[XNNPACK][Partitioner] enable src based partitioner

backends/xnnpack/partition/config/__init__.py

@@ -11,6 +11,7 @@
     AddmmConfig,
     ConvolutionConfig,
     LinearConfig,
+    MMConfig,
 )
 
 from executorch.backends.xnnpack.partition.config.generic_node_configs import (
@@ -79,6 +80,7 @@
     MaxPool2dConfig,
     MeanDimConfig,
     MinimumConfig,
+    MMConfig,
     MulConfig,
     NegConfig,
     PermuteConfig,

backends/xnnpack/partition/config/gemm_configs.py

@@ -29,6 +29,10 @@
     format_target_name,
 )
 from torch.export import ExportedProgram
+from torch.fx.passes.utils.source_matcher_utils import (
+    get_source_partitions,
+    SourcePartition,
+)
 
 
 class GEMMConfig(XNNPartitionerConfig):
@@ -52,20 +56,14 @@ def check_constraints(self, node: torch.fx.Node, ep: ExportedProgram) -> bool:
             # short circuit if we don't pass common constraints
             return False
 
-        precision = self._detect_precision(node)
-        if precision not in self.enabled_precision_types:
-            # detected precision but it is either disabled or not supported
-            return False
-
-        is_valid, _ = self.get_deps(node, ep, precision)
+        is_valid, _ = self.get_deps(node, ep)
         return is_valid
 
     def get_node_and_deps(
         self, node: torch.fx.Node, ep: ExportedProgram
     ) -> List[torch.fx.Node]:
         partition = [node]
-        precision = self._detect_precision(node)
-        _, deps = self.get_deps(node, ep, precision)
+        _, deps = self.get_deps(node, ep)
         partition.extend(deps)
 
         return partition
@@ -86,13 +84,20 @@ def _detect_precision(self, node: torch.fx.Node) -> ConfigPrecisionType:
         return ConfigPrecisionType.STATIC_QUANT
 
     def get_deps(
-        self, node: torch.fx.Node, ep: ExportedProgram, precision: ConfigPrecisionType
+        self,
+        node: torch.fx.Node,
+        ep: ExportedProgram,
     ) -> Tuple[bool, List[torch.fx.Node]]:
         """
         Gets all dependencies for this gemm partition. Returns a tuple of
         a bool indicating if the deps are valid and a list of all the
         dep nodes
         """
+        precision = self._detect_precision(node)
+        if precision not in self.supported_precision_types():
+            # detected precision but it is either disabled or not supported
+            return (False, [])
+
         valid_bias, bias_deps = self._get_bias_deps(node, ep, precision)
         valid_weight, weight_deps = self._get_weight_deps(node, ep, precision)
         valid_act, act_deps = self._get_act_deps(node, ep, precision)
@@ -178,7 +183,7 @@ def _get_bias_deps(
         self, node: torch.fx.Node, ep: ExportedProgram, precision: ConfigPrecisionType
     ) -> Tuple[bool, List[torch.fx.Node]]:
         gemm_deps = []
-        if len(node.all_input_nodes) > 2:
+        if len(node.all_input_nodes) > 2 and self.bias_idx:
             bias_node = get_input_node(node, self.bias_idx)
             if bias_node:
                 if not is_param_node(ep, bias_node):
@@ -251,11 +256,16 @@ def supported_precision_types(self):
         ]
 
 
-class AddmmConfig(GEMMConfig):
-    target_name = "addmm.default"
+class ConvolutionConfig(GEMMConfig):
+    target_name = "convolution.default"
 
     def __init__(self):
-        super().__init__(weight_idx=2, bias_idx=0, act_idx=1, fused_acts=[])
+        super().__init__(
+            weight_idx=1,
+            bias_idx=2,
+            act_idx=0,
+            fused_acts=["relu.default", "hardtanh.default"],
+        )
 
     def supported_precision_types(self):
         return [
@@ -264,19 +274,126 @@ def supported_precision_types(self):
         ]
 
 
-class ConvolutionConfig(GEMMConfig):
-    target_name = "convolution.default"
+class AddmmConfig(GEMMConfig):
+    """
+    We will handle the legacy form of addmm partitioning which will include
+    partitioning using source partitions.
+    """
+
+    target_name = "addmm.default"
 
     def __init__(self):
         super().__init__(
-            weight_idx=1,
-            bias_idx=2,
-            act_idx=0,
+            weight_idx=2,
+            bias_idx=0,
+            act_idx=1,
             fused_acts=["relu.default", "hardtanh.default"],
         )
+        self.src_partitions = None
+        self.linear_modules = [torch.nn.functional.linear, torch.nn.Linear]
+
+    def get_deps(
+        self,
+        node: torch.fx.Node,
+        ep: ExportedProgram,
+    ) -> Tuple[bool, List[torch.fx.Node]]:
+        """
+        Gets all dependencies for this gemm partition. Returns a tuple of
+        a bool indicating if the deps are valid and a list of all the
+        dep nodes. This handles the src partition for
+        """
+        if self.src_partitions is None:
+            # Cache src partitions so we don't have to recompute them every time
+            self.src_partitions = get_source_partitions(ep.graph, self.linear_modules)
+
+        # src_partition is None if node is not in source partition,
+        # otherwise gives us the linear source partition it belongs to
+        src_partition = None
+        for partition_list in self.src_partitions.values():
+            for partition in partition_list:
+                if node in partition.nodes:
+                    src_partition = partition
+
+        if src_partition:
+            # if addmm belongs to linear src partition, then partition the
+            # src partition and get its deps
+            return self.get_deps_from_src_partition(node, ep, src_partition)
+
+        return super().get_deps(node, ep)
+
+    def get_deps_from_src_partition(
+        self, node: torch.fx.Node, ep: ExportedProgram, src_partition: SourcePartition
+    ):
+        """
+        Gets all the dependencies for the src partition. This is done by simulating the
+        linear node from the src partition. We find the associated weights, act, bias
+        from the linear src partition, and plug those in as the addmm node's args. We also
+        take the users of the src partitions output node as the addmm node's users. Finally
+        we just run the GEMMConfig's get_deps method no this faked linear node. After
+        getting the deps, we return the addmm nodes users and args back.
+        """
+
+        def find_partition_args(input_node):
+            while (
+                len(input_node.all_input_nodes) != 0
+                and input_node not in src_partition.input_nodes
+            ):
+                input_node = input_node.all_input_nodes[0]
+            return input_node
+
+        old_args, old_users = node.args, node.users
+
+        fake_args = []
+        for arg in node.args:
+            # map addmm's args to the source partition's inputs
+            # basically simulating what the args of the linear node would be
+            fake_args.append(find_partition_args(arg))
+
+        # validate source partition
+        if (
+            # bias must be in source partition
+            (self.bias_idx and fake_args[self.bias_idx] not in src_partition.nodes)
+            # activation input must be an input node to partition
+            or fake_args[self.act_idx] not in src_partition.input_nodes
+            # weight can either be in the nodes or input_nodes
+            or fake_args[self.weight_idx]
+            not in (src_partition.nodes + src_partition.input_nodes)
+            # there can only be a single output node in partition
+            or len(src_partition.output_nodes) != 1
+        ):
+            return (False, [])
+
+        # map addmm's args to the source partition linear's inputs and users
+        node.args = tuple(fake_args)
+        node.users = src_partition.output_nodes[0].users
+        valid_deps, deps = super().get_deps(node, ep)
+
+        # Reset addmm node back to old args and users
+        node.args = old_args
+        node.users = old_users
+
+        return valid_deps, list(set(deps) | set(src_partition.nodes))
 
     def supported_precision_types(self):
         return [
             ConfigPrecisionType.FP32,
             ConfigPrecisionType.STATIC_QUANT,
+            ConfigPrecisionType.DYNAMIC_QUANT,
+        ]
+
+
+class MMConfig(AddmmConfig):
+    target_name = "mm.default"
+
+    def __init__(self):
+        super().__init__()
+        self.bias_idx = None
+        self.weight_idx = 1
+        self.act_idx = 0
+
+    def supported_precision_types(self):
+        return [
+            ConfigPrecisionType.FP32,
+            ConfigPrecisionType.STATIC_QUANT,
+            ConfigPrecisionType.DYNAMIC_QUANT,
         ]

backends/xnnpack/partition/config/node_configs.py

@@ -16,6 +16,9 @@
     FuseBatchNormWithConvPass,
 )
 from executorch.backends.xnnpack.utils.utils import is_param_node
+from executorch.exir.backend.canonical_partitioners.config_partitioner import (
+    format_target_name,
+)
 from torch.export import ExportedProgram
 
 
@@ -29,6 +32,14 @@ def check_constraints(self, node: torch.fx.Node, ep: ExportedProgram) -> bool:
         bn = node
         conv = node.all_input_nodes[0]
 
+        if conv.op != "call_function":
+            return False
+
+        conv_name = format_target_name(conv.target.__name__)  # pyre-ignore
+
+        if conv_name not in ["convolution.default"]:
+            return False
+
         return FuseBatchNormWithConvPass.can_fuse(conv, bn, ep)
 
     def get_node_and_deps(