mirror of
https://github.com/rasbt/LLMs-from-scratch.git
synced 2026-04-10 12:33:42 +00:00
276 lines
9.4 KiB
Python
276 lines
9.4 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 sys
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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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def _import_qwen3_5_classes():
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try:
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from transformers.models.qwen3_5.configuration_qwen3_5 import Qwen3_5TextConfig
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from transformers.models.qwen3_5.modeling_qwen3_5 import Qwen3_5ForCausalLM
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return Qwen3_5TextConfig, Qwen3_5ForCausalLM
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except Exception:
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repo_root = Path(__file__).resolve().parents[3]
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local_src = repo_root / "transformers-main" / "src"
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if not local_src.exists():
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raise
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for name in list(sys.modules):
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if name == "transformers" or name.startswith("transformers."):
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del sys.modules[name]
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sys.path.insert(0, str(local_src))
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from transformers.models.qwen3_5.configuration_qwen3_5 import Qwen3_5TextConfig
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from transformers.models.qwen3_5.modeling_qwen3_5 import Qwen3_5ForCausalLM
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return Qwen3_5TextConfig, Qwen3_5ForCausalLM
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try:
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Qwen3_5TextConfig, Qwen3_5ForCausalLM = _import_qwen3_5_classes()
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except Exception:
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Qwen3_5TextConfig = None
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Qwen3_5ForCausalLM = 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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"qk_norm": True,
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"n_kv_groups": 2,
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"rope_base": 1_000_000.0,
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"partial_rotary_factor": 1.0,
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"rms_norm_eps": 1e-6,
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"linear_conv_kernel_dim": 2,
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"linear_key_head_dim": 8,
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"linear_value_head_dim": 8,
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"linear_num_key_heads": 2,
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"linear_num_value_heads": 2,
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"layer_types": ["linear_attention", "full_attention"],
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"dtype": torch.float32,
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}
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def _hf_config_from_dict(cfg):
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if Qwen3_5TextConfig is None:
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raise ImportError("Qwen3.5 classes are required for the layer debugger.")
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hf_cfg = Qwen3_5TextConfig(
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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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head_dim=cfg["head_dim"],
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num_key_value_heads=cfg["n_kv_groups"],
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layer_types=cfg["layer_types"],
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linear_conv_kernel_dim=cfg["linear_conv_kernel_dim"],
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linear_key_head_dim=cfg["linear_key_head_dim"],
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linear_value_head_dim=cfg["linear_value_head_dim"],
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linear_num_key_heads=cfg["linear_num_key_heads"],
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linear_num_value_heads=cfg["linear_num_value_heads"],
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tie_word_embeddings=False,
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use_cache=False,
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attention_bias=False,
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attention_dropout=0.0,
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rms_norm_eps=cfg.get("rms_norm_eps", 1e-6),
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rope_parameters={
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"rope_type": "default",
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"rope_theta": cfg["rope_base"],
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"partial_rotary_factor": cfg.get("partial_rotary_factor", 1.0),
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"mrope_interleaved": True,
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"mrope_section": [2, 1, 1],
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},
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torch_dtype=cfg.get("dtype", torch.float32),
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)
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hf_cfg._attn_implementation = "eager"
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return hf_cfg
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def load_notebook_defs(nb_name="qwen3.5.ipynb"):
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nb_dir = Path(__file__).resolve().parents[1]
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if str(nb_dir) not in sys.path:
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sys.path.insert(0, str(nb_dir))
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return import_definitions_from_notebook(nb_dir, nb_name)
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def build_qwen3_5_pair(import_notebook_defs, cfg, hf_checkpoint=None):
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if Qwen3_5ForCausalLM is None:
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raise ImportError("Qwen3.5 classes are required for the layer debugger.")
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ours = import_notebook_defs.Qwen3_5Model(cfg)
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if hf_checkpoint:
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hf_model = Qwen3_5ForCausalLM.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_cfg = _hf_config_from_dict(cfg)
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hf_model = Qwen3_5ForCausalLM(hf_cfg)
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import_notebook_defs.load_weights_into_qwen3_5(
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ours,
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{"n_layers": cfg["n_layers"], "layer_types": cfg["layer_types"]},
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hf_model.state_dict(),
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)
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hf_model.config.use_cache = False
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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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if isinstance(output, tuple):
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output = output[0]
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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 Qwen3.5 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, use_cache=False)
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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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if Qwen3_5ForCausalLM is None:
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raise SystemExit(
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"Qwen3.5 classes are unavailable. Install a recent transformers version or use local transformers-main."
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)
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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_qwen3_5_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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