update reduce_sum op on xpu (#29367)

* update reduce_sum op on xpu

* update reduce_sum op on xpu

* support running on xpu

paddle/fluid/operators/reduce_ops/reduce_sum_op_xpu.cc

@@ -16,6 +16,8 @@
 #include "paddle/fluid/operators/reduce_ops/reduce_sum_op.h"
 #include <memory>
 #include <string>
+#include "paddle/fluid/platform/xpu_header.h"
+
 namespace paddle {
 namespace operators {
 
@@ -27,87 +29,121 @@ class ReduceSumXPUKernel : public framework::OpKernel<T> {
         platform::is_xpu_place(context.GetPlace()), true,
         platform::errors::Unavailable("This kernel only runs on XPU."));
     bool reduce_all = context.Attr<bool>("reduce_all");
-    auto* input = context.Input<Tensor>("X");
-    auto* output = context.Output<Tensor>("Out");
-    output->mutable_data<T>(context.GetPlace());
+    auto dims = context.Attr<std::vector<int>>("dim");
+    auto* x = context.Input<Tensor>("X");
+    auto* y = context.Output<Tensor>("Out");
+    y->mutable_data<T>(context.GetPlace());
     auto& dev_ctx = context.template device_context<DeviceContext>();
+
+    int out_dtype = context.Attr<int>("out_dtype");
+    PADDLE_ENFORCE_EQ(
+        out_dtype == -1, true,
+        platform::errors::InvalidArgument(
+            "XPU only support out_dtype == -1 in reduce_sum op."));
+
+    const auto* x_data = x->data<T>();
+    auto* y_data = y->data<T>();
+    const auto& input_dim_size = x->dims().size();
+    std::vector<int> true_dims;
+    for (size_t i = 0; i < dims.size(); ++i) {
+      if (dims[i] < 0) {
+        true_dims.push_back(dims[i] + input_dim_size);
+      } else {
+        true_dims.push_back(dims[i]);
+      }
+    }
+
+    std::vector<int> reduce_dims;
+    std::vector<int> xdims((input_dim_size));
+    for (int i = 0; i < input_dim_size; ++i) {
+      xdims[i] = x->dims()[i];
+    }
     if (reduce_all) {
-      int input_len = input->numel();
-      int r = xpu::sum(dev_ctx.x_context(), input->data<T>(), output->data<T>(),
-                       input_len);
-      PADDLE_ENFORCE_EQ(r == xpu::Error_t::SUCCESS, true,
-                        platform::errors::External("XPU kernel error!"));
+      for (int i = 0; i < input_dim_size; ++i) {
+        reduce_dims.push_back(i);
+      }
     } else {
-      int ndim = input->dims().size();
-      std::vector<int> idims;
-      for (int i = 0; i < input->dims().size(); i++) {
-        idims.push_back(input->dims()[i]);
+      std::set<int> dims_set(true_dims.begin(), true_dims.end());
+      for (auto i = 0; i < input_dim_size; i++) {
+        if (dims_set.find(i) != dims_set.end()) {
+          if (x->dims()[i] != 1) {
+            reduce_dims.push_back(i);
           }
-      auto dims = context.Attr<std::vector<int>>("dim");
-      int rdim = dims.size();
-      int r =
-          xpu::reduce(dev_ctx.x_context(), input->data<T>(), output->data<T>(),
-                      idims.data(), ndim, dims.data(), rdim, xpu::REDUCE_SUM);
-      PADDLE_ENFORCE_EQ(r == xpu::Error_t::SUCCESS, true,
-                        platform::errors::External("XPU kernel error!"));
+        }
+      }
+    }
+
+    if (reduce_dims.size() == 0) {
+      int r = xpu::copy<T>(dev_ctx.x_context(), x_data, y_data,
+                           x->numel() * sizeof(T));
+      PADDLE_ENFORCE_EQ(
+          r == xpu::Error_t::SUCCESS, true,
+          platform::errors::External("XPU copy in reduce_sum op return "
+                                     "wrong value[%d %s].",
+                                     r, XPUAPIErrorMsg[r]));
+    } else {
+      int r = xpu::reduce_sum<T>(dev_ctx.x_context(), x_data, y_data, xdims,
+                                 reduce_dims);
+      PADDLE_ENFORCE_EQ(
+          r == xpu::Error_t::SUCCESS, true,
+          platform::errors::External("XPU reduce_sum in reduce_sum op return"
+                                     " wrong value[%d %s].",
+                                     r, XPUAPIErrorMsg[r]));
     }
   }
 };
+
 template <typename DeviceContext, typename T>
 class ReduceSumGradXPUKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& context) const override {
     auto dims = context.Attr<std::vector<int>>("dim");
     bool reduce_all = context.Attr<bool>("reduce_all");
-    auto* input0 = context.Input<Tensor>("X");
-    auto* input2 = context.Input<Tensor>(framework::GradVarName("Out"));
-    auto* output = context.Output<Tensor>(framework::GradVarName("X"));
-    output->mutable_data<T>(context.GetPlace());
-    const auto* input2_d = input2->data<T>();
-    auto* output_d = output->data<T>();
+    auto* x = context.Input<Tensor>("X");
+    auto* out = context.Input<Tensor>(framework::GradVarName("Out"));
+    auto* x_grad = context.Output<Tensor>(framework::GradVarName("X"));
+
+    int in_dtype = context.Attr<int>("in_dtype");
+    PADDLE_ENFORCE_EQ(
+        in_dtype == -1, true,
+        platform::errors::InvalidArgument(
+            "XPU only support in_dtype == -1 in reduce_sum_grad op."));
+
     auto& dev_ctx = context.template device_context<DeviceContext>();
-    int r = 0;
-    std::vector<int> idims;
-    int reduce_dim = 0;
-    if (reduce_all) {
-      idims.push_back(input0->numel());
-      idims.push_back(1);
-      idims.push_back(1);
-      r = xpu::reduce_grad(dev_ctx.x_context(), input2_d, output_d,
-                           idims.data(), idims.size(), &reduce_dim, 1,
-                           xpu::REDUCE_SUM);
-      PADDLE_ENFORCE_EQ(r == xpu::Error_t::SUCCESS, true,
-                        platform::errors::External("XPU kernel error!"));
-    } else if (dims.size() == 1) {
-      // handle reduce by one dimension
-      int reduce_dim_index = dims[0];
-      if (reduce_dim_index < 0) {
-        reduce_dim_index += input0->dims().size();
-      }
-      auto& input_dim = input0->dims();
-      int before_dim = 1;
-      for (int i = 0; i < reduce_dim_index; ++i) {
-        before_dim *= input_dim[i];
+    x_grad->mutable_data<T>(context.GetPlace());
+    const auto* out_data = out->data<T>();
+    auto* x_grad_data = x_grad->data<T>();
+
+    const auto& input_dim_size = x->dims().size();
+    std::vector<int> true_dims;
+    for (size_t i = 0; i < dims.size(); ++i) {
+      if (dims[i] < 0) {
+        true_dims.push_back(dims[i] + input_dim_size);
+      } else {
+        true_dims.push_back(dims[i]);
       }
-      int reduce_dim = input_dim[reduce_dim_index];
-      int after_dim = 1;
-      for (int i = reduce_dim_index + 1; i < input_dim.size(); ++i) {
-        after_dim *= input_dim[i];
     }
-      idims.push_back(before_dim);
-      idims.push_back(input_dim[reduce_dim_index]);
-      idims.push_back(after_dim);
-      reduce_dim = 1;
-      r = xpu::reduce_grad(dev_ctx.x_context(), input2_d, output_d,
-                           idims.data(), idims.size(), &reduce_dim, 1,
-                           xpu::REDUCE_SUM);
-      PADDLE_ENFORCE_EQ(r == xpu::Error_t::SUCCESS, true,
-                        platform::errors::External("XPU kernel error!"));
+
+    std::vector<int> ydims(input_dim_size);
+    std::vector<int> xdims((input_dim_size));
+    std::set<int> dims_set(true_dims.begin(), true_dims.end());
+    for (auto i = 0; i < input_dim_size; i++) {
+      xdims[i] = x->dims()[i];
+      if (dims_set.find(i) != dims_set.end() || reduce_all) {
+        ydims[i] = 1;
       } else {
-      PADDLE_THROW(
-          platform::errors::Unimplemented("unsupport reduce sum grad"));
+        ydims[i] = x->dims()[i];
       }
     }
+
+    int r = xpu::broadcast<T>(dev_ctx.x_context(), out_data, x_grad_data, ydims,
+                              xdims);
+    PADDLE_ENFORCE_EQ(
+        r == xpu::Error_t::SUCCESS, true,
+        platform::errors::External("XPU broadcast in reduce_sum_grad op return"
+                                   " wrong value[%d %s].",
+                                   r, XPUAPIErrorMsg[r]));
+  }
 };
 
 }  // namespace operators

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

@@ -18,7 +18,8 @@ import unittest
 import numpy as np
 import sys
 sys.path.append("..")
-from op_test import OpTest, skip_check_grad_ci
+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
@@ -26,180 +27,128 @@ from paddle.fluid import compiler, Program, program_guard
 from paddle.fluid.framework import convert_np_dtype_to_dtype_
 
 
-class TestSumOp(OpTest):
+class TestXPUReduceSumOp(XPUOpTest):
     def setUp(self):
-        self.op_type = "reduce_sum"
-        self.inputs = {'X': np.random.random((5, 6, 10)).astype("float64")}
-        self.attrs = {'use_xpu': True}
-        self.outputs = {'Out': self.inputs['X'].sum(axis=0)}
-
-    def test_check_output(self):
-        if paddle.is_compiled_with_xpu():
-            place = paddle.XPUPlace(0)
-            self.check_output_with_place(place)
-
-    def check_grad_(self):
-        self.check_grad(['X'], 'Out')
-
-
-class TestSumOp5D(OpTest):
-    def setUp(self):
-        self.op_type = "reduce_sum"
-        self.inputs = {
-            'X': np.random.random((1, 2, 5, 6, 10)).astype("float64")
+        self.init_op_type()
+        self.initTestCase()
+        self.use_xpu = True
+        self.use_mkldnn = False
+        self.attrs = {
+            'dim': self.axis,
+            'keep_dim': self.keep_dim,
+            'reduce_all': self.reduce_all
+        }
+        self.inputs = {'X': np.random.random(self.shape).astype("float32")}
+        if self.attrs['reduce_all']:
+            self.outputs = {'Out': self.inputs['X'].sum()}
+        else:
+            self.outputs = {
+                'Out': self.inputs['X'].sum(axis=self.axis,
+                                            keepdims=self.attrs['keep_dim'])
             }
-        self.attrs = {'use_xpu': True}
-        self.outputs = {'Out': self.inputs['X'].sum(axis=0)}
 
     def test_check_output(self):
         if paddle.is_compiled_with_xpu():
+            paddle.enable_static()
             place = paddle.XPUPlace(0)
             self.check_output_with_place(place)
 
     def test_check_grad(self):
-        self.check_grad(['X'], 'Out')
-
-
-class TestSumOp6D(OpTest):
-    def setUp(self):
-        self.op_type = "reduce_sum"
-        self.inputs = {
-            'X': np.random.random((1, 1, 2, 5, 6, 10)).astype("float64")
-        }
-        self.attrs = {'use_xpu': True}
-        self.outputs = {'Out': self.inputs['X'].sum(axis=0)}
-
-    def test_check_output(self):
         if paddle.is_compiled_with_xpu():
+            paddle.enable_static()
             place = paddle.XPUPlace(0)
-            self.check_output_with_place(place)
+            self.check_grad_with_place(place, ['X'], 'Out')
 
-    def test_check_grad(self):
-        self.check_grad(['X'], 'Out')
+    def init_op_type(self):
+        self.op_type = "reduce_sum"
+        self.use_mkldnn = False
+        self.keep_dim = False
+        self.reduce_all = False
 
+    def initTestCase(self):
+        self.shape = (5, 6, 10)
+        self.axis = (0, )
 
-class TestSumOp8D(OpTest):
-    def setUp(self):
-        self.op_type = "reduce_sum"
-        self.inputs = {
-            'X': np.random.random((1, 3, 1, 2, 1, 4, 3, 10)).astype("float64")
-        }
-        self.attrs = {'dim': (0, 3), 'use_xpu': True}
-        self.outputs = {'Out': self.inputs['X'].sum(axis=(0, 3))}
 
-    def test_check_output(self):
-        if paddle.is_compiled_with_xpu():
-            place = paddle.XPUPlace(0)
-            self.check_output_with_place(place)
+class TestSumOp5D(TestXPUReduceSumOp):
+    def initTestCase(self):
+        self.shape = (1, 2, 5, 6, 10)
+        self.axis = (0, )
 
-    def test_check_grad(self):
-        self.check_grad(['X'], 'Out')
 
+class TestSumOp6D(TestXPUReduceSumOp):
+    def initTestCase(self):
+        self.shape = (1, 1, 2, 5, 6, 10)
+        self.axis = (0, )
 
-class Test1DReduce(OpTest):
-    def setUp(self):
-        self.op_type = "reduce_sum"
-        self.inputs = {'X': np.random.random(120).astype("float64")}
-        self.attrs = {'use_xpu': True}
-        self.outputs = {'Out': self.inputs['X'].sum(axis=0)}
 
-    def test_check_output(self):
-        if paddle.is_compiled_with_xpu():
-            place = paddle.XPUPlace(0)
-            self.check_output_with_place(place)
+class TestSumOp8D(TestXPUReduceSumOp):
+    def initTestCase(self):
+        self.shape = (1, 3, 1, 2, 1, 4, 3, 10)
+        self.axis = (0, 3)
 
-    def test_check_grad(self):
-        self.check_grad(['X'], 'Out')
 
+class Test1DReduce(TestXPUReduceSumOp):
+    def initTestCase(self):
+        self.shape = 120
+        self.axis = (0, )
 
-class Test2DReduce0(Test1DReduce):
-    def setUp(self):
-        self.op_type = "reduce_sum"
-        self.attrs = {'dim': [0], 'use_xpu': True}
-        self.inputs = {'X': np.random.random((20, 10)).astype("float64")}
-        self.outputs = {'Out': self.inputs['X'].sum(axis=0)}
 
+class Test2DReduce0(TestXPUReduceSumOp):
+    def initTestCase(self):
+        self.shape = (20, 10)
+        self.axis = (0, )
 
-class Test2DReduce1(Test1DReduce):
-    def setUp(self):
-        self.op_type = "reduce_sum"
-        self.attrs = {'dim': [1], 'use_xpu': True}
-        self.inputs = {'X': np.random.random((20, 10)).astype("float64")}
-        self.outputs = {
-            'Out': self.inputs['X'].sum(axis=tuple(self.attrs['dim']))
-        }
 
+class Test2DReduce1(TestXPUReduceSumOp):
+    def initTestCase(self):
+        self.shape = (20, 10)
+        self.axis = (1, )
 
-class Test3DReduce0(Test1DReduce):
-    def setUp(self):
-        self.op_type = "reduce_sum"
-        self.attrs = {'dim': [1], 'use_xpu': True}
-        self.inputs = {'X': np.random.random((5, 6, 7)).astype("float64")}
-        self.outputs = {
-            'Out': self.inputs['X'].sum(axis=tuple(self.attrs['dim']))
-        }
 
+class Test3DReduce0(TestXPUReduceSumOp):
+    def initTestCase(self):
+        self.shape = (5, 6, 7)
+        self.axis = (1, )
 
-class Test3DReduce1(Test1DReduce):
-    def setUp(self):
-        self.op_type = "reduce_sum"
-        self.attrs = {'dim': [2], 'use_xpu': True}
-        self.inputs = {'X': np.random.random((5, 6, 7)).astype("float64")}
-        self.outputs = {
-            'Out': self.inputs['X'].sum(axis=tuple(self.attrs['dim']))
-        }
 
+class Test3DReduce1(TestXPUReduceSumOp):
+    def initTestCase(self):
+        self.shape = (5, 6, 7)
+        self.axis = (2, )
 
-class Test3DReduce2(Test1DReduce):
-    def setUp(self):
-        self.op_type = "reduce_sum"
-        self.attrs = {'dim': [-2], 'use_xpu': True}
-        self.inputs = {'X': np.random.random((5, 6, 7)).astype("float64")}
-        self.outputs = {
-            'Out': self.inputs['X'].sum(axis=tuple(self.attrs['dim']))
-        }
 
+class Test3DReduce2(TestXPUReduceSumOp):
+    def initTestCase(self):
+        self.shape = (5, 6, 7)
+        self.axis = (-2, )
 
-class Test3DReduce3(Test1DReduce):
-    def setUp(self):
-        self.op_type = "reduce_sum"
-        self.attrs = {'dim': [1, 2], 'use_xpu': True}
-        self.inputs = {'X': np.random.random((5, 6, 7)).astype("float64")}
-        self.outputs = {
-            'Out': self.inputs['X'].sum(axis=tuple(self.attrs['dim']))
-        }
 
+class Test3DReduce3(TestXPUReduceSumOp):
+    def initTestCase(self):
+        self.shape = (5, 6, 7)
+        self.axis = (1, 2)
 
-class TestKeepDimReduce(Test1DReduce):
-    def setUp(self):
-        self.op_type = "reduce_sum"
-        self.inputs = {'X': np.random.random((5, 6, 10)).astype("float64")}
-        self.attrs = {'dim': [1], 'keep_dim': True, 'use_xpu': True}
-        self.outputs = {
-            'Out': self.inputs['X'].sum(axis=tuple(self.attrs['dim']),
-                                        keepdims=self.attrs['keep_dim'])
-        }
 
+class TestKeepDimReduce(TestXPUReduceSumOp):
+    def initTestCase(self):
+        self.shape = (5, 6, 10)
+        self.axis = (1, )
+        self.keep_dim = True
 
-class TestKeepDim8DReduce(Test1DReduce):
-    def setUp(self):
-        self.op_type = "reduce_sum"
-        self.inputs = {
-            'X': np.random.random((2, 5, 3, 2, 2, 3, 4, 2)).astype("float64")
-        }
-        self.attrs = {'dim': (3, 4, 5), 'keep_dim': True, 'use_xpu': True}
-        self.outputs = {
-            'Out': self.inputs['X'].sum(axis=tuple(self.attrs['dim']),
-                                        keepdims=self.attrs['keep_dim'])
-        }
 
+class TestKeepDim8DReduce(TestXPUReduceSumOp):
+    def initTestCase(self):
+        self.shape = (2, 5, 3, 2, 2, 3, 4, 2)
+        self.axis = (3, 4, 5)
+        self.keep_dim = True
 
-class TestReduceAll(Test1DReduce):
-    def setUp(self):
-        self.op_type = "reduce_sum"
-        self.inputs = {'X': np.random.random((5, 6, 2, 10)).astype("float64")}
-        self.attrs = {'reduce_all': True, 'use_xpu': True}
-        self.outputs = {'Out': self.inputs['X'].sum()}
+
+class TestReduceAll(TestXPUReduceSumOp):
+    def initTestCase(self):
+        self.shape = (5, 6, 2, 10)
+        self.axis = (0, )
+        self.reduce_all = True
 
 
 if __name__ == '__main__':