2498d297ae
This PR add custom ascendc kernel vocabparallelembedding support in vllm-ascend, related CMakeLists and setuptools is also added in this PR. pytest -s benchmarks/ops/ben_vocabparallelembedding.py pytest -s tests/ops/test_vocabparallelembedding.py --------- Signed-off-by: ttanzhiqiang <389825161@qq.com>
76 lines
2.7 KiB
C++
76 lines
2.7 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 launch_advance_step_flashattn(
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void* stream,
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int64_t num_seqs,
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int64_t num_queries,
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int64_t block_size,
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int64_t* input_tokens_ptr,
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int64_t* sampled_token_ids_ptr,
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int64_t* input_positions_ptr,
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int32_t* seq_lens_ptr,
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int32_t* slot_mapping_ptr,
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int32_t* block_tables_ptr,
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int64_t block_tables_stride);
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}
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