mirror of
https://github.com/pytorch/pytorch.git
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548c460bf1
See https://github.com/pytorch/pytorch/pull/129751#issue-2380881501. Most changes are auto-generated by linter. You can review these PRs via: ```bash git diff --ignore-all-space --ignore-blank-lines HEAD~1 ``` Pull Request resolved: https://github.com/pytorch/pytorch/pull/129758 Approved by: https://github.com/ezyang
394 lines
15 KiB
Python
394 lines
15 KiB
Python
import os
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import shutil
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import unittest
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import warnings
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from collections import namedtuple
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import torch
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import torch.testing._internal.common_nn as common_nn
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import torch.utils.cpp_extension
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from torch.testing._internal.common_cuda import TEST_CUDA
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# Note that this namedtuple is for C++ parity test mechanism's internal use.
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# For guidance on how to add a new C++ parity test, please see
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# NOTE [How to check NN module / functional API parity between Python and C++ frontends]
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TorchNNModuleTestParams = namedtuple(
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"TorchNNModuleTestParams",
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[
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# NN module name (e.g. "BCELoss")
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"module_name",
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# Unique identifier for this module config (e.g. "BCELoss_weights_cuda")
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"module_variant_name",
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# An instance of an NN test class (e.g. `CriterionTest`) which stores
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# necessary information (e.g. input / target / extra_args) for running the Python test
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"test_instance",
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# Constructor arguments passed to the C++ module constructor, which must be
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# strictly equivalent to the Python module constructor arguments
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# (e.g. `torch::nn::BCELossOptions().weight(torch::rand(10))`,
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# which is strictly equivalent to passing `torch.rand(10)` to `torch.nn.BCELoss`
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# constructor in Python)
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"cpp_constructor_args",
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# All arguments used in NN module's forward pass.
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# Please see `compute_arg_dict` function for details on how we construct this dict.
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# (e.g.
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# ```
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# arg_dict = {
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# 'input': [python_input_tensor],
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# 'target': [python_target_tensor],
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# 'extra_args': [],
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# 'other': [],
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# }
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# ```
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# )
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"arg_dict",
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# Whether we expect this NN module test to pass the Python/C++ parity test
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# (e.g. `True`)
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"has_parity",
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# Device (e.g. "cuda")
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"device",
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# Temporary folder to store C++ outputs (to be compared with Python outputs later)
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"cpp_tmp_folder",
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],
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)
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# Note that this namedtuple is for C++ parity test mechanism's internal use.
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# For guidance on how to add a new C++ parity test, please see
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# NOTE [How to check NN module / functional API parity between Python and C++ frontends]
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TorchNNFunctionalTestParams = namedtuple(
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"TorchNNFunctionalTestParams",
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[
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# NN functional name (e.g. "binary_cross_entropy")
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"functional_name",
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# Unique identifier for this functional config (e.g. "BCELoss_no_reduce_cuda")
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"functional_variant_name",
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# An instance of an NN test class (e.g. `NewModuleTest`) which stores
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# necessary information (e.g. input / target / extra_args) for running the Python test
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"test_instance",
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# The C++ function call that is strictly equivalent to the Python function call
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# (e.g. "F::binary_cross_entropy(
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# i, t.to(i.options()),F::BinaryCrossEntropyFuncOptions().reduction(torch::kNone))",
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# which is strictly equivalent to `F.binary_cross_entropy(i, t.type_as(i), reduction='none')` in Python)
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"cpp_function_call",
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# All arguments used in NN functional's function call.
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# Please see `compute_arg_dict` function for details on how we construct this dict.
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# (e.g.
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# ```
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# arg_dict = {
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# 'input': [python_input_tensor],
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# 'target': [python_target_tensor],
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# 'extra_args': [],
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# 'other': [],
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# }
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# ```
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# )
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"arg_dict",
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# Whether we expect this NN functional test to pass the Python/C++ parity test
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# (e.g. `True`)
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"has_parity",
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# Device (e.g. "cuda")
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"device",
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# Temporary folder to store C++ outputs (to be compared with Python outputs later)
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"cpp_tmp_folder",
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],
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)
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CppArg = namedtuple("CppArg", ["name", "value"])
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TORCH_NN_COMMON_TEST_HARNESS = """
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#include <torch/script.h>
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void write_ivalue_to_file(const torch::IValue& ivalue, const std::string& file_path) {
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auto bytes = torch::jit::pickle_save(ivalue);
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std::ofstream fout(file_path, std::ios::out | std::ios::binary);
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fout.write(bytes.data(), bytes.size());
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fout.close();
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}
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c10::Dict<std::string, torch::Tensor> load_dict_from_file(const std::string& file_path) {
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c10::Dict<std::string, torch::Tensor> arg_dict;
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auto arg_dict_module = torch::jit::load(file_path);
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for (const auto& p : arg_dict_module.named_buffers(/*recurse=*/false)) {
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arg_dict.insert(p.name, p.value);
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}
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return arg_dict;
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}
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// Generates rand tensor with non-equal values. This ensures that duplicate
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// values won't be causing test failure for modules like MaxPooling.
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// size should be small, otherwise randperm fails / long overflows.
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torch::Tensor _rand_tensor_non_equal(torch::IntArrayRef size) {
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int64_t total = 1;
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for (int64_t elem : size) {
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total *= elem;
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}
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return torch::randperm(total).view(size).to(torch::kDouble);
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}
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"""
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def compile_cpp_code_inline(name, cpp_sources, functions):
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cpp_module = torch.utils.cpp_extension.load_inline(
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name=name,
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cpp_sources=cpp_sources,
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extra_cflags=[
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"-g"
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], # Enable debug symbols by default for debugging test failures.
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functions=functions,
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verbose=False,
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)
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return cpp_module
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def compute_temp_file_path(cpp_tmp_folder, variant_name, file_suffix):
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return os.path.join(cpp_tmp_folder, f"{variant_name}_{file_suffix}.pt")
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def is_torch_nn_functional_test(test_params_dict):
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return "wrap_functional" in str(test_params_dict.get("constructor", ""))
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def convert_to_list(python_input):
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if isinstance(python_input, torch.Tensor):
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return [python_input]
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else:
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return list(python_input)
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def set_python_tensors_requires_grad(python_tensors):
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return [
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tensor.requires_grad_(True) if tensor.dtype != torch.long else tensor
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for tensor in python_tensors
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]
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def move_python_tensors_to_device(python_tensors, device):
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return [tensor.to(device) for tensor in python_tensors]
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def has_test(unit_test_class, test_name):
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return hasattr(unit_test_class, test_name)
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def add_test(unit_test_class, test_name, test_fn):
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if has_test(unit_test_class, test_name):
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raise RuntimeError("Found two tests with the same name: " + test_name)
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setattr(unit_test_class, test_name, test_fn)
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def set_cpp_tensors_requires_grad(cpp_tensor_stmts, python_tensors):
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assert len(cpp_tensor_stmts) == len(python_tensors)
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return [
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f"{tensor_stmt}.requires_grad_(true)"
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if tensor.dtype != torch.long
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else tensor_stmt
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for tensor_stmt, (_, tensor) in zip(cpp_tensor_stmts, python_tensors)
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]
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def move_cpp_tensors_to_device(cpp_tensor_stmts, device):
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return [f'{tensor_stmt}.to("{device}")' for tensor_stmt in cpp_tensor_stmts]
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def is_criterion_test(test_instance):
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return isinstance(test_instance, common_nn.CriterionTest)
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# This function computes the following:
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# - What variable declaration statements should show up in the C++ parity test function
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# - What arguments should be passed into the C++ module/functional's forward function
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#
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# For example, for the "L1Loss" test, the return values from this function are:
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# ```
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# // Note that `arg_dict` stores all tensor values we transfer from Python to C++
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# cpp_args_construction_stmts = [
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# "auto i0 = arg_dict.at("i0").to("cpu").requires_grad_(true)",
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# "auto t0 = arg_dict.at("t0").to("cpu")",
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# ],
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# cpp_forward_args_symbols = [
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# "i0",
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# "t0",
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# ]
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# ```
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def compute_cpp_args_construction_stmts_and_forward_arg_symbols(test_params):
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device = test_params.device
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cpp_forward_args_symbols = []
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def add_cpp_forward_args(args):
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args_stmts = []
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for arg_name, _ in args:
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args_stmts.append(f'auto {arg_name} = arg_dict.at("{arg_name}")')
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cpp_forward_args_symbols.append(arg_name)
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return args_stmts
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cpp_forward_input_args_stmts = set_cpp_tensors_requires_grad(
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move_cpp_tensors_to_device(
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add_cpp_forward_args(test_params.arg_dict["input"]), device
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),
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test_params.arg_dict["input"],
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)
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cpp_forward_target_args_stmts = move_cpp_tensors_to_device(
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add_cpp_forward_args(test_params.arg_dict["target"]), device
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)
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cpp_forward_extra_args_stmts = move_cpp_tensors_to_device(
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add_cpp_forward_args(test_params.arg_dict["extra_args"]), device
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)
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# Build the list of other arguments needed
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cpp_other_args_stmts = []
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for arg_name, _ in test_params.arg_dict["other"]:
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cpp_other_args_stmts.append(f'auto {arg_name} = arg_dict.at("{arg_name}")')
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cpp_other_args_stmts = move_cpp_tensors_to_device(cpp_other_args_stmts, device)
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cpp_args_construction_stmts = (
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cpp_forward_input_args_stmts
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+ cpp_forward_target_args_stmts
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+ cpp_forward_extra_args_stmts
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+ cpp_other_args_stmts
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)
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return cpp_args_construction_stmts, cpp_forward_args_symbols
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def serialize_arg_dict_as_script_module(arg_dict):
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arg_dict_flat = dict(
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arg_dict["input"]
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+ arg_dict["target"]
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+ arg_dict["extra_args"]
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+ arg_dict["other"]
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)
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arg_dict_module = torch.nn.Module()
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for arg_name, arg_value in arg_dict_flat.items():
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assert isinstance(arg_value, torch.Tensor)
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arg_dict_module.register_buffer(arg_name, arg_value)
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return torch.jit.script(arg_dict_module)
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# NOTE: any argument symbol used in `cpp_constructor_args` / `cpp_options_args` / `cpp_function_call`
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# must have a mapping in `cpp_var_map`.
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#
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# The mapping can take one of the following formats:
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#
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# 1. `argument_name` -> Python value
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# 2. `argument_name` -> '_get_input()' (which means `argument_name` in C++ will be bound to `test_instance._get_input()`)
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#
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# For example:
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# ```
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# def bceloss_weights_no_reduce_test():
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# t = torch.randn(15, 10).gt(0).double()
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# weights = torch.rand(10)
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# return dict(
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# fullname='BCELoss_weights_no_reduce',
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# constructor=wrap_functional(
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# lambda i: F.binary_cross_entropy(i, t.type_as(i),
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# weight=weights.type_as(i), reduction='none')),
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# cpp_function_call='''F::binary_cross_entropy(
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# i, t.to(i.options()),
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# F::BinaryCrossEntropyFuncOptions()
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# .weight(weights.to(i.options()))
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# .reduction(torch::kNone))''',
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# input_fn=lambda: torch.rand(15, 10).clamp_(2.8e-2, 1 - 2.8e-2),
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# cpp_var_map={'i': '_get_input()', 't': t, 'weights': weights},
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# reference_fn=lambda i, p, m: -(t * i.log() + (1 - t) * (1 - i).log()) * weights,
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# )
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# ```
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def compute_arg_dict(test_params_dict, test_instance):
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arg_dict = {
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"input": [],
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"target": [],
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"extra_args": [],
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"other": [],
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}
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def put_args_into_arg_dict(arg_type, arg_type_prefix, args):
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for i, arg in enumerate(args):
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arg_dict[arg_type].append(CppArg(name=arg_type_prefix + str(i), value=arg))
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put_args_into_arg_dict("input", "i", convert_to_list(test_instance._get_input()))
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if is_criterion_test(test_instance):
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put_args_into_arg_dict(
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"target", "t", convert_to_list(test_instance._get_target())
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)
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if test_instance.extra_args:
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put_args_into_arg_dict(
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"extra_args", "e", convert_to_list(test_instance.extra_args)
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)
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cpp_var_map = test_params_dict.get("cpp_var_map", {})
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for arg_name, arg_value in cpp_var_map.items():
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if isinstance(arg_value, str):
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if arg_value == "_get_input()":
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arg_dict["other"].append(
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CppArg(name=arg_name, value=test_instance._get_input())
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)
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else:
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raise RuntimeError(
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f"`{arg_name}` has unsupported string value: {arg_value}"
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)
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elif isinstance(arg_value, torch.Tensor):
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arg_dict["other"].append(CppArg(name=arg_name, value=arg_value))
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else:
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raise RuntimeError(f"`{arg_name}` has unsupported value: {arg_value}")
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return arg_dict
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def decorate_test_fn(test_fn, test_cuda, has_impl_parity, device):
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if device == "cuda":
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test_fn = unittest.skipIf(not TEST_CUDA, "CUDA unavailable")(test_fn)
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test_fn = unittest.skipIf(not test_cuda, "Excluded from CUDA tests")(test_fn)
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# If `Implementation Parity` entry in parity table for this module is `No`,
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# or `has_parity` entry in test params dict is `False`, we mark the test as
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# expected failure.
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if not has_impl_parity:
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test_fn = unittest.expectedFailure(test_fn)
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return test_fn
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MESSAGE_HOW_TO_FIX_CPP_PARITY_TEST_FAILURE = """
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What should I do when C++ API parity test is failing?
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- If you are changing the implementation of an existing `torch.nn` module / `torch.nn.functional` function:
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Answer: Ideally you should also change the C++ API implementation for that module / function
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(you can start by searching for the module / function name in `torch/csrc/api/` folder).
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- If you are adding a new test for an existing `torch.nn` module / `torch.nn.functional` function:
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Answer: Ideally you should fix the C++ API implementation for that module / function
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to exactly match the Python API implementation (you can start by searching for the module /
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function name in `torch/csrc/api/` folder).
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- If you are adding a test for a *new* `torch.nn` module / `torch.nn.functional` function:
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Answer: Ideally you should add the corresponding C++ API implementation for that module / function,
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and it should exactly match the Python API implementation. (We have done a large effort on this
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which is tracked at https://github.com/pytorch/pytorch/issues/25883.)
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However, if any of the above is proven to be too complicated, you can just add
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`test_cpp_api_parity=False` to any failing test in `torch/testing/_internal/common_nn.py`,
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and the C++ API parity test will be skipped accordingly. Note that you should
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also file an issue when you do this.
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For more details on how to add a C++ API parity test, please see:
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NOTE [How to check NN module / functional API parity between Python and C++ frontends]
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"""
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def generate_error_msg(name, cpp_value, python_value):
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return (
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f"Parity test failed: {name} in C++ has value: {cpp_value}, "
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f"which does not match the corresponding value in Python: {python_value}.\n{MESSAGE_HOW_TO_FIX_CPP_PARITY_TEST_FAILURE}"
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)
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def try_remove_folder(folder_path):
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if os.path.exists(folder_path):
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# Don't block the process if this fails, but show the error message as warning.
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try:
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shutil.rmtree(folder_path)
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except Exception as e:
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warnings.warn(
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f"Non-blocking folder removal fails with the following error:\n{str(e)}"
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)
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