Update base for Update on "Refactor attention v2"

Pull attention creation out of Transformer/TransformerBlock. Instead, pass the layers into Transformer.

The motivation is to customize linear layers in attention for LoRA (eg. make wq into a LoraLinear instead of a regular linear). In the next diff (D73517350), we pull wq,wk,wv,wo out of the attention and pass those in as well.

This allows us to customize attention parameters without passing in ModelArgs and doing the customization deep inside attention.py.

I think this modularizes our attention/transformer components, though also means that users have to do some more work to construct the attention layers and pass it to transformer.

It follows the torchtune structure more closely, eg. https://github.com/pytorch/torchtune/blob/main/torchtune/models/llama3_2/_component_builders.py#L221

Differential Revision: [D73538697](https://our.internmc.facebook.com/intern/diff/D73538697/)

[ghstack-poisoned]

Author: lucylq

Package.swift

@@ -96,8 +96,10 @@ let package = Package(
         .copy("resources/add.pte")
       ],
       linkerSettings: [
+        .linkedLibrary("c++"),
         .unsafeFlags([
-          "-Xlinker", "-all_load",
+          "-Xlinker", "-force_load",
+          "-Xlinker", "cmake-out/kernels_portable.xcframework/macos-arm64/libkernels_portable_macos.a",
         ])
       ]
     )

backends/apple/mps/setup.md

@@ -76,12 +76,12 @@ cd executorch
 ## Run the mv3 generated model using the mps_executor_runner
 
 ```bash
-./cmake-out/examples/apple/mps/mps_executor_runner --model_path mv3_mps_bundled_fp16.pte --bundled_program
+./cmake-out/examples/apple/mps/mps_executor_runner --model_path mv3_mps_float16_bundled.pte --bundled_program
 ```
 
 - You should see the following results. Note that no output file will be generated in this example:
 ```
-I 00:00:00.003290 executorch:mps_executor_runner.mm:286] Model file mv3_mps_bundled_fp16.pte is loaded.
+I 00:00:00.003290 executorch:mps_executor_runner.mm:286] Model file mv3_mps_float16_bundled.pte is loaded.
 I 00:00:00.003306 executorch:mps_executor_runner.mm:292] Program methods: 1
 I 00:00:00.003308 executorch:mps_executor_runner.mm:294] Running method forward
 I 00:00:00.003311 executorch:mps_executor_runner.mm:349] Setting up non-const buffer 1, size 606112.
@@ -118,7 +118,7 @@ python3 -m examples.apple.mps.scripts.mps_example --model_name="mv3" --generate_
 ```
 2. Run your Program on the ExecuTorch runtime and generate an [ETDump](../../../docs/source/etdump.md).
 ```
-./cmake-out/examples/apple/mps/mps_executor_runner --model_path mv3_mps_bundled_fp16.pte --bundled_program --dump-outputs
+./cmake-out/examples/apple/mps/mps_executor_runner --model_path mv3_mps_float16_bundled.pte --bundled_program --dump-outputs
 ```
 3. Create an instance of the Inspector API by passing in the ETDump you have sourced from the runtime along with the optionally generated ETRecord from step 1.
 ```bash

backends/arm/operator_support/convolution_support.py

@@ -11,7 +11,11 @@
     register_tosa_support_check,
     SupportedTOSAOperatorCheck,
 )
-from executorch.backends.arm.tosa_specification import Tosa_0_80, TosaSpecification
+from executorch.backends.arm.tosa_specification import (
+    Tosa_0_80,
+    Tosa_1_00,
+    TosaSpecification,
+)
 from executorch.exir.dialects._ops import ops as exir_ops
 
 
@@ -43,6 +47,9 @@ def is_node_tosa_supported(self, node: fx.Node, tosa_spec: TosaSpecification):
 
         # Hardware specific constraints
         if not (isinstance(tosa_spec, Tosa_0_80) and tosa_spec.is_U55_subset):
+            # TODO remove this once TOSA 1.0 support for u55 is added.
+            if isinstance(tosa_spec, Tosa_1_00) and "u55" in tosa_spec.extensions:
+                return False
             return True
         else:
             return self._is_node_supported_u55(node)

backends/arm/operators/op_abs.py

@@ -4,12 +4,11 @@
 # LICENSE file in the root directory of this source tree.
 
 # pyre-unsafe
-from typing import List
+from typing import Any, List
 
 import executorch.backends.arm.tosa_quant_utils as tqutils
 import executorch.backends.arm.tosa_utils as tutils
 
-import tosa_tools.v0_80.serializer.tosa_serializer as ts  # type: ignore
 from executorch.backends.arm.operators.node_visitor import (
     NodeVisitor,
     register_node_visitor,
@@ -33,10 +32,13 @@ def __init__(self, *args):
     def define_node(
         self,
         node: Node,
-        tosa_graph: ts.TosaSerializer,
+        tosa_graph: Any,
         inputs: List[TosaArg],
         output: TosaArg,
     ) -> None:
+
+        import tosa_tools.v0_80.serializer.tosa_serializer as ts  # type: ignore
+
         # Specification (0.80) states that input and output types
         # should all be the same
         if not (inputs[0].dtype == output.dtype):
@@ -53,7 +55,7 @@ def define_node(
         if inputs[0].dtype == ts.DType.INT8:
             rescaled_inputs, scale_back = tqutils.insert_rescale_ops_to_int32(
                 tosa_graph, inputs, node
-            )
+            )  # type: ignore[possibly-undefined]
         else:
             # input[0].dtype == ts.DType.INT32
             # Non quantized input, natively support by TOSA.abs
@@ -96,10 +98,13 @@ def __init__(self, *args):
     def define_node(
         self,
         node: Node,
-        tosa_graph: ts.TosaSerializer,
+        tosa_graph: Any,
         inputs: List[TosaArg],
         output: TosaArg,
     ) -> None:
+
+        import tosa_tools.v0_80.serializer.tosa_serializer as ts  # type: ignore
+
         # Specification (0.80) states that input and output types
         # should all be the same
         if not (inputs[0].dtype == output.dtype):
@@ -129,3 +134,122 @@ def define_node(
                 [output.name],
                 None,
             )
+
+
+@register_node_visitor
+class AbsVisitor_INT(NodeVisitor):
+    target = "aten.abs.default"
+
+    tosa_specs = [
+        TosaSpecification.create_from_string("TOSA-1.0+INT"),
+    ]
+
+    def __init__(self, *args):
+        super().__init__(*args)
+
+    def define_node(
+        self,
+        node: Node,
+        tosa_graph: Any,
+        inputs: List[TosaArg],
+        output: TosaArg,
+    ) -> None:
+
+        import serializer.tosa_serializer as ts  # type: ignore
+
+        # Specification (1.0) states that input and output types
+        # should all be the same
+        if not (inputs[0].dtype == output.dtype):
+            raise ValueError(
+                "All inputs and outputs need same dtype."
+                f"Got {inputs[0].dtype=}, {output.dtype=}"
+            )
+        # Handle int8 (quantized) and int32
+        if not (inputs[0].dtype in [ts.DType.INT8, ts.DType.INT32]):
+            raise ValueError(
+                "All inputs need to be INT8 or INT32." f"Got {inputs[0].dtype=}"
+            )
+
+        scale_back = 1.0
+        if inputs[0].dtype == ts.DType.INT8:
+            rescaled_inputs, scale_back = tqutils.insert_rescale_ops_to_int32(
+                tosa_graph, inputs, node, self.tosa_specs
+            )  # type: ignore[possibly-undefined]
+        else:
+            # input[0].dtype == ts.DType.INT32
+            # Non quantized input, natively support by TOSA.abs
+            rescaled_inputs = inputs
+
+        if output.dtype == ts.DType.INT8:
+            broadcasted_shape = tutils.tosa_shape(output.shape, output.dim_order)
+            abs_output = tosa_graph.addIntermediate(broadcasted_shape, ts.DType.INT32)
+        else:
+            # output.dtype == ts.DType.INT32
+            abs_output = output
+
+        # Do the INT32 Abs
+        tosa_graph.addOperator(
+            ts.TosaOp.Op().ABS,
+            [
+                rescaled_inputs[0].name,
+            ],
+            [abs_output.name],
+            None,
+        )
+
+        if output.dtype == ts.DType.INT8:
+            # Scale output back to 8 bit
+            # pyre-ignore
+            tqutils.insert_rescale_op_to_int8(
+                tosa_graph, abs_output, scale_back, node, self.tosa_specs
+            )  # type: ignore[possibly-undefined]
+
+
+@register_node_visitor
+class AbsVisitor_FP(AbsVisitor_INT):
+    # inheriting 'target' from BI class
+
+    tosa_specs = [TosaSpecification.create_from_string("TOSA-1.0+FP")]
+
+    def __init__(self, *args):
+        super().__init__(*args)
+
+    def define_node(
+        self,
+        node: Node,
+        tosa_graph: Any,
+        inputs: List[TosaArg],
+        output: TosaArg,
+    ) -> None:
+
+        import serializer.tosa_serializer as ts  # type: ignore
+
+        # Specification (1.0) states that input and output types
+        # should all be the same
+        if not (inputs[0].dtype == output.dtype):
+            raise ValueError(
+                "All inputs and output need same dtype."
+                f"Got {inputs[0].dtype=}, {output.dtype=}"
+            )
+
+        if inputs[0].dtype in [ts.DType.INT8, ts.DType.INT32]:
+            # Call the inherited define_node for handling integers
+            super().define_node(node, tosa_graph, inputs, output)
+        else:
+            # FP32 Abs lowering
+
+            if not (inputs[0].dtype == ts.DType.FP32):
+                raise ValueError(
+                    "All inputs need to be FP32." f"Got {inputs[0].dtype=}"
+                )
+
+            if not (output.dtype == ts.DType.FP32):
+                raise ValueError("All outputs need to be FP32." f"Got {output.dtype=}")
+
+            # MI lowering
+            tosa_graph.addOperator(
+                ts.TosaOp.Op().ABS,
+                [inputs[0].name],
+                [output.name],
+                None,
+            )