Align max_pool1d Error Checking between CPU and CUDA/CPU requires_grad (pytorch#90211)

Fixes pytorch#85712

Standardizes error checking for max_pool1d between CPU and CPU requires_grad/CUDA.

Pull Request resolved: pytorch#90211
Approved by: https://github.com/mruberry

Author: deandyu

aten/src/ATen/native/MaxPooling.cpp

@@ -24,14 +24,13 @@ DEFINE_DISPATCH(max_pool1d_stub);
 
 namespace {
 
-Tensor max_pool1d_impl(
+static void check_max_pool1d(
     const Tensor& self,
     IntArrayRef kernel_size,
     IntArrayRef stride,
     IntArrayRef padding,
     IntArrayRef dilation,
     bool ceil_mode) {
-  NoNamesGuard guard;
 
   TORCH_CHECK(
       self.dim() == 2 || self.dim() == 3,
@@ -58,33 +57,54 @@ Tensor max_pool1d_impl(
     stride = kernel_size;
   }
 
-  const int64_t NB = self.dim() == 3 ? self.size(-3) : 1;
-  const int64_t NC = self.size(-2);
-  const int64_t IW = self.size(-1);
-  const int64_t KW = kernel_size[0];
-  const int64_t SJ = stride[0];
-  const int64_t PJ = padding[0];
-  const int64_t DJ = dilation[0];
-
   TORCH_CHECK(
-      KW > 0,
+      kernel_size[0] > 0,
       "max_pool1d() kernel_size must be greater than zero, but got ",
-      KW);
+      kernel_size[0]);
   TORCH_CHECK(
-      SJ > 0, "max_pool1d() stride must be greater than zero, but got ", SJ);
+      stride[0] > 0, "max_pool1d() stride must be greater than zero, but got ", stride[0]);
   TORCH_CHECK(
-      PJ >= 0, "max_pool1d() padding must be non-negative, but got ", PJ);
+      padding[0] >= 0, "max_pool1d() padding must be non-negative, but got ", padding[0]);
   TORCH_CHECK(
-      PJ <= KW / 2,
+      padding[0] <= kernel_size[0] / 2,
       "max_pool1d() padding should be at most half of kernel size, but got padding=",
-      PJ,
+      padding[0],
       " and kernel_size=",
-      KW);
+      kernel_size[0]);
   TORCH_CHECK(
-      DJ > 0, "max_pool1d() dilation must be greater than zero, but got ", DJ);
+      dilation[0] > 0, "max_pool1d() dilation must be greater than zero, but got ", dilation[0]);
 
-  const int64_t OW = pooling_output_shape(IW, KW, PJ, SJ, DJ, ceil_mode);
+  const int64_t OW = pooling_output_shape(self.size(-1), kernel_size[0], padding[0], stride[0], dilation[0], ceil_mode);
   TORCH_CHECK(OW >= 0, "max_pool1d() Invalid computed output size: ", OW);
+}
+
+} // namespace
+
+namespace {
+
+Tensor max_pool1d_impl(
+    const Tensor& self,
+    IntArrayRef kernel_size,
+    IntArrayRef stride,
+    IntArrayRef padding,
+    IntArrayRef dilation,
+    bool ceil_mode) {
+  NoNamesGuard guard;
+
+  // If stride=None then set it to kernel_size
+  if (stride.empty()) {
+    stride = kernel_size;
+  }
+
+  const int64_t NB = self.dim() == 3 ? self.size(-3) : 1;
+  const int64_t NC = self.size(-2);
+  const int64_t IW = self.size(-1);
+  const int64_t KW = kernel_size[0];
+  const int64_t SJ = stride[0];
+  const int64_t PJ = padding[0];
+  const int64_t DJ = dilation[0];
+
+  const int64_t OW = pooling_output_shape(IW, KW, PJ, SJ, DJ, ceil_mode);
   Tensor output = at::empty({NB, NC, OW}, self.options());
 
   PoolingParams1D params{NB, NC, IW, OW, KW, SJ, PJ, DJ};
@@ -121,6 +141,8 @@ Tensor max_pool1d(
     return at::quantized_max_pool1d(
         self, kernel_size, stride, padding, dilation, ceil_mode);
   }
+
+  check_max_pool1d(self, kernel_size, stride, padding, dilation, ceil_mode);
   if ((self.requires_grad() && at::GradMode::is_enabled()) ||
       self._fw_grad(/*level */ 0).defined() ||
       !self.device().is_cpu() ||

torch/testing/_internal/common_methods_invocations.py

@@ -3032,40 +3032,29 @@ def error_inputs_max_pool1d(op_info, device, **kwargs):
                          error_regex=error_msg)
 
         # error inputs for empty input with stride=0
-        # NOTE: CPU vs (CPU with requires_grad and CUDA) error messages are different.
-        error_msg = 'stride must be greater than zero, but got 0' if torch.device(
-            device).type == 'cpu' and not requires_grad else 'stride should not be zero'
+        error_msg = 'stride must be greater than zero, but got 0'
         yield ErrorInput(SampleInput(make_arg((3, 3, 3)), kwargs={'kernel_size': 1, 'stride': 0}),
                          error_regex=error_msg)
 
         # error inputs for empty input with dilation=0
-        # NOTE: CPU vs (CPU with requires_grad and CUDA) error messages are different.
-        error_msg = 'dilation must be greater than zero, but got 0' if torch.device(
-            device).type == 'cpu' and not requires_grad else 'dilation should be greater than zero, but got dilation'
+        error_msg = 'dilation must be greater than zero, but got 0'
         yield ErrorInput(SampleInput(make_arg((3, 3, 3)),
                                      kwargs={'kernel_size': 1, 'stride': 1, 'padding': 0, 'dilation': 0}),
                          error_regex=error_msg)
 
         # error inputs for invalid output size
-        # NOTE: CPU vs (CPU with requires_grad and CUDA) error messages are different.
-        error_msg = 'Invalid computed output size: -2' if torch.device(device).type == 'cpu' and not requires_grad \
-            else \
-            r'Given input size: \(2x1x2\). Calculated output size: \(2x1x-2\). Output size is too small'
+        error_msg = 'Invalid computed output size: -2'
         yield ErrorInput(SampleInput(make_arg((2, 2, 2)),
                                      kwargs={'kernel_size': 5, 'stride': 1, 'padding': 0, 'dilation': 1}),
                          error_regex=error_msg)
 
         # error inputs when kernel_size=0
-        # NOTE: CPU vs (CPU with requires_grad and CUDA) error messages are different.
-        error_msg = 'kernel_size must be greater than zero' if torch.device(
-            device).type == 'cpu' and not requires_grad else r'stride should not be zero'
+        error_msg = 'kernel_size must be greater than zero'
         yield ErrorInput(SampleInput(x, kwargs={'kernel_size': 0}),
                          error_regex=error_msg)
 
         # error inputs for strides > 0
-        # NOTE: CPU vs (CPU with requires_grad and CUDA) error messages are different.
-        error_msg = 'stride must be greater than zero' if torch.device(
-            device).type == 'cpu' and not requires_grad else r'stride should not be zero'
+        error_msg = 'stride must be greater than zero'
         yield ErrorInput(SampleInput(x, kwargs={'kernel_size': 2, 'stride': 0}),
                          error_regex=error_msg)