Arm backend: Remove instance checks for Tosa_1.00 (#13219)

With tosa 0.80 removed there's no need to check which tosa version's
being used.

Signed-off-by: Sebastian Larsson <[email protected]>

Author: Sebastian-Larsson

backends/arm/process_node.py

@@ -8,11 +8,12 @@
 from typing import Any, cast, Dict
 
 import numpy as np
+import serializer.tosa_serializer as ts
 import torch
 import torch.fx
 from executorch.backends.arm.operators.node_visitor import NodeVisitor
 from executorch.backends.arm.tosa_mapping import TosaArg
-from executorch.backends.arm.tosa_specification import Tosa_1_00, TosaSpecification
+from executorch.backends.arm.tosa_specification import TosaSpecification
 from executorch.backends.arm.tosa_utils import getNodeArgs, tosa_shape
 from torch._export.utils import (
     get_buffer,
@@ -81,11 +82,6 @@ def process_inputs(
             "Is the original torch function supported?"
         ) from e
 
-    if isinstance(tosa_spec, Tosa_1_00):
-        import serializer.tosa_serializer as ts
-    else:
-        raise ValueError(f"Unsupported TOSA spec: {tosa_spec}")
-
     input_shape = tosa_arg.shape
     input_dim_order = tosa_arg.dim_order
     tensor = ts.TosaSerializerTensor(

backends/arm/tosa_backend.py

@@ -13,8 +13,7 @@
 import logging
 from typing import cast, final, List
 
-import executorch.backends.arm.tosa_specification as tosa_specification
-
+import serializer.tosa_serializer as ts  # type: ignore
 from executorch.backends.arm.arm_backend import get_tosa_spec
 from executorch.backends.arm.operators.node_visitor import get_node_visitors
 from executorch.backends.arm._passes import (
@@ -85,13 +84,6 @@ def preprocess(  # noqa: C901
 
         # Converted output for this subgraph, serializer needs path early as it emits
         # const data directly. Path created and data written only in debug builds.
-        if isinstance(tosa_spec, tosa_specification.Tosa_1_00):
-            import serializer.tosa_serializer as ts  # type: ignore
-        else:
-            raise RuntimeError(
-                f"Unknown TOSA version {tosa_spec}, no pip package installed to handle serialization to that version."
-            )
-
         tosa_graph = ts.TosaSerializer(artifact_path)
 
         assert (

backends/arm/tosa_mapping.py

@@ -13,8 +13,10 @@
 
 from typing import Any, Optional, Sequence
 
+import serializer.tosa_serializer as ts  # type: ignore
+
 import torch
-from executorch.backends.arm.tosa_specification import Tosa_1_00, TosaSpecification
+from executorch.backends.arm.tosa_specification import TosaSpecification
 
 UNSUPPORTED_DTYPES = (
     torch.float64,
@@ -32,10 +34,6 @@
 def map_dtype(data_type: torch.dtype, tosa_spec: TosaSpecification) -> Any:
     if data_type in UNSUPPORTED_DTYPES:
         raise ValueError(f"Unsupported type: {data_type}")
-    if isinstance(tosa_spec, Tosa_1_00):
-        import serializer.tosa_serializer as ts  # type: ignore
-    else:
-        raise RuntimeError(f"Unsupported tosa_spec: {tosa_spec}")
 
     dtype_map = {
         torch.float32: ts.DType.FP32,
@@ -134,10 +132,6 @@ def __repr__(self):
             if self.name is not None:
                 attrs.append(f"name={self.name!r}")
             if self.dtype is not None:
-                if isinstance(self.tosa_spec, Tosa_1_00):
-                    import serializer.tosa_serializer as ts  # type: ignore
-                else:
-                    raise RuntimeError(f"Unsupported tosa_spec: {self.tosa_spec}")
                 attrs.append(f"dtype={ts.DTypeNames[self.dtype]}")
             if self.shape is not None:
                 attrs.append(f"shape={self.shape!r}")

backends/arm/tosa_quant_utils.py

@@ -11,8 +11,7 @@
 
 from typing import Any, Tuple
 
-import executorch.backends.arm.tosa_specification as tosa_specification
-
+import serializer.tosa_serializer as ts  # type: ignore
 import torch.fx
 import torch.fx.node
 
@@ -247,25 +246,18 @@ def build_rescale_to_int32(
 ) -> Any:
     input_A_rescaled_to_int32 = None
 
-    if isinstance(tosa_spec, tosa_specification.Tosa_1_00):
-        # For TOSA v1.0 multipliers, shifts, input_zp and output_zp are now inputs
-        # to the RESCALE op see: https://www.mlplatform.org/tosa/tosa_spec.html#_rescale
-        import serializer.tosa_serializer as ts  # type: ignore
-
-        input_A_rescaled_to_int32 = tosa_fb.addIntermediate(
-            input_arg.shape, ts.DType.INT32
-        )
+    input_A_rescaled_to_int32 = tosa_fb.addIntermediate(input_arg.shape, ts.DType.INT32)
 
-        build_rescale(
-            tosa_fb,
-            [rescale_scale],
-            input_arg,
-            input_A_rescaled_to_int32.name,
-            ts.DType.INT32,
-            [input_zp],
-            [0],
-            rounding_mode=RoundingMode.SINGLE_ROUND,
-        )  # type: ignore[call-arg]
+    build_rescale(
+        tosa_fb,
+        [rescale_scale],
+        input_arg,
+        input_A_rescaled_to_int32.name,
+        ts.DType.INT32,
+        [input_zp],
+        [0],
+        rounding_mode=RoundingMode.SINGLE_ROUND,
+    )  # type: ignore[call-arg]
 
     return input_A_rescaled_to_int32
 
@@ -281,21 +273,19 @@ def build_rescale_from_int32(
     per_channel: bool = False,
     tosa_spec=None,
 ) -> None:
-    if isinstance(tosa_spec, tosa_specification.Tosa_1_00):
-        import serializer.tosa_serializer as ts  # type: ignore
-
-        # For TOSA v1.0 multipliers, shifts, input_zp and output_zp are now inputs
-        # to the RESCALE op see: https://www.mlplatform.org/tosa/tosa_spec.html#_rescale
-        build_rescale(
-            tosa_fb,
-            [rescale_scale],
-            input_node,
-            output_name=output_name,
-            output_type=ts.DType.INT8,
-            input_zp=[0],
-            output_zp=[output_zp],
-            rounding_mode=RoundingMode.SINGLE_ROUND,
-        )  # type: ignore[call-arg]
+    # For TOSA v1.0 multipliers, shifts, input_zp and output_zp are now inputs
+    # to the RESCALE op see: https://www.mlplatform.org/tosa/tosa_spec.html#_rescale
+    build_rescale(
+        tosa_fb,
+        [rescale_scale],
+        input_node,
+        output_name=output_name,
+        output_type=ts.DType.INT8,
+        input_zp=[0],
+        output_zp=[output_zp],
+        rounding_mode=RoundingMode.SINGLE_ROUND,
+    )  # type: ignore[call-arg]
+
     return
 
 
@@ -318,18 +308,17 @@ def build_rescale_conv_output(
         (inp * w) / out for inp, w, out in zip(input_scale, weight_scale, output_scale)
     ]
 
-    if isinstance(tosa_spec[0], tosa_specification.Tosa_1_00):
-        # For TOSA v1.0 multipliers, shifts, input_zp and output_zp are now inputs
-        # to the RESCALE op see: https://www.mlplatform.org/tosa/tosa_spec.html#_rescale
-        build_rescale(
-            tosa_fb=tosa_fb,
-            scale=post_conv2d_scale,
-            input_node=op,
-            output_name=output_name,
-            output_type=output_type,
-            input_zp=[0],
-            output_zp=output_zp,
-            rounding_mode=RoundingMode.SINGLE_ROUND,
-            per_channel=isinstance(weight_scale, torch.Tensor),
-        )  # type: ignore[call-arg]
+    # For TOSA v1.0 multipliers, shifts, input_zp and output_zp are now inputs
+    # to the RESCALE op see: https://www.mlplatform.org/tosa/tosa_spec.html#_rescale
+    build_rescale(
+        tosa_fb=tosa_fb,
+        scale=post_conv2d_scale,
+        input_node=op,
+        output_name=output_name,
+        output_type=output_type,
+        input_zp=[0],
+        output_zp=output_zp,
+        rounding_mode=RoundingMode.SINGLE_ROUND,
+        per_channel=isinstance(weight_scale, torch.Tensor),
+    )  # type: ignore[call-arg]
     return

backends/arm/tosa_utils.py

@@ -18,7 +18,7 @@
 
 from executorch.backends.arm.tosa_mapping import extract_tensor_meta, TosaArg
 
-from executorch.backends.arm.tosa_specification import Tosa_1_00, TosaSpecification
+from executorch.backends.arm.tosa_specification import TosaSpecification
 from executorch.exir.dialects._ops import ops as exir_ops
 from executorch.exir.print_program import inspect_node
 
@@ -169,14 +169,6 @@ def broadcast_tensors(
         for broadcast. However this function also performs the broadcast and
         does not have a limit on only two input tensors.
     """
-
-    if isinstance(tosa_spec, Tosa_1_00):
-        import serializer.tosa_serializer as ts
-
-        reshape_helper = build_reshape_tosa_1_0
-    else:
-        raise ValueError(f"Unsupported TOSA spec: {tosa_spec}")
-
     index_fake_tensors = [node.meta["val"] for node in nodes]
     broadcastable, common_shape = are_fake_tensors_broadcastable(index_fake_tensors)
     if not broadcastable:
@@ -198,26 +190,25 @@ def broadcast_tensors(
             tens_dtype,
         )
 
-        reshape_helper(tosa_fb, node.name, new_shape, reshaped.name)
+        build_reshape_tosa_1_0(tosa_fb, node.name, new_shape, reshaped.name)
 
         tiled = tosa_fb.addIntermediate(common_shape, tens_dtype)
         multipliers = [
             comm if curr == 1 else 1 for comm, curr in zip(common_shape, new_shape)
         ]
-        if isinstance(tosa_spec, Tosa_1_00):
-            multiple_shapes = tosa_fb.addConst(
-                (len(multipliers),),
-                ts.DType.SHAPE,
-                multipliers,
-                name=f"{node.name}_multiples",
-            )
+        multiple_shapes = tosa_fb.addConst(
+            (len(multipliers),),
+            ts.DType.SHAPE,
+            multipliers,
+            name=f"{node.name}_multiples",
+        )
 
-            tosa_fb.addOperator(
-                ts.TosaOp.Op().TILE,
-                [reshaped.name, multiple_shapes.name],
-                [tiled.name],
-                None,
-            )
+        tosa_fb.addOperator(
+            ts.TosaOp.Op().TILE,
+            [reshaped.name, multiple_shapes.name],
+            [tiled.name],
+            None,
+        )
 
         broadcast_tensors.append(tiled)
 
@@ -227,19 +218,17 @@ def broadcast_tensors(
 def build_reshape_tosa_1_0(
     tosa_graph, input_name, new_shape, output_name, shape_name_override=""
 ):
-    import serializer.tosa_serializer as ts_  # type: ignore
-
     shape = tosa_graph.addConst(
         np.array(new_shape).shape,
-        ts_.DType.SHAPE,
+        ts.DType.SHAPE,
         np.array(new_shape),
         name=shape_name_override if shape_name_override else output_name + "_shape",
     )
 
-    attr = ts_.TosaSerializerAttribute()
+    attr = ts.TosaSerializerAttribute()
     attr.ReshapeAttribute()
     tosa_graph.addOperator(
-        ts_.TosaOp.Op().RESHAPE,
+        ts.TosaOp.Op().RESHAPE,
         [input_name, shape.name],
         [output_name],
         attr,