diff --git a/mindspore/ccsrc/frontend/parallel/auto_parallel/operator_costmodel.h b/mindspore/ccsrc/frontend/parallel/auto_parallel/operator_costmodel.h index 9606241ef38..8cd1370fb5e 100644 --- a/mindspore/ccsrc/frontend/parallel/auto_parallel/operator_costmodel.h +++ b/mindspore/ccsrc/frontend/parallel/auto_parallel/operator_costmodel.h @@ -170,6 +170,8 @@ class ActivationCost : public OperatorCost { using ActivationCostPtr = std::shared_ptr; using TransposeCost = ActivationCost; using TransposeCostPtr = std::shared_ptr; +using StridedSliceCost = ActivationCost; +using StridedSliceCostPtr = std::shared_ptr; class SoftmaxCost : public OperatorCost { public: diff --git a/mindspore/ccsrc/frontend/parallel/dynamic_creator.h b/mindspore/ccsrc/frontend/parallel/dynamic_creator.h index 4fca2dfe61b..619f17f080f 100644 --- a/mindspore/ccsrc/frontend/parallel/dynamic_creator.h +++ b/mindspore/ccsrc/frontend/parallel/dynamic_creator.h @@ -134,6 +134,7 @@ REGISTER(SquareInfo); REGISTER(GatherV2PInfo); REGISTER(EmbeddingLookupInfo); REGISTER(TileInfo); +REGISTER(StridedSliceInfo); } // namespace parallel } // namespace mindspore diff --git a/mindspore/ccsrc/frontend/parallel/ops_info/ops_info_head_files.h b/mindspore/ccsrc/frontend/parallel/ops_info/ops_info_head_files.h index 4c8d92c4ba4..de9481c4d5b 100644 --- a/mindspore/ccsrc/frontend/parallel/ops_info/ops_info_head_files.h +++ b/mindspore/ccsrc/frontend/parallel/ops_info/ops_info_head_files.h @@ -38,5 +38,6 @@ #include "frontend/parallel/ops_info/virtual_dataset_info.h" #include "frontend/parallel/ops_info/gather_v2_p_info.h" #include "frontend/parallel/ops_info/tile_info.h" +#include "frontend/parallel/ops_info/strided_slice_info.h" #endif // MINDSPORE_CCSRC_FRONTEND_PARALLEL_OPS_INFO_HEAD_FILES_H_ diff --git a/mindspore/ccsrc/frontend/parallel/ops_info/ops_utils.h b/mindspore/ccsrc/frontend/parallel/ops_info/ops_utils.h index 9193476e22b..c013c998dea 100644 --- a/mindspore/ccsrc/frontend/parallel/ops_info/ops_utils.h +++ b/mindspore/ccsrc/frontend/parallel/ops_info/ops_utils.h @@ -29,6 +29,11 @@ constexpr int32_t NO_SPLIT_STRATEGY = 1; constexpr int32_t SPLIT_FLAG = 1; constexpr int32_t NO_SPLIT_FLAG = 0; constexpr size_t MATMUL_ATTRS_SIZE = 2; +constexpr size_t STRIDED_SLICE_ATTRS_SIZE = 5; +constexpr size_t STRIDED_SLICE_INPUTS_SIZE = 4; +constexpr size_t STRIDED_SLICE_BEGIN_INDEX = 1; +constexpr size_t STRIDED_SLICE_END_INDEX = 2; +constexpr size_t STRIDED_SLICE_STRIDES_INDEX = 3; constexpr size_t MATMUL_INPUTS_SIZE = 2; constexpr size_t MATMUL_OUTPUTS_SIZE = 1; constexpr size_t ACTIVATION_ATTR_SIZE = 1; diff --git a/mindspore/ccsrc/frontend/parallel/ops_info/strided_slice_info.cc b/mindspore/ccsrc/frontend/parallel/ops_info/strided_slice_info.cc new file mode 100644 index 00000000000..4e6d8ab8710 --- /dev/null +++ b/mindspore/ccsrc/frontend/parallel/ops_info/strided_slice_info.cc @@ -0,0 +1,305 @@ +/** + * Copyright 2020 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "frontend/parallel/ops_info/strided_slice_info.h" + +#include +#include +#include +#include + +#include "frontend/parallel/device_matrix.h" +#include "frontend/parallel/strategy.h" +#include "frontend/parallel/tensor_layout/tensor_redistribution.h" +#include "pipeline/jit/resource.h" + +namespace mindspore { +namespace parallel { +Status StridedSliceInfo::GetMask(const std::string &mask_name, int32_t *mask_value) { + if (mask_value == nullptr) { + return FAILED; + } + auto mask_iter = attrs_.find(mask_name); + if (mask_iter != attrs_.end()) { + MS_EXCEPTION_IF_NULL(mask_iter->second); + if (mask_iter->second->isa()) { + *mask_value = mask_iter->second->cast()->value(); + } else { + MS_LOG(ERROR) << name_ << ": The value of " << mask_name << " is not int"; + return FAILED; + } + } + return SUCCESS; +} + +Status GetInput(const ValuePtr &input_value, std::vector *input) { + MS_EXCEPTION_IF_NULL(input_value); + ValueTuplePtr value_tuple = input_value->cast(); + if (value_tuple == nullptr) { + MS_LOG(ERROR) << "Input value must be ValueTuplePtr."; + return FAILED; + } + + for (auto &element : value_tuple->value()) { + MS_EXCEPTION_IF_NULL(element); + if (element->isa()) { + int32_t value = element->cast()->value(); + input->push_back(value); + } else { + MS_LOG(ERROR) << "The value must be int32"; + return FAILED; + } + } + + return SUCCESS; +} + +Status StridedSliceInfo::GetAttrs() { + if (attrs_.size() < STRIDED_SLICE_ATTRS_SIZE) { + MS_LOG(ERROR) << name_ << ": The size of attrs small than " << STRIDED_SLICE_ATTRS_SIZE; + return FAILED; + } + + if ((GetMask(BEGIN_MASK, &begin_mask_) != SUCCESS) || (GetMask(END_MASK, &end_mask_) != SUCCESS) || + (GetMask(ELLIPSIS_MASK, &ellipsis_mask_) != SUCCESS) || (GetMask(NEW_AXIS_MASK, &new_axis_mask_) != SUCCESS) || + (GetMask(SHRINK_AXIS_MASK, &shrink_axis_mask_) != SUCCESS)) { + return FAILED; + } + has_mask_ = ((begin_mask_ != 0) || (end_mask_ != 0) || (ellipsis_mask_ != 0) || (new_axis_mask_ != 0) || + (shrink_axis_mask_ != 0)); + + if (input_value_.size() != STRIDED_SLICE_INPUTS_SIZE) { + MS_LOG(ERROR) << name_ << ": The size of input value must be " << STRIDED_SLICE_INPUTS_SIZE << ", but got " + << input_value_.size(); + return FAILED; + } + + if ((GetInput(input_value_[STRIDED_SLICE_BEGIN_INDEX], &begin_) != SUCCESS) || + (GetInput(input_value_[STRIDED_SLICE_END_INDEX], &end_) != SUCCESS) || + (GetInput(input_value_[STRIDED_SLICE_STRIDES_INDEX], &strides_) != SUCCESS)) { + return FAILED; + } + + return SUCCESS; +} + +Status StridedSliceInfo::CheckStrategy(const StrategyPtr &strategy) { + MS_EXCEPTION_IF_NULL(strategy); + if (CheckStrategyValue(strategy, inputs_shape_, is_auto_parallel_) != SUCCESS) { + MS_LOG(ERROR) << name_ << ": Invalid strategy"; + return FAILED; + } + + std::vector stra = strategy->GetInputDim(); + if (stra.empty()) { + MS_LOG(ERROR) << name_ << ": The strategy is empty"; + return FAILED; + } + + Dimensions strategy_value = stra[0]; + bool has_split = std::any_of(strategy_value.begin(), strategy_value.end(), [](int32_t v) { return v > 1; }); + + if (has_split && has_mask_) { + MS_LOG(ERROR) << name_ << ": When there is a mask, the input is not supported to be split"; + return FAILED; + } + + if (strategy_value.size() < strides_.size()) { + MS_LOG(ERROR) << name_ << ": The size of strategy must be larger or equal to the size of strides"; + return FAILED; + } + for (size_t i = 0; i < strides_.size(); ++i) { + if ((strides_[i] != 1) && (strategy_value[i] > 1)) { + MS_LOG(ERROR) << name_ << ": When a certain dimension is split, now does not support that the stride is not 1"; + return FAILED; + } + } + + if ((begin_.size() != end_.size()) || (begin_.size() != strides_.size())) { + MS_LOG(ERROR) << name_ << ": The size of begin " << begin_.size() << ", end " << end_.size() << " and strides " + << strides_.size() << " must be equal"; + return FAILED; + } + + for (size_t i = 0; i < begin_.size(); ++i) { + bool no_fully_fetch = ((begin_[i] != 0) || (end_[i] < inputs_shape_[0][i])); + if (no_fully_fetch && (strategy_value[i] != 1)) { + MS_LOG(ERROR) << name_ << "When a dimension is not fully fetched, the dimension can not be split now"; + return FAILED; + } + } + + return SUCCESS; +} + +Status StridedSliceInfo::InferDevMatrixShape() { + MS_EXCEPTION_IF_NULL(strategy_); + std::vector stra = strategy_->GetInputDim(); + if (stra.empty()) { + MS_LOG(ERROR) << name_ << "The strategy is empty"; + return FAILED; + } + + dev_matrix_shape_ = stra[0]; + return SUCCESS; +} + +Status StridedSliceInfo::InferTensorMap() { + TensorMap tensor_map; + if (inputs_shape_.empty()) { + MS_LOG(ERROR) << name_ << "The inputs shape is empty"; + return FAILED; + } + + // cannot use dev_matrix_shape_ replace inputs_shape_[0], because it may not be fully split in all devices. + int32_t size = SizeToInt(inputs_shape_[0].size()); + for (int i = 0; i < size; ++i) { + tensor_map.push_back(size - i - 1); + } + + inputs_tensor_map_.push_back(tensor_map); + outputs_tensor_map_.push_back(tensor_map); + return SUCCESS; +} + +Status StridedSliceInfo::InferMirrorOps() { + mirror_ops_.clear(); + if (inputs_tensor_map_.empty()) { + MS_LOG(ERROR) << name_ << ": The inputs tensor map is empty"; + return FAILED; + } + Shape input_tensor_map = inputs_tensor_map_[0]; + std::vector group; + if (CreateGroupByTensorMap(input_tensor_map, &group) != SUCCESS) { + MS_LOG(ERROR) << name_ << ": Create group for input failed."; + return FAILED; + } + + if (group.empty()) { + MS_LOG(INFO) << name_ << ": The mirror group is empty."; + return SUCCESS; + } + + OperatorVector input_op, begin_op, end_op, strides_op; + input_op = CreateMirrorOps(group[0].name(), group[0].GetDevNum()); + mirror_ops_.push_back(input_op); + mirror_ops_.push_back(begin_op); + mirror_ops_.push_back(end_op); + mirror_ops_.push_back(strides_op); + return SUCCESS; +} + +Status StridedSliceInfo::InferTensorInfo() { + if (inputs_shape_.empty() || outputs_shape_.empty() || inputs_tensor_map_.empty() || outputs_tensor_map_.empty()) { + MS_LOG(ERROR) << name_ << ": Invalid args"; + return FAILED; + } + // infer tensor layout + TensorLayout input_layout, output_layout; + if (input_layout.InitFromVector(dev_matrix_shape_, inputs_tensor_map_[0], inputs_shape_[0]) != SUCCESS) { + MS_LOG(ERROR) << name_ << ": Infer input tensor layout failed."; + return FAILED; + } + if (output_layout.InitFromVector(dev_matrix_shape_, outputs_tensor_map_[0], outputs_shape_[0]) != SUCCESS) { + MS_LOG(ERROR) << name_ << ": Infer output tensor layout failed."; + return FAILED; + } + + TensorInfo input_tensor_info(input_layout); + TensorInfo output_tensor_info(output_layout); + + inputs_tensor_info_.push_back(input_tensor_info); + outputs_tensor_info_.push_back(output_tensor_info); + return SUCCESS; +} + +// Note: if the batch dimension is not fully fetched, the batch strategy may not work. +std::shared_ptr>> StridedSliceInfo::GenerateBatchStrategies() { + split_flag_list_ = {true}; + return GenerateBatchStrategiesBySplitFlag(inputs_shape_, split_flag_list_); +} + +Status StridedSliceInfo::SetCostUnderStrategy(const StrategyPtr &strategy) { + if (SetCostUnderStrategyBase(strategy) != SUCCESS) { + MS_LOG(ERROR) << name_ << ": Set cost under strategy failed."; + return FAILED; + } + + return SUCCESS; +} + +Status StridedSliceInfo::GenerateStrategies(int32_t stage_id) { + if (InferAttrs() != SUCCESS) { + MS_LOG(ERROR) << name_ << ": Infer attrs failed"; + return FAILED; + } + if (inputs_shape_.empty()) { + MS_LOG(ERROR) << name_ << ": The inputs shape is empty"; + return FAILED; + } + Shape input_split(inputs_shape_[0].size(), 1); + if (has_mask_) { + for (size_t i = 0; i < inputs_shape_[0].size(); ++i) { + input_split[i] = 0; + } + } else { + for (size_t i = 0; i < begin_.size(); ++i) { + bool no_fully_fetch = ((begin_[i] != 0) || (end_[i] < inputs_shape_[0][i])); + if (no_fully_fetch || (strides_[i] != 1)) { + input_split[i] = 0; + } + } + } + Shapes splittable_inputs = {input_split}; + + std::vector sp_vector; + is_auto_parallel_ = true; + if (GenerateStrategiesForIndependentInputs(stage_id, inputs_shape_, splittable_inputs, &sp_vector) != SUCCESS) { + return FAILED; + } + + size_t success = 0; + for (auto &sp : sp_vector) { + PrintStrategy(sp); + if (SetCostUnderStrategy(sp) == SUCCESS) { + success++; + MS_LOG(INFO) << name_ << ": Successfully generated " << success << " strategy."; + PrintStrategy(sp); + } + } + return SUCCESS; +} + +Status StridedSliceInfo::Init(const StrategyPtr &strategy) { + if (InitWithAutoRepeatCalc(strategy) != SUCCESS) { + MS_LOG(ERROR) << name_ << ": Init failed."; + return FAILED; + } + MS_LOG(INFO) << name_ << ": Init success."; + return SUCCESS; +} + +Status StridedSliceInfo::InitForCostModel(const StrategyPtr &strategy) { + if (InitForCostModelWithAutoRepeatCalc(strategy) != SUCCESS) { + MS_LOG(ERROR) << name_ << ": Init for cost model failed."; + return FAILED; + } + + MS_LOG(INFO) << name_ << ": Init for cost model success."; + return SUCCESS; +} +} // namespace parallel +} // namespace mindspore diff --git a/mindspore/ccsrc/frontend/parallel/ops_info/strided_slice_info.h b/mindspore/ccsrc/frontend/parallel/ops_info/strided_slice_info.h new file mode 100644 index 00000000000..aa289b11823 --- /dev/null +++ b/mindspore/ccsrc/frontend/parallel/ops_info/strided_slice_info.h @@ -0,0 +1,72 @@ +/** + * Copyright 2020 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef MINDSPORE_CCSRC_FRONTEND_PARALLEL_OPS_INFO_STRIDED_SLICE_INFO_H_ +#define MINDSPORE_CCSRC_FRONTEND_PARALLEL_OPS_INFO_STRIDED_SLICE_INFO_H_ + +#include + +#include +#include +#include + +#include "ir/value.h" +#include "frontend/parallel/auto_parallel/operator_costmodel.h" +#include "frontend/parallel/ops_info/operator_info.h" +#include "frontend/parallel/strategy.h" + +namespace mindspore { +namespace parallel { +class StridedSliceInfo : public OperatorInfo { + public: + StridedSliceInfo(const std::string &operator_name, const Shapes &inputs_shape, const Shapes &outputs_shape, + const PrimitiveAttrs &attrs) + : OperatorInfo(operator_name, inputs_shape, outputs_shape, attrs, std::make_shared(false)) {} + ~StridedSliceInfo() override = default; + + Status Init(const StrategyPtr &strategy) override; + Status InitForCostModel(const StrategyPtr &strategy) override; + Status GenerateStrategies(int32_t) override; + Status SetCostUnderStrategy(const StrategyPtr &) override; + std::shared_ptr>> GenerateBatchStrategies() override; + + protected: + Status GetAttrs() override; + Status CheckStrategy(const StrategyPtr &strategy) override; + Status InferMirrorOps() override; + Status InferForwardCommunication() override { return SUCCESS; } + Status InferTensorInfo() override; + Status InferDevMatrixShape() override; + Status InferTensorMap() override; + Status GetMask(const std::string &mask_name, int32_t *mask_value); + + private: + std::vector begin_; + std::vector end_; + std::vector strides_; + int32_t begin_mask_ = 0; + int32_t end_mask_ = 0; + int32_t ellipsis_mask_ = 0; + int32_t new_axis_mask_ = 0; + int32_t shrink_axis_mask_ = 0; + bool has_mask_ = false; +}; + +using StridedSliceInfoPtr = std::shared_ptr; +} // namespace parallel +} // namespace mindspore + +#endif // MINDSPORE_CCSRC_FRONTEND_PARALLEL_OPS_INFO_STRIDED_SLICE_INFO_H_ diff --git a/tests/ut/python/parallel/test_stridedslice.py b/tests/ut/python/parallel/test_stridedslice.py new file mode 100644 index 00000000000..9ee190b14a0 --- /dev/null +++ b/tests/ut/python/parallel/test_stridedslice.py @@ -0,0 +1,164 @@ +# Copyright 2020 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +import numpy as np +import pytest + +import mindspore as ms +from mindspore import context, Tensor, Parameter +from mindspore.common.api import _executor +from mindspore.nn import Cell, TrainOneStepCell, Momentum +from mindspore.ops import operations as P + + +class Net(Cell): + def __init__(self, weight, w2, begin, end, strides, strategy1=None, strategy2=None, is_parameter=True, mask=0): + super().__init__() + self.mul = P.Mul().set_strategy(strategy1) + self.strided_slice = P.StridedSlice(begin_mask=mask).set_strategy(strategy2) + if is_parameter: + self.weight = Parameter(weight, "w1") + else: + self.weight = weight + self.mul2 = P.Mul() + self.weight2 = Parameter(w2, "w2") + self.begin = begin + self.end = end + self.strides = strides + + def construct(self, x, b): + out = self.strided_slice(self.weight, self.begin, self.end, self.strides) + out = self.mul(x, out) + out = self.mul2(out, self.weight2) + return out + + +class Net2(Cell): + def __init__(self, weight2, begin, end, strides, strategy1=None, strategy2=None): + super().__init__() + self.mul = P.Mul().set_strategy(strategy1) + self.strided_slice = P.StridedSlice().set_strategy(strategy2) + self.weight2 = Parameter(weight2, "w2") + self.begin = begin + self.end = end + self.strides = strides + + def construct(self, x, b): + out = self.mul(x, self.weight2) + out = self.strided_slice(out, self.begin, self.end, self.strides) + return out + + +_x = Tensor(np.ones([128, 64, 1]), dtype=ms.float32) +_w1 = Tensor(np.ones([256, 64, 32]), dtype=ms.float32) +_w2 = Tensor(np.ones([128, 64, 1]), dtype=ms.float32) +_b = Tensor(np.ones([128, 64, 32]), dtype=ms.float32) + + +def compile_net(net): + context.set_context(save_graphs=True) + optimizer = Momentum(net.trainable_params(), learning_rate=0.1, momentum=0.9) + train_net = TrainOneStepCell(net, optimizer) + train_net.set_auto_parallel() + _executor.compile(train_net, _x, _b) + context.reset_auto_parallel_context() + + +def test_stridedslice_no_fully_fetch_split_error(): + context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0) + strategy1 = ((2, 2, 2), (2, 2, 2)) + strategy2 = ((2, 2, 2),) + net = Net(_w1, _w2, (0, 0, 0), (128, 64, 32), (1, 1, 1), strategy1, strategy2, is_parameter=True) + with pytest.raises(RuntimeError): + compile_net(net) + + +def test_stridedslice_strides_no_1_split_error(): + context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0) + strategy1 = ((2, 2, 2), (2, 2, 2)) + strategy2 = ((1, 2, 2),) + net = Net(_w1, _w2, (0, 0, 0), (128, 64, 32), (1, 1, 2), strategy1, strategy2, is_parameter=True) + with pytest.raises(RuntimeError): + compile_net(net) + + +def test_stridedslice_mask_no_0_split_error(): + context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0) + strategy1 = ((2, 2, 2), (2, 2, 2)) + strategy2 = ((1, 2, 2),) + net = Net(_w1, _w2, (0, 0, 0), (128, 64, 32), (1, 1, 1), strategy1, strategy2, is_parameter=True, mask=1) + with pytest.raises(RuntimeError): + compile_net(net) + + +def test_stridedslice_begin_size_smaller(): + context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0) + strategy1 = ((1, 4, 1), (1, 4, 2)) + strategy2 = ((1, 4, 2),) + net = Net(_w1, _w2, (0, 0), (128, 64), (1, 1), strategy1, strategy2, is_parameter=True) + compile_net(net) + + +def test_stridedslice_parameter(): + context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0) + strategy1 = ((1, 4, 1), (1, 4, 2)) + strategy2 = ((1, 4, 2),) + net = Net(_w1, _w2, (0, 0, 0), (128, 64, 32), (1, 1, 1), strategy1, strategy2, is_parameter=True) + compile_net(net) + + +def test_stridedslice_tensor(): + context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0) + strategy1 = ((1, 4, 1), (1, 4, 2)) + strategy2 = ((1, 4, 2),) + net = Net(_w1, _w2, (0, 0, 0), (128, 64, 32), (1, 1, 1), strategy1, strategy2, is_parameter=False) + compile_net(net) + + +def test_stridedslice_parameter_no_full_split(): + context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0) + strategy1 = ((1, 4, 1), (1, 4, 2)) + strategy2 = ((1, 2, 2),) + net = Net(_w1, _w2, (0, 0, 0), (128, 64, 32), (1, 1, 1), strategy1, strategy2, is_parameter=True) + compile_net(net) + + +def test_stridedslice_output(): + context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0) + strategy1 = ((1, 8, 1), (1, 8, 1)) + strategy2 = ((1, 8, 1),) + net = Net2(_w2, (0, 0, 0), (64, 64, 1), (1, 1, 1), strategy1, strategy2) + compile_net(net) + + +def test_stridedslice_output_no_full_split(): + context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0) + strategy1 = ((1, 8, 1), (1, 8, 1)) + strategy2 = ((1, 4, 1),) + net = Net2(_w2, (0, 0, 0), (64, 64, 1), (1, 1, 1), strategy1, strategy2) + compile_net(net) + + +def test_stridedslice_no_strategy(): + context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0) + strategy1 = ((1, 8, 1), (1, 8, 1)) + strategy2 = None + net = Net2(_w2, (0, 0, 0), (128, 64, 1), (1, 1, 1), strategy1, strategy2) + compile_net(net) + + +def test_stridedslice_auto_parallel(): + context.set_auto_parallel_context(parallel_mode="auto_parallel", device_num=8, global_rank=0) + net = Net2(_w2, (0, 0, 0), (32, 64, 1), (1, 1, 1)) + compile_net(net)