[ExecuTorch][XNNPACK] Rename linear weight partitioning flag for clarity

backends/xnnpack/partition/config/gemm_configs.py

@@ -97,9 +97,9 @@ def _detect_precision(self, node: torch.fx.Node) -> ConfigPrecisionType:
     def _overwrite_precision(self, node: torch.fx.Node):
         precision = self._detect_precision(node)
         if precision not in self.enabled_precision_types:
-            # detected precision is not enabled, lets try to partition it as fp32
+            # detected precision is not enabled, try to partition it as fp32
             if self.enabled_precision_types == [ConfigPrecisionType.FP32]:
-                # if only fp32 is enabled, then we can still partition fp32 gemms
+                # when only fp32 is enabled, then we can still partition fp32 gemms
                 # even with in a quantized graph
                 if precision in [
                     ConfigPrecisionType.STATIC_QUANT,
@@ -108,6 +108,7 @@ def _overwrite_precision(self, node: torch.fx.Node):
                     precision = ConfigPrecisionType.FP32
                     logging.info(f"Overwriting precision, partitioning {node} as FP32")
                     return True, precision
+
         return False, precision
 
     def get_deps(
@@ -210,8 +211,11 @@ def _get_bias_deps(
         self, node: torch.fx.Node, ep: ExportedProgram, precision: ConfigPrecisionType
     ) -> Tuple[bool, List[torch.fx.Node]]:
         gemm_deps = []
-        if precision == ConfigPrecisionType.FP32 and self.force_fp32_dynamic_linear:
-            # if force force_fp32_dynamic_linear is enabled, then we
+        if (
+            precision == ConfigPrecisionType.FP32
+            and self.force_non_static_weights_for_f32_linear
+        ):
+            # if force_non_static_weights_for_f32_linear is enabled, then we
             # do not partition the weight node
             return (True, gemm_deps)
 
@@ -287,8 +291,11 @@ def get_original_aten(self) -> Optional[torch._ops.OpOverload]:
     def _get_weight_deps(
         self, node: torch.fx.Node, ep: ExportedProgram, precision: ConfigPrecisionType
     ) -> Tuple[bool, List[torch.fx.Node]]:
-        if precision == ConfigPrecisionType.FP32 and self.force_fp32_dynamic_linear:
-            # if force fp32_dynamic_linear is enabled, then we
+        if (
+            precision == ConfigPrecisionType.FP32
+            and self.force_non_static_weights_for_f32_linear
+        ):
+            # if force_non_static_weights_for_f32_linear is enabled, then we
             # do not partition the weight node
             return (True, [])
 
@@ -394,9 +401,11 @@ def __init__(self, **kwargs):
     def _get_weight_deps(
         self, node: torch.fx.Node, ep: ExportedProgram, precision: ConfigPrecisionType
     ) -> Tuple[bool, List[torch.fx.Node]]:
-        # TODO(maxren, T210537195):
-        if precision == ConfigPrecisionType.FP32 and self.force_fp32_dynamic_linear:
-            # if force fp32_dynamic_linear is on and we detected this as fp32, then we
+        if (
+            precision == ConfigPrecisionType.FP32
+            and self.force_non_static_weights_for_f32_linear
+        ):
+            # if force_non_static_weights_for_f32_linear is on and we detected this as fp32, then we
             # do not partition the weight node
             return (True, [])
 
@@ -482,11 +491,11 @@ def find_partition_args(input_node):
         node.args = old_args
         node.users = old_users
 
-        # When using force_fp32_dynamic_linear, we want to get_deps to overwrite the source partition nodes.
+        # When using force_non_static_weights_for_f32_linear, we want to get_deps to overwrite the source partition nodes.
         # Else we want to be greedy.
         ret_deps = (
             list(set(deps) & set(src_partition.nodes))
-            if self.force_fp32_dynamic_linear
+            if self.force_non_static_weights_for_f32_linear
             else list(set(deps) | set(src_partition.nodes))
         )
 
@@ -512,8 +521,11 @@ def __init__(self, **kwargs):
     def _get_weight_deps(
         self, node: torch.fx.Node, ep: ExportedProgram, precision: ConfigPrecisionType
     ) -> Tuple[bool, List[torch.fx.Node]]:
-        if precision == ConfigPrecisionType.FP32 and self.force_fp32_dynamic_linear:
-            # if force fp32_dynamic_linear is on and we detected this as fp32, then we
+        if (
+            precision == ConfigPrecisionType.FP32
+            and self.force_non_static_weights_for_f32_linear
+        ):
+            # if force_non_static_weights_for_f32_linear is on and we detected this as fp32, then we
             # do not partition the weight node
             return (True, [])
 

backends/xnnpack/partition/config/xnnpack_config.py

@@ -41,7 +41,9 @@ def __init__(self, **kwargs):
         super().__init__()
         self.enabled_precision_types = self.supported_precision_types()
         # Flag used in GEMMConfig()
-        self.force_fp32_dynamic_linear = kwargs.get("force_fp32_dynamic_linear", False)
+        self.force_non_static_weights_for_f32_linear = kwargs.get(
+            "force_non_static_weights_for_f32_linear", False
+        )
 
     def get_partition(
         self, node: torch.fx.Node, ep: ExportedProgram

backends/xnnpack/test/ops/test_linear.py

@@ -874,7 +874,7 @@ def test_linear_qd8_as_fp32(self):
                 },
             )
 
-    def test_linear_fp32_with_force_as_mm(self):
+    def test_linear_with_force_non_static_weights_for_f32_linear(self):
         def check_signature(
             signature: ExportGraphSignature,
             force_flag: bool,
@@ -907,7 +907,7 @@ def check_signature(
                     inputs = module.get_inputs()
                     tester = Tester(module, inputs).export()
                     partitioner = XnnpackPartitioner(
-                        force_fp32_dynamic_linear=force_flag
+                        force_non_static_weights_for_f32_linear=force_flag
                     )
                     if legacy_mode:
                         tester.to_edge()

backends/xnnpack/test/ops/test_lstm.py

@@ -43,18 +43,20 @@ def test_fp32_lstm(self):
             .run_method_and_compare_outputs()
         )
 
-    def test_fp32_lstm_force_dynamic_linear(self):
+    def test_lstm_with_force_non_static_weights_for_f32_linear(self):
         (
             Tester(self.LSTMLinear(32, 32, 10), (torch.rand(1, 32, 32),))
             .export()
             .to_edge_transform_and_lower(
                 ToEdgeTransformAndLower(
-                    partitioners=[XnnpackPartitioner(force_fp32_dynamic_linear=True)]
+                    partitioners=[
+                        XnnpackPartitioner(force_non_static_weights_for_f32_linear=True)
+                    ]
                 )
             )
             .check_not(["executorch_exir_dialects_edge__ops_aten_addmm_default"])
             # Weights are supplied as input to linears
-            # Biases are not owned by delegates when force_fp32_dynamic_linear is set
+            # Biases are not owned by delegates when force_non_static_weights_for_f32_linear is set
             .check(["p_lstm_weight_hh_l0", "p_lstm_weight_ih_l0", "p_lstm_bias"])
             .to_executorch()
             .serialize()