捆绑程序 – ExecuTorch 模型验证工具¶

介绍¶

BundledProgram是核心 ExecuTorch 程序的包装器，旨在帮助用户使用他们部署的模型包装测试用例。不一定是程序的核心部分，也不是程序执行所必需的，但对于各种其他用例（例如模型正确性评估，包括模型启动过程中的 E2E 测试）尤为重要。BundledProgram

总体而言，该程序可以分为两个阶段，在每个阶段我们都支持：

Emit 阶段：将测试 I/O 案例与 ExecuTorch 程序捆绑在一起，序列化到 flatbuffer 中。
运行时阶段：在运行时访问、执行和验证捆绑的测试用例。

Emit 阶段¶

此阶段主要侧重于创建 a 并将其作为 flatbuffer 文件转储到磁盘上。主要步骤如下：BundledProgram

创建一个模型并发出其 ExecuTorch 程序。
构造 a 来记录所有需要捆绑的测试用例。List[MethodTestSuite]
使用发出的模型和生成。BundledProgramList[MethodTestSuite]
序列化并将其转储到磁盘。BundledProgram

步骤 1：创建一个模型并发出其 ExecuTorch 程序。¶

ExecuTorch 程序可以通过 ExecuTorch API 从用户的模型发出。按照生成示例 ExecuTorch 程序或导出到 ExecuTorch 教程进行操作。

第 2 步：构造以保存测试信息`List[MethodTestSuite]`¶

在中，我们创建了两个新类和，用于保存 ExecuTorch 程序验证的基本信息。BundledProgramMethodTestCaseMethodTestSuite

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

MethodTestSuite包含单个方法的所有测试信息，包括表示方法名称的 str 和所有测试用例的 STR：List[MethodTestCase]

由于每个模型可能有多种推理方法，因此我们需要生成来保存所有基本信息。List[MethodTestSuite]

第 3 步：生成`BundledProgram`¶

我们提供 class under 来捆绑 -like 变量，包括、或、和：BundledProgramexecutorch/devtools/bundled_program/core.pyExecutorchProgramExecutorchProgramMultiMethodExecutorchProgramExecutorchProgramManagerList[MethodTestSuite]

的 Construtor 将在内部进行 sannity 检查，以查看给定的 Program 是否符合给定的 Program 的要求。具体说来：BundledProgram List[MethodTestSuite]

每个 in for 的 method_names 也应为 in 程序。请注意，无需为 Program 中的每个方法设置测试用例。MethodTestSuiteList[MethodTestSuite]
每个测试用例的元数据应满足相应推理方法输入的要求。

第 4 步：序列化为 Flatbuffer。`BundledProgram`¶

为了序列化以使运行时 API 使用它，我们提供了两个 API，它们都位于 .BundledProgramexecutorch/devtools/bundled_program/serialize/__init__.py

发出示例¶

这是一个流程，重点介绍了如何生成给定的 PyTorch 模型以及我们要测试该模型的代表性输入。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¶

此阶段主要侧重于执行具有捆绑输入的模型，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 来处理它的关键部分。

从缓冲区获取 ExecuTorch 程序指针`BundledProgram`¶

我们需要指向 ExecuTorch 程序的指针来执行。为了统一加载和执行过程以及 Program flatbuffer，我们创建了一个 API：BundledProgram

以下是如何使用 API 的示例：get_program_data

// 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¶

要在捆绑的 input 上执行程序，我们需要将捆绑的 input 加载到方法中。这里我们提供了一个名为：executorch::bundled_program::load_bundled_input

验证方法的输出。¶

我们调用以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：executorch::bundled_program::verify_method_outputs

运行时示例¶

这里我们提供了一个有关如何逐步运行捆绑程序的示例。大部分代码是从 executor_runner 借来的，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¶

如果与 .以下是两种常见情况：List[MethodTestSuites]Program

测试输入与模型的要求不匹配。¶

PyTorch 模型的每种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 dtype 等。如果测试输入不满足要求，将引发错误。BundledProgram

以下是测试输入的 dtype 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

中的方法名称不存在。`BundleConfig`¶

另一个常见错误是 any 中的方法名称在 Model 中不存在。将引发错误并显示不存在的方法名称：MethodTestSuiteBundledProgram

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)

捆绑程序 – ExecuTorch 模型验证工具¶

介绍¶

Emit 阶段¶

步骤 1：创建一个模型并发出其 ExecuTorch 程序。¶

第 2 步：构造以保存测试信息`List[MethodTestSuite]`¶

第 3 步：生成`BundledProgram`¶

第 4 步：序列化为 Flatbuffer。`BundledProgram`¶

发出示例¶

运行时阶段¶

从缓冲区获取 ExecuTorch 程序指针`BundledProgram`¶

将捆绑的输入加载到方法¶

验证方法的输出。¶

运行时示例¶

常见错误¶

测试输入与模型的要求不匹配。¶

中的方法名称不存在。`BundleConfig`¶

文档

教程

资源

捆绑程序 – ExecuTorch 模型验证工具¶

介绍¶

Emit 阶段¶

步骤 1：创建一个模型并发出其 ExecuTorch 程序。¶

第 2 步：构造以保存测试信息List[MethodTestSuite]¶

第 3 步：生成BundledProgram¶

第 4 步：序列化为 Flatbuffer。BundledProgram¶

发出示例¶

运行时阶段¶

从缓冲区获取 ExecuTorch 程序指针BundledProgram¶

将捆绑的输入加载到方法¶

验证方法的输出。¶

运行时示例¶

常见错误¶

测试输入与模型的要求不匹配。¶

中的方法名称 不存在。BundleConfig¶

文档

教程

资源

第 2 步：构造以保存测试信息`List[MethodTestSuite]`¶

第 3 步：生成`BundledProgram`¶

第 4 步：序列化为 Flatbuffer。`BundledProgram`¶

从缓冲区获取 ExecuTorch 程序指针`BundledProgram`¶

中的方法名称不存在。`BundleConfig`¶