Add support for channels_last contig format in cat.

Summary:
Existing cat implementation produces output tensor in contig format
disregarding in the input memory format. This PR fixes the kernel as
well as op implementation to account for that.

Test Plan:
CI

Reviewers:

Subscribers:

Tasks:

Tags:

aten/src/ATen/native/TensorShape.cpp

@@ -145,6 +145,7 @@ Tensor & _cat_out_cpu(Tensor& result, TensorList tensors, int64_t dim) {
 
   // compute size of the result in the cat dimension
   int64_t cat_dim_size = 0;
+  auto first_tensor_mem_format = tensors[0].suggest_memory_format();
   for (int i = 0; i < tensors.size(); i++) {
     auto const &tensor = tensors[i];
     if (should_skip(tensor)) {
@@ -155,7 +156,7 @@ Tensor & _cat_out_cpu(Tensor& result, TensorList tensors, int64_t dim) {
     check_cat_shape_except_dim(notSkippedTensor, tensor, dim, i);
     cat_dim_size += tensor.size(dim);
 
-    if (!tensor.is_contiguous()) {
+    if (!tensor.is_contiguous(first_tensor_mem_format)) {
       allContiguous = false;
     }
 
@@ -170,19 +171,24 @@ Tensor & _cat_out_cpu(Tensor& result, TensorList tensors, int64_t dim) {
   // compute the size of the result
   auto result_size = notSkippedTensor.sizes().vec();
   result_size[dim] = cat_dim_size;
-  result.resize_(result_size);
+  result.resize_(result_size, first_tensor_mem_format);
+  if (result.numel() == 0) {
+    return result;
+  }
 
   // fast path for single thread when both inputs and result are contiguous and not empty
+  allContiguous = allContiguous && result.is_contiguous(first_tensor_mem_format);
   bool use_serial_kernel = result.numel() < at::internal::GRAIN_SIZE || at::get_num_threads() == 1;
-  allContiguous = allContiguous && result.is_contiguous();
   ScalarType dtype = notSkippedTensor.scalar_type();
   if (use_serial_kernel && allContiguous && no_type_promotion && (dtype == ScalarType::Double || dtype == ScalarType::Float)) {
     cat_serial_stub(kCPU, result, tensors, dim);
     return result;
   }
 
   int64_t offset = 0;
-  if (reuse_iterator && result.is_contiguous() && no_type_promotion) {
+  if (reuse_iterator &&
+      result.is_contiguous(first_tensor_mem_format) &&
+      no_type_promotion) {
     auto source_slice = notSkippedTensor;
     auto slice_dim_size = source_slice.size(dim);
     auto result_slice = result.narrow(dim, 0, slice_dim_size);

aten/src/ATen/native/cpu/CatKernel.cpp

@@ -20,27 +20,19 @@ struct InputMeta {
 
 template <typename scalar_t>
 void cat_serial_kernel_impl(Tensor& result, TensorList tensors, int64_t dim) {
-  auto size = result.sizes().vec();
-  int64_t outer = 1, inner = 1;
-  for (int64_t i = 0; i < dim; i++) {
-    outer *= size[i];
-  }
-  for (int64_t i = dim + 1; i < size.size(); i++) {
-    inner *= size[i];
-  }
+  int64_t outer = result.numel() / (result.size(dim) * result.stride(dim));
   scalar_t* result_data = result.data_ptr<scalar_t>();
   int64_t ninputs = tensors.size();
   std::vector<InputMeta> inputs;
   inputs.reserve(ninputs);
   for (auto const &tensor : tensors) {
-    inputs.emplace_back(tensor, dim, inner);
+    inputs.emplace_back(tensor, dim, tensor.stride(dim));
   }
- 
+
   using Vec = vec256::Vec256<scalar_t>;
-  int64_t offset = 0;
-  for (int64_t i = 0; i < outer; i++) {
+  scalar_t* result_ptr = result_data;
+  for (int64_t i = 0; i < outer; ++i) {
     for (int64_t j = 0; j < ninputs; j++) {
-      scalar_t* result_ptr = result_data + offset;
       int64_t local_inner = inputs[j].inner_size;
       scalar_t* input_ptr = (scalar_t*)(inputs[j].data_ptr) + i * local_inner;
       if (local_inner < Vec::size()) {
@@ -57,7 +49,7 @@ void cat_serial_kernel_impl(Tensor& result, TensorList tensors, int64_t dim) {
             input_ptr,
             local_inner);
       }
-      offset += local_inner;
+      result_ptr += local_inner;
     }
   }
 }

test/test_torch.py

@@ -7065,6 +7065,28 @@ def test_cat_out_channels_last(self, device):
         res2 = torch.cat((x, y), out=z)
         self.assertEqual(res1, res2)
 
+    @onlyCPU
+    def test_cat_in_channels_last(self, device):
+        for dim in range(4):
+            x = torch.randn((4, 15, 8, 8), device=device)
+            y = torch.randn(x.shape, device=device)
+            res1 = torch.cat((x, y), dim=dim)
+            x = x.clone().contiguous(memory_format=torch.channels_last)
+            y = y.clone().contiguous(memory_format=torch.channels_last)
+            res2 = torch.cat((x, y), dim=dim)
+            self.assertTrue(res2.is_contiguous(memory_format=torch.channels_last))
+            self.assertEqual(res1, res2)
+
+            # Size larger than grain size.
+            x = torch.randn((4, 15, 256, 256), device=device)
+            y = torch.randn(x.shape, device=device)
+            res1 = torch.cat((x, y), dim=dim)
+            x = x.clone().contiguous(memory_format=torch.channels_last)
+            y = y.clone().contiguous(memory_format=torch.channels_last)
+            res2 = torch.cat((x, y), dim=dim)
+            self.assertTrue(res2.is_contiguous(memory_format=torch.channels_last))
+            self.assertEqual(res1, res2)
+
     @onlyCUDA
     def test_cat_preserve_channels_last(self, device):
         x = torch.randn((4, 3, 8, 8), device=device)