Skip to content

feat: optimize the groupmatmul operator. #3

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 1 commit into from
Aug 21, 2025
Merged

feat: optimize the groupmatmul operator. #3

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
2 changes: 1 addition & 1 deletion third_party/xllm_ops
Open in desktop
Submodule xllm_ops updated from 73746a to a5b84a
76 changes: 60 additions & 16 deletions xllm/core/kernels/ascend/a2/atb_layers/operations/aclnn/ops/grouped_matmul_operation.cpp
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -25,6 +25,7 @@
#include "atb_speed/log.h"
#include "atb_speed/utils/timer.h"
#include "aclnnop/aclnn_grouped_matmul_v4.h"
#include "aclnn_index_group_matmul.h"
#include "operations/aclnn/utils/utils.h"
#include "atb_speed/utils/check_util.h"

Expand All @@ -50,7 +51,7 @@ atb::Status GroupedMatmulOperation::InferShape(
ACL_BF16 : ACL_FLOAT16;
outTensorDescs.at(DIM0).shape.dimNum = inTensorDescs.at(DIM0).shape.dimNum;

int nDim = param_.transposeB ? DIM1 : DIM2;
int nDim = DIM2;
ATB_SPEED_LOG_DEBUG(opName_ << "GroupedMatmulOperation infer shape origin inTensorDescs.at(DIM0).shape.dims[DIM0]"
<< inTensorDescs.at(DIM0).shape.dims[DIM0]);
ATB_SPEED_LOG_DEBUG(opName_ << "GroupedMatmulOperation infer shape origin inTensorDescs.at(DIM1).shape.dims[nDim]"
Expand Down Expand Up @@ -95,9 +96,9 @@ atb::Dims GetWeightStorageShape(const atb::TensorDesc atbTensorDesc)
// (group_size, n, k) => (group_size, n / 16, k / 16, 16, 16)
storageTensorDims.dims[0] = atbTensorDesc.shape.dims[0];
storageTensorDims.dims[1] = 1 + ((atbTensorDesc.shape.dims[1] - 1) / 16); // 1, 16：1: 维度, 16: padding大小
storageTensorDims.dims[2] = 1 + ((atbTensorDesc.shape.dims[2] - 1) / 16); // 2, 16：1: 维度, 16: padding大小
storageTensorDims.dims[2] = 1 + ((atbTensorDesc.shape.dims[2] - 1) / 32); // 2, 16：1: 维度, 16: padding大小
storageTensorDims.dims[3] = 16; // 3, 16：NZ格式要求
storageTensorDims.dims[4] = 16; // 4, 16：NZ格式要求
storageTensorDims.dims[4] = 32; // 4, 16：NZ格式要求
}
return storageTensorDims;
}
Expand All @@ -124,10 +125,18 @@ atb::Status GroupedMatmulOperation::CreateAclNNInTensorVariantPack(const atb::Va
AclNNVariantPack &aclnnVariantPack = this->aclnnOpCache_->aclnnVariantPack;
aclnnVariantPack.aclInTensors.resize(GetInputNum());
uint32_t inTensorCount = aclnnVariantPack.aclInTensors.size();
atb::Tensor x = variantPack.inTensors.at(DIM0);
atb::Tensor y = variantPack.inTensors.at(DIM1);
int index = 7;
if(!(x.desc.shape.dims[0] < 2048 && x.desc.shape.dims[0] > 8 && y.desc.shape.dims[2]==7168)){
index = 7;
}else{
index = 4;
}
for (size_t i = 0; i < inTensorCount; i++) {
std::shared_ptr<AclNNTensor> aclnnTensor = std::make_shared<AclNNTensor>();
if (i == inTensorCount - 1) {
aclnnTensor->tensorIdx = 7; // 7 : for the last tensor
aclnnTensor->tensorIdx = index; // 7 : for the last tensor
} else {
aclnnTensor->tensorListidx = i;
aclnnTensor->tensorIdx = 0;
Expand All @@ -154,16 +163,16 @@ atb::Status GroupedMatmulOperation::CreateAclNNInTensorVariantPack(const atb::Va
storageTensorDims = GetWeightStorageShape(squeezedAtbTensor.desc);
}

// ViewShape and Stride

atb::Dims viewDims = squeezedAtbTensor.desc.shape;
if (squeezedAtbTensor.desc.shape.dimNum >= 3 && this->param_.transposeB) { // 3: 维度
aclnnTensor->strides = GetTransposeTensorStride(viewDims);
viewDims.dims[0] = squeezedAtbTensor.desc.shape.dims[0];
viewDims.dims[1] = squeezedAtbTensor.desc.shape.dims[2]; // 1, 2: 后两维转置
viewDims.dims[2] = squeezedAtbTensor.desc.shape.dims[1]; // 1, 2: 后两维转置
} else {
// if (squeezedAtbTensor.desc.shape.dimNum >= 3 && this->param_.transposeB) { // 3: 维度
// aclnnTensor->strides = GetTransposeTensorStride(viewDims);
// viewDims.dims[0] = squeezedAtbTensor.desc.shape.dims[0];
// viewDims.dims[1] = squeezedAtbTensor.desc.shape.dims[2]; // 1, 2: 后两维转置
// viewDims.dims[2] = squeezedAtbTensor.desc.shape.dims[1]; // 1, 2: 后两维转置
// } else {
aclnnTensor->strides = GetCopyTensorStride(viewDims);
}
// }

CHECK_OPERATION_STATUS_RETURN(CallAclCreateTensor(viewDims, storageTensorDims, squeezedAtbTensor, aclnnTensor));
aclnnVariantPack.aclInTensors[i] = aclnnTensor;
Expand Down Expand Up @@ -266,9 +275,26 @@ int GroupedMatmulOperation::CreateA16(AclNNVariantPack &aclnnVariantPack)
&this->aclnnOpCache_->aclExecutor);
return ret;
}
int GroupedMatmulOperation::IndexGmmQuant(AclNNVariantPack &aclnnVariantPack)
{
int ret = aclnnIndexGroupMatmulGetWorkspaceSize(aclnnVariantPack.aclInTensorList.at(DIM0),
aclnnVariantPack.aclInTensorList.at(DIM1),
param_.hasBias ? aclnnVariantPack.aclInTensorList.at(DIM3) :
aclnnVariantPack.aclInTensorList.at(DIM2),
param_.hasBias ? aclnnVariantPack.aclInTensorList.at(4) : // 5 : index of input tensor
aclnnVariantPack.aclInTensorList.at(3), // 4 : index of input tensor
param_.hasBias ? aclnnVariantPack.aclInTensors.at(5)->tensor : // 6 : index of input tensor
aclnnVariantPack.aclInTensors.at(4)->tensor, // 5 : index of input tensor
aclnnVariantPack.aclOutTensorList.at(DIM0),
&this->aclnnOpCache_->workspaceSize,
&this->aclnnOpCache_->aclExecutor);
return ret;
// splitItem, groupType, groupListType, actType,
}


int GroupedMatmulOperation::CreateW8A8Token(AclNNVariantPack &aclnnVariantPack)
{
{
int ret = aclnnGroupedMatmulV4GetWorkspaceSize(aclnnVariantPack.aclInTensorList.at(DIM0),
aclnnVariantPack.aclInTensorList.at(DIM1),
param_.hasBias ? aclnnVariantPack.aclInTensorList.at(DIM2) : nullptr,
Expand Down Expand Up @@ -313,7 +339,13 @@ int GroupedMatmulOperation::SetAclNNWorkspaceExecutor()
} else if (param_.quantType == GmmQuantType::W4A8_GROUP) {
ret = CreateW4A8(aclnnVariantPack);
} else {
ret = CreateW8A8Token(aclnnVariantPack);
atb::Tensor x = aclnnVariantPack.aclInTensors.at(DIM0)->atbTensor;
atb::Tensor y = aclnnVariantPack.aclInTensors.at(DIM1)->atbTensor;
if(!(x.desc.shape.dims[0] < 2048 && x.desc.shape.dims[0] > 8 && y.desc.shape.dims[2]==7168)){
ret = CreateW8A8Token(aclnnVariantPack);
}else{
ret = IndexGmmQuant(aclnnVariantPack);
}
}

ATB_SPEED_LOG_DEBUG(opName_ << " SetAclNNWorkspaceExecutor end, ret:" << ret
Expand All @@ -325,8 +357,20 @@ int GroupedMatmulOperation::SetAclNNWorkspaceExecutor()
int GroupedMatmulOperation::ExecuteAclNNOp(uint8_t *workspace, aclrtStream &stream)
{
ATB_SPEED_LOG_DEBUG(opName_ << " aclnnGroupedMatmul start");
int ret = aclnnGroupedMatmulV4(
workspace, this->aclnnOpCache_->workspaceSize, this->aclnnOpCache_->aclExecutor, stream);

AclNNVariantPack &aclnnVariantPack = this->aclnnOpCache_->aclnnVariantPack;
atb::Tensor x = aclnnVariantPack.aclInTensors.at(DIM0)->atbTensor;
atb::Tensor y = aclnnVariantPack.aclInTensors.at(DIM1)->atbTensor;
int ret = 0;
if(!(x.desc.shape.dims[0] < 2048 && x.desc.shape.dims[0] > 8 && y.desc.shape.dims[2]==7168)){
ret = aclnnGroupedMatmulV4(
workspace, this->aclnnOpCache_->workspaceSize, this->aclnnOpCache_->aclExecutor, stream);
}
else{
ret = aclnnIndexGroupMatmul(
workspace, this->aclnnOpCache_->workspaceSize, this->aclnnOpCache_->aclExecutor, stream);
}

ATB_SPEED_LOG_DEBUG(opName_ << " aclnnGroupedMatmul end, ret:" << ret);
return ret;
}
Expand Down
1 change: 1 addition & 0 deletions xllm/core/kernels/ascend/a2/atb_layers/operations/aclnn/ops/grouped_matmul_operation.h
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -105,6 +105,7 @@ class GroupedMatmulOperation : public AclNNOperation {
int CreateA16(AclNNVariantPack &aclnnVariantPack);
int CreateW8A8Token(AclNNVariantPack &aclnnVariantPack);
int CreateW4A8(AclNNVariantPack &aclnnVariantPack);
int IndexGmmQuant(AclNNVariantPack &aclnnVariantPack);

std::vector<aclTensor *> yTensorVector;
std::vector<std::vector<aclTensor *>> inputVectorOfTensor;
Expand Down
8 changes: 4 additions & 4 deletions xllm/core/layers/ascend/deepseek_v2_decoder_layer.cpp
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -1067,11 +1067,11 @@ void DeepseekV2DecoderImpl::merge_experts_weights() {

torch::Tensor mlp_down_weight =
merge_experts_weights(experts_weights_["down_proj.weight"],
/*transpose=*/false);
// at_weight_tensors_[IN_MLP_DOWN_WEIGHT_EXPERT] =
// at_npu::native::npu_format_cast(mlp_down_weight, 29);
/*transpose=*/true);
at_weight_tensors_[IN_MLP_DOWN_WEIGHT_EXPERT] =
at_npu::native::npu_format_cast(mlp_down_weight, 2).contiguous();
at_npu::native::npu_format_cast(mlp_down_weight, 29);
// at_weight_tensors_[IN_MLP_DOWN_WEIGHT_EXPERT] =
// at_npu::native::npu_format_cast(mlp_down_weight, 2).contiguous();
if (quantize_type_ == "w8a8_dynamic") {
at_weight_tensors_[IN_MLP_DOWN_OFFSET_EXPERT] =
merge_experts_weights(experts_weights_["down_proj.weight_offset"]);
Expand Down