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1debbd5 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 | #!/usr/bin/env python3
import argparse
import os
import sys
import importlib
import torch
import numpy as np
from transformers import AutoTokenizer, AutoModelForCausalLM, AutoModelForImageTextToText, AutoConfig
# Add parent directory to path for imports
sys.path.insert(0, os.path.join(os.path.dirname(__file__), '..'))
from utils.common import debug_hook, save_output_data
def parse_arguments():
parser = argparse.ArgumentParser(description="Process model with specified path")
parser.add_argument("--model-path", "-m", help="Path to the model")
parser.add_argument("--prompt-file", "-f", help="Optional prompt file", required=False)
parser.add_argument("--verbose", "-v", action="store_true", help="Enable verbose debug output")
parser.add_argument("--device", "-d", help="Device to use (cpu, cuda, mps, auto)", default="auto")
return parser.parse_args()
def load_model_and_tokenizer(model_path, device="auto"):
print("Loading model and tokenizer using AutoTokenizer:", model_path)
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
config = AutoConfig.from_pretrained(model_path, trust_remote_code=True)
multimodal = False
full_config = config
# Determine device_map based on device argument
if device == "cpu":
device_map = {"": "cpu"}
print("Forcing CPU usage")
elif device == "auto":
device_map = "auto"
else:
device_map = {"": device}
print("Model type: ", config.model_type)
if "vocab_size" not in config and "text_config" in config:
config = config.text_config
multimodal = True
def print_if_exists(label, obj, attr, default="N/A"):
val = getattr(obj, attr) if hasattr(obj, attr) else default
print(f"{label}", val)
print_if_exists("Vocab size: ", config, "vocab_size")
print_if_exists("Hidden size: ", config, "hidden_size")
print_if_exists("Number of layers: ", config, "num_hidden_layers")
print_if_exists("BOS token id: ", config, "bos_token_id")
print_if_exists("EOS token id: ", config, "eos_token_id")
unreleased_model_name = os.getenv("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}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,
device_map=device_map,
offload_folder="offload",
trust_remote_code=True,
config=config
)
except (ImportError, AttributeError) as e:
print(f"Failed to import or load model: {e}")
exit(1)
else:
if multimodal:
model = AutoModelForImageTextToText.from_pretrained(
model_path,
device_map=device_map,
offload_folder="offload",
trust_remote_code=True,
config=full_config
)
else:
model = AutoModelForCausalLM.from_pretrained(
model_path,
device_map=device_map,
offload_folder="offload",
trust_remote_code=True,
config=config
)
print(f"Model class: {model.__class__.__name__}")
return model, tokenizer, config
def enable_torch_debugging(model):
for name, module in model.named_modules():
if len(list(module.children())) == 0: # only leaf modules
module.register_forward_hook(debug_hook(name))
def get_prompt(args):
if args.prompt_file:
with open(args.prompt_file, encoding='utf-8') as f:
return f.read()
elif os.getenv("MODEL_TESTING_PROMPT"):
return os.getenv("MODEL_TESTING_PROMPT")
else:
return "Hello, my name is"
def main():
args = parse_arguments()
model_path = os.environ.get("MODEL_PATH", args.model_path)
if model_path is None:
print("Error: Model path must be specified either via --model-path argument or MODEL_PATH environment variable")
sys.exit(1)
model, tokenizer, config = load_model_and_tokenizer(model_path, args.device)
if args.verbose:
enable_torch_debugging(model)
model_name = os.path.basename(model_path)
# Iterate over the model parameters (the tensors) and get the first one
# and use it to get the device the model is on.
device = next(model.parameters()).device
prompt = get_prompt(args)
input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to(device)
token_ids = input_ids[0].cpu().tolist()
print(f"Input tokens: {input_ids}")
print(f"Input text: {repr(prompt)}")
print(f"Tokenized: {tokenizer.convert_ids_to_tokens(input_ids[0])}")
batch_size = 512
with torch.no_grad():
past = None
outputs = None
for i in range(0, input_ids.size(1), batch_size):
print(f"Processing chunk with tokens {i} to {i + batch_size}")
chunk = input_ids[:, i:i + batch_size]
outputs = model(chunk.to(model.device), past_key_values=past, use_cache=True)
past = outputs.past_key_values
logits = outputs.logits # type: ignore
# Extract logits for the last token (next token prediction)
last_logits = logits[0, -1, :].float().cpu().numpy()
print(f"Logits shape: {logits.shape}")
print(f"Last token logits shape: {last_logits.shape}")
print(f"Vocab size: {len(last_logits)}")
# Print some sample logits for quick verification
print(f"First 10 logits: {last_logits[:10]}")
print(f"Last 10 logits: {last_logits[-10:]}")
# Show top 5 predicted tokens
top_indices = np.argsort(last_logits)[-5:][::-1]
print("Top 5 predictions:")
for idx in top_indices:
token = tokenizer.decode([idx])
print(f" Token {idx} ({repr(token)}): {last_logits[idx]:.6f}")
save_output_data(last_logits, token_ids, prompt, model_name)
if __name__ == "__main__":
main()
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