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* examples : add model conversion tool/example This commit adds an "example/tool" that is intended to help in the process of converting models to GGUF. Currently it supports normal causal models and embedding models. The readme contains instructions and command to guide through the process. The motivation for this to have a structured and repeatable process for model conversions and hopefully with time improve upon it to make the process easier and more reliable. We have started to use this for new model conversions internally and will continue doing so and improve it as we go along. Perhaps with time this should be placed in a different directory than the examples directory, but for now it seems like a good place to keep it while we are still developing it. * squash! examples : add model conversion tool/example Remove dependency on scikit-learn in model conversion example. * squash! examples : add model conversion tool/example Update transformer dep to use non-dev version. And also import `AutoModelForCausalLM` instead of `AutoModel` to ensure compatibility with the latest version. * squash! examples : add model conversion tool/example Remove the logits requirements file from the all requirements file.
116 lines
4.1 KiB
Python
Executable file
116 lines
4.1 KiB
Python
Executable file
#!/usr/bin/env python3
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import argparse
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import os
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import numpy as np
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import importlib
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from pathlib import Path
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from transformers import AutoTokenizer, AutoConfig, AutoModel
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import torch
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unreleased_model_name = os.getenv('UNRELEASED_MODEL_NAME')
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parser = argparse.ArgumentParser(description='Process model with specified path')
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parser.add_argument('--model-path', '-m', help='Path to the model')
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args = parser.parse_args()
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model_path = os.environ.get('EMBEDDING_MODEL_PATH', args.model_path)
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if model_path is None:
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parser.error("Model path must be specified either via --model-path argument or EMBEDDING_MODEL_PATH environment variable")
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tokenizer = AutoTokenizer.from_pretrained(model_path)
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if unreleased_model_name:
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model_name_lower = unreleased_model_name.lower()
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unreleased_module_path = f"transformers.models.{model_name_lower}.modular_{model_name_lower}"
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class_name = f"{unreleased_model_name}Model"
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print(f"Importing unreleased model module: {unreleased_module_path}")
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try:
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model_class = getattr(importlib.import_module(unreleased_module_path), class_name)
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model = model_class.from_pretrained(model_path) # Note: from_pretrained, not fromPretrained
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except (ImportError, AttributeError) as e:
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print(f"Failed to import or load model: {e}")
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exit(1)
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else:
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model = AutoModel.from_pretrained(model_path)
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print(f"Model class: {type(model)}")
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#print(f"Model file: {type(model).__module__}")
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config = AutoConfig.from_pretrained(model_path)
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model_name = os.path.basename(model_path)
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texts = [ "Hello world today" ]
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encoded = tokenizer(
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texts,
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padding=True,
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truncation=True,
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return_tensors="pt"
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)
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tokens = encoded['input_ids'][0]
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token_strings = tokenizer.convert_ids_to_tokens(tokens)
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for i, (token_id, token_str) in enumerate(zip(tokens, token_strings)):
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print(f"{token_id:6d} -> '{token_str}'")
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with torch.no_grad():
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outputs = model(**encoded)
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hidden_states = outputs.last_hidden_state # Shape: [batch_size, seq_len, hidden_size]
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# Extract embeddings for each token (matching LLAMA_POOLING_TYPE_NONE behavior)
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all_embeddings = hidden_states[0].cpu().numpy() # Shape: [seq_len, hidden_size]
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print(f"Hidden states shape: {hidden_states.shape}")
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print(f"All embeddings shape: {all_embeddings.shape}")
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print(f"Embedding dimension: {all_embeddings.shape[1]}")
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# Print embeddings exactly like embedding.cpp does for LLAMA_POOLING_TYPE_NONE
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n_embd = all_embeddings.shape[1]
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n_embd_count = all_embeddings.shape[0]
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print() # Empty line to match C++ output
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for j in range(n_embd_count):
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embedding = all_embeddings[j]
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print(f"embedding {j}: ", end="")
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# Print first 3 values
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for i in range(min(3, n_embd)):
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print(f"{embedding[i]:9.6f} ", end="")
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print(" ... ", end="")
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# Print last 3 values
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for i in range(n_embd - 3, n_embd):
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print(f"{embedding[i]:9.6f} ", end="")
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print() # New line
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print() # Final empty line to match C++ output
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data_dir = Path("data")
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data_dir.mkdir(exist_ok=True)
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bin_filename = data_dir / f"pytorch-{model_name}-embeddings.bin"
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txt_filename = data_dir / f"pytorch-{model_name}-embeddings.txt"
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# Save all embeddings flattened (matching what embedding.cpp would save if it did)
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flattened_embeddings = all_embeddings.flatten()
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flattened_embeddings.astype(np.float32).tofile(bin_filename)
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with open(txt_filename, "w") as f:
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f.write(f"# Model class: {model_name}\n")
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f.write(f"# Tokens: {token_strings}\n")
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f.write(f"# Shape: {all_embeddings.shape}\n")
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f.write(f"# n_embd_count: {n_embd_count}, n_embd: {n_embd}\n\n")
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for j in range(n_embd_count):
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f.write(f"# Token {j} ({token_strings[j]}):\n")
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for i, value in enumerate(all_embeddings[j]):
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f.write(f"{j}_{i}: {value:.6f}\n")
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f.write("\n")
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print(f"Total values: {len(flattened_embeddings)} ({n_embd_count} tokens × {n_embd} dimensions)")
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print("")
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print(f"Saved bin embeddings to: {bin_filename}")
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print(f"Saved txt embeddings to: {txt_filename}")
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