Merge branch 'main' into android-config-api-2

Author: kirklandsign

.github/workflows/doc-build.yml

@@ -21,12 +21,12 @@ jobs:
       - name: Check URLs
         run: bash ./scripts/check_urls.sh
 
-  check-links:
+  check-xrefs:
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v3
       - name: Check Links
-        run: bash ./scripts/check_links.sh
+        run: bash ./scripts/check_xrefs.sh
 
   build:
     uses: pytorch/test-infra/.github/workflows/linux_job_v2.yml@main

backends/arm/__init__.py

@@ -0,0 +1,10 @@
+# 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.
+
+from .arm_backend import ArmCompileSpecBuilder  # noqa  # usort: skip
+from .tosa_backend import TOSABackend  # noqa  # usort: skip
+from .tosa_partitioner import TOSAPartitioner  # noqa  # usort: skip
+from .ethosu_backend import EthosUBackend  # noqa  # usort: skip
+from .ethosu_partitioner import EthosUPartitioner  # noqa  # usort: skip

backends/arm/_passes/convert_expand_copy_to_repeat.py

@@ -1,16 +1,18 @@
-# Copyright 2024 Arm Limited and/or its affiliates.
-# All rights reserved.
+# Copyright 2024-2025 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 logging
 from typing import cast
 
 from executorch.exir.dialects._ops import ops as exir_ops
 from executorch.exir.pass_base import ExportPass
 
+logger = logging.getLogger(__name__)
+
 
 class ConvertExpandCopyToRepeatPass(ExportPass):
     """
@@ -41,6 +43,14 @@ def call_operator(self, op, args, kwargs, meta):
             multiples[i] if multiples[i] != -1 and extended_shape[i] == 1 else 1
             for i in range(expanded_rank)
         ]
+
+        if all((x == 1 for x in multiples)):
+            # All dimensions/repetitions occur only once. Remove node
+            # altogether since it's in practice just a copy.
+            logger.warning("Found redundant expand node (no-op). Removing it.")
+
+            return args[0]
+
         return super().call_operator(
             op=self.repeat, args=(args[0], multiples), kwargs=kwargs, meta=meta
         )

backends/arm/ethosu_backend.py

@@ -14,9 +14,9 @@
 import logging
 from typing import final, List
 
-from executorch.backends.arm.arm_vela import vela_compile
+from executorch.backends.arm import TOSABackend
 
-from executorch.backends.arm.tosa_backend import TOSABackend
+from executorch.backends.arm.arm_vela import vela_compile
 from executorch.exir.backend.backend_details import BackendDetails, PreprocessResult
 from executorch.exir.backend.compile_spec_schema import CompileSpec
 from torch.export.exported_program import ExportedProgram

backends/arm/ethosu_partitioner.py

@@ -10,8 +10,7 @@
 from executorch.backends.arm.arm_backend import (
     is_ethosu,
 )  # usort: skip
-from executorch.backends.arm.ethosu_backend import EthosUBackend
-from executorch.backends.arm.tosa_partitioner import TOSAPartitioner
+from executorch.backends.arm import EthosUBackend, TOSAPartitioner
 from executorch.exir.backend.compile_spec_schema import CompileSpec
 from executorch.exir.backend.partitioner import DelegationSpec
 from torch.fx.passes.operator_support import OperatorSupportBase

backends/arm/operator_support/tosa_supported_operators.py

@@ -207,6 +207,7 @@ def is_node_supported(
             exir_ops.edge.aten._log_softmax.default,
             exir_ops.edge.aten.sub.Tensor,
             exir_ops.edge.aten.tanh.default,
+            exir_ops.edge.aten.upsample_bilinear2d.vec,
             exir_ops.edge.aten.upsample_nearest2d.vec,
             exir_ops.edge.aten.var.correction,
             exir_ops.edge.aten.var.dim,
@@ -365,6 +366,7 @@ def is_node_supported(
             exir_ops.edge.aten.sigmoid.default,
             exir_ops.edge.aten.sub.Tensor,
             exir_ops.edge.aten.tanh.default,
+            exir_ops.edge.aten.upsample_bilinear2d.vec,
             exir_ops.edge.aten.upsample_nearest2d.vec,
             exir_ops.edge.aten.gelu.default,
         ):

backends/arm/operators/__init__.py

@@ -46,6 +46,7 @@
     op_to_copy,
     op_to_dim_order_copy,
     op_transpose,
+    op_upsample_bilinear2d,
     op_upsample_nearest2d,
     op_view,
     op_where,

backends/arm/operators/op_mul.py

@@ -41,7 +41,15 @@ def define_node(
         inputs: List[TosaArg],
         output: TosaArg,
     ) -> None:
-        assert inputs[0].dtype == inputs[1].dtype == output.dtype == ts.DType.INT8
+        if (
+            inputs[0].dtype != ts.DType.INT8
+            or inputs[1].dtype != ts.DType.INT8
+            or output.dtype != ts.DType.INT8
+        ):
+            raise ValueError(
+                f"Inputs and output for {self.target} need to be INT8, got "
+                f"{inputs[0].dtype=}, {inputs[1].dtype=} and {output.dtype=}"
+            )
 
         dim_order = (
             inputs[0].dim_order

backends/arm/operators/op_upsample_bilinear2d.py

@@ -0,0 +1,100 @@
+# 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
+from typing import List
+
+import torch
+
+import tosa_tools.v0_80.serializer.tosa_serializer as ts  # type: ignore
+
+from executorch.backends.arm.operators.node_visitor import (
+    NodeVisitor,
+    register_node_visitor,
+)
+from executorch.backends.arm.tosa_mapping import TosaArg
+from executorch.backends.arm.tosa_quant_utils import build_rescale
+from executorch.backends.arm.tosa_utils import get_resize_parameters, tosa_shape
+from tosa_tools.v0_80.tosa.ResizeMode import ResizeMode  # type: ignore
+
+
+@register_node_visitor
+class UpsampleBilinear2dVisitor_0_80(NodeVisitor):
+    target = "aten.upsample_bilinear2d.vec"
+
+    def __init__(self, *args):
+        super().__init__(*args)
+
+    def define_node(
+        self,
+        node: torch.fx.Node,
+        tosa_graph: ts.TosaSerializer,
+        inputs: List[TosaArg],
+        output: TosaArg,
+    ) -> None:
+        assert (
+            inputs[0].shape is not None and output.shape is not None
+        ), "Only static shapes are supported"
+
+        input_dtype = inputs[0].dtype
+
+        # tosa_shape output is NHWC, take HW
+        input_size_yx = torch.tensor(
+            tosa_shape(inputs[0].shape, inputs[0].dim_order)[1:3]
+        )
+        # Ignore scale and size parameters, directly use the output size as
+        # we only support static shapes currently
+        output_size_yx = torch.tensor(tosa_shape(output.shape, output.dim_order)[1:3])
+
+        scale_n_yx, scale_d_yx, offset_yx, border_yx = get_resize_parameters(
+            input_size_yx, output_size_yx, ResizeMode.NEAREST, align_corners=True
+        )
+
+        def in_int16_range(x):
+            return torch.all(x >= -(2**15)) and torch.all(x <= 2**15 - 1)
+
+        assert in_int16_range(scale_n_yx)
+        assert in_int16_range(scale_d_yx)
+        assert in_int16_range(border_yx)
+
+        attr = ts.TosaSerializerAttribute()
+        attr.ResizeAttribute(
+            scale=[scale_n_yx[0], scale_d_yx[0], scale_n_yx[1], scale_d_yx[1]],
+            offset=offset_yx.tolist(),
+            border=border_yx.tolist(),
+            mode=ResizeMode.BILINEAR,
+        )
+
+        if input_dtype == output.dtype == ts.DType.FP32:
+            tosa_graph.addOperator(
+                ts.TosaOp.Op().RESIZE, [inputs[0].name], [output.name], attr
+            )
+            return
+        elif input_dtype == output.dtype == ts.DType.INT8:
+            intermediate = tosa_graph.addIntermediate(
+                tosa_shape(output.shape, output.dim_order), ts.DType.INT32
+            )
+
+            tosa_graph.addOperator(
+                ts.TosaOp.Op().RESIZE, [inputs[0].name], [intermediate.name], attr
+            )
+
+            final_output_scale = float(1 / (scale_n_yx[0] * scale_n_yx[1]))
+
+            build_rescale(
+                tosa_fb=tosa_graph,
+                scale=[final_output_scale],
+                input_node=intermediate,
+                output_name=output.name,
+                output_type=ts.DType.INT8,
+                output_shape=output.shape,
+                input_zp=0,
+                output_zp=0,
+                is_double_round=False,
+            )
+        else:
+            raise ValueError(
+                "Input/output dtype not in {float32, int8}: {input_dtype=} {output.dtype=}"
+            )

backends/arm/quantizer/__init__.py

@@ -1,4 +1,12 @@
-# Copyright 2024 Arm Limited and/or its affiliates.
+# Copyright 2024-2025 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.
+
+
+from .quantization_config import QuantizationConfig  # noqa  # usort: skip
+from .arm_quantizer import (  # noqa
+    EthosUQuantizer,
+    get_symmetric_quantization_config,
+    TOSAQuantizer,
+)

backends/arm/quantizer/arm_quantizer.py

@@ -19,15 +19,14 @@
 import torch
 from executorch.backends.arm._passes import ArmPassManager
 
-from executorch.backends.arm.quantizer import arm_quantizer_utils
+from executorch.backends.arm.quantizer import arm_quantizer_utils, QuantizationConfig
 from executorch.backends.arm.quantizer.arm_quantizer_utils import (  # type: ignore[attr-defined]
     mark_node_as_annotated,
 )
 from executorch.backends.arm.quantizer.quantization_annotator import (  # type: ignore[import-not-found]
     annotate_graph,
 )
 
-from executorch.backends.arm.quantizer.quantization_config import QuantizationConfig
 from executorch.backends.arm.tosa_specification import TosaSpecification
 from executorch.backends.arm.arm_backend import (
     get_tosa_spec,

backends/arm/quantizer/quantization_annotator.py

@@ -10,8 +10,7 @@
 
 import torch
 import torch.fx
-from executorch.backends.arm.quantizer import arm_quantizer_utils
-from executorch.backends.arm.quantizer.quantization_config import QuantizationConfig
+from executorch.backends.arm.quantizer import arm_quantizer_utils, QuantizationConfig
 from executorch.backends.arm.tosa_utils import get_node_debug_info
 from torch.ao.quantization.quantizer import QuantizationSpecBase, SharedQuantizationSpec
 from torch.ao.quantization.quantizer.utils import (
@@ -215,6 +214,7 @@ def _match_pattern(
     torch.ops.aten.flip.default,
     torch.ops.aten.chunk.default,
     torch.ops.aten.contiguous.default,
+    torch.ops.aten.upsample_bilinear2d.vec,
     torch.ops.aten.upsample_nearest2d.vec,
     torch.ops.aten.pad.default,
     torch.ops.aten.amax.default,

backends/arm/test/ops/test_expand.py

@@ -1,5 +1,4 @@
 # Copyright 2024-2025 Arm Limited and/or its affiliates.
-# All rights reserved.
 #
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
@@ -16,7 +15,7 @@
 
 import torch
 
-from executorch.backends.arm.quantizer.arm_quantizer import (
+from executorch.backends.arm.quantizer import (
     EthosUQuantizer,
     get_symmetric_quantization_config,
     TOSAQuantizer,
@@ -37,14 +36,14 @@ class Expand(torch.nn.Module):
         # (input tensor, multiples)
         test_parameters = [
             (torch.rand(1), (2,)),
-            (torch.randn(1, 4), (1, -1)),
             (torch.randn(1), (2, 2, 4)),
             (torch.randn(1, 1, 1, 5), (1, 4, -1, -1)),
-            (torch.randn(1, 1, 192), (1, -1, -1)),
             (torch.randn(1, 1), (1, 2, 2, 4)),
             (torch.randn(1, 1), (2, 2, 2, 4)),
             (torch.randn(10, 1, 1, 97), (-1, 4, -1, -1)),
             (torch.rand(1, 1, 2, 2), (4, 3, -1, 2)),
+            (torch.randn(1, 4), (1, -1)),
+            (torch.randn(1, 1, 192), (1, -1, -1)),
         ]
 
         def forward(self, x: torch.Tensor, m: Sequence):
@@ -117,34 +116,52 @@ def test_expand_tosa_MI(self, test_input, multiples):
     def test_expand_tosa_BI(self, test_input, multiples):
         self._test_expand_tosa_BI_pipeline(self.Expand(), (test_input, multiples))
 
-    @parameterized.expand(Expand.test_parameters[:-3])
+    @parameterized.expand(Expand.test_parameters[:-5])
     @pytest.mark.corstone_fvp
     def test_expand_u55_BI(self, test_input, multiples):
         self._test_expand_ethosu_BI_pipeline(
             common.get_u55_compile_spec(), self.Expand(), (test_input, multiples)
         )
 
     # MLETORCH-629: Expand does not work on FVP with batch>1
-    @parameterized.expand(Expand.test_parameters[-3:])
+    @parameterized.expand(Expand.test_parameters[-5:-2])
     @pytest.mark.corstone_fvp
     @conftest.expectedFailureOnFVP
+    def test_expand_u55_BI_xfails_on_fvp(self, test_input, multiples):
+        self._test_expand_ethosu_BI_pipeline(
+            common.get_u55_compile_spec(), self.Expand(), (test_input, multiples)
+        )
+
+    @parameterized.expand(Expand.test_parameters[-2:])
+    @pytest.mark.xfail(
+        reason="MLETORCH-716: Node will be optimized away and Vela can't handle empty graphs"
+    )
     def test_expand_u55_BI_xfails(self, test_input, multiples):
         self._test_expand_ethosu_BI_pipeline(
             common.get_u55_compile_spec(), self.Expand(), (test_input, multiples)
         )
 
-    @parameterized.expand(Expand.test_parameters[:-3])
+    @parameterized.expand(Expand.test_parameters[:-5])
     @pytest.mark.corstone_fvp
     def test_expand_u85_BI(self, test_input, multiples):
         self._test_expand_ethosu_BI_pipeline(
             common.get_u85_compile_spec(), self.Expand(), (test_input, multiples)
         )
 
     # MLETORCH-629: Expand does not work on FVP with batch>1
-    @parameterized.expand(Expand.test_parameters[-3:])
+    @parameterized.expand(Expand.test_parameters[-5:-2])
     @pytest.mark.corstone_fvp
     @conftest.expectedFailureOnFVP
-    def test_expand_u85_BI_xfails(self, test_input, multiples):
+    def test_expand_u85_BI_xfails_on_fvp(self, test_input, multiples):
+        self._test_expand_ethosu_BI_pipeline(
+            common.get_u85_compile_spec(), self.Expand(), (test_input, multiples)
+        )
+
+    @parameterized.expand(Expand.test_parameters[-2:])
+    @pytest.mark.xfail(
+        reason="MLETORCH-716: Node will be optimized away and Vela can't handle empty graphs"
+    )
+    def test_expand_u85_xfails(self, test_input, multiples):
         self._test_expand_ethosu_BI_pipeline(
             common.get_u85_compile_spec(), self.Expand(), (test_input, multiples)
         )

backends/arm/test/ops/test_hardtanh.py

@@ -13,7 +13,7 @@
 
 import torch
 
-from executorch.backends.arm.quantizer.arm_quantizer import (
+from executorch.backends.arm.quantizer import (
     EthosUQuantizer,
     get_symmetric_quantization_config,
     TOSAQuantizer,

backends/arm/test/ops/test_max_pool.py

@@ -12,7 +12,7 @@
 import pytest
 
 import torch
-from executorch.backends.arm.quantizer.arm_quantizer import (
+from executorch.backends.arm.quantizer import (
     EthosUQuantizer,
     get_symmetric_quantization_config,
     TOSAQuantizer,

backends/arm/test/ops/test_permute.py

@@ -13,7 +13,7 @@
 
 import torch
 
-from executorch.backends.arm.quantizer.arm_quantizer import (
+from executorch.backends.arm.quantizer import (
     EthosUQuantizer,
     get_symmetric_quantization_config,
     TOSAQuantizer,