Update base for Update on "[ET-VK] Fixing conv2d dw incorrect output when stride != dilation issue."

This diff moves current implementation of conv2d dw as a special case when stride equals dilation in the Vulkan backend of Executorch, since that's the only time this kind of caching is possible.

If stride does not equal dilation the old implementation is used.

Additional test cases are added to ensure computation is correct when stride != dilation.

Differential Revision: [D67908916](https://our.internmc.facebook.com/intern/diff/D67908916/)

[ghstack-poisoned]

Author: trivedivivek

.gitmodules

@@ -66,7 +66,7 @@
 	url = https://github.com/pybind/pybind11.git
 [submodule "backends/cadence/fusion_g3/third-party/nnlib/nnlib-FusionG3"]
 	path = backends/cadence/fusion_g3/third-party/nnlib/nnlib-FusionG3
-	url = https://github.com/foss-xtensa/nnlib-FusionG3/
+	url = https://github.com/foss-xtensa/nnlib-FusionG3.git
 [submodule "third-party/ao"]
 	path = third-party/ao
 	url = https://github.com/pytorch/ao.git

backends/apple/mps/mps_preprocess.py

@@ -32,6 +32,9 @@
     CompileSpec,
     PreprocessResult,
 )
+
+from executorch.exir.passes.memory_format_ops_pass import DimOrderOpsRevertPass
+from executorch.exir.program._program import _transform
 from torch.export.exported_program import ExportedProgram
 
 FORMAT = "[%(levelname)s %(asctime)s %(filename)s:%(lineno)s] %(message)s"
@@ -83,6 +86,9 @@ def preprocess(
         #    FlatBuffer graph, process the `output` nodes and add their id to
         #    the `output_ids` array in the schema.
 
+        # TODO: Remove this once we have a better support for the dim-order ops.
+        edge_program = _transform(edge_program, DimOrderOpsRevertPass())
+
         mps_graph = MPSGraph(
             version="0",
             mps_nodes=[],

backends/apple/mps/operators/constant_ops.py

@@ -79,6 +79,25 @@ def define_node(
         )
 
 
+@register_node_visitor
+class ToDimOrderEmptyVisitor(NodeVisitor):
+    target = ["dim_order_ops._empty_dim_order.default"]
+
+    def __init__(self, *args) -> None:
+        super().__init__(*args)
+
+    def define_node(
+        self,
+        node: torch.fx.Node,
+        mps_graph: MPSGraph,
+    ) -> None:
+        # We should never get here, because DimOrderOpsRevertPass replaces this with an aten.empty.memory_format op
+        # But if we do, we can't handle it ATM, so raise an exception
+        raise NotImplementedError(
+            "dim_order_ops._empty_dim_order.default is not supported yet"
+        )
+
+
 @register_node_visitor
 class FullLikeVisitor(NodeVisitor):
     target = "aten.full_like.default"

backends/apple/mps/operators/op_clone.py

@@ -33,3 +33,22 @@ def define_node(
                 )
         input_id = self.define_tensor(get_input_node(node, 0), mps_graph)
         self.tensor_to_id[node] = input_id
+
+
+@register_node_visitor
+class ToDimOrderCopyVisitor(NodeVisitor):
+    target = ["dim_order_ops._to_dim_order_copy.default"]
+
+    def __init__(self, *args) -> None:
+        super().__init__(*args)
+
+    def define_node(
+        self,
+        node: torch.fx.Node,
+        mps_graph: MPSGraph,
+    ) -> None:
+        # We should never get here, because DimOrderOpsRevertPass replaces this with an aten._to_copy op
+        # But if we do, we can't handle it ATM, so raise an exception
+        raise NotImplementedError(
+            "dim_order_ops._to_dim_order_copy.default is not supported yet"
+        )

backends/apple/mps/test/test_mps.py

@@ -1829,6 +1829,21 @@ def forward(self, x):
             Clone(), model_inputs, func_name=inspect.stack()[0].function[5:]
         )
 
+    def test_mps_backend_to_copy(self):
+        class Copy(torch.nn.Module):
+            def forward(self, x):
+                return (
+                    torch.ops.aten._to_copy.default(
+                        x + 2, memory_format=torch.contiguous_format
+                    )
+                    + x
+                )
+
+        model_inputs = (torch.randn(1, 3, 3),)
+        self.lower_and_test_with_partitioner(
+            Copy(), model_inputs, func_name=inspect.stack()[0].function[5:]
+        )
+
     def test_mps_backend_floor(self):
         class Floor(torch.nn.Module):
             def forward(self, x):

backends/apple/mps/test/test_mps_utils.py

@@ -26,10 +26,7 @@
 
 # Config for Capturing the weights, will be moved in the future
 
-# TODO(T182928844): Delegate dim order op to backend.
-_EDGE_COMPILE_CONFIG = exir.EdgeCompileConfig(
-    _check_ir_validity=False, _skip_dim_order=True
-)
+_EDGE_COMPILE_CONFIG = exir.EdgeCompileConfig(_check_ir_validity=False)
 
 
 class ansi_colors:
@@ -219,7 +216,6 @@ def lower_module_and_test_output(
             dynamic_shapes=dynamic_shapes,
             edge_compile_config=EdgeCompileConfig(
                 _check_ir_validity=False,
-                _skip_dim_order=True,  # TODO(T182928844): Delegate dim order op to backend.
             ),
         )
 
@@ -250,7 +246,6 @@ def lower_module_and_test_output(
                 export(delegated_program, sample_inputs, strict=True),
                 compile_config=exir.EdgeCompileConfig(
                     _check_ir_validity=False,
-                    _skip_dim_order=True,  # TODO(T182928844): Delegate dim order op to backend.
                 ),
             ).to_executorch(
                 config=ExecutorchBackendConfig(extract_delegate_segments=False)

backends/cadence/aot/functions_fusion_g3.yaml

@@ -50,12 +50,12 @@
 - op: div.out
   kernels:
     - arg_meta: null
-      kernel_name: torch::executor::div_out
+      kernel_name: cadence::impl::G3::div_out
 
 - op: div.out_mode
   kernels:
     - arg_meta: null
-      kernel_name: torch::executor::div_out_mode
+      kernel_name: cadence::impl::G3::div_out_mode
 
 - op: embedding.out
   kernels:
@@ -71,7 +71,6 @@
   kernels:
     - arg_meta: null
       kernel_name: cadence::impl::G3::mul_out
-
 - op: mul.Scalar_out
   kernels:
     - arg_meta: null
@@ -80,7 +79,7 @@
 - op: permute_copy.out
   kernels:
     - arg_meta: null
-      kernel_name: torch::executor::permute_copy_out
+      kernel_name: cadence::impl::G3::permute_copy_out
 
 - op: sigmoid.out
   kernels:
@@ -90,7 +89,7 @@
 - op: slice_copy.Tensor_out
   kernels:
     - arg_meta: null
-      kernel_name: torch::executor::slice_copy_Tensor_out
+      kernel_name: cadence::impl::G3::slice_copy_Tensor_out
 
 - op: split_with_sizes_copy.out
   kernels:
@@ -100,7 +99,12 @@
 - op: sub.out
   kernels:
     - arg_meta: null
-      kernel_name: torch::executor::sub_out
+      kernel_name: cadence::impl::G3::sub_out
+
+- op: sub.Scalar_out
+  kernels:
+    - arg_meta: null
+      kernel_name: cadence::impl::G3::sub_scalar_out
 
 - op: view_copy.out
   kernels:
@@ -117,6 +121,16 @@
     - arg_meta: null
       kernel_name: cadence::impl::G3::native_layer_norm_out
 
+- op: mean.out
+  kernels:
+    - arg_meta: null
+      kernel_name:  cadence::impl::G3::mean_dim_out
+
+- op: exp.out
+  kernels:
+    - arg_meta: null
+      kernel_name: cadence::impl::G3::exp_out
+
 # custom ops
 - func: cadence::quantize_per_tensor.out(Tensor input, float scale, int zero_point, int quant_min, int quant_max, ScalarType dtype, *, Tensor(a!) out) -> Tensor(a!)
   variants: function

backends/cadence/fusion_g3/operators/CMakeLists.txt

@@ -36,6 +36,12 @@ set(_aten_ops__srcs
     "${CMAKE_CURRENT_SOURCE_DIR}/op_native_layer_norm.cpp"
     "${CMAKE_CURRENT_SOURCE_DIR}/op_quantize.cpp"
     "${CMAKE_CURRENT_SOURCE_DIR}/op_dequantize.cpp"
+    "${CMAKE_CURRENT_SOURCE_DIR}/op_sub.cpp"
+    "${CMAKE_CURRENT_SOURCE_DIR}/op_div.cpp"
+    "${CMAKE_CURRENT_SOURCE_DIR}/op_mean.cpp"
+    "${CMAKE_CURRENT_SOURCE_DIR}/op_slice_copy.cpp"
+    "${CMAKE_CURRENT_SOURCE_DIR}/op_permute_copy.cpp"
+    "${CMAKE_CURRENT_SOURCE_DIR}/op_exp.cpp"
     "${EXECUTORCH_ROOT}/kernels/portable/cpu/op_bmm.cpp"
     "${EXECUTORCH_ROOT}/kernels/portable/cpu/op_clone.cpp"
     "${EXECUTORCH_ROOT}/kernels/portable/cpu/op_div.cpp"
@@ -51,6 +57,7 @@ set(_aten_ops__srcs
     "${EXECUTORCH_ROOT}/kernels/portable/cpu/op_where.cpp"
     "${EXECUTORCH_ROOT}/kernels/portable/cpu/util/dtype_util.cpp"
     "${EXECUTORCH_ROOT}/kernels/portable/cpu/util/normalization_ops_util.cpp"
+    "${EXECUTORCH_ROOT}/kernels/portable/cpu/pattern/unary_ufunc_realhbbf16_to_floathbf16.cpp"
 )
 add_library(aten_ops_cadence ${_aten_ops__srcs})
 target_link_libraries(aten_ops_cadence PUBLIC executorch)

backends/cadence/fusion_g3/operators/op_add.cpp

@@ -39,6 +39,7 @@ Tensor& add_out(
   ScalarType common_type =
       executorch::runtime::promoteTypes(a.scalar_type(), b.scalar_type());
 
+#ifdef OP_ARG_CHECK
   // Check Common Dtype
   ET_KERNEL_CHECK(
       ctx,
@@ -62,12 +63,12 @@ Tensor& add_out(
       torch::executor::resize_to_broadcast_target_size(a, b, out) == Error::Ok,
       InvalidArgument,
       out);
+#endif
 
   // Compute Dtype
   ScalarType compute_type =
       torch::executor::native::utils::get_compute_type(common_type);
 
-  // @lint-ignore CLANGTIDY facebook-hte-CArray
   static constexpr const char op_name[] = "add.out";
 
   int kTensorDimensionLimit = 5;
@@ -253,6 +254,7 @@ Tensor& add_scalar_out(
       torch::executor::native::utils::promote_type_with_scalar(
           a.scalar_type(), b);
 
+#ifdef OP_ARG_CHECK
   // Check Common Dtype
   ET_KERNEL_CHECK(
       ctx,
@@ -276,7 +278,7 @@ Tensor& add_scalar_out(
       executorch::runtime::resize_tensor(out, a.sizes()) == Error::Ok,
       InvalidArgument,
       out);
-
+#endif
   // Compute Dtype
   ScalarType compute_type =
       torch::executor::native::utils::get_compute_type(common_type);