xpu mul unittest *test=kunlun (#41140)

paddle/fluid/operators/mul_op_xpu.cc

@@ -19,6 +19,8 @@ limitations under the License. */
 #include <unordered_map>
 #include <vector>
 #include "paddle/fluid/framework/op_registry.h"
+#include "paddle/fluid/operators/xpu_api_wrapper.h"
+#include "paddle/fluid/platform/device/device_wrapper.h"
 
 namespace paddle {
 namespace operators {
@@ -28,6 +30,8 @@ using framework::Tensor;
 
 template <typename DeviceContext, typename T>
 class MulXPUKernel : public framework::OpKernel<T> {
+  using XPUType = typename XPUTypeTrait<T>::Type;
+
  public:
   void Compute(const framework::ExecutionContext& context) const override {
     const Tensor* x = context.Input<Tensor>("X");
@@ -62,14 +66,15 @@ class MulXPUKernel : public framework::OpKernel<T> {
     const T* data_b = y_matrix.data<T>();
     T* data_c = z->data<T>();
     auto& dev_ctx = context.template device_context<DeviceContext>();
-    int ret = xpu::fc_int16(dev_ctx.x_context(), trans_a, trans_b, m, n, k,
-                            alpha, data_a, data_b, beta, data_c);
-    PADDLE_ENFORCE_EQ(
-        ret, XPU_SUCCESS,
-        platform::errors::External(
-            "XPU API return wrong value[%d], please check whether "
-            "Baidu Kunlun Card is properly installed.",
-            ret));
+
+    int ret = xpu_fc_wrapper<XPUType, int16_t>(
+        dev_ctx.x_context(), reinterpret_cast<const XPUType*>(data_a),
+        reinterpret_cast<const XPUType*>(data_b),
+        reinterpret_cast<XPUType*>(data_c), m, n, k, trans_a, trans_b, nullptr,
+        nullptr, nullptr, k, n, n, alpha, beta, nullptr,
+        xpu::Activation_t::LINEAR);
+    PADDLE_ENFORCE_XDNN_SUCCESS(ret, "xpu_fc_wrapper");
+
     if (z_dim.size() != 2) {
       z->Resize(z_dim);
     }
@@ -78,6 +83,8 @@ class MulXPUKernel : public framework::OpKernel<T> {
 
 template <typename DeviceContext, typename T>
 class MulGradXPUKernel : public framework::OpKernel<T> {
+  using XPUType = typename XPUTypeTrait<T>::Type;
+
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
     int x_num_col_dims = ctx.template Attr<int>("x_num_col_dims");
@@ -126,14 +133,14 @@ class MulGradXPUKernel : public framework::OpKernel<T> {
       const T* data_a = dout->data<T>();
       const T* data_b = y_matrix.data<T>();
       T* data_c = dx_matrix.data<T>();
-      int ret =
-          xpu::gemm_int16(dev_ctx.x_context(), trans_a, trans_b, m, n, k, alpha,
-                          data_a, lda, data_b, ldb, beta, data_c, ldc);
-      PADDLE_ENFORCE_EQ(ret, XPU_SUCCESS,
-                        platform::errors::External(
-                            "XPU API return wrong value[%d], please check "
-                            "where Baidu Kunlun Card is properly installed.",
-                            ret));
+
+      int ret = xpu_fc_wrapper<XPUType, int16_t>(
+          dev_ctx.x_context(), reinterpret_cast<const XPUType*>(data_a),
+          reinterpret_cast<const XPUType*>(data_b),
+          reinterpret_cast<XPUType*>(data_c), m, n, k, trans_a, trans_b,
+          nullptr, nullptr, nullptr, lda, ldb, ldc, alpha, beta, nullptr,
+          xpu::Activation_t::LINEAR);
+      PADDLE_ENFORCE_XDNN_SUCCESS(ret, "xpu_fc_wrapper");
     }
 
     if (dy) {
@@ -159,14 +166,14 @@ class MulGradXPUKernel : public framework::OpKernel<T> {
       const T* data_a = x_matrix.data<T>();
       const T* data_b = dout->data<T>();
       T* data_c = dy_matrix.data<T>();
-      int ret =
-          xpu::gemm_int16(dev_ctx.x_context(), trans_a, trans_b, m, n, k, alpha,
-                          data_a, lda, data_b, ldb, beta, data_c, ldc);
-      PADDLE_ENFORCE_EQ(ret, XPU_SUCCESS,
-                        platform::errors::External(
-                            "XPU API return wrong value[%d], please check "
-                            "where Baidu Kunlun Card is properly installed.",
-                            ret));
+
+      int ret = xpu_fc_wrapper<XPUType, int16_t>(
+          dev_ctx.x_context(), reinterpret_cast<const XPUType*>(data_a),
+          reinterpret_cast<const XPUType*>(data_b),
+          reinterpret_cast<XPUType*>(data_c), m, n, k, trans_a, trans_b,
+          nullptr, nullptr, nullptr, lda, ldb, ldc, alpha, beta, nullptr,
+          xpu::Activation_t::LINEAR);
+      PADDLE_ENFORCE_XDNN_SUCCESS(ret, "xpu_fc_wrapper");
     }
   }
 };
@@ -175,9 +182,12 @@ class MulGradXPUKernel : public framework::OpKernel<T> {
 }  // namespace paddle
 
 namespace ops = paddle::operators;
+namespace plat = paddle::platform;
 
 REGISTER_OP_XPU_KERNEL(
-    mul, ops::MulXPUKernel<paddle::platform::XPUDeviceContext, float>);
+    mul, ops::MulXPUKernel<paddle::platform::XPUDeviceContext, float>,
+    ops::MulXPUKernel<paddle::platform::XPUDeviceContext, plat::float16>);
 REGISTER_OP_XPU_KERNEL(
-    mul_grad, ops::MulGradXPUKernel<paddle::platform::XPUDeviceContext, float>)
+    mul_grad, ops::MulGradXPUKernel<paddle::platform::XPUDeviceContext, float>,
+    ops::MulGradXPUKernel<paddle::platform::XPUDeviceContext, plat::float16>)
 #endif

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

@@ -70,8 +70,10 @@ XPUOpMap& get_kl2_ops() {
        XPUKernelSet({pOpKernelType(vartype::FP32, XPUPlace()),
                      pOpKernelType(vartype::FP16, XPUPlace())})},
       {"dropout_grad",
-       XPUKernelSet({pOpKernelType(vartype::FP32, XPUPlace())})},
-      {"dropout", XPUKernelSet({pOpKernelType(vartype::FP32, XPUPlace())})},
+       XPUKernelSet({pOpKernelType(vartype::FP32, XPUPlace()),
+                     pOpKernelType(vartype::FP16, XPUPlace())})},
+      {"dropout", XPUKernelSet({pOpKernelType(vartype::FP32, XPUPlace()),
+                                pOpKernelType(vartype::FP16, XPUPlace())})},
       {"elementwise_add_grad",
        XPUKernelSet({pOpKernelType(vartype::FP32, XPUPlace()),
                      pOpKernelType(vartype::FP16, XPUPlace())})},
@@ -249,6 +251,8 @@ XPUOpMap& get_kl2_ops() {
       {"momentum", XPUKernelSet({pOpKernelType(vartype::FP32, XPUPlace())})},
       {"mul", XPUKernelSet({pOpKernelType(vartype::FP32, XPUPlace()),
                             pOpKernelType(vartype::FP16, XPUPlace())})},
+      {"mul_grad", XPUKernelSet({pOpKernelType(vartype::FP32, XPUPlace()),
+                                 pOpKernelType(vartype::FP16, XPUPlace())})},
       {"nearest_interp_v2",
        XPUKernelSet({pOpKernelType(vartype::FP32, XPUPlace())})},
       {"nearest_interp_v2_grad",

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

@@ -27,104 +27,120 @@ import time
 
 paddle.enable_static()
 
+from xpu.get_test_cover_info import create_test_class, get_xpu_op_support_types, XPUOpTestWrapper
+
 
-@unittest.skipIf(not paddle.is_compiled_with_xpu(),
-                 "core is not compiled with XPU")
 class TestMulOpError(unittest.TestCase):
     def test_errors(self):
         with program_guard(Program(), Program()):
             # The input type of mul_op must be Variable.
             x1 = fluid.create_lod_tensor(
-                np.array([[-1]]), [[1]], fluid.CPUPlace())
+                np.array([[-1]]), [[1]], fluid.XPUPlace(0))
             x2 = fluid.create_lod_tensor(
-                np.array([[-1]]), [[1]], fluid.CPUPlace())
+                np.array([[-1]]), [[1]], fluid.XPUPlace(0))
             self.assertRaises(TypeError, fluid.layers.mul, x1, x2)
-            # The input dtype of mul_op must be float32 or float64.
+            # The input dtype of mul_op must be float32.
             x3 = fluid.layers.data(name='x3', shape=[4], dtype="int32")
             x4 = fluid.layers.data(name='x4', shape=[4], dtype="int32")
             self.assertRaises(TypeError, fluid.layers.mul, x3, x4)
 
 
-@unittest.skipIf(not paddle.is_compiled_with_xpu(),
-                 "core is not compiled with XPU")
-class TestXPUMulOp1(XPUOpTest):
-    def setUp(self):
-        self.op_type = "mul"
-        self.dtype = np.float32
-        self.use_xpu = True
-        self.init_dtype_type()
-        self.inputs = {
-            'X': np.random.random((3, 4, 2, 9)).astype(self.dtype),
-            'Y': np.random.random((3, 6, 1, 2, 3)).astype(self.dtype)
-        }
-        self.attrs = {
-            'x_num_col_dims': 2,
-            'y_num_col_dims': 2,
-        }
-        result = np.dot(self.inputs['X'].reshape(3 * 4, 2 * 9),
-                        self.inputs['Y'].reshape(3 * 6, 1 * 2 * 3))
-        result = result.reshape(3, 4, 1, 2, 3)
-        self.outputs = {'Out': result}
-
-    def init_dtype_type(self):
-        pass
-
-    def test_check_output(self):
-        place = paddle.XPUPlace(0)
-        self.check_output_with_place(place, atol=0.01)
-
-    def test_check_grad_normal(self):
-        place = paddle.XPUPlace(0)
-        self.check_grad_with_place(
-            place, ['X', 'Y'], 'Out', max_relative_error=0.1)
-
-    def test_check_grad_ingore_x(self):
-        place = paddle.XPUPlace(0)
-        self.check_grad_with_place(
-            place, ['Y'], 'Out', max_relative_error=0.1, no_grad_set=set("X"))
-
-    def test_check_grad_ignore_y(self):
-        place = paddle.XPUPlace(0)
-        self.check_grad_with_place(
-            place, ['X'], 'Out', max_relative_error=0.1, no_grad_set=set('Y'))
-
-
-@unittest.skipIf(not paddle.is_compiled_with_xpu(),
-                 "core is not compiled with XPU")
-class TestXPUMulOp2(XPUOpTest):
-    def setUp(self):
-        self.op_type = "mul"
-        self.use_xpu = True
-        self.dtype = np.float32
-        self.init_dtype_type()
-        self.inputs = {
-            'X': np.random.random((20, 5)).astype(self.dtype),
-            'Y': np.random.random((5, 21)).astype(self.dtype)
-        }
-        self.outputs = {'Out': np.dot(self.inputs['X'], self.inputs['Y'])}
-
-    def init_dtype_type(self):
-        self.dtype = np.float32
-
-    def test_check_output(self):
-        place = paddle.XPUPlace(0)
-        self.check_output_with_place(place, atol=0.01)
-
-    def test_check_grad_normal(self):
-        place = paddle.XPUPlace(0)
-        self.check_grad_with_place(
-            place, ['X', 'Y'], 'Out', max_relative_error=0.1)
-
-    def test_check_grad_ingore_x(self):
-        place = paddle.XPUPlace(0)
-        self.check_grad_with_place(
-            place, ['Y'], 'Out', max_relative_error=0.1, no_grad_set=set("X"))
-
-    def test_check_grad_ingore_y(self):
-        place = paddle.XPUPlace(0)
-        self.check_grad_with_place(
-            place, ['X'], 'Out', max_relative_error=0.1, no_grad_set=set('Y'))
-
+class XPUTestMulOp(XPUOpTestWrapper):
+    def __init__(self):
+        self.op_name = 'mul'
+        self.use_dynamic_create_class = False
+
+    class TestXPUMulOp1(XPUOpTest):
+        def setUp(self):
+            self.op_type = "mul"
+            self.dtype = self.in_type
+            self.inputs = {
+                'X': np.random.random((3, 4, 2, 9)).astype(self.in_type_str),
+                'Y': np.random.random((3, 6, 1, 2, 3)).astype(self.in_type_str)
+            }
+            self.attrs = {
+                'x_num_col_dims': 2,
+                'y_num_col_dims': 2,
+            }
+            result = np.dot(self.inputs['X'].reshape(3 * 4, 2 * 9),
+                            self.inputs['Y'].reshape(3 * 6, 1 * 2 * 3))
+            result = result.reshape(3, 4, 1, 2, 3)
+            self.outputs = {'Out': result}
+
+        def test_check_output(self):
+            paddle.enable_static()
+            place = paddle.XPUPlace(0)
+            self.check_output_with_place(place, atol=0.01)
+
+        def test_check_grad_normal(self):
+            place = paddle.XPUPlace(0)
+            paddle.enable_static()
+            self.check_grad_with_place(
+                place, ['X', 'Y'], 'Out', max_relative_error=0.1)
+
+        def test_check_grad_ingore_x(self):
+            place = paddle.XPUPlace(0)
+            paddle.enable_static()
+            self.check_grad_with_place(
+                place, ['Y'],
+                'Out',
+                max_relative_error=0.1,
+                no_grad_set=set("X"))
+
+        def test_check_grad_ignore_y(self):
+            place = paddle.XPUPlace(0)
+            paddle.enable_static()
+            self.check_grad_with_place(
+                place, ['X'],
+                'Out',
+                max_relative_error=0.1,
+                no_grad_set=set('Y'))
+
+    class TestXPUMulOp2(XPUOpTest):
+        def setUp(self):
+            self.op_type = "mul"
+            self.use_xpu = True
+            self.dtype = self.in_type
+            self.inputs = {
+                'X': np.random.random((20, 5)).astype(self.in_type_str),
+                'Y': np.random.random((5, 21)).astype(self.in_type_str)
+            }
+            self.outputs = {'Out': np.dot(self.inputs['X'], self.inputs['Y'])}
+
+        def test_check_output(self):
+            place = paddle.XPUPlace(0)
+            paddle.enable_static()
+            self.check_output_with_place(place, atol=0.01)
+
+        def test_check_grad_normal(self):
+            place = paddle.XPUPlace(0)
+            paddle.enable_static()
+            self.check_grad_with_place(
+                place, ['X', 'Y'], 'Out', max_relative_error=0.1)
+
+        def test_check_grad_ingore_x(self):
+            place = paddle.XPUPlace(0)
+            paddle.enable_static()
+            self.check_grad_with_place(
+                place, ['Y'],
+                'Out',
+                max_relative_error=0.1,
+                no_grad_set=set("X"))
+
+        def test_check_grad_ingore_y(self):
+            place = paddle.XPUPlace(0)
+            paddle.enable_static()
+            self.check_grad_with_place(
+                place, ['X'],
+                'Out',
+                max_relative_error=0.1,
+                no_grad_set=set('Y'))
+
+
+support_types = get_xpu_op_support_types('mul')
+for stype in support_types:
+    create_test_class(globals(), XPUTestMulOp, stype)
 
 if __name__ == "__main__":
+    paddle.enable_static()
     unittest.main()