Fixes#131865. Addresses the issue seen when running llama v3.1 8B parameter model on MPS backend where the batch matmul output size can go over the 32-bit indexing limit of MPS tensors, causing an assert.
Test case to reproduce the issue with the dimensions encountered in llama v3.1 and verify this fix works around it:
```
import torch
device='mps'
a = torch.randn([32, 20064, 128], dtype=torch.float32,device=device)
b = torch.randn([32, 128, 20064], dtype=torch.float32, device=device)
res = torch.bmm(a, b)
```
Notably the current change only works as long as the individual output matrix in the bmm does not exceed the number of elements 2**32. This lets us split up the computation along the batch axis to avoid going over the limit.
Added a TORCH_CHECK to raise an error if the individual matrix dimensions are too large to handle for this op until a more general workaround tiling the matmuls is available.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133430
Approved by: https://github.com/malfet
Co-authored-by: Nikita Shulga <2453524+malfet@users.noreply.github.com>
Which fixes BatchNorm behavior for if called with empty tensors on MPS backed. Removed `expectedFailureMPS` in test_nn.py, deleted expected failure in `test_mps.py` and adjusted `skipIfMPS` to `expectedFailureMPS` in BatchNorm2d OpInfo decorator, but restrict it only to the memory format tests
Test Plan: CI + `python3 -c "import torch; print(torch.nn.BatchNorm2d(3, device='mps')(torch.rand(0, 3, 2, 2, device='mps')))"`
Fixes https://github.com/pytorch/pytorch/issues/134423
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134540
Approved by: https://github.com/Skylion007, https://github.com/albanD
Add similar semantics for creating a buffer object similar to creating a parameter. This is done by introducing a new Buffer class that can be used for type disambiguation. The underlying functionality of registering a buffer remains the same as the register_buffer method has not been changed. The persistent parameter in the Buffer type is to indicate whether a buffer object should be persistent or not. Other non-test changes have to do with getting the new Buffer type recognized by inductor and dynamo. Remaining changes are test changes to make sure that the Buffer type can be used as a drop in replacement for register_buffer as it just leads to register_buffer being called. The addition of this new functionality still allows for normal tensors to be used as buffers so these changes are intended to be backwards compatible.
Fixes#35735
Co-authored-by: Mikayla Gawarecki <mikaylagawarecki@gmail.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/125971
Approved by: https://github.com/albanD, https://github.com/anijain2305, https://github.com/mlazos
Partial fix to issue #130295
Moves min and max ops to use the NaN propagating API in MPS to align with the pytorch convention. Adds a regression test to validate the fix achieves parity with cpu backend.
Co-authored-by: Nikita Shulga <2453524+malfet@users.noreply.github.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/130445
Approved by: https://github.com/malfet
The bug causing the correctness problem will be fixed in future OS release. Root cause of the problem is in a bug in an optimization to MPSGraph reshape operation in MacOS 14_4 that results in a correctness issue with the shapes the LSTM gradient operation has when num_layers > 2.
Solves silentness of issue #125803.
Co-authored-by: Nikita Shulga <2453524+malfet@users.noreply.github.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/130038
Approved by: https://github.com/malfet
0.12.0 Major Updates:
- Add context manager to temporarily set the dictionary sorting mode
- Add accessor APIs
- Use `stable` tag for `pybind11` for Python 3.13 support
- Fix potential segmentation fault for pickling support
0.12.1 Updates:
- Fix warning regression during import when launch with strict warning filters
Closes#130155
Pull Request resolved: https://github.com/pytorch/pytorch/pull/130139
Approved by: https://github.com/zou3519
ghstack dependencies: #130895
0.12.0 Major Updates:
- Add context manager to temporarily set the dictionary sorting mode
- Add accessor APIs
- Use `stable` tag for `pybind11` for Python 3.13 support
- Fix potential segmentation fault for pickling support
0.12.1 Updates:
- Fix warning regression during import when launch with strict warning filters
Closes#130155
Pull Request resolved: https://github.com/pytorch/pytorch/pull/130139
Approved by: https://github.com/zou3519
Workaround bug in `reductionAndWithTensor:` that kills app with the
following assert if 5+D tensor as an input
```
Assertion failed: (0 <= mpsAxis && mpsAxis < 4 && "Runtime canonicalization must simplify reduction axes to minor 4 dimensions."), function encodeNDArrayOp, file GPUReductionOps.mm, line 76.
```
by reshaping the tensor to 2D/3D one before running the reduction.
Refactored common code into `all_any_common_impl_mps` as both `reductionOrWithTensor:` and `reductionAndWithTensor:` suffer from the same issue
Enabled `test_reduction_ops_5D` and added regression test to it
Pull Request resolved: https://github.com/pytorch/pytorch/pull/130542
Approved by: https://github.com/Skylion007, https://github.com/albanD
ghstack dependencies: #130541
This PR is to update the input `weight` of `_convert_weight_to_int4pack` from `[n][k] int32` to `[n][k / 2] uint8`, both for CPU, CUDA and MPS, which can help decouple int4 model checkpoint with different ISAs and different platforms in `gpt-fast`. The advantage is int4 model checkpoint can be shared in different test machines, without re-generating in one certain platform. Meanwhile, the size of input `weight` can be reduced to `1 / 8`.
Before this PR, packed weight stored in CUDA specific layout: `[n/8][k/(InnerKTiles*16)][32][InnerKTiles/2]`, dtype int32, where InnerKTiles = 2, 4, 8. CPU packed weight viewed as the SAME shape but stored in different layout: `[n/64][k][32]`, dtype uint8. Weight is strongly coupled with platforms (CPU/CUDA) and ISAs (AVX512/AVX2/scalar). And users cannot use a generated weight in another different ISA or platform, because when loading weight into devices, the compute format is different.
Now, we use common serialized layout (`[n][k/2] uint8`) for different devices or ISAs as input `weight` of `_convert_weight_to_int4pack`, and each back chooses how to interpret as compute layout.
### Performance
Intel (R) Xeon (R) CPU Max 9480, single socket (56 cores)
There is no obvious regression of this PR.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/129940
Approved by: https://github.com/jgong5, https://github.com/lezcano, https://github.com/mingfeima
Summary:
1. Fixed#130201 by adding type promotion.
2. Added proper tests.
3. Found torch's type promotion is different from numpy as follows:
```python
import torch
import numpy as np
np.clip(np.array([1], dtype=np.float32), np.array([1], dtype=np.int32), None).dtype # dtype('float64')
torch.clamp(torch.tensor([1], dtype=torch.float32), torch.tensor([1], dtype=torch.int32)).dtype # torch.float32
```
~Not sure the proper way to handle it, it causes numpy ref tests to fail.~
Reason here, so think I'm gonna xfail it:
3c1cf03fde/test/test_ops.py (L260-L264)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/130226
Approved by: https://github.com/malfet
This PR is to update the input `weight` of `_convert_weight_to_int4pack` from `[n][k] int32` to `[n][k / 2] uint8`, both for CPU, CUDA and MPS, which can help decouple int4 model checkpoint with different ISAs and different platforms in `gpt-fast`. The advantage is int4 model checkpoint can be shared in different test machines, without re-generating in one certain platform. Meanwhile, the size of input `weight` can be reduced to `1 / 8`.
Before this PR, packed weight stored in CUDA specific layout: `[n/8][k/(InnerKTiles*16)][32][InnerKTiles/2]`, dtype int32, where InnerKTiles = 2, 4, 8. CPU packed weight viewed as the SAME shape but stored in different layout: `[n/64][k][32]`, dtype uint8. Weight is strongly coupled with platforms (CPU/CUDA) and ISAs (AVX512/AVX2/scalar). And users cannot use a generated weight in another different ISA or platform, because when loading weight into devices, the compute format is different.
Now, we use common serialized layout (`[n][k/2] uint8`) for different devices or ISAs as input `weight` of `_convert_weight_to_int4pack`, and each back chooses how to interpret as compute layout.
### Performance
Intel (R) Xeon (R) CPU Max 9480, single socket (56 cores)
There is no obvious regression of this PR.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/129940
Approved by: https://github.com/jgong5, https://github.com/lezcano, https://github.com/mingfeima
This PR:
* Sets a random seed before generating each sample for an OpInfo test. It does this by intercepting the sample input iterator via `TrackedInputIter`, optionally setting the seed to a test name specific seed before each iterator call (default is to set the seed).
* Some quick and dirty benchmarking shows (hopefully) negligible overhead from setting the random seed before each sample input generation. For a trivial (single assert) test that uses `@ops`:
* Uncovered a bunch of test issues:
* Test breakdown (>100 total)
* A lot of tolerance issues (tweaked tolerance values to fix)
* 1 broken OpInfo (`sample_inputs_masked_fill` was generating a sample of the wrong dtype)
* 3 actually broken semantics (for masked tensor; added xfails)
* 4 Jacobian mismatches (added xfails)
* 2 nan results (skip for now, need fixing)
* 3 results too far from reference result (add xfails)
* Skips MPS tests for now (there are so many failures!). Those will default to the old behavior.
**before (no seed setting):**
```
real 0m21.306s
user 0m19.053s
sys 0m5.192s
```
**after (with seed setting):**
```
real 0m21.905s
user 0m19.578s
sys 0m5.390s
```
* Utilizing the above for reproducible sample input generation, adds support for restricting the iterator to a single sample input. This is done via an env var `PYTORCH_OPINFO_SAMPLE_INPUT_INDEX` and its usage is included in the repro command.
```
======================================================================
ERROR: test_bar_add_cuda_uint8 (__main__.TestFooCUDA.test_bar_add_cuda_uint8)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/jbschlosser/branches/testing_updates/torch/testing/_internal/common_device_type.py", line 971, in test_wrapper
return test(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "/home/jbschlosser/branches/testing_updates/test/test_ops.py", line 2671, in test_bar
self.assertFalse(True)
AssertionError: True is not false
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/home/jbschlosser/branches/testing_updates/torch/testing/_internal/common_utils.py", line 2816, in wrapper
method(*args, **kwargs)
File "/home/jbschlosser/branches/testing_updates/torch/testing/_internal/common_utils.py", line 2816, in wrapper
method(*args, **kwargs)
File "/home/jbschlosser/branches/testing_updates/torch/testing/_internal/common_device_type.py", line 419, in instantiated_test
result = test(self, **param_kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jbschlosser/branches/testing_updates/torch/testing/_internal/common_utils.py", line 1426, in wrapper
fn(*args, **kwargs)
File "/home/jbschlosser/branches/testing_updates/torch/testing/_internal/common_device_type.py", line 982, in test_wrapper
raise new_e from e
Exception: Caused by sample input at index 3: SampleInput(input=Tensor[size=(10, 5), device="cuda:0", dtype=torch.uint8], args=TensorList[Tensor[size=(), device="cuda:0", dtype=torch.uint8]], kwargs={}, broadcasts_input=False, name='')
To execute this test, run the following from the base repo dir:
PYTORCH_OPINFO_SAMPLE_INPUT_INDEX=3 python test/test_ops.py -k TestFooCUDA.test_bar_add_cuda_uint8
This message can be suppressed by setting PYTORCH_PRINT_REPRO_ON_FAILURE=0
----------------------------------------------------------------------
Ran 1 test in 0.037s
FAILED (errors=1)
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/128238
Approved by: https://github.com/janeyx99, https://github.com/justinchuby
