139 lines
6 KiB
Python
139 lines
6 KiB
Python
#
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# Copyright 2016 The BigDL Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import os
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import torch
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import transformers
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import deepspeed
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def get_int_from_env(env_keys, default):
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"""Returns the first positive env value found in the `env_keys` list or the default."""
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for e in env_keys:
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val = int(os.environ.get(e, -1))
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if val >= 0:
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return val
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return int(default)
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local_rank = get_int_from_env(["LOCAL_RANK","PMI_RANK"], "0")
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world_size = get_int_from_env(["WORLD_SIZE","PMI_SIZE"], "1")
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os.environ["RANK"] = str(local_rank)
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os.environ["WORLD_SIZE"] = str(world_size)
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os.environ["MASTER_PORT"] = os.environ.get("MASTER_PORT", "29500")
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from ipex_llm import optimize_model
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import torch
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import time
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import argparse
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from transformers import AutoModelForCausalLM # export AutoModelForCausalLM from transformers so that deepspeed use it
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from transformers import LlamaTokenizer, AutoTokenizer
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from deepspeed.accelerator.cpu_accelerator import CPU_Accelerator
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from deepspeed.accelerator import set_accelerator, get_accelerator
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from intel_extension_for_deepspeed import XPU_Accelerator
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='Predict Tokens using `generate()` API for Llama2 model')
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parser.add_argument('--repo-id-or-model-path', type=str, default="meta-llama/Llama-2-7b-chat-hf",
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help='The huggingface repo id for the Llama2 (e.g. `meta-llama/Llama-2-7b-chat-hf`, `meta-llama/Llama-2-13b-chat-hf` and `meta-llama/Llama-2-70b-chat-hf`) to be downloaded'
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', or the path to the huggingface checkpoint folder')
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parser.add_argument('--prompt', type=str, default="Once upon a time, there existed a little girl who liked to have adventures. She wanted to go to places and meet new people, and have fun",
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help='Prompt to infer')
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parser.add_argument('--n-predict', type=int, default=32,
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help='Max tokens to predict')
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parser.add_argument('--low-bit', type=str, default='sym_int4',
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help='The quantization type the model will convert to.')
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args = parser.parse_args()
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model_path = args.repo_id_or_model_path
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low_bit = args.low_bit
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# First use CPU as accelerator
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# Convert to deepspeed model and apply IPEX-LLM optimization on CPU to decrease GPU memory usage
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current_accel = CPU_Accelerator()
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set_accelerator(current_accel)
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model = AutoModelForCausalLM.from_pretrained(args.repo_id_or_model_path,
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device_map={"": "cpu"},
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low_cpu_mem_usage=True,
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torch_dtype=torch.float16,
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trust_remote_code=True,
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use_cache=True)
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model = deepspeed.init_inference(
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model,
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mp_size=world_size,
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dtype=torch.float16,
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replace_method="auto",
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)
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# Use IPEX-LLM `optimize_model` to convert the model into optimized low bit format
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# Convert the rest of the model into float16 to reduce allreduce traffic
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model = optimize_model(model.module.to(f'cpu'), low_bit=low_bit).to(torch.float16)
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# Next, use XPU as accelerator to speed up inference
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current_accel = XPU_Accelerator()
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set_accelerator(current_accel)
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# Move model back to xpu
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model = model.to(f'xpu:{local_rank}')
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# Modify backend related settings
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if world_size > 1:
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get_accelerator().set_device(local_rank)
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dist_backend = get_accelerator().communication_backend_name()
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import deepspeed.comm.comm
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deepspeed.comm.comm.cdb = None
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from deepspeed.comm.comm import init_distributed
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init_distributed()
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print(model)
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# Load tokenizer
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tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
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# Generate predicted tokens
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with torch.inference_mode():
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prompt = args.prompt
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input_ids = tokenizer.encode(prompt, return_tensors="pt").to(f'xpu:{local_rank}')
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# ipex_llm model needs a warmup, then inference time can be accurate
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output = model.generate(input_ids,
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max_new_tokens=args.n_predict,
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use_cache=True)
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# start inference
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st = time.time()
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# if your selected model is capable of utilizing previous key/value attentions
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# to enhance decoding speed, but has `"use_cache": false` in its model config,
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# it is important to set `use_cache=True` explicitly in the `generate` function
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# to obtain optimal performance with IPEX-LLM INT4 optimizations
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output = model.generate(input_ids,
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do_sample=False,
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max_new_tokens=args.n_predict)
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torch.xpu.synchronize()
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end = time.time()
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if local_rank == 0:
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output = output.cpu()
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actual_output_len = output.shape[1] - input_ids.shape[1]
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output_str = tokenizer.decode(output[0], skip_special_tokens=True)
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avg_time = (end - st) / actual_output_len * 1000
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print(f'Inference time of generating {actual_output_len} tokens: {end-st} s, average token latency is {avg_time} ms/token.')
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print('-'*20, 'Prompt', '-'*20)
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print(prompt)
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print('-'*20, 'Output', '-'*20)
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print(output_str)
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deepspeed.comm.destroy_process_group()
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print("process group destroyed, exiting...")
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