540336edc9
### What this PR does / why we need it?
Add two custom kernels(bgmv_shrink and bgmv expand) to solve the
performance of LoRA
### Does this PR introduce _any_ user-facing change?
no user-facing change
### How was this patch tested?
we add Unit Test file to test the custom ascendc kernel. See
vllm-ascend/tests/e2e/singlecard/ops/test_bgmv_expand.py and
vllm-ascend/tests/e2e/singlecard/ops/test_bgmv_expand.py
Based on the actual test of the QWen2.5 7B model using vllm-ascend
version v0.9.2.rc1, the TTFT, TPOT and throughput have increased by
about 70%.
- vLLM version: v0.9.2
- vLLM main:
40d86ee412
---------
Signed-off-by: taoxudonghaha <justsheldon@163.com>
92 lines
2.9 KiB
C++
92 lines
2.9 KiB
C++
/*
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* Copyright (c) Huawei Technologies Co., Ltd. 2024. 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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#pragma once
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#include <optional>
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#include <torch/library.h>
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#include <vector>
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#include "kernels/types.h"
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#include "torch_npu/csrc/aten/common/from_blob.h"
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namespace vllm_ascend {
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extern void rotary_embedding_impl(AscendType type, bool isNeox, void *stream, int64_t *positions, void *queryDst,
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void *keyDst, void *query, void *key, void *cosSinCache, const int rotDim,
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const int64_t queryStride, const int64_t keyStride, const int64_t dstQueryStride,
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const int64_t dstKeyStride, const int numHeads, const int numKvHeads,
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const int headSize, const int64_t numTokens, const uint32_t loopCnt,
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uint32_t aivNum);
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extern void get_masked_input_and_mask_impl(
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void* stream,
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void* input,
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void* masked_input,
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void* mask_out,
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const int64_t org_vocab_start_index,
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const int64_t org_vocab_end_index,
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const int64_t num_org_vocab_padding,
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const int64_t added_vocab_start_index,
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const int64_t added_vocab_end_index,
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const int64_t size,
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const uint32_t loop_cnt,
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const uint32_t aiv_num);
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torch::Tensor weak_ref_tensor(torch::Tensor& tensor) {
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if (!tensor.is_privateuseone()) {
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throw std::runtime_error("Tensor must be on NPU device");
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}
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// Get the raw data pointer
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void* data_ptr = tensor.data_ptr();
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// Get tensor sizes and strides
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std::vector<int64_t> sizes = tensor.sizes().vec();
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std::vector<int64_t> strides = tensor.strides().vec();
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// Get tensor options (dtype, device)
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auto options = tensor.options();
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// Create a new tensor from the raw data pointer
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auto new_tensor = at_npu::native::from_blob(data_ptr, sizes, strides, options);
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return new_tensor;
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}
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extern void bgmv_shrink_impl(
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AscendType type,
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void *stream,
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void *x,
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void *weight,
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void *indices,
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void *y,
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uint32_t batch_size,
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uint32_t num_tokens_per_core,
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uint32_t input_hidden_dim,
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uint32_t lora_rank,
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float scale);
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extern void bgmv_expand_impl(
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AscendType type,
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void *stream,
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void *x,
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void *weight,
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void *indices,
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void *y,
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void *y_out,
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uint32_t batch_size,
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uint32_t num_tokens_per_core,
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uint32_t lora_rank,
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uint32_t output_hidden_dim,
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uint32_t slice_offset,
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uint32_t output_full_dim);
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}
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