2119 Commits

Author	SHA1	Message	Date
PyTorch MergeBot	15fd1ea118	Revert "[Reland] Update mypy to 1.4.1 (#105227)" ... This reverts commit c9c4f8efc3. Reverted https://github.com/pytorch/pytorch/pull/105227 on behalf of https://github.com/atalman due to trying to mitigate ci sev #105248 ([comment](https://github.com/pytorch/pytorch/pull/105227#issuecomment-1636510935))	2023-07-14 22:28:35 +00:00
Nikita Karetnikov	0c89596e4f	[OpInfo] add reference and error inputs for multi_margin_loss (#104850) ... Pull Request resolved: https://github.com/pytorch/pytorch/pull/104850 Approved by: https://github.com/ezyang	2023-07-14 21:16:09 +00:00
Nikita Shulga	c9c4f8efc3	[Reland] Update mypy to 1.4.1 (#105227) ... This PR re-lands - [Typing] Fix PEP 484 Violation (#105022) - Update mypy to 1.4.1 (#91983) That were reverted due to the conflict with internal source repo. Mostly fixes for PEP-484 violation (i.e. when default arg is set to None, but type is not annotated as optional) Plus few real fixes: - Add missing `_get_upgraders_entry_map` to `torch/_C/__init__.pyi` - Add missing return statement to `torch._export. deserialize_graph` - Fix error message in `torch.ao.ns.fx.weight_utils.get_lstm_mod_weights` - Add assert it `torch/optim/optimizer.py` that Optional list is not None TODO (in followup PR): - Fix erroneous `isinstance` check in `torch/ao/quantization/_pt2e/qat_utils.py` Pull Request resolved: https://github.com/pytorch/pytorch/pull/105227 Approved by: https://github.com/atalman, https://github.com/albanD, https://github.com/Skylion007	2023-07-14 20:45:12 +00:00
PyTorch MergeBot	3c5a494d7a	Revert "Update mypy to 1.4.1 (#91983)" ... This reverts commit 634659e262. Reverted https://github.com/pytorch/pytorch/pull/91983 on behalf of https://github.com/malfet due to It's dependent change was reverted, so reverting this one as well, to keep CI clean ([comment](https://github.com/pytorch/pytorch/pull/91983#issuecomment-1636059709))	2023-07-14 15:59:16 +00:00
Kurt Mohler	f987d11fa7	Reland: Make torch.empty* deterministic by filling with NaN or max int (#104995) ... Relands #101849 after #104302 reverted it. torchrec PR https://github.com/pytorch/torchrec/pull/1269 fixes the torchrec failure that caused #101849 to be reverted Part of #82004 Pull Request resolved: https://github.com/pytorch/pytorch/pull/104995 Approved by: https://github.com/albanD	2023-07-13 22:18:03 +00:00
Nikita Shulga	634659e262	Update mypy to 1.4.1 (#91983) ... Mostly fixes for PEP-484 violation (i.e. when default arg is set to None, but type is not annotated as optional) Plus few real fixes: - Add missing `_get_upgraders_entry_map` to `torch/_C/__init__.pyi` - Add missing return statement to `torch._export. deserialize_graph` - Fix error message in `torch.ao.ns.fx.weight_utils.get_lstm_mod_weights` - TODO (in followup PR): - Fix erroneous `isinstance` check in `torch/ao/quantization/_pt2e/qat_utils.py` Pull Request resolved: https://github.com/pytorch/pytorch/pull/91983 Approved by: https://github.com/kit1980, https://github.com/ZainRizvi, https://github.com/huydhn, https://github.com/thiagocrepaldi, https://github.com/aaronenyeshi	2023-07-13 16:30:36 +00:00
yanbing-j	053654b9cf	Optimize scatter_add/scatter_reduce in BFloat16/Half data type in CPU backend (#103427) ... ### Description This PR is to optimize scatter_add/scatter_reduce of BFloat16/Half data type in CPU backend, which is one task in https://github.com/pyg-team/pytorch_geometric/issues/7057. Main point is creating a buffer among threads to accumulate intermediate data as fp32 data type. Next step: - [x] Add benchmarks - [x] Extend to Half - [x] Simplify code ### Performance test (Updated) Test BFloat16 in Intel(R) Xeon(R) Platinum 8380 CPU @ 2.30GHz With jemalloc and iomp Single socket (40C)  Single core  Pull Request resolved: https://github.com/pytorch/pytorch/pull/103427 Approved by: https://github.com/mingfeima, https://github.com/albanD	2023-07-13 09:34:29 +00:00
Aaron Gokaslan	2f95a3d0fc	[BE]: Apply ruff PERF fixes to torch (#104917) ... Applies automated ruff fixes in the PERF modules and enables all automatic ones. I also updated ruff which applied some additional fixes. Pull Request resolved: https://github.com/pytorch/pytorch/pull/104917 Approved by: https://github.com/ezyang, https://github.com/albanD	2023-07-11 20:45:21 +00:00
Kurt Mohler	0ccdbbe233	Add deterministic path for Tensor.resize_ (#104300) ... New elements added to a tensor by `torch.Tensor.resize_` are set to NaN/MAX_INT when deterministic mode is turned on. When `torch.Tensor.resize_` is called on a quantized tensor and deterministic mode is turned on, a nondeterministic error is raised. Part of #82004 Pull Request resolved: https://github.com/pytorch/pytorch/pull/104300 Approved by: https://github.com/albanD	2023-07-07 00:22:13 +00:00
Nikita Shulga	ddd7da7546	Enable more tests (#104437) ... Remove `test_segment_reductions` from list of blocklisted tests Remove `@onlyCPU` qualifier from test_segment_reductions as it has CUDA specific parts Fixes https://github.com/pytorch/pytorch/issues/104410 Pull Request resolved: https://github.com/pytorch/pytorch/pull/104437 Approved by: https://github.com/atalman, https://github.com/huydhn	2023-06-30 16:26:11 +00:00
Amr Elshennawy	a78bddac01	Revert D46920584: Multisect successfully blamed D46920584 for test or build failures (#104269) (#104302) ... Summary: This diff is reverting D46920584 D46920584: Make `torch.empty*` deterministic by filling with NaN or max int value (#101849) by generatedunixname499836121 has been identified to be causing the following test or build failures: Tests affected: - [torchrec/distributed/composable/tests:test_fsdp - torchrec.distributed.composable.tests.test_fsdp.FullyShardTest: test_composable_checkpoint](https://www.internalfb.com/intern/test/281475062923125/) Here's the Multisect link: https://www.internalfb.com/multisect/2341386 Here are the tasks that are relevant to this breakage: We're generating a revert to back out the changes in this diff, please note the backout may land if someone accepts it. If you believe this diff has been generated in error you may Commandeer and Abandon it. Test Plan: NA Reviewed By: huydhn, osalpekar Differential Revision: D46997394 Pull Request resolved: https://github.com/pytorch/pytorch/pull/104302 Approved by: https://github.com/osalpekar	2023-06-29 20:20:58 +00:00
Richard Barnes	8cad411d3d	Fix UntypedStorage pin error (#104355) ... Summary: Fixes: ``` TypeError: cannot pin 'torch.storage.UntypedStorage' only CPU memory can be pinned ``` Test Plan: Sandcastle Differential Revision: D47093797 Pull Request resolved: https://github.com/pytorch/pytorch/pull/104355 Approved by: https://github.com/malfet	2023-06-29 16:06:52 +00:00
Kurt Mohler	2642f31e4c	Make torch.empty* deterministic by filling with NaN or max int value (#101849) ... Part of #82004 Pull Request resolved: https://github.com/pytorch/pytorch/pull/101849 Approved by: https://github.com/lezcano, https://github.com/albanD, https://github.com/kulinseth	2023-06-21 02:53:22 +00:00
Elias Ellison	40d70ba7ed	Remove a number of fixed skips (#103162) ... Also adds `PYTORCH_TEST_WITH_AOT_EAGER` to distinguish errors coming from aot_autograd and not inductor (not tested in ci, but useful for local debugging) Pull Request resolved: https://github.com/pytorch/pytorch/pull/103162 Approved by: https://github.com/desertfire	2023-06-08 17:37:59 +00:00
ts	d2d03f0f44	Make index_add_ error if input source shape is wrong (#100321) ... Fixes #92576 , checking the following as described in the documentation: "source.shape[dim] == len(index) and source.shape[i] == self.shape[i] for i != dim" Would be happy to iterate on this if there are any issues, and would be happy to implement the checking for the CUDA and MPS implementations of index_add_. Pull Request resolved: https://github.com/pytorch/pytorch/pull/100321 Approved by: https://github.com/lezcano	2023-06-08 06:51:10 +00:00
Lu Fang	1237502213	Introduce fast path for cuda_equal (#102714) ... We introduce the same trick for cuda_equal. Assuming in cuda_equal, the flags are already handled correctly. Added the tests for cuda part. Pull Request resolved: https://github.com/pytorch/pytorch/pull/102714 Approved by: https://github.com/ezyang	2023-06-03 05:49:49 +00:00
Shiyan Deng	685505353a	Back out "Add PyObject preservation for UntypedStorage (#97470)" (#102553) ... Summary: Original commit changeset: c24708d18ccb Original Phabricator Diff: D46159983 Test Plan: SL tests and CI Differential Revision: D46284986 Pull Request resolved: https://github.com/pytorch/pytorch/pull/102553 Approved by: https://github.com/DanilBaibak	2023-06-01 17:23:43 +00:00
Edward Z. Yang	818d92f58c	Support resize on meta storage (#101988) ... Signed-off-by: Edward Z. Yang <ezyang@meta.com> Pull Request resolved: https://github.com/pytorch/pytorch/pull/101988 Approved by: https://github.com/albanD, https://github.com/bdhirsh	2023-05-25 04:41:45 +00:00
PyTorch MergeBot	210fc28d5e	Revert "Support resize on meta storage (#101988)" ... This reverts commit 7d1ba0a92a. Reverted https://github.com/pytorch/pytorch/pull/101988 on behalf of https://github.com/osalpekar due to Need to revert and rebase this in order to unblock train import ([comment](https://github.com/pytorch/pytorch/pull/101988#issuecomment-1561970230))	2023-05-24 21:51:33 +00:00
Wang, Eikan	2e18dd2bdc	Improve bf16 neg by bypassing the convertion between BF16 and FP32 (#99711) ... Pull Request resolved: https://github.com/pytorch/pytorch/pull/99711 Approved by: https://github.com/mingfeima, https://github.com/jgong5, https://github.com/desertfire	2023-05-24 03:25:23 +00:00
Kazuaki Ishizaki	be5e77ca4c	Make _StorageBase.byteswap faster ( > 10000x) (#101925) ... This PR addresses #101690. This PR implement faster data elements swap in `_StorageBase` using C++ rather than using Python. This PR helps such a situation that a large model saved on a little-endian machine will be loaded on a big-endian machine. TODO: - [x] Add test cases - [x] Add performance comparison before and after the PR - [ ] (Optional) Investigate further opportunities for performance improvements by [SIMDization](https://dev.to/wunk/fast-array-reversal-with-simd-j3p) Fixes #101690 Pull Request resolved: https://github.com/pytorch/pytorch/pull/101925 Approved by: https://github.com/mikaylagawarecki	2023-05-24 00:13:41 +00:00
Edward Z. Yang	7d1ba0a92a	Support resize on meta storage (#101988) ... Signed-off-by: Edward Z. Yang <ezyang@meta.com> Pull Request resolved: https://github.com/pytorch/pytorch/pull/101988 Approved by: https://github.com/albanD, https://github.com/bdhirsh	2023-05-23 16:49:17 +00:00
Kurt Mohler	5fe629e314	Add PyObject preservation for UntypedStorage (#97470) ... Part of #91395 Pull Request resolved: https://github.com/pytorch/pytorch/pull/97470 Approved by: https://github.com/ezyang	2023-05-23 01:27:30 +00:00
drisspg	6f13d6892a	Add meta support for multinomial (#101324) ... # Summary Found this when trying to compile the text gen loop of nanogpt here: b33289942b/torchbenchmark/models/nanogpt_generate/model.py (L322) Pull Request resolved: https://github.com/pytorch/pytorch/pull/101324 Approved by: https://github.com/ngimel	2023-05-19 00:04:26 +00:00
Edward Z. Yang	c567748e16	Make interpolate_bilinear deterministic using decomposition (#101115) ... Signed-off-by: Edward Z. Yang <ezyang@meta.com> Pull Request resolved: https://github.com/pytorch/pytorch/pull/101115 Approved by: https://github.com/ngimel	2023-05-11 22:48:01 +00:00
Yu, Guangye	14964b3aa5	Add is_xpu to torch type (#101072) ... # Motivate Without this PR: ```python >>>import torch >>>torch.IntTensor.is_cuda False >>>torch.IntTensor.is_xpu <attribute 'is_xpu' of 'torch._C._TensorBase' objects> ``` With this PR: ```python >>>import torch >>>torch.IntTensor.is_xpu False ``` Align to CUDA, some customer code use is_xpu to check the backend. Without this PR, the check is always True which result in an unexpected behavior Pull Request resolved: https://github.com/pytorch/pytorch/pull/101072 Approved by: https://github.com/mikaylagawarecki	2023-05-11 17:50:59 +00:00
vfdev-5	622e582a2b	Register get_cpu_capability for jit (#100723) ... Description: Context: In torchvision we ensure that functional ops are torchscriptable. Recently exposed `torch.backends.cpu.get_cpu_capability()` in https://github.com/pytorch/pytorch/pull/100164 is failing in torchvision CI ``` RuntimeError: Python builtin <built-in function _get_cpu_capability> is currently not supported in Torchscript: File "/usr/local/lib/python3.10/dist-packages/torch/backends/cpu/__init__.py", line 17 - "AVX512" """ return torch._C._get_cpu_capability() ~~~~~~~~~~~~~~~~~~~~~~~~~~~~ <--- HERE ``` Ref: https://github.com/pytorch/vision/pull/7557 In this PR, `torch._C._get_cpu_capability()` is explicitly registered for JIT and tested. Pull Request resolved: https://github.com/pytorch/pytorch/pull/100723 Approved by: https://github.com/albanD	2023-05-09 09:52:29 +00:00
Valentin Andrei	9bc68fcd25	[pytorch] Accelerate indexing_backward_kernel with duplicates (#99441 attempt 2) (#100505) ... By knowing the stride value ahead of time, we can simplify the kernel code as follows: If stride == 1 we can use the whole warp to reduce the gradients If stride < warp_size we don't need the internal while (start_feature < stride) loop as blockDim.x is always 32 This changes improve the performance of the kernel when duplicates are present and do not affect the performance with low amount of duplicates. The implementation is deterministic. The proposed implementation uses opmath_t to accumulate in registers the gradient values so when using FP16/BF16 it may overflow if the number of elements is large. This is different from the initial implementation who accumulates in scalar_t and does not overflow. In addition, when the stride is 1, we are using warp shuffles to sum the gradient so the order of the addition is slightly different than a reference implementation which causes some minor numerical differences when compared to a reference. TEST CODE: ``` # The first element is the number of iterations. # The second represents the number of unique elements. If # set to 0, the number of unique elements is equal to the # number of elements. # The remaining elements are the tensor dimensions. basic_indexing_tests = [ [10, 0, 12345], [10, 4, 12345], [10, 16, 512, 512, 32], [10, 0, 4, 4], [10, 0, 32, 32], [10, 8, 32, 32], [10, 8, 64, 32, 16], [10, 0, 64, 32, 16], [10, 16, 512, 512, 32], [10, 0, 675, 999, 13], [10, 0, 123, 456, 31], [10, 0, 512, 512, 32], [10, 4, 512, 512, 32], [10, 2, 512, 512, 32], [10, 0, 128, 128, 16, 16], [10, 8, 128, 126, 16, 16], [10, 4, 128, 126, 16, 16], [10, 0, 64, 64, 16, 16, 16], [10, 8, 64, 64, 16, 16, 16], [10, 2, 64, 64, 16, 16, 16], [10, 1, 64, 64, 16, 16, 16], ] def run_basic_indexing_on_device(x, index, expected, device_string, iters): x_dev = x.to(device_string) x_dev = x_dev.detach().requires_grad_() index_dev = index.to(device_string) # Run backward pass; keep gradients and measure time torch.cuda.synchronize() t_bw_s = time() for _ in range(iters): y = x_dev[index_dev] z = y.sum() z.backward() torch.cuda.synchronize() t_bw_s = (time() - t_bw_s) / iters return (x_dev.grad, t_bw_s) def run_basic_indexing_test(test_input): tensor_size = tuple(test_input[:5]) niters = test_input[0] num_unique = test_input[1] tensor_size = tuple(test_input[2:]) numel = 1 for dim in tensor_size: numel *= dim if num_unique == 0: num_unique = numel index = torch.randint(0, num_unique, tensor_size, dtype=torch.long, device="cpu") x = torch.randn((numel,), dtype=torch.float32, device="cuda") index = index.detach() x = x.detach().requires_grad_() (cpu_grad, t_bw_cpu) = run_basic_indexing_on_device(x, index, numel / 2, "cpu", 1) (gpu_grad, t_bw_gpu) = run_basic_indexing_on_device(x, index, numel / 2, "cuda", 1) max_delta = torch.max(torch.abs(cpu_grad - gpu_grad.to("cpu"))) missmatches = torch.nonzero(torch.abs(cpu_grad - gpu_grad.to("cpu"))) (gpu_grad_perf, t_gpu) = run_basic_indexing_on_device( x, index, numel / 2, "cuda", niters ) print( "test = {}, delta = {:.5f}, missmatches = {} duration_ms = {:.3f}".format( tuple(test_input), max_delta, missmatches, t_gpu * 1000.0 ) ) if torch.numel(missmatches) > 0: print("cpu grad = {}", cpu_grad[missmatches]) print("gpu grad = {}", gpu_grad[missmatches]) ``` RESULTS: ``` Default Implementation test = (1, 0, 12345), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 0.726 test = (1, 4, 12345), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 0.867 test = (1, 16, 512, 512, 32), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 80.514 test = (1, 0, 4, 4), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 0.689 test = (1, 0, 32, 32), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 0.547 test = (1, 8, 32, 32), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 0.537 test = (1, 8, 64, 32, 16), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 1.199 test = (1, 0, 64, 32, 16), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 0.584 test = (1, 16, 512, 512, 32), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 80.055 test = (1, 0, 675, 999, 13), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 8.411 test = (1, 0, 123, 456, 31), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 2.419 test = (1, 0, 512, 512, 32), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 8.048 test = (1, 4, 512, 512, 32), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 307.633 test = (1, 2, 512, 512, 32), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 606.403 test = (1, 0, 128, 128, 16, 16), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 4.099 test = (1, 8, 128, 126, 16, 16), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 76.813 test = (1, 4, 128, 126, 16, 16), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 148.760 test = (1, 0, 64, 64, 16, 16, 16), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 16.547 test = (1, 8, 64, 64, 16, 16, 16), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 317.583 test = (1, 2, 64, 64, 16, 16, 16), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 1204.800 test = (1, 1, 64, 64, 16, 16, 16), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 2412.133 Small Stride Kernel Version test = (1, 0, 12345), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 0.904 test = (1, 4, 12345), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 2.156 test = (1, 16, 512, 512, 32), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 308.878 test = (1, 0, 4, 4), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 0.566 test = (1, 0, 32, 32), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 0.540 test = (1, 8, 32, 32), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 0.550 test = (1, 8, 64, 32, 16), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 2.868 test = (1, 0, 64, 32, 16), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 0.656 test = (1, 16, 512, 512, 32), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 307.856 test = (1, 0, 675, 999, 13), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 6.624 test = (1, 0, 123, 456, 31), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 1.837 test = (1, 0, 512, 512, 32), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 6.274 test = (1, 4, 512, 512, 32), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 1127.040 test = (1, 2, 512, 512, 32), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 2123.942 test = (1, 0, 128, 128, 16, 16), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 3.282 test = (1, 8, 128, 126, 16, 16), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 288.997 test = (1, 4, 128, 126, 16, 16), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 547.267 test = (1, 0, 64, 64, 16, 16, 16), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 12.844 test = (1, 8, 64, 64, 16, 16, 16), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 1178.934 test = (1, 2, 64, 64, 16, 16, 16), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 4262.042 test = (1, 1, 64, 64, 16, 16, 16), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 8172.318 Stride 1 Kernel Version test = (1, 0, 12345), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 0.692 test = (1, 4, 12345), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 0.834 test = (1, 16, 512, 512, 32), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 81.023 test = (1, 0, 4, 4), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 0.631 test = (100, 0, 32, 32), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 0.491 test = (100, 8, 32, 32), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 0.477 test = (50, 8, 64, 32, 16), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 0.561 test = (50, 0, 64, 32, 16), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 0.516 test = (16, 10, 512, 512, 32), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 126.455 test = (10, 0, 675, 999, 13), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 8.238 test = (10, 0, 123, 456, 31), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 1.520 test = (10, 0, 512, 512, 32), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 7.854 test = (10, 4, 512, 512, 32), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 306.327 test = (10, 2, 512, 512, 32), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 610.498 test = (5, 0, 128, 128, 16, 16), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 3.684 test = (5, 8, 128, 126, 16, 16), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 75.604 test = (5, 4, 128, 126, 16, 16), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 148.679 test = (1, 0, 64, 64, 16, 16, 16), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 16.525 test = (1, 8, 64, 64, 16, 16, 16), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 315.095 test = (1, 2, 64, 64, 16, 16, 16), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 1214.715 ``` Pull Request resolved: https://github.com/pytorch/pytorch/pull/100505 Approved by: https://github.com/ngimel	2023-05-03 23:52:58 +00:00
vfdev-5	6a12f10b08	Publicly exposing torch.backends.cpu.get_cpu_capability() (#100164) ... Description: - As suggested by Nikita, created `torch.backends.cpu` submodule and exposed `get_cpu_capability`. - In torchvision Resize method we want to know current cpu capability in order to pick appropriate codepath depending on cpu capablities Newly coded vectorized resize of uint8 images on AVX2 supported CPUs is now faster than older way (uint8->float->resize->uint8). However, on non-avx hardware (e.g. Mac M1) certain configs are slower using native uint8. Pull Request resolved: https://github.com/pytorch/pytorch/pull/100164 Approved by: https://github.com/albanD, https://github.com/malfet	2023-05-03 19:02:07 +00:00
PyTorch MergeBot	1114673c90	Revert "[pytorch] Accelerate indexing_backward_kernel with duplicates (#99441)" ... This reverts commit 97afbcbc80. Reverted https://github.com/pytorch/pytorch/pull/99441 on behalf of https://github.com/ngimel due to breaks ROCM ([comment](https://github.com/pytorch/pytorch/pull/99441#issuecomment-1531804487))	2023-05-02 16:46:04 +00:00
Lu Fang	090ec55f8d	Only skip in torch inductor test ... Differential Revision: D45464303nnPull Request resolved: https://github.com/pytorch/pytorch/pull/100435	2023-05-01 22:21:37 -07:00
Lu Fang	429155b3c8	Disable some check to get the test pass ... Differential Revision: D45437730nnPull Request resolved: https://github.com/pytorch/pytorch/pull/100364	2023-05-01 16:28:12 -07:00
valentinandrei	97afbcbc80	[pytorch] Accelerate indexing_backward_kernel with duplicates (#99441) ... By knowing the stride value ahead of time, we can simplify the kernel code as follows: If `stride == 1` we can use the whole warp to reduce the gradients If `stride < warp_size` we don't need the internal `while (start_feature < stride)` loop as `blockDim.x` is always 32 This changes improve the performance of the kernel when duplicates are present and do not affect the performance with low amount of duplicates. The implementation is deterministic. The proposed implementation uses `opmath_t` to accumulate in registers the gradient values so when using FP16/BF16 it may overflow if the number of elements is large. This is different from the initial implementation who accumulates in `scalar_t` and does not overflow. In addition, when the stride is 1, we are using warp shuffles to sum the gradient so the order of the addition is slightly different than a reference implementation which causes some minor numerical differences when compared to a reference. TEST CODE: ``` # The first element is the number of iterations. # The second represents the number of unique elements. If # set to 0, the number of unique elements is equal to the # number of elements. # The remaining elements are the tensor dimensions. basic_indexing_tests = [ [10, 0, 12345], [10, 4, 12345], [10, 16, 512, 512, 32], [10, 0, 4, 4], [10, 0, 32, 32], [10, 8, 32, 32], [10, 8, 64, 32, 16], [10, 0, 64, 32, 16], [10, 16, 512, 512, 32], [10, 0, 675, 999, 13], [10, 0, 123, 456, 31], [10, 0, 512, 512, 32], [10, 4, 512, 512, 32], [10, 2, 512, 512, 32], [10, 0, 128, 128, 16, 16], [10, 8, 128, 126, 16, 16], [10, 4, 128, 126, 16, 16], [10, 0, 64, 64, 16, 16, 16], [10, 8, 64, 64, 16, 16, 16], [10, 2, 64, 64, 16, 16, 16], [10, 1, 64, 64, 16, 16, 16], ] def run_basic_indexing_on_device(x, index, expected, device_string, iters): x_dev = x.to(device_string) x_dev = x_dev.detach().requires_grad_() index_dev = index.to(device_string) # Run backward pass; keep gradients and measure time torch.cuda.synchronize() t_bw_s = time() for _ in range(iters): y = x_dev[index_dev] z = y.sum() z.backward() torch.cuda.synchronize() t_bw_s = (time() - t_bw_s) / iters return (x_dev.grad, t_bw_s) def run_basic_indexing_test(test_input): tensor_size = tuple(test_input[:5]) niters = test_input[0] num_unique = test_input[1] tensor_size = tuple(test_input[2:]) numel = 1 for dim in tensor_size: numel *= dim if num_unique == 0: num_unique = numel index = torch.randint(0, num_unique, tensor_size, dtype=torch.long, device="cpu") x = torch.randn((numel,), dtype=torch.float32, device="cuda") index = index.detach() x = x.detach().requires_grad_() (cpu_grad, t_bw_cpu) = run_basic_indexing_on_device(x, index, numel / 2, "cpu", 1) (gpu_grad, t_bw_gpu) = run_basic_indexing_on_device(x, index, numel / 2, "cuda", 1) max_delta = torch.max(torch.abs(cpu_grad - gpu_grad.to("cpu"))) missmatches = torch.nonzero(torch.abs(cpu_grad - gpu_grad.to("cpu"))) (gpu_grad_perf, t_gpu) = run_basic_indexing_on_device( x, index, numel / 2, "cuda", niters ) print( "test = {}, delta = {:.5f}, missmatches = {} duration_ms = {:.3f}".format( tuple(test_input), max_delta, missmatches, t_gpu * 1000.0 ) ) if torch.numel(missmatches) > 0: print("cpu grad = {}", cpu_grad[missmatches]) print("gpu grad = {}", gpu_grad[missmatches]) ``` RESULTS: ``` Default Implementation test = (1, 0, 12345), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 0.726 test = (1, 4, 12345), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 0.867 test = (1, 16, 512, 512, 32), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 80.514 test = (1, 0, 4, 4), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 0.689 test = (1, 0, 32, 32), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 0.547 test = (1, 8, 32, 32), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 0.537 test = (1, 8, 64, 32, 16), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 1.199 test = (1, 0, 64, 32, 16), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 0.584 test = (1, 16, 512, 512, 32), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 80.055 test = (1, 0, 675, 999, 13), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 8.411 test = (1, 0, 123, 456, 31), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 2.419 test = (1, 0, 512, 512, 32), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 8.048 test = (1, 4, 512, 512, 32), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 307.633 test = (1, 2, 512, 512, 32), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 606.403 test = (1, 0, 128, 128, 16, 16), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 4.099 test = (1, 8, 128, 126, 16, 16), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 76.813 test = (1, 4, 128, 126, 16, 16), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 148.760 test = (1, 0, 64, 64, 16, 16, 16), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 16.547 test = (1, 8, 64, 64, 16, 16, 16), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 317.583 test = (1, 2, 64, 64, 16, 16, 16), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 1204.800 test = (1, 1, 64, 64, 16, 16, 16), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 2412.133 Small Stride Kernel Version test = (1, 0, 12345), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 0.904 test = (1, 4, 12345), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 2.156 test = (1, 16, 512, 512, 32), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 308.878 test = (1, 0, 4, 4), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 0.566 test = (1, 0, 32, 32), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 0.540 test = (1, 8, 32, 32), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 0.550 test = (1, 8, 64, 32, 16), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 2.868 test = (1, 0, 64, 32, 16), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 0.656 test = (1, 16, 512, 512, 32), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 307.856 test = (1, 0, 675, 999, 13), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 6.624 test = (1, 0, 123, 456, 31), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 1.837 test = (1, 0, 512, 512, 32), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 6.274 test = (1, 4, 512, 512, 32), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 1127.040 test = (1, 2, 512, 512, 32), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 2123.942 test = (1, 0, 128, 128, 16, 16), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 3.282 test = (1, 8, 128, 126, 16, 16), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 288.997 test = (1, 4, 128, 126, 16, 16), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 547.267 test = (1, 0, 64, 64, 16, 16, 16), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 12.844 test = (1, 8, 64, 64, 16, 16, 16), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 1178.934 test = (1, 2, 64, 64, 16, 16, 16), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 4262.042 test = (1, 1, 64, 64, 16, 16, 16), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 8172.318 Stride 1 Kernel Version test = (1, 0, 12345), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 0.692 test = (1, 4, 12345), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 0.834 test = (1, 16, 512, 512, 32), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 81.023 test = (1, 0, 4, 4), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 0.631 test = (100, 0, 32, 32), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 0.491 test = (100, 8, 32, 32), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 0.477 test = (50, 8, 64, 32, 16), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 0.561 test = (50, 0, 64, 32, 16), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 0.516 test = (16, 10, 512, 512, 32), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 126.455 test = (10, 0, 675, 999, 13), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 8.238 test = (10, 0, 123, 456, 31), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 1.520 test = (10, 0, 512, 512, 32), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 7.854 test = (10, 4, 512, 512, 32), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 306.327 test = (10, 2, 512, 512, 32), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 610.498 test = (5, 0, 128, 128, 16, 16), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 3.684 test = (5, 8, 128, 126, 16, 16), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 75.604 test = (5, 4, 128, 126, 16, 16), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 148.679 test = (1, 0, 64, 64, 16, 16, 16), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 16.525 test = (1, 8, 64, 64, 16, 16, 16), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 315.095 test = (1, 2, 64, 64, 16, 16, 16), delta = 0.00000, missmatches = tensor([], size=(0, 1), dtype=torch.int64) duration_ms = 1214.715 ``` Pull Request resolved: https://github.com/pytorch/pytorch/pull/99441 Approved by: https://github.com/ngimel	2023-05-01 22:41:00 +00:00
Lu Fang	d7fa7fa8cf	Introduce fast path in the CPU equal op ... Differential Revision: D45282119nnPull Request resolved: https://github.com/pytorch/pytorch/pull/100024	2023-04-28 16:00:17 -07:00
kshitij12345	61dffa61c3	[fix] masked_scatter_: non-contiguous self (#100232) ... Fixes https://github.com/pytorch/pytorch/issues/99638 Pull Request resolved: https://github.com/pytorch/pytorch/pull/100232 Approved by: https://github.com/ngimel	2023-04-28 18:12:23 +00:00
dujinhang	9cd48b0575	Add warning information for dtypetensor. (#99521) ... Fixes #ISSUE_NUMBER Without affecting the existing cpu/cuda logic, a separate interface is provided for the custom backend and users can choose whether to use the interface function which provides 10 tensor types with custom backend variations. Therefore, users can use torch.set_deafult_tensor_type to set the default device tensor type, or use torch.xxx.dtypetensor to create a tensor.For example，torch.set_deafult_tensor_type(torch.foo.DoubleTensor) or torch.foo.DoubleTensor([]). @albanD , please review my changes. Pull Request resolved: https://github.com/pytorch/pytorch/pull/99521 Approved by: https://github.com/albanD	2023-04-28 18:01:45 +00:00
Larry Liu	687afeb686	[dynamo][numpy] Add NumpyTensorVariable to translate ndarray attribute calls to tensor attributes (#95849) ... Issue: #93684 # Problem Reduce graph breaks when dynamo compiles python functions containing numpy functions and ndarray operations. # Design (as I know it) * Use torch_np.ndarray(a wrapper of tensor) to back a `VariableTracker`: `NumpyTensorVariable`. * Translate all attributes and methods calls, on ndarray, to torch_np.ndarray equivalent. This PR adds `NumpyTensorVariable` and supports: 1. tensor to ndarray, ndarray to tensor 2. numpy functions such as numpy.meshgrid() 3. ndarray attributes such as `itemsize`, `stride` Next PR will handle returning `np.ndarray` and add support for ndarray methods Pull Request resolved: https://github.com/pytorch/pytorch/pull/95849 Approved by: https://github.com/ezyang	2023-04-27 16:18:35 +00:00
Jiong Gong	e5c9a0fcf5	[dynamo] avoid graph break on repeat_interleave.self_int (#99528) ... Address convit_base failure: https://github.com/pytorch/torchdynamo/issues/1886 mentioned in https://github.com/pytorch/pytorch/issues/93777 Also for models like EleutherAI/gpt-j-6B. Pull Request resolved: https://github.com/pytorch/pytorch/pull/99528 Approved by: https://github.com/ezyang	2023-04-25 04:47:39 +00:00
BJ Hargrave	555ab310dc	Add itemsize and nbytes properties to Tensor (#98322) ... Adds properties for itemsize and nbytes to Tensor matching the properties in NumPy. Fixes https://github.com/pytorch/pytorch/issues/12728 Pull Request resolved: https://github.com/pytorch/pytorch/pull/98322 Approved by: https://github.com/ezyang	2023-04-05 12:11:55 +00:00
Jason Ansel	b96fe9b61c	Fix issues related to ClassInstantier in HF models (#97997) ... Pull Request resolved: https://github.com/pytorch/pytorch/pull/97997 Approved by: https://github.com/anijain2305	2023-04-04 00:01:08 +00:00
Jason Ansel	71d850a100	[inductor] Fallback on complex64 kernels (#98155) ... Later PRs in this stack fixe graph breaks in GoogleFnet which triggers errors from inductor trying to compile torch.complex64, this fixes that. Pull Request resolved: https://github.com/pytorch/pytorch/pull/98155 Approved by: https://github.com/anijain2305, https://github.com/ngimel	2023-04-03 01:06:43 +00:00
Nikita Shulga	2af09393f9	masked_scatter should accept only bool masks (#97999) ... Modify test_torch to check that assert is raised in this case torch.uint8 usage has been deprecated for a few releases, and errors has been raised for other dtypes on CUDA device, but not on CPU. This PR finally restricts mask to just `torch.bool` See https://github.com/pytorch/pytorch/pull/96594 as an example doing it for `torch.masked_fill` Fixes https://github.com/pytorch/pytorch/issues/94634 Pull Request resolved: https://github.com/pytorch/pytorch/pull/97999 Approved by: https://github.com/ngimel	2023-04-01 23:25:25 +00:00
Nikita Shulga	a1dc2b1774	[BE] Remove bool dtype from masked_scatter (#98015) ... <!-- copilot:summary --> ### <samp>🤖 Generated by Copilot at a9fa438</samp> Simplified a test function for `torch.masked_scatter` in `test/test_torch.py` by removing redundant and unnecessary code. Pull Request resolved: https://github.com/pytorch/pytorch/pull/98015 Approved by: https://github.com/ezyang	2023-03-31 01:45:57 +00:00
Aleksei Nikiforov	8289120ef0	Revert "test/test_torch.py: fix TestTorch::test_from_buffer test (#96952)" (#97759) ... Tests were already fixed in https://github.com/pytorch/pytorch/pull/92834, and these changes instead of also fixing tests are now breaking them again. This reverts commit 7f94ea8492. Pull Request resolved: https://github.com/pytorch/pytorch/pull/97759 Approved by: https://github.com/janeyx99	2023-03-28 18:43:08 +00:00
Nikita Shulga	542fb0b1fa	Specify file encoding in test_torch.py (#97628) ... Attempt to fix ``` UnicodeDecodeError: 'ascii' codec can't decode byte 0xe4 in position 5260: ordinal not in range(128) ``` in https://github.com/pytorch/pytorch/actions/runs/4522628359/jobs/7965372405 In general, it's a good practice to explicitly specify encoding, as otherwise it depends on environment variable and makes tests failures unpredicatble Pull Request resolved: https://github.com/pytorch/pytorch/pull/97628 Approved by: https://github.com/dagitses, https://github.com/kit1980	2023-03-26 20:03:25 +00:00
Edward Z. Yang	37faa48844	DCE inference graphs too (#97275) ... I added a bunch of asserts to verify that I didn't accidentally kill copy_ in the graph, hopefully this combined with our existing tests is good enough. Signed-off-by: Edward Z. Yang <ezyang@meta.com> Pull Request resolved: https://github.com/pytorch/pytorch/pull/97275 Approved by: https://github.com/bdhirsh	2023-03-23 07:02:52 +00:00
Kurt Mohler	fbc803df0c	Only warn once for TypedStorage deprecation (#97379) ... Fixes #97207 Pull Request resolved: https://github.com/pytorch/pytorch/pull/97379 Approved by: https://github.com/ezyang	2023-03-23 05:40:23 +00:00
Aleksei Nikiforov	7f94ea8492	test/test_torch.py: fix TestTorch::test_from_buffer test (#96952) ... Use opposite encoding on big endian systems Pull Request resolved: https://github.com/pytorch/pytorch/pull/96952 Approved by: https://github.com/ezyang	2023-03-17 14:36:33 +00:00
mingfeima	06054d7df0	fix random output issue on index_select when src is scalar and index is empty (#96408) ... Fix https://github.com/pytorch/pytorch/issues/94340 Pull Request resolved: https://github.com/pytorch/pytorch/pull/96408 Approved by: https://github.com/ngimel	2023-03-16 05:30:45 +00:00
Kurt Mohler	06b7285163	Add torch._check* functions analogous to C++ TORCH_CHECK* (#88725) ... Adds `_check`, `_check_index`, `_check_value`, `_check_type`, `_check_not_implemented`, `_check_tensor_all` Part of #72948 Pull Request resolved: https://github.com/pytorch/pytorch/pull/88725 Approved by: https://github.com/albanD	2023-03-14 20:44:50 +00:00
kvathupo	2b9d9bcb85	Deprecate non-bool masks in masked_fill (#96594) ... __What?__ Per discussion at #94634, deprecate `masked_fill` with non-bool masks. Deprecation warnings were previously added by #22261, but not for Apple MPS. I can revert the MPS changes if deprecation warnings are wanted first tho. See also #96112. Fixes #85063 and #89320. __Further Development?__ - Fixed the mask dtype checking for the cuda dispatch for `masked_fill` in `aten/src/ATen/native/cuda/Indexing.cu` Pull Request resolved: https://github.com/pytorch/pytorch/pull/96594 Approved by: https://github.com/malfet, https://github.com/ngimel	2023-03-13 01:41:47 +00:00
Nikita Shulga	1cd0929bf7	[BC] Allow only bool tensors as mask in masked_select (#96112) ... `byte` support was marked as deprecated in 1.8, so it's fine to remove this in 2.1 (or even 2.0) Deprecation warning was added by https://github.com/pytorch/pytorch/pull/22261 Also, fix bunch of syntactic errors in comments Pull Request resolved: https://github.com/pytorch/pytorch/pull/96112 Approved by: https://github.com/ezyang	2023-03-07 01:43:14 +00:00
puririshi98	8aa34602f7	Jetson Update for CI Redo (#94549) ... Pull Request resolved: https://github.com/pytorch/pytorch/pull/94549 Approved by: https://github.com/ezyang, https://github.com/malfet	2023-02-21 17:13:38 +00:00
Yuxin Wu	9bb2fe3eae	fix numpy1.24 deprecations in unittests (#93997) ... Fixes https://github.com/pytorch/pytorch/issues/91329 Pull Request resolved: https://github.com/pytorch/pytorch/pull/93997 Approved by: https://github.com/ngimel, https://github.com/jerryzh168	2023-02-18 00:59:09 +00:00
Xuehai Pan	b005ec62b9	[BE] Remove dependency on six and future (#94709) ... Remove the Python 2 and 3 compatibility library [six](https://pypi.org/project/six) and [future](https://pypi.org/project/future) and `torch._six`. We only support Python 3.8+ now. It's time to retire them. Pull Request resolved: https://github.com/pytorch/pytorch/pull/94709 Approved by: https://github.com/malfet, https://github.com/Skylion007	2023-02-14 09:14:14 +00:00
Brian Hirsh	ceb0f1576b	turn functionalization on in aot_autograd inference (#92857) ... still waiting for CI fallout fixes #90759 Pull Request resolved: https://github.com/pytorch/pytorch/pull/92857 Approved by: https://github.com/ezyang	2023-02-13 17:48:00 +00:00
Nikita Shulga	4869929f32	Update Triton hash (#94249) ... That includes MLIR + latest packaging changes (that also download ptxas from CUDA-12) Tweak CI to install gcc-9 to build trition Disable a few tests to make everything be correct Pull Request resolved: https://github.com/pytorch/pytorch/pull/94249 Approved by: https://github.com/Skylion007, https://github.com/ngimel, https://github.com/weiwangmeta	2023-02-13 13:17:36 +00:00
Aaron Gokaslan	9171f7d4cd	[BE] Modernize PyTorch even more for 3.8 with pyupgrade (#94520) ... Applies some more pyupgrade fixits to PyTorch Pull Request resolved: https://github.com/pytorch/pytorch/pull/94520 Approved by: https://github.com/ezyang	2023-02-10 18:02:50 +00:00
ganler	db6cfff827	fix: forbid multi-index for index_select over scalar (#94347) ... Fixes #88940 According to the [doc](https://pytorch.org/docs/stable/generated/torch.index_select.html): 1. "The returned tensor has the same number of dimensions as the original tensor (`input`). " 2. "The `dim`th dimension has the same size as the length of `index`; other dimensions have the same size as in the original tensor." These two conditions cannot be satisfied at the same time if the `input` is a scalar && `index` has multiple values: because a scalar at most holds one element (according to property 1, the output is a scalar), it is impossible to satisfy "The `dim`th dimension has the same size as the length of `index`" when `index` has multiple values. However, currently, if we do so we either get: 1. Buffer overflow with ASAN; 2. Or (w/o ASAN) silently returns outputs that is not consistent with the doc (`x.index_select(0, torch.Tensor([0, 0, 0]).int())` returns `x`). As a result, we should explicitly reject such cases. Pull Request resolved: https://github.com/pytorch/pytorch/pull/94347 Approved by: https://github.com/malfet	2023-02-10 17:17:09 +00:00
min-jean-cho	900e09c872	[Dynamo] Support torch.Tensor.fn as TorchVariable, not UserDefinedObjectVariable, preventing graph break (#93243) ... As found in #92709, thanks to @ngimel and @jansel, currently `torch.Tensor.fn` points to `UserDefinedObjectVariable` rather than `TorchVariable`. The root cause is due to https://github.com/pytorch/pytorch/pull/92709#pullrequestreview-1273357406. To prevent this, build `TorchVariable` of `torch.Tensor.fn` pointing to `torch.ops.aten.fn`. This issue propagates to `torch.Tensor.fn` causing graph break with `nopython=True`. ```python import torch import torch._dynamo as dynamo #op = torch.ops.aten.abs_ # no graph break op = torch.Tensor.abs_ # graph break args = torch.empty(10) def foo(args): return op(args) opt_foo = dynamo.optimize("inductor", nopython=True)(foo) y_ = opt_foo(args) ``` Pull Request resolved: https://github.com/pytorch/pytorch/pull/93243 Approved by: https://github.com/jansel	2023-02-07 09:26:50 +00:00
min-jean-cho	6e1cfcdf4b	cauchy_ few fixes (1) check gamma > 0 (2) better dtype error log (#93314) ... Related #92047 (1) `torch.Tensor.cauchy_` is missing check for `gamma > 0` (`torch.distributions.cauchy.Cauchy` correctly checks `gamma > 0`). (2) add better error log on dtype similar to exponential_ Pull Request resolved: https://github.com/pytorch/pytorch/pull/93314 Approved by: https://github.com/jgong5, https://github.com/fritzo, https://github.com/lezcano	2023-02-03 11:56:28 +00:00
min-jean-cho	2f0b0c5dd7	exponential_ few fixes (1) lambda > 0 (2) mkl kernel to continuous (3) better error log on dtype (#92891) ... Exponential distribution is continuous. Fixes CPU MKL exponential implementation to exclude integer dtypes. ```python import torch dtypes = [torch.uint8, torch.int8, torch.int16, torch.int32, torch.int64] for dtype in dtypes: x = torch.empty(10000, dtype=dtype).exponential_() # should fail ! print("dtype: ", x.dtype, "sum: ", x.sum()) ``` ### Additional Context Related to #92709. This issue propagates to OpInfo of exponential. ``` AssertionError: The supported dtypes for exponential on device type cpu are incorrect! The following dtypes worked in forward but are not listed by the OpInfo: {torch.int64, torch.uint8, torch.int8, torch.int16, torch.int32}. ``` Pull Request resolved: https://github.com/pytorch/pytorch/pull/92891 Approved by: https://github.com/CaoE, https://github.com/jgong5, https://github.com/ngimel	2023-01-28 02:27:16 +00:00
Yanbo Liang	a6b51448f5	[Dynamo] Supports if condition on user defined object (#90892) ... Fixes Meta internal user case, see the pattern in unit test. Pull Request resolved: https://github.com/pytorch/pytorch/pull/90892 Approved by: https://github.com/jansel, https://github.com/mlazos	2023-01-26 04:19:32 +00:00
PyTorch MergeBot	9b23fd378f	Revert "Logcumsumexp for complex in CPU and CUDA (#90847)" ... This reverts commit 64985123e4. Reverted https://github.com/pytorch/pytorch/pull/90847 on behalf of https://github.com/malfet due to Reverting to decrease build time, let's discuss the alternatives here	2023-01-24 20:49:08 +00:00
pierreHaslee	1c30844eaa	where() function added as a Tensor method as well (#92849) ... Fixes #88470 I added the "method" keyword in `aten/src/ATen/native/native_functions.yaml` for the function `where` with Scalar Overload. This way, you can now use `Tensor.where()` with a scalar parameter the same way `torch.where()` can. I added a test in `test/test_torch.py` as requested. It uses the `where()` method on a tensor and then checks it has the same results as the `torch.where()` function. The test is roughly the same as the one provided by the author of the issue. PS: this is the second PR I make to resolve this issue, the first one is #92747. I had troubles with commit signatures and is therefore closed. Pull Request resolved: https://github.com/pytorch/pytorch/pull/92849 Approved by: https://github.com/albanD	2023-01-24 03:09:33 +00:00
mfkasim1	64985123e4	Logcumsumexp for complex in CPU and CUDA (#90847) ... Another PR towards solving #89205. What's in this PR: * The implementation of forward `logcumsumexp` for complex numbers in CPU & CUDA * The tests on forward call of `logcumsumexp` for complex numbers * The implementation of backward `logcumsumexp` for complex numbers What's missing: * The test on backward gradient of `logcumsumexp` (it complaints `RuntimeError: logcumsumexp does not support automatic differentiation for outputs with complex dtype.` and I don't know how to solve the error and I don't know where to put the test for the backward computation). If possible, I'd like this to be done in this PR. It's really tricky to handle the edge cases here (i.e. the ones involving `inf`), but I've tried my best to put some comments explaining the reasonings of my decisions in this PR. Pull Request resolved: https://github.com/pytorch/pytorch/pull/90847 Approved by: https://github.com/albanD	2023-01-20 15:10:50 +00:00
Kurt Mohler	647b8f8e3e	Add TORCH_CHECK_TENSOR_ALL (#89097) ... `TORCH_CHECK_TENSOR_ALL(cond, ...)` is a wrapper around `TORCH_CHECK` which allows the condition argument to be a tensor, batched or unbatched. `cond` can be a boolean tensor of any size. If any element is False, or if `cond.numel() == 0`, then `TORCH_CHECK_TENSOR_ALL` raises an error Part of #72948 Pull Request resolved: https://github.com/pytorch/pytorch/pull/89097 Approved by: https://github.com/zou3519	2023-01-19 21:04:09 +00:00
BowenBao	a72bcb3388	Do not leak SkipFrame exception to parent frames (#91059) ... Discovered by https://github.com/pytorch/torchdynamo/issues/2000, we noticed the exception `SkipFrame` to avoid repeatedly compiling frame of loop with graph breaks could leak to parent frames while inlining, which then prevents compiling. This PR checks at inlining if such exception is raised and would instead raise an `Unsupported` to the outer frame. The original behavior and goal of #88857 is unaffected: the inner frame that has loop would still be skipped. Pull Request resolved: https://github.com/pytorch/pytorch/pull/91059 Approved by: https://github.com/jansel, https://github.com/thiagocrepaldi	2023-01-13 17:11:22 +00:00
XiaobingSuper	1892c75a45	fix norrow_copy correctness issue for non-contiguous input for cpu path(reland) (#91883) ... This PR is about re-land https://github.com/pytorch/pytorch/pull/91789. Pull Request resolved: https://github.com/pytorch/pytorch/pull/91883 Approved by: https://github.com/lezcano	2023-01-10 10:56:18 +00:00
PyTorch MergeBot	d85f3c8237	Revert "fix norrow_copy correctness issue for non-contiguous input for cpu path (#91789)" ... This reverts commit 136dadd689. Reverted https://github.com/pytorch/pytorch/pull/91789 on behalf of https://github.com/huydhn due to This breaks trunk with XPASS test_vmap_exhaustive_narrow_copy_cpu_float32 136dadd689	2023-01-09 06:50:20 +00:00
XiaobingSuper	136dadd689	fix norrow_copy correctness issue for non-contiguous input for cpu path (#91789) ... Fix https://github.com/pytorch/pytorch/issues/91690. Pull Request resolved: https://github.com/pytorch/pytorch/pull/91789 Approved by: https://github.com/jgong5, https://github.com/lezcano	2023-01-09 00:55:03 +00:00
PyTorch MergeBot	b3603f8129	Revert "Deduplicate c10 error and PyTorchError hierarchy (#87855)" ... This reverts commit 34f2d3e6ae. Reverted https://github.com/pytorch/pytorch/pull/87855 on behalf of https://github.com/osalpekar due to perf regression in quantization tests	2023-01-06 19:56:35 +00:00
William Phetsinorath	34f2d3e6ae	Deduplicate c10 error and PyTorchError hierarchy (#87855) ... Fixes #53370 Pull Request resolved: https://github.com/pytorch/pytorch/pull/87855 Approved by: https://github.com/albanD	2023-01-02 15:53:36 +00:00
ecao	274d3b24c3	use scatter_add for index_add when dim is the most inner dim (#88729) ... ### Motivation When dim is -1 and the slice of source or result is noncontiguous, original `index_add` is slow as it uses add for the sliced tensor, which is serial on index and parallel on sliced tensor to avoid write conflict. Doing parallel on the sliced tensor is not optimal as the size of sliced tensor may be not big enough to parallel and also causes multiple parallelizations. `scatter_add ` is used to speedup for this case as `scatter_add ` parallels on the outer dimension of input and is serial on the inner dimension to avoid write conflict. `scatter_add ` only need one parallel and the size of outer dimensions is bigger to do parallel. ### Testing - Single core: Before: shape | fp32 / s | bf16 / s -- | -- | -- [10, 128, 20, 20] | 2.82E-03 | 2.11E-03 [10, 128, 50, 50] | 0.023604 | 0.023794 After: shape | fp32 / s | bf16 / s -- | -- | -- [10, 128, 20, 20] | 9.30E-04 | 1.66E-03 [10, 128, 50, 50] | 0.005995 | 0.010003 - Single socket (28 cores): Before: shape | fp32 / s | bf16 / s -- | -- | -- [10, 128, 20, 20] | 2.96E-03 | 2.52E-03 [10, 128, 50, 50] | 0.012208 | 0.012568 After: shape | fp32 / s | bf16 / s -- | -- | -- [10, 128, 20, 20] | 7.44E-05 | 1.33E-04 [10, 128, 50, 50] | 0.000333 | 0.000469 Pull Request resolved: https://github.com/pytorch/pytorch/pull/88729 Approved by: https://github.com/mingfeima, https://github.com/jgong5, https://github.com/malfet	2022-12-28 12:04:17 +00:00
PyTorch MergeBot	eadd557266	Revert "use scatter_add for index_add when dim is the most inner dim (#88729)" ... This reverts commit 68e9da68cb. Reverted https://github.com/pytorch/pytorch/pull/88729 on behalf of https://github.com/atalman due to Break internal build	2022-12-22 18:06:45 +00:00
ecao	68e9da68cb	use scatter_add for index_add when dim is the most inner dim (#88729) ... ### Motivation When dim is -1 and the slice of source or result is noncontiguous, original `index_add` is slow as it uses add for the sliced tensor, which is serial on index and parallel on sliced tensor to avoid write conflict. Doing parallel on the sliced tensor is not optimal as the size of sliced tensor may be not big enough to parallel and also causes multiple parallelizations. `scatter_add ` is used to speedup for this case as `scatter_add ` parallels on the outer dimension of input and is serial on the inner dimension to avoid write conflict. `scatter_add ` only need one parallel and the size of outer dimensions is bigger to do parallel. ### Testing - Single core: Before: shape | fp32 / s | bf16 / s -- | -- | -- [10, 128, 20, 20] | 2.82E-03 | 2.11E-03 [10, 128, 50, 50] | 0.023604 | 0.023794 After: shape | fp32 / s | bf16 / s -- | -- | -- [10, 128, 20, 20] | 9.30E-04 | 1.66E-03 [10, 128, 50, 50] | 0.005995 | 0.010003 - Single socket (28 cores): Before: shape | fp32 / s | bf16 / s -- | -- | -- [10, 128, 20, 20] | 2.96E-03 | 2.52E-03 [10, 128, 50, 50] | 0.012208 | 0.012568 After: shape | fp32 / s | bf16 / s -- | -- | -- [10, 128, 20, 20] | 7.44E-05 | 1.33E-04 [10, 128, 50, 50] | 0.000333 | 0.000469 Pull Request resolved: https://github.com/pytorch/pytorch/pull/88729 Approved by: https://github.com/mingfeima, https://github.com/jgong5, https://github.com/malfet	2022-12-22 01:13:35 +00:00
PyTorch MergeBot	3194281ca7	Revert "use scatter_add for index_add when dim is the most inner dim (#88729)" ... This reverts commit 13dbad6369. Reverted https://github.com/pytorch/pytorch/pull/88729 on behalf of https://github.com/desertfire due to causing inductor test failure	2022-12-20 15:19:54 +00:00
ecao	13dbad6369	use scatter_add for index_add when dim is the most inner dim (#88729) ... ### Motivation When dim is -1 and the slice of source or result is noncontiguous, original `index_add` is slow as it uses add for the sliced tensor, which is serial on index and parallel on sliced tensor to avoid write conflict. Doing parallel on the sliced tensor is not optimal as the size of sliced tensor may be not big enough to parallel and also causes multiple parallelizations. `scatter_add ` is used to speedup for this case as `scatter_add ` parallels on the outer dimension of input and is serial on the inner dimension to avoid write conflict. `scatter_add ` only need one parallel and the size of outer dimensions is bigger to do parallel. ### Testing - Single core: Before: shape | fp32 / s | bf16 / s -- | -- | -- [10, 128, 20, 20] | 2.82E-03 | 2.11E-03 [10, 128, 50, 50] | 0.023604 | 0.023794 After: shape | fp32 / s | bf16 / s -- | -- | -- [10, 128, 20, 20] | 9.30E-04 | 1.66E-03 [10, 128, 50, 50] | 0.005995 | 0.010003 - Single socket (28 cores): Before: shape | fp32 / s | bf16 / s -- | -- | -- [10, 128, 20, 20] | 2.96E-03 | 2.52E-03 [10, 128, 50, 50] | 0.012208 | 0.012568 After: shape | fp32 / s | bf16 / s -- | -- | -- [10, 128, 20, 20] | 7.44E-05 | 1.33E-04 [10, 128, 50, 50] | 0.000333 | 0.000469 Pull Request resolved: https://github.com/pytorch/pytorch/pull/88729 Approved by: https://github.com/mingfeima, https://github.com/jgong5, https://github.com/malfet	2022-12-20 13:12:36 +00:00
Yanbo Liang	511fbad830	[Dynamo] Fix builder for class with metaclass (#90807) ... Fixes Meta internal user case: a class with metaclass can't be identified as ```UserDefinedClassVariable```. Pull Request resolved: https://github.com/pytorch/pytorch/pull/90807 Approved by: https://github.com/jansel	2022-12-20 05:02:28 +00:00
Edward Z. Yang	e686a442b4	If a torch.* returns non-Tensor, make this unimplemented rather than assert. (#89918) ... Signed-off-by: Edward Z. Yang <ezyang@fb.com> Pull Request resolved: https://github.com/pytorch/pytorch/pull/89918 Approved by: https://github.com/albanD	2022-12-15 21:53:54 +00:00
Edward Z. Yang	283cf718ed	Fix _fix_weakref memory leak (#90823) ... Signed-off-by: Edward Z. Yang <ezyang@fb.com> Pull Request resolved: https://github.com/pytorch/pytorch/pull/90823 Approved by: https://github.com/eellison, https://github.com/albanD	2022-12-15 01:07:29 +00:00
Edward Z. Yang	cc504ce292	Restore test_warn_types (#90810) ... Signed-off-by: Edward Z. Yang <ezyang@fb.com> Pull Request resolved: https://github.com/pytorch/pytorch/pull/90810 Approved by: https://github.com/ngimel	2022-12-14 05:15:32 +00:00
Bin Bao	7035bcdd0f	[inductor] Enable test_torch (#90518) ... Summary: Skipping failures in those tests so that CI can guard other passing cases. Pull Request resolved: https://github.com/pytorch/pytorch/pull/90518 Approved by: https://github.com/jansel	2022-12-13 16:21:35 +00:00
Yuxin Wu	5d8618dfbd	Some memory saving in large unittests (#90148) ... Two tests test_large_cumsum, test_large_cumprod use a lot of memory. This PR: * Reduces their memory usage by: avoid `self.assertEqual` and avoid a temporary python variable * Mark their memory requirement by decorator. related to https://github.com/pytorch/pytorch/issues/84944 Pull Request resolved: https://github.com/pytorch/pytorch/pull/90148 Approved by: https://github.com/soumith	2022-12-11 21:04:38 +00:00
Edward Z. Yang	2ad6ed8ac9	Fix some typed storage is deprecated warnings. (#89867) ... Signed-off-by: Edward Z. Yang <ezyang@fb.com> Pull Request resolved: https://github.com/pytorch/pytorch/pull/89867 Approved by: https://github.com/albanD	2022-12-07 20:09:57 +00:00
eqy	f7520cb51e	Reduce memory usage requirement of test_pdist_norm_large in test_torch.py (#90075) ... Basically the same fix as #85373, `/usr/bin/time` indicates that the memory requirement on the host-side was actually ~64GiB before the workaround and ~30GiB after. CC @ptrblck @davidberard98 Pull Request resolved: https://github.com/pytorch/pytorch/pull/90075 Approved by: https://github.com/davidberard98	2022-12-03 05:28:21 +00:00
Yu, Guangye	4144ad16af	add XPU backend to support torch.save and torch.load (#89679) ... # Motivate We need to add XPU backend to support torch.save and torch.load when parameter _use_new_zipfile_serialization=False. # Solution We give a design via wrap data as a tensor: >1. and use an in-place copy for H2D >2. directly call a tensor.to() for D2H. This can help us: >1. unify the generic code for all backends. >2. support all the non-CPU device backends. # Additional Context No need more UT. test/test_serialization.py will cover this code change. Pull Request resolved: https://github.com/pytorch/pytorch/pull/89679 Approved by: https://github.com/ezyang	2022-11-30 20:38:02 +00:00
albanD	8713119c89	Stream actually overrides __new__ so we need to patch it as well (#89592) ... Avoids ``` $ python foo.py Traceback (most recent call last): File "foo.py", line 3, in <module> a = torch.cuda.Stream() File "/home/albandes/local/pytorch/3.8_debug_source/torch/cuda/streams.py", line 34, in __new__ return super(Stream, cls).__new__(cls, priority=priority, **kwargs) TypeError: object.__new__() takes exactly one argument (the type to instantiate) ``` And now gets ``` $ python foo.py Traceback (most recent call last): File "foo.py", line 3, in <module> a = torch.cuda.Stream() File "/home/albandes/local/pytorch/3.8_debug_source/torch/cuda/streams.py", line 34, in __new__ return super(Stream, cls).__new__(cls, priority=priority, **kwargs) File "/home/albandes/local/pytorch/3.8_debug_source/torch/cuda/_utils.py", line 44, in err_fn raise RuntimeError( RuntimeError: Tried to instantiate dummy base class Stream ``` Pull Request resolved: https://github.com/pytorch/pytorch/pull/89592 Approved by: https://github.com/soumith	2022-11-29 21:43:23 +00:00
David Berard	a029ec2c88	Move gpu slow tests to sm86 (#87880) ... NVFuser tests (which are slow tests) would be better to run on more modern GPU hardware. Pull Request resolved: https://github.com/pytorch/pytorch/pull/87880 Approved by: https://github.com/malfet	2022-11-29 19:29:59 +00:00
Nikita Karetnikov	57af0c8245	Bug fix: make sure copy_impl doesn't read out of bounds (#88544) ... Fixes #88543. Pull Request resolved: https://github.com/pytorch/pytorch/pull/88544 Approved by: https://github.com/lezcano	2022-11-16 13:23:38 +00:00
PyTorch MergeBot	8441443132	Revert "Add nondeterministic error for scatter (#88244)" ... This reverts commit e940a2f8e2. Reverted https://github.com/pytorch/pytorch/pull/88244 on behalf of https://github.com/mehtanirav due to Internal test failures	2022-11-10 23:56:49 +00:00
Kurt Mohler	ee28b865ee	Deprecate TypedStorage, its derived classes, and all of their public methods (#85303) ... Part of #85302 Pull Request resolved: https://github.com/pytorch/pytorch/pull/85303 Approved by: https://github.com/ezyang	2022-11-08 18:11:01 +00:00
Kurt Mohler	e940a2f8e2	Add nondeterministic error for scatter (#88244) ... Fixes #88096 Pull Request resolved: https://github.com/pytorch/pytorch/pull/88244 Approved by: https://github.com/ezyang, https://github.com/mruberry	2022-11-04 20:23:59 +00:00
Nikolay Korovaiko	0f6304ef1e	disable the out variants in test_cumprod test for inductor (#88328) ... `out=` variants aren't supported by autograd and it's not a must fix, so disabling the test (https://github.com/pytorch/torchdynamo/issues/1798) for now. Pull Request resolved: https://github.com/pytorch/pytorch/pull/88328 Approved by: https://github.com/desertfire	2022-11-03 16:52:37 +00:00
Nikolay Korovaiko	529ba076c6	add an exclude for test_constructor for inductor (#88143) ... This test (https://github.com/pytorch/torchdynamo/issues/1800) fails since none of the c-tor ops support `pin_memory=True`. Natalia suggests it's not a priority to fix. Pull Request resolved: https://github.com/pytorch/pytorch/pull/88143 Approved by: https://github.com/desertfire	2022-11-03 16:21:18 +00:00
Edward Z. Yang	f884e817d4	Make Python op registration work with torchdeploy/multipy (#87162) ... See strategy at PythonOpRegistrationTrampoline.cpp for the big picture. Along the way, I made OperatorHandle support == and hashing, and slightly changed the low level python_dispatch impl API to disallow empty strings for dispatch key, which had the knock on effect of requiring us to explicitly make sure we pass in CompositeImplicitAutograd if we would have passed in "" (I didn't apply this to the rest of the file because I'm lazy.) Test strategy is we delete the logic for preventing Python op registrations in torch from being skipped in a torchdeploy context and show CI still works. Signed-off-by: Edward Z. Yang <ezyang@fb.com> Pull Request resolved: https://github.com/pytorch/pytorch/pull/87162 Approved by: https://github.com/anjali411, https://github.com/bdhirsh	2022-11-03 12:56:44 +00:00
Philip Meier	bc73affdad	prepare removal of deprecated functionality in torch.testing (#87969) ... _Redo of #86586 with all BC breaking changes granularly placed into separate commits._ --- Per title. Deprecation happened on Feb 25, 2022 in c6f1bbc0ac, which made it into the 1.12 release. Since it is now 245 days later and the next release will be 1.14, the removals later in the stack comply with the [BC policy](https://github.com/pytorch/pytorch/wiki/PyTorch's-Python-Frontend-Backward-and-Forward-Compatibility-Policy#minimizing-the-disruption-of-bc-breaking-changes). Pull Request resolved: https://github.com/pytorch/pytorch/pull/87969 Approved by: https://github.com/mruberry	2022-11-02 14:04:48 +00:00
Kurt Mohler	1dbc8ad3b7	Add Warning class and refactor C++ warnings to use it (#84101) ... Also adds `TORCH_WARN_WITH` and `TORCH_WARN_DEPRECATION` macros Part of #72948 Pull Request resolved: https://github.com/pytorch/pytorch/pull/84101 Approved by: https://github.com/albanD	2022-10-18 20:02:42 +00:00
Natalia Gimelshein	1704256b10	Enables where to have cpu scalar args (#87022) ... This is for decompositions only, no attempt made to have good performance for this case. Pull Request resolved: https://github.com/pytorch/pytorch/pull/87022 Approved by: https://github.com/ezyang, https://github.com/eellison, https://github.com/mruberry	2022-10-17 17:08:47 +00:00
Mikayla Gawarecki	afaee00fec	Add python nested_tensor and as_nested_tensor constructors in torch.nested (#85593) ... Remove `torch.nested_tensor` which has erroneous behavior wrt gradients (could be either leaf or not leaf). Introduce `torch.nested.nested_tensor` and `torch.nested.as_nested_tensor` in the vein of `torch.tensor` and `torch.as_tensor`. Done in nested `__init__.py` for now but can move to pybind in future (when we want to load from numpy/nested lists ). Discussed offline with @cpuhrsch and pybind constructor (https://github.com/pytorch/pytorch/pull/85536) was more gnarly than expected, so we can move to that when we do need loading from numpy etc. Differential Revision: [D39806622](https://our.internmc.facebook.com/intern/diff/D39806622) Pull Request resolved: https://github.com/pytorch/pytorch/pull/85593 Approved by: https://github.com/drisspg, https://github.com/cpuhrsch	2022-09-28 20:15:02 +00:00
Kurt Mohler	b0a631cd14	Add nondeterministic alert for MaxUnpool1d/2d/3d (#84766) ... Part of #80827 Part of #78249 Pull Request resolved: https://github.com/pytorch/pytorch/pull/84766 Approved by: https://github.com/Lezcano, https://github.com/mruberry, https://github.com/nikitaved	2022-09-17 11:58:18 +00:00
soulitzer	02f654abca	Disable torch.library.Library with PYTORCH_DISABLE_LIBRARY (#85190) ... Pull Request resolved: https://github.com/pytorch/pytorch/pull/85190 Approved by: https://github.com/d4l3k	2022-09-17 03:05:43 +00:00
Khushi Agrawal	a9258eba8e	[Testing] Port bernoulli and multinomial to ErrorInputs. (#74683) ... Hi, The PR aims to port `bernoulli` and `multinomial` to error inputs. Thanks! cc: @kshitij12345! :) Pull Request resolved: https://github.com/pytorch/pytorch/pull/74683 Approved by: https://github.com/kshitij12345, https://github.com/mruberry	2022-09-16 21:24:09 +00:00
Elias Ellison	f37069aac7	Re-enable fixed dynamo tests (#84969) ... Pull Request resolved: https://github.com/pytorch/pytorch/pull/84969 Approved by: https://github.com/bdhirsh, https://github.com/ezyang	2022-09-16 15:36:52 +00:00
Kurt Mohler	95a2c3df31	Replace expectedAlertNondeterministic with simpler check function (#84808) ... Fixes #84807 Pull Request resolved: https://github.com/pytorch/pytorch/pull/84808 Approved by: https://github.com/mruberry	2022-09-16 01:10:12 +00:00
Kurt Mohler	5b58140d1a	Add deterministic impl of scatter_add CUDA for all input sizes (#79466) ... Fixes #50469 Pull Request resolved: https://github.com/pytorch/pytorch/pull/79466 Approved by: https://github.com/ngimel	2022-09-07 03:12:49 +00:00
Natalia Gimelshein	0b363c5c5c	don't synchronize single element any/all reductions (#84465) ... Fixes #84291 Pull Request resolved: https://github.com/pytorch/pytorch/pull/84465 Approved by: https://github.com/ezyang	2022-09-02 21:18:58 +00:00
Elias Ellison	f701cb04fb	Test Dynamo CI w Fake Tensors (#84282) ... Pull Request resolved: https://github.com/pytorch/pytorch/pull/84282 Approved by: https://github.com/anijain2305	2022-09-01 00:15:05 +00:00
mattip	4dfa6d28a1	Normalize DLPack stride to 1 where shape < 2 (#83158) ... Fixes #83069. Also move all the dlpack tests to a new file., `test_dlpack.py`. The fix involves always allocating a "strides" int array when converting to dlPack and deleting the strides when the capsule descructor is called. Then the strides are copied from the tensor, and `strides[i]` is set to `1` where `shape[i] < 2`. Pull Request resolved: https://github.com/pytorch/pytorch/pull/83158 Approved by: https://github.com/ezyang	2022-08-23 15:03:29 +00:00
Brian Hirsh	0c24af4985	Always allow tensor metadata changes (#83590) ... Make it so that it is valid to set metadata after detach calls, like `x.detach().resize_(...)`. This technically lifts some restrictions around `.data`. This PR means that you can now technically call `x.data.resize_(...)`, which can now directly resize `x` instead of erroring. My understanding: Before the tensor-variable merge, when `x` and `x.data` were really different tensors, you could resize `x.data` independently of `x`, and during the merge, this error was added to avoid silent confusing behavior changes. It was agreed that this error has been around long enough (several years) that it's acceptable to drop. cc @albanD @ezyang. (Ed already had a prototype PR [here](https://github.com/pytorch/pytorch/pull/83545) - I ended up making one to try to slog through test failures). Pull Request resolved: https://github.com/pytorch/pytorch/pull/83590 Approved by: https://github.com/ezyang	2022-08-19 23:30:43 +00:00
Nikita Shulga	1a09b05c94	Fix torch.equal on CPU (#83350) ... `torch.equal` should not raise an exception when comparing tensors of different types I.e. `torch.equal(torch.tensor([1, 2]), torch.tensor([1, 2], dtype=torch.float)))` should return True rather than raise an exception. Also, this makes it consistent with GPU behaviour Fixes https://github.com/pytorch/pytorch/issues/83314 Pull Request resolved: https://github.com/pytorch/pytorch/pull/83350 Approved by: https://github.com/albanD	2022-08-17 03:22:56 +00:00
Nikita Karetnikov	4010f96121	[primTorch] Fix off by 1 in canonicalize_dim (#83198) ... Also fix an issue in the `unsqueeze` ref due to this change. Pull Request resolved: https://github.com/pytorch/pytorch/pull/83198 Approved by: https://github.com/ngimel	2022-08-16 17:57:01 +00:00
PyTorch MergeBot	f534b2c627	Revert "Remove split functional wrapper (#74727)" ... This reverts commit a58876ace7. Reverted https://github.com/pytorch/pytorch/pull/74727 on behalf of https://github.com/seemethere due to Fails internal use cases, might extend out to external use cases as well. Need to assess overall impact of this change more widely	2022-08-10 19:45:23 +00:00
Peter Bell	a58876ace7	Remove split functional wrapper (#74727) ... Pull Request resolved: https://github.com/pytorch/pytorch/pull/74727 Approved by: https://github.com/albanD, https://github.com/khabinov	2022-08-10 17:57:48 +00:00
Kurt Mohler	c379915969	Add nondeterministic alert to CUDA cumsum (#75693) ... Part of #75240 Pull Request resolved: https://github.com/pytorch/pytorch/pull/75693 Approved by: https://github.com/ngimel	2022-08-04 01:58:29 +00:00
Kurt Mohler	14d0296e5c	Rename _Typed/_UntypedStorage to Typed/UntypedStorage and update docs (#82438) ... ### Description Since the major changes for `_TypedStorage` and `_UntypedStorage` are now complete, they can be renamed to be public. `TypedStorage._untyped()` is renamed to `TypedStorage.untyped()`. Documentation for storages is improved as well. ### Issue Fixes #82436 ### Testing N/A Pull Request resolved: https://github.com/pytorch/pytorch/pull/82438 Approved by: https://github.com/ezyang	2022-07-30 19:37:08 +00:00
Fabio Rocha	fd84c458f4	Add torch.unflatten and improve its docs (#81399) ... unflatten now has a free function version in torch.flatten in addition to the method in torch.Tensor.flatten. Updated docs to reflect this and polished them a little. For consistency, changed the signature of the int version of unflatten in native_functions.yaml. Some override tests were failing because unflatten has unusual characteristics in terms of the .int and .Dimname versions having different number of arguments so this required some changes to test/test_override.py Removed support for using mix of integer and string arguments when specifying dimensions in unflatten. Pull Request resolved: https://github.com/pytorch/pytorch/pull/81399 Approved by: https://github.com/Lezcano, https://github.com/ngimel	2022-07-29 15:02:42 +00:00
ecao	1ebe98220c	Optimize the copy of BFloat16 to Float and Float to BFloat16 (#79685) ... Optimize the copy of BFloat16 to Float and Float to BFloat16. * Vectorize the copy of BFLoat16 <-> Float * Use `at::internal::serial_for_each` instead of directly using `cpu_kernel_vec` as `cpu_kernel_vec` can't handle that input and output has different data types. single socket (28cores): ``` before: torch.Size([10, 128, 10, 124]) bf16 -> fp32: 4.18e-05 ms; fp32 -> bf16: 5.04e-05 ms torch.Size([10, 128, 30, 124]) bf16 -> fp32: 0.00011868 ms; fp32 -> bf16: 0.0001476 ms after: torch.Size([10, 128, 10, 124]) bf16 -> fp32: 1.35e-05 ms; fp32 -> bf16: 1.97e-05 ms torch.Size([10, 128, 30, 124]) bf16 -> fp32: 7.32e-05 ms; fp32 -> bf16: 5.70e-05 ms ``` single core: ``` before: torch.Size([10, 128, 10, 124]) bf16 -> fp32: 0.000848 ms; fp32 -> bf16: 0.00105 ms torch.Size([10, 128, 30, 124]) bf16 -> fp32: 0.00269 ms; fp32 -> bf16: 0.00321 ms after: torch.Size([10, 128, 10, 124]) bf16 -> fp32: 0.000370 ms; fp32 -> bf16: 0.000382 ms torch.Size([10, 128, 30, 124]) bf16 -> fp32: 0.00153 ms; fp32 -> bf16: 0.00113 ms ``` Pull Request resolved: https://github.com/pytorch/pytorch/pull/79685 Approved by: https://github.com/malfet	2022-07-28 14:34:08 +00:00
Huy Do	edf1868e67	Fix test_doc_template regex (#81755) ... ### The problem This original regex abuses .* in combination with `re.DOTALL` and leads to a catastrophic backtracking perf issue when there is no match. When it happens, test_doc_template will run "forever" and timeout. Here is an example timeout test https://github.com/pytorch/pytorch/runs/7413337595 Another minor issue with this regex is that it won't matches concatenated doc string like `"""FOO""" + """BAR"""`, which is used for some API `_torch_docs.py` ### The fix * Remove most of the match all .* usage. I have tested to make sure that the test finishes even when there is no match, i.e. it fails successfully * Update the regex to match all the following cases before and after linting (You can also try it out on https://pythex.org): BEFORE ``` add_docstr(torch.abs, r""" abs(input, *, out=None) -> Tensor Computes the absolute value of each element in :attr:`input`. .. math:: \text{out}_{i} = |\text{input}_{i}| """ + r""" Args: {input} Keyword args: {out} Example:: >>> torch.abs(torch.tensor([-1, -2, 3])) tensor([ 1, 2, 3]) """.format(**common_args)) add_docstr(torch.absolute, r""" absolute(input, *, out=None) -> Tensor Alias for :func:`torch.abs` """) ``` AFTER ``` add_docstr( torch.abs, r""" abs(input, *, out=None) -> Tensor Computes the absolute value of each element in :attr:`input`. .. math:: \text{out}_{i} = |\text{input}_{i}| """ + r""" Args: {input} Keyword args: {out} Example:: >>> torch.abs(torch.tensor([-1, -2, 3])) tensor([ 1, 2, 3]) """.format( **common_args ), ) add_docstr( torch.absolute, r""" absolute(input, *, out=None) -> Tensor Alias for :func:`torch.abs` """, ) ``` This will unblock https://github.com/pytorch/pytorch/pull/81643 Pull Request resolved: https://github.com/pytorch/pytorch/pull/81755 Approved by: https://github.com/atalman	2022-07-21 16:28:29 +00:00
Animesh Jain	1d90d6ee60	Setup for running PyTorch tests with TorchDynamo and skips for known failing tests (#80106) ... @ezyang I am going to keep adding more skips in this PR for now. And once we have the CI running, I will replace with the appropriate decorators. cc @mlazos , we should add those tests in test_ops.py in this PR as well cc @jansel Pull Request resolved: https://github.com/pytorch/pytorch/pull/80106 Approved by: https://github.com/ezyang, https://github.com/jansel	2022-07-07 18:57:33 +00:00
Kurt Mohler	4c279994fd	Fix Module.share_memory error (#80843) ... Fixes #80733 Pull Request resolved: https://github.com/pytorch/pytorch/pull/80843 Approved by: https://github.com/malfet	2022-07-05 15:17:36 +00:00
PyTorch MergeBot	f668b7ecb0	Add integer support to index_reduce (#80464) ... Pull Request resolved: https://github.com/pytorch/pytorch/pull/80464 Approved by: https://github.com/cpuhrsch	2022-06-30 12:54:51 +00:00
PyTorch MergeBot	d7847ed23e	Add integer support to scatter_reduce (#80324) ... Pull Request resolved: https://github.com/pytorch/pytorch/pull/80324 Approved by: https://github.com/cpuhrsch	2022-06-29 21:10:26 +00:00
Alexander Grund	71d9592a72	Only sync CUDA if the operation is run on GPU (#80328) ... This fixes test failures when PyTorch is build without CUDA Fixes https://github.com/pytorch/pytorch/issues/58563 I used the same is_cuda check that is used in test_nn.py CC @ailzhang after #58564 Pull Request resolved: https://github.com/pytorch/pytorch/pull/80328 Approved by: https://github.com/mruberry	2022-06-27 14:49:39 +00:00
Alexander Grund	3b8589ac44	Copy Tensor for tests to avoid in-place transform modifying the original tensor (#80331) ... Fixes #48591 CC @mruberry after #60256 Pull Request resolved: https://github.com/pytorch/pytorch/pull/80331 Approved by: https://github.com/mruberry	2022-06-27 14:47:52 +00:00
lezcano	f54e7b4ad6	More forward AD formulas ... This PR: - Corrects the forward AD formula of `torch.sgn`. - The reason why we can't use `auto_element_wise` for this operations is rather subtle. I left a comment. - This, in turn, fixes a problem we had in forward-over-backward for `linalg.svd` and other spectral decompositions (and `norm`, `linalg.norm`, `linalg.matrix_norm`) that were using `torch.abs` (whose derivative is given by `torch.sgn`. - Implement the formula for a number of missing operations `nansum`, `amax`, `amin`... - Simplified a few formulas, most notably the forward AD for `div` and the derivative of `norm`, `linalg.norm` and `vector_norm` for `ord=+-inf`. - Correct the formula for `mean`, `std_mean`, `var_mean` when `dim` is provided and equal to `()` (or `None`) - A few minor improvements to `sum_backward`, `unsqueeze_multiple` and formulas depending on them - Fix the derivatives of `std_mean` and `std_var` (complex support, ASAN, forward AD...) Fixes: https://github.com/pytorch/pytorch/issues/67539 Pull Request resolved: https://github.com/pytorch/pytorch/pull/80082 Approved by: https://github.com/zou3519	2022-06-23 01:31:08 +00:00
Alex Hedges	cb2b7b1e57	Fix code that triggers BytesWarning (#79868) ... Fixes #74812. I have fixed the multiple instances in the repository that trigger `BytesWarning`, and I have enabled the `-bb` option when tests are run to prevent regressions. Pull Request resolved: https://github.com/pytorch/pytorch/pull/79868 Approved by: https://github.com/janeyx99	2022-06-21 01:12:21 +00:00
PyTorch MergeBot	e10cbe3880	Revert "Fix BytesWarning in torch.load() (#74813)" ... This reverts commit 6c2e8119dd. Reverted https://github.com/pytorch/pytorch/pull/74813 on behalf of https://github.com/janeyx99 due to Broke slow tests in cuda 10.2 https://github.com/pytorch/pytorch/runs/6944238177?check_suite_focus=true	2022-06-18 03:53:54 +00:00
Alex Hedges	6c2e8119dd	Fix BytesWarning in torch.load() (#74813) ... Fixes #74812. I have enabled the `-bb` option when tests are run to prevent regressions. I don't think it will make CI run more slowly, but I'm not entirely sure. Pull Request resolved: https://github.com/pytorch/pytorch/pull/74813 Approved by: https://github.com/kit1980	2022-06-17 22:56:43 +00:00
drisspg	bdcee8f995	update is_same_size to work with nested tensor dispatch ... Pull Request resolved: https://github.com/pytorch/pytorch/pull/79297 Approved by: https://github.com/soulitzer	2022-06-11 00:07:27 +00:00
Brian Hirsh	7b3a0ff87a	Port index.Tensor to structured kernels. ... Tracking issue: #55070 Pull Request resolved: https://github.com/pytorch/pytorch/pull/69607 Approved by: https://github.com/bdhirsh	2022-06-10 17:27:47 +00:00
Peter Bell	7843a5e882	Move Tensor.grad back into C++ ... `Tensor.grad` was moved to python in #30531 to add a warning. However, that warning has since been lowered into C++ so this wrapper is no longer necessary. Pull Request resolved: https://github.com/pytorch/pytorch/pull/76675 Approved by: https://github.com/albanD	2022-06-10 13:44:45 +00:00
PyTorch MergeBot	4b82ef7928	Revert "Port index.Tensor to structured kernels." ... This reverts commit cfd84125bd. Reverted https://github.com/pytorch/pytorch/pull/69607 on behalf of https://github.com/zengk95 due to This is breaking mac trunk tests cfd84125bd	2022-06-08 20:16:10 +00:00
Brian Hirsh	cfd84125bd	Port index.Tensor to structured kernels. ... Tracking issue: #55070 Pull Request resolved: https://github.com/pytorch/pytorch/pull/69607 Approved by: https://github.com/bdhirsh	2022-06-08 18:17:52 +00:00
Kshiteej K	497ae27050	[chalf] warn once on creating a chalf tensor (#78245) ... `chalf` is experimental as the op coverage is low. Following script raises 6 warnings if `set_warn_always(True)` else raises only 1 warning. ```python import torch torch.set_warn_always(True) device='cpu' t = torch.randn(3, dtype=torch.chalf, device=device) y = torch.rand(3, dtype=torch.chalf, device=device) # Allocates new tensor for result t + y device='cuda' t = torch.randn(3, dtype=torch.chalf, device=device) y = torch.rand(3, dtype=torch.chalf, device=device) # Allocates new tensor for result t + y ``` Pull Request resolved: https://github.com/pytorch/pytorch/pull/78245 Approved by: https://github.com/anjali411	2022-06-01 18:38:31 +00:00
yuguo68	efdb4192bc	set data permits requires_grad=True on integer tensor ... Pull Request resolved: https://github.com/pytorch/pytorch/pull/78436 Approved by: https://github.com/albanD, https://github.com/soulitzer	2022-06-01 15:56:32 +00:00
PyTorch MergeBot	fca1f495c2	Revert "Port index.Tensor to structured kernels." ... This reverts commit 9fe6f1baf5. Reverted https://github.com/pytorch/pytorch/pull/69607 on behalf of https://github.com/suo due to this broke master, see: 9fe6f1baf5	2022-06-01 00:12:15 +00:00
Brian Hirsh	9fe6f1baf5	Port index.Tensor to structured kernels. ... Tracking issue: #55070 Pull Request resolved: https://github.com/pytorch/pytorch/pull/69607 Approved by: https://github.com/bdhirsh	2022-05-31 22:15:20 +00:00
Kurt Mohler	e9afb43676	Add meta device support to _UntypedStorage and _TypedStorage (#78008) ... Fixes #77885 Pull Request resolved: https://github.com/pytorch/pytorch/pull/78008 Approved by: https://github.com/ezyang	2022-05-28 15:33:45 +00:00
Yu Guo	f69c990ecc	fix index_select when source tensor is empty ... Pull Request resolved: https://github.com/pytorch/pytorch/pull/77881 Approved by: https://github.com/ezyang	2022-05-26 03:10:47 +00:00
Kurt Mohler	cecb2ad95e	Restore old names for private funcs in legacy storages (#77861) ... Followup from #75459 Pull Request resolved: https://github.com/pytorch/pytorch/pull/77861 Approved by: https://github.com/ezyang	2022-05-20 02:03:34 +00:00
Eric Sauser	2d4291fb81	[torch] Fixed a few test for Windows & Linux GPUs (#77531) ... Summary: While running those tests on - my local windows GPU machine - a dev server - an on-demand GPU I noticed a few test failures and here's some tentative fixes Test Plan: Ran tests on: - my local windows GPU machine - a linux dev server w/o GPU - an linux on-demand GPU server Note that when using CUDA11, the tests crashes (segfaults) on calls to torch.nn.ConvTranspose3d. Fails on master, but works with CUDA10. Differential Revision: D36377288 Pull Request resolved: https://github.com/pytorch/pytorch/pull/77531 Approved by: https://github.com/ezyang	2022-05-19 14:04:13 +00:00
Kurt Mohler	aea6e2c396	Merge torch.cuda._UntypedStorage into torch._UntypedStorage (#75459) ... Fixes #74933 Pull Request resolved: https://github.com/pytorch/pytorch/pull/75459 Approved by: https://github.com/ezyang	2022-05-19 13:54:39 +00:00
Mikayla Gawarecki	841c65f499	Unprivate _index_reduce and add documentation ... Pull Request resolved: https://github.com/pytorch/pytorch/pull/76997 Approved by: https://github.com/cpuhrsch	2022-05-13 19:48:38 +00:00
Kulin Seth	e011a8e18b	Enable PyTorch operations on MPS Backend. (#77343) ... Add PyTorch operations to MPS backend. - https://github.com/pytorch/pytorch/issues/77394 Pull Request resolved: https://github.com/pytorch/pytorch/pull/77343 Approved by: https://github.com/albanD	2022-05-13 18:28:53 +00:00
Mikayla Gawarecki	1141b45e7a	Index reduction CUDA support ... Pull Request resolved: https://github.com/pytorch/pytorch/pull/76296 Approved by: https://github.com/cpuhrsch, https://github.com/ngimel	2022-05-13 14:47:52 +00:00
Christian Puhrsch	ce9a477fdf	Support torch.Tensor.to for CSR ... Fixes #76379 Pull Request resolved: https://github.com/pytorch/pytorch/pull/76400 Approved by: https://github.com/pearu, https://github.com/davidberard98	2022-05-05 21:59:50 +00:00
Natalia Gimelshein	ce76244200	fix where type promotion ... Fixes #73298 I don't know whether `where` kernel actually supports type promotion, nor am I in the mood to find out, so it's manual type promotion. Edit: nah, i can't tell TI to "promote to common dtype" because of bool condition, so manual type promotion is our only option. I'll see what tests start failing and fix. Uses some parts from #62084 Pull Request resolved: https://github.com/pytorch/pytorch/pull/76691 Approved by: https://github.com/mruberry	2022-05-03 04:40:04 +00:00
kshitij12345	e36d25fbae	[complex32] support printing the tensor ... Reference: #74537 Pull Request resolved: https://github.com/pytorch/pytorch/pull/76614 Approved by: https://github.com/anjali411	2022-05-01 12:46:09 +00:00
Mikayla Gawarecki	676a4a3969	Prototype _index_reduce (CPU-only) ... Pull Request resolved: https://github.com/pytorch/pytorch/pull/75981 Approved by: https://github.com/cpuhrsch	2022-04-27 23:01:00 +00:00
kshitij12345	aa51704ce5	[complex32] add chalf alias for complex32 and chalf method ... Reference: https://github.com/pytorch/pytorch/issues/74537 Adds chalf alias for complex32 and also adds method `chalf` similar to `cfloat, cdouble` TODO: * [x] Add docs * [x] Add override Pull Request resolved: https://github.com/pytorch/pytorch/pull/75320 Approved by: https://github.com/anjali411	2022-04-20 23:44:47 +00:00
Edward Z. Yang	ee955b8bb9	Cannibalize noarch CI job into crossref CI job ... crossref is a new strategy for performing tests when you want to run a normal PyTorch API call, separately run some variation of the API call (e.g., same thing but all the arguments are meta tensors) and then cross-reference the results to see that they are consistent. Any logic you add to CrossRefMode will get run on *every* PyTorch API call that is called in the course of PyTorch's test suite. This can be a good choice for correctness testing if OpInfo testing is not exhaustive enough. For now, the crossref test doesn't do anything except verify that we can validly push a mode onto the torch function mode stack for all functions. Signed-off-by: Edward Z. Yang <ezyangfb.com> Pull Request resolved: https://github.com/pytorch/pytorch/pull/75988 Approved by: https://github.com/seemethere	2022-04-20 11:56:25 +00:00
Edward Z. Yang	30943d1610	Remove noarchTest decorator ... These tests are cheap so it doesn't matter if we run them on all configs. This is in preparation for removing the noarch build configuration entirely. Signed-off-by: Edward Z. Yang <ezyangfb.com> Pull Request resolved: https://github.com/pytorch/pytorch/pull/75985 Approved by: https://github.com/seemethere, https://github.com/cbalioglu	2022-04-19 00:48:49 +00:00
Beilei Zheng	332086c08d	Add BFloat16 support for multinomial and poisson on CPU ... Add BFloat16 support for multinomial and poisson on CPU Pull Request resolved: https://github.com/pytorch/pytorch/pull/63215 Approved by: https://github.com/frank-wei, https://github.com/bigfootjon	2022-04-14 15:42:18 +00:00
Jagadish Krishnamoorthy	26ba7a9297	ROCm: Enable test_masked_scatter_large_tensor ... #68487 fixes the issue #60190 for ROCm >= 5.0 release. Signed-off-by: Jagadish Krishnamoorthy <jagdish.krishna@gmail.com> Fixes #60190 Pull Request resolved: https://github.com/pytorch/pytorch/pull/75455 Approved by: https://github.com/ezyang	2022-04-08 15:59:40 +00:00
Nikita Karetnikov	936a65056e	Use the same checks in all grid_sampler functions ... Fixes #73187. Pull Request resolved: https://github.com/pytorch/pytorch/pull/75164 Approved by: https://github.com/albanD	2022-04-04 15:21:44 +00:00
Alban Desmaison	0ce02ea52d	Revert D35284563: Use the same checks in all grid_sampler functions ... Test Plan: revert-hammer Differential Revision: D35284563 (835cc66e5d) Original commit changeset: 1477c506b875 Original Phabricator Diff: D35284563 (835cc66e5d) fbshipit-source-id: 7260f4dfda23bd60200e5ba2c5bf3e4f833c2646 (cherry picked from commit fbe082905ef678e7dd70dbc9520dca644383ce01)	2022-04-01 16:45:46 +00:00
kshitij12345	65b65af236	[complex32] cat, fill_(partial), item ... Reference : #74537 `cat_backwards` (on CUDA) requires support for `fill`, have added support for `fill`. (Also `fill` requires `item` support) Now `fill` backward requires `sum` (will add it in later PR). Pull Request resolved: https://github.com/pytorch/pytorch/pull/75010 Approved by: https://github.com/anjali411	2022-04-01 15:19:05 +00:00
Nikita Karetnikov	835cc66e5d	Use the same checks in all grid_sampler functions (#74635) ... Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/74635 Fixes #73187. Test Plan: Imported from OSS Reviewed By: bdhirsh Differential Revision: D35284563 Pulled By: albanD fbshipit-source-id: 1477c506b8755d864ca902ee140bee7bdb0069b0 (cherry picked from commit dcbd5242baaae11f9e323d99a9596e5b88e86bd7)	2022-04-01 14:26:16 +00:00
Mikayla Gawarecki	2bfa018462	[BC-breaking] Use ScatterGatherKernel for scatter_reduce (CPU-only) (#74226) ... Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/74226 Update signature of `scatter_reduce_` to match `scatter_/scatter_add_` `Tensor.scatter_reduce_(int64 dim, Tensor index, Tensor src, str reduce)` - Add new reduction options in ScatterGatherKernel.cpp and update `scatter_reduce` to call into the cpu kernel for `scatter.reduce` - `scatter_reduce` now has the same shape constraints as `scatter_` and `scatter_add_` - Migrate `test/test_torch.py:test_scatter_reduce` to `test/test_scatter_gather_ops.py` Test Plan: Imported from OSS Reviewed By: ngimel Differential Revision: D35222842 Pulled By: mikaylagawarecki fbshipit-source-id: 84930add2ad30baf872c495251373313cb7428bd (cherry picked from commit 1b45139482e22eb0dc8b6aec2a7b25a4b58e31df)	2022-04-01 05:57:45 +00:00
Nikita Shulga	bfac65dfe5	[testing] Update dispatch macros (#74977) ... This PR is reland of #74289 Co-authored-by: Khushi Agrawal <khushiagrawal411@gmail.com>	2022-03-30 14:13:21 -07:00
PyTorch MergeBot	2e4152b118	Revert "[testing] Update dispatch macros" ... This reverts commit eed19a0f38. Reverted https://github.com/pytorch/pytorch/pull/74289 on behalf of https://github.com/malfet	2022-03-30 19:52:37 +00:00
Khushi Agrawal	eed19a0f38	[testing] Update dispatch macros ... Hi, This PR is the follow-up PR of #71561. (the previous PR had a couple of merge conflicts and was reverted, this PR resolves that). Please take a look. Thanks! cc: @pmeier @mruberry @kshitij12345 Pull Request resolved: https://github.com/pytorch/pytorch/pull/74289 Approved by: https://github.com/pmeier, https://github.com/mruberry	2022-03-30 16:10:16 +00:00
Edward Z. Yang	51e7a3406c	Fix formatting of scalar tensors (don't call item) ... Signed-off-by: Edward Z. Yang <ezyangfb.com> Pull Request resolved: https://github.com/pytorch/pytorch/pull/74376 Approved by: https://github.com/bdhirsh	2022-03-25 02:22:25 +00:00
Jane Xu	3f9115dc7a	Decorate test_pdist_large for requiring large memory (#74574) ... Summary: Fixes https://github.com/pytorch/pytorch/issues/74154 Pull Request resolved: https://github.com/pytorch/pytorch/pull/74574 Reviewed By: george-qi Differential Revision: D35100229 Pulled By: janeyx99 fbshipit-source-id: d7df377318e45c7f5447c034aa025b1422fcc06e (cherry picked from commit 335a76d9f2a721b30e1b9e1c869bfbe431f01a2a)	2022-03-24 17:25:37 +00:00
kshitij12345	f7ee308dfb	[complex-half] support casting (by updating copy_) ... Reference https://github.com/pytorch/pytorch/issues/71680 Pull Request resolved: https://github.com/pytorch/pytorch/pull/73847 Approved by: https://github.com/anjali411	2022-03-23 21:42:59 +00:00
Kurt Mohler	79ddc72b85	Virtualize <type>Storage classes (#66970) ... Summary: Fixes https://github.com/pytorch/pytorch/issues/66228 cc ezyang bhosmer smessmer ljk53 bdhirsh Pull Request resolved: https://github.com/pytorch/pytorch/pull/66970 Reviewed By: bdhirsh Differential Revision: D33245612 Pulled By: ezyang fbshipit-source-id: 4c61c2cb029e2b94b0e68927c377d3e1c358dd7c (cherry picked from commit d29fcdfb4bc2cc17b1795d4349e4b56fa0d1cf12)	2022-03-22 23:44:48 +00:00
Saketh Are	46a88036af	Refactor error input tests in test_torch.py to OpInfos (#73981) ... Summary: This PR ports several tests in `test/test_torch.py` over to OpInfo ErrorInputs. Some tests commented "convert to ErrorInputs" still remain in `test_torch.py`. They fall under two categories: - Memory overlap tests which specifically test the in-place version of an operator (e.g. [this test](424a054d53/test/test_torch.py (L3788)) for index_add_). - Tests with non-trivial behavior calling `torch.cuda.synchronize()` after calling the operator being tested (e.g. [this test](424a054d53/test/test_torch.py (L4948)) for torch.multinomial). Pull Request resolved: https://github.com/pytorch/pytorch/pull/73981 Reviewed By: qihqi Differential Revision: D35016669 Pulled By: saketh-are fbshipit-source-id: bc0016d2b2bfb566a9dfef81ecf44e0adb9e4b14 (cherry picked from commit 99bcbdb05f2c10a717a269b0010aa3a3e24fe5c0)	2022-03-21 22:31:37 +00:00
Nikita Shulga	ef066f0832	Revert D34856571: [pytorch][PR] Replace get_all_ type macros with the ATen dispatch macros. ... Test Plan: revert-hammer Differential Revision: D34856571 (3ded7b1da3) Original commit changeset: 0dca038bcad5 Original Phabricator Diff: D34856571 (3ded7b1da3) fbshipit-source-id: 594553fa0b710d78beba59d5d2b646f1f1270386 (cherry picked from commit 8090eb9b12dcf452a9e7dc01792a66fb91b563b6)	2022-03-15 22:07:11 +00:00
Khushi Agrawal	3ded7b1da3	Replace get_all_ type macros with the ATen dispatch macros. (#71561) ... Summary: Hi, Team! The PR is motivated from https://github.com/pytorch/pytorch/pull/71153#discussion_r782446738. It aims to replace `get_all` type macros with the ATen dispatch macros. The files it iterates over are: (Thanks, Lezcano, for the idea!!) <details> <summary> `test/test_autograd.py`</summary> <p> ```python 43:from torch.testing._internal.common_dtype import get_all_dtypes 8506: floating_dt = [dt for dt in get_all_dtypes() if dt.is_floating_point] ``` </p> </details> <details> <summary> `test/test_binary_ufuncs.py`</summary> <p> ```python 26: all_types_and_complex_and, integral_types_and, get_all_dtypes, get_all_int_dtypes, get_all_math_dtypes, 27: get_all_complex_dtypes, get_all_fp_dtypes, 935: dtypes(*get_all_dtypes(include_bool=False, include_complex=False)) 1035: dtypes(*get_all_dtypes( 1488: dtypes(*(get_all_dtypes(include_bool=False, include_bfloat16=False))) 1879: dtypes(*product(get_all_dtypes(include_complex=False), get_all_dtypes(include_complex=False))) 1887: dtypes(*(get_all_int_dtypes() + [torch.bool])) 1913: dtypes(*(get_all_fp_dtypes())) 1941: dtypes(*(get_all_fp_dtypes())) 1977: dtypes(*product(get_all_complex_dtypes(), get_all_dtypes())) 2019: dtypes(*product(get_all_fp_dtypes(), get_all_fp_dtypes())) 2048: dtypes(*get_all_dtypes()) 2110: dtypes(*product(get_all_dtypes(include_complex=False), 2111: get_all_dtypes(include_complex=False))) 2128: types = [torch.bool, torch.bfloat16] + get_all_int_dtypes() 2173: if dtypes[1] in get_all_fp_dtypes(): 2178: dtypes(*product(get_all_fp_dtypes(), 2179: get_all_fp_dtypes())) 2260: dtypesIfCUDA(*set(get_all_math_dtypes('cuda')) - {torch.complex64, torch.complex128}) 2261: dtypes(*set(get_all_math_dtypes('cpu')) - {torch.complex64, torch.complex128}) 2273: dtypesIfCUDA(*set(get_all_math_dtypes('cuda')) - {torch.complex64, torch.complex128}) 2274: dtypes(*set(get_all_math_dtypes('cpu')) - {torch.complex64, torch.complex128}) 2307: dtypes(*get_all_math_dtypes('cpu')) 2319: dtypes(*get_all_fp_dtypes(include_bfloat16=False)) 2331: dtypes(*get_all_int_dtypes()) 2356: dtypes(*get_all_dtypes(include_bfloat16=False, include_bool=False, include_complex=False)) 2393: if dtype in get_all_int_dtypes(): 2614: dtypes(*get_all_dtypes()) 2624: dtypes(*tuple(itertools.combinations_with_replacement(get_all_dtypes(), 2))) 2806: dtypes(*list(product(get_all_dtypes(include_complex=False), 2807: get_all_dtypes(include_complex=False)))) 2866: dtypes(*list(product(get_all_complex_dtypes(), 2867: get_all_complex_dtypes()))) 2902: dtypes(*product(get_all_dtypes(), get_all_dtypes())) 2906: dtypes(*product(get_all_dtypes(), get_all_dtypes())) 2910: dtypes(*product(get_all_dtypes(), get_all_dtypes())) 3019: dtypes = [torch.float, torch.double] + get_all_complex_dtypes() 3221: dtypes(*get_all_dtypes(include_complex=False)) 3407: dtypes(*list(product(get_all_dtypes(include_bool=False), 3408: get_all_dtypes(include_bool=False)))) 3504: dtypes(*product(get_all_dtypes(include_complex=False, include_bfloat16=False), 3505: get_all_dtypes(include_complex=False, include_bfloat16=False))) 3516: if x.dtype in get_all_int_dtypes() + [torch.bool]: 3643: dtypes(*product(get_all_dtypes(include_complex=False, 3645: get_all_dtypes(include_complex=False, ``` </p> </details> <details> <summary> `test/test_complex.py`</summary> <p> ```python 6:from torch.testing._internal.common_dtype import get_all_complex_dtypes 11: dtypes(*get_all_complex_dtypes()) ``` </p> </details> <details> <summary> `test/test_foreach.py`</summary> <p> ```python 18: get_all_dtypes, get_all_int_dtypes, get_all_complex_dtypes, get_all_fp_dtypes, 142: if dtype in get_all_int_dtypes(): 179: disable_fastpath = op.ref == torch.div and dtype in get_all_int_dtypes() + [torch.bool] 201: disable_fastpath = op.ref == torch.div and dtype in get_all_int_dtypes() + [torch.bool] 205: disable_fastpath |= dtype in get_all_int_dtypes() + [torch.bool] 211: disable_fastpath |= dtype not in get_all_complex_dtypes() 241: bool_int_div = op.ref == torch.div and dtype in get_all_int_dtypes() + [torch.bool] 246: disable_fastpath |= dtype in get_all_int_dtypes() + [torch.bool] 248: disable_fastpath |= dtype not in get_all_complex_dtypes() 250: disable_fastpath |= True and dtype not in get_all_complex_dtypes() 307: disable_fastpath = dtype in get_all_int_dtypes() + [torch.bool] 365: if opinfo.name == "_foreach_abs" and dtype in get_all_complex_dtypes(): 376: ops(foreach_unary_op_db, dtypes=get_all_dtypes()) 393: dtypes=get_all_dtypes(include_half=True, include_bfloat16=True, include_complex=False)) 401: ops(foreach_minmax_op_db, dtypes=get_all_fp_dtypes(include_bfloat16=True, include_half=True)) 426: if ord in (1, 2) and dtype in torch.testing.get_all_fp_dtypes(): 439: dtypes(*get_all_dtypes()) 449: ops(foreach_binary_op_db, dtypes=get_all_dtypes()) 481: ops(foreach_binary_op_db, dtypes=get_all_dtypes()) 536: if dtype in get_all_int_dtypes() + [torch.bool] and foreach_op == torch._foreach_div: 545: ops(foreach_binary_op_db, dtypes=get_all_dtypes()) 637: ops(foreach_pointwise_op_db, allowed_dtypes=get_all_fp_dtypes(include_half=False, include_bfloat16=False)) ``` </p> </details> <details> <summary> `test/test_linalg.py`</summary> <p> ```python 29: all_types, floating_types, floating_and_complex_types, get_all_dtypes, get_all_int_dtypes, get_all_complex_dtypes, 30: get_all_fp_dtypes, 111: dtypes(*(get_all_dtypes())) 794: float_and_complex_dtypes = get_all_fp_dtypes() + get_all_complex_dtypes() 807: dtypes(*(get_all_int_dtypes())) 828: dtypes(*(get_all_fp_dtypes() + get_all_complex_dtypes())) 841: if dtype in get_all_complex_dtypes(): 844: dtypes(*itertools.product(get_all_dtypes(), 845: get_all_dtypes())) 855: for dtypes0, dtypes1, dtypes2 in product(get_all_dtypes(), repeat=3): 5607: *get_all_fp_dtypes(include_half=not CUDA9, include_bfloat16=(CUDA11OrLater and SM53OrLater))) 5608: dtypes(*(set(get_all_dtypes()) - {torch.half, torch.bool})) 5644: dtypes(*(get_all_complex_dtypes() + get_all_fp_dtypes())) 6255: dtypesIfCUDA(*get_all_complex_dtypes(), 6256: *get_all_fp_dtypes(include_bfloat16=(TEST_WITH_ROCM or (CUDA11OrLater and SM53OrLater)), 6292: dtypesIfCUDA(*get_all_fp_dtypes(include_bfloat16=(TEST_WITH_ROCM or (CUDA11OrLater and SM53OrLater)))) 6323: dtypesIfCUDA(*get_all_complex_dtypes(), 6324: *get_all_fp_dtypes(include_bfloat16=(TEST_WITH_ROCM or (CUDA11OrLater and SM53OrLater)))) 6325: dtypes(*get_all_complex_dtypes(), *get_all_fp_dtypes()) 6358: dtypesIfCUDA(*([torch.float, torch.double] + get_all_complex_dtypes())) 6556: dtypes(*get_all_fp_dtypes(), *get_all_complex_dtypes()) 6668: dtypes(*get_all_fp_dtypes(), *get_all_complex_dtypes()) 6741: dtypes(*get_all_fp_dtypes(), *get_all_complex_dtypes()) ``` </p> </details> <details> <summary> `test/test_nn.py`</summary> <p> ```python 37:from torch.testing._internal.common_dtype import integral_types, get_all_fp_dtypes, get_all_math_dtypes 50: onlyNativeDeviceTypes, deviceCountAtLeast, largeTensorTest, expectedFailureMeta, skipMeta, get_all_device_types, \ 8862: for device in get_all_device_types(): 9629: for dt1 in get_all_math_dtypes(device): 9630: for dt2 in get_all_math_dtypes(device): 9631: for dt3 in get_all_math_dtypes(device): 9648: for input_dtype in get_all_math_dtypes(device): 9664: for input_dtype in get_all_math_dtypes(device): 13015: dtypes(*get_all_fp_dtypes(include_bfloat16=AMPERE_OR_ROCM)) 13034: dtypes(*get_all_fp_dtypes(include_bfloat16=AMPERE_OR_ROCM)) 13159: dtypes(*get_all_fp_dtypes(include_bfloat16=AMPERE_OR_ROCM)) 17400: dtypesIfCUDA(*get_all_fp_dtypes(include_bfloat16=AMPERE_OR_ROCM)) 17768: dtypesIfCUDA(*get_all_fp_dtypes()) 17773: dtypesIfCUDA(*get_all_fp_dtypes()) 17778: dtypesIfCUDA(*get_all_fp_dtypes()) 17783: dtypesIfCUDA(*get_all_fp_dtypes()) 17788: dtypesIfCUDA(*get_all_fp_dtypes()) 17793: dtypesIfCUDA(*get_all_fp_dtypes()) 17798: dtypesIfCUDA(*get_all_fp_dtypes()) 17963: dtypesIfCUDA(*get_all_fp_dtypes()) 17977: dtypesIfCUDA(*get_all_fp_dtypes()) 18684: def test_cross_entropy_loss_prob_target_all_reductions(self, device): ``` </p> </details> <details> <summary> `test/test_numpy_interop.py`</summary> <p> ```python 12:from torch.testing._internal.common_dtype import get_all_dtypes 399: dtypes(*get_all_dtypes()) ``` </p> </details> <details> <summary> `test/test_ops.py`</summary> <p> ```python 12:from torch.testing._internal.common_dtype import floating_and_complex_types_and, get_all_dtypes 86: for dtype in get_all_dtypes(): ``` </p> </details> <details> <summary> `test/test_reductions.py`</summary> <p> ```python 16: get_all_dtypes, get_all_math_dtypes, get_all_int_dtypes, get_all_complex_dtypes, get_all_fp_dtypes, 360: allowed_dtypes=get_all_dtypes(include_bfloat16=False)) 366: allowed_dtypes=get_all_dtypes(include_bfloat16=False)) 394: allowed_dtypes=get_all_dtypes(include_bfloat16=False)) 750: for dtype in [dtype for dtype in get_all_math_dtypes('cpu') if dtype != torch.float16]: 1404: dtypes(*get_all_dtypes(include_bool=False, include_complex=False)) 1457: dtypes(*(get_all_int_dtypes() + get_all_fp_dtypes(include_bfloat16=False) + 1458: get_all_complex_dtypes())) 1465: return dtype in get_all_int_dtypes() 1494: dtypes(*(get_all_int_dtypes() + get_all_fp_dtypes(include_bfloat16=False))) 1501: dtypes(*(get_all_int_dtypes() + get_all_fp_dtypes(include_bfloat16=False))) 1507: dtypes(*(get_all_complex_dtypes())) 1514: dtypes = list(get_all_int_dtypes() + get_all_fp_dtypes(include_bfloat16=False)) 1523: dtypes(*(get_all_int_dtypes() + get_all_fp_dtypes(include_bfloat16=False))) 1531: if dtype in get_all_fp_dtypes(): 1608: dtypes(*(get_all_dtypes(include_half=True, include_bfloat16=False, 1837: dtypes(*get_all_dtypes(include_bool=False, include_complex=False)) 1855: dtypes(*(set(get_all_dtypes(include_bool=False, include_complex=False)) - {torch.uint8})) 3219: for dtype in get_all_dtypes(include_half=True, include_bfloat16=False, ``` </p> </details> <details> <summary> `test/test_serialization.py`</summary> <p> ```python 26:from torch.testing._internal.common_dtype import get_all_dtypes 586: for device, dtype in product(devices, get_all_dtypes()): 589: for other_dtype in get_all_dtypes(): ``` </p> </details> <details> <summary> `test/test_shape_ops.py`</summary> <p> ```python 18:from torch.testing._internal.common_dtype import get_all_dtypes 230: dtypes(*get_all_dtypes(include_complex=False, include_bool=False, include_half=False, 232: dtypesIfCUDA(*get_all_dtypes(include_complex=False, include_bool=False, include_bfloat16=False)) 344: dtypes(*get_all_dtypes()) 443: dtypes(*get_all_dtypes()) 461: dtypes(*get_all_dtypes()) 570: dtypes(*get_all_dtypes(include_complex=False)) ``` </p> </details> <details> <summary> `test/test_sort_and_select.py`</summary> <p> ```python 12: all_types, all_types_and, floating_types_and, get_all_dtypes, get_all_int_dtypes, get_all_fp_dtypes, 136: dtypes(*set(get_all_dtypes()) - {torch.bool, torch.complex64, torch.complex128}) 231: dtypes(*set(get_all_dtypes()) - {torch.bool, torch.complex64, torch.complex128}) 296: dtypes(*(get_all_int_dtypes() + get_all_fp_dtypes())) 647: dtypesIfCUDA(*get_all_fp_dtypes()) 678: dtypesIfCUDA(*(get_all_dtypes(include_complex=False, 682: dtypes(*(get_all_dtypes(include_complex=False, include_bool=False, include_half=False, include_bfloat16=False))) 739: dtypesIfCPU(*set(get_all_dtypes()) - {torch.complex64, torch.complex128}) 740: dtypes(*set(get_all_dtypes()) - {torch.bfloat16, torch.complex64, torch.complex128}) 799: dtypesIfCPU(*set(get_all_dtypes()) - {torch.complex64, torch.complex128}) 800: dtypes(*set(get_all_dtypes()) - {torch.bfloat16, torch.complex64, torch.complex128}) ``` </p> </details> <details> <summary> `test/test_sparse.py`</summary> <p> ```python 20:from torch.testing import get_all_complex_dtypes, get_all_fp_dtypes 29: floating_and_complex_types, floating_and_complex_types_and, get_all_dtypes, get_all_int_dtypes, 1963: return dtype in get_all_int_dtypes() 1994: dtypes(*get_all_dtypes(include_bool=False, include_half=False, 2103: return dtype in get_all_int_dtypes() 2138: dtypes(*get_all_dtypes(include_bool=False, include_half=False, 2626: all_sparse_dtypes = get_all_dtypes(include_complex=True) 2633: all_sparse_dtypes = get_all_dtypes(include_complex=True) 3230: dtypes(*get_all_complex_dtypes(), 3231: *get_all_fp_dtypes(include_half=False, include_bfloat16=False)) 3234: *get_all_fp_dtypes( ``` </p> </details> <details> <summary> `test/test_sparse_csr.py`</summary> <p> ```python 7:from torch.testing import get_all_complex_dtypes, get_all_fp_dtypes, floating_and_complex_types, make_tensor 17:from torch.testing._internal.common_dtype import floating_types, get_all_dtypes 120: dtypes(*get_all_dtypes()) 133: dtypes(*get_all_dtypes()) 150: dtypes(*get_all_dtypes()) 180: dtypes(*get_all_dtypes()) 201: dtypes(*get_all_dtypes()) 210: dtypes(*get_all_dtypes()) 225: dtypes(*get_all_dtypes()) 244: dtypes(*get_all_dtypes()) 263: dtypes(*get_all_dtypes()) 285: dtypes(*get_all_dtypes()) 411: dtypes(*get_all_dtypes()) 482: dtypes(*get_all_dtypes()) 502: dtypes(*get_all_dtypes()) 562: dtypes(*get_all_dtypes()) 588: dtypesIfCUDA(*get_all_complex_dtypes(), 589: *get_all_fp_dtypes(include_half=SM53OrLater, include_bfloat16=SM80OrLater)) 745: dtypesIfCUDA(*get_all_complex_dtypes(), 746: *get_all_fp_dtypes(include_half=SM53OrLater and TEST_CUSPARSE_GENERIC, 765: dtypesIfCUDA(*get_all_complex_dtypes(), 766: *get_all_fp_dtypes(include_half=SM53OrLater and TEST_CUSPARSE_GENERIC, 801: *torch.testing.get_all_fp_dtypes(include_bfloat16=SM80OrLater, 841: *torch.testing.get_all_fp_dtypes(include_bfloat16=SM80OrLater, 1182: dtypes(*get_all_dtypes()) 1276: dtypes(*get_all_dtypes(include_bool=False, include_half=False, include_bfloat16=False)) 1286: dtypes(*get_all_dtypes()) ``` </p> </details> <details> <summary> `test/test_tensor_creation_ops.py`</summary> <p> ```python 21: onlyCUDA, skipCPUIf, dtypesIfCUDA, skipMeta, get_all_device_types) 23: get_all_dtypes, get_all_math_dtypes, get_all_int_dtypes, get_all_fp_dtypes, get_all_complex_dtypes 150: for dt in get_all_dtypes(): 160: for dt in get_all_dtypes(): 314: dtypes = [dtype for dtype in get_all_dtypes() if dtype != torch.bfloat16] 1012: dtypes(*(get_all_int_dtypes() + get_all_fp_dtypes(include_bfloat16=False) + 1013: get_all_complex_dtypes())) 1032: dtypes(*(get_all_int_dtypes() + get_all_fp_dtypes(include_bfloat16=False) + 1033: get_all_complex_dtypes())) 1050: dtypes(*(get_all_int_dtypes() + get_all_fp_dtypes(include_bfloat16=False) + 1051: get_all_complex_dtypes())) 1745: dtypes(*(get_all_int_dtypes() + get_all_fp_dtypes())) 1779: dtypes(*(get_all_int_dtypes() + get_all_fp_dtypes())) 1868: dtypes(*(get_all_int_dtypes() + get_all_fp_dtypes())) 1926: dtypes(*(get_all_int_dtypes() + get_all_fp_dtypes())) 1954: do_test_empty_full(self, get_all_math_dtypes('cpu'), torch.strided, torch_device) 1956: do_test_empty_full(self, get_all_math_dtypes('cpu'), torch.strided, None) 1957: do_test_empty_full(self, get_all_math_dtypes('cpu'), torch.strided, torch_device) 2538: for device in get_all_device_types(): 2645: for dtype in get_all_dtypes(): 2678: dtypes(*(get_all_fp_dtypes(include_half=False, include_bfloat16=False) + 2679: get_all_complex_dtypes())) 2716: dtypes(*get_all_fp_dtypes(include_half=False, include_bfloat16=False)) 2827: for dt in get_all_dtypes(): 2913: dtypes(*get_all_dtypes(include_bool=False, include_half=False)) 2914: dtypesIfCUDA(*get_all_dtypes(include_bool=False, include_half=True)) 3028: dtypes(*(get_all_fp_dtypes() + get_all_complex_dtypes())) 3033: dtypes(*(get_all_fp_dtypes() + get_all_complex_dtypes())) 3074: dtypes(*get_all_dtypes(include_bool=False, include_half=False, include_complex=False)) 3075: dtypesIfCUDA(*((get_all_int_dtypes() + [torch.float32, torch.float16, torch.bfloat16]) 3077: else get_all_dtypes(include_bool=False, include_half=True, include_complex=False))) 3873: dtypes(*get_all_dtypes()) 3884: dtypes(*get_all_dtypes(include_bool=False)) 3916: for other in get_all_dtypes(): 3922: dtypes(*get_all_dtypes()) 3932: dtypes(*get_all_dtypes(include_bool=False)) 3955: dtypes(*get_all_dtypes(include_bool=False)) 3961: dtypes(*get_all_dtypes(include_bool=False)) 3965: dtypes(*get_all_dtypes()) ``` </p> </details> <details> <summary> `test/test_testing.py`</summary> <p> ```python 25:from torch.testing._internal.common_dtype import get_all_dtypes 31: dtypes(*(get_all_dtypes(include_half=True, include_bfloat16=False, ``` </p> </details> <details> <summary> `test/test_torch.py`</summary> <p> ```python 51: expectedAlertNondeterministic, get_all_device_types, skipXLA) 57: get_all_fp_dtypes, get_all_int_dtypes, get_all_math_dtypes, get_all_dtypes, get_all_complex_dtypes 296: for d in get_all_device_types(): 323: for device in get_all_device_types(): 324: for dt1 in get_all_dtypes(): 325: for dt2 in get_all_dtypes(): 343: all_dtypes = get_all_dtypes() 350: all_dtypes = get_all_dtypes() 781: for dtype in get_all_dtypes(): 986: for device in get_all_device_types(): 1017: for device in get_all_device_types(): 1018: for dtype in get_all_math_dtypes(device): 2792: for device in get_all_device_types(): 3186: dtypes(*get_all_dtypes()) 3195: for error_dtype in get_all_dtypes(): 3203: dtypes(*get_all_dtypes()) 3212: for error_dtype in get_all_dtypes(): 4539: dtypes(*get_all_fp_dtypes()) 4545: dtypes(*(get_all_int_dtypes() + get_all_fp_dtypes())) 4577: dtypes(*get_all_fp_dtypes(include_half=False, include_bfloat16=False)) 4578: dtypesIfCPU(*(get_all_fp_dtypes(include_half=False, include_bfloat16=True))) 4579: dtypesIfCUDA(*(get_all_fp_dtypes(include_bfloat16=False))) 4599: dtypes(*(get_all_fp_dtypes(include_half=False, include_bfloat16=False))) 4600: dtypesIfCPU(*(get_all_dtypes(include_half=False, include_bfloat16=False, include_complex=False))) 4601: dtypesIfCUDA(*(get_all_dtypes(include_bfloat16=False, include_complex=False))) 4613: for p_dtype in get_all_fp_dtypes(include_half=device.startswith('cuda'), include_bfloat16=False): 4628: dtypes(*(get_all_fp_dtypes(include_half=False, include_bfloat16=False))) 4629: dtypesIfCUDA(*(get_all_fp_dtypes(include_bfloat16=False))) 4640: dtypes(*get_all_fp_dtypes()) 4723: dtypes(*get_all_fp_dtypes()) 4735: dtypes(*get_all_fp_dtypes(include_bfloat16=False)) 4736: dtypesIfCUDA(*get_all_fp_dtypes()) 4747: dtypes(*get_all_fp_dtypes()) 4761: dtypes(*get_all_fp_dtypes()) 4771: dtypes(*get_all_fp_dtypes()) 4792: dtypes(*(get_all_int_dtypes() + get_all_fp_dtypes())) 5302: dtypes(*get_all_dtypes(include_bfloat16=False)) 5322: dtypes(*get_all_dtypes(include_half=False, include_bfloat16=False)) 5323: dtypesIfCPU(*get_all_dtypes(include_bfloat16=False)) 5324: dtypesIfCUDA(*get_all_dtypes(include_bfloat16=False)) 5591: for dt in get_all_dtypes(): 5611: for dt in get_all_dtypes(): 5678: for dt in get_all_dtypes(): 5696: dtypesIfCUDA(*set(get_all_math_dtypes('cuda'))) 5697: dtypes(*set(get_all_math_dtypes('cpu'))) 5746: dtypes(*get_all_dtypes()) 5780: dtypes(*get_all_dtypes()) 5885: dtypes(*get_all_dtypes()) 5902: dtypes(*get_all_dtypes()) 5945: dtypes(*get_all_dtypes()) 5979: dtypes(*get_all_dtypes(include_bool=False)) 6049: dtypes(*get_all_dtypes(include_bool=False)) 6092: dtypes(*(get_all_fp_dtypes(include_bfloat16=False, include_half=False) + 6093: get_all_complex_dtypes())) 6094: dtypesIfCPU(*get_all_dtypes()) 6095: dtypesIfCUDA(*get_all_dtypes()) 6122: dtypes(*(get_all_fp_dtypes(include_bfloat16=False, include_half=False) + 6123: get_all_complex_dtypes())) 6124: dtypesIfCPU(*get_all_dtypes()) 6125: dtypesIfCUDA(*get_all_dtypes()) 6163: dtypes(*(get_all_fp_dtypes(include_bfloat16=False, include_half=False) + 6164: get_all_complex_dtypes())) 6165: dtypesIfCPU(*get_all_dtypes()) 6166: dtypesIfCUDA(*get_all_dtypes()) 6190: dtypes(*(get_all_complex_dtypes() + 6191: get_all_int_dtypes())) 6238: dtypes(*get_all_dtypes()) 6323: dtypes(*get_all_dtypes()) 6389: dtypes(*product(get_all_dtypes(), (torch.uint8, torch.bool))) 6699: dtypesIfCUDA(*set(get_all_math_dtypes('cuda'))) 6700: dtypes(*set(get_all_math_dtypes('cpu'))) 7452: dtypes(*get_all_dtypes(include_bool=False)) 7461: dtypes(*get_all_dtypes(include_bool=False)) 7477: dtypes(*get_all_dtypes(include_bool=False)) 7496: dtypes(*get_all_dtypes(include_bool=False)) 7538: dtypes(*get_all_dtypes(include_bool=False)) 8162: dtypes(*(get_all_int_dtypes() + get_all_fp_dtypes() + 8163: get_all_complex_dtypes())) 8175: dtypes(*(get_all_int_dtypes() + get_all_fp_dtypes() + 8176: get_all_complex_dtypes())) ``` </p> </details> <details> <summary> `test/test_type_promotion.py`</summary> <p> ```python 14: get_all_dtypes, get_all_math_dtypes, get_all_int_dtypes, get_all_fp_dtypes 187: for dtype in get_all_dtypes(): 262: dtypes1 = get_all_math_dtypes('cuda') 263: dtypes2 = get_all_math_dtypes(device) 339: dtypes(*itertools.product(get_all_dtypes(), get_all_dtypes())) 468: for dt1 in get_all_math_dtypes(device): 469: for dt2 in get_all_math_dtypes(device): 519: for dt1 in get_all_math_dtypes(device): 520: for dt2 in get_all_math_dtypes(device): 528: for dt in get_all_math_dtypes(device): 561: for dtype in get_all_dtypes(): 766: dtypes=get_all_math_dtypes(device)) 771: dtypes=get_all_math_dtypes(device)) 782: dtypes=get_all_math_dtypes(device)) 879: dtypes = get_all_dtypes(include_bfloat16=False) 898: dtypes = get_all_dtypes(include_bfloat16=False, include_bool=False) 965: dtypesIfCUDA(*itertools.product(get_all_dtypes(include_bfloat16=False, include_complex=False), 966: get_all_dtypes(include_bfloat16=False, include_complex=False))) 967: dtypes(*itertools.product(get_all_dtypes(include_half=False, include_bfloat16=False, 969: get_all_dtypes(include_half=False, include_bfloat16=False, 976: return dtype in get_all_int_dtypes() + [torch.bool] 979: return dtype in get_all_fp_dtypes(include_half=True, include_bfloat16=False) ``` </p> </details> <details> <summary> `test/test_unary_ufuncs.py`</summary> <p> ```python 24: floating_types_and, all_types_and_complex_and, floating_and_complex_types_and, get_all_dtypes, get_all_math_dtypes, 25: get_all_int_dtypes, get_all_fp_dtypes, get_all_complex_dtypes 517: dtypes(*(get_all_int_dtypes() + [torch.bool] + 518: get_all_fp_dtypes(include_bfloat16=False))) 596: dtypes(*get_all_fp_dtypes(include_half=True, include_bfloat16=False)) 611: invalid_input_dtypes = get_all_int_dtypes() + \ 612: get_all_complex_dtypes() + \ 619: for dtype in get_all_fp_dtypes(include_half=True, include_bfloat16=False): 1048: dtypes(*get_all_math_dtypes('cpu')) 1182: dtypesIfCUDA(*get_all_fp_dtypes()) 1190: dtypesIfCUDA(*get_all_fp_dtypes()) 1205: dtypesIfCUDA(*get_all_fp_dtypes()) 1215: dtypesIfCUDA(*get_all_fp_dtypes()) 1307: dtypes(*(get_all_dtypes(include_bool=False))) 1349: dtypes(*(get_all_fp_dtypes(include_half=False) + 1350: get_all_complex_dtypes())) 1351: dtypesIfCUDA(*(get_all_fp_dtypes(include_half=True) + 1352: get_all_complex_dtypes())) ``` </p> </details> <details> <summary> `test/test_view_ops.py`</summary> <p> ```python 19: get_all_dtypes, get_all_int_dtypes, get_all_fp_dtypes, get_all_complex_dtypes 124: dtypes(*(get_all_int_dtypes() + get_all_fp_dtypes())) 131: dtypes(*get_all_dtypes(include_bfloat16=False)) 213: for view_dtype in [*get_all_fp_dtypes(), *get_all_complex_dtypes()]: 220: dtypes(*get_all_dtypes()) 224: for view_dtype in get_all_dtypes(): 305: dtypes(*get_all_complex_dtypes(include_complex32=True)) 343: dtypes(*get_all_dtypes()) 354: dtypes(*get_all_dtypes()) 364: dtypes(*get_all_dtypes()) 374: dtypes(*get_all_dtypes()) 384: dtypes(*(get_all_int_dtypes() + get_all_fp_dtypes())) 395: dtypes(*get_all_complex_dtypes()) 426: dtypes(*get_all_complex_dtypes()) 451: dtypes(*product(get_all_complex_dtypes(), get_all_dtypes())) 1263: dtypes(*(torch.testing.get_all_dtypes())) 1279: dtypes(*(torch.testing.get_all_dtypes())) 1405: dtypes(*(get_all_int_dtypes() + get_all_fp_dtypes(include_bfloat16=False) + 1406: get_all_complex_dtypes())) 1471: dtypes(*get_all_dtypes(include_bfloat16=False)) 1574: dtypes(*get_all_dtypes()) 1601: dtypes(*get_all_dtypes(include_bfloat16=False)) 1632: dtypes(*get_all_dtypes(include_bfloat16=False)) 1711: for dt in get_all_dtypes(): 1717: for dt in get_all_dtypes(): 1724: for dt in get_all_dtypes(): ``` </p> </details> I'm looking forward to your viewpoints. Thanks :) cc: mruberry kshitij12345 anjali411 Pull Request resolved: https://github.com/pytorch/pytorch/pull/71561 Reviewed By: samdow Differential Revision: D34856571 Pulled By: mruberry fbshipit-source-id: 0dca038bcad5cf69906245c496d2e61ac3876335 (cherry picked from commit b058f67b4313143efa714ab105f36e74083131b9)	2022-03-15 20:31:41 +00:00
Natalia Gimelshein	967606124a	port torch cov tests to error inputs (#73977) ... Summary: Per title Pull Request resolved: https://github.com/pytorch/pytorch/pull/73977 Reviewed By: malfet Differential Revision: D34779552 Pulled By: ngimel fbshipit-source-id: b4191101a029981eb27c75e1b56d739db046f819 (cherry picked from commit 2c2af726ffdba68f358a4ff0ee07580609bccc34)	2022-03-10 19:04:44 +00:00
Natalia Gimelshein	e47a5a64bb	Back out "Revert D34524207: [pytorch][PR] remove _s_where" (#73579) ... Summary: Original commit changeset: 87b1220d851c Original Phabricator Diff: D34524207 (4eb2482568) (4eb2482568) Pull Request resolved: https://github.com/pytorch/pytorch/pull/73579 Test Plan: OSS tests tested with canary https://www.internalfb.com/intern/ads/canary/441912928798660873 Reviewed By: ezyang Differential Revision: D34688237 Pulled By: ngimel fbshipit-source-id: 32f3a0046053ef52e95ab45a26bfc1de17e7e061 (cherry picked from commit d1c0acbe3e0ff884c429072923a468ee1d3d447d)	2022-03-08 19:15:30 +00:00
anjali411	37e0d2e361	Fix segfault while real and imaginary attributes are set to a number (#73867) ... Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/73867 Fixes https://github.com/pytorch/pytorch/issues/72947 Test Plan: Imported from OSS Reviewed By: davidberard98 Differential Revision: D34695956 Pulled By: anjali411 fbshipit-source-id: 2f3eda272a5214335eae506bd387ce8da4d81b8c (cherry picked from commit fdb07354cac22c30aa047e65fbac9840608db811)	2022-03-08 18:58:26 +00:00
Natalia Gimelshein	55525632ab	Revert D34554432: Back out "Revert D34524207: [pytorch][PR] remove _s_where" ... Test Plan: revert-hammer Differential Revision: D34554432 (9c03c6163f) Original commit changeset: 2f3601d3d426 Original Phabricator Diff: D34554432 (9c03c6163f) fbshipit-source-id: db434750f44c6e6ec545a248c462d8fdcbefbaf8 (cherry picked from commit 866d4d0c795edd7ef519925683b5e57dd9b116ad)	2022-03-04 20:32:39 +00:00
Natalia Gimelshein	9c03c6163f	Back out "Revert D34524207: [pytorch][PR] remove _s_where" (#73579) ... Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/73579 Original commit changeset: 87b1220d851c Original Phabricator Diff: D34524207 (4eb2482568) Test Plan: OSS tests Reviewed By: malfet Differential Revision: D34554432 fbshipit-source-id: 2f3601d3d4261ebcebb05b4b1aec0c9a8a00ea04 (cherry picked from commit b9cad3f2bc54e12b275567454336cf4d9dcb78c4)	2022-03-04 19:35:41 +00:00
Nikita Karetnikov	eb0d370f14	Write explicit meta-kernels for normal (#70089) ... Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/70089 See #69386. Test Plan: Imported from OSS Reviewed By: ejguan Differential Revision: D34089964 Pulled By: bdhirsh fbshipit-source-id: eb88eb7c4830545d3d43c82b6f3abb98617cee8e (cherry picked from commit 89c9c02a0fb1c780495fee6370961104f4b1dcd1)	2022-03-01 23:28:14 +00:00
Nikita Shulga	dd9517cc4a	Revert D34524207: [pytorch][PR] remove _s_where ... Test Plan: revert-hammer Differential Revision: D34524207 (4eb2482568) Original commit changeset: bc71e27b6d3f Original Phabricator Diff: D34524207 (4eb2482568) fbshipit-source-id: 87b1220d851c3d2b51bdd1cf2f8a493c58ab9b14 (cherry picked from commit af1f0cc9e032b00619a7979bbbd2281f69e0fdf0)	2022-03-01 17:43:16 +00:00
Natalia Gimelshein	4eb2482568	remove _s_where (#73468) ... Summary: Per title Fixes https://github.com/pytorch/pytorch/issues/73135 Pull Request resolved: https://github.com/pytorch/pytorch/pull/73468 Reviewed By: albanD Differential Revision: D34524207 Pulled By: ngimel fbshipit-source-id: bc71e27b6d3fa50de6737533c92375266d9eadc5 (cherry picked from commit 047b925849370e6e4cbe9e3a722db52bb1e965b9)	2022-03-01 07:30:34 +00:00
Philip Meier	0973c5a1cc	align signature of make_tensor with other creation ops (#72702) ... Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/72702 Test Plan: Imported from OSS Reviewed By: mrshenli Differential Revision: D34457729 Pulled By: mruberry fbshipit-source-id: 83d580c4201eef946dc9cf4b9e28a3d36be55609 (cherry picked from commit aa4cf20fbeb4b795595729b8ac2e6ba7707d8283)	2022-02-25 06:30:31 +00:00
Nikita Shulga	cfb6c942fe	scatter_reduce documentation (#73125) ... Summary: Reland of https://github.com/pytorch/pytorch/issues/68580 (which were milestoned for 1.11) plus partial revert of https://github.com/pytorch/pytorch/pull/72543 Pull Request resolved: https://github.com/pytorch/pytorch/pull/73125 Reviewed By: bdhirsh Differential Revision: D34355217 Pulled By: malfet fbshipit-source-id: 325ecdeaf53183d653b44ee5e6e8839ceefd9200 (cherry picked from commit 71db31748a)	2022-02-22 19:33:46 +00:00
Philip Meier	1f74e082e2	only compare attributes for meta tensors (#72508) ... Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/72508 Todo: - [x] document this behavior - [x] add tests Test Plan: Imported from OSS Reviewed By: zou3519 Differential Revision: D34262452 Pulled By: ezyang fbshipit-source-id: bc5c9653d5c3ad5c6efccc9c8e0efc0d28e15104 (cherry picked from commit 233142c88e)	2022-02-17 02:33:08 +00:00
Brian Hirsh	f87f753bb9	avoiding adding some functions to the public python API before 1.11 release (#72543) ... Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/72543 Test Plan: Imported from OSS Reviewed By: ejguan Differential Revision: D34085724 Pulled By: bdhirsh fbshipit-source-id: 941d5a90a6fa5328268d623e0e2b01577e4132ca (cherry picked from commit 6676a0c79a)	2022-02-14 19:49:01 +00:00
Kurt Mohler	47c6993355	Update from_dlpack tests and documentation (#70543) ... Summary: Part of https://github.com/pytorch/pytorch/issues/58742 Pull Request resolved: https://github.com/pytorch/pytorch/pull/70543 Reviewed By: soulitzer Differential Revision: D34172475 Pulled By: mruberry fbshipit-source-id: d498764b8651a8b7a19181b3421aeebf28a5db2b (cherry picked from commit 05332f164c)	2022-02-14 03:35:17 +00:00
anjali411	f607af126e	Set correct device id on efficientzerotensors (#71611) ... Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/71611 Fixes https://github.com/pytorch/pytorch/issues/71160 https://github.com/pytorch/pytorch/issues/69925 #69913 Test Plan: Imported from OSS Reviewed By: VitalyFedyunin Differential Revision: D33897543 Pulled By: anjali411 fbshipit-source-id: f1d8608c351876b8c2619da5ef891f74bad30ab5 (cherry picked from commit 643e666ea3)	2022-02-02 21:51:32 +00:00
Anjali Chourdia	1e4aefaa2f	Revert D33834916: Set correct device id on efficientzerotensors ... Test Plan: revert-hammer Differential Revision: D33834916 (a18cfb790d) Original commit changeset: 11cec343e95e Original Phabricator Diff: D33834916 (a18cfb790d) fbshipit-source-id: 3d3f60b760b445383768161b1d21ea4dadbe5d7c (cherry picked from commit eba41aa646)	2022-01-31 03:49:56 +00:00
anjali411	a18cfb790d	Set correct device id on efficientzerotensors (#71611) ... Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/71611 Fixes https://github.com/pytorch/pytorch/issues/71160 https://github.com/pytorch/pytorch/issues/69925 Test Plan: Imported from OSS Reviewed By: george-qi Differential Revision: D33834916 Pulled By: anjali411 fbshipit-source-id: 11cec343e95e2ee188ab7576f26f64aa19317891 (cherry picked from commit f6e86f8a6b)	2022-01-30 20:53:15 +00:00
Mikayla Gawarecki	09c417ae65	Add new reduce options and autograd support for scatter_reduce (#71788) ... Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/71788 Test Plan: Imported from OSS Reviewed By: mikaylagawarecki Differential Revision: D33778525 Pulled By: cpuhrsch fbshipit-source-id: 47b8544e29df3075bc6ede894c59499a7ffec876 (cherry picked from commit ddcddac726)	2022-01-27 17:38:50 +00:00
Mikayla Gawarecki	fdec94504f	Rename _scatter_reduce to scatter_reduce and make it unstructured (#71787) ... Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/71787 Test Plan: Imported from OSS Reviewed By: mikaylagawarecki Differential Revision: D33778524 Pulled By: cpuhrsch fbshipit-source-id: 55a330e1c2227c0eaaa1c0d2f9205a4dee24a11b (cherry picked from commit 6e4a8a91da)	2022-01-27 16:29:13 +00:00
Mike Ruberry	0891c908bb	Revert D33768645: Set correct device id on efficientzerotensors ... Test Plan: revert-hammer Differential Revision: D33768645 (5dd6cd55ba) Original commit changeset: 66ce9907630b Original Phabricator Diff: D33768645 (5dd6cd55ba) fbshipit-source-id: 4bb1ad46f01cd33aeb813bdc123741cf665194a8 (cherry picked from commit 8ca385b1d8)	2022-01-26 17:01:32 +00:00
anjali411	5dd6cd55ba	Set correct device id on efficientzerotensors (#71611) ... Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/71611 Fixes https://github.com/pytorch/pytorch/issues/71160 Test Plan: Imported from OSS Reviewed By: pbelevich, ngimel Differential Revision: D33768645 Pulled By: anjali411 fbshipit-source-id: 66ce9907630b65a12c0775077147a7e72ff4cee4 (cherry picked from commit 3af98a4d70)	2022-01-25 23:32:11 +00:00
Jonathan Colen	33403f4848	edge_order check in torch.gradient only applies to dim argument (#67926) ... Summary: Fixes https://github.com/pytorch/pytorch/issues/67919 The compatibility check on `edge_order` in `pre_check_gradient` now looks only at dim argument if it is present, otherwise it checks all dimensions. Previously, it would check all dimensions regardless of the dim argument and throw unnecessary errors. Pull Request resolved: https://github.com/pytorch/pytorch/pull/67926 Reviewed By: albanD Differential Revision: D33760621 Pulled By: mruberry fbshipit-source-id: d490cd8610c68ff3787e670fc947de3cbf2db062 (cherry picked from commit 45bc56de9e)	2022-01-25 21:29:31 +00:00
Mike Ruberry	e0d829a266	Kill the test_torch.py mixin and creates test_scatter_gather_ops (#71691) ... Summary: Per title. Also annotates test_torch.py with additional cleanup tasks and adds empty sample inputs to elementwise unary and binary OpInfos. Pull Request resolved: https://github.com/pytorch/pytorch/pull/71691 Reviewed By: ngimel Differential Revision: D33735126 Pulled By: mruberry fbshipit-source-id: 8cc097a7581a8b620540c95b2a5889c1165ecf23 (cherry picked from commit 5c6a245a3f)	2022-01-24 09:32:32 +00:00
Mike Ruberry	7680a0ae9d	Deprecates _aminmax (#71576) ... Summary: Replaces https://github.com/pytorch/pytorch/pull/62432. Existing callsites are updated. Pull Request resolved: https://github.com/pytorch/pytorch/pull/71576 Reviewed By: ngimel Differential Revision: D33689960 Pulled By: mruberry fbshipit-source-id: fad1ba78347ecec7fd48f21862c3eb606662b8f4 (cherry picked from commit 6cd438e9a1)	2022-01-21 09:23:29 +00:00
Peter Bell	17bb68618f	Copy: Fix CPU transpose path ignoring neg and conj bits (#69026) ... Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/69026 Test Plan: Imported from OSS Reviewed By: ngimel Differential Revision: D33064533 Pulled By: anjali411 fbshipit-source-id: 98c25586a1707ac2324f69f652ce5a14dd59c0ad	2022-01-14 10:13:33 -08:00
Emilio Castillo	8dfff8b2e2	Fix scatter for empty indexes (#70662) ... Summary: This PR fixes an issue with `scatter` where the output is garbage for zero-sized indexes. ```py import torch null_index = torch.zeros((0, 4), dtype=torch.int64) null_arr = torch.zeros((0, 4)) zeros_arr = torch.zeros((1, 4)) result = zeros_arr.scatter(0, null_index, null_arr) print(null_index) print(null_arr) print(zeros_arr) print(result) ``` ``` tensor([], size=(0, 4), dtype=torch.int64) tensor([], size=(0, 4)) tensor([[0., 0., 0., 0.]]) tensor([[1.7036e+19, 2.9965e+32, 3.9133e-14, 1.3585e-19]]) ``` the out array is never filled if `index` arg has 0 elements. Pull Request resolved: https://github.com/pytorch/pytorch/pull/70662 Reviewed By: dagitses Differential Revision: D33476807 Pulled By: albanD fbshipit-source-id: 97dbdd9c0133899e58828c43ecba81838807b8af	2022-01-07 09:20:43 -08:00
Peter Bell	917d56a7e4	Copy: Fix conj bit being ignored on type mismatch (#68963) ... Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/68963 Test Plan: Imported from OSS Reviewed By: ngimel Differential Revision: D33064492 Pulled By: anjali411 fbshipit-source-id: 043f927d6bfff46bf5f8ea6fce9409f250bf8ff8	2022-01-05 17:59:32 -08:00
Brian Hirsh	457ba1dd3e	Porting index_add to structured kernels, add an out variant (#65993) ... Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/65993 This PR attempts to port `index_add` to structured kernels, but does more than that: * Adds an `out=` variant to `index_add` * Revises `native_functions.yaml` registrations, to not have multiple entries and instead pass default value to `alpha`. * Changes in `derivatives.yaml` file for autograd functioning * Revises error messages, please see: https://github.com/pytorch/pytorch/pull/65993#issuecomment-945441615 Follow-up PRs in near future will attempt to refactor the OpInfo test, and will give another look at tests in `test/test_torch.py` for this function. (hence the use of ghstack for this) ~This is WIP because there are tests failing for `Dimname` variant on mobile/android builds, and I'm working on fixing them.~ Issue tracker: https://github.com/pytorch/pytorch/issues/55070 Test Plan: Imported from OSS Reviewed By: ejguan Differential Revision: D32646426 fbshipit-source-id: b035ecf843a9a27d4d1e18b202b035adc2a49ab5	2021-12-14 11:57:13 -08:00
kshitij12345	5b2586fe09	[testing] Ignore expected_regex in assertRaisesRegex for non-native device (#68723) ... Summary: Fixes https://github.com/pytorch/pytorch/issues/29719 Pull Request resolved: https://github.com/pytorch/pytorch/pull/68723 Reviewed By: zou3519 Differential Revision: D32797061 Pulled By: mruberry fbshipit-source-id: 3bcae6d3d62d180059dbe39be520b0e7f9aea19f	2021-12-02 14:52:27 -08:00
Emilio Castillo	533e72e0a4	Fix DLPack CUDA stream convention (#67618) ... Summary: Apparently for the array API, cuda default stream and per thread stream should be 1 and 2 instead of 0 and 1: https://data-apis.org/array-api/latest/API_specification/array_object.html?dlpack-self-stream-none#dlpack-self-stream-none. This caused a problem in the interop with CuPy https://github.com/cupy/cupy/pull/5970#discussion_r739912926. cc rgommers leofang mruberry Pull Request resolved: https://github.com/pytorch/pytorch/pull/67618 Reviewed By: albanD Differential Revision: D32521805 Pulled By: mruberry fbshipit-source-id: 95777e4014e5edf1f88ba10adc03c6e34c13248d	2021-11-18 08:36:05 -08:00
kshitij12345	d5d2096dab	[testing] make @dtypes mandatory when using @dtypesIf (#68186) ... Summary: Fixes https://github.com/pytorch/pytorch/issues/53647 With this if a test forgets to add `dtypes` while using `dtypesIf`, following error is raised ``` AssertionError: dtypes is mandatory when using dtypesIf however 'test_exponential_no_zero' didn't specify it ``` **Tested Locally** Pull Request resolved: https://github.com/pytorch/pytorch/pull/68186 Reviewed By: VitalyFedyunin Differential Revision: D32468581 Pulled By: mruberry fbshipit-source-id: 805e0855f988b77a5d8d4cd52b31426c04c2200b	2021-11-18 08:29:31 -08:00