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Revert "[Perf][1/N] w8a8c8 support in dsv3.2/glm5 (#7029)" (#7288)

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### What this PR does / why we need it?
This reverts commit 7ed9e9de69, which
introduces an issue that the patch doesn't work with recompute scheduler
enabled.
- vLLM version: v0.17.0
- vLLM main:
4034c3d32e
---------
Signed-off-by: MengqingCao <cmq0113@163.com>
This commit is contained in:
Mengqing Cao
2026-03-15 20:19:09 +08:00
committed by GitHub
parent 29f195a91c
commit 0c299f79b9
24 changed files with 79 additions and 4281 deletions
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81
csrc/lightning_indexer_quant/lightning_indexer_quant_torch_adpt.h
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@@ -1,81 +0,0 @@
/*
* Copyright (c) Huawei Technologies Co., Ltd. 2026. All rights reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#ifndef LIGHTING_INDEXER_QUANT_VLLM_TORCH_ADPT_H
#define LIGHTING_INDEXER_QUANT_VLLM_TORCH_ADPT_H
namespace vllm_ascend {
at::Tensor npu_lightning_indexer_quant(
const at::Tensor &query, const at::Tensor &key, const at::Tensor &weights,
const at::Tensor &query_dequant_scale, const at::Tensor &key_dequant_scale,
const c10::optional<at::Tensor> &actual_seq_lengths_query,
const c10::optional<at::Tensor> &actual_seq_lengths_key,
const c10::optional<at::Tensor> &block_table, int64_t query_quant_mode, int64_t key_quant_mode,
c10::string_view layout_query, c10::string_view layout_key, int64_t sparse_count, int64_t sparse_mode)
{
std::string query_layout_str = std::string(layout_query);
std::string key_layout_str = std::string(layout_key);
const int SIZE = 8;
const int DIM_0 = 0;
const int DIM_1 = 1;
const int DIM_2 = 2;
const int DIM_3 = 3;
at::SmallVector<int64_t, SIZE> output_size;
for (size_t i = 0; i < query.sizes().size(); i++) {
TORCH_CHECK(query.size(i) > 0, "All values within query's shape should be greater "
"than 0, but shape[", i, "] is ", query.size(i));
}
for (size_t i = 0; i < key.sizes().size(); i++) {
TORCH_CHECK(key.size(i) > 0, "All values within key's shape should be greater "
"than 0, but shape[", i, "] is ", key.size(i));
}
TORCH_CHECK(sparse_count > 0, "sparse count should be greater than 0, but now is ", sparse_count);
int64_t keyHeadNum = (key_layout_str == "TND")? key.size(DIM_1) : key.size(DIM_2);
if (query_layout_str == "BSND") {
output_size = {query.size(DIM_0), query.size(DIM_1), keyHeadNum, sparse_count};
} else {
output_size = {query.size(DIM_0), keyHeadNum, sparse_count};
}
at::Tensor lightning_indexer_quant_output = at::empty(output_size, query.options().dtype(at::kInt));
// convert str
char *query_layout_ptr = const_cast<char *>(query_layout_str.c_str());
char *key_layout_ptr = const_cast<char *>(key_layout_str.c_str());
EXEC_NPU_CMD(aclnnLightningIndexerQuant,
query,
key,
weights,
query_dequant_scale,
key_dequant_scale,
actual_seq_lengths_query,
actual_seq_lengths_key,
block_table,
query_quant_mode,
key_quant_mode,
query_layout_ptr,
key_layout_ptr,
sparse_count,
sparse_mode,
lightning_indexer_quant_output
);
return lightning_indexer_quant_output;
}
}
#endif

41
csrc/lightning_indexer_quant/op_host/CMakeLists.txt
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@@ -1,41 +0,0 @@
# This program is free software, you can redistribute it and/or modify it.
# Copyright (c) 2025 Huawei Technologies Co., Ltd.
# This file is a part of the CANN Open Software.
# Licensed under CANN Open Software License Agreement Version 2.0 (the "License").
# Please refer to the License for details. You may not use this file except in compliance with the License.
# THIS SOFTWARE IS PROVIDED ON AN "AS IS" BASIS, WITHOUT WARRANTIES OF ANY KIND, EITHER EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO NON-INFRINGEMENT, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE.
# See LICENSE in the root of the software repository for the full text of the License.
# ======================================================================================================================
add_ops_compile_options(
OP_NAME LightningIndexerQuant
OPTIONS --cce-auto-sync=off
-Wno-deprecated-declarations
-Werror
-mllvm -cce-aicore-hoist-movemask=false
--op_relocatable_kernel_binary=true
)
set(lightning_indexer_quant_depends transformer/attention/lightning_indexer_quant PARENT_SCOPE)
target_sources(op_host_aclnn PRIVATE
lightning_indexer_quant_def.cpp
)
target_sources(optiling PRIVATE
lightning_indexer_quant_tiling.cpp
)
if (NOT BUILD_OPEN_PROJECT)
target_sources(opmaster_ct PRIVATE
lightning_indexer_quant_tiling.cpp
)
endif ()
target_include_directories(optiling PRIVATE
${CMAKE_CURRENT_SOURCE_DIR}/op_host
)
target_sources(opsproto PRIVATE
lightning_indexer_quant_proto.cpp
)

85
csrc/lightning_indexer_quant/op_host/lightning_indexer_quant_def.cpp
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@@ -1,85 +0,0 @@
/**
* This program is free software, you can redistribute it and/or modify it.
* Copyright (c) 2025 Huawei Technologies Co., Ltd.
* This file is a part of the CANN Open Software.
* Licensed under CANN Open Software License Agreement Version 2.0 (the "License").
* Please refer to the License for details. You may not use this file except in compliance with the License.
* THIS SOFTWARE IS PROVIDED ON AN "AS IS" BASIS, WITHOUT WARRANTIES OF ANY KIND, EITHER EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO NON-INFRINGEMENT, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE.
* See LICENSE in the root of the software repository for the full text of the License.
*/
/*!
* \file lightning_indexer_quant_def.cpp
* \brief
*/
#include <cstdint>
#include "register/op_def_registry.h"
namespace ops {
class LightningIndexerQuant : public OpDef {
public:
explicit LightningIndexerQuant(const char *name) : OpDef(name)
{
this->Input("query")
.ParamType(REQUIRED)
.DataType({ge::DT_INT8})
.Format({ge::FORMAT_ND})
.AutoContiguous();
this->Input("key")
.ParamType(REQUIRED)
.DataType({ge::DT_INT8})
.Format({ge::FORMAT_ND})
.AutoContiguous();
this->Input("weights")
.ParamType(REQUIRED)
.DataType({ge::DT_FLOAT16})
.Format({ge::FORMAT_ND})
.AutoContiguous();
this->Input("query_dequant_scale")
.ParamType(REQUIRED)
.DataType({ge::DT_FLOAT16})
.Format({ge::FORMAT_ND})
.AutoContiguous();
this->Input("key_dequant_scale")
.ParamType(REQUIRED)
.DataType({ge::DT_FLOAT16})
.Format({ge::FORMAT_ND})
.AutoContiguous();
this->Input("actual_seq_lengths_query")
.ParamType(OPTIONAL)
.DataType({ge::DT_INT32})
.Format({ge::FORMAT_ND})
.AutoContiguous();
this->Input("actual_seq_lengths_key")
.ParamType(OPTIONAL)
.DataType({ge::DT_INT32})
.Format({ge::FORMAT_ND})
.AutoContiguous();
this->Input("block_table")
.ParamType(OPTIONAL)
.DataType({ge::DT_INT32})
.Format({ge::FORMAT_ND})
.AutoContiguous();
this->Output("sparse_indices").ParamType(REQUIRED).DataType({ge::DT_INT32}).Format({ge::FORMAT_ND});
this->Attr("query_quant_mode").AttrType(REQUIRED).Int(0); // 0: 默认值，per-token-head
this->Attr("key_quant_mode").AttrType(REQUIRED).Int(0); // 0: 默认值，per-token-head
this->Attr("layout_query").AttrType(OPTIONAL).String("BSND");
this->Attr("layout_key").AttrType(OPTIONAL).String("PA_BSND");
this->Attr("sparse_count").AttrType(OPTIONAL).Int(2048); // 2048: 默认值，筛选前2048
this->Attr("sparse_mode").AttrType(OPTIONAL).Int(3); // 3: 默认值，只计算下三角
OpAICoreConfig aicore_config;
aicore_config.DynamicCompileStaticFlag(true)
.DynamicFormatFlag(true)
.DynamicRankSupportFlag(true)
.DynamicShapeSupportFlag(true)
.NeedCheckSupportFlag(false)
.PrecisionReduceFlag(true)
.ExtendCfgInfo("aclnnSupport.value", "support_aclnn")
.ExtendCfgInfo("jitCompile.flag", "static_false,dynamic_false");
this->AICore().AddConfig("ascend910b", aicore_config);
this->AICore().AddConfig("ascend910_93", aicore_config);
}
};
OP_ADD(LightningIndexerQuant);
} // namespace ops

91
csrc/lightning_indexer_quant/op_host/lightning_indexer_quant_proto.cpp
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@@ -1,91 +0,0 @@
/**
* This program is free software, you can redistribute it and/or modify it.
* Copyright (c) 2025 Huawei Technologies Co., Ltd.
* This file is a part of the CANN Open Software.
* Licensed under CANN Open Software License Agreement Version 2.0 (the "License").
* Please refer to the License for details. You may not use this file except in compliance with the License.
* THIS SOFTWARE IS PROVIDED ON AN "AS IS" BASIS, WITHOUT WARRANTIES OF ANY KIND, EITHER EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO NON-INFRINGEMENT, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE.
* See LICENSE in the root of the software repository for the full text of the License.
*/
/*!
* \file lightning_indexer_quant_proto.cpp
* \brief
*/
#include <graph/utils/type_utils.h>
#include <register/op_impl_registry.h>
#include "error/ops_error.h"
using namespace ge;
namespace ops {
constexpr uint32_t QUERY_INDEX = 0;
constexpr uint32_t KEY_INDEX = 1;
constexpr uint32_t ATTR_QUERY_LAYOUT_INDEX = 2;
constexpr uint32_t ATTR_KV_LAYOUT_INDEX = 3;
constexpr uint32_t ATTR_SPARSE_COUNT_INDEX = 4;
static ge::graphStatus InferShapeLightningIndexerQuant(gert::InferShapeContext *context)
{
if (context == nullptr) {
OPS_LOG_E("LightningIndexerQuant", "context is nullptr!");
return ge::GRAPH_FAILED;
}
const gert::Shape *queryShape = context->GetInputShape(QUERY_INDEX);
OPS_LOG_E_IF_NULL(context, queryShape, return ge::GRAPH_FAILED);
const gert::Shape *keyShape = context->GetInputShape(KEY_INDEX);
OPS_LOG_E_IF_NULL(context, keyShape, return ge::GRAPH_FAILED);
gert::Shape *outShape = context->GetOutputShape(0);
auto attrs = context->GetAttrs();
OPS_LOG_E_IF_NULL(context, attrs, return ge::GRAPH_FAILED);
const char *inputLayoutQueryPtr = attrs->GetAttrPointer<char>(ATTR_QUERY_LAYOUT_INDEX);
OPS_LOG_E_IF_NULL(context, inputLayoutQueryPtr, return ge::GRAPH_FAILED);
const char *inputLayoutKeyPtr = attrs->GetAttrPointer<char>(ATTR_KV_LAYOUT_INDEX);
OPS_LOG_E_IF_NULL(context, inputLayoutKeyPtr, return ge::GRAPH_FAILED);
const int64_t *sparse_count = attrs->GetInt(ATTR_SPARSE_COUNT_INDEX);
OPS_LOG_E_IF_NULL(context, sparse_count, return ge::GRAPH_FAILED);
std::string inputLayoutQueryPtrStr = std::string(inputLayoutQueryPtr);
std::string inputLayoutKeyPtrStr = std::string(inputLayoutKeyPtr);
if (inputLayoutQueryPtrStr != "TND" && inputLayoutQueryPtrStr != "BSND") {
OPS_LOG_E(context, "The input layout query should be TND or BSND, but got %s.", inputLayoutQueryPtrStr.c_str());
return GRAPH_FAILED;
}
outShape->SetDimNum(queryShape->GetDimNum());
int64_t keyHeadNum = (inputLayoutKeyPtrStr == "TND") ? keyShape->GetDim(1) : keyShape->GetDim(2);
if (inputLayoutQueryPtrStr == "BSND") {
outShape->SetDim(0, queryShape->GetDim(0)); // 0:Dim B
outShape->SetDim(1, queryShape->GetDim(1)); // 1:Dim S
outShape->SetDim(2, keyHeadNum); // 2:Dim N
outShape->SetDim(3, *sparse_count); // 3:Dim K
} else {
outShape->SetDim(0, queryShape->GetDim(0)); // 0:Dim T
outShape->SetDim(1, keyHeadNum); // 1:output shape's N Dim, 2: key shape's N Dim
outShape->SetDim(2, *sparse_count); // 2:Dim K
}
OPS_LOG_D(context->GetNodeName(), "LightningIndexerQuant InferShape end.");
return ge::GRAPH_SUCCESS;
}
static ge::graphStatus InferDataTypeLightningIndexerQuant(gert::InferDataTypeContext *context)
{
if (context == nullptr) {
OPS_LOG_E("LightningIndexerQuant", "InferDataTypeContext context is nullptr!");
return ge::GRAPH_FAILED;
}
OPS_LOG_D(context->GetNodeName(), "Enter LightningIndexerQuant InferDataType impl.");
// default index data type is int32
ge::DataType outputType = ge::DT_INT32;
context->SetOutputDataType(0, outputType);
OPS_LOG_D(context->GetNodeName(), "LightningIndexerQuant InferDataType end.");
return GRAPH_SUCCESS;
}
IMPL_OP_INFERSHAPE(LightningIndexerQuant)
.InferShape(InferShapeLightningIndexerQuant)
.InferDataType(InferDataTypeLightningIndexerQuant);
} // namespace ops

828
csrc/lightning_indexer_quant/op_host/lightning_indexer_quant_tiling.cpp
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@@ -1,828 +0,0 @@
/**
* This program is free software, you can redistribute it and/or modify it.
* Copyright (c) 2025 Huawei Technologies Co., Ltd.
* This file is a part of the CANN Open Software.
* Licensed under CANN Open Software License Agreement Version 2.0 (the "License").
* Please refer to the License for details. You may not use this file except in compliance with the License.
* THIS SOFTWARE IS PROVIDED ON AN "AS IS" BASIS, WITHOUT WARRANTIES OF ANY KIND, EITHER EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO NON-INFRINGEMENT, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE.
* See LICENSE in the root of the software repository for the full text of the License.
*/
/*!
* \file lightning_indexer_quant_tiling.cpp
* \brief
*/
#include "lightning_indexer_quant_tiling.h"
#include "../op_kernel/lightning_indexer_quant_template_tiling_key.h"
using namespace ge;
using namespace AscendC;
using std::map;
using std::string;
namespace optiling {
// --------------------------LIQInfoParser类成员函数定义-------------------------------------
ge::graphStatus LIQInfoParser::CheckRequiredInOutExistence() const
{
OPS_ERR_IF(opParamInfo_.query.shape == nullptr, OPS_LOG_E(opName_, "Shape of tensor query is nullptr"),
return ge::GRAPH_FAILED);
OPS_ERR_IF(opParamInfo_.query.desc == nullptr, OPS_LOG_E(opName_, "Desc of tensor query is nullptr"),
return ge::GRAPH_FAILED);
OPS_ERR_IF(opParamInfo_.key.shape == nullptr, OPS_LOG_E(opName_, "Shape of tensor key is nullptr"),
return ge::GRAPH_FAILED);
OPS_ERR_IF(opParamInfo_.key.desc == nullptr, OPS_LOG_E(opName_, "Desc of tensor key is nullptr"),
return ge::GRAPH_FAILED);
OPS_ERR_IF(opParamInfo_.weights.shape == nullptr, OPS_LOG_E(opName_, "Shape of tensor weights is nullptr"),
return ge::GRAPH_FAILED);
OPS_ERR_IF(opParamInfo_.weights.desc == nullptr, OPS_LOG_E(opName_, "Desc of tensor weights is nullptr"),
return ge::GRAPH_FAILED);
OPS_ERR_IF(opParamInfo_.query_dequant_scale.shape == nullptr,
OPS_LOG_E(opName_, "Shape of tensor query_dequant_scale is nullptr"), return ge::GRAPH_FAILED);
OPS_ERR_IF(opParamInfo_.query_dequant_scale.desc == nullptr,
OPS_LOG_E(opName_, "Desc of tensor query_dequant_scale is nullptr"), return ge::GRAPH_FAILED);
OPS_ERR_IF(opParamInfo_.key_dequant_scale.shape == nullptr,
OPS_LOG_E(opName_, "Shape of tensor key_dequant_scale is nullptr"), return ge::GRAPH_FAILED);
OPS_ERR_IF(opParamInfo_.key_dequant_scale.desc == nullptr,
OPS_LOG_E(opName_, "Desc of tensor key_dequant_scale is nullptr"), return ge::GRAPH_FAILED);
OPS_ERR_IF(opParamInfo_.attenOut.shape == nullptr, OPS_LOG_E(opName_, "Shape of tensor output is nullptr"),
return ge::GRAPH_FAILED);
OPS_ERR_IF(opParamInfo_.attenOut.desc == nullptr, OPS_LOG_E(opName_, "Desc of tensor output is nullptr"),
return ge::GRAPH_FAILED);
return ge::GRAPH_SUCCESS;
}
ge::graphStatus LIQInfoParser::CheckRequiredAttrExistence() const
{
OPS_ERR_IF(opParamInfo_.layOutQuery == nullptr, OPS_LOG_E(opName_, "attr layout_query is nullptr"),
return ge::GRAPH_FAILED);
OPS_ERR_IF(opParamInfo_.layOutKey == nullptr, OPS_LOG_E(opName_, "attr layout_key is nullptr"),
return ge::GRAPH_FAILED);
OPS_ERR_IF(opParamInfo_.sparseCount == nullptr, OPS_LOG_E(opName_, "attr sparse_count is nullptr"),
return ge::GRAPH_FAILED);
OPS_ERR_IF(opParamInfo_.sparseMode == nullptr, OPS_LOG_E(opName_, "attr sparse_mode is nullptr"),
return ge::GRAPH_FAILED);
OPS_ERR_IF(opParamInfo_.queryQuantMode == nullptr, OPS_LOG_E(opName_, "query_quant_mode is nullptr"),
return ge::GRAPH_FAILED);
OPS_ERR_IF(opParamInfo_.keyQuantMode == nullptr, OPS_LOG_E(opName_, "key_quant_mode is nullptr"),
return ge::GRAPH_FAILED);
return ge::GRAPH_SUCCESS;
}
ge::graphStatus LIQInfoParser::CheckRequiredParaExistence() const
{
if (CheckRequiredInOutExistence() != ge::GRAPH_SUCCESS || CheckRequiredAttrExistence() != ge::GRAPH_SUCCESS) {
return ge::GRAPH_FAILED;
}
return ge::GRAPH_SUCCESS;
}
ge::graphStatus LIQInfoParser::GetOpName()
{
if (context_->GetNodeName() == nullptr) {
OPS_LOG_E("LightningIndexerQuant", "opName got from TilingContext is nullptr");
return ge::GRAPH_FAILED;
}
opName_ = context_->GetNodeName();
return ge::GRAPH_SUCCESS;
}
ge::graphStatus LIQInfoParser::GetNpuInfo()
{
platformInfo_ = context_->GetPlatformInfo();
OPS_ERR_IF(platformInfo_ == nullptr, OPS_LOG_E(opName_, "GetPlatformInfo is nullptr."), return ge::GRAPH_FAILED);
auto ascendcPlatform = platform_ascendc::PlatformAscendC(platformInfo_);
uint32_t aivNum = ascendcPlatform.GetCoreNumAiv();
uint32_t aicNum = ascendcPlatform.GetCoreNumAic();
OPS_ERR_IF(aicNum == 0 || aivNum == 0, OPS_LOG_E(opName_, "num of core obtained is 0."), return GRAPH_FAILED);
socVersion_ = ascendcPlatform.GetSocVersion();
if ((socVersion_ != platform_ascendc::SocVersion::ASCEND910B) &&
(socVersion_ != platform_ascendc::SocVersion::ASCEND910_93)) {
OPS_LOG_E(opName_, "SOC Version[%d] is not support.", (int32_t)socVersion_);
return GRAPH_FAILED;
}
OPS_ERR_IF(context_->GetWorkspaceSizes(1) == nullptr, OPS_LOG_E(opName_, "workSpaceSize got from ge is nullptr"),
return ge::GRAPH_FAILED);
OPS_ERR_IF(context_->GetRawTilingData() == nullptr,
OPS_LOG_E(context_->GetNodeName(), "RawTilingData got from GE context is nullptr."),
return ge::GRAPH_FAILED);
return ge::GRAPH_SUCCESS;
}
void LIQInfoParser::GetOptionalInputParaInfo()
{
opParamInfo_.actualSeqLengthsQ.tensor = context_->GetOptionalInputTensor(ACTUAL_SEQ_Q_INDEX);
opParamInfo_.actualSeqLengthsQ.desc = context_->GetOptionalInputDesc(ACTUAL_SEQ_Q_INDEX);
opParamInfo_.actualSeqLengthsK.tensor = context_->GetOptionalInputTensor(ACTUAL_SEQ_K_INDEX);
opParamInfo_.actualSeqLengthsK.desc = context_->GetOptionalInputDesc(ACTUAL_SEQ_K_INDEX);
opParamInfo_.blockTable.tensor = context_->GetOptionalInputTensor(BLOCK_TABLE_INDEX);
opParamInfo_.blockTable.desc = context_->GetOptionalInputDesc(BLOCK_TABLE_INDEX);
}
void LIQInfoParser::GetInputParaInfo()
{
opParamInfo_.query.desc = context_->GetInputDesc(QUERY_INDEX);
opParamInfo_.query.shape = context_->GetInputShape(QUERY_INDEX);
opParamInfo_.key.desc = context_->GetInputDesc(KEY_INDEX);
opParamInfo_.key.shape = context_->GetInputShape(KEY_INDEX);
opParamInfo_.weights.desc = context_->GetInputDesc(WEIGTHS_INDEX);
opParamInfo_.weights.shape = context_->GetInputShape(WEIGTHS_INDEX);
opParamInfo_.query_dequant_scale.desc = context_->GetInputDesc(QUERY_DEQUANT_SCALE_INDEX);
opParamInfo_.query_dequant_scale.shape = context_->GetInputShape(QUERY_DEQUANT_SCALE_INDEX);
opParamInfo_.key_dequant_scale.desc = context_->GetInputDesc(KEY_DEQUANT_SCALE_INDEX);
opParamInfo_.key_dequant_scale.shape = context_->GetInputShape(KEY_DEQUANT_SCALE_INDEX);
GetOptionalInputParaInfo();
}
void LIQInfoParser::GetOutputParaInfo()
{
opParamInfo_.attenOut.desc = context_->GetOutputDesc(LIGHTNING_INDEXER_QUANT);
opParamInfo_.attenOut.shape = context_->GetOutputShape(LIGHTNING_INDEXER_QUANT);
}
ge::graphStatus LIQInfoParser::GetAttrParaInfo()
{
auto attrs = context_->GetAttrs();
OPS_ERR_IF(attrs == nullptr, OPS_REPORT_VECTOR_INNER_ERR(context_->GetNodeName(), "attrs got from ge is nullptr"),
return ge::GRAPH_FAILED);
OPS_LOG_I(context_->GetNodeName(), "GetAttrParaInfo start");
opParamInfo_.layOutQuery = attrs->GetStr(ATTR_QUERY_LAYOUT_INDEX);
opParamInfo_.layOutKey = attrs->GetStr(ATTR_KEY_LAYOUT_INDEX);
opParamInfo_.queryQuantMode = attrs->GetAttrPointer<int32_t>(ATTR_QUERY_QUANT_MODE_INDEX);
opParamInfo_.keyQuantMode = attrs->GetAttrPointer<int32_t>(ATTR_KEY_QUANT_MODE_INDEX);
opParamInfo_.layOutQuery = attrs->GetStr(ATTR_QUERY_LAYOUT_INDEX);
opParamInfo_.layOutKey = attrs->GetStr(ATTR_KEY_LAYOUT_INDEX);
opParamInfo_.sparseCount = attrs->GetAttrPointer<int32_t>(ATTR_SPARSE_COUNT_INDEX);
opParamInfo_.sparseMode = attrs->GetAttrPointer<int32_t>(ATTR_SPARSE_MODE_INDEX);
if (opParamInfo_.layOutQuery != nullptr) {
OPS_LOG_I(context_->GetNodeName(), "layout_query is:%s", opParamInfo_.layOutQuery);
}
if (opParamInfo_.layOutKey != nullptr) {
OPS_LOG_I(context_->GetNodeName(), "layout_key is:%s", opParamInfo_.layOutKey);
}
if (opParamInfo_.sparseCount != nullptr) {
OPS_LOG_I(context_->GetNodeName(), "selscted count is:%d", *opParamInfo_.sparseCount);
}
if (opParamInfo_.sparseMode != nullptr) {
OPS_LOG_I(context_->GetNodeName(), "sparse mode is:%d", *opParamInfo_.sparseMode);
}
if (opParamInfo_.queryQuantMode != nullptr) {
OPS_LOG_I(context_->GetNodeName(), "query_quant_mode mode is:%d", *opParamInfo_.queryQuantMode);
}
if (opParamInfo_.keyQuantMode != nullptr) {
OPS_LOG_I(context_->GetNodeName(), "key_quant_mode mode is:%d", *opParamInfo_.keyQuantMode);
}
OPS_LOG_I(context_->GetNodeName(), "GetAttrParaInfo end");
return ge::GRAPH_SUCCESS;
}
ge::graphStatus LIQInfoParser::CheckAttrParaInfo()
{
std::string layout_key(opParamInfo_.layOutKey);
std::string layout_query(opParamInfo_.layOutQuery);
OPS_ERR_IF(
((std::string(opParamInfo_.layOutKey) == "BNSD") || (std::string(opParamInfo_.layOutKey) == "PA_BBND")),
OPS_LOG_E(opName_, "input attr layout_key only supported PA_BSND, PA_BBND, BSND or TND"
"but now layout_key is %s.", layout_key.c_str()),
return ge::GRAPH_FAILED);
OPS_ERR_IF(((std::string(opParamInfo_.layOutQuery) != "BSND") && (std::string(opParamInfo_.layOutQuery) != "TND")),
OPS_LOG_E(opName_, "input attr layout_query only supported BSND or TND."), return ge::GRAPH_FAILED);
OPS_ERR_IF(
((std::string(opParamInfo_.layOutKey) != "PA_BSND") &&
(std::string(opParamInfo_.layOutQuery)) != (std::string(opParamInfo_.layOutKey))),
OPS_LOG_E(opName_, "outside of PA, input attr layout_query and input attr layout_key must be the same, but now layout_key is %s, layout_query is %s.",
layout_key.c_str(), layout_query.c_str()), return ge::GRAPH_FAILED);
OPS_ERR_IF(!((*opParamInfo_.sparseCount > 0) && (*opParamInfo_.sparseCount <= SPARSE_LIMIT)),
OPS_LOG_E(opName_, "input attr sparse_count must > 0 and <= 2048."), return ge::GRAPH_FAILED);
OPS_ERR_IF(!((*opParamInfo_.sparseMode == 0) || (*opParamInfo_.sparseMode == SPARSE_MODE_LOWER)),
OPS_LOG_E(opName_, "input attr sparse_mode only supported 0 or 3, but now is %u.",
*opParamInfo_.sparseMode), return ge::GRAPH_FAILED);
OPS_ERR_IF(*opParamInfo_.queryQuantMode != 0, OPS_LOG_E(opName_, "input attr query_quant_mode only supported 0."),
return ge::GRAPH_FAILED);
OPS_ERR_IF(*opParamInfo_.keyQuantMode != 0, OPS_LOG_E(opName_, "input attr key_quant_mode only supported 0."),
return ge::GRAPH_FAILED);
return ge::GRAPH_SUCCESS;
}
ge::graphStatus LIQInfoParser::GetOpParaInfo()
{
GetInputParaInfo();
GetOutputParaInfo();
if (ge::GRAPH_SUCCESS != GetAttrParaInfo()) {
return ge::GRAPH_FAILED;
}
if (ge::GRAPH_SUCCESS != CheckAttrParaInfo()) {
return ge::GRAPH_FAILED;
}
return ge::GRAPH_SUCCESS;
}
ge::graphStatus LIQInfoParser::GetAndCheckInOutDataType()
{
inputQType_ = opParamInfo_.query.desc->GetDataType();
inputKType_ = opParamInfo_.key.desc->GetDataType();
weightsType_ = opParamInfo_.weights.desc->GetDataType();
inputQueryScaleType_ = opParamInfo_.query_dequant_scale.desc->GetDataType();
inputKeyScaleType_ = opParamInfo_.key_dequant_scale.desc->GetDataType();
outputType_ = opParamInfo_.attenOut.desc->GetDataType();
OPS_ERR_IF(!(inputQType_ == inputKType_),
OPS_LOG_E(opName_, "The data types of the input query and key must be the same, but now is %s, %s respectively.",
inputQType_, inputKType_),
return ge::GRAPH_FAILED);
OPS_ERR_IF(
!(inputQueryScaleType_ == inputKeyScaleType_),
OPS_LOG_E(opName_, "The data types of the input query_dequant_scale and key_dequant_scale must be the same."),
return ge::GRAPH_FAILED);
OPS_ERR_IF(inputQType_ != ge::DT_INT8,
OPS_LOG_E(opName_, "The data types of the input query and key must be int8."), return ge::GRAPH_FAILED);
OPS_ERR_IF(weightsType_ != ge::DT_FLOAT16,
OPS_LOG_E(opName_, "The data types of the input weights must be float16."), return ge::GRAPH_FAILED);
OPS_ERR_IF(
inputQueryScaleType_ != ge::DT_FLOAT16,
OPS_LOG_E(opName_, "The data types of the input query_dequant_scale and key_dequant_scale must be float16."),
return ge::GRAPH_FAILED);
OPS_ERR_IF(outputType_ != ge::DT_INT32,
OPS_LOG_E(opName_, "The data types of the output sparse_indices must be int32."),
return ge::GRAPH_FAILED);
return ge::GRAPH_SUCCESS;
}
ge::graphStatus LIQInfoParser::GetQueryKeyAndOutLayout()
{
// 获取query,key的Layout基准值
const map<string, DataLayout> layoutQueryMap = {{"BSND", DataLayout::BSND}, {"TND", DataLayout::TND}};
std::string layout_query(opParamInfo_.layOutQuery);
auto QLayout_ = layoutQueryMap.find(layout_query);
if (QLayout_ != layoutQueryMap.end()) {
qLayout_ = QLayout_->second;
}
const map<string, DataLayout> layoutKeyMap = {
{"BSND", DataLayout::BSND}, {"TND", DataLayout::TND},
{"PA_BSND", DataLayout::PA_BSND}, {"PA_BBND", DataLayout::PA_BSND}};
std::string layout_key(opParamInfo_.layOutKey);
auto KLayout = layoutKeyMap.find(layout_key);
if (KLayout != layoutKeyMap.end()) {
kLayout_ = KLayout->second;
}
return ge::GRAPH_SUCCESS;
}
ge::graphStatus LIQInfoParser::GetAndCheckOptionalInput()
{
if (kLayout_ == DataLayout::PA_BSND) {
OPS_ERR_IF(opParamInfo_.blockTable.tensor == nullptr,
OPS_LOG_E(opName_, "key layout only supported PA_BSND, input block_table must not be null"),
return ge::GRAPH_FAILED);
OPS_ERR_IF(
opParamInfo_.actualSeqLengthsK.tensor == nullptr,
OPS_LOG_E(opName_, "key layout only supported PA_BSND, input actual_seq_lengths_key must not be null"),
return ge::GRAPH_FAILED);
OPS_ERR_IF(opParamInfo_.blockTable.desc->GetDataType() != ge::DT_INT32,
OPS_LOG_E(opName_, "input block_table data type only support int32"), return ge::GRAPH_FAILED);
} else {
OPS_ERR_IF(opParamInfo_.blockTable.tensor != nullptr,
OPS_LOG_E(opName_, "key layout is not PA_BSND, input block_table must be null"),
return ge::GRAPH_FAILED);
}
if (kLayout_ == DataLayout::TND) {
OPS_ERR_IF(opParamInfo_.actualSeqLengthsK.tensor == nullptr,
OPS_LOG_E(opName_, "when layout_key is TND, input actual_seq_lengths_key must not be null"),
return ge::GRAPH_FAILED);
}
OPS_ERR_IF(opParamInfo_.actualSeqLengthsK.tensor != nullptr &&
opParamInfo_.actualSeqLengthsK.desc->GetDataType() != ge::DT_INT32,
OPS_LOG_E(opName_, "input actual_seq_lengths_key data type only support int32"),
return ge::GRAPH_FAILED);
if (qLayout_ == DataLayout::TND) {
OPS_ERR_IF(opParamInfo_.actualSeqLengthsQ.tensor == nullptr,
OPS_LOG_E(opName_, "when layout_query is TND, input actual_seq_lengths_query must not be null"),
return ge::GRAPH_FAILED);
}
OPS_ERR_IF(opParamInfo_.actualSeqLengthsQ.tensor != nullptr &&
opParamInfo_.actualSeqLengthsQ.desc->GetDataType() != ge::DT_INT32,
OPS_LOG_E(opName_, "input actual_seq_lengths_query data type only support int32"),
return ge::GRAPH_FAILED);
return ge::GRAPH_SUCCESS;
}
ge::graphStatus LIQInfoParser::CheckShapeDim()
{
OPS_ERR_IF((opParamInfo_.blockTable.tensor != nullptr) &&
(opParamInfo_.blockTable.tensor->GetStorageShape().GetDimNum() != DIM_NUM_TWO),
OPS_LOG_E(opName_, "the dim num of block_table's shape should be 2, but now is %u",
opParamInfo_.blockTable.tensor->GetStorageShape().GetDimNum()), return ge::GRAPH_FAILED);
OPS_ERR_IF(
(kLayout_ == DataLayout::PA_BSND) && (opParamInfo_.key.shape->GetStorageShape().GetDimNum() != DIM_NUM_FOUR),
OPS_LOG_E(opName_, "the dim num of key's shape should be 4, but now is %u",
opParamInfo_.key.shape->GetStorageShape().GetDimNum()), return ge::GRAPH_FAILED);
uint32_t qShapeDim = opParamInfo_.query.shape->GetStorageShape().GetDimNum();
uint32_t weightsShapeDim = opParamInfo_.weights.shape->GetStorageShape().GetDimNum();
uint32_t outShapeDim = opParamInfo_.attenOut.shape->GetStorageShape().GetDimNum();
uint32_t expectShapeDim = DIM_NUM_FOUR;
if (qLayout_ == DataLayout::TND) {
expectShapeDim = DIM_NUM_THREE;
}
OPS_ERR_IF(
qShapeDim != expectShapeDim,
OPS_LOG_E(opName_, "the dim num of query's shape should be %u, but now is %u", expectShapeDim, qShapeDim),
return ge::GRAPH_FAILED);
OPS_ERR_IF(outShapeDim != expectShapeDim,
OPS_LOG_E(opName_, "the dim num of sparse_indices's shape should be %u, but now is %u", expectShapeDim,
outShapeDim),
return ge::GRAPH_FAILED);
OPS_ERR_IF(!(weightsShapeDim == expectShapeDim - 1),
OPS_LOG_E(opName_, "the dim num of weights's shape should be %u, but now is %u", expectShapeDim - 1,
weightsShapeDim),
return ge::GRAPH_FAILED);
return ge::GRAPH_SUCCESS;
}
ge::graphStatus LIQInfoParser::GetN1Size()
{
if (qLayout_ == DataLayout::BSND) {
n1Size_ = static_cast<uint32_t>(opParamInfo_.query.shape->GetStorageShape().GetDim(DIM_IDX_TWO));
} else {
// TND
n1Size_ = static_cast<uint32_t>(opParamInfo_.query.shape->GetStorageShape().GetDim(DIM_IDX_ONE));
}
OPS_LOG_I(context_->GetNodeName(), "n1Size is %d", n1Size_);
return ge::GRAPH_SUCCESS;
}
ge::graphStatus LIQInfoParser::GetActualSeqLenSize(uint32_t &size, const gert::Tensor *tensor,
const std::string &actualSeqLenName)
{
size = static_cast<uint32_t>(tensor->GetShapeSize());
if (size <= 0) {
OPS_LOG_E(opName_, "%s's shape size is %u, it should be greater than 0.", actualSeqLenName.c_str(), size);
return ge::GRAPH_FAILED;
}
return ge::GRAPH_SUCCESS;
}
ge::graphStatus LIQInfoParser::GetAndCheckN2Size()
{
// PA_BSND
if (kLayout_ == DataLayout::TND) {
n2Size_ = static_cast<uint32_t>(opParamInfo_.key.shape->GetStorageShape().GetDim(DIM_IDX_ONE));
} else {
n2Size_ = static_cast<uint32_t>(opParamInfo_.key.shape->GetStorageShape().GetDim(DIM_IDX_TWO));
}
OPS_LOG_I(context_->GetNodeName(), "N2 is %d", n2Size_);
OPS_ERR_IF(n2Size_ != 1, OPS_LOG_E(opName_, "key shape[2] is numhead, only support 1."), return ge::GRAPH_FAILED);
return ge::GRAPH_SUCCESS;
}
ge::graphStatus LIQInfoParser::GetGSize()
{
if (n1Size_ % n2Size_ != 0) {
OPS_LOG_E(opName_, "input query's head_num %u can not be a multiple of key's head_num %u.", n1Size_, n2Size_);
return ge::GRAPH_FAILED;
}
gSize_ = n1Size_ / n2Size_;
return ge::GRAPH_SUCCESS;
}
ge::graphStatus LIQInfoParser::GetBatchSize()
{
// 获取B基准值
// 1、非TND/NTD时, 以query的batch_size维度为基准;
// 2、TND/NTD时, actual_seq_lens_q必须传入, 以actual_seq_lens_q数组的长度为B轴大小
if (qLayout_ == DataLayout::TND) {
return GetActualSeqLenSize(bSize_, opParamInfo_.actualSeqLengthsQ.tensor, "input actual_seq_lengths_query");
} else { // BSND
bSize_ = opParamInfo_.query.shape->GetStorageShape().GetDim(DIM_IDX_ZERO);
OPS_LOG_I(context_->GetNodeName(), "b: %d, s: %d, n: %d,d :%d",
opParamInfo_.query.shape->GetStorageShape().GetDim(DIM_IDX_ZERO),
opParamInfo_.query.shape->GetStorageShape().GetDim(DIM_IDX_ONE),
opParamInfo_.query.shape->GetStorageShape().GetDim(DIM_IDX_TWO),
opParamInfo_.query.shape->GetStorageShape().GetDim(DIM_IDX_THREE));
return ge::GRAPH_SUCCESS;
}
}
ge::graphStatus LIQInfoParser::GetHeadDim()
{
// 以query的D维度为基准
uint32_t dIndex = DIM_IDX_TWO;
// 根据layout确定D维度在shape中的位置
switch (qLayout_) {
case DataLayout::TND:
// TND格式: [Total, N, D] -> D是第2维(索引2)
dIndex = DIM_IDX_TWO;
break;
case DataLayout::BSND:
// BSND格式: [Batch, SeqLen, N, D] -> D是第3维(索引3)
dIndex = DIM_IDX_THREE;
break;
default:
OPS_LOG_E(opName_, "unsupported layout for getting head dim.");
return ge::GRAPH_FAILED;
}
headDim_ = opParamInfo_.query.shape->GetStorageShape().GetDim(dIndex);
OPS_ERR_IF(headDim_ != HEAD_DIM_LIMIT, OPS_LOG_E(opName_, "input query's last dim head_dim only support 128, but now is %u.", headDim_),
return ge::GRAPH_FAILED);
return ge::GRAPH_SUCCESS;
}
ge::graphStatus LIQInfoParser::GetS1Size()
{
if (qLayout_ == DataLayout::BSND) {
s1Size_ = opParamInfo_.query.shape->GetStorageShape().GetDim(1);
}
return ge::GRAPH_SUCCESS;
}
ge::graphStatus LIQInfoParser::GetAndCheckBlockSize()
{
blockSize_ = static_cast<uint32_t>(opParamInfo_.key.shape->GetStorageShape().GetDim(1));
OPS_LOG_I(context_->GetNodeName(), "blockSize_ is %d", blockSize_);
OPS_ERR_IF(
((blockSize_ % BLOCK_SIZE_FACTOR != 0) || (blockSize_ == 0) || (blockSize_ > BLOCK_SIZE_LIMIT)),
OPS_LOG_E(opName_, "input key's block_size must be a multiple of 16 and belong to (0, 1024], but now is %u.",
blockSize_),
return ge::GRAPH_FAILED);
return ge::GRAPH_SUCCESS;
}
ge::graphStatus LIQInfoParser::GetS2SizeForPageAttention()
{
if (GetAndCheckBlockSize() != ge::GRAPH_SUCCESS) {
return ge::GRAPH_FAILED;
}
int32_t blockCount_ = static_cast<uint32_t>(opParamInfo_.key.shape->GetStorageShape().GetDim(0));
OPS_ERR_IF((blockCount_ == 0), OPS_LOG_E(opName_, "input key's block_count cannot be 0."), return ge::GRAPH_FAILED);
maxBlockNumPerBatch_ = opParamInfo_.blockTable.tensor->GetStorageShape().GetDim(1);
s2Size_ = maxBlockNumPerBatch_ * blockSize_;
OPS_LOG_I(context_->GetNodeName(), "maxBlockNumPerBatch_ is %d, blockSize_ is %d, s2Size_ is %d",
maxBlockNumPerBatch_, blockSize_, s2Size_);
return ge::GRAPH_SUCCESS;
}
ge::graphStatus LIQInfoParser::GetS2SizeForBatchContinuous()
{
std::string layout_key(opParamInfo_.layOutKey);
if (kLayout_ == DataLayout::BSND) {
s2Size_ = opParamInfo_.key.shape->GetStorageShape().GetDim(DIM_IDX_ONE);
} else if (kLayout_ == DataLayout::TND) {
s2Size_ = opParamInfo_.key.shape->GetStorageShape().GetDim(DIM_IDX_ZERO);
}
OPS_ERR_IF((kLayout_ != DataLayout::BSND) && (kLayout_ != DataLayout::TND),
OPS_LOG_E(opName_, "the layout of key is %s, it is unsupported.", layout_key.c_str()),
return ge::GRAPH_FAILED);
return ge::GRAPH_SUCCESS;
}
ge::graphStatus LIQInfoParser::GetS2Size()
{
// 获取S2基准值
// 1、BATCH_CONTINUOUS时, 从key的S轴获取
// 3、PAGE_ATTENTION时, S2 = block_table.dim1 * block_size
if (kLayout_ == DataLayout::PA_BSND) {
return GetS2SizeForPageAttention();
}
return GetS2SizeForBatchContinuous();
}
ge::graphStatus LIQInfoParser::ValidateInputShapesMatch()
{
/*
TND:
query [T,N1,D],
key [BlockNum,BlockSize,N2,D],
weight [T,N1],
block_table [BatchSize, BatchMaxBlockNum],
act_seq_k [BatchSize]
act_seq_q [BatchSize],
out [T,N2,topk]
----------------------
BSND:
query [BatchSize,S1,N1,D],
key [BlockNum,BlockSize,N2,D],
weight [BatchSize,S1,N1],
block_table [BatchSize, BatchMaxBlockNum],
act_seq_k [BatchSize]
act_seq_q [BatchSize] 可选
out [BatchSize,S1,N2,topk]
*/
uint32_t queryWeightsN1Dim = 1;
uint32_t outN2Dim = 1;
if (qLayout_ == DataLayout::TND) {
// -----------------------check BatchSize-------------------
// bSize_ 来源于act_seq_q
OPS_ERR_IF((kLayout_ == DataLayout::PA_BSND) &&
((opParamInfo_.actualSeqLengthsK.tensor->GetShapeSize() != bSize_) ||
(opParamInfo_.blockTable.tensor != nullptr &&
opParamInfo_.blockTable.tensor->GetStorageShape().GetDim(0) != bSize_)),
OPS_LOG_E(
opName_,
"TND case input actual_seq_lengths_query, actual_seq_lengths_key, block_table dim 0 are %u, %u, %u respectively, they must be same.",
bSize_, opParamInfo_.actualSeqLengthsK.tensor->GetShapeSize(),
opParamInfo_.blockTable.tensor->GetStorageShape().GetDim(0)),
return ge::GRAPH_FAILED);
OPS_ERR_IF((kLayout_ != DataLayout::PA_BSND) &&
(opParamInfo_.actualSeqLengthsK.tensor->GetShapeSize() != bSize_),
OPS_LOG_E(
opName_,
"TND case input actual_seq_lengths_query, actual_seq_lengths_key, are %u, %u respectively, they must be same.",
bSize_, opParamInfo_.actualSeqLengthsK.tensor->GetShapeSize()),
return ge::GRAPH_FAILED);
// -----------------------check T-------------------
uint32_t qTsize = opParamInfo_.query.shape->GetStorageShape().GetDim(0);
OPS_ERR_IF((opParamInfo_.weights.shape->GetStorageShape().GetDim(0) != qTsize) ||
(opParamInfo_.attenOut.shape->GetStorageShape().GetDim(0) != qTsize),
OPS_LOG_E(opName_,
"TND case input query, weights, sparse_indices dim 0 are %u, %u, %u respectively, they must be same.",
qTsize, opParamInfo_.weights.shape->GetStorageShape().GetDim(0),
opParamInfo_.attenOut.shape->GetStorageShape().GetDim(0)),
return ge::GRAPH_FAILED);
} else {
// -----------------------check BatchSize-------------------
// bSize_ 来源于query
OPS_ERR_IF((kLayout_ == DataLayout::PA_BSND) &&
((opParamInfo_.weights.shape->GetStorageShape().GetDim(0) != bSize_) ||
(opParamInfo_.blockTable.tensor != nullptr &&
opParamInfo_.blockTable.tensor->GetStorageShape().GetDim(0) != bSize_) ||
(opParamInfo_.actualSeqLengthsK.tensor->GetShapeSize() != bSize_) ||
(opParamInfo_.attenOut.shape->GetStorageShape().GetDim(0) != bSize_)),
OPS_LOG_E(opName_,
"BSND case input query, weight, actual_seq_lengths_key, block_table, sparse_indices dim 0 are %u, %u, %u, %u, %u respectively, they must be same.",
bSize_, opParamInfo_.weights.shape->GetStorageShape().GetDim(0),
opParamInfo_.actualSeqLengthsK.tensor->GetShapeSize(),
opParamInfo_.blockTable.tensor->GetStorageShape().GetDim(0),
opParamInfo_.attenOut.shape->GetStorageShape().GetDim(0)),
return ge::GRAPH_FAILED);
OPS_ERR_IF((kLayout_ != DataLayout::PA_BSND) &&
((opParamInfo_.weights.shape->GetStorageShape().GetDim(0) != bSize_) ||
(opParamInfo_.actualSeqLengthsK.tensor != nullptr &&
opParamInfo_.actualSeqLengthsK.tensor->GetShapeSize() != bSize_) ||
(opParamInfo_.attenOut.shape->GetStorageShape().GetDim(0) != bSize_)),
OPS_LOG_E(opName_,
"BSND case input query, weight, actual_seq_lengths_key, sparse_indices dim 0 are %u, %u, %u, %u respectively, they must be same.",
bSize_, opParamInfo_.weights.shape->GetStorageShape().GetDim(0),
opParamInfo_.actualSeqLengthsK.tensor->GetShapeSize(),
opParamInfo_.attenOut.shape->GetStorageShape().GetDim(0)),
return ge::GRAPH_FAILED);
OPS_ERR_IF(
(opParamInfo_.actualSeqLengthsQ.tensor != nullptr) &&
(opParamInfo_.actualSeqLengthsQ.tensor->GetShapeSize() != bSize_),
OPS_LOG_E(
opName_,
"BSND case input query, actual_seq_lengths_query dim 0 are %u, %u respectively, they must be same",
bSize_, opParamInfo_.actualSeqLengthsQ.tensor->GetShapeSize()),
return ge::GRAPH_FAILED);
// -----------------------check S1-------------------
OPS_ERR_IF(
(opParamInfo_.weights.shape->GetStorageShape().GetDim(1) != s1Size_) ||
(opParamInfo_.attenOut.shape->GetStorageShape().GetDim(1) != s1Size_),
OPS_LOG_E(opName_, "BSND case input query, weight, sparse_indices dim 1 are %u, %u, %u, they must be same.",
s1Size_, opParamInfo_.weights.shape->GetStorageShape().GetDim(1),
opParamInfo_.attenOut.shape->GetStorageShape().GetDim(1)),
return ge::GRAPH_FAILED);
queryWeightsN1Dim = DIM_IDX_TWO;
outN2Dim = DIM_IDX_TWO;
}
// -----------------------check N1-------------------
OPS_ERR_IF((opParamInfo_.weights.shape->GetStorageShape().GetDim(queryWeightsN1Dim) != n1Size_),
OPS_LOG_E(opName_, "input query, weight shape dim N1 must be same, but now are %u, %u respectively.",
opParamInfo_.weights.shape->GetStorageShape().GetDim(queryWeightsN1Dim), n1Size_),
return ge::GRAPH_FAILED);
// -----------------------check D-------------------
OPS_ERR_IF(
((kLayout_ != DataLayout::TND && opParamInfo_.key.shape->GetStorageShape().GetDim(DIM_IDX_THREE) != headDim_)
|| (kLayout_ == DataLayout::TND && opParamInfo_.key.shape->GetStorageShape().GetDim(DIM_IDX_TWO) != headDim_)),
OPS_LOG_E(opName_, "input query, key shape last dim must be same, now are %u, %u respectively.",
headDim_, opParamInfo_.key.shape->GetStorageShape().GetDim(DIM_IDX_THREE)),
return ge::GRAPH_FAILED);
// -----------------------check N2-------------------
OPS_ERR_IF((opParamInfo_.attenOut.shape->GetStorageShape().GetDim(outN2Dim) != n2Size_),
OPS_LOG_E(opName_, "input query and output sparse_indices shape n2 dim must be same."),
return ge::GRAPH_FAILED);
// -----------------------check sparse_count-------------------
OPS_ERR_IF((opParamInfo_.attenOut.shape->GetStorageShape().GetDim(outN2Dim + 1) != *opParamInfo_.sparseCount),
OPS_LOG_E(opName_, "output sparse_indices shape last dim must be same as attr sparse_count."),
return ge::GRAPH_FAILED);
return ge::GRAPH_SUCCESS;
}
ge::graphStatus LIQInfoParser::CheckScaleShape()
{
uint32_t qShapeDim = opParamInfo_.query.shape->GetStorageShape().GetDimNum();
uint32_t kShapeDim = opParamInfo_.key.shape->GetStorageShape().GetDimNum();
uint32_t qDequantScaleShapeDim = opParamInfo_.query_dequant_scale.shape->GetStorageShape().GetDimNum();
uint32_t kDequantScaleShapeDim = opParamInfo_.key_dequant_scale.shape->GetStorageShape().GetDimNum();
OPS_ERR_IF(qDequantScaleShapeDim != (qShapeDim - 1),
OPS_LOG_E(opName_, "the dim num of query_dequant_scale's shape should be %u, but now is %u",
qShapeDim - 1, qDequantScaleShapeDim),
return ge::GRAPH_FAILED);
OPS_ERR_IF(kDequantScaleShapeDim != (kShapeDim - 1),
OPS_LOG_E(opName_, "the dim num of key_dequant_scale's shape should be %u, but now is %u", kShapeDim - 1,
kDequantScaleShapeDim),
return ge::GRAPH_FAILED);
// check q scale
for (uint32_t i = 0; i < (qShapeDim - 1); i++) {
uint32_t dimValueQueryScale = opParamInfo_.query_dequant_scale.shape->GetStorageShape().GetDim(i);
uint32_t dimValueQuery = opParamInfo_.query.shape->GetStorageShape().GetDim(i);
OPS_ERR_IF(dimValueQueryScale != dimValueQuery,
OPS_LOG_E(opName_, "query_dequant_scale's shape[%u] %u and query's shape[%u] %u is not same", i,
dimValueQueryScale, i, dimValueQuery),
return ge::GRAPH_FAILED);
}
// check k scale
for (uint32_t i = 0; i < (kShapeDim - 1); i++) {
uint32_t dimValueKeyScale = opParamInfo_.key_dequant_scale.shape->GetStorageShape().GetDim(i);
uint32_t dimValueKey = opParamInfo_.key.shape->GetStorageShape().GetDim(i);
OPS_ERR_IF(dimValueKeyScale != dimValueKey,
OPS_LOG_E(opName_, "key_dequant_scale's shape[%u] %u and key's shape[%u] %u is not same", i,
dimValueKeyScale, i, dimValueKey),
return ge::GRAPH_FAILED);
}
return ge::GRAPH_SUCCESS;
}
void LIQInfoParser::GenerateInfo(LIQTilingInfo &liqInfo)
{
liqInfo.opName = opName_;
liqInfo.platformInfo = platformInfo_;
liqInfo.opParamInfo = opParamInfo_;
liqInfo.socVersion = socVersion_;
liqInfo.bSize = bSize_;
liqInfo.n1Size = n1Size_;
liqInfo.n2Size = n2Size_;
liqInfo.s1Size = s1Size_;
liqInfo.s2Size = s2Size_;
liqInfo.gSize = gSize_;
liqInfo.inputQType = inputQType_;
liqInfo.inputKType = inputKType_;
liqInfo.outputType = outputType_;
liqInfo.blockSize = blockSize_;
liqInfo.maxBlockNumPerBatch = maxBlockNumPerBatch_;
liqInfo.pageAttentionFlag = (kLayout_ == DataLayout::PA_BSND);
liqInfo.sparseMode = *opParamInfo_.sparseMode;
liqInfo.sparseCount = *opParamInfo_.sparseCount;
liqInfo.inputQLayout = qLayout_;
liqInfo.inputKLayout = kLayout_;
}
ge::graphStatus LIQInfoParser::ParseAndCheck(LIQTilingInfo &liqInfo)
{
if (ge::GRAPH_SUCCESS != GetOpName() || ge::GRAPH_SUCCESS != GetNpuInfo() || ge::GRAPH_SUCCESS != GetOpParaInfo() ||
ge::GRAPH_SUCCESS != CheckRequiredParaExistence()) {
return ge::GRAPH_FAILED;
}
if (ge::GRAPH_SUCCESS != GetAndCheckInOutDataType() || ge::GRAPH_SUCCESS != GetQueryKeyAndOutLayout() ||
ge::GRAPH_SUCCESS != GetAndCheckOptionalInput()) {
return ge::GRAPH_FAILED;
}
if (ge::GRAPH_SUCCESS != CheckShapeDim() || ge::GRAPH_SUCCESS != GetN1Size() ||
ge::GRAPH_SUCCESS != GetAndCheckN2Size() || ge::GRAPH_SUCCESS != GetGSize()) {
return ge::GRAPH_FAILED;
}
if (ge::GRAPH_SUCCESS != GetBatchSize() || ge::GRAPH_SUCCESS != GetS1Size() || ge::GRAPH_SUCCESS != GetHeadDim() ||
ge::GRAPH_SUCCESS != GetS2Size()) {
return ge::GRAPH_FAILED;
}
if (ge::GRAPH_SUCCESS != ValidateInputShapesMatch() || ge::GRAPH_SUCCESS != CheckScaleShape()) {
return ge::GRAPH_FAILED;
}
GenerateInfo(liqInfo);
return ge::GRAPH_SUCCESS;
}
// --------------------------TilingPrepare函数定义-------------------------------------
static ge::graphStatus TilingPrepareForLightningIndexerQuant(gert::TilingParseContext * /* context */)
{
return ge::GRAPH_SUCCESS;
}
// --------------------------LightningIndexerQuantTiling类成员函数定义-----------------------
ge::graphStatus LightningIndexerQuantTiling::DoTiling(LIQTilingInfo *tilingInfo)
{
// -------------set blockdim-----------------
auto ascendcPlatform = platform_ascendc::PlatformAscendC(tilingInfo->platformInfo);
uint32_t aivNum = ascendcPlatform.GetCoreNumAiv();
uint32_t aicNum = ascendcPlatform.GetCoreNumAic();
uint32_t blockDim = ascendcPlatform.CalcTschBlockDim(aivNum, aicNum, aivNum);
context_->SetBlockDim(blockDim);
// -------------set workspacesize-----------------
constexpr uint32_t MM1_RES_ELEM_SIZE = 4; // 4: fp32
constexpr uint32_t DOUBLE_BUFFER = 2; // 双Buffer
constexpr uint32_t M_BASE_SIZE = 512; // m轴基本块大小
constexpr uint32_t S2_BASE_SIZE = 512; // S2轴基本块大小
constexpr uint32_t V1_RES_ELEM_SIZE = 4; // 4: int32
constexpr uint32_t V1_RES_ELEM_TYPE = 2; // 保留Index和Value 2种数据
constexpr uint32_t V1_DECODE_PARAM_ELEM_SIZE = 8; // 8: int64
constexpr uint32_t V1_DECODE_PARAM_NUM = 16; // Decode参数个数
constexpr uint32_t V1_DECODE_DATA_NUM = 2; // Decode每个核需要存储头和尾部两块数据
constexpr uint32_t S1_BASE_SIZE = 8; // S1轴基本块的大小
constexpr uint32_t TOPK_MAX_SIZE = 2048; // TopK选取个数
uint32_t workspaceSize = ascendcPlatform.GetLibApiWorkSpaceSize();
// 主流程需Workspace大小
uint32_t mm1ResSize = M_BASE_SIZE * S2_BASE_SIZE;
workspaceSize += mm1ResSize * MM1_RES_ELEM_SIZE * DOUBLE_BUFFER * aicNum;
// Decode流程(LD)需要Workspace大小
// 临时存储Decode中间结果大小: 2(头/尾)*8(s1Base)*2(idx/value)*2048(K)*sizeof(int32)*24=6M
workspaceSize += V1_DECODE_DATA_NUM * S1_BASE_SIZE * V1_RES_ELEM_TYPE * TOPK_MAX_SIZE * V1_RES_ELEM_SIZE * aicNum;
// 临时存储Decode中间参数信息大小: 2(头/尾)*8(s1Base)*16(paramNum)*sizeof(int64_t)*24=48k
workspaceSize += V1_DECODE_DATA_NUM * S1_BASE_SIZE * V1_DECODE_PARAM_NUM * V1_DECODE_PARAM_ELEM_SIZE * aicNum;
size_t *workSpaces = context_->GetWorkspaceSizes(1);
workSpaces[0] = workspaceSize;
// -------------set tilingdata-----------------
tilingData_.set_bSize(tilingInfo->bSize);
tilingData_.set_s2Size(tilingInfo->s2Size);
tilingData_.set_s1Size(tilingInfo->s1Size);
tilingData_.set_sparseCount(tilingInfo->sparseCount);
tilingData_.set_gSize(tilingInfo->gSize);
tilingData_.set_blockSize(tilingInfo->blockSize);
tilingData_.set_maxBlockNumPerBatch(tilingInfo->maxBlockNumPerBatch);
tilingData_.set_sparseMode(tilingInfo->sparseMode);
tilingData_.set_usedCoreNum(blockDim);
tilingData_.SaveToBuffer(context_->GetRawTilingData()->GetData(), context_->GetRawTilingData()->GetCapacity());
context_->GetRawTilingData()->SetDataSize(tilingData_.GetDataSize());
// -------------set tilingkey-----------------
// DT_Q, DT_KV, DT_OUT, PAGE_ATTENTION, FLASH_DECODE, LAYOUT_T, KV_LAYOUT_T
uint32_t inputQType = static_cast<uint32_t>(tilingInfo->inputQType);
uint32_t inputKType = static_cast<uint32_t>(tilingInfo->inputKType);
uint32_t outputType = static_cast<uint32_t>(tilingInfo->outputType);
uint32_t pageAttentionFlag = static_cast<uint32_t>(tilingInfo->pageAttentionFlag);
uint32_t inputQLayout = static_cast<uint32_t>(tilingInfo->inputQLayout);
uint32_t inputKLayout = static_cast<uint32_t>(tilingInfo->inputKLayout);
uint32_t tilingKey =
GET_TPL_TILING_KEY(inputQType, inputKType, outputType, pageAttentionFlag, inputQLayout, inputKLayout);
context_->SetTilingKey(tilingKey);
return ge::GRAPH_SUCCESS;
}
// --------------------------Tiling函数定义---------------------------
ge::graphStatus TilingForLightningIndexerQuant(gert::TilingContext *context)
{
OPS_ERR_IF(context == nullptr, OPS_REPORT_VECTOR_INNER_ERR("LightningIndexerQuant", "Tiling context is null."),
return ge::GRAPH_FAILED);
LIQTilingInfo liqInfo;
LIQInfoParser LIQInfoParser(context);
if (LIQInfoParser.ParseAndCheck(liqInfo) != ge::GRAPH_SUCCESS) {
return ge::GRAPH_FAILED;
}
LightningIndexerQuantTiling liqTiling(context);
return liqTiling.DoTiling(&liqInfo);
}
// --------------------------Tiling及函数TilingPrepare函数注册--------
IMPL_OP_OPTILING(LightningIndexerQuant)
.Tiling(TilingForLightningIndexerQuant)
.TilingParse<LIQCompileInfo>(TilingPrepareForLightningIndexerQuant);
} // namespace optiling

234
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@@ -1,234 +0,0 @@
/**
* This program is free software, you can redistribute it and/or modify it.
* Copyright (c) 2025 Huawei Technologies Co., Ltd.
* This file is a part of the CANN Open Software.
* Licensed under CANN Open Software License Agreement Version 2.0 (the "License").
* Please refer to the License for details. You may not use this file except in compliance with the License.
* THIS SOFTWARE IS PROVIDED ON AN "AS IS" BASIS, WITHOUT WARRANTIES OF ANY KIND, EITHER EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO NON-INFRINGEMENT, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE.
* See LICENSE in the root of the software repository for the full text of the License.
*/
/*!
* \file lightning_indexer_quant_tiling.h
* \brief
*/
#ifndef LIGHTNING_INDEXER_QUANT_TILING_H_
#define LIGHTNING_INDEXER_QUANT_TILING_H_
#include "error/ops_error.h"
#include "exe_graph/runtime/tiling_context.h"
#include "platform/platform_info.h"
#include "register/op_def_registry.h"
#include "register/tilingdata_base.h"
#include "tiling/platform/platform_ascendc.h"
#include "tiling/tiling_api.h"
namespace optiling {
// ------------------公共定义--------------------------
struct TilingRequiredParaInfo {
const gert::CompileTimeTensorDesc *desc;
const gert::StorageShape *shape;
};
struct TilingOptionalParaInfo {
const gert::CompileTimeTensorDesc *desc;
const gert::Tensor *tensor;
};
enum class DataLayout : uint32_t {
BSND = 0,
TND = 1,
PA_BSND = 2
};
// ------------------算子原型索引常量定义----------------
// Inputs Index
constexpr uint32_t QUERY_INDEX = 0;
constexpr uint32_t KEY_INDEX = 1;
constexpr uint32_t WEIGTHS_INDEX = 2;
constexpr uint32_t QUERY_DEQUANT_SCALE_INDEX = 3;
constexpr uint32_t KEY_DEQUANT_SCALE_INDEX = 4;
constexpr uint32_t ACTUAL_SEQ_Q_INDEX = 5;
constexpr uint32_t ACTUAL_SEQ_K_INDEX = 6;
constexpr uint32_t BLOCK_TABLE_INDEX = 7;
constexpr uint32_t LIGHTNING_INDEXER_QUANT = 0;
// Attributes Index
constexpr uint32_t ATTR_QUERY_QUANT_MODE_INDEX = 0;
constexpr uint32_t ATTR_KEY_QUANT_MODE_INDEX = 1;
constexpr uint32_t ATTR_QUERY_LAYOUT_INDEX = 2;
constexpr uint32_t ATTR_KEY_LAYOUT_INDEX = 3;
constexpr uint32_t ATTR_SPARSE_COUNT_INDEX = 4;
constexpr uint32_t ATTR_SPARSE_MODE_INDEX = 5;
// Dim Index
constexpr uint32_t DIM_IDX_ZERO = 0;
constexpr uint32_t DIM_IDX_ONE = 1;
constexpr uint32_t DIM_IDX_TWO = 2;
constexpr uint32_t DIM_IDX_THREE = 3;
// Dim Num
constexpr uint32_t DIM_NUM_TWO = 2;
constexpr uint32_t DIM_NUM_THREE = 3;
constexpr uint32_t DIM_NUM_FOUR = 4;
// 入参限制常量
constexpr uint32_t HEAD_DIM_LIMIT = 128;
constexpr uint32_t SPARSE_LIMIT = 2048;
constexpr uint32_t G_SIZE_LIMIT = 64;
constexpr uint32_t BLOCK_SIZE_LIMIT = 1024;
constexpr uint32_t BLOCK_SIZE_FACTOR = 16;
constexpr uint32_t SPARSE_MODE_LOWER = 3;
// -----------算子TilingData定义---------------
BEGIN_TILING_DATA_DEF(LIQTilingData)
TILING_DATA_FIELD_DEF(uint32_t, bSize)
TILING_DATA_FIELD_DEF(uint32_t, n2Size)
TILING_DATA_FIELD_DEF(uint32_t, gSize)
TILING_DATA_FIELD_DEF(uint32_t, s1Size)
TILING_DATA_FIELD_DEF(uint32_t, s2Size)
TILING_DATA_FIELD_DEF(uint32_t, sparseCount)
TILING_DATA_FIELD_DEF(uint32_t, usedCoreNum)
TILING_DATA_FIELD_DEF(uint32_t, blockSize)
TILING_DATA_FIELD_DEF(uint32_t, maxBlockNumPerBatch)
TILING_DATA_FIELD_DEF(uint32_t, sparseMode)
END_TILING_DATA_DEF
REGISTER_TILING_DATA_CLASS(LightningIndexerQuant, LIQTilingData)
// -----------算子CompileInfo定义-------------------
struct LIQCompileInfo {};
// -----------算子Tiling入参结构体定义---------------
struct LIQParaInfo {
TilingRequiredParaInfo query = {nullptr, nullptr};
TilingRequiredParaInfo key = {nullptr, nullptr};
TilingRequiredParaInfo weights = {nullptr, nullptr};
TilingRequiredParaInfo query_dequant_scale = {nullptr, nullptr};
TilingRequiredParaInfo key_dequant_scale = {nullptr, nullptr};
TilingOptionalParaInfo actualSeqLengthsQ = {nullptr, nullptr};
TilingOptionalParaInfo actualSeqLengthsK = {nullptr, nullptr};
TilingOptionalParaInfo blockTable = {nullptr, nullptr};
TilingRequiredParaInfo attenOut = {nullptr, nullptr};
const int32_t *queryQuantMode = nullptr;
const int32_t *keyQuantMode = nullptr;
const char *layOutQuery = nullptr;
const char *layOutKey = nullptr;
const int32_t *blockSize = nullptr;
const int32_t *sparseMode = nullptr;
const int32_t *sparseCount = nullptr;
};
// -----------算子Tiling入参信息类---------------
class LIQTilingInfo {
public:
const char *opName = nullptr;
fe::PlatFormInfos *platformInfo = nullptr;
LIQParaInfo opParamInfo;
// Base Param
platform_ascendc::SocVersion socVersion = platform_ascendc::SocVersion::ASCEND910B;
uint32_t bSize = 0;
uint32_t n1Size = 0;
uint32_t n2Size = 0;
uint32_t s1Size = 0;
int64_t s2Size = 0;
uint32_t qkHeadDim = 0;
uint32_t gSize = 0;
// PageAttention
bool pageAttentionFlag = false;
int32_t blockSize = 0;
uint32_t maxBlockNumPerBatch = 0;
// Mask
int32_t sparseMode = 0;
// Others Flag
uint32_t sparseCount = 0;
// DType
ge::DataType inputQType = ge::DT_FLOAT16;
ge::DataType inputKType = ge::DT_FLOAT16;
ge::DataType outputType = ge::DT_INT32;
// Layout
DataLayout inputQLayout = DataLayout::BSND;
DataLayout inputKLayout = DataLayout::PA_BSND;
};
// -----------算子Tiling入参信息解析及Check类---------------
class LIQInfoParser {
public:
explicit LIQInfoParser(gert::TilingContext *context) : context_(context) {}
~LIQInfoParser() = default;
ge::graphStatus CheckRequiredInOutExistence() const;
ge::graphStatus CheckRequiredAttrExistence() const;
ge::graphStatus CheckRequiredParaExistence() const;
ge::graphStatus GetActualSeqLenSize(uint32_t &size, const gert::Tensor *tensor,
const std::string &actualSeqLenName);
ge::graphStatus GetOpName();
ge::graphStatus GetNpuInfo();
void GetOptionalInputParaInfo();
void GetInputParaInfo();
void GetOutputParaInfo();
ge::graphStatus GetAttrParaInfo();
ge::graphStatus CheckAttrParaInfo();
ge::graphStatus GetOpParaInfo();
ge::graphStatus ValidateInputShapesMatch();
ge::graphStatus CheckScaleShape();
ge::graphStatus GetAndCheckInOutDataType();
ge::graphStatus GetBatchSize();
ge::graphStatus GetHeadDim();
ge::graphStatus GetS1Size();
ge::graphStatus GetAndCheckOptionalInput();
ge::graphStatus CheckShapeDim();
ge::graphStatus GetAndCheckBlockSize();
ge::graphStatus GetS2SizeForPageAttention();
ge::graphStatus GetS2SizeForBatchContinuous();
ge::graphStatus GetS2Size();
ge::graphStatus GetQueryKeyAndOutLayout();
ge::graphStatus GetN1Size();
ge::graphStatus GetAndCheckN2Size();
ge::graphStatus GetGSize();
ge::graphStatus GetAttenMaskInfo();
ge::graphStatus GetActualSeqInfo();
void GenerateInfo(LIQTilingInfo &liqInfo);
ge::graphStatus ParseAndCheck(LIQTilingInfo &liqInfo);
public:
gert::TilingContext *context_ = nullptr;
const char *opName_;
fe::PlatFormInfos *platformInfo_;
LIQParaInfo opParamInfo_;
// BaseParams
uint32_t bSize_ = 0;
uint32_t n1Size_ = 0;
uint32_t n2Size_ = 0;
uint32_t gSize_ = 0;
uint32_t s1Size_ = 0;
int64_t s2Size_ = 0;
uint32_t headDim_ = 0;
// Layout
DataLayout qLayout_ = DataLayout::BSND;
DataLayout kLayout_ = DataLayout::PA_BSND;
// PageAttention
uint32_t maxBlockNumPerBatch_ = 0;
int32_t blockSize_ = 0;
platform_ascendc::SocVersion socVersion_ = platform_ascendc::SocVersion::ASCEND910B;
ge::DataType inputQType_ = ge::DT_FLOAT16;
ge::DataType inputKType_ = ge::DT_FLOAT16;
ge::DataType weightsType_ = ge::DT_FLOAT16;
ge::DataType inputQueryScaleType_ = ge::DT_FLOAT16;
ge::DataType inputKeyScaleType_ = ge::DT_FLOAT16;
ge::DataType blockTableType_ = ge::DT_FLOAT16;
ge::DataType inputKRopeType_ = ge::DT_FLOAT16;
ge::DataType outputType_ = ge::DT_FLOAT16;
};
// ---------------算子Tiling类---------------
class LightningIndexerQuantTiling {
public:
explicit LightningIndexerQuantTiling(gert::TilingContext *context) : context_(context) {};
ge::graphStatus DoTiling(LIQTilingInfo *tilingInfo);
private:
gert::TilingContext *context_ = nullptr;
LIQTilingData tilingData_;
};
} // namespace optiling
#endif // LIGHTNING_INDEXER_QUANT_TILING_H_

50
csrc/lightning_indexer_quant/op_kernel/lightning_indexer_quant.cpp
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@@ -1,50 +0,0 @@
/**
* This program is free software, you can redistribute it and/or modify it.
* Copyright (c) 2025 Huawei Technologies Co., Ltd.
* This file is a part of the CANN Open Software.
* Licensed under CANN Open Software License Agreement Version 2.0 (the "License").
* Please refer to the License for details. You may not use this file except in compliance with the License.
* THIS SOFTWARE IS PROVIDED ON AN "AS IS" BASIS, WITHOUT WARRANTIES OF ANY KIND, EITHER EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO NON-INFRINGEMENT, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE.
* See LICENSE in the root of the software repository for the full text of the License.
*/
/*!
* \file lightning_indexer_quant.cpp
* \brief
*/
#include "kernel_operator.h"
#include "lib/matmul_intf.h"
#include "lightning_indexer_quant_kernel.h"
#include "lightning_indexer_quant_template_tiling_key.h"
using namespace LIQKernel;
#define INVOKE_LI_NO_KFC_OP_IMPL(templateClass, ...) \
do { \
templateClass<LIQType<__VA_ARGS__>> op; \
GET_TILING_DATA_WITH_STRUCT(LIQTilingData, tiling_data_in, tiling); \
const LIQTilingData *__restrict tiling_data = &tiling_data_in; \
op.Init(query, key, weights, queryScale, keyScale, actualSeqLengthsQ, actualSeqLengthsK, blocktable, \
sparseIndices, user, tiling_data, &tPipe); \
op.Process(); \
} while (0)
template <int DT_Q, int DT_K, int DT_OUT, int PAGE_ATTENTION, int Q_LAYOUT_T, int K_LAYOUT_T>
__global__ __aicore__ void lightning_indexer_quant(__gm__ uint8_t *query, __gm__ uint8_t *key, __gm__ uint8_t *weights,
__gm__ uint8_t *queryScale, __gm__ uint8_t *keyScale,
__gm__ uint8_t *actualSeqLengthsQ, __gm__ uint8_t *actualSeqLengthsK,
__gm__ uint8_t *blocktable, __gm__ uint8_t *sparseIndices,
__gm__ uint8_t *workspace, __gm__ uint8_t *tiling)
{
#if (__CCE_AICORE__ == 310) || (defined __DAV_310R6__) || (__CCE_AICORE__ == 200)
#else
TPipe tPipe;
__gm__ uint8_t *user = GetUserWorkspace(workspace);
KERNEL_TASK_TYPE_DEFAULT(KERNEL_TYPE_MIX_AIC_1_2);
INVOKE_LI_NO_KFC_OP_IMPL(LIQPreload, int8_t, int8_t, int32_t,
PAGE_ATTENTION, LI_LAYOUT(Q_LAYOUT_T), LI_LAYOUT(K_LAYOUT_T));
#endif
}

146
csrc/lightning_indexer_quant/op_kernel/lightning_indexer_quant_common.h
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@@ -1,146 +0,0 @@
/**
* This program is free software, you can redistribute it and/or modify it.
* Copyright (c) 2025 Huawei Technologies Co., Ltd.
* This file is a part of the CANN Open Software.
* Licensed under CANN Open Software License Agreement Version 2.0 (the "License").
* Please refer to the License for details. You may not use this file except in compliance with the License.
* THIS SOFTWARE IS PROVIDED ON AN "AS IS" BASIS, WITHOUT WARRANTIES OF ANY KIND, EITHER EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO NON-INFRINGEMENT, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE.
* See LICENSE in the root of the software repository for the full text of the License.
*/
/*!
* \file lightning_indexer_quant_common.h
* \brief
*/
#ifndef LIGHTNING_INDEXER_QUANT_COMMON_H
#define LIGHTNING_INDEXER_QUANT_COMMON_H
namespace LIQCommon {
// 与tiling的layout保持一致
enum class LI_LAYOUT : uint32_t {
BSND = 0,
TND = 1,
PA_BSND = 2
};
template <typename Q_T, typename K_T, typename OUT_T, const bool PAGE_ATTENTION = false,
LI_LAYOUT Q_LAYOUT_T = LI_LAYOUT::BSND, LI_LAYOUT K_LAYOUT_T = LI_LAYOUT::PA_BSND, typename... Args>
struct LIQType {
using queryType = Q_T;
using keyType = K_T;
using outputType = OUT_T;
static constexpr bool pageAttention = PAGE_ATTENTION;
static constexpr LI_LAYOUT layout = Q_LAYOUT_T;
static constexpr LI_LAYOUT keyLayout = K_LAYOUT_T;
};
struct RunInfo {
uint32_t loop;
uint32_t bN2Idx;
uint32_t bIdx;
uint32_t n2Idx = 0;
uint32_t gS1Idx;
uint32_t s2Idx;
uint32_t actS1Size = 1;
uint32_t actS2Size = 1;
uint32_t actMBaseSize;
uint32_t actualSingleProcessSInnerSize;
uint32_t actualSingleProcessSInnerSizeAlign;
uint64_t tensorQueryOffset;
uint64_t tensorKeyOffset;
uint64_t tensorKeyScaleOffset;
uint64_t tensorWeightsOffset;
uint64_t indiceOutOffset;
bool isFirstS2InnerLoop;
bool isLastS2InnerLoop;
bool isAllLoopEnd = false;
bool isValid = false;
};
struct ConstInfo {
// CUBE与VEC核间同步的模式
static constexpr uint32_t FIA_SYNC_MODE2 = 2;
// BUFFER的字节数
static constexpr uint32_t BUFFER_SIZE_BYTE_32B = 32;
static constexpr uint32_t BUFFER_SIZE_BYTE_64B = 64;
static constexpr uint32_t BUFFER_SIZE_BYTE_256B = 256;
static constexpr uint32_t BUFFER_SIZE_BYTE_512B = 512;
static constexpr uint32_t BUFFER_SIZE_BYTE_1K = 1024;
static constexpr uint32_t BUFFER_SIZE_BYTE_2K = 2048;
static constexpr uint32_t BUFFER_SIZE_BYTE_4K = 4096;
static constexpr uint32_t BUFFER_SIZE_BYTE_8K = 8192;
static constexpr uint32_t BUFFER_SIZE_BYTE_16K = 16384;
static constexpr uint32_t BUFFER_SIZE_BYTE_32K = 32768;
// 无效索引
static constexpr int INVALID_IDX = -1;
// CUBE和VEC的核间同步EventID
uint32_t syncC1V1 = 0U;
uint32_t syncC1V0 = 2U;
uint32_t syncV1C1 = 0U;
uint32_t syncV0C1 = 1U;
// 基本块大小
uint32_t mBaseSize = 1ULL;
uint32_t s1BaseSize = 1ULL;
uint32_t s2BaseSize = 1ULL;
uint64_t batchSize = 0ULL;
uint64_t gSize = 0ULL;
uint64_t qHeadNum = 0ULL;
uint64_t kHeadNum;
uint64_t headDim;
uint64_t sparseCount; // topK选取大小
uint64_t kSeqSize = 0ULL; // kv最大S长度
uint64_t qSeqSize = 1ULL; // q最大S长度
uint32_t kCacheBlockSize = 0; // PA场景的block size
uint32_t maxBlockNumPerBatch = 0; // PA场景的最大单batch block number
LI_LAYOUT outputLayout; // 输出的格式
bool attenMaskFlag = false;
uint32_t actualLenQDims = 0U; // query的actualSeqLength 的维度
uint32_t actualLenDims = 0U; // KV 的actualSeqLength 的维度
bool isAccumSeqS1 = false; // 是否累加模式
bool isAccumSeqS2 = false; // 是否累加模式
};
struct SplitCoreInfo {
uint32_t s2Start = 0U; // S2的起始位置
uint32_t s2End = 0U; // S2循环index上限
uint32_t bN2Start = 0U;
uint32_t bN2End = 0U;
uint32_t gS1Start = 0U;
uint32_t gS1End = 0U;
bool isLD = false; // 当前核是否需要进行Decode归约任务
};
template <typename T>
__aicore__ inline T Align(T num, T rnd)
{
return (((rnd) == 0) ? 0 : (((num) + (rnd)-1) / (rnd) * (rnd)));
}
template <typename T1, typename T2>
__aicore__ inline T1 Min(T1 a, T2 b)
{
return (a > b) ? (b) : (a);
}
template <typename T1, typename T2>
__aicore__ inline T1 Max(T1 a, T2 b)
{
return (a > b) ? (a) : (b);
}
template <typename T>
__aicore__ inline T CeilDiv(T num, T rnd)
{
return (((rnd) == 0) ? 0 : (((num) + (rnd)-1) / (rnd)));
}
} // namespace LIQCommon
#endif // LIGHTNING_INDEXER_QUANT_COMMON_H

714
csrc/lightning_indexer_quant/op_kernel/lightning_indexer_quant_kernel.h
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@@ -1,714 +0,0 @@
/**
* This program is free software, you can redistribute it and/or modify it.
* Copyright (c) 2025 Huawei Technologies Co., Ltd.
* This file is a part of the CANN Open Software.
* Licensed under CANN Open Software License Agreement Version 2.0 (the "License").
* Please refer to the License for details. You may not use this file except in compliance with the License.
* THIS SOFTWARE IS PROVIDED ON AN "AS IS" BASIS, WITHOUT WARRANTIES OF ANY KIND, EITHER EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO NON-INFRINGEMENT, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE.
* See LICENSE in the root of the software repository for the full text of the License.
*/
/*!
* \file lightning_indexer_quant_kernel.h
* \brief
*/
#ifndef LIGHTNING_INDEXER_QUANT_KERNEL_H
#define LIGHTNING_INDEXER_QUANT_KERNEL_H
#include "kernel_operator.h"
#include "kernel_operator_list_tensor_intf.h"
#include "kernel_tiling/kernel_tiling.h"
#include "lib/matmul_intf.h"
#include "lib/matrix/matmul/tiling.h"
#include "lightning_indexer_quant_common.h"
#include "lightning_indexer_quant_service_vector.h"
#include "lightning_indexer_quant_service_cube.h"
namespace LIQKernel {
using namespace LIQCommon;
using namespace LIQServiceVec;
using namespace matmul;
using AscendC::CacheMode;
using AscendC::CrossCoreSetFlag;
using AscendC::CrossCoreWaitFlag;
// 由于S2循环前，RunInfo还没有赋值，使用TempLoopInfo临时存放B、N、S1轴相关的信息；同时减少重复计算
struct TempLoopInfo {
uint32_t bN2Idx = 0;
uint32_t bIdx = 0U;
uint32_t n2Idx = 0U;
uint32_t gS1Idx = 0U;
uint32_t gS1LoopEnd = 0U; // gS1方向循环的结束Idx
uint32_t s2LoopEnd = 0U; // S2方向循环的结束Idx
uint32_t actS1Size = 1ULL; // 当前Batch循环处理的S1轴的实际大小
uint32_t actS2Size = 0ULL;
bool curActSeqLenIsZero = false;
bool needDealActS1LessThanS1 = false; // S1的实际长度小于shape的S1长度时，是否需要清理输出
uint32_t actMBaseSize = 0U; // m轴(gS1)方向实际大小
uint32_t mBasicSizeTail = 0U; // gS1方向循环的尾基本块大小
uint32_t s2BasicSizeTail = 0U; // S2方向循环的尾基本块大小
};
template <typename LIQT>
class LIQPreload {
public:
__aicore__ inline LIQPreload(){};
__aicore__ inline void Init(__gm__ uint8_t *query, __gm__ uint8_t *key, __gm__ uint8_t *weights,
__gm__ uint8_t *queryScale, __gm__ uint8_t *keyScale, __gm__ uint8_t *actualSeqLengthsQ,
__gm__ uint8_t *actualSeqLengthsK, __gm__ uint8_t *blockTable,
__gm__ uint8_t *sparseIndices, __gm__ uint8_t *workspace,
const LIQTilingData *__restrict tiling, TPipe *tPipe);
__aicore__ inline void Process();
// =================================类型定义区=================================
using Q_T = typename LIQT::queryType;
using K_T = typename LIQT::keyType;
using OUT_T = typename LIQT::outputType;
static constexpr bool PAGE_ATTENTION = LIQT::pageAttention;
static constexpr LI_LAYOUT Q_LAYOUT_T = LIQT::layout;
static constexpr LI_LAYOUT K_LAYOUT_T = LIQT::keyLayout;
using MM1_OUT_T = float;
LIQMatmul<LIQT> matmulService;
LIQVector<LIQT> vectorService;
// =================================常量区=================================
static constexpr uint32_t SYNC_C1_V1_FLAG = 4;
static constexpr uint32_t SYNC_V1_C1_FLAG = 5;
static constexpr uint32_t M_BASE_SIZE = 256;
static constexpr uint32_t S2_BASE_SIZE = 2048;
static constexpr uint32_t HEAD_DIM = 128;
static constexpr uint32_t K_HEAD_NUM = 1;
static constexpr uint32_t GM_ALIGN_BYTES = 512;
static constexpr uint32_t LI_QUANT_PRELOAD_TASK_CACHE_SIZE = 2;
static constexpr int64_t LD_PREFETCH_LEN = 2;
// for workspace double
static constexpr uint32_t WS_DOBULE = 2;
protected:
TPipe *pipe = nullptr;
// offset
uint64_t queryCoreOffset = 0ULL;
uint64_t keyCoreOffset = 0ULL;
uint64_t keyScaleCoreOffset = 0ULL;
uint64_t weightsCoreOffset = 0ULL;
uint64_t indiceOutCoreOffset = 0ULL;
// ================================Global Buffer区=================================
GlobalTensor<Q_T> queryGm;
GlobalTensor<K_T> keyGm;
GlobalTensor<half> weightsGm;
GlobalTensor<int32_t> indiceOutGm;
GlobalTensor<int32_t> blockTableGm;
GlobalTensor<uint32_t> actualSeqLengthsGmQ;
GlobalTensor<uint32_t> actualSeqLengthsGm;
// ================================类成员变量====================================
// aic、aiv核信息
uint32_t tmpBlockIdx = 0U;
uint32_t aiCoreIdx = 0U;
uint32_t usedCoreNum = 0U;
LIQCommon::ConstInfo constInfo{};
TempLoopInfo tempLoopInfo{};
LIQCommon::SplitCoreInfo splitCoreInfo{};
// ================================Init functions==================================
__aicore__ inline void InitTilingData(const LIQTilingData *__restrict tilingData);
__aicore__ inline void InitBuffers();
__aicore__ inline void InitActualSeqLen(__gm__ uint8_t *actualSeqLengthsQ, __gm__ uint8_t *actualSeqLengthsK);
// ================================Split Core================================
__aicore__ inline void SplitCore(uint32_t curCoreIdx, uint32_t &coreNum, LIQCommon::SplitCoreInfo &info);
__aicore__ inline uint32_t GetS2BaseBlockNumOnMask(uint32_t s1gIdx, uint32_t actS1Size, uint32_t actS2Size);
__aicore__ inline uint32_t GetTotalBaseBlockNum();
// ================================Process functions================================
__aicore__ inline void ProcessMain();
__aicore__ inline void ProcessBaseBlock(uint32_t loop, uint64_t s2LoopIdx,
LIQCommon::RunInfo runInfo[LI_QUANT_PRELOAD_TASK_CACHE_SIZE]);
__aicore__ inline void ProcessDecode();
__aicore__ inline void ProcessInvalid();
// ================================Params Calc=====================================
__aicore__ inline void CalcGS1LoopParams(uint32_t bN2Idx);
__aicore__ inline void GetBN2Idx(uint32_t bN2Idx);
__aicore__ inline uint32_t GetActualSeqLen(uint32_t bIdx, uint32_t actualLenDims, bool isAccumSeq,
GlobalTensor<uint32_t> &actualSeqLengthsGm, uint32_t defaultSeqLen);
__aicore__ inline void GetS1S2ActualSeqLen(uint32_t bIdx, uint32_t &actS1Size, uint32_t &actS2Size);
__aicore__ inline void CalcS2LoopParams(uint32_t bN2LoopIdx, uint32_t gS1LoopIdx);
__aicore__ inline void CalcRunInfo(uint32_t loop, uint32_t s2LoopIdx, LIQCommon::RunInfo &runInfo);
__aicore__ inline void DealActSeqLenIsZero(uint32_t bIdx, uint32_t n2Idx, uint32_t s1Start);
};
template <typename LIQT>
__aicore__ inline void LIQPreload<LIQT>::InitTilingData(const LIQTilingData *__restrict tilingData)
{
usedCoreNum = tilingData->usedCoreNum;
constInfo.batchSize = tilingData->bSize;
constInfo.qHeadNum = constInfo.gSize = tilingData->gSize;
constInfo.kSeqSize = tilingData->s2Size;
constInfo.qSeqSize = tilingData->s1Size;
constInfo.attenMaskFlag = (tilingData->sparseMode == 3);
constInfo.kCacheBlockSize = tilingData->blockSize;
constInfo.maxBlockNumPerBatch = tilingData->maxBlockNumPerBatch;
constInfo.sparseCount = tilingData->sparseCount;
constInfo.outputLayout = Q_LAYOUT_T; // 输出和输入形状一致
if (Q_LAYOUT_T == LI_LAYOUT::TND) {
constInfo.isAccumSeqS1 = true;
}
if (K_LAYOUT_T == LI_LAYOUT::TND) {
constInfo.isAccumSeqS2 = true;
}
constInfo.kHeadNum = K_HEAD_NUM;
constInfo.headDim = HEAD_DIM;
constInfo.mBaseSize = M_BASE_SIZE;
constInfo.s2BaseSize = S2_BASE_SIZE;
constInfo.s1BaseSize = (constInfo.mBaseSize + constInfo.gSize - 1) / constInfo.gSize;
}
template <typename LIQT>
__aicore__ inline void LIQPreload<LIQT>::InitBuffers()
{
if ASCEND_IS_AIV {
vectorService.InitBuffers(pipe);
} else {
matmulService.InitBuffers(pipe);
}
}
template <typename LIQT>
__aicore__ inline void LIQPreload<LIQT>::InitActualSeqLen(__gm__ uint8_t *actualSeqLengthsQ,
__gm__ uint8_t *actualSeqLengthsK)
{
if (actualSeqLengthsQ == nullptr) {
constInfo.actualLenQDims = 0;
} else {
constInfo.actualLenQDims = constInfo.batchSize;
actualSeqLengthsGmQ.SetGlobalBuffer((__gm__ uint32_t *)actualSeqLengthsQ, constInfo.actualLenQDims);
}
if (actualSeqLengthsK == nullptr) {
constInfo.actualLenDims = 0;
} else {
constInfo.actualLenDims = constInfo.batchSize;
actualSeqLengthsGm.SetGlobalBuffer((__gm__ uint32_t *)actualSeqLengthsK, constInfo.actualLenDims);
}
}
template <typename LIQT>
__aicore__ inline uint32_t LIQPreload<LIQT>::GetActualSeqLen(uint32_t bIdx, uint32_t actualLenDims, bool isAccumSeq,
GlobalTensor<uint32_t> &actualSeqLengthsGm,
uint32_t defaultSeqLen)
{
if (actualLenDims == 0) {
return defaultSeqLen;
} else if (isAccumSeq && bIdx > 0) {
return actualSeqLengthsGm.GetValue(bIdx) - actualSeqLengthsGm.GetValue(bIdx - 1);
} else {
return actualSeqLengthsGm.GetValue(bIdx);
}
}
template <typename LIQT>
__aicore__ inline void LIQPreload<LIQT>::GetS1S2ActualSeqLen(uint32_t bIdx, uint32_t &actS1Size, uint32_t &actS2Size)
{
actS1Size = GetActualSeqLen(bIdx, constInfo.actualLenQDims, constInfo.isAccumSeqS1, actualSeqLengthsGmQ,
constInfo.qSeqSize);
actS2Size =
GetActualSeqLen(bIdx, constInfo.actualLenDims, constInfo.isAccumSeqS2, actualSeqLengthsGm, constInfo.kSeqSize);
}
template <typename LIQT>
__aicore__ inline uint32_t LIQPreload<LIQT>::GetS2BaseBlockNumOnMask(uint32_t s1gIdx, uint32_t actS1Size,
uint32_t actS2Size)
{
if (actS2Size == 0) {
return 0;
}
uint32_t s1Offset = constInfo.s1BaseSize * s1gIdx;
int32_t validS2LenBase = static_cast<int32_t>(actS2Size) - static_cast<int32_t>(actS1Size);
int32_t validS2Len = s1Offset + validS2LenBase + constInfo.s1BaseSize;
validS2Len = Min(validS2Len, static_cast<int32_t>(actS2Size));
validS2Len = Max(validS2Len, 1);
return (validS2Len + constInfo.s2BaseSize - 1) / constInfo.s2BaseSize;
}
template <typename LIQT>
__aicore__ inline uint32_t LIQPreload<LIQT>::GetTotalBaseBlockNum()
{
uint32_t totalBlockNum = 0;
uint32_t actS1Size, actS2Size;
uint32_t s1GBaseNum, s2BaseNum;
for (uint32_t bIdx = 0; bIdx < constInfo.batchSize; bIdx++) {
GetS1S2ActualSeqLen(bIdx, actS1Size, actS2Size);
s1GBaseNum = CeilDiv(actS1Size, constInfo.s1BaseSize);
if (!constInfo.attenMaskFlag) {
s2BaseNum = CeilDiv(actS2Size, constInfo.s2BaseSize);
totalBlockNum += s1GBaseNum * s2BaseNum * constInfo.kHeadNum;
continue;
}
for (uint32_t s1gIdx = 0; s1gIdx < s1GBaseNum; s1gIdx++) {
s2BaseNum = GetS2BaseBlockNumOnMask(s1gIdx, actS1Size, actS2Size);
totalBlockNum += s2BaseNum * constInfo.kHeadNum;
}
}
return totalBlockNum;
}
// 多核版本，双闭区间。基本原则：计算每个核最少处理的块数, 剩余的部分前面的核每个核多处理一块
template <typename LIQT>
__aicore__ void inline LIQPreload<LIQT>::SplitCore(uint32_t curCoreIdx, uint32_t &coreNum,
LIQCommon::SplitCoreInfo &info)
{
uint32_t totalBlockNum = GetTotalBaseBlockNum();
uint32_t minBlockPerCore = totalBlockNum / coreNum;
uint32_t deal1MoreBlockCoreNum = totalBlockNum % coreNum;
uint32_t coreIdx = 0;
uint32_t lastGS1RemainBlockCnt = 0;
uint32_t coreDealBlockCnt = coreIdx < deal1MoreBlockCoreNum ? minBlockPerCore + 1 : minBlockPerCore;
coreNum = minBlockPerCore == 0 ? deal1MoreBlockCoreNum : coreNum;
bool findLastCoreEnd = true;
uint32_t actS1Size, actS2Size;
uint32_t s1GBaseNum, s2BaseNum;
for (uint32_t bN2Idx = 0; bN2Idx < constInfo.batchSize * constInfo.kHeadNum; bN2Idx++) {
uint32_t bIdx = bN2Idx / constInfo.kHeadNum;
if (bN2Idx % constInfo.kHeadNum == 0) {
GetS1S2ActualSeqLen(bIdx, actS1Size, actS2Size);
s1GBaseNum = CeilDiv(actS1Size, constInfo.s1BaseSize);
s2BaseNum = CeilDiv(actS2Size, constInfo.s2BaseSize);
}
if constexpr (Q_LAYOUT_T == LI_LAYOUT::BSND) {
if (findLastCoreEnd && (s1GBaseNum == 0U || s2BaseNum == 0U)) {
info.bN2Start = bN2Idx;
info.gS1Start = 0;
info.s2Start = 0;
findLastCoreEnd = false;
}
}
for (uint32_t gS1Idx = 0; gS1Idx < s1GBaseNum; gS1Idx++) {
if (constInfo.attenMaskFlag) {
s2BaseNum = GetS2BaseBlockNumOnMask(gS1Idx, actS1Size, actS2Size);
}
if (findLastCoreEnd && s2BaseNum == 0U) {
info.bN2Start = bN2Idx;
info.gS1Start = gS1Idx;
info.s2Start = 0;
findLastCoreEnd = false;
}
for (uint32_t s2Idx = 0; s2Idx < s2BaseNum;) {
if (findLastCoreEnd) {
info.bN2Start = bN2Idx;
info.gS1Start = gS1Idx;
info.s2Start = s2Idx;
findLastCoreEnd = false;
}
uint32_t s2RemainBaseNum = s2BaseNum - s2Idx;
if (lastGS1RemainBlockCnt + s2RemainBaseNum >= coreDealBlockCnt) {
info.bN2End = bN2Idx;
info.gS1End = gS1Idx;
info.s2End = s2Idx + coreDealBlockCnt - lastGS1RemainBlockCnt - 1;
if (coreIdx == curCoreIdx) {
// S2被切N核，那么只有第一个核需要处理LD，其他核不用
if (s2Idx == 0 && info.s2End + 1 < s2BaseNum) {
info.isLD = true;
}
// 最后一个核处理的不是最后一个Batch，表明后面的Batch为空块(S2=0), 调整终点坐标以便清理输出
if (coreIdx == coreNum - 1 && info.bN2End != constInfo.batchSize - 1) {
info.bN2End = constInfo.batchSize - 1;
info.gS1End = 0;
info.s2End = 0;
}
return;
}
coreIdx++;
findLastCoreEnd = true;
s2Idx = info.s2End + 1;
lastGS1RemainBlockCnt = 0;
coreDealBlockCnt = coreIdx < deal1MoreBlockCoreNum ? minBlockPerCore + 1 : minBlockPerCore;
} else {
lastGS1RemainBlockCnt += s2RemainBaseNum;
break;
}
}
}
}
}
template <typename LIQT>
__aicore__ inline void LIQPreload<LIQT>::DealActSeqLenIsZero(uint32_t bIdx, uint32_t n2Idx, uint32_t s1Start)
{
if ASCEND_IS_AIV {
if (constInfo.outputLayout == LI_LAYOUT::TND) {
uint32_t tSize = actualSeqLengthsGmQ.GetValue(constInfo.batchSize - 1);
uint32_t tBase = bIdx == 0 ? 0 : actualSeqLengthsGmQ.GetValue(bIdx - 1);
uint32_t s1Count = tempLoopInfo.actS1Size;
for (uint32_t s1Idx = s1Start; s1Idx < s1Count; s1Idx++) {
uint64_t indiceOutOffset =
(tBase + s1Idx) * constInfo.kHeadNum * constInfo.sparseCount + // T轴、s1轴偏移
n2Idx * constInfo.sparseCount; // N2轴偏移
vectorService.CleanInvalidOutput(indiceOutOffset);
}
} else if (constInfo.outputLayout == LI_LAYOUT::BSND) {
for (uint32_t s1Idx = s1Start; s1Idx < constInfo.qSeqSize; s1Idx++) {
// B,S1,N2,K
uint64_t indiceOutOffset = bIdx * constInfo.qSeqSize * constInfo.kHeadNum * constInfo.sparseCount +
s1Idx * constInfo.kHeadNum * constInfo.sparseCount + // B轴、S1轴偏移
n2Idx * constInfo.sparseCount; // N2轴偏移
vectorService.CleanInvalidOutput(indiceOutOffset);
}
}
}
}
template <typename LIQT>
__aicore__ inline void LIQPreload<LIQT>::Init(__gm__ uint8_t *query, __gm__ uint8_t *key, __gm__ uint8_t *weights,
__gm__ uint8_t *queryScale, __gm__ uint8_t *keyScale,
__gm__ uint8_t *actualSeqLengthsQ, __gm__ uint8_t *actualSeqLengthsK,
__gm__ uint8_t *blockTable, __gm__ uint8_t *sparseIndices,
__gm__ uint8_t *workspace, const LIQTilingData *__restrict tiling,
TPipe *tPipe)
{
if ASCEND_IS_AIV {
tmpBlockIdx = GetBlockIdx(); // vec:0-47
aiCoreIdx = tmpBlockIdx / 2;
} else {
tmpBlockIdx = GetBlockIdx(); // cube:0-23
aiCoreIdx = tmpBlockIdx;
}
InitTilingData(tiling);
InitActualSeqLen(actualSeqLengthsQ, actualSeqLengthsK);
// 计算分核
SplitCore(aiCoreIdx, usedCoreNum, splitCoreInfo);
pipe = tPipe;
// workspace 内存排布
// |mm1ResGm(存S)|vec1ResGm(存LD中间结果)|vec1ParamGm(存LD参数)
// |Core0_mm1ResDB0-Core0_mm1ResDB1-Core1_mm1ResDB0....Core23_mm1ResDB0-Core23_mm1ResDB1|Core0_vec1Res...
uint64_t offset = 0;
// mm1开DoubleBuffer
GlobalTensor<MM1_OUT_T> mm1ResGm; // 存放S
uint64_t singleCoreMm1ResSize = WS_DOBULE * constInfo.s1BaseSize * constInfo.s2BaseSize * sizeof(MM1_OUT_T);
mm1ResGm.SetGlobalBuffer((__gm__ MM1_OUT_T *)(workspace + aiCoreIdx * singleCoreMm1ResSize));
offset += GetBlockNum() * singleCoreMm1ResSize;
// ld流程需要ws大小: [aicnum, 2, CeilDiv(constInfo.mBaseSize, constInfo.gSize), topkOut_*2]
// (aic, 8, 2, 2, 2048)
// (aic, s1_cube, 头尾, idx/value, K)
GlobalTensor<float> vec1ResGm; // 存放TopK计算中间结果
vec1ResGm.SetGlobalBuffer((__gm__ float *)(workspace + offset));
offset += GetBlockNum() * constInfo.s1BaseSize * WS_DOBULE * WS_DOBULE * BASE_TOPK * sizeof(float);
// (aic, 8, 2, 16)
// (aic, s1_cube, 头尾，16ele)
GlobalTensor<int64_t> vec1ParamGm; // 存放LD参数信息
vec1ParamGm.SetGlobalBuffer((__gm__ int64_t *)(workspace + offset));
offset += GetBlockNum() * constInfo.s1BaseSize * WS_DOBULE * LD_PARAM_NUM * sizeof(int64_t);
GlobalTensor<half> weightWorkspaceGm; // v1阶段处理w*scale后的结果
uint64_t weightMemSize = BLOCK_CUBE * constInfo.mBaseSize * WS_DOBULE * sizeof(half);
weightWorkspaceGm.SetGlobalBuffer((__gm__ half *)(workspace + offset + aiCoreIdx * weightMemSize));
offset += GetBlockNum() * weightMemSize;
GlobalTensor<half> qScaleGm;
GlobalTensor<half> kScaleGm;
if ASCEND_IS_AIV {
vectorService.InitParams(constInfo, tiling);
indiceOutGm.SetGlobalBuffer((__gm__ int32_t *)sparseIndices);
weightsGm.SetGlobalBuffer((__gm__ half *)weights);
qScaleGm.SetGlobalBuffer((__gm__ half *)queryScale);
kScaleGm.SetGlobalBuffer((__gm__ half *)keyScale);
blockTableGm.SetGlobalBuffer((__gm__ int32_t *)blockTable);
vectorService.InitVecInputTensor(weightsGm, qScaleGm, kScaleGm, indiceOutGm, blockTableGm);
vectorService.InitVecWorkspaceTensor(weightWorkspaceGm, mm1ResGm, vec1ResGm, vec1ParamGm);
} else {
matmulService.InitParams(constInfo);
queryGm.SetGlobalBuffer((__gm__ Q_T *)query);
if constexpr (PAGE_ATTENTION) {
blockTableGm.SetGlobalBuffer((__gm__ int32_t *)blockTable);
}
keyGm.SetGlobalBuffer((__gm__ K_T *)key);
matmulService.InitMm1GlobalTensor(blockTableGm, keyGm, queryGm, mm1ResGm, weightWorkspaceGm);
}
InitBuffers();
}
template <typename LIQT>
__aicore__ inline void LIQPreload<LIQT>::GetBN2Idx(uint32_t bN2Idx)
{
tempLoopInfo.bN2Idx = bN2Idx;
tempLoopInfo.bIdx = bN2Idx / constInfo.kHeadNum;
tempLoopInfo.n2Idx = bN2Idx % constInfo.kHeadNum;
}
template <typename LIQT>
__aicore__ inline void LIQPreload<LIQT>::CalcS2LoopParams(uint32_t bN2LoopIdx, uint32_t gS1LoopIdx)
{
tempLoopInfo.gS1Idx = gS1LoopIdx;
tempLoopInfo.actMBaseSize = constInfo.mBaseSize;
uint32_t remainedGS1Size = tempLoopInfo.actS1Size * constInfo.gSize - tempLoopInfo.gS1Idx * constInfo.mBaseSize;
if (remainedGS1Size <= constInfo.mBaseSize && remainedGS1Size > 0) {
tempLoopInfo.actMBaseSize = tempLoopInfo.mBasicSizeTail;
}
bool isEnd = (bN2LoopIdx == splitCoreInfo.bN2End) && (gS1LoopIdx == splitCoreInfo.gS1End);
uint32_t s2BlockNum;
if (constInfo.attenMaskFlag) {
s2BlockNum = GetS2BaseBlockNumOnMask(gS1LoopIdx, tempLoopInfo.actS1Size, tempLoopInfo.actS2Size);
} else {
s2BlockNum = (tempLoopInfo.actS2Size + constInfo.s2BaseSize - 1) / constInfo.s2BaseSize;
}
tempLoopInfo.s2LoopEnd = isEnd ? splitCoreInfo.s2End : s2BlockNum - 1;
}
template <typename LIQT>
__aicore__ inline void LIQPreload<LIQT>::CalcGS1LoopParams(uint32_t bN2LoopIdx)
{
GetBN2Idx(bN2LoopIdx);
GetS1S2ActualSeqLen(tempLoopInfo.bIdx, tempLoopInfo.actS1Size, tempLoopInfo.actS2Size);
if ((tempLoopInfo.actS2Size == 0) || (tempLoopInfo.actS1Size == 0)) {
tempLoopInfo.curActSeqLenIsZero = true;
return;
}
tempLoopInfo.curActSeqLenIsZero = false;
tempLoopInfo.s2BasicSizeTail = tempLoopInfo.actS2Size % constInfo.s2BaseSize;
tempLoopInfo.s2BasicSizeTail =
(tempLoopInfo.s2BasicSizeTail == 0) ? constInfo.s2BaseSize : tempLoopInfo.s2BasicSizeTail;
tempLoopInfo.mBasicSizeTail = (tempLoopInfo.actS1Size * constInfo.gSize) % constInfo.mBaseSize;
tempLoopInfo.mBasicSizeTail =
(tempLoopInfo.mBasicSizeTail == 0) ? constInfo.mBaseSize : tempLoopInfo.mBasicSizeTail;
uint32_t gS1SplitNum = (tempLoopInfo.actS1Size * constInfo.gSize + constInfo.mBaseSize - 1) / constInfo.mBaseSize;
tempLoopInfo.gS1LoopEnd = (bN2LoopIdx == splitCoreInfo.bN2End) ? splitCoreInfo.gS1End : gS1SplitNum - 1;
if constexpr (Q_LAYOUT_T == LI_LAYOUT::BSND) {
if (tempLoopInfo.gS1LoopEnd == gS1SplitNum - 1 && constInfo.qSeqSize > tempLoopInfo.actS1Size) {
tempLoopInfo.needDealActS1LessThanS1 = true;
}
}
}
template <typename LIQT>
__aicore__ inline void LIQPreload<LIQT>::CalcRunInfo(uint32_t loop, uint32_t s2LoopIdx, LIQCommon::RunInfo &runInfo)
{
runInfo.loop = loop;
runInfo.bIdx = tempLoopInfo.bIdx;
runInfo.gS1Idx = tempLoopInfo.gS1Idx;
runInfo.s2Idx = s2LoopIdx;
runInfo.bN2Idx = tempLoopInfo.bN2Idx;
runInfo.isValid = s2LoopIdx <= tempLoopInfo.s2LoopEnd;
if (!runInfo.isValid) {
return; // 需要验证， v1 时候需要runInfo
}
runInfo.actS1Size = tempLoopInfo.actS1Size;
runInfo.actS2Size = tempLoopInfo.actS2Size;
// 计算实际基本块size
runInfo.actMBaseSize = tempLoopInfo.actMBaseSize;
runInfo.actualSingleProcessSInnerSize = constInfo.s2BaseSize;
uint32_t s2SplitNum = (tempLoopInfo.actS2Size + constInfo.s2BaseSize - 1) / constInfo.s2BaseSize;
if (runInfo.s2Idx == s2SplitNum - 1) {
runInfo.actualSingleProcessSInnerSize = tempLoopInfo.s2BasicSizeTail;
}
runInfo.actualSingleProcessSInnerSizeAlign =
LIQCommon::Align((uint32_t)runInfo.actualSingleProcessSInnerSize, LIQCommon::ConstInfo::BUFFER_SIZE_BYTE_32B);
runInfo.isFirstS2InnerLoop = s2LoopIdx == splitCoreInfo.s2Start;
runInfo.isLastS2InnerLoop = s2LoopIdx == tempLoopInfo.s2LoopEnd;
runInfo.isAllLoopEnd = (runInfo.bN2Idx == splitCoreInfo.bN2End) && (runInfo.gS1Idx == splitCoreInfo.gS1End) &&
(runInfo.s2Idx == splitCoreInfo.s2End);
if (runInfo.isFirstS2InnerLoop) {
uint64_t actualSeqQPrefixSum;
if constexpr (Q_LAYOUT_T == LI_LAYOUT::TND) {
actualSeqQPrefixSum = (runInfo.bIdx <= 0) ? 0 : actualSeqLengthsGmQ.GetValue(runInfo.bIdx - 1);
} else { // BSND
actualSeqQPrefixSum = (runInfo.bIdx <= 0) ? 0 : runInfo.bIdx * constInfo.qSeqSize;
}
uint64_t tndBIdxOffset = actualSeqQPrefixSum * constInfo.qHeadNum * constInfo.headDim;
// B,S1,N1(N2,G),D
queryCoreOffset = tndBIdxOffset + runInfo.gS1Idx * constInfo.mBaseSize * constInfo.headDim;
// B,S1,N1(N2,G)/T,N1(N2,G)
weightsCoreOffset = actualSeqQPrefixSum * constInfo.qHeadNum + runInfo.n2Idx * constInfo.gSize;
// B,S1,N2,k/T,N2,k
indiceOutCoreOffset =
actualSeqQPrefixSum * constInfo.kHeadNum * constInfo.sparseCount + runInfo.n2Idx * constInfo.sparseCount;
}
uint64_t actualSeqKPrefixSum;
if constexpr (K_LAYOUT_T == LI_LAYOUT::TND) { // T N2 D
actualSeqKPrefixSum = (runInfo.bIdx <= 0) ? 0 : actualSeqLengthsGm.GetValue(runInfo.bIdx - 1);
} else {
actualSeqKPrefixSum = (runInfo.bIdx <= 0) ? 0 : runInfo.bIdx * constInfo.kSeqSize;
}
uint64_t tndBIdxOffsetForK = actualSeqKPrefixSum * constInfo.kHeadNum * constInfo.headDim;
keyCoreOffset = tndBIdxOffsetForK + runInfo.s2Idx * constInfo.s2BaseSize * constInfo.kHeadNum * constInfo.headDim;
keyScaleCoreOffset = (actualSeqKPrefixSum + runInfo.s2Idx * constInfo.s2BaseSize) * constInfo.kHeadNum;
runInfo.tensorQueryOffset = queryCoreOffset;
runInfo.tensorKeyOffset = keyCoreOffset;
runInfo.tensorKeyScaleOffset = keyScaleCoreOffset;
runInfo.tensorWeightsOffset = weightsCoreOffset;
runInfo.indiceOutOffset = indiceOutCoreOffset;
}
template <typename LIQT>
__aicore__ inline void LIQPreload<LIQT>::Process()
{
if (usedCoreNum == 0) {
// 没有计算任务，直接清理输出
ProcessInvalid();
return;
}
ProcessMain();
ProcessDecode();
}
template <typename LIQT>
__aicore__ inline void LIQPreload<LIQT>::ProcessInvalid()
{
if ASCEND_IS_AIV {
uint32_t aivCoreNum = GetBlockNum() * 2; // 2 means c:v = 1:2
uint64_t totalOutputSize =
constInfo.batchSize * constInfo.qSeqSize * constInfo.kHeadNum * constInfo.sparseCount;
uint64_t singleCoreSize =
LIQCommon::Align((totalOutputSize + aivCoreNum - 1) / aivCoreNum, GM_ALIGN_BYTES / sizeof(OUT_T));
uint64_t baseSize = tmpBlockIdx * singleCoreSize;
if (baseSize < totalOutputSize) {
uint64_t dealSize =
(baseSize + singleCoreSize <= totalOutputSize) ? singleCoreSize : totalOutputSize - baseSize;
GlobalTensor<OUT_T> output = indiceOutGm[baseSize];
AscendC::InitGlobalMemory(output, dealSize, constInfo.INVALID_IDX);
}
}
}
template <typename LIQT>
__aicore__ inline void LIQPreload<LIQT>::ProcessMain()
{
if (aiCoreIdx >= usedCoreNum) {
// 无任务核直接返回
return;
}
if ASCEND_IS_AIV {
vectorService.AllocEventID();
CrossCoreSetFlag<LIQCommon::ConstInfo::FIA_SYNC_MODE2, PIPE_MTE2>(constInfo.syncV1C1);
CrossCoreSetFlag<LIQCommon::ConstInfo::FIA_SYNC_MODE2, PIPE_MTE2>(constInfo.syncV1C1);
} else {
matmulService.AllocEventID();
CrossCoreSetFlag<LIQCommon::ConstInfo::FIA_SYNC_MODE2, PIPE_FIX>(constInfo.syncC1V0);
CrossCoreSetFlag<LIQCommon::ConstInfo::FIA_SYNC_MODE2, PIPE_FIX>(constInfo.syncC1V0);
}
LIQCommon::RunInfo runInfo[LI_QUANT_PRELOAD_TASK_CACHE_SIZE];
uint32_t gloop = 0;
for (uint32_t bN2LoopIdx = splitCoreInfo.bN2Start; bN2LoopIdx <= splitCoreInfo.bN2End; bN2LoopIdx++) {
CalcGS1LoopParams(bN2LoopIdx);
if (tempLoopInfo.curActSeqLenIsZero) {
DealActSeqLenIsZero(tempLoopInfo.bIdx, tempLoopInfo.n2Idx, 0U);
if ASCEND_IS_AIV {
if (bN2LoopIdx == splitCoreInfo.bN2End && gloop > 0) {
CrossCoreWaitFlag(constInfo.syncC1V1);
vectorService.ProcessVec1(runInfo[1 - gloop % LI_QUANT_PRELOAD_TASK_CACHE_SIZE]);
CrossCoreSetFlag<LIQCommon::ConstInfo::FIA_SYNC_MODE2, PIPE_MTE3>(
constInfo.syncV1C1); // 反向同步 1
}
}
continue;
}
for (uint32_t gS1LoopIdx = splitCoreInfo.gS1Start; gS1LoopIdx <= tempLoopInfo.gS1LoopEnd; gS1LoopIdx++) {
CalcS2LoopParams(bN2LoopIdx, gS1LoopIdx);
bool isEnd = (bN2LoopIdx == splitCoreInfo.bN2End) && (gS1LoopIdx == splitCoreInfo.gS1End);
uint32_t extraLoop = isEnd ? LI_QUANT_PRELOAD_TASK_CACHE_SIZE - 1 : 0;
for (int s2LoopIdx = splitCoreInfo.s2Start; s2LoopIdx <= (tempLoopInfo.s2LoopEnd + extraLoop);
s2LoopIdx++) {
ProcessBaseBlock(gloop, s2LoopIdx, runInfo);
++gloop;
}
splitCoreInfo.s2Start = 0;
}
if (tempLoopInfo.needDealActS1LessThanS1) {
DealActSeqLenIsZero(tempLoopInfo.bIdx, tempLoopInfo.n2Idx, tempLoopInfo.actS1Size);
}
splitCoreInfo.gS1Start = 0;
}
if ASCEND_IS_AIV {
vectorService.FreeEventID();
CrossCoreWaitFlag(constInfo.syncC1V0);
CrossCoreWaitFlag(constInfo.syncC1V0);
} else {
matmulService.FreeEventID();
CrossCoreWaitFlag(constInfo.syncV1C1);
CrossCoreWaitFlag(constInfo.syncV1C1);
}
}
template <typename LIQT>
__aicore__ inline void LIQPreload<LIQT>::ProcessBaseBlock(uint32_t loop, uint64_t s2LoopIdx,
LIQCommon::RunInfo runInfo[LI_QUANT_PRELOAD_TASK_CACHE_SIZE])
{
int32_t curTaskId = loop % LI_QUANT_PRELOAD_TASK_CACHE_SIZE;
LIQCommon::RunInfo &curRunInfo = runInfo[curTaskId];
LIQCommon::RunInfo &lastRunInfo = runInfo[1 - curTaskId];
CalcRunInfo(loop, s2LoopIdx, curRunInfo);
if (curRunInfo.isValid) {
if ASCEND_IS_AIC {
if (curRunInfo.isFirstS2InnerLoop) {
CrossCoreWaitFlag(constInfo.syncV0C1);
}
CrossCoreWaitFlag(constInfo.syncV1C1); // 反向同步 1
matmulService.ComputeMm1(curRunInfo);
CrossCoreSetFlag<LIQCommon::ConstInfo::FIA_SYNC_MODE2, PIPE_FIX>(constInfo.syncC1V1);
if (curRunInfo.isLastS2InnerLoop) {
CrossCoreSetFlag<LIQCommon::ConstInfo::FIA_SYNC_MODE2, PIPE_FIX>(constInfo.syncC1V0); // 反向同步 0
}
} else {
if (curRunInfo.isFirstS2InnerLoop) {
CrossCoreWaitFlag(constInfo.syncC1V0); // 反向同步 0
vectorService.ProcessVec0(curRunInfo);
CrossCoreSetFlag<LIQCommon::ConstInfo::FIA_SYNC_MODE2, PIPE_MTE3>(constInfo.syncV0C1);
}
}
}
if (lastRunInfo.isValid) {
if ASCEND_IS_AIV {
CrossCoreWaitFlag(constInfo.syncC1V1);
vectorService.ProcessVec1(lastRunInfo);
CrossCoreSetFlag<LIQCommon::ConstInfo::FIA_SYNC_MODE2, PIPE_MTE3>(constInfo.syncV1C1); // 反向同步 1
}
lastRunInfo.isValid = false;
}
}
template <typename LIQT>
__aicore__ inline void LIQPreload<LIQT>::ProcessDecode()
{
if ASCEND_IS_AIV {
vectorService.InitLDBuffers(pipe);
ICachePreLoad(LD_PREFETCH_LEN);
SyncAll();
if (splitCoreInfo.isLD) {
vectorService.ProcessLD();
}
}
}
} // namespace LIQKernel
#endif // LIGHTNING_INDEXER_QUANT_KERNEL_H

613
csrc/lightning_indexer_quant/op_kernel/lightning_indexer_quant_service_cube.h
View File

@@ -1,613 +0,0 @@
/**
* This program is free software, you can redistribute it and/or modify it.
* Copyright (c) 2025 Huawei Technologies Co., Ltd.
* This file is a part of the CANN Open Software.
* Licensed under CANN Open Software License Agreement Version 2.0 (the "License").
* Please refer to the License for details. You may not use this file except in compliance with the License.
* THIS SOFTWARE IS PROVIDED ON AN "AS IS" BASIS, WITHOUT WARRANTIES OF ANY KIND, EITHER EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO NON-INFRINGEMENT, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE.
* See LICENSE in the root of the software repository for the full text of the License.
*/
/*!
* \file lightning_indexer_quant_service_cube.h
* \brief use 5 buffer for matmul l1, better pipeline
*/
#ifndef LIGHTNING_INDEXER_QUANT_SERVICE_CUBE_H
#define LIGHTNING_INDEXER_QUANT_SERVICE_CUBE_H
#include "kernel_operator.h"
#include "kernel_operator_list_tensor_intf.h"
#include "kernel_tiling/kernel_tiling.h"
#include "lib/matmul_intf.h"
#include "lib/matrix/matmul/tiling.h"
#include "lightning_indexer_quant_common.h"
namespace LIQKernel {
using namespace LIQCommon;
struct MmInfo {
int64_t s2L0LoopId;
int64_t s1gL0LoopId;
int64_t s2L0RealSize;
int64_t s2GmOffset;
};
template <typename LIQT>
class LIQMatmul {
public:
using Q_T = typename LIQT::queryType;
using K_T = typename LIQT::keyType;
__aicore__ inline LIQMatmul(){};
__aicore__ inline void InitBuffers(TPipe *pipe);
__aicore__ inline void InitMm1GlobalTensor(const GlobalTensor<int32_t> &blkTableGm, const GlobalTensor<K_T> &keyGm,
const GlobalTensor<Q_T> &queryGm, const GlobalTensor<float> &mm1ResGm,
const GlobalTensor<half> &weightWorkspaceGm);
__aicore__ inline void InitParams(const ConstInfo &constInfo);
__aicore__ inline void AllocEventID();
__aicore__ inline void FreeEventID();
__aicore__ inline void ComputeMm1(const LIQCommon::RunInfo &runInfo);
static constexpr IsResetLoad3dConfig LOAD3DV2_CONFIG = {true, true}; // isSetFMatrix isSetPadding;
static constexpr uint64_t DOUBLE_BUF_NUM = 2;
static constexpr uint64_t L0AB_BUF_NUM = 4;
static constexpr uint32_t KEY_MTE1_MTE2_EVENT = EVENT_ID2;
static constexpr uint32_t QW_MTE1_MTE2_EVENT = EVENT_ID5; // KEY_MTE1_MTE2_EVENT + DOUBLE_BUF_NUM;
static constexpr uint32_t M_MTE1_EVENT = EVENT_ID3;
static constexpr uint32_t M_FIX_EVENT = EVENT_ID0;
static constexpr uint32_t FIX_M_EVENT = EVENT_ID2;
static constexpr uint32_t FIX_MTE1_EVENT = EVENT_ID4;
static constexpr uint64_t S8_BLOCK_CUBE = 32;
static constexpr uint32_t MTE2_MTE1_EVENT = EVENT_ID2;
static constexpr uint32_t MTE1_M_EVENT = EVENT_ID2;
static constexpr uint64_t D_BASIC_BLOCK = 128;
static constexpr uint64_t S1G_BASIC_BLOCK_L1 = 256;
static constexpr uint64_t S1G_BASIC_BLOCK_L0 = 128;
static constexpr uint64_t S2_BASIC_BLOCK_L0 = 128;
static constexpr uint64_t QUERY_BUFFER_OFFSET = S1G_BASIC_BLOCK_L1 * D_BASIC_BLOCK;
static constexpr uint64_t SL1_BUFFER_OFFSET = S1G_BASIC_BLOCK_L0 * S2_BASIC_BLOCK_L0;
static constexpr uint64_t KEY_BUFFER_OFFSET = S2_BASIC_BLOCK_L0 * D_BASIC_BLOCK;
static constexpr uint64_t WEIGHT_BUFFER_OFFSET = S1G_BASIC_BLOCK_L1 * BLOCK_CUBE;
static constexpr uint64_t L0AB_BUFFER_OFFSET_S8_16K = 16 * 1024;
static constexpr uint64_t L0AB_BUFFER_OFFSET_FP16_16K = 16 * 512;
static constexpr uint64_t L0C_BUFFER_OFFSET = 64 * 256;
private:
__aicore__ inline void WeightDmaCopy(uint64_t s1gL1RealSize, const LIQCommon::RunInfo &runInfo);
__aicore__ inline void LoadKeyToL0b(uint64_t s2L0RealSize);
__aicore__ inline void LoadQueryToL0a(uint64_t s1gL1Offset, uint64_t s1gL1RealSize, uint64_t s1gL0RealSize);
__aicore__ inline void QueryNd2Nz(uint64_t s1gL1RealSize, const LIQCommon::RunInfo &runInfo);
__aicore__ inline void KeyNd2NzForPA(uint64_t s2L1RealSize, uint64_t s2GmOffset, const LIQCommon::RunInfo &runInfo);
__aicore__ inline void KeyNd2Nz(uint64_t s2L1RealSize, const MmInfo &mmInfo, const LIQCommon::RunInfo &runInfo);
__aicore__ inline void FixpSToL1(uint64_t s1gL0RealSize, uint64_t s2L0RealSize);
__aicore__ inline void LoadSToL0b(uint64_t s1gL1RealSize, uint64_t s2L0RealSize, uint64_t sL1BufIdx,
int64_t mStartPt);
__aicore__ inline void LoadWeightToL0a(uint64_t s1gL1Offset);
__aicore__ inline void ComputeWs(uint64_t s1gL0RealSize, uint64_t s2L0RealSize, int64_t s1gOffset);
__aicore__ inline void FixpResToGm(uint64_t s1L0RealCount, uint64_t s2L0RealSize, uint64_t s1GmOffset,
uint64_t s2GmOffset, const LIQCommon::RunInfo &runInfo);
__aicore__ inline void ComputeQk(uint64_t s1gL0RealSize, uint64_t s2L0RealSize);
__aicore__ inline void ProcessWs(uint64_t s1gL0RealSize, uint64_t s1gL1Offset, uint64_t sL1BufIdx,
const MmInfo &mmInfo, const LIQCommon::RunInfo &runInfo);
__aicore__ inline void ProcessQk(uint64_t s1gL0RealSize, uint64_t s1gL1Offset, uint64_t s1L0LoopCnt,
const MmInfo &mmInfo, const LIQCommon::RunInfo &runInfo);
__aicore__ inline void CalcMmInfo(MmInfo &mmInfo, uint64_t loopIdx, uint64_t s1L0LoopCnt, const MmInfo &lastMmInfo,
const LIQCommon::RunInfo &runInfo);
static constexpr LI_LAYOUT Q_LAYOUT_T = LIQT::layout;
static constexpr LI_LAYOUT K_LAYOUT_T = LIQT::keyLayout;
GlobalTensor<int32_t> blkTableGm_;
GlobalTensor<K_T> keyGm_;
GlobalTensor<Q_T> queryGm_;
GlobalTensor<half> weightGm_;
GlobalTensor<float> mm1ResGm_;
TBuf<TPosition::A1> bufQL1_;
LocalTensor<Q_T> queryL1_;
TBuf<TPosition::B1> bufKeyL1_;
LocalTensor<K_T> keyL1_;
TBuf<TPosition::A1> bufWeightL1_;
LocalTensor<half> weightL1_;
TBuf<TPosition::B1> bufSL1_;
LocalTensor<half> sL1_;
TBuf<TPosition::A2> bufL0A_;
LocalTensor<Q_T> l0a_;
TBuf<TPosition::B2> bufL0B_;
LocalTensor<K_T> l0b_;
TBuf<TPosition::CO1> bufL0C_;
LocalTensor<int32_t> cL0_;
uint64_t keyL1BufIdx_ = 0;
uint64_t qwL1Mte2BufIdx_ = 0;
uint64_t sL1BufIdx_ = 0;
uint64_t l0BufIdx_ = 0;
uint64_t l0cBufIdx_ = 0;
ConstInfo constInfo_;
};
template <typename LIQT>
__aicore__ inline void LIQMatmul<LIQT>::InitParams(const ConstInfo &constInfo)
{
constInfo_ = constInfo;
}
template <typename LIQT>
__aicore__ inline void LIQMatmul<LIQT>::InitBuffers(TPipe *pipe)
{
pipe->InitBuffer(bufQL1_, DOUBLE_BUF_NUM * S1G_BASIC_BLOCK_L1 * D_BASIC_BLOCK * sizeof(Q_T));
queryL1_ = bufQL1_.Get<Q_T>();
pipe->InitBuffer(bufKeyL1_, DOUBLE_BUF_NUM * S2_BASIC_BLOCK_L0 * D_BASIC_BLOCK * sizeof(K_T));
keyL1_ = bufKeyL1_.Get<K_T>();
pipe->InitBuffer(bufWeightL1_, DOUBLE_BUF_NUM * S1G_BASIC_BLOCK_L1 * BLOCK_CUBE * sizeof(half));
weightL1_ = bufWeightL1_.Get<half>();
pipe->InitBuffer(bufSL1_, DOUBLE_BUF_NUM * S2_BASIC_BLOCK_L0 * S1G_BASIC_BLOCK_L0 * sizeof(half));
sL1_ = bufSL1_.Get<half>();
pipe->InitBuffer(bufL0A_, 64 * 1024);
l0a_ = bufL0A_.Get<Q_T>();
pipe->InitBuffer(bufL0B_, 64 * 1024);
l0b_ = bufL0B_.Get<K_T>();
pipe->InitBuffer(bufL0C_, 128 * 1024);
cL0_ = bufL0C_.Get<int32_t>();
}
template <typename LIQT>
__aicore__ inline void LIQMatmul<LIQT>::InitMm1GlobalTensor(const GlobalTensor<int32_t> &blkTableGm,
const GlobalTensor<K_T> &keyGm,
const GlobalTensor<Q_T> &queryGm,
const GlobalTensor<float> &mm1ResGm,
const GlobalTensor<half> &weightWorkspaceGm)
{
blkTableGm_ = blkTableGm;
keyGm_ = keyGm;
queryGm_ = queryGm;
mm1ResGm_ = mm1ResGm;
weightGm_ = weightWorkspaceGm;
}
template <typename LIQT>
__aicore__ inline void LIQMatmul<LIQT>::ProcessWs(uint64_t s1gL0RealSize, uint64_t s1gL1Offset, uint64_t sL1BufIdx,
const MmInfo &mmInfo, const LIQCommon::RunInfo &runInfo)
{
WaitFlag<HardEvent::FIX_M>(FIX_M_EVENT + l0cBufIdx_ % DOUBLE_BUF_NUM);
for (int64_t s1gOffset = 0; s1gOffset < s1gL0RealSize; s1gOffset += constInfo_.gSize) {
WaitFlag<HardEvent::M_MTE1>(M_MTE1_EVENT + l0BufIdx_ % L0AB_BUF_NUM);
LoadSToL0b(s1gL0RealSize, mmInfo.s2L0RealSize, sL1BufIdx, s1gOffset);
LoadWeightToL0a(s1gOffset + s1gL1Offset);
ComputeWs(s1gL0RealSize, mmInfo.s2L0RealSize, s1gOffset);
SetFlag<HardEvent::M_MTE1>(M_MTE1_EVENT + l0BufIdx_ % L0AB_BUF_NUM);
l0BufIdx_++;
}
FixpResToGm(s1gL0RealSize / constInfo_.gSize, mmInfo.s2L0RealSize, s1gL1Offset / constInfo_.gSize,
mmInfo.s2L0LoopId * S2_BASIC_BLOCK_L0, runInfo);
SetFlag<HardEvent::FIX_M>(FIX_M_EVENT + l0cBufIdx_ % DOUBLE_BUF_NUM);
l0cBufIdx_++;
}
template <typename LIQT>
__aicore__ inline void LIQMatmul<LIQT>::ProcessQk(uint64_t s1gL0RealSize, uint64_t s1gL1Offset, uint64_t s1L0LoopCnt,
const MmInfo &mmInfo, const LIQCommon::RunInfo &runInfo)
{
if (mmInfo.s1gL0LoopId == 0) {
WaitFlag<HardEvent::MTE1_MTE2>(KEY_MTE1_MTE2_EVENT + keyL1BufIdx_ % DOUBLE_BUF_NUM);
if constexpr (K_LAYOUT_T == LI_LAYOUT::PA_BSND) {
KeyNd2NzForPA(mmInfo.s2L0RealSize, runInfo.s2Idx * constInfo_.s2BaseSize + mmInfo.s2GmOffset, runInfo);
} else {
KeyNd2Nz(mmInfo.s2L0RealSize, mmInfo, runInfo);
}
SetFlag<HardEvent::MTE2_MTE1>(MTE2_MTE1_EVENT);
WaitFlag<HardEvent::MTE2_MTE1>(MTE2_MTE1_EVENT);
}
WaitFlag<HardEvent::M_MTE1>(M_MTE1_EVENT + l0BufIdx_ % L0AB_BUF_NUM);
LoadQueryToL0a(s1gL1Offset, runInfo.actMBaseSize, s1gL0RealSize);
LoadKeyToL0b(mmInfo.s2L0RealSize);
if (mmInfo.s1gL0LoopId + 1 >= s1L0LoopCnt) {
SetFlag<HardEvent::MTE1_MTE2>(KEY_MTE1_MTE2_EVENT + keyL1BufIdx_ % DOUBLE_BUF_NUM);
keyL1BufIdx_++;
}
WaitFlag<HardEvent::FIX_M>(FIX_M_EVENT + l0cBufIdx_ % DOUBLE_BUF_NUM);
ComputeQk(s1gL0RealSize, mmInfo.s2L0RealSize);
SetFlag<HardEvent::M_MTE1>(M_MTE1_EVENT + l0BufIdx_ % L0AB_BUF_NUM);
FixpSToL1(s1gL0RealSize, mmInfo.s2L0RealSize);
SetFlag<HardEvent::FIX_M>(FIX_M_EVENT + l0cBufIdx_ % DOUBLE_BUF_NUM);
l0BufIdx_++;
l0cBufIdx_++;
}
template <typename LIQT>
__aicore__ inline void LIQMatmul<LIQT>::CalcMmInfo(MmInfo &mmInfo, uint64_t loopIdx, uint64_t s1L0LoopCnt,
const MmInfo &lastMmInfo, const LIQCommon::RunInfo &runInfo)
{
mmInfo.s2L0LoopId = loopIdx / s1L0LoopCnt;
mmInfo.s1gL0LoopId = loopIdx % s1L0LoopCnt;
if (mmInfo.s1gL0LoopId == 0) {
mmInfo.s2GmOffset = mmInfo.s2L0LoopId * S2_BASIC_BLOCK_L0;
mmInfo.s2L0RealSize = mmInfo.s2GmOffset + S2_BASIC_BLOCK_L0 > runInfo.actualSingleProcessSInnerSize
? runInfo.actualSingleProcessSInnerSize - mmInfo.s2GmOffset
: S2_BASIC_BLOCK_L0;
} else {
mmInfo.s2L0RealSize = lastMmInfo.s2L0RealSize;
}
}
template <typename LIQT>
__aicore__ inline void LIQMatmul<LIQT>::ComputeMm1(const LIQCommon::RunInfo &runInfo)
{
if (runInfo.isFirstS2InnerLoop) {
WaitFlag<HardEvent::MTE1_MTE2>(QW_MTE1_MTE2_EVENT + qwL1Mte2BufIdx_ % DOUBLE_BUF_NUM);
QueryNd2Nz(runInfo.actMBaseSize, runInfo); // 256 * 128 // L1BasicBlock
WeightDmaCopy(runInfo.actMBaseSize, runInfo);
}
int64_t loopIdx = 0;
int64_t s2L0LoopCnt = CeilDiv(runInfo.actualSingleProcessSInnerSize, S2_BASIC_BLOCK_L0); // 2048取128
int64_t s1L0LoopCnt = CeilDiv(runInfo.actMBaseSize, S1G_BASIC_BLOCK_L0); // 256取128
int64_t s1gL1Offset[2] = {0, static_cast<int64_t>(S1G_BASIC_BLOCK_L0)};
int64_t s1gL0RealSize[2] = {s1L0LoopCnt > 1 ? static_cast<int64_t>(S1G_BASIC_BLOCK_L0) : runInfo.actMBaseSize,
runInfo.actMBaseSize - s1gL1Offset[1]};
MmInfo mmInfo[2];
CalcMmInfo(mmInfo[loopIdx & 1], loopIdx, s1L0LoopCnt, mmInfo[(loopIdx + 1) & 1], runInfo);
ProcessQk(s1gL0RealSize[mmInfo[loopIdx & 1].s1gL0LoopId % s1L0LoopCnt],
s1gL1Offset[mmInfo[loopIdx & 1].s1gL0LoopId % s1L0LoopCnt], s1L0LoopCnt, mmInfo[loopIdx & 1],
runInfo);
SetFlag<HardEvent::FIX_MTE1>(FIX_MTE1_EVENT + sL1BufIdx_ % DOUBLE_BUF_NUM);
sL1BufIdx_++;
loopIdx++;
while (loopIdx < s2L0LoopCnt * s1L0LoopCnt) {
CalcMmInfo(mmInfo[loopIdx & 1], loopIdx, s1L0LoopCnt, mmInfo[(loopIdx + 1) & 1], runInfo);
ProcessQk(s1gL0RealSize[mmInfo[loopIdx & 1].s1gL0LoopId % s1L0LoopCnt],
s1gL1Offset[mmInfo[loopIdx & 1].s1gL0LoopId % s1L0LoopCnt], s1L0LoopCnt, mmInfo[loopIdx & 1],
runInfo);
SetFlag<HardEvent::FIX_MTE1>(FIX_MTE1_EVENT + sL1BufIdx_ % DOUBLE_BUF_NUM);
sL1BufIdx_++;
WaitFlag<HardEvent::FIX_MTE1>(FIX_MTE1_EVENT + sL1BufIdx_ % DOUBLE_BUF_NUM);
ProcessWs(s1gL0RealSize[mmInfo[(loopIdx + 1) & 1].s1gL0LoopId % s1L0LoopCnt],
s1gL1Offset[mmInfo[(loopIdx + 1) & 1].s1gL0LoopId % s1L0LoopCnt], sL1BufIdx_,
mmInfo[(loopIdx + 1) & 1], runInfo);
loopIdx++;
}
WaitFlag<HardEvent::FIX_MTE1>(FIX_MTE1_EVENT + (sL1BufIdx_ + 1) % DOUBLE_BUF_NUM);
ProcessWs(s1gL0RealSize[mmInfo[(loopIdx + 1) & 1].s1gL0LoopId % s1L0LoopCnt],
s1gL1Offset[mmInfo[(loopIdx + 1) & 1].s1gL0LoopId % s1L0LoopCnt], sL1BufIdx_ - 1,
mmInfo[(loopIdx + 1) & 1], runInfo);
if (runInfo.isLastS2InnerLoop) {
SetFlag<HardEvent::MTE1_MTE2>(QW_MTE1_MTE2_EVENT + qwL1Mte2BufIdx_ % DOUBLE_BUF_NUM);
qwL1Mte2BufIdx_++;
}
}
// blkNum, blkSize, N2, D
template <typename LIQT>
__aicore__ inline void LIQMatmul<LIQT>::KeyNd2NzForPA(uint64_t s2L1RealSize, uint64_t s2GmOffset,
const LIQCommon::RunInfo &runInfo)
{
uint64_t s2L1Offset = 0;
while (s2L1Offset < s2L1RealSize) {
uint64_t s2BlkId = (s2L1Offset + s2GmOffset) / constInfo_.kCacheBlockSize;
uint64_t s2BlkOffset = (s2L1Offset + s2GmOffset) % constInfo_.kCacheBlockSize;
uint64_t keyGmOffset = blkTableGm_.GetValue(runInfo.bIdx * constInfo_.maxBlockNumPerBatch + s2BlkId) *
constInfo_.kCacheBlockSize * constInfo_.kHeadNum * constInfo_.headDim +
s2BlkOffset * constInfo_.headDim;
uint64_t s2Mte2Size = s2L1RealSize - s2L1Offset;
s2Mte2Size = s2BlkOffset + s2Mte2Size >= constInfo_.kCacheBlockSize ? constInfo_.kCacheBlockSize - s2BlkOffset
: s2Mte2Size;
Nd2NzParams nd2nzPara;
nd2nzPara.ndNum = 1;
nd2nzPara.nValue = s2Mte2Size; // 行数
nd2nzPara.dValue = constInfo_.headDim;
nd2nzPara.srcDValue = constInfo_.headDim;
nd2nzPara.dstNzC0Stride = CeilAlign(s2L1RealSize, (uint64_t)BLOCK_CUBE); // 对齐到16 单位block
nd2nzPara.dstNzNStride = 1;
nd2nzPara.srcNdMatrixStride = 0;
nd2nzPara.dstNzMatrixStride = 0;
DataCopy(keyL1_[(keyL1BufIdx_ % DOUBLE_BUF_NUM) * KEY_BUFFER_OFFSET + s2L1Offset * S8_BLOCK_CUBE],
keyGm_[keyGmOffset], nd2nzPara);
s2L1Offset += s2Mte2Size;
}
}
template <typename LIQT>
__aicore__ inline void LIQMatmul<LIQT>::KeyNd2Nz(uint64_t s2L1RealSize, const MmInfo &mmInfo,
const LIQCommon::RunInfo &runInfo)
{
uint64_t dStride = constInfo_.headDim;
if constexpr (K_LAYOUT_T == LI_LAYOUT::BSND || K_LAYOUT_T == LI_LAYOUT::TND) {
dStride = constInfo_.headDim * constInfo_.kHeadNum; // constInfo_.kHeadNum
}
Nd2NzParams nd2nzPara;
nd2nzPara.ndNum = 1;
nd2nzPara.nValue = s2L1RealSize; // 行数
nd2nzPara.dValue = constInfo_.headDim;
nd2nzPara.srcDValue = dStride;
nd2nzPara.dstNzC0Stride = CeilAlign(s2L1RealSize, (uint64_t)BLOCK_CUBE); // 对齐到16 单位block
nd2nzPara.dstNzNStride = 1;
nd2nzPara.srcNdMatrixStride = 0;
nd2nzPara.dstNzMatrixStride = 0;
// 默认一块buf最多放两份
DataCopy(keyL1_[(keyL1BufIdx_ % DOUBLE_BUF_NUM) * KEY_BUFFER_OFFSET],
keyGm_[runInfo.tensorKeyOffset + mmInfo.s2GmOffset * constInfo_.headDim], nd2nzPara);
}
// batch, s1, g, 1
template <typename LIQT>
__aicore__ inline void LIQMatmul<LIQT>::WeightDmaCopy(uint64_t s1gL1RealSize, const LIQCommon::RunInfo &runInfo)
{
DataCopyParams copyInParams;
copyInParams.blockCount = 1;
copyInParams.blockLen = s1gL1RealSize;
copyInParams.srcStride = 0;
copyInParams.dstStride = 0;
DataCopy(weightL1_[(qwL1Mte2BufIdx_ % DOUBLE_BUF_NUM) * WEIGHT_BUFFER_OFFSET],
weightGm_[runInfo.loop % DOUBLE_BUF_NUM * BLOCK_CUBE * constInfo_.mBaseSize], copyInParams);
}
// batch, s1, n2, g, d
template <typename LIQT>
__aicore__ inline void LIQMatmul<LIQT>::QueryNd2Nz(uint64_t s1gL1RealSize, const LIQCommon::RunInfo &runInfo)
{
Nd2NzParams nd2nzPara;
nd2nzPara.ndNum = 1;
nd2nzPara.nValue = s1gL1RealSize; // 行数
nd2nzPara.dValue = constInfo_.headDim;
nd2nzPara.srcDValue = constInfo_.headDim;
nd2nzPara.dstNzC0Stride = CeilAlign(s1gL1RealSize, (uint64_t)BLOCK_CUBE); // 对齐到16 单位block
nd2nzPara.dstNzNStride = 1;
nd2nzPara.srcNdMatrixStride = 0;
nd2nzPara.dstNzMatrixStride = 0;
// 默认一块buf最多放两份
DataCopy(queryL1_[(qwL1Mte2BufIdx_ % DOUBLE_BUF_NUM) * QUERY_BUFFER_OFFSET], queryGm_[runInfo.tensorQueryOffset],
nd2nzPara);
}
// s1g, d
template <typename LIQT>
__aicore__ inline void LIQMatmul<LIQT>::LoadQueryToL0a(uint64_t s1gL1Offset, uint64_t s1gL1RealSize,
uint64_t s1gL0RealSize)
{
LoadData3DParamsV2<Q_T> loadData3DParams;
// SetFmatrixParams
loadData3DParams.l1H = CeilDiv(s1gL1RealSize, BLOCK_CUBE); // Hin=M1=8
loadData3DParams.l1W = BLOCK_CUBE; // Win=M0
loadData3DParams.channelSize = constInfo_.headDim; // Cin=K
loadData3DParams.padList[0] = 0;
loadData3DParams.padList[1] = 0;
loadData3DParams.padList[2] = 0;
loadData3DParams.padList[3] = 255; // 尾部数据不影响滑窗的结果
// SetLoadToA0Params
loadData3DParams.mExtension = CeilAlign(s1gL0RealSize, BLOCK_CUBE); // M height维度目的
loadData3DParams.kExtension = constInfo_.headDim; // K width维度目的
loadData3DParams.mStartPt = s1gL1Offset;
loadData3DParams.kStartPt = 0;
loadData3DParams.strideW = 1;
loadData3DParams.strideH = 1;
loadData3DParams.filterW = 1;
loadData3DParams.filterSizeW = (1 >> 8) & 255;
loadData3DParams.filterH = 1;
loadData3DParams.filterSizeH = (1 >> 8) & 255;
loadData3DParams.dilationFilterW = 1;
loadData3DParams.dilationFilterH = 1;
loadData3DParams.enTranspose = 0;
loadData3DParams.fMatrixCtrl = 0;
LoadData<Q_T, LOAD3DV2_CONFIG>(l0a_[(l0BufIdx_ % L0AB_BUF_NUM) * L0AB_BUFFER_OFFSET_S8_16K],
queryL1_[(qwL1Mte2BufIdx_ % DOUBLE_BUF_NUM) * QUERY_BUFFER_OFFSET],
loadData3DParams);
}
// s1, g, s2 --> 2 * 64* 128
template <typename LIQT>
__aicore__ inline void LIQMatmul<LIQT>::LoadSToL0b(uint64_t s1gL1RealSize, uint64_t s2L0RealSize, uint64_t sL1BufIdx,
int64_t mStartPt)
{
LoadData3DParamsV2<half> loadData3DParams;
// SetFmatrixParams
loadData3DParams.l1H = S1G_BASIC_BLOCK_L0 / BLOCK_CUBE; // Hin=M1=8
loadData3DParams.l1W = BLOCK_CUBE; // Win=M0
loadData3DParams.channelSize = CeilAlign(s2L0RealSize, BLOCK_CUBE); // Cin=K
loadData3DParams.padList[0] = 0;
loadData3DParams.padList[1] = 0;
loadData3DParams.padList[2] = 0;
loadData3DParams.padList[3] = 255; // 尾部数据不影响滑窗的结果
// SetLoadToA0Params
loadData3DParams.mExtension = constInfo_.gSize; // M height维度目的
loadData3DParams.kExtension = CeilAlign(s2L0RealSize, BLOCK_CUBE); // K width维度目的
loadData3DParams.kStartPt = 0;
loadData3DParams.strideW = 1;
loadData3DParams.strideH = 1;
loadData3DParams.filterW = 1;
loadData3DParams.filterSizeW = (1 >> 8) & 255;
loadData3DParams.filterH = 1;
loadData3DParams.filterSizeH = (1 >> 8) & 255;
loadData3DParams.dilationFilterW = 1;
loadData3DParams.dilationFilterH = 1;
loadData3DParams.enTranspose = 1;
loadData3DParams.fMatrixCtrl = 0;
loadData3DParams.mStartPt = mStartPt;
LoadData<half, LOAD3DV2_CONFIG>(
l0b_.template ReinterpretCast<half>()[(l0BufIdx_ % L0AB_BUF_NUM) * L0AB_BUFFER_OFFSET_FP16_16K],
sL1_[(sL1BufIdx % DOUBLE_BUF_NUM) * SL1_BUFFER_OFFSET], loadData3DParams);
}
// s1,g,1(16), 2,64,16
template <typename LIQT>
__aicore__ inline void LIQMatmul<LIQT>::LoadWeightToL0a(uint64_t s1gL1Offset)
{
LoadData2DParams loadData2DParams;
loadData2DParams.startIndex = 0;
loadData2DParams.repeatTimes = CeilDiv(constInfo_.gSize, BLOCK_CUBE);
loadData2DParams.srcStride = 1;
loadData2DParams.dstGap = 0;
loadData2DParams.ifTranspose = true;
LoadData(l0a_.template ReinterpretCast<half>()[(l0BufIdx_ % L0AB_BUF_NUM) * L0AB_BUFFER_OFFSET_FP16_16K],
weightL1_[(qwL1Mte2BufIdx_ % DOUBLE_BUF_NUM) * WEIGHT_BUFFER_OFFSET + s1gL1Offset* BLOCK_CUBE],
loadData2DParams);
}
// s2, d -> 128,128
template <typename LIQT>
__aicore__ inline void LIQMatmul<LIQT>::LoadKeyToL0b(uint64_t s2L0RealSize)
{
LoadData2DParams loadData2DParams;
loadData2DParams.startIndex = 0;
loadData2DParams.repeatTimes = CeilDiv(s2L0RealSize, BLOCK_CUBE) * CeilDiv(constInfo_.headDim, S8_BLOCK_CUBE);
loadData2DParams.srcStride = 1;
loadData2DParams.dstGap = 0;
loadData2DParams.ifTranspose = false;
LoadData(l0b_[(l0BufIdx_ % L0AB_BUF_NUM) * L0AB_BUFFER_OFFSET_S8_16K],
keyL1_[(keyL1BufIdx_ % DOUBLE_BUF_NUM) * KEY_BUFFER_OFFSET], loadData2DParams);
}
// A: s1,g,1(16) B: s1,g,s2 C: s1, 1(16), s2
template <typename LIQT>
__aicore__ inline void LIQMatmul<LIQT>::ComputeWs(uint64_t s1gL0RealSize, uint64_t s2L0RealSize, int64_t s1gOffset)
{
SetFlag<HardEvent::MTE1_M>(MTE1_M_EVENT);
WaitFlag<HardEvent::MTE1_M>(MTE1_M_EVENT);
MmadParams mmadParams;
mmadParams.m = BLOCK_CUBE;
mmadParams.n = s2L0RealSize;
mmadParams.k = constInfo_.gSize;
mmadParams.cmatrixInitVal = true;
mmadParams.cmatrixSource = false;
Mmad(cL0_.template ReinterpretCast<float>()[(l0cBufIdx_ % DOUBLE_BUF_NUM) * L0C_BUFFER_OFFSET +
s1gOffset * S2_BASIC_BLOCK_L0],
l0a_.template ReinterpretCast<half>()[(l0BufIdx_ % L0AB_BUF_NUM) * L0AB_BUFFER_OFFSET_FP16_16K],
l0b_.template ReinterpretCast<half>()[(l0BufIdx_ % L0AB_BUF_NUM) * L0AB_BUFFER_OFFSET_FP16_16K],
mmadParams);
}
template <typename LIQT>
__aicore__ inline void LIQMatmul<LIQT>::ComputeQk(uint64_t s1gL0RealSize, uint64_t s2L0RealSize)
{
SetFlag<HardEvent::MTE1_M>(MTE1_M_EVENT);
WaitFlag<HardEvent::MTE1_M>(MTE1_M_EVENT);
MmadParams mmadParams;
mmadParams.m = CeilAlign(s1gL0RealSize, BLOCK_CUBE);
mmadParams.n = s2L0RealSize;
mmadParams.k = constInfo_.headDim;
mmadParams.cmatrixInitVal = true;
mmadParams.cmatrixSource = false;
Mmad(cL0_[(l0cBufIdx_ % DOUBLE_BUF_NUM) * L0C_BUFFER_OFFSET],
l0a_[(l0BufIdx_ % L0AB_BUF_NUM) * L0AB_BUFFER_OFFSET_S8_16K],
l0b_[(l0BufIdx_ % L0AB_BUF_NUM) * L0AB_BUFFER_OFFSET_S8_16K], mmadParams);
if ((mmadParams.m / 16) * (mmadParams.n / 16) < 10) {
PipeBarrier<PIPE_M>();
}
}
template <typename LIQT>
__aicore__ inline void LIQMatmul<LIQT>::FixpSToL1(uint64_t s1gL0RealSize, uint64_t s2L0RealSize)
{
SetFlag<HardEvent::M_FIX>(M_FIX_EVENT);
WaitFlag<HardEvent::M_FIX>(M_FIX_EVENT);
DataCopyCO12DstParams params;
params.mSize = CeilAlign(s1gL0RealSize, BLOCK_CUBE);
params.nSize = CeilAlign(s2L0RealSize, BLOCK_CUBE);
params.dstStride = S1G_BASIC_BLOCK_L0;
params.srcStride = params.mSize;
params.quantPre = QuantMode_t::DEQF16;
params.reluPre = 1;
params.channelSplit = 0;
params.nz2ndEn = 0;
SetFixpipePreQuantFlag(0x3a800000);
DataCopy(sL1_[(sL1BufIdx_ % DOUBLE_BUF_NUM) * SL1_BUFFER_OFFSET],
cL0_[(l0cBufIdx_ % DOUBLE_BUF_NUM) * L0C_BUFFER_OFFSET], params);
}
template <typename LIQT>
__aicore__ inline void LIQMatmul<LIQT>::FixpResToGm(uint64_t s1L0RealCount, uint64_t s2L0RealSize, uint64_t s1GmOffset,
uint64_t s2GmOffset, const LIQCommon::RunInfo &runInfo)
{
SetFlag<HardEvent::M_FIX>(M_FIX_EVENT);
WaitFlag<HardEvent::M_FIX>(M_FIX_EVENT);
AscendC::DataCopyCO12DstParams intriParams;
intriParams.mSize = 1;
intriParams.nSize = s2L0RealSize;
intriParams.dstStride = constInfo_.s2BaseSize;
intriParams.srcStride = 16;
// set mode according to dtype
intriParams.quantPre = QuantMode_t::NoQuant;
intriParams.nz2ndEn = true;
intriParams.reluPre = 0;
AscendC::SetFixpipeNz2ndFlag(s1L0RealCount, CeilDiv(constInfo_.gSize, BLOCK_CUBE) * S2_BASIC_BLOCK_L0 / BLOCK_CUBE,
2048);
AscendC::DataCopy(mm1ResGm_[(runInfo.loop % 2) * constInfo_.mBaseSize / constInfo_.gSize * constInfo_.s2BaseSize +
s1GmOffset * intriParams.dstStride + s2GmOffset],
cL0_.template ReinterpretCast<float>()[(l0cBufIdx_ % DOUBLE_BUF_NUM) * L0C_BUFFER_OFFSET],
intriParams);
}
template <typename LIQT>
__aicore__ inline void LIQMatmul<LIQT>::AllocEventID()
{
SetFlag<HardEvent::MTE1_MTE2>(KEY_MTE1_MTE2_EVENT + 0);
SetFlag<HardEvent::MTE1_MTE2>(KEY_MTE1_MTE2_EVENT + 1);
SetFlag<HardEvent::MTE1_MTE2>(KEY_MTE1_MTE2_EVENT + 2);
SetFlag<HardEvent::MTE1_MTE2>(QW_MTE1_MTE2_EVENT + 0);
SetFlag<HardEvent::MTE1_MTE2>(QW_MTE1_MTE2_EVENT + 1);
SetFlag<HardEvent::M_MTE1>(M_MTE1_EVENT + 0);
SetFlag<HardEvent::M_MTE1>(M_MTE1_EVENT + 1);
SetFlag<HardEvent::M_MTE1>(M_MTE1_EVENT + 2);
SetFlag<HardEvent::M_MTE1>(M_MTE1_EVENT + 3);
SetFlag<HardEvent::FIX_M>(FIX_M_EVENT + 0);
SetFlag<HardEvent::FIX_M>(FIX_M_EVENT + 1);
}
template <typename LIQT>
__aicore__ inline void LIQMatmul<LIQT>::FreeEventID()
{
WaitFlag<HardEvent::MTE1_MTE2>(KEY_MTE1_MTE2_EVENT + 0);
WaitFlag<HardEvent::MTE1_MTE2>(KEY_MTE1_MTE2_EVENT + 1);
WaitFlag<HardEvent::MTE1_MTE2>(KEY_MTE1_MTE2_EVENT + 2);
WaitFlag<HardEvent::MTE1_MTE2>(QW_MTE1_MTE2_EVENT + 0);
WaitFlag<HardEvent::MTE1_MTE2>(QW_MTE1_MTE2_EVENT + 1);
WaitFlag<HardEvent::M_MTE1>(M_MTE1_EVENT + 0);
WaitFlag<HardEvent::M_MTE1>(M_MTE1_EVENT + 1);
WaitFlag<HardEvent::M_MTE1>(M_MTE1_EVENT + 2);
WaitFlag<HardEvent::M_MTE1>(M_MTE1_EVENT + 3);
WaitFlag<HardEvent::FIX_M>(FIX_M_EVENT + 0);
WaitFlag<HardEvent::FIX_M>(FIX_M_EVENT + 1);
}
} // namespace LIQKernel
#endif

665
csrc/lightning_indexer_quant/op_kernel/lightning_indexer_quant_service_vector.h
View File

@@ -1,665 +0,0 @@
/**
* This program is free software, you can redistribute it and/or modify it.
* Copyright (c) 2025 Huawei Technologies Co., Ltd.
* This file is a part of the CANN Open Software.
* Licensed under CANN Open Software License Agreement Version 2.0 (the "License").
* Please refer to the License for details. You may not use this file except in compliance with the License.
* THIS SOFTWARE IS PROVIDED ON AN "AS IS" BASIS, WITHOUT WARRANTIES OF ANY KIND, EITHER EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO NON-INFRINGEMENT, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE.
* See LICENSE in the root of the software repository for the full text of the License.
*/
/*!
* \file lightning_indexer_quant_service_vector.h
* \brief
*/
#ifndef LIGHTNING_INDEXER_QUANT_SERVICE_VECTOR_H
#define LIGHTNING_INDEXER_QUANT_SERVICE_VECTOR_H
#include "kernel_operator.h"
#include "kernel_operator_list_tensor_intf.h"
#include "kernel_tiling/kernel_tiling.h"
#include "lib/matmul_intf.h"
#include "lib/matrix/matmul/tiling.h"
#include "lightning_indexer_quant_common.h"
#include "lightning_indexer_quant_vector.h"
namespace LIQKernel {
using namespace LIQCommon;
using namespace LIQServiceVec;
constexpr uint32_t BASE_TOPK = 2048;
constexpr uint32_t BASE_TOPK_VALUE_IDX_SIZE = 4096;
constexpr uint32_t LD_PARAM_NUM = 16;
template <typename LIQT>
class LIQVector {
public:
// =================================类型定义区=================================
static constexpr LI_LAYOUT Q_LAYOUT_T = LIQT::layout;
static constexpr LI_LAYOUT K_LAYOUT_T = LIQT::keyLayout;
static constexpr bool PAGE_ATTENTION = LIQT::pageAttention;
// MM输出数据类型, 当前只支持float
using MM1_OUT_T = float;
__aicore__ inline LIQVector(){};
__aicore__ inline void ProcessVec0(const LIQCommon::RunInfo &info);
__aicore__ inline void ProcessVec1(const LIQCommon::RunInfo &info);
__aicore__ inline void ProcessLD();
__aicore__ inline void InitBuffers(TPipe *pipe);
__aicore__ inline void InitParams(const struct LIQCommon::ConstInfo &constInfo,
const LIQTilingData *__restrict tilingData);
__aicore__ inline void InitVecWorkspaceTensor(GlobalTensor<half> vec0OutGm, GlobalTensor<MM1_OUT_T> mm1ResGm,
GlobalTensor<float> vec1ResGm, GlobalTensor<int64_t> vec1ParamGm);
__aicore__ inline void InitVecInputTensor(GlobalTensor<half> weightsGm, GlobalTensor<half> qScaleGm,
GlobalTensor<half> kScaleGm, GlobalTensor<int32_t> indiceOutGm,
GlobalTensor<int32_t> blockTableGm);
__aicore__ inline void CleanInvalidOutput(int64_t invalidS1offset);
__aicore__ inline void AllocEventID();
__aicore__ inline void FreeEventID();
__aicore__ inline void InitLDBuffers(TPipe *pipe);
protected:
GlobalTensor<MM1_OUT_T> mm1ResGm;
GlobalTensor<float> vec1ResGm;
GlobalTensor<int64_t> vec1ParamGm;
GlobalTensor<half> weightsGm;
GlobalTensor<half> qScaleGm;
GlobalTensor<half> kScaleGm;
GlobalTensor<half> vec0OutGm;
GlobalTensor<int32_t> indiceOutGm;
GlobalTensor<int32_t> blockTableGm;
// =================================常量区=================================
private:
__aicore__ inline void GetKeyScale(const LIQCommon::RunInfo &runInfo, const LocalTensor<half> &resUb,
int64_t batchId, int64_t startS2, int64_t getLen);
// ================================Local Buffer区====================================
// queue
TQue<QuePosition::VECIN, 1> inQueue_;
TQue<QuePosition::VECOUT, 1> outQueue_;
// tmp buff for vector
TBuf<TPosition::VECCALC> sortOutBuf_;
TBuf<TPosition::VECCALC> indexBuf_;
TBuf<TPosition::VECCALC> paramBuf_;
TBuf<TPosition::VECCALC> tmpBuf_;
// tmp buff for LD
TBuf<> ldToBeMrgBuf_;
TBuf<> ldTmpBuf_;
TBuf<> ldOutValueBuf_;
TBuf<> ldOutIdxBuf_;
LocalTensor<int32_t> globalTopkIndice_;
LocalTensor<float> globalTopkUb_;
int32_t blockId_ = -1;
// para for vector
int32_t groupInner_ = 0;
int32_t globalTopkNum_ = 0;
int64_t blockS2StartIdx_ = 0;
int32_t gSize_ = 0;
int32_t kSeqSize_ = 0;
int32_t kHeadNum_ = 0;
int32_t qHeadNum_ = 0;
int32_t s1BaseSize_ = 0;
int32_t s2BaseSize_ = 0;
int32_t kCacheBlockSize_ = 0;
int32_t maxBlockNumPerBatch_ = 0;
// para for LD
uint32_t mrgListNum_ = 4;
uint32_t paramNum_ = 16;
struct LIQCommon::ConstInfo constInfo_;
};
template <typename LIQT>
__aicore__ inline void LIQVector<LIQT>::GetKeyScale(const LIQCommon::RunInfo &runInfo, const LocalTensor<half> &resUb,
int64_t batchId, int64_t startS2, int64_t getLen)
{
// startS2一定能整除kCacheBlockSize_
AscendC::DataCopyPadExtParams<half> padParams{false, 0, 0, 0};
AscendC::DataCopyExtParams copyInParams;
if constexpr (PAGE_ATTENTION) {
int32_t startBlockTableIdx = startS2 / kCacheBlockSize_;
int32_t startBlockTableOffset = startS2 % kCacheBlockSize_;
int32_t blockTableBatchOffset = batchId * maxBlockNumPerBatch_;
copyInParams.blockCount = 1;
copyInParams.srcStride = 0;
copyInParams.dstStride = 0;
copyInParams.rsv = 0;
int32_t resUbBaseOffset = 0;
if (startBlockTableOffset > 0) {
int32_t firstPartLen =
kCacheBlockSize_ - startBlockTableOffset > getLen ? getLen : kCacheBlockSize_ - startBlockTableOffset;
copyInParams.blockLen = firstPartLen * sizeof(half);
int32_t blockId = blockTableGm.GetValue(blockTableBatchOffset + startBlockTableIdx);
SetWaitFlag<HardEvent::S_MTE2>(HardEvent::S_MTE2);
AscendC::DataCopyPad(resUb, kScaleGm[blockId * kCacheBlockSize_ + startBlockTableOffset],
copyInParams, padParams);
startBlockTableIdx++;
getLen = getLen - firstPartLen;
resUbBaseOffset = firstPartLen;
}
int32_t getLoopNum = CeilDiv(getLen, kCacheBlockSize_);
copyInParams.blockLen = kCacheBlockSize_ * sizeof(half);
for (int32_t i = 0; i < getLoopNum; i++) {
if (i == getLoopNum - 1) {
copyInParams.blockLen = (getLen - i * kCacheBlockSize_) * sizeof(half);
}
int32_t blockId = blockTableGm.GetValue(blockTableBatchOffset + startBlockTableIdx + i);
SetWaitFlag<HardEvent::S_MTE2>(HardEvent::S_MTE2);
AscendC::DataCopyPad(resUb[resUbBaseOffset + i * kCacheBlockSize_], kScaleGm[blockId * kCacheBlockSize_],
copyInParams, padParams);
}
} else {
copyInParams.blockCount = 1;
copyInParams.blockLen = getLen * sizeof(half);
copyInParams.srcStride = 0;
copyInParams.dstStride = 0;
copyInParams.rsv = 0;
AscendC::DataCopyPad(resUb, kScaleGm[runInfo.tensorKeyScaleOffset], copyInParams, padParams);
}
}
template <typename LIQT>
__aicore__ inline void LIQVector<LIQT>::InitBuffers(TPipe *pipe)
{
pipe->InitBuffer(paramBuf_, LD_PARAM_NUM * sizeof(int64_t)); // 1 KB
pipe->InitBuffer(inQueue_, 2, s2BaseSize_ * sizeof(float) * 2); // 32KB
pipe->InitBuffer(outQueue_, 1, BASE_TOPK * sizeof(float)); // 8 KB
pipe->InitBuffer(indexBuf_, s2BaseSize_ * sizeof(int32_t)); // 8 KB
pipe->InitBuffer(tmpBuf_, 64 * 1024); // 64KB
pipe->InitBuffer(sortOutBuf_, CeilDiv(s1BaseSize_, 2) * BASE_TOPK_VALUE_IDX_SIZE * sizeof(float)); // 32KB
globalTopkIndice_ = indexBuf_.Get<int32_t>();
globalTopkUb_ = sortOutBuf_.Get<float>();
globalTopkNum_ = 0;
// 基本块执行前初始化UB和GM
// step1. 初始化一个有序索引 0 - s2BaseSize_
ArithProgression<int32_t>(globalTopkIndice_, 0, 1, s2BaseSize_);
// step2. globalTopkUb_ [CeilDiv(s1BaseSize_, 2), BASE_TOPK, 2] -inf,-1
InitSortOutBuf(globalTopkUb_, CeilDiv(s1BaseSize_, 2) * BASE_TOPK_VALUE_IDX_SIZE);
// step3. 初始化vec1ParamGm，是否进行LD的标志位设为-1(needFd=-1)
// vec1ResIn32Gm = [aic, 2, s1BaseSize_, 16] int32
// ws清零 [needFd, s2AcSeq, s2Start, s2End, isS2End, bn2idx, s1Idx, ......]
LocalTensor<float> tmpfBuff = outQueue_.AllocTensor<float>();
Duplicate(tmpfBuff.template ReinterpretCast<int32_t>(), -1, 2 * (s1BaseSize_ / 2) * paramNum_ * 2);
SetWaitFlag<HardEvent::V_MTE3>(HardEvent::V_MTE3);
int64_t wsInfoOffset = (blockId_ / 2) * s1BaseSize_ * 2 * paramNum_ + // 2个AIV共同地址偏移
(blockId_ % 2) * (s1BaseSize_ / 2) * 2 * paramNum_; // 每个AIV的地址偏移，S1方向
DataCopyPad(vec1ParamGm[wsInfoOffset], tmpfBuff.template ReinterpretCast<int64_t>(),
{1, static_cast<uint16_t>((s1BaseSize_ / 2) * 2 * paramNum_ * sizeof(int64_t)), 0, 0});
SetWaitFlag<HardEvent::MTE3_V>(HardEvent::MTE3_V);
outQueue_.FreeTensor(tmpfBuff);
}
template <typename LIQT>
__aicore__ inline void LIQVector<LIQT>::InitLDBuffers(TPipe *pipe)
{
pipe->Reset();
pipe->InitBuffer(ldToBeMrgBuf_, BASE_TOPK_VALUE_IDX_SIZE * mrgListNum_ * sizeof(float));
pipe->InitBuffer(ldTmpBuf_, BASE_TOPK_VALUE_IDX_SIZE * mrgListNum_ * sizeof(float));
pipe->InitBuffer(ldOutValueBuf_, BASE_TOPK * sizeof(float));
pipe->InitBuffer(ldOutIdxBuf_, BASE_TOPK * sizeof(int32_t));
}
template <typename LIQT>
__aicore__ inline void LIQVector<LIQT>::InitParams(const struct LIQCommon::ConstInfo &constInfo,
const LIQTilingData *__restrict tilingData)
{
this->constInfo_ = constInfo;
blockS2StartIdx_ = 0;
gSize_ = constInfo.gSize;
kSeqSize_ = constInfo.kSeqSize;
// define N2 para
kHeadNum_ = constInfo.kHeadNum;
qHeadNum_ = constInfo.qHeadNum;
// define MMBase para
s1BaseSize_ = constInfo.s1BaseSize; // 4
s2BaseSize_ = constInfo.s2BaseSize; // 2048
kCacheBlockSize_ = constInfo.kCacheBlockSize;
maxBlockNumPerBatch_ = constInfo.maxBlockNumPerBatch;
blockId_ = GetBlockIdx();
}
template <typename LIQT>
__aicore__ inline void LIQVector<LIQT>::InitVecInputTensor(GlobalTensor<half> weightsGm, GlobalTensor<half> qScaleGm,
GlobalTensor<half> kScaleGm,
GlobalTensor<int32_t> indiceOutGm,
GlobalTensor<int32_t> blockTableGm)
{
this->weightsGm = weightsGm;
this->qScaleGm = qScaleGm;
this->kScaleGm = kScaleGm;
this->indiceOutGm = indiceOutGm;
this->blockTableGm = blockTableGm;
}
template <typename LIQT>
__aicore__ inline void LIQVector<LIQT>::InitVecWorkspaceTensor(GlobalTensor<half> vec0OutGm,
GlobalTensor<MM1_OUT_T> mm1ResGm,
GlobalTensor<float> vec1ResGm,
GlobalTensor<int64_t> vec1ParamGm)
{
this->mm1ResGm = mm1ResGm;
this->vec1ResGm = vec1ResGm;
this->vec0OutGm = vec0OutGm;
this->vec1ParamGm = vec1ParamGm;
}
template <typename LIQT>
__aicore__ inline void LIQVector<LIQT>::AllocEventID()
{
}
template <typename LIQT>
__aicore__ inline void LIQVector<LIQT>::FreeEventID()
{
}
template <typename LIQT>
__aicore__ inline void LIQVector<LIQT>::CleanInvalidOutput(int64_t invalidS1offset)
{
// init -1 and copy to output
LocalTensor<float> valueULocal = outQueue_.AllocTensor<float>();
LocalTensor<int32_t> idxULocal1 = valueULocal.template ReinterpretCast<int32_t>();
Duplicate(idxULocal1, constInfo_.INVALID_IDX, constInfo_.sparseCount);
outQueue_.EnQue<float>(valueULocal);
valueULocal = outQueue_.DeQue<float>();
LIQServiceVec::CopyOut(indiceOutGm[invalidS1offset], idxULocal1, constInfo_.sparseCount);
outQueue_.FreeTensor(valueULocal);
}
template <typename LIQT>
__aicore__ inline void LIQVector<LIQT>::ProcessVec0(const LIQCommon::RunInfo &info)
{
// 只需要一个v核做
if (blockId_ % 2 != 0) {
return;
}
int32_t cuBaseS1Idx = info.gS1Idx * s1BaseSize_;
// 计算输出w基地址偏移 偶数循环 -> 0 + aic_offset 奇数循环 -> 4*64 + aic_offset
int64_t vec0OutGmOffset = (info.loop % 2) * ((s1BaseSize_ * gSize_ * BLOCK_CUBE));
// 计算输入weight的地址偏移，qScale的地址偏移与weight相同
int64_t weightGmOffset = info.tensorWeightsOffset + cuBaseS1Idx * qHeadNum_;
// 当前需要计算的S1行数，处理尾块场景
int32_t cuS1ProcNum = cuBaseS1Idx + s1BaseSize_ > info.actS1Size ? info.actS1Size % s1BaseSize_ : s1BaseSize_;
int32_t cuProcEleNum = cuS1ProcNum * gSize_;
LocalTensor<half> inWeightsUb = inQueue_.AllocTensor<half>();
LocalTensor<half> inQScaleUb = inWeightsUb[cuProcEleNum];
AscendC::DataCopyPadExtParams<half> padParams{false, 0, 0, 0};
AscendC::DataCopyExtParams copyInParams;
copyInParams.blockCount = 1;
copyInParams.blockLen = cuProcEleNum * sizeof(half);
copyInParams.srcStride = 0;
copyInParams.dstStride = 0;
copyInParams.rsv = 0;
AscendC::DataCopyPad(inWeightsUb, weightsGm[weightGmOffset], copyInParams, padParams);
AscendC::DataCopyPad(inQScaleUb, qScaleGm[weightGmOffset], copyInParams, padParams);
inQueue_.EnQue<half>(inWeightsUb);
inWeightsUb = inQueue_.DeQue<half>();
AscendC::Mul(inWeightsUb, inWeightsUb, inQScaleUb, cuProcEleNum);
PipeBarrier<PIPE_V>();
LocalTensor<half> resUb = outQueue_.AllocTensor<half>();
AscendC::Brcb(resUb, inWeightsUb, static_cast<uint8_t>(cuProcEleNum / 8), {1, 8});
inQueue_.FreeTensor(inWeightsUb);
outQueue_.EnQue<half>(resUb);
resUb = outQueue_.DeQue<half>();
AscendC::DataCopyParams copyOutParams;
copyOutParams.blockCount = 1;
copyOutParams.blockLen = cuProcEleNum * BLOCK_CUBE * sizeof(half);
copyOutParams.srcStride = 0;
copyOutParams.dstStride = 0;
AscendC::DataCopyPad(vec0OutGm[vec0OutGmOffset], resUb, copyOutParams);
outQueue_.FreeTensor(resUb);
}
template <typename LIQT>
__aicore__ inline void LIQVector<LIQT>::ProcessVec1(const LIQCommon::RunInfo &info)
{
int32_t cuBaseS1Idx = info.gS1Idx * s1BaseSize_;
int32_t cuBaseS2Idx = info.s2Idx * s2BaseSize_;
// 计算基本块基地址偏移 偶数循环 -> 0 + aic_offset 奇数循环 -> 4*2048 + aic_offset
int64_t mmGmOffset = (info.loop % 2) * (s1BaseSize_ * s2BaseSize_);
// cuS1BeginIdxPerAiv: 每个AIV的S1起始偏移
int32_t cuS1BeginIdxPerAiv = cuBaseS1Idx;
int32_t cuS1ProcNum =
cuS1BeginIdxPerAiv + s1BaseSize_ > info.actS1Size ? info.actS1Size % s1BaseSize_ : s1BaseSize_;
// cuS1ProcNumPerAiv: 每个AIv的S1计算量
int32_t cuS1ProcNumPerAiv = blockId_ % 2 == 0 ? CeilDiv(cuS1ProcNum, 2) : (cuS1ProcNum / 2);
cuS1BeginIdxPerAiv += (blockId_ % 2) * CeilDiv(cuS1ProcNum, 2);
// 基本块基地址偏移奇数核加一个S1地址偏移
mmGmOffset += (blockId_ % 2) * CeilDiv(cuS1ProcNum, 2) * s2BaseSize_;
// 非首个基本块, M(S1)轴发生切换需要初始化
if (info.loop != 0 && info.s2Idx == 0) {
// globalTopkUb_ value,index=-inf,-1
InitSortOutBuf(globalTopkUb_, CeilDiv(s1BaseSize_, 2) * BASE_TOPK_VALUE_IDX_SIZE);
blockS2StartIdx_ = 0;
} else if (info.loop == 0) {
blockS2StartIdx_ = info.s2Idx;
}
// cuRealAcSeq: 当前基本块S1对应的AcSeq
int32_t cuRealAcSeq = info.actS2Size;
if (constInfo_.attenMaskFlag) {
// attenMask true场景
cuRealAcSeq = info.actS2Size - (info.actS1Size - cuS1BeginIdxPerAiv);
}
// LD输出S1方向偏移，保证2个Vector输出的内容连续
uint32_t ldS1Offset = (blockId_ % 2 == 0) ? s1BaseSize_ / 2 - cuS1ProcNumPerAiv : 0;
for (int innerS1Idx = 0; innerS1Idx < cuS1ProcNumPerAiv; innerS1Idx++) {
if (constInfo_.attenMaskFlag) {
cuRealAcSeq += 1;
}
int32_t cuS2Len = cuBaseS2Idx + s2BaseSize_ >= cuRealAcSeq ? cuRealAcSeq - cuBaseS2Idx : s2BaseSize_;
int32_t cuS1Idx = cuS1BeginIdxPerAiv + innerS1Idx;
if (cuRealAcSeq > 0 && cuS2Len > 0) {
int32_t cuS2LenVecAlign = CeilDiv(cuS2Len, s2BaseSize_) * s2BaseSize_;
LocalTensor<float> mmInUb = inQueue_.AllocTensor<float>();
LocalTensor<float> kScaleUb = mmInUb[cuS2LenVecAlign];
LocalTensor<half> kScaleTUb = kScaleUb.template ReinterpretCast<half>()[cuS2LenVecAlign];
AscendC::DataCopyPadExtParams<float> padParams{false, 0, 0, 0};
AscendC::DataCopyPadExtParams<half> padTParams{false, 0, 0, 0};
AscendC::DataCopyExtParams copyInParams;
copyInParams.blockCount = 1;
copyInParams.blockLen = cuS2Len * sizeof(float);
copyInParams.srcStride = 0;
copyInParams.dstStride = 0;
copyInParams.rsv = 0;
AscendC::DataCopyPad(mmInUb, mm1ResGm[mmGmOffset + innerS1Idx * s2BaseSize_], copyInParams, padParams);
GetKeyScale(info, kScaleTUb, info.bIdx, cuBaseS2Idx, cuS2Len);
inQueue_.EnQue<float>(mmInUb);
mmInUb = inQueue_.DeQue<float>();
AscendC::Cast(kScaleUb, kScaleTUb, RoundMode::CAST_NONE, cuS2Len);
PipeBarrier<PIPE_V>();
AscendC::Mul(mmInUb, mmInUb, kScaleUb, cuS2Len);
PipeBarrier<PIPE_V>();
LocalTensor<float> sortBuff = tmpBuf_.Get<float>();
LocalTensor<float> sortScoreUb = sortBuff;
LocalTensor<float> sortIndiceUb = sortBuff[cuS2LenVecAlign];
PipeBarrier<PIPE_V>();
Duplicate(sortScoreUb.template ReinterpretCast<int32_t>(), LIQServiceVec::NEG_INF, cuS2LenVecAlign);
PipeBarrier<PIPE_V>();
Adds(sortScoreUb, mmInUb, 0.0f, cuS2Len);
PipeBarrier<PIPE_V>();
inQueue_.FreeTensor(mmInUb);
LocalTensor<int32_t> sortIndiceUbInt = sortIndiceUb.template ReinterpretCast<int32_t>();
// 无效数据索引填充为-1
if (cuS2LenVecAlign != cuS2Len) {
Duplicate(sortIndiceUbInt, -1, cuS2LenVecAlign);
PipeBarrier<PIPE_V>();
}
Adds(sortIndiceUbInt, globalTopkIndice_, static_cast<int32_t>(cuBaseS2Idx), cuS2Len);
PipeBarrier<PIPE_V>();
LocalTensor<float> tmpSortBuf = sortBuff[2 * cuS2LenVecAlign];
LIQServiceVec::SortAll(sortBuff, tmpSortBuf, cuS2LenVecAlign);
PipeBarrier<PIPE_V>();
LIQServiceVec::MergeSort(globalTopkUb_[innerS1Idx * BASE_TOPK_VALUE_IDX_SIZE], BASE_TOPK, sortBuff,
cuS2LenVecAlign, tmpSortBuf);
PipeBarrier<PIPE_V>();
bool isS2End = cuBaseS2Idx + s2BaseSize_ >= cuRealAcSeq;
bool needCopyOutGm = blockS2StartIdx_ == 0 && isS2End;
// 中间结果保存
bool needCopyWsGm = info.isAllLoopEnd || isS2End;
if (needCopyOutGm) {
LocalTensor<uint32_t> idxULocal = outQueue_.AllocTensor<uint32_t>();
ExtractIndex(idxULocal,
globalTopkUb_[innerS1Idx * BASE_TOPK_VALUE_IDX_SIZE].template ReinterpretCast<uint32_t>(),
BASE_TOPK);
PipeBarrier<PIPE_V>();
InitSortOutBuf(globalTopkUb_[innerS1Idx * BASE_TOPK_VALUE_IDX_SIZE], BASE_TOPK_VALUE_IDX_SIZE);
outQueue_.EnQue<uint32_t>(idxULocal);
idxULocal = outQueue_.DeQue<uint32_t>();
LIQServiceVec::CopyOut(indiceOutGm[info.indiceOutOffset + cuS1Idx * constInfo_.sparseCount],
idxULocal.template ReinterpretCast<int32_t>(), constInfo_.sparseCount);
outQueue_.FreeTensor(idxULocal);
} else if (needCopyWsGm) {
// vec1Res Gm = [aic, s1BaseSize_, 2, 2, topkOut_] float32
// vec1Param Gm = [aic, s1BaseSize_, 2, 16] int64
// 16 = [needFd, s2AcSeq, s2Start, s2End, isS2End, bn2idx, s1Idx, S1ProcNum, ......]
int64_t wsOffset =
(blockId_ / 2) * s1BaseSize_ * 2 * BASE_TOPK_VALUE_IDX_SIZE + // 2个AIV共同地址偏移
(blockId_ % 2) * (s1BaseSize_ / 2) * 2 * BASE_TOPK_VALUE_IDX_SIZE + // 每个AIV的地址偏移，S1方向
(ldS1Offset + innerS1Idx) * 2 * BASE_TOPK_VALUE_IDX_SIZE;
int64_t wsInfoOffset =
(blockId_ / 2) * s1BaseSize_ * 2 * paramNum_ + // 2个AIV共同地址偏移
(blockId_ % 2) * (s1BaseSize_ / 2) * 2 * paramNum_ + // 每个AIV的地址偏移，S1方向
(ldS1Offset + innerS1Idx) * 2 * paramNum_;
LocalTensor<int64_t> tmpiBuff = paramBuf_.Get<int64_t>();
SetWaitFlag<HardEvent::MTE3_S>(HardEvent::MTE3_S);
tmpiBuff.SetValue(0, static_cast<int64_t>(1));
tmpiBuff.SetValue(1, static_cast<int64_t>(cuRealAcSeq));
tmpiBuff.SetValue(2, static_cast<int64_t>(blockS2StartIdx_));
tmpiBuff.SetValue(3, static_cast<int64_t>(cuBaseS2Idx + cuS2Len));
tmpiBuff.SetValue(4, static_cast<int64_t>(isS2End));
tmpiBuff.SetValue(5, static_cast<int64_t>(info.bN2Idx));
tmpiBuff.SetValue(6, static_cast<int64_t>(cuS1Idx));
tmpiBuff.SetValue(7, static_cast<int64_t>(cuS1ProcNum));
tmpiBuff.SetValue(8, static_cast<int64_t>(info.indiceOutOffset + cuS1Idx * constInfo_.sparseCount));
// 写入头尾判断
// [head, tail]
// head: 与前面规约，与前后规约
// tail: 与后面规约
bool isTailReduce = blockS2StartIdx_ == 0; // 一定是isLastTile
// WS偏移规则 blockS2StartIdx_ != 0
// 跟前面块做规约 写到0偏移 不用做计算 blockS2StartIdx_ == 0 and !isS2End
// 跟后面块做规约 写到1偏移 需要 + s1BaseSize_, BASE_TOPK*2
if (isTailReduce) { // S2不是最后结束的数据就需要往后做规约，放入第二块ws
wsInfoOffset += paramNum_;
wsOffset += BASE_TOPK_VALUE_IDX_SIZE;
}
SetWaitFlag<HardEvent::S_MTE3>(HardEvent::S_MTE3);
LIQServiceVec::CopyOut(vec1ParamGm[wsInfoOffset], tmpiBuff, 16);
SetWaitFlag<HardEvent::V_MTE3>(HardEvent::V_MTE3);
LIQServiceVec::CopyOut(vec1ResGm[wsOffset], globalTopkUb_[innerS1Idx * BASE_TOPK_VALUE_IDX_SIZE],
BASE_TOPK_VALUE_IDX_SIZE);
SetWaitFlag<HardEvent::MTE3_V>(HardEvent::MTE3_V);
}
} else if (cuRealAcSeq <= 0) {
CleanInvalidOutput(info.indiceOutOffset + cuS1Idx * constInfo_.sparseCount);
}
}
// BNSD场景无效S1 输出-1
if (Q_LAYOUT_T == LI_LAYOUT::BSND) {
// 最后一个S1的基本块, 需要 >= info.actS1Size
bool isS1LoopEnd = (cuBaseS1Idx + s1BaseSize_) >= info.actS1Size;
int32_t invalidS1Num = constInfo_.qSeqSize - info.actS1Size;
// blockS2StartIdx_ == 0 控制S2从开始的核去做冗余清理
if (invalidS1Num > 0 && isS1LoopEnd && blockS2StartIdx_ == 0) {
int32_t s1NumPerAiv = blockId_ % 2 == 0 ? CeilDiv(invalidS1Num, 2) : (invalidS1Num / 2);
int32_t s1OffsetPerAiv = info.actS1Size + (blockId_ % 2) * CeilDiv(invalidS1Num, 2);
for (int innerS1Idx = 0; innerS1Idx < s1NumPerAiv; innerS1Idx++) {
CleanInvalidOutput(info.indiceOutOffset + (s1OffsetPerAiv + innerS1Idx) * constInfo_.sparseCount);
}
}
int32_t invalidS1Num2 = info.actS1Size - info.actS2Size;
if (invalidS1Num2 > 0 && isS1LoopEnd && blockS2StartIdx_ == 0 && constInfo_.attenMaskFlag) {
int32_t s1NumPerAiv = blockId_ % 2 == 0 ? CeilDiv(invalidS1Num2, 2) : (invalidS1Num2 / 2);
int32_t s1OffsetPerAiv = (blockId_ % 2) * CeilDiv(invalidS1Num2, 2);
for (int innerS1Idx = 0; innerS1Idx < s1NumPerAiv; innerS1Idx++) {
CleanInvalidOutput((info.bN2Idx * constInfo_.qSeqSize + s1OffsetPerAiv + innerS1Idx) *
constInfo_.sparseCount);
}
}
}
if (info.isLastS2InnerLoop) {
// S2最后一个Loop后, 下一个基本块初始从0开始
blockS2StartIdx_ = 0;
}
}
template <typename LIQT>
__aicore__ inline void LIQVector<LIQT>::ProcessLD()
{
int32_t curCubeId = blockId_ / 2;
int32_t tmpCubeId = curCubeId;
int64_t s2ActSeq;
int64_t s2Start;
int64_t s2End;
int64_t isS2End;
int64_t bn2Idx;
int64_t s1Idx;
uint32_t acc_list_num = 0;
int64_t bIdx = 0;
int64_t needFd;
int64_t wsOffset;
int64_t wsInfoOffset = 0;
int64_t nextneedFd;
int64_t valueOffset = 0;
int64_t outOffset = 0;
LocalTensor<float> curValueIdxUb = ldToBeMrgBuf_.Get<float>();
LocalTensor<float> tmpUb = ldTmpBuf_.Get<float>();
// S2开头信息
// 开始必然没有头规约，因此从尾规约开始处理，while循环读取下一个核的头规约
// 存满4个list或者遇到S2结尾，则做merge，直到做完S2
// 每个核都忽略自己的头规约，因为必然由前面的核做完
uint32_t s1LdStartIdx = 0;
uint32_t s1ProcNum = 0;
uint64_t paramGmCoreOffset = tmpCubeId * s1BaseSize_ * 2 * paramNum_;
for (uint32_t innerS1Idx = 0; innerS1Idx < s1BaseSize_; innerS1Idx++) {
needFd = vec1ParamGm.GetValue(paramGmCoreOffset + innerS1Idx * 2 * paramNum_ + paramNum_);
if (needFd == 1) {
s1LdStartIdx = (s1ProcNum == 0) ? innerS1Idx : s1LdStartIdx;
s1ProcNum++;
}
}
if (s1ProcNum == 0) {
return;
}
// S1逐行计算
uint32_t s1VecNum = CeilDiv(s1ProcNum, 2);
if (blockId_ % 2 == 1) {
s1LdStartIdx = s1LdStartIdx + s1VecNum;
s1VecNum = s1ProcNum - s1VecNum;
}
for (uint32_t innerS1Idx = s1LdStartIdx; innerS1Idx < s1LdStartIdx + s1VecNum; innerS1Idx++) {
// 重置偏移
tmpCubeId = curCubeId;
acc_list_num = 0;
valueOffset = 0;
// 搬入数据
wsOffset = tmpCubeId * s1BaseSize_ * 2 * BASE_TOPK_VALUE_IDX_SIZE + // 2个AIV共同地址偏移
innerS1Idx * 2 * BASE_TOPK_VALUE_IDX_SIZE + BASE_TOPK_VALUE_IDX_SIZE;
SetWaitFlag<HardEvent::V_MTE2>(HardEvent::V_MTE2);
SetWaitFlag<HardEvent::S_MTE2>(HardEvent::S_MTE2);
DataCopyPad(curValueIdxUb, vec1ResGm[wsOffset],
{1, static_cast<uint16_t>(BASE_TOPK_VALUE_IDX_SIZE * sizeof(int32_t)), 0, 0}, {true, 0, 0, 0});
acc_list_num++;
valueOffset += BASE_TOPK_VALUE_IDX_SIZE;
// 获取下一个核规约信息
tmpCubeId++;
wsInfoOffset = tmpCubeId * s1BaseSize_ * 2 * paramNum_ + innerS1Idx * 2 * paramNum_;
needFd = vec1ParamGm.GetValue(wsInfoOffset);
isS2End = vec1ParamGm.GetValue(wsInfoOffset + 4);
s1Idx = vec1ParamGm.GetValue(wsInfoOffset + 6);
outOffset = vec1ParamGm.GetValue(wsInfoOffset + 8);
while (needFd == 1) {
// 搬入头规约数据
wsOffset = tmpCubeId * s1BaseSize_ * 2 * BASE_TOPK_VALUE_IDX_SIZE + // 2个AIV共同地址偏移
innerS1Idx * 2 * BASE_TOPK_VALUE_IDX_SIZE;
SetWaitFlag<HardEvent::V_MTE2>(HardEvent::V_MTE2);
SetWaitFlag<HardEvent::S_MTE2>(HardEvent::S_MTE2);
DataCopyPad(curValueIdxUb[valueOffset], vec1ResGm[wsOffset],
{1, static_cast<uint16_t>(BASE_TOPK_VALUE_IDX_SIZE * sizeof(int32_t)), 0, 0}, {true, 0, 0, 0});
valueOffset += BASE_TOPK_VALUE_IDX_SIZE;
acc_list_num++;
// 每满4个list，聚合 前2K为mrg结果
if (acc_list_num == mrgListNum_) {
// MrgSort 四条2048的队列，Mrg成一条
AscendC::MrgSort4Info params;
params.elementLengths[0] = BASE_TOPK;
params.elementLengths[1] = BASE_TOPK;
params.elementLengths[2] = BASE_TOPK;
params.elementLengths[3] = BASE_TOPK;
params.ifExhaustedSuspension = true;
params.validBit = 0b1111;
params.repeatTimes = 1;
AscendC::MrgSortSrcList<float> srcList;
srcList.src1 = curValueIdxUb[0];
srcList.src2 = curValueIdxUb[BASE_TOPK_VALUE_IDX_SIZE];
srcList.src3 = curValueIdxUb[2 * BASE_TOPK_VALUE_IDX_SIZE];
srcList.src4 = curValueIdxUb[3 * BASE_TOPK_VALUE_IDX_SIZE];
SetWaitFlag<HardEvent::MTE2_V>(HardEvent::MTE2_V);
MrgSort(tmpUb, srcList, params);
PipeBarrier<PIPE_V>();
DataCopy(curValueIdxUb, tmpUb, BASE_TOPK_VALUE_IDX_SIZE);
PipeBarrier<PIPE_V>();
acc_list_num = 1;
valueOffset = BASE_TOPK_VALUE_IDX_SIZE;
}
// reduce到S2末尾，则跳出
if (isS2End == 1) {
break;
}
tmpCubeId++;
wsInfoOffset = tmpCubeId * s1BaseSize_ * 2 * paramNum_ + innerS1Idx * 2 * paramNum_;
needFd = vec1ParamGm.GetValue(wsInfoOffset);
isS2End = vec1ParamGm.GetValue(wsInfoOffset + 4);
}
// mrg不足4个list的数据
if (acc_list_num != 1) {
AscendC::MrgSort4Info params;
params.elementLengths[0] = BASE_TOPK;
params.elementLengths[1] = BASE_TOPK;
params.elementLengths[2] = BASE_TOPK;
params.elementLengths[3] = BASE_TOPK;
params.ifExhaustedSuspension = true;
if (acc_list_num == 2) {
params.validBit = 0b0011;
} else if (acc_list_num == 3) {
params.validBit = 0b0111;
}
params.repeatTimes = 1;
AscendC::MrgSortSrcList<float> srcList;
srcList.src1 = curValueIdxUb[0];
srcList.src2 = curValueIdxUb[BASE_TOPK_VALUE_IDX_SIZE];
srcList.src3 = curValueIdxUb[2 * BASE_TOPK_VALUE_IDX_SIZE];
srcList.src4 = curValueIdxUb[3 * BASE_TOPK_VALUE_IDX_SIZE];
SetWaitFlag<HardEvent::MTE2_V>(HardEvent::MTE2_V);
MrgSort(tmpUb, srcList, params);
PipeBarrier<PIPE_V>();
DataCopy(curValueIdxUb, tmpUb, BASE_TOPK_VALUE_IDX_SIZE);
PipeBarrier<PIPE_V>();
}
// 搬出
LocalTensor<float> outValueUb = ldOutValueBuf_.Get<float>();
LocalTensor<uint32_t> outIdxUb = ldOutIdxBuf_.Get<uint32_t>();
Extract(outValueUb, outIdxUb, curValueIdxUb, (BASE_TOPK / 32));
LocalTensor<int32_t> idxULocal1 = outIdxUb.template ReinterpretCast<int32_t>();
SetWaitFlag<HardEvent::V_MTE3>(HardEvent::V_MTE3);
SetWaitFlag<HardEvent::S_MTE3>(HardEvent::S_MTE3);
DataCopyPad(indiceOutGm[outOffset], idxULocal1,
{1, static_cast<uint16_t>(constInfo_.sparseCount * sizeof(int32_t)), 0, 0});
SetWaitFlag<HardEvent::MTE3_V>(HardEvent::MTE3_V);
}
}
} // namespace LIQKernel
#endif

53
csrc/lightning_indexer_quant/op_kernel/lightning_indexer_quant_template_tiling_key.h
View File

@@ -1,53 +0,0 @@
/**
* This program is free software, you can redistribute it and/or modify it.
* Copyright (c) 2025 Huawei Technologies Co., Ltd.
* This file is a part of the CANN Open Software.
* Licensed under CANN Open Software License Agreement Version 2.0 (the "License").
* Please refer to the License for details. You may not use this file except in compliance with the License.
* THIS SOFTWARE IS PROVIDED ON AN "AS IS" BASIS, WITHOUT WARRANTIES OF ANY KIND, EITHER EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO NON-INFRINGEMENT, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE.
* See LICENSE in the root of the software repository for the full text of the License.
*/
/*!
* \file lightning_indexer_quant_template_tiling_key.h
* \brief
*/
#ifndef TEMPLATE_TILING_KEY_LI_H_
#define TEMPLATE_TILING_KEY_LI_H_
#include "ascendc/host_api/tiling/template_argument.h"
#define LI_TPL_FP16 1
#define LI_TPL_IN8 2
#define LI_TPL_INT32 3
#define LI_TPL_BF16 27
#define LIQ_LAYOUT_BSND 0
#define LIQ_LAYOUT_TND 1
#define LIQ_LAYOUT_PA_BSND 2
#define ASCENDC_TPL_4_BW 4
// 模板参数支持的范围定义
ASCENDC_TPL_ARGS_DECL(LightningIndexerQuant, // 算子OpType
ASCENDC_TPL_DTYPE_DECL(DT_Q, LI_TPL_IN8), ASCENDC_TPL_DTYPE_DECL(DT_K, LI_TPL_IN8),
ASCENDC_TPL_DTYPE_DECL(DT_OUT, LI_TPL_INT32), ASCENDC_TPL_BOOL_DECL(PAGE_ATTENTION, 1, 0),
ASCENDC_TPL_UINT_DECL(Q_LAYOUT_T, ASCENDC_TPL_4_BW, ASCENDC_TPL_UI_LIST, LIQ_LAYOUT_BSND,
LIQ_LAYOUT_TND),
ASCENDC_TPL_UINT_DECL(K_LAYOUT_T, ASCENDC_TPL_4_BW, ASCENDC_TPL_UI_LIST,
LIQ_LAYOUT_PA_BSND, LIQ_LAYOUT_BSND, LIQ_LAYOUT_TND), );
// 支持的模板参数组合
// 用于调用GET_TPL_TILING_KEY获取TilingKey时，接口内部校验TilingKey是否合法
ASCENDC_TPL_SEL(
ASCENDC_TPL_ARGS_SEL(ASCENDC_TPL_DTYPE_SEL(DT_Q, LI_TPL_IN8), ASCENDC_TPL_DTYPE_SEL(DT_K, LI_TPL_IN8),
ASCENDC_TPL_DTYPE_SEL(DT_OUT, LI_TPL_INT32), ASCENDC_TPL_BOOL_SEL(PAGE_ATTENTION, 1),
ASCENDC_TPL_UINT_SEL(Q_LAYOUT_T, ASCENDC_TPL_UI_LIST, LIQ_LAYOUT_BSND, LIQ_LAYOUT_TND),
ASCENDC_TPL_UINT_SEL(K_LAYOUT_T, ASCENDC_TPL_UI_LIST, LIQ_LAYOUT_PA_BSND), ),
ASCENDC_TPL_ARGS_SEL(ASCENDC_TPL_DTYPE_SEL(DT_Q, LI_TPL_IN8), ASCENDC_TPL_DTYPE_SEL(DT_K, LI_TPL_IN8),
ASCENDC_TPL_DTYPE_SEL(DT_OUT, LI_TPL_INT32), ASCENDC_TPL_BOOL_SEL(PAGE_ATTENTION, 0),
ASCENDC_TPL_UINT_SEL(Q_LAYOUT_T, ASCENDC_TPL_UI_LIST, LIQ_LAYOUT_BSND, LIQ_LAYOUT_TND),
ASCENDC_TPL_UINT_SEL(K_LAYOUT_T, ASCENDC_TPL_UI_LIST, LIQ_LAYOUT_BSND, LIQ_LAYOUT_TND), ), );
#endif

193
csrc/lightning_indexer_quant/op_kernel/lightning_indexer_quant_vector.h
View File

@@ -1,193 +0,0 @@
/**
* This program is free software, you can redistribute it and/or modify it.
* Copyright (c) 2025 Huawei Technologies Co., Ltd.
* This file is a part of the CANN Open Software.
* Licensed under CANN Open Software License Agreement Version 2.0 (the "License").
* Please refer to the License for details. You may not use this file except in compliance with the License.
* THIS SOFTWARE IS PROVIDED ON AN "AS IS" BASIS, WITHOUT WARRANTIES OF ANY KIND, EITHER EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO NON-INFRINGEMENT, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE.
* See LICENSE in the root of the software repository for the full text of the License.
*/
/*!
* \file lightning_indexer_quant_vector.h
* \brief
*/
#ifndef LIGHTNING_INDEXER_QUANT_VECTOR_H
#define LIGHTNING_INDEXER_QUANT_VECTOR_H
#include "kernel_operator.h"
#include "lightning_indexer_quant_vector.h"
namespace LIQServiceVec {
using namespace AscendC;
constexpr int32_t NEG_INF = 0xFF800000;
constexpr int32_t INVALID_INDEX = -1;
constexpr uint8_t VEC_REPEAT_MAX = 255;
constexpr uint8_t B32_VEC_ELM_NUM = 64;
constexpr uint8_t B32_BLOCK_ALIGN_NUM = 8;
constexpr uint8_t B32_VEC_REPEAT_STRIDE = 8;
constexpr uint64_t VEC_REPEAT_BYTES = 256;
constexpr int32_t CONST_TWO = 2;
constexpr int64_t VALUE_AND_INDEX_NUM = 2;
constexpr int64_t BLOCK_BYTES = 32;
constexpr int64_t MRG_QUE_0 = 0;
constexpr int64_t MRG_QUE_1 = 1;
constexpr int64_t MRG_QUE_2 = 2;
constexpr int64_t MRG_QUE_3 = 3;
constexpr int64_t MRG_BLOCK_2 = 2;
constexpr int64_t MRG_BLOCK_3 = 3;
constexpr int64_t MRG_BLOCK_4 = 4;
template <typename T>
__aicore__ inline void CopyOut(const GlobalTensor<T> &dstGm, const LocalTensor<T> &srcUb, int64_t copyCount)
{
AscendC::DataCopyParams dataCopyOutyParams;
dataCopyOutyParams.blockCount = 1;
dataCopyOutyParams.blockLen = copyCount * sizeof(T);
dataCopyOutyParams.srcStride = 0;
dataCopyOutyParams.dstStride = 0;
AscendC::DataCopyPad(dstGm, srcUb, dataCopyOutyParams);
}
/**
src: 传入的初始化空间
eleNum: 需要初始化的元素个数需为64整数倍，元素将被初始化为交错排布的-inf，-1
*/
__aicore__ inline void InitSortOutBuf(const LocalTensor<float> &src, int64_t eleNum)
{
uint64_t mask1[2] = {0x5555555555555555, 0};
uint64_t mask0[2] = {0xaaaaaaaaaaaaaaaa, 0};
int64_t repeatNum = eleNum / B32_VEC_ELM_NUM;
int64_t forLoop = repeatNum / VEC_REPEAT_MAX;
int64_t forRemain = repeatNum % VEC_REPEAT_MAX;
for (int i = 0; i < forLoop; i++) {
AscendC::Duplicate(src.template ReinterpretCast<int32_t>(), NEG_INF, mask1, VEC_REPEAT_MAX, 1,
B32_VEC_REPEAT_STRIDE);
AscendC::Duplicate(src.template ReinterpretCast<int32_t>(), INVALID_INDEX, mask0, VEC_REPEAT_MAX, 1,
B32_VEC_REPEAT_STRIDE);
}
if (forRemain > 0) {
AscendC::Duplicate(src.template ReinterpretCast<int32_t>()[forLoop * VEC_REPEAT_MAX * B32_VEC_ELM_NUM], NEG_INF,
mask1, forRemain, 1, B32_VEC_REPEAT_STRIDE);
AscendC::Duplicate(src.template ReinterpretCast<int32_t>()[forLoop * VEC_REPEAT_MAX * B32_VEC_ELM_NUM],
INVALID_INDEX, mask0, forRemain, 1, B32_VEC_REPEAT_STRIDE);
}
AscendC::PipeBarrier<PIPE_V>();
}
/**
src: logits和索引，前logitsNum为logits，后logitsNum为索引
tmp: 计算使用到的临时空间，大小与src一致
logitsNum: 排序的元素个数, 暂只支持[128,256,384,512,1024,2048]
*/
__aicore__ inline void SortAll(LocalTensor<float> &src, LocalTensor<float> &tmp, int64_t logitsNum)
{
int64_t sort32Repeats = logitsNum / BLOCK_BYTES;
AscendC::Sort32(tmp, src, src[logitsNum].ReinterpretCast<uint32_t>(), sort32Repeats);
AscendC::PipeBarrier<PIPE_V>();
int64_t mrgGroups = sort32Repeats;
int64_t mrgElements = BLOCK_BYTES;
int64_t i = 0;
AscendC::LocalTensor<float> srcTensor;
AscendC::LocalTensor<float> dstTensor;
while (true) {
if (i % CONST_TWO == 0) {
srcTensor = tmp;
dstTensor = src;
} else {
srcTensor = src;
dstTensor = tmp;
}
AscendC::MrgSort4Info params;
params.elementLengths[0] = mrgElements;
params.elementLengths[MRG_QUE_1] = mrgElements;
params.elementLengths[MRG_QUE_2] = mrgElements;
params.elementLengths[MRG_QUE_3] = mrgElements;
params.ifExhaustedSuspension = false;
params.validBit = 0b1111;
AscendC::MrgSortSrcList<float> srcList;
srcList.src1 = srcTensor[0];
srcList.src2 = srcTensor[MRG_QUE_1 * VALUE_AND_INDEX_NUM * mrgElements];
srcList.src3 = srcTensor[MRG_QUE_2 * VALUE_AND_INDEX_NUM * mrgElements];
srcList.src4 = srcTensor[MRG_QUE_3 * VALUE_AND_INDEX_NUM * mrgElements];
if (mrgGroups <= MRG_BLOCK_4) {
params.repeatTimes = 1;
if (mrgGroups == 1) {
break;
} else if (mrgGroups == MRG_BLOCK_2) {
params.validBit = 0b0011;
} else if (mrgGroups == MRG_BLOCK_3) {
params.validBit = 0b0111;
} else if (mrgGroups == MRG_BLOCK_4) {
params.validBit = 0b1111;
}
AscendC::MrgSort<float>(dstTensor, srcList, params);
i += 1;
break;
} else {
params.repeatTimes = mrgGroups / MRG_BLOCK_4;
AscendC::MrgSort<float>(dstTensor, srcList, params);
i += 1;
mrgElements = mrgElements * MRG_BLOCK_4;
mrgGroups = mrgGroups / MRG_BLOCK_4;
}
AscendC::PipeBarrier<PIPE_V>();
}
if (i % CONST_TWO == 0) {
AscendC::DataCopy(src, tmp, logitsNum * VALUE_AND_INDEX_NUM);
AscendC::PipeBarrier<PIPE_V>();
}
}
/**
mrgDst: 合并进的Tensor
mrgSrc: 待合并的Tensor
tmpTensor：空间为mrgDst+mrgSrc
*/
__aicore__ inline void MergeSort(const LocalTensor<float> &mrgDst, int32_t mrgDstNum, LocalTensor<float> &mrgSrc,
int32_t mrgSrcNum, LocalTensor<float> &tmpTensor)
{
AscendC::MrgSort4Info params;
params.elementLengths[0] = mrgSrcNum;
params.elementLengths[1] = mrgDstNum;
params.ifExhaustedSuspension = false;
params.validBit = 0b0011;
params.repeatTimes = 1;
AscendC::MrgSortSrcList<float> srcList;
srcList.src1 = mrgSrc;
srcList.src2 = mrgDst;
AscendC::MrgSort<float>(tmpTensor, srcList, params);
AscendC::PipeBarrier<PIPE_V>();
AscendC::DataCopy(mrgDst, tmpTensor, mrgDstNum * VALUE_AND_INDEX_NUM);
AscendC::PipeBarrier<PIPE_V>();
}
__aicore__ inline void ExtractIndex(const LocalTensor<uint32_t> &idxULocal, const LocalTensor<uint32_t> &sortLocal,
int64_t extractNum)
{
AscendC::GatherMaskParams gatherMaskParams;
gatherMaskParams.repeatTimes = Ceil(extractNum * sizeof(float) * VALUE_AND_INDEX_NUM, VEC_REPEAT_BYTES);
gatherMaskParams.src0BlockStride = 1;
gatherMaskParams.src0RepeatStride = B32_VEC_REPEAT_STRIDE;
gatherMaskParams.src1RepeatStride = 0;
uint64_t rsvdCnt = 0; // 用于保存筛选后保留下来的元素个数
uint8_t src1Pattern = 2; // 固定模式2,表示筛选出奇数索引的数
AscendC::GatherMask(idxULocal, sortLocal, src1Pattern, false, static_cast<uint32_t>(0), gatherMaskParams, rsvdCnt);
AscendC::PipeBarrier<PIPE_V>();
}
template <HardEvent event>
__aicore__ inline void SetWaitFlag(HardEvent evt)
{
event_t eventId = static_cast<event_t>(GetTPipePtr()->FetchEventID(evt));
AscendC::SetFlag<event>(eventId);
AscendC::WaitFlag<event>(eventId);
}
} // namespace LIQServiceVec
#endif // LIGHTNING_INDEXER_QUANT_VECTOR_H