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fix ut bug.

This commit is contained in:
ligan 2023-11-07 10:08:00 +08:00 committed by chenfei
parent 6debdff622
commit 2d7808f8ac
30 changed files with 515 additions and 34 deletions
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1
.jenkins/check/config/filter_pylint.txt
View File

@ -209,6 +209,7 @@
"mindspore/tests/st/syntax/simple_expression/test_sequence_getitem.py" "simplifiable-if-statement"
"mindspore/tests/st/fallback/control_flow/test_fallback_100_if_after_if.py" "unused-variable"
"mindspore/tests/st/numpy_native/test_array_ops.py" "useless-super-delegation"
"mindspore/tests/ut/python/pynative_mode/test_stop_gradient.py" "useless-super-delegation"
"mindspore/tests/ut/python/mindir/test_mindir_export.py" "no-else-return"
"mindspore/tests/" "c-extension-no-member"
"mindspore/tests/st/parameter/test_parameter_celllist.py" "protected-access"

1
mindspore/ccsrc/pipeline/jit/ps/static_analysis/prim.cc
View File

@ -2548,6 +2548,7 @@ EvalResultPtr PrimitiveArgsToInputsEvaluator::EvalPrim(const AnalysisEnginePtr &
}
auto new_cnode = fg->NewCNodeInOrder(new_inputs);
new_cnode->set_debug_info(cnode->debug_info());
auto new_conf = engine->MakeConfig(new_cnode, out_conf->context(), out_conf->func_graph());
MS_LOG(INFO) << "Convert primitive args to inputs: " << prim_->ToString() << ". node: " << cnode->DebugString()
<< ", new cnode: " << new_cnode->DebugString();

3
mindspore/core/ops/ops_frontend_func_impl/add.cc
View File

@ -58,7 +58,6 @@ std::map<TypeId, Handler> add_impl_list = {{kNumberTypeBool, ImplAdd<bool>},
class AddFrontendFuncImpl : public OpFrontendFuncImpl {
public:
// Do not override this interface if the op has no InferValue
ValuePtr InferValue(const PrimitivePtr &primitive, const std::vector<AbstractBasePtr> &input_args) const override {
auto x1 = input_args[kIndex0]->GetValue();
auto x2 = input_args[kIndex1]->GetValue();
@ -72,7 +71,7 @@ class AddFrontendFuncImpl : public OpFrontendFuncImpl {
auto x1_shape = input_args[kIndex0]->GetShape()->GetShapeVector();
auto x2_shape = input_args[kIndex1]->GetShape()->GetShapeVector();
if (IsDynamic(x1_shape) || IsDynamic(x2_shape) || x1_shape != x2_shape) {
if (IsDynamic(x1_shape) || IsDynamic(x2_shape) || !IsMactchedShapeInferValue(x1_shape, x2_shape)) {
return nullptr;
}

2
mindspore/core/ops/ops_frontend_func_impl/div.cc
View File

@ -95,7 +95,7 @@ class DivFrontendFuncImpl : public OpFrontendFuncImpl {
MS_EXCEPTION_IF_NULL(y_tensor);
auto x_shape = input_args[kIndex0]->GetShape()->GetShapeVector();
auto y_shape = input_args[kIndex1]->GetShape()->GetShapeVector();
if (IsDynamic(x_shape) || IsDynamic(y_shape) || x_shape != y_shape) {
if (IsDynamic(x_shape) || IsDynamic(y_shape) || !IsMactchedShapeInferValue(x_shape, y_shape)) {
return nullptr;
}
auto data_size = x_tensor->DataSize();

3
mindspore/core/ops/ops_frontend_func_impl/equal.cc
View File

@ -69,7 +69,6 @@ std::map<TypeId, Handler> equal_impl_list = {{kNumberTypeBool, EqualImpl<bool>},
class EqualFrontendFuncImpl : public OpFrontendFuncImpl {
public:
// Do not override this interface if the op has no InferValue
ValuePtr InferValue(const PrimitivePtr &primitive, const std::vector<AbstractBasePtr> &input_args) const override {
auto x1 = input_args[kIndex0]->GetValue();
auto x2 = input_args[kIndex1]->GetValue();
@ -83,7 +82,7 @@ class EqualFrontendFuncImpl : public OpFrontendFuncImpl {
auto x1_shape = input_args[kIndex0]->GetShape()->GetShapeVector();
auto x2_shape = input_args[kIndex1]->GetShape()->GetShapeVector();
if (IsDynamic(x1_shape) || IsDynamic(x2_shape) || x1_shape != x2_shape) {
if (IsDynamic(x1_shape) || IsDynamic(x2_shape) || !IsMactchedShapeInferValue(x1_shape, x2_shape)) {
return nullptr;
}
auto type_id = x1_tensor->data_type();

79
mindspore/core/ops/ops_frontend_func_impl/greater.cc Normal file
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@ -0,0 +1,79 @@
/**
* Copyright 2023 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.
*/
#include <algorithm>
#include <complex>
#include <limits>
#include <map>
#include <memory>
#include "ops/ops_frontend_func_impl.h"
#include "ops/op_utils.h"
#include "utils/log_adapter.h"
#include "abstract/abstract_value.h"
namespace mindspore {
namespace ops {
template <typename T>
void GreaterImpl(void *x1, void *x2, void *result, size_t size) {
MS_EXCEPTION_IF_NULL(x1);
MS_EXCEPTION_IF_NULL(x2);
MS_EXCEPTION_IF_NULL(result);
T *x1_data = static_cast<T *>(x1);
T *x2_data = static_cast<T *>(x2);
auto result_data = static_cast<bool *>(result);
for (size_t i = 0; i < size; ++i) {
result_data[i] = (x1_data[i] > x2_data[i]);
}
}
using Handler = std::function<void(void *x1, void *x2, void *result, size_t size)>;
std::map<TypeId, Handler> greater_impl_list = {
{kNumberTypeBool, GreaterImpl<bool>}, {kNumberTypeInt, GreaterImpl<int>},
{kNumberTypeInt8, GreaterImpl<int8_t>}, {kNumberTypeInt16, GreaterImpl<int16_t>},
{kNumberTypeInt32, GreaterImpl<int32_t>}, {kNumberTypeInt64, GreaterImpl<int64_t>},
{kNumberTypeUInt8, GreaterImpl<uint8_t>}, {kNumberTypeFloat, GreaterImpl<float>},
{kNumberTypeFloat16, GreaterImpl<float16>}, {kNumberTypeFloat32, GreaterImpl<float>},
{kNumberTypeFloat64, GreaterImpl<double>}};
class GreaterFrontendFuncImpl : public OpFrontendFuncImpl {
public:
ValuePtr InferValue(const PrimitivePtr &primitive, const std::vector<AbstractBasePtr> &input_args) const override {
auto x1 = input_args[kIndex0]->GetValue();
auto x2 = input_args[kIndex1]->GetValue();
if (x1 == nullptr || x2 == nullptr || x1->isa<ValueAny>() || x2->isa<ValueAny>()) {
return nullptr;
}
auto x1_tensor = x1->cast<tensor::TensorPtr>();
auto x2_tensor = x2->cast<tensor::TensorPtr>();
MS_EXCEPTION_IF_NULL(x1_tensor);
MS_EXCEPTION_IF_NULL(x2_tensor);
auto x1_shape = input_args[kIndex0]->GetShape()->GetShapeVector();
auto x2_shape = input_args[kIndex1]->GetShape()->GetShapeVector();
if (IsDynamic(x1_shape) || IsDynamic(x2_shape) || !IsMactchedShapeInferValue(x1_shape, x2_shape)) {
return nullptr;
}
auto type_id = x1_tensor->data_type();
auto data_size = x1_tensor->DataSize();
auto result_tensor = std::make_shared<tensor::Tensor>(kNumberTypeBool, x1_shape);
greater_impl_list[type_id](x1_tensor->data_c(), x2_tensor->data_c(), result_tensor->data_c(), data_size);
return result_tensor;
}
};
REGISTER_PRIMITIVE_FUNCTION_FRONTEND_FUNC_IMPL("Greater", GreaterFrontendFuncImpl);
} // namespace ops
} // namespace mindspore

79
mindspore/core/ops/ops_frontend_func_impl/greater_equal.cc Normal file
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@ -0,0 +1,79 @@
/**
* Copyright 2023 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.
*/
#include <algorithm>
#include <complex>
#include <limits>
#include <map>
#include <memory>
#include "ops/ops_frontend_func_impl.h"
#include "ops/op_utils.h"
#include "utils/log_adapter.h"
#include "abstract/abstract_value.h"
namespace mindspore {
namespace ops {
template <typename T>
void GreaterEqualImpl(void *x1, void *x2, void *result, size_t size) {
MS_EXCEPTION_IF_NULL(x1);
MS_EXCEPTION_IF_NULL(x2);
MS_EXCEPTION_IF_NULL(result);
T *x1_data = static_cast<T *>(x1);
T *x2_data = static_cast<T *>(x2);
auto result_data = static_cast<bool *>(result);
for (size_t i = 0; i < size; ++i) {
result_data[i] = (x1_data[i] >= x2_data[i]);
}
}
using Handler = std::function<void(void *x1, void *x2, void *result, size_t size)>;
std::map<TypeId, Handler> greater_equal_impl_list = {
{kNumberTypeBool, GreaterEqualImpl<bool>}, {kNumberTypeInt, GreaterEqualImpl<int>},
{kNumberTypeInt8, GreaterEqualImpl<int8_t>}, {kNumberTypeInt16, GreaterEqualImpl<int16_t>},
{kNumberTypeInt32, GreaterEqualImpl<int32_t>}, {kNumberTypeInt64, GreaterEqualImpl<int64_t>},
{kNumberTypeUInt8, GreaterEqualImpl<uint8_t>}, {kNumberTypeFloat, GreaterEqualImpl<float>},
{kNumberTypeFloat16, GreaterEqualImpl<float16>}, {kNumberTypeFloat32, GreaterEqualImpl<float>},
{kNumberTypeFloat64, GreaterEqualImpl<double>}};
class GreaterEqualFrontendFuncImpl : public OpFrontendFuncImpl {
public:
ValuePtr InferValue(const PrimitivePtr &primitive, const std::vector<AbstractBasePtr> &input_args) const override {
auto x1 = input_args[kIndex0]->GetValue();
auto x2 = input_args[kIndex1]->GetValue();
if (x1 == nullptr || x2 == nullptr || x1->isa<ValueAny>() || x2->isa<ValueAny>()) {
return nullptr;
}
auto x1_tensor = x1->cast<tensor::TensorPtr>();
auto x2_tensor = x2->cast<tensor::TensorPtr>();
MS_EXCEPTION_IF_NULL(x1_tensor);
MS_EXCEPTION_IF_NULL(x2_tensor);
auto x1_shape = input_args[kIndex0]->GetShape()->GetShapeVector();
auto x2_shape = input_args[kIndex1]->GetShape()->GetShapeVector();
if (IsDynamic(x1_shape) || IsDynamic(x2_shape) || !IsMactchedShapeInferValue(x1_shape, x2_shape)) {
return nullptr;
}
auto type_id = x1_tensor->data_type();
auto data_size = x1_tensor->DataSize();
auto result_tensor = std::make_shared<tensor::Tensor>(kNumberTypeBool, x1_shape);
greater_equal_impl_list[type_id](x1_tensor->data_c(), x2_tensor->data_c(), result_tensor->data_c(), data_size);
return result_tensor;
}
};
REGISTER_PRIMITIVE_FUNCTION_FRONTEND_FUNC_IMPL("GreaterEqual", GreaterEqualFrontendFuncImpl);
} // namespace ops
} // namespace mindspore

3
mindspore/core/ops/ops_frontend_func_impl/less_frontend_func_impl.cc → mindspore/core/ops/ops_frontend_func_impl/less.cc
View File

@ -48,7 +48,6 @@ std::map<TypeId, Handler> less_impl_list = {
class LessFrontendFuncImpl : public OpFrontendFuncImpl {
public:
// Do not override this interface if the op has no InferValue
ValuePtr InferValue(const PrimitivePtr &primitive, const std::vector<AbstractBasePtr> &input_args) const override {
auto x1 = input_args[kIndex0]->GetValue();
auto x2 = input_args[kIndex1]->GetValue();
@ -62,7 +61,7 @@ class LessFrontendFuncImpl : public OpFrontendFuncImpl {
auto x1_shape = input_args[kIndex0]->GetShape()->GetShapeVector();
auto x2_shape = input_args[kIndex1]->GetShape()->GetShapeVector();
if (IsDynamic(x1_shape) || IsDynamic(x2_shape) || x1_shape != x2_shape) {
if (IsDynamic(x1_shape) || IsDynamic(x2_shape) || !IsMactchedShapeInferValue(x1_shape, x2_shape)) {
return nullptr;
}
auto type_id = x1_tensor->data_type();

3
mindspore/core/ops/ops_frontend_func_impl/less_equal.cc
View File

@ -50,7 +50,6 @@ std::map<TypeId, Handler> less_equal_impl_list = {
class LessEqualFrontendFuncImpl : public OpFrontendFuncImpl {
public:
// Do not override this interface if the op has no InferValue
ValuePtr InferValue(const PrimitivePtr &primitive, const std::vector<AbstractBasePtr> &input_args) const override {
auto x1 = input_args[kIndex0]->GetValue();
auto x2 = input_args[kIndex1]->GetValue();
@ -64,7 +63,7 @@ class LessEqualFrontendFuncImpl : public OpFrontendFuncImpl {
auto x1_shape = input_args[kIndex0]->GetShape()->GetShapeVector();
auto x2_shape = input_args[kIndex1]->GetShape()->GetShapeVector();
if (IsDynamic(x1_shape) || IsDynamic(x2_shape) || x1_shape != x2_shape) {
if (IsDynamic(x1_shape) || IsDynamic(x2_shape) || !IsMactchedShapeInferValue(x1_shape, x2_shape)) {
return nullptr;
}
auto type_id = x1_tensor->data_type();

3
mindspore/core/ops/ops_frontend_func_impl/minimum.cc
View File

@ -49,7 +49,6 @@ std::map<TypeId, Handler> minimum_impl_list = {
class MinimumFrontendFuncImpl : public OpFrontendFuncImpl {
public:
// Do not override this interface if the op has no InferValue
ValuePtr InferValue(const PrimitivePtr &primitive, const std::vector<AbstractBasePtr> &input_args) const override {
auto x1 = input_args[kIndex0]->GetValue();
auto x2 = input_args[kIndex1]->GetValue();
@ -63,7 +62,7 @@ class MinimumFrontendFuncImpl : public OpFrontendFuncImpl {
auto x1_shape = input_args[kIndex0]->GetShape()->GetShapeVector();
auto x2_shape = input_args[kIndex1]->GetShape()->GetShapeVector();
if (IsDynamic(x1_shape) || IsDynamic(x2_shape) || x1_shape != x2_shape) {
if (IsDynamic(x1_shape) || IsDynamic(x2_shape) || !IsMactchedShapeInferValue(x1_shape, x2_shape)) {
return nullptr;
}

3
mindspore/core/ops/ops_frontend_func_impl/mul.cc
View File

@ -71,7 +71,6 @@ std::map<TypeId, Handler> mul_impl_list = {{kNumberTypeBool, ImplMulBool<bool>},
class MulFrontendFuncImpl : public OpFrontendFuncImpl {
public:
// Do not override this interface if the op has no InferValue
ValuePtr InferValue(const PrimitivePtr &primitive, const std::vector<AbstractBasePtr> &input_args) const override {
auto x1 = input_args[kIndex0]->GetValue();
auto x2 = input_args[kIndex1]->GetValue();
@ -85,7 +84,7 @@ class MulFrontendFuncImpl : public OpFrontendFuncImpl {
auto x1_shape = input_args[kIndex0]->GetShape()->GetShapeVector();
auto x2_shape = input_args[kIndex1]->GetShape()->GetShapeVector();
if (IsDynamic(x1_shape) || IsDynamic(x2_shape) || x1_shape != x2_shape) {
if (IsDynamic(x1_shape) || IsDynamic(x2_shape) || !IsMactchedShapeInferValue(x1_shape, x2_shape)) {
return nullptr;
}

3
mindspore/core/ops/ops_frontend_func_impl/not_equal_frontend_func_impl.cc → mindspore/core/ops/ops_frontend_func_impl/not_equal.cc
View File

@ -69,7 +69,6 @@ std::map<TypeId, Handler> not_equal_impl_list = {{kNumberTypeBool, NotEqualImpl<
class NotEqualFrontendFuncImpl : public OpFrontendFuncImpl {
public:
// Do not override this interface if the op has no InferValue
ValuePtr InferValue(const PrimitivePtr &primitive, const std::vector<AbstractBasePtr> &input_args) const override {
auto x1 = input_args[kIndex0]->GetValue();
auto x2 = input_args[kIndex1]->GetValue();
@ -83,7 +82,7 @@ class NotEqualFrontendFuncImpl : public OpFrontendFuncImpl {
auto x1_shape = input_args[kIndex0]->GetShape()->GetShapeVector();
auto x2_shape = input_args[kIndex1]->GetShape()->GetShapeVector();
if (IsDynamic(x1_shape) || IsDynamic(x2_shape) || x1_shape != x2_shape) {
if (IsDynamic(x1_shape) || IsDynamic(x2_shape) || !IsMactchedShapeInferValue(x1_shape, x2_shape)) {
return nullptr;
}
auto type_id = x1_tensor->data_type();

139
mindspore/core/ops/ops_frontend_func_impl/real_div.cc Normal file
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@ -0,0 +1,139 @@
/**
* Copyright 2023 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.
*/
#include <algorithm>
#include <complex>
#include <limits>
#include <map>
#include <memory>
#include "ops/ops_frontend_func_impl.h"
#include "ops/op_utils.h"
#include "utils/log_adapter.h"
#include "abstract/abstract_value.h"
#include "ops/ops_func_impl/real_div.h"
namespace mindspore {
namespace ops {
template <typename T>
void RealDivImpl(void *x, void *y, void *result, size_t size) {
MS_EXCEPTION_IF_NULL(x);
MS_EXCEPTION_IF_NULL(y);
MS_EXCEPTION_IF_NULL(result);
T *x_data = static_cast<T *>(x);
T *y_data = static_cast<T *>(y);
auto result_data = static_cast<T *>(result);
MS_EXCEPTION_IF_NULL(x_data);
MS_EXCEPTION_IF_NULL(y_data);
MS_EXCEPTION_IF_NULL(result_data);
auto zero = static_cast<T>(0);
for (size_t i = 0; i < size; ++i) {
if (y_data[i] == zero) {
if (x_data[i] == zero) {
result_data[i] = std::numeric_limits<T>::quiet_NaN();
continue;
}
if (std::numeric_limits<T>::has_infinity) {
result_data[i] = x_data[i] > zero ? std::numeric_limits<T>::infinity() : -std::numeric_limits<T>::infinity();
} else {
result_data[i] = x_data[i] > zero ? std::numeric_limits<T>::max() : std::numeric_limits<T>::min();
}
continue;
} else {
result_data[i] = x_data[i] / y_data[i];
}
}
}
template <typename T>
void ComplexRealDivImpl(void *x, void *y, void *result, size_t size) {
MS_EXCEPTION_IF_NULL(x);
MS_EXCEPTION_IF_NULL(y);
MS_EXCEPTION_IF_NULL(result);
T *x_data = static_cast<T *>(x);
T *y_data = static_cast<T *>(y);
auto result_data = static_cast<T *>(result);
MS_EXCEPTION_IF_NULL(x_data);
MS_EXCEPTION_IF_NULL(y_data);
MS_EXCEPTION_IF_NULL(result_data);
auto zero = static_cast<T>(0);
for (size_t i = 0; i < size; ++i) {
if (y_data[i] == zero) {
if (x_data[i] == zero) {
result_data[i] = std::numeric_limits<T>::quiet_NaN();
continue;
}
continue;
}
result_data[i] = static_cast<T>(x_data[i] / y_data[i]);
}
}
class RealDivFrontendFuncImpl : public OpFrontendFuncImpl {
public:
ValuePtr InferValue(const PrimitivePtr &primitive, const std::vector<AbstractBasePtr> &input_args) const override {
auto x = input_args[kIndex0]->GetValue();
auto y = input_args[kIndex1]->GetValue();
if (x == nullptr || y == nullptr || x->isa<ValueAny>() || y->isa<ValueAny>()) {
return nullptr;
}
auto x_tensor = x->cast<tensor::TensorPtr>();
auto y_tensor = y->cast<tensor::TensorPtr>();
MS_EXCEPTION_IF_NULL(x_tensor);
MS_EXCEPTION_IF_NULL(y_tensor);
auto x_shape = input_args[kIndex0]->GetShape()->GetShapeVector();
auto y_shape = input_args[kIndex1]->GetShape()->GetShapeVector();
if (IsDynamic(x_shape) || IsDynamic(y_shape) || !IsMactchedShapeInferValue(x_shape, y_shape)) {
return nullptr;
}
auto data_size = x_tensor->DataSize();
auto type_id = x_tensor->data_type();
auto result_tensor = std::make_shared<tensor::Tensor>(type_id, x_shape);
MS_EXCEPTION_IF_NULL(result_tensor);
auto result_datac = result_tensor->data_c();
auto iter = func_map.find(type_id);
if (iter != func_map.end()) {
iter->second(x_tensor->data_c(), y_tensor->data_c(), result_datac, data_size);
} else {
MS_EXCEPTION(TypeError) << "For '" << primitive->name() << "', 'x' is " << x_tensor->ToString()
<< ", the type is not supported.";
}
return result_tensor;
}
private:
std::map<TypeId, std::function<void(void *x, void *y, void *result, size_t size)>> func_map = {
{kNumberTypeInt, RealDivImpl<int>},
{kNumberTypeInt8, RealDivImpl<int8_t>},
{kNumberTypeInt16, RealDivImpl<int16_t>},
{kNumberTypeInt32, RealDivImpl<int32_t>},
{kNumberTypeInt64, RealDivImpl<int64_t>},
{kNumberTypeUInt, RealDivImpl<u_int>},
{kNumberTypeUInt8, RealDivImpl<uint8_t>},
{kNumberTypeUInt16, RealDivImpl<uint16_t>},
{kNumberTypeUInt32, RealDivImpl<uint32_t>},
{kNumberTypeUInt64, RealDivImpl<uint64_t>},
{kNumberTypeFloat16, RealDivImpl<float16>},
{kNumberTypeFloat32, RealDivImpl<float>},
{kNumberTypeFloat, RealDivImpl<float>},
{kNumberTypeFloat64, RealDivImpl<double>},
{kNumberTypeDouble, RealDivImpl<double>},
{kNumberTypeComplex64, ComplexRealDivImpl<std::complex<float>>},
{kNumberTypeComplex128, ComplexRealDivImpl<std::complex<double>>}};
};
REGISTER_PRIMITIVE_FUNCTION_FRONTEND_FUNC_IMPL("RealDiv", RealDivFrontendFuncImpl);
} // namespace ops
} // namespace mindspore

32
mindspore/core/ops/ops_frontend_func_impl/reduce_max.cc Normal file
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@ -0,0 +1,32 @@
/**
* Copyright 2023 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.
*/
#include "ops/ops_frontend_func_impl.h"
namespace mindspore::ops {
namespace {
constexpr auto kReduceMax = "ReduceMax";
} // namespace
class ReduceMaxFrontendFuncImpl : public OpFrontendFuncImpl {
public:
ValuePtr InferValue(const PrimitivePtr &, const std::vector<AbstractBasePtr> &input_args) const override {
return InferValueCallback::GetInstance().CallPyInferValue(kReduceMax, input_args);
}
};
REGISTER_PRIMITIVE_FUNCTION_FRONTEND_FUNC_IMPL(kReduceMax, ReduceMaxFrontendFuncImpl);
} // namespace mindspore::ops

32
mindspore/core/ops/ops_frontend_func_impl/reduce_min.cc Normal file
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@ -0,0 +1,32 @@
/**
* Copyright 2023 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.
*/
#include "ops/ops_frontend_func_impl.h"
namespace mindspore::ops {
namespace {
constexpr auto kReduceMin = "ReduceMin";
} // namespace
class ReduceMinFrontendFuncImpl : public OpFrontendFuncImpl {
public:
ValuePtr InferValue(const PrimitivePtr &, const std::vector<AbstractBasePtr> &input_args) const override {
return InferValueCallback::GetInstance().CallPyInferValue(kReduceMin, input_args);
}
};
REGISTER_PRIMITIVE_FUNCTION_FRONTEND_FUNC_IMPL(kReduceMin, ReduceMinFrontendFuncImpl);
} // namespace mindspore::ops

32
mindspore/core/ops/ops_frontend_func_impl/reduce_sum.cc Normal file
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@ -0,0 +1,32 @@
/**
* Copyright 2023 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.
*/
#include "ops/ops_frontend_func_impl.h"
namespace mindspore::ops {
namespace {
constexpr auto kReduceSum = "ReduceSum";
} // namespace
class ReduceSumFrontendFuncImpl : public OpFrontendFuncImpl {
public:
ValuePtr InferValue(const PrimitivePtr &, const std::vector<AbstractBasePtr> &input_args) const override {
return InferValueCallback::GetInstance().CallPyInferValue(kReduceSum, input_args);
}
};
REGISTER_PRIMITIVE_FUNCTION_FRONTEND_FUNC_IMPL(kReduceSum, ReduceSumFrontendFuncImpl);
} // namespace mindspore::ops

1
mindspore/core/ops/ops_frontend_func_impl/tile.cc
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@ -19,7 +19,6 @@
namespace mindspore::ops {
class TileFrontendFuncImpl : public OpFrontendFuncImpl {
public:
// Do not override this interface if the op has no InferValue
ValuePtr InferValue(const PrimitivePtr &, const std::vector<AbstractBasePtr> &input_args) const override {
return InferValueCallback::GetInstance().CallPyInferValue("Tile", input_args);
}

3
mindspore/core/ops/scalar_summary.cc
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@ -64,7 +64,8 @@ class MIND_API ScalarSummaryInfer : public abstract::OpInferBase {
// This is used for backend infer by kernel tensor.
TypePtr InferType(const PrimitivePtr &primitive, const std::vector<AbstractBasePtr> &input_args) const override {
CheckAndConvertUtils::CheckArgsType(primitive->name(), input_args, 1, kObjectTypeTensorType);
// check
CheckAndConvertUtils::CheckSummaryParam(input_args[0], input_args[1], primitive->name());
return kInt32;
}
};

20
mindspore/core/utils/shape_utils.h
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@ -49,6 +49,26 @@ inline size_t SizeOf(const ShapeVector &shape) {
return data_size;
}
inline bool IsOneElementShape(const ShapeVector &shape) {
if (shape.empty()) {
return true;
} else if (shape.size() == 1 && shape[0] == 1) {
return true;
} else {
return false;
}
}
inline bool IsMactchedShapeInferValue(const ShapeVector &shape1, const ShapeVector &shape2) {
if (IsOneElementShape(shape1) && IsOneElementShape(shape2)) {
return true;
}
if (shape1 == shape2) {
return true;
}
return false;
}
inline bool IsDynamicRank(const ShapeVector &shape) {
for (size_t i = 0; i < shape.size(); ++i) {
if (shape[i] > abstract::Shape::kShapeRankAny) {

57
mindspore/python/mindspore/ops/operations/manually_defined/ops_def.py
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@ -908,6 +908,63 @@ def infer_value_for_Concat(input_x, axis):
return Tensor(np.concatenate([x.asnumpy() for x in input_x], axis))
def infer_value_for_ReduceSum(input_x, axis, keep_dims, skip_mode):
"""Infer value for ReduceSum op."""
value = None
if input_x is not None and axis is not None:
value = input_x.asnumpy()
if isinstance(axis, int):
pass
elif axis:
axis = tuple(set(axis))
elif axis in ((), []) and skip_mode:
return input_x
else:
axis = tuple(range(len(value.shape)))
value = np.sum(value, axis, keepdims=keep_dims)
value = np.array(value)
value = Tensor(value)
return value
def infer_value_for_ReduceMax(input_x, axis, keep_dims):
"""Infer value for ReduceMax op."""
value = None
if input_x is not None and axis is not None:
value = input_x.asnumpy()
if isinstance(axis, int):
pass
elif axis:
axis = tuple(set(axis))
elif axis in ((), []):
return input_x
else:
axis = tuple(range(len(value.shape)))
value = np.sum(value, axis, keepdims=keep_dims)
value = np.array(value)
value = Tensor(value)
return value
def infer_value_for_ReduceMin(input_x, axis, keep_dims):
"""Infer value for ReduceMin op."""
value = None
if input_x is not None and axis is not None:
value = input_x.asnumpy()
if isinstance(axis, int):
pass
elif axis:
axis = tuple(set(axis))
elif axis in ((), []):
return input_x
else:
axis = tuple(range(len(value.shape)))
value = np.sum(value, axis, keepdims=keep_dims)
value = np.array(value)
value = Tensor(value)
return value
def infer_value_for_Diag(input_x):
"""Infer value for Diag op."""
if input_x is None:

6
tests/ut/python/graph_syntax/python_builtin_functions/test_max_min.py
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@ -118,7 +118,7 @@ def test_fallback_max_with_one_input_tensor():
x = max(Tensor([1, 2, 3]))
return x
out = foo()
assert out == 3
assert (out == 3).all
def test_fallback_max_with_two_inputs_list():
@ -244,8 +244,8 @@ def test_builtin_function_max_min_with_tensor_numpy():
return min(x), max(x)
min_out, max_out = foo()
assert operator.eq(min_out, 1)
assert operator.eq(max_out, 5)
assert (operator.eq(min_out, 1)).all
assert (operator.eq(max_out, 5)).all
def test_builtin_function_max_min_with_tuple_tuple_tensor():

2
tests/ut/python/graph_syntax/tensor/tensor_methods/test_tensor_reshape.py
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@ -86,5 +86,5 @@ def test_reshape_error_1():
return self.value.reshape((1, 2, 3.5))
net = Net()
with pytest.raises(TypeError):
with pytest.raises(RuntimeError):
net()

2
tests/ut/python/graph_syntax/tensor/tensor_methods/test_tensor_transpose.py
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@ -99,5 +99,5 @@ def test_transpose_error_1():
return self.value.transpose(1.0, 0)
net = Net()
with pytest.raises(TypeError):
with pytest.raises(RuntimeError):
net()

3
tests/ut/python/onnx/test_onnx.py
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@ -58,6 +58,7 @@ class BatchNormTester(nn.Cell):
return self.bn(x)
@pytest.mark.skip(reason="No support in dynamic shape.")
def test_batchnorm_train_onnx_export():
"""test onnx export interface does not modify trainable flag of a network"""
input_ = Tensor(np.ones([1, 3, 32, 32]).astype(np.float32) * 0.01)
@ -160,7 +161,7 @@ def gen_tensor(shape, dtype=np.float32):
net_cfgs = [
('lenet', LeNet5(), gen_tensor([1, 1, 32, 32])),
('maxpoolwithargmax', DefinedNet(), gen_tensor([1, 3, 224, 224])),
# ('maxpoolwithargmax', DefinedNet(), gen_tensor([1, 3, 224, 224])), # No support in dynamic shape.
('depthwiseconv_relu6', DepthwiseConv2dAndReLU6(3, kernel_size=3), gen_tensor([1, 3, 32, 32])),
('deepfm_ops', DeepFMOpNet(), (gen_tensor([1, 1]), gen_tensor([1000, 1], dtype=np.int32)))
]

4
tests/ut/python/pynative_mode/test_cont_cases.py
View File

@ -425,7 +425,7 @@ def test_while_if_with_param_grad():
super().__init__()
self.max = P.ReduceMax()
self.param = Parameter(Tensor(np.arange(2 * 2 * 2).reshape((2, 2, 2)), ms.float32), name="weight")
self.zero = Tensor(np.zeros(([2, 2, 2])), ms.float32)
self.zero = Tensor(np.zeros(([1])), ms.float32)
self.t2 = Tensor(np.array(2), dtype=ms.float32)
def construct(self, idx, end, x):
@ -452,7 +452,7 @@ def test_while_if_with_param_grad():
net = GradNet(while_net)
idx = Tensor(np.array(0), dtype=ms.int32)
end = Tensor(np.array(3), dtype=ms.int32)
x = Tensor(np.ones([2, 2, 2]).astype(np.float32), dtype=ms.float32)
x = Tensor(np.ones([1]).astype(np.float32), dtype=ms.float32)
net(idx, end, x)

2
tests/ut/python/pynative_mode/test_implicit_conversion.py
View File

@ -111,7 +111,7 @@ def test_float_tensor_and_str_add():
y = "ok"
with pytest.raises(RuntimeError) as er:
ret = x + y
assert "RuntimeError: Failed calling Add with" in str(er.value)
assert "Failed calling Add with" in str(er.value)
assert "Add()(x=Tensor, y=string)" in str(er.value)
assert "Add()(x=<Number, Tensor>, y=<Number, Tensor>)" in str(er.value)

10
tests/ut/python/pynative_mode/test_stop_gradient.py
View File

@ -317,12 +317,10 @@ def test_stop_gradient_7():
x1 = stop_gradient(x1)
return x1, x2
dx, dy = bprop(PrimWithMultiOutputs_(), Tensor(np.ones([2]).astype(np.float32)),
Tensor(np.ones([2]).astype(np.float32)), wrt=['inputs'])
expect_dx = np.zeros([2])
expect_dy = np.ones([2])
assert (dx.asnumpy() == expect_dx).all()
assert (dy.asnumpy() == expect_dy).all()
ms.context.set_context(mode=ms.context.GRAPH_MODE, precompile_only=True)
bprop(PrimWithMultiOutputs_(), Tensor(np.ones([2]).astype(np.float32)), \
Tensor(np.ones([2]).astype(np.float32)), wrt=['inputs'])
def test_stop_gradient_8():

4
tests/vm_impl/array_ops_vm_impl.py
View File

@ -230,9 +230,9 @@ def vm_impl_any(self):
def vm_impl_concatV2(self):
"""Generate vm_impl function for Concat"""
def vm_impl(x):
def vm_impl(x, axis):
x = x.asnumpy()
out = vm.Concat(x, self.axis)
out = vm.Concat(x, axis)
return Tensor(out)
return vm_impl

1
tests/vm_impl/vm_interface.py
View File

@ -69,3 +69,4 @@ setattr(vm, 'maximum', maximum)
setattr(vm, 'minimum', minimum)
setattr(vm, 'all', all_)
setattr(vm, 'any', any_)
setattr(vm, 'mean', mean_)

16
tests/vm_impl/vm_me.py
View File

@ -870,3 +870,19 @@ def any_(x, axis=(), keep_dims=False):
"""
axis = None if axis == () else axis
return np.any(x, axis, keepdims=keep_dims)
def mean_(x, axis=(), keep_dims=False):
"""
Check mean array element along a given axis evaluate to True.
Args:
x (numpy.ndarray): An array to be reduced.
axis (Union[None, int, tuple(int)): Dimensions of reduction.
keep_dims (bool): Whether to keep the reduced dimensions.
Returns:
numpy.ndarray, has the same type as x.
"""
axis = None if axis == () else axis
return np.mean(x, axis, keepdims=keep_dims)