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#!/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}")
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