llama.cpp/examples/model-conversion/scripts/embedding/run-original-model.py

#!/usr/bin/env python3

import argparse
import os
import numpy as np
import importlib
from pathlib import Path

from transformers import AutoTokenizer, AutoConfig, AutoModel
import torch

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('EMBEDDING_MODEL_PATH', args.model_path)
if model_path is None:
    parser.error("Model path must be specified either via --model-path argument or EMBEDDING_MODEL_PATH environment variable")

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}Model"
    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)  # Note: from_pretrained, not fromPretrained
    except (ImportError, AttributeError) as e:
        print(f"Failed to import or load model: {e}")
        exit(1)
else:
    model = AutoModel.from_pretrained(model_path)
print(f"Model class: {type(model)}")
#print(f"Model file: {type(model).__module__}")
config = AutoConfig.from_pretrained(model_path)

model_name = os.path.basename(model_path)

texts = [ "Hello world today" ]

encoded = tokenizer(
    texts,
    padding=True,
    truncation=True,
    return_tensors="pt"
)

tokens = encoded['input_ids'][0]
token_strings = tokenizer.convert_ids_to_tokens(tokens)
for i, (token_id, token_str) in enumerate(zip(tokens, token_strings)):
    print(f"{token_id:6d} -> '{token_str}'")

with torch.no_grad():
    outputs = model(**encoded)
    hidden_states = outputs.last_hidden_state  # Shape: [batch_size, seq_len, hidden_size]

    # Extract embeddings for each token (matching LLAMA_POOLING_TYPE_NONE behavior)
    all_embeddings = hidden_states[0].cpu().numpy()  # Shape: [seq_len, hidden_size]

    print(f"Hidden states shape: {hidden_states.shape}")
    print(f"All embeddings shape: {all_embeddings.shape}")
    print(f"Embedding dimension: {all_embeddings.shape[1]}")

    # Print embeddings exactly like embedding.cpp does for LLAMA_POOLING_TYPE_NONE
    n_embd = all_embeddings.shape[1]
    n_embd_count = all_embeddings.shape[0]

    print()  # Empty line to match C++ output

    for j in range(n_embd_count):
        embedding = all_embeddings[j]
        print(f"embedding {j}: ", end="")

        # Print first 3 values
        for i in range(min(3, n_embd)):
            print(f"{embedding[i]:9.6f} ", end="")

        print(" ... ", end="")

        # Print last 3 values
        for i in range(n_embd - 3, n_embd):
            print(f"{embedding[i]:9.6f} ", end="")

        print()  # New line

    print()  # Final empty line to match C++ output

    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 embeddings flattened (matching what embedding.cpp would save if it did)
    flattened_embeddings = all_embeddings.flatten()
    flattened_embeddings.astype(np.float32).tofile(bin_filename)

    with open(txt_filename, "w") as f:
        f.write(f"# Model class: {model_name}\n")
        f.write(f"# Tokens: {token_strings}\n")
        f.write(f"# Shape: {all_embeddings.shape}\n")
        f.write(f"# n_embd_count: {n_embd_count}, n_embd: {n_embd}\n\n")

        for j in range(n_embd_count):
            f.write(f"# Token {j} ({token_strings[j]}):\n")
            for i, value in enumerate(all_embeddings[j]):
                f.write(f"{j}_{i}: {value:.6f}\n")
            f.write("\n")
    print(f"Total values: {len(flattened_embeddings)} ({n_embd_count} tokens × {n_embd} dimensions)")
    print("")
    print(f"Saved bin embeddings to: {bin_filename}")
    print(f"Saved txt embeddings to: {txt_filename}")