Update base for Update on "[backend_api] Delete partitioner tags after lowering"

There was a slight bug i found when lowering with:

XnnpackDynamicallyQuantizedPartitioner --> XnnpackPartitioner. The issue arrises because the second time we partition, delegation tags from the previous partitioner still exist. Specifically they exist on the getitem node because the metadata was propagated.

See:
https://github.com/pytorch/pytorch/blob/main/torch/fx/passes/utils/fuser_utils.py#L235

Since the getitem nodes created in by the XnnpackDynamicallyQuantizedPartitioner still have the delegation tags, they sneak into partitions from XnnpackPartitioner. This shouldn't happen because delegation tags from previous Partitioner should not exist after the previous lowering.

We update the code in to_backend here to erase all the "delegation_tags" from node.meta after we've performed all `_partition_and_lower`

Differential Revision: [D71518362](https://our.internmc.facebook.com/intern/diff/D71518362/)

[ghstack-poisoned]

Author: mcr229

.ci/docker/common/install_android.sh

@@ -70,6 +70,7 @@ install_sdk() {
   # These are the tools needed to build Android apps
   yes | /opt/cmdline-tools/bin/sdkmanager --sdk_root="${SDK_INSTALLATION_DIR}" --install "platforms;android-34"
   yes | /opt/cmdline-tools/bin/sdkmanager --sdk_root="${SDK_INSTALLATION_DIR}" --install "build-tools;33.0.1"
+  yes | /opt/cmdline-tools/bin/sdkmanager --sdk_root="${SDK_INSTALLATION_DIR}" --install "build-tools;35.0.0"
   # And some more tools for future emulator tests
   yes | /opt/cmdline-tools/bin/sdkmanager --sdk_root="${SDK_INSTALLATION_DIR}" --install "platform-tools"
   yes | /opt/cmdline-tools/bin/sdkmanager --sdk_root="${SDK_INSTALLATION_DIR}" --install "tools"

build/test_ios_ci.sh renamed to .ci/scripts/test_ios_ci.sh

@@ -14,7 +14,7 @@ on:
       - build/build_apple_frameworks.sh
       - build/build_apple_llm_demo.sh
       - build/create_frameworks.sh
-      - build/test_ios_ci.sh
+      - .ci/scripts/test_ios_ci.sh
       - examples/demo-apps/apple_ios/**
       - extension/apple/**
       - extension/benchmark/apple/**
@@ -75,7 +75,7 @@ jobs:
 
         # Build and test iOS Demo App
         PYTHON_EXECUTABLE=python ${CONDA_RUN} --no-capture-output \
-        build/test_ios_ci.sh "${ARTIFACTS_DIR_NAME}"
+        .ci/scripts/test_ios_ci.sh "${ARTIFACTS_DIR_NAME}"
 
   # Upload the test demo app to S3
   upload-demo-ios:

.github/workflows/apple.yml

@@ -34,6 +34,9 @@ def is_node_tosa_supported(self, node: fx.Node, tosa_spec: TosaSpecification):
 
         for pad in output_padding:
             if pad != 0:
+                self.reporter.report_reject(
+                    node, "Convolutions with non-zero output padding not implemented."
+                )
                 return False
 
         # Hardware specific constraints
@@ -56,19 +59,33 @@ def _is_node_supported_u55(self, node: fx.Node):
             # Depthwise convolution
             for dim in shape_in[1:]:
                 if not 1 <= dim <= 65536:
+                    self.reporter.report_reject(
+                        node,
+                        f"Depthwise convolution must have CWH <= 65536, got {dim})",
+                    )
                     return False
         else:
             # Convolution
             if not 1 <= C_in <= 65536:
+                self.reporter.report_reject(
+                    node, f"Convolution must have C <= 65536, got {C_in})"
+                )
                 return False
 
         kernel_w = kernel[2]
         kernel_h = kernel[3] if len(kernel) > 3 else 1
         # Kernel condition misses constraint on sum of absolute weights
         if not 1 <= kernel_h <= 64 or not 1 <= kernel_w * kernel_h <= 4096:
+            self.reporter.report_reject(
+                node,
+                f"Convolution needs to have kernel_y<=64, kernel_x*kernel_y<=4096, got kernel ({kernel_w}, {kernel_h})",
+            )
             return False
 
         if not self._stride_condition(node):
+            self.reporter.report_reject(
+                node, "Failed condition on stride, pad and dilation combination."
+            )
             return False
 
         return True

backends/arm/operator_support/convolution_support.py

@@ -54,12 +54,35 @@ def is_node_tosa_supported(self, node: fx.Node, tosa_spec: TosaSpecification):
         if len(node.args) > 3:
             # Padding case
             if not all(1 <= k <= 8 for k in kernel):
+                self.reporter.report_reject(
+                    node, f"Avgpool2d with padding needs kernel dims < 8, got {kernel}"
+                )
                 return False
         else:
             if not kernel_check(kernel):
+                self.reporter.report_reject(
+                    node,
+                    f"Avgpool2d needs kernel_y < 256, kernel_x*kernel_y<=65536, got {kernel}",
+                )
                 return False
 
-        return dim_check(shape) and shape[0] == 1 and stride_check(stride)
+        if not dim_check(shape):
+            self.reporter.report_reject(
+                node,
+                f"Avgpool2d needs N == 1, rest dims <= 65536, got shape {list(shape)}",
+            )
+            return False
+        if not stride_check(stride):
+            self.reporter.report_reject(
+                node, f"Avgpool2d needs stride <= 3, got {stride}"
+            )
+            return False
+        if not shape[0] == 1:
+            self.reporter.report_reject(
+                node, f"Avgpool2d needs N==1, got N=={shape[0]}"
+            )
+            return False
+        return True
 
 
 @register_tosa_support_check
@@ -82,4 +105,21 @@ def is_node_tosa_supported(self, node: fx.Node, tosa_spec: TosaSpecification):
         kernel = cast(tuple[int, int], node.args[1])
         stride = cast(tuple[int, int], node.args[2])
 
-        return kernel_check(kernel) and dim_check(shape) and stride_check(stride)
+        if not kernel_check(kernel):
+            self.reporter.report_reject(
+                node,
+                f"Maxpool2d needs kernel_y < 256, kernel_x*kernel_y<=65536, got {kernel}",
+            )
+            return False
+        if not dim_check(shape):
+            self.reporter.report_reject(
+                node,
+                f"Maxpool2d needs N == 1, rest dims <= 65536, got shape {list(shape)}",
+            )
+            return False
+        if not stride_check(stride):
+            self.reporter.report_reject(
+                node, f"Maxpool2d needs stride <= 3, got {stride}"
+            )
+            return False
+        return True

backends/arm/operator_support/pool_2d_support.py

@@ -34,6 +34,9 @@ def is_node_tosa_supported(self, node: fx.Node, tosa_spec: TosaSpecification):
 
         for dim in dim_list:
             if not 1 <= input_shape[dim] <= 65536:
+                self.reporter.report_reject(
+                    node, f"sum needs dims < 65536, got shape {input_shape}"
+                )
                 return False
 
             # We can't be certain of which dim is the last in memory yet,
@@ -45,7 +48,9 @@ def is_node_tosa_supported(self, node: fx.Node, tosa_spec: TosaSpecification):
             for length in input_shape[dim + 1 :]:
                 post_R_product *= length
             if not 1 <= pre_R_product <= 65536:
+                self.reporter.report_reject(node, "Failed dim check")
                 return False
             if not 1 <= post_R_product <= 65536:
+                self.reporter.report_reject(node, "Failed dim check")
                 return False
         return True

backends/arm/operator_support/reduce_sum_support.py

@@ -75,9 +75,6 @@ def is_node_tosa_supported(
     ) -> bool:
         assert node.target in self.targets
 
-        if tosa_spec not in self.tosa_specs:
-            return False
-
         assert tosa_spec.support_integer()
         supported_dtypes = (
             self.ALL_SUPPORTED_TYPES
@@ -97,30 +94,32 @@ def is_node_tosa_supported(
         assert isinstance(input_val, torch._subclasses.FakeTensor)
         input_dtype = input_val.dtype
         if input_dtype not in supported_dtypes:
-            logger.info(
-                f"Input dtype {input_val.dtype} is not supported in "
-                f"{node.target.name()}."  # type: ignore[union-attr]  # pyre-ignore[16]
+            self.reporter.report_reject(
+                node,
+                f"Input dtype {input_val.dtype} is not supported in {node.target}.",
             )
             return False
 
         # Check output type
         output_val = node.meta["val"]
         assert isinstance(output_val, torch._subclasses.FakeTensor)
         if output_val.dtype not in supported_dtypes[input_dtype]:
-            logger.info(
+            self.reporter.report_reject(
+                node,
                 f"Output dtype {output_val.dtype} is not supported in "
-                f"{node.target.name()} for input dtype {input_dtype}. "  # type: ignore[union-attr]  # pyre-ignore[16]
+                f"{node.target} for input dtype {input_dtype}. "
                 f"Supported output types: "
-                f"{''.join(str(t) for t in supported_dtypes[input_dtype])}"
+                f"{''.join(str(t) for t in supported_dtypes[input_dtype])}",
             )
             return False
 
         # Check memory format (to_copy)
         if "memory_format" in node.kwargs:
             if node.kwargs["memory_format"] in (torch.preserve_format,):
-                logger.info(
+                self.reporter.report_reject(
+                    node,
                     f"Argument 'memory_format' is not supported for "
-                    f"{node.target.name()} right now."  # type: ignore[union-attr]  # pyre-ignore[16]
+                    f"{node.target} right now.",
                 )
                 return False
 
@@ -129,9 +128,10 @@ def is_node_tosa_supported(
             dim_order = node.kwargs["dim_order"]
             # pyre-ignore[6]
             if dim_order != list(range(len(dim_order))):  # type: ignore[arg-type]
-                logger.info(
+                self.reporter.report_reject(
+                    node,
                     f"Argument {dim_order=} is not supported for "
-                    f"{node.target.name()} right now."  # type: ignore[union-attr]  # pyre-ignore[16]
+                    f"{node.target} right now.",
                 )
                 return False