Address pyre checks from Meta internal tools

Signed-off-by: Per Åstrand <[email protected]>
Change-Id: I60fb32ad55a3f6d3617993481ab0c1ed46cf778c

Author: per

backends/arm/_passes/fold_qdq_with_annotated_qparams_pass.py

@@ -99,7 +99,6 @@ def call(self, graph_module: GraphModule) -> PassResult:
             for i, arg in enumerate(n.args):
                 if not isinstance(arg, Node):
                     continue
-                arg = cast(Node, arg)
 
                 # Make sure arg has requires_grad set to False
                 # For parameters that are not quantized, sometimes (i.e. convolution)
@@ -125,7 +124,8 @@ def call(self, graph_module: GraphModule) -> PassResult:
                 )
 
                 # arg.args[0] is the tensor input, replace the input usage
-                n.replace_input_with(arg, arg.args[0])
+                tensor_input = cast(Node, arg.args[0])
+                n.replace_input_with(arg, tensor_input)
                 graph_module.graph.erase_node(arg)
 
             # Copy the users, since we are modifying it.

backends/arm/operators/op_avg_pool2d.py

@@ -8,6 +8,8 @@
 
 import serializer.tosa_serializer as ts
 import torch
+
+# pyre-fixme[21]: ' Could not find a module corresponding to import `executorch.backends.arm._passes.fold_qdq_with_annotated_qparams_pass`
 from executorch.backends.arm._passes.fold_qdq_with_annotated_qparams_pass import (
     get_input_qparams,
     get_output_qparams,
@@ -80,10 +82,10 @@ def define_node(
 
         accumulator_type = ts.DType.INT32
 
-        input_qargs = get_input_qparams(node)
+        input_qargs = get_input_qparams(node)  # pyre-ignore[16]
         input_zp = input_qargs[0].zp
 
-        output_qargs = get_output_qparams(node)
+        output_qargs = get_output_qparams(node)  # pyre-ignore[16]
         output_zp = output_qargs[0].zp
 
         self._build_generic_avgpool2d(

backends/arm/operators/op_conv2d.py

@@ -82,7 +82,7 @@ def define_node(
         input_zp = 0
         if inputs[0].dtype == ts.DType.INT8:
             # int8 input requires quantization information
-            input_qparams = get_input_qparams(node)
+            input_qparams = get_input_qparams(node)  # pyre-ignore[16]
             input_zp = input_qparams[0].zp
 
         attr.ConvAttribute(
@@ -164,9 +164,9 @@ def define_node(
         # integer value domain of the next op. Otherwise return float32 output.
         if inputs[0].dtype == ts.DType.INT8:
             # Get scale_factor from input, weight, and output.
-            input_scale = input_qparams[0].scale
-            weight_scale = input_qparams[1].scale
-            output_qargs = get_output_qparams(node)
+            input_scale = input_qparams[0].scale  # pyre-ignore [61]
+            weight_scale = input_qparams[1].scale  # pyre-ignore [61]
+            output_qargs = get_output_qparams(node)  # pyre-ignore [16]
             build_rescale_conv_output(
                 tosa_graph,
                 # pyre-fixme[61]: Uninitialized local [61]: Local variable `conv2d_res` is undefined, or not always defined.

backends/arm/operators/op_max.py

@@ -9,6 +9,8 @@
 
 import executorch.backends.arm.tosa_quant_utils as tqutils
 import serializer.tosa_serializer as ts
+
+# pyre-fixme[21]: 'Could not find a module corresponding to import `executorch.backends.arm._passes.fold_qdq_with_annotated_qparams_pass`.'
 from executorch.backends.arm._passes.fold_qdq_with_annotated_qparams_pass import (
     get_input_qparams,
 )
@@ -43,7 +45,9 @@ def define_node(
         scale_back = 1.0
         max_output = output
         if inputs[0].dtype == ts.DType.INT8:
-            input_qparams = get_input_qparams(node)
+            input_qparams = get_input_qparams(
+                node
+            )  # pyre-ignore[16]: 'Module `executorch.backends.arm` has no attribute `_passes`.'
             assert (
                 len(input_qparams) == 2
             ), f"Both inputs needs to have quantization information for {node}"

backends/arm/operators/op_min.py

@@ -10,6 +10,8 @@
 import executorch.backends.arm.tosa_quant_utils as tqutils
 
 import serializer.tosa_serializer as ts
+
+# pyre-fixme[21]: 'Could not find a module corresponding to import `executorch.backends.arm._passes.fold_qdq_with_annotated_qparams_pass`.'
 from executorch.backends.arm._passes.fold_qdq_with_annotated_qparams_pass import (
     get_input_qparams,
 )
@@ -44,7 +46,9 @@ def define_node(
         scale_back = 1.0
         min_output = output
         if inputs[0].dtype == ts.DType.INT8:
-            input_qparams = get_input_qparams(node)
+            input_qparams = get_input_qparams(
+                node
+            )  # pyre-ignore[16]: 'Module `executorch.backends.arm` has no attribute `_passes`.'
             assert (
                 len(input_qparams) == 2
             ), f"Both inputs needs to have quantization information for {node}"

backends/arm/process_node.py

@@ -11,6 +11,8 @@
 import serializer.tosa_serializer as ts
 import torch
 import torch.fx
+
+# pyre-fixme[21]: 'Could not find a module corresponding to import `executorch.backends.arm._passes.fold_qdq_with_annotated_qparams_pass`.'
 from executorch.backends.arm._passes.fold_qdq_with_annotated_qparams_pass import (
     get_input_qparams,
 )
@@ -112,7 +114,9 @@ def process_quantized_bias(
         _,
     ) = consumer_node.all_input_nodes
 
-    input_qargs = get_input_qparams(consumer_node)
+    input_qargs = get_input_qparams(
+        consumer_node
+    )  # pyre-ignore[16]: Module `executorch.backends.arm` has no attribute `_passes`.
 
     input_node_scale = input_qargs[0].scale
     weight_node_scale = input_qargs[1].scale

backends/arm/tosa_quant_utils.py

@@ -57,6 +57,7 @@ def insert_rescale_ops_to_int32(
     the graph upstream for DQ nodes.
     """
 
+    # pyre-fixme[21]: 'Could not find a module corresponding to import `executorch.backends.arm._passes.fold_qdq_with_annotated_qparams_pass`.'
     from executorch.backends.arm._passes.fold_qdq_with_annotated_qparams_pass import (
         get_input_qparams,
     )
@@ -68,7 +69,7 @@ def insert_rescale_ops_to_int32(
         dim_order = tensor.dim_order
         tensor.shape = [tensor.shape[i] for i in dim_order]
 
-    input_qparams = get_input_qparams(node)
+    input_qparams = get_input_qparams(node)  # pyre-ignore[16]
     qargs = input_qparams.values()
 
     # Scale the int8 quantized input to a common scale in the integer
@@ -107,11 +108,12 @@ def insert_rescale_op_to_int8(
     in the node meta dict as opposed to 'rescale_node_back_to_int8' which search
     the graph downstream for Q nodes.
     """
+    # pyre-fixme[21]: 'Could not find a module corresponding to import `executorch.backends.arm._passes.fold_qdq_with_annotated_qparams_pass`.'
     from executorch.backends.arm._passes.fold_qdq_with_annotated_qparams_pass import (
         get_output_qparams,
     )
 
-    output_qparams = get_output_qparams(node)
+    output_qparams = get_output_qparams(node)  # pyre-ignore[16]
     assert len(output_qparams) == 1, "More than one output not supported"
 
     qargs_out = output_qparams[0]