Fix pyre issues

Address issues from pyre and add similar # pyre-ignores as in
#7362.

Signed-off-by: Oscar Andersson <[email protected]>
Change-Id: I6feaa611dcd539b3b0d21a6a7dd696ef7db691ef

Author: oscarandersson8218

backends/arm/_passes/annotate_decomposed_matmul.py

@@ -5,18 +5,14 @@
 # LICENSE file in the root directory of this source tree.
 
 import itertools
-from typing import Any, Dict, List
 
 import torch
 from executorch.backends.arm._passes.arm_pass_utils import create_node
 from executorch.backends.arm.tosa_quant_utils import dq_op, q_op
 from executorch.exir.dialects._ops import ops as exir_ops
 from executorch.exir.pass_base import ExportPass, PassResult
 from torch.fx import GraphModule
-from torch.fx.passes.utils.source_matcher_utils import (
-    get_source_partitions,
-    SourcePartition,
-)
+from torch.fx.passes.utils.source_matcher_utils import get_source_partitions
 
 
 class AnnotateDecomposedMatmulPass(ExportPass):
@@ -28,8 +24,8 @@ class AnnotateDecomposedMatmulPass(ExportPass):
     matmul-op (can be mm or bmm).
     """
 
-    def call(self, graph_module: GraphModule):
-        matmul_partitions: Dict[Any, List[SourcePartition]] = get_source_partitions(
+    def call(self, graph_module: GraphModule) -> PassResult:
+        matmul_partitions = get_source_partitions(
             graph_module.graph,
             [
                 torch.matmul,
@@ -56,7 +52,7 @@ def call(self, graph_module: GraphModule):
                     input_node = partition.input_nodes[i]
                     matmul_input_node = matmul_args[i]
                     # Remove partition input dq-node
-                    input_node.replace_all_uses_with(input_node.args[0])
+                    input_node.replace_all_uses_with(input_node.all_input_nodes[0])
                     graph_module.graph.erase_node(input_node)
                     input_node_qargs = input_node.args[1:]
                     with graph_module.graph.inserting_before(matmul_node):
@@ -81,7 +77,9 @@ def call(self, graph_module: GraphModule):
                     matmul_node.replace_all_uses_with(q_node)
                     q_node.args = (matmul_node, *output_node_qargs)
                 # Remove partition output q-node
-                partition_output.replace_all_uses_with(partition_output.args[0])
+                partition_output.replace_all_uses_with(
+                    partition_output.all_input_nodes[0]
+                )
                 graph_module.graph.erase_node(partition_output)
 
         # retrace the graph to update the fake tensor types

backends/arm/_passes/fold_qdq_with_annotated_qparams_pass.py

@@ -6,14 +6,20 @@
 
 import copy
 
-from typing import cast, Iterable
+from typing import cast, Dict, Iterable, Set, Tuple
 
 from executorch.backends.arm.tosa_quant_utils import QuantArgs
 
 from executorch.exir.dialects._ops import ops as exir_ops
 from executorch.exir.dialects.edge._ops import EdgeOpOverload
 
-from executorch.exir.pass_base import ExportPass, PassResult
+from executorch.exir.pass_base import (
+    Argument,
+    ExportPass,
+    NodeMetadata,
+    PassResult,
+    ProxyValue,
+)
 from torch.fx import GraphModule, Node
 
 q_op: EdgeOpOverload = exir_ops.edge.quantized_decomposed.quantize_per_tensor.default
@@ -82,7 +88,7 @@ def __init__(self, targeted_ops: Iterable[EdgeOpOverload]) -> None:
 
     def fold_and_annotate_arg(
         self, graph_module: GraphModule, node: Node, arg_list: list[Node], i: int
-    ):
+    ) -> None:
         input_qparams = None
         nodes_to_remove = set()
         for arg in arg_list:
@@ -210,11 +216,17 @@ class RetraceFoldedDtypesPass(ExportPass):
     the output type of that matches the type of the output_qparams.
     """
 
-    targeted_ops = {
+    targeted_ops: Set[EdgeOpOverload] = {
         exir_ops.edge.aten.sum.dim_IntList,
     }
 
-    def call_operator(self, op, args, kwargs, meta):
+    def call_operator(
+        self,
+        op,  # pyre-ignore
+        args: Tuple[Argument, ...],
+        kwargs: Dict[str, Argument],
+        meta: NodeMetadata,
+    ) -> ProxyValue:
         if op not in self.targeted_ops:
             return super().call_operator(op, args, kwargs, meta)
 

backends/arm/_passes/insert_table_ops.py

@@ -4,14 +4,15 @@
 # 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 Callable
+from typing import Callable, Dict
 
 import torch
 from executorch.backends.arm._passes.arm_pass_utils import create_node
 from executorch.backends.arm.tosa_quant_utils import QuantArgs
 from executorch.exir import ExportedProgram
 
 from executorch.exir.dialects._ops import ops as exir_ops
+from executorch.exir.dialects.edge._ops import EdgeOpOverload
 
 from executorch.exir.pass_base import ExportPass, PassResult
 from torch.fx import GraphModule
@@ -22,7 +23,7 @@
 
 
 @impl(lib, "_table")
-def _table_impl(*args, **kwargs):
+def _table_impl(*args, **kwargs):  # pyre-ignore
     return args[0]
 
 
@@ -34,7 +35,7 @@ class InsertTableOpsPass(ExportPass):
     which will be used to produce the table values in operators/op_table.py.
     """
 
-    table_ops = {
+    table_ops: Dict[EdgeOpOverload, Callable[[torch.Tensor], torch.Tensor]] = {
         exir_ops.edge.aten.exp.default: torch.exp,
         exir_ops.edge.aten.log.default: torch.log,
         exir_ops.edge.aten.reciprocal.default: torch.reciprocal,
@@ -43,7 +44,7 @@ class InsertTableOpsPass(ExportPass):
         exir_ops.edge.aten.tanh.default: torch.tanh,
     }
 
-    def __init__(self, exported_program: ExportedProgram):
+    def __init__(self, exported_program: ExportedProgram) -> None:
         super().__init__()
         self.exported_program = exported_program
 

backends/arm/operators/op_bmm.py

@@ -9,6 +9,8 @@
 
 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,
@@ -49,7 +51,7 @@ def define_node(
         # for a later rescale.
 
         if inputs[0].dtype == ts.DType.INT8:
-            input_qparams = get_input_qparams(node)
+            input_qparams = get_input_qparams(node)  # pyre-ignore[16]
             input0_zp = input_qparams[0].zp
             input1_zp = input_qparams[1].zp
             bmm_result = tosa_graph.addIntermediate(output.shape, ts.DType.INT32)
@@ -71,9 +73,9 @@ def define_node(
 
         # As INT8 accumulates into INT32, we need to rescale it back to INT8
         if output.dtype == ts.DType.INT8:
-            output_qparams = get_output_qparams(node)[0]
+            output_qparams = get_output_qparams(node)[0]  # pyre-ignore[16]
             final_output_scale = (
-                input_qparams[0].scale * input_qparams[1].scale
+                input_qparams[0].scale * input_qparams[1].scale  # pyre-ignore[61]
             ) / output_qparams.scale
 
             build_rescale(

backends/arm/operators/op_hardtanh.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,
 )
@@ -39,7 +41,7 @@ def define_node(
 
         if inputs[0].dtype == ts.DType.INT8:
             # Get quant parameters
-            input_qparams = get_input_qparams(node)
+            input_qparams = get_input_qparams(node)  # pyre-ignore[16]
             qargs = input_qparams[0]
             # Convert to quantized representation
             clamp_min_qs = quantize_value(inputs[1].number, qargs)

backends/arm/operators/op_max_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,
@@ -49,12 +51,12 @@ def define_node(
         # Initilize zero point to zero.
         input_zp = 0
         if inputs[0].dtype == ts.DType.INT8:
-            input_qparams = get_input_qparams(node)
+            input_qparams = get_input_qparams(node)  # pyre-ignore[16]
             input_zp = input_qparams[0].zp
 
         output_zp = 0
         if output.dtype == ts.DType.INT8:
-            output_qparams = get_output_qparams(node)
+            output_qparams = get_output_qparams(node)  # pyre-ignore[16]
             output_zp = output_qparams[0].zp
 
         attr = ts.TosaSerializerAttribute()

backends/arm/operators/op_mm.py

@@ -9,6 +9,8 @@
 
 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,
@@ -52,7 +54,7 @@ def define_node(
         # The output also needs to be rank 3
         output_new_shape = (1, output.shape[0], output.shape[1])
 
-        input_qparams = get_input_qparams(node)
+        input_qparams = get_input_qparams(node)  # pyre-ignore[16]
         assert len(input_qparams) == 2
         input0_qparams = input_qparams[0]
         input1_qparams = input_qparams[1]
@@ -78,7 +80,7 @@ def define_node(
         )
 
         # As INT8 accumulates into INT32, we need to rescale it back to INT8
-        output_qparams = get_output_qparams(node)
+        output_qparams = get_output_qparams(node)  # pyre-ignore[16]
         assert len(output_qparams) == 1
 
         final_output_scale = (

backends/arm/operators/op_mul.py

@@ -12,6 +12,8 @@
 
 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,
 )
@@ -43,7 +45,7 @@ def define_node(
         assert inputs[0].dtype == inputs[1].dtype == output.dtype == ts.DType.INT8
         input_A = inputs[0]
         input_B = inputs[1]
-        input_qparams = get_input_qparams(node)
+        input_qparams = get_input_qparams(node)  # pyre-ignore[16]
         input_A_qargs = input_qparams[0]
         input_B_qargs = input_qparams[1]
         input_A.shape = tutils.tosa_shape(input_A.shape, input_A.dim_order)

backends/arm/operators/op_relu.py

@@ -8,6 +8,8 @@
 import executorch.backends.arm.tosa_quant_utils as tqutils
 import serializer.tosa_serializer as ts
 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_output_qparams,
 )
@@ -40,7 +42,7 @@ def define_node(
         clamp_min_qs = 0
         clamp_max_qs = 0
         if inputs[0].dtype == ts.DType.INT8:
-            out_qargs = get_output_qparams(node)
+            out_qargs = get_output_qparams(node)  # pyre-ignore[16]
             clamp_min_qs = tqutils.quantize_value(0, out_qargs[0])
             clamp_max_qs = tqutils.quantize_value(float("inf"), out_qargs[0])
 

backends/arm/tosa_quant_utils.py

@@ -145,7 +145,7 @@ def quantize_value(self, x):
             self.qmax,
         ).to(self.dtype)
 
-    def dequantize_value(self, qx: int) -> float:
+    def dequantize_value(self, qx: torch.Tensor) -> torch.Tensor:
         return (qx - self.zp) * self.scale
 
     def __eq__(self, other):