Felipe97/llama-cpp-compiled

1#!/usr/bin/env python32 3import argparse4import os5import sys6import importlib7import torch8import numpy as np9 10from transformers import AutoTokenizer, AutoConfig, AutoModelForCausalLM11 12sys.path.insert(0, os.path.join(os.path.dirname(__file__), '..'))13from utils.common import save_output_data14 15unreleased_model_name = os.getenv('UNRELEASED_MODEL_NAME')16 17parser = argparse.ArgumentParser(description='Process model with specified path')18parser.add_argument('--model-path', '-m', help='Path to the model')19args = parser.parse_args()20 21model_path = os.environ.get('MODEL_PATH', args.model_path)22if model_path is None:23    parser.error("Model path must be specified either via --model-path argument or MODEL_PATH environment variable")24 25config = AutoConfig.from_pretrained(model_path)26 27print("Model type:       ", config.model_type)28print("Vocab size:       ", config.vocab_size)29print("Hidden size:      ", config.hidden_size)30print("Number of layers: ", config.num_hidden_layers)31print("BOS token id:     ", config.bos_token_id)32print("EOS token id:     ", config.eos_token_id)33 34print("Loading model and tokenizer using AutoTokenizer:", model_path)35tokenizer = AutoTokenizer.from_pretrained(model_path)36 37if unreleased_model_name:38    model_name_lower = unreleased_model_name.lower()39    unreleased_module_path = f"transformers.models.{model_name_lower}.modular_{model_name_lower}"40    class_name = f"{unreleased_model_name}ForCausalLM"41    print(f"Importing unreleased model module: {unreleased_module_path}")42 43    try:44        model_class = getattr(importlib.import_module(unreleased_module_path), class_name)45        model = model_class.from_pretrained(model_path)46    except (ImportError, AttributeError) as e:47        print(f"Failed to import or load model: {e}")48        print("Falling back to AutoModelForCausalLM")49        model = AutoModelForCausalLM.from_pretrained(model_path)50else:51    model = AutoModelForCausalLM.from_pretrained(model_path)52print(f"Model class: {type(model)}")53#print(f"Model file: {type(model).__module__}")54 55model_name = os.path.basename(model_path)56print(f"Model name: {model_name}")57 58prompt = "Hello world today"59input_ids = tokenizer(prompt, return_tensors="pt").input_ids  # ty: ignore[call-non-callable]60token_ids = input_ids[0].cpu().tolist()61print(f"Input tokens: {input_ids}")62print(f"Input text: {repr(prompt)}")63print(f"Tokenized: {tokenizer.convert_ids_to_tokens(input_ids[0])}")  # ty: ignore[unresolved-attribute]64 65with torch.no_grad():66    outputs = model(input_ids, output_hidden_states=True)67 68    # Extract hidden states from the last layer69    # outputs.hidden_states is a tuple of (num_layers + 1) tensors70    # Index -1 gets the last layer, shape: [batch_size, seq_len, hidden_size]71    last_hidden_states = outputs.hidden_states[-1]72 73    # Get embeddings for all tokens74    token_embeddings = last_hidden_states[0].float().cpu().numpy()  # Remove batch dimension75 76    print(f"Hidden states shape: {last_hidden_states.shape}")77    print(f"Token embeddings shape: {token_embeddings.shape}")78    print(f"Hidden dimension: {token_embeddings.shape[-1]}")79    print(f"Number of tokens: {token_embeddings.shape[0]}")80 81    print(token_embeddings)82    save_output_data(token_embeddings, token_ids, prompt, model_name, type_suffix="-embeddings")83 84    # Print embeddings per token in the requested format85    print("\nToken embeddings:")86    tokens = tokenizer.convert_ids_to_tokens(input_ids[0])  # ty: ignore[unresolved-attribute]87    for i, embedding in enumerate(token_embeddings):88        # Format: show first few values, ..., then last few values89        if len(embedding) > 10:90            # Show first 3 and last 3 values with ... in between91            first_vals = " ".join(f"{val:8.6f}" for val in embedding[:3])92            last_vals = " ".join(f"{val:8.6f}" for val in embedding[-3:])93            print(f"embedding {i}: {first_vals}  ... {last_vals}")94        else:95            # If embedding is short, show all values96            vals = " ".join(f"{val:8.6f}" for val in embedding)97            print(f"embedding {i}: {vals}")98 99    # Also show token info for reference100    print(f"\nToken reference:")101    for i, token in enumerate(tokens):102        print(f"  Token {i}: {repr(token)}")103 104