导出到 ExecuTorch 教程

作者： Angela Yi

ExecuTorch 是一个统一的 ML 堆栈，用于将 PyTorch 模型降低到边缘设备。 它引入了改进的入口点来执行模型、设备和/或用例 特定优化，例如后端委托、用户定义的编译器 转换、默认或用户定义的内存规划等。

概括地说，工作流如下所示：

在本教程中，我们将介绍“程序准备”步骤中的 API，以 将 PyTorch 模型降低为可加载到设备并在 ExecuTorch 运行时。

先决条件

要运行本教程，您首先需要设置 ExecuTorch 环境。

导出模型

注意：Export API 仍在进行更改，以更好地与 长期出口状态。有关更多详细信息，请参阅此问题。

降级到 ExecuTorch 的第一步是将给定的模型（任何 callable 或 ） 转换为图形表示形式。这是通过 ，它接受一个 ， 一个 positional arguments，也可以选择关键字参数的字典（未显示 ）和动态形状列表（稍后介绍）。torch.nn.Moduletorch.exporttorch.nn.Module

import torch
from torch.export import export, ExportedProgram


class SimpleConv(torch.nn.Module):
    def __init__(self) -> None:
        super().__init__()
        self.conv = torch.nn.Conv2d(
            in_channels=3, out_channels=16, kernel_size=3, padding=1
        )
        self.relu = torch.nn.ReLU()

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        a = self.conv(x)
        return self.relu(a)


example_args = (torch.randn(1, 3, 256, 256),)
aten_dialect: ExportedProgram = export(SimpleConv(), example_args)
print(aten_dialect)

ExportedProgram:
    class GraphModule(torch.nn.Module):
        def forward(self, p_conv_weight: "f32[16, 3, 3, 3]", p_conv_bias: "f32[16]", x: "f32[1, 3, 256, 256]"):
            # File: /pytorch/executorch/docs/source/tutorials_source/export-to-executorch-tutorial.py:64 in forward, code: a = self.conv(x)
            conv2d: "f32[1, 16, 256, 256]" = torch.ops.aten.conv2d.default(x, p_conv_weight, p_conv_bias, [1, 1], [1, 1]);  x = p_conv_weight = p_conv_bias = None

            # File: /pytorch/executorch/docs/source/tutorials_source/export-to-executorch-tutorial.py:65 in forward, code: return self.relu(a)
            relu: "f32[1, 16, 256, 256]" = torch.ops.aten.relu.default(conv2d);  conv2d = None
            return (relu,)

Graph signature: ExportGraphSignature(input_specs=[InputSpec(kind=<InputKind.PARAMETER: 2>, arg=TensorArgument(name='p_conv_weight'), target='conv.weight', persistent=None), InputSpec(kind=<InputKind.PARAMETER: 2>, arg=TensorArgument(name='p_conv_bias'), target='conv.bias', persistent=None), InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='x'), target=None, persistent=None)], output_specs=[OutputSpec(kind=<OutputKind.USER_OUTPUT: 1>, arg=TensorArgument(name='relu'), target=None)])
Range constraints: {}

的输出是一个完全扁平化的图形（意味着 Graph 不包含任何 Module 层次结构，Control 除外 流运算符）。此外，该图是纯函数式的，这意味着它确实 不包含具有副作用（如突变或别名）的操作。torch.export.export

有关结果的更多规范，请参见 此处 .torch.export

返回的图形仅包含函数式 ATen 运算符 （~2000 次操作），我们将其称为 .torch.exportATen Dialect

表达活力

默认情况下，导出流将跟踪程序，假设所有输入 形状是静态的，因此如果我们使用具有以下 与我们在 tracing 时使用的 Settings 不同，我们将遇到一个错误：

import traceback as tb


class Basic(torch.nn.Module):
    def __init__(self):
        super().__init__()

    def forward(self, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
        return x + y


example_args = (torch.randn(3, 3), torch.randn(3, 3))
aten_dialect: ExportedProgram = export(Basic(), example_args)

# Works correctly
print(aten_dialect.module()(torch.ones(3, 3), torch.ones(3, 3)))

# Errors
try:
    print(aten_dialect.module()(torch.ones(3, 2), torch.ones(3, 2)))
except Exception:
    tb.print_exc()

tensor([[2., 2., 2.],
        [2., 2., 2.],
        [2., 2., 2.]])
Traceback (most recent call last):
  File "/pytorch/executorch/docs/source/tutorials_source/export-to-executorch-tutorial.py", line 111, in <module>
    print(aten_dialect.module()(torch.ones(3, 2), torch.ones(3, 2)))
  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/fx/graph_module.py", line 738, in call_wrapped
    return self._wrapped_call(self, *args, **kwargs)
  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/fx/graph_module.py", line 316, in __call__
    raise e
  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/fx/graph_module.py", line 303, in __call__
    return super(self.cls, obj).__call__(*args, **kwargs)  # type: ignore[misc]
  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1582, in _call_impl
    args_kwargs_result = hook(self, args, kwargs)  # type: ignore[misc]
  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py", line 600, in _fn
    return fn(*args, **kwargs)
  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/export/_unlift.py", line 33, in _check_input_constraints_pre_hook
    return _check_input_constraints_for_graph(
  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_export/utils.py", line 155, in _check_input_constraints_for_graph
    raise RuntimeError(
RuntimeError: Expected input at *args[0].shape[1] to be equal to 3, but got 2

为了表示某些输入形状是动态的，我们可以插入动态

shapes 添加到导出流程中。这是通过 API 完成的：Dim

from torch.export import Dim


class Basic(torch.nn.Module):
    def __init__(self):
        super().__init__()

    def forward(self, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
        return x + y


example_args = (torch.randn(3, 3), torch.randn(3, 3))
dim1_x = Dim("dim1_x", min=1, max=10)
dynamic_shapes = {"x": {1: dim1_x}, "y": {1: dim1_x}}
aten_dialect: ExportedProgram = export(
    Basic(), example_args, dynamic_shapes=dynamic_shapes
)
print(aten_dialect)

ExportedProgram:
    class GraphModule(torch.nn.Module):
        def forward(self, x: "f32[3, s0]", y: "f32[3, s0]"):
            # File: /pytorch/executorch/docs/source/tutorials_source/export-to-executorch-tutorial.py:127 in forward, code: return x + y
            add: "f32[3, s0]" = torch.ops.aten.add.Tensor(x, y);  x = y = None
            return (add,)

Graph signature: ExportGraphSignature(input_specs=[InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='x'), target=None, persistent=None), InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='y'), target=None, persistent=None)], output_specs=[OutputSpec(kind=<OutputKind.USER_OUTPUT: 1>, arg=TensorArgument(name='add'), target=None)])
Range constraints: {s0: VR[1, 10]}

请注意，输入 和 现在具有形状 （3， s0）， 替换为一个符号，表示此维度可以是范围 的值。arg0_1arg1_1s0

此外，我们可以在 Range 约束中看到 value of 具有 范围 [1， 10]，由我们的动态形状指定。s0

现在让我们尝试运行具有不同形状的模型：

# Works correctly
print(aten_dialect.module()(torch.ones(3, 3), torch.ones(3, 3)))
print(aten_dialect.module()(torch.ones(3, 2), torch.ones(3, 2)))

# Errors because it violates our constraint that input 0, dim 1 <= 10
try:
    print(aten_dialect.module()(torch.ones(3, 15), torch.ones(3, 15)))
except Exception:
    tb.print_exc()

# Errors because it violates our constraint that input 0, dim 1 == input 1, dim 1
try:
    print(aten_dialect.module()(torch.ones(3, 3), torch.ones(3, 2)))
except Exception:
    tb.print_exc()

tensor([[2., 2., 2.],
        [2., 2., 2.],
        [2., 2., 2.]])
tensor([[2., 2.],
        [2., 2.],
        [2., 2.]])
Traceback (most recent call last):
  File "/pytorch/executorch/docs/source/tutorials_source/export-to-executorch-tutorial.py", line 154, in <module>
    print(aten_dialect.module()(torch.ones(3, 15), torch.ones(3, 15)))
  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/fx/graph_module.py", line 738, in call_wrapped
    return self._wrapped_call(self, *args, **kwargs)
  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/fx/graph_module.py", line 316, in __call__
    raise e
  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/fx/graph_module.py", line 303, in __call__
    return super(self.cls, obj).__call__(*args, **kwargs)  # type: ignore[misc]
  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1582, in _call_impl
    args_kwargs_result = hook(self, args, kwargs)  # type: ignore[misc]
  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py", line 600, in _fn
    return fn(*args, **kwargs)
  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/export/_unlift.py", line 33, in _check_input_constraints_pre_hook
    return _check_input_constraints_for_graph(
  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_export/utils.py", line 145, in _check_input_constraints_for_graph
    raise RuntimeError(
RuntimeError: Expected input at *args[0].shape[1] to be <= 10, but got 15
Traceback (most recent call last):
  File "/pytorch/executorch/docs/source/tutorials_source/export-to-executorch-tutorial.py", line 160, in <module>
    print(aten_dialect.module()(torch.ones(3, 3), torch.ones(3, 2)))
  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/fx/graph_module.py", line 738, in call_wrapped
    return self._wrapped_call(self, *args, **kwargs)
  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/fx/graph_module.py", line 316, in __call__
    raise e
  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/fx/graph_module.py", line 303, in __call__
    return super(self.cls, obj).__call__(*args, **kwargs)  # type: ignore[misc]
  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1582, in _call_impl
    args_kwargs_result = hook(self, args, kwargs)  # type: ignore[misc]
  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py", line 600, in _fn
    return fn(*args, **kwargs)
  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/export/_unlift.py", line 33, in _check_input_constraints_pre_hook
    return _check_input_constraints_for_graph(
  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_export/utils.py", line 107, in _check_input_constraints_for_graph
    raise RuntimeError(
RuntimeError: Expected input at *args[1].shape[1] to be equal to 3, but got 2

寻址无法追踪的代码

由于我们的目标是从 PyTorch 捕获整个计算图 程序中，我们最终可能会遇到程序中无法追踪的部分。自 解决这些问题，torch.export 文档， 或者 torch.export 教程将是最好的查看位置。

执行量化

要量化模型，我们首先需要使用 捕获图形，执行量化，然后 叫。 返回 graph 中包含 Autograd 安全的 ATen 运算符，这意味着它们是 safe 用于量化所需的 Eager Mode 训练。我们将调用 此级别的图形，即图形。torch._export.capture_pre_autograd_graphtorch.exporttorch._export.capture_pre_autograd_graphPre-Autograd ATen Dialect

与 FX Graph Mode 量化相比， 我们需要调用两个新的 API：and，而不是 和 。它的不同之处在于，它采用特定于后端的参数，该 将使用量化 model。prepare_pt2econvert_pt2eprepare_fxconvert_fxprepare_pt2eQuantizer

from torch._export import capture_pre_autograd_graph

example_args = (torch.randn(1, 3, 256, 256),)
pre_autograd_aten_dialect = capture_pre_autograd_graph(SimpleConv(), example_args)
print("Pre-Autograd ATen Dialect Graph")
print(pre_autograd_aten_dialect)

from torch.ao.quantization.quantize_pt2e import convert_pt2e, prepare_pt2e
from torch.ao.quantization.quantizer.xnnpack_quantizer import (
    get_symmetric_quantization_config,
    XNNPACKQuantizer,
)

quantizer = XNNPACKQuantizer().set_global(get_symmetric_quantization_config())
prepared_graph = prepare_pt2e(pre_autograd_aten_dialect, quantizer)
# calibrate with a sample dataset
converted_graph = convert_pt2e(prepared_graph)
print("Quantized Graph")
print(converted_graph)

aten_dialect: ExportedProgram = export(converted_graph, example_args)
print("ATen Dialect Graph")
print(aten_dialect)

Pre-Autograd ATen Dialect Graph
GraphModule()



def forward(self, x):
    arg0, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec)
    arg0_1 = arg0
    _param_constant0 = self.conv_weight
    _param_constant1 = self.conv_bias
    conv2d = torch.ops.aten.conv2d.default(arg0_1, _param_constant0, _param_constant1, [1, 1], [1, 1]);  arg0_1 = _param_constant0 = _param_constant1 = None
    relu = torch.ops.aten.relu.default(conv2d);  conv2d = None
    return pytree.tree_unflatten([relu], self._out_spec)

# To see more debug info, please use `graph_module.print_readable()`
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/ao/quantization/observer.py:1289: UserWarning: must run observer before calling calculate_qparams.                                    Returning default scale and zero point
  warnings.warn(
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/ao/quantization/utils.py:376: UserWarning: must run observer before calling calculate_qparams. Returning default values.
  warnings.warn(
Quantized Graph
GraphModule()



def forward(self, x):
    arg0, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec)
    arg0_1 = arg0
    quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(arg0_1, 1.0, 0, -128, 127, torch.int8);  arg0_1 = None
    dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 1.0, 0, -128, 127, torch.int8);  quantize_per_tensor_default = None
    _frozen_param0 = self._frozen_param0
    dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(_frozen_param0, 1.0, 0, -127, 127, torch.int8);  _frozen_param0 = None
    _param_constant1 = self.conv_bias
    conv2d = torch.ops.aten.conv2d.default(dequantize_per_tensor_default, dequantize_per_tensor_default_1, _param_constant1, [1, 1], [1, 1]);  dequantize_per_tensor_default = dequantize_per_tensor_default_1 = _param_constant1 = None
    relu = torch.ops.aten.relu.default(conv2d);  conv2d = None
    quantize_per_tensor_default_2 = torch.ops.quantized_decomposed.quantize_per_tensor.default(relu, 1.0, 0, -128, 127, torch.int8);  relu = None
    dequantize_per_tensor_default_2 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_2, 1.0, 0, -128, 127, torch.int8);  quantize_per_tensor_default_2 = None
    return pytree.tree_unflatten([dequantize_per_tensor_default_2], self._out_spec)

# To see more debug info, please use `graph_module.print_readable()`
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_subclasses/functional_tensor.py:362: UserWarning: At pre-dispatch tracing, we will assume that any custom op that is marked with CompositeImplicitAutograd and functional are safe to not decompose. We found quantized_decomposed.quantize_per_tensor.default to be one such op.
  warnings.warn(
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_subclasses/functional_tensor.py:362: UserWarning: At pre-dispatch tracing, we will assume that any custom op that is marked with CompositeImplicitAutograd and functional are safe to not decompose. We found quantized_decomposed.dequantize_per_tensor.default to be one such op.
  warnings.warn(
ATen Dialect Graph
ExportedProgram:
    class GraphModule(torch.nn.Module):
        def forward(self, p_conv_bias: "f32[16]", b__frozen_param0: "i8[16, 3, 3, 3]", x: "f32[1, 3, 256, 256]"):
            # File: <eval_with_key>.169:7 in forward, code: quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(arg0_1, 1.0, 0, -128, 127, torch.int8);  arg0_1 = None
            quantize_per_tensor: "i8[1, 3, 256, 256]" = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 1.0, 0, -128, 127, torch.int8);  x = None

            # File: /pytorch/executorch/docs/source/tutorials_source/export-to-executorch-tutorial.py:64 in forward, code: a = self.conv(x)
            dequantize_per_tensor: "f32[1, 3, 256, 256]" = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor, 1.0, 0, -128, 127, torch.int8);  quantize_per_tensor = None
            dequantize_per_tensor_1: "f32[16, 3, 3, 3]" = torch.ops.quantized_decomposed.dequantize_per_tensor.default(b__frozen_param0, 1.0, 0, -127, 127, torch.int8);  b__frozen_param0 = None
            conv2d: "f32[1, 16, 256, 256]" = torch.ops.aten.conv2d.default(dequantize_per_tensor, dequantize_per_tensor_1, p_conv_bias, [1, 1], [1, 1]);  dequantize_per_tensor = dequantize_per_tensor_1 = p_conv_bias = None

            # File: /pytorch/executorch/docs/source/tutorials_source/export-to-executorch-tutorial.py:65 in forward, code: return self.relu(a)
            relu: "f32[1, 16, 256, 256]" = torch.ops.aten.relu.default(conv2d);  conv2d = None
            quantize_per_tensor_1: "i8[1, 16, 256, 256]" = torch.ops.quantized_decomposed.quantize_per_tensor.default(relu, 1.0, 0, -128, 127, torch.int8);  relu = None

            # File: <eval_with_key>.169:15 in forward, code: dequantize_per_tensor_default_2 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_2, 1.0, 0, -128, 127, torch.int8);  quantize_per_tensor_default_2 = None
            dequantize_per_tensor_2: "f32[1, 16, 256, 256]" = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_1, 1.0, 0, -128, 127, torch.int8);  quantize_per_tensor_1 = None
            return (dequantize_per_tensor_2,)

Graph signature: ExportGraphSignature(input_specs=[InputSpec(kind=<InputKind.PARAMETER: 2>, arg=TensorArgument(name='p_conv_bias'), target='conv_bias', persistent=None), InputSpec(kind=<InputKind.BUFFER: 3>, arg=TensorArgument(name='b__frozen_param0'), target='_frozen_param0', persistent=True), InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='x'), target=None, persistent=None)], output_specs=[OutputSpec(kind=<OutputKind.USER_OUTPUT: 1>, arg=TensorArgument(name='dequantize_per_tensor_2'), target=None)])
Range constraints: {}

有关如何量化模型以及后端如何实现 的更多信息，请参见 此处.Quantizer

降低到 Edge Dialect

导出图表并将其降低到 后，下一步 是降低到 ，其中有用的特化 对于边缘设备，但对于一般（服务器）环境不是必需的 应用的。 其中一些专业包括：ATen DialectEdge Dialect

• DType 专业化

• 标量到张量的转换

• 将所有操作转换为命名空间。executorch.exir.dialects.edge

请注意，此方言仍然与后端（或目标）无关。

降低是通过 API 完成的。to_edge

from executorch.exir import EdgeProgramManager, to_edge

example_args = (torch.randn(1, 3, 256, 256),)
aten_dialect: ExportedProgram = export(SimpleConv(), example_args)

edge_program: EdgeProgramManager = to_edge(aten_dialect)
print("Edge Dialect Graph")
print(edge_program.exported_program())

Edge Dialect Graph
ExportedProgram:
    class GraphModule(torch.nn.Module):
        def forward(self, p_conv_weight: "f32[16, 3, 3, 3]", p_conv_bias: "f32[16]", x: "f32[1, 3, 256, 256]"):
            # File: /pytorch/executorch/docs/source/tutorials_source/export-to-executorch-tutorial.py:64 in forward, code: a = self.conv(x)
            aten_convolution_default: "f32[1, 16, 256, 256]" = executorch_exir_dialects_edge__ops_aten_convolution_default(x, p_conv_weight, p_conv_bias, [1, 1], [1, 1], [1, 1], False, [0, 0], 1);  x = p_conv_weight = p_conv_bias = None

            # File: /pytorch/executorch/docs/source/tutorials_source/export-to-executorch-tutorial.py:65 in forward, code: return self.relu(a)
            aten_relu_default: "f32[1, 16, 256, 256]" = executorch_exir_dialects_edge__ops_aten_relu_default(aten_convolution_default);  aten_convolution_default = None
            return (aten_relu_default,)

Graph signature: ExportGraphSignature(input_specs=[InputSpec(kind=<InputKind.PARAMETER: 2>, arg=TensorArgument(name='p_conv_weight'), target='conv.weight', persistent=None), InputSpec(kind=<InputKind.PARAMETER: 2>, arg=TensorArgument(name='p_conv_bias'), target='conv.bias', persistent=None), InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='x'), target=None, persistent=None)], output_specs=[OutputSpec(kind=<OutputKind.USER_OUTPUT: 1>, arg=TensorArgument(name='aten_relu_default'), target=None)])
Range constraints: {}

to_edge()返回一个对象，其中包含 导出的程序，这些程序将被放置在此设备上。此数据结构 允许用户导出多个程序并将它们合并为一个二进制文件。如果 只有一个程序，默认情况下，它将保存为“forward”名称。EdgeProgramManager

class Encode(torch.nn.Module):
    def forward(self, x):
        return torch.nn.functional.linear(x, torch.randn(5, 10))


class Decode(torch.nn.Module):
    def forward(self, x):
        return torch.nn.functional.linear(x, torch.randn(10, 5))


encode_args = (torch.randn(1, 10),)
aten_encode: ExportedProgram = export(Encode(), encode_args)

decode_args = (torch.randn(1, 5),)
aten_decode: ExportedProgram = export(Decode(), decode_args)

edge_program: EdgeProgramManager = to_edge(
    {"encode": aten_encode, "decode": aten_decode}
)
for method in edge_program.methods:
    print(f"Edge Dialect graph of {method}")
    print(edge_program.exported_program(method))

Edge Dialect graph of encode
ExportedProgram:
    class GraphModule(torch.nn.Module):
        def forward(self, x: "f32[1, 10]"):
            # File: /pytorch/executorch/docs/source/tutorials_source/export-to-executorch-tutorial.py:261 in forward, code: return torch.nn.functional.linear(x, torch.randn(5, 10))
            aten_randn_default: "f32[5, 10]" = executorch_exir_dialects_edge__ops_aten_randn_default([5, 10], device = device(type='cpu'), pin_memory = False)
            aten_permute_copy_default: "f32[10, 5]" = executorch_exir_dialects_edge__ops_aten_permute_copy_default(aten_randn_default, [1, 0]);  aten_randn_default = None
            aten_mm_default: "f32[1, 5]" = executorch_exir_dialects_edge__ops_aten_mm_default(x, aten_permute_copy_default);  x = aten_permute_copy_default = None
            return (aten_mm_default,)

Graph signature: ExportGraphSignature(input_specs=[InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='x'), target=None, persistent=None)], output_specs=[OutputSpec(kind=<OutputKind.USER_OUTPUT: 1>, arg=TensorArgument(name='aten_mm_default'), target=None)])
Range constraints: {}

Edge Dialect graph of decode
ExportedProgram:
    class GraphModule(torch.nn.Module):
        def forward(self, x: "f32[1, 5]"):
            # File: /pytorch/executorch/docs/source/tutorials_source/export-to-executorch-tutorial.py:266 in forward, code: return torch.nn.functional.linear(x, torch.randn(10, 5))
            aten_randn_default: "f32[10, 5]" = executorch_exir_dialects_edge__ops_aten_randn_default([10, 5], device = device(type='cpu'), pin_memory = False)
            aten_permute_copy_default: "f32[5, 10]" = executorch_exir_dialects_edge__ops_aten_permute_copy_default(aten_randn_default, [1, 0]);  aten_randn_default = None
            aten_mm_default: "f32[1, 10]" = executorch_exir_dialects_edge__ops_aten_mm_default(x, aten_permute_copy_default);  x = aten_permute_copy_default = None
            return (aten_mm_default,)

Graph signature: ExportGraphSignature(input_specs=[InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='x'), target=None, persistent=None)], output_specs=[OutputSpec(kind=<OutputKind.USER_OUTPUT: 1>, arg=TensorArgument(name='aten_mm_default'), target=None)])
Range constraints: {}

我们还可以通过 API 的 API 创建。有关如何编写的深入文档 可在此处找到转换。transform

请注意，由于图形现在位于 Edge Dialect 中，因此所有通道也必须 生成有效的 Edge Dialect 图（具体需要指出的一点是 运算符现在位于命名空间中， 而不是命名空间。executorch.exir.dialects.edgetorch.ops.aten

example_args = (torch.randn(1, 3, 256, 256),)
aten_dialect: ExportedProgram = export(SimpleConv(), example_args)
edge_program: EdgeProgramManager = to_edge(aten_dialect)
print("Edge Dialect Graph")
print(edge_program.exported_program())

from executorch.exir.dialects._ops import ops as exir_ops
from executorch.exir.pass_base import ExportPass


class ConvertReluToSigmoid(ExportPass):
    def call_operator(self, op, args, kwargs, meta):
        if op == exir_ops.edge.aten.relu.default:
            return super().call_operator(
                exir_ops.edge.aten.sigmoid.default, args, kwargs, meta
            )
        else:
            return super().call_operator(op, args, kwargs, meta)


transformed_edge_program = edge_program.transform((ConvertReluToSigmoid(),))
print("Transformed Edge Dialect Graph")
print(transformed_edge_program.exported_program())

Edge Dialect Graph
ExportedProgram:
    class GraphModule(torch.nn.Module):
        def forward(self, p_conv_weight: "f32[16, 3, 3, 3]", p_conv_bias: "f32[16]", x: "f32[1, 3, 256, 256]"):
            # File: /pytorch/executorch/docs/source/tutorials_source/export-to-executorch-tutorial.py:64 in forward, code: a = self.conv(x)
            aten_convolution_default: "f32[1, 16, 256, 256]" = executorch_exir_dialects_edge__ops_aten_convolution_default(x, p_conv_weight, p_conv_bias, [1, 1], [1, 1], [1, 1], False, [0, 0], 1);  x = p_conv_weight = p_conv_bias = None

            # File: /pytorch/executorch/docs/source/tutorials_source/export-to-executorch-tutorial.py:65 in forward, code: return self.relu(a)
            aten_relu_default: "f32[1, 16, 256, 256]" = executorch_exir_dialects_edge__ops_aten_relu_default(aten_convolution_default);  aten_convolution_default = None
            return (aten_relu_default,)

Graph signature: ExportGraphSignature(input_specs=[InputSpec(kind=<InputKind.PARAMETER: 2>, arg=TensorArgument(name='p_conv_weight'), target='conv.weight', persistent=None), InputSpec(kind=<InputKind.PARAMETER: 2>, arg=TensorArgument(name='p_conv_bias'), target='conv.bias', persistent=None), InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='x'), target=None, persistent=None)], output_specs=[OutputSpec(kind=<OutputKind.USER_OUTPUT: 1>, arg=TensorArgument(name='aten_relu_default'), target=None)])
Range constraints: {}

Transformed Edge Dialect Graph
ExportedProgram:
    class GraphModule(torch.nn.Module):
        def forward(self, p_conv_weight: "f32[16, 3, 3, 3]", p_conv_bias: "f32[16]", x: "f32[1, 3, 256, 256]"):
            # File: /pytorch/executorch/docs/source/tutorials_source/export-to-executorch-tutorial.py:64 in forward, code: a = self.conv(x)
            aten_convolution_default: "f32[1, 16, 256, 256]" = executorch_exir_dialects_edge__ops_aten_convolution_default(x, p_conv_weight, p_conv_bias, [1, 1], [1, 1], [1, 1], False, [0, 0], 1);  x = p_conv_weight = p_conv_bias = None

            # File: /pytorch/executorch/docs/source/tutorials_source/export-to-executorch-tutorial.py:65 in forward, code: return self.relu(a)
            aten_sigmoid_default: "f32[1, 16, 256, 256]" = executorch_exir_dialects_edge__ops_aten_sigmoid_default(aten_convolution_default);  aten_convolution_default = None
            return (aten_sigmoid_default,)

Graph signature: ExportGraphSignature(input_specs=[InputSpec(kind=<InputKind.PARAMETER: 2>, arg=TensorArgument(name='p_conv_weight'), target='conv.weight', persistent=None), InputSpec(kind=<InputKind.PARAMETER: 2>, arg=TensorArgument(name='p_conv_bias'), target='conv.bias', persistent=None), InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='x'), target=None, persistent=None)], output_specs=[OutputSpec(kind=<OutputKind.USER_OUTPUT: 1>, arg=TensorArgument(name='aten_sigmoid_default'), target=None)])
Range constraints: {}

注意：如果您看到类似 ， 请在 https://github.com/pytorch/executorch/issues 中提交问题，我们很乐意提供帮助！torch._export.verifier.SpecViolationError: Operator torch._ops.aten._native_batch_norm_legit_functional.default is not Aten Canonical

委托给后端

现在，我们可以将图形的一部分或整个图形委托给第三方 backend 的 API 进行验证。有关 后端委托的细节，包括如何委托给后端和 如何实现后端，可以在这里找到。to_backend

有三种方法可以使用此 API：

1. 我们可以降低整个模块。

2. 我们可以将降低的模块插入到另一个更大的模块中。

3. 我们可以将模块划分为可降低的子图，然后 将这些子图降低到后端。

降低整个模块

要降低整个模块，我们可以将后端名称 模块，以及一个编译规范列表，以帮助后端使用 降低过程。to_backend

class LowerableModule(torch.nn.Module):
    def __init__(self):
        super().__init__()

    def forward(self, x):
        return torch.sin(x)


# Export and lower the module to Edge Dialect
example_args = (torch.ones(1),)
aten_dialect: ExportedProgram = export(LowerableModule(), example_args)
edge_program: EdgeProgramManager = to_edge(aten_dialect)
to_be_lowered_module = edge_program.exported_program()

from executorch.exir.backend.backend_api import LoweredBackendModule, to_backend

# Import the backend
from executorch.exir.backend.test.backend_with_compiler_demo import (  # noqa
    BackendWithCompilerDemo,
)

# Lower the module
lowered_module: LoweredBackendModule = to_backend(
    "BackendWithCompilerDemo", to_be_lowered_module, []
)
print(lowered_module)
print(lowered_module.backend_id)
print(lowered_module.processed_bytes)
print(lowered_module.original_module)

# Serialize and save it to a file
save_path = "delegate.pte"
with open(save_path, "wb") as f:
    f.write(lowered_module.buffer())

LoweredBackendModule()
BackendWithCompilerDemo
b'1#op:demo::aten.sin.default, numel:1, dtype:torch.float32<debug_handle>1#'
ExportedProgram:
    class GraphModule(torch.nn.Module):
        def forward(self, x: "f32[1]"):
            # File: /pytorch/executorch/docs/source/tutorials_source/export-to-executorch-tutorial.py:355 in forward, code: return torch.sin(x)
            aten_sin_default: "f32[1]" = executorch_exir_dialects_edge__ops_aten_sin_default(x);  x = None
            return (aten_sin_default,)

Graph signature: ExportGraphSignature(input_specs=[InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='x'), target=None, persistent=None)], output_specs=[OutputSpec(kind=<OutputKind.USER_OUTPUT: 1>, arg=TensorArgument(name='aten_sin_default'), target=None)])
Range constraints: {}

在此调用中，将返回一个 .一些 的重要属性包括：to_backendLoweredBackendModuleLoweredBackendModule

• backend_id：此降低的模块将在 中运行的后端的名称 运行时

• processed_bytes：一个二进制 blob，它将告诉后端如何运行 运行时中的此程序

• original_module：原始导出的模块

将降低的模块组合成另一个模块

如果我们想在多个程序中重用这个降低的模块，我们 可以将这个降低的模块与另一个模块组合在一起。

class NotLowerableModule(torch.nn.Module):
    def __init__(self, bias):
        super().__init__()
        self.bias = bias

    def forward(self, a, b):
        return torch.add(torch.add(a, b), self.bias)


class ComposedModule(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.non_lowerable = NotLowerableModule(torch.ones(1) * 0.3)
        self.lowerable = lowered_module

    def forward(self, x):
        a = self.lowerable(x)
        b = self.lowerable(a)
        ret = self.non_lowerable(a, b)
        return a, b, ret


example_args = (torch.ones(1),)
aten_dialect: ExportedProgram = export(ComposedModule(), example_args)
edge_program: EdgeProgramManager = to_edge(aten_dialect)
exported_program = edge_program.exported_program()
print("Edge Dialect graph")
print(exported_program)
print("Lowered Module within the graph")
print(exported_program.graph_module.lowered_module_0.backend_id)
print(exported_program.graph_module.lowered_module_0.processed_bytes)
print(exported_program.graph_module.lowered_module_0.original_module)

Edge Dialect graph
ExportedProgram:
    class GraphModule(torch.nn.Module):
        def forward(self, c_non_lowerable_bias: "f32[1]", x: "f32[1]"):
            # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/executorch/exir/lowered_backend_module.py:337 in forward, code: return executorch_call_delegate(self, *args)
            lowered_module_0 = self.lowered_module_0
            executorch_call_delegate: "f32[1]" = torch.ops.higher_order.executorch_call_delegate(lowered_module_0, x);  lowered_module_0 = x = None

            # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/executorch/exir/lowered_backend_module.py:337 in forward, code: return executorch_call_delegate(self, *args)
            lowered_module_1 = self.lowered_module_0
            executorch_call_delegate_1: "f32[1]" = torch.ops.higher_order.executorch_call_delegate(lowered_module_1, executorch_call_delegate);  lowered_module_1 = None

            # File: /pytorch/executorch/docs/source/tutorials_source/export-to-executorch-tutorial.py:409 in forward, code: return torch.add(torch.add(a, b), self.bias)
            aten_add_tensor: "f32[1]" = executorch_exir_dialects_edge__ops_aten_add_Tensor(executorch_call_delegate, executorch_call_delegate_1)
            aten_add_tensor_1: "f32[1]" = executorch_exir_dialects_edge__ops_aten_add_Tensor(aten_add_tensor, c_non_lowerable_bias);  aten_add_tensor = c_non_lowerable_bias = None
            return (executorch_call_delegate, executorch_call_delegate_1, aten_add_tensor_1)

Graph signature: ExportGraphSignature(input_specs=[InputSpec(kind=<InputKind.CONSTANT_TENSOR: 4>, arg=TensorArgument(name='c_non_lowerable_bias'), target='non_lowerable.bias', persistent=True), InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='x'), target=None, persistent=None)], output_specs=[OutputSpec(kind=<OutputKind.USER_OUTPUT: 1>, arg=TensorArgument(name='executorch_call_delegate'), target=None), OutputSpec(kind=<OutputKind.USER_OUTPUT: 1>, arg=TensorArgument(name='executorch_call_delegate_1'), target=None), OutputSpec(kind=<OutputKind.USER_OUTPUT: 1>, arg=TensorArgument(name='aten_add_tensor_1'), target=None)])
Range constraints: {}

Lowered Module within the graph
BackendWithCompilerDemo
b'1#op:demo::aten.sin.default, numel:1, dtype:torch.float32<debug_handle>1#'
ExportedProgram:
    class GraphModule(torch.nn.Module):
        def forward(self, x: "f32[1]"):
            # File: /pytorch/executorch/docs/source/tutorials_source/export-to-executorch-tutorial.py:355 in forward, code: return torch.sin(x)
            aten_sin_default: "f32[1]" = executorch_exir_dialects_edge__ops_aten_sin_default(x);  x = None
            return (aten_sin_default,)

Graph signature: ExportGraphSignature(input_specs=[InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='x'), target=None, persistent=None)], output_specs=[OutputSpec(kind=<OutputKind.USER_OUTPUT: 1>, arg=TensorArgument(name='aten_sin_default'), target=None)])
Range constraints: {}

请注意，现在 graph 调用。此外，其内容与我们创建的相同 以前。torch.ops.higher_order.executorch_call_delegatelowered_module_0lowered_module_0lowered_module

模块的分区和下部

一个单独的下降流是将我们想要的模块 lower 和特定于后端的分区程序。 将使用 特定于后端的分区器来标记模块中可降低的节点， 将这些节点划分为子图，然后为每个子图创建一个。to_backendto_backendLoweredBackendModule

class Foo(torch.nn.Module):
    def forward(self, a, x, b):
        y = torch.mm(a, x)
        z = y + b
        a = z - a
        y = torch.mm(a, x)
        z = y + b
        return z


example_args = (torch.randn(2, 2), torch.randn(2, 2), torch.randn(2, 2))
aten_dialect: ExportedProgram = export(Foo(), example_args)
edge_program: EdgeProgramManager = to_edge(aten_dialect)
exported_program = edge_program.exported_program()
print("Edge Dialect graph")
print(exported_program)

from executorch.exir.backend.test.op_partitioner_demo import AddMulPartitionerDemo

delegated_program = to_backend(exported_program, AddMulPartitionerDemo())
print("Delegated program")
print(delegated_program)
print(delegated_program.graph_module.lowered_module_0.original_module)
print(delegated_program.graph_module.lowered_module_1.original_module)

Edge Dialect graph
ExportedProgram:
    class GraphModule(torch.nn.Module):
        def forward(self, a: "f32[2, 2]", x: "f32[2, 2]", b: "f32[2, 2]"):
            # File: /pytorch/executorch/docs/source/tutorials_source/export-to-executorch-tutorial.py:455 in forward, code: y = torch.mm(a, x)
            aten_mm_default: "f32[2, 2]" = executorch_exir_dialects_edge__ops_aten_mm_default(a, x)

            # File: /pytorch/executorch/docs/source/tutorials_source/export-to-executorch-tutorial.py:456 in forward, code: z = y + b
            aten_add_tensor: "f32[2, 2]" = executorch_exir_dialects_edge__ops_aten_add_Tensor(aten_mm_default, b);  aten_mm_default = None

            # File: /pytorch/executorch/docs/source/tutorials_source/export-to-executorch-tutorial.py:457 in forward, code: a = z - a
            aten_sub_tensor: "f32[2, 2]" = executorch_exir_dialects_edge__ops_aten_sub_Tensor(aten_add_tensor, a);  aten_add_tensor = a = None

            # File: /pytorch/executorch/docs/source/tutorials_source/export-to-executorch-tutorial.py:458 in forward, code: y = torch.mm(a, x)
            aten_mm_default_1: "f32[2, 2]" = executorch_exir_dialects_edge__ops_aten_mm_default(aten_sub_tensor, x);  aten_sub_tensor = x = None

            # File: /pytorch/executorch/docs/source/tutorials_source/export-to-executorch-tutorial.py:459 in forward, code: z = y + b
            aten_add_tensor_1: "f32[2, 2]" = executorch_exir_dialects_edge__ops_aten_add_Tensor(aten_mm_default_1, b);  aten_mm_default_1 = b = None
            return (aten_add_tensor_1,)

Graph signature: ExportGraphSignature(input_specs=[InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='a'), target=None, persistent=None), InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='x'), target=None, persistent=None), InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='b'), target=None, persistent=None)], output_specs=[OutputSpec(kind=<OutputKind.USER_OUTPUT: 1>, arg=TensorArgument(name='aten_add_tensor_1'), target=None)])
Range constraints: {}

Delegated program
ExportedProgram:
    class GraphModule(torch.nn.Module):
        def forward(self, a: "f32[2, 2]", x: "f32[2, 2]", b: "f32[2, 2]"):
            # No stacktrace found for following nodes
            lowered_module_0 = self.lowered_module_0
            executorch_call_delegate = torch.ops.higher_order.executorch_call_delegate(lowered_module_0, a, x, b);  lowered_module_0 = None
            getitem: "f32[2, 2]" = executorch_call_delegate[0];  executorch_call_delegate = None

            # File: /pytorch/executorch/docs/source/tutorials_source/export-to-executorch-tutorial.py:457 in forward, code: a = z - a
            aten_sub_tensor: "f32[2, 2]" = executorch_exir_dialects_edge__ops_aten_sub_Tensor(getitem, a);  getitem = a = None

            # No stacktrace found for following nodes
            lowered_module_1 = self.lowered_module_1
            executorch_call_delegate_1 = torch.ops.higher_order.executorch_call_delegate(lowered_module_1, aten_sub_tensor, x, b);  lowered_module_1 = aten_sub_tensor = x = b = None
            getitem_1: "f32[2, 2]" = executorch_call_delegate_1[0];  executorch_call_delegate_1 = None
            return (getitem_1,)

Graph signature: ExportGraphSignature(input_specs=[InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='a'), target=None, persistent=None), InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='x'), target=None, persistent=None), InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='b'), target=None, persistent=None)], output_specs=[OutputSpec(kind=<OutputKind.USER_OUTPUT: 1>, arg=TensorArgument(name='getitem_1'), target=None)])
Range constraints: {}

ExportedProgram:
    class GraphModule(torch.nn.Module):
        def forward(self, a: "f32[2, 2]", x: "f32[2, 2]", b: "f32[2, 2]"):
            # File: /pytorch/executorch/docs/source/tutorials_source/export-to-executorch-tutorial.py:455 in forward, code: y = torch.mm(a, x)
            aten_mm_default: "f32[2, 2]" = executorch_exir_dialects_edge__ops_aten_mm_default(a, x);  a = x = None

            # File: /pytorch/executorch/docs/source/tutorials_source/export-to-executorch-tutorial.py:456 in forward, code: z = y + b
            aten_add_tensor: "f32[2, 2]" = executorch_exir_dialects_edge__ops_aten_add_Tensor(aten_mm_default, b);  aten_mm_default = b = None
            return (aten_add_tensor,)

Graph signature: ExportGraphSignature(input_specs=[InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='a'), target=None, persistent=None), InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='x'), target=None, persistent=None), InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='b'), target=None, persistent=None)], output_specs=[OutputSpec(kind=<OutputKind.USER_OUTPUT: 1>, arg=TensorArgument(name='aten_add_tensor'), target=None)])
Range constraints: {}

ExportedProgram:
    class GraphModule(torch.nn.Module):
        def forward(self, aten_sub_tensor: "f32[2, 2]", x: "f32[2, 2]", b: "f32[2, 2]"):
            # File: /pytorch/executorch/docs/source/tutorials_source/export-to-executorch-tutorial.py:458 in forward, code: y = torch.mm(a, x)
            aten_mm_default_1: "f32[2, 2]" = executorch_exir_dialects_edge__ops_aten_mm_default(aten_sub_tensor, x);  aten_sub_tensor = x = None

            # File: /pytorch/executorch/docs/source/tutorials_source/export-to-executorch-tutorial.py:459 in forward, code: z = y + b
            aten_add_tensor_1: "f32[2, 2]" = executorch_exir_dialects_edge__ops_aten_add_Tensor(aten_mm_default_1, b);  aten_mm_default_1 = b = None
            return (aten_add_tensor_1,)

Graph signature: ExportGraphSignature(input_specs=[InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='aten_sub_tensor'), target=None, persistent=None), InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='x'), target=None, persistent=None), InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='b'), target=None, persistent=None)], output_specs=[OutputSpec(kind=<OutputKind.USER_OUTPUT: 1>, arg=TensorArgument(name='aten_add_tensor_1'), target=None)])
Range constraints: {}

请注意，现在 graph 中，一个包含操作 add、mul，另一个包含 操作 mul， add.torch.ops.higher_order.executorch_call_delegate

或者，一个更内聚的 API 来降低模块的下部部分是 directly 调用 IT：to_backend

class Foo(torch.nn.Module):
    def forward(self, a, x, b):
        y = torch.mm(a, x)
        z = y + b
        a = z - a
        y = torch.mm(a, x)
        z = y + b
        return z


example_args = (torch.randn(2, 2), torch.randn(2, 2), torch.randn(2, 2))
aten_dialect: ExportedProgram = export(Foo(), example_args)
edge_program: EdgeProgramManager = to_edge(aten_dialect)
exported_program = edge_program.exported_program()
delegated_program = edge_program.to_backend(AddMulPartitionerDemo())

print("Delegated program")
print(delegated_program.exported_program())

Delegated program
ExportedProgram:
    class GraphModule(torch.nn.Module):
        def forward(self, a: "f32[2, 2]", x: "f32[2, 2]", b: "f32[2, 2]"):
            # No stacktrace found for following nodes
            lowered_module_0 = self.lowered_module_0
            executorch_call_delegate = torch.ops.higher_order.executorch_call_delegate(lowered_module_0, a, x, b);  lowered_module_0 = None
            getitem: "f32[2, 2]" = executorch_call_delegate[0];  executorch_call_delegate = None

            # File: /pytorch/executorch/docs/source/tutorials_source/export-to-executorch-tutorial.py:491 in forward, code: a = z - a
            aten_sub_tensor: "f32[2, 2]" = executorch_exir_dialects_edge__ops_aten_sub_Tensor(getitem, a);  getitem = a = None

            # No stacktrace found for following nodes
            lowered_module_1 = self.lowered_module_1
            executorch_call_delegate_1 = torch.ops.higher_order.executorch_call_delegate(lowered_module_1, aten_sub_tensor, x, b);  lowered_module_1 = aten_sub_tensor = x = b = None
            getitem_1: "f32[2, 2]" = executorch_call_delegate_1[0];  executorch_call_delegate_1 = None
            return (getitem_1,)

Graph signature: ExportGraphSignature(input_specs=[InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='a'), target=None, persistent=None), InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='x'), target=None, persistent=None), InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='b'), target=None, persistent=None)], output_specs=[OutputSpec(kind=<OutputKind.USER_OUTPUT: 1>, arg=TensorArgument(name='getitem_1'), target=None)])
Range constraints: {}

运行 User-Defined 走路和内存规划

作为降低 API 的最后一步，我们可以使用 API 传入 特定于后端的传递，例如将运算符集替换为自定义 backend 运算符和内存规划 pass 来告诉运行时如何 在运行程序时提前分配内存。to_executorch()

提供了默认的内存规划过程，但我们也可以选择一个 特定于后端的内存规划通过（如果存在）。更多信息 可在此处找到编写自定义内存规划过程

from executorch.exir import ExecutorchBackendConfig, ExecutorchProgramManager
from executorch.exir.passes import MemoryPlanningPass

executorch_program: ExecutorchProgramManager = edge_program.to_executorch(
    ExecutorchBackendConfig(
        passes=[],  # User-defined passes
        memory_planning_pass=MemoryPlanningPass(
            "greedy"
        ),  # Default memory planning pass
    )
)

print("ExecuTorch Dialect")
print(executorch_program.exported_program())

import executorch.exir as exir

ExecuTorch Dialect
ExportedProgram:
    class GraphModule(torch.nn.Module):
        def forward(self, a: "f32[2, 2]", x: "f32[2, 2]", b: "f32[2, 2]"):
            # No stacktrace found for following nodes
            alloc: "f32[2, 2]" = executorch_exir_memory_alloc(((2, 2), torch.float32))

            # File: /pytorch/executorch/docs/source/tutorials_source/export-to-executorch-tutorial.py:489 in forward, code: y = torch.mm(a, x)
            aten_mm_default: "f32[2, 2]" = torch.ops.aten.mm.out(a, x, out = alloc);  alloc = None

            # No stacktrace found for following nodes
            alloc_1: "f32[2, 2]" = executorch_exir_memory_alloc(((2, 2), torch.float32))

            # File: /pytorch/executorch/docs/source/tutorials_source/export-to-executorch-tutorial.py:490 in forward, code: z = y + b
            aten_add_tensor: "f32[2, 2]" = torch.ops.aten.add.out(aten_mm_default, b, out = alloc_1);  aten_mm_default = alloc_1 = None

            # No stacktrace found for following nodes
            alloc_2: "f32[2, 2]" = executorch_exir_memory_alloc(((2, 2), torch.float32))

            # File: /pytorch/executorch/docs/source/tutorials_source/export-to-executorch-tutorial.py:491 in forward, code: a = z - a
            aten_sub_tensor: "f32[2, 2]" = torch.ops.aten.sub.out(aten_add_tensor, a, out = alloc_2);  aten_add_tensor = a = alloc_2 = None

            # No stacktrace found for following nodes
            alloc_3: "f32[2, 2]" = executorch_exir_memory_alloc(((2, 2), torch.float32))

            # File: /pytorch/executorch/docs/source/tutorials_source/export-to-executorch-tutorial.py:492 in forward, code: y = torch.mm(a, x)
            aten_mm_default_1: "f32[2, 2]" = torch.ops.aten.mm.out(aten_sub_tensor, x, out = alloc_3);  aten_sub_tensor = x = alloc_3 = None

            # No stacktrace found for following nodes
            alloc_4: "f32[2, 2]" = executorch_exir_memory_alloc(((2, 2), torch.float32))

            # File: /pytorch/executorch/docs/source/tutorials_source/export-to-executorch-tutorial.py:493 in forward, code: z = y + b
            aten_add_tensor_1: "f32[2, 2]" = torch.ops.aten.add.out(aten_mm_default_1, b, out = alloc_4);  aten_mm_default_1 = b = alloc_4 = None
            return (aten_add_tensor_1,)

Graph signature: ExportGraphSignature(input_specs=[InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='a'), target=None, persistent=None), InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='x'), target=None, persistent=None), InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='b'), target=None, persistent=None)], output_specs=[OutputSpec(kind=<OutputKind.USER_OUTPUT: 1>, arg=TensorArgument(name='aten_add_tensor_1'), target=None)])
Range constraints: {}

请注意，在图形中，我们现在看到的是类似 and 而不是 and 的运算符。torch.ops.aten.sub.outtorch.ops.aten.div.outtorch.ops.aten.sub.Tensortorch.ops.aten.div.Tensor

这是因为在运行后端传递和内存规划传递之间， 为了准备图形以进行内存规划，在 将所有运算符转换为其 Out 变体的图表。而不是 在内核实现中分配返回的张量，运算符的变体将采用预同种异体化的张量到其输出的 kwarg，并且 将结果存储在那里，使内存规划者更容易执行 Tensor 寿命分析。out

我们还将节点插入到包含对特殊运算符的调用的图中。这告诉我们内存量是多少 需要分配 out-variant 运算符输出的每个张量。allocexecutorch.exir.memory.alloc

保存到文件

最后，我们可以将 ExecuTorch 程序保存到文件中，并将其加载到设备上 运行。

以下是整个端到端工作流的示例：

import torch
from torch._export import capture_pre_autograd_graph
from torch.export import export, ExportedProgram


class M(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.param = torch.nn.Parameter(torch.rand(3, 4))
        self.linear = torch.nn.Linear(4, 5)

    def forward(self, x):
        return self.linear(x + self.param).clamp(min=0.0, max=1.0)


example_args = (torch.randn(3, 4),)
pre_autograd_aten_dialect = capture_pre_autograd_graph(M(), example_args)
# Optionally do quantization:
# pre_autograd_aten_dialect = convert_pt2e(prepare_pt2e(pre_autograd_aten_dialect, CustomBackendQuantizer))
aten_dialect: ExportedProgram = export(pre_autograd_aten_dialect, example_args)
edge_program: exir.EdgeProgramManager = exir.to_edge(aten_dialect)
# Optionally do delegation:
# edge_program = edge_program.to_backend(CustomBackendPartitioner)
executorch_program: exir.ExecutorchProgramManager = edge_program.to_executorch(
    ExecutorchBackendConfig(
        passes=[],  # User-defined passes
    )
)

with open("model.pte", "wb") as file:
    file.write(executorch_program.buffer)

结论

在本教程中，我们介绍了降低 PyTorch 所需的 API 和步骤 program 复制到可以在 ExecuTorch 运行时上运行的文件。

提及的链接

• torch.export 文档

• 量化文档

• 红外规格

• 编写编译器传递 + 分区程序文档

• 后端委托文档

• 内存规划文档