support kunlun matmul_v2 (PaddlePaddle#27910)

*test=kunlun

paddle/fluid/operators/matmul_v2_op_xpu.cc

@@ -0,0 +1,367 @@
+//   Copyright (c) 2020 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/matmul_v2_op.h"
+#include <string>
+#include <vector>
+
+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,
+                       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();
+
+  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;
+  }
+
+  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."));
+    } else {
+      PADDLE_ENFORCE_EQ(
+          x_dims[x_ndim - 1], N,
+          platform::errors::InvalidArgument("Input(X) has error dim."));
+    }
+    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));
+      }
+    } 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));
+    }
+    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."));
+  } else {
+    PADDLE_ENFORCE_EQ(y_dims[y_ndim - 2], K, platform::errors::InvalidArgument(
+                                                 "Input(X) has error dim."));
+  }
+  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");
+    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);
+  }
+};
+
+template <typename T>
+class MatMulV2XPUGradKernel : public framework::OpKernel<T> {
+ public:
+  void MatMul(const framework::ExecutionContext& context,
+              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);
+  }
+
+  void CalcInputGrad(const framework::ExecutionContext& context,
+                     const framework::Tensor& a, bool trans_a,
+                     bool is_fold_init_dims_a, const framework::Tensor& b,
+                     bool trans_b, bool is_fold_init_dims_b,
+                     framework::Tensor* out) const {
+    if (out == nullptr) return;
+    bool need_combine = (a.dims().size() == 3 || b.dims().size() == 3) &&
+                        out->dims().size() == 2;
+    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) {
+      ReshapeXYOutIntoMatrixSequence(&x, &y, &dout, transpose_x, transpose_y);
+      framework::DDim dx_dims;
+      if (dx) {
+        dx_dims = dx->dims();
+        if (dx_dims != x.dims()) {
+          dx->Resize(x.dims());
+        }
+      }
+
+      framework::DDim dy_dims;
+      if (dy) {
+        dy_dims = dy->dims();
+        if (dy_dims != y.dims()) {
+          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);
+      } else if (transpose_x) {
+        CalcInputGrad(ctx, y, false, false, dout, true, false, dx);
+        CalcInputGrad(ctx, 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);
+      } else {
+        CalcInputGrad(ctx, dout, false, false, y, true, false, dx);
+        CalcInputGrad(ctx, x, true, true, dout, false, true, dy);
+      }
+
+      if (dx) {
+        if (dx_dims != x.dims()) {
+          dx->Resize(dx_dims);
+        }
+      }
+      if (dy) {
+        if (dy_dims != y.dims()) {
+          dy->Resize(dy_dims);
+        }
+      }
+    }
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+
+REGISTER_OP_XPU_KERNEL(matmul_v2, ops::MatMulV2XPUKernel<float>);
+REGISTER_OP_XPU_KERNEL(matmul_v2_grad, ops::MatMulV2XPUGradKernel<float>);
+
+#endif