!11636 add bce cpu ops

From: @zhangzhewei01
Reviewed-by: 
Signed-off-by:

mindspore/ccsrc/backend/kernel_compiler/cpu/binary_cross_entropy_cpu_kernel.cc

@@ -0,0 +1,100 @@
+/**
+ * Copyright 2021 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/binary_cross_entropy_cpu_kernel.h"
+
+namespace mindspore {
+namespace kernel {
+template <typename T>
+void BinaryCrossEntropyCpuKernel::LaunchToScalar(const int &input_size, const int &reduction, T *loss, T *tmp_loss) {
+  if (input_size % 2 == 1) {
+    tmp_loss[0] += tmp_loss[input_size - 1];
+  }
+
+  for (int stride = input_size / 2; stride > 0; stride >>= 1) {
+    for (int i = 0; i < stride; i++) {
+      tmp_loss[i] += tmp_loss[i + stride];
+    }
+    if (stride > 2 && stride % 2 == 1) {
+      tmp_loss[0] += tmp_loss[stride - 1];
+    }
+  }
+
+  loss[0] += tmp_loss[0];
+  if (reduction == 1) {
+    loss[0] /= static_cast<T>(input_size);
+  }
+}
+
+template <typename T>
+void BinaryCrossEntropyCpuKernel::Launchkernel(const std::vector<AddressPtr> &inputs,
+                                               const std::vector<AddressPtr> &workspace,
+                                               const std::vector<AddressPtr> &outputs) {
+  T *input_x = reinterpret_cast<T *>(inputs[0]->addr);
+  T *input_y = reinterpret_cast<T *>(inputs[1]->addr);
+  T *weight = reinterpret_cast<T *>(inputs[2]->addr);
+  T *loss = reinterpret_cast<T *>(outputs[0]->addr);
+  std::vector<T> tmp_loss(input_size_);
+
+  T epsilon = static_cast<T>(1e-12);
+  T one = static_cast<T>(1);
+  if (reduction_ == 0) {
+    for (size_t i = 0; i < input_size_; i++) {
+      T value =
+        -weight[i] * (input_y[i] * log(input_x[i] + epsilon) + (one - input_y[i]) * log(one - input_x[i] + epsilon));
+      loss[i] = value;
+    }
+  } else {
+    for (size_t i = 0; i < input_size_; i++) {
+      T value =
+        -weight[i] * (input_y[i] * log(input_x[i] + epsilon) + (one - input_y[i]) * log(one - input_x[i] + epsilon));
+      tmp_loss[i] = value;
+    }
+  }
+
+  if (reduction_ != 0) {
+    LaunchToScalar<T>(input_size_, reduction_, loss, tmp_loss.data());
+  }
+}
+
+bool BinaryCrossEntropyCpuKernel::Launch(const std::vector<AddressPtr> &inputs,
+                                         const std::vector<AddressPtr> &workspace,
+                                         const std::vector<AddressPtr> &outputs) {
+  if (input_size_ > 0) {
+    if (dtype_ == kNumberTypeFloat32) {
+      Launchkernel<float>(inputs, workspace, outputs);
+    } else if (dtype_ == kNumberTypeFloat16) {
+      Launchkernel<float16>(inputs, workspace, outputs);
+    }
+  }
+  return true;
+}
+
+void BinaryCrossEntropyCpuKernel::InitKernel(const CNodePtr &kernel_node) {
+  auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0);
+  for (size_t i = 0; i < input_shape.size(); i++) {
+    input_size_ *= input_shape[i];
+  }
+  string reduction = AnfAlgo::GetNodeAttr<string>(kernel_node, "reduction");
+  if (reduction == "none") {
+    reduction_ = 0;
+  } else if (reduction == "sum") {
+    reduction_ = 2;
+  }
+
+  dtype_ = AnfAlgo::GetInputDeviceDataType(kernel_node, 0);
+}
+}  // namespace kernel
+}  // namespace mindspore

mindspore/ccsrc/backend/kernel_compiler/cpu/binary_cross_entropy_cpu_kernel.h

@@ -0,0 +1,62 @@
+/**
+ * Copyright 2021 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_NN_BINARY_CROSS_ENTROPY_KERNEL_H
+#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_CPU_NN_BINARY_CROSS_ENTROPY_KERNEL_H
+
+#include <vector>
+#include <string>
+#include "backend/kernel_compiler/cpu/cpu_kernel.h"
+#include "backend/kernel_compiler/cpu/cpu_kernel_factory.h"
+
+namespace mindspore {
+namespace kernel {
+class BinaryCrossEntropyCpuKernel : public CPUKernel {
+ public:
+  BinaryCrossEntropyCpuKernel() : input_size_(1), reduction_(1) {}
+  ~BinaryCrossEntropyCpuKernel() override = default;
+
+  void InitKernel(const CNodePtr &kernel_node) override;
+  bool Launch(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &workspace,
+              const std::vector<AddressPtr> &outputs) override;
+
+ private:
+  template <typename T>
+  void LaunchToScalar(const int &input_size, const int &reduction, T *loss, T *tmp_loss);
+  template <typename T>
+  void Launchkernel(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &workspace,
+                    const std::vector<AddressPtr> &outputs);
+
+  TypeId dtype_{kTypeUnknown};
+  size_t input_size_;
+  int reduction_;
+};
+MS_REG_CPU_KERNEL(BinaryCrossEntropy,
+                  KernelAttr()
+                    .AddInputAttr(kNumberTypeFloat16)
+                    .AddInputAttr(kNumberTypeFloat16)
+                    .AddInputAttr(kNumberTypeFloat16)
+                    .AddOutputAttr(kNumberTypeFloat16),
+                  BinaryCrossEntropyCpuKernel);
+MS_REG_CPU_KERNEL(BinaryCrossEntropy,
+                  KernelAttr()
+                    .AddInputAttr(kNumberTypeFloat32)
+                    .AddInputAttr(kNumberTypeFloat32)
+                    .AddInputAttr(kNumberTypeFloat32)
+                    .AddOutputAttr(kNumberTypeFloat32),
+                  BinaryCrossEntropyCpuKernel);
+}  // namespace kernel
+}  // namespace mindspore
+#endif  // MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_CPU_NN_BINARY_CROSS_ENTROPY_KERNEL_H

mindspore/ccsrc/backend/kernel_compiler/cpu/binary_cross_entropy_grad_kernel.cc

@@ -0,0 +1,78 @@
+/**
+ * Copyright 2021 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/binary_cross_entropy_grad_kernel.h"
+
+namespace mindspore {
+namespace kernel {
+template <typename T>
+void BinaryCrossEntropyGradCpuKernel::Launchkernel(const std::vector<AddressPtr> &inputs,
+                                                   const std::vector<AddressPtr> &outputs) {
+  T *input_x = reinterpret_cast<T *>(inputs[0]->addr);
+  T *input_y = reinterpret_cast<T *>(inputs[1]->addr);
+  T *dloss = reinterpret_cast<T *>(inputs[2]->addr);
+  T *weight = reinterpret_cast<T *>(inputs[3]->addr);
+  T *dx = reinterpret_cast<T *>(outputs[0]->addr);
+
+  T epsilon = static_cast<T>(1e-12);
+  T one = static_cast<T>(1);
+  if (reduction_ == 0) {
+    for (size_t i = 0; i < input_size_; i++) {
+      T denominator = ((input_x[i] * (one - input_x[i])) > epsilon) ? (input_x[i] * (one - input_x[i])) : epsilon;
+      T value = weight[i] * (input_x[i] - input_y[i]) / denominator;
+      dx[i] = value * dloss[i];
+    }
+  } else {
+    T dloss1 = dloss[0];
+    if (reduction_ == 1) {
+      dloss1 = dloss[0] / static_cast<T>(input_size_);
+    }
+    for (size_t i = 0; i < input_size_; i++) {
+      T denominator = ((input_x[i] * (one - input_x[i])) > epsilon) ? (input_x[i] * (one - input_x[i])) : epsilon;
+      T value = weight[i] * (input_x[i] - input_y[i]) / denominator;
+      dx[i] = value * dloss1;
+    }
+  }
+}
+
+bool BinaryCrossEntropyGradCpuKernel::Launch(const std::vector<AddressPtr> &inputs,
+                                             const std::vector<AddressPtr> &workspace,
+                                             const std::vector<AddressPtr> &outputs) {
+  if (input_size_ > 0) {
+    if (dtype_ == kNumberTypeFloat32) {
+      Launchkernel<float>(inputs, outputs);
+    } else if (dtype_ == kNumberTypeFloat16) {
+      Launchkernel<float16>(inputs, outputs);
+    }
+  }
+  return true;
+}
+
+void BinaryCrossEntropyGradCpuKernel::InitKernel(const CNodePtr &kernel_node) {
+  auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0);
+  for (size_t i = 0; i < input_shape.size(); i++) {
+    input_size_ *= input_shape[i];
+  }
+  string reduction = AnfAlgo::GetNodeAttr<string>(kernel_node, "reduction");
+  if (reduction == "none") {
+    reduction_ = 0;
+  } else if (reduction == "sum") {
+    reduction_ = 2;
+  }
+
+  dtype_ = AnfAlgo::GetInputDeviceDataType(kernel_node, 0);
+}
+}  // namespace kernel
+}  // namespace mindspore

mindspore/ccsrc/backend/kernel_compiler/cpu/binary_cross_entropy_grad_kernel.h

@@ -0,0 +1,61 @@
+/**
+ * Copyright 2021 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_NN_BINARY_CROSS_ENTROPY_GRAD_KERNEL_H
+#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_CPU_NN_BINARY_CROSS_ENTROPY_GRAD_KERNEL_H
+
+#include <vector>
+#include <string>
+#include "backend/kernel_compiler/cpu/cpu_kernel.h"
+#include "backend/kernel_compiler/cpu/cpu_kernel_factory.h"
+
+namespace mindspore {
+namespace kernel {
+class BinaryCrossEntropyGradCpuKernel : public CPUKernel {
+ public:
+  BinaryCrossEntropyGradCpuKernel() : input_size_(1), reduction_(1) {}
+  ~BinaryCrossEntropyGradCpuKernel() override = default;
+
+  void InitKernel(const CNodePtr &kernel_node) override;
+  bool Launch(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &workspace,
+              const std::vector<AddressPtr> &outputs) override;
+
+ private:
+  template <typename T>
+  void Launchkernel(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &outputs);
+
+  TypeId dtype_{kTypeUnknown};
+  size_t input_size_;
+  int reduction_;
+};
+MS_REG_CPU_KERNEL(BinaryCrossEntropyGrad,
+                  KernelAttr()
+                    .AddInputAttr(kNumberTypeFloat16)
+                    .AddInputAttr(kNumberTypeFloat16)
+                    .AddInputAttr(kNumberTypeFloat16)
+                    .AddInputAttr(kNumberTypeFloat16)
+                    .AddOutputAttr(kNumberTypeFloat16),
+                  BinaryCrossEntropyGradCpuKernel);
+MS_REG_CPU_KERNEL(BinaryCrossEntropyGrad,
+                  KernelAttr()
+                    .AddInputAttr(kNumberTypeFloat32)
+                    .AddInputAttr(kNumberTypeFloat32)
+                    .AddInputAttr(kNumberTypeFloat32)
+                    .AddInputAttr(kNumberTypeFloat32)
+                    .AddOutputAttr(kNumberTypeFloat32),
+                  BinaryCrossEntropyGradCpuKernel);
+}  // namespace kernel
+}  // namespace mindspore
+#endif  // MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_CPU_NN_BINARY_CROSS_ENTROPY_GRAD_KERNEL_H

mindspore/ops/operations/nn_ops.py

@@ -4619,7 +4619,7 @@ class BinaryCrossEntropy(PrimitiveWithInfer):
         Otherwise, the output is a scalar.
 
     Supported Platforms:
-        ``Ascend`` ``GPU``
+        ``Ascend`` ``GPU`` ``CPU``
 
     Examples:
         >>> import mindspore

tests/st/ops/cpu/test_binary_cross_entropy_op.py

@@ -0,0 +1,141 @@
+# Copyright 2021 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 import composite as C
+from mindspore.ops import operations as P
+
+context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
+
+class Net(nn.Cell):
+    def __init__(self, reduction="none"):
+        super(Net, self).__init__()
+        self.BinaryCrossEntropy = P.BinaryCrossEntropy(reduction)
+
+    def construct(self, x, y, weight):
+        return self.BinaryCrossEntropy(x, y, weight)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu
+@pytest.mark.env_onecard
+def test_binary_cross_entropy_loss():
+    np.random.seed(42)
+    prediction = np.random.rand(20).astype(np.float32)
+    target = np.random.rand(20).astype(np.float32)
+    weight = np.random.rand(20).astype(np.float32)
+    reduction = "none"
+    net = Net(reduction)
+    loss = net(Tensor(prediction), Tensor(target), Tensor(weight))
+    expect = [0.09555826, 1.2861121, 0.03518666, 0.6969416, 0.24313456, 0.99062896,
+              0.19205657, 0.5465214, 0.36964455, 0.21999404, 2.2953863, 2.2566645,
+              1.5803775, 1.3266402, 0.9883408, 1.2997618, 0.05439841, 0.14389999,
+              0.03405444, 0.23934692]
+    assert np.allclose(loss.asnumpy(), expect)
+
+def test_binary_cross_entropy_loss_mean():
+    np.random.seed(42)
+    prediction = np.random.rand(20).astype(np.float32)
+    target = np.random.rand(20).astype(np.float32)
+    weight = np.random.rand(20).astype(np.float32)
+    reduction = "mean"
+    net = Net(reduction)
+    loss = net(Tensor(prediction), Tensor(target), Tensor(weight))
+    expect = [0.7447324991226196]
+    assert loss.asnumpy() == expect
+
+def test_binary_cross_entropy_loss_sum():
+    np.random.seed(42)
+    prediction = np.random.rand(20).astype(np.float32)
+    target = np.random.rand(20).astype(np.float32)
+    weight = np.random.rand(20).astype(np.float32)
+    reduction = "sum"
+    net = Net(reduction)
+    loss = net(Tensor(prediction), Tensor(target), Tensor(weight))
+    expect = [14.894649505615234]
+    assert loss.asnumpy() == expect
+
+def test_binary_cross_entropy_loss_16():
+    np.random.seed(42)
+    prediction = np.random.rand(20).astype(np.float16)
+    target = np.random.rand(20).astype(np.float16)
+    weight = np.random.rand(20).astype(np.float16)
+    reduction = "none"
+    net = Net(reduction)
+    loss = net(Tensor(prediction), Tensor(target), Tensor(weight))
+    expect = [0.09552, 1.28613, 0.0351868, 0.696777, 0.243164, 0.990234,
+              0.192139, 0.546875, 0.370117, 0.219971, 2.29492, 2.25391,
+              1.58105, 1.32812, 0.987305, 1.30078, 0.0544434, 0.143921,
+              0.0340576, 0.239258]
+    assert np.allclose(loss.asnumpy(), expect)
+
+def test_binary_cross_entropy_loss_mean_16():
+    np.random.seed(42)
+    prediction = np.random.rand(20).astype(np.float16)
+    target = np.random.rand(20).astype(np.float16)
+    weight = np.random.rand(20).astype(np.float16)
+    reduction = "mean"
+    net = Net(reduction)
+    loss = net(Tensor(prediction), Tensor(target), Tensor(weight))
+    expect = [0.74462890625]
+    assert loss.asnumpy() == expect
+
+def test_binary_cross_entropy_loss_sum_16():
+    np.random.seed(42)
+    prediction = np.random.rand(20).astype(np.float16)
+    target = np.random.rand(20).astype(np.float16)
+    weight = np.random.rand(20).astype(np.float16)
+    reduction = "sum"
+    net = Net(reduction)
+    loss = net(Tensor(prediction), Tensor(target), Tensor(weight))
+    expect = [14.890625]
+    assert loss.asnumpy() == expect
+
+class Grad(nn.Cell):
+    def __init__(self, network):
+        super(Grad, self).__init__()
+        self.grad = C.GradOperation(get_all=True, sens_param=True)
+        self.network = network
+
+    def construct(self, x1, x2, sens, weight):
+        gout = self.grad(self.network)(x1, x2, sens, weight)
+        return gout
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu
+@pytest.mark.env_onecard
+def test_binary_cross_entropy_loss_grad():
+    np.random.seed(42)
+    prediction = np.random.rand(20).astype(np.float32)
+    target = np.random.rand(20).astype(np.float32)
+    sens = np.random.rand(20).astype(np.float32)
+    weight = np.random.rand(20).astype(np.float32)
+    reduction = "none"
+    grad = Grad(Net(reduction))
+    dx = grad(Tensor(prediction), Tensor(target), Tensor(sens), Tensor(weight))
+
+    dx1_expect = [-4.80516590e-02, 2.32625079e+00, 6.38972521e-02, 3.13642323e-01,
+                  -1.65661633e-01, -1.71821892e+00, -1.13685496e-01, 1.26669514e+00,
+                  1.47891801e-03, 5.83921909e-01, -2.17992840e+01, 4.21899414e+00,
+                  2.85430793e-02, -3.21346498e+00, -2.22674108e+00, -2.80453944e+00,
+                  -1.19787852e-04, 2.48514321e-02, -1.66696273e-02, -2.71965731e-02]
+
+    assert np.allclose(dx[0].asnumpy(), dx1_expect)