add sigmoid_cross_entropy_with_logits to kl1 (#38586)

* add sigmoid cross entropy with logits to kl1. test=kunlun

* add sigmoid cross entropy with logits to kl1. test=kunlun

paddle/fluid/operators/sigmoid_cross_entropy_with_logits_op_xpu.cc

+// Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+//
+// 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.
+
+#ifdef PADDLE_WITH_XPU
+
+#include <memory>
+#include <vector>
+
+#include "paddle/fluid/operators/sigmoid_cross_entropy_with_logits_op.h"
+#include "paddle/fluid/platform/device/xpu/xpu_header.h"
+
+namespace paddle {
+namespace operators {
+
+template <typename DeviceContext, typename T>
+class SigmoidCrossEntropyWithLogitsXPUKernel : public framework::OpKernel<T> {
+  using XPUType = typename XPUTypeTrait<T>::Type;
+
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    PADDLE_ENFORCE_EQ(
+        platform::is_xpu_place(context.GetPlace()), true,
+        platform::errors::Unavailable("This kernel only runs on XPU."));
+
+    // input and output data
+    auto* input = context.Input<Tensor>("X");
+    auto* label = context.Input<Tensor>("Label");
+    auto* output = context.Output<Tensor>("Out");
+    output->mutable_data<T>(context.GetPlace());
+    auto& dev_ctx = context.template device_context<DeviceContext>();
+
+    // attrs
+    bool normalize = context.Attr<bool>("normalize");
+    PADDLE_ENFORCE_EQ(
+        normalize, false,
+        platform::errors::InvalidArgument("normalize only support true now."));
+    int ignore_index = context.Attr<int>("ignore_index");
+    PADDLE_ENFORCE_EQ(ignore_index, kIgnoreIndex,
+                      platform::errors::InvalidArgument(
+                          "ignore_index only support %d now.", kIgnoreIndex));
+
+    int r = xpu::sigmoid_cross_entropy_with_logits(
+        dev_ctx.x_context(), reinterpret_cast<const XPUType*>(input->data<T>()),
+        reinterpret_cast<const XPUType*>(label->data<T>()),
+        reinterpret_cast<XPUType*>(output->data<T>()), 1, input->numel());
+    PADDLE_ENFORCE_EQ(
+        r, XPU_SUCCESS,
+        platform::errors::External("XPU sigmoid_cross_entropy_with_logits "
+                                   "kernel return wrong value[%d %s]",
+                                   r, XPUAPIErrorMsg[r]));
+  }
+};
+
+template <typename DeviceContext, typename T>
+class SigmoidCrossEntropyWithLogitsGradXPUKernel
+    : public framework::OpKernel<T> {
+  using XPUType = typename XPUTypeTrait<T>::Type;
+
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    PADDLE_ENFORCE_EQ(
+        platform::is_xpu_place(context.GetPlace()), true,
+        platform::errors::Unavailable("This kernel only runs on XPU."));
+
+    // input and output data
+    auto* input = context.Input<Tensor>("X");
+    auto* label = context.Input<Tensor>("Label");
+    auto* dy = context.Input<Tensor>(framework::GradVarName("Out"));
+    auto* dx = context.Output<Tensor>(framework::GradVarName("X"));
+    dx->mutable_data<T>(context.GetPlace());
+    auto& dev_ctx = context.template device_context<DeviceContext>();
+
+    int r = xpu::sigmoid_cross_entropy_with_logits_grad(
+        dev_ctx.x_context(), reinterpret_cast<const XPUType*>(input->data<T>()),
+        reinterpret_cast<const XPUType*>(label->data<T>()),
+        reinterpret_cast<const XPUType*>(dy->data<T>()),
+        reinterpret_cast<XPUType*>(dx->data<T>()), 1, input->numel());
+    PADDLE_ENFORCE_EQ(
+        r, XPU_SUCCESS,
+        platform::errors::External("XPU sigmoid_cross_entropy_with_logits_grad "
+                                   "kernel return wrong value[%d %s]",
+                                   r, XPUAPIErrorMsg[r]));
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+
+REGISTER_OP_XPU_KERNEL(sigmoid_cross_entropy_with_logits,
+                       ops::SigmoidCrossEntropyWithLogitsXPUKernel<
+                           paddle::platform::XPUDeviceContext, float>);
+
+REGISTER_OP_XPU_KERNEL(sigmoid_cross_entropy_with_logits_grad,
+                       ops::SigmoidCrossEntropyWithLogitsGradXPUKernel<
+                           paddle::platform::XPUDeviceContext, float>);
+
+#endif

paddle/fluid/platform/device/xpu/xpu1_op_list.h

@@ -249,6 +249,10 @@ XPUOpMap& get_kl1_ops() {
                               pOpKernelType(vartype::INT32, XPUPlace()),
                               pOpKernelType(vartype::BOOL, XPUPlace()),
                               pOpKernelType(vartype::FP32, XPUPlace())})},
+      {"sigmoid_cross_entropy_with_logits_grad",
+       XPUKernelSet({pOpKernelType(vartype::FP32, XPUPlace())})},
+      {"sigmoid_cross_entropy_with_logits",
+       XPUKernelSet({pOpKernelType(vartype::FP32, XPUPlace())})},
       {"sigmoid_grad",
        XPUKernelSet({pOpKernelType(vartype::FP32, XPUPlace())})},
       {"sigmoid", XPUKernelSet({pOpKernelType(vartype::FP32, XPUPlace())})},

python/paddle/fluid/tests/unittests/xpu/test_sigmoid_cross_entropy_with_logits_op_xpu.py

+#   Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+#
+# 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.
+
+from __future__ import print_function
+
+import unittest
+import numpy as np
+import sys
+sys.path.append("..")
+from op_test_xpu import OpTest, XPUOpTest
+from op_test import skip_check_grad_ci
+import paddle
+import paddle.fluid.core as core
+import paddle.fluid as fluid
+from paddle.fluid import compiler, Program, program_guard
+from paddle.fluid.framework import convert_np_dtype_to_dtype_
+
+from scipy.special import logit
+from scipy.special import expit
+
+paddle.enable_static()
+
+
+class TestSigmoidCrossEntropyWithLogitsOp1(XPUOpTest):
+    """Test sigmoid_cross_entropy_with_logit_op with binary label
+    """
+
+    def setUp(self):
+        self.op_type = "sigmoid_cross_entropy_with_logits"
+        self.set_xpu()
+        self.init_dtype()
+
+        batch_size = 64
+        num_classes = 20
+        self.inputs = {
+            'X': logit(
+                np.random.uniform(0, 1, (batch_size, num_classes))
+                .astype(self.dtype)),
+            'Label': np.random.randint(0, 2, (batch_size, num_classes))
+            .astype(self.dtype)
+        }
+
+        # Fw Pass is implemented as elementwise sigmoid followed by
+        # elementwise logistic loss
+        # Label * -log(sigmoid(X)) + (1 - label) * -log(1 - sigmoid(X))
+        sigmoid_X = expit(self.inputs['X'])
+        term1 = self.inputs['Label'] * np.log(sigmoid_X)
+        term2 = (1 - self.inputs['Label']) * np.log(1 - sigmoid_X)
+        self.outputs = {'Out': -term1 - term2}
+
+    def test_check_output(self):
+        self.check_output_with_place(self.place)
+
+    def test_check_grad(self):
+        self.check_grad_with_place(self.place, ['X'], 'Out')
+
+    def set_xpu(self):
+        self.__class__.use_xpu = True
+        self.place = paddle.XPUPlace(0)
+
+    def init_dtype(self):
+        self.dtype = np.float32
+
+
+class TestSigmoidCrossEntropyWithLogitsOp3(
+        TestSigmoidCrossEntropyWithLogitsOp1):
+    """Test sigmoid_cross_entropy_with_logit_op with probabalistic label
+    """
+
+    def setUp(self):
+        self.op_type = "sigmoid_cross_entropy_with_logits"
+        self.set_xpu()
+        self.init_dtype()
+
+        batch_size = 64
+        num_classes = 20
+        self.inputs = {
+            'X': logit(
+                np.random.uniform(0, 1, (batch_size, num_classes))
+                .astype(self.dtype)),
+            'Label': np.random.uniform(0, 1, (batch_size, num_classes))
+            .astype(self.dtype)
+        }
+
+        # Fw Pass is implemented as elementwise sigmoid followed by
+        # elementwise logistic loss
+        # Label * -log(sigmoid(X)) + (1 - label) * -log(1 - sigmoid(X))
+        sigmoid_X = expit(self.inputs['X'])
+        term1 = self.inputs['Label'] * np.log(sigmoid_X)
+        term2 = (1 - self.inputs['Label']) * np.log(1 - sigmoid_X)
+        self.outputs = {'Out': -term1 - term2}
+
+
+class TestSigmoidCrossEntropyWithLogitsOp5(
+        TestSigmoidCrossEntropyWithLogitsOp1):
+    """Test sigmoid_cross_entropy_with_logit_op with probabalistic label
+    """
+
+    def setUp(self):
+        self.op_type = "sigmoid_cross_entropy_with_logits"
+        self.set_xpu()
+        self.init_dtype()
+
+        batch_size = [10, 10]
+        num_classes = 20
+        self.inputs = {
+            'X': logit(
+                np.random.uniform(0, 1, tuple(batch_size + [num_classes]))
+                .astype(self.dtype)),
+            'Label': np.random.uniform(0, 1, tuple(batch_size + [num_classes]))
+            .astype(self.dtype)
+        }
+
+        # Fw Pass is implemented as elementwise sigmoid followed by
+        # elementwise logistic loss
+        # Label * -log(sigmoid(X)) + (1 - label) * -log(1 - sigmoid(X))
+        sigmoid_X = expit(self.inputs['X'])
+        term1 = self.inputs['Label'] * np.log(sigmoid_X)
+        term2 = (1 - self.inputs['Label']) * np.log(1 - sigmoid_X)
+        self.outputs = {'Out': -term1 - term2}
+
+
+class TestSigmoidCrossEntropyWithLogitsOp6(
+        TestSigmoidCrossEntropyWithLogitsOp1):
+    """Test sigmoid_cross_entropy_with_logit_op with binary label
+    """
+
+    def setUp(self):
+        self.op_type = "sigmoid_cross_entropy_with_logits"
+        self.set_xpu()
+        self.init_dtype()
+
+        batch_size = [10, 10]
+        num_classes = 20
+        self.inputs = {
+            'X': logit(
+                np.random.uniform(0, 1, tuple(batch_size + [num_classes]))
+                .astype(self.dtype)),
+            'Label': np.random.randint(0, 2, tuple(batch_size + [num_classes]))
+            .astype(self.dtype)
+        }
+
+        # Fw Pass is implemented as elementwise sigmoid followed by
+        # elementwise logistic loss
+        # Label * -log(sigmoid(X)) + (1 - label) * -log(1 - sigmoid(X))
+        sigmoid_X = expit(self.inputs['X'])
+        term1 = self.inputs['Label'] * np.log(sigmoid_X)
+        term2 = (1 - self.inputs['Label']) * np.log(1 - sigmoid_X)
+        self.outputs = {'Out': -term1 - term2}
+
+
+if __name__ == '__main__':
+    unittest.main()