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捆绑程序 – ExecuTorch 模型验证工具

简介

BundledProgram 是围绕核心 ExecuTorch 程序的包装器,旨在帮助用户使用其部署的模型包装测试用例。BundledProgram 不一定是程序的核心部分,也不需要用于执行,但在各种其他用例中尤其重要,例如模型正确性评估,包括模型启动过程中的端到端测试。

总的来说,该过程可以分为两个阶段,并且在每个阶段我们都支持

  • **发射阶段**: 将测试 I/O 用例与 ExecuTorch 程序捆绑在一起,序列化为 FlatBuffer。

  • **运行时阶段**: 在运行时访问、执行和验证捆绑的测试用例。

发射阶段

此阶段主要侧重于创建 BundledProgram 并将其作为 FlatBuffer 文件转储到磁盘。主要过程如下

  1. 创建模型并发出其 ExecuTorch 程序。

  2. 构造 List[MethodTestSuite] 以记录所有需要捆绑的测试用例。

  3. 使用发出的模型和 List[MethodTestSuite] 生成 BundledProgram

  4. 序列化 BundledProgram 并将其转储到磁盘。

步骤 1:创建模型并发出其 ExecuTorch 程序。

可以使用 ExecuTorch API 从用户的模型发出 ExecuTorch 程序。请遵循 生成示例 ExecuTorch 程序导出到 ExecuTorch 教程

步骤 2:构造 List[MethodTestSuite] 以保存测试信息

BundledProgram 中,我们创建了两个新类 MethodTestCaseMethodTestSuite,以保存 ExecuTorch 程序验证的基本信息。

MethodTestCase 表示单个测试用例。每个 MethodTestCase 包含单个执行的输入和预期输出。

MethodTestCase
executorch.devtools.bundled_program.config.MethodTestCase.__init__(self, inputs, expected_outputs=None)

验证特定方法的单个测试用例

参数
  • inputs

    eager_model 使用特定推理方法一次执行所需的所有输入。

    值得一提的是,尽管捆绑程序和 ET 运行时 API 都支持设置除 torch.tensor 类型之外的输入,但只有 torch.tensor 类型的输入才会在方法中实际更新,其余输入只会进行健全性检查,以查看它们是否与方法中的默认值匹配。

  • expected_outputs – 给定输入的预期输出,用于验证。如果用户只想将测试用例用于分析,则可以为 None。

返回值

self

MethodTestSuite 包含单个方法的所有测试信息,包括表示方法名称的字符串和 List[MethodTestCase] 中的所有测试用例

MethodTestSuite
executorch.devtools.bundled_program.config.MethodTestSuite(method_name, test_cases)[source]

与验证方法相关的所有测试信息

executorch.devtools.bundled_program.config.method_name

要验证的方法的名称。

executorch.devtools.bundled_program.config.test_cases

验证该方法的所有测试用例。

由于每个模型可能有多个推理方法,因此我们需要生成 List[MethodTestSuite] 来保存所有必要的信息。

步骤 3:生成 BundledProgram

我们在 executorch/devtools/bundled_program/core.py 下提供了 BundledProgram 类,用于将 ExecutorchProgram 类变量(包括 ExecutorchProgramMultiMethodExecutorchProgramExecutorchProgramManager)与 List[MethodTestSuite] 捆绑在一起。

BundledProgram
executorch.devtools.bundled_program.core.BundledProgram.__init__(self, executorch_program, method_test_suites)

通过将给定的程序和方法测试套件捆绑在一起创建 BundledProgram。

参数
  • executorch_program – 要捆绑的程序。

  • method_test_suites – 要捆绑的某些方法的测试用例。

BundledProgram 的构造函数会在内部进行完整性检查,以查看给定的List[MethodTestSuite]是否与给定程序的要求匹配。具体来说

  1. List[MethodTestSuite]中每个MethodTestSuite的方法名称也应该在程序中。请注意,不需要为程序中的每个方法设置测试用例。

  2. 每个测试用例的元数据应满足相应推理方法输入的要求。

步骤 4:将BundledProgram序列化为 Flatbuffer。

为了序列化BundledProgram以便运行时 API 使用它,我们提供了两个 API,都位于executorch/devtools/bundled_program/serialize/__init__.py下。

序列化和反序列化
executorch.devtools.bundled_program.serialize.serialize_from_bundled_program_to_flatbuffer(bundled_program)[source]

将 BundledProgram 序列化为 FlatBuffer 二进制格式。

参数

bundled_program (BundledProgram) – 要序列化的BundledProgram变量。

返回值

以字节为单位的序列化 FlatBuffer 二进制数据。

executorch.devtools.bundled_program.serialize.deserialize_from_flatbuffer_to_bundled_program(flatbuffer)[source]

将 FlatBuffer 二进制格式反序列化为 BundledProgram。

参数

flatbuffer (bytes) – 以字节为单位的 FlatBuffer 二进制数据。

返回值

一个BundledProgram实例。

发出示例

这是一个流程,突出显示了如何在给定 PyTorch 模型和我们想要测试的代表性输入的情况下生成BundledProgram

import torch

from executorch.exir import to_edge
from executorch.devtools import BundledProgram

from executorch.devtools.bundled_program.config import MethodTestCase, MethodTestSuite
from executorch.devtools.bundled_program.serialize import (
    serialize_from_bundled_program_to_flatbuffer,
)
from torch.export import export, export_for_training


# Step 1: ExecuTorch Program Export
class SampleModel(torch.nn.Module):
    """An example model with multi-methods. Each method has multiple input and single output"""

    def __init__(self) -> None:
        super().__init__()
        self.a: torch.Tensor = 3 * torch.ones(2, 2, dtype=torch.int32)
        self.b: torch.Tensor = 2 * torch.ones(2, 2, dtype=torch.int32)

    def forward(self, x: torch.Tensor, q: torch.Tensor) -> torch.Tensor:
        z = x.clone()
        torch.mul(self.a, x, out=z)
        y = x.clone()
        torch.add(z, self.b, out=y)
        torch.add(y, q, out=y)
        return y


# Inference method name of SampleModel we want to bundle testcases to.
# Notices that we do not need to bundle testcases for every inference methods.
method_name = "forward"
model = SampleModel()

# Inputs for graph capture.
capture_input = (
    (torch.rand(2, 2) - 0.5).to(dtype=torch.int32),
    (torch.rand(2, 2) - 0.5).to(dtype=torch.int32),
)

# Export method's FX Graph.
method_graph = export(
    export_for_training(model, capture_input).module(),
    capture_input,
)


# Emit the traced method into ET Program.
et_program = to_edge(method_graph).to_executorch()

# Step 2: Construct MethodTestSuite for Each Method

# Prepare the Test Inputs.

# Number of input sets to be verified
n_input = 10

# Input sets to be verified.
inputs = [
    # Each list below is a individual input set.
    # The number of inputs, dtype and size of each input follow Program's spec.
    [
        (torch.rand(2, 2) - 0.5).to(dtype=torch.int32),
        (torch.rand(2, 2) - 0.5).to(dtype=torch.int32),
    ]
    for _ in range(n_input)
]

# Generate Test Suites
method_test_suites = [
    MethodTestSuite(
        method_name=method_name,
        test_cases=[
            MethodTestCase(
                inputs=input,
                expected_outputs=(getattr(model, method_name)(*input), ),
            )
            for input in inputs
        ],
    ),
]

# Step 3: Generate BundledProgram
bundled_program = BundledProgram(et_program, method_test_suites)

# Step 4: Serialize BundledProgram to flatbuffer.
serialized_bundled_program = serialize_from_bundled_program_to_flatbuffer(
    bundled_program
)
save_path = "bundled_program.bpte"
with open(save_path, "wb") as f:
    f.write(serialized_bundled_program)

如果需要,我们还可以从 flatbuffer 文件重新生成BundledProgram

from executorch.devtools.bundled_program.serialize import deserialize_from_flatbuffer_to_bundled_program
save_path = "bundled_program.bpte"
with open(save_path, "rb") as f:
    serialized_bundled_program = f.read()

regenerate_bundled_program = deserialize_from_flatbuffer_to_bundled_program(serialized_bundled_program)

运行时阶段

此阶段主要侧重于使用捆绑的输入执行模型,并将模型的输出与捆绑的预期输出进行比较。我们提供了多个 API 来处理其中的关键部分。

BundledProgram缓冲区获取 ExecuTorch 程序指针

我们需要 ExecuTorch 程序的指针来执行。为了统一加载和执行BundledProgram和程序 flatbuffer 的过程,我们创建了一个 API

get_program_data

警告

doxygenfunction:在来自目录:../build/xml/的项目“ExecuTorch”的 doxygen xml 输出中找不到函数“::executorch::bundled_program::get_program_data”。

以下是如何使用get_program_data API 的示例

// Assume that the user has read the contents of the file into file_data using
// whatever method works best for their application. The file could contain
// either BundledProgram data or Program data.
void* file_data = ...;
size_t file_data_len = ...;

// If file_data contains a BundledProgram, get_program_data() will return a
// pointer to the Program data embedded inside it. Otherwise it will return
// file_data, which already pointed to Program data.
const void* program_ptr;
size_t program_len;
status = executorch::bundled_program::get_program_data(
    file_data, file_data_len, &program_ptr, &program_len);
ET_CHECK_MSG(
    status == Error::Ok,
    "get_program_data() failed with status 0x%" PRIx32,
    status);

将捆绑的输入加载到方法中

要对捆绑的输入执行程序,我们需要将捆绑的输入加载到方法中。这里我们提供了一个名为executorch::bundled_program::load_bundled_input的 API

load_bundled_input

警告

doxygenfunction:在来自目录:../build/xml/的项目“ExecuTorch”的 doxygen xml 输出中找不到函数“::executorch::bundled_program::load_bundled_input”。

验证方法的输出。

我们调用executorch::bundled_program::verify_method_outputs来验证方法的输出与捆绑的预期输出。以下是此 API 的详细信息

verify_method_outputs

警告

doxygenfunction:在来自目录:../build/xml/的项目“ExecuTorch”的 doxygen xml 输出中找不到函数“::executorch::bundled_program::verify_method_outputs”。

运行时示例

在这里,我们提供了一个关于如何逐步运行捆绑程序的示例。大部分代码借鉴自executor_runner,如果您需要更多信息和上下文,请查看该文件

// method_name is the name for the method we want to test
// memory_manager is the executor::MemoryManager variable for executor memory allocation.
// program is the ExecuTorch program.
Result<Method> method = program->load_method(method_name, &memory_manager);

ET_CHECK_MSG(
    method.ok(),
    "load_method() failed with status 0x%" PRIx32,
    method.error());

// Load testset_idx-th input in the buffer to plan
status = executorch::bundled_program::load_bundled_input(
        *method,
        program_data.bundled_program_data(),
        FLAGS_testset_idx);
ET_CHECK_MSG(
    status == Error::Ok,
    "load_bundled_input failed with status 0x%" PRIx32,
    status);

// Execute the plan
status = method->execute();
ET_CHECK_MSG(
    status == Error::Ok,
    "method->execute() failed with status 0x%" PRIx32,
    status);

// Verify the result.
status = executorch::bundled_program::verify_method_outputs(
        *method,
        program_data.bundled_program_data(),
        FLAGS_testset_idx,
        FLAGS_rtol,
        FLAGS_atol);
ET_CHECK_MSG(
    status == Error::Ok,
    "Bundle verification failed with status 0x%" PRIx32,
    status);

常见错误

如果List[MethodTestSuites]Program不匹配,则会引发错误。以下是两种常见情况

测试输入与模型的要求不匹配。

PyTorch 模型的每个推理方法对输入都有自己的要求,例如输入的数量、每个输入的数据类型等。BundledProgram如果测试输入不满足要求,则会引发错误。

以下是如何测试输入的数据类型不满足模型要求的示例

import torch

from executorch.exir import to_edge
from executorch.devtools import BundledProgram

from executorch.devtools.bundled_program.config import MethodTestCase, MethodTestSuite
from torch.export import export, export_for_training


class Module(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.a = 3 * torch.ones(2, 2, dtype=torch.float)
        self.b = 2 * torch.ones(2, 2, dtype=torch.float)

    def forward(self, x):
        out_1 = torch.ones(2, 2, dtype=torch.float)
        out_2 = torch.ones(2, 2, dtype=torch.float)
        torch.mul(self.a, x, out=out_1)
        torch.add(out_1, self.b, out=out_2)
        return out_2


model = Module()
method_names = ["forward"]

inputs = (torch.ones(2, 2, dtype=torch.float), )

# Find each method of model needs to be traced my its name, export its FX Graph.
method_graph = export(
    export_for_training(model, inputs).module(),
    inputs,
)

# Emit the traced methods into ET Program.
et_program = to_edge(method_graph).to_executorch()

# number of input sets to be verified
n_input = 10

# Input sets to be verified for each inference methods.
# To simplify, here we create same inputs for all methods.
inputs = {
    # Inference method name corresponding to its test cases.
    m_name: [
        # NOTE: executorch program needs torch.float, but here is torch.int
        [
            torch.randint(-5, 5, (2, 2), dtype=torch.int),
        ]
        for _ in range(n_input)
    ]
    for m_name in method_names
}

# Generate Test Suites
method_test_suites = [
    MethodTestSuite(
        method_name=m_name,
        test_cases=[
            MethodTestCase(
                inputs=input,
                expected_outputs=(getattr(model, m_name)(*input),),
            )
            for input in inputs[m_name]
        ],
    )
    for m_name in method_names
]

# Generate BundledProgram

bundled_program = BundledProgram(et_program, method_test_suites)
引发的错误
The input tensor tensor([[-2,  0],
        [-2, -1]], dtype=torch.int32) dtype shall be torch.float32, but now is torch.int32
---------------------------------------------------------------------------
AssertionError                            Traceback (most recent call last)
Cell In[1], line 72
     56 method_test_suites = [
     57     MethodTestSuite(
     58         method_name=m_name,
   (...)
     67     for m_name in method_names
     68 ]
     70 # Step 3: Generate BundledProgram
---> 72 bundled_program = create_bundled_program(program, method_test_suites)
File /executorch/devtools/bundled_program/core.py:276, in create_bundled_program(program, method_test_suites)
    264 """Create bp_schema.BundledProgram by bundling the given program and method_test_suites together.
    265
    266 Args:
   (...)
    271     The `BundledProgram` variable contains given ExecuTorch program and test cases.
    272 """
    274 method_test_suites = sorted(method_test_suites, key=lambda x: x.method_name)
--> 276 assert_valid_bundle(program, method_test_suites)
    278 bundled_method_test_suites: List[bp_schema.BundledMethodTestSuite] = []
    280 # Emit data and metadata of bundled tensor
File /executorch/devtools/bundled_program/core.py:219, in assert_valid_bundle(program, method_test_suites)
    215 # type of tensor input should match execution plan
    216 if type(cur_plan_test_inputs[j]) == torch.Tensor:
    217     # pyre-fixme[16]: Undefined attribute [16]: Item `bool` of `typing.Union[bool, float, int, torch._tensor.Tensor]`
    218     # has no attribute `dtype`.
--> 219     assert cur_plan_test_inputs[j].dtype == get_input_dtype(
    220         program, program_plan_id, j
    221     ), "The input tensor {} dtype shall be {}, but now is {}".format(
    222         cur_plan_test_inputs[j],
    223         get_input_dtype(program, program_plan_id, j),
    224         cur_plan_test_inputs[j].dtype,
    225     )
    226 elif type(cur_plan_test_inputs[j]) in (
    227     int,
    228     bool,
    229     float,
    230 ):
    231     assert type(cur_plan_test_inputs[j]) == get_input_type(
    232         program, program_plan_id, j
    233     ), "The input primitive dtype shall be {}, but now is {}".format(
    234         get_input_type(program, program_plan_id, j),
    235         type(cur_plan_test_inputs[j]),
    236     )
AssertionError: The input tensor tensor([[-2,  0],
        [-2, -1]], dtype=torch.int32) dtype shall be torch.float32, but now is torch.int32

BundleConfig中的方法名不存在。

另一个常见的错误是任何MethodTestSuite中的方法名在模型中不存在。BundledProgram会引发错误并显示不存在的方法名

import torch

from executorch.exir import to_edge
from executorch.devtools import BundledProgram

from executorch.devtools.bundled_program.config import MethodTestCase, MethodTestSuite
from torch.export import export, export_for_training


class Module(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.a = 3 * torch.ones(2, 2, dtype=torch.float)
        self.b = 2 * torch.ones(2, 2, dtype=torch.float)

    def forward(self, x):
        out_1 = torch.ones(2, 2, dtype=torch.float)
        out_2 = torch.ones(2, 2, dtype=torch.float)
        torch.mul(self.a, x, out=out_1)
        torch.add(out_1, self.b, out=out_2)
        return out_2


model = Module()
method_names = ["forward"]

inputs = (torch.ones(2, 2, dtype=torch.float),)

# Find each method of model needs to be traced my its name, export its FX Graph.
method_graph = export(
    export_for_training(model, inputs).module(),
    inputs,
)

# Emit the traced methods into ET Program.
et_program = to_edge(method_graph).to_executorch()

# number of input sets to be verified
n_input = 10

# Input sets to be verified for each inference methods.
# To simplify, here we create same inputs for all methods.
inputs = {
    # Inference method name corresponding to its test cases.
    m_name: [
        [
            torch.randint(-5, 5, (2, 2), dtype=torch.float),
        ]
        for _ in range(n_input)
    ]
    for m_name in method_names
}

# Generate Test Suites
method_test_suites = [
    MethodTestSuite(
        method_name=m_name,
        test_cases=[
            MethodTestCase(
                inputs=input,
                expected_outputs=(getattr(model, m_name)(*input),),
            )
            for input in inputs[m_name]
        ],
    )
    for m_name in method_names
]

# NOTE: MISSING_METHOD_NAME is not an inference method in the above model.
method_test_suites[0].method_name = "MISSING_METHOD_NAME"

# Generate BundledProgram
bundled_program = BundledProgram(et_program, method_test_suites)

引发的错误
All method names in bundled config should be found in program.execution_plan,          but {'MISSING_METHOD_NAME'} does not include.
---------------------------------------------------------------------------
AssertionError                            Traceback (most recent call last)
Cell In[3], line 73
     70 method_test_suites[0].method_name = "MISSING_METHOD_NAME"
     72 # Generate BundledProgram
---> 73 bundled_program = create_bundled_program(program, method_test_suites)
File /executorch/devtools/bundled_program/core.py:276, in create_bundled_program(program, method_test_suites)
    264 """Create bp_schema.BundledProgram by bundling the given program and method_test_suites together.
    265
    266 Args:
   (...)
    271     The `BundledProgram` variable contains given ExecuTorch program and test cases.
    272 """
    274 method_test_suites = sorted(method_test_suites, key=lambda x: x.method_name)
--> 276 assert_valid_bundle(program, method_test_suites)
    278 bundled_method_test_suites: List[bp_schema.BundledMethodTestSuite] = []
    280 # Emit data and metadata of bundled tensor
File /executorch/devtools/bundled_program/core.py:141, in assert_valid_bundle(program, method_test_suites)
    138 method_name_of_program = {e.name for e in program.execution_plan}
    139 method_name_of_test_suites = {t.method_name for t in method_test_suites}
--> 141 assert method_name_of_test_suites.issubset(
    142     method_name_of_program
    143 ), f"All method names in bundled config should be found in program.execution_plan, \
    144      but {str(method_name_of_test_suites - method_name_of_program)} does not include."
    146 # check if method_tesdt_suites has been sorted in ascending alphabetical order of method name.
    147 for test_suite_id in range(1, len(method_test_suites)):
AssertionError: All method names in bundled config should be found in program.execution_plan,          but {'MISSING_METHOD_NAME'} does not include.

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