mirror of
https://github.com/ggml-org/llama.cpp.git
synced 2025-10-27 08:21:30 +00:00
115 lines
4.4 KiB
Python
Executable File
115 lines
4.4 KiB
Python
Executable File
#!/usr/bin/env python3
|
|
|
|
import argparse
|
|
import os
|
|
import importlib
|
|
import torch
|
|
import numpy as np
|
|
|
|
from transformers import AutoTokenizer, AutoConfig, AutoModelForCausalLM
|
|
from pathlib import Path
|
|
|
|
unreleased_model_name = os.getenv('UNRELEASED_MODEL_NAME')
|
|
|
|
parser = argparse.ArgumentParser(description='Process model with specified path')
|
|
parser.add_argument('--model-path', '-m', help='Path to the model')
|
|
args = parser.parse_args()
|
|
|
|
model_path = os.environ.get('MODEL_PATH', args.model_path)
|
|
if model_path is None:
|
|
parser.error("Model path must be specified either via --model-path argument or MODEL_PATH environment variable")
|
|
|
|
config = AutoConfig.from_pretrained(model_path)
|
|
|
|
print("Model type: ", config.model_type)
|
|
print("Vocab size: ", config.vocab_size)
|
|
print("Hidden size: ", config.hidden_size)
|
|
print("Number of layers: ", config.num_hidden_layers)
|
|
print("BOS token id: ", config.bos_token_id)
|
|
print("EOS token id: ", config.eos_token_id)
|
|
|
|
print("Loading model and tokenizer using AutoTokenizer:", model_path)
|
|
tokenizer = AutoTokenizer.from_pretrained(model_path)
|
|
|
|
if unreleased_model_name:
|
|
model_name_lower = unreleased_model_name.lower()
|
|
unreleased_module_path = f"transformers.models.{model_name_lower}.modular_{model_name_lower}"
|
|
class_name = f"{unreleased_model_name}ForCausalLM"
|
|
print(f"Importing unreleased model module: {unreleased_module_path}")
|
|
|
|
try:
|
|
model_class = getattr(importlib.import_module(unreleased_module_path), class_name)
|
|
model = model_class.from_pretrained(model_path)
|
|
except (ImportError, AttributeError) as e:
|
|
print(f"Failed to import or load model: {e}")
|
|
print("Falling back to AutoModelForCausalLM")
|
|
model = AutoModelForCausalLM.from_pretrained(model_path)
|
|
else:
|
|
model = AutoModelForCausalLM.from_pretrained(model_path)
|
|
print(f"Model class: {type(model)}")
|
|
#print(f"Model file: {type(model).__module__}")
|
|
|
|
model_name = os.path.basename(model_path)
|
|
print(f"Model name: {model_name}")
|
|
|
|
prompt = "Hello world today"
|
|
input_ids = tokenizer(prompt, return_tensors="pt").input_ids
|
|
print(f"Input tokens: {input_ids}")
|
|
print(f"Input text: {repr(prompt)}")
|
|
print(f"Tokenized: {tokenizer.convert_ids_to_tokens(input_ids[0])}")
|
|
|
|
with torch.no_grad():
|
|
outputs = model(input_ids, output_hidden_states=True)
|
|
|
|
# Extract hidden states from the last layer
|
|
# outputs.hidden_states is a tuple of (num_layers + 1) tensors
|
|
# Index -1 gets the last layer, shape: [batch_size, seq_len, hidden_size]
|
|
last_hidden_states = outputs.hidden_states[-1]
|
|
|
|
# Get embeddings for all tokens
|
|
token_embeddings = last_hidden_states[0].cpu().numpy() # Remove batch dimension
|
|
|
|
print(f"Hidden states shape: {last_hidden_states.shape}")
|
|
print(f"Token embeddings shape: {token_embeddings.shape}")
|
|
print(f"Hidden dimension: {token_embeddings.shape[-1]}")
|
|
print(f"Number of tokens: {token_embeddings.shape[0]}")
|
|
|
|
# Save raw token embeddings
|
|
data_dir = Path("data")
|
|
data_dir.mkdir(exist_ok=True)
|
|
bin_filename = data_dir / f"pytorch-{model_name}-embeddings.bin"
|
|
txt_filename = data_dir / f"pytorch-{model_name}-embeddings.txt"
|
|
|
|
# Save all token embeddings as binary
|
|
print(token_embeddings)
|
|
token_embeddings.astype(np.float32).tofile(bin_filename)
|
|
|
|
# Save as text for inspection
|
|
with open(txt_filename, "w") as f:
|
|
for i, embedding in enumerate(token_embeddings):
|
|
for j, val in enumerate(embedding):
|
|
f.write(f"{i} {j} {val:.6f}\n")
|
|
|
|
# Print embeddings per token in the requested format
|
|
print("\nToken embeddings:")
|
|
tokens = tokenizer.convert_ids_to_tokens(input_ids[0])
|
|
for i, embedding in enumerate(token_embeddings):
|
|
# Format: show first few values, ..., then last few values
|
|
if len(embedding) > 10:
|
|
# Show first 3 and last 3 values with ... in between
|
|
first_vals = " ".join(f"{val:8.6f}" for val in embedding[:3])
|
|
last_vals = " ".join(f"{val:8.6f}" for val in embedding[-3:])
|
|
print(f"embedding {i}: {first_vals} ... {last_vals}")
|
|
else:
|
|
# If embedding is short, show all values
|
|
vals = " ".join(f"{val:8.6f}" for val in embedding)
|
|
print(f"embedding {i}: {vals}")
|
|
|
|
# Also show token info for reference
|
|
print(f"\nToken reference:")
|
|
for i, token in enumerate(tokens):
|
|
print(f" Token {i}: {repr(token)}")
|
|
|
|
print(f"Saved bin logits to: {bin_filename}")
|
|
print(f"Saved txt logist to: {txt_filename}")
|