Instead of relaxing tolerances for certain unit tests that exercise TF32 on MI300, skip the tests until hipblaslt accuracy is improved.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/166478
Approved by: https://github.com/jeffdaily
Co-authored-by: Jeff Daily <jeff.daily@amd.com>
Co-authored-by: Jagadish Krishnamoorthy <jagadish.krishnamoorthy@amd.com>
It was found that the integration of miopen batchnorm was causing the output to always be in default contig memory format even when the input was channels last. This also unskips a number of related unit tests.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/162112
Approved by: https://github.com/jeffdaily
Co-authored-by: Jeff Daily <jeff.daily@amd.com>
Co-authored-by: Dmitry Nikolaev <dmitry.nikolaev@amd.com>
Co-authored-by: Jithun Nair <37884920+jithunnair-amd@users.noreply.github.com>
Fixes#160053
The previous error message `Only 2D, 3D, 4D, 5D padding with non-constant padding are supported for now` was not clear
now we have
```
python3
Python 3.13.5 | packaged by conda-forge | (main, Jun 16 2025, 08:27:50) [GCC 13.3.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import torch
... import torch.nn.functional as F
... a = torch.empty(2,2,2,2)
... F.pad(a, (1,1), mode="circular")
...
Traceback (most recent call last):
File "<python-input-0>", line 4, in <module>
F.pad(a, (1,1), mode="circular")
~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/rrathaur/Desktop/pytorch/torch/nn/functional.py", line 5294, in pad
return torch._C._nn.pad(input, pad, mode, value)
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
NotImplementedError: Padding size 2 is not supported for 4D input tensor.
Supported combinations for non-constant padding:
- 2D or 3D input: padding size = 2 (pads last dimension)
- 3D or 4D input: padding size = 4 (pads last 2 dimensions)
- 4D or 5D input: padding size = 6 (pads last 3 dimensions)
>>>
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/160866
Approved by: https://github.com/mikaylagawarecki
This would allow me to enable more opinfo tests against MPS device eventually and supposed to be a very simple test, but actually required minor adjustments to lots of test files, namely:
- Introduce `all_mps_types_and` that is very similar to `all_types_and`, but skips `float64`
- Decorate lots of tests with `@dtypesIfMPS(*all_mps_types())`
- Skip `test_from_dlpack_noncontinguous` as it currently crashes (need to be fixed)
- Add lots of `expectedFailureIfMPS`
- Delete all `@onlyNativeDeviceTypesAnd("mps")`
<sarcasm> I love how well documented this variable are </sarcasm>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/153835
Approved by: https://github.com/Skylion007
### Description
This PR is to enable TF32 as fp32 internal precision for matmul/linear/conv in `mkldnn backend`. Since we have refined fp32 precision API in https://github.com/pytorch/pytorch/pull/125888, we can easily extend the API to support TF32 for `mkldnn backend`.
```
torch.backends.mkldnn.matmul.fp32_precision = 'tf32'
torch.backends.mkldnn.conv.fp32_precision = "tf32"
```
Related kernel update and UTs update are done. And the wrapper `bf32_on_and _off` is updated to `reduced_f32_on_and_off`, and it can run tests 3 times, one is reduced_f32 OFF, the other two are reduced_f32 ON (including `bf32 ON` and `tf32 ON`).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/157520
Approved by: https://github.com/mingfeima, https://github.com/jansel
Fixes#154978
## Test Result
```python
>>> import torch
>>> import numpy as np
>>> import torch.nn as nn
>>> import torch.distributions.normal as norm
>>> device = torch.device(('cuda' if torch.cuda.is_available() else 'cpu'))
>>> print('Using {}'.format(device))
Using cuda
>>> m = nn.Sequential(nn.Linear(1, 128).cuda(), nn.Tanh(), nn.Linear(128, 128).cuda(), nn.Tanh(), nn.Linear(128, 128).cuda(), nn.Tanh())
>>> m.to(device, dtype=None, non_blocking=False)
Sequential(
(0): Linear(in_features=1, out_features=128, bias=True)
(1): Tanh()
(2): Linear(in_features=128, out_features=128, bias=True)
(3): Tanh()
(4): Linear(in_features=128, out_features=128, bias=True)
(5): Tanh()
)
>>> opt = torch.optim.Adam(m.parameters(), lr=0.001)
>>> print('Number of trainable parameters: ', sum((p.numel() for p in m.parameters() if p.requires_grad)))
Number of trainable parameters: 33280
>>> input_tensor = torch.tensor(77.0, device=device)
>>> target = torch.tensor(66.0)
>>> loss_function = nn.MSELoss()
>>> print('Loss Function: ', loss_function)
Loss Function: MSELoss()
>>> loss = loss_function(input_tensor, target)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/zong/code/pytorch/torch/nn/modules/module.py", line 1767, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/zong/code/pytorch/torch/nn/modules/module.py", line 1778, in _call_impl
return forward_call(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/zong/code/pytorch/torch/nn/modules/loss.py", line 610, in forward
return F.mse_loss(input, target, reduction=self.reduction)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/zong/code/pytorch/torch/nn/functional.py", line 3903, in mse_loss
return torch._C._nn.mse_loss(
^^^^^^^^^^^^^^^^^^^^^^
RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cuda:0 and cpu!
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/155089
Approved by: https://github.com/cyyever, https://github.com/albanD
New set of batchnorm tests to verify NCHW 2D/3D BatchNorm
This test also allows to add and configure different BatchNorm tests (dtypes, NCHW/NHWC, Mixed) in the future
based on:
- Train [test_batchnorm_cudnn_nhwc](1051b93192/test/test_nn.py (L4985))
- Inference [test_batchnorm_nhwc_cuda](1051b93192/test/test_nn.py (L5130))
```
test_batchnorm_3D_inference_NCHW_vs_cpu_float32 (__main__.TestNN.test_batchnorm_3D_inference_NCHW_vs_cpu_float32) ... ok (0.113s)
test_batchnorm_3D_inference_NCHW_vs_cpu_mixed_bfloat16 (__main__.TestNN.test_batchnorm_3D_inference_NCHW_vs_cpu_mixed_bfloat16) ... ok (0.057s)
test_batchnorm_3D_inference_NCHW_vs_cpu_mixed_float16 (__main__.TestNN.test_batchnorm_3D_inference_NCHW_vs_cpu_mixed_float16) ... ok (0.063s)
test_batchnorm_3D_inference_NCHW_vs_native_float32 (__main__.TestNN.test_batchnorm_3D_inference_NCHW_vs_native_float32) ... ok (0.059s)
test_batchnorm_3D_inference_NCHW_vs_native_mixed_bfloat16 (__main__.TestNN.test_batchnorm_3D_inference_NCHW_vs_native_mixed_bfloat16) ... ok (0.006s)
test_batchnorm_3D_inference_NCHW_vs_native_mixed_float16 (__main__.TestNN.test_batchnorm_3D_inference_NCHW_vs_native_mixed_float16) ... ok (0.006s)
test_batchnorm_3D_train_NCHW_vs_cpu_float32 (__main__.TestNN.test_batchnorm_3D_train_NCHW_vs_cpu_float32) ... ok (0.007s)
test_batchnorm_3D_train_NCHW_vs_cpu_mixed_bfloat16 (__main__.TestNN.test_batchnorm_3D_train_NCHW_vs_cpu_mixed_bfloat16) ... ok (0.005s)
test_batchnorm_3D_train_NCHW_vs_cpu_mixed_float16 (__main__.TestNN.test_batchnorm_3D_train_NCHW_vs_cpu_mixed_float16) ... ok (0.005s)
test_batchnorm_3D_train_NCHW_vs_native_float32 (__main__.TestNN.test_batchnorm_3D_train_NCHW_vs_native_float32) ... ok (0.003s)
test_batchnorm_3D_train_NCHW_vs_native_mixed_bfloat16 (__main__.TestNN.test_batchnorm_3D_train_NCHW_vs_native_mixed_bfloat16) ... skip: bfloat16 NCHW train failed due to native tolerance issue (0.001s)
test_batchnorm_3D_train_NCHW_vs_native_mixed_float16 (__main__.TestNN.test_batchnorm_3D_train_NCHW_vs_native_mixed_float16) ... skip: 3D float16 NCHW train failed on ROCm<7.0 (0.001s)
test_batchnorm_2D_inference_NCHW_vs_cpu_float32 (__main__.TestNN.test_batchnorm_2D_inference_NCHW_vs_cpu_float32) ... ok (0.016s)
test_batchnorm_2D_inference_NCHW_vs_cpu_mixed_bfloat16 (__main__.TestNN.test_batchnorm_2D_inference_NCHW_vs_cpu_mixed_bfloat16) ... ok (0.003s)
test_batchnorm_2D_inference_NCHW_vs_cpu_mixed_float16 (__main__.TestNN.test_batchnorm_2D_inference_NCHW_vs_cpu_mixed_float16) ... ok (0.003s)
test_batchnorm_2D_inference_NCHW_vs_native_float32 (__main__.TestNN.test_batchnorm_2D_inference_NCHW_vs_native_float32) ... ok (0.054s)
test_batchnorm_2D_inference_NCHW_vs_native_mixed_bfloat16 (__main__.TestNN.test_batchnorm_2D_inference_NCHW_vs_native_mixed_bfloat16) ... ok (0.002s)
test_batchnorm_2D_inference_NCHW_vs_native_mixed_float16 (__main__.TestNN.test_batchnorm_2D_inference_NCHW_vs_native_mixed_float16) ... ok (0.001s)
test_batchnorm_2D_train_NCHW_vs_cpu_float32 (__main__.TestNN.test_batchnorm_2D_train_NCHW_vs_cpu_float32) ... ok (0.007s)
test_batchnorm_2D_train_NCHW_vs_cpu_mixed_bfloat16 (__main__.TestNN.test_batchnorm_2D_train_NCHW_vs_cpu_mixed_bfloat16) ... ok (0.004s)
test_batchnorm_2D_train_NCHW_vs_cpu_mixed_float16 (__main__.TestNN.test_batchnorm_2D_train_NCHW_vs_cpu_mixed_float16) ... ok (0.004s)
test_batchnorm_2D_train_NCHW_vs_native_float32 (__main__.TestNN.test_batchnorm_2D_train_NCHW_vs_native_float32) ... ok (0.003s)
test_batchnorm_2D_train_NCHW_vs_native_mixed_bfloat16 (__main__.TestNN.test_batchnorm_2D_train_NCHW_vs_native_mixed_bfloat16) ... skip: bfloat16 NCHW train failed due to native tolerance issue (0.001s)
test_batchnorm_2D_train_NCHW_vs_native_mixed_float16 (__main__.TestNN.test_batchnorm_2D_train_NCHW_vs_native_mixed_float16) ... ok (0.002s)
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/156498
Approved by: https://github.com/jeffdaily
Don't call `sum()` on a tensor that is default constructed.
Previously we could call `sum()` on a tensor that was default-contructed. That would lead to an error like this:
```
Traceback (most recent call last):
File "/home/ahmads/.conda/envs/pt3/lib/python3.12/unittest/case.py", line 58, in testPartExecutor
yield
File "/home/ahmads/.conda/envs/pt3/lib/python3.12/unittest/case.py", line 634, in run
self._callTestMethod(testMethod)
File "/home/ahmads/.conda/envs/pt3/lib/python3.12/unittest/case.py", line 589, in _callTestMethod
if method() is not None:
^^^^^^^^
File "/home/ahmads/personal/pytorch/torch/testing/_internal/common_utils.py", line 3191, in wrapper
method(*args, **kwargs)
File "/home/ahmads/personal/pytorch/test/test_nn.py", line 7235, in test_layer_norm_backwards_eps
ln_out_cuda.backward(grad_output_cuda)
File "/home/ahmads/personal/pytorch/torch/_tensor.py", line 647, in backward
torch.autograd.backward(
File "/home/ahmads/personal/pytorch/torch/autograd/__init__.py", line 354, in backward
_engine_run_backward(
File "/home/ahmads/personal/pytorch/torch/autograd/graph.py", line 829, in _engine_run_backward
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
RuntimeError: tensor does not have a device
Exception raised from device_default at /home/ahmads/personal/pytorch/c10/core/TensorImpl.h:1265 (most recent call first):
C++ CapturedTraceback:
#4 std::_Function_handler<std::shared_ptr<c10::LazyValue<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > > const> (), c10::SetStackTraceFetcher(std::function<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > ()>)::{lambda()#1}>::_M_invoke(std::_Any_data const&) from Logging.cpp:0
#5 c10::Error::Error(c10::SourceLocation, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >) from ??:0
#6 c10::detail::torchCheckFail(char const*, char const*, unsigned int, char const*) from ??:0
#7 at::TensorBase::options() const from :0
#8 at::meta::resize_reduction(at::impl::MetaBase&, at::Tensor const&, c10::OptionalArrayRef<long>, bool, c10::ScalarType, bool) from :0
#9 at::meta::structured_sum_dim_IntList::meta(at::Tensor const&, c10::OptionalArrayRef<long>, bool, std::optional<c10::ScalarType>) from ??:0
#10 at::(anonymous namespace)::wrapper_CompositeExplicitAutogradNonFunctional_sum_dim_IntList(at::Tensor const&, c10::OptionalArrayRef<long>, bool, std::optional<c10::ScalarType>) from RegisterCompositeExplicitAutogradNonFunctional_0.cpp:0
#11 c10::impl::wrap_kernel_functor_unboxed_<c10::impl::detail::WrapFunctionIntoFunctor_<c10::CompileTimeFunctionPointer<at::Tensor (at::Tensor const&, c10::OptionalArrayRef<long>, bool, std::optional<c10::ScalarType>), &at::(anonymous namespace)::wrapper_CompositeExplicitAutogradNonFunctional_sum_dim_IntList>, at::Tensor, c10::guts::typelist::typelist<at::Tensor const&, c10::OptionalArrayRef<long>, bool, std::optional<c10::ScalarType> > >, at::Tensor (at::Tensor const&, c10::OptionalArrayRef<long>, bool, std::optional<c10::ScalarType>)>::call(c10::OperatorKernel*, c10::DispatchKeySet, at::Tensor const&, c10::OptionalArrayRef<long>, bool, std::optional<c10::ScalarType>) from RegisterCompositeExplicitAutogradNonFunctional_0.cpp:0
#12 at::_ops::sum_dim_IntList::call(at::Tensor const&, c10::OptionalArrayRef<long>, bool, std::optional<c10::ScalarType>) from ??:0
#13 void at::native::(anonymous namespace)::LaunchGammaBetaBackwardCUDAKernel<float, float>(float const*, float const*, float const*, float const*, long, long, at::Tensor*, at::Tensor*, CUstream_st*) from ??:0
#14 void at::native::(anonymous namespace)::LayerNormBackwardKernelImplInternal<float>(at::Tensor const&, at::Tensor const&, at::Tensor const&, at::Tensor const&, at::Tensor const&, long, long, at::Tensor*, at::Tensor*, at::Tensor*) from ??:0
#15 at::native::(anonymous namespace)::LayerNormBackwardKernelImpl(at::Tensor const&, at::Tensor const&, at::Tensor const&, at::Tensor const&, at::Tensor const&, long, long, at::Tensor*, at::Tensor*, at::Tensor*) from ??:0
#16 at::native::layer_norm_backward_cuda(at::Tensor const&, at::Tensor const&, c10::ArrayRef<long>, at::Tensor const&, at::Tensor const&, std::optional<at::Tensor> const&, std::optional<at::Tensor> const&, std::array<bool, 3ul>) from ??:0
#17 at::(anonymous namespace)::(anonymous namespace)::wrapper_CUDA__native_layer_norm_backward(at::Tensor const&, at::Tensor const&, c10::ArrayRef<c10::SymInt>, at::Tensor const&, at::Tensor const&, std::optional<at::Tensor> const&, std::optional<at::Tensor> const&, std::array<bool, 3ul>) from RegisterCUDA_0.cpp:0
```
Now we only call `sum(0)` on tensors that are defined and properly guard the `sum(0)` and assignment.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/156600
Approved by: https://github.com/eqy, https://github.com/ngimel
