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## Describe your changes Add float16 configuration to Mistral. ## Checklist before requesting a review - [ ] Add unit tests for this change. - [ ] Make sure all tests can pass. - [x] Update documents if necessary. - [x] Lint and apply fixes to your code by running `lintrunner -a` - [ ] Is this a user-facing change? If yes, give a description of this change to be included in the release notes. - [ ] Is this PR including examples changes? If yes, please remember to update [example documentation](https://github.com/microsoft/Olive/blob/main/docs/source/examples.md) in a follow-up PR. ## (Optional) Issue link
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| # ------------------------------------------------------------------------- | ||
| # Copyright (c) Microsoft Corporation. All rights reserved. | ||
| # Licensed under the MIT License. | ||
| # -------------------------------------------------------------------------- | ||
| import argparse | ||
| import json | ||
| import shutil | ||
| from pathlib import Path | ||
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| import onnxruntime as ort | ||
| import torch | ||
| from transformers import AutoConfig, LlamaTokenizer | ||
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| from olive.workflows import run as olive_run | ||
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| # ruff: noqa: T201, T203 | ||
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| def optimize(model_name: str, optimized_model_des: Path): | ||
| ort.set_default_logger_severity(4) | ||
| cur_dir = Path(__file__).resolve().parent | ||
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| # Optimize the model with Olive | ||
| print(f"\nOptimizing {model_name}") | ||
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| olive_config = None | ||
| with (cur_dir / "mistral_optimize.json").open() as fin: | ||
| olive_config = json.load(fin) | ||
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| olive_config["input_model"]["config"]["model_path"] = model_name | ||
| olive_run(olive_config) | ||
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| def main(): | ||
| parser = argparse.ArgumentParser() | ||
| parser.add_argument("--optimize", action="store_true", help="Runs the optimization step") | ||
| parser.add_argument( | ||
| "--model-id", | ||
| dest="model_id", | ||
| type=str, | ||
| default="mistralai/Mistral-7B-v0.1", | ||
| help="Model Id to load", | ||
| ) | ||
| parser.add_argument("--inference", action="store_true", help="Runs the inference step") | ||
| args = parser.parse_args() | ||
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| script_dir = Path(__file__).resolve().parent | ||
| optimized_model_dir = script_dir / "models" / "convert-optimize-perf_tuning" / "mistral_gpu-cuda_model" | ||
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| if args.optimize: | ||
| shutil.rmtree(optimized_model_dir, ignore_errors=True) | ||
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| if args.optimize or not optimized_model_dir.exists(): | ||
| optimize(args.model_id, optimized_model_dir) | ||
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| if args.inference: | ||
| prompt = "Is it normal to have a dark ring around the iris of my eye?" | ||
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| tokenizer = LlamaTokenizer.from_pretrained(args.model_id) | ||
| tokens = tokenizer(prompt, return_tensors="pt") | ||
| tokenizer = None | ||
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| config = AutoConfig.from_pretrained(args.model_id) | ||
| num_heads = config.num_key_value_heads | ||
| head_size = config.hidden_size // config.num_attention_heads | ||
| past_seq_len = 0 | ||
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| position_ids = tokens.attention_mask.long().cumsum(-1) - 1 | ||
| position_ids.masked_fill_(tokens.attention_mask == 0, 1) | ||
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| onnx_inputs = { | ||
| "input_ids": tokens.input_ids.numpy(), | ||
| "attention_mask": tokens.attention_mask.numpy(), | ||
| "position_ids": position_ids.numpy(), | ||
| } | ||
| for i in range(config.num_hidden_layers): | ||
| onnx_inputs[f"past_key_values.{i}.key"] = torch.rand( | ||
| 1, num_heads // 1, past_seq_len, head_size, dtype=torch.float16 | ||
| ).numpy() | ||
| onnx_inputs[f"past_key_values.{i}.value"] = torch.rand( | ||
| 1, num_heads // 1, past_seq_len, head_size, dtype=torch.float16 | ||
| ).numpy() | ||
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| model_path = optimized_model_dir / "model.onnx" | ||
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| session = ort.InferenceSession(model_path, providers=["CUDAExecutionProvider"]) | ||
| session.run(None, onnx_inputs)[0] | ||
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| print("Inference test completed successfully!") | ||
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| if __name__ == "__main__": | ||
| main() |
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| { | ||
| "input_model": { | ||
| "type": "PyTorchModel", | ||
| "config": { | ||
| "hf_config": { | ||
| "model_name": "mistralai/Mistral-7B-v0.1", | ||
| "model_class": "MistralForCausalLM" | ||
| } | ||
| } | ||
| }, | ||
| "evaluators": { | ||
| "common_evaluator": { | ||
| "metrics": [ | ||
| { | ||
| "name": "latency", | ||
| "type": "latency", | ||
| "sub_types": [ | ||
| { | ||
| "name": "avg", | ||
| "priority": 1 | ||
| } | ||
| ], | ||
| "user_config": { | ||
| "user_script": "user_script.py", | ||
| "dataloader_func": "create_dataloader", | ||
| "batch_size": 1, | ||
| "inference_settings": { | ||
| "onnx": { | ||
| "session_options": { | ||
| "enable_profiling": false | ||
| } | ||
| } | ||
| } | ||
| } | ||
| } | ||
| ] | ||
| } | ||
| }, | ||
| "passes": { | ||
| "convert": { | ||
| "type": "OptimumConversion", | ||
| "config": { | ||
| "target_opset": 14, | ||
| "extra_args": { | ||
| "legacy": false, | ||
| "no_post_process": false | ||
| } | ||
| } | ||
| }, | ||
| "optimize": { | ||
| "type": "OrtTransformersOptimization", | ||
| "config": { | ||
| "model_type": "gpt2", | ||
| "use_gpu": true, | ||
| "keep_io_types": false, | ||
| "num_heads": 32, | ||
| "hidden_size": 4096, | ||
| "opt_level": 0, | ||
| "optimization_options": { | ||
| "use_multi_head_attention": false | ||
| }, | ||
| "save_as_external_data": true, | ||
| "all_tensors_to_one_file": true, | ||
| "float16": true, | ||
| "use_gqa": true | ||
| } | ||
| }, | ||
| "perf_tuning": { | ||
| "type": "OrtPerfTuning", | ||
| "config": { | ||
| "user_script": "user_script.py", | ||
| "dataloader_func": "create_dataloader", | ||
| "batch_size": 1, | ||
| "enable_profiling": false | ||
| } | ||
| } | ||
| }, | ||
| "pass_flows": [ | ||
| [ | ||
| "convert", | ||
| "optimize", | ||
| "perf_tuning" | ||
| ] | ||
| ], | ||
| "engine": { | ||
| "evaluate_input_model": false, | ||
| "evaluator": "common_evaluator", | ||
| "cache_dir": "cache", | ||
| "output_name": "mistral", | ||
| "output_dir": "models", | ||
| "execution_providers": [ | ||
| "CUDAExecutionProvider" | ||
| ], | ||
| "clean_cache": false, | ||
| "log_severity_level": 0, | ||
| "log_to_file": true | ||
| } | ||
| } |
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