[Hackathon 3rd No.22 ] add paddle.incubate.sparse.reshape (PaddlePadd…

…le#46694)

* add sparse reshape

* change the dtype in all test cases to int64

* just one test case

* modify comments

* Update test_sparse_reshape_op.py

* chang the type of "shape"  from  vector<int64_t>  to  IntArray

* check whether sp_out.to_dense() is the cause  of error

* print sp_out

* Update reshape_kernel.cc

* use numpy to generate the equal paddle tensor

* just check dense_tensor.numpy()

* check cpu and cuda versions

* Update test_sparse_reshape_op.py

* supply all test cases for cpu forward coo kernel

* test forward coo cuda kernel

* change configuration of cuda kernel

* keep only one test case

* test coo cpu kernel (forward and backward)

* row major or column major ???

* test cuda coo forward kernel

* complete declaration and registration

* Update __init__.py

* rebuild

* retrigger CI

* add cudaMalloc and cudaMemcpy  in  ReshapeCooKernel  and change back to row major order in a cuda dense tensor

* midify minor error

* test only cpu coo forward kernel

* add all test cases for coo forward kernel  (both cpu and gpu)

* test all forward kernels (coo, csr; cpu, gpu)

* add all test cases for all kinds of kernels

* just retrigger CI

* Update sparse_ops.yaml

* Update sparse_ops.yaml

* Update sparse_ops.yaml

* resolve conflicts

* Update sparse_ops.yaml

* don't specify tensor place

* new shape has -1 or 0 in it

* Update unary_grad_kernel.h

* correct lvalue error

* code style

* Update sparse_backward.yaml

* Update sparse_ops.yaml

* Update unary_kernel.h

* Update unary.py

* Update sparse_backward.yaml

* Update unary.py

* code style

* code style

* code style

* Update unary.py

* specify tensor place explicitly

* do not use numpy array

* use numpy array in unit test again

* modify example code in docstring

paddle/phi/api/yaml/sparse_backward.yaml

@@ -260,6 +260,17 @@
     func : relu_coo_grad {sparse_coo, sparse_coo -> sparse_coo},
            relu_csr_grad {sparse_csr, sparse_csr -> sparse_csr}
 
+- backward_op : reshape_grad
+  forward : reshape(Tensor x, IntArray shape) -> Tensor(out)
+  args : (Tensor x, Tensor out_grad)
+  output : Tensor(x_grad)
+  infer_meta :
+    func : UnchangedInferMeta
+    param : [x]
+  kernel :
+    func : reshape_coo_grad {sparse_coo, sparse_coo -> sparse_coo},
+           reshape_csr_grad {sparse_csr, sparse_csr -> sparse_csr}
+
 - backward_op : scale_grad
   forward : scale(Tensor x, float scale, float bias, bool bias_after_scale) -> Tensor(out)
   args : (Tensor out_grad, float scale)

paddle/phi/api/yaml/sparse_ops.yaml

@@ -469,3 +469,24 @@
            transpose_csr{sparse_csr -> sparse_csr}
     layout : x
   backward : transpose_grad
+
+- op : sync_batch_norm
+  args : (Tensor x, Tensor scale, Tensor bias, Tensor mean, Tensor variance, float momentum, float epsilon, str data_layout, bool is_test, bool use_global_stats, bool trainable_statistics, bool fuse_with_relu)
+  output : Tensor(out), Tensor(mean_out), Tensor(variance_out), Tensor(saved_mean), Tensor(saved_variance), Tensor(reserve_space)
+  infer_meta :
+    func : BatchNormInferMeta
+  kernel :
+    func : sync_batch_norm_coo{sparse_coo, dense, dense, dense, dense -> sparse_coo, dense, dense, dense, dense, dense}
+    data_type : x
+  backward : sync_batch_norm_grad
+
+- op : reshape
+  args : (Tensor x,  IntArray shape)
+  output : Tensor(out)
+  infer_meta :
+    func : ReshapeInferMeta
+  kernel :
+    func : reshape_coo{sparse_coo -> sparse_coo},
+           reshape_csr{sparse_csr -> sparse_csr}
+    layout : x
+  backward : reshape_grad

paddle/phi/kernels/sparse/cpu/reshape_grad_kernel.cc

@@ -0,0 +1,73 @@
+// 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/sparse/unary_grad_kernel.h"
+#include "paddle/phi/kernels/sparse/unary_kernel.h"
+
+#include "paddle/phi/backends/cpu/cpu_context.h"
+#include "paddle/phi/core/kernel_registry.h"
+#include "paddle/phi/kernels/sparse/empty_kernel.h"
+#include "paddle/phi/kernels/sparse/impl/unary_grad_kernel_impl.h"
+
+namespace phi {
+namespace sparse {
+
+template <typename T, typename Context>
+void ReshapeCooGradKernel(const Context& dev_ctx,
+                          const SparseCooTensor& x,
+                          const SparseCooTensor& dout,
+                          SparseCooTensor* dx) {
+  EmptyLikeCooKernel<T, Context>(dev_ctx, x, dx);
+  phi::IntArray x_shape(phi::vectorize(x.dims()));
+  ReshapeCooKernel<T, Context>(dev_ctx, dout, x_shape, dx);
+}
+
+template <typename T, typename Context>
+void ReshapeCsrGradKernel(const Context& dev_ctx,
+                          const SparseCsrTensor& x,
+                          const SparseCsrTensor& dout,
+                          SparseCsrTensor* dx) {
+  EmptyLikeCsrKernel<T, Context>(dev_ctx, x, dx);
+  phi::IntArray x_shape(phi::vectorize(x.dims()));
+  ReshapeCsrKernel<T, Context>(dev_ctx, dout, x_shape, dx);
+}
+
+}  // namespace sparse
+}  // namespace phi
+
+PD_REGISTER_KERNEL(reshape_coo_grad,
+                   CPU,
+                   ALL_LAYOUT,
+                   phi::sparse::ReshapeCooGradKernel,
+                   float,
+                   double,
+                   int8_t,
+                   uint8_t,
+                   int16_t,
+                   int,
+                   int64_t,
+                   bool) {}
+
+PD_REGISTER_KERNEL(reshape_csr_grad,
+                   CPU,
+                   ALL_LAYOUT,
+                   phi::sparse::ReshapeCsrGradKernel,
+                   float,
+                   double,
+                   int8_t,
+                   uint8_t,
+                   int16_t,
+                   int,
+                   int64_t,
+                   bool) {}

paddle/phi/kernels/sparse/cpu/reshape_kernel.cc

@@ -0,0 +1,117 @@
+// 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/sparse/unary_kernel.h"
+
+#include "paddle/phi/core/ddim.h"
+#include "paddle/phi/kernels/sparse/sparse_utils_kernel.h"
+
+#include "paddle/phi/backends/cpu/cpu_context.h"
+#include "paddle/phi/core/kernel_registry.h"
+#include "paddle/phi/kernels/funcs/eigen/common.h"
+#include "paddle/phi/kernels/funcs/eigen/eigen_function.h"
+#include "paddle/phi/kernels/sparse/empty_kernel.h"
+#include "paddle/phi/kernels/sparse/impl/unary_grad_kernel_impl.h"
+#include "paddle/phi/kernels/sparse/impl/unary_kernel_impl.h"
+
+namespace phi {
+namespace sparse {
+
+template <typename T, typename Context>
+void ReshapeCooKernel(const Context& dev_ctx,
+                      const SparseCooTensor& x,
+                      const phi::IntArray& shape,
+                      SparseCooTensor* out) {
+  // TODO(OccupyMars2025): Currently, reshape is only applicable to sparse dims
+  int64_t x_nnz = x.nnz();
+
+  // Use DDim::reshape to handle -1 and 0 in the argument "shape"
+  std::vector<int> new_shape(shape.GetData().begin(), shape.GetData().end());
+  phi::DDim out_dims = x.dims().reshape(new_shape);
+  // get sparse part dimensions of x and out
+  std::vector<int64_t> x_sparse_part_dims;
+  std::vector<int64_t> out_sparse_part_dims;
+  for (int i = 0; i < x.sparse_dim(); ++i) {
+    x_sparse_part_dims.push_back(x.dims()[i]);
+  }
+  for (int i = 0; i < out_dims.size() - x.dense_dim(); ++i) {
+    out_sparse_part_dims.push_back(out_dims[i]);
+  }
+  DenseTensor out_indices = Empty<int64_t, Context>(
+      dev_ctx, {static_cast<int64_t>(out_sparse_part_dims.size()), x_nnz});
+  DenseTensor out_values(x.values());
+  out->SetMember(out_indices, out_values, out_dims, x.coalesced());
+
+  // compute values of indices
+  const DenseTensor& x_indices = x.indices();
+  const auto* x_indices_data = x_indices.data<int64_t>();
+  auto* out_indices_data = out_indices.data<int64_t>();
+
+  const phi::DDim& x_sparse_part_strides =
+      phi::stride(phi::make_ddim(x_sparse_part_dims));
+  const phi::DDim& out_sparse_part_strides =
+      phi::stride(phi::make_ddim(out_sparse_part_dims));
+  int64_t location = 0;
+  for (int64_t j = 0; j < x_nnz; ++j) {
+    location = 0;
+    for (int i = 0; i < x.sparse_dim(); ++i) {
+      location += x_indices_data[i * x_nnz + j] * x_sparse_part_strides[i];
+    }
+    for (size_t i = 0; i < out_sparse_part_dims.size(); ++i) {
+      out_indices_data[i * x_nnz + j] = location / out_sparse_part_strides[i];
+      location %= out_sparse_part_strides[i];
+    }
+  }
+}
+
+template <typename T, typename Context>
+void ReshapeCsrKernel(const Context& dev_ctx,
+                      const SparseCsrTensor& x,
+                      const phi::IntArray& shape,
+                      SparseCsrTensor* out) {
+  // transform csr format to coo format, and then use coo kernel
+  const SparseCooTensor x_coo = CsrToCoo<T, Context>(dev_ctx, x);
+  SparseCooTensor out_coo;
+  ReshapeCooKernel<T, Context>(dev_ctx, x_coo, shape, &out_coo);
+  CooToCsrKernel<T, Context>(dev_ctx, out_coo, out);
+}
+
+}  // namespace sparse
+}  // namespace phi
+
+PD_REGISTER_KERNEL(reshape_coo,
+                   CPU,
+                   ALL_LAYOUT,
+                   phi::sparse::ReshapeCooKernel,
+                   float,
+                   double,
+                   int8_t,
+                   uint8_t,
+                   int16_t,
+                   int,
+                   int64_t,
+                   bool) {}
+
+PD_REGISTER_KERNEL(reshape_csr,
+                   CPU,
+                   ALL_LAYOUT,
+                   phi::sparse::ReshapeCsrKernel,
+                   float,
+                   double,
+                   int8_t,
+                   uint8_t,
+                   int16_t,
+                   int,
+                   int64_t,
+                   bool) {}

paddle/phi/kernels/sparse/cpu/sparse_utils_kernel.cc

@@ -329,7 +329,8 @@ PD_REGISTER_KERNEL(csr_to_coo,
                    int8_t,
                    int16_t,
                    int,
-                   int64_t) {}
+                   int64_t,
+                   bool) {}
 
 PD_REGISTER_KERNEL(coo_to_csr,
                    CPU,
@@ -342,7 +343,8 @@ PD_REGISTER_KERNEL(coo_to_csr,
                    int8_t,
                    int16_t,
                    int,
-                   int64_t) {}
+                   int64_t,
+                   bool) {}
 
 PD_REGISTER_KERNEL(dense_to_csr,
                    CPU,

paddle/phi/kernels/sparse/gpu/reshape_grad_kernel.cu

@@ -0,0 +1,77 @@
+// 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/sparse/unary_grad_kernel.h"
+#include "paddle/phi/kernels/sparse/unary_kernel.h"
+
+#include "paddle/phi/backends/gpu/gpu_context.h"
+#include "paddle/phi/core/kernel_registry.h"
+#include "paddle/phi/kernels/sparse/empty_kernel.h"
+#include "paddle/phi/kernels/sparse/impl/unary_grad_kernel_impl.h"
+
+namespace phi {
+namespace sparse {
+
+// just copy from paddle\phi\kernels\sparse\cpu\reshape_grad_kernel.cc
+template <typename T, typename Context>
+void ReshapeCooGradKernel(const Context& dev_ctx,
+                          const SparseCooTensor& x,
+                          const SparseCooTensor& dout,
+                          SparseCooTensor* dx) {
+  EmptyLikeCooKernel<T, Context>(dev_ctx, x, dx);
+  phi::IntArray x_shape(phi::vectorize(x.dims()));
+  ReshapeCooKernel<T, Context>(dev_ctx, dout, x_shape, dx);
+}
+
+// just copy from paddle\phi\kernels\sparse\cpu\reshape_grad_kernel.cc
+template <typename T, typename Context>
+void ReshapeCsrGradKernel(const Context& dev_ctx,
+                          const SparseCsrTensor& x,
+                          const SparseCsrTensor& dout,
+                          SparseCsrTensor* dx) {
+  EmptyLikeCsrKernel<T, Context>(dev_ctx, x, dx);
+  phi::IntArray x_shape(phi::vectorize(x.dims()));
+  ReshapeCsrKernel<T, Context>(dev_ctx, dout, x_shape, dx);
+}
+
+}  // namespace sparse
+}  // namespace phi
+
+PD_REGISTER_KERNEL(reshape_coo_grad,
+                   GPU,
+                   ALL_LAYOUT,
+                   phi::sparse::ReshapeCooGradKernel,
+                   phi::dtype::float16,
+                   float,
+                   double,
+                   int8_t,
+                   uint8_t,
+                   int16_t,
+                   int,
+                   int64_t,
+                   bool) {}
+
+PD_REGISTER_KERNEL(reshape_csr_grad,
+                   GPU,
+                   ALL_LAYOUT,
+                   phi::sparse::ReshapeCsrGradKernel,
+                   phi::dtype::float16,
+                   float,
+                   double,
+                   int8_t,
+                   uint8_t,
+                   int16_t,
+                   int,
+                   int64_t,
+                   bool) {}