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adb73d3dcf
pytorch/test/cpp/api
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Laurence Rouesnel adb73d3dcf Removed overhead from reshape() call if tensor doesn't need to be changed (#61466) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/61466

## Goal

Per #55126 the performance of `reshape` is worse than `alias` in cases where they are performing the same operation (i.e. where reshape is returning a view) because `reshape` delegates to `view` and duplicates some of the operations (specifically `infer_size_dv` and `computeStride`).

The goal of this pull-request is to reduce or remove the additional overhead that `reshape` has.

### Proposed Implementation

Instead of using `view` we implement a private/internal operator (`_reshape_alias`) that `reshape` dispatches to which skips the relevant checks. This is functionally equivalent to `as_strided` however it is a lot simpler because it's specialized to this use-case, and importantly the `backward` implementation is a lot faster.

Note that we have to dispatch (`reshape` is a composite operator) because `reshape` can return either a view or a copy of the Tensor depending on the parameters, and this complicates implementing a derivative/backward for `reshape`.

### Why not `as_strided`?

Using `as_strided` directly slows down autograd. If we use a custom function equivalent to `_reshape_alias` but with a simpler backward function then `view` has the same performance as `reshape`. If we delegate to `as_strided` it is about 56% slower (and this holds against our custom function).

This is also the reason we make an internal operator named `_reshape_alias` instead of exposing a new operator since this should only be used in the `reshape` case and it is effectively a more limited version of `view`, `alias`, and `as_strided`.

## Benchmarks
In a micro-benchmark for `backward` running:

```cpp
// Setup
at::Tensor x=torch::empty({2,2}, torch::requires_grad(true));

// Benchmark loop
// `reshape(-1)` replaced with a call to view(-1) for view baseline
x.pow(4).reshape(-1).mean().backward();
```

I also benchmarked simple operations without gradients using:

```cpp
// Setup
at::Tensor x=torch::empty({2,2}, torch::requires_grad(true));

// Benchmark loop
x.reshape(-1) // replaced with a call to view(-1) for view baseline
```

Baselined to `view`:

* Original `reshape`: `+3.3%` (without gradients `+20.8%`)
* Using `as_strided`: `+55.1%` (without gradients `+1.0%`)
* Using custom `_reshape_view`: `-1.0%` (without gradients `+6.2%`)

In absolute terms (note the percentages above were generated comparing between runs/tests rather than to a single baseline):

* Original `view`: `53.66 us` (without gradients `582.78 ns`)
* Original `reshape`: `55.46 us` (without gradients `704.24 ns`)
* Using `as_strided`: `83.24 us` (without gradients `576.49 ns`)
* Using custom `_reshape_view`: `53.13 us` (without gradients `536.01 ns`)

Note that these benchmarks perform a backwards operation as well. When compared without using gradient computation at all the performance differneces are more pronounced as this takes up more of the time.

### Original performance

<details>
  <summary>Benchmark results</summary>

```
[<torch.utils.benchmark.utils.common.Measurement object at 0x7f0e4d393160>
x.pow(4).view(-1).mean().backward();
setup: at::Tensor x=torch::empty({2,2}, torch::requires_grad(true));
  Median: 53.66 us
  IQR:    2.70 us (52.54 to 55.24)
  884 measurements, 100 runs per measurement, 1 thread]

[<torch.utils.benchmark.utils.common.Measurement object at 0x7f0e2ebd4fa0>
x.pow(4).reshape(-1).mean().backward();
setup: at::Tensor x=torch::empty({2,2}, torch::requires_grad(true));
  Median: 55.46 us
  IQR:    2.61 us (54.39 to 57.01)
  889 measurements, 100 runs per measurement, 1 thread]

2276116
2286256

<torch.utils.benchmark.utils.valgrind_wrapper.timer_interface.FunctionCounts object at 0x7f0e5b2e3e20>
   2640  ???:at::detail::computeStride(c10::ArrayRef<long>, c10::ArrayRef<long>, c10::SmallVector<long, 5u> const&)
   1920  ???:at::native::reshape(at::Tensor const&, c10::ArrayRef<long>)
   1520  ???:at::_ops::reshape::call(at::Tensor const&, c10::ArrayRef<long>)
   1040  ???:c10::SmallVectorImpl<long>::operator=(c10::SmallVectorImpl<long>&&)
    980  ???:void at::infer_size_impl<c10::SmallVector<long, 5u> >(c10::ArrayRef<long>, long, c10::SmallVector<long, 5u>&)
    720  ???:__tls_get_addr
    520  ???:at::shouldRunRecordFunction(bool*)
    520  ???:__memcpy_avx_unaligned_erms
    200  ???:c10::impl::wrap_kernel_functor_unboxed_<c10::impl::detail::WrapFunctionIntoFunctor_<c10:: ... g>)>::call(c10::OperatorKernel*, c10::DispatchKeySet, at::Tensor const&, c10::ArrayRef<long>)
    100  ???:c10::TensorImpl::strides() const
    100  ???:c10::TensorImpl::sizes() const
    100  ???:at::(anonymous namespace)::manager()
     77  /tmp/benchmark_utils_jit_build__1626465284__8a34e7ff-cd37-4a82-be28-7f19e081e771/timer_cpp_7815557938202456331/timer_src.cpp:main
     40  ???:c10::TensorImpl::numel() const
    -77  /tmp/benchmark_utils_jit_build__1626465284__8a34e7ff-cd37-4a82-be28-7f19e081e771/timer_cpp_8055217880649990171/timer_src.cpp:main
   -260  ???:at::native::view(at::Tensor const&, c10::ArrayRef<long>)

Total: 10140
```

```
[<torch.utils.benchmark.utils.common.Measurement object at 0x7f850dd66c10>
x.view(-1);
setup: at::Tensor x=torch::empty({2,2});
  Median: 582.78 ns
  IQR:    33.80 ns (573.80 to 607.61)
  833 measurements, 10000 runs per measurement, 1 thread]

[<torch.utils.benchmark.utils.common.Measurement object at 0x7f850de31e20>
x.reshape(-1);
setup: at::Tensor x=torch::empty({2,2});
  Median: 704.24 ns
  IQR:    24.42 ns (697.20 to 721.62)
  679 measurements, 10000 runs per measurement, 1 thread]

56896
67036

<torch.utils.benchmark.utils.valgrind_wrapper.timer_interface.FunctionCounts object at 0x7f84e1930bb0>
   2640  ???:at::detail::computeStride(c10::ArrayRef<long>, c10::ArrayRef<long>, c10::SmallVector<long, 5u> const&)
   1920  ???:at::native::reshape(at::Tensor const&, c10::ArrayRef<long>)
   1520  ???:at::_ops::reshape::call(at::Tensor const&, c10::ArrayRef<long>)
   1040  ???:c10::SmallVectorImpl<long>::operator=(c10::SmallVectorImpl<long>&&)
    980  ???:void at::infer_size_impl<c10::SmallVector<long, 5u> >(c10::ArrayRef<long>, long, c10::SmallVector<long, 5u>&)
    720  ???:__tls_get_addr
    520  ???:at::shouldRunRecordFunction(bool*)
    520  ???:__memcpy_avx_unaligned_erms
    200  ???:c10::impl::wrap_kernel_functor_unboxed_<c10::impl::detail::WrapFunctionIntoFunctor_<c10:: ... g>)>::call(c10::OperatorKernel*, c10::DispatchKeySet, at::Tensor const&, c10::ArrayRef<long>)
    100  ???:c10::TensorImpl::strides() const
    100  ???:c10::TensorImpl::sizes() const
    100  ???:at::(anonymous namespace)::manager()
     76  /tmp/benchmark_utils_jit_build__1626466038__15fbbac0-2072-4459-8f8e-08121a905b99/timer_cpp_547407365342278353/timer_src.cpp:main
     40  ???:c10::TensorImpl::numel() const
    -76  /tmp/benchmark_utils_jit_build__1626466038__15fbbac0-2072-4459-8f8e-08121a905b99/timer_cpp_3457873755756181226/timer_src.cpp:main
   -260  ???:at::native::view(at::Tensor const&, c10::ArrayRef<long>)

Total: 10140
```

</details>

### Using `as_strided`

<details>
  <summary>Benchmark results</summary>

```
[<torch.utils.benchmark.utils.common.Measurement object at 0x7f8b13bb5b50>
x.pow(4).view(-1).mean().backward();
setup: at::Tensor x=torch::empty({2,2}, torch::requires_grad(true));
  Median: 53.37 us
  IQR:    3.15 us (51.73 to 54.88)
  936 measurements, 100 runs per measurement, 1 thread]

[<torch.utils.benchmark.utils.common.Measurement object at 0x7f8af55f8490>
x.pow(4).reshape(-1).mean().backward();
setup: at::Tensor x=torch::empty({2,2}, torch::requires_grad(true));
  Median: 83.24 us
  IQR:    4.05 us (81.20 to 85.25)
  609 measurements, 100 runs per measurement, 1 thread]

2267916
2525061

<torch.utils.benchmark.utils.valgrind_wrapper.timer_interface.FunctionCounts object at 0x7f8af55f8e50>
   31930  ???:_int_free
   15940  ???:malloc
   11595  ???:_int_malloc
   10100  ???:torch::autograd::generated::details::as_strided_backward(at::Tensor, at::TensorGeometry, c10::ArrayRef<long>, c10::ArrayRef<long>, c10::optional<long>)
    9360  ???:__tls_get_addr
    8280  ???:free
    8100  ???:torch::autograd::VariableType::(anonymous namespace)::as_strided(c10::DispatchKeySet, at::Tensor const&, c10::ArrayRef<long>, c10::ArrayRef<long>, c10::optional<long>)
    4520  ???:c10::intrusive_ptr<c10::TensorImpl, c10::UndefinedTensorImpl>::reset_()
    4080  ???:operator new(unsigned long)
     ...
    -780  ???:at::_ops::view::redispatch(c10::DispatchKeySet, at::Tensor const&, c10::ArrayRef<long>)
    -920  ???:c10::SmallVectorImpl<long>::operator=(c10::SmallVectorImpl<long> const&)
   -1220  ???:torch::autograd::generated::ViewBackward::apply(std::vector<at::Tensor, std::allocator<at::Tensor> >&&)
   -1520  ???:at::_ops::view::call(at::Tensor const&, c10::ArrayRef<long>)
   -1580  ???:torch::ADInplaceOrView::(anonymous namespace)::view(c10::DispatchKeySet, at::Tensor const&, c10::ArrayRef<long>)
   -1680  ???:at::Tensor at::native::alias_with_sizes_and_strides<c10::SmallVector<long, 5u> >(at::Tensor const&, c10::SmallVector<long, 5u> const&, c10::SmallVector<long, 5u> const&)
   -2560  ???:at::detail::computeStride(c10::ArrayRef<long>, c10::ArrayRef<long>, c10::SmallVector<long, 5u> const&)
   -2640  ???:at::native::view(at::Tensor const&, c10::ArrayRef<long>)
   -4860  ???:torch::autograd::VariableType::(anonymous namespace)::view(c10::DispatchKeySet, at::Tensor const&, c10::ArrayRef<long>)

Total: 257145
```

```

[<torch.utils.benchmark.utils.common.Measurement object at 0x7f93176a0160>
x.view(-1);
setup: at::Tensor x=torch::empty({2,2});
  Median: 570.55 ns
  IQR:    32.69 ns (552.87 to 585.56)
  874 measurements, 10000 runs per measurement, 1 thread]

[<torch.utils.benchmark.utils.common.Measurement object at 0x7f92f8f29490>
x.reshape(-1);
setup: at::Tensor x=torch::empty({2,2});
  Median: 576.49 ns
  IQR:    37.95 ns (559.51 to 597.46)
  861 measurements, 10000 runs per measurement, 1 thread]

56896
58556

<torch.utils.benchmark.utils.valgrind_wrapper.timer_interface.FunctionCounts object at 0x7f932556ca60>
    2140  ???:at::native::reshape(at::Tensor const&, c10::ArrayRef<long>)
    1940  ???:torch::autograd::VariableType::(anonymous namespace)::as_strided(c10::DispatchKeySet, at::Tensor const&, c10::ArrayRef<long>, c10::ArrayRef<long>, c10::optional<long>)
    1880  ???:torch::ADInplaceOrView::(anonymous namespace)::as_strided(c10::DispatchKeySet, at::Tensor const&, c10::ArrayRef<long>, c10::ArrayRef<long>, c10::optional<long>)
    1720  ???:at::_ops::as_strided::call(at::Tensor const&, c10::ArrayRef<long>, c10::ArrayRef<long>, c10::optional<long>)
    1520  ???:at::_ops::reshape::call(at::Tensor const&, c10::ArrayRef<long>)
    1400  ???:at::native::as_strided_tensorimpl(at::Tensor const&, c10::ArrayRef<long>, c10::ArrayRef<long>, c10::optional<long>)
    1260  ???:at::_ops::as_strided::redispatch(c10::DispatchKeySet, at::Tensor const&, c10::ArrayRef<long>, c10::ArrayRef<long>, c10::optional<long>)'2
    1260  ???:at::_ops::as_strided::redispatch(c10::DispatchKeySet, at::Tensor const&, c10::ArrayRef<long>, c10::ArrayRef<long>, c10::optional<long>)
     980  ???:void at::infer_size_impl<c10::SmallVector<long, 5u> >(c10::ArrayRef<long>, long, c10::SmallVector<long, 5u>&)
     ...
    -620  ???:at::Tensor c10::Dispatcher::redispatch<at::Tensor, at::Tensor const&, c10::ArrayRef<long ... ::ArrayRef<long>)> const&, c10::DispatchKeySet, at::Tensor const&, c10::ArrayRef<long>) const
    -780  ???:at::_ops::view::redispatch(c10::DispatchKeySet, at::Tensor const&, c10::ArrayRef<long>)'2
    -780  ???:at::_ops::view::redispatch(c10::DispatchKeySet, at::Tensor const&, c10::ArrayRef<long>)
    -920  ???:c10::SmallVectorImpl<long>::operator=(c10::SmallVectorImpl<long> const&)
   -1520  ???:at::_ops::view::call(at::Tensor const&, c10::ArrayRef<long>)
   -1580  ???:torch::ADInplaceOrView::(anonymous namespace)::view(c10::DispatchKeySet, at::Tensor const&, c10::ArrayRef<long>)
   -1680  ???:at::Tensor at::native::alias_with_sizes_and_strides<c10::SmallVector<long, 5u> >(at::Tensor const&, c10::SmallVector<long, 5u> const&, c10::SmallVector<long, 5u> const&)
   -1740  ???:torch::autograd::VariableType::(anonymous namespace)::view(c10::DispatchKeySet, at::Tensor const&, c10::ArrayRef<long>)
   -2640  ???:at::native::view(at::Tensor const&, c10::ArrayRef<long>)

Total: 1660

```

</details>

### Using custom function (`_reshape_alias`)

<details>
  <summary>Benchmark results</summary>

```
[<torch.utils.benchmark.utils.common.Measurement object at 0x7f16861d6b50>
x.pow(4).view(-1).mean().backward();
setup: at::Tensor x=torch::empty({2,2}, torch::requires_grad(true));
  Median: 53.50 us
  IQR:    2.64 us (52.32 to 54.96)
  906 measurements, 100 runs per measurement, 1 thread]

[<torch.utils.benchmark.utils.common.Measurement object at 0x7f1667b2ed60>
x.pow(4).reshape(-1).mean().backward();
setup: at::Tensor x=torch::empty({2,2}, torch::requires_grad(true));
  Median: 53.13 us
  IQR:    3.40 us (51.72 to 55.13)
  914 measurements, 100 runs per measurement, 1 thread]

2269736
2273236

<torch.utils.benchmark.utils.valgrind_wrapper.timer_interface.FunctionCounts object at 0x7f1693f8dc10>
    5060  ???:torch::autograd::VariableType::(anonymous namespace)::_reshape_alias(c10::DispatchKeySet, at::Tensor const&, c10::ArrayRef<long>, c10::ArrayRef<long>)
    2000  ???:at::native::reshape(at::Tensor const&, c10::ArrayRef<long>)
    1780  ???:torch::ADInplaceOrView::(anonymous namespace)::_reshape_alias(c10::DispatchKeySet, at::Tensor const&, c10::ArrayRef<long>, c10::ArrayRef<long>)
    1660  ???:at::_ops::_reshape_alias::call(at::Tensor const&, c10::ArrayRef<long>, c10::ArrayRef<long>)
    1600  ???:at::Tensor at::native::alias_with_sizes_and_strides<c10::ArrayRef<long> >(at::Tensor const&, c10::ArrayRef<long> const&, c10::ArrayRef<long> const&)
    1520  ???:at::_ops::reshape::call(at::Tensor const&, c10::ArrayRef<long>)
    1240  ???:at::_ops::_reshape_alias::redispatch(c10::DispatchKeySet, at::Tensor const&, c10::ArrayRef<long>, c10::ArrayRef<long>)'2
    1240  ???:at::_ops::_reshape_alias::redispatch(c10::DispatchKeySet, at::Tensor const&, c10::ArrayRef<long>, c10::ArrayRef<long>)
    1220  ???:torch::autograd::generated::AliasToShapeBackward::apply(std::vector<at::Tensor, std::allocator<at::Tensor> >&&)
     ...
    -780  ???:at::_ops::view::redispatch(c10::DispatchKeySet, at::Tensor const&, c10::ArrayRef<long>)'2
    -780  ???:at::_ops::view::redispatch(c10::DispatchKeySet, at::Tensor const&, c10::ArrayRef<long>)
    -920  ???:c10::SmallVectorImpl<long>::operator=(c10::SmallVectorImpl<long> const&)
   -1220  ???:torch::autograd::generated::ViewBackward::apply(std::vector<at::Tensor, std::allocator<at::Tensor> >&&)
   -1520  ???:at::_ops::view::call(at::Tensor const&, c10::ArrayRef<long>)
   -1580  ???:torch::ADInplaceOrView::(anonymous namespace)::view(c10::DispatchKeySet, at::Tensor const&, c10::ArrayRef<long>)
   -1680  ???:at::Tensor at::native::alias_with_sizes_and_strides<c10::SmallVector<long, 5u> >(at::Tensor const&, c10::SmallVector<long, 5u> const&, c10::SmallVector<long, 5u> const&)
   -2640  ???:at::native::view(at::Tensor const&, c10::ArrayRef<long>)
   -4860  ???:torch::autograd::VariableType::(anonymous namespace)::view(c10::DispatchKeySet, at::Tensor const&, c10::ArrayRef<long>)

Total: 3500
```

```

[<torch.utils.benchmark.utils.common.Measurement object at 0x7f5287adfb20>
x.view(-1);
setup: at::Tensor x=torch::empty({2,2});
  Median: 505.10 ns
  IQR:    20.04 ns (500.41 to 520.45)
  944 measurements, 10000 runs per measurement, 1 thread]

[<torch.utils.benchmark.utils.common.Measurement object at 0x7f526951b430>
x.reshape(-1);
setup: at::Tensor x=torch::empty({2,2});
  Median: 536.01 ns
  IQR:    17.81 ns (531.34 to 549.16)
  916 measurements, 10000 runs per measurement, 1 thread]

56896
60376

<torch.utils.benchmark.utils.valgrind_wrapper.timer_interface.FunctionCounts object at 0x7f5295896c10>
    2000  ???:at::native::reshape(at::Tensor const&, c10::ArrayRef<long>)
    1860  ???:torch::autograd::VariableType::(anonymous namespace)::_reshape_alias(c10::DispatchKeySet, at::Tensor const&, c10::ArrayRef<long>, c10::ArrayRef<long>)
    1780  ???:torch::ADInplaceOrView::(anonymous namespace)::_reshape_alias(c10::DispatchKeySet, at::Tensor const&, c10::ArrayRef<long>, c10::ArrayRef<long>)
    1660  ???:at::_ops::_reshape_alias::call(at::Tensor const&, c10::ArrayRef<long>, c10::ArrayRef<long>)
    1600  ???:at::Tensor at::native::alias_with_sizes_and_strides<c10::ArrayRef<long> >(at::Tensor const&, c10::ArrayRef<long> const&, c10::ArrayRef<long> const&)
    1520  ???:at::_ops::reshape::call(at::Tensor const&, c10::ArrayRef<long>)
    1240  ???:at::_ops::_reshape_alias::redispatch(c10::DispatchKeySet, at::Tensor const&, c10::ArrayRef<long>, c10::ArrayRef<long>)'2
    1240  ???:at::_ops::_reshape_alias::redispatch(c10::DispatchKeySet, at::Tensor const&, c10::ArrayRef<long>, c10::ArrayRef<long>)
     980  ???:void at::infer_size_impl<c10::SmallVector<long, 5u> >(c10::ArrayRef<long>, long, c10::SmallVector<long, 5u>&)
     ...
    -620  ???:at::Tensor c10::Dispatcher::redispatch<at::Tensor, at::Tensor const&, c10::ArrayRef<long ... ::ArrayRef<long>)> const&, c10::DispatchKeySet, at::Tensor const&, c10::ArrayRef<long>) const
    -780  ???:at::_ops::view::redispatch(c10::DispatchKeySet, at::Tensor const&, c10::ArrayRef<long>)'2
    -780  ???:at::_ops::view::redispatch(c10::DispatchKeySet, at::Tensor const&, c10::ArrayRef<long>)
    -920  ???:c10::SmallVectorImpl<long>::operator=(c10::SmallVectorImpl<long> const&)
   -1520  ???:at::_ops::view::call(at::Tensor const&, c10::ArrayRef<long>)
   -1580  ???:torch::ADInplaceOrView::(anonymous namespace)::view(c10::DispatchKeySet, at::Tensor const&, c10::ArrayRef<long>)
   -1680  ???:at::Tensor at::native::alias_with_sizes_and_strides<c10::SmallVector<long, 5u> >(at::Tensor const&, c10::SmallVector<long, 5u> const&, c10::SmallVector<long, 5u> const&)
   -1740  ???:torch::autograd::VariableType::(anonymous namespace)::view(c10::DispatchKeySet, at::Tensor const&, c10::ArrayRef<long>)
   -2640  ???:at::native::view(at::Tensor const&, c10::ArrayRef<long>)

Total: 3480

```

</details>

Test Plan: Imported from OSS

Reviewed By: ejguan

Differential Revision: D29792126

Pulled By: laurencer

fbshipit-source-id: f0519b45b65f868aa3e8651679354558bd761dfd
2021-07-21 14:05:35 -07:00
..
any.cpp [clang-tidy] Exclude cppcoreguidelines-avoid-magic-numbers (#57841) 2021-05-07 20:02:33 -07:00
autograd.cpp supports non-leaf inputs for autograd.backward() function (#60521) 2021-06-25 18:57:26 -07:00
CMakeLists.txt generate C++ API for meta functions using at::meta:: (#58570) 2021-06-15 16:54:46 -07:00
dataloader.cpp [clang-tidy] Exclude cppcoreguidelines-avoid-magic-numbers (#57841) 2021-05-07 20:02:33 -07:00
dispatch.cpp
enum.cpp Make PyTorch code-base clang-tidy compliant (#56892) 2021-04-28 14:10:25 -07:00
expanding-array.cpp [clang-tidy] Exclude cppcoreguidelines-avoid-magic-numbers (#57841) 2021-05-07 20:02:33 -07:00
fft.cpp [clang-tidy] Exclude cppcoreguidelines-avoid-magic-numbers (#57841) 2021-05-07 20:02:33 -07:00
functional.cpp ENH Adds nn.ReflectionPad3d (#59791) 2021-06-21 10:53:14 -07:00
grad_mode.cpp Revert "Make grad mode error just a warning (#56401)" (#57640) 2021-05-05 13:07:29 -07:00
imethod.cpp Add IMethod interface 2021-06-30 11:28:24 -07:00
inference_mode.cpp Warn when backward() is called with create_graph=True (#59412) 2021-06-08 17:19:04 -07:00
init_baseline.h Lint trailing newlines (#54737) 2021-03-30 13:09:52 -07:00
init_baseline.py
init.cpp [clang-tidy] Exclude cppcoreguidelines-avoid-magic-numbers (#57841) 2021-05-07 20:02:33 -07:00
integration.cpp [clang-tidy] Exclude cppcoreguidelines-avoid-magic-numbers (#57841) 2021-05-07 20:02:33 -07:00
jit.cpp [clang-tidy] Exclude cppcoreguidelines-avoid-magic-numbers (#57841) 2021-05-07 20:02:33 -07:00
memory.cpp [clang-tidy] Exclude cppcoreguidelines-avoid-magic-numbers (#57841) 2021-05-07 20:02:33 -07:00
meta_tensor.cpp generate C++ API for meta functions using at::meta:: (#58570) 2021-06-15 16:54:46 -07:00
misc.cpp [clang-tidy] Exclude cppcoreguidelines-avoid-magic-numbers (#57841) 2021-05-07 20:02:33 -07:00
module.cpp [clang-tidy] Exclude cppcoreguidelines-avoid-magic-numbers (#57841) 2021-05-07 20:02:33 -07:00
moduledict.cpp [clang-tidy] Exclude cppcoreguidelines-avoid-magic-numbers (#57841) 2021-05-07 20:02:33 -07:00
modulelist.cpp [clang-tidy] Exclude cppcoreguidelines-avoid-magic-numbers (#57841) 2021-05-07 20:02:33 -07:00
modules.cpp ENH Adds nn.ReflectionPad3d (#59791) 2021-06-21 10:53:14 -07:00
namespace.cpp Make PyTorch code-base clang-tidy compliant (#56892) 2021-04-28 14:10:25 -07:00
nn_utils.cpp [clang-tidy] Exclude cppcoreguidelines-avoid-magic-numbers (#57841) 2021-05-07 20:02:33 -07:00
operations.cpp [Codemod][GleanFbcode] Remove dead includes in caffe2/test (#43953) 2020-09-01 21:48:28 -07:00
optim_baseline.h
optim_baseline.py Remove legacy constructor calls from pytorch codebase. (#54142) 2021-04-11 15:45:17 -07:00
optim.cpp [clang-tidy] Exclude cppcoreguidelines-avoid-magic-numbers (#57841) 2021-05-07 20:02:33 -07:00
ordered_dict.cpp Make PyTorch code-base clang-tidy compliant (#56892) 2021-04-28 14:10:25 -07:00
parallel_benchmark.cpp [clang-tidy] Exclude cppcoreguidelines-avoid-magic-numbers (#57841) 2021-05-07 20:02:33 -07:00
parallel.cpp
parameterdict.cpp [clang-tidy] Exclude cppcoreguidelines-avoid-magic-numbers (#57841) 2021-05-07 20:02:33 -07:00
parameterlist.cpp Make PyTorch code-base clang-tidy compliant (#56892) 2021-04-28 14:10:25 -07:00
README.md
rnn.cpp [clang-tidy] Exclude cppcoreguidelines-avoid-magic-numbers (#57841) 2021-05-07 20:02:33 -07:00
sequential.cpp [clang-tidy] Exclude cppcoreguidelines-avoid-magic-numbers (#57841) 2021-05-07 20:02:33 -07:00
serialize.cpp [clang-tidy] Exclude cppcoreguidelines-avoid-magic-numbers (#57841) 2021-05-07 20:02:33 -07:00
special.cpp [clang-tidy] Exclude cppcoreguidelines-avoid-magic-numbers (#57841) 2021-05-07 20:02:33 -07:00
static.cpp [clang-tidy] Exclude cppcoreguidelines-avoid-magic-numbers (#57841) 2021-05-07 20:02:33 -07:00
support.cpp Make PyTorch code-base clang-tidy compliant (#56892) 2021-04-28 14:10:25 -07:00
support.h Implement public API InferenceMode and its error handling (#55008) 2021-03-31 10:48:00 -07:00
tensor_cuda.cpp Make PyTorch code-base clang-tidy compliant (#56892) 2021-04-28 14:10:25 -07:00
tensor_flatten.cpp fix unflatten_dense_tensor when there is empty tensor inside (#50321) 2021-01-23 12:14:34 -08:00
tensor_indexing.cpp [clang-tidy] Exclude cppcoreguidelines-avoid-magic-numbers (#57841) 2021-05-07 20:02:33 -07:00
tensor_options_cuda.cpp [clang-tidy] Exclude cppcoreguidelines-avoid-magic-numbers (#57841) 2021-05-07 20:02:33 -07:00
tensor_options.cpp [clang-tidy] Exclude cppcoreguidelines-avoid-magic-numbers (#57841) 2021-05-07 20:02:33 -07:00
tensor.cpp Removed overhead from reshape() call if tensor doesn't need to be changed (#61466) 2021-07-21 14:05:35 -07:00
torch_include.cpp Make PyTorch code-base clang-tidy compliant (#56892) 2021-04-28 14:10:25 -07:00
transformer.cpp [clang-tidy] Exclude cppcoreguidelines-avoid-magic-numbers (#57841) 2021-05-07 20:02:33 -07:00

README.md

C++ Frontend Tests

In this folder live the tests for PyTorch's C++ Frontend. They use the GoogleTest test framework.

CUDA Tests

To make a test runnable only on platforms with CUDA, you should suffix your test with _CUDA, e.g.

TEST(MyTestSuite, MyTestCase_CUDA) { }

To make it runnable only on platforms with at least two CUDA machines, suffix it with _MultiCUDA instead of _CUDA, e.g.

TEST(MyTestSuite, MyTestCase_MultiCUDA) { }

There is logic in main.cpp that detects the availability and number of CUDA devices and supplies the appropriate negative filters to GoogleTest.

Integration Tests

Integration tests use the MNIST dataset. You must download it by running the following command from the PyTorch root folder:

$ python tools/download_mnist.py -d test/cpp/api/mnist

The required paths will be referenced as test/cpp/api/mnist/... in the test code, so you must run the integration tests from the PyTorch root folder.