mirror of
https://github.com/ggml-org/llama.cpp.git
synced 2025-11-03 09:22:01 +00:00
232 lines
8.0 KiB
Python
Executable File
232 lines
8.0 KiB
Python
Executable File
#!/usr/bin/env python3
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import argparse
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import os
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import importlib
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from pathlib import Path
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from transformers import AutoTokenizer, AutoModelForCausalLM, AutoConfig
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import torch
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import numpy as np
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### If you want to dump RoPE activations, apply this monkey patch to the model
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### class from Transformers that you are running (replace apertus.modeling_apertus
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### with the proper package and class for your model
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### === START ROPE DEBUG ===
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# from transformers.models.apertus.modeling_apertus import apply_rotary_pos_emb
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# orig_rope = apply_rotary_pos_emb
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# torch.set_printoptions(threshold=float('inf'))
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# torch.set_printoptions(precision=6, sci_mode=False)
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# def debug_rope(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
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# # log inputs
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# summarize(q, "RoPE.q_in")
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# summarize(k, "RoPE.k_in")
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# # call original
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# q_out, k_out = orig_rope(q, k, cos, sin, position_ids, unsqueeze_dim)
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# # log outputs
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# summarize(q_out, "RoPE.q_out")
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# summarize(k_out, "RoPE.k_out")
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# return q_out, k_out
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# # Patch it
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# import transformers.models.apertus.modeling_apertus as apertus_mod # noqa: E402
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# apertus_mod.apply_rotary_pos_emb = debug_rope
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### == END ROPE DEBUG ===
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def summarize(tensor: torch.Tensor, name: str, max_seq: int = 3, max_vals: int = 3):
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"""
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Print a tensor in llama.cpp debug style.
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Supports:
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- 2D tensors (seq, hidden)
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- 3D tensors (batch, seq, hidden)
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- 4D tensors (batch, seq, heads, dim_per_head) via flattening heads × dim_per_head
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Shows first and last max_vals of each vector per sequence position.
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"""
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t = tensor.detach().to(torch.float32).cpu()
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# Determine dimensions
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if t.ndim == 3:
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_, s, _ = t.shape
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elif t.ndim == 2:
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_, s = 1, t.shape[0]
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t = t.unsqueeze(0)
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elif t.ndim == 4:
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_, s, _, _ = t.shape
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else:
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print(f"Skipping tensor due to unsupported dimensions: {t.ndim}")
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return
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ten_shape = t.shape
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print(f"ggml_debug: {name} = (f32) ... = {{{ten_shape}}}")
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print(" [")
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print(" [")
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# Determine indices for first and last sequences
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first_indices = list(range(min(s, max_seq)))
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last_indices = list(range(max(0, s - max_seq), s))
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# Check if there's an overlap between first and last indices or if we're at the edge case of s = 2 * max_seq
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has_overlap = bool(set(first_indices) & set(last_indices)) or (max_seq * 2 == s)
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# Combine indices
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if has_overlap:
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# If there's overlap, just use the combined unique indices
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indices = sorted(list(set(first_indices + last_indices)))
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separator_index = None
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else:
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# If no overlap, we'll add a separator between first and last sequences
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indices = first_indices + last_indices
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separator_index = len(first_indices)
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for i, si in enumerate(indices):
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# Add separator if needed
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if separator_index is not None and i == separator_index:
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print(" ...")
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# Extract appropriate slice
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vec = t[0, si]
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if vec.ndim == 2: # 4D case: flatten heads × dim_per_head
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flat = vec.flatten().tolist()
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else: # 2D or 3D case
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flat = vec.tolist()
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# First and last slices
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first = flat[:max_vals]
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last = flat[-max_vals:] if len(flat) >= max_vals else flat
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first_str = ", ".join(f"{v:12.4f}" for v in first)
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last_str = ", ".join(f"{v:12.4f}" for v in last)
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print(f" [{first_str}, ..., {last_str}]")
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print(" ],")
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print(" ]")
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print(f" sum = {t.sum().item():.6f}\n")
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def debug_hook(name):
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def fn(_m, input, output):
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if isinstance(input, torch.Tensor):
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summarize(input, name + "_in")
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elif isinstance(input, (tuple, list)) and isinstance(input[0], torch.Tensor):
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summarize(input[0], name + "_in")
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if isinstance(output, torch.Tensor):
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summarize(output, name + "_out")
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elif isinstance(output, (tuple, list)) and isinstance(output[0], torch.Tensor):
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summarize(output[0], name + "_out")
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return fn
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unreleased_model_name = os.getenv("UNRELEASED_MODEL_NAME")
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parser = argparse.ArgumentParser(description="Process model with specified path")
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parser.add_argument("--model-path", "-m", help="Path to the model")
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args = parser.parse_args()
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model_path = os.environ.get("MODEL_PATH", args.model_path)
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if model_path is None:
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parser.error(
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"Model path must be specified either via --model-path argument or MODEL_PATH environment variable"
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)
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config = AutoConfig.from_pretrained(model_path)
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print("Model type: ", config.model_type)
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print("Vocab size: ", config.vocab_size)
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print("Hidden size: ", config.hidden_size)
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print("Number of layers: ", config.num_hidden_layers)
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print("BOS token id: ", config.bos_token_id)
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print("EOS token id: ", config.eos_token_id)
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print("Loading model and tokenizer using AutoTokenizer:", model_path)
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tokenizer = AutoTokenizer.from_pretrained(model_path)
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config = AutoConfig.from_pretrained(model_path)
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if unreleased_model_name:
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model_name_lower = unreleased_model_name.lower()
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unreleased_module_path = (
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f"transformers.models.{model_name_lower}.modular_{model_name_lower}"
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)
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class_name = f"{unreleased_model_name}ForCausalLM"
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print(f"Importing unreleased model module: {unreleased_module_path}")
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try:
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model_class = getattr(
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importlib.import_module(unreleased_module_path), class_name
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)
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model = model_class.from_pretrained(
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model_path
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) # Note: from_pretrained, not fromPretrained
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except (ImportError, AttributeError) as e:
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print(f"Failed to import or load model: {e}")
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exit(1)
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else:
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model = AutoModelForCausalLM.from_pretrained(
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model_path, device_map="auto", offload_folder="offload"
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)
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for name, module in model.named_modules():
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if len(list(module.children())) == 0: # only leaf modules
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module.register_forward_hook(debug_hook(name))
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model_name = os.path.basename(model_path)
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# Printing the Model class to allow for easier debugging. This can be useful
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# when working with models that have not been publicly released yet and this
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# migth require that the concrete class is imported and used directly instead
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# of using AutoModelForCausalLM.
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print(f"Model class: {model.__class__.__name__}")
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prompt = "Hello, my name is"
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input_ids = tokenizer(prompt, return_tensors="pt").input_ids
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print(f"Input tokens: {input_ids}")
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print(f"Input text: {repr(prompt)}")
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print(f"Tokenized: {tokenizer.convert_ids_to_tokens(input_ids[0])}")
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with torch.no_grad():
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outputs = model(input_ids.to(model.device))
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logits = outputs.logits
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# Extract logits for the last token (next token prediction)
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last_logits = logits[0, -1, :].cpu().numpy()
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print(f"Logits shape: {logits.shape}")
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print(f"Last token logits shape: {last_logits.shape}")
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print(f"Vocab size: {len(last_logits)}")
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data_dir = Path("data")
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data_dir.mkdir(exist_ok=True)
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bin_filename = data_dir / f"pytorch-{model_name}.bin"
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txt_filename = data_dir / f"pytorch-{model_name}.txt"
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# Save to file for comparison
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last_logits.astype(np.float32).tofile(bin_filename)
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# Also save as text file for easy inspection
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with open(txt_filename, "w") as f:
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for i, logit in enumerate(last_logits):
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f.write(f"{i}: {logit:.6f}\n")
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# Print some sample logits for quick verification
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print(f"First 10 logits: {last_logits[:10]}")
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print(f"Last 10 logits: {last_logits[-10:]}")
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# Show top 5 predicted tokens
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top_indices = np.argsort(last_logits)[-5:][::-1]
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print("Top 5 predictions:")
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for idx in top_indices:
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token = tokenizer.decode([idx])
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print(f" Token {idx} ({repr(token)}): {last_logits[idx]:.6f}")
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print(f"Saved bin logits to: {bin_filename}")
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print(f"Saved txt logist to: {txt_filename}")
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