
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return "\n".join(lines)
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if __name__ == "__main__":
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transformers_available = importlib.util.find_spec("transformers") is not None
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if not transformers_available:
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raise SystemExit("transformers is not installed; install it to run the debugger.")
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import_notebook_defs = load_notebook_defs()
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cfg = tiny_debug_config()
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ours_model, hf_model = build_tiny_aya_pair(import_notebook_defs, cfg)
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torch.manual_seed(0)
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input_ids = torch.randint(0, cfg["vocab_size"], (1, cfg["context_length"]), dtype=torch.long)
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diffs = layerwise_differences(ours_model, hf_model, input_ids)
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print(format_report(diffs))
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