6ea2afe5fa
### What this PR does / why we need it?
This PR implement the basic framework for batch invariant, please see
https://github.com/vllm-project/vllm-ascend/issues/5487.
### Does this PR introduce _any_ user-facing change?
we reuse the function `vllm_is_batch_invariant` in vllm to judge if
batch invariant is enabled.
- vLLM version: v0.13.0
- vLLM main:
45c1ca1ca1
---------
Signed-off-by: Ronald1995 <ronaldautomobile@163.com>
Signed-off-by: Lord_of_Ironhill <suiweiyi@huawei.com>
Signed-off-by: zjchenn <zjchenn@gmail.com>
Signed-off-by: wangx700 <wangxin700@huawei.com>
Co-authored-by: Lord_of_Ironhill <suiweiyi@huawei.com>
Co-authored-by: zjchenn <zjchenn@gmail.com>
Co-authored-by: wangx700 <wangxin700@huawei.com>
83 lines
3.0 KiB
Python
83 lines
3.0 KiB
Python
# Adapt from https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/layers/batch_invariant.py
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# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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# Copyright (c) 2026 Huawei Technologies Co., Ltd. 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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# This file is a part of the vllm-ascend project.
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#
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import os
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import torch
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from vllm.logger import init_logger
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from vllm.model_executor.layers.batch_invariant import vllm_is_batch_invariant
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from vllm.triton_utils import HAS_TRITON
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logger = init_logger(__name__)
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if HAS_TRITON:
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from vllm_ascend.ops.triton.batch_invariant.matmul import (
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addmm_batch_invariant, bmm_batch_invariant, linear_batch_invariant,
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matmul_batch_invariant, mm_batch_invariant)
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def override_envs_for_invariance():
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# TODO(Ronald) set attntion backend to deterministic mode
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# enabling NZ mode introduces NZ format input to the triton operator,
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# resulting in accuracy anomalies.
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os.environ["VLLM_ASCEND_ENABLE_NZ"] = "0"
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# communication determinism settings
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os.environ["HCCL_DETERMINISTIC"] = "true"
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os.environ["LCCL_DETERMINISTIC"] = "1"
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_batch_invariant_LIB = None
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def enable_batch_invariant_mode():
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global _batch_invariant_LIB
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_batch_invariant_LIB = torch.library.Library("aten", "IMPL")
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_batch_invariant_LIB.impl("aten::mm", mm_batch_invariant, "NPU")
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_batch_invariant_LIB.impl("aten::addmm", addmm_batch_invariant, "NPU")
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_batch_invariant_LIB.impl("aten::matmul", matmul_batch_invariant, "NPU")
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_batch_invariant_LIB.impl("aten::linear", linear_batch_invariant, "NPU")
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_batch_invariant_LIB.impl("aten::bmm", bmm_batch_invariant, "NPU")
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def init_batch_invariance():
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"""
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Initialize batch-invariant mode for vLLM on Ascend NPU.
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This function:
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1. Sets environment variables for deterministic computation
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2. Registers batch-invariant implementations for torch operators
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3. Enables batch-invariant flash attention
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Call this function early in your application, or set VLLM_BATCH_INVARIANT=1
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environment variable to enable automatically.
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"""
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if vllm_is_batch_invariant():
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if HAS_TRITON:
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logger.info(
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"Enabling batch-invariant mode for vLLM on Ascend NPU.", )
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override_envs_for_invariance()
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enable_batch_invariant_mode()
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else:
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logger.warning(
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"Batch-invariant mode requested but Triton is not available."
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"skipping batch-invariant initialization.", )
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