2024-10-16 nightly release (8673567)

.ci/docker/build.sh

@@ -41,7 +41,7 @@ case "${IMAGE_NAME}" in
     LINTRUNNER=""
     CLANG_VERSION=12
     # From https://developer.android.com/ndk/downloads
-    ANDROID_NDK_VERSION=r26c
+    ANDROID_NDK_VERSION=r27b
     ;;
   *)
     echo "Invalid image name ${IMAGE_NAME}"

.github/workflows/android-release-artifacts.yml

@@ -13,8 +13,24 @@ concurrency:
   cancel-in-progress: true
 
 jobs:
+  check-if-aar-exists:
+    name: check-if-aar-exists
+    runs-on: ubuntu-22.04
+    timeout-minutes: 10
+    steps:
+      - name: Check if this RC version is already in S3
+        shell: bash
+        run: |
+          VERSION="${{ inputs.version }}"
+          if curl -I "https://ossci-android.s3.amazonaws.com/executorch/release/${VERSION}/executorch.aar" | grep "200 OK"; then
+            echo "AAR already exists at https://ossci-android.s3.amazonaws.com/executorch/release/${VERSION}/executorch.aar"
+            echo "Will skip build/upload"
+            exit 1
+          fi
+
   build-aar:
     name: build-aar
+    needs: check-if-aar-exists
     uses: pytorch/test-infra/.github/workflows/linux_job.yml@main
     with:
       runner: linux.2xlarge

.github/workflows/android.yml

@@ -53,7 +53,7 @@ jobs:
     # NB: Use metal install for KVM support to run the emulator faster
     runs-on: linux.24xl.spr-metal
     env:
-      ANDROID_NDK_VERSION: r26c
+      ANDROID_NDK_VERSION: r27b
       API_LEVEL: 34
     steps:
       - name: Setup SSH (Click me for login details)

backends/arm/_passes/arm_pass_manager.py

@@ -19,6 +19,9 @@
     ConvertSplitToSlicePass,
 )
 from executorch.backends.arm._passes.decompose_div_pass import DecomposeDivPass
+from executorch.backends.arm._passes.insert_squeeze_after_sum_pass import (
+    InsertSqueezeAfterSumPass,
+)
 from executorch.backends.arm._passes.meandim_to_averagepool_pass import (
     ConvertMeanDimToAveragePool,
 )
@@ -47,6 +50,7 @@ def transform_to_backend_pipeline(
         self.add_pass(ConvertExpandCopyToRepeatPass())
         self.add_pass(ConvertMeanDimToAveragePool())
         self.add_pass(DecomposeDivPass())
+        self.add_pass(InsertSqueezeAfterSumPass())
         self.add_pass(ConvertSplitToSlicePass())
         for spec in compile_spec:
             if spec.key == "permute_memory_format":

backends/arm/_passes/insert_squeeze_after_sum_pass.py

@@ -0,0 +1,69 @@
+# 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
+
+import torch
+import torch.fx
+from executorch.backends.arm._passes.arm_pass_utils import create_node, insert_q_dq_pair
+
+from executorch.backends.arm.tosa_quant_utils import get_quant_node_args, is_quant_node
+from executorch.exir.dialects._ops import ops as exir_ops
+from executorch.exir.pass_base import ExportPass, PassResult
+
+
+class InsertSqueezeAfterSumPass(ExportPass):
+    """
+    In Pytorch, the default behaviour of Tensor.sum is to squeeze
+    the dimension that is summed (keep_dim = False).
+    However, in TOSA, REDUCE_SUM always preserves the
+    rank of the input (keep_dim = True).
+    To get a 1-1 mapping in the sum lowering, normalize the
+    keep_dim = False case to keep_dim = True and add squeeze ops.
+
+    Original:
+        sum(dims, keep_dim = False)
+    After pass:
+        sum(dims, keep_dim = True)
+        (q)
+        (dq)
+        squeeze(dim = dims)
+    """
+
+    def call(self, graph_module: torch.fx.GraphModule):
+        for node in graph_module.graph.nodes:
+            if node.op != "call_function":
+                continue
+            if node.target != exir_ops.edge.aten.sum.dim_IntList:
+                continue
+            sum_node = cast(torch.fx.Node, node)
+            keep_dim = cast(bool, sum_node.args[2] if len(sum_node.args) > 2 else False)
+            if keep_dim:
+                continue
+
+            dim_list = cast(list[int], sum_node.args[1])
+            quantized = is_quant_node(sum_node)
+            if quantized:
+                qparams = get_quant_node_args(sum_node.all_input_nodes[0])
+                qparams = qparams + (torch.int8,)
+            else:
+                qparams = None
+
+            # Add keep_dim = True arg to sum node.
+            sum_node.args = sum_node.args[0:2] + (True,)
+
+            with graph_module.graph.inserting_after(sum_node):
+                squeeze_node = create_node(
+                    graph_module.graph, exir_ops.edge.aten.squeeze_copy.dims, ()
+                )
+                sum_node.replace_all_uses_with(squeeze_node)
+                squeeze_node.args = (sum_node, dim_list)
+                if quantized:
+                    sum_node = insert_q_dq_pair(graph_module.graph, sum_node, qparams)
+        graph_module.graph.eliminate_dead_code()
+        graph_module.recompile()
+        graph_module = super().call(graph_module).graph_module
+        return PassResult(graph_module, True)

backends/arm/arm_partitioner.py

@@ -63,6 +63,7 @@ def is_node_supported(self, submodules, node: torch.fx.Node) -> bool:
             exir_ops.edge.aten._softmax.default,
             exir_ops.edge.aten.slice_copy.Tensor,
             exir_ops.edge.aten.sub.Tensor,
+            exir_ops.edge.aten.sum.dim_IntList,
             exir_ops.edge.aten.view_copy.default,
             exir_ops.edge.aten.clone.default,
             exir_ops.edge.aten.mean.dim,

backends/arm/operators/__init__.py

@@ -34,6 +34,7 @@
     op_softmax,
     op_squeeze,
     op_sub,
+    op_sum,
     op_unsqueeze,
     op_view,
 )

backends/arm/operators/op_sum.py

@@ -0,0 +1,96 @@
+# Copyright 2023-2024 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 typing import cast, List
+
+import executorch.backends.arm.tosa_quant_utils as tqutils
+import executorch.backends.arm.tosa_utils as tutils
+
+import serializer.tosa_serializer as ts
+from executorch.backends.arm.operators.node_visitor import (
+    NodeVisitor,
+    register_node_visitor,
+)
+from executorch.backends.arm.tosa_mapping import TosaArg
+from serializer.tosa_serializer import TosaOp
+from torch.fx import Node
+
+
+@register_node_visitor
+class AddVisitor(NodeVisitor):
+    target = "aten.sum.dim_IntList"
+
+    def __init__(self, *args):
+        super().__init__(*args)
+
+    def define_node(
+        self,
+        node: Node,
+        tosa_graph: ts.TosaSerializer,
+        inputs: List[TosaArg],
+        output: TosaArg,
+        is_quant_node: bool,
+    ) -> None:
+        input_node = inputs[0]
+        input_shape = list(input_node.shape)
+        dim_list = cast(list[int], inputs[1].special)
+        dim_list = [dim % len(input_node.shape) for dim in dim_list]
+        keep_dim = cast(bool, inputs[2].number if len(inputs) > 2 else False)
+        assert keep_dim, "This case should be handled by InsertSqueezeAfterSumPass"
+
+        if is_quant_node:
+
+            # Rescale input to 32 bit
+            rescaled_inputs, scale = tqutils.rescale_nodes_to_int32(
+                [node.all_input_nodes[0]], tosa_graph
+            )
+
+            prev_node = rescaled_inputs[0]
+            reduced_shape = input_shape
+
+            # Reduce all dims in dim_list one-by-one.
+            for dim in dim_list:
+                # When reduced, the size of the dim becomes 1.
+                reduced_shape[dim] = 1
+
+                attr = ts.TosaSerializerAttribute()
+                attr.AxisAttribute(input_node.dim_order.index(dim))
+
+                next_node = tosa_graph.addIntermediate(
+                    tutils.tosa_shape(reduced_shape, input_node.dim_order),
+                    dtype=ts.DType.INT32,
+                )
+
+                tosa_graph.addOperator(
+                    TosaOp.Op().REDUCE_SUM, [prev_node.name], [next_node.name], attr
+                )
+
+                prev_node = next_node
+            tqutils.rescale_node_back_to_int8(node, prev_node, scale, tosa_graph)
+        else:
+            input_name = input_node.name
+            reduced_shape = input_shape
+
+            # Reduce all dims in dim_list one-by-one.
+            for dim in dim_list:
+                # When reduced, the size of the dim becomes 1
+                reduced_shape[dim] = 1
+
+                attr = ts.TosaSerializerAttribute()
+                attr.AxisAttribute(input_node.dim_order.index(dim))
+
+                if dim == dim_list[-1]:
+                    output_name = output.name
+                else:
+                    output_name = tosa_graph.addIntermediate(
+                        tutils.tosa_shape(reduced_shape, input_node.dim_order),
+                        dtype=ts.DType.FP32,
+                    ).name
+
+                tosa_graph.addOperator(
+                    TosaOp.Op().REDUCE_SUM, [input_name], [output_name], attr
+                )
+
+                input_name = output_name

backends/arm/quantizer/arm_quantizer.py

@@ -267,11 +267,11 @@ class ArmQuantizer(Quantizer):
         "add",
         "sub",
         "mul",
-        "sigmoid",
         "mm",
         "cat",
         "one_to_one",
         "generic",
+        "sum",
     ]
 
     def __init__(self) -> None:

backends/arm/quantizer/quantization_annotation/__init__.py

@@ -59,6 +59,6 @@ def decorator(annotator: AnnotatorType):
     mm_annotator,
     mul_annotator,
     one_to_one_annotator,
-    sigmoid_annotator,
     sub_annotator,
+    sum_annotator,
 )

backends/arm/quantizer/quantization_annotation/one_to_one_annotator.py

@@ -40,6 +40,7 @@ def _annotate_one_to_one(
         torch.ops.aten.log.default,
         torch.ops.aten.reciprocal.default,
         torch.ops.aten.rsqrt.default,
+        torch.ops.aten.sigmoid.default,
     )
     for node in gm.graph.nodes:
         if node.op != "call_function" or node.target not in one_to_one_ops:

backends/arm/quantizer/quantization_annotation/sum_annotator.py

@@ -0,0 +1,57 @@
+# Copyright 2024 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 typing import Callable, cast, List, Optional
+
+import torch
+from executorch.backends.arm.quantizer import arm_quantizer_utils
+from executorch.backends.arm.quantizer.quantization_annotation import register_annotator
+from executorch.backends.arm.quantizer.quantization_config import QuantizationConfig
+
+from torch.ao.quantization.quantizer import (
+    QuantizationAnnotation,
+    QuantizationSpecBase,
+    SharedQuantizationSpec,
+)
+from torch.fx import Node
+
+
+@register_annotator("sum")
+def _annotate_sum(
+    gm: torch.fx.GraphModule,
+    quantization_config: QuantizationConfig,
+    filter_fn: Optional[Callable[[Node], bool]] = None,
+) -> Optional[List[List[Node]]]:
+    annotated_partitions = []
+    for node in gm.graph.nodes:
+        if node.target is not torch.ops.aten.sum.dim_IntList:
+            continue
+        if filter_fn and not filter_fn(node):
+            continue
+
+        sum_node = node
+        if arm_quantizer_utils.is_annotated(sum_node):
+            continue
+
+        input_act = sum_node.args[0]
+
+        if not isinstance(input_act, Node):
+            continue
+        if not arm_quantizer_utils.is_input_ok_for_quantization(input_act, gm):
+            continue
+
+        input_act_qspec = cast(
+            Optional[QuantizationSpecBase], quantization_config.get_input_act_qspec()
+        )
+        input_qspec_map = {input_act: input_act_qspec}
+        shared_with_input0_qspec = SharedQuantizationSpec((input_act, sum_node))
+
+        sum_node.meta["quantization_annotation"] = QuantizationAnnotation(
+            input_qspec_map=input_qspec_map,
+            output_qspec=shared_with_input0_qspec,
+            _annotated=True,
+        )
+        annotated_partitions.append([sum_node])
+    return annotated_partitions