[MLU] add huber_loss kernel. (#46455)

paddle/fluid/operators/huber_loss_op_mlu.cc

+/* Copyright (c) 2022 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. */
+
+#include "paddle/fluid/framework/op_registry.h"
+#include "paddle/fluid/operators/mlu/mlu_baseop.h"
+
+namespace paddle {
+namespace operators {
+
+using Tensor = phi::DenseTensor;
+
+template <typename T>
+class HuberLossMLUKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    auto& dev_ctx = GetDevCtxFromCTX(ctx);
+    auto* x = ctx.Input<Tensor>("X");
+    auto* y = ctx.Input<Tensor>("Y");
+    auto* residual = ctx.Output<Tensor>("Residual");
+    auto* out = ctx.Output<Tensor>("Out");
+    auto delta = ctx.Attr<float>("delta");
+
+    auto place = ctx.GetPlace();
+
+    // compute y-x
+    cnnlDataType_t data_type = ToCnnlDataType<T>();
+    residual->mutable_data<T>(x->dims(), place);
+    MLUCnnlTensorDesc x_desc(*x);
+    MLUCnnlOpTensorDesc sub_op_desc(
+        CNNL_OP_TENSOR_SUB, data_type, CNNL_NOT_PROPAGATE_NAN);
+    MLUCnnl::OpTensor(ctx,
+                      sub_op_desc.get(),
+                      x_desc.get(),
+                      GetBasePtr(y),
+                      x_desc.get(),
+                      GetBasePtr(x),
+                      x_desc.get(),
+                      GetBasePtr(residual),
+                      data_type);
+
+    // compute smoothl1loss
+    out->mutable_data<T>(x->dims(), place);
+    cnnlSmoothL1LossAlgorithm_t smoothl1_algo =
+        CNNL_SMOOTHL1LOSS_REDUCTION_NONE;  // defines whether to do reduction
+                                           // here
+    MLUCnnl::SmoothL1LossForward(ctx,
+                                 x_desc.get(),
+                                 GetBasePtr(x),
+                                 x_desc.get(), /* target has same shape as x */
+                                 GetBasePtr(y),
+                                 static_cast<float>(delta),
+                                 smoothl1_algo,
+                                 x_desc.get(), /* out has same shape as x */
+                                 GetBasePtr(out));
+
+    // compute multiply by delta
+    framework::Tensor scale_tensor, bias_tensor;
+    scale_tensor = ctx.AllocateTmpTensor<T, MLUDeviceContext>({1}, dev_ctx);
+    bias_tensor = ctx.AllocateTmpTensor<T, MLUDeviceContext>({1}, dev_ctx);
+    FillMLUTensorWithHostValue(ctx, static_cast<T>(delta), &scale_tensor);
+    FillMLUTensorWithHostValue(ctx, static_cast<T>(0.f), &bias_tensor);
+    const int axis = std::max(out->dims().size() - 1, 0);
+
+    MLUCnnlTensorDesc scale_desc(scale_tensor);
+    MLUCnnlTensorDesc bias_desc(bias_tensor);
+    MLUCnnlTensorDesc out_desc(*out);
+    MLUCnnl::Scale(ctx,
+                   axis,
+                   out_desc.get(),
+                   GetBasePtr(out),
+                   scale_desc.get(),
+                   GetBasePtr(&scale_tensor),
+                   bias_desc.get(),
+                   GetBasePtr(&bias_tensor),
+                   out_desc.get(),
+                   GetBasePtr(out));
+  }
+};
+
+template <typename T>
+class HuberLossGradMLUKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    auto& dev_ctx = GetDevCtxFromCTX(ctx);
+    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");
+
+    auto place = ctx.GetPlace();
+
+    Tensor t_grad_rd;
+    t_grad_rd =
+        ctx.AllocateTmpTensor<T, MLUDeviceContext>(residual->dims(), dev_ctx);
+    MLUCnnlTensorDesc t_grad_rd_desc(t_grad_rd);
+    if (dx || dy) {
+      Tensor t_zero;
+      t_zero =
+          ctx.AllocateTmpTensor<T, MLUDeviceContext>(residual->dims(), dev_ctx);
+      FillMLUTensorWithHostValue(ctx, static_cast<T>(0.f), &t_zero);
+
+      MLUCnnlTensorDesc residual_desc(*residual);
+      MLUCnnlTensorDesc dout_desc(*dout);
+
+      cnnlSmoothL1LossAlgorithm_t smoothl1_algo =
+          CNNL_SMOOTHL1LOSS_REDUCTION_NONE;  // defines whether to do reduction
+                                             // here
+      MLUCnnl::SmoothL1LossBackward(ctx,
+                                    residual_desc.get(),
+                                    GetBasePtr(residual),
+                                    residual_desc.get(),
+                                    GetBasePtr(&t_zero),
+                                    dout_desc.get(),
+                                    GetBasePtr(dout),
+                                    static_cast<float>(delta),
+                                    smoothl1_algo,
+                                    t_grad_rd_desc.get(),
+                                    GetBasePtr(&t_grad_rd));
+    }
+    // compute multiply by delta
+    framework::Tensor scale_tensor, bias_tensor;
+    scale_tensor = ctx.AllocateTmpTensor<T, MLUDeviceContext>({1}, dev_ctx);
+    bias_tensor = ctx.AllocateTmpTensor<T, MLUDeviceContext>({1}, dev_ctx);
+
+    FillMLUTensorWithHostValue(ctx, static_cast<T>(0.f), &bias_tensor);
+    const int axis = std::max(t_grad_rd.dims().size() - 1, 0);
+
+    MLUCnnlTensorDesc scale_desc(scale_tensor);
+    MLUCnnlTensorDesc bias_desc(bias_tensor);
+
+    if (dx) {
+      dx->mutable_data<T>(place);
+      FillMLUTensorWithHostValue(ctx, static_cast<T>(-delta), &scale_tensor);
+      MLUCnnlTensorDesc out_desc(*dx);
+      MLUCnnl::Scale(ctx,
+                     axis,
+                     t_grad_rd_desc.get(),
+                     GetBasePtr(&t_grad_rd),
+                     scale_desc.get(),
+                     GetBasePtr(&scale_tensor),
+                     bias_desc.get(),
+                     GetBasePtr(&bias_tensor),
+                     out_desc.get(),
+                     GetBasePtr(dx));
+    }
+    if (dy) {
+      dy->mutable_data<T>(place);
+      FillMLUTensorWithHostValue(ctx, static_cast<T>(delta), &scale_tensor);
+      MLUCnnlTensorDesc out_desc(*dy);
+      MLUCnnl::Scale(ctx,
+                     axis,
+                     t_grad_rd_desc.get(),
+                     GetBasePtr(&t_grad_rd),
+                     scale_desc.get(),
+                     GetBasePtr(&scale_tensor),
+                     bias_desc.get(),
+                     GetBasePtr(&bias_tensor),
+                     out_desc.get(),
+                     GetBasePtr(dy));
+    }
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+namespace plat = paddle::platform;
+
+REGISTER_OP_MLU_KERNEL(huber_loss,
+                       ops::HuberLossMLUKernel<float>,
+                       ops::HuberLossMLUKernel<plat::float16>);
+REGISTER_OP_MLU_KERNEL(huber_loss_grad,
+                       ops::HuberLossGradMLUKernel<float>,
+                       ops::HuberLossGradMLUKernel<plat::float16>);

paddle/fluid/operators/mlu/mlu_baseop.cc

@@ -4725,6 +4725,78 @@ MLURNNDesc::~MLURNNDesc() {
                                                  output));
 }
 
+/* static */ void MLUCnnl::SmoothL1LossForward(
+    const ExecutionContext& ctx,
+    const cnnlTensorDescriptor_t x_desc,
+    const void* x,
+    const cnnlTensorDescriptor_t t_desc,
+    const void* target,
+    const float beta,
+    const cnnlSmoothL1LossAlgorithm_t algorithm,
+    const cnnlTensorDescriptor_t y_desc,
+    void* y) {
+  cnnlHandle_t handle = GetHandleFromCTX(ctx);
+
+  size_t workspace_size;
+  PADDLE_ENFORCE_MLU_SUCCESS(cnnlGetSmoothL1LossForwardWorkspaceSize(
+      handle, x_desc, algorithm, &workspace_size));
+
+  auto& dev_ctx = GetDevCtxFromCTX(ctx);
+  Tensor workspace = ctx.AllocateTmpTensor<int8_t, MLUDeviceContext>(
+      {static_cast<int64_t>(workspace_size)}, dev_ctx);
+  void* workspace_ptr = workspace.mutable_data(ctx.GetPlace());
+
+  PADDLE_ENFORCE_MLU_SUCCESS(cnnlSmoothL1LossForward_v2(handle,
+                                                        x_desc,
+                                                        x,
+                                                        t_desc,
+                                                        target,
+                                                        beta,
+                                                        algorithm,
+                                                        workspace_ptr,
+                                                        workspace_size,
+                                                        y_desc,
+                                                        y));
+}
+
+/* static */ void MLUCnnl::SmoothL1LossBackward(
+    const ExecutionContext& ctx,
+    const cnnlTensorDescriptor_t x_desc,
+    const void* x,
+    const cnnlTensorDescriptor_t target_desc,
+    const void* target,
+    const cnnlTensorDescriptor_t dy_desc,
+    const void* dy,
+    const float beta,
+    const cnnlSmoothL1LossAlgorithm_t algorithm,
+    const cnnlTensorDescriptor_t dx_desc,
+    void* dx) {
+  cnnlHandle_t handle = GetHandleFromCTX(ctx);
+
+  size_t workspace_size;
+  PADDLE_ENFORCE_MLU_SUCCESS(cnnlGetSmoothL1LossBackwardWorkspaceSize(
+      handle, x_desc, algorithm, &workspace_size));
+
+  auto& dev_ctx = GetDevCtxFromCTX(ctx);
+  Tensor workspace = ctx.AllocateTmpTensor<int8_t, MLUDeviceContext>(
+      {static_cast<int64_t>(workspace_size)}, dev_ctx);
+  void* workspace_ptr = workspace.mutable_data(ctx.GetPlace());
+
+  PADDLE_ENFORCE_MLU_SUCCESS(cnnlSmoothL1LossBackward_v2(handle,
+                                                         x_desc,
+                                                         x,
+                                                         target_desc,
+                                                         target,
+                                                         dy_desc,
+                                                         dy,
+                                                         beta,
+                                                         algorithm,
+                                                         workspace_ptr,
+                                                         workspace_size,
+                                                         dx_desc,
+                                                         dx));
+}
+
 /* static */ void MLUCnnl::EmbeddingForward(
     const ExecutionContext& ctx,
     const int padding_idx,

paddle/fluid/operators/mlu/mlu_baseop.h

@@ -2042,6 +2042,28 @@ class MLUCnnl {
                               const cnnlTensorDescriptor_t output_desc,
                               void* output);
 
+  static void SmoothL1LossForward(const ExecutionContext& ctx,
+                                  const cnnlTensorDescriptor_t x_desc,
+                                  const void* x,
+                                  const cnnlTensorDescriptor_t t_desc,
+                                  const void* target,
+                                  const float beta,
+                                  const cnnlSmoothL1LossAlgorithm_t algorithm,
+                                  const cnnlTensorDescriptor_t y_desc,
+                                  void* y);
+
+  static void SmoothL1LossBackward(const ExecutionContext& ctx,
+                                   const cnnlTensorDescriptor_t x_desc,
+                                   const void* x,
+                                   const cnnlTensorDescriptor_t target_desc,
+                                   const void* target,
+                                   const cnnlTensorDescriptor_t dy_desc,
+                                   const void* dy,
+                                   const float beta,
+                                   const cnnlSmoothL1LossAlgorithm_t algorithm,
+                                   const cnnlTensorDescriptor_t dx_desc,
+                                   void* dx);
+
   static void EmbeddingForward(const ExecutionContext& ctx,
                                const int padding_idx,
                                const cnnlTensorDescriptor_t weight_desc,

python/paddle/fluid/tests/unittests/mlu/test_huber_loss_op_mlu.py

+#   Copyright (c) 2022 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
+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(OpTest):
+
+    def setUp(self):
+        self.op_type = 'huber_loss'
+        self.set_mlu()
+        self.python_api = paddle.fluid.layers.huber_loss
+        self.python_out_sig = ["Out"]
+        self.delta = 1.0
+        self.init_input()
+        shape = self.set_shape()
+        residual = self.inputs['Y'] - self.inputs['X']
+        loss = np.vectorize(huber_loss_forward)(residual,
+                                                self.delta).astype('float32')
+        self.attrs = {'delta': self.delta}
+        self.outputs = {'Residual': residual, 'Out': loss.reshape(shape)}
+
+    def init_input(self):
+        shape = self.set_shape()
+        self.inputs = {
+            'X': np.random.uniform(0, 1., shape).astype('float32'),
+            'Y': np.random.uniform(0, 1., shape).astype('float32'),
+        }
+
+    def set_mlu(self):
+        self.__class__.use_mlu = True
+        self.place = paddle.MLUPlace(0)
+
+    def set_shape(self):
+        return (100, 1)
+
+    def test_check_output(self):
+        self.check_output_with_place(self.place, atol=1e-3)
+
+    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',
+                                   max_relative_error=0.008,
+                                   no_grad_set=set("residual"))
+
+    def test_check_grad_ingore_y(self):
+        self.check_grad_with_place(self.place, ['X'],
+                                   'Out',
+                                   max_relative_error=0.008,
+                                   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)
+
+
+class TestHuberLossOpError(unittest.TestCase):
+
+    def test_errors(self):
+        with program_guard(Program(), Program()):
+            # the input and label must be Variable
+            xw = np.random.random((6, 6)).astype("float32")
+            xr = fluid.data(name='xr', shape=[None, 6], dtype="float32")
+            lw = np.random.random((6, 6)).astype("float32")
+            lr = fluid.data(name='lr', shape=[None, 6], dtype="float32")
+            delta = 1.0
+            self.assertRaises(TypeError, fluid.layers.huber_loss, xr, lw, delta)
+            self.assertRaises(TypeError, fluid.layers.huber_loss, xw, lr, delta)
+
+            # the dtype of input and label must be float32 or float64
+            xw2 = fluid.data(name='xw2', shape=[None, 6], dtype="int32")
+            lw2 = fluid.data(name='lw2', shape=[None, 6], dtype="int32")
+            self.assertRaises(TypeError, fluid.layers.huber_loss, xw2, lr,
+                              delta)
+            self.assertRaises(TypeError, fluid.layers.huber_loss, xr, lw2,
+                              delta)
+
+
+if __name__ == '__main__':
+    unittest.main()