diff --git a/mindspore/ccsrc/frontend/parallel/ops_info/operator_info.cc b/mindspore/ccsrc/frontend/parallel/ops_info/operator_info.cc index ca0fdb8e2f6..2f58d6bd744 100644 --- a/mindspore/ccsrc/frontend/parallel/ops_info/operator_info.cc +++ b/mindspore/ccsrc/frontend/parallel/ops_info/operator_info.cc @@ -56,6 +56,14 @@ std::string StrategyToString(const Strategys &strategy) { return strategy_str; } +Status OperatorInfo::CheckOutputStrategy(const StrategyPtr &out_strategy) { + if (out_strategy) { + MS_LOG(ERROR) << name_ << ": It does not support to set output strategy now, please modify the shard set"; + return FAILED; + } + return SUCCESS; +} + Status OperatorInfo::CheckStrategyValue(const StrategyPtr &strategy, const Shapes &inputs_shape) { if (strategy == nullptr) { MS_LOG(ERROR) << name_ << ": The strategy is null."; diff --git a/mindspore/ccsrc/frontend/parallel/ops_info/operator_info.h b/mindspore/ccsrc/frontend/parallel/ops_info/operator_info.h index d89c6a65a4a..5ccff8e9197 100644 --- a/mindspore/ccsrc/frontend/parallel/ops_info/operator_info.h +++ b/mindspore/ccsrc/frontend/parallel/ops_info/operator_info.h @@ -209,7 +209,7 @@ class OperatorInfo { virtual Status InferMirrorOps(); virtual Status InferTensorInfo(); virtual void InferReplaceOps() {} - virtual Status CheckOutputStrategy(const StrategyPtr &out_strategy) { return SUCCESS; } + virtual Status CheckOutputStrategy(const StrategyPtr &out_strategy); Status CheckStrategyValue(const StrategyPtr &strategy, const Shapes &inputs_shape); void SetRepeatedCalcDevMatrix(); void ResetTensorMapIfRepeatedCalc(); diff --git a/mindspore/ccsrc/frontend/parallel/ops_info/resizebilinear_info.cc b/mindspore/ccsrc/frontend/parallel/ops_info/resizebilinear_info.cc index 1f54bfcd8d5..8031c2cad09 100644 --- a/mindspore/ccsrc/frontend/parallel/ops_info/resizebilinear_info.cc +++ b/mindspore/ccsrc/frontend/parallel/ops_info/resizebilinear_info.cc @@ -126,6 +126,25 @@ void ResizeBilinearInfo::ReplaceNodeInputOrAttrs() { Status ResizeNearestNeighborInfo::CheckStrategy(const StrategyPtr &strategy) { MS_EXCEPTION_IF_NULL(strategy); + if (align_corners_) { + std::vector stra = strategy->GetInputDim(); + if (stra.size() != 1) { + MS_LOG(ERROR) << name_ << ": The size of strategy must be 1, but got " << stra.size(); + return FAILED; + } + + Dimensions input_strategy = stra[0]; + if (input_strategy.size() != 4) { + MS_LOG(ERROR) << name_ << ": The size of input strategy must be 4, but got" << input_strategy.size(); + return FAILED; + } + + if (input_strategy[2] != 1 || input_strategy[3] != 1) { + MS_LOG(ERROR) << name_ << ": The align_corners is True, do not support split from H or W"; + return FAILED; + } + } + // check input strategy if (CheckStrategyValue(strategy, inputs_shape_) != SUCCESS) { MS_LOG(ERROR) << name_ << ": Check input strategy failed"; @@ -143,6 +162,10 @@ Status ResizeNearestNeighborInfo::CheckStrategy(const StrategyPtr &strategy) { std::vector ResizeNearestNeighborInfo::GenerateOpStrategies(int64_t stage_id) { Shape multiples_split(inputs_shape_[0].size(), 1); + if (align_corners_) { + multiples_split[2] = 0; + multiples_split[3] = 0; + } Shapes splittable_inputs = {multiples_split}; std::vector sp_vector; diff --git a/tests/ut/python/parallel/test_resizebilinear.py b/tests/ut/python/parallel/test_resizebilinear.py index 77991009629..2c3e3127480 100644 --- a/tests/ut/python/parallel/test_resizebilinear.py +++ b/tests/ut/python/parallel/test_resizebilinear.py @@ -13,6 +13,7 @@ # limitations under the License. '''ResizeBilinear and ResizeNearestNeigbor ut''' import numpy as np +import pytest import mindspore as ms from mindspore import context, Tensor, Parameter @@ -43,13 +44,13 @@ class Net2(Cell): ''' create the test Net ''' - def __init__(self, conv2d_weight, mul_weight, out_channel, kernel_size, pad_mode, stride, - strategy1=None, strategy2=None): + def __init__(self, conv2d_weight, mul_weight, out_channel, kernel_size, pad_mode, stride, align_corners=False, + strategy1=None, strategy2=None, out_strategy=None): super(Net2, self).__init__() self.conv2d = P.Conv2D(out_channel=out_channel, kernel_size=kernel_size, pad_mode=pad_mode, stride=stride).shard(strategy1) self.conv2d_weight = Parameter(conv2d_weight, "w1") - self.resize_neighbor = P.ResizeNearestNeighbor((16, 16)).shard(strategy2) + self.resize_neighbor = P.ResizeNearestNeighbor((16, 16), align_corners).shard(strategy2, out_strategy) self.mul = P.Mul() self.mul_weight = Parameter(mul_weight, "w2") @@ -59,6 +60,7 @@ class Net2(Cell): out = self.mul(out, self.mul_weight) return out + class Net3(Cell): ''' create the test Net @@ -92,6 +94,11 @@ def compile_net(net, inputs=_x): def test_bililear_data_parallel(): + """ + Feature: test ResizeBilinear data parallel strategy + Description: only shard batch dimension + Expectation: compile success + """ context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0) strategy1 = ((8, 1, 1, 1), (1, 1, 1, 1)) strategy2 = ((8, 1, 1, 1),) @@ -101,6 +108,11 @@ def test_bililear_data_parallel(): def test_bilinear_model_parallel1(): + """ + Feature: test ResizeBilinear model parallel strategy + Description: shard N/C + Expectation: compile success + """ context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0) strategy1 = ((2, 2, 1, 1), (2, 2, 1, 1)) strategy2 = ((4, 2, 1, 1),) @@ -109,7 +121,12 @@ def test_bilinear_model_parallel1(): compile_net(net) -def test_bilinear_model_parallel2(): +def test_bilinear_repeated_calc(): + """ + Feature: test ResizeBilinear repeated calculation parallel strategy + Description: only shard batch dimension, but shard num smaller than device num + Expectation: compile success + """ context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0) strategy1 = ((2, 2, 1, 1), (2, 2, 1, 1)) strategy2 = ((2, 1, 1, 1),) @@ -119,18 +136,33 @@ def test_bilinear_model_parallel2(): def test_bilinear_auto_parallel(): + """ + Feature: test ResizeBilinear auto parallel + Description: + Expectation: compile success + """ context.set_auto_parallel_context(parallel_mode="auto_parallel", device_num=8, global_rank=0) net = Net(_w1, out_channel=8, kernel_size=2, pad_mode="same", stride=1) compile_net(net) def test_bilinear_no_strategy(): - context.set_auto_parallel_context(parallel_mode="auto_parallel", device_num=8, global_rank=0) + """ + Feature: test ResizeBilinear semi auto parallel, and has not set strategy for it + Description: + Expectation: compile success + """ + context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0) net = Net3(_w1, out_channel=8, kernel_size=2, pad_mode="same", stride=1) compile_net(net) def test_neighbor_data_parallel(): + """ + Feature: test ResizeNearestNeighbor data parallel strategy + Description: only shard batch dimension + Expectation: compile success + """ context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0) strategy1 = ((8, 1, 1, 1), (1, 1, 1, 1)) strategy2 = ((8, 1, 1, 1),) @@ -139,7 +171,57 @@ def test_neighbor_data_parallel(): compile_net(net) -def test_neighbor_model_parallel1(): +def test_neighbor_model_parallel_align_corners_shard_HW(): + """ + Feature: test ResizeNearestNeighbor model parallel strategy + Description: the align_corners is True, and shard N/C/H/W + Expectation: compile failed + """ + context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0) + strategy1 = ((2, 2, 1, 1), (2, 2, 1, 1)) + strategy2 = ((2, 2, 2, 2),) + net = Net2(_w1, _w2, out_channel=8, kernel_size=2, pad_mode="same", stride=1, align_corners=True, + strategy1=strategy1, strategy2=strategy2) + with pytest.raises(RuntimeError): + compile_net(net) + + +def test_neighbor_out_strategy(): + """ + Feature: test ResizeNearestNeighbor to set output parallel strategy + Description: + Expectation: compile failed + """ + context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0) + strategy1 = ((2, 2, 1, 1), (2, 2, 1, 1)) + strategy2 = ((2, 2, 2, 2),) + out_strategy = ((2, 2, 2, 2),) + net = Net2(_w1, _w2, out_channel=8, kernel_size=2, pad_mode="same", stride=1, + strategy1=strategy1, strategy2=strategy2, out_strategy=out_strategy) + with pytest.raises(RuntimeError): + compile_net(net) + + +def test_neighbor_model_parallel_align_corners_shard_NC(): + """ + Feature: test ResizeNearestNeighbor model parallel strategy + Description: the align_corners is True, and shard N/C + Expectation: compile success + """ + context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0) + strategy1 = ((2, 2, 1, 1), (2, 2, 1, 1)) + strategy2 = ((4, 4, 1, 1),) + net = Net2(_w1, _w2, out_channel=8, kernel_size=2, pad_mode="same", stride=1, align_corners=True, + strategy1=strategy1, strategy2=strategy2) + compile_net(net) + + +def test_neighbor_model_parallel_align_corners_is_false(): + """ + Feature: test ResizeNearestNeighbor model parallel strategy + Description: the align_corners is False, and shard N/C/H/W + Expectation: compile success + """ context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0) strategy1 = ((2, 2, 1, 1), (2, 2, 1, 1)) strategy2 = ((2, 2, 2, 2),) @@ -149,6 +231,11 @@ def test_neighbor_model_parallel1(): def test_neighbor_auto_parallel(): + """ + Feature: test ResizeNearestNeighbor auto parallel + Description: + Expectation: compile success + """ context.set_auto_parallel_context(parallel_mode="auto_parallel", device_num=8, global_rank=0) net = Net2(_w1, _w2, out_channel=8, kernel_size=2, pad_mode="same", stride=1) compile_net(net)