From b333b06772c89d96aacb5490d6a219fba7c09cc6 Mon Sep 17 00:00:00 2001 From: Mitja Felicijan Date: Thu, 12 Feb 2026 20:57:17 +0100 Subject: Engage! --- .../causal/run-casual-gen-embeddings-org.py | 114 +++++++++++++++++++++ 1 file changed, 114 insertions(+) create mode 100755 llama.cpp/examples/model-conversion/scripts/causal/run-casual-gen-embeddings-org.py (limited to 'llama.cpp/examples/model-conversion/scripts/causal/run-casual-gen-embeddings-org.py') diff --git a/llama.cpp/examples/model-conversion/scripts/causal/run-casual-gen-embeddings-org.py b/llama.cpp/examples/model-conversion/scripts/causal/run-casual-gen-embeddings-org.py new file mode 100755 index 0000000..4ab778f --- /dev/null +++ b/llama.cpp/examples/model-conversion/scripts/causal/run-casual-gen-embeddings-org.py @@ -0,0 +1,114 @@ +#!/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].float().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}") -- cgit v1.2.3