9df70d95eb
* Rename bigdl/llm to ipex_llm * rm python/llm/src/bigdl * from bigdl.llm to from ipex_llm
87 lines
3.7 KiB
Python
87 lines
3.7 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 torch
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from ipex_llm.transformers import AutoModelForCausalLM
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from transformers import AutoTokenizer
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import argparse
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import time
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import numpy as np
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torch.nn.Linear.reset_parameters = lambda x: None
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seed=42
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torch.manual_seed(seed)
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np.random.seed(seed)
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STARCODER_PROMPT_FORMAT = "{prompt}"
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prompt = "def dfs_print_Fibonacci_sequence(n):"
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='Predict Tokens using `generate()` API for Mistral model')
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parser.add_argument('--repo-id-or-model-path', type=str, default="bigcode/starcoder",
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help='The huggingface repo id for the Mistral (e.g. `bigcode/starcoder` and `bigcode/tiny_starcoder_py`) 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=prompt,
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help='Prompt to infer')
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parser.add_argument('--n-predict', type=int, default=128,
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help='Max tokens to predict')
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args = parser.parse_args()
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model_path = args.repo_id_or_model_path
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# Load model in optimized bf16 here.
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# Set `speculative=True`` to enable speculative decoding,
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# it only works when load_in_low_bit="fp16" on Intel GPU or load_in_low_bit="bf16" on latest Intel Xeon CPU
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model = AutoModelForCausalLM.from_pretrained(model_path,
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optimize_model=True,
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torch_dtype=torch.bfloat16,
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load_in_low_bit="bf16",
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speculative=True,
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torchscript=True,
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trust_remote_code=True,
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use_cache=True)
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tokenizer = AutoTokenizer.from_pretrained(model_path)
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with torch.inference_mode():
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prompt = STARCODER_PROMPT_FORMAT.format(prompt=args.prompt)
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inputs = tokenizer(prompt, return_tensors='pt')
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input_ids = inputs.input_ids.to(model.device)
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actual_in_len = input_ids.shape[1]
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print("actual input_ids length:" + str(actual_in_len))
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attention_mask = inputs.attention_mask.to(model.device)
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# warmup
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output = model.generate(input_ids,
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max_new_tokens=args.n_predict,
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attention_mask=attention_mask,
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do_sample=False)
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output_str = tokenizer.decode(output[0])
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# speculative decoding
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st = time.perf_counter()
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output = model.generate(input_ids,
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max_new_tokens=args.n_predict,
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attention_mask=attention_mask,
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do_sample=False)
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output_str = tokenizer.decode(output[0], skip_special_tokens=True)
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end = time.perf_counter()
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print(output_str)
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print(f"Tokens generated {model.n_token_generated}")
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print(f"E2E Generation time {(end - st):.4f}s")
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print(f"First token latency {model.first_token_time:.4f}s")
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