[Phi] Migrate solve kernel to phi (#44363)

* draft version

* draft version

* draft version

* migrate solve kernel to phi

* polish

* polish

* re useless header file, fix a bug in grad_kernel_impl

* add header file in need

paddle/fluid/operators/solve_op.cc

@@ -12,13 +12,13 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 See the License for the specific language governing permissions and
 limitations under the License. */
 
-#include "paddle/fluid/operators/solve_op.h"
-
 #include <memory>
 #include <string>
 #include <unordered_map>
 #include <vector>
 
+#include "paddle/fluid/framework/op_registry.h"
+#include "paddle/fluid/framework/op_version_registry.h"
 #include "paddle/phi/core/ddim.h"
 
 namespace paddle {
@@ -220,10 +220,3 @@ REGISTER_OPERATOR(solve,
                   ops::SolveOpGradMaker<paddle::imperative::OpBase>);
 
 REGISTER_OPERATOR(solve_grad, ops::SolveGradOp);
-
-REGISTER_OP_CPU_KERNEL(solve,
-                       ops::SolveKernel<phi::CPUContext, float>,
-                       ops::SolveKernel<phi::CPUContext, double>);
-REGISTER_OP_CPU_KERNEL(solve_grad,
-                       ops::SolveGradKernel<phi::CPUContext, float>,
-                       ops::SolveGradKernel<phi::CPUContext, double>);

paddle/phi/infermeta/unary.cc

@@ -3211,15 +3211,18 @@ void UnsqueezeInferMeta(const MetaTensor& x,
     }
     out->set_dtype(x.dtype());
   }
-  // set xshape dims.
-  std::vector<int64_t> xshape_dims(x_dims.size() + 1);
-  xshape_dims[0] = 0;
-  for (int i = 0; i < x_dims.size(); ++i) {
-    xshape_dims[i + 1] = x_dims[i];
+  if (xshape) {
+    // set xshape dims.
+    std::vector<int64_t> xshape_dims(x_dims.size() + 1);
+    xshape_dims[0] = 0;
+    for (int i = 0; i < x_dims.size(); ++i) {
+      xshape_dims[i + 1] = x_dims[i];
+    }
+
+    xshape->set_dims(phi::make_ddim(xshape_dims));
+    xshape->share_lod(x);
+    xshape->set_dtype(x.dtype());
   }
-  xshape->set_dims(phi::make_ddim(xshape_dims));
-  xshape->share_lod(x);
-  xshape->set_dtype(x.dtype());
 }
 
 void UnStackInferMeta(const MetaTensor& x,

paddle/phi/kernels/CMakeLists.txt

@@ -62,6 +62,7 @@ set(COMMON_KERNEL_DEPS
     pooling
     maxouting
     matrix_inverse
+    matrix_solve
     phi_dynload_warpctc
     sequence_padding
     sequence_scale)

paddle/phi/kernels/cpu/solve_grad_kernel.cc

+/* Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#include "paddle/phi/kernels/solve_grad_kernel.h"
+#include "paddle/phi/core/kernel_registry.h"
+#include "paddle/phi/kernels/impl/solve_grad_kernel_impl.h"
+
+PD_REGISTER_KERNEL(
+    solve_grad, CPU, ALL_LAYOUT, phi::SolveGradKernel, float, double) {}

paddle/phi/kernels/cpu/solve_kernel.cc

+/* Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#include "paddle/phi/kernels/solve_kernel.h"
+#include "paddle/phi/core/kernel_registry.h"
+#include "paddle/phi/kernels/impl/solve_kernel_impl.h"
+
+PD_REGISTER_KERNEL(solve, CPU, ALL_LAYOUT, phi::SolveKernel, float, double) {}

paddle/phi/kernels/gpu/solve_grad_kernel.cu

+/* Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#include "paddle/phi/kernels/solve_grad_kernel.h"
+#include "paddle/phi/core/kernel_registry.h"
+#include "paddle/phi/kernels/impl/solve_grad_kernel_impl.h"
+
+PD_REGISTER_KERNEL(
+    solve_grad, GPU, ALL_LAYOUT, phi::SolveGradKernel, float, double) {}

paddle/phi/kernels/gpu/solve_kernel.cu

+/* Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#include "paddle/phi/kernels/solve_kernel.h"
+#include "paddle/phi/core/kernel_registry.h"
+#include "paddle/phi/kernels/impl/solve_kernel_impl.h"
+
+PD_REGISTER_KERNEL(solve, GPU, ALL_LAYOUT, phi::SolveKernel, float, double) {}

paddle/phi/kernels/impl/solve_grad_kernel_impl.h

+/* Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#pragma once
+
+#include "paddle/phi/backends/cpu/cpu_context.h"
+#include "paddle/phi/backends/gpu/gpu_context.h"
+#include "paddle/phi/kernels/cpu/reduce.h"
+#include "paddle/phi/kernels/expand_as_kernel.h"
+#include "paddle/phi/kernels/funcs/blas/blas.h"
+#include "paddle/phi/kernels/funcs/math_function.h"
+#include "paddle/phi/kernels/funcs/matrix_solve.h"
+#include "paddle/phi/kernels/funcs/reduce_functor.h"
+#include "paddle/phi/kernels/impl/solve_kernel_impl.h"
+#include "paddle/phi/kernels/squeeze_kernel.h"
+#include "paddle/phi/kernels/unsqueeze_kernel.h"
+
+#if defined(__NVCC__) || defined(__HIPCC__)
+#include "paddle/phi/kernels/gpu/reduce.h"
+#endif
+
+namespace phi {
+
+template <typename Context, typename T>
+struct ReduceSumForSolvelGrad {
+  void operator()(const Context& dev_ctx,
+                  const DenseTensor& input,
+                  DenseTensor* output,
+                  const std::vector<int>& reduce_dims,
+                  bool keep_dims);
+};
+
+template <typename T>
+struct ReduceSumForSolvelGrad<CPUContext, T> {
+  void operator()(const CPUContext& dev_ctx,
+                  const DenseTensor& input,
+                  DenseTensor* output,
+                  const std::vector<int>& reduce_dims,
+                  bool keep_dims) {
+    std::vector<int64_t> reduce_dims_tmp(reduce_dims.begin(),
+                                         reduce_dims.end());
+    phi::ReduceKernelImpl<CPUContext, T, T, phi::funcs::SumFunctor>(
+        dev_ctx, input, output, reduce_dims_tmp, keep_dims, false);
+  }
+};
+
+#if defined(__NVCC__) || defined(__HIPCC__)
+template <typename T>
+struct ReduceSumForSolvelGrad<GPUContext, T> {
+  void operator()(const GPUContext& dev_ctx,
+                  const DenseTensor& input,
+                  DenseTensor* output,
+                  const std::vector<int>& reduce_dims,
+                  bool keep_dims) {
+    phi::funcs::ReduceKernel<T, T, kps::AddFunctor, kps::IdentityFunctor<T>>(
+        dev_ctx, input, output, kps::IdentityFunctor<T>(), reduce_dims);
+  }
+};
+#endif
+
+template <typename T, typename Context>
+void SolveGradKernel(const Context& dev_ctx,
+                     const DenseTensor& x,
+                     const DenseTensor& y,
+                     const DenseTensor& dout,
+                     const DenseTensor& out,
+                     DenseTensor* dx,
+                     DenseTensor* dy) {
+  bool is_vector = false;
+  is_vector = is_vector_rhs(x, y);
+  DenseTensor tmp_y;
+  if (is_vector) {
+    dev_ctx.Alloc(&tmp_y, y.dtype());
+    phi::Unsqueeze<T, Context>(dev_ctx, y, {-1}, &tmp_y, nullptr);
+  } else {
+    tmp_y.Resize(y.dims());
+    dev_ctx.Alloc(&tmp_y, y.dtype());
+    phi::Copy(dev_ctx, y, dev_ctx.GetPlace(), false, &tmp_y);
+  }
+  DenseTensor tmp_x;
+  tmp_x.Resize(x.dims());
+  dev_ctx.Alloc(&tmp_x, x.dtype());
+  phi::Copy(dev_ctx, x, dev_ctx.GetPlace(), false, &tmp_x);
+
+  std::vector<int64_t> x_broadcast_dims;
+  std::vector<int64_t> y_broadcast_dims;
+  std::tie(x_broadcast_dims, y_broadcast_dims) =
+      get_broadcast_dims(tmp_x, tmp_y);
+  // tmp_dx
+  DenseTensor tmp_dx;
+  tmp_dx.Resize(phi::make_ddim(x_broadcast_dims));
+  dev_ctx.template Alloc<T>(&tmp_dx);
+
+  // tmp_dy
+  DenseTensor tmp_dy;
+  tmp_dy.Resize(phi::make_ddim(y_broadcast_dims));
+  dev_ctx.template Alloc<T>(&tmp_dy);
+
+  DenseTensor tmp_input(x.dtype());
+  const auto& new_dims_vec = phi::funcs::getNewDimsVec(x.dims());
+  tmp_input.Resize(phi::make_ddim(new_dims_vec));
+  dev_ctx.template Alloc<T>(&tmp_input);
+
+  phi::funcs::TransposeNormal<Context, T> trans;
+  std::vector<int> new_axis = phi::funcs::getNewAxis(x.dims().size());
+  trans(dev_ctx, x, &tmp_input, new_axis);
+
+  if (dy) {
+    dev_ctx.template Alloc<T>(dy);
+    linalg_solve<Context, T>(dev_ctx, tmp_input, dout, &tmp_dy);
+  }
+
+  if (dx) {
+    dev_ctx.template Alloc<T>(dx);
+
+    // to get dx
+    auto blas = phi::funcs::GetBlas<Context, T>(dev_ctx);
+    if (x.dims().size() == 2 && y.dims().size() == 2) {
+      auto mat_dim_a1 =
+          phi::funcs::CreateMatrixDescriptor(tmp_dy.dims(), 0, false);
+      auto mat_dim_b1 = phi::funcs::CreateMatrixDescriptor(out.dims(), 0, true);
+      blas.MatMul(tmp_dy, mat_dim_a1, out, mat_dim_b1, T(-1), &tmp_dx, T(0));
+
+    } else if (is_vector_rhs(x, y)) {
+      DenseTensor tmp_dy_;
+      dev_ctx.Alloc(&tmp_dy_, y.dtype());
+
+      phi::Unsqueeze<T, Context>(dev_ctx,
+                                 tmp_dy,
+                                 paddle::experimental::IntArray({-1}),
+                                 &tmp_dy_,
+                                 nullptr);
+
+      DenseTensor tmp_out_;
+      dev_ctx.Alloc(&tmp_out_, out.dtype());
+
+      phi::Unsqueeze<T, Context>(dev_ctx,
+                                 out,
+                                 paddle::experimental::IntArray({-1}),
+                                 &tmp_out_,
+                                 nullptr);
+
+      auto mat_dim_a1 =
+          phi::funcs::CreateMatrixDescriptor(tmp_dy_.dims(), 0, false);
+      auto mat_dim_b1 =
+          phi::funcs::CreateMatrixDescriptor(tmp_out_.dims(), 0, true);
+      blas.MatMul(
+          tmp_dy_, mat_dim_a1, tmp_out_, mat_dim_b1, T(-1), &tmp_dx, T(0));
+
+    } else {
+      auto mat_dim_a1 =
+          phi::funcs::CreateMatrixDescriptor(tmp_dy.dims(), 0, false);
+      auto mat_dim_b1 = phi::funcs::CreateMatrixDescriptor(out.dims(), 0, true);
+      blas.MatMul(tmp_dy, mat_dim_a1, out, mat_dim_b1, T(-1), &tmp_dx, T(0));
+    }
+  }
+  if (y.dims() != tmp_dy.dims()) {
+    DenseTensor dy_help;
+    dy_help.Resize(tmp_dy.dims());
+    dev_ctx.Alloc(&dy_help, tmp_dy.dtype());
+
+    phi::Copy(dev_ctx, tmp_dy, dev_ctx.GetPlace(), false, &dy_help);
+
+    // 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());
+
+    if (is_vector_rhs(x, y)) {
+      dout_dims.push_back(1);
+    }
+
+    int y_ndim = y_dims.size();
+    int ndim = dout_dims.size();
+
+    const std::vector<std::int64_t> dy_help_dims = vectorize(dy_help.dims());
+    std::vector<std::int64_t> dy_broadcast_dims(ndim);
+
+    std::fill(
+        dy_broadcast_dims.data(), dy_broadcast_dims.data() + ndim - y_ndim, 1);
+    std::copy(y_dims.data(),
+              y_dims.data() + y_ndim,
+              dy_broadcast_dims.data() + ndim - y_ndim);
+
+    std::vector<int> dy_reduce_dims;
+    for (int idx = 0; idx <= ndim - 3; idx++) {
+      if (dy_help_dims[idx] != 1 && dy_broadcast_dims[idx] == 1) {
+        dy_reduce_dims.push_back(idx);
+      }
+    }
+    // reduce sum to get grad by ReduceSum
+    if (dy) {
+      if (dy_reduce_dims.empty()) {
+        *dy = std::move(dy_help);
+      } else {
+        bool keep_dim = true;
+        if (dy_help.dims().size() != dy->dims().size()) {
+          keep_dim = false;
+        }
+        ReduceSumForSolvelGrad<Context, T>()(
+            dev_ctx, dy_help, dy, dy_reduce_dims, keep_dim);
+      }
+      dy->Resize(y.dims());
+    }
+  } else {
+    phi::Copy(dev_ctx, tmp_dy, dev_ctx.GetPlace(), false, dy);
+  }
+
+  if (x.dims() != tmp_dx.dims()) {
+    DenseTensor dx_help;
+    dx_help.Resize(tmp_dx.dims());
+    dev_ctx.Alloc(&dx_help, tmp_dx.dtype());
+    phi::Copy(dev_ctx, tmp_dx, dev_ctx.GetPlace(), false, &dx_help);
+    // get dims
+    std::vector<std::int64_t> x_dims = vectorize(x.dims());
+    std::vector<std::int64_t> y_dims = vectorize(y.dims());
+
+    int x_ndim = x_dims.size();
+    int ndim = x_broadcast_dims.size();
+
+    const std::vector<std::int64_t> dx_help_dims = vectorize(dx_help.dims());
+    std::vector<std::int64_t> dx_broadcast_dims(ndim);
+    std::fill(
+        dx_broadcast_dims.data(), dx_broadcast_dims.data() + ndim - x_ndim, 1);
+    std::copy(x_dims.data(),
+              x_dims.data() + x_ndim,
+              dx_broadcast_dims.data() + ndim - x_ndim);
+
+    std::vector<int> dx_reduce_dims;
+    for (int idx = 0; idx <= ndim - 3; idx++) {
+      if (dx_help_dims[idx] != 1 && dx_broadcast_dims[idx] == 1) {
+        dx_reduce_dims.push_back(idx);
+      }
+    }
+    // reduce sum to get grad by ReduceSum
+    if (dx) {
+      dev_ctx.template Alloc<T>(dx);
+
+      if (dx_reduce_dims.empty()) {
+        *dx = std::move(dx_help);
+      } else {
+        bool keep_dim = true;
+        if (dx_help.dims().size() != dx->dims().size()) {
+          keep_dim = false;
+        }
+        ReduceSumForSolvelGrad<Context, T>()(
+            dev_ctx, dx_help, dx, dx_reduce_dims, keep_dim);
+      }
+      dx->Resize(x.dims());
+    }
+  } else {
+    phi::Copy(dev_ctx, tmp_dx, dev_ctx.GetPlace(), false, dx);
+  }
+}
+
+}  // namespace phi

paddle/phi/kernels/impl/solve_kernel_impl.h

+/* Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#include "paddle/phi/core/tensor_utils.h"
+#include "paddle/phi/kernels/expand_as_kernel.h"
+#include "paddle/phi/kernels/funcs/matrix_solve.h"
+#include "paddle/phi/kernels/funcs/reduce_functor.h"
+#include "paddle/phi/kernels/squeeze_kernel.h"
+#include "paddle/phi/kernels/unsqueeze_kernel.h"
+
+namespace phi {
+
+using Tensor = DenseTensor;
+
+// check the input other is vector_case or not
+static inline bool is_vector_rhs(const DenseTensor& input,
+                                 const DenseTensor& other) {
+  auto x_dim = input.dims();
+  auto y_dim = other.dims();
+  auto x_dim_size = x_dim.size();
+  auto y_dim_size = y_dim.size();
+  std::vector<int64_t> x_dims_vec = phi::vectorize(x_dim);
+  std::vector<int64_t> y_dims_vec = phi::vectorize(y_dim);
+
+  std::vector<int64_t>::const_iterator f = x_dims_vec.begin();
+  std::vector<int64_t>::const_iterator l = x_dims_vec.end() - 1;
+  std::vector<int64_t> x_dims_vec_cut(f, l);  // input.shape[:-1]
+
+  std::vector<int64_t> expected_batched_rhs_shape(x_dims_vec_cut);
+  bool vector_case =
+      y_dim_size == 1 || (x_dim_size - 1 == y_dim_size &&
+                          y_dims_vec == (expected_batched_rhs_shape));
+
+  return vector_case;
+}
+
+// Prepared for the broadcast operation
+static std::vector<int64_t> get_broadcast_batch_portion(
+    std::vector<int64_t> x, std::vector<int64_t> y) {
+  size_t size_x = x.size();
+  size_t size_y = y.size();
+  size_t size = std::max(size_x, size_y);
+  std::vector<int64_t> batchPortion(size);
+  ptrdiff_t i = (ptrdiff_t)size - 1;
+  for (; i >= 0; --i) {
+    ptrdiff_t offset = size - i - 1;
+    ptrdiff_t dim_x = size_x - offset - 1;
+    ptrdiff_t dim_y = size_y - offset - 1;
+    int64_t x_size = (dim_x >= 0) ? x[dim_x] : 1;
+    int64_t y_size = (dim_y >= 0) ? y[dim_y] : 1;
+    PADDLE_ENFORCE_EQ(
+        (x_size == y_size || x_size == 1 || y_size == 1),
+        true,
+        phi::errors::PreconditionNotMet(
+            "The size of tensor x (%d) must match the size of tensor y "
+            "(%d) at non-singleton dimension %d.",
+            x_size,
+            y_size,
+            i));
+
+    batchPortion[i] = x_size != 1 ? x_size : y_size;
+  }
+  return batchPortion;
+}
+
+static inline std::vector<int> convert_to_int_vec(std::vector<int64_t> a) {
+  std::vector<int> ret;
+  for (size_t i = 0; i < a.size(); i++) {
+    ret.emplace_back(int(a[i]));
+  }
+
+  return ret;
+}
+
+// broadcast the batch dimensions of tensor x and tensor y.
+static inline std::tuple<std::vector<int64_t>, std::vector<int64_t>>
+get_broadcast_dims(const Tensor& x, const Tensor& y) {
+  std::vector<int64_t> x_dims_vec = phi::vectorize(x.dims());
+  std::vector<int64_t> y_dims_vec = phi::vectorize(y.dims());
+  std::vector<int64_t>::const_iterator f1 = x_dims_vec.begin();
+  std::vector<int64_t>::const_iterator l1 = x_dims_vec.end() - 2;
+  std::vector<int64_t> x_dims_vec_cut(f1, l1);
+
+  std::vector<int64_t>::const_iterator f2 = y_dims_vec.begin();
+  std::vector<int64_t>::const_iterator l2 = y_dims_vec.end() - 2;
+  std::vector<int64_t> y_dims_vec_cut(f2, l2);
+
+  std::vector<int64_t> expand_batch_portion =
+      get_broadcast_batch_portion(x_dims_vec_cut, y_dims_vec_cut);
+  std::vector<int64_t> x_expand_size({expand_batch_portion});
+  x_expand_size.insert(x_expand_size.end(),
+                       {x_dims_vec[static_cast<int>(x_dims_vec.size()) - 2],
+                        x_dims_vec[static_cast<int>(x_dims_vec.size()) - 1]});
+  std::vector<int64_t> y_expand_size({expand_batch_portion});
+  y_expand_size.insert(y_expand_size.end(),
+                       {y_dims_vec[static_cast<int>(y_dims_vec.size()) - 2],
+                        y_dims_vec[static_cast<int>(y_dims_vec.size()) - 1]});
+
+  return std::make_tuple(x_expand_size, y_expand_size);
+}
+
+template <typename Context, typename T>
+static void linalg_solve(const Context& dev_ctx,
+                         const DenseTensor& x,
+                         const DenseTensor& y,
+                         DenseTensor* out) {
+  dev_ctx.template Alloc<T>(out);
+  phi::funcs::MatrixSolveFunctor<Context, T> mat_solve;
+
+  // input y can be vector or matrix
+  // but need to be unsqueezed if y is a vector
+  bool is_vector = false;
+  is_vector = is_vector_rhs(x, y);
+
+  Tensor tmp_y;
+  if (is_vector) {
+    dev_ctx.Alloc(&tmp_y, y.dtype());
+
+    phi::Unsqueeze<T, Context>(dev_ctx, y, {-1}, &tmp_y, nullptr);
+  } else {
+    tmp_y.Resize(y.dims());
+    dev_ctx.Alloc(&tmp_y, y.dtype());
+
+    phi::Copy(dev_ctx, y, dev_ctx.GetPlace(), false, &tmp_y);
+  }
+
+  Tensor tmp_x;
+  tmp_x.Resize(x.dims());
+  dev_ctx.Alloc(&tmp_x, x.dtype());
+  phi::Copy(dev_ctx, x, dev_ctx.GetPlace(), false, &tmp_x);
+
+  std::vector<int64_t> x_broadcast_dims;
+  std::vector<int64_t> y_broadcast_dims;
+  std::tie(x_broadcast_dims, y_broadcast_dims) =
+      get_broadcast_dims(tmp_x, tmp_y);
+
+  Tensor tmp_x_bc;
+
+  phi::ExpandAsKernel<T, Context>(
+      dev_ctx, tmp_x, nullptr, convert_to_int_vec(x_broadcast_dims), &tmp_x_bc);
+
+  Tensor tmp_y_bc;
+  phi::ExpandAsKernel<T, Context>(
+      dev_ctx, tmp_y, nullptr, convert_to_int_vec(y_broadcast_dims), &tmp_y_bc);
+
+  auto x_dim = x.dims();
+  auto y_dim = y.dims();
+  auto x_dim_size = x_dim.size();
+  auto y_dim_size = y_dim.size();
+
+  if (is_vector) {                 // vector case
+    out->Resize(tmp_y_bc.dims());  // out.unsqueeze(-1)
+    mat_solve(dev_ctx, tmp_x_bc, tmp_y_bc, out);
+
+    Tensor out_tmp;
+    out_tmp.Resize(out->dims());
+    out_tmp = *out;
+
+    phi::SqueezeKernel<T, Context>(dev_ctx, out_tmp, {-1}, out, nullptr);
+  } else {
+    PADDLE_ENFORCE_EQ(
+        x_dim[x_dim_size - 1],
+        y_dim[y_dim_size - 2],
+        phi::errors::InvalidArgument(
+            "Matrix X1 with dimension greater than 2 and any matrix Y1,"
+            "the matrix X1's width must be equal with matrix Y1's "
+            "height. But received X's shape = [%s], X1's shape = [%s], X1's "
+            "width = %s; Y's shape = [%s], Y1's shape = [%s], Y1's height = "
+            "%s.",
+            x_dim,
+            x_dim,
+            x_dim[x_dim_size - 1],
+            y_dim,
+            y_dim,
+            y_dim[y_dim_size - 2]));
+    mat_solve(dev_ctx, tmp_x_bc, tmp_y_bc, out);
+  }
+}
+
+template <typename T, typename Context>
+void SolveKernel(const Context& dev_ctx,
+                 const DenseTensor& x,
+                 const DenseTensor& y,
+                 DenseTensor* out) {
+  linalg_solve<Context, T>(dev_ctx, x, y, out);
+}
+
+}  // namespace phi

paddle/fluid/operators/solve_op.cu → paddle/phi/kernels/solve_grad_kernel.h

-/* Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+/* Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
 
 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.
@@ -12,14 +12,19 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 See the License for the specific language governing permissions and
 limitations under the License. */
 
-#include "paddle/fluid/operators/solve_op.h"
+#pragma once
 
-namespace ops = paddle::operators;
-namespace plat = paddle::platform;
-REGISTER_OP_CUDA_KERNEL(solve,
-                        ops::SolveKernel<plat::CUDADeviceContext, float>,
-                        ops::SolveKernel<plat::CUDADeviceContext, double>);
+#include "paddle/phi/core/dense_tensor.h"
 
-REGISTER_OP_CUDA_KERNEL(solve_grad,
-                        ops::SolveGradKernel<plat::CUDADeviceContext, float>,
-                        ops::SolveGradKernel<plat::CUDADeviceContext, double>);
+namespace phi {
+
+template <typename T, typename Context>
+void SolveGradKernel(const Context& dev_ctx,
+                     const DenseTensor& x,
+                     const DenseTensor& y,
+                     const DenseTensor& dout,
+                     const DenseTensor& out,
+                     DenseTensor* dx,
+                     DenseTensor* dy);
+
+}  // namespace phi

paddle/phi/kernels/solve_kernel.h

+/* Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#pragma once
+
+#include "paddle/phi/core/dense_tensor.h"
+
+namespace phi {
+
+template <typename T, typename Context>
+void SolveKernel(const Context& dev_ctx,
+                 const DenseTensor& x,
+                 const DenseTensor& y,
+                 DenseTensor* out);
+
+}  // namespace phi

paddle/phi/kernels/unsqueeze_kernel.h

@@ -17,6 +17,7 @@
 
 #include "paddle/phi/common/int_array.h"
 #include "paddle/phi/core/dense_tensor.h"
+#include "paddle/phi/infermeta/unary.h"
 
 namespace phi {
 
@@ -26,4 +27,16 @@ void UnsqueezeKernel(const Context& dev_ctx,
                      const IntArray& axes,
                      DenseTensor* out,
                      DenseTensor* xshape);
+
+template <typename T, typename Context>
+void Unsqueeze(const Context& dev_ctx,
+               const DenseTensor& x,
+               const IntArray& axes,
+               DenseTensor* out,
+               DenseTensor* xshape) {
+  MetaTensor meta_out(out);
+  UnsqueezeInferMeta(x, axes, &meta_out, nullptr, MetaConfig());
+  UnsqueezeKernel<T, Context>(dev_ctx, x, axes, out, nullptr);
+}
+
 }  // namespace phi

paddle/phi/ops/compat/solve_sig.cc

+/* Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#include "paddle/phi/core/compat/op_utils.h"
+
+namespace phi {
+
+KernelSignature SolveGradOpArgumentMapping(const ArgumentMappingContext& ctx) {
+  return KernelSignature(
+      "solve_grad", {"X", "Y", "Out@GRAD", "Out"}, {}, {"X@GRAD", "Y@GRAD"});
+}
+
+}  // namespace phi
+
+PD_REGISTER_ARG_MAPPING_FN(solve_grad, phi::SolveGradOpArgumentMapping);