5cf9ff18e9
### What this PR does / why we need it? - According to https://github.com/vllm-project/vllm-ascend/issues/807, we pull request for customer ascendc kernel of multi-step. - also a bug we found in multi_step_runner.py is fixed when we use multi-step on V0 Engine. ### Does this PR introduce _any_ user-facing change? no user-facing change ### How was this patch tested? we add Unit Test file and offline inference file to test the custom ascendc kernel. See test/ops/test_multi_step.py and examples/offline_multi_step.py --------- Signed-off-by: wan_danfeng <wonderful199082@126.com>
54 lines
1.9 KiB
Python
54 lines
1.9 KiB
Python
#
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# Copyright (c) 2025 China Merchants Bank Co., Ltd. All Rights Reserved.
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# This file is a part of the vllm-ascend project.
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# Adapted from vllm-project/vllm/examples/offline_inference/basic.py
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# Copyright 2023 The vLLM team.
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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 vllm import LLM, SamplingParams
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import vllm_ascend.platform as pf
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pf.CUSTOM_OP_ENABLED = True # set True for custom Ops of Multi-Step.
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prompts = [
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"Hello, my name is",
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"The president of the United States is",
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"The capital of France is",
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"The future of AI is",
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"China is",
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]
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# Create a sampling params object.
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sampling_params = SamplingParams(max_tokens=100, temperature=0.0)
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# Create an LLM.
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llm = LLM(
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model="Qwen/Qwen2.5-0.5B",
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block_size=128,
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max_model_len=1024, # max length of prompt
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tensor_parallel_size=1, # number of NPUs to be used
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max_num_seqs=26, # max batch number
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enforce_eager=
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True, # Force PyTorch eager execution to debug intermediate tensors (disables graph optimizations)
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trust_remote_code=
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True, # If the model is a cuscd tom model not yet available in the HuggingFace transformers library
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num_scheduler_steps=8,
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gpu_memory_utilization=0.5)
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outputs = llm.generate(prompts, sampling_params)
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for output in outputs:
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prompt = output.prompt
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generated_text = output.outputs[0].text
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print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
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