Arm backend: Add additional unsupported checks to Ethos-U55 backend

backends/arm/operator_support/__init__.py

@@ -7,6 +7,7 @@
 
 from . import (  # noqa
     convolution_support,
+    ethos_u55_support,
     minmax_support,
     pool_2d_support,
     reduce_sum_support,

backends/arm/operator_support/ethos_u55_support.py

@@ -0,0 +1,276 @@
+# Copyright 2025 Arm Limited and/or its affiliates.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+# pyre-unsafe
+
+import typing
+
+import torch
+import torch.fx as fx
+from executorch.backends.arm._passes.arm_pass_utils import get_first_fake_tensor
+from executorch.backends.arm._passes.insert_table_ops import TableOps
+from executorch.backends.arm.operators.op_permute import transform_permutation_vector
+from executorch.backends.arm.tosa_utils import tosa_shape
+from executorch.exir.backend.utils import WhyNoPartitionReporter
+
+from executorch.exir.dialects._ops import ops as exir_ops
+from torch.fx.passes.operator_support import OperatorSupportBase
+
+
+def _try_determine_dtype(node: fx.Node) -> torch.dtype | None:
+    dtype = get_first_fake_tensor(node).dtype
+    if not dtype.is_floating_point:
+        return dtype
+    if node.target is exir_ops.edge.quantized_decomposed.dequantize_per_tensor.default:
+        return get_first_fake_tensor(node.all_input_nodes[0]).dtype
+    q_node = list(node.users)[0]
+    if q_node.target is exir_ops.edge.quantized_decomposed.quantize_per_tensor.default:
+        return typing.cast(torch.dtype, q_node.args[-1])
+    # We can't easily figure out dtype, return None
+    return None
+
+
+class EthosU55DtypeSupport(OperatorSupportBase):
+
+    def __init__(self, reporter: WhyNoPartitionReporter):
+        super().__init__()
+        self.reporter = reporter
+
+    targeted_ops_i8_i16_i32 = [
+        exir_ops.edge.aten.cat.default,
+        exir_ops.edge.aten.repeat.default,
+        exir_ops.edge.aten.constant_pad_nd.default,
+        exir_ops.edge.aten.view.default,
+        exir_ops.edge.aten.permute.default,
+    ]
+
+    target_ops_i8 = tuple(TableOps.included_ops())
+
+    def is_node_supported(  # noqa: C901
+        self, submodules: typing.Mapping[str, torch.nn.Module], node: fx.Node
+    ) -> bool:
+
+        dtype = _try_determine_dtype(node)
+        if dtype is None:
+            # If we couldn't determine dtype, just return ok.
+            return True
+
+        if node.target in self.targeted_ops_i8_i16_i32:
+            if dtype not in (torch.int8, torch.int16, torch.int32):
+                self.reporter.report_reject(
+                    node, f"Unsupported dtype {dtype} (Supports i8, i16, i32)."
+                )
+                return False
+
+        if node.target in self.target_ops_i8:
+            if dtype not in (torch.int8,):
+                self.reporter.report_reject(
+                    node, f"Unsupported dtype {dtype} (Supports i8)."
+                )
+                return False
+
+        if node.target == exir_ops.edge.aten.convolution.default:
+            ifm, weight = node.all_input_nodes[0:2]
+            ifm_dtype = _try_determine_dtype(ifm)
+            if ifm_dtype is not None and ifm_dtype not in (torch.int8, torch.int16):
+                self.reporter.report_reject(
+                    node, f"Unsupported input dtype {dtype} (Supports i8, i16)."
+                )
+                return False
+            weight_dtype = _try_determine_dtype(weight)
+            if weight_dtype is not None and weight_dtype not in (torch.int8,):
+                self.reporter.report_reject(
+                    node, f"Unsupported weight dtype {dtype} (Supports i8)."
+                )
+                return False
+            if len(node.all_input_nodes) > 2:
+                bias = node.all_input_nodes[2]
+                bias_dtype = _try_determine_dtype(bias)
+                if bias_dtype is not None and bias_dtype not in (torch.int32,):
+                    self.reporter.report_reject(
+                        node, f"Unsupported bias dtype {dtype} (Supports i32)."
+                    )
+                    return False
+
+        if node.target in (
+            exir_ops.edge.aten.mm.default,
+            exir_ops.edge.aten.bmm.default,
+        ):
+            for input_node in node.all_input_nodes:
+                dtype = _try_determine_dtype(input_node)
+                if dtype is not None and dtype != torch.int8:
+                    self.reporter.report_reject(
+                        input_node,
+                        f"Input {input_node.name} has unsupported dtype {dtype} (Supports i8).",
+                    )
+                    return False
+
+        return True
+
+
+class EthosU55NotSupported(OperatorSupportBase):
+    """
+    Certain operators are not supported on U55. These are listed in `unsupported_ops`.
+    The comment mentions the unsupported TOSA operator that the aten operator maps to where it is not obvious.
+    For unimplemented operators, this is the anticipated mapping, and it might be incorrect.
+    """
+
+    unsupported_ops = [
+        exir_ops.edge.aten.any.default,  # REDUCE_ANY
+        exir_ops.edge.aten.any.dim,  # REDUCE_ANY
+        exir_ops.edge.aten.any.dims,  # REDUCE_ANY
+        exir_ops.edge.aten.bitwise_and.Tensor,
+        exir_ops.edge.aten.bitwise_or.Tensor,
+        exir_ops.edge.aten.bitwise_xor.Tensor,
+        exir_ops.edge.aten.bitwise_not,
+        exir_ops.edge.aten.logical_and.default,
+        exir_ops.edge.aten.logical_or.default,
+        exir_ops.edge.aten.logical_xor.default,
+        exir_ops.edge.aten.logical_not.default,
+        exir_ops.edge.aten.amax.default,  # REDUCE_MAX
+        exir_ops.edge.aten.amin.default,  # REDUCE_MIN
+        exir_ops.edge.aten.eq.Tensor,
+        exir_ops.edge.aten.eq.Scalar,
+        exir_ops.edge.aten.ge.Tensor,
+        exir_ops.edge.aten.gt.Tensor,
+        exir_ops.edge.aten.le.Tensor,
+        exir_ops.edge.aten.lt.Tensor,
+        exir_ops.edge.aten.flip.default,  # REVERSE
+        exir_ops.edge.aten.grid_sampler_2d,  # GATHER
+        exir_ops.edge.aten.scatter.src,
+        exir_ops.edge.aten.scatter.value,
+        exir_ops.edge.aten.select_scatter.default,
+        exir_ops.edge.aten.scatter_reduce.two,
+        exir_ops.edge.aten.scatter_add.default,
+        exir_ops.edge.aten.upsample_nearest2d.vec,  # RESIZE
+        exir_ops.edge.aten.upsample_bilinear2d.vec,  # RESIZE
+        exir_ops.edge.aten.reflection_pad1d.default,  # REVERSE
+        exir_ops.edge.aten.reflection_pad2d.default,  # REVERSE
+        exir_ops.edge.aten.reflection_pad3d.default,  # REVERSE
+    ]
+
+    def __init__(self, reporter: WhyNoPartitionReporter):
+        self.reporter = reporter
+
+    def is_node_supported(
+        self, submodules: typing.Mapping[str, torch.nn.Module], node: fx.Node
+    ) -> bool:
+
+        if node.target in self.unsupported_ops:
+            self.reporter.report_reject(node, "Op is not supported on U55.")
+            return False
+
+        return True
+
+
+shape_t = list[int]
+
+
+class EthosU55TransposeCheck(OperatorSupportBase):
+
+    def __init__(self, reporter: WhyNoPartitionReporter):
+        super().__init__()
+        self.reporter = reporter
+
+    def _pad_to_rank_4(
+        self, shape: shape_t, permutation: list[int]
+    ) -> tuple[shape_t, shape_t]:
+        diff = 4 - len(shape)
+        padded_shape = [1] * diff + shape
+        for i in range(len(permutation)):
+            permutation[i] += diff
+        padded_permutation = list(range(diff)) + permutation
+        return padded_shape, padded_permutation
+
+    def axes_product(self, nhwc_shape: shape_t) -> int:
+        product = 1
+        for axes in nhwc_shape:
+            product *= axes
+        return product
+
+    def _permute_constraint_i8_i16(
+        self, nhwc_shape: list[int], permutation: list[int]
+    ) -> bool:
+        """Returns True if the constraints are ok."""
+        N, H, W, C = nhwc_shape
+        match permutation:
+            case (0, 1, 2, 3):  # NHWC -> NHWC
+                return True
+            case (0, 2, 1, 3) | (0, 1, 3, 2) | (0, 3, 1, 2):  # NHWC -> NWHC, NHCW, NCWH
+                return N * H <= 65536 and W <= 65536 and C <= 65536
+            case _:
+                return self.axes_product(nhwc_shape) <= 65536
+
+    def _permute_constraint_i32(
+        self, nhwc_shape: list[int], permutation: list[int]
+    ) -> bool:
+        """Returns True if the constraints are ok."""
+        N, H, W, C = nhwc_shape
+        match permutation:
+            case (0, 1, 2, 3):  # NHWC -> NHWC
+                return C <= 32768
+            case (0, 2, 1, 3):  # NHWC -> NHWC
+                return N == 1 and H <= 65536 and W <= 65536 and C <= 16384
+            case (0, 1, 3, 2):  # NHWC -> NHCW
+                return N * H <= 65536 and W <= 65536 and C <= 65536
+            case _:
+                return False
+
+    def _permute_constraint(self, shape, permutation, dtype):
+        if dtype in (torch.int8, torch.int16):
+            return self._permute_constraint_i8_i16(shape, permutation)
+        if dtype == torch.int32:
+            return not self._permute_constraint_i32(shape, permutation)
+        return True
+
+    def is_node_supported(
+        self, submodules: typing.Mapping[str, torch.nn.Module], node: fx.Node
+    ) -> bool:
+
+        if not node.target == exir_ops.edge.aten.permute_copy.default:
+            return True
+
+        shape = list(get_first_fake_tensor(node).shape)
+        dtype = _try_determine_dtype(node)
+        permutation = list(typing.cast(list[int], node.args[1]))
+
+        rank = len(shape)
+        if rank > 4:
+            if dtype == torch.int32:
+                self.reporter.report_reject(
+                    node, f"No support for {permutation=} in int32."
+                )
+                return False
+            if dtype in (torch.int8, torch.int16):
+                if self.axes_product(shape) > 65536:
+                    self.reporter.report_reject(
+                        node,
+                        f"No support for {shape=}, {dtype=}. Product of axes must be <65536",
+                    )
+                    return False
+            return True
+
+        shape, permutation = self._pad_to_rank_4(shape, permutation)
+        if rank == 3 or rank == 4:
+            # For rank 3 and 4, we can have channels first or channels last dim order.
+            # Since we don't know which at partition-time, test both.
+
+            nhwc_shape = tosa_shape(shape, [0, 2, 3, 1])
+            nhwc_permutation = transform_permutation_vector(permutation, [0, 2, 3, 1])
+
+            if not self._permute_constraint(nhwc_shape, nhwc_permutation, dtype):
+                self.reporter.report_reject(
+                    node,
+                    f"Unsupported NHWC {nhwc_shape=} for {nhwc_permutation=}, {dtype=}",
+                )
+                return False
+
+        if not self._permute_constraint(shape, permutation, dtype):
+            self.reporter.report_reject(
+                node, f"Unsupported NCHW {shape=} for {permutation=}, {dtype=}"
+            )
+            return False
+
+        return True

backends/arm/operator_support/tosa_supported_operators.py

@@ -18,6 +18,11 @@
 from executorch.backends.arm._passes.fuse_quantized_activation_pass import (
     FuseQuantizedActivationPass,
 )
+from executorch.backends.arm.operator_support.ethos_u55_support import (
+    EthosU55DtypeSupport,
+    EthosU55NotSupported,
+    EthosU55TransposeCheck,
+)
 from executorch.backends.arm.tosa_specification import Tosa_0_80, TosaSpecification
 from executorch.exir import ExportedProgram
 from executorch.exir.backend.utils import WhyNoPartitionReporter
@@ -118,6 +123,8 @@ def tosa_support_factory(
         negative_checks.append(CheckProperQuantization(reporter))
     if isinstance(tosa_spec, Tosa_0_80) and tosa_spec.is_U55_subset:
         negative_checks.append(EthosU55NotSupported(reporter))
+        negative_checks.append(EthosU55DtypeSupport(reporter))
+        negative_checks.append(EthosU55TransposeCheck(reporter))
 
     return chain(
         reporter.wrap_check(
@@ -216,61 +223,6 @@ def is_node_supported(
         return supported
 
 
-class EthosU55NotSupported(OperatorSupportBase):
-    """
-    Certain operators are not supported on U55. These are listed in `unsupported_ops`.
-    The comment mentions the unsupported TOSA operator that the aten operator maps to where it is not obvious.
-    For unimplemented operators, this is the anticipated mapping, and it might be incorrect.
-    """
-
-    unsupported_ops = [
-        exir_ops.edge.aten.any.default,  # REDUCE_ANY
-        exir_ops.edge.aten.any.dim,  # REDUCE_ANY
-        exir_ops.edge.aten.any.dims,  # REDUCE_ANY
-        exir_ops.edge.aten.bitwise_and.Tensor,
-        exir_ops.edge.aten.bitwise_or.Tensor,
-        exir_ops.edge.aten.bitwise_xor.Tensor,
-        exir_ops.edge.aten.bitwise_not,
-        exir_ops.edge.aten.logical_and.default,
-        exir_ops.edge.aten.logical_or.default,
-        exir_ops.edge.aten.logical_xor.default,
-        exir_ops.edge.aten.logical_not.default,
-        exir_ops.edge.aten.amax.default,  # REDUCE_MAX
-        exir_ops.edge.aten.amin.default,  # REDUCE_MIN
-        exir_ops.edge.aten.eq.Tensor,
-        exir_ops.edge.aten.eq.Scalar,
-        exir_ops.edge.aten.ge.Tensor,
-        exir_ops.edge.aten.gt.Tensor,
-        exir_ops.edge.aten.le.Tensor,
-        exir_ops.edge.aten.lt.Tensor,
-        exir_ops.edge.aten.flip.default,  # REVERSE
-        exir_ops.edge.aten.grid_sampler_2d,  # GATHER
-        exir_ops.edge.aten.scatter.src,
-        exir_ops.edge.aten.scatter.value,
-        exir_ops.edge.aten.select_scatter.default,
-        exir_ops.edge.aten.scatter_reduce.two,
-        exir_ops.edge.aten.scatter_add.default,
-        exir_ops.edge.aten.upsample_nearest2d.vec,  # RESIZE
-        exir_ops.edge.aten.upsample_bilinear2d.vec,  # RESIZE
-        exir_ops.edge.aten.reflection_pad1d.default,  # REVERSE
-        exir_ops.edge.aten.reflection_pad2d.default,  # REVERSE
-        exir_ops.edge.aten.reflection_pad3d.default,  # REVERSE
-    ]
-
-    def __init__(self, reporter: WhyNoPartitionReporter):
-        self.reporter = reporter
-
-    def is_node_supported(
-        self, submodules: typing.Mapping[str, torch.nn.Module], node: fx.Node
-    ) -> bool:
-
-        if node.target in self.unsupported_ops:
-            self.reporter.report_reject(node, "Op is not supported on U55.")
-            return False
-
-        return True
-
-
 class NeedsDecompositionCheck(OperatorSupportBase):
     """
     Targeted operators need to be decomposed prior to quantization in order to get a pair of q-dq-nodes surrounding