Arm backend: Make TOSA backend NCHW-compatible

- Moves the needed input/output transposes into the
  delegated graph to run on Ethos-U rather than requiring
  the EthosUBackend to implement transposes on CPU.

- Renames the annotate_channels_last_dim_order_pass to
  to_tosa_memory_format_pass since to be more descriptive
  and future proof.

This changes additionally enables running muliple batches
since the EtohsU transpose supports that natively, whereas
the CPU implementation did not.

Change-Id: I676e5915b15cbcc370a03d70bfa2ea2fe20b2210
Signed-off-by: Adrian Lundell <[email protected]>

Author: AdrianLundell

backends/arm/_passes/__init__.py

@@ -7,7 +7,6 @@
 from . import arm_pass_utils  # noqa
 from .arm_pass import ArmPass  # noqa  # usort: skip
 from .add_bias_pass import AddBiasPass  # noqa
-from .annotate_channels_last_dim_order_pass import AnnotateChannelsLastDimOrder  # noqa
 from .annotate_decomposed_matmul import AnnotateDecomposedMatmulPass  # noqa
 from .broadcast_args_pass import BroadcastArgsPass  # noqa
 from .cast_bool_to_int8_pass import CastBoolToInt8Pass  # noqa
@@ -80,6 +79,7 @@
 )
 from .scalars_to_attribute_pass import ScalarsToAttributePass  # noqa
 from .size_adjust_input_pass import SizeAdjustInputPass  # noqa
+from .to_tosa_memory_format_pass import ToTosaMemoryFormatPass  # noqa
 from .unsqueeze_before_repeat_pass import UnsqueezeBeforeRepeatPass  # noqa
 from .unsqueeze_scalar_placeholders_pass import UnsqueezeScalarPlaceholdersPass  # noqa
 from .replace_inf_values_pass import ReplaceInfValues  # noqa  # usort: skip

backends/arm/_passes/arm_pass_manager.py

@@ -10,7 +10,6 @@
 import executorch.backends.arm.tosa.dialect  # noqa: unused
 from executorch.backends.arm._passes import (
     AddBiasPass,
-    AnnotateChannelsLastDimOrder,
     AnnotateDecomposedMatmulPass,
     BroadcastArgsPass,
     CastBoolToInt8Pass,
@@ -79,6 +78,7 @@
     RetraceFoldedDtypesPass,
     ScalarsToAttributePass,
     SizeAdjustInputPass,
+    ToTosaMemoryFormatPass,
     UnsqueezeBeforeRepeatPass,
     UnsqueezeScalarPlaceholdersPass,
 )
@@ -156,7 +156,7 @@ def _tosa_INT_pipeline(self, exported_program: ExportedProgram) -> GraphModule:
 
         self.add_pass(InsertTableOpsPass(exported_program))
         self.add_pass(FuseEqualPlaceholdersPass(exported_program))
-        self.add_pass(AnnotateChannelsLastDimOrder())
+        self.add_pass(ToTosaMemoryFormatPass(exported_program))
         self.add_pass(InsertRescalePass())
 
         return self._transform(exported_program.graph_module)
@@ -230,7 +230,7 @@ def _tosa_FP_pipeline(self, exported_program: ExportedProgram) -> GraphModule:
         self.add_pass(AddBiasPass(exported_program))
         self.add_pass(InsertTableOpsPass(exported_program))
         self.add_pass(FuseEqualPlaceholdersPass(exported_program))
-        self.add_pass(AnnotateChannelsLastDimOrder())
+        self.add_pass(ToTosaMemoryFormatPass(exported_program))
         self.add_pass(InsertRescalePass())
 
         return self._transform(exported_program.graph_module)

backends/arm/_passes/decompose_select.py

@@ -7,7 +7,10 @@
 # pyre-unsafe
 
 import torch
-from executorch.backends.arm._passes.arm_pass_utils import create_node
+from executorch.backends.arm._passes.arm_pass_utils import (
+    create_node,
+    get_first_fake_tensor,
+)
 from executorch.exir.dialects._ops import ops as exir_ops
 from executorch.exir.pass_base import ExportPass, PassResult
 
@@ -34,8 +37,9 @@ def call(self, graph_module: torch.fx.GraphModule):
 
             input_node, dim, index = node.args
 
-            rank = len(input_node.meta["val"].size())
-            shape = input_node.meta["val"].shape
+            input_tensor = get_first_fake_tensor(input_node)
+            rank = len(input_tensor.size())
+            shape = input_tensor.shape
             dim = dim % rank if dim < 0 else dim
             index = index % shape[dim] if index < 0 else index
 
@@ -44,7 +48,7 @@ def call(self, graph_module: torch.fx.GraphModule):
                     graph_module.graph, slice_op, (input_node, dim, index, index + 1)
                 )
                 squeeze_node = create_node(
-                    graph_module.graph, squeeze_op, (slice_node, [dim])
+                    graph_module.graph, squeeze_op, (slice_node, [dim]), from_node=node
                 )
 
             node.replace_all_uses_with(squeeze_node)

backends/arm/_passes/annotate_channels_last_dim_order_pass.py renamed to backends/arm/_passes/to_tosa_memory_format_pass.py

@@ -10,8 +10,10 @@
 from executorch.backends.arm._passes.arm_pass_utils import (
     create_node,
     get_first_fake_tensor,
+    is_param_node,
 )
 from executorch.backends.arm.tosa_utils import is_consumer_node_depthwise_conv2d
+from executorch.exir import ExportedProgram
 from executorch.exir.dialects._ops import ops as exir_ops
 from executorch.exir.pass_base import ExportPass, PassResult
 from torch.library import impl, Library
@@ -40,7 +42,14 @@ def _transpose_impl(*args, **kwargs):
     return args[0]
 
 
-class AnnotateChannelsLastDimOrder(ExportPass):
+def _is_input(node: torch.fx.Node, exported_program: ExportedProgram) -> bool:
+    """
+    Returns True if the node is an input node, i.e. a placeholder or a parameter.
+    """
+    return node.op == "placeholder" and not is_param_node(exported_program, node)
+
+
+class ToTosaMemoryFormatPass(ExportPass):
     """
     Annotates each node with a tosa_dim_order. tosa_dim_order can be seen as a channels-last dim-order
     that in most cases will be (0, 2, 3, 1) for nodes with 4D-shapes. The pass also inserts passthrough_to_tosa._transpose
@@ -54,6 +63,10 @@ class AnnotateChannelsLastDimOrder(ExportPass):
     NNHWC_order = (0, 1, 3, 4, 2)
     NNHWC_inverse_order = (0, 1, 4, 2, 3)
 
+    def __init__(self, exported_program: ExportedProgram) -> None:
+        self.exported_program = exported_program
+        super().__init__()
+
     def is_weight_node_for_depthwise_conv2d(self, node: torch.fx.Node):
         """
         returns True for w in the following sequence;
@@ -70,6 +83,7 @@ def is_weight_node_for_depthwise_conv2d(self, node: torch.fx.Node):
 
         return False
 
+    @staticmethod
     @staticmethod
     def memory_format_differs(shape):
         """Returns true if the shape will have a different memory layout in (N)NCHW and (N)NHWC format"""
@@ -116,25 +130,30 @@ def is_channel_reshape(input_shape, output_shape):
 
     @staticmethod
     def insert_input_transpose(node, input_node, graph_module):
+        if input_node.target == torch.ops.passthrough_to_tosa._transpose.default:
+            pre_permute_node = input_node.all_input_nodes[0]
+            node.replace_input_with(input_node, pre_permute_node)
+            return
+
         with graph_module.graph.inserting_before(node):
             permute_node = create_node(
                 graph_module.graph,
                 torch.ops.passthrough_to_tosa._transpose.default,
                 args=(
                     input_node,
                     list(
-                        AnnotateChannelsLastDimOrder.NNHWC_inverse_order
+                        ToTosaMemoryFormatPass.NNHWC_inverse_order
                         if len(get_first_fake_tensor(input_node).size()) == 5
-                        else AnnotateChannelsLastDimOrder.NHWC_inverse_order
+                        else ToTosaMemoryFormatPass.NHWC_inverse_order
                     ),
                 ),
+                from_node=node,
             )
             node.replace_input_with(input_node, permute_node)
 
             permute_node.meta["tosa_dim_order"] = tuple(
                 range(len(input_node.meta["val"].size()))
             )
-            permute_node.meta["val"] = input_node.meta["val"]
 
     @staticmethod
     def insert_output_transpose(node, graph_module):
@@ -145,25 +164,23 @@ def insert_output_transpose(node, graph_module):
                 args=(
                     node,
                     list(
-                        AnnotateChannelsLastDimOrder.NNHWC_order
+                        ToTosaMemoryFormatPass.NNHWC_order
                         if len(get_first_fake_tensor(node).size()) == 5
-                        else AnnotateChannelsLastDimOrder.NHWC_order
+                        else ToTosaMemoryFormatPass.NHWC_order
                     ),
                 ),
+                from_node=node,
             )
+
             permute_node.meta["tosa_dim_order"] = (
-                AnnotateChannelsLastDimOrder.NNHWC_order
+                ToTosaMemoryFormatPass.NNHWC_order
                 if len(get_first_fake_tensor(node).size()) == 5
-                else AnnotateChannelsLastDimOrder.NHWC_order
-            )
-            permute_node.meta["val"] = get_first_fake_tensor(node).permute(
-                AnnotateChannelsLastDimOrder.NNHWC_order
-                if len(get_first_fake_tensor(node).size()) == 5
-                else AnnotateChannelsLastDimOrder.NHWC_order
+                else ToTosaMemoryFormatPass.NHWC_order
             )
             node.meta["tosa_dim_order"] = tuple(
                 range(len(get_first_fake_tensor(node).size()))
             )
+
             users = [user for user in node.users if user != permute_node]
             for user in users:
                 user.replace_input_with(node, permute_node)
@@ -174,20 +191,23 @@ def _insert_view_transpose(
     ):
         nchw_to_nhwc = len(input_shape) < 4 and len(output_shape) >= 4
         nhwc_to_nchw = len(input_shape) >= 4 and len(output_shape) < 4
-        channel_reshape = AnnotateChannelsLastDimOrder.is_channel_reshape(
+        channel_reshape = ToTosaMemoryFormatPass.is_channel_reshape(
             output_shape, input_shape
         )
 
         if (
             channel_reshape or nhwc_to_nchw
-        ) and AnnotateChannelsLastDimOrder.memory_format_differs(input_shape):
-            AnnotateChannelsLastDimOrder.insert_input_transpose(
+        ) and ToTosaMemoryFormatPass.memory_format_differs(input_shape):
+
+            ToTosaMemoryFormatPass.insert_input_transpose(
                 node, input_node, graph_module
             )
+
         if (
             channel_reshape or nchw_to_nhwc
-        ) and AnnotateChannelsLastDimOrder.memory_format_differs(output_shape):
-            AnnotateChannelsLastDimOrder.insert_output_transpose(node, graph_module)
+        ) and ToTosaMemoryFormatPass.memory_format_differs(output_shape):
+
+            ToTosaMemoryFormatPass.insert_output_transpose(node, graph_module)
 
     def insert_tosa_transposes(self, graph_module: torch.fx.GraphModule):
         """
@@ -205,9 +225,10 @@ def insert_tosa_transposes(self, graph_module: torch.fx.GraphModule):
         for node in graph_module.graph.nodes:
             # call_function and placeholder allowed due to
             # index.Tensor being able to come in as both
-            if node.op not in ["call_function", "placeholder"]:
+            if node.op not in ["call_function", "placeholder", "output"]:
                 continue
 
+            # Transpose views
             elif node.target in (
                 exir_ops.edge.aten.view_copy.default,
                 exir_ops.edge.aten.index.Tensor,
@@ -218,25 +239,48 @@ def insert_tosa_transposes(self, graph_module: torch.fx.GraphModule):
                 input_node = node.args[0]
                 input_shape = input_node.meta["val"].shape
                 output_shape = node.meta["val"].shape
-
                 self._insert_view_transpose(
-                    input_shape, output_shape, node, input_node, graph_module
+                    input_shape,
+                    output_shape,
+                    node,
+                    input_node,
+                    graph_module,
                 )
 
+            # Transpose inputs
+            elif _is_input(node, self.exported_program):
+                input_shape = get_first_fake_tensor(node).size()
+                if len(input_shape) in (4, 5):
+                    ToTosaMemoryFormatPass.insert_output_transpose(node, graph_module)
+
+            # Transpose outputs
+            elif node.op == "output":
+                output_shape = get_first_fake_tensor(node).size()
+
+                if len(output_shape) in (4, 5):
+                    for input_node in node.all_input_nodes:
+                        ToTosaMemoryFormatPass.insert_input_transpose(
+                            node, input_node, graph_module
+                        )
+
     def call(self, graph_module: torch.fx.GraphModule):
         for node in graph_module.graph.nodes:
             node_data = get_first_fake_tensor(node).data
 
-            if node_data.dim() == 4:
+            # Inputs and outputs are always in (N)NCHW format
+            if _is_input(node, self.exported_program) or node.op == "output":
+                dim_order = tuple(range(node_data.dim()))
+            elif node_data.dim() == 4:
                 dim_order = self.NHWC_order
                 if self.is_weight_node_for_depthwise_conv2d(node):
                     # The weights of TOSA DEPTHWISE_CONV2D have shape (H, W, C, M) which corresponds to
                     # dim_order = (2, 3, 0, 1) (https://www.mlplatform.org/tosa/tosa_spec.html#_depthwise_conv2d).
                     dim_order = self.HWCM_order
             elif node_data.dim() == 5:
-                dim_order = self.NNHWC_order  # type: ignore[assignment]
+                dim_order = self.NNHWC_order
             else:
                 dim_order = tuple(range(node_data.dim()))  # type: ignore[assignment]
+
             node.meta["tosa_dim_order"] = dim_order
         # Insert TOSA transposes to convert between (N)NCHW and (N)NHWC format.
         # See insert_tosa_transposes for insertion conditions.

backends/arm/operators/op_transpose.py

@@ -47,7 +47,14 @@ def define_node(
         validate_valid_dtype(
             self.target,
             [inputs[0], output],
-            [ts.DType.INT8, ts.DType.INT32, ts.DType.FP32],
+            [
+                ts.DType.INT8,
+                ts.DType.INT16,
+                ts.DType.INT32,
+                ts.DType.FP32,
+                ts.DType.BOOL,
+                ts.DType.FP16,
+            ],
             output.tosa_spec,
         )