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Update on "[0.6 documentation] Fix Page Developer Tools: Bundled Program"
#10193 - Update the hyperlink of “Generate Sample ExecuTorch program”, as this part has been heavily revamped in v0.6: - Update to https://pytorch.org/executorch/0.6/getting-started.html#exporting - Fixed the emit stage sample code: - Use `register_buffer` as warning suggests. - Update `to_edge` to be `to_edge_transform_and_lower`, as updated in https://pytorch.org/executorch/0.6/getting-started.html. - Remove function blocks for `get_program_data`, `load_bundled_input` and `load_bundled_output`, as there are no other similar usage in the doc. - Changed to links of real-world examples. - Remove hard-coded cpp code examples. - For APIs, provide links to real-world examples. - For runtime example, change to a ready-to-use snippet for people to quickly try out. Differential Revision: [D73027728](https://our.internmc.facebook.com/intern/diff/D73027728/) [ghstack-poisoned]
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‎.ci/scripts/gather_benchmark_configs.py‎

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"samsung_galaxy_s22": "arn:aws:devicefarm:us-west-2:308535385114:devicepool:02a2cf0f-6d9b-45ee-ba1a-a086587469e6/e59f866a-30aa-4aa1-87b7-4510e5820dfa",
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"samsung_galaxy_s24": "arn:aws:devicefarm:us-west-2:308535385114:devicepool:02a2cf0f-6d9b-45ee-ba1a-a086587469e6/98f8788c-2e25-4a3c-8bb2-0d1e8897c0db",
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"google_pixel_8_pro": "arn:aws:devicefarm:us-west-2:308535385114:devicepool:02a2cf0f-6d9b-45ee-ba1a-a086587469e6/d65096ab-900b-4521-be8b-a3619b69236a",
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"google_pixel_3_private_rooted": "arn:aws:devicefarm:us-west-2:308535385114:devicepool:02a2cf0f-6d9b-45ee-ba1a-a086587469e6/98d23ca8-ea9e-4fb7-b725-d402017b198d",
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}
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# Predefined benchmark configurations

‎.github/workflows/android-perf-private-device-experiment.yml‎

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name: android-perf (private devices)
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on:
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schedule:
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- cron: 0 0,4,8,12,16,20 * * *
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pull_request:
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paths:
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- .github/workflows/android-perf-private-device-experiment.yml
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push:
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branches:
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- main
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paths:
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- .github/workflows/android-perf-private-device-experiment.yml
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# Note: GitHub has an upper limit of 10 inputs
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workflow_dispatch:
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inputs:
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models:
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description: Models to be benchmarked
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required: false
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type: string
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default: mv3,meta-llama/Llama-3.2-1B-Instruct-SpinQuant_INT4_EO8,meta-llama/Llama-3.2-1B-Instruct-QLORA_INT4_EO8
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devices:
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description: Target devices to run benchmark
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required: false
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type: string
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default: google_pixel_3_private_rooted
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benchmark_configs:
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description: The list of configs used the benchmark
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required: false
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type: string
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workflow_call:
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inputs:
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models:
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description: Models to be benchmarked
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required: false
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type: string
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default: mv3,meta-llama/Llama-3.2-1B-Instruct-SpinQuant_INT4_EO8,meta-llama/Llama-3.2-1B-Instruct-QLORA_INT4_EO8
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devices:
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description: Target devices to run benchmark
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required: false
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type: string
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default: google_pixel_3_private_rooted
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benchmark_configs:
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description: The list of configs used the benchmark
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required: false
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type: string
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concurrency:
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group: android-perf-private-devices-${{ github.event.pull_request.number || github.ref_name }}-${{ github.ref_type == 'branch' && github.sha }}-${{ github.event_name == 'workflow_dispatch' }}-${{ github.event_name == 'schedule' }}
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cancel-in-progress: true
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jobs:
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android:
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uses: ./.github/workflows/android-perf.yml
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secrets: inherit
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permissions:
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id-token: write
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contents: read
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with:
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models: ${{ inputs.models }}
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devices: google_pixel_3_private_rooted
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benchmark_configs: ${{ inputs.benchmark_configs }}

‎.github/workflows/android-release-artifacts.yml‎

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contents: read
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with:
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secrets-env: EXECUTORCH_MAVEN_SIGNING_KEYID EXECUTORCH_MAVEN_SIGNING_PASSWORD EXECUTORCH_MAVEN_CENTRAL_PASSWORD EXECUTORCH_MAVEN_CENTRAL_USERNAME EXECUTORCH_MAVEN_SIGNING_GPG_KEY_CONTENTS
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runner: linux.2xlarge
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# As this job has access to Maven credential, run this on a fresh ephemeral runner
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runner: ephemeral.linux.2xlarge
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docker-image: executorch-ubuntu-22.04-clang12-android
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submodules: 'recursive'
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ref: ${{ github.sha }}

‎backends/arm/operator_support/tosa_supported_operators.py‎

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exir_ops.edge.aten.__lshift__.Scalar,
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torch.ops.aten.scalar_tensor.default,
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exir_ops.edge.aten.gelu.default,
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exir_ops.edge.aten.alias_copy.default,
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]
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return supported

‎backends/arm/operators/__init__.py‎

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op_erf,
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op_exp,
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op_ge,
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op_get_item,
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op_gt,
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op_le,
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op_log,
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op_view,
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op_where,
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ops_binary,
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ops_identity,
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ops_unary,
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)

‎backends/arm/operators/op_get_item.py‎

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‎backends/arm/operators/ops_identity.py‎

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# Copyright 2025 Arm Limited and/or its affiliates.
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#
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# This source code is licensed under the BSD-style license found in the
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# LICENSE file in the root directory of this source tree.
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# pyre-unsafe
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from typing import List
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import torch
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import torch.fx
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import tosa_tools.v0_80.serializer.tosa_serializer as ts
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from executorch.backends.arm.operators.node_visitor import (
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NodeVisitor,
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register_node_visitor,
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)
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from executorch.backends.arm.tosa_mapping import TosaArg
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def identity_operator_factory(identity_target: str):
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"""
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Creates and registers NodeVisitors for operators that map directly
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to a TOSA IDENTITY op.
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"""
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class IdentityOperatorVisitor(NodeVisitor):
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target = identity_target
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def define_node(
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self,
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node: torch.fx.Node,
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tosa_graph: ts.TosaSerializer,
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inputs: List[TosaArg],
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output: TosaArg,
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) -> None:
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# Simply add an identityOp
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tosa_graph.addOperator(
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ts.TosaOp.Op().IDENTITY, [inputs[0].name], [output.name]
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)
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register_node_visitor(IdentityOperatorVisitor)
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identity_operator_factory("getitem")
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identity_operator_factory("aten.alias_copy.default")

‎backends/arm/quantizer/quantization_annotator.py‎

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operator.getitem,
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]
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_one_to_one_shared_input_or_input_act_qspec = [
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torch.ops.aten.adaptive_avg_pool2d.default,
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torch.ops.aten.alias_copy.default,
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]
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def get_quant_properties( # noqa: C901
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node: Node, gm: torch.fx.GraphModule, quantization_config
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_QuantProperty(2, shared_qspec), # type: ignore[arg-type]
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]
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quant_properties.quant_output = _QuantProperty(0, shared_qspec) # type: ignore[arg-type]
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elif node.target == torch.ops.aten.adaptive_avg_pool2d.default:
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elif node.target in _one_to_one_shared_input_or_input_act_qspec:
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input_qspec = (
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SharedQuantizationSpec(node.args[0]) # type: ignore[arg-type]
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if arm_quantizer_utils.is_output_annotated(node.args[0]) # type: ignore

‎backends/arm/test/ops/test_alias_copy.py‎

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# Copyright 2025 Arm Limited and/or its affiliates.
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#
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# This source code is licensed under the BSD-style license found in the
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# LICENSE file in the root directory of this source tree.
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from typing import Tuple
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import torch
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from executorch.backends.arm.test import common
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from executorch.backends.arm.test.tester.test_pipeline import (
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EthosU55PipelineBI,
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EthosU85PipelineBI,
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TosaPipelineBI,
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TosaPipelineMI,
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)
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input_t1 = Tuple[torch.Tensor]
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class AliasCopy(torch.nn.Module):
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"""
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Tests proper handling of alias_copy when used directly.
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alias_copy can also appear from PyTorch/ExecuTorch optimizations
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such as `x.transpose(0, 0)`. This is optimized to an alias_copy but
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not before dq/q operators are added.
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"""
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aten_op = "torch.ops.aten.alias_copy.default"
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exir_op = "executorch_exir_dialects_edge__ops_aten_alias_copy_default"
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test_data: dict[input_t1] = {
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"1d_ramp": (torch.arange(-16, 16, 0.2),),
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"2d_ones": (torch.ones(5, 5),),
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"3d_rand": (torch.rand(3, 5, 5),),
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"4d_zeros": (torch.zeros(1, 10, 10, 10),),
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}
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def __init__(self):
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super().__init__()
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def forward(self, x: torch.Tensor):
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return torch.alias_copy(x)
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@common.parametrize("test_data", AliasCopy.test_data)
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def test_alias_copy_tosa_MI(test_data: input_t1):
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TosaPipelineMI[input_t1](
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AliasCopy(),
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test_data,
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AliasCopy.aten_op,
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AliasCopy.exir_op,
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).run()
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@common.parametrize("test_data", AliasCopy.test_data)
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def test_alias_copy_tosa_BI(test_data: input_t1):
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TosaPipelineBI[input_t1](
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AliasCopy(),
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test_data,
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AliasCopy.aten_op,
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AliasCopy.exir_op,
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).run()
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@common.parametrize("test_data", AliasCopy.test_data)
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def test_alias_copy_u55_BI(test_data: input_t1):
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EthosU55PipelineBI[input_t1](
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AliasCopy(),
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test_data,
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AliasCopy.aten_op,
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AliasCopy.exir_op,
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).run()
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@common.parametrize("test_data", AliasCopy.test_data)
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def test_alias_copy_u85_BI(test_data: input_t1):
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EthosU85PipelineBI[input_t1](
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AliasCopy(),
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test_data,
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AliasCopy.aten_op,
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AliasCopy.exir_op,
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).run()

‎docs/source/bundled-io.md‎

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./cmake-out/examples/devtools/example_runner --bundled_program_path {your-bpte-file} --output_verification
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```
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It is expected to see no input from running the above mentioned snippet.
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It is expected to see no output from running the above mentioned snippet.
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For a detailed example of how the runner should be like, please refer to our [example runner](https://github.com/pytorch/executorch/blob/release/0.6/examples/devtools/example_runner/example_runner.cpp).
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