Fix scalar arithemetic and add test cases

backends/arm/_passes/annotate_channels_last_dim_order_pass.py

@@ -9,6 +9,7 @@
 from typing import cast
 
 import torch
+from executorch.backends.arm._passes.arm_pass_utils import get_first_fake_tensor
 from executorch.backends.arm.tosa_quant_utils import dq_op
 from executorch.backends.arm.tosa_utils import is_consumer_node_depthwise_conv2d
 from executorch.exir.pass_base import ExportPass, PassResult
@@ -52,12 +53,7 @@ def call(self, graph_module: torch.fx.GraphModule):
         NHWC_Order = (0, 2, 3, 1)
         HWCM_Order = (2, 3, 0, 1)
         for node in graph_module.graph.nodes:
-            if isinstance(
-                node.meta["val"], (tuple, torch.fx.immutable_collections.immutable_list)
-            ):
-                node_data = node.meta["val"][0].data
-            else:
-                node_data = node.meta["val"].data
+            node_data = get_first_fake_tensor(node).data
 
             if len(node_data.shape) == 4:
                 dim_order = NHWC_Order

backends/arm/_passes/arm_pass_manager.py

@@ -22,6 +22,7 @@
 from executorch.backends.arm._passes.insert_squeeze_after_sum_pass import (
     InsertSqueezeAfterSumPass,
 )
+from executorch.backends.arm._passes.match_arg_ranks_pass import MatchArgRanksPass
 from executorch.backends.arm._passes.meandim_to_averagepool_pass import (
     ConvertMeanDimToAveragePool,
 )
@@ -30,6 +31,9 @@
     ScalarsToAttributePass,
 )
 from executorch.backends.arm._passes.size_adjust_conv2d_pass import SizeAdjustConv2DPass
+from executorch.backends.arm._passes.unsqueeze_scalar_placeholders_pass import (
+    UnsqueezeScalarPlaceholdersPass,
+)
 from executorch.exir import ExportedProgram
 from executorch.exir.backend.compile_spec_schema import CompileSpec
 from executorch.exir.pass_manager import PassManager
@@ -45,10 +49,12 @@ def transform_to_backend_pipeline(
     ):
         """Apply passes before transforming program to backend"""
         self.add_pass(CastInt64ToInt32Pass(exported_program))
+        self.add_pass(UnsqueezeScalarPlaceholdersPass(exported_program))
         self.add_pass(SizeAdjustConv2DPass())
         self.add_pass(RemoveClonePass())
         self.add_pass(ConvertExpandCopyToRepeatPass())
         self.add_pass(ConvertMeanDimToAveragePool())
+        self.add_pass(MatchArgRanksPass(exported_program))
         self.add_pass(DecomposeDivPass())
         self.add_pass(InsertSqueezeAfterSumPass())
         self.add_pass(ConvertSplitToSlicePass())
@@ -61,6 +67,6 @@ def transform_to_backend_pipeline(
         return self._transform(exported_program.graph_module)
 
     def transform_for_annotation_pipeline(self, graph_module: torch.fx.GraphModule):
-        self.add_pass(DecomposeDivPass())
         self.add_pass(ScalarsToAttributePass())
+        self.add_pass(DecomposeDivPass())
         return self._transform(graph_module)

backends/arm/_passes/arm_pass_utils.py

@@ -7,9 +7,11 @@
 from typing import Optional
 
 import torch
+import torch.fx
 
 from executorch.exir.dialects._ops import ops as exir_ops
 from torch._ops import OpOverload
+from torch._subclasses.fake_tensor import FakeTensor
 
 
 def create_node(
@@ -64,3 +66,21 @@ def insert_q_dq_pair(
     # node's first use
     q.args = (anchor,) + q_params
     return dq
+
+
+def get_first_fake_tensor(node: torch.fx.Node) -> FakeTensor:
+    """
+    Returns a FakeTensor from the meta field of 'node'.
+    If the node contains many fake tensors, return the first one.
+    """
+    if isinstance(
+        node.meta["val"], (tuple, torch.fx.immutable_collections.immutable_list)
+    ):
+        fake_tensor = node.meta["val"][0]
+    else:
+        fake_tensor = node.meta["val"]
+
+    assert isinstance(
+        fake_tensor, FakeTensor
+    ), f'Found {fake_tensor} in meta["val"] of {node}, expected to find FakeTensor.'
+    return fake_tensor

backends/arm/_passes/decompose_div_pass.py

@@ -8,15 +8,18 @@
 from executorch.exir.dialects._ops import ops as exir_ops
 from executorch.exir.pass_base import ExportPass
 
+edge_div_ops = (exir_ops.edge.aten.div.Tensor,)
+aten_div_ops = (torch.ops.aten.div.Tensor, torch.ops.aten.div_.Tensor)
+
 
 def get_div_decomposition(op) -> tuple:
     """
     Returns the the (reciprocal_op, mul_op), where the ops depends on if
     the div op is in exir_ops torch.ops.aten.
     """
-    if op == exir_ops.edge.aten.div.Tensor:
+    if op in edge_div_ops:
         return (exir_ops.edge.aten.reciprocal.default, exir_ops.edge.aten.mul.Tensor)
-    if op == torch.ops.aten.div.Tensor:
+    if op in aten_div_ops:
         return (torch.ops.aten.reciprocal.default, torch.ops.aten.mul.Tensor)
     raise RuntimeError(f"Can't get div decomposition for op {op}")
 
@@ -33,7 +36,7 @@ class DecomposeDivPass(ExportPass):
     """
 
     def call_operator(self, op, args, kwargs, meta):
-        if op not in (exir_ops.edge.aten.div.Tensor, torch.ops.aten.div.Tensor):
+        if op not in (edge_div_ops + aten_div_ops):
             return super().call_operator(op, args, kwargs, meta)
 
         reciprocal_op, mul_op = get_div_decomposition(op)

backends/arm/_passes/match_arg_ranks_pass.py

@@ -0,0 +1,126 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# Copyright 2024 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.
+
+from typing import cast
+
+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
+from torch.fx import GraphModule, Node
+
+
+class MatchArgRanksPass(ExportPass):
+    """
+    For ops in 'targeted_ops', make sure that the inputs share the same rank.
+    New dimensions are inserted at from the beginning of the
+    """
+
+    def __init__(self, exported_program):
+        super().__init__()
+        self.exported_program = exported_program
+
+    targeted_ops = [
+        exir_ops.edge.aten.add.Tensor,
+        exir_ops.edge.aten.sub.Tensor,
+        exir_ops.edge.aten.mul.Tensor,
+        exir_ops.edge.aten.div.Tensor,
+    ]
+
+    def _match_op_rank(self, graph_module, node, arg, max_rank):
+        """
+        In graph_module, insert a view between arg and node to make the
+        rank of arg match the other args to node.
+        """
+        shape = get_first_fake_tensor(arg).shape
+        rank = len(shape)
+        new_shape = list([1] * (max_rank - rank) + list(shape))
+        with graph_module.graph.inserting_before(node):
+            view = create_node(
+                graph_module.graph,
+                exir_ops.edge.aten.view_copy.default,
+                args=(arg, new_shape),
+                kwargs={},
+            )
+            node.replace_input_with(arg, view)
+
+    def _match_buffer_rank(self, arg, max_rank):
+        """
+        Change arg's fake tensor meta to match max_rank if:
+            - arg is found in inputs_to_buffers or inputs_to_parameters.
+        """
+        fake_tensor = get_first_fake_tensor(arg)
+        shape = fake_tensor.shape
+        rank = len(shape)
+        new_shape = list([1] * (max_rank - rank) + list(shape))
+
+        buffer_name = None
+        if arg.name in self.exported_program.graph_signature.inputs_to_buffers:
+            buffer_name = self.exported_program.graph_signature.inputs_to_buffers[
+                arg.name
+            ]
+        elif arg.name in self.exported_program.graph_signature.inputs_to_parameters:
+            buffer_name = self.exported_program.graph_signature.inputs_to_parameters[
+                arg.name
+            ]
+        if buffer_name:
+            new_tensor = self.exported_program.state_dict[buffer_name].reshape(
+                new_shape
+            )
+            self.exported_program.state_dict[buffer_name] = new_tensor
+            arg.meta["val"] = fake_tensor.fake_mode.from_tensor(
+                new_tensor, static_shapes=True
+            )
+
+    def call(self, graph_module: GraphModule) -> PassResult:
+        for node in graph_module.graph.nodes:
+            node = cast(Node, node)
+
+            if node.op != "call_function" or node.target not in self.targeted_ops:
+                continue
+
+            # Calculate max rank of all inputs to node
+            max_rank = 1
+            for arg in node.args:
+                if isinstance(arg, Node):
+                    shape = get_first_fake_tensor(arg).shape
+                    max_rank = max(max_rank, len(shape))
+
+            # Adjust output shape of args if needed.
+            for arg in node.args:
+                if not isinstance(arg, Node):
+                    continue
+                shape = get_first_fake_tensor(arg).shape
+                rank = len(shape)
+                if rank == max_rank:
+                    continue
+
+                # If the argument is call_function, match shape by inserting view node.
+                if arg.op == "call_function":
+                    self._match_op_rank(graph_module, node, arg, max_rank)
+                else:
+                    # If the argument is a buffer or parameter, adjust shape by changing the fake tensor meta.
+                    self._match_buffer_rank(arg, max_rank)
+
+        graph_module.recompile()
+        graph_module = super().call(graph_module).graph_module
+        return PassResult(graph_module, True)
+
+    def ensures(self, graph_module):
+        for node in graph_module.graph.nodes:
+            if node.op != "call_function" or node.target not in self.targeted_ops:
+                continue
+            arg0_rank = node.args[0].meta["val"].dim()
+            arg1_rank = node.args[1].meta["val"].dim()
+            if arg0_rank != arg1_rank:
+                raise ValueError(
+                    "Arguments of arithmetic operators need to have the same rank!"
+                )

backends/arm/_passes/scalars_to_attribute_pass.py

@@ -7,7 +7,7 @@
 from typing import cast, Union
 
 import torch
-from executorch.backends.arm.tosa_mapping import extract_tensor_meta
+from executorch.backends.arm._passes.arm_pass_utils import get_first_fake_tensor
 
 from executorch.exir.pass_base import ExportPass, PassResult
 from torch.ao.quantization.fx.utils import get_new_attr_name_with_prefix
@@ -22,10 +22,14 @@ class ScalarsToAttributePass(ExportPass):
 
     targeted_ops = [
         torch.ops.aten.add.Tensor,
+        torch.ops.aten.add_.Tensor,
         torch.ops.aten.sub.Tensor,
         torch.ops.aten.sub_.Tensor,
+        torch.ops.aten.rsub.Scalar,
         torch.ops.aten.mul.Tensor,
+        torch.ops.aten.mul_.Tensor,
         torch.ops.aten.div.Tensor,
+        torch.ops.aten.div_.Tensor,
     ]
 
     def call(self, graph_module: GraphModule) -> PassResult:
@@ -37,7 +41,7 @@ def call(self, graph_module: GraphModule) -> PassResult:
             biggest_rank = 1
             for arg in n.args:
                 if isinstance(arg, Node):
-                    _, shape, _ = extract_tensor_meta(arg.meta)
+                    shape = get_first_fake_tensor(arg).shape
                     biggest_rank = max(biggest_rank, len(shape))
 
             new_args = []

backends/arm/_passes/unsqueeze_scalar_placeholders_pass.py

@@ -0,0 +1,53 @@
+# Copyright 2024 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.
+
+import torch
+from executorch.exir.pass_base import ExportPass, PassResult
+
+
+class UnsqueezeScalarPlaceholdersPass(ExportPass):
+    """
+    Placeholders that have node.meta["val"].shape = () cause issues later in the lowering.
+    This pass unsqueezes the placeholders to make sure shape is at least (1,).
+    """
+
+    def __init__(self, exported_program):
+        self.exported_program = exported_program
+        super().__init__()
+
+    def call(self, graph_module: torch.fx.GraphModule):
+        for node in graph_module.graph.nodes:
+            if node.op != "placeholder":
+                continue
+            rank = node.meta["val"].dim()
+            if rank == 0:
+                if not (
+                    node.name in self.exported_program.graph_signature.inputs_to_buffers
+                    or node.name
+                    in self.exported_program.graph_signature.inputs_to_parameters
+                ):
+                    continue
+                tensor = self.exported_program.state_dict[node.name]
+                if tensor.dim() == 0:
+                    self.exported_program.state_dict[node.name] = tensor.unsqueeze(0)
+                    node.meta["val"] = node.meta["val"].fake_mode.from_tensor(
+                        tensor.unsqueeze(0), static_shapes=True
+                    )
+                else:
+                    node.meta["val"] = node.meta["val"].fake_mode.from_tensor(
+                        tensor, static_shapes=True
+                    )
+
+        graph_module.recompile()
+        graph_module = super().call(graph_module).graph_module
+        return PassResult(graph_module, True)
+
+    def ensures(self, graph_module: torch.fx.GraphModule):
+        for node in graph_module.graph.nodes:
+            if node.op == "placeholder":
+                rank = node.meta["val"].dim()
+                if rank == 0:
+                    raise ValueError("Placeholders of rank 0 are not supported!")

backends/arm/quantizer/quantization_annotation/mul_annotator.py

@@ -24,7 +24,7 @@ def _annotate_mul(
 
     annotated_partitions = []
     for node in gm.graph.nodes:
-        if node.target not in (torch.ops.aten.mul.Tensor,):
+        if node.target not in (torch.ops.aten.mul.Tensor, torch.ops.aten.mul_.Tensor):
             continue
         mul_node = node
         annotated_partitions.append([mul_node])

backends/arm/quantizer/quantization_annotation/sub_annotator.py

@@ -6,8 +6,6 @@
 
 # pyre-unsafe
 
-import itertools
-import operator
 from typing import Callable, List, Optional
 
 import torch
@@ -16,7 +14,6 @@
 from executorch.backends.arm.quantizer.quantization_config import QuantizationConfig
 from torch.ao.quantization.quantizer import QuantizationAnnotation
 from torch.fx import GraphModule, Node
-from torch.fx.passes.utils.source_matcher_utils import get_source_partitions
 
 
 @register_annotator("sub")
@@ -25,14 +22,12 @@ def _annotate_sub(
     quantization_config: QuantizationConfig,
     filter_fn: Optional[Callable[[Node], bool]] = None,
 ) -> Optional[List[List[Node]]]:
-    sub_partitions = get_source_partitions(
-        gm.graph, [operator.sub, torch.sub, operator.isub], filter_fn
-    )
-    sub_partitions = list(itertools.chain.from_iterable(sub_partitions.values()))
     annotated_partitions = []
-    for sub_partition in sub_partitions:
-        annotated_partitions.append(sub_partition.nodes)
-        sub_node = sub_partition.output_nodes[0]
+    for node in gm.graph.nodes:
+        if node.target not in (torch.ops.aten.sub.Tensor, torch.ops.aten.sub_.Tensor):
+            continue
+        annotated_partitions.append(node)
+        sub_node = node
         if arm_quantizer_utils.is_annotated(sub_node):
             continue