# Copyright (c) Sebastian Raschka under Apache License 2.0 (see LICENSE.txt). # Source for "Build a Large Language Model From Scratch" # - https://www.manning.com/books/build-a-large-language-model-from-scratch # Code: https://github.com/rasbt/LLMs-from-scratch import importlib from pathlib import Path import torch from llms_from_scratch.utils import import_definitions_from_notebook try: from transformers import Olmo3Config, Olmo3ForCausalLM except ImportError: Olmo3Config = None Olmo3ForCausalLM = None def tiny_debug_config(): return { "vocab_size": 257, "context_length": 8, "emb_dim": 32, "n_heads": 4, "n_layers": 2, "hidden_dim": 64, "head_dim": 8, "qk_norm": True, "n_kv_heads": 2, "sliding_window": 4, "layer_types": ["full_attention", "full_attention"], "dtype": torch.float32, "query_pre_attn_scalar": 256, "attention_bias": False, "rms_norm_eps": 1e-6, "rope_base": 1_000_000.0, "rope_attention_factor": 1.0, "rope_type": "default", "rope_factor": 1.0, "rope_orig_max": 8, "rope_local_base": 10_000.0, } def yarn_debug_config(): return { "vocab_size": 257, "context_length": 8, "emb_dim": 32, "n_heads": 4, "n_layers": 2, "hidden_dim": 64, "head_dim": 8, "qk_norm": True, "n_kv_heads": 2, "sliding_window": 4, "layer_types": ["full_attention", "full_attention"], "dtype": torch.float32, "query_pre_attn_scalar": 256, "attention_bias": False, "rms_norm_eps": 1e-6, "rope_base": 500_000.0, "rope_attention_factor": 1.2079441541679836, "rope_type": "yarn", "rope_factor": 8.0, "rope_orig_max": 8192, "beta_fast": 32.0, "beta_slow": 1.0, "rope_local_base": 10_000.0, } def _hf_config_from_dict(cfg): if Olmo3Config is None: raise ImportError("transformers is required for the Olmo-3 debugger.") rope_type = cfg.get("rope_type", "default") rope_scaling = {"rope_type": rope_type} if rope_type == "yarn": rope_scaling.update( { "attention_factor": cfg.get("rope_attention_factor", 1.0), "beta_fast": cfg.get("beta_fast", 32.0), "beta_slow": cfg.get("beta_slow", 1.0), "factor": cfg.get("rope_factor", 1.0), "original_max_position_embeddings": cfg.get("rope_orig_max", 8192), } ) return Olmo3Config( vocab_size=cfg["vocab_size"], max_position_embeddings=cfg["context_length"], hidden_size=cfg["emb_dim"], num_attention_heads=cfg["n_heads"], num_hidden_layers=cfg["n_layers"], intermediate_size=cfg["hidden_dim"], head_dim=cfg["head_dim"], num_key_value_heads=cfg["n_kv_heads"], rope_theta=cfg["rope_base"], rope_local_base_freq=cfg.get("rope_local_base", 10_000.0), layer_types=cfg["layer_types"], sliding_window=cfg["sliding_window"], tie_word_embeddings=False, attn_implementation="eager", torch_dtype=cfg.get("dtype", torch.float32), query_pre_attn_scalar=cfg.get("query_pre_attn_scalar", 256), rope_scaling=rope_scaling, qk_norm=cfg.get("qk_norm", False), rms_norm_eps=cfg.get("rms_norm_eps", 1e-5), ) def load_notebook_defs(nb_name="standalone-olmo3.ipynb"): nb_dir = Path(__file__).resolve().parents[1] return import_definitions_from_notebook(nb_dir, nb_name) def build_olmo3_pair(nb_imports, cfg, hf_checkpoint=None): if Olmo3ForCausalLM is None: raise ImportError("transformers is required for the Olmo-3 debugger.") ours = nb_imports.Olmo3Model(cfg) hf_cfg = _hf_config_from_dict(cfg) if hf_checkpoint: hf_model = Olmo3ForCausalLM.from_pretrained( hf_checkpoint, torch_dtype=cfg.get("dtype", torch.float32), attn_implementation="eager", ) else: hf_model = Olmo3ForCausalLM(hf_cfg) param_config = {"n_layers": cfg["n_layers"], "hidden_dim": cfg["hidden_dim"]} nb_imports.load_weights_into_olmo(ours, param_config, hf_model.state_dict()) ours.eval() hf_model.eval() return ours, hf_model def _attach_debug_hooks(model, is_hf): traces = {} handles = [] def hook(name): def _record(_, __, output): traces[name] = output.detach().to(torch.float32).cpu() return _record if is_hf: core = model.model handles.append(core.embed_tokens.register_forward_hook(hook("embedding"))) for idx, layer in enumerate(core.layers): handles.append(layer.register_forward_hook(hook(f"block_{idx}"))) handles.append(core.norm.register_forward_hook(hook("final_norm"))) handles.append(model.lm_head.register_forward_hook(hook("logits"))) else: handles.append(model.tok_emb.register_forward_hook(hook("embedding"))) for idx, block in enumerate(model.blocks): handles.append(block.register_forward_hook(hook(f"block_{idx}"))) handles.append(model.final_norm.register_forward_hook(hook("final_norm"))) handles.append(model.out_head.register_forward_hook(hook("logits"))) return traces, handles def _layer_sort_key(name): if name == "embedding": return (0, 0) if name.startswith("block_"): idx = int(name.split("_")[1]) return (1, idx) if name == "final_norm": return (2, 0) if name == "logits": return (3, 0) return (4, name) def layerwise_differences(ours, hf_model, input_ids, rtol=1e-5, atol=1e-5): ours_traces, ours_handles = _attach_debug_hooks(ours, is_hf=False) hf_traces, hf_handles = _attach_debug_hooks(hf_model, is_hf=True) try: with torch.inference_mode(): ours(input_ids) hf_model(input_ids) finally: for h in ours_handles + hf_handles: h.remove() layer_names = sorted(set(ours_traces) | set(hf_traces), key=_layer_sort_key) results = [] for name in layer_names: ours_tensor = ours_traces.get(name) hf_tensor = hf_traces.get(name) if ours_tensor is None or hf_tensor is None: results.append( { "name": name, "status": "missing", "ours_shape": None if ours_tensor is None else tuple(ours_tensor.shape), "hf_shape": None if hf_tensor is None else tuple(hf_tensor.shape), "max_diff": None, "mean_abs_diff": None, } ) continue shapes_match = ours_tensor.shape == hf_tensor.shape if not shapes_match: results.append( { "name": name, "status": "shape_mismatch", "ours_shape": tuple(ours_tensor.shape), "hf_shape": tuple(hf_tensor.shape), "max_diff": None, "mean_abs_diff": None, } ) continue diff = (ours_tensor - hf_tensor).abs() max_diff = float(diff.max().item()) mean_diff = float(diff.mean().item()) allclose = torch.allclose(ours_tensor, hf_tensor, rtol=rtol, atol=atol) results.append( { "name": name, "status": "ok" if allclose else "mismatch", "ours_shape": tuple(ours_tensor.shape), "hf_shape": tuple(hf_tensor.shape), "max_diff": max_diff, "mean_abs_diff": mean_diff, } ) return results def first_mismatch(differences): for diff in differences: if diff["status"] != "ok": return diff return None def format_report(differences): lines = [] for diff in sorted(differences, key=lambda d: _layer_sort_key(d["name"])): if diff["status"] == "ok": lines.append(f"[OK] {diff['name']}: max={diff['max_diff']:.2e}, mean={diff['mean_abs_diff']:.2e}") elif diff["status"] == "mismatch": lines.append( f"[DIFF] {diff['name']}: max={diff['max_diff']:.2e}, mean={diff['mean_abs_diff']:.2e}" ) elif diff["status"] == "shape_mismatch": lines.append( f"[SHAPE] {diff['name']}: ours={diff['ours_shape']}, hf={diff['hf_shape']}" ) else: lines.append(f"[MISSING] {diff['name']}: ours={diff['ours_shape']}, hf={diff['hf_shape']}") return "\n".join(lines) if __name__ == "__main__": transformers_available = importlib.util.find_spec("transformers") is not None if not transformers_available: raise SystemExit("transformers is not installed; install it to run the debugger.") nb_imports = load_notebook_defs() cfg = yarn_debug_config() ours_model, hf_model = build_olmo3_pair(nb_imports, cfg) torch.manual_seed(0) input_ids = torch.randint(0, cfg["vocab_size"], (1, cfg["context_length"]), dtype=torch.long) diffs = layerwise_differences(ours_model, hf_model, input_ids) print(format_report(diffs))