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aa3ee0eb0b
This commit adds support for passing a prompt file to the model conversion targets/scripts. It also updates the logits.cpp to print out embedding information in the same format as when running the original embedding model. The motivation for this is that it allows us to pass files of different sizes when running the converted models and validating the logits. This can be particularly important when testing the sliding window functionality of models where the sequence length needs to exceed a certain number of tokens to trigger the sliding window logic.
68 lines
2.3 KiB
Python
Executable File
68 lines
2.3 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 json
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from safetensors import safe_open
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from collections import defaultdict
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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('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 MODEL_PATH environment variable")
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# Check if there's an index file (multi-file model)
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index_path = os.path.join(model_path, "model.safetensors.index.json")
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single_file_path = os.path.join(model_path, "model.safetensors")
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if os.path.exists(index_path):
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# Multi-file model
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print("Multi-file model detected")
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with open(index_path, 'r') as f:
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index_data = json.load(f)
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# Get the weight map (tensor_name -> file_name)
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weight_map = index_data.get("weight_map", {})
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# Group tensors by file for efficient processing
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file_tensors = defaultdict(list)
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for tensor_name, file_name in weight_map.items():
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file_tensors[file_name].append(tensor_name)
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print("Tensors in model:")
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# Process each shard file
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for file_name, tensor_names in file_tensors.items():
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file_path = os.path.join(model_path, file_name)
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print(f"\n--- From {file_name} ---")
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with safe_open(file_path, framework="pt") as f:
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for tensor_name in sorted(tensor_names):
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tensor = f.get_tensor(tensor_name)
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print(f"- {tensor_name} : shape = {tensor.shape}, dtype = {tensor.dtype}")
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elif os.path.exists(single_file_path):
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# Single file model (original behavior)
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print("Single-file model detected")
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with safe_open(single_file_path, framework="pt") as f:
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keys = f.keys()
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print("Tensors in model:")
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for key in sorted(keys):
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tensor = f.get_tensor(key)
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print(f"- {key} : shape = {tensor.shape}, dtype = {tensor.dtype}")
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else:
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print(f"Error: Neither 'model.safetensors.index.json' nor 'model.safetensors' found in {model_path}")
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print("Available files:")
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if os.path.exists(model_path):
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for item in sorted(os.listdir(model_path)):
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print(f" {item}")
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else:
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print(f" Directory {model_path} does not exist")
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exit(1)
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