!2235 add SigmoidCrossEntropyWithLogitsGrad operation

Merge pull request !2235 from zyli2020/add_sigmoid_cross_entropy_with_logits_grad

mindspore/ccsrc/kernel/gpu/cuda_impl/sigmoid_cross_entropy_with_logits_grad_impl.cu

+/**
+ * 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 "kernel/gpu/cuda_impl/sigmoid_cross_entropy_with_logits_grad_impl.cuh"
+
+template <typename T, typename S>
+__global__ void SigmoidCrossEntropyWithLogitsGradKernel(const size_t size, const T *logits, const S *labels,
+                                                        T *outputs) {
+  for (size_t i = blockIdx.x * blockDim.x + threadIdx.x; i < size; i += gridDim.x * blockDim.x) {
+    if (logits[i] >= 0) {
+      outputs[i] = 1. / (1. + exp(-logits[i])) - labels[i];
+    } else {
+      const T exp_val = exp(logits[i]);
+      outputs[i] = exp_val / (1. + exp_val) - labels[i];
+    }
+  }
+}
+
+template <typename T, typename S>
+void SigmoidCrossEntropyWithLogitsGrad(const size_t size, const T *logits, const S *labels, T *outputs,
+                                       cudaStream_t cuda_stream) {
+  SigmoidCrossEntropyWithLogitsGradKernel<<<GET_BLOCKS(size), GET_THREADS, 0, cuda_stream>>>(size, logits, labels,
+                                                                                             outputs);
+}
+
+template void SigmoidCrossEntropyWithLogitsGrad<float, float>(const size_t size, const float *logits,
+                                                              const float *labels, float *outputs,
+                                                              cudaStream_t cuda_stream);

mindspore/ccsrc/kernel/gpu/cuda_impl/sigmoid_cross_entropy_with_logits_grad_impl.cuh

+/**
+ * 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_KERNEL_GPU_CUDA_IMP_SIGMOID_CROSS_ENTROPY_WITH_LOGITS_GRAD_IMPL_H_
+#define MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMP_SIGMOID_CROSS_ENTROPY_WITH_LOGITS_GRAD_IMPL_H_
+
+#include "device/gpu/cuda_common.h"
+template <typename T, typename S>
+void SigmoidCrossEntropyWithLogitsGrad(const size_t size, const T *logits, const S *labels, T *outputs,
+                                       cudaStream_t cuda_stream);
+
+#endif  // MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMP_SIGMOID_CROSS_ENTROPY_WITH_LOGITS_GRAD_IMPL_H_

mindspore/ccsrc/kernel/gpu/nn/sigmoid_cross_entropy_with_logits_grad_gpu_kernel.cc

+/**
+ * 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 "kernel/gpu/nn/sigmoid_cross_entropy_with_logits_grad_gpu_kernel.h"
+
+namespace mindspore {
+namespace kernel {
+MS_REG_GPU_KERNEL_TWO(SigmoidCrossEntropyWithLogitsGrad,
+                      KernelAttr()
+                        .AddInputAttr(kNumberTypeFloat32)
+                        .AddInputAttr(kNumberTypeFloat32)
+                        .AddInputAttr(kNumberTypeFloat32)
+                        .AddOutputAttr(kNumberTypeFloat32),
+                      SigmoidCrossEntropyWithLogitsGradGpuKernel, float, float)
+}  // namespace kernel
+}  // namespace mindspore

mindspore/ccsrc/kernel/gpu/nn/sigmoid_cross_entropy_with_logits_grad_gpu_kernel.h

+/**
+ * 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_KERNEL_GPU_NN_SIGMOID_CROSS_ENTROPY_WITH_LOGITS_GRAD_GPU_KERNEL_H_
+#define MINDSPORE_CCSRC_KERNEL_GPU_NN_SIGMOID_CROSS_ENTROPY_WITH_LOGITS_GRAD_GPU_KERNEL_H_
+
+#include <vector>
+#include "kernel/gpu/gpu_kernel.h"
+#include "kernel/gpu/gpu_kernel_factory.h"
+#include "kernel/gpu/cuda_impl/sigmoid_cross_entropy_with_logits_grad_impl.cuh"
+
+namespace mindspore {
+namespace kernel {
+template <typename T, typename S>
+class SigmoidCrossEntropyWithLogitsGradGpuKernel : public GpuKernel {
+ public:
+  SigmoidCrossEntropyWithLogitsGradGpuKernel() : logits_size_(0), labels_size_(0), outputs_size_(0) {}
+  ~SigmoidCrossEntropyWithLogitsGradGpuKernel() override = default;
+
+  const std::vector<size_t> &GetInputSizeList() const override { return input_size_list_; }
+  const std::vector<size_t> &GetOutputSizeList() const override { return output_size_list_; }
+  const std::vector<size_t> &GetWorkspaceSizeList() const override { return workspace_size_list_; }
+
+  bool Launch(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &,
+              const std::vector<AddressPtr> &outputs, void *stream_ptr) override {
+    T *logits_addr = GetDeviceAddress<T>(inputs, 0);
+    S *labels_addr = GetDeviceAddress<S>(inputs, 1);
+    T *outputs_addr = GetDeviceAddress<T>(outputs, 0);
+
+    SigmoidCrossEntropyWithLogitsGrad(inputs[0]->size / sizeof(T), logits_addr, labels_addr, outputs_addr,
+                                      reinterpret_cast<cudaStream_t>(stream_ptr));
+    return true;
+  }
+
+  bool Init(const CNodePtr &kernel_node) override {
+    size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node);
+    if (input_num != 3) {
+      MS_LOG(ERROR) << "Input number is " << input_num << ", but SigmoidCrossEntropyWithLogitsGrad needs 3 inputs.";
+      return false;
+    }
+    logits_size_ = sizeof(T);
+    labels_size_ = sizeof(S);
+    outputs_size_ = sizeof(T);
+
+    auto logits_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0);
+    for (size_t i = 0; i < logits_shape.size(); i++) {
+      logits_size_ *= logits_shape[i];
+    }
+
+    auto labels_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1);
+    for (size_t i = 0; i < labels_shape.size(); i++) {
+      labels_size_ *= labels_shape[i];
+    }
+
+    auto output_shape = AnfAlgo::GetOutputInferShape(kernel_node, 0);
+    for (size_t i = 0; i < output_shape.size(); i++) {
+      outputs_size_ *= output_shape[i];
+    }
+
+    InitSizeLists();
+    return true;
+  }
+
+ protected:
+  void InitSizeLists() override {
+    input_size_list_.push_back(logits_size_);
+    input_size_list_.push_back(labels_size_);
+    output_size_list_.push_back(outputs_size_);
+  }
+
+ private:
+  size_t logits_size_;
+  size_t labels_size_;
+  size_t outputs_size_;
+
+  std::vector<size_t> input_size_list_;
+  std::vector<size_t> output_size_list_;
+  std::vector<size_t> workspace_size_list_;
+};
+}  // namespace kernel
+}  // namespace mindspore
+
+#endif  // MINDSPORE_CCSRC_KERNEL_GPU_NN_SIGMOID_CROSS_ENTROPY_WITH_LOGITS_GRAD_GPU_KERNEL_H_

tests/st/ops/gpu/test_sigmoid_cross_entropy_with_logits_grad_op.py

+# 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
+import mindspore.nn as nn
+from mindspore import Tensor
+from mindspore.ops.operations import _grad_ops as G
+
+
+class NetSigmoidCrossEntropyWithLogits(nn.Cell):
+    def __init__(self):
+        super(NetSigmoidCrossEntropyWithLogits, self).__init__()
+        self.sigmoid_cross_entropy_with_logits_grad = G.SigmoidCrossEntropyWithLogitsGrad()
+
+    def construct(self, logits, labels, dout):
+        return self.sigmoid_cross_entropy_with_logits_grad(logits, labels, dout)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_sigmoid_cross_entropy_with_logits():
+    logits = Tensor(np.array([[1, 1, 2],
+                              [1, 2, 1],
+                              [2, 1, 1]]).astype(np.float32))
+    labels = Tensor(np.array([[0, 0, 1],
+                              [0, 1, 0],
+                              [1, 0, 0]]).astype(np.float32))
+    dout = Tensor(np.ones(shape=[3, 3]).astype(np.float32))
+
+    expect = np.array([[0.731059, 0.731059, -0.119203],
+                       [0.731059, -0.119203, 0.731059],
+                       [-0.119203, 0.731059, 0.731059]]).astype(np.float32)
+
+    error = np.ones(shape=[3, 3]) * 1.0e-6
+
+    context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
+    sigmoid_cross_entropy_with_logits = NetSigmoidCrossEntropyWithLogits()
+    output = sigmoid_cross_entropy_with_logits(logits, labels, dout)
+    diff = output.asnumpy() - expect
+    assert np.all(abs(diff) < error)
+
+    context.set_context(mode=context.PYNATIVE_MODE, device_target='GPU')
+    sigmoid_cross_entropy_with_logits = NetSigmoidCrossEntropyWithLogits()
+    output = sigmoid_cross_entropy_with_logits(logits, labels, dout)
+    diff = output.asnumpy() - expect
+    assert np.all(abs(diff) < error)