提交 cfe9c356 编写于 作者: D dayschan

GraphKernel support akg batchmatmul

上级 12a359b9
akg @ df57a6cf
Subproject commit c460176523d039c8995f1d71089753725ebc0792
Subproject commit df57a6cf9450e347d1854687d1fe66a420ee3b35
......@@ -23,6 +23,7 @@
#include "kernel/tbe/tbe_kernel_select/tbe_kernel_select.h"
#include "kernel/akg/akg_kernel_metadata.h"
#include "session/anf_runtime_algorithm.h"
#include "utils/context/ms_context.h"
namespace mindspore {
namespace kernel {
......@@ -96,6 +97,12 @@ void KernelQuery(const CNodePtr &kernel_node, std::vector<std::shared_ptr<kernel
std::string op_name = AnfAlgo::GetCNodeName(kernel_node);
auto context_ptr = MsContext::GetInstance();
MS_EXCEPTION_IF_NULL(context_ptr);
if (context_ptr->enable_graph_kernel() && IsPrimitiveCNode(kernel_node, prim::kPrimBatchMatMul)) {
kernel_type = KernelType::AKG_KERNEL;
}
switch (kernel_type) {
case KernelType::AKG_KERNEL:
AkgMetadataInfo(kernel_node, kernel_info_list);
......
......@@ -47,6 +47,7 @@ from .gather_v2 import _gather_v2_akg
from .less import _less_akg
from .log import _log_akg
from .matmul import _matmul_akg
from .batchmatmul import _batchmatmul_akg
from .max_pool_grad_with_argmax import _max_pool_grad_with_argmax_akg
from .max_pool_with_argmax import _max_pool_with_argmax_akg
from .max import _max_akg
......
# Copyright 2020 Huawei Technologies Co., Ltd
#
# 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.
# ============================================================================
"""BatchMatMul op"""
from mindspore.ops.op_info_register import op_info_register
@op_info_register("""{
"op_name": "BatchMatMul",
"imply_type": "AutoDiff",
"fusion_type": "OPAQUE",
"attr": [
{
"name": "transpose_a",
"param_type": "optional",
"type": "bool"
},
{
"name": "transpose_b",
"param_type": "optional",
"type": "bool"
}
],
"inputs": [
{
"index": 0,
"dtype": [
"float16"
],
"format": [
"FRACTAL_NZ"
],
"name": "x1"
},
{
"index": 1,
"dtype": [
"float16"
],
"format": [
"FRACTAL_NZ"
],
"name": "x2"
}
],
"outputs": [
{
"index": 0,
"dtype": [
"float16"
],
"format": [
"FRACTAL_NZ"
],
"name": "output"
}
]
}""")
def _batchmatmul_akg():
"""BatchMatMul AKG register"""
return
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