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429dd2b1db
* [Intel HPU] add example benchmark scripts for hpu * Revise the code based on the copilot comments * update code based on comments * update ci ops version
54 lines
1.7 KiB
Python
54 lines
1.7 KiB
Python
"""
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# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
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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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from fastdeploy.engine.sampling_params import SamplingParams
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from fastdeploy.entrypoints.llm import LLM
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model_name_or_path = "ERNIE-4.5-21B-A3B-Paddle"
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# model_name_or_path = "ERNIE-4.5-300B-A47B-Paddle"
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# Hyperparameter settings
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input_bs = 1
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input_seq = None # 1000
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max_out_tokens = 128
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server_max_bs = 128
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TP = 1
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# num_gpu_blocks_override = ceil((input_seq + max_out_tokens) / 128) * server_max_bs
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num_gpu_blocks_override = 2000
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sampling_params = SamplingParams(max_tokens=max_out_tokens)
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graph_optimization_config = {"use_cudagraph": False}
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llm = LLM(
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model=model_name_or_path,
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tensor_parallel_size=TP,
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engine_worker_queue_port=8602,
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num_gpu_blocks_override=num_gpu_blocks_override,
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block_size=128,
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max_model_len=32768,
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max_num_seqs=server_max_bs,
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graph_optimization_config=graph_optimization_config,
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)
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if input_seq is None:
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prompt = "user: who are you?"
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else:
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prompt = "hi " * input_seq
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prompts = [prompt] * input_bs
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for i in range(2):
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output = llm.generate(prompts=prompts, use_tqdm=True, sampling_params=sampling_params)
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print(output)
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