Summary: Fixed a bunch of fbcode imports that happened to work but confused autodeps. After this autodeps still suggests "improvements" to TARGETS (which breaks our builds) but at least it can find all the imports.
Test Plan:
```
fbpython fbcode/tools/build/buck/linters/lint_autoformat.py --linter=autodeps --default-exec-timeout=1800 -- fbcode/caffe2/TARGETS fbcode/caffe2/test/TARGETS
```
Before:
```
ERROR while processing caffe2/test/TARGETS: Cannot find an owner for "test_export" (from caffe2/test/export/testing.py:229) when processing rule "test_export". Please make sure it's listed in the srcs parameter of another rule. See https://fbur$
ERROR while processing caffe2/test/TARGETS: Cannot find an owner for "testing" (from caffe2/test/export/test_export.py:87) when processing rule "test_export". Please make sure it's listed in the srcs parameter of another rule. See https://fburl$
ERROR while processing caffe2/test/TARGETS: Cannot find an owner for "test_export" (from caffe2/test/export/test_serdes.py:9) when processing rule "test_export". Please make sure it's listed in the srcs parameter of another rule. See https://fb$
ERROR while processing caffe2/test/TARGETS: Cannot find an owner for "testing" (from caffe2/test/export/test_serdes.py:10) when processing rule "test_export". Please make sure it's listed in the srcs parameter of another rule. See https://fburl$
ERROR while processing caffe2/test/TARGETS: Cannot find an owner for "testing" (from caffe2/test/export/test_retraceability.py:7) when processing rule "test_export". Please make sure it's listed in the srcs parameter of another rule. See https:$
ERROR while processing caffe2/test/TARGETS: Cannot find an owner for "test_export" (from caffe2/test/export/test_retraceability.py:6) when processing rule "test_export". Please make sure it's listed in the srcs parameter of another rule. See ht$
ERROR while processing caffe2/test/TARGETS: Cannot find an owner for "testing" (from caffe2/test/export/test_export_nonstrict.py:7) when processing rule "test_export". Please make sure it's listed in the srcs parameter of another rule. See http$
ERROR while processing caffe2/test/TARGETS: Cannot find an owner for "test_export" (from caffe2/test/export/test_export_nonstrict.py:6) when processing rule "test_export". Please make sure it's listed in the srcs parameter of another rule. See $
ERROR while processing caffe2/test/TARGETS: Cannot find an owner for "test_export" (from caffe2/test/export/test_export_training_ir_to_run_decomp.py:8) when processing rule "test_export". Please make sure it's listed in the srcs parameter of an$
ERROR while processing caffe2/test/TARGETS: Cannot find an owner for "testing" (from caffe2/test/export/test_export_training_ir_to_run_decomp.py:10) when processing rule "test_export". Please make sure it's listed in the srcs parameter of anoth$
ERROR while processing caffe2/test/TARGETS: Found "//python/typeshed_internal:typeshed_internal_library" owner for "cv2" but it is protected by visibility rules: [] (from caffe2/test/test_bundled_images.py:7) when processing rule "test_bundled_$
ERROR while processing caffe2/test/TARGETS: Cannot find an owner for "caffe2.test.profiler_test_cpp_thread_lib" (from caffe2/test/profiler/test_cpp_thread.py:29) when processing rule "profiler_test_cpp_thread". Please make sure it's listed in t$
ERROR while processing caffe2/test/TARGETS: Cannot find an owner for "torch._utils_internal.get_file_path_2" (from caffe2/test/test_custom_ops.py:23) when processing rule "custom_ops". Please make sure it's listed in the srcs parameter of anoth$
ERROR while processing caffe2/test/TARGETS: Cannot find an owner for "torch._utils_internal.get_file_path_2" (from caffe2/test/test_public_bindings.py:13) when processing rule "public_bindings". Please make sure it's listed in the srcs paramete$
ERROR while processing caffe2/test/TARGETS: Cannot find an owner for "torch._C._profiler.symbolize_tracebacks" (from caffe2/test/test_cuda.py:3348) when processing rule "test_cuda". Please make sure it's listed in the srcs parameter of another $
ERROR while processing caffe2/test/TARGETS: Cannot find an owner for "torch._C._profiler.gather_traceback" (from caffe2/test/test_cuda.py:3348) when processing rule "test_cuda". Please make sure it's listed in the srcs parameter of another rule$
ERROR while processing caffe2/test/TARGETS: Cannot find an owner for include <torch/csrc/autograd/profiler_kineto.h> (from caffe2/test/profiler/test_cpp_thread.cpp:2) when processing profiler_test_cpp_thread_lib. Some things to try:
```
Differential Revision: D62049222
Pull Request resolved: https://github.com/pytorch/pytorch/pull/135614
Approved by: https://github.com/oulgen, https://github.com/laithsakka
Fixes#131337
- add `arg_type` for workspace_arg, the type is consistent with the type in `generate_workspace_allocation()`.
- do not generate example tensors for `workspace`, and use `generate_workspace_allocation()` instead.
- add workspace allocation generation code to `kernel_autotune_calls`. e.g.
```python
workspace = empty_strided_cuda((1280, ), (1, ), torch.uint8)
workspace.zero_()
.....
triton_spl_fused_add_cumprod_0.run(buf2, arg0_1, arg1_1, workspace, 1, 10000, grid=split_scan_grid(1, 10000), stream=stream0)
del buf2, arg0_1, arg1_1, workspace
```
- add `empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda` to the header of triton autotune code.
The generated cpp has lines like below, so we also implement a `zero_()` for ` AtenTensorHandle `.
```cpp
static constexpr int64_t int_array_0[] = {1280L, };
static constexpr int64_t int_array_1[] = {1L, };
AtenTensorHandle workspace_handle;
AOTI_TORCH_ERROR_CODE_CHECK(aoti_torch_empty_strided(1, int_array_0, int_array_1, cached_torch_dtype_uint8, cached_torch_device_type_cuda, 0, &workspace_handle));
RAIIAtenTensorHandle workspace(workspace_handle);
workspace.zero_();
```
- Fix handle grid_fn for grid computation. Pass in "RBLOCK" to `split_scan_grid`
- Fix dynamic shapes:
Without the fix we generate code that looks like this `workspace = empty_strided_cuda((32*((255 + s0) // 256), ), (1, ), torch.uint8)` when doing triton autotune and `s0` is not defined.
The solution approach is to use `V.graph.sizevars.size_hint(nbytes)` to realize the workspace size for triton autotune. Note that we only realize it for triton autotune code, but not for the cpp cuda code.
- We also generate slightly different cpp code depending on if `abi_compatible` is turned on.
```cpp
RAIIAtenTensorHandle workspace(workspace_handle);
AOTI_TORCH_ERROR_CODE_CHECK(aoti_torch_zero_(workspace.get()));
```
vs
```cpp
at::Tensor workspace = at::detail::empty_strided_cuda({8L*(c10::div_floor_integer(static_cast<int64_t>((255L + s0)), static_cast<int64_t>(256L))), }, {1L, }, at::kByte, c10::DeviceType::CUDA);
workspace.zero_();
```
Test Plan:
```
TORCHINDUCTOR_ABI_COMPATIBLE=1 TORCHINDUCTOR_CPP_WRAPPER=1 python test/inductor/test_torchinductor.py -k GPUTests.test_consecutive_split_cumprod_cuda
python test/inductor/test_cuda_cpp_wrapper.py TestCudaWrapper.test_consecutive_split_cumprod_cuda_cuda_wrapper
python test/inductor/test_cuda_cpp_wrapper.py DynamicShapesCudaWrapperCudaTests.test_consecutive_split_cumprod_cuda_dynamic_shapes_cuda_wrapper
TORCHINDUCTOR_ABI_COMPATIBLE=1 python test/inductor/test_cuda_cpp_wrapper.py TestCudaWrapper.test_consecutive_split_cumprod_cuda_cuda_wrapper
TORCHINDUCTOR_CPP_WRAPPER=1 python test/inductor/test_torchinductor.py -k GPUTests.test_consecutive_split_cumprod_cuda
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/135552
Approved by: https://github.com/desertfire
Previously we were accomodating `torch._dynamo.mark_dynamic()` for export's dynamic shapes. Here we clean things up and ignore it, requiring users to specify an export input for `dynamic_shapes`.
Note: there's 4 decorators relevant to export, `mark_dynamic, maybe_mark_dynamic, mark_static, mark_unbacked`. User calls that involve export have only been `mark_dynamic()`, and we use `maybe_mark_dynamic` under the hood for `Dim.AUTO`, but we could start using others. One reason I decided to not warn and just silently ignore is these decorators cause the tensors to carry dynamic info, and it'll be hard to tell whether the markers are from export or user calls when re-exporting with the same inputs.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/135536
Approved by: https://github.com/avikchaudhuri
Optimized dynamic quantization for aarch64 was enabled by #126687 and #134897
This PR fixes an issue for aarch64 where on a [cache miss](https://github.com/pytorch/pytorch/blob/main/aten/src/ATen/native/quantized/cpu/qlinear_dynamic.cpp#L592) (e.g. if input dimensions change) [ideep::matmul_forward::compute ](https://github.com/intel/ideep/blob/pytorch-rls-v3.5.3-2/include/ideep/operators/matmul.hpp#L160) (wrongly) runs with the [default lowp_kind (u8s8)](https://github.com/intel/ideep/blob/pytorch-rls-v3.5.3-2/include/ideep/operators/matmul.hpp#L174) which is not supported by oneDNN+ACL (Arm Compute Library), causing the workload to fall back to a much slower oneDNN gemm:jit kernel
Example:
```python
import torch
DIM = 4096
INPUT_SIZE1 = 32
INPUT_SIZE2 = 16
class LinearNet(torch.nn.Module):
def __init__(self):
super().__init__()
self.fc1 = torch.nn.Linear(DIM, DIM, bias=False)
def forward(self, x):
x = self.fc1(x)
return x
input1 = torch.randn(size=(INPUT_SIZE1, DIM))
input2 = torch.randn(size=(INPUT_SIZE2, DIM))
with torch.no_grad():
model = LinearNet()
model = torch.ao.quantization.quantize_dynamic(model,{torch.nn.Linear})
model(input1) # this goes to ACL lowp_gemm
print("="*50)
model(input2) # this goes to gemm:jit without this PR, and to ACL with this PR
```
In the code snippet above:
- The matmul from `model(input1)` goes to oneDNN+ACL (in both cases, with and without the PR)
- The matmul from `model(input2)`: **Without this PR**: there's a cache miss (different input shapes) and matmul_forward::compute is run with the default lowp_kind (u8s8). Hence the matmul falls back to gemm:jit in oneDNN. However, **With this PR** the matmul goes to oneDNN+ACL which is around 10x faster than oneDNN+jit.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/135058
Approved by: https://github.com/jondea, https://github.com/malfet
Summary:
Sometimes we only want to generate a replacement for a matched pattern
once we know some information about the nodes in the pattern.
So far, we have found this the most useful to do matches based on specific
shapes of tensors flowing into functions.
Use a callback function similar to `match_filters`. By default this isn't used.
Had to make `replacement` a None-able parameter because Callable was
already used to detect a case where a graph needed to be traced.
Differential Revision: D62412628
Pull Request resolved: https://github.com/pytorch/pytorch/pull/135553
Approved by: https://github.com/SherlockNoMad
This PR:
* Implements the pre-existing `nt.to_padded_tensor(padding_val)` ATen op via the FBGEMM kernel + appropriate view gymnastics (since that kernel only handles 2D values)
* Introduces a new `_nested_from_padded_tensor` op for the reverse conversion, implemented via the reverse FBGEMM kernel + view gymnastics
* Note: there is currently no public API for this; design booted to a future PR
TODO:
* ~~Propagate min / max sequence length via the new factory function `_nested_from_padded_tensor`~~
* ~~Verify that Inductor does computation fusion via test logic~~
Pull Request resolved: https://github.com/pytorch/pytorch/pull/125947
Approved by: https://github.com/soulitzer
Fix https://github.com/pytorch/pytorch/issues/134095
This fix distributed state dict full_state_dict option hang during set_state_dict. We switch `_distribute_tensors` in _state_dict_utils.py to use `DTensor.from_local` instead of `distribute_tensor` to support FSDP2+TP 2D strided sharding use case, as `distribute_tensor` cannot handle strided sharding yet. `distribute_tensor` incurs a scatter behind the scenes, while `DTensor.from_local` takes the local slice from the full tensor on each rank to create the DTensor (no collective). This means it's the user's responsibility to make sure the full_tensor from the full_state_dict is the same across all ranks.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/135725
Approved by: https://github.com/fegin
This PR resolves#134408. Add an additional test and have passed the local test.
Do you think we should add a post-check to ensure `args` and `kwargs` are not both `None`? It seems to be possible to have modules without inputs.
This PR does not include any such post-check.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134643
Approved by: https://github.com/zou3519
**Summary**
1. This PR removes the public API `compute_local_shape` and replace its use with the more general API `compute_local_shape_and_global_offset`.
2. To keep `compute_local_shape_and_global_offset` consistent with `compute_local_shape` on empty shards, it now returns local tensor shape `(0,)` for empty shards which is more aligned with DTensor's semantics on non-participating ranks.
**Test**
`pytest test/distributed/_tensor/test_dtensor.py`
`pytest test/distributed/_tensor/test_init.py`
`pytest test/distributed/_tensor/test_tensor_ops.py`
Differential Revision: [D62415591](https://our.internmc.facebook.com/intern/diff/D62415591)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/135554
Approved by: https://github.com/tianyu-l, https://github.com/wz337
When the input format for group norm is NHWC and the device is privateuseone, it introduces an additional transpose operation. To avoid this issue, a check for the privateuseone device needs to be added here.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/135575
Approved by: https://github.com/ezyang
