Pd2.0 (#30532)

* support transformer v2.0

* fix range op crash in dygraph xpu place

cmake/external/xpu.cmake

@@ -10,7 +10,7 @@ if (WITH_AARCH64)
 elseif(WITH_SUNWAY)
     SET(XPU_URL    "https://baidu-kunlun-public.su.bcebos.com/paddle_depence/sunway/xpu_2020_1227.tar.gz" CACHE STRING "" FORCE)
 else()
-    SET(XPU_URL    "https://baidu-kunlun-public.su.bcebos.com/paddle_depence/xpu_2021_0105.tar.gz" CACHE STRING "" FORCE)
+    SET(XPU_URL    "https://baidu-kunlun-public.su.bcebos.com/paddle_depence/xpu_2021_01_13.tar.gz" CACHE STRING "" FORCE)
 endif()
 
 SET(XPU_SOURCE_DIR              "${THIRD_PARTY_PATH}/xpu")

paddle/fluid/operators/layer_norm_op_xpu.cc

@@ -45,15 +45,13 @@ class LayerNormXPUKernel : public framework::OpKernel<T> {
     auto* mean_data = mean->mutable_data<T>(ctx.GetPlace());
     auto* variance_data = variance->mutable_data<T>(ctx.GetPlace());
     auto& dev_ctx = ctx.template device_context<DeviceContext>();
-    int r = xpu::layer_norm(dev_ctx.x_context(), left, right, x_data, y_data,
-                            scale_data, bias_data, epsilon, mean_data,
-                            variance_data, false);
-    PADDLE_ENFORCE_EQ(
-        r, XPU_SUCCESS,
-        platform::errors::External("XPU API(layer_norm) return wrong "
-                                   "value[%d], please check whether Baidu "
-                                   "Kunlun Card is properly installed.",
-                                   r));
+    int r = xpu::layer_norm(dev_ctx.x_context(), x_data, y_data, left, right,
+                            epsilon, scale_data, bias_data, mean_data,
+                            variance_data);
+    PADDLE_ENFORCE_EQ(r, XPU_SUCCESS,
+                      platform::errors::External(
+                          "XPU layer_norm kernel return wrong value[%d %s]", r,
+                          XPUAPIErrorMsg[r]));
   }
 };
 
@@ -87,15 +85,14 @@ class LayerNormGradXPUKernel : public framework::OpKernel<T> {
     auto* dx_data =
         (dx == nullptr ? nullptr : dx->mutable_data<T>(ctx.GetPlace()));
     auto& dev_ctx = ctx.template device_context<DeviceContext>();
-    int r = xpu::layer_norm_backward(
-        dev_ctx.x_context(), left, right, x_data, scale_data, variance_data,
-        mean_data, dy_data, dx_data, dscale_data, dbias_data, epsilon);
+    int r = xpu::layer_norm_grad(dev_ctx.x_context(), x_data, dy_data, dx_data,
+                                 left, right, epsilon, scale_data, mean_data,
+                                 variance_data, dscale_data, dbias_data);
     PADDLE_ENFORCE_EQ(
         r, XPU_SUCCESS,
-        platform::errors::External("XPU API(layer_norm_backward) return wrong "
-                                   "value[%d], please check whether Baidu "
-                                   "Kunlun Card is properly installed.",
-                                   r));
+        platform::errors::External(
+            "XPU layer_norm_grad kernel return wrong value[%d %s]", r,
+            XPUAPIErrorMsg[r]));
   }
 };
 

paddle/fluid/operators/matmul_op_xpu.cc

@@ -24,6 +24,8 @@ limitations under the License. */
 namespace paddle {
 namespace operators {
 
+using framework::Tensor;
+
 static framework::DDim RowMatrixFromVector(const framework::DDim &x_dim) {
   if (x_dim.size() > 1) {
     return x_dim;
@@ -97,26 +99,23 @@ static void ReshapeXYOutIntoMatrixSequence(framework::Tensor *x,
   ReshapeTensorIntoMatrixSequence(y, mat_dim_y);
 }
 
-template <typename DeviceContext, typename T>
-class MatMulXPUKernel : public framework::OpKernel<T> {
- public:
-  void Compute(const framework::ExecutionContext &context) const override {
-    auto *x = context.Input<framework::Tensor>("X");
-    auto *y = context.Input<framework::Tensor>("Y");
-    auto *out = context.Output<framework::Tensor>("Out");
-    out->mutable_data<T>(context.GetPlace());
+template <typename T, typename FCT>
+static void MatMulXPUFunction(const Tensor *x, const Tensor *y, Tensor *out,
+                              bool trans_x, bool trans_y,
+                              const paddle::framework::ExecutionContext &ctx) {
+  const auto &x_dims = x->dims();
+  const auto &y_dims = y->dims();
+  auto &dev_ctx =
+      ctx.template device_context<paddle::platform::XPUDeviceContext>();
 
-    auto mat_dim_a = math::CreateMatrixDescriptor(
-        RowMatrixFromVector(x->dims()), 0, context.Attr<bool>("transpose_X"));
+  auto mat_dim_a =
+      math::CreateMatrixDescriptor(RowMatrixFromVector(x_dims), 0, trans_x);
   auto mat_dim_b =
-        math::CreateMatrixDescriptor(ColumnMatrixFromVector(y->dims()), 0,
-                                     context.Attr<bool>("transpose_Y"));
+      math::CreateMatrixDescriptor(ColumnMatrixFromVector(y_dims), 0, trans_y);
 
-    const auto &x_dims = x->dims();
-    const auto &y_dims = y->dims();
   if (x_dims.size() == 3 && y_dims.size() <= 2) {
     // if transpose_X is true, the transpose cost much time
-      if (!context.Attr<bool>("transpose_X")) {
+    if (!trans_x) {
       mat_dim_a.height_ *= mat_dim_a.batch_size_;
       mat_dim_a.batch_size_ = 0;
     } else {
@@ -124,7 +123,6 @@ class MatMulXPUKernel : public framework::OpKernel<T> {
       mat_dim_b.height_ = mat_dim_b.height_ / mat_dim_b.batch_size_;
     }
   }
-
   PADDLE_ENFORCE_EQ(
       mat_dim_a.width_, mat_dim_b.height_,
       platform::errors::InvalidArgument("Shape mistake in matmul_op, the "
@@ -139,9 +137,9 @@ class MatMulXPUKernel : public framework::OpKernel<T> {
                         "tensor batch_size:%d, second "
                         "tensor batch_size:%d",
                         mat_dim_a.batch_size_, mat_dim_b.batch_size_));
-    T alpha = static_cast<T>(context.Attr<float>("alpha"));
 
-    auto &dev_ctx = context.template device_context<DeviceContext>();
+  T alpha = static_cast<T>(ctx.Attr<float>("alpha"));
+
   float *data_c = out->data<T>();
   int m = mat_dim_a.height_;
   int n = mat_dim_b.width_;
@@ -150,11 +148,12 @@ class MatMulXPUKernel : public framework::OpKernel<T> {
   int ldy = mat_dim_b.trans_ ? k : n;
   int ldout = n;
   int batch_size = mat_dim_a.batch_size_;
-    if (batch_size == 0 || batch_size == 1) {
-      int r = xpu::fc_fusion<float, float, float, int16_t>(
+
+  if (batch_size == 0) {
+    int r = xpu::fc_fusion<float, float, float, FCT>(
         dev_ctx.x_context(), x->data<T>(), y->data<T>(), data_c, m, n, k,
-          mat_dim_a.trans_, mat_dim_b.trans_, nullptr, nullptr, nullptr, ldx,
-          ldy, ldout, alpha, 0, nullptr, xpu::Activation_t::LINEAR);
+        mat_dim_a.trans_, mat_dim_b.trans_, nullptr, nullptr, nullptr, ldx, ldy,
+        ldout, alpha, 0, nullptr, xpu::Activation_t::LINEAR);
     PADDLE_ENFORCE_EQ(r, XPU_SUCCESS,
                       platform::errors::External(
                           "XPU fc_fusion kernel return wrong value[%d %s]", r,
@@ -168,16 +167,33 @@ class MatMulXPUKernel : public framework::OpKernel<T> {
       const float *x_data = x->data<T>() + x_stride * i;
       const float *y_data = y->data<T>() + y_stride * i;
       float *out_data = data_c + out_stride * i;
-        int r = xpu::fc_fusion<float, float, float, int16_t>(
+      int r = xpu::fc_fusion<float, float, float, FCT>(
           dev_ctx.x_context(), x_data, y_data, out_data, m, n, k,
           mat_dim_a.trans_, mat_dim_b.trans_, nullptr, nullptr, nullptr, ldx,
           ldy, ldout, alpha, 0, nullptr, xpu::Activation_t::LINEAR);
       PADDLE_ENFORCE_EQ(r, XPU_SUCCESS,
                         platform::errors::External(
-                              "XPU fc_fusion kernel return wrong value[%d %s]",
-                              r, XPUAPIErrorMsg[r]));
+                            "XPU fc_fusion kernel return wrong value[%d %s]", r,
+                            XPUAPIErrorMsg[r]));
     }
   }
+}
+
+template <typename DeviceContext, typename T>
+class MatMulXPUKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext &context) const override {
+    auto *x = context.Input<framework::Tensor>("X");
+    auto *y = context.Input<framework::Tensor>("Y");
+    auto *out = context.Output<framework::Tensor>("Out");
+    out->mutable_data<T>(context.GetPlace());
+    bool trans_x = context.Attr<bool>("transpose_X");
+    bool trans_y = context.Attr<bool>("transpose_Y");
+    if (std::getenv("XPU_PADDLE_MAT_MUL_FCINT32") != nullptr) {
+      MatMulXPUFunction<T, int32_t>(x, y, out, trans_x, trans_y, context);
+    } else {
+      MatMulXPUFunction<T, int16_t>(x, y, out, trans_x, trans_y, context);
+    }
   }
 };
 
@@ -244,75 +260,10 @@ class MatMulGradXPUKernel : public framework::OpKernel<T> {
               const framework::Tensor &b, bool trans_b,
               framework::Tensor *out) const {
     out->mutable_data<T>(context.GetPlace());
-    auto mat_dim_a = math::CreateMatrixDescriptor(a.dims(), 0, trans_a);
-    auto mat_dim_b = math::CreateMatrixDescriptor(b.dims(), 0, trans_b);
-    const auto &a_dims = a.dims();
-    const auto &b_dims = b.dims();
-    if (a_dims.size() == 3 && b_dims.size() <= 2) {
-      // if transpose_X is true, the transpose cost much time
-      if (!context.Attr<bool>("transpose_X")) {
-        mat_dim_a.height_ *= mat_dim_a.batch_size_;
-        mat_dim_a.batch_size_ = 0;
+    if (std::getenv("XPU_PADDLE_MAT_MUL_GRAD_FCINT32") != nullptr) {
+      MatMulXPUFunction<T, int32_t>(&a, &b, out, trans_a, trans_b, context);
     } else {
-        mat_dim_b.batch_size_ = mat_dim_a.batch_size_;
-        mat_dim_b.height_ = mat_dim_b.height_ / mat_dim_b.batch_size_;
-      }
-    }
-
-    PADDLE_ENFORCE_EQ(mat_dim_a.width_, mat_dim_b.height_,
-                      platform::errors::InvalidArgument(
-                          "Shape mistake in matmul_grad_op, the "
-                          "first tensor width must be same as second tensor "
-                          "height, but received "
-                          "width:%d, height:%d",
-                          mat_dim_a.width_, mat_dim_b.height_));
-    PADDLE_ENFORCE_EQ(mat_dim_a.batch_size_, mat_dim_b.batch_size_,
-                      platform::errors::InvalidArgument(
-                          "Shape mistake in matmul_grad_op, the two input"
-                          "tensor batch_size must be same, but received first "
-                          "tensor batch_size:%d, second "
-                          "tensor batch_size:%d",
-                          mat_dim_a.batch_size_, mat_dim_b.batch_size_));
-
-    T alpha = static_cast<T>(context.Attr<float>("alpha"));
-
-    auto &dev_ctx = context.template device_context<DeviceContext>();
-    float *data_c = out->data<T>();
-
-    int m = mat_dim_a.height_;
-    int n = mat_dim_b.width_;
-    int k = mat_dim_a.width_;
-    int ldx = mat_dim_a.trans_ ? m : k;
-    int ldy = mat_dim_b.trans_ ? k : n;
-    int ldout = n;
-    int batch_size = mat_dim_a.batch_size_;
-    if (batch_size == 0 || batch_size == 1) {
-      int r = xpu::fc_fusion<float, float, float, int16_t>(
-          dev_ctx.x_context(), a.data<T>(), b.data<T>(), data_c, m, n, k,
-          mat_dim_a.trans_, mat_dim_b.trans_, nullptr, nullptr, nullptr, ldx,
-          ldy, ldout, alpha, 0, nullptr, xpu::Activation_t::LINEAR);
-      PADDLE_ENFORCE_EQ(r, XPU_SUCCESS,
-                        platform::errors::External(
-                            "XPU fc_fusion kernel return wrong value[%d %s]", r,
-                            XPUAPIErrorMsg[r]));
-    } else {
-      // batch matmul
-      int x_stride = mat_dim_a.stride_;
-      int y_stride = mat_dim_b.stride_;
-      int out_stride = m * n;
-      for (int i = 0; i < batch_size; ++i) {
-        const float *x_data = a.data<T>() + x_stride * i;
-        const float *y_data = b.data<T>() + y_stride * i;
-        float *out_data = data_c + out_stride * i;
-        int r = xpu::fc_fusion<float, float, float, int16_t>(
-            dev_ctx.x_context(), x_data, y_data, out_data, m, n, k,
-            mat_dim_a.trans_, mat_dim_b.trans_, nullptr, nullptr, nullptr, ldx,
-            ldy, ldout, alpha, 0, nullptr, xpu::Activation_t::LINEAR);
-        PADDLE_ENFORCE_EQ(r, XPU_SUCCESS,
-                          platform::errors::External(
-                              "XPU fc_fusion kernel return wrong value[%d %s]",
-                              r, XPUAPIErrorMsg[r]));
-      }
+      MatMulXPUFunction<T, int16_t>(&a, &b, out, trans_a, trans_b, context);
     }
   }
 

paddle/fluid/operators/matmul_v2_op_xpu.cc

@@ -21,211 +21,141 @@
 namespace paddle {
 namespace operators {
 
-template <typename T>
-void MatMulXPUFunction(const Tensor* X, const Tensor* Y,
-                       const std::vector<std::int64_t>& x_dims,
-                       const std::vector<std::int64_t>& y_dims, Tensor* Out,
+template <typename T, typename FCT>
+static void MatMulXPUFunction(const Tensor* x, const Tensor* y, Tensor* out,
                               bool trans_x, bool trans_y,
                               const paddle::framework::ExecutionContext& ctx) {
-  const int x_ndim = x_dims.size();
-  const int y_ndim = y_dims.size();
-
+  const auto& x_dims = x->dims();
+  const auto& y_dims = y->dims();
   auto& dev_ctx =
       ctx.template device_context<paddle::platform::XPUDeviceContext>();
 
-  // currently only support x_ndim == y_dim and non-broadcast case
-  PADDLE_ENFORCE_EQ(x_ndim, y_ndim, platform::errors::InvalidArgument(
-                                        "Shape mistake in matmul_v2_op"));
-  for (int i = 0; i < x_ndim - 2; i++) {
-    PADDLE_ENFORCE_EQ(
-        x_dims.data()[i], y_dims.data()[i],
-        platform::errors::InvalidArgument("Shape mistake in matmul_v2_op"));
-  }
-
-  int ret = 0;
-  if (x_ndim == 1 && y_ndim == 1) {
-    PADDLE_ENFORCE_EQ(X->numel(), Y->numel(),
-                      platform::errors::InvalidArgument(
-                          "X's numbers is not equal to Y's numbers,"
-                          "when X/Y's dims =1"));
-    VLOG(3) << "MatMul's case 1";
-    Out->Resize({1});
-    Out->mutable_data<T>(ctx.GetPlace());
-    ret = baidu::xpu::api::fc_int16(dev_ctx.x_context(), false, false, 1, 1,
-                                    X->numel(), 1.0f, X->data<T>(),
-                                    Y->data<T>(), 0.0f, Out->data<T>());
-    PADDLE_ENFORCE_EQ(
-        ret, XPU_SUCCESS,
-        platform::errors::External(
-            "XPU API return wrong value[%d] in matmul_v2, please check whether "
-            "Baidu Kunlun Card is properly installed.",
-            ret));
-    return;
-  }
-
-  if (x_ndim == 1) {
-    const int N = X->numel();
-    if (trans_y) {
-      PADDLE_ENFORCE_EQ(
-          y_dims[y_ndim - 1], N,
-          platform::errors::InvalidArgument("Input(Y) has error dim."));
-    } else {
-      PADDLE_ENFORCE_EQ(
-          y_dims[y_ndim - 2], N,
-          platform::errors::InvalidArgument("Input(Y) has error dim."));
-    }
-    std::vector<std::int64_t> out_dims(y_ndim - 1);
-    if (trans_y) {
-      std::copy_n(y_dims.cbegin(), y_ndim - 1, out_dims.begin());
-    } else {
-      std::copy_n(y_dims.cbegin(), y_ndim - 2, out_dims.begin());
-      out_dims.back() = y_dims.back();
-    }
-    Out->Resize(framework::make_ddim(out_dims));
-    Out->mutable_data<T>(ctx.GetPlace());
-    if (trans_y) {
-      const int M = Y->numel() / N;
-      VLOG(3) << "MatMul's case 2";
-      ret = baidu::xpu::api::fc_int16(dev_ctx.x_context(), false, true, 1, M, N,
-                                      1.0f, X->data<T>(), Y->data<T>(), 0.0f,
-                                      Out->data<T>());
-      PADDLE_ENFORCE_EQ(
-          ret, XPU_SUCCESS,
-          platform::errors::External("XPU API return wrong value[%d] in "
-                                     "matmul_v2, please check whether "
-                                     "Baidu Kunlun Card is properly installed.",
-                                     ret));
-    } else {
-      const int M = y_dims[y_ndim - 1];
-      const int batch_size = Y->numel() / (M * N);
-      for (int i = 0; i < batch_size; i++) {
-        ret = baidu::xpu::api::fc_int16(
-            dev_ctx.x_context(), false, false, 1, M, N, 1.0f, X->data<T>(),
-            Y->data<T>() + i * M * N, 0.0f, Out->data<T>() + i * M);
-        PADDLE_ENFORCE_EQ(ret, XPU_SUCCESS,
-                          platform::errors::External(
-                              "XPU API return wrong value[%d] in matmul_v2, "
-                              "please check whether "
-                              "Baidu Kunlun Card is properly installed.",
-                              ret));
-      }
-    }
-    return;
-  }
+  auto mat_dim_a =
+      math::CreateMatrixDescriptor(RowMatrixFromVector(x_dims), 0, trans_x);
+  auto mat_dim_b =
+      math::CreateMatrixDescriptor(ColumnMatrixFromVector(y_dims), 0, trans_y);
 
-  if (y_ndim == 1) {
-    const int N = Y->numel();
-    if (trans_x) {
-      PADDLE_ENFORCE_EQ(
-          x_dims[x_ndim - 2], N,
-          platform::errors::InvalidArgument("Input(X) has error dim."));
+  if (x_dims.size() == 3 && y_dims.size() <= 2) {
+    // if transpose_X is true, the transpose cost much time
+    if (!trans_x) {
+      mat_dim_a.height_ *= mat_dim_a.batch_size_;
+      mat_dim_a.batch_size_ = 0;
     } else {
-      PADDLE_ENFORCE_EQ(
-          x_dims[x_ndim - 1], N,
-          platform::errors::InvalidArgument("Input(X) has error dim."));
+      mat_dim_b.batch_size_ = mat_dim_a.batch_size_;
+      mat_dim_b.height_ = mat_dim_b.height_ / mat_dim_b.batch_size_;
     }
-    std::vector<std::int64_t> out_dims(x_ndim - 1);
-    if (trans_x) {
-      std::copy_n(x_dims.cbegin(), x_ndim - 2, out_dims.begin());
-      out_dims.back() = x_dims.back();
-    } else {
-      std::copy_n(x_dims.cbegin(), x_ndim - 1, out_dims.begin());
   }
-    Out->Resize(framework::make_ddim(out_dims));
-    Out->mutable_data<T>(ctx.GetPlace());
 
-    if (trans_x) {
-      const int M = x_dims[x_ndim - 1];
-      const int batch_size = X->numel() / (M * N);
-      for (int i = 0; i < batch_size; i++) {
-        ret = baidu::xpu::api::fc_int16(dev_ctx.x_context(), true, false, M, 1,
-                                        N, 1.0f, X->data<T>() + i * M * N,
-                                        Y->data<T>(), 0.0f,
-                                        Out->data<T>() + i * M);
-        PADDLE_ENFORCE_EQ(ret, XPU_SUCCESS,
-                          platform::errors::External(
-                              "XPU API return wrong value[%d] in matmul_v2, "
-                              "please check whether "
-                              "Baidu Kunlun Card is properly installed.",
-                              ret));
+  if (mat_dim_a.width_ == mat_dim_b.height_) {
+    if (mat_dim_a.batch_size_ == 0 && mat_dim_b.batch_size_ == 1) {
+      mat_dim_a.batch_size_ = mat_dim_b.batch_size_ = 0;
     }
-    } else {
-      const int M = X->numel() / N;
-      VLOG(3) << "MatMul's case 7";
-      ret = baidu::xpu::api::fc_int16(dev_ctx.x_context(), false, false, M, 1,
-                                      N, 1.0f, X->data<T>(), Y->data<T>(), 0.0f,
-                                      Out->data<T>());
-      PADDLE_ENFORCE_EQ(
-          ret, XPU_SUCCESS,
-          platform::errors::External("XPU API return wrong value[%d] in "
-                                     "matmul_v2, please check whether "
-                                     "Baidu Kunlun Card is properly installed.",
-                                     ret));
+    if (mat_dim_a.batch_size_ == 1 && mat_dim_b.batch_size_ == 0) {
+      mat_dim_a.batch_size_ = mat_dim_b.batch_size_ = 0;
     }
-    return;
   }
 
-  const int M = trans_x ? x_dims[x_ndim - 1] : x_dims[x_ndim - 2];
-  const int K = trans_x ? x_dims[x_ndim - 2] : x_dims[x_ndim - 1];
-  if (trans_y) {
-    PADDLE_ENFORCE_EQ(y_dims[y_ndim - 1], K, platform::errors::InvalidArgument(
-                                                 "Input(X) has error dim."));
+  PADDLE_ENFORCE_EQ(mat_dim_a.width_, mat_dim_b.height_,
+                    platform::errors::InvalidArgument(
+                        "Shape mistake in matmul_v2_op xdims = %s ydims = %s",
+                        x_dims.to_str(), y_dims.to_str()));
+  PADDLE_ENFORCE_EQ(mat_dim_a.batch_size_, mat_dim_b.batch_size_,
+                    platform::errors::InvalidArgument(
+                        "Shape mistake in matmul_v2_op xdims = %s ydims = %s",
+                        x_dims.to_str(), y_dims.to_str()));
+
+  float* data_c = out->data<T>();
+  int m = mat_dim_a.height_;
+  int n = mat_dim_b.width_;
+  int k = mat_dim_a.width_;
+  int batch_size = mat_dim_a.batch_size_;
+
+  if (batch_size == 0) {
+    int r = xpu::fc<float, float, float, FCT>(
+        dev_ctx.x_context(), x->data<T>(), y->data<T>(), data_c, m, n, k,
+        mat_dim_a.trans_, mat_dim_b.trans_, nullptr, nullptr, nullptr);
+    PADDLE_ENFORCE_EQ(r, XPU_SUCCESS,
+                      platform::errors::External(
+                          "XPU fc_fusion kernel return wrong value[%d %s]", r,
+                          XPUAPIErrorMsg[r]));
   } else {
-    PADDLE_ENFORCE_EQ(y_dims[y_ndim - 2], K, platform::errors::InvalidArgument(
-                                                 "Input(X) has error dim."));
+    // batch matmul
+    int x_stride = mat_dim_a.stride_;
+    int y_stride = mat_dim_b.stride_;
+    int out_stride = m * n;
+    for (int i = 0; i < batch_size; ++i) {
+      const float* x_data = x->data<T>() + x_stride * i;
+      const float* y_data = y->data<T>() + y_stride * i;
+      float* out_data = data_c + out_stride * i;
+      int r = xpu::fc<float, float, float, FCT>(
+          dev_ctx.x_context(), x_data, y_data, out_data, m, n, k,
+          mat_dim_a.trans_, mat_dim_b.trans_, nullptr, nullptr, nullptr);
+      PADDLE_ENFORCE_EQ(r, XPU_SUCCESS,
+                        platform::errors::External(
+                            "XPU fc_fusion kernel return wrong value[%d %s]", r,
+                            XPUAPIErrorMsg[r]));
     }
-  const int N = trans_y ? y_dims[y_ndim - 2] : y_dims[y_ndim - 1];
-  const int ndim = (std::max)(x_ndim, y_ndim);
-  std::vector<std::int64_t> out_broadcast_dims(ndim);
-  int batch_size = 1;
-  for (int i = 0; i < ndim - 2; i++) {
-    PADDLE_ENFORCE_EQ(
-        x_dims.data()[i], y_dims.data()[i],
-        platform::errors::InvalidArgument("Shape mistake in matmul_v2_op"));
-    out_broadcast_dims[i] = x_dims.data()[i];
-    batch_size *= x_dims.data()[i];
   }
-
-  out_broadcast_dims[ndim - 2] = M;
-  out_broadcast_dims[ndim - 1] = N;
-
-  Out->Resize(framework::make_ddim(out_broadcast_dims));
-  Out->mutable_data<T>(ctx.GetPlace());
-  ret = baidu::xpu::api::batched_gemm_int16(
-      dev_ctx.x_context(), trans_x, trans_y, batch_size, M, N, K, 1.0f,
-      X->data<T>(), Y->data<T>(), Out->data<T>(), nullptr, nullptr);
-  PADDLE_ENFORCE_EQ(
-      ret, XPU_SUCCESS,
-      platform::errors::External(
-          "XPU API return wrong value[%d] in matmul_v2, please check whether "
-          "Baidu Kunlun Card is properly installed.",
-          ret));
 }
 
 template <typename T>
 class MatMulV2XPUKernel : public framework::OpKernel<T> {
  public:
   void Compute(const paddle::framework::ExecutionContext& ctx) const override {
-    auto* X = ctx.Input<Tensor>("X");
-    auto* Y = ctx.Input<Tensor>("Y");
-    auto* Out = ctx.Output<Tensor>("Out");
+    auto* x = ctx.Input<Tensor>("X");
+    auto* y = ctx.Input<Tensor>("Y");
+    auto* out = ctx.Output<Tensor>("Out");
     bool trans_x = ctx.Attr<bool>("trans_x");
     bool trans_y = ctx.Attr<bool>("trans_y");
-    MatMulXPUFunction<T>(X, Y, vectorize(X->dims()), vectorize(Y->dims()), Out,
-                         trans_x, trans_y, ctx);
+    out->mutable_data<T>(ctx.GetPlace());
+    if (std::getenv("XPU_PADDLE_MAT_MUL_V2_FCINT32") != nullptr) {
+      MatMulXPUFunction<T, int32_t>(x, y, out, trans_x, trans_y, ctx);
+    } else {
+      MatMulXPUFunction<T, int16_t>(x, y, out, trans_x, trans_y, ctx);
+    }
   }
 };
 
+template <typename DeviceContext, typename T>
+static framework::Tensor XPUFoldHeadAndLastDims(
+    const DeviceContext& context, const framework::Tensor& input) {
+  auto in_dims = input.dims();
+  if (in_dims.size() != 3) {
+    return input;
+  }
+
+  framework::Tensor output;
+  output.Resize({in_dims[1], in_dims[0], in_dims[2]});
+  output.mutable_data<T>(context.GetPlace());
+  std::vector<int> in_shape_host = {static_cast<int>(in_dims[0]),
+                                    static_cast<int>(in_dims[1]),
+                                    static_cast<int>(in_dims[2])};
+  std::vector<int> axis_host = {1, 0, 2};
+
+  int r = xpu::transpose(context.x_context(), input.data<T>(), output.data<T>(),
+                         in_shape_host.data(), axis_host.data(), /*ndims=*/3);
+  PADDLE_ENFORCE_EQ(r, XPU_SUCCESS,
+                    platform::errors::External(
+                        "XPU transpose kernel return wrong value[%d %s]", r,
+                        XPUAPIErrorMsg[r]));
+  output.Resize({in_dims[1], in_dims[0] * in_dims[2]});
+
+  return output;
+}
+
 template <typename T>
 class MatMulV2XPUGradKernel : public framework::OpKernel<T> {
  public:
-  void MatMul(const framework::ExecutionContext& context,
+  void MatMul(const framework::ExecutionContext& ctx,
               const framework::Tensor& a, bool trans_a,
               const framework::Tensor& b, bool trans_b,
               framework::Tensor* out) const {
-    out->mutable_data<T>(context.GetPlace());
-    MatMulXPUFunction<T>(&a, &b, vectorize(a.dims()), vectorize(b.dims()), out,
-                         trans_a, trans_b, context);
+    out->mutable_data<T>(ctx.GetPlace());
+    if (std::getenv("XPU_PADDLE_MAT_MUL_GRAD_V2_FCINT32") != nullptr) {
+      MatMulXPUFunction<T, int32_t>(&a, &b, out, trans_a, trans_b, ctx);
+    } else {
+      MatMulXPUFunction<T, int16_t>(&a, &b, out, trans_a, trans_b, ctx);
+    }
   }
 
   void CalcInputGrad(const framework::ExecutionContext& context,
@@ -239,79 +169,33 @@ class MatMulV2XPUGradKernel : public framework::OpKernel<T> {
     if (!need_combine) {
       MatMul(context, a, trans_a, b, trans_b, out);
     } else {
-      // currently not support this case
-    }
-  }
-
-  void Compute(const framework::ExecutionContext& ctx) const override {
-    bool transpose_x = ctx.Attr<bool>("trans_x");
-    bool transpose_y = ctx.Attr<bool>("trans_y");
-
-    auto x = *ctx.Input<framework::Tensor>("X");
-    auto y = *ctx.Input<framework::Tensor>("Y");
-    auto dout = *ctx.Input<framework::Tensor>(framework::GradVarName("Out"));
-
-    // get dims
-    std::vector<std::int64_t> x_dims = vectorize(x.dims());
-    std::vector<std::int64_t> y_dims = vectorize(y.dims());
-    std::vector<std::int64_t> dout_dims = vectorize(dout.dims());
-
-    int x_ndim = x_dims.size();
-    int y_ndim = y_dims.size();
-
-    auto* dx = ctx.Output<Tensor>(framework::GradVarName("X"));
-    auto* dy = ctx.Output<Tensor>(framework::GradVarName("Y"));
-
       auto& dev_ctx =
-        ctx.template device_context<paddle::platform::XPUDeviceContext>();
-    // Case1 : x's or y's dim = 1
-    int ret = 0;
-    if (x_ndim == 1 && y_ndim == 1) {
-      if (dx) {
-        dx->mutable_data<T>(ctx.GetPlace());
-        ret = baidu::xpu::api::fc_int16(dev_ctx.x_context(), false, false,
-                                        dx->numel(), 1, 1, 1.0f, y.data<T>(),
-                                        dout.data<T>(), 0.0f, dx->data<T>());
-        PADDLE_ENFORCE_EQ(ret, XPU_SUCCESS,
-                          platform::errors::External(
-                              "XPU API return wrong value[%d] in "
-                              "matmul_v2_grad, please check whether "
-                              "Baidu Kunlun Card is properly installed.",
-                              ret));
-      }
-      if (dy) {
-        dy->mutable_data<T>(ctx.GetPlace());
-        ret = baidu::xpu::api::fc_int16(dev_ctx.x_context(), false, false,
-                                        dy->numel(), 1, 1, 1.0f, x.data<T>(),
-                                        dout.data<T>(), 0.0f, dy->data<T>());
-        PADDLE_ENFORCE_EQ(ret, XPU_SUCCESS,
-                          platform::errors::External(
-                              "XPU API return wrong value[%d] in "
-                              "matmul_v2_grad, please check whether "
-                              "Baidu Kunlun Card is properly installed.",
-                              ret));
-      }
-      return;
-    }
-
-    bool is_broadcast = true;
-    if (x_ndim <= 2 || y_ndim <= 2) {
-      is_broadcast = false;
-    } else if (x_ndim != y_ndim) {
-      is_broadcast = true;
-    } else {
-      is_broadcast = !std::equal(x_dims.cbegin(), x_dims.cbegin() + x_ndim - 2,
-                                 y_dims.cbegin());
-    }
-
-    // currently only support non-broadcast case
-    PADDLE_ENFORCE_EQ(
-        is_broadcast, false,
-        platform::errors::InvalidArgument("Shape mistake in matmul_v2_op"));
-
-    // Case2: no broadcast or no batch size, it aims to speed and it is same as
-    // matmul in old version.
-    if (!is_broadcast) {
+          context.template device_context<paddle::platform::XPUDeviceContext>();
+      MatMul(
+          context,
+          is_fold_init_dims_a
+              ? FoldInitDims(a)
+              : XPUFoldHeadAndLastDims<paddle::platform::XPUDeviceContext, T>(
+                    dev_ctx, a),
+          trans_a,
+          is_fold_init_dims_b
+              ? FoldInitDims(b)
+              : XPUFoldHeadAndLastDims<paddle::platform::XPUDeviceContext, T>(
+                    dev_ctx, b),
+          trans_b, out);
+    }
+  }
+
+  void Compute(const framework::ExecutionContext& context) const override {
+    bool transpose_x = context.Attr<bool>("trans_x");
+    bool transpose_y = context.Attr<bool>("trans_y");
+
+    auto x = *context.Input<framework::Tensor>("X");
+    auto y = *context.Input<framework::Tensor>("Y");
+    auto dout =
+        *context.Input<framework::Tensor>(framework::GradVarName("Out"));
+    auto* dx = context.Output<framework::Tensor>(framework::GradVarName("X"));
+    auto* dy = context.Output<framework::Tensor>(framework::GradVarName("Y"));
     ReshapeXYOutIntoMatrixSequence(&x, &y, &dout, transpose_x, transpose_y);
     framework::DDim dx_dims;
     if (dx) {
@@ -328,18 +212,19 @@ class MatMulV2XPUGradKernel : public framework::OpKernel<T> {
         dy->Resize(y.dims());
       }
     }
+
     if (transpose_x && transpose_y) {
-        CalcInputGrad(ctx, y, true, true, dout, true, false, dx);
-        CalcInputGrad(ctx, dout, true, true, x, true, false, dy);
+      CalcInputGrad(context, y, true, true, dout, true, false, dx);
+      CalcInputGrad(context, dout, true, true, x, true, false, dy);
     } else if (transpose_x) {
-        CalcInputGrad(ctx, y, false, false, dout, true, false, dx);
-        CalcInputGrad(ctx, x, false, false, dout, false, true, dy);
+      CalcInputGrad(context, y, false, false, dout, true, false, dx);
+      CalcInputGrad(context, x, false, false, dout, false, true, dy);
     } else if (transpose_y) {
-        CalcInputGrad(ctx, dout, false, false, y, false, true, dx);
-        CalcInputGrad(ctx, dout, true, true, x, false, true, dy);
+      CalcInputGrad(context, dout, false, false, y, false, true, dx);
+      CalcInputGrad(context, dout, true, true, x, false, true, dy);
     } else {
-        CalcInputGrad(ctx, dout, false, false, y, true, false, dx);
-        CalcInputGrad(ctx, x, true, true, dout, false, true, dy);
+      CalcInputGrad(context, dout, false, false, y, true, false, dx);
+      CalcInputGrad(context, x, true, true, dout, false, true, dy);
     }
 
     if (dx) {
@@ -347,13 +232,13 @@ class MatMulV2XPUGradKernel : public framework::OpKernel<T> {
         dx->Resize(dx_dims);
       }
     }
+
     if (dy) {
       if (dy_dims != y.dims()) {
         dy->Resize(dy_dims);
       }
     }
   }
-  }
 };
 
 }  // namespace operators

paddle/fluid/operators/one_hot_op_xpu.cc

@@ -35,7 +35,7 @@ class OneHotXPUKernel : public framework::OpKernel<T> {
     if (context.HasInput("depth_tensor")) {
       auto* depth_tensor = context.Input<Tensor>("depth_tensor");
       auto* depth_data = depth_tensor->data<int32_t>();
-      if (depth_tensor->place() == platform::XPUPlace()) {
+      if (platform::is_xpu_place(depth_tensor->place())) {
         xpu_memcpy(static_cast<void*>(&depth),
                    static_cast<const void*>(depth_data), sizeof(int32_t),
                    XPU_DEVICE_TO_HOST);

paddle/fluid/operators/one_hot_v2_op_xpu.cc

+//   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.
+
+#ifdef PADDLE_WITH_XPU
+#include <string>
+#include <vector>
+
+#include "paddle/fluid/framework/framework.pb.h"
+#include "paddle/fluid/operators/one_hot_op.h"
+
+namespace paddle {
+namespace operators {
+
+using LoDTensor = framework::LoDTensor;
+using Tensor = framework::Tensor;
+
+template <typename DeviceContext, typename T>
+class OneHotV2XPUKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    auto* in = context.Input<LoDTensor>("X");
+    auto* out = context.Output<LoDTensor>("Out");
+    int depth = context.Attr<int>("depth");
+    if (context.HasInput("depth_tensor")) {
+      auto* depth_tensor = context.Input<Tensor>("depth_tensor");
+      auto* depth_data = depth_tensor->data<int32_t>();
+      if (platform::is_xpu_place(depth_tensor->place())) {
+        xpu_memcpy(static_cast<void*>(&depth),
+                   static_cast<const void*>(depth_data), sizeof(int32_t),
+                   XPU_DEVICE_TO_HOST);
+      } else {
+        depth = depth_data[0];
+      }
+      auto out_dims = out->dims();
+      out_dims[out_dims.size() - 1] = depth;
+      out->Resize(out_dims);
+    }
+
+    auto& dev_ctx = context.template device_context<DeviceContext>();
+    int len = in->numel();
+    int ret = xpu::one_hot<T>(dev_ctx.x_context(), in->data<T>(),
+                              out->mutable_data<float>(context.GetPlace()), len,
+                              depth, 1.0, 0.0);
+
+    PADDLE_ENFORCE_EQ(ret, XPU_SUCCESS,
+                      platform::errors::External(
+                          "XPU one_hot kernel return wrong value[%d %s]", ret,
+                          XPUAPIErrorMsg[ret]));
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OP_XPU_KERNEL(
+    one_hot_v2, ops::OneHotV2XPUKernel<paddle::platform::XPUDeviceContext, int>,
+    ops::OneHotV2XPUKernel<paddle::platform::XPUDeviceContext, int64_t>);
+#endif

paddle/fluid/operators/range_op_xpu.cc

+/* Copyright (c) 2016 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/range_op.h"
+#include "paddle/fluid/framework/op_registry.h"
+
+namespace paddle {
+namespace operators {
+
+template <typename T>
+class XPURangeKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    auto* start_t = context.Input<framework::Tensor>("Start");
+    auto* end_t = context.Input<framework::Tensor>("End");
+    auto* step_t = context.Input<framework::Tensor>("Step");
+    auto* out = context.Output<framework::Tensor>("Out");
+
+    framework::Tensor n;
+    framework::TensorCopy(*start_t, platform::CPUPlace(), &n);
+    T start = n.data<T>()[0];
+    framework::TensorCopy(*end_t, platform::CPUPlace(), &n);
+    T end = n.data<T>()[0];
+    framework::TensorCopy(*step_t, platform::CPUPlace(), &n);
+    T step = n.data<T>()[0];
+
+    int64_t size = 0;
+    GetSize(start, end, step, &size);
+    out->Resize(framework::make_ddim({size}));
+
+    T* out_data = out->mutable_data<T>(context.GetPlace());
+
+    framework::Tensor out_cpu;
+    T* out_cpu_data_ptr =
+        out_cpu.mutable_data<T>(platform::CPUPlace(), out->numel() * sizeof(T));
+    T value = start;
+    for (int64_t i = 0; i < size; ++i) {
+      out_cpu_data_ptr[i] = value;
+      value += step;
+    }
+    int ret = xpu_memcpy(out_data, out_cpu_data_ptr, out->numel() * sizeof(T),
+                         XPUMemcpyKind::XPU_HOST_TO_DEVICE);
+    PADDLE_ENFORCE_EQ(ret, XPU_SUCCESS,
+                      platform::errors::External("XPU xpu_memcpy return wrong "
+                                                 "value[%d %s]",
+                                                 ret, XPUAPIErrorMsg[ret]));
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OP_XPU_KERNEL(range, ops::XPURangeKernel<int>,
+                       ops::XPURangeKernel<int64_t>, ops::XPURangeKernel<float>,
+                       ops::XPURangeKernel<double>);
+#endif  // PADDLE_WITH_XPU

paddle/fluid/operators/scale_op_xpu.cc

@@ -46,10 +46,13 @@ class ScaleXPUKernel : public framework::OpKernel<T> {
                                           in->dims().to_str().c_str(),
                                           out->dims().to_str().c_str()));
     auto& dev_ctx = ctx.template device_context<DeviceContext>();
-    int r = xpu::scale(dev_ctx.x_context(), in->numel(), scale, bias,
-                       bias_after_scale, in->data<float>(), out->data<float>());
-    PADDLE_ENFORCE_EQ(r, xpu::Error_t::SUCCESS,
-                      platform::errors::Fatal("XPU scale kernel error!"));
+    int r =
+        xpu::scale(dev_ctx.x_context(), in->data<float>(), out->data<float>(),
+                   in->numel(), bias_after_scale, scale, bias);
+    PADDLE_ENFORCE_EQ(
+        r, XPU_SUCCESS,
+        platform::errors::External("XPU scale kernel return wrong value[%d %s]",
+                                   r, XPUAPIErrorMsg[r]));
   }
 };
 

paddle/fluid/operators/softmax_op_xpu.cc

@@ -41,8 +41,21 @@ class SoftmaxXPUKernel : public framework::OpKernel<T> {
     }
 
     auto& dev_ctx = context.template device_context<DeviceContext>();
-    int r = xpu::softmax<T>(dev_ctx.x_context(), x->data<float>(),
-                            out->data<float>(), x_dims, axis);
+
+    int r = XPU_SUCCESS;
+    Tensor clip_x;
+    int len = x->numel();
+    T* clip_x_data =
+        clip_x.mutable_data<T>(platform::XPUPlace(), len * sizeof(T));
+    r = xpu::clip(dev_ctx.x_context(), x->data<float>(), clip_x_data, len,
+                  -1e30, 1e30);
+    PADDLE_ENFORCE_EQ(r, XPU_SUCCESS,
+                      platform::errors::External("XPU API(clip) return wrong "
+                                                 "value[%d %s]",
+                                                 r, XPUAPIErrorMsg[r]));
+
+    r = xpu::softmax<T>(dev_ctx.x_context(), clip_x_data, out->data<float>(),
+                        x_dims, axis);
     PADDLE_ENFORCE_EQ(
         r, XPU_SUCCESS,
         platform::errors::External("XPU API(softmax2d_forward) return wrong "

python/paddle/fluid/tests/unittests/xpu/test_matmul_v2_op_xpu.py

@@ -13,12 +13,11 @@
 # 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 unittest
+import numpy as np
+from op_test_xpu import XPUOpTest
 import paddle.fluid.core as core
 
 import paddle
@@ -57,9 +56,7 @@ def reference_matmul(X, Y, transpose_X=False, transpose_Y=False):
     return Out
 
 
-@unittest.skipIf(not paddle.is_compiled_with_xpu(),
-                 "core is not compiled with XPU")
-class TestMatMulV2Op(OpTest):
+class TestMatMulV2Op(XPUOpTest):
     """
     case 1
     """
@@ -74,10 +71,10 @@ class TestMatMulV2Op(OpTest):
         self.dtype = "float32"
 
     def setUp(self):
+        self.use_xpu = True
         self.init_kernel_type()
         self.config()
         self.op_type = "matmul_v2"
-        self.use_xpu = True
         x = np.random.random(self.x_shape).astype(self.dtype)
         y = np.random.random(self.y_shape).astype(self.dtype)
         # -0.1 ~ 0.1
@@ -94,31 +91,25 @@ class TestMatMulV2Op(OpTest):
 
     def test_check_output(self):
         place = paddle.XPUPlace(0)
-        self.check_output_with_place(place, atol=0.01)
+        self.check_output_with_place(place)
 
     def test_check_grad(self):
         place = paddle.XPUPlace(0)
-        self.check_grad_with_place(
-            place, ['X', 'Y'], 'Out', max_relative_error=0.1)
+        self.check_grad_with_place(place, ['X', 'Y'], 'Out')
 
 
-'''
-@unittest.skipIf(not paddle.is_compiled_with_xpu(),
-                 "core is not compiled with XPU")
-class TestMatMuklOp2(TestMatMulV2Op):
-    """
-    case 2
-    """
+# class TestMatMuklOp2(TestMatMulV2Op):
+#     """
+#     case 2
+#     """
 
-    def config(self):
-        self.x_shape = (100, )
-        self.y_shape = (1, 3, 2, 100)
-        self.trans_x = False
-        self.trans_y = True
+#     def config(self):
+#         self.x_shape = (100, )
+#         self.y_shape = (1, 3, 2, 100)
+#         self.trans_x = False
+#         self.trans_y = True
 
 
-@unittest.skipIf(not paddle.is_compiled_with_xpu(),
-                 "core is not compiled with XPU")
 class TestMatMuklOp3(TestMatMulV2Op):
     """
     case 3
@@ -131,21 +122,18 @@ class TestMatMuklOp3(TestMatMulV2Op):
         self.trans_y = False
 
 
-@unittest.skipIf(not paddle.is_compiled_with_xpu(),
-                 "core is not compiled with XPU")
-class TestMatMuklOp4(TestMatMulV2Op):
-    """
-    case 4
-    """
+# class TestMatMuklOp4(TestMatMulV2Op):
+#     """
+#     case 4
+#     """
+
+#     def config(self):
+#         self.x_shape = (100, )
+#         self.y_shape = (1, 2, 100, 2)
+#         self.trans_x = False
+#         self.trans_y = False
 
-    def config(self):
-        self.x_shape = (100, )
-        self.y_shape = (1, 2, 100, 2)
-        self.trans_x = False
-        self.trans_y = False
 
-@unittest.skipIf(not paddle.is_compiled_with_xpu(),
-                 "core is not compiled with XPU")
 class TestMatMuklOp5(TestMatMulV2Op):
     """
     case 5
@@ -158,37 +146,29 @@ class TestMatMuklOp5(TestMatMulV2Op):
         self.trans_y = False
 
 
-@unittest.skipIf(not paddle.is_compiled_with_xpu(),
-                 "core is not compiled with XPU")
-class TestMatMuklOp6(TestMatMulV2Op):
-    """
-    case 6
-    """
-
-    def config(self):
-        self.x_shape = (1, 2, 100, 1)
-        self.y_shape = (100, )
-        self.trans_x = True
-        self.trans_y = False
+# class TestMatMuklOp6(TestMatMulV2Op):
+#     """
+#     case 6
+#     """
 
+#     def config(self):
+#         self.x_shape = (1, 2, 102, 1)
+#         self.y_shape = (102, )
+#         self.trans_x = True
+#         self.trans_y = False
 
-@unittest.skipIf(not paddle.is_compiled_with_xpu(),
-                 "core is not compiled with XPU")
-class TestMatMuklOp7(TestMatMulV2Op):
-    """
-    case 7
-    """
+# class TestMatMuklOp7(TestMatMulV2Op):
+#     """
+#     case 7
+#     """
 
-    def config(self):
-        self.x_shape = (1, 2, 1, 100)
-        self.y_shape = (100, )
-        self.trans_x = False
-        self.trans_y = False
-'''
+#     def config(self):
+#         self.x_shape = (1, 2, 1, 100)
+#         self.y_shape = (100, )
+#         self.trans_x = False
+#         self.trans_y = False
 
 
-@unittest.skipIf(not paddle.is_compiled_with_xpu(),
-                 "core is not compiled with XPU")
 class TestMatMuklOp8(TestMatMulV2Op):
     """
     case 8
@@ -201,37 +181,97 @@ class TestMatMuklOp8(TestMatMulV2Op):
         self.trans_y = False
 
 
-@unittest.skipIf(not paddle.is_compiled_with_xpu(),
-                 "core is not compiled with XPU")
+# class TestMatMuklOp9(TestMatMulV2Op):
+#     """
+#     case 9
+#     """
+
+#     def config(self):
+#         self.x_shape = (1, 1, 1, 100)
+#         self.y_shape = (2, 1, 2, 100)
+#         self.trans_x = False
+#         self.trans_y = True
+
+# class TestMatMuklOp10(TestMatMulV2Op):
+#     """
+#     case 10
+#     """
+
+#     def config(self):
+#         self.x_shape = (1, 1, 25, 4)
+#         self.y_shape = (1, 2, 4, 25)
+#         self.trans_x = False
+#         self.trans_y = False
+
+# class TestMatMuklOp11(TestMatMulV2Op):
+#     """
+#     case 11
+#     """
+
+#     def config(self):
+#         self.x_shape = (2, 1, 2, 100)
+#         self.y_shape = (1, 1, 100, 2)
+#         self.trans_x = False
+#         self.trans_y = False
+
+# class TestMatMuklOp12(TestMatMulV2Op):
+#     """
+#     case 12
+#     """
+
+#     def config(self):
+#         self.x_shape = (2, 1, 4, 25)
+#         self.y_shape = (1, 1, 4, 25)
+#         self.trans_x = True
+#         self.trans_y = False
+
+
 class TestMatMuklOp13(TestMatMulV2Op):
     """
     case 13
     """
 
     def config(self):
-        self.x_shape = (2, 2, 2, 50)
-        self.y_shape = (2, 2, 2, 50)
+        self.x_shape = (2, 2, 10, 10)
+        self.y_shape = (2, 2, 10, 10)
         self.trans_x = True
         self.trans_y = False
 
 
-'''
-@unittest.skipIf(not paddle.is_compiled_with_xpu(),
-                 "core is not compiled with XPU")
-class TestMatMuklOp16(TestMatMulV2Op):
-    """
-    case 16 : to check the gradient for special case
-    """
+# class TestMatMuklOp14(TestMatMulV2Op):
+#     """
+#     case 14_1
+#     """
 
-    def config(self):
-        self.x_shape = (100)
-        self.y_shape = (1, 2, 2, 100, 2)
-        self.trans_x = False
-        self.trans_y = False
+#     def config(self):
+#         self.x_shape = (3, 1, 6, 6)
+#         self.y_shape = (1, 2, 6, 9)
+#         self.trans_x = True
+#         self.trans_y = False
+
+# class TestMatMuklOp15(TestMatMulV2Op):
+#     """
+#     case 14_2
+#     """
+
+#     def config(self):
+#         self.x_shape = (3, 1, 6, 6)
+#         self.y_shape = (1, 2, 6, 9)
+#         self.trans_x = False
+#         self.trans_y = False
+
+# class TestMatMuklOp16(TestMatMulV2Op):
+#     """
+#     case 16 : to check the gradient for special case
+#     """
+
+#     def config(self):
+#         self.x_shape = (100)
+#         self.y_shape = (1, 2, 2, 100, 2)
+#         self.trans_x = False
+#         self.trans_y = False
 
 
-@unittest.skipIf(not paddle.is_compiled_with_xpu(),
-                 "core is not compiled with XPU")
 class TestMatMuklOp17(TestMatMulV2Op):
     """
     case 17 : to check the gradient for special case
@@ -242,36 +282,30 @@ class TestMatMuklOp17(TestMatMulV2Op):
         self.y_shape = (100)
         self.trans_x = False
         self.trans_y = False
-'''
-
-
-@unittest.skipIf(not paddle.is_compiled_with_xpu(),
-                 "core is not compiled with XPU")
-class TestMatMulV2API(unittest.TestCase):
-    def setUp(self):
-        self.places = [fluid.CPUPlace()]
-        self.places.append(fluid.XPUPlace(0))
-
-    def check_static_result(self, place):
-        with fluid.program_guard(fluid.Program(), fluid.Program()):
-            input_x = fluid.data(name="input_x", shape=[4, 3], dtype="float32")
-            input_y = fluid.data(name="input_y", shape=[3, 4], dtype="float32")
 
-            result = paddle.matmul(input_x, input_y)
 
-            x_np = np.random.random([4, 3]).astype("float32")
-            y_np = np.random.random([3, 4]).astype("float32")
+# class TestMatMuklOpBroadcast1(TestMatMulV2Op):
+#     """
+#     case 14_3
+#     """
 
-            exe = fluid.Executor(place)
-            fetches = exe.run(fluid.default_main_program(),
-                              feed={"input_x": x_np,
-                                    "input_y": y_np},
-                              fetch_list=[result])
+#     def config(self):
+#         self.x_shape = (3, 1, 10, 10)
+#         self.y_shape = (1, 2, 10, 10)
+#         self.trans_x = True
+#         self.trans_y = True
 
-    def test_static(self):
-        for place in self.places:
-            self.check_static_result(place=place)
+# class TestMatMuklOpBroadcast2(TestMatMulV2Op):
+#     """
+#     case 14_4
+#     """
 
+#     def config(self):
+#         self.x_shape = (3, 1, 10, 10)
+#         self.y_shape = (1, 2, 10, 10)
+#         self.trans_x = False
+#         self.trans_y = True
 
 if __name__ == "__main__":
+    paddle.enable_static()
     unittest.main()

python/paddle/fluid/tests/unittests/xpu/test_one_hot_v2_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 paddle
+import paddle.fluid.core as core
+import sys
+sys.path.append("..")
+from op_test_xpu import XPUOpTest
+import paddle.fluid as fluid
+from paddle.fluid import Program, program_guard
+import time
+
+paddle.enable_static()
+
+
+class TestOneHotOp(XPUOpTest):
+    def setUp(self):
+        self.use_xpu = True
+        self.op_type = 'one_hot_v2'
+        depth = 10
+        depth_np = np.array(10).astype('int32')
+        # dimension = 12
+        x_lod = [[4, 1, 3, 3]]
+        x = [np.random.randint(0, depth - 1) for i in range(sum(x_lod[0]))]
+        x = np.array(x).astype('int32').reshape([sum(x_lod[0])])
+
+        out = np.zeros(shape=(np.product(x.shape), depth)).astype('float32')
+
+        for i in range(np.product(x.shape)):
+            out[i, x[i]] = 1.0
+
+        self.inputs = {'X': (x, x_lod), 'depth_tensor': depth_np}
+        self.attrs = {'dtype': int(core.VarDesc.VarType.FP32)}
+        self.outputs = {'Out': (out, x_lod)}
+
+    def test_check_output(self):
+        place = paddle.XPUPlace(0)
+        self.check_output_with_place(place, check_dygraph=False)
+
+
+class TestOneHotOp_attr(XPUOpTest):
+    def setUp(self):
+        self.op_type = 'one_hot_v2'
+        depth = 10
+        dimension = 12
+        x_lod = [[4, 1, 3, 3]]
+        x = [np.random.randint(0, depth - 1) for i in range(sum(x_lod[0]))]
+        x = np.array(x).astype('int32').reshape([sum(x_lod[0]), 1])
+
+        out = np.zeros(shape=(np.product(x.shape[:-1]), 1,
+                              depth)).astype('float32')
+
+        for i in range(np.product(x.shape)):
+            out[i, 0, x[i]] = 1.0
+
+        self.inputs = {'X': (x, x_lod)}
+        self.attrs = {'dtype': int(core.VarDesc.VarType.FP32), 'depth': depth}
+        self.outputs = {'Out': (out, x_lod)}
+
+    def test_check_output(self):
+        place = paddle.XPUPlace(0)
+        self.check_output_with_place(place, check_dygraph=False)
+
+
+class TestOneHotOp_default_dtype(XPUOpTest):
+    def setUp(self):
+        self.op_type = 'one_hot_v2'
+        depth = 10
+        depth_np = np.array(10).astype('int32')
+        dimension = 12
+        x_lod = [[4, 1, 3, 3]]
+        x = [np.random.randint(0, depth - 1) for i in range(sum(x_lod[0]))]
+        x = np.array(x).astype('int32').reshape([sum(x_lod[0])])
+
+        out = np.zeros(shape=(np.product(x.shape), depth)).astype('float32')
+
+        for i in range(np.product(x.shape)):
+            out[i, x[i]] = 1.0
+
+        self.inputs = {'X': (x, x_lod), 'depth_tensor': depth_np}
+        self.attrs = {}
+        self.outputs = {'Out': (out, x_lod)}
+
+    def test_check_output(self):
+        place = paddle.XPUPlace(0)
+        self.check_output_with_place(place, check_dygraph=False)
+
+
+class TestOneHotOp_default_dtype_attr(XPUOpTest):
+    def setUp(self):
+        self.op_type = 'one_hot_v2'
+        depth = 10
+        dimension = 12
+        x_lod = [[4, 1, 3, 3]]
+        x = [np.random.randint(0, depth - 1) for i in range(sum(x_lod[0]))]
+        x = np.array(x).astype('int32').reshape([sum(x_lod[0]), 1])
+
+        out = np.zeros(shape=(np.product(x.shape[:-1]), 1,
+                              depth)).astype('float32')
+
+        for i in range(np.product(x.shape)):
+            out[i, 0, x[i]] = 1.0
+
+        self.inputs = {'X': (x, x_lod)}
+        self.attrs = {'depth': depth}
+        self.outputs = {'Out': (out, x_lod)}
+
+    def test_check_output(self):
+        place = paddle.XPUPlace(0)
+        self.check_output_with_place(place, check_dygraph=False)
+
+
+class TestOneHotOp_out_of_range(XPUOpTest):
+    def setUp(self):
+        self.op_type = 'one_hot_v2'
+        depth = 10
+        x_lod = [[4, 1, 3, 3]]
+        x = [np.random.choice([-1, depth]) for i in range(sum(x_lod[0]))]
+        x = np.array(x).astype('int32').reshape([sum(x_lod[0])])
+
+        out = np.zeros(shape=(np.product(x.shape), depth)).astype('float32')
+
+        self.inputs = {'X': (x, x_lod)}
+        self.attrs = {'depth': depth, 'allow_out_of_range': True}
+        self.outputs = {'Out': (out, x_lod)}
+
+    def test_check_output(self):
+        place = paddle.XPUPlace(0)
+        self.check_output_with_place(place, check_dygraph=False)
+
+
+class TestOneHotOpApi(unittest.TestCase):
+    def test_api(self):
+        depth = 10
+        self._run(depth)
+
+    def test_api_with_depthTensor(self):
+        depth = fluid.layers.assign(input=np.array([10], dtype=np.int32))
+        self._run(depth)
+
+    def test_api_with_dygraph(self):
+        depth = 10
+        label = np.array([np.random.randint(0, depth - 1)
+                          for i in range(6)]).reshape([6, 1])
+        with fluid.dygraph.guard():
+            one_hot_label = fluid.one_hot(
+                input=fluid.dygraph.to_variable(label), depth=depth)
+
+    def _run(self, depth):
+        label = fluid.layers.data(name="label", shape=[1], dtype="int64")
+        one_hot_label = fluid.one_hot(input=label, depth=depth)
+
+        place = fluid.XPUPlace(0)
+        label_data = np.array([np.random.randint(0, 10 - 1)
+                               for i in range(6)]).reshape([6, 1])
+
+        exe = fluid.Executor(place)
+        exe.run(fluid.default_startup_program())
+        ret = exe.run(feed={'label': label_data, },
+                      fetch_list=[one_hot_label],
+                      return_numpy=False)
+
+
+class BadInputTestOnehotV2(unittest.TestCase):
+    def test_error(self):
+        with fluid.program_guard(fluid.Program()):
+
+            def test_bad_x():
+                label = fluid.layers.data(
+                    name="label",
+                    shape=[4],
+                    append_batch_size=False,
+                    dtype="float32")
+                one_hot_label = fluid.one_hot(input=label, depth=4)
+
+            self.assertRaises(TypeError, test_bad_x)
+
+
+if __name__ == '__main__':
+    paddle.enable_static()
+    unittest.main()

python/paddle/fluid/tests/unittests/xpu/test_range_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 paddle
+import numpy as np
+import sys
+sys.path.append("..")
+from op_test_xpu import XPUOpTest
+
+paddle.enable_static()
+
+
+class TestRangeOp(XPUOpTest):
+    def setUp(self):
+        self.op_type = "range"
+        self.init_config()
+        self.inputs = {
+            'Start': np.array([self.case[0]]).astype(self.dtype),
+            'End': np.array([self.case[1]]).astype(self.dtype),
+            'Step': np.array([self.case[2]]).astype(self.dtype)
+        }
+
+        self.outputs = {
+            'Out': np.arange(self.case[0], self.case[1],
+                             self.case[2]).astype(self.dtype)
+        }
+
+    def init_config(self):
+        self.dtype = np.float32
+        self.case = (0, 1, 0.2)
+
+    def test_check_output(self):
+        place = paddle.XPUPlace(0)
+        self.check_output_with_place(place, check_dygraph=False)
+
+
+class TestFloatRangeOpCase0(TestRangeOp):
+    def init_config(self):
+        self.dtype = np.float32
+        self.case = (0, 5, 1)
+
+
+class TestInt32RangeOpCase0(TestRangeOp):
+    def init_config(self):
+        self.dtype = np.int32
+        self.case = (0, 5, 2)
+
+
+class TestInt32RangeOpCase1(TestRangeOp):
+    def init_config(self):
+        self.dtype = np.int32
+        self.case = (10, 1, -2)
+
+
+class TestInt32RangeOpCase2(TestRangeOp):
+    def init_config(self):
+        self.dtype = np.int32
+        self.case = (-1, -10, -2)
+
+
+if __name__ == "__main__":
+    unittest.main()