Div, Mul, Elu Partitioner Configs

backends/xnnpack/operators/op_conv2d.py

@@ -52,6 +52,9 @@ def define_node(
         )  # NHWC input
         kwargs["input1_id"] = vals_to_ids[get_input_node(node, 0)]
 
+        # filter shape for pytorch convolution is (oc, inc/groups, height, width)
+        # shape for xnnpack convolution is (oc, height, width, inc/groups), to convert
+        # to the proper shape, this is essentially a NCHW to NHWC conversion
         kernel_node = get_input_node(node, 1)
         kernel_shape = get_shape(kernel_node)
         groups = cast(int, node.args[8])
@@ -65,19 +68,13 @@ def define_node(
         is_depthwise_conv = (group_input_channels == 1) and (
             group_output_channels % group_input_channels == 0
         )
-        # filter
-        # filter shape for pytorch convolution is (oc, inc/groups, height, width)
-        # shape for xnnpack convolution is (oc, height, width, inc/groups), to convert
-        # to the proper shape, this is essentially a NCHW to NHWC conversion
-        weight_node = get_input_node(node, 1)
         weight_quant_params = QuantParams.from_weights(
-            weight_node, self._exported_program
+            kernel_node, self._exported_program
         )
-
-        fp32_static_weights = weight_node.meta["val"].dtype == torch.float16
+        fp32_static_weights = kernel_node.meta["val"].dtype == torch.float16
 
         self.define_tensor(
-            weight_node,
+            kernel_node,
             xnn_graph,
             vals_to_ids,
             convert_to_nhwc=True,

backends/xnnpack/partition/TARGETS

@@ -6,6 +6,7 @@ runtime.python_library(
     name = "xnnpack_partitioner",
     srcs = [
         "xnnpack_partitioner.py",
+        "xnnpack_partitioner2.py",
     ],
     visibility = [
         "//executorch/...",
@@ -15,6 +16,7 @@ runtime.python_library(
         ":configs",
         ":partitioner_graphs",
         "//executorch/backends/xnnpack:xnnpack_preprocess",
+        "//executorch/backends/xnnpack/partition/config:xnnpack_partitioner_configs",
         "//executorch/exir:delegate",
         "//executorch/exir:lib",
         "//executorch/exir/backend:partitioner",

backends/xnnpack/partition/config/TARGETS

@@ -0,0 +1,20 @@
+load("@fbsource//xplat/executorch/build:runtime_wrapper.bzl", "runtime")
+
+oncall("executorch")
+
+runtime.python_library(
+    name = "xnnpack_partitioner_configs",
+    srcs = glob([
+        "*.py",
+    ]),
+    visibility = [
+        "//executorch/...",
+        "@EXECUTORCH_CLIENTS",
+    ],
+    deps = [
+        "//executorch/exir:lib",
+        "//executorch/exir/backend:partitioner",
+        "//executorch/exir/backend:utils",
+        "//executorch/exir/backend/canonical_partitioners:config_partitioner_lib",
+    ],
+)

backends/xnnpack/partition/config/__init__.py

@@ -0,0 +1,58 @@
+# Copyright (c) Meta Platforms, Inc. and 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 List, Type
+
+from executorch.backends.xnnpack.partition.config.gemm_configs import (
+    AddmmConfig,
+    ConvolutionConfig,
+    LinearConfig,
+)
+
+from executorch.backends.xnnpack.partition.config.generic_node_configs import (
+    AbsConfig,
+    AddConfig,
+    AvgPoolingConfig,
+    CatConfig,
+    CeilConfig,
+    ClampConfig,
+    DeQuantizedPerTensorConfig,
+    DivConfig,
+    # EluConfig,
+    HardtanhConfig,
+    MulConfig,
+    QuantizedPerTensorConfig,
+    ReLUConfig,
+)
+from executorch.backends.xnnpack.partition.config.node_configs import BatchNormConfig
+from executorch.backends.xnnpack.partition.config.xnnpack_config import (
+    XNNPartitionerConfig,
+)
+
+ALL_PARTITIONER_CONFIGS: List[Type[XNNPartitionerConfig]] = [
+    # GEMM-like Configs
+    AddmmConfig,
+    LinearConfig,
+    ConvolutionConfig,
+    # BatchNorm Config
+    BatchNormConfig,
+    # Single Node Configs
+    HardtanhConfig,
+    AbsConfig,
+    AvgPoolingConfig,
+    AddConfig,
+    CatConfig,
+    CeilConfig,
+    ClampConfig,
+    DivConfig,
+    MulConfig,
+    # EluConfig, # Waiting for PyTorch Pin Update
+    ReLUConfig,
+    # Quantization Op Configs
+    QuantizedPerTensorConfig,
+    DeQuantizedPerTensorConfig,
+]