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!42469 update grad for dynamic_shape 

Merge pull request !42469 from chengbin/ds_r1.9
This commit is contained in:
i-robot 2022-09-21 12:51:23 +00:00 committed by Gitee
parent 2a9718bf30 e89ab5408e
commit 520dfd5c6e
No known key found for this signature in database
GPG Key ID: 173E9B9CA92EEF8F
94 changed files with 2696 additions and 552 deletions
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31
mindspore/ccsrc/backend/common/pass/reduce_sum_optimizer.cc
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@ -17,38 +17,12 @@
#include "backend/common/pass/reduce_sum_optimizer.h" #include "backend/common/pass/reduce_sum_optimizer.h"
#include <vector> #include <vector>
#include "include/common/utils/anfalgo.h" #include "include/common/utils/anfalgo.h"
#include "utils/ms_context.h"
namespace mindspore { namespace mindspore {
namespace opt { namespace opt {
namespace { namespace {
const int axis_input_index = 2; const int axis_input_index = 2;
bool IsNeedComputeRank(const CNodePtr &cnode) {
MS_EXCEPTION_IF_NULL(cnode);
auto axis_input = cnode->input(axis_input_index);
MS_EXCEPTION_IF_NULL(axis_input);
if (!IsValueNode<ValueTuple>(axis_input)) {
return false;
}
auto value_node = axis_input->cast<ValueNodePtr>();
MS_EXCEPTION_IF_NULL(value_node);
auto value = value_node->value();
MS_EXCEPTION_IF_NULL(value);
if (value->isa<ValueTuple>()) {
auto value_tuple = value->cast<ValueTuplePtr>();
MS_EXCEPTION_IF_NULL(value_tuple);
if (value_tuple->value().empty()) {
return true;
}
for (auto &iter : value_tuple->value()) {
auto item = GetValue<int64_t>(iter->cast<ScalarPtr>());
if (item < 0) {
return true;
}
}
}
return false;
}
} // namespace } // namespace
AnfNodePtr ReduceSumOptimizer::NewRankOp(const AnfNodePtr &cnode, const KernelGraphPtr &kernel_graph) const { AnfNodePtr ReduceSumOptimizer::NewRankOp(const AnfNodePtr &cnode, const KernelGraphPtr &kernel_graph) const {
@ -168,9 +142,6 @@ const AnfNodePtr ReduceSumOptimizer::Process(const FuncGraphPtr &func_graph, con
common::AnfAlgo::SetNodeAttr(kAttrVisited, MakeValue(true), node); common::AnfAlgo::SetNodeAttr(kAttrVisited, MakeValue(true), node);
auto kernel_graph = func_graph->cast<std::shared_ptr<session::KernelGraph>>(); auto kernel_graph = func_graph->cast<std::shared_ptr<session::KernelGraph>>();
MS_EXCEPTION_IF_NULL(kernel_graph); MS_EXCEPTION_IF_NULL(kernel_graph);
if (AnfUtils::IsDimUnknown(cnode) && IsNeedComputeRank(cnode)) {
return InsertAssistNode(cnode, kernel_graph);
}
return NewAssistValueNode(cnode, kernel_graph); return NewAssistValueNode(cnode, kernel_graph);
} }

7
mindspore/ccsrc/plugin/device/cpu/kernel/bias_add_grad_cpu_kernel.cc
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@ -28,7 +28,12 @@ constexpr size_t kBiasAddGradOutputsNum = 1;
void BiasAddGradCpuKernelMod::InitKernel(const CNodePtr &kernel_node) { void BiasAddGradCpuKernelMod::InitKernel(const CNodePtr &kernel_node) {
MS_EXCEPTION_IF_NULL(kernel_node); MS_EXCEPTION_IF_NULL(kernel_node);
kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node); kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node);
input_shape_ = Convert2SizeTClipNeg(AnfAlgo::GetInputDeviceShape(kernel_node, 0)); auto shape = AnfAlgo::GetInputDeviceShape(kernel_node, 0);
if (IsDynamic(shape)) {
return;
}
input_shape_ = Convert2SizeT(shape);
if (input_shape_.size() < 2) { if (input_shape_.size() < 2) {
MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', input tensor's dimension must be at least 2, but got " MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', input tensor's dimension must be at least 2, but got "
<< input_shape_.size(); << input_shape_.size();

1
mindspore/ccsrc/plugin/device/cpu/kernel/topk_cpu_kernel.cc
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@ -97,6 +97,7 @@ void TopKCpuKernelMod::InitKernel(const CNodePtr &kernel_node) {
MS_LOG(EXCEPTION) << "For '" << kernel_name_ MS_LOG(EXCEPTION) << "For '" << kernel_name_
<< "', the dimension of input must be greater than 0, but got empty input."; << "', the dimension of input must be greater than 0, but got empty input.";
} }
outer_size_ = 1;
for (size_t i = 0; i < x_shape_.size() - 1; ++i) { for (size_t i = 0; i < x_shape_.size() - 1; ++i) {
outer_size_ *= x_shape_[i]; outer_size_ *= x_shape_[i];
} }

6
mindspore/ccsrc/plugin/device/gpu/kernel/math/linspace.cc
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@ -50,9 +50,9 @@ int LinSpaceGpuKernelMod::Resize(const BaseOperatorPtr &base_operator, const std
if (auto ret = KernelMod::Resize(base_operator, inputs, outputs); ret != KRET_OK) { if (auto ret = KernelMod::Resize(base_operator, inputs, outputs); ret != KRET_OK) {
return ret; return ret;
} }
auto input1_shape = Convert2SizeTClipNeg(inputs[kIndex0]->GetShapeVector()); auto input1_shape = inputs[kIndex0]->GetShapeVector();
auto input2_shape = Convert2SizeTClipNeg(inputs[kIndex1]->GetShapeVector()); auto input2_shape = inputs[kIndex1]->GetShapeVector();
auto output_shape = Convert2SizeTClipNeg(outputs[kIndex0]->GetShapeVector()); auto output_shape = outputs[kIndex0]->GetShapeVector();
is_null_input_ = CHECK_SHAPE_NULL(input1_shape, kernel_name_, "start") || is_null_input_ = CHECK_SHAPE_NULL(input1_shape, kernel_name_, "start") ||
CHECK_SHAPE_NULL(input2_shape, kernel_name_, "stop") || CHECK_SHAPE_NULL(input2_shape, kernel_name_, "stop") ||
CHECK_SHAPE_NULL(output_shape, kernel_name_, "output"); CHECK_SHAPE_NULL(output_shape, kernel_name_, "output");

14
mindspore/ccsrc/plugin/device/gpu/kernel/math/svd_gpu_kernel.cc
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@ -43,18 +43,22 @@ bool SvdGpuKernelMod::Init(const BaseOperatorPtr &base_operator, const std::vect
int SvdGpuKernelMod::Resize(const BaseOperatorPtr &base_operator, const std::vector<KernelTensorPtr> &inputs, int SvdGpuKernelMod::Resize(const BaseOperatorPtr &base_operator, const std::vector<KernelTensorPtr> &inputs,
const std::vector<KernelTensorPtr> &outputs, const std::vector<KernelTensorPtr> &outputs,
const std::map<uint32_t, tensor::TensorPtr> &) { const std::map<uint32_t, tensor::TensorPtr> &) {
if (int ret = KernelMod::Resize(base_operator, inputs, outputs); ret != KRET_OK) {
return ret;
}
auto input_shape = inputs[kIndex0]->GetShapeVector();
if (IsDynamicRank(input_shape)) {
return KRET_OK;
}
DestroyResource(); DestroyResource();
ResetResource(); ResetResource();
input_shape_ = Convert2SizeTClipNeg(input_shape);
input_shape_ = std::vector<size_t>(inputs.at(kIndex0)->GetDeviceShapeAdaptively().begin(),
inputs.at(kIndex0)->GetDeviceShapeAdaptively().end());
total_size_ = std::accumulate(input_shape_.begin(), input_shape_.end(), size_t(1), std::multiplies<size_t>()); total_size_ = std::accumulate(input_shape_.begin(), input_shape_.end(), size_t(1), std::multiplies<size_t>());
is_null_input_ = (total_size_ == 0); is_null_input_ = (total_size_ == 0);
if (is_null_input_) { if (is_null_input_) {
init_size_lists_func_(this); init_size_lists_func_(this);
return 0; return KRET_OK;
} }
dims_ = input_shape_.size(); dims_ = input_shape_.size();
if (dims_ < kDim2) { if (dims_ < kDim2) {
MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', dimensions must >= 2, but got [" << dims_; MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', dimensions must >= 2, but got [" << dims_;

14
mindspore/ccsrc/plugin/device/gpu/kernel/nn/conv3d_grad_filter_gpu_kernel.cc
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@ -26,5 +26,19 @@ MS_REG_GPU_KERNEL_ONE(
Conv3DBackpropFilter, Conv3DBackpropFilter,
KernelAttr().AddInputAttr(kNumberTypeFloat16).AddInputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeFloat32), KernelAttr().AddInputAttr(kNumberTypeFloat16).AddInputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeFloat32),
Conv3dGradFilterGpuKernelMod, half) Conv3dGradFilterGpuKernelMod, half)
MS_REG_GPU_KERNEL_ONE(Conv3DBackpropFilter,
KernelAttr()
.AddInputAttr(kNumberTypeFloat32)
.AddInputAttr(kNumberTypeFloat32)
.AddInputAttr(kNumberTypeInt64)
.AddOutputAttr(kNumberTypeFloat32),
Conv3dGradFilterGpuKernelMod, float)
MS_REG_GPU_KERNEL_ONE(Conv3DBackpropFilter,
KernelAttr()
.AddInputAttr(kNumberTypeFloat16)
.AddInputAttr(kNumberTypeFloat16)
.AddInputAttr(kNumberTypeInt64)
.AddOutputAttr(kNumberTypeFloat32),
Conv3dGradFilterGpuKernelMod, half)
} // namespace kernel } // namespace kernel
} // namespace mindspore } // namespace mindspore

7
mindspore/ccsrc/plugin/device/gpu/kernel/nn/conv3d_grad_filter_gpu_kernel.h
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@ -31,6 +31,9 @@
namespace mindspore { namespace mindspore {
namespace kernel { namespace kernel {
constexpr int kStaticInputNum = 2;
constexpr int kDynamicInputNum = 3;
constexpr size_t kInputDimSize = 5; constexpr size_t kInputDimSize = 5;
constexpr size_t kInDimIdxForN = 0; constexpr size_t kInDimIdxForN = 0;
constexpr size_t kInDimIdxForC = 1; constexpr size_t kInDimIdxForC = 1;
@ -127,8 +130,8 @@ class Conv3dGradFilterGpuKernelMod : public NativeGpuKernelMod {
InitResource(); InitResource();
size_t input_num = inputs.size(); size_t input_num = inputs.size();
if (input_num != 2) { if (input_num != kStaticInputNum && input_num != kDynamicInputNum) {
MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the number of inputs must be 2, but got " << input_num; MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the number of inputs must be 2 or 3, but got " << input_num;
} }
size_t output_num = outputs.size(); size_t output_num = outputs.size();
if (output_num != 1) { if (output_num != 1) {

14
mindspore/ccsrc/plugin/device/gpu/kernel/nn/conv3d_grad_input_gpu_kernel.cc
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@ -26,5 +26,19 @@ MS_REG_GPU_KERNEL_ONE(
Conv3DBackpropInput, Conv3DBackpropInput,
KernelAttr().AddInputAttr(kNumberTypeFloat16).AddInputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeFloat16), KernelAttr().AddInputAttr(kNumberTypeFloat16).AddInputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeFloat16),
Conv3dGradInputGpuKernelMod, half) Conv3dGradInputGpuKernelMod, half)
MS_REG_GPU_KERNEL_ONE(Conv3DBackpropInput,
KernelAttr()
.AddInputAttr(kNumberTypeFloat32)
.AddInputAttr(kNumberTypeFloat32)
.AddInputAttr(kNumberTypeInt64)
.AddOutputAttr(kNumberTypeFloat32),
Conv3dGradInputGpuKernelMod, float)
MS_REG_GPU_KERNEL_ONE(Conv3DBackpropInput,
KernelAttr()
.AddInputAttr(kNumberTypeFloat16)
.AddInputAttr(kNumberTypeFloat16)
.AddInputAttr(kNumberTypeInt64)
.AddOutputAttr(kNumberTypeFloat16),
Conv3dGradInputGpuKernelMod, half)
} // namespace kernel } // namespace kernel
} // namespace mindspore } // namespace mindspore

7
mindspore/ccsrc/plugin/device/gpu/kernel/nn/conv3d_grad_input_gpu_kernel.h
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@ -32,7 +32,8 @@ namespace mindspore {
namespace kernel { namespace kernel {
constexpr int kNumDims = 5; constexpr int kNumDims = 5;
constexpr int kConvDims = 3; constexpr int kConvDims = 3;
constexpr int kInputNum = 2; constexpr int kStaticInputNum = 2;
constexpr int kDynamicInputNum = 3;
constexpr size_t kInDimIdxForN = 0; constexpr size_t kInDimIdxForN = 0;
constexpr size_t kInDimIdxForC = 1; constexpr size_t kInDimIdxForC = 1;
constexpr size_t kInDimIdxForD = 2; constexpr size_t kInDimIdxForD = 2;
@ -115,8 +116,8 @@ class Conv3dGradInputGpuKernelMod : public NativeGpuKernelMod {
InitResource(); InitResource();
size_t input_num = inputs.size(); size_t input_num = inputs.size();
if (input_num != kInputNum) { if (input_num != kStaticInputNum && input_num != kDynamicInputNum) {
MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the number of inputs must be 2, but got " << input_num; MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the number of inputs must be 2 or 3, but got " << input_num;
} }
size_t output_num = outputs.size(); size_t output_num = outputs.size();
if (output_num != 1) { if (output_num != 1) {

1
mindspore/ccsrc/plugin/device/gpu/optimizer/insert_cast_gpu.cc
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@ -37,6 +37,7 @@ void InsertCast(const FuncGraphPtr &graph, const AnfNodePtr &node, size_t i, con
std::vector<AnfNodePtr> inputs = {NewValueNode(prim), common::AnfAlgo::GetInputNode(utils::cast<CNodePtr>(node), i)}; std::vector<AnfNodePtr> inputs = {NewValueNode(prim), common::AnfAlgo::GetInputNode(utils::cast<CNodePtr>(node), i)};
auto cast = graph->NewCNode(inputs); auto cast = graph->NewCNode(inputs);
MS_EXCEPTION_IF_NULL(cast); MS_EXCEPTION_IF_NULL(cast);
common::AnfAlgo::SetNodeAttr(kAttrDstType, TypeIdToType(cast_type), cast);
auto cast_shape = {common::AnfAlgo::GetPrevNodeOutputDetailShape(node, i)}; auto cast_shape = {common::AnfAlgo::GetPrevNodeOutputDetailShape(node, i)};
common::AnfAlgo::SetOutputTypeAndDetailShape({cast_type}, cast_shape, cast.get()); common::AnfAlgo::SetOutputTypeAndDetailShape({cast_type}, cast_shape, cast.get());
FuncGraphManagerPtr manager = graph->manager(); FuncGraphManagerPtr manager = graph->manager();

55
mindspore/core/abstract/ops/prim_maths.cc
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@ -221,10 +221,20 @@ AbstractBasePtr InferImplMatMul(const AnalysisEnginePtr &, const PrimitivePtr &p
MS_EXCEPTION_IF_NULL(y->shape()); MS_EXCEPTION_IF_NULL(y->shape());
auto x_shp = x->shape()->shape(); auto x_shp = x->shape()->shape();
auto y_shp = y->shape()->shape(); auto y_shp = y->shape()->shape();
const size_t SHAPE_SIZE = 2;
if (x_shp.size() != SHAPE_SIZE || y_shp.size() != SHAPE_SIZE) { TypePtr x_type = x->element()->GetTypeTrack();
MS_LOG(EXCEPTION) << "MatMul inputs should have the same dimension size and equal to 2."; if (x_type->type_id() == TypeId::kNumberTypeInt8) {
x_type = kInt32;
} }
if (primitive->HasAttr("cast_type")) {
auto out_type = primitive->GetAttr("cast_type");
MS_EXCEPTION_IF_NULL(out_type);
if (!out_type->isa<Type>()) {
MS_EXCEPTION(ValueError) << "MatMul cast_type must be a `Type`";
}
x_type = out_type->cast<TypePtr>();
}
ValuePtr transpose_a_ptr = primitive->GetAttr("transpose_a"); ValuePtr transpose_a_ptr = primitive->GetAttr("transpose_a");
ValuePtr transpose_b_ptr = primitive->GetAttr("transpose_b"); ValuePtr transpose_b_ptr = primitive->GetAttr("transpose_b");
bool transpose_a = GetValue<bool>(transpose_a_ptr); bool transpose_a = GetValue<bool>(transpose_a_ptr);
@ -233,18 +243,23 @@ AbstractBasePtr InferImplMatMul(const AnalysisEnginePtr &, const PrimitivePtr &p
ShapeVector x_max_shape = x->shape()->max_shape(); ShapeVector x_max_shape = x->shape()->max_shape();
ShapeVector y_min_shape = y->shape()->min_shape(); ShapeVector y_min_shape = y->shape()->min_shape();
ShapeVector y_max_shape = y->shape()->max_shape(); ShapeVector y_max_shape = y->shape()->max_shape();
// Additional check for dynamic shape
// Last infer will be real shape values if (IsDynamicRank(x_shp) || IsDynamicRank(y_shp)) {
bool x_not_dyn = std::all_of(x_shp.begin(), x_shp.end(), [](int64_t value) { return value != Shape::SHP_ANY; }); ShapeVector ret_shape{UNKNOWN_RANK};
bool y_not_dyn = std::all_of(y_shp.begin(), y_shp.end(), [](int64_t value) { return value != Shape::SHP_ANY; }); return std::make_shared<AbstractTensor>(x_type, std::make_shared<Shape>(ret_shape));
if (x_not_dyn && y_not_dyn) {
auto x_col = x_shp[(transpose_a ? 0 : 1)];
auto y_row = y_shp[(transpose_b ? 1 : 0)];
if (x_col != y_row) {
MS_LOG(EXCEPTION) << "MatMul shape error, got x_col: " << x_col << ", y_row: " << y_row
<< ". In MatMul x_col and y_row should be equal.";
}
} }
const size_t SHAPE_SIZE = 2;
if (x_shp.size() != SHAPE_SIZE || y_shp.size() != SHAPE_SIZE) {
MS_LOG(EXCEPTION) << "MatMul inputs should have the same dimension size and equal to 2.";
}
auto x_col = x_shp[(transpose_a ? 0 : 1)];
auto y_row = y_shp[(transpose_b ? 1 : 0)];
if (x_col != y_row && x_col >= 0 && y_row >= 0) {
MS_LOG(EXCEPTION) << "MatMul shape error, got x_col: " << x_col << ", y_row: " << y_row
<< ". In MatMul x_col and y_row should be equal.";
}
ShapeVector ret_shape; ShapeVector ret_shape;
ShapeVector ret_min_shape; ShapeVector ret_min_shape;
ShapeVector ret_max_shape; ShapeVector ret_max_shape;
@ -259,18 +274,6 @@ AbstractBasePtr InferImplMatMul(const AnalysisEnginePtr &, const PrimitivePtr &p
make_shape(ret_shape, x_shp, y_shp); make_shape(ret_shape, x_shp, y_shp);
make_shape(ret_min_shape, x_min_shape, y_min_shape); make_shape(ret_min_shape, x_min_shape, y_min_shape);
make_shape(ret_max_shape, x_max_shape, y_max_shape); make_shape(ret_max_shape, x_max_shape, y_max_shape);
TypePtr x_type = x->element()->GetTypeTrack();
if (x_type->type_id() == TypeId::kNumberTypeInt8) {
x_type = kInt32;
}
if (primitive->HasAttr("cast_type")) {
auto out_type = primitive->GetAttr("cast_type");
MS_EXCEPTION_IF_NULL(out_type);
if (!out_type->isa<Type>()) {
MS_EXCEPTION(ValueError) << "MatMul cast_type must be a `Type`";
}
x_type = out_type->cast<TypePtr>();
}
return std::make_shared<AbstractTensor>(x_type, std::make_shared<Shape>(ret_shape, ret_min_shape, ret_max_shape)); return std::make_shared<AbstractTensor>(x_type, std::make_shared<Shape>(ret_shape, ret_min_shape, ret_max_shape));
} }

25
mindspore/core/ops/batch_matmul.cc
View File

@ -1,5 +1,5 @@
/** /**
* Copyright 2021 Huawei Technologies Co., Ltd * Copyright 2021-2022 Huawei Technologies Co., Ltd
* *
* Licensed under the Apache License, Version 2.0 (the "License"); * Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License. * you may not use this file except in compliance with the License.
@ -83,16 +83,15 @@ abstract::ShapePtr BatchMatmulInferShape(const PrimitivePtr &primitive,
if (IsDynamicRank(x_shp) || IsDynamicRank(y_shp)) { if (IsDynamicRank(x_shp) || IsDynamicRank(y_shp)) {
return std::make_shared<abstract::Shape>(ShapeVector({UNKNOWN_RANK})); return std::make_shared<abstract::Shape>(ShapeVector({UNKNOWN_RANK}));
} }
auto context = MsContext::GetInstance();
constexpr size_t x_dim_limit = 3; constexpr size_t x_dim_limit = 3;
constexpr size_t y_dim_limit = 2; constexpr size_t y_dim_limit = 2;
if ((!IsDynamic(x_shp)) && (!IsDynamic(y_shp))) {
if (x_shp.size() < x_dim_limit || y_shp.size() < y_dim_limit) { bool not_dynamic_shape = (!IsDynamic(x_shp)) && !(IsDynamic(y_shp));
MS_EXCEPTION(ValueError) if (not_dynamic_shape && (x_shp.size() < x_dim_limit || y_shp.size() < y_dim_limit)) {
<< "For '" << prim_name MS_EXCEPTION(ValueError)
<< "', input 'x' must be greater or equal to 3, input 'y' must be greater or equal to 2. But got 'x': " << "For '" << prim_name
<< x_shp.size() << ", 'y': " << y_shp.size() << "."; << "', input 'x' must be greater or equal to 3, input 'y' must be greater or equal to 2. But got 'x': "
} << x_shp.size() << ", 'y': " << y_shp.size() << ".";
} }
constexpr size_t offset = 2; constexpr size_t offset = 2;
@ -106,7 +105,7 @@ abstract::ShapePtr BatchMatmulInferShape(const PrimitivePtr &primitive,
int64_t y_row = y_last[static_cast<size_t>(transpose_b)]; int64_t y_row = y_last[static_cast<size_t>(transpose_b)];
if (std::find(x_shp.begin(), x_shp.end(), -1) == x_shp.end() && if (std::find(x_shp.begin(), x_shp.end(), -1) == x_shp.end() &&
std::find(y_shp.begin(), y_shp.end(), -1) == y_shp.end()) { std::find(y_shp.begin(), y_shp.end(), -1) == y_shp.end()) {
if (x_col != y_row) { if (not_dynamic_shape && x_col != y_row) {
MS_EXCEPTION(ValueError) << "For " << prim_name << " evaluator shapes of inputs can not do this operator, " MS_EXCEPTION(ValueError) << "For " << prim_name << " evaluator shapes of inputs can not do this operator, "
<< "got " << x_col << " and " << y_row << " , with x1 shape " << x_shp << "got " << x_col << " and " << y_row << " , with x1 shape " << x_shp
<< "(transpose_a=" << transpose_a << "})" << "(transpose_a=" << transpose_a << "})"
@ -117,11 +116,7 @@ abstract::ShapePtr BatchMatmulInferShape(const PrimitivePtr &primitive,
(void)primitive->AddAttr("transpose_x2", transpose_b_ptr); (void)primitive->AddAttr("transpose_x2", transpose_b_ptr);
// Additional check for dynamic shape // Additional check for dynamic shape
// Last infer will be real shape values // Last infer will be real shape values
bool x_not_dyn = if (not_dynamic_shape) {
std::all_of(x_shp.begin(), x_shp.end(), [](int64_t value) { return value != abstract::Shape::SHP_ANY; });
bool y_not_dyn =
std::all_of(y_shp.begin(), y_shp.end(), [](int64_t value) { return value != abstract::Shape::SHP_ANY; });
if (x_not_dyn && y_not_dyn) {
size_t x_offset = x_shp.size() - offset; size_t x_offset = x_shp.size() - offset;
size_t y_offset = y_shp.size() - offset; size_t y_offset = y_shp.size() - offset;
auto x_c = x_shp[x_offset + (transpose_a ? 0 : 1)]; auto x_c = x_shp[x_offset + (transpose_a ? 0 : 1)];

5
mindspore/core/ops/bias_add.cc
View File

@ -40,11 +40,14 @@ abstract::ShapePtr BiasAddInferShape(const PrimitivePtr &primitive, const std::v
(void)CheckAndConvertUtils::CheckInteger("arg size", SizeToLong(input_args.size()), kEqual, input_num, prim_name); (void)CheckAndConvertUtils::CheckInteger("arg size", SizeToLong(input_args.size()), kEqual, input_num, prim_name);
auto shape_map = CheckAndConvertUtils::ConvertShapePtrToShapeMap(input_args[0]->BuildShape()); auto shape_map = CheckAndConvertUtils::ConvertShapePtrToShapeMap(input_args[0]->BuildShape());
auto input_shape = shape_map[kShape]; auto input_shape = shape_map[kShape];
auto bias_shape = CheckAndConvertUtils::ConvertShapePtrToShapeMap(input_args[1]->BuildShape())[kShape];
if (IsDynamicRank(input_shape) || IsDynamicRank(bias_shape)) {
return std::make_shared<abstract::Shape>(ShapeVector{UNKNOWN_RANK});
}
const int64_t x_min_rank = 2; const int64_t x_min_rank = 2;
const int64_t x_max_rank = 5; const int64_t x_max_rank = 5;
CheckAndConvertUtils::CheckInRange("dims of input_x", input_shape.size(), kIncludeBoth, {x_min_rank, x_max_rank}, CheckAndConvertUtils::CheckInRange("dims of input_x", input_shape.size(), kIncludeBoth, {x_min_rank, x_max_rank},
prim_name); prim_name);
auto bias_shape = CheckAndConvertUtils::ConvertShapePtrToShapeMap(input_args[1]->BuildShape())[kShape];
(void)CheckAndConvertUtils::CheckInteger("bias rank", SizeToLong(bias_shape.size()), kEqual, 1, prim_name); (void)CheckAndConvertUtils::CheckInteger("bias rank", SizeToLong(bias_shape.size()), kEqual, 1, prim_name);
const int64_t x_size = 2; const int64_t x_size = 2;
(void)CheckAndConvertUtils::CheckInteger("x rank", SizeToLong(input_shape.size()), kGreaterEqual, x_size, prim_name); (void)CheckAndConvertUtils::CheckInteger("x rank", SizeToLong(input_shape.size()), kGreaterEqual, x_size, prim_name);

3
mindspore/core/ops/grad/conv3d_backprop_filter.cc
View File

@ -52,7 +52,7 @@ void SetConv3DBackpropFilterPadList(const PrimitivePtr &primitive, const std::ve
CheckAndConvertUtils::CheckIntOrTupleInt("attribute[dilation]", primitive->GetAttr(kDilation), prim_name); CheckAndConvertUtils::CheckIntOrTupleInt("attribute[dilation]", primitive->GetAttr(kDilation), prim_name);
// default pad mode is valid // default pad mode is valid
int64_t pad_mode; int64_t pad_mode;
CheckAndConvertUtils::GetPadModEnumValue(primitive->GetAttr(kPadMode), &pad_mode, true); CheckAndConvertUtils::GetPadModEnumValue(primitive->GetAttr(kPadMode), &pad_mode, false);
ShapeVector pad_list = {0, 0, 0, 0, 0, 0}; ShapeVector pad_list = {0, 0, 0, 0, 0, 0};
auto attr_pad_list_prt = primitive->GetAttr(kPadList); auto attr_pad_list_prt = primitive->GetAttr(kPadList);
if ((attr_pad_list_prt != nullptr) && !attr_pad_list_prt->isa<None>()) { if ((attr_pad_list_prt != nullptr) && !attr_pad_list_prt->isa<None>()) {
@ -261,6 +261,7 @@ class Conv3DBackpropFilterInfer : public abstract::OpInferBase {
std::set<TypePtr> valid_x_type = {kInt8, kInt32, kFloat16, kFloat32}; std::set<TypePtr> valid_x_type = {kInt8, kInt32, kFloat16, kFloat32};
return CheckAndConvertUtils::CheckTensorTypeSame(types, valid_x_type, prim_name); return CheckAndConvertUtils::CheckTensorTypeSame(types, valid_x_type, prim_name);
} }
std::set<int64_t> GetValueDependArgIndices() const override { return {kConv3DBackpropFilterFilterSizeIndex}; }
}; };
REGISTER_PRIMITIVE_OP_INFER_IMPL(Conv3DBackpropFilter, prim::kPrimConv3DBackpropFilter, Conv3DBackpropFilterInfer, REGISTER_PRIMITIVE_OP_INFER_IMPL(Conv3DBackpropFilter, prim::kPrimConv3DBackpropFilter, Conv3DBackpropFilterInfer,

3
mindspore/core/ops/grad/conv3d_backprop_input.cc
View File

@ -45,7 +45,7 @@ void SetConv3DBackpropInputPadList(const PrimitivePtr &primitive, const std::vec
CheckAndConvertUtils::CheckIntOrTupleInt("attribute[dilation]", primitive->GetAttr(kDilation), prim_name); CheckAndConvertUtils::CheckIntOrTupleInt("attribute[dilation]", primitive->GetAttr(kDilation), prim_name);
// default pad mode is valid // default pad mode is valid
int64_t pad_mode; int64_t pad_mode;
CheckAndConvertUtils::GetPadModEnumValue(primitive->GetAttr(kPadMode), &pad_mode, true); CheckAndConvertUtils::GetPadModEnumValue(primitive->GetAttr(kPadMode), &pad_mode, false);
ShapeVector pad_list = {0, 0, 0, 0, 0, 0}; ShapeVector pad_list = {0, 0, 0, 0, 0, 0};
auto attr_pad_list_prt = primitive->GetAttr(kPadList); auto attr_pad_list_prt = primitive->GetAttr(kPadList);
if ((attr_pad_list_prt != nullptr) && (!attr_pad_list_prt->isa<None>())) { if ((attr_pad_list_prt != nullptr) && (!attr_pad_list_prt->isa<None>())) {
@ -258,6 +258,7 @@ class Conv3DBackpropInputInfer : public abstract::OpInferBase {
std::set<TypePtr> valid_x_type = {kInt8, kInt32, kFloat16, kFloat32}; std::set<TypePtr> valid_x_type = {kInt8, kInt32, kFloat16, kFloat32};
return CheckAndConvertUtils::CheckTensorTypeSame(types, valid_x_type, prim_name); return CheckAndConvertUtils::CheckTensorTypeSame(types, valid_x_type, prim_name);
} }
std::set<int64_t> GetValueDependArgIndices() const override { return {kConv3DBackpropInputSizeIndex}; }
}; };
REGISTER_PRIMITIVE_OP_INFER_IMPL(Conv3DBackpropInput, prim::kPrimConv3DBackpropInput, Conv3DBackpropInputInfer, false); REGISTER_PRIMITIVE_OP_INFER_IMPL(Conv3DBackpropInput, prim::kPrimConv3DBackpropInput, Conv3DBackpropInputInfer, false);

4
mindspore/core/ops/matrix_diag_v3.cc
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@ -121,6 +121,10 @@ abstract::ShapePtr MatrixDiagV3InferShape(const PrimitivePtr &primitive,
auto padding_value_rank = SizeToLong(padding_shape.size()); auto padding_value_rank = SizeToLong(padding_shape.size());
constexpr int64_t number_one = 1; constexpr int64_t number_one = 1;
constexpr int64_t number_two = 2; constexpr int64_t number_two = 2;
if (IsDynamicRank(x_shape)) {
ShapeVector out_shape = {UNKNOWN_RANK};
return std::make_shared<abstract::Shape>(out_shape);
}
CheckAndConvertUtils::CheckInRange<int64_t>("rank of 'k'", k_rank, kIncludeBoth, {0, number_one}, prim_name); CheckAndConvertUtils::CheckInRange<int64_t>("rank of 'k'", k_rank, kIncludeBoth, {0, number_one}, prim_name);
(void)CheckAndConvertUtils::CheckInteger("rank of 'num_rows'", row_rank, kEqual, 0, prim_name); (void)CheckAndConvertUtils::CheckInteger("rank of 'num_rows'", row_rank, kEqual, 0, prim_name);
(void)CheckAndConvertUtils::CheckInteger("rank of 'num_cols'", col_rank, kEqual, 0, prim_name); (void)CheckAndConvertUtils::CheckInteger("rank of 'num_cols'", col_rank, kEqual, 0, prim_name);

10
mindspore/core/ops/svd.cc
View File

@ -1,5 +1,5 @@
/** /**
* Copyright 2020 Huawei Technologies Co., Ltd * Copyright 2020-2022 Huawei Technologies Co., Ltd
* *
* Licensed under the Apache License, Version 2.0 (the "License"); * Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License. * you may not use this file except in compliance with the License.
@ -34,6 +34,14 @@ abstract::BaseShapePtr SvdInferShape(const PrimitivePtr &prim, const std::vector
auto full_matrices = GetValue<bool>(prim->GetAttr(kAttrFullMatrices)); auto full_matrices = GetValue<bool>(prim->GetAttr(kAttrFullMatrices));
auto a_shape = CheckAndConvertUtils::ConvertShapePtrToShapeMap(input_args[kInputIndex0]->BuildShape())[kShape]; auto a_shape = CheckAndConvertUtils::ConvertShapePtrToShapeMap(input_args[kInputIndex0]->BuildShape())[kShape];
if (IsDynamicRank(a_shape) || IsDynamic(a_shape)) {
ShapeVector dyn_shape{UNKNOWN_RANK};
std::vector<abstract::BaseShapePtr> shape_tuple;
(void)shape_tuple.emplace_back(std::make_shared<abstract::Shape>(dyn_shape));
(void)shape_tuple.emplace_back(std::make_shared<abstract::Shape>(dyn_shape));
(void)shape_tuple.emplace_back(std::make_shared<abstract::Shape>(dyn_shape));
return std::make_shared<abstract::TupleShape>(shape_tuple);
}
auto ndim = a_shape.size(); auto ndim = a_shape.size();
(void)CheckAndConvertUtils::CheckInteger("ndim", SizeToLong(ndim), kGreaterEqual, kSizeTwo, prim->name()); (void)CheckAndConvertUtils::CheckInteger("ndim", SizeToLong(ndim), kGreaterEqual, kSizeTwo, prim->name());
auto m = a_shape[ndim - kIndexTwo]; auto m = a_shape[ndim - kIndexTwo];

10
mindspore/core/ops/topk.cc
View File

@ -36,16 +36,16 @@ abstract::TupleShapePtr TopKInferShape(const PrimitivePtr &primitive, const std:
auto prim_name = primitive->name(); auto prim_name = primitive->name();
auto shape_map = CheckAndConvertUtils::ConvertShapePtrToShapeMap(input_args[kInputIndex0]->BuildShape()); auto shape_map = CheckAndConvertUtils::ConvertShapePtrToShapeMap(input_args[kInputIndex0]->BuildShape());
auto x_shape = shape_map[kShape]; auto x_shape = shape_map[kShape];
if (IsDynamicRank(x_shape)) {
abstract::BaseShapePtr out_shape_ptr = std::make_shared<abstract::Shape>(ShapeVector{UNKNOWN_RANK});
return std::make_shared<abstract::TupleShape>(std::vector<abstract::BaseShapePtr>{out_shape_ptr, out_shape_ptr});
}
int64_t k_v = 0; int64_t k_v = 0;
// 2rd input is a Tensor when TopK is a dynamic shape operator // 2rd input is a Tensor when TopK is a dynamic shape operator
if (input_args[kInputIndex1]->isa<abstract::AbstractTensor>()) { if (input_args[kInputIndex1]->isa<abstract::AbstractTensor>()) {
auto k_ptr = input_args[kInputIndex1]->BuildValue(); auto k_ptr = input_args[kInputIndex1]->BuildValue();
MS_EXCEPTION_IF_NULL(k_ptr); MS_EXCEPTION_IF_NULL(k_ptr);
if (k_ptr->isa<tensor::Tensor>()) { k_v = CheckAndConvertUtils::CheckTensorIntValue("k", k_ptr, prim_name)[0];
auto k_tensor_ptr = k_ptr->cast<tensor::TensorPtr>();
MS_EXCEPTION_IF_NULL(k_tensor_ptr);
k_v = *static_cast<int64_t *>(k_tensor_ptr->data_c());
}
} else if (input_args[kInputIndex1]->isa<abstract::AbstractScalar>()) { } else if (input_args[kInputIndex1]->isa<abstract::AbstractScalar>()) {
k_v = GetValue<int64_t>(input_args[kInputIndex1]->BuildValue()); k_v = GetValue<int64_t>(input_args[kInputIndex1]->BuildValue());
} else { } else {

50
mindspore/python/mindspore/ops/_grad/grad_array_ops.py
View File

@ -29,6 +29,7 @@ from mindspore.ops.primitive import constexpr
from mindspore.common import dtype as mstype from mindspore.common import dtype as mstype
from mindspore.common.tensor import RowTensor from mindspore.common.tensor import RowTensor
from mindspore.ops._utils.utils import range_op, get_1d_shape, generate_shape_index, is_shape_unknown from mindspore.ops._utils.utils import range_op, get_1d_shape, generate_shape_index, is_shape_unknown
from .._grad.grad_base import dyn_rank, convert_to_tensor, dyn_invert_permutation, dyn_size, dyn_ones, dyn_fill
reduce_sum = P.ReduceSum() reduce_sum = P.ReduceSum()
unsorted_segment_sum = P.UnsortedSegmentSum() unsorted_segment_sum = P.UnsortedSegmentSum()
@ -370,11 +371,20 @@ def _transpose_perm_positive(perm):
return tuple(res) return tuple(res)
def _dyn_transpose_perm_positive(perm):
return (perm + dyn_size(perm)) % (dyn_size(perm))
@bprop_getters.register(P.Transpose) @bprop_getters.register(P.Transpose)
def get_bprop_transpose(self): def get_bprop_transpose(self):
"""Generate bprop for Transpose""" """Generate bprop for Transpose"""
def bprop(x, perm, out, dout): def bprop(x, perm, out, dout):
is_mutable, perm = convert_to_tensor(perm)
if is_mutable:
perm = _dyn_transpose_perm_positive(perm)
return transpose(dout, dyn_invert_permutation(perm)), zeros_like(perm)
perm = _transpose_perm_positive(perm) perm = _transpose_perm_positive(perm)
return transpose(dout, invert_permutation(perm)), zeros_like(perm) return transpose(dout, invert_permutation(perm)), zeros_like(perm)
@ -1019,18 +1029,36 @@ def _gather_drop_negatives(params,
select = P.Select() select = P.Select()
if zero_clipped_indices is None: if zero_clipped_indices is None:
zero_clipped_indices = maximum(ids, zeros_like(ids)) if is_shape_unknown(shape_op(ids)):
zero_ids = dyn_fill(ids.dtype, dyn_shape_op(ids), 0)
else:
zero_ids = zeros_like(ids)
zero_clipped_indices = maximum(ids, zero_ids)
gathered = gather(params, zero_clipped_indices, 0) gathered = gather(params, zero_clipped_indices, 0)
zero_slice = zeros_like(gathered)
if is_positive is None: if is_positive is None:
is_positive = greater_equal(ids, 0) is_positive = greater_equal(ids, 0)
is_positive_shape = shape_op(is_positive) is_positive_shape = shape_op(is_positive)
broadcastable_shape = is_positive_shape
for _ in range(rank(gathered) - rank(is_positive)):
broadcastable_shape += (1,)
is_positive = reshape(is_positive, broadcastable_shape)
gathered_shape = shape_op(gathered) gathered_shape = shape_op(gathered)
is_positive = logical_and(is_positive, fill(mstype.bool_, gathered_shape, 1)) if is_shape_unknown(gathered_shape) or is_shape_unknown(is_positive_shape):
zero_slice = zeros_like(gathered) gathered_shape = dyn_shape_op(gathered)
rank_gathered = dyn_rank(gathered)
fill_gathered = dyn_fill(mstype.int64, gathered_shape, 1)
is_positive_shape = dyn_shape_op(is_positive)
rank_positive = dyn_rank(is_positive)
if rank_gathered - rank_positive > 0:
padded_size = F.expand_dims(rank_gathered - rank_positive, 0)
padded_shape = dyn_ones(padded_size, is_positive_shape.dtype)
is_positive_shape = P.Concat(-1)((is_positive_shape, padded_shape))
is_positive = reshape(is_positive, is_positive_shape)
is_positive = logical_and(is_positive, F.cast(fill_gathered, mstype.bool_))
zero_slice = dyn_fill(gathered.dtype, gathered_shape, 0)
else:
broadcastable_shape = is_positive_shape
for _ in range(rank(gathered) - rank(is_positive)):
broadcastable_shape += (1,)
is_positive = reshape(is_positive, broadcastable_shape)
is_positive = logical_and(is_positive, fill(mstype.bool_, gathered_shape, 1))
return (select(is_positive, gathered, zero_slice), zero_clipped_indices, is_positive) return (select(is_positive, gathered, zero_slice), zero_clipped_indices, is_positive)
@ -1059,7 +1087,13 @@ def get_bprop_unsorted_segment_sum(self):
"""Generate bprop for UnsortedSegmentSum""" """Generate bprop for UnsortedSegmentSum"""
def bprop(x, segment_ids, num_segments, out, dout): def bprop(x, segment_ids, num_segments, out, dout):
return _gather_drop_negatives(dout, segment_ids, None, None)[0], zeros_like(segment_ids), \ segment_shape = shape_op(segment_ids)
if is_shape_unknown(segment_shape):
segment_shape = dyn_shape_op(segment_ids)
zeros_segment = dyn_fill(segment_ids.dtype, segment_shape, 0)
else:
zeros_segment = zeros_like(segment_ids)
return _gather_drop_negatives(dout, segment_ids, None, None)[0], zeros_segment, \
zeros_like(num_segments) zeros_like(num_segments)
return bprop return bprop

6
mindspore/python/mindspore/ops/_grad/grad_base.py
View File

@ -97,12 +97,12 @@ def dyn_rank(tensor):
return dyn_shape(dyn_shape(tensor))[0] return dyn_shape(dyn_shape(tensor))[0]
def dyn_size(tensor): def dyn_size(tensor, dtype=mstype.int64):
"""get the size of tensor""" """get the size of tensor"""
shape = dyn_shape(tensor) shape = dyn_shape(tensor)
shape = cast(shape, mstype.float32) shape = cast(shape, mstype.float32)
size = P.ReduceProd()(shape) size = P.ReduceProd()(shape)
size = cast(size, mstype.int32) size = cast(size, dtype)
return size return size
@ -117,7 +117,7 @@ def create_tensor_by_element(ori_tuple, data_type=mstype.int64):
return ori_tuple return ori_tuple
def dyn_invert_premutation(prem): def dyn_invert_permutation(prem):
"""get the invert premutation of tensor""" """get the invert premutation of tensor"""
indices = P.ExpandDims()(prem, -1) indices = P.ExpandDims()(prem, -1)
end = dyn_size(prem) end = dyn_size(prem)

16
mindspore/python/mindspore/ops/_grad/grad_math_ops.py
View File

@ -53,8 +53,20 @@ def dyn_binop_grad_common(x, y, dx, dy):
shape_of_x = dyn_shape_op(x) shape_of_x = dyn_shape_op(x)
shape_of_y = dyn_shape_op(y) shape_of_y = dyn_shape_op(y)
rx, ry = DynamicBroadcastGradientArgs()(shape_of_x, shape_of_y) rx, ry = DynamicBroadcastGradientArgs()(shape_of_x, shape_of_y)
dx = reduce_sum(dx, rx) dx_origin_dtype = dx.dtype
dy = reduce_sum(dy, ry) if dx_origin_dtype in (mstype.int16, mstype.int32, mstype.int64):
dx = F.cast(dx, mstype.float32)
dx = reduce_sum(dx, rx)
dx = F.cast(dx, dx_origin_dtype)
else:
dx = reduce_sum(dx, rx)
dy_origin_dtype = dy.dtype
if dy_origin_dtype in (mstype.int16, mstype.int32, mstype.int64):
dy = F.cast(dy, mstype.float32)
dy = reduce_sum(dy, ry)
dy = F.cast(dy, dy_origin_dtype)
else:
dy = reduce_sum(dy, ry)
reduce_dx = reshape(dx, shape_of_x) reduce_dx = reshape(dx, shape_of_x)
reduce_dy = reshape(dy, shape_of_y) reduce_dy = reshape(dy, shape_of_y)
return reduce_dx, reduce_dy return reduce_dx, reduce_dy

88
mindspore/python/mindspore/ops/_grad/grad_nn_ops.py
View File

@ -18,7 +18,7 @@ import numpy as np
from mindspore.ops.primitive import constexpr from mindspore.ops.primitive import constexpr
from mindspore.ops.operations import nn_ops as nps from mindspore.ops.operations import nn_ops as nps
from mindspore.common import dtype as mstype from mindspore.common import dtype as mstype
from .grad_base import bprop_getters from .grad_base import bprop_getters, dyn_size, create_tensor_by_element
from .. import functional as F from .. import functional as F
from .. import operations as P from .. import operations as P
from ..composite.multitype_ops.zeros_like_impl import zeros_like from ..composite.multitype_ops.zeros_like_impl import zeros_like
@ -61,6 +61,17 @@ def bias_add_gradgrad_helper(shape, bias_shape, data_format):
return tuple(expanded_shape), tuple(tile_mults) return tuple(expanded_shape), tuple(tile_mults)
def bias_add_gradgrad_helper_dynamic(shape, bias_shape, data_format):
"""Helper function of BiasGradGrad to calculate expanded shape(dynamic version)."""
if data_format == "NCHW":
expanded_shape = P.Concat(0)((P.OnesLike()(shape[:1]), bias_shape, P.OnesLike()(shape[2:])))
tile_mults = P.Concat(0)((shape[:1], [1], shape[2:]))
else:
expanded_shape = P.Concat(0)((P.OnesLike()(shape[:-1]), bias_shape))
tile_mults = P.Concat(0)((shape[:-1], [1]))
return tuple(expanded_shape), tuple(tile_mults)
@bprop_getters.register(G.BiasAddGrad) @bprop_getters.register(G.BiasAddGrad)
def get_bprop_bias_add_grad(self): def get_bprop_bias_add_grad(self):
"""Grad definition for `BiasAddGrad` operation.""" """Grad definition for `BiasAddGrad` operation."""
@ -70,10 +81,19 @@ def get_bprop_bias_add_grad(self):
def bprop(dy, out, dout): def bprop(dy, out, dout):
reshape = P.Reshape() reshape = P.Reshape()
tile = P.Tile() tile = P.Tile()
dyn_shape = P.TensorShape()
expanded_shape, tile_mults = bias_add_gradgrad_helper(dy.shape, dout.shape, data_format) if is_shape_unknown(dy) or is_shape_unknown(dout):
expanded_grad = reshape(dout, expanded_shape) dy_shape = dyn_shape(dy)
tiled_grad = tile(expanded_grad, tile_mults) dout_shape = dyn_shape(dout)
expanded_shape, tile_mults = bias_add_gradgrad_helper_dynamic(dy_shape, dout_shape, data_format)
expanded_grad = reshape(dout, create_tensor_by_element(expanded_shape))
tiled_grad = tile(expanded_grad, create_tensor_by_element(tile_mults))
else:
dy_shape = dy.shape
dout_shape = dout.shape
expanded_shape, tile_mults = bias_add_gradgrad_helper(dy_shape, dout_shape, data_format)
expanded_grad = reshape(dout, expanded_shape)
tiled_grad = tile(expanded_grad, tile_mults)
return (tiled_grad,) return (tiled_grad,)
return bprop return bprop
@ -122,8 +142,14 @@ def get_bprop_conv3d(self):
pad=self.pad, stride=self.stride, dilation=self.dilation, group=self.group, data_format=self.data_format pad=self.pad, stride=self.stride, dilation=self.dilation, group=self.group, data_format=self.data_format
) )
get_shape = P.Shape() get_shape = P.Shape()
get_dyn_shape = P.TensorShape()
def bprop(x, w, out, dout): def bprop(x, w, out, dout):
if is_shape_unknown(get_shape(x)) or is_shape_unknown(get_shape(w)):
dx = input_grad(w, dout, get_dyn_shape(x))
dw = filter_grad(x, dout, get_dyn_shape(w))
return dx, dw
dx = input_grad(w, dout, get_shape(x)) dx = input_grad(w, dout, get_shape(x))
dw = filter_grad(x, dout, get_shape(w)) dw = filter_grad(x, dout, get_shape(w))
return dx, dw return dx, dw
@ -145,8 +171,14 @@ def get_bprop_conv3d_transpose(self):
out_channel=self.in_channel, kernel_size=self.kernel_size, mode=self.mode, pad_mode="pad", out_channel=self.in_channel, kernel_size=self.kernel_size, mode=self.mode, pad_mode="pad",
pad=pad_list, stride=self.stride, dilation=self.dilation, group=self.group, data_format=self.data_format pad=pad_list, stride=self.stride, dilation=self.dilation, group=self.group, data_format=self.data_format
) )
get_dyn_shape = P.TensorShape()
def bprop(x, w, out, dout): def bprop(x, w, out, dout):
if is_shape_unknown(F.shape(w)):
dx = input_grad(dout, w)
dw = filter_grad(dout, x, get_dyn_shape(w))
return dx, dw
dx = input_grad(dout, w) dx = input_grad(dout, w)
dw = filter_grad(dout, x, F.shape(w)) dw = filter_grad(dout, x, F.shape(w))
return dx, dw return dx, dw
@ -928,10 +960,12 @@ def get_bprop_top_kv2(self):
scatter = P.ScatterNd() scatter = P.ScatterNd()
expand_dims = P.ExpandDims() expand_dims = P.ExpandDims()
shape_op = P.Shape() shape_op = P.Shape()
dyn_shape = P.TensorShape()
reshape_op = P.Reshape() reshape_op = P.Reshape()
dtype = P.DType() dtype = P.DType()
cast = P.Cast()
def bprop(input_x, k, out, dout): def _bprop_static(input_x, k, out, dout):
in_shape = shape_op(input_x) in_shape = shape_op(input_x)
in_lastdim = in_shape[-1] in_lastdim = in_shape[-1]
@ -958,6 +992,37 @@ def get_bprop_top_kv2(self):
in_shape) in_shape)
return out_grad, zeros_like(k) return out_grad, zeros_like(k)
def _bprop_dynshape(input_x, k, out, dout):
in_shape = dyn_shape(input_x)
in_lastdim = in_shape[-1]
indices = out[1]
ind_shape = dyn_shape(indices)
ind_lastdim = ind_shape[-1]
ind_2d = reshape_op(indices, create_tensor_by_element((-1, ind_lastdim)))
outerdim = dyn_shape(ind_2d)[0]
# [0, outterdim, 2*outerdim, ..., (k-1)*outerdim]
range_flatten_index = P.Range()(cast(0, mstype.int64), outerdim * in_lastdim, in_lastdim)
# expand_dims to (k, 1), then broadcast
ind = reshape_op(ind_2d + expand_dims(range_flatten_index, -1), create_tensor_by_element((-1,)))
in_shape_1d = expand_dims(dyn_size(input_x, mstype.int64), -1)
out_grad = reshape_op(
scatter(
expand_dims(ind, -1),
reshape_op(dout[0], create_tensor_by_element((-1,))),
in_shape_1d),
in_shape)
return out_grad, zeros_like(k)
def bprop(input_x, k, out, dout):
if is_shape_unknown(shape_op(input_x)):
return _bprop_dynshape(input_x, k, out, dout)
return _bprop_static(input_x, k, out, dout)
return bprop return bprop
@ -1239,7 +1304,7 @@ def get_bprop_binary_cross_entropy(self):
@bprop_getters.register(P.BCEWithLogitsLoss) @bprop_getters.register(P.BCEWithLogitsLoss)
def get_bprop_ce_with_logits_loss(self): def get_bprop_bce_with_logits_loss(self):
"""Grad definition for `BCEWithLogitsLoss` operation.""" """Grad definition for `BCEWithLogitsLoss` operation."""
reduction = self.reduction reduction = self.reduction
mul = P.Mul() mul = P.Mul()
@ -1249,6 +1314,7 @@ def get_bprop_ce_with_logits_loss(self):
size = P.Size() size = P.Size()
neg = P.Neg() neg = P.Neg()
log = P.Log() log = P.Log()
shape = P.Shape()
def bprop(predict, target, weight, pos_weight, out, dout): def bprop(predict, target, weight, pos_weight, out, dout):
sigmoid_input = sigmoid(predict) sigmoid_input = sigmoid(predict)
@ -1263,8 +1329,10 @@ def get_bprop_ce_with_logits_loss(self):
dx = mul(dx, weight) dx = mul(dx, weight)
grad_target = mul(grad_target, weight) grad_target = mul(grad_target, weight)
if reduction == 'mean': if reduction == 'mean':
dx = dx / size(dx) dx_size = dyn_size(dx) if is_shape_unknown(shape(dx)) else size(dx)
grad_target = grad_target / size(target) target_size = dyn_size(target) if is_shape_unknown(shape(target)) else size(target)
dx = dx / dx_size
grad_target = grad_target / target_size
return dx, grad_target, zeros_like(weight), zeros_like(pos_weight) return dx, grad_target, zeros_like(weight), zeros_like(pos_weight)
return bprop return bprop
@ -1414,7 +1482,7 @@ def get_bprop_conv2d_backprop_filter(self):
def bprop(dy, x, filter_size, out, dout): def bprop(dy, x, filter_size, out, dout):
x_shape = get_shape(x) x_shape = get_shape(x)
if -1 in x_shape: if is_shape_unknown(x_shape):
x_shape = get_dyn_shape(x) x_shape = get_dyn_shape(x)
dw_dx = input_grad(dy, dout, x_shape) dw_dx = input_grad(dy, dout, x_shape)
dw_dy = filter_grad(x, dout) dw_dy = filter_grad(x, dout)

46
mindspore/python/mindspore/ops/_grad_experimental/grad_linalg_ops.py
View File

@ -19,14 +19,19 @@ import mindspore
from .. import Tensor from .. import Tensor
from .. import functional as F from .. import functional as F
from .. import operations as P
from ..composite.multitype_ops.zeros_like_impl import zeros_like from ..composite.multitype_ops.zeros_like_impl import zeros_like
from ..operations import math_ops as math from ..operations import math_ops as math
from ..operations import linalg_ops as linalg from ..operations import linalg_ops as linalg
from ..operations import array_ops as arrays from ..operations import array_ops as arrays
from ..primitive import constexpr from ..primitive import constexpr
from .._grad.grad_base import bprop_getters from .._grad.grad_base import bprop_getters
from .._grad.grad_base import dyn_rank
from .._utils.utils import is_shape_unknown
_shape = arrays.Shape() _shape = arrays.Shape()
_dyn_shape = arrays.TensorShape()
_dtype = arrays.DType() _dtype = arrays.DType()
_cast = arrays.Cast() _cast = arrays.Cast()
_transpose = arrays.Transpose() _transpose = arrays.Transpose()
@ -47,12 +52,19 @@ def _raise_value_error(*info):
def _matrix_transpose(a): def _matrix_transpose(a):
dims = a.ndim """Transpose last two axes"""
if dims < 2: if is_shape_unknown(_shape(a)):
_raise_value_error( dims = dyn_rank(a)
"To do _matrix_transpose for input a's ndim is not greater or equal to 2, which is invalid.") axes = P.Range()(P.Cast()(0, mindspore.int64), dims, P.Cast()(1, mindspore.int64))
axes = F.make_range(0, dims) axes = P.Concat(axis=-1)((axes[:-2], axes[-1:], axes[-2:-1]))
axes = axes[:-2] + (axes[-1],) + (axes[-2],) else:
dims = a.ndim
if dims < 2:
_raise_value_error(
"To do _matrix_transpose for input a's ndim is not greater or equal to 2, which is invalid: {}."
.format(dims))
axes = F.make_range(0, dims)
axes = axes[:-2] + (axes[-1],) + (axes[-2],)
return _transpose(a, axes) return _transpose(a, axes)
@ -76,14 +88,26 @@ def _make_zero_matrix(shape, dtype):
def _matrix_diag(diagonal): def _matrix_diag(diagonal):
"""Do matrix diagnoal"""
diagonal_shape = _shape(diagonal) diagonal_shape = _shape(diagonal)
if is_shape_unknown(diagonal_shape):
diagonal_shape = _dyn_shape(diagonal)
row = P.Cast()(diagonal_shape[-1], mindspore.int32)
return arrays.MatrixDiagV3()(diagonal, _k_0, row, row, P.Cast()(0, _dtype(diagonal)))
row = _make_tensor(diagonal_shape[-1], mindspore.int32) row = _make_tensor(diagonal_shape[-1], mindspore.int32)
return arrays.MatrixDiagV3()(diagonal, _k_0, row, row, _make_tensor(0, _dtype(diagonal))) return arrays.MatrixDiagV3()(diagonal, _k_0, row, row, _make_tensor(0, _dtype(diagonal)))
def _mat_mul(x, y): def _mat_mul(x, y):
"""Do matmul"""
shape = _shape(x) shape = _shape(x)
if len(shape) > 2: if is_shape_unknown(shape):
shape = _dyn_shape(x)
tensor_rank = dyn_rank(x)
else:
tensor_rank = len(shape)
if tensor_rank > 2:
return math.BatchMatMul()(x, y) return math.BatchMatMul()(x, y)
return math.MatMul()(x, y) return math.MatMul()(x, y)
@ -106,12 +130,16 @@ def get_bprop_svd(self):
return (da,) return (da,)
a_shape = _shape(a) a_shape = _shape(a)
if len(a_shape) < 2: if is_shape_unknown(a_shape):
a_shape = _dyn_shape(a)
tensor_rank = dyn_rank(a)
else:
tensor_rank = len(a_shape)
if tensor_rank < 2:
_raise_value_error( _raise_value_error(
"For input a's ndim is not greater or equal to 2, which is invalid.") "For input a's ndim is not greater or equal to 2, which is invalid.")
m = a_shape[-2] m = a_shape[-2]
n = a_shape[-1] n = a_shape[-1]
s, u, v = out s, u, v = out
ds, du, dv = dout ds, du, dv = dout
use_adjoint = False use_adjoint = False

46
mindspore/python/mindspore/ops/_grad_experimental/grad_math_ops.py
View File

@ -1,4 +1,4 @@
# Copyright 2021 Huawei Technologies Co., Ltd # Copyright 2021-2022 Huawei Technologies Co., Ltd
# #
# Licensed under the Apache License, Version 2.0 (the "License"); # Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License. # you may not use this file except in compliance with the License.
@ -17,6 +17,7 @@
from mindspore.common import dtype as mstype from mindspore.common import dtype as mstype
from mindspore.ops.operations.math_ops import Trace, Bernoulli, Renorm from mindspore.ops.operations.math_ops import Trace, Bernoulli, Renorm
from mindspore.ops._utils.utils import is_shape_unknown
from mindspore import nn from mindspore import nn
import mindspore.numpy as mnp import mindspore.numpy as mnp
import numpy as np import numpy as np
@ -27,7 +28,7 @@ from ..operations.math_ops import Real, Imag, Complex, Angle
from ..operations.math_ops import ComplexAbs from ..operations.math_ops import ComplexAbs
from ..operations.math_ops import Sinc from ..operations.math_ops import Sinc
from ..functional import broadcast_gradient_args from ..functional import broadcast_gradient_args
from .._grad.grad_base import bprop_getters from .._grad.grad_base import bprop_getters, create_tensor_by_element, dyn_rank
from .._grad.grad_math_ops import binop_grad_common from .._grad.grad_math_ops import binop_grad_common
from ..composite.multitype_ops.zeros_like_impl import zeros_like from ..composite.multitype_ops.zeros_like_impl import zeros_like
from ..operations import _grad_ops as G from ..operations import _grad_ops as G
@ -55,9 +56,11 @@ from ..operations.math_ops import CholeskySolve
from ..operations.math_ops import AddV2 from ..operations.math_ops import AddV2
from ..operations.math_ops import TridiagonalMatMul from ..operations.math_ops import TridiagonalMatMul
from ..operations.math_ops import Logit from ..operations.math_ops import Logit
from .._utils.utils import is_shape_unknown
transpose = P.Transpose() transpose = P.Transpose()
dyn_shape_op = P.TensorShape()
_conj = P.Conj() _conj = P.Conj()
@ -67,6 +70,11 @@ def _generate_perm(x_dim):
return perm return perm
def _dyn_generate_perm(x_dim):
perm = P.Range()(P.Cast()(0, x_dim.dtype), x_dim - 2, P.Cast()(1, x_dim.dtype))
return perm
def _adjoint(a): def _adjoint(a):
return cholesky_transpose(_conj(a)) return cholesky_transpose(_conj(a))
@ -110,10 +118,20 @@ def get_bprop_cdist(self):
def bprop(input_x, input_y, out, dout): def bprop(input_x, input_y, out, dout):
dout_shape = F.shape(dout) dout_shape = F.shape(dout)
dout_dim = len(dout_shape) if is_shape_unknown(dout_shape):
dout_perm_part1 = _generate_perm(dout_dim) dout_dim = dyn_rank(dout)
dout_perm_part2 = (dout_dim - 1, dout_dim - 2) dout_perm_part2 = create_tensor_by_element(
dout_perm = dout_perm_part1 + dout_perm_part2 (dout_dim - 1, dout_dim - 2))
if dout_dim <= 2:
dout_perm = dout_perm_part2
else:
dout_perm_part1 = _dyn_generate_perm(dout_dim)
dout_perm = P.Concat(0)((dout_perm_part1, dout_perm_part2))
else:
dout_dim = len(dout_shape)
dout_perm_part1 = _generate_perm(dout_dim)
dout_perm_part2 = (dout_dim - 1, dout_dim - 2)
dout_perm = dout_perm_part1 + dout_perm_part2
out_perm = dout_perm out_perm = dout_perm
dout_transpose = transpose(dout, dout_perm) dout_transpose = transpose(dout, dout_perm)
out_transpose = transpose(out, out_perm) out_transpose = transpose(out, out_perm)
@ -484,8 +502,16 @@ def get_bprop_matrix_determinant(self):
inverse_op = P.MatrixInverse(adjoint=True) inverse_op = P.MatrixInverse(adjoint=True)
shape_op = P.Shape() shape_op = P.Shape()
reshape = P.Reshape() reshape = P.Reshape()
concat = P.Concat(0)
def bprop(x, out, dout): def bprop(x, out, dout):
if is_shape_unknown(shape_op(x)):
x_adj_inv = inverse_op(x)
out_shape = dyn_shape_op(out)
ones = create_tensor_by_element((1, 1))
multipliers = reshape(dout * out, concat((out_shape, ones)))
dx = multipliers * x_adj_inv
return (dx,)
x_adj_inv = inverse_op(x) x_adj_inv = inverse_op(x)
multipliers = reshape(dout * out, shape_op(out) + (1, 1)) multipliers = reshape(dout * out, shape_op(out) + (1, 1))
dx = multipliers * x_adj_inv dx = multipliers * x_adj_inv
@ -902,7 +928,11 @@ def get_bprop_trace(self):
def bprop(x, out, dout): def bprop(x, out, dout):
shape = shape_op(x) shape = shape_op(x)
dx = input_grad(dout, cast(to_array(shape), mstype.int64)) if is_shape_unknown(shape):
shape = dyn_shape_op(x)
dx = input_grad(dout, shape)
else:
dx = input_grad(dout, cast(to_array(shape), mstype.int64))
return (dx,) return (dx,)
return bprop return bprop
@ -1041,7 +1071,7 @@ def get_bprop_tridiagonal_matmul(self):
maindiag_grad = reduce_sum(rhs_conj * grad, -1) maindiag_grad = reduce_sum(rhs_conj * grad, -1)
subdiag_grad = reduce_sum(_rightshift(rhs_conj) * grad, -1) subdiag_grad = reduce_sum(_rightshift(rhs_conj) * grad, -1)
rhs_grad = _rightshift(superdiag_conj * grad) + maindiag_conj * grad + \ rhs_grad = _rightshift(superdiag_conj * grad) + maindiag_conj * grad + \
_leftshift(subdiag_conj * grad) _leftshift(subdiag_conj * grad)
superdiag_grad = expand_dims(superdiag_grad, -2) superdiag_grad = expand_dims(superdiag_grad, -2)
maindiag_grad = expand_dims(maindiag_grad, -2) maindiag_grad = expand_dims(maindiag_grad, -2)
subdiag_grad = expand_dims(subdiag_grad, -2) subdiag_grad = expand_dims(subdiag_grad, -2)

15
mindspore/python/mindspore/ops/_grad_experimental/grad_sparse_ops.py
View File

@ -25,6 +25,7 @@ from mindspore.ops.operations.sparse_ops import SparseSegmentSumWithNumSegments
from mindspore.ops.operations.sparse_ops import SparseSegmentSqrtN from mindspore.ops.operations.sparse_ops import SparseSegmentSqrtN
from mindspore.ops.operations.sparse_ops import SparseSegmentSqrtNWithNumSegments from mindspore.ops.operations.sparse_ops import SparseSegmentSqrtNWithNumSegments
from mindspore.ops.operations.sparse_ops import SparseSegmentMeanWithNumSegments from mindspore.ops.operations.sparse_ops import SparseSegmentMeanWithNumSegments
from mindspore.ops._utils.utils import is_shape_unknown
from mindspore.common import dtype as mstype from mindspore.common import dtype as mstype
from mindspore import Tensor from mindspore import Tensor
from mindspore.ops.primitive import constexpr from mindspore.ops.primitive import constexpr
@ -36,6 +37,7 @@ from .._grad.grad_base import bprop_getters
from .._utils.utils import is_shape_unknown from .._utils.utils import is_shape_unknown
# Unused parameters are placeholders. # Unused parameters are placeholders.
dyn_shape_op = P.TensorShape()
@constexpr @constexpr
@ -50,6 +52,7 @@ def get_bprop_sparse_softmax(self):
sparse_dense_cwise_add = SparseDenseCwiseAdd() sparse_dense_cwise_add = SparseDenseCwiseAdd()
reduce_sum = P.ReduceSum(keep_dims=True) reduce_sum = P.ReduceSum(keep_dims=True)
mul = P.Mul() mul = P.Mul()
def bprop(indices, values, shape, out, dout): def bprop(indices, values, shape, out, dout):
default_values = _create_tensor(0, values.dtype) default_values = _create_tensor(0, values.dtype)
out_dout = mul(out, dout) out_dout = mul(out, dout)
@ -106,7 +109,6 @@ def get_bprop_sparse_segment_sqrt_n(self):
"""Grad definition for `SparseSegmentSqrtN` operation.""" """Grad definition for `SparseSegmentSqrtN` operation."""
input_grad = G.SparseSegmentSqrtNGrad() input_grad = G.SparseSegmentSqrtNGrad()
shape = P.Shape() shape = P.Shape()
dyn_shape_op = P.TensorShape()
def bprop(x, indices, segment_ids, out, dout): def bprop(x, indices, segment_ids, out, dout):
shape_x = shape(x) shape_x = shape(x)
@ -127,7 +129,6 @@ def get_bprop_sparse_segment_sqrt_n_with_num_segments(self):
"""Grad definition for `SparseSegmentSqrtNWithNumSegments` operation.""" """Grad definition for `SparseSegmentSqrtNWithNumSegments` operation."""
input_grad = G.SparseSegmentSqrtNGrad() input_grad = G.SparseSegmentSqrtNGrad()
shape = P.Shape() shape = P.Shape()
dyn_shape_op = P.TensorShape()
def bprop(x, indices, segment_ids, num_segments, out, dout): def bprop(x, indices, segment_ids, num_segments, out, dout):
shape_x = shape(x) shape_x = shape(x)
@ -148,7 +149,6 @@ def get_bprop_sparse_segment_sum(self):
"""Grad definition for `SparseSegmentSum` operation.""" """Grad definition for `SparseSegmentSum` operation."""
input_grad = G.SparseSegmentSumGrad() input_grad = G.SparseSegmentSumGrad()
shape = P.Shape() shape = P.Shape()
dyn_shape_op = P.TensorShape()
def bprop(x, indices, segment_ids, out, dout): def bprop(x, indices, segment_ids, out, dout):
shape_x = shape(x) shape_x = shape(x)
@ -169,7 +169,6 @@ def get_bprop_sparse_segment_sum_with_num_segments(self):
"""Grad definition for `SparseSegmentSumWithNumSegments` operation.""" """Grad definition for `SparseSegmentSumWithNumSegments` operation."""
input_grad = G.SparseSegmentSumGrad() input_grad = G.SparseSegmentSumGrad()
shape = P.Shape() shape = P.Shape()
dyn_shape_op = P.TensorShape()
def bprop(x, indices, segment_ids, num_segments, out, dout): def bprop(x, indices, segment_ids, num_segments, out, dout):
shape_x = shape(x) shape_x = shape(x)
@ -192,7 +191,12 @@ def get_bprop_sparse_segment_mean_with_num_segments(self):
shape = P.Shape() shape = P.Shape()
def bprop(x, indices, segment_ids, num_segments, out, dout): def bprop(x, indices, segment_ids, num_segments, out, dout):
output_dim0 = F.scalar_to_tensor(shape(x)[0], mstype.int32) x_shp = shape(x)
if is_shape_unknown(x_shp):
x_shp = dyn_shape_op(x)
output_dim0 = F.cast(x_shp[0], mstype.int32)
else:
output_dim0 = F.scalar_to_tensor(x_shp[0], mstype.int32)
indices = F.cast(indices, mstype.int32) indices = F.cast(indices, mstype.int32)
segment_ids = F.cast(segment_ids, mstype.int32) segment_ids = F.cast(segment_ids, mstype.int32)
dx = input_grad(dout, indices, segment_ids, output_dim0) dx = input_grad(dout, indices, segment_ids, output_dim0)
@ -208,6 +212,7 @@ def get_bprop_sparse_reorder(self):
sparse_reorder_op = SparseReorder() sparse_reorder_op = SparseReorder()
range_op = P.Range() range_op = P.Range()
gather_op = P.Gather() gather_op = P.Gather()
def bprop(indices, values, shape, out, dout): def bprop(indices, values, shape, out, dout):
num_entries = F.shape(indices)[0] num_entries = F.shape(indices)[0]
start = Tensor(0, dtype=mstype.int32) start = Tensor(0, dtype=mstype.int32)

24
mindspore/python/mindspore/ops/bprop_mindir/ApproximateEqual_bprop.mindir
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@ -1,20 +1,20 @@
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20
mindspore/python/mindspore/ops/bprop_mindir/Argmax_bprop.mindir
View File

@ -1,14 +1,14 @@
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mindspore/python/mindspore/ops/bprop_mindir/Argmin_bprop.mindir
View File

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30
mindspore/python/mindspore/ops/bprop_mindir/AssignAdd_bprop.mindir
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bprop.1:y* bprop.22:[CNode]24:3bprop.22:[CNode]25:4bprop.22:[CNode]25:4"REF::S-Prim-MakeTuple:5:Default/S-Prim-MakeTuple-op17bprop.22*
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bprop.1:[CNode]4:4:@51014516712342426367430e7abdbb8b5454385a02c91339e89ad18f19803ee3PbH
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bprop.22:dout2
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S-Prim-MakeTuple:5S-Prim-MakeTupleh S-Prim-MakeTuple:5S-Prim-MakeTupleh

34
mindspore/python/mindspore/ops/bprop_mindir/AssignSub_bprop.mindir
View File

@ -1,16 +1,20 @@
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bprop.5:xbprop.5:[CNode]6:1bprop.5:[CNode]6:1"(REF::S-Prim-hyper_map[zeros_like_leaf]:2:-Default/S-Prim-hyper_map[zeros_like_leaf]-op3
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bprop.5:ybprop.5:[CNode]7:3bprop.5:[CNode]7:3"(REF::S-Prim-hyper_map[zeros_like_leaf]:2:-Default/S-Prim-hyper_map[zeros_like_leaf]-op4 ’
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bprop.5:[CNode]7:3bprop.5:[CNode]8:4bprop.5:[CNode]8:4"REF::S-Prim-MakeTuple:5:Default/S-Prim-MakeTuple-op5bprop.5* <EFBFBD>
bprop.5:x* bprop.26:[CNode]27:1
bprop.5:y* bprop.26:[CNode]28:3bprop.26:[CNode]29:4bprop.26:[CNode]29:4"REF::S-Prim-MakeTuple:5:Default/S-Prim-MakeTuple-op20bprop.26*
bprop.5:out*
bprop.5:dout2 bprop.26:x*
bprop.5:[CNode]8:4:@51014516712342426367430e7abdbb8b5454385a02c91339e89ad18f19803ee3Pb&
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S-Prim-MakeTuple:5S-Prim-MakeTupleh

16
mindspore/python/mindspore/ops/bprop_mindir/DType_bprop.mindir
View File

@ -2,13 +2,13 @@
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z z
bprop.20:[CNode]21:1bprop.20:[CNode]22:3bprop.20:[CNode]22:3"REF::S-Prim-MakeTuple:4:Default/S-Prim-MakeTuple-op14bprop.20* bprop.37:[CNode]38:1bprop.37:[CNode]39:3bprop.37:[CNode]39:3"REF::S-Prim-MakeTuple:4:Default/S-Prim-MakeTuple-op28bprop.37*
bprop.20:x* bprop.37:x*
bprop.20:out* bprop.37:out*
bprop.20:dout2 bprop.37:dout2
bprop.20:[CNode]22:3:@da20d6cb5b4c7c7cced90d19d6551099a88fa59be3bc1e215683bed4c1b029abPb& bprop.37:[CNode]39:3:@030ffb7e684c8e2604e69bfc02a6e32af82543a97f4aa0a6bc7cab6275ee9b9dPbH
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#S-Prim-hyper_map[zeros_like_leaf]:2!S-Prim-hyper_map[zeros_like_leaf]h S-Prim-MakeTuple:4S-Prim-MakeTupleh

34
mindspore/python/mindspore/ops/bprop_mindir/DropoutDoMask_bprop.mindir
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@ -1,27 +1,27 @@
0.1.1 MindSpore*1.9.0:° 0.1.1 MindSpore*1.9.0:°
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bprop.13:dout bprop.32:dout
bprop.13:y bprop.32:y
bprop.13:keep_probbprop.13:[CNode]14:1bprop.13:[CNode]14:1"REF::S-Prim-DropoutDoMask:2:!Default/S-Prim-DropoutDoMask-op10 bprop.32:keep_probbprop.32:[CNode]33:1bprop.32:[CNode]33:1"REF::S-Prim-DropoutDoMask:2:!Default/S-Prim-DropoutDoMask-op23
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bprop.13:x* bprop.32:x*
bprop.13:y* bprop.32:y*
bprop.13:keep_prob* bprop.32:keep_prob*
bprop.13:out* bprop.32:out*
bprop.13:dout2 bprop.32:dout2
bprop.13:[CNode]17:6:@de4408af9f648bc43794b496de642f2ba8fef52099d138465267bb08b5fd7e8fPb& bprop.32:[CNode]36:6:@320a603a26f1cc5c0174957a4eea3494bfd5f1c2f20ad64ef6e0ee49a91b62baPb&
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24
mindspore/python/mindspore/ops/bprop_mindir/DropoutGenMask_bprop.mindir
View File

@ -1,16 +1,16 @@
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16
mindspore/python/mindspore/ops/bprop_mindir/DynamicShape_bprop.mindir
View File

@ -2,13 +2,13 @@
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bprop.26:out* bprop.43:out*
bprop.26:dout2 bprop.43:dout2
bprop.26:[CNode]28:3:@da20d6cb5b4c7c7cced90d19d6551099a88fa59be3bc1e215683bed4c1b029abPbH bprop.43:[CNode]45:3:@030ffb7e684c8e2604e69bfc02a6e32af82543a97f4aa0a6bc7cab6275ee9b9dPb&
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22
mindspore/python/mindspore/ops/bprop_mindir/Equal_bprop.mindir
View File

@ -2,19 +2,19 @@
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30
mindspore/python/mindspore/ops/bprop_mindir/FloorDiv_bprop.mindir
View File

@ -1,20 +1,16 @@
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22
mindspore/python/mindspore/ops/bprop_mindir/GreaterEqual_bprop.mindir
View File

@ -2,19 +2,19 @@
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22
mindspore/python/mindspore/ops/bprop_mindir/Greater_bprop.mindir
View File

@ -2,19 +2,19 @@
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mindspore/python/mindspore/ops/bprop_mindir/Identity_bprop.mindir
View File

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20
mindspore/python/mindspore/ops/bprop_mindir/IsFinite_bprop.mindir
View File

@ -1,12 +1,14 @@
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bprop.9:x* bprop.30:[CNode]31:1bprop.30:[CNode]32:3bprop.30:[CNode]32:3"REF::S-Prim-MakeTuple:4:Default/S-Prim-MakeTuple-op22bprop.30*
bprop.9:out*
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bprop.30:[CNode]32:3:@157abcda70ad669686a320be427dedf2a17498d59a38b65d04c272c1a40296e0PbH
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S-Prim-MakeTuple:4S-Prim-MakeTupleh S-Prim-MakeTuple:4S-Prim-MakeTupleh

20
mindspore/python/mindspore/ops/bprop_mindir/IsInf_bprop.mindir
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@ -1,14 +1,12 @@
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bprop.164:xbprop.164:[CNode]165:1bprop.164:[CNode]165:1"(REF::S-Prim-hyper_map[zeros_like_leaf]:2:/Default/S-Prim-hyper_map[zeros_like_leaf]-op120
bprop.91:xbprop.91:[CNode]92:1bprop.91:[CNode]92:1"(REF::S-Prim-hyper_map[zeros_like_leaf]:2:.Default/S-Prim-hyper_map[zeros_like_leaf]-op67 <EFBFBD>
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bprop.91:[CNode]92:1bprop.91:[CNode]93:3bprop.91:[CNode]93:3"REF::S-Prim-MakeTuple:4:Default/S-Prim-MakeTuple-op68bprop.91* bprop.164:x*
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bprop.91:x* bprop.164:dout2
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S-Prim-MakeTuple:4S-Prim-MakeTupleh S-Prim-MakeTuple:4S-Prim-MakeTupleh

20
mindspore/python/mindspore/ops/bprop_mindir/IsNan_bprop.mindir
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@ -1,14 +1,12 @@
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bprop.161:xbprop.161:[CNode]162:1bprop.161:[CNode]162:1"(REF::S-Prim-hyper_map[zeros_like_leaf]:2:/Default/S-Prim-hyper_map[zeros_like_leaf]-op118
bprop.88:xbprop.88:[CNode]89:1bprop.88:[CNode]89:1"(REF::S-Prim-hyper_map[zeros_like_leaf]:2:.Default/S-Prim-hyper_map[zeros_like_leaf]-op65 <EFBFBD>
z bprop.161:[CNode]162:1bprop.161:[CNode]163:3bprop.161:[CNode]163:3"REF::S-Prim-MakeTuple:4:Default/S-Prim-MakeTuple-op119 bprop.161*
bprop.88:[CNode]89:1bprop.88:[CNode]90:3bprop.88:[CNode]90:3"REF::S-Prim-MakeTuple:4:Default/S-Prim-MakeTuple-op66bprop.88* bprop.161:x*
bprop.161:out*
bprop.88:x* bprop.161:dout2
bprop.88:out* bprop.161:[CNode]163:3:@157abcda70ad669686a320be427dedf2a17498d59a38b65d04c272c1a40296e0Pb&
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22
mindspore/python/mindspore/ops/bprop_mindir/LessEqual_bprop.mindir
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@ -2,19 +2,19 @@
0.1.1 MindSpore*1.9.0:Ţ 0.1.1 MindSpore*1.9.0:Ţ
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bprop.50:xbprop.50:[CNode]51:1bprop.50:[CNode]51:1"(REF::S-Prim-hyper_map[zeros_like_leaf]:2:.Default/S-Prim-hyper_map[zeros_like_leaf]-op36 bprop.81:xbprop.81:[CNode]82:1bprop.81:[CNode]82:1"(REF::S-Prim-hyper_map[zeros_like_leaf]:2:.Default/S-Prim-hyper_map[zeros_like_leaf]-op59
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bprop.50:ybprop.50:[CNode]52:3bprop.50:[CNode]52:3"(REF::S-Prim-hyper_map[zeros_like_leaf]:2:.Default/S-Prim-hyper_map[zeros_like_leaf]-op37 bprop.81:ybprop.81:[CNode]83:3bprop.81:[CNode]83:3"(REF::S-Prim-hyper_map[zeros_like_leaf]:2:.Default/S-Prim-hyper_map[zeros_like_leaf]-op60
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bprop.50:[CNode]51:1 bprop.81:[CNode]82:1
bprop.50:[CNode]52:3bprop.50:[CNode]53:4bprop.50:[CNode]53:4"REF::S-Prim-MakeTuple:5:Default/S-Prim-MakeTuple-op38bprop.50* bprop.81:[CNode]83:3bprop.81:[CNode]84:4bprop.81:[CNode]84:4"REF::S-Prim-MakeTuple:5:Default/S-Prim-MakeTuple-op61bprop.81*
bprop.50:x* bprop.81:x*
bprop.50:y* bprop.81:y*
bprop.50:out* bprop.81:out*
bprop.50:dout2 bprop.81:dout2
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#S-Prim-hyper_map[zeros_like_leaf]:2!S-Prim-hyper_map[zeros_like_leaf]b& S-Prim-MakeTuple:5S-Prim-MakeTuplebH
S-Prim-MakeTuple:5S-Prim-MakeTupleh #S-Prim-hyper_map[zeros_like_leaf]:2!S-Prim-hyper_map[zeros_like_leaf]h

22
mindspore/python/mindspore/ops/bprop_mindir/Less_bprop.mindir
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@ -2,19 +2,19 @@
0.1.1 MindSpore*1.9.0:Ţ 0.1.1 MindSpore*1.9.0:Ţ
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bprop.46:xbprop.46:[CNode]47:1bprop.46:[CNode]47:1"(REF::S-Prim-hyper_map[zeros_like_leaf]:2:.Default/S-Prim-hyper_map[zeros_like_leaf]-op33 bprop.77:xbprop.77:[CNode]78:1bprop.77:[CNode]78:1"(REF::S-Prim-hyper_map[zeros_like_leaf]:2:.Default/S-Prim-hyper_map[zeros_like_leaf]-op56
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bprop.46:ybprop.46:[CNode]48:3bprop.46:[CNode]48:3"(REF::S-Prim-hyper_map[zeros_like_leaf]:2:.Default/S-Prim-hyper_map[zeros_like_leaf]-op34 bprop.77:ybprop.77:[CNode]79:3bprop.77:[CNode]79:3"(REF::S-Prim-hyper_map[zeros_like_leaf]:2:.Default/S-Prim-hyper_map[zeros_like_leaf]-op57
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bprop.46:[CNode]47:1 bprop.77:[CNode]78:1
bprop.46:[CNode]48:3bprop.46:[CNode]49:4bprop.46:[CNode]49:4"REF::S-Prim-MakeTuple:5:Default/S-Prim-MakeTuple-op35bprop.46* bprop.77:[CNode]79:3bprop.77:[CNode]80:4bprop.77:[CNode]80:4"REF::S-Prim-MakeTuple:5:Default/S-Prim-MakeTuple-op58bprop.77*
bprop.46:x* bprop.77:x*
bprop.46:y* bprop.77:y*
bprop.46:out* bprop.77:out*
bprop.46:dout2 bprop.77:dout2
bprop.46:[CNode]49:4:@51014516712342426367430e7abdbb8b5454385a02c91339e89ad18f19803ee3Pb& bprop.77:[CNode]80:4:@157abcda70ad669686a320be427dedf2a17498d59a38b65d04c272c1a40296e0PbH
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#S-Prim-hyper_map[zeros_like_leaf]:2!S-Prim-hyper_map[zeros_like_leaf]h S-Prim-MakeTuple:5S-Prim-MakeTupleh

28
mindspore/python/mindspore/ops/bprop_mindir/LinSpace_bprop.mindir
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@ -1,20 +1,20 @@
0.1.1 MindSpore*1.9.0:© 0.1.1 MindSpore*1.9.0:©
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bprop.25:startbprop.25:[CNode]26:1bprop.25:[CNode]26:1"(REF::S-Prim-hyper_map[zeros_like_leaf]:2:.Default/S-Prim-hyper_map[zeros_like_leaf]-op17 bprop.46:startbprop.46:[CNode]47:1bprop.46:[CNode]47:1"(REF::S-Prim-hyper_map[zeros_like_leaf]:2:.Default/S-Prim-hyper_map[zeros_like_leaf]-op32
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bprop.25:stopbprop.25:[CNode]27:3bprop.25:[CNode]27:3"(REF::S-Prim-hyper_map[zeros_like_leaf]:2:.Default/S-Prim-hyper_map[zeros_like_leaf]-op18 bprop.46:stopbprop.46:[CNode]48:3bprop.46:[CNode]48:3"(REF::S-Prim-hyper_map[zeros_like_leaf]:2:.Default/S-Prim-hyper_map[zeros_like_leaf]-op33
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bprop.25:numbprop.25:[CNode]28:4bprop.25:[CNode]28:4"(REF::S-Prim-hyper_map[zeros_like_leaf]:2:.Default/S-Prim-hyper_map[zeros_like_leaf]-op19 bprop.46:numbprop.46:[CNode]49:4bprop.46:[CNode]49:4"(REF::S-Prim-hyper_map[zeros_like_leaf]:2:.Default/S-Prim-hyper_map[zeros_like_leaf]-op34
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bprop.25:start* bprop.46:start*
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bprop.25:num* bprop.46:num*
bprop.25:out* bprop.46:out*
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18
mindspore/python/mindspore/ops/bprop_mindir/LogicalAnd_bprop.mindir
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@ -2,19 +2,19 @@
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bprop.54:xbprop.54:[CNode]55:1bprop.54:[CNode]55:1"(REF::S-Prim-hyper_map[zeros_like_leaf]:2:.Default/S-Prim-hyper_map[zeros_like_leaf]-op39 bprop.85:xbprop.85:[CNode]86:1bprop.85:[CNode]86:1"(REF::S-Prim-hyper_map[zeros_like_leaf]:2:.Default/S-Prim-hyper_map[zeros_like_leaf]-op62
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bprop.54:[CNode]55:1 bprop.85:[CNode]86:1
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bprop.54:x* bprop.85:x*
bprop.54:y* bprop.85:y*
bprop.54:out* bprop.85:out*
bprop.54:dout2 bprop.85:dout2
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12
mindspore/python/mindspore/ops/bprop_mindir/LogicalNot_bprop.mindir
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@ -2,13 +2,13 @@
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bprop.19:x* bprop.40:x*
bprop.19:out* bprop.40:out*
bprop.19:dout2 bprop.40:dout2
bprop.19:[CNode]21:3:@51014516712342426367430e7abdbb8b5454385a02c91339e89ad18f19803ee3Pb& bprop.40:[CNode]42:3:@157abcda70ad669686a320be427dedf2a17498d59a38b65d04c272c1a40296e0Pb&
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22
mindspore/python/mindspore/ops/bprop_mindir/LogicalOr_bprop.mindir
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@ -2,19 +2,19 @@
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bprop.58:xbprop.58:[CNode]59:1bprop.58:[CNode]59:1"(REF::S-Prim-hyper_map[zeros_like_leaf]:2:.Default/S-Prim-hyper_map[zeros_like_leaf]-op42 bprop.89:xbprop.89:[CNode]90:1bprop.89:[CNode]90:1"(REF::S-Prim-hyper_map[zeros_like_leaf]:2:.Default/S-Prim-hyper_map[zeros_like_leaf]-op65
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bprop.58:[CNode]59:1 bprop.89:[CNode]90:1
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bprop.58:x* bprop.89:x*
bprop.58:y* bprop.89:y*
bprop.58:out* bprop.89:out*
bprop.58:dout2 bprop.89:dout2
bprop.58:[CNode]61:4:@51014516712342426367430e7abdbb8b5454385a02c91339e89ad18f19803ee3PbH bprop.89:[CNode]92:4:@157abcda70ad669686a320be427dedf2a17498d59a38b65d04c272c1a40296e0Pb&
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mindspore/python/mindspore/ops/bprop_mindir/Maximum_bprop.mindir
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BIN
mindspore/python/mindspore/ops/bprop_mindir/Minimum_bprop.mindir
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22
mindspore/python/mindspore/ops/bprop_mindir/NotEqual_bprop.mindir
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@ -2,19 +2,19 @@
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bprop.34:[CNode]35:1 bprop.65:[CNode]66:1
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bprop.34:y* bprop.65:y*
bprop.34:out* bprop.65:out*
bprop.34:dout2 bprop.65:dout2
bprop.34:[CNode]37:4:@51014516712342426367430e7abdbb8b5454385a02c91339e89ad18f19803ee3PbH bprop.65:[CNode]68:4:@157abcda70ad669686a320be427dedf2a17498d59a38b65d04c272c1a40296e0Pb&
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40
mindspore/python/mindspore/ops/bprop_mindir/OneHot_bprop.mindir
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@ -1,24 +1,24 @@
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bprop.7:off_valuebprop.7:[CNode]11:5bprop.7:[CNode]11:5"(REF::S-Prim-hyper_map[zeros_like_leaf]:2:-Default/S-Prim-hyper_map[zeros_like_leaf]-op8 bprop.26:depthbprop.26:[CNode]28:3bprop.26:[CNode]28:3"(REF::S-Prim-hyper_map[zeros_like_leaf]:2:.Default/S-Prim-hyper_map[zeros_like_leaf]-op19
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bprop.7:depth* bprop.26:[CNode]28:3
bprop.7:on_value* bprop.26:[CNode]29:4
bprop.7:off_value* bprop.26:[CNode]30:5bprop.26:[CNode]31:6bprop.26:[CNode]31:6"REF::S-Prim-MakeTuple:7:Default/S-Prim-MakeTuple-op22bprop.26*
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mindspore/python/mindspore/ops/bprop_mindir/OnesLike_bprop.mindir
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@ -1,12 +1,14 @@
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bprop.8:[CNode]9:1bprop.8:[CNode]10:3bprop.8:[CNode]10:3"REF::S-Prim-MakeTuple:4:Default/S-Prim-MakeTuple-op6bprop.8* y
bprop.8:x* bprop.10:[CNode]11:1bprop.10:[CNode]12:3bprop.10:[CNode]12:3"REF::S-Prim-MakeTuple:4:Default/S-Prim-MakeTuple-op8bprop.10*
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24
mindspore/python/mindspore/ops/bprop_mindir/Range_bprop.mindir
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@ -1,20 +1,20 @@
0.1.1 MindSpore*1.9.0:Š 0.1.1 MindSpore*1.9.0:Š
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bprop.3:limitbprop.3:[CNode]5:3bprop.3:[CNode]5:3"(REF::S-Prim-hyper_map[zeros_like_leaf]:2:-Default/S-Prim-hyper_map[zeros_like_leaf]-op2 bprop.5:limitbprop.5:[CNode]7:3bprop.5:[CNode]7:3"(REF::S-Prim-hyper_map[zeros_like_leaf]:2:-Default/S-Prim-hyper_map[zeros_like_leaf]-op4
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bprop.3:start* bprop.5:start*
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12
mindspore/python/mindspore/ops/bprop_mindir/Rank_bprop.mindir
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@ -2,13 +2,13 @@
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bprop.29:[CNode]30:1bprop.29:[CNode]31:3bprop.29:[CNode]31:3"REF::S-Prim-MakeTuple:4:Default/S-Prim-MakeTuple-op20bprop.29* bprop.46:[CNode]47:1bprop.46:[CNode]48:3bprop.46:[CNode]48:3"REF::S-Prim-MakeTuple:4:Default/S-Prim-MakeTuple-op34bprop.46*
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mindspore/python/mindspore/ops/bprop_mindir/ReLUV2_bprop.mindir
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6
mindspore/python/mindspore/ops/bprop_mindir/ReLU_bprop.mindir
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@ -8,9 +8,9 @@ m
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S-Prim-MakeTuple:4S-Prim-MakeTuplebr
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18
mindspore/python/mindspore/ops/bprop_mindir/ReduceAll_bprop.mindir
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@ -2,17 +2,17 @@
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bprop.62:[CNode]63:1 bprop.93:[CNode]94:1
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bprop.62:x* bprop.93:x*
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26
mindspore/python/mindspore/ops/bprop_mindir/ReduceAny_bprop.mindir
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@ -1,18 +1,18 @@
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24
mindspore/python/mindspore/ops/bprop_mindir/ReluGrad_bprop.mindir
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@ -1,24 +1,24 @@
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12
mindspore/python/mindspore/ops/bprop_mindir/Round_bprop.mindir
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@ -2,13 +2,13 @@
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mindspore/python/mindspore/ops/bprop_mindir/ScatterMax_bprop.mindir
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mindspore/python/mindspore/ops/bprop_mindir/ScatterMin_bprop.mindir
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mindspore/python/mindspore/ops/bprop_mindir/ScatterUpdate_bprop.mindir
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42
mindspore/python/mindspore/ops/bprop_mindir/Select_bprop.mindir
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@ -1,35 +1,35 @@
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12
mindspore/python/mindspore/ops/bprop_mindir/Shape_bprop.mindir
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@ -2,13 +2,13 @@
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12
mindspore/python/mindspore/ops/bprop_mindir/Sign_bprop.mindir
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@ -2,13 +2,13 @@
0.1.1 MindSpore*1.9.0:¤ 0.1.1 MindSpore*1.9.0:¤
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bprop.16:x* bprop.37:x*
bprop.16:out* bprop.37:out*
bprop.16:dout2 bprop.37:dout2
bprop.16:[CNode]18:3:@51014516712342426367430e7abdbb8b5454385a02c91339e89ad18f19803ee3PbH bprop.37:[CNode]39:3:@157abcda70ad669686a320be427dedf2a17498d59a38b65d04c272c1a40296e0PbH
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34
mindspore/python/mindspore/ops/bprop_mindir/TruncateDiv_bprop.mindir
View File

@ -1,20 +1,16 @@
0.1.1 MindSpore*1.9.0:Þ 0.1.1 MindSpore*1.9.0:ú
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20
mindspore/python/mindspore/ops/bprop_mindir/ZerosLike_bprop.mindir
View File

@ -1,14 +1,14 @@
0.1.1 MindSpore*1.9.0:¢ 0.1.1 MindSpore*1.9.0:£
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1
mindspore/python/mindspore/ops/operations/math_ops.py
View File

@ -2503,7 +2503,6 @@ class HistogramFixedWidth(PrimitiveWithInfer):
self.add_prim_attr('dtype', 3) self.add_prim_attr('dtype', 3)
class Log(Primitive): class Log(Primitive):
""" """
Returns the natural logarithm of a tensor element-wise. Returns the natural logarithm of a tensor element-wise.

64
tests/st/ops/dynamic_shape/grad/test_addcmul.py Normal file
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@ -0,0 +1,64 @@
# Copyright 2022 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.
# ============================================================================
import numpy as np
import pytest
import mindspore as ms
from mindspore import ops, nn, context, Tensor
from .test_grad_of_dynamic import TestDynamicGrad
context.set_context(mode=context.PYNATIVE_MODE)
class NetAddcmul(nn.Cell):
def __init__(self):
super(NetAddcmul, self).__init__()
self.add = ops.Addcmul()
def construct(self, a, x1, x2, v):
return self.add(a, x1, x2, v)
def addcmul_test(is_dyn_rank):
a = Tensor(np.array([1, 1, 1]).astype(np.float32))
x1 = Tensor(np.array([[1], [2], [3]]).astype(np.float32))
x2 = Tensor(np.array([[1, 2, 3]]).astype(np.float32))
v = Tensor([1], ms.float32)
tester = TestDynamicGrad(NetAddcmul())
tester.test_dynamic_grad_net([a, x1, x2, v], is_dyn_rank)
@pytest.mark.level1
@pytest.mark.env_onecard
@pytest.mark.platform_x86_gpu_training
def test_addcmul_dyn_shape():
"""
Feature: Addcmul Grad DynamicShape.
Description: Test case of dynamic shape for Addcmul grad operator.
Expectation: success.
"""
addcmul_test(False)
@pytest.mark.level1
@pytest.mark.env_onecard
@pytest.mark.platform_x86_gpu_training
def test_addcmul_dyn_rank():
"""
Feature: Addcmul Grad DynamicShape.
Description: Test case of dynamic rank for Addcmul grad operator.
Expectation: success.
"""
addcmul_test(True)

97
tests/st/ops/dynamic_shape/grad/test_atan2.py Normal file
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@ -0,0 +1,97 @@
# Copyright 2022 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.
# ============================================================================
import numpy as np
import pytest
import mindspore.nn as nn
from mindspore import Tensor, context
from mindspore.ops import operations as P
from .test_grad_of_dynamic import TestDynamicGrad
class Atan2Net(nn.Cell):
def __init__(self):
super(Atan2Net, self).__init__()
self.atan2 = P.Atan2()
def construct(self, x, y):
return self.atan2(x, y)
def dynamic_shape():
test_dynamic = TestDynamicGrad(Atan2Net())
x = Tensor(np.array([0, 1]).astype(np.float32))
y = Tensor(np.array([1, 1]).astype(np.float32))
test_dynamic.test_dynamic_grad_net((x, y))
def dynamic_rank():
test_dynamic = TestDynamicGrad(Atan2Net())
x = Tensor(np.array([0, 1]).astype(np.float32))
y = Tensor(np.array([1, 1]).astype(np.float32))
test_dynamic.test_dynamic_grad_net((x, y), True)
def test_dynamic_atan2_cpu():
"""
Feature: Atan2 Grad DynamicShape.
Description: Test case of dynamic shape for Atan2 grad operator on CPU.
Expectation: success.
"""
# Graph mode
context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
dynamic_shape()
dynamic_rank()
# PyNative mode
context.set_context(mode=context.PYNATIVE_MODE, device_target="CPU")
dynamic_shape()
dynamic_rank()
@pytest.mark.level1
@pytest.mark.platform_x86_gpu_training
def test_dynamic_atan2_gpu():
"""
Feature: Atan2 Grad DynamicShape.
Description: Test case of dynamic shape for Atan2 grad operator on GPU.
Expectation: success.
"""
# Graph mode
context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
dynamic_shape()
dynamic_rank()
# PyNative mode
context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
dynamic_shape()
dynamic_rank()
@pytest.mark.level1
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
def test_dynamic_atan2_ascend():
"""
Feature: Atan2 Grad DynamicShape.
Description: Test case of dynamic shape for Atan2 grad operator on Ascend.
Expectation: success.
"""
# Graph mode
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
dynamic_shape()
dynamic_rank()
# PyNative mode
context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
dynamic_shape()
dynamic_rank()

63
tests/st/ops/dynamic_shape/grad/test_batch_matmul.py Normal file
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@ -0,0 +1,63 @@
# Copyright 2022 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.
# ============================================================================
import numpy as np
import pytest
import mindspore
import mindspore.ops.operations.math_ops as M
from mindspore import nn, context, Tensor
from .test_grad_of_dynamic import TestDynamicGrad
context.set_context(mode=context.PYNATIVE_MODE)
class NetBatchMatMul(nn.Cell):
def __init__(self):
super(NetBatchMatMul, self).__init__()
self.batchmatmul = M.BatchMatMul()
def construct(self, x, y):
return self.batchmatmul(x, y)
@pytest.mark.level1
@pytest.mark.env_onecard
@pytest.mark.platform_x86_gpu_training
def test_batch_matmul_dynamic_shape():
"""
Feature: BatchMatMul Grad DynamicShape.
Description: Test case of dynamic shape for BatchMatMul grad operator on GPU.
Expectation: success.
"""
test_dynamic = TestDynamicGrad(NetBatchMatMul(), skip_convert_out_ids=[0])
x = Tensor(np.ones(shape=[2, 4, 1, 3]), mindspore.float32)
y = Tensor(np.ones(shape=[2, 4, 3, 4]), mindspore.float32)
inputs = [x, y]
test_dynamic.test_dynamic_grad_net(inputs, False)
@pytest.mark.level1
@pytest.mark.env_onecard
@pytest.mark.platform_x86_gpu_training
def test_batch_matmul_dynamic_rank():
"""
Feature: BatchMatMul Grad DynamicShape.
Description: Test case of dynamic rank for BatchMatMul grad operator on GPU.
Expectation: success.
"""
test_dynamic = TestDynamicGrad(NetBatchMatMul(), skip_convert_out_ids=[0])
x = Tensor(np.ones(shape=[2, 4, 1, 3]), mindspore.float32)
y = Tensor(np.ones(shape=[2, 4, 3, 4]), mindspore.float32)
inputs = [x, y]
test_dynamic.test_dynamic_grad_net(inputs, True)

99
tests/st/ops/dynamic_shape/grad/test_bias_add.py Normal file
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@ -0,0 +1,99 @@
# Copyright 2022 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.
# ============================================================================
import numpy as np
import pytest
import mindspore as ms
import mindspore.nn as nn
from mindspore import Tensor, context
from mindspore.ops import operations as P
from .test_grad_of_dynamic import TestDynamicGrad
class BiasAddNet(nn.Cell):
def __init__(self):
super(BiasAddNet, self).__init__()
self.bias_add = P.BiasAdd()
def construct(self, x, y):
return self.bias_add(x, y)
def run_dynamic_shape():
test_dynamic = TestDynamicGrad(BiasAddNet())
input_x = Tensor(np.arange(6).reshape((2, 3)), ms.float32)
bias = Tensor(np.random.random(3).reshape((3,)), ms.float32)
test_dynamic.test_dynamic_grad_net([input_x, bias])
def run_dynamic_rank():
test_dynamic = TestDynamicGrad(BiasAddNet())
input_x = Tensor(np.arange(6).reshape((2, 3)), ms.float32)
bias = Tensor(np.random.random(3).reshape((3,)), ms.float32)
test_dynamic.test_dynamic_grad_net([input_x, bias], True)
@pytest.mark.level1
@pytest.mark.platform_x86_cpu
def test_dynamic_bias_add_cpu():
"""
Feature: BiasAdd Grad DynamicShape.
Description: Test case of dynamic shape for BiasAdd grad operator on CPU.
Expectation: success.
"""
# Graph mode
context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
run_dynamic_shape()
run_dynamic_rank()
# PyNative mode
context.set_context(mode=context.PYNATIVE_MODE, device_target="CPU")
run_dynamic_shape()
run_dynamic_rank()
@pytest.mark.level1
@pytest.mark.platform_x86_gpu_training
def test_dynamic_bias_add_gpu():
"""
Feature: BiasAdd Grad DynamicShape.
Description: Test case of dynamic shape for BiasAdd grad operator on GPU.
Expectation: success.
"""
# Graph mode
context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
run_dynamic_shape()
run_dynamic_rank()
# PyNative mode
context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
run_dynamic_shape()
run_dynamic_rank()
@pytest.mark.level1
@pytest.mark.platform_arm_ascend_training
def test_dynamic_bias_add_ascend():
"""
Feature: BiasAdd Grad DynamicShape.
Description: Test case of dynamic shape for BiasAdd grad operator on Ascend.
Expectation: success.
"""
# Graph mode
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
run_dynamic_shape()
run_dynamic_rank()
# PyNative mode
context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
run_dynamic_shape()
run_dynamic_rank()

63
tests/st/ops/dynamic_shape/grad/test_cdist.py Normal file
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@ -0,0 +1,63 @@
# Copyright 2022 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.
# ============================================================================
import numpy as np
import pytest
from mindspore import nn
from mindspore.ops import operations as P
from mindspore import Tensor
from mindspore import context
from .test_grad_of_dynamic import TestDynamicGrad
class NetCdist(nn.Cell):
def __init__(self, p):
super(NetCdist, self).__init__()
self.cdist = P.Cdist(p)
def construct(self, x1, x2):
return self.cdist(x1, x2)
@pytest.mark.level1
@pytest.mark.env_onecard
@pytest.mark.platform_x86_cpu
def test_dynamic_shape_cdist():
"""
Feature: Cdist Grad DynamicShape.
Description: Test case of dynamic shape for Cdist grad operator on CPU.
Expectation: success.
"""
context.set_context(mode=context.PYNATIVE_MODE)
test_dynamic = TestDynamicGrad(NetCdist(2.))
x1 = Tensor(np.array([[[1.0, 1.0], [2.0, 2.0]]]).astype(np.float32))
x2 = Tensor(np.array([[[3.0, 3.0], [3.0, 3.0]]]).astype(np.float32))
test_dynamic.test_dynamic_grad_net([x1, x2], False)
@pytest.mark.level1
@pytest.mark.env_onecard
@pytest.mark.platform_x86_cpu
def test_dynamic_rank_cdist():
"""
Feature: Cdist Grad DynamicShape.
Description: Test case of dynamic rank for Cdist grad operator on CPU.
Expectation: success.
"""
context.set_context(mode=context.PYNATIVE_MODE)
test_dynamic = TestDynamicGrad(NetCdist(2.))
x1 = Tensor(np.array([[[1.0, 1.0], [2.0, 2.0]]]).astype(np.float32))
x2 = Tensor(np.array([[[3.0, 3.0], [3.0, 3.0]]]).astype(np.float32))
test_dynamic.test_dynamic_grad_net([x1, x2], True)

111
tests/st/ops/dynamic_shape/grad/test_conv2d_back_prop_filter.py Normal file
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@ -0,0 +1,111 @@
# Copyright 2022 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.
# ============================================================================
import numpy as np
import pytest
from mindspore import nn, context, Tensor
from mindspore.ops.operations import _grad_ops as G
from .test_grad_of_dynamic import TestDynamicGrad
class NetConv2DBackpropFilter(nn.Cell):
def __init__(self):
super(NetConv2DBackpropFilter, self).__init__()
self.conv_filter = G.Conv2DBackpropFilter(1, 3, pad_mode="valid", pad=0, mode=1, stride=(1, 1),
dilation=(1, 1, 1, 1), group=1)
self.w_shape = (1, 1, 3, 3)
def construct(self, out, x):
return self.conv_filter(out, x, self.w_shape)
def grad_dyn_case(is_dynamic_rank):
test_dynamic = TestDynamicGrad(NetConv2DBackpropFilter())
np.random.seed(1)
out = Tensor(np.random.normal(0, 1, (1, 1, 4, 4)).astype(np.float32))
x = Tensor(np.random.normal(0, 2, (1, 1, 6, 6)).astype(np.float32))
test_dynamic.test_dynamic_grad_net([out, x], is_dynamic_rank)
@pytest.mark.level1
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_gpu_grad_dynamic_shape():
"""
Feature: test Conv2DBackpropFilter dynamic shape on GPU.
Description: input is dynamic shape.
Expectation: the result match with static shape
"""
context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
grad_dyn_case(False)
@pytest.mark.level1
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_gpu_grad_dynamic_rank():
"""
Feature: test Conv2DBackpropFilter dynamic rank on GPU.
Description: input is dynamic rank.
Expectation: the result match with static shape
"""
context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
grad_dyn_case(True)
@pytest.mark.level1
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_cpu_grad_dynamic_shape():
"""
Feature: test Conv2DBackpropFilter dynamic shape on CPU.
Description: input is dynamic shape.
Expectation: the result match with static shape
"""
context.set_context(mode=context.PYNATIVE_MODE, device_target="CPU")
grad_dyn_case(False)
@pytest.mark.level1
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_cpu_grad_dynamic_rank():
"""
Feature: test Conv2DBackpropFilter dynamic rank on CPU.
Description: input is dynamic rank.
Expectation: the result match with static shape
"""
context.set_context(mode=context.PYNATIVE_MODE, device_target="CPU")
grad_dyn_case(True)
def test_ascend_grad_dynamic_shape():
"""
Feature: test Conv2DBackpropFilter dynamic shape on Ascend.
Description: input is dynamic shape.
Expectation: the result match with static shape
"""
context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
grad_dyn_case(False)
def test_ascend_grad_dynamic_rank():
"""
Feature: test Conv2DBackpropFilter dynamic rank on Ascend.
Description: input is dynamic rank.
Expectation: the result match with static shape
"""
context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
grad_dyn_case(True)

125
tests/st/ops/dynamic_shape/grad/test_conv3d.py Normal file
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@ -0,0 +1,125 @@
# Copyright 2022 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.
# ============================================================================
import numpy as np
import pytest
from mindspore import nn, context, Tensor
from mindspore.ops.operations import Conv3D
from .test_grad_of_dynamic import TestDynamicGrad
class NetConv3d(nn.Cell):
def __init__(self):
super(NetConv3d, self).__init__()
out_channel = 4
kernel_size = 2
self.conv = Conv3D(out_channel,
kernel_size,
mode=1,
pad_mode="valid",
pad=0,
stride=1,
dilation=1,
group=1)
def construct(self, x, w):
return self.conv(x, w)
def grad_dyn_case(is_dynamic_rank):
test_dynamic = TestDynamicGrad(NetConv3d())
input_np = np.arange(1 * 3 * 3 * 3 * 3).reshape(1, 3, 3, 3, 3).astype(np.float32)
weight_np = np.arange(4 * 3 * 2 * 2 * 2).reshape(4, 3, 2, 2, 2).astype(np.float32)
test_dynamic.test_dynamic_grad_net([Tensor(input_np), Tensor(weight_np)], is_dynamic_rank)
@pytest.mark.level1
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_gpu_grad_dynamic_shape_1():
"""
Feature: test Conv3D grad dynamic shape on GPU.
Description: input is dynamic shape.
Expectation: the result match with static shape
"""
context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
grad_dyn_case(False)
@pytest.mark.level1
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_gpu_grad_dynamic_rank_1():
"""
Feature: test Conv3D grad dynamic rank on GPU.
Description: input is dynamic rank.
Expectation: the result match with static shape
"""
context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
grad_dyn_case(True)
@pytest.mark.level1
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_gpu_grad_dynamic_shape_2():
"""
Feature: test Conv3D grad dynamic shape on GPU.
Description: input is dynamic shape.
Expectation: the result match with static shape
"""
context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
grad_dyn_case(False)
@pytest.mark.level1
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_gpu_grad_dynamic_rank_2():
"""
Feature: test Conv3D grad dynamic shape on GPU.
Description: input is dynamic shape.
Expectation: the result match with static shape
"""
context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
grad_dyn_case(True)
@pytest.mark.skip(reason="CPU无Conv3DBackpropFilter, Conv3DBackpropInput, kernel实现")
@pytest.mark.level1
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_cpu_grad_dynamic_shape():
"""
Feature: test Conv3D grad dynamic shape on CPU.
Description: input is dynamic shape.
Expectation: the result match with static shape
"""
context.set_context(mode=context.PYNATIVE_MODE, device_target="CPU")
grad_dyn_case(False)
@pytest.mark.skip(reason="CPU无Conv3DBackpropFilter, Conv3DBackpropInput, kernel实现")
@pytest.mark.level1
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_cpu_grad_dynamic_rank():
"""
Feature: test Conv3D grad dynamic rank on CPU.
Description: input is dynamic rank.
Expectation: the result match with static shape
"""
context.set_context(mode=context.PYNATIVE_MODE, device_target="CPU")
grad_dyn_case(True)

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tests/st/ops/dynamic_shape/grad/test_conv3d_transpose.py Normal file
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# Copyright 2022 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.
# ============================================================================
import numpy as np
import pytest
from mindspore import nn, context, Tensor
from mindspore.ops.operations import Conv3DTranspose
from .test_grad_of_dynamic import TestDynamicGrad
class NetConv3dTranspose(nn.Cell):
def __init__(self):
super(NetConv3dTranspose, self).__init__()
in_channel = 2
out_channel = 2
kernel_size = 2
self.conv_trans = Conv3DTranspose(in_channel, out_channel,
kernel_size,
pad_mode="pad",
pad=1,
stride=1,
dilation=1,
group=1)
def construct(self, x, w):
return self.conv_trans(x, w)
def grad_dyn_case(is_dynamic_rank):
x = Tensor(np.arange(1 * 2 * 3 * 3 * 3).reshape(1, 2, 3, 3, 3).astype(np.float32))
w = Tensor(np.ones((2, 2, 2, 2, 2)).astype(np.float32))
test_dynamic = TestDynamicGrad(NetConv3dTranspose())
test_dynamic.test_dynamic_grad_net([x, w], is_dynamic_rank)
@pytest.mark.level1
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_gpu_grad_dynamic_shape_1():
"""
Feature: test Conv3DTranspose grad dynamic shape on GPU.
Description: input is dynamic shape.
Expectation: the result match with static shape
"""
context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
grad_dyn_case(False)
@pytest.mark.level1
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_gpu_grad_dynamic_rank_1():
"""
Feature: test Conv3DTranspose grad dynamic rank on GPU.
Description: input is dynamic rank.
Expectation: the result match with static shape
"""
context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
grad_dyn_case(True)
@pytest.mark.level1
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_gpu_grad_dynamic_shape_2():
"""
Feature: test Conv3DTranspose grad dynamic shape on GPU.
Description: input is dynamic shape.
Expectation: the result match with static shape
"""
context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
grad_dyn_case(False)
@pytest.mark.level1
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_gpu_grad_dynamic_rank_2():
"""
Feature: test Conv3DTranspose grad dynamic shape on GPU.
Description: input is dynamic shape.
Expectation: the result match with static shape
"""
context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
grad_dyn_case(True)
@pytest.mark.skip(reason="CPU无Conv3DBackpropFilter, Conv3DBackpropInput, kernel实现")
@pytest.mark.level1
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_cpu_grad_dynamic_shape():
"""
Feature: test Conv3DTranspose grad dynamic shape on CPU.
Description: input is dynamic shape.
Expectation: the result match with static shape
"""
context.set_context(mode=context.PYNATIVE_MODE, device_target="CPU")
grad_dyn_case(False)
@pytest.mark.skip(reason="CPU无Conv3DBackpropFilter, Conv3DBackpropInput, kernel实现")
@pytest.mark.level1
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_cpu_grad_dynamic_rank():
"""
Feature: test Conv3DTranspose grad dynamic rank on CPU.
Description: input is dynamic rank.
Expectation: the result match with static shape
"""
context.set_context(mode=context.PYNATIVE_MODE, device_target="CPU")
grad_dyn_case(True)

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tests/st/ops/dynamic_shape/grad/test_divnonan.py Normal file
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# Copyright 2022 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.
# ============================================================================
import numpy as np
import pytest
from mindspore import ops, nn, context, Tensor
from .test_grad_of_dynamic import TestDynamicGrad
context.set_context(mode=context.PYNATIVE_MODE)
class NetDivNoNan(nn.Cell):
def __init__(self):
super(NetDivNoNan, self).__init__()
self.div = ops.DivNoNan()
def construct(self, x, y):
return self.div(x, y)
def divnonan_test(is_dyn_rank):
x = Tensor(np.array([[1, 2, 3], [4, 5, 6]]).astype(np.float32))
y = Tensor(np.array([[7, 8, 9]]).astype(np.float32))
tester = TestDynamicGrad(NetDivNoNan())
tester.test_dynamic_grad_net([x, y], is_dyn_rank)
@pytest.mark.level1
@pytest.mark.env_onecard
@pytest.mark.platform_x86_gpu_training
@pytest.mark.platform_arm_ascend_training
def test_divnonan_dyn_shape():
"""
Feature: DivNoNan Grad DynamicShape.
Description: Test case of dynamic shape for DivNoNan grad operator.
Expectation: success.
"""
divnonan_test(False)
@pytest.mark.level1
@pytest.mark.env_onecard
@pytest.mark.platform_x86_gpu_training
@pytest.mark.platform_arm_ascend_training
def test_divnonan_dyn_rank():
"""
Feature: DivNoNan Grad DynamicShape.
Description: Test case of dynamic rank for DivNoNan grad operator.
Expectation: success.
"""
divnonan_test(True)

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tests/st/ops/dynamic_shape/grad/test_maskedfill.py Normal file
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# Copyright 2022 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.
# ============================================================================
import numpy as np
import pytest
import mindspore as ms
import mindspore.nn as nn
from mindspore import Tensor, context
from mindspore.ops import operations as P
from .test_grad_of_dynamic import TestDynamicGrad
class MaskedFillNet(nn.Cell):
def __init__(self):
super(MaskedFillNet, self).__init__()
self.maskedfill = P.MaskedFill()
def construct(self, input_0, mask, value):
return self.maskedfill(input_0, mask, value)
def run_dynamic_shape():
test_dynamic = TestDynamicGrad(MaskedFillNet())
input_0 = Tensor(np.array([1., 2., 3., 4.]), ms.float32)
mask = Tensor(np.array([True, True, False, True]), ms.bool_)
value = Tensor(0.5, ms.float32)
test_dynamic.test_dynamic_grad_net([input_0, mask, value])
def run_dynamic_rank():
test_dynamic = TestDynamicGrad(MaskedFillNet())
input_0 = Tensor(np.array([1., 2., 3., 4.]), ms.float32)
mask = Tensor(np.array([True, True, False, True]), ms.bool_)
value = Tensor(0.5, ms.float32)
test_dynamic.test_dynamic_grad_net([input_0, mask, value], True)
@pytest.mark.level1
@pytest.mark.platform_x86_gpu_training
def test_dynamic_maskedfill_gpu():
"""
Feature: MaskedFill Grad DynamicShape.
Description: Test case of dynamic shape for MaskedFill grad operator on GPU.
Expectation: success.
"""
# Graph mode
context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
run_dynamic_shape()
run_dynamic_rank()
# PyNative mode
context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
run_dynamic_shape()
run_dynamic_rank()
@pytest.mark.level1
@pytest.mark.platform_arm_ascend_training
def test_dynamic_maskedfill_ascend():
"""
Feature: MaskedFill Grad DynamicShape.
Description: Test case of dynamic shape for MaskedFill grad operator on Ascend.
Expectation: success.
"""
# Graph mode
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
run_dynamic_shape()
run_dynamic_rank()
# PyNative mode
context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
run_dynamic_shape()
run_dynamic_rank()

78
tests/st/ops/dynamic_shape/grad/test_matrix_determinant.py Normal file
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# Copyright 2022 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.
# ============================================================================
import numpy as np
import pytest
import mindspore.nn as nn
from mindspore import Tensor, context
from mindspore.ops import operations as P
from .test_grad_of_dynamic import TestDynamicGrad
class MatrixDeterminantNet(nn.Cell):
def __init__(self):
super(MatrixDeterminantNet, self).__init__()
self.matrix_determinant = P.MatrixDeterminant()
def construct(self, x):
return self.matrix_determinant(x)
def dynamic_shape():
test_dynamic = TestDynamicGrad(MatrixDeterminantNet())
x = Tensor(np.array([[[-4.5, -1.5], [7.0, 6.0]], [[2.5, 0.5], [3.0, 9.0]]]).astype(np.float32))
test_dynamic.test_dynamic_grad_net(x)
def dynamic_rank():
test_dynamic = TestDynamicGrad(MatrixDeterminantNet())
x = Tensor(np.array([[[-4.5, -1.5], [7.0, 6.0]], [[2.5, 0.5], [3.0, 9.0]]]).astype(np.float32))
test_dynamic.test_dynamic_grad_net(x, True)
@pytest.mark.level1
@pytest.mark.platform_x86_cpu
def test_dynamic_matrix_determinant_cpu():
"""
Feature: MatrixDeterminant Grad DynamicShape.
Description: Test case of dynamic shape for MatrixDeterminant grad operator on CPU.
Expectation: success.
"""
# Graph mode
context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
dynamic_shape()
dynamic_rank()
# PyNative mode
context.set_context(mode=context.PYNATIVE_MODE, device_target="CPU")
dynamic_shape()
dynamic_rank()
@pytest.mark.level1
@pytest.mark.platform_x86_gpu_training
def test_dynamic_matrix_determinant_gpu():
"""
Feature: MatrixDeterminant Grad DynamicShape.
Description: Test case of dynamic shape for MatrixDeterminant grad operator on GPU.
Expectation: success.
"""
# Graph mode
context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
dynamic_shape()
dynamic_rank()
# PyNative mode
context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
dynamic_shape()
dynamic_rank()

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tests/st/ops/dynamic_shape/grad/test_matrix_diag_partv3.py Normal file
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# Copyright 2022 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.
# ============================================================================
import numpy as np
import pytest
from mindspore import nn
from mindspore.ops import operations as P
from mindspore import Tensor
from mindspore import context
from mindspore import dtype as mstype
from .test_grad_of_dynamic import TestDynamicGrad
class MatrixDiagPartV3Net(nn.Cell):
def __init__(self, align='LEFT_RIGHT'):
super(MatrixDiagPartV3Net, self).__init__()
self.matrix_diag_dart_v3 = P.array_ops.MatrixDiagPartV3(align=align)
def construct(self, x, k, padding_value):
return self.matrix_diag_dart_v3(x, k, padding_value)
@pytest.mark.level1
@pytest.mark.env_onecard
@pytest.mark.platform_x86_cpu
@pytest.mark.platform_x86_gpu_training
def test_dynamic_shape_matrix_diag_partv3():
"""
Feature: MatrixDiagPartV3 Grad DynamicShape.
Description: Test case of dynamic shape for MatrixDiagPartV3 grad operator on CPU and GPU.
Expectation: success.
"""
context.set_context(mode=context.PYNATIVE_MODE)
align = 'RIGHT_LEFT'
test_dynamic = TestDynamicGrad(MatrixDiagPartV3Net(align))
input_x = Tensor(np.array([[[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 8, 7, 6]],
[[5, 4, 3, 2],
[1, 2, 3, 4],
[5, 6, 7, 8]]]), mstype.float32)
k = Tensor(1, mstype.int32)
padding_value = Tensor(0, mstype.float32)
test_dynamic.test_dynamic_grad_net([input_x, k, padding_value], False)
@pytest.mark.level1
@pytest.mark.env_onecard
@pytest.mark.platform_x86_cpu
@pytest.mark.platform_x86_gpu_training
def test_dynamic_rank_matrix_diag_partv3():
"""
Feature: MatrixDiagPartV3 Grad DynamicShape.
Description: Test case of dynamic rank for MatrixDiagPartV3 grad operator on CPU and GPU.
Expectation: success.
"""
context.set_context(mode=context.PYNATIVE_MODE)
align = 'RIGHT_LEFT'
test_dynamic = TestDynamicGrad(MatrixDiagPartV3Net(align))
input_x = Tensor(np.array([[[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 8, 7, 6]],
[[5, 4, 3, 2],
[1, 2, 3, 4],
[5, 6, 7, 8]]]), mstype.float32)
k = Tensor(1, mstype.int32)
padding_value = Tensor(0, mstype.float32)
test_dynamic.test_dynamic_grad_net([input_x, k, padding_value], True)

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tests/st/ops/dynamic_shape/grad/test_matrix_diag_v3.py Normal file
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# Copyright 2022 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.
# ============================================================================
import numpy as np
import pytest
import mindspore as ms
import mindspore.nn as nn
from mindspore import Tensor, context
from mindspore.ops.operations.array_ops import MatrixDiagV3
from .test_grad_of_dynamic import TestDynamicGrad
class MatrixDiagV3Net(nn.Cell):
def __init__(self):
super(MatrixDiagV3Net, self).__init__()
self.matrix_diag_v3 = MatrixDiagV3(align='LEFT_RIGHT')
def construct(self, x, k, num_rows, num_cols, padding_value):
return self.matrix_diag_v3(x, k, num_rows, num_cols, padding_value)
def run_dynamic_shape():
test_dynamic = TestDynamicGrad(MatrixDiagV3Net())
x = Tensor(np.array([[8, 9, 0], [1, 2, 3], [0, 4, 5]]), ms.float32)
k = Tensor(np.array([-1, 1]), ms.int32)
num_rows = Tensor(np.array(3), ms.int32)
num_cols = Tensor(np.array(3), ms.int32)
padding_value = Tensor(np.array(11), ms.float32)
test_dynamic.test_dynamic_grad_net(
[x, k, num_rows, num_cols, padding_value])
def run_dynamic_rank():
test_dynamic = TestDynamicGrad(MatrixDiagV3Net())
x = Tensor(np.array([[8, 9, 0],
[1, 2, 3],
[0, 4, 5]]), ms.float32)
k = Tensor(np.array([-1, 1]), ms.int32)
num_rows = Tensor(np.array(3), ms.int32)
num_cols = Tensor(np.array(3), ms.int32)
padding_value = Tensor(np.array(11), ms.float32)
test_dynamic.test_dynamic_grad_net(
[x, k, num_rows, num_cols, padding_value], True)
@pytest.mark.level1
@pytest.mark.platform_x86_cpu
def test_dynamic_matrix_diag_v3_cpu():
"""
Feature: MatrixDiagV3 Grad DynamicShape.
Description: Test case of dynamic shape for MatrixDiagV3 grad operator on CPU.
Expectation: success.
"""
# PyNative mode
context.set_context(mode=context.PYNATIVE_MODE, device_target="CPU")
run_dynamic_shape()
run_dynamic_rank()
@pytest.mark.level1
@pytest.mark.platform_x86_gpu_training
def test_dynamic_matrix_diag_v3_gpu():
"""
Feature: MatrixDiagV3 Grad DynamicShape.
Description: Test case of dynamic shape for MatrixDiagV3 grad operator on GPU.
Expectation: success.
"""
# PyNative mode
context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
run_dynamic_shape()
run_dynamic_rank()

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tests/st/ops/dynamic_shape/grad/test_sparse_add.py Normal file
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# Copyright 2022 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.
# ============================================================================
import pytest
import mindspore as ms
from mindspore import nn, context, Tensor
from mindspore.ops.operations.sparse_ops import SparseAdd
from .test_grad_of_dynamic import TestDynamicGrad
class NetSparseAdd(nn.Cell):
def __init__(self):
super(NetSparseAdd, self).__init__()
self.sparse_add = SparseAdd()
def construct(self, a_indices, a_values, a_shape, b_indices, b_values, b_shape, thresh):
return self.sparse_add(a_indices, a_values, a_shape, b_indices, b_values, b_shape, thresh)
def grad_dyn_case(is_dynamic_rank):
test_dynamic = TestDynamicGrad(NetSparseAdd())
value_type = ms.float32
thresh_type = ms.float32
thresh_value = 0
a_indices = Tensor([[0, 1], [1, 2]], ms.int64)
a_values = Tensor([1, 2], value_type)
a_shape = Tensor([3, 4], ms.int64)
b_indices = Tensor([[0, 1], [1, 2]], ms.int64)
b_values = Tensor([1, 2], value_type)
b_shape = Tensor([3, 4], ms.int64)
thresh = Tensor(thresh_value, thresh_type)
test_dynamic.test_dynamic_grad_net([a_indices, a_values, a_shape, b_indices, b_values, b_shape, thresh],
is_dynamic_rank)
@pytest.mark.level1
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_gpu_grad_dynamic_shape():
"""
Feature: test SparseAdd dynamic shape on GPU.
Description: input is dynamic shape.
Expectation: the result match with static shape
"""
context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
grad_dyn_case(False)
@pytest.mark.level1
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_gpu_grad_dynamic_rank():
"""
Feature: test SparseAdd dynamic rank on GPU.
Description: input is dynamic rank.
Expectation: the result match with static shape
"""
context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
grad_dyn_case(True)
@pytest.mark.level1
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_cpu_grad_dynamic_shape():
"""
Feature: test SparseAdd dynamic shape on CPU.
Description: input is dynamic shape.
Expectation: the result match with static shape
"""
context.set_context(mode=context.PYNATIVE_MODE, device_target="CPU")
grad_dyn_case(False)
@pytest.mark.level1
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_cpu_grad_dynamic_rank():
"""
Feature: test SparseAdd dynamic rank on CPU.
Description: input is dynamic rank.
Expectation: the result match with static shape
"""
context.set_context(mode=context.PYNATIVE_MODE, device_target="CPU")
grad_dyn_case(True)

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tests/st/ops/dynamic_shape/grad/test_sparse_seg_sqrt_n.py Normal file
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# Copyright 2022 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.
# ============================================================================
import pytest
import numpy as np
from mindspore import nn, context, Tensor
from mindspore.ops.operations.sparse_ops import SparseSegmentSqrtN
from .test_grad_of_dynamic import TestDynamicGrad
class NetSparseSegmentSqrtN(nn.Cell):
def __init__(self):
super(NetSparseSegmentSqrtN, self).__init__()
self.sparse_seg_sqrt_n = SparseSegmentSqrtN()
def construct(self, x, indices, seg):
return self.sparse_seg_sqrt_n(x, indices, seg)
def grad_dyn_case(is_dynamic_rank):
test_dynamic = TestDynamicGrad(NetSparseSegmentSqrtN())
x = Tensor(np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]).astype(np.float32))
indices = Tensor(np.array([0, 1, 2]).astype(np.int32))
segment_ids = Tensor(np.array([0, 1, 2]).astype(np.int32))
test_dynamic.test_dynamic_grad_net((x, indices, segment_ids), is_dynamic_rank)
@pytest.mark.level1
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_cpu_grad_dynamic_shape():
"""
Feature: test SparseSegmentSqrtN dynamic shape on CPU.
Description: input is dynamic shape.
Expectation: the result match with static shape
"""
context.set_context(mode=context.PYNATIVE_MODE, device_target="CPU")
grad_dyn_case(False)
@pytest.mark.level1
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_cpu_grad_dynamic_rank():
"""
Feature: test SparseSegmentSqrtN dynamic rank on CPU.
Description: input is dynamic rank.
Expectation: the result match with static shape
"""
context.set_context(mode=context.PYNATIVE_MODE, device_target="CPU")
grad_dyn_case(True)

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tests/st/ops/dynamic_shape/grad/test_sparse_seg_sqrt_n_with_num_seg.py Normal file
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# Copyright 2022 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.
# ============================================================================
import pytest
import mindspore as ms
from mindspore import nn, context, Tensor
from mindspore.ops.operations.sparse_ops import SparseSegmentSqrtNWithNumSegments
from .test_grad_of_dynamic import TestDynamicGrad
class NetSparseSegmentSqrtNWithNumSegments(nn.Cell):
def __init__(self):
super(NetSparseSegmentSqrtNWithNumSegments, self).__init__()
self.sparse_seg_sqrt_n_with_n_seg = SparseSegmentSqrtNWithNumSegments()
def construct(self, x, indices, seg, num):
return self.sparse_seg_sqrt_n_with_n_seg(x, indices, seg, num)
def grad_dyn_case(is_dynamic_rank):
test_dynamic = TestDynamicGrad(NetSparseSegmentSqrtNWithNumSegments(), skip_convert_out_ids=[0])
x = Tensor([[0, 1, 0, 0], [0, 1, 1, 0], [1, 0, 1, 0]], dtype=ms.float16)
indices = Tensor([0, 2, 1], dtype=ms.int32)
segment_ids = Tensor([0, 1, 2], dtype=ms.int32)
num_segments = Tensor([4], dtype=ms.int32)
test_dynamic.test_dynamic_grad_net((x, indices, segment_ids, num_segments), is_dynamic_rank)
@pytest.mark.level1
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_cpu_grad_dynamic_shape():
"""
Feature: test SparseSegmentSqrtNWithNumSegments dynamic shape on CPU.
Description: input is dynamic shape.
Expectation: the result match with static shape
"""
context.set_context(mode=context.PYNATIVE_MODE, device_target="CPU")
grad_dyn_case(False)
@pytest.mark.level1
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_cpu_grad_dynamic_rank():
"""
Feature: test SparseSegmentSqrtNWithNumSegments dynamic rank on CPU.
Description: input is dynamic rank.
Expectation: the result match with static shape
"""
context.set_context(mode=context.PYNATIVE_MODE, device_target="CPU")
grad_dyn_case(True)

67
tests/st/ops/dynamic_shape/grad/test_sparse_segment_mean_with_num_segments.py Normal file
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# Copyright 2022 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.
# ============================================================================
import pytest
import mindspore
import mindspore.ops.operations.sparse_ops as S
from mindspore import nn, context, Tensor
from .test_grad_of_dynamic import TestDynamicGrad
context.set_context(mode=context.PYNATIVE_MODE, device_target="CPU")
class NetSparseSegmentMeanWithNumSegments(nn.Cell):
def __init__(self):
super(NetSparseSegmentMeanWithNumSegments, self).__init__()
self.sparse_segment_mean_with_num_segments = S.SparseSegmentMeanWithNumSegments()
def construct(self, x, indices, seg_ids, num_segments):
return self.sparse_segment_mean_with_num_segments(x, indices, seg_ids, num_segments)
@pytest.mark.level1
@pytest.mark.env_onecard
@pytest.mark.platform_x86_cpu
def test_sparse_segment_mean_with_num_segments_dynamic_shape():
"""
Feature: SparseSegmentMeanWithNumSegments Grad DynamicShape.
Description: Test case of dynamic shape for SparseSegmentMeanWithNumSegments grad operator on CPU.
Expectation: success.
"""
test_dynamic = TestDynamicGrad(NetSparseSegmentMeanWithNumSegments(), skip_convert_out_ids=[0])
x = Tensor([[0, 2, 0, 0], [0, 1, 1, 0], [2, 0, 2, 0]], dtype=mindspore.float32)
indices = Tensor([0, 2, 1], dtype=mindspore.int32)
segment_ids = Tensor([0, 0, 2], dtype=mindspore.int32)
num_segments = Tensor([4], dtype=mindspore.int32)
inputs = [x, indices, segment_ids, num_segments]
test_dynamic.test_dynamic_grad_net(inputs, False)
@pytest.mark.level1
@pytest.mark.env_onecard
@pytest.mark.platform_x86_cpu
def test_sparse_segment_mean_with_num_segments_dynamic_rank():
"""
Feature: SparseSegmentMeanWithNumSegments Grad DynamicShape.
Description: Test case of dynamic rank for SparseSegmentMeanWithNumSegments grad operator on CPU.
Expectation: success.
"""
x = Tensor([[0, 2, 0, 0], [0, 1, 1, 0], [2, 0, 2, 0]], dtype=mindspore.float32)
indices = Tensor([0, 2, 1], dtype=mindspore.int32)
segment_ids = Tensor([0, 0, 2], dtype=mindspore.int32)
num_segments = Tensor([4], dtype=mindspore.int32)
test_dynamic = TestDynamicGrad(NetSparseSegmentMeanWithNumSegments(), skip_convert_out_ids=[0])
inputs = [x, indices, segment_ids, num_segments]
test_dynamic.test_dynamic_grad_net(inputs, True)

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tests/st/ops/dynamic_shape/grad/test_squared_diff.py Normal file
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# Copyright 2022 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.
# ============================================================================
import numpy as np
import pytest
from mindspore import nn, ops, context, Tensor
from .test_grad_of_dynamic import TestDynamicGrad
class NetSquaredDifference(nn.Cell):
def __init__(self):
super(NetSquaredDifference, self).__init__()
self.squared_diff = ops.SquaredDifference()
def construct(self, x, y):
return self.squared_diff(x, y)
def grad_dyn_case(is_dynamic_rank):
test_dynamic = TestDynamicGrad(NetSquaredDifference())
np.random.seed(1)
x = Tensor(np.random.uniform(10, 20, (3, 4, 5, 2)).astype(np.float16))
y = Tensor(np.random.uniform(40, 50, (3, 4, 5, 2)).astype(np.float16))
test_dynamic.test_dynamic_grad_net([x, y], is_dynamic_rank)
@pytest.mark.level1
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_gpu_grad_dynamic_shape():
"""
Feature: test SquaredDifference dynamic shape on GPU.
Description: input is dynamic shape.
Expectation: the result match with static shape
"""
context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
grad_dyn_case(False)
@pytest.mark.level1
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_gpu_grad_dynamic_rank():
"""
Feature: test SquaredDifference dynamic rank on GPU.
Description: input is dynamic rank.
Expectation: the result match with static shape
"""
context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
grad_dyn_case(True)
def test_cpu_grad_dynamic_shape():
"""
Feature: test SquaredDifference dynamic shape on CPU.
Description: input is dynamic shape.
Expectation: the result match with static shape
"""
context.set_context(mode=context.PYNATIVE_MODE, device_target="CPU")
grad_dyn_case(False)
def test_cpu_grad_dynamic_rank():
"""
Feature: test SquaredDifference dynamic rank on CPU.
Description: input is dynamic rank.
Expectation: the result match with static shape
"""
context.set_context(mode=context.PYNATIVE_MODE, device_target="CPU")
grad_dyn_case(True)
@pytest.mark.level1
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.env_onecard
def test_ascend_grad_dynamic_shape():
"""
Feature: test SquaredDifference dynamic shape on Ascend.
Description: input is dynamic shape.
Expectation: the result match with static shape
"""
context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
grad_dyn_case(False)
@pytest.mark.level1
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.env_onecard
def test_ascend_grad_dynamic_rank():
"""
Feature: test SquaredDifference dynamic rank on Ascend.
Description: input is dynamic rank.
Expectation: the result match with static shape
"""
context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
grad_dyn_case(True)

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tests/st/ops/dynamic_shape/grad/test_sub.py Normal file
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# Copyright 2022 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.
# ============================================================================
import numpy as np
import pytest
from mindspore import ops, nn, context, Tensor
from .test_grad_of_dynamic import TestDynamicGrad
context.set_context(mode=context.PYNATIVE_MODE)
class NetSub(nn.Cell):
def __init__(self):
super(NetSub, self).__init__()
self.sub = ops.Sub()
def construct(self, x, y):
return self.sub(x, y)
def sub_test(is_dyn_rank):
x = Tensor(np.array([[1, 2, 3], [4, 5, 6]]).astype(np.float32))
y = Tensor(np.array([[7, 8, 9]]).astype(np.float32))
tester = TestDynamicGrad(NetSub())
tester.test_dynamic_grad_net([x, y], is_dyn_rank)
@pytest.mark.level1
@pytest.mark.env_onecard
@pytest.mark.platform_x86_gpu_training
@pytest.mark.platform_arm_ascend_training
def test_sub_dyn_shape():
"""
Feature: Sub Grad DynamicShape.
Description: Test case of dynamic shape for Sub grad operator.
Expectation: success.
"""
sub_test(False)
@pytest.mark.level1
@pytest.mark.env_onecard
@pytest.mark.platform_x86_gpu_training
@pytest.mark.platform_arm_ascend_training
def test_sub_dyn_rank():
"""
Feature: Sub Grad DynamicShape.
Description: Test case of dynamic rank for Sub grad operator.
Expectation: success.
"""
sub_test(True)

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tests/st/ops/dynamic_shape/grad/test_topk.py Normal file
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# Copyright 2022 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.
# ============================================================================
import numpy as np
import pytest
from mindspore import ops, nn, context, Tensor
from .test_grad_of_dynamic import TestDynamicGrad
context.set_context(mode=context.PYNATIVE_MODE)
class NetTopK(nn.Cell):
def __init__(self, k):
super(NetTopK, self).__init__()
self.topk = ops.TopK()
self.k = k
def construct(self, x):
return self.topk(x, self.k)
def topk_test(is_dyn_rank):
x = Tensor(np.array([[1, 2, 3, 4, 5]]).astype(np.float32))
k = 3
tester = TestDynamicGrad(NetTopK(k))
tester.test_dynamic_grad_net([x], is_dyn_rank)
@pytest.mark.level1
@pytest.mark.env_onecard
@pytest.mark.platform_x86_cpu
def test_topk_dyn_shape():
"""
Feature: TopK Grad DynamicShape.
Description: Test case of dynamic shape for TopK grad operator.
Expectation: success.
"""
topk_test(False)
@pytest.mark.level1
@pytest.mark.env_onecard
@pytest.mark.platform_x86_cpu
def test_topk_dyn_rank():
"""
Feature: TopK Grad DynamicShape.
Description: Test case of dynamic rank for TopK grad operator.
Expectation: success.
"""
topk_test(True)

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tests/st/ops/dynamic_shape/grad/test_trace.py Normal file
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# Copyright 2022 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.
# ============================================================================
import numpy as np
import pytest
import mindspore
import mindspore.ops.operations.math_ops as M
from mindspore import nn, context, Tensor
from .test_grad_of_dynamic import TestDynamicGrad
context.set_context(mode=context.PYNATIVE_MODE)
class NetTrace(nn.Cell):
def __init__(self):
super(NetTrace, self).__init__()
self.trace = M.Trace()
def construct(self, x):
return self.trace(x)
@pytest.mark.level1
@pytest.mark.env_onecard
@pytest.mark.platform_x86_cpu
@pytest.mark.platform_x86_gpu_training
def test_trace_dynamic_shape():
"""
Feature: Trace Grad DynamicShape.
Description: Test case of dynamic shape for Trace grad operator on CPU and GPU.
Expectation: success.
"""
test_dynamic = TestDynamicGrad(NetTrace())
x = Tensor(np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]), mindspore.float32)
test_dynamic.test_dynamic_grad_net(x, False)
@pytest.mark.level1
@pytest.mark.env_onecard
@pytest.mark.platform_x86_cpu
@pytest.mark.platform_x86_gpu_training
def test_trace_dynamic_shape_rank():
"""
Feature: Trace Grad DynamicShape.
Description: Test case of dynamic rank for Trace grad operator on CPU and GPU.
Expectation: success.
"""
test_dynamic = TestDynamicGrad(NetTrace())
x = Tensor(np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]), mindspore.float32)
test_dynamic.test_dynamic_grad_net(x, True)

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tests/st/ops/dynamic_shape/grad/test_transpose.py Normal file
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# Copyright 2022 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.
# ============================================================================
import numpy as np
import pytest
from mindspore import nn
from mindspore.ops import operations as P
from mindspore import Tensor
from mindspore import context
from .test_grad_of_dynamic import TestDynamicGrad
class NetTranspose(nn.Cell):
def __init__(self):
super(NetTranspose, self).__init__()
self.transpose = P.Transpose()
def construct(self, x, perm):
return self.transpose(x, perm)
@pytest.mark.level1
@pytest.mark.env_onecard
@pytest.mark.platform_x86_cpu
@pytest.mark.platform_x86_gpu_training
@pytest.mark.platform_arm_ascend_training
def test_dynamic_shape_transpose():
"""
Feature: Transpose Grad DynamicShape.
Description: Test case of dynamic shape for Transpose grad operator on CPU, GPU and Ascend.
Expectation: success.
"""
context.set_context(mode=context.PYNATIVE_MODE)
test_dynamic = TestDynamicGrad(NetTranspose())
x = Tensor(np.array([[1, 2, 3], [3, 2, 1]]))
perm = (0, 1)
test_dynamic.test_dynamic_grad_net([x, perm], False)
@pytest.mark.level1
@pytest.mark.env_onecard
@pytest.mark.platform_x86_cpu
@pytest.mark.platform_x86_gpu_training
@pytest.mark.platform_arm_ascend_training
def test_dynamic_rank_transpose():
"""
Feature: Transpose Grad DynamicShape.
Description: Test case of dynamic rank for Transpose grad operator on CPU, GPU and Ascend.
Expectation: success.
"""
context.set_context(mode=context.PYNATIVE_MODE)
test_dynamic = TestDynamicGrad(NetTranspose())
x = Tensor(np.array([[1, 2, 3], [3, 2, 1]]))
perm = (0, 1)
test_dynamic.test_dynamic_grad_net([x, perm], True)

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tests/st/ops/dynamic_shape/grad/test_truncate_mod.py Normal file
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# Copyright 2022 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.
# ============================================================================
import numpy as np
import pytest
import mindspore as ms
import mindspore.nn as nn
from mindspore import Tensor, context
from mindspore.ops import operations as P
from .test_grad_of_dynamic import TestDynamicGrad
class TruncateModNet(nn.Cell):
def __init__(self):
super(TruncateModNet, self).__init__()
self.truncate_mode = P.TruncateMod()
def construct(self, x, y):
return self.truncate_mode(x, y)
def run_dynamic_shape():
test_dynamic = TestDynamicGrad(TruncateModNet())
x = Tensor(np.array([2, 4, -1]), ms.int32)
y = Tensor(np.array([3, 3, 3]), ms.int32)
test_dynamic.test_dynamic_grad_net([x, y])
def run_dynamic_rank():
test_dynamic = TestDynamicGrad(TruncateModNet())
x = Tensor(np.array([2, 4, -1]), ms.int32)
y = Tensor(np.array([3, 3, 3]), ms.int32)
test_dynamic.test_dynamic_grad_net([x, y], True)
@pytest.mark.level1
@pytest.mark.platform_x86_gpu_training
def test_dynamic_truncate_mode_gpu():
"""
Feature: TruncateMod Grad DynamicShape.
Description: Test case of dynamic shape for TruncateMod grad operator on GPU.
Expectation: success.
"""
# Graph mode
context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
run_dynamic_shape()
run_dynamic_rank()
# PyNative mode
context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
run_dynamic_shape()
run_dynamic_rank()
@pytest.mark.level1
@pytest.mark.platform_arm_ascend_training
def test_dynamic_truncate_mode_ascend():
"""
Feature: TruncateMod Grad DynamicShape.
Description: Test case of dynamic shape for TruncateMod grad operator on Ascend.
Expectation: success.
"""
# Graph mode
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
run_dynamic_shape()
run_dynamic_rank()
# PyNative mode
context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
run_dynamic_shape()
run_dynamic_rank()

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tests/st/ops/dynamic_shape/grad/test_unsorted_segment_max.py Normal file
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# Copyright 2022 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.
# ============================================================================
import numpy as np
import pytest
from mindspore import nn
from mindspore.ops import operations as P
from mindspore import Tensor
from mindspore import context
from .test_grad_of_dynamic import TestDynamicGrad
class UnsortedSegmentMaxNet(nn.Cell):
def __init__(self, num_segments):
super(UnsortedSegmentMaxNet, self).__init__()
self.unsorted_segment_max = P.UnsortedSegmentMax()
self.num_segments = num_segments
def construct(self, data, ids):
return self.unsorted_segment_max(data, ids, self.num_segments)
@pytest.mark.level1
@pytest.mark.env_onecard
@pytest.mark.platform_x86_cpu
@pytest.mark.platform_x86_gpu_training
def test_dynamic_shape_unsorted_segment_max():
"""
Feature: UnsortedSegmentMax Grad DynamicShape.
Description: Test case of dynamic shape for UnsortedSegmentMax grad operator on CPU and GPU.
Expectation: success.
"""
context.set_context(mode=context.PYNATIVE_MODE)
num_segments = 2
test_dynamic = TestDynamicGrad(UnsortedSegmentMaxNet(num_segments))
input_x = Tensor(
np.array([[1, 2, 3], [4, 5, 6], [4, 2, 1]]).astype(np.float32))
segment_ids = Tensor(np.array([0, 1, 1]).astype(np.int32))
test_dynamic.test_dynamic_grad_net([input_x, segment_ids], False)
@pytest.mark.level1
@pytest.mark.env_onecard
@pytest.mark.platform_x86_cpu
@pytest.mark.platform_x86_gpu_training
def test_dynamic_rank_unsorted_segment_max():
"""
Feature: UnsortedSegmentMax Grad DynamicShape.
Description: Test case of dynamic rank for UnsortedSegmentMax grad operator on CPU and GPU.
Expectation: success.
"""
context.set_context(mode=context.PYNATIVE_MODE)
num_segments = 2
test_dynamic = TestDynamicGrad(UnsortedSegmentMaxNet(num_segments))
input_x = Tensor(
np.array([[1, 2, 3], [4, 5, 6], [4, 2, 1]]).astype(np.float32))
segment_ids = Tensor(np.array([0, 1, 1]).astype(np.int32))
test_dynamic.test_dynamic_grad_net([input_x, segment_ids], True)