Support non-default dim orders in pybindings + aten bridge

Summary: Now that we have better dim order support, we can pass inputs into models in non-default dim orders, such as channels last. This works in the runtime, but the python layer current asserts that input tensors are in default dim order. This PR relaxes this restriction to allow dense input tensors with alternative dim orders.

Differential Revision: D71716100

Author: GregoryComer

extension/aten_util/aten_bridge.cpp

@@ -8,6 +8,8 @@
 
 #include <executorch/extension/aten_util/aten_bridge.h>
 
+#include <executorch/runtime/core/exec_aten/util/dim_order_util.h>
+#include <executorch/runtime/core/exec_aten/util/tensor_dimension_limit.h>
 #include <executorch/runtime/platform/assert.h>
 #include <cstring>
 
@@ -40,7 +42,12 @@ ET_CHECK_MSG(
         ssize_t(a.size(i)),
         ssize_t(b.size(i)));
   }
-  // check strides
+  // check strides and dim order
+  std::array<exec_aten::StridesType, executorch::runtime::kTensorDimensionLimit>
+      expected_strides;
+  runtime::dim_order_to_stride_nocheck(
+      b.sizes().data(), b.dim_order().data(), b.dim(), expected_strides.data());
+
   for (size_t i = 0, dims = a.dim(); i < dims; ++i) {
     // Dont match strides if the size is 1.
     // Why? Because tensor is non-contig only if
@@ -52,6 +59,12 @@ ET_CHECK_MSG(
         i,
         ssize_t(a.stride(i)),
         ssize_t(b.strides()[i]));
+    ET_CHECK_MSG(
+        (b.size(i) == 1 || (b.strides()[i] == expected_strides[i])),
+        "Strides don't match dim order at index:%zd, stride: %zd != expected %zd",
+        i,
+        ssize_t(a.stride(i)),
+        ssize_t(expected_strides[i]));
   }
   // check dtype
   ET_CHECK_MSG(
@@ -109,13 +122,6 @@ c10::ScalarType executorch_to_torch_scalar_type(
 void alias_etensor_to_attensor(
     at::Tensor& aten_tensor,
     torch::executor::Tensor& mutable_et) {
-  // TODO(kimishpatel): contiguous according to memformat
-  // Right now we assume everything is channels first contiguous
-  // Note that input tensor must be contiguous for us to alias.
-  // Mixing aliasing and copying is dangerous since if we aliased
-  // the instance of mutatble_et to aten_tensor in the previous call,
-  // then in the next call copying will not be the correct behavior.
-  ET_CHECK_MSG(aten_tensor.is_contiguous(), "Input tensor must be contiguous");
   check_tensor_meta(aten_tensor, mutable_et);
   mutable_et.unsafeGetTensorImpl()->set_data(aten_tensor.mutable_data_ptr());
 }

extension/aten_util/test/aten_bridge_test.cpp

@@ -154,3 +154,83 @@ TEST(ATenBridgeTest, AliasTensorPtrToATenTensor) {
   alias_etensor_to_attensor(at_tensor, *et_tensor_ptr);
   EXPECT_EQ(at_tensor.const_data_ptr(), et_tensor_ptr->const_data_ptr());
 }
+
+TEST(ATenBridgeTest, AliasATTensorToETensorChannelsLast) {
+  auto at_tensor = at::randn({2, 3, 4, 5}).to(at::MemoryFormat::ChannelsLast);
+  std::vector<Tensor::SizesType> sizes(
+      at_tensor.sizes().begin(), at_tensor.sizes().end());
+  std::vector<Tensor::DimOrderType> dim_order = {0, 2, 3, 1};
+  std::vector<Tensor::StridesType> strides(
+      at_tensor.strides().begin(), at_tensor.strides().end());
+  auto dtype = torchToExecuTorchScalarType(at_tensor.options().dtype());
+  std::vector<uint8_t> etensor_data(at_tensor.nbytes());
+  torch::executor::TensorImpl tensor_impl(
+      dtype,
+      at_tensor.dim(),
+      sizes.data(),
+      etensor_data.data(),
+      dim_order.data(),
+      strides.data());
+  torch::executor::Tensor etensor(&tensor_impl);
+  auto aliased_at_tensor = alias_attensor_to_etensor(etensor);
+  EXPECT_EQ(aliased_at_tensor.const_data_ptr(), etensor_data.data());
+}
+
+TEST(ATenBridgeTest, AliasATTensorToETensorFailDimOrder) {
+  auto at_tensor = at::randn({2, 3, 4, 5}).to(at::MemoryFormat::ChannelsLast);
+  std::vector<Tensor::SizesType> sizes(
+      at_tensor.sizes().begin(), at_tensor.sizes().end());
+  std::vector<Tensor::DimOrderType> dim_order = {0, 1, 2, 3};
+  std::vector<Tensor::StridesType> strides(
+      at_tensor.strides().begin(), at_tensor.strides().end());
+  auto dtype = torchToExecuTorchScalarType(at_tensor.options().dtype());
+  std::vector<uint8_t> etensor_data(at_tensor.nbytes());
+  torch::executor::TensorImpl tensor_impl(
+      dtype,
+      at_tensor.dim(),
+      sizes.data(),
+      etensor_data.data(),
+      dim_order.data(),
+      strides.data());
+  torch::executor::Tensor etensor(&tensor_impl);
+  ET_EXPECT_DEATH(alias_attensor_to_etensor(etensor), "");
+}
+
+TEST(ATenBridgeTest, AliasETensorToATenTensorChannelsLast) {
+  auto at_tensor = at::randn({2, 3, 4, 5}).to(at::MemoryFormat::ChannelsLast);
+  std::vector<Tensor::SizesType> sizes(
+      at_tensor.sizes().begin(), at_tensor.sizes().end());
+  std::vector<Tensor::DimOrderType> dim_order = {0, 2, 3, 1};
+  std::vector<Tensor::StridesType> strides(
+      at_tensor.strides().begin(), at_tensor.strides().end());
+  auto dtype = torchToExecuTorchScalarType(at_tensor.options().dtype());
+  torch::executor::TensorImpl tensor_impl(
+      dtype,
+      at_tensor.dim(),
+      sizes.data(),
+      nullptr,
+      dim_order.data(),
+      strides.data());
+  torch::executor::Tensor etensor(&tensor_impl);
+  alias_etensor_to_attensor(at_tensor, etensor);
+  EXPECT_EQ(at_tensor.const_data_ptr(), etensor.const_data_ptr());
+}
+
+TEST(ATenBridgeTest, AliasETensorToATenTensorFailDimOrder) {
+  auto at_tensor = at::randn({2, 3, 4, 5}).to(at::MemoryFormat::ChannelsLast);
+  std::vector<Tensor::SizesType> sizes(
+      at_tensor.sizes().begin(), at_tensor.sizes().end());
+  std::vector<Tensor::DimOrderType> dim_order = {0, 1, 2, 3};
+  std::vector<Tensor::StridesType> strides(
+      at_tensor.strides().begin(), at_tensor.strides().end());
+  auto dtype = torchToExecuTorchScalarType(at_tensor.options().dtype());
+  torch::executor::TensorImpl tensor_impl(
+      dtype,
+      at_tensor.dim(),
+      sizes.data(),
+      nullptr,
+      dim_order.data(),
+      strides.data());
+  torch::executor::Tensor etensor(&tensor_impl);
+  ET_EXPECT_DEATH(alias_etensor_to_attensor(at_tensor, etensor), "");
+}

extension/pybindings/pybindings.cpp

@@ -703,13 +703,6 @@ struct PyModule final {
       const std::string& type_str = py::str(python_input.get_type());
       if (type_str == "<class 'torch.Tensor'>") {
         auto at_tensor = python_input.cast<at::Tensor>();
-        // alias_etensor_to_attensor will assert on this later, so to better
-        // propogate up to python we check early and throw an exception.
-        if (!at_tensor.is_contiguous()) {
-          auto error_msg = "Input " + std::to_string(i) + "for method " +
-              method_name + " is not contiguous.";
-          throw std::runtime_error(error_msg);
-        }
 
 #ifdef USE_ATEN_LIB
         EValue evalue(at_tensor);
@@ -725,10 +718,24 @@ struct PyModule final {
         input_strides.emplace_back(
             at_tensor.strides().begin(), at_tensor.strides().end());
 
-        // Only works for MemoryFormat::Contiguous inputs
+        // Only works for MemoryFormat::Contiguous or MemoryFormat::ChannelsLast
+        // inputs
         std::vector<torch::executor::Tensor::DimOrderType> dim_order;
-        for (size_t cur_dim = 0; cur_dim < dim; cur_dim++) {
-          dim_order.push_back(cur_dim);
+        if (at_tensor.is_contiguous()) {
+          for (size_t cur_dim = 0; cur_dim < dim; cur_dim++) {
+            dim_order.push_back(cur_dim);
+          }
+        } else if (
+            at_tensor.is_contiguous(at::MemoryFormat::ChannelsLast) &&
+            at_tensor.dim() == 4) {
+          dim_order.push_back(0);
+          dim_order.push_back(2);
+          dim_order.push_back(3);
+          dim_order.push_back(1);
+        } else {
+          auto error_msg = "Input " + std::to_string(i) + "for method " +
+              method_name + " should be contiguous or channels-last.";
+          throw std::runtime_error(error_msg);
         }
         input_dim_order.push_back(std::move(dim_order));
         input_tensors.emplace_back(

extension/pybindings/test/make_test.py

@@ -32,6 +32,40 @@ def get_inputs(self):
         return (torch.ones(2, 2), torch.ones(2, 2))
 
 
+class ModuleChannelsLast(torch.nn.Module):
+    """The module to serialize and execute."""
+
+    def forward(self, x):
+        return torch.nn.functional.interpolate(
+            x,
+            scale_factor=2,
+            mode="nearest",
+        )
+
+    def get_methods_to_export(self):
+        return ("forward",)
+
+    def get_inputs(self):
+        return (torch.ones(1, 2, 3, 4).to(memory_format=torch.channels_last),)
+
+
+class ModuleChannelsLastInDefaultOut(torch.nn.Module):
+    """The module to serialize and execute."""
+
+    def forward(self, x):
+        return torch.nn.functional.interpolate(
+            x,
+            scale_factor=2,
+            mode="nearest",
+        ).to(memory_format=torch.contiguous_format)
+
+    def get_methods_to_export(self):
+        return ("forward",)
+
+    def get_inputs(self):
+        return (torch.ones(1, 2, 3, 4).to(memory_format=torch.channels_last),)
+
+
 class ModuleMulti(torch.nn.Module):
     """The module to serialize and execute."""
 
@@ -298,11 +332,43 @@ def test_constant_output_not_memory_planned(tester):
             # The test module adds the input to torch.ones(2,2), so its output should be the same
             # as adding them directly.
             expected = torch.ones(2, 2) + torch.ones(2, 2)
-            tester.assertEqual(str(expected), str(executorch_output[0]))
+            tester.assertTrue(torch.allclose(expected, executorch_output[0]))
 
             # The test module returns the state. Check that its value is correct.
             tester.assertEqual(str(torch.ones(2, 2)), str(executorch_output[1]))
 
+        def test_channels_last(tester) -> None:
+            # Create an ExecuTorch program from ModuleChannelsLast.
+            model = ModuleChannelsLast()
+            exported_program, inputs = create_program(model)
+
+            # Use pybindings to load and execute the program.
+            executorch_module = load_fn(exported_program.buffer)
+            # Inovke the callable on executorch_module instead of calling module.forward.
+            # Use only one input to test this case.
+            executorch_output = executorch_module(inputs[0])[0]
+
+            # The test module adds the two inputs, so its output should be the same
+            # as adding them directly.
+            expected = model(inputs[0])
+            tester.assertTrue(torch.allclose(expected, executorch_output))
+
+        def test_channels_last_in_default_out(tester) -> None:
+            # Create an ExecuTorch program from ModuleChannelsLastInDefaultOut.
+            model = ModuleChannelsLastInDefaultOut()
+            exported_program, inputs = create_program(model)
+
+            # Use pybindings to load and execute the program.
+            executorch_module = load_fn(exported_program.buffer)
+            # Inovke the callable on executorch_module instead of calling module.forward.
+            # Use only one input to test this case.
+            executorch_output = executorch_module(inputs[0])[0]
+
+            # The test module adds the two inputs, so its output should be the same
+            # as adding them directly.
+            expected = model(inputs[0])
+            tester.assertTrue(torch.allclose(expected, executorch_output))
+
         def test_method_meta(tester) -> None:
             exported_program, inputs = create_program(ModuleAdd())
 
@@ -388,6 +454,8 @@ def test_verification_config(tester) -> None:
         test_module_single_input(tester)
         test_stderr_redirect(tester)
         test_quantized_ops(tester)
+        test_channels_last(tester)
+        test_channels_last_in_default_out(tester)
         test_constant_output_not_memory_planned(tester)
         test_method_meta(tester)
         test_bad_name(tester)