#!/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') parser.add_argument('--prompts-file', '-p', help='Path to file containing prompts (one per line)') parser.add_argument('--use-sentence-transformers', action='store_true', help='Use SentenceTransformer to apply all numbered layers (01_Pooling, 02_Dense, 03_Dense, 04_Normalize)') args = parser.parse_args() def read_prompt_from_file(file_path): try: with open(file_path, 'r', encoding='utf-8') as f: return f.read().strip() except FileNotFoundError: print(f"Error: Prompts file '{file_path}' not found") exit(1) except Exception as e: print(f"Error reading prompts file: {e}") exit(1) 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") # Determine if we should use SentenceTransformer use_sentence_transformers = args.use_sentence_transformers or os.environ.get('USE_SENTENCE_TRANSFORMERS', '').lower() in ('1', 'true', 'yes') if use_sentence_transformers: from sentence_transformers import SentenceTransformer print("Using SentenceTransformer to apply all numbered layers") model = SentenceTransformer(model_path) tokenizer = model.tokenizer config = model[0].auto_model.config # type: ignore else: tokenizer = AutoTokenizer.from_pretrained(model_path) config = AutoConfig.from_pretrained(model_path) # This can be used to override the sliding window size for manual testing. This # can be useful to verify the sliding window attention mask in the original model # and compare it with the converted .gguf model. if hasattr(config, 'sliding_window'): original_sliding_window = config.sliding_window #original_sliding_window = 6 print(f"Modified sliding window: {original_sliding_window} -> {config.sliding_window}") print(f"Using unreleased model: {unreleased_model_name}") 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, config=config) except (ImportError, AttributeError) as e: print(f"Failed to import or load model: {e}") exit(1) else: model = AutoModel.from_pretrained(model_path, config=config) print(f"Model class: {type(model)}") print(f"Model file: {type(model).__module__}") # Verify the model is using the correct sliding window if not use_sentence_transformers: if hasattr(model.config, 'sliding_window'): # type: ignore print(f"Model's sliding_window: {model.config.sliding_window}") # type: ignore else: print("Model config does not have sliding_window attribute") model_name = os.path.basename(model_path) if args.prompts_file: prompt_text = read_prompt_from_file(args.prompts_file) texts = [prompt_text] else: texts = ["Hello world today"] with torch.no_grad(): if use_sentence_transformers: embeddings = model.encode(texts, convert_to_numpy=True) all_embeddings = embeddings # Shape: [batch_size, hidden_size] 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}'") print(f"Embeddings shape (after all SentenceTransformer layers): {all_embeddings.shape}") print(f"Embedding dimension: {all_embeddings.shape[1] if len(all_embeddings.shape) > 1 else all_embeddings.shape[0]}") # type: ignore else: # Standard approach: use base model output only 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}'") outputs = model(**encoded) hidden_states = outputs.last_hidden_state # Shape: [batch_size, seq_len, hidden_size] 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]}") if len(all_embeddings.shape) == 1: n_embd = all_embeddings.shape[0] # type: ignore n_embd_count = 1 all_embeddings = all_embeddings.reshape(1, -1) else: n_embd = all_embeddings.shape[1] # type: ignore n_embd_count = all_embeddings.shape[0] # type: ignore print() 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() 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" flattened_embeddings = all_embeddings.flatten() flattened_embeddings.astype(np.float32).tofile(bin_filename) with open(txt_filename, "w") as f: idx = 0 for j in range(n_embd_count): for value in all_embeddings[j]: f.write(f"{idx}: {value:.6f}\n") idx += 1 print(f"Total values: {len(flattened_embeddings)} ({n_embd_count} embeddings × {n_embd} dimensions)") print("") print(f"Saved bin embeddings to: {bin_filename}") print(f"Saved txt embeddings to: {txt_filename}")