add huber_loss for kunlun (#38589)

* add huber_loss for kunlun

* update xpu.cmake

* update unitests

* update unitests

* update elementwise_add

* update elementwise_add

* update elementwise_add

paddle/fluid/operators/huber_loss_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 "paddle/fluid/operators/huber_loss_op.h"
+
+namespace paddle {
+namespace operators {
+
+using Tensor = framework::Tensor;
+
+template <typename T>
+class HuberLossXPUKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    auto* in0 = ctx.Input<Tensor>("X");
+    auto* in1 = ctx.Input<Tensor>("Y");
+    auto* residual = ctx.Output<Tensor>("Residual");
+    auto* out = ctx.Output<Tensor>("Out");
+    auto delta = ctx.Attr<float>("delta");
+
+    auto residual_data = residual->mutable_data<T>(ctx.GetPlace());
+    auto out_data = out->mutable_data<T>(ctx.GetPlace());
+    auto in0_data = in0->data<T>();
+    auto in1_data = in1->data<T>();
+
+    auto& dev_ctx =
+        ctx.template device_context<paddle::platform::XPUDeviceContext>();
+    int r = xpu::huber_loss<T>(dev_ctx.x_context(), in0_data, in1_data,
+                               residual_data, out_data, in0->numel(), 1, delta);
+    PADDLE_ENFORCE_EQ(r, XPU_SUCCESS, platform::errors::External(
+                                          "XPU API(huber_loss) return wrong "
+                                          "value[%d %s]",
+                                          r, XPUAPIErrorMsg[r]));
+  }
+};
+
+template <typename T>
+class HuberLossGradXPUKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    auto* residual = ctx.Input<Tensor>("Residual");
+    auto* dout = ctx.Input<Tensor>(framework::GradVarName("Out"));
+    auto* dx = ctx.Output<Tensor>(framework::GradVarName("X"));
+    auto* dy = ctx.Output<Tensor>(framework::GradVarName("Y"));
+    auto delta = ctx.Attr<float>("delta");
+
+    T* dx_data = nullptr;
+    T* dy_data = nullptr;
+    if (dx) {
+      dx_data = dx->mutable_data<T>(ctx.GetPlace());
+    }
+    if (dy) {
+      dy_data = dy->mutable_data<T>(ctx.GetPlace());
+    }
+    auto dout_data = dout->data<T>();
+    auto residual_data = residual->data<T>();
+    auto& dev_ctx =
+        ctx.template device_context<paddle::platform::XPUDeviceContext>();
+    int r =
+        xpu::huber_loss_grad<T>(dev_ctx.x_context(), residual_data, dout_data,
+                                dx_data, dy_data, dout->numel(), 1, delta);
+    PADDLE_ENFORCE_EQ(
+        r, XPU_SUCCESS,
+        platform::errors::External("XPU API(huber_loss_grad) return wrong "
+                                   "value[%d %s]",
+                                   r, XPUAPIErrorMsg[r]));
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+namespace plat = paddle::platform;
+
+REGISTER_OP_XPU_KERNEL(huber_loss, ops::HuberLossXPUKernel<float>);
+REGISTER_OP_XPU_KERNEL(huber_loss_grad, ops::HuberLossGradXPUKernel<float>);
+
+#endif

paddle/fluid/platform/device/xpu/xpu2_op_list.h

@@ -192,6 +192,9 @@ XPUOpMap& get_kl2_ops() {
       {"hard_swish_grad",
        XPUKernelSet({pOpKernelType(vartype::FP32, XPUPlace()),
                      pOpKernelType(vartype::FP16, XPUPlace())})},
+      {"huber_loss_grad",
+       XPUKernelSet({pOpKernelType(vartype::FP32, XPUPlace())})},
+      {"huber_loss", XPUKernelSet({pOpKernelType(vartype::FP32, XPUPlace())})},
       {"iou_similarity",
        XPUKernelSet({pOpKernelType(vartype::FP32, XPUPlace())})},
       {"label_smooth",

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

+#   Copyright (c) 2018 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 import OpTest
+from op_test_xpu import XPUOpTest
+import paddle
+import paddle.fluid as fluid
+from paddle.fluid import compiler, Program, program_guard
+
+paddle.enable_static()
+
+
+def huber_loss_forward(val, delta):
+    abs_val = abs(val)
+    if abs_val <= delta:
+        return 0.5 * val * val
+    else:
+        return delta * (abs_val - 0.5 * delta)
+
+
+class TestHuberLossOp(XPUOpTest):
+    def setUp(self):
+        self.set_xpu()
+        self.op_type = 'huber_loss'
+        self.place = paddle.XPUPlace(0)
+
+        self.init_dtype()
+
+        self.set_inputs()
+        self.set_attrs()
+        self.set_outputs()
+
+    def set_inputs(self):
+        shape = self.set_shape()
+        x = np.random.uniform(0, 1., shape).astype(self.dtype)
+        y = np.random.uniform(0, 1., shape).astype(self.dtype)
+        self.inputs = {
+            'X': OpTest.np_dtype_to_fluid_dtype(x),
+            'Y': OpTest.np_dtype_to_fluid_dtype(y)
+        }
+
+    def set_attrs(self):
+        self.attrs = {'delta': 0.5}
+
+    def set_outputs(self):
+        delta = self.attrs['delta']
+        shape = self.set_shape()
+        residual = self.inputs['Y'] - self.inputs['X']
+        loss = np.vectorize(huber_loss_forward)(residual,
+                                                delta).astype(self.dtype)
+        self.outputs = {'Residual': residual, 'Out': loss.reshape(shape)}
+
+    def set_shape(self):
+        return (100, 1)
+
+    def set_xpu(self):
+        self.__class__.use_xpu = True
+
+    def init_dtype(self):
+        self.dtype = np.float32
+
+    def test_check_output(self):
+        self.check_output_with_place(self.place)
+
+    def test_check_grad_normal(self):
+        self.check_grad_with_place(self.place, ['X', 'Y'], 'Out')
+
+    def test_check_grad_ingore_x(self):
+        self.check_grad_with_place(
+            self.place, ['Y'], 'Out', no_grad_set=set("residual"))
+
+    def test_check_grad_ingore_y(self):
+        self.check_grad_with_place(
+            self.place, ['X'], 'Out', no_grad_set=set('residual'))
+
+
+def TestHuberLossOp1(TestHuberLossOp):
+    def set_shape(self):
+        return (64)
+
+
+def TestHuberLossOp2(TestHuberLossOp):
+    def set_shape(self):
+        return (6, 6)
+
+
+def TestHuberLossOp3(TestHuberLossOp):
+    def set_shape(self):
+        return (6, 6, 1)
+
+
+if __name__ == '__main__':
+    unittest.main()