7006835977
### What this PR does / why we need it?
Fix device of tensors created in `AscendAttentionBackendImpl`.
While specifying device to cards except card-0, there'll cause an
**device conflict** because the tensors (such as `attn_mask`) will be
put on card-0 by default.
This pr creates these tensors on the correct card corresponding to the
input.
### Does this PR introduce _any_ user-facing change?
User could specify device with local rank by this pr, and a modify on
vLLM is also needed, will related to this pr when created.
### How was this patch tested?
This is tested by the following code locally. Will add a test case when
the modify in vLLM is also completed.
```python
from vllm import LLM, SamplingParams
prompts = [
"Hello, my name is",
"The president of the United States is",
"The capital of France is",
"The future of AI is",
]
# Create a sampling params object.
sampling_params = SamplingParams(max_tokens=100, temperature=0.0)
# Create an LLM.
llm = LLM(model="~/.cache/modelscope/hub/Qwen/Qwen2___5-7B-Instruct", device="npu:1")
# Generate texts from the prompts.
outputs = llm.generate(prompts, sampling_params)
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
```
Signed-off-by: MengqingCao <cmq0113@163.com>
45 lines
1.4 KiB
Python
45 lines
1.4 KiB
Python
#
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# Copyright (c) 2025 Huawei Technologies 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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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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]
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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="facebook/opt-125m",
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tensor_parallel_size=2,
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distributed_executor_backend="ray",
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trust_remote_code=True,
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)
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# Generate texts from the prompts.
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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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