diff --git a/mindspore/ccsrc/backend/kernel_compiler/cpu/maximum_cpu_kernel.cc b/mindspore/ccsrc/backend/kernel_compiler/cpu/maximum_cpu_kernel.cc index 68af12668f2..5fa83d8487c 100644 --- a/mindspore/ccsrc/backend/kernel_compiler/cpu/maximum_cpu_kernel.cc +++ b/mindspore/ccsrc/backend/kernel_compiler/cpu/maximum_cpu_kernel.cc @@ -22,7 +22,7 @@ namespace kernel { template void MaximumCPUKernel::InitKernel(const CNodePtr &kernel_node) { - MS_EXCEPTION_IF_NULL(kernel_node); + CheckParam(kernel_node); input_x_shape_ = AnfAlgo::GetInputDeviceShape(kernel_node, 0); input_y_shape_ = AnfAlgo::GetInputDeviceShape(kernel_node, 1); output_shape_ = AnfAlgo::GetOutputDeviceShape(kernel_node, 0); @@ -43,6 +43,18 @@ void MaximumCPUKernel::InitKernel(const CNodePtr &kernel_node) { } } +template +void MaximumCPUKernel::CheckParam(const CNodePtr &kernel_node) { + size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + if (input_num != 2) { + MS_LOG(EXCEPTION) << "Input number is " << input_num << ", but MaximumCPUKernel needs 2 input."; + } + size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + if (output_num != 1) { + MS_LOG(EXCEPTION) << "Output number is " << output_num << ", but MaximumCPUKernel needs 1 output."; + } +} + template void MaximumCPUKernel::InitInputTensorAndScalar(size_t max_input_shape_size) { if (max_input_shape_size != output_shape_.size()) { @@ -70,7 +82,6 @@ bool MaximumCPUKernel::Launch(const std::vector &inputs, T *input_x_ = reinterpret_cast(inputs[0]->addr); T *input_y_ = reinterpret_cast(inputs[1]->addr); T *output_ = reinterpret_cast(outputs[0]->addr); - BroadcastArith(input_x_, input_y_, output_); return true; } diff --git a/mindspore/ccsrc/backend/kernel_compiler/cpu/maximum_cpu_kernel.h b/mindspore/ccsrc/backend/kernel_compiler/cpu/maximum_cpu_kernel.h index 4342821fbbc..813c01e6b9f 100644 --- a/mindspore/ccsrc/backend/kernel_compiler/cpu/maximum_cpu_kernel.h +++ b/mindspore/ccsrc/backend/kernel_compiler/cpu/maximum_cpu_kernel.h @@ -34,6 +34,8 @@ class MaximumCPUKernel : public CPUKernel { const std::vector &outputs) override; private: + void CheckParam(const CNodePtr &kernel_node); + bool IsBroadcast(); size_t Index(const size_t &index, const size_t &dim); @@ -87,22 +89,6 @@ MS_REG_CPU_KERNEL_T( KernelAttr().AddInputAttr(kNumberTypeFloat32).AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32), MaximumCPUKernel, float); -MS_REG_CPU_KERNEL_T( - Maximum, KernelAttr().AddInputAttr(kNumberTypeBool).AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeInt32), - MaximumCPUKernel, int32_t); - -MS_REG_CPU_KERNEL_T( - Maximum, KernelAttr().AddInputAttr(kNumberTypeInt32).AddInputAttr(kNumberTypeBool).AddOutputAttr(kNumberTypeInt32), - MaximumCPUKernel, int32_t); - -MS_REG_CPU_KERNEL_T( - Maximum, KernelAttr().AddInputAttr(kNumberTypeBool).AddInputAttr(kNumberTypeInt64).AddOutputAttr(kNumberTypeInt64), - MaximumCPUKernel, int64_t); - -MS_REG_CPU_KERNEL_T( - Maximum, KernelAttr().AddInputAttr(kNumberTypeInt64).AddInputAttr(kNumberTypeBool).AddOutputAttr(kNumberTypeInt64), - MaximumCPUKernel, int64_t); - MS_REG_CPU_KERNEL_T( Maximum, KernelAttr().AddInputAttr(kNumberTypeInt64).AddInputAttr(kNumberTypeInt64).AddOutputAttr(kNumberTypeInt64), MaximumCPUKernel, int64_t); diff --git a/mindspore/ccsrc/backend/kernel_compiler/cpu/maximum_grad_cpu_kernel.cc b/mindspore/ccsrc/backend/kernel_compiler/cpu/maximum_grad_cpu_kernel.cc new file mode 100644 index 00000000000..1dd67b378ed --- /dev/null +++ b/mindspore/ccsrc/backend/kernel_compiler/cpu/maximum_grad_cpu_kernel.cc @@ -0,0 +1,147 @@ +/** + * 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 "backend/kernel_compiler/cpu/maximum_grad_cpu_kernel.h" +#include +#include "runtime/device/cpu/cpu_device_address.h" + +namespace mindspore { +namespace kernel { +void MaximumGradCPUKernel::InitKernel(const CNodePtr &kernel_node) { + CheckParam(kernel_node); + x_shape_ = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); + y_shape_ = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); + dout_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); + dx_shape = AnfAlgo::GetOutputInferShape(kernel_node, 0); + dy_shape = AnfAlgo::GetOutputInferShape(kernel_node, 1); + dtype_ = AnfAlgo::GetPrevNodeOutputInferDataType(kernel_node, 0); + if (!x_shape_.size() || !y_shape_.size() || !dout_shape.size()) { + MS_LOG(EXCEPTION) << "Input NULL"; + } +} + +bool MaximumGradCPUKernel::Launch(const std::vector &inputs, + const std::vector & /*workspace*/, + const std::vector &outputs) { + if (dtype_ == kNumberTypeInt32) { + LaunchKernel(inputs, outputs); + } else if (dtype_ == kNumberTypeUInt32) { + LaunchKernel(inputs, outputs); + } else if (dtype_ == kNumberTypeFloat32) { + LaunchKernel(inputs, outputs); + } else if (dtype_ == kNumberTypeInt64) { + LaunchKernel(inputs, outputs); + } else if (dtype_ == kNumberTypeUInt64) { + LaunchKernel(inputs, outputs); + } else if (dtype_ == kNumberTypeFloat64) { + LaunchKernel(inputs, outputs); + } + return true; +} + +template +void MaximumGradRecTask(T *x, T *y, T *dout, T *dx, T *dy, size_t dim, size_t x_index, size_t y_index, + size_t dout_index, const std::vector &x_cargo, const std::vector &y_cargo, + const std::vector &dout_cargo, const std::vector &x_shape, + const std::vector &y_shape, const std::vector &dout_shape) { + for (size_t i = 0; i < dout_shape[dim]; i++) { + size_t x_i = x_shape[dim] == dout_shape[dim] ? i * x_cargo[dim] : 0; + size_t y_i = y_shape[dim] == dout_shape[dim] ? i * y_cargo[dim] : 0; + size_t dout_i = i * dout_cargo[dim]; + + if (dim == dout_shape.size() - 1) { + if (*(x + x_index + x_i) >= *(y + y_index + y_i)) { + *(dx + x_index + x_i) += *(dout + dout_index + i); + } else { + *(dy + y_index + y_i) += *(dout + dout_index + i); + } + } else { + MaximumGradRecTask(x, y, dout, dx, dy, dim + 1, x_index + x_i, y_index + y_i, dout_index + dout_i, x_cargo, + y_cargo, dout_cargo, x_shape, y_shape, dout_shape); + } + } +} + +void GetCargo(std::vector *cargo, const std::vector &shape, const std::vector &dout_shape) { + int i = dout_shape.size() - 1; + int j = shape.size() - 1; + (*cargo)[i] = 1; + for (--i; j >= 1; --i, --j) { + (*cargo)[i] = shape[j] * (*cargo)[i + 1]; + } + for (; i >= 0; i--) { + (*cargo)[i] = 1; + } +} + +size_t GetTensorLen(const std::vector &shape) { + size_t len = 1; + for (size_t i = 0; i < shape.size(); i++) { + len *= shape[i]; + } + return len; +} + +void GetShape(std::vector *shape, const std::vector &shape_, const std::vector &dout_shape) { + int k = dout_shape.size() - 1; + int i = shape_.size() - 1; + for (; i >= 0; i--, k--) { + (*shape)[k] = shape_[i]; + } +} + +template +void MaximumGradCPUKernel::LaunchKernel(const std::vector &inputs, const std::vector &outputs) { + auto x_addr = reinterpret_cast(inputs[0]->addr); + auto y_addr = reinterpret_cast(inputs[1]->addr); + auto dout_addr = reinterpret_cast(inputs[2]->addr); + auto dx_addr = reinterpret_cast(outputs[0]->addr); + auto dy_addr = reinterpret_cast(outputs[1]->addr); + + size_t x_tensor_len = GetTensorLen(x_shape_); + size_t y_tensor_len = GetTensorLen(y_shape_); + memset(dx_addr, 0, x_tensor_len * sizeof(T)); + memset(dy_addr, 0, y_tensor_len * sizeof(T)); + + std::vector x_shape(dout_shape.size(), 1); + std::vector y_shape(dout_shape.size(), 1); + std::vector x_cargo(dout_shape.size(), 0); + std::vector y_cargo(dout_shape.size(), 0); + std::vector dout_cargo(dout_shape.size(), 0); + + GetShape(&x_shape, x_shape_, dout_shape); + GetShape(&y_shape, y_shape_, dout_shape); + + GetCargo(&x_cargo, x_shape, dout_shape); + GetCargo(&y_cargo, y_shape, dout_shape); + GetCargo(&dout_cargo, dout_shape, dout_shape); + + MaximumGradRecTask(x_addr, y_addr, dout_addr, dx_addr, dy_addr, 0, 0, 0, 0, x_cargo, y_cargo, dout_cargo, x_shape, + y_shape, dout_shape); +} + +void MaximumGradCPUKernel::CheckParam(const CNodePtr &kernel_node) { + size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node); + if (input_num != 3) { + MS_LOG(EXCEPTION) << "Input number is " << input_num << ", but MaximumGradCPUKernel needs 3 input."; + } + size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node); + if (output_num != 2) { + MS_LOG(EXCEPTION) << "Output number is " << output_num << ", but MaximumGradCPUKernel needs 2 output."; + } +} +} // namespace kernel +} // namespace mindspore diff --git a/mindspore/ccsrc/backend/kernel_compiler/cpu/maximum_grad_cpu_kernel.h b/mindspore/ccsrc/backend/kernel_compiler/cpu/maximum_grad_cpu_kernel.h new file mode 100644 index 00000000000..07f0f834c76 --- /dev/null +++ b/mindspore/ccsrc/backend/kernel_compiler/cpu/maximum_grad_cpu_kernel.h @@ -0,0 +1,105 @@ +/** + * 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_BACKEND_KERNEL_COMPILER_CPU_MAXIMUMGRAD_CPU_KERNEL_H_ +#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_CPU_MAXIMUMGRAD_CPU_KERNEL_H_ +#include +#include +#include +#include "backend/kernel_compiler/cpu/cpu_kernel.h" +#include "backend/kernel_compiler/cpu/cpu_kernel_factory.h" + +namespace mindspore { +namespace kernel { +class MaximumGradCPUKernel : public CPUKernel { + public: + MaximumGradCPUKernel() = default; + ~MaximumGradCPUKernel() override = default; + + void InitKernel(const CNodePtr &kernel_node) override; + + bool Launch(const std::vector &inputs, const std::vector &workspace, + const std::vector &outputs) override; + + template + void LaunchKernel(const std::vector &inputs, const std::vector &outputs); + + private: + void CheckParam(const CNodePtr &kernel_node); + std::vector x_shape_; + std::vector y_shape_; + std::vector dout_shape; + std::vector dx_shape; + std::vector dy_shape; + TypeId dtype_{kTypeUnknown}; +}; + +MS_REG_CPU_KERNEL(MaximumGrad, + KernelAttr() + .AddInputAttr(kNumberTypeInt32) + .AddInputAttr(kNumberTypeInt32) + .AddInputAttr(kNumberTypeInt32) + .AddOutputAttr(kNumberTypeInt32) + .AddOutputAttr(kNumberTypeInt32), + MaximumGradCPUKernel); + +MS_REG_CPU_KERNEL(MaximumGrad, + KernelAttr() + .AddInputAttr(kNumberTypeUInt32) + .AddInputAttr(kNumberTypeUInt32) + .AddInputAttr(kNumberTypeUInt32) + .AddOutputAttr(kNumberTypeUInt32) + .AddOutputAttr(kNumberTypeUInt32), + MaximumGradCPUKernel); + +MS_REG_CPU_KERNEL(MaximumGrad, + KernelAttr() + .AddInputAttr(kNumberTypeFloat32) + .AddInputAttr(kNumberTypeFloat32) + .AddInputAttr(kNumberTypeFloat32) + .AddOutputAttr(kNumberTypeFloat32) + .AddOutputAttr(kNumberTypeFloat32), + MaximumGradCPUKernel); + +MS_REG_CPU_KERNEL(MaximumGrad, + KernelAttr() + .AddInputAttr(kNumberTypeInt64) + .AddInputAttr(kNumberTypeInt64) + .AddInputAttr(kNumberTypeInt64) + .AddOutputAttr(kNumberTypeInt64) + .AddOutputAttr(kNumberTypeInt64), + MaximumGradCPUKernel); + +MS_REG_CPU_KERNEL(MaximumGrad, + KernelAttr() + .AddInputAttr(kNumberTypeUInt64) + .AddInputAttr(kNumberTypeUInt64) + .AddInputAttr(kNumberTypeUInt64) + .AddOutputAttr(kNumberTypeUInt64) + .AddOutputAttr(kNumberTypeUInt64), + MaximumGradCPUKernel); + +MS_REG_CPU_KERNEL(MaximumGrad, + KernelAttr() + .AddInputAttr(kNumberTypeFloat64) + .AddInputAttr(kNumberTypeFloat64) + .AddInputAttr(kNumberTypeFloat64) + .AddOutputAttr(kNumberTypeFloat64) + .AddOutputAttr(kNumberTypeFloat64), + MaximumGradCPUKernel); +} // namespace kernel +} // namespace mindspore +#endif // MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_CPU_MaximumGrad_CPU_KERNEL_H_ diff --git a/mindspore/ops/operations/_grad_ops.py b/mindspore/ops/operations/_grad_ops.py index 423d3f12d3e..95d5869c544 100644 --- a/mindspore/ops/operations/_grad_ops.py +++ b/mindspore/ops/operations/_grad_ops.py @@ -950,9 +950,6 @@ class MaximumGrad(Primitive): def __init__(self, grad_x=True, grad_y=True): """Initialize MaximumGrad""" - def __call__(self, x, y, dout): - raise NotImplementedError - class MaxPoolGradWithArgmax(_PoolGrad): """Computes the gradients of MaxPoolWithArgmax.""" diff --git a/tests/st/ops/cpu/test_maximum_grad.py b/tests/st/ops/cpu/test_maximum_grad.py new file mode 100644 index 00000000000..1b06024aa88 --- /dev/null +++ b/tests/st/ops/cpu/test_maximum_grad.py @@ -0,0 +1,170 @@ +# 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.context as context +from mindspore import Tensor +from mindspore.nn import Cell +import mindspore.ops.operations._grad_ops as G + +context.set_context(mode=context.GRAPH_MODE, enable_graph_kernel=True, device_target="CPU") + + +class MaxmumGradNet(Cell): + def __init__(self): + super(MaxmumGradNet, self).__init__() + self.maximum_grad = G.MaximumGrad() + + def construct(self, x, y, dy): + return self.maximum_grad(x, y, dy) + + +@pytest.mark.level0 +@pytest.mark.platform_x86_cpu_training +@pytest.mark.env_onecard +def test_maximum_grad_random(): + np.random.seed(0) + input_x = np.random.normal(0, 1, [2, 3]).astype(np.float32) + input_y = np.random.normal(0, 1, [2, 3]).astype(np.float32) + input_dout = np.maximum(input_x, input_y).astype(np.float32) + net = MaxmumGradNet() + result = net(Tensor(input_x), Tensor(input_y), Tensor(input_dout)) + dx = input_dout * (input_x >= input_y) + dy = input_dout - dx + assert np.allclose(result[0].asnumpy(), dx, rtol=1.e-4, atol=1.e-8, equal_nan=True) + assert np.allclose(result[1].asnumpy(), dy, rtol=1.e-4, atol=1.e-8, equal_nan=True) + + +@pytest.mark.level0 +@pytest.mark.platform_x86_cpu_training +@pytest.mark.env_onecard +def test_broadcast_grad_cpu(): + x = np.array([[[[0.659578], + [0.49113268], + [0.75909054], + [0.71681815], + [0.30421826]]], + [[[0.30322495], + [0.02858258], + [0.06398096], + [0.09519596], + [0.12498625]]], + [[[0.7347768], + [0.166469], + [0.328553], + [0.54908437], + [0.23673844]]]]).astype(np.float32) + y = np.array([[[[0.9154968, 0.29014662, 0.6492294, 0.39918253, 0.1648203, 0.00861965]], + [[0.996885, 0.24152198, 0.3601213, 0.51664376, 0.7933056, 0.84706444]], + [[0.75606346, 0.974512, 0.3939527, 0.69697475, 0.83400667, 0.6348955]], + [[0.68492866, 0.24609096, 0.4924665, 0.22500521, 0.38474053, 0.5586104]]]]).astype(np.float32) + dout = np.array([[[[0.42891738, 0.03434946, 0.06192983, 0.21216309, 0.37450036, 0.6619524], + [0.8583447, 0.5765161, 0.1468952, 0.9975385, 0.6908136, 0.4903796], + [0.68952006, 0.39336833, 0.9049695, 0.66886294, 0.2338471, 0.913618], + [0.0428149, 0.6243054, 0.8519898, 0.12088962, 0.9735885, 0.45661286], + [0.41563734, 0.41607043, 0.4754915, 0.32207987, 0.33823156, 0.47422352]], + + [[0.64478457, 0.22430937, 0.7682554, 0.46082005, 0.8938723, 0.20490853], + [0.44393885, 0.08278944, 0.4734108, 0.5543551, 0.39428464, 0.44424313], + [0.12612297, 0.76566416, 0.71133816, 0.81280327, 0.20583127, 0.54058075], + [0.41341263, 0.48118508, 0.00401995, 0.37259838, 0.05435474, 0.5240658], + [0.4081956, 0.48718935, 0.9132831, 0.67969185, 0.0119757, 0.8328054]], + + [[0.91695577, 0.95370644, 0.263782, 0.7477626, 0.6448147, 0.8080634], + [0.15576603, 0.9104615, 0.3778708, 0.6912833, 0.2092224, 0.67462957], + [0.7087075, 0.7888326, 0.4672294, 0.98221505, 0.25210258, 0.98920417], + [0.7466197, 0.22702982, 0.01991269, 0.6846591, 0.7515228, 0.5890395], + [0.04531088, 0.21740614, 0.8406235, 0.36480767, 0.37733936, 0.02914464]], + + [[0.33069974, 0.5497569, 0.9896345, 0.4167176, 0.78057563, 0.04659131], + [0.7747768, 0.21427679, 0.29893255, 0.7706969, 0.9755185, 0.42388415], + [0.3910244, 0.39381978, 0.37065396, 0.15558061, 0.05012341, 0.15870963], + [0.17791101, 0.47219893, 0.13899496, 0.32323205, 0.3628809, 0.02580585], + [0.30274773, 0.62890774, 0.11024303, 0.6980051, 0.35346958, 0.062852]]], + + [[[0.6925081, 0.74668753, 0.80145043, 0.06598313, 0.665123, 0.15073007], + [0.11784806, 0.6385372, 0.5228278, 0.5349848, 0.84671104, 0.8096436], + [0.09516156, 0.63298017, 0.52382874, 0.36734378, 0.66497755, 0.6019127], + [0.46438488, 0.0194377, 0.9388292, 0.7286089, 0.29178405, 0.11872514], + [0.22101837, 0.6164887, 0.6139798, 0.11711904, 0.6227745, 0.09701069]], + + [[0.80480653, 0.90034056, 0.8633447, 0.97415197, 0.08309154, 0.8446033], + [0.9473769, 0.791024, 0.26339203, 0.01155075, 0.2673186, 0.7116369], + [0.9687511, 0.24281934, 0.37777108, 0.09802654, 0.2421312, 0.87095344], + [0.6311381, 0.23368953, 0.0998995, 0.4364419, 0.9187446, 0.5043872], + [0.35226053, 0.09357589, 0.41317305, 0.85930043, 0.16249318, 0.5478765]], + + [[0.14338651, 0.24859418, 0.4246941, 0.73034066, 0.47172204, 0.8717199], + [0.05415315, 0.78556925, 0.99214983, 0.7415298, 0.673708, 0.87817156], + [0.616975, 0.42843062, 0.05179814, 0.1566958, 0.04536059, 0.70166487], + [0.15493333, 0.776598, 0.4361967, 0.40253627, 0.89210516, 0.8144414], + [0.04816005, 0.29696834, 0.4586605, 0.3419852, 0.5595613, 0.74093205]], + + [[0.1388035, 0.9168704, 0.64287645, 0.83864623, 0.48026922, 0.78323376], + [0.12724937, 0.83034366, 0.42557436, 0.50578654, 0.25630295, 0.15349793], + [0.27256685, 0.04547984, 0.5385756, 0.39270344, 0.7661698, 0.23722854], + [0.24620503, 0.25431684, 0.71564585, 0.01161419, 0.846467, 0.7043044], + [0.63272387, 0.11857849, 0.3772076, 0.16758402, 0.46743023, 0.05919575]]], + + [[[0.18827082, 0.8912264, 0.6841404, 0.74436826, 0.9582085, 0.1083683], + [0.60695344, 0.09742349, 0.25074378, 0.87940735, 0.21116392, 0.39418384], + [0.744686, 0.35679692, 0.01308284, 0.45166633, 0.68166, 0.8634658], + [0.7331758, 0.21113694, 0.3935488, 0.87934476, 0.70728546, 0.09309767], + [0.12128611, 0.93696386, 0.81177396, 0.85402405, 0.5827289, 0.9776509]], + + [[0.54069614, 0.66651285, 0.10646132, 0.17342485, 0.88795924, 0.03551182], + [0.25531697, 0.87946486, 0.74267226, 0.89230734, 0.95171434, 0.94697934], + [0.3708397, 0.507355, 0.97099817, 0.4918163, 0.17212386, 0.5008048], + [0.62530744, 0.25210327, 0.73966664, 0.71555346, 0.82484317, 0.6094874], + [0.4589691, 0.1386695, 0.27448782, 0.20373994, 0.27805242, 0.23292768]], + + [[0.7414099, 0.2270226, 0.90431255, 0.47035843, 0.9581062, 0.5359226], + [0.79603523, 0.45549425, 0.80858237, 0.7705133, 0.017761, 0.98001194], + [0.06013146, 0.99240226, 0.33515573, 0.04110833, 0.41470334, 0.7130743], + [0.5687417, 0.5788611, 0.00722461, 0.6603336, 0.3420471, 0.75181854], + [0.4699261, 0.51390815, 0.343182, 0.81498754, 0.8942413, 0.46532857]], + + [[0.4589523, 0.5534698, 0.2825786, 0.8205943, 0.78258514, 0.43154418], + [0.27020997, 0.01667354, 0.60871965, 0.90670526, 0.3208025, 0.96995634], + [0.85337156, 0.9711295, 0.1381724, 0.53670496, 0.7347996, 0.73380876], + [0.6137464, 0.54751194, 0.9037335, 0.23134394, 0.61411524, 0.26583543], + [0.70770144, 0.01813207, 0.24718016, 0.70329237, 0.7062925, 0.14399007]]]]).astype(np.float32) + + dx = np.array([[[[6.6534014], + [5.649811], + [10.071739], + [6.6798244], + [3.0426278]]], + [[[4.2183976], + [0.8096436], + [0.6019127], + [0.11872514], + [0.09701069]]], + [[[9.573029], + [0.60534775], + [3.917112], + [5.9021177], + [2.263672]]]]).astype(np.float32) + + dy = np.array([[[[6.4205275, 2.941831, 5.492452, 4.3212175, 2.4262471, 0.]], + [[7.991917, 2.3792431, 4.9190216, 5.2013817, 6.348791, 8.351772]], + [[5.518505, 8.401285, 4.691043, 6.463884, 7.504318, 7.620938]], + [[5.2708025, 1.2835244, 4.1031275, 1.9843934, 4.9320035, 4.537787]]]]).astype(np.float32) + + net = MaxmumGradNet() + result = net(Tensor(x), Tensor(y), Tensor(dout)) + assert np.allclose(result[0].asnumpy(), dx, rtol=1.e-4, atol=1.e-8, equal_nan=True) + assert np.allclose(result[1].asnumpy(), dy, rtol=1.e-4, atol=1.e-8, equal_nan=True) diff --git a/tests/st/ops/cpu/test_maximum_op.py b/tests/st/ops/cpu/test_maximum_op.py index 3a34af59bcb..a18676e3b9d 100644 --- a/tests/st/ops/cpu/test_maximum_op.py +++ b/tests/st/ops/cpu/test_maximum_op.py @@ -125,40 +125,6 @@ def test_maximum_two_tensors_notBroadcast_all_oneDimension_int(): assert np.all(-diff < error) -@pytest.mark.level0 -@pytest.mark.platform_x86_cpu_training -@pytest.mark.env_onecard -def test_maximum_two_tensors_Broadcast_bool(): - x = Tensor(np.array([[2, 2]]).astype(np.int32)) - y = Tensor(np.array([[True, False], [False, False]]).astype(np.bool_)) - expect = [[2, 2], [2, 2]] - error = np.ones(shape=[2, 2]) * 1.0e-5 - - context.set_context(mode=context.GRAPH_MODE, device_target="CPU") - max_op = TwoTensorsMaximum() - output = max_op(x, y) - diff = output.asnumpy() - expect - assert np.all(diff < error) - assert np.all(-diff < error) - - -@pytest.mark.level0 -@pytest.mark.platform_x86_cpu_training -@pytest.mark.env_onecard -def test_maximum_two_tensors_notBroadcast_bool(): - x = Tensor(np.array([[2, 2], [-1, 100]]).astype(np.int32)) - y = Tensor(np.array([[True, False], [False, False]]).astype(np.bool_)) - expect = [[2, 2], [0, 100]] - error = np.ones(shape=[2, 2]) * 1.0e-5 - - context.set_context(mode=context.GRAPH_MODE, device_target="CPU") - max_op = TwoTensorsMaximum() - output = max_op(x, y) - diff = output.asnumpy() - expect - assert np.all(diff < error) - assert np.all(-diff < error) - - @pytest.mark.level0 @pytest.mark.platform_x86_cpu_training @pytest.mark.env_onecard