Serial Number
int64
1
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Issue Number
int64
75.6k
112k
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74.5k
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int64
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867
2,901
99,037
AttributeError: type object 'torch._C._profiler.ProfilerActivity' has no attribute 'MPS'
triaged, oncall: profiler, module: mps
### 🚀 The feature, motivation and pitch Add support for 'MPS' in Pytorch profiler ``` In [1]: import torch In [2]: from torch.profiler import profile, record_function, ProfilerActivity ...: In [9]: with profile(activities=[ProfilerActivity.CPU, ProfilerActivity.MPS], re ...: cord_shapes=True) as prof: ...: for epoch in range(num_epochs): ...: i = i + 1 ``` ### Alternatives _No response_ ### Additional context _No response_ cc @robieta @chaekit @aaronenyeshi @ngimel @nbcsm @guotuofeng @guyang3532 @gaoteng-git @tiffzhaofb @dzhulgakov @davidberard98 @kulinseth @albanD @malfet @DenisVieriu97 @razarmehr @abhudev
2
2,902
99,035
Issue on building from source: Remove -mfpu=neon option on MacOS with Apple silicon
module: build, triaged
### 🐛 Describe the bug Get pytorch: ``` git clone -b v2.0.0 --recursive https://github.com/pytorch/pytorch ``` Install dependencies as said in Readme. I tried compiling with bot gcc(real gcc, not alias for clang) and clang, they all told me about -mfpu=neon option. GCC: ``` gcc-12: error: unrecognized command-line option '-mfpu=neon' ``` Clang: ``` clang: warning: argument unused during compilation: '-mfpu=neon' [-Wunused-command-line-argument] ``` This option seems to be useless in this situation. ### Versions pytorch 2.0.0 cc @malfet @seemethere
1
2,903
99,025
Is there a way to get the full call stack of pytorch from python to C/C++?
triaged
### 🚀 The feature, motivation and pitch I can get the python call stack of pytorch with PYCG or other package. I can get C/C++ call stack with perf. But how to link them together? Pytorch calls C/C++ functions/operators with dynamic dispatching. It's hard to know what C/C++ functions/operators is called by a pytorch operator ,e.g. bmm operator. Is there any tools that can profile the call stack or trace from pytorch(up) to C/C++ operators/functions(down)? ### Alternatives _No response_ ### Additional context _No response_
4
2,904
99,023
Dtype changes while going from FX graph -> Torchscript
triaged, FX-TorchScript Compatibility, module: fx
### 🐛 Describe the bug Python: ``` rand_idx = torch.randint(sy*sx, size=(hsy, wsx, 1), device=metric.device, dtype=torch.int64) idx_buffer_view = torch.zeros(hsy, wsx, sy*sx, device=metric.device, dtype=torch.int64) idx_buffer_view.scatter_(dim=2, index=rand_idx, src=-torch.ones_like(rand_idx, dtype=rand_idx.dtype)) ``` The `FX graph` : ``` %randint : [#users=2] = call_function[target=torch.ops.aten.randint.default](args = (4, [32, 32, 1]), kwargs = {device: cpu, pin_memory: False}) %zeros : [#users=1] = call_function[target=torch.ops.aten.zeros.default](args = ([32, 32, 4],), kwargs = {dtype: torch.int64, device: cpu, pin_memory: False}) %ones_like : [#users=1] = call_function[target=torch.ops.aten.ones_like.default](args = (%randint,), kwargs = {dtype: torch.int64, pin_memory: False}) %neg : [#users=1] = call_function[target=torch.ops.aten.neg.default](args = (%ones_like,), kwargs = {}) %scatter_ : [#users=1] = call_function[target=torch.ops.aten.scatter_.src](args = (%zeros, 2, %randint, %neg), kwargs = {}) ``` The `Torchscript` IR : ``` %836 = torch.aten.randint %int4, %835, %int4, %none_3, %cpu, %false : !torch.int, !torch.list<int>, !torch.int, !torch.none, !torch.Device, !torch.bool -> !torch.tensor %838 = torch.aten.zeros %837, %int5, %none_3, %cpu, %false : !torch.list<int>, !torch.int, !torch.none, !torch.Device, !torch.bool -> !torch.tensor %839 = torch.aten.ones_like %836, %int4, %none_3, %none_3, %false, %none_3 : !torch.tensor, !torch.int, !torch.none, !torch.none, !torch.bool, !torch.none -> !torch.tensor %840 = torch.aten.neg %839 : !torch.tensor -> !torch.tensor %841 = torch.aten.scatter_.src %838, %int2, %836, %840 : !torch.tensor, !torch.int, !torch.tensor, !torch.tensor -> !torch.tensor ``` `torch.aten.randint ` : 3rd argument is `dtype`, in this case it's `%int4` (int64) `torch.aten.zeros` : 2nd argument is `dtype`, in this case it's `%int5`. (half) `torch.aten.ones_like` : 2nd argument is `dtype`, in this case it's `%int4`. (int64) The reason behind `torch.aten.zeros` being set to have dtype as`fp16` despite having `int64` in the Python code is because when an FX graph is converted to TorchScript and imported into Torch-MLIR, a Python representation of the graph is included as a string parameter in the MLIR module. All the `torch.ops.aten.zeros` in this Python representation are being set to have `dtyp = torch.float16` (that's a bug!). You can observe that in the [Torchscript](https://drive.google.com/file/d/167bzYzDKfv6G1WR5Gnv0vAHDSUoGIVrN/view?usp=sharing) IR file. The following is what you'd observe :- ``` zeros = torch.ops.aten.zeros([32, 32, 4], dtype = torch.float16, device = device(type='cpu'), pin_memory = False) zeros_1 = torch.ops.aten.zeros([2, 4096, 320], dtype = torch.float16, device = device(type='cpu'), pin_memory = False) zeros_2 = torch.ops.aten.zeros([32, 32, 4], dtype = torch.float16, device = device(type='cpu'), pin_memory = False) zeros_3 = torch.ops.aten.zeros([2, 4096, 320], dtype = torch.float16, device = device(type='cpu'), pin_memory = False) zeros_4 = torch.ops.aten.zeros([32, 32, 4], dtype = torch.float16, device = device(type='cpu'), pin_memory = False) zeros_5 = torch.ops.aten.zeros([2, 4096, 320], dtype = torch.float16, device = device(type='cpu'), pin_memory = False) zeros_6 = torch.ops.aten.zeros([32, 32, 4], dtype = torch.float16, device = device(type='cpu'), pin_memory = False) zeros_7 = torch.ops.aten.zeros([2, 4096, 320], dtype = torch.float16, device = device(type='cpu'), pin_memory = False) zeros_8 = torch.ops.aten.zeros([32, 32, 4], dtype = torch.float16, device = device(type='cpu'), pin_memory = False) zeros_9 = torch.ops.aten.zeros([2, 4096, 320], dtype = torch.float16, device = device(type='cpu'), pin_memory = False) ``` Also, here is the corresponding [FX graph](https://drive.google.com/file/d/1KQrOEtDzoUjf-H_oee_Un30QME05Lwfk/view?usp=sharing) file just for your reference. ### Versions ``` Collecting environment information... PyTorch version: 2.1.0.dev20230403+cu118 Is debug build: False CUDA used to build PyTorch: 11.8 ROCM used to build PyTorch: N/A OS: Ubuntu 22.04.1 LTS (x86_64) GCC version: (Ubuntu 11.3.0-1ubuntu1~22.04) 11.3.0 Clang version: 14.0.0-1ubuntu1 CMake version: version 3.26.3 Libc version: glibc-2.35 Python version: 3.11.2 (main, Feb 8 2023, 14:49:25) [GCC 11.3.0] (64-bit runtime) Python platform: Linux-5.15.0-1030-gcp-x86_64-with-glibc2.35 Is CUDA available: True CUDA runtime version: 12.0.76 CUDA_MODULE_LOADING set to: LAZY GPU models and configuration: GPU 0: NVIDIA A100-SXM4-40GB Nvidia driver version: 525.85.12 cuDNN version: Probably one of the following: /usr/lib/x86_64-linux-gnu/libcudnn.so.8.7.0 /usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.7.0 /usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.7.0 /usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.7.0 /usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.7.0 /usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.7.0 /usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.7.0 /usr/local/cuda-12.0/targets/x86_64-linux/lib/libcudnn.so.8.7.0 /usr/local/cuda-12.0/targets/x86_64-linux/lib/libcudnn_adv_infer.so.8.7.0 /usr/local/cuda-12.0/targets/x86_64-linux/lib/libcudnn_adv_train.so.8.7.0 /usr/local/cuda-12.0/targets/x86_64-linux/lib/libcudnn_cnn_infer.so.8.7.0 /usr/local/cuda-12.0/targets/x86_64-linux/lib/libcudnn_cnn_train.so.8.7.0 /usr/local/cuda-12.0/targets/x86_64-linux/lib/libcudnn_ops_infer.so.8.7.0 /usr/local/cuda-12.0/targets/x86_64-linux/lib/libcudnn_ops_train.so.8.7.0 HIP runtime version: N/A MIOpen runtime version: N/A Is XNNPACK available: True CPU: Architecture: x86_64 CPU op-mode(s): 32-bit, 64-bit Address sizes: 46 bits physical, 48 bits virtual Byte Order: Little Endian CPU(s): 12 On-line CPU(s) list: 0-11 Vendor ID: GenuineIntel Model name: Intel(R) Xeon(R) CPU @ 2.20GHz CPU family: 6 Model: 85 Thread(s) per core: 2 Core(s) per socket: 6 Socket(s): 1 Stepping: 7 BogoMIPS: 4400.39 Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch invpcid_single ssbd ibrs ibpb stibp ibrs_enhanced fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm mpx avx512f avx512dq rdseed adx smap clflushopt clwb avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves arat avx512_vnni md_clear arch_capabilities Hypervisor vendor: KVM Virtualization type: full L1d cache: 192 KiB (6 instances) L1i cache: 192 KiB (6 instances) L2 cache: 6 MiB (6 instances) L3 cache: 38.5 MiB (1 instance) NUMA node(s): 1 NUMA node0 CPU(s): 0-11 Vulnerability Itlb multihit: Not affected Vulnerability L1tf: Not affected Vulnerability Mds: Mitigation; Clear CPU buffers; SMT Host state unknown Vulnerability Meltdown: Not affected Vulnerability Mmio stale data: Vulnerable: Clear CPU buffers attempted, no microcode; SMT Host state unknown Vulnerability Retbleed: Mitigation; Enhanced IBRS Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization Vulnerability Spectre v2: Mitigation; Enhanced IBRS, IBPB conditional, RSB filling, PBRSB-eIBRS SW sequence Vulnerability Srbds: Not affected Vulnerability Tsx async abort: Mitigation; Clear CPU buffers; SMT Host state unknown Versions of relevant libraries: [pip3] mypy-extensions==1.0.0 [pip3] numpy==1.24.2 [pip3] pytorch-lightning==2.0.1.post0 [pip3] pytorch-triton==2.1.0+46672772b4 [pip3] torch==2.1.0.dev20230403+cu118 [pip3] torch-mlir==20230411.805 [pip3] torchmetrics==0.11.4 [pip3] torchvision==0.16.0.dev20230403+cu118 [pip3] triton==2.0.0 [conda] Could not collect ``` cc @ezyang @SherlockNoMad @soumith @EikanWang @jgong5 @wenzhe-nrv
0
2,905
99,012
[BUG]Float32 attention mask not working with torch.autocast("cpu")
triaged, oncall: transformer/mha
### 🐛 Describe the bug **Here is a minimal example of the bug, torch==2.0.0** ``` import torch # torch==2.0.0, cuda=11.7 b = 1 n = 64 d = 256 query = torch.randn(n, b, d) key = torch.randn(n, b, d) value = torch.randn(n, b, d) attn_mask = torch.zeros(n, n) attention = torch.nn.MultiheadAttention(d, 8) with torch.no_grad(), torch.autocast("cpu"): output = attention(query, key, value, attn_mask=attn_mask, need_weights=False) ``` **RuntimeError: Expected attn_mask dtype to be bool or to match query dtype, but got attn_mask.dtype: float and query.dtype: c10::BFloat16 instead.** ### Versions [pip3] numpy==1.23.4 [pip3] open-clip-torch==2.16.0 [pip3] torch==2.0.0 [pip3] torchvision==0.15.1 [pip3] triton==2.0.0 [conda] numpy 1.23.4 pypi_0 pypi [conda] open-clip-torch 2.16.0 pypi_0 pypi [conda] torch 2.0.0 pypi_0 pypi [conda] torchvision 0.15.1 pypi_0 pypi [conda] triton 2.0.0 pypi_0 pypi cc @jbschlosser @bhosmer @cpuhrsch @erichan1
0
2,906
99,007
create_graph_input and add_grapharg should be combined into one function
triaged, module: dynamo
### 🐛 Describe the bug @awgu came up with this: https://github.com/pytorch/pytorch/pull/98775#issuecomment-1503293308 It seems to me that the correct invariant is that they should always be called in lockstep. Maybe there is some funny business with constant source but that could be toggled with a kwarg. We should combine these two methods. ### Versions master cc @soumith @voznesenskym @penguinwu @anijain2305 @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @Xia-Weiwen @wenzhe-nrv @jiayisunx @desertfire
3
2,907
98,978
[torch.compile] makes `linear(permute(input))` succeed for integer input in `torch.no_grad` context
triaged, module: inductor
### 🐛 Describe the bug `torch.compile` makes `linear(permute(input))` succeed for integer input in `torch.no_grad` context ```py import torch import torch.nn as nn torch.manual_seed(420) class Net(nn.Module): def __init__(self): super(Net, self).__init__() self.fc1 = nn.Linear(3, 3) def forward(self, x): x = self.fc1(x.permute(1, 2, 0)) return x input_tensor = torch.tensor([[[1, 2, 3], [4, 5, 6], [7, 8, 9]], [[10, 11, 12], [13, 14, 15], [16, 17, 18]], [[19, 20, 21], [22, 23, 24], [25, 26, 27]]]) func = Net().to('cpu') with torch.no_grad(): jit_func = torch.compile(func) print(jit_func(input_tensor)) #tensor([[[ 6.8708, -10.1139, -2.9715], # [ 7.4253, -11.1465, -2.5976], # [ 7.9799, -12.1791, -2.2237]], # [[ 8.5344, -13.2118, -1.8498], # [ 9.0889, -14.2444, -1.4759], # [ 9.6435, -15.2770, -1.1020]], # [[ 10.1980, -16.3097, -0.7281], # [ 10.7526, -17.3423, -0.3542], # [ 11.3071, -18.3750, 0.0197]]]) print(func(input_tensor)) # RuntimeError: expected scalar type Long but found Float ``` In the `torch.no_grad` context, `torch.compile` does some optimization to make it succeed even the dtypes are mismatched. But without `torch.no_grad`, `torch.compile` will just raise an exception ```py import torch import torch.nn as nn torch.manual_seed(420) class Net(nn.Module): def __init__(self): super(Net, self).__init__() self.fc1 = nn.Linear(3, 3) def forward(self, x): x = self.fc1(x.permute(1, 2, 0)) return x input_tensor = torch.tensor([[[1, 2, 3], [4, 5, 6], [7, 8, 9]], [[10, 11, 12], [13, 14, 15], [16, 17, 18]], [[19, 20, 21], [22, 23, 24], [25, 26, 27]]]) func = Net().to('cpu') jit_func = torch.compile(func) print(jit_func(input_tensor)) # torch._dynamo.exc.TorchRuntimeError ``` ### Versions ``` Collecting environment information... PyTorch version: 2.1.0.dev20230404+cu118 Is debug build: False CUDA used to build PyTorch: 11.8 ROCM used to build PyTorch: N/A OS: Ubuntu 22.04.1 LTS (x86_64) GCC version: (Ubuntu 11.3.0-1ubuntu1~22.04) 11.3.0 Clang version: 14.0.0-1ubuntu1 CMake version: Could not collect Libc version: glibc-2.35 Python version: 3.9.16 (main, Mar 8 2023, 14:00:05) [GCC 11.2.0] (64-bit runtime) Python platform: Linux-5.19.5-051905-generic-x86_64-with-glibc2.35 Is CUDA available: True CUDA runtime version: 11.5.119 CUDA_MODULE_LOADING set to: LAZY GPU models and configuration: GPU 0: NVIDIA GeForce RTX 3060 Nvidia driver version: 510.108.03 cuDNN version: Could not collect HIP runtime version: N/A MIOpen runtime version: N/A Is XNNPACK available: True CPU: Architecture: x86_64 CPU op-mode(s): 32-bit, 64-bit Address sizes: 46 bits physical, 48 bits virtual Byte Order: Little Endian CPU(s): 24 On-line CPU(s) list: 0-23 Vendor ID: GenuineIntel Model name: 12th Gen Intel(R) Core(TM) i9-12900K CPU family: 6 Model: 151 Thread(s) per core: 2 Core(s) per socket: 16 Socket(s): 1 Stepping: 2 CPU max MHz: 6700.0000 CPU min MHz: 800.0000 BogoMIPS: 6374.40 Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf tsc_known_freq pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault cat_l2 invpcid_single cdp_l2 ssbd ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid rdt_a rdseed adx smap clflushopt clwb intel_pt sha_ni xsaveopt xsavec xgetbv1 xsaves split_lock_detect avx_vnni dtherm ida arat pln pts hwp hwp_notify hwp_act_window hwp_epp hwp_pkg_req hfi umip pku ospke waitpkg gfni vaes vpclmulqdq tme rdpid movdiri movdir64b fsrm md_clear serialize pconfig arch_lbr ibt flush_l1d arch_capabilities Virtualization: VT-x L1d cache: 640 KiB (16 instances) L1i cache: 768 KiB (16 instances) L2 cache: 14 MiB (10 instances) L3 cache: 30 MiB (1 instance) NUMA node(s): 1 NUMA node0 CPU(s): 0-23 Vulnerability Itlb multihit: Not affected Vulnerability L1tf: Not affected Vulnerability Mds: Not affected Vulnerability Meltdown: Not affected Vulnerability Mmio stale data: Not affected Vulnerability Retbleed: Not affected Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization Vulnerability Spectre v2: Mitigation; Enhanced IBRS, IBPB conditional, RSB filling, PBRSB-eIBRS SW sequence Vulnerability Srbds: Not affected Vulnerability Tsx async abort: Not affected Versions of relevant libraries: [pip3] numpy==1.24.1 [pip3] pytorch-triton==2.1.0+46672772b4 [pip3] torch==2.1.0.dev20230404+cu118 [pip3] torchaudio==2.1.0.dev20230404+cu118 [pip3] torchvision==0.16.0.dev20230404+cu118 [conda] numpy 1.24.1 pypi_0 pypi [conda] pytorch-triton 2.1.0+46672772b4 pypi_0 pypi [conda] torch 2.1.0.dev20230404+cu118 pypi_0 pypi [conda] torchaudio 2.1.0.dev20230404+cu118 pypi_0 pypi [conda] torchvision 0.16.0.dev20230404+cu118 pypi_0 pypi ``` cc @soumith @voznesenskym @penguinwu @anijain2305 @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @Xia-Weiwen @wenzhe-nrv @jiayisunx @peterbell10 @desertfire
1
2,908
98,977
[BE] Dedup the functorch skipOps mechanism and the common_method_invocations one
triaged, module: testing
This code was original in functorch out-of-tree. Then, we [copy-pasted it into PyTorch](https://github.com/pytorch/pytorch/blob/1149ba5553dc3467afed7e1867dffc7065c742c7/torch/testing/_internal/common_methods_invocations.py#L20288-L20321). Since then, there's been a number of improvements to the functorch version and functorch was upstreamed into PyTorch. However, we have not yet consolidated [the functorch version](https://github.com/pytorch/pytorch/blob/master/test/functorch/common_utils.py#L355) with the one in common_method_invocations. We should consolidate the two, likely just by moving the functorch pieces into common_method_invocations.
0
2,909
98,976
Sparse Tensor: in-place operation on detached tensors no longer raised error
module: sparse, triaged
### 🐛 Describe the bug According to the [documentation](https://pytorch.org/docs/stable/generated/torch.Tensor.detach.html), in-place operations to detached sparse tensors would raise an error. However, it did not. This has implications for modifying the `.state_dict` (which detaches the tensor by default) in place. ### Versions [pip3] mypy==1.1.1 [pip3] mypy-extensions==1.0.0 [pip3] numpy==1.23.5 [pip3] torch==2.0.0 [pip3] torchaudio==2.0.0 [pip3] torchdata==0.6.0 [pip3] torchelastic==0.2.2 [pip3] torchtext==0.15.0 [pip3] torchvision==0.15.0 [pip3] triton==2.0.0 [conda] blas 1.0 mkl [conda] ffmpeg 4.3 hf484d3e_0 pytorch [conda] magma-cuda117 2.6.1 1 pytorch [conda] mkl 2021.4.0 h06a4308_640 [conda] mkl-service 2.4.0 py310h7f8727e_0 [conda] mkl_fft 1.3.1 py310hd6ae3a3_0 [conda] mkl_random 1.2.2 py310h00e6091_0 [conda] numpy 1.23.5 py310hd5efca6_0 [conda] numpy-base 1.23.5 py310h8e6c178_0 [conda] pytorch 2.0.0 py3.10_cuda11.7_cudnn8.5.0_0 pytorch [conda] pytorch-cuda 11.7 h778d358_3 pytorch [conda] pytorch-mutex 1.0 cuda pytorch [conda] torchaudio 2.0.0 py310_cu117 pytorch [conda] torchdata 0.6.0 py310 pytorch [conda] torchelastic 0.2.2 pypi_0 pypi [conda] torchtext 0.15.0 py310 pytorch [conda] torchtriton 2.0.0 py310 pytorch [conda] torchvision 0.15.0 py310_cu117 pytorch cc @alexsamardzic @nikitaved @pearu @cpuhrsch @amjames @bhosmer
5
2,910
98,970
[torch.compile] `replace_fx`
triaged, module: inductor
### 🐛 Describe the bug `torch.compile` will replace `dropout` to some other implementation for performance. However, the original `dropout` will raise an exception if the input `dtype` is integer. By contrast, the compiled version will accept it and return the value without any error. Notably, I think the dropout value returned by compiled version is wrong. Please correct me if I am wrong. ```py import torch import torch.nn as nn torch.manual_seed(420) class MyModel(torch.nn.Module): def forward(self, x): x = x * 2 x = torch.nn.functional.dropout(x, p=0.5) x = torch.relu(x) return x example_inputs = torch.tensor([[1, 2, 3], [4, 5, 6]]) func = MyModel() jit_func = torch.compile(func) print(jit_func(example_inputs)) # tensor([[ 0, 0, 12], # [16, 0, 0]]) print(func(example_inputs)) # RuntimeError: result type Float can't be cast to the desired output type Long ``` This is caused by `replace_fx` in `overrides.py`. ### Versions ``` Collecting environment information... PyTorch version: 2.1.0.dev20230404+cu118 Is debug build: False CUDA used to build PyTorch: 11.8 ROCM used to build PyTorch: N/A OS: Ubuntu 22.04.1 LTS (x86_64) GCC version: (Ubuntu 11.3.0-1ubuntu1~22.04) 11.3.0 Clang version: 14.0.0-1ubuntu1 CMake version: Could not collect Libc version: glibc-2.35 Python version: 3.9.16 (main, Mar 8 2023, 14:00:05) [GCC 11.2.0] (64-bit runtime) Python platform: Linux-5.19.5-051905-generic-x86_64-with-glibc2.35 Is CUDA available: True CUDA runtime version: 11.5.119 CUDA_MODULE_LOADING set to: LAZY GPU models and configuration: GPU 0: NVIDIA GeForce RTX 3060 Nvidia driver version: 510.108.03 cuDNN version: Could not collect HIP runtime version: N/A MIOpen runtime version: N/A Is XNNPACK available: True CPU: Architecture: x86_64 CPU op-mode(s): 32-bit, 64-bit Address sizes: 46 bits physical, 48 bits virtual Byte Order: Little Endian CPU(s): 24 On-line CPU(s) list: 0-23 Vendor ID: GenuineIntel Model name: 12th Gen Intel(R) Core(TM) i9-12900K CPU family: 6 Model: 151 Thread(s) per core: 2 Core(s) per socket: 16 Socket(s): 1 Stepping: 2 CPU max MHz: 6700.0000 CPU min MHz: 800.0000 BogoMIPS: 6374.40 Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf tsc_known_freq pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault cat_l2 invpcid_single cdp_l2 ssbd ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid rdt_a rdseed adx smap clflushopt clwb intel_pt sha_ni xsaveopt xsavec xgetbv1 xsaves split_lock_detect avx_vnni dtherm ida arat pln pts hwp hwp_notify hwp_act_window hwp_epp hwp_pkg_req hfi umip pku ospke waitpkg gfni vaes vpclmulqdq tme rdpid movdiri movdir64b fsrm md_clear serialize pconfig arch_lbr ibt flush_l1d arch_capabilities Virtualization: VT-x L1d cache: 640 KiB (16 instances) L1i cache: 768 KiB (16 instances) L2 cache: 14 MiB (10 instances) L3 cache: 30 MiB (1 instance) NUMA node(s): 1 NUMA node0 CPU(s): 0-23 Vulnerability Itlb multihit: Not affected Vulnerability L1tf: Not affected Vulnerability Mds: Not affected Vulnerability Meltdown: Not affected Vulnerability Mmio stale data: Not affected Vulnerability Retbleed: Not affected Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization Vulnerability Spectre v2: Mitigation; Enhanced IBRS, IBPB conditional, RSB filling, PBRSB-eIBRS SW sequence Vulnerability Srbds: Not affected Vulnerability Tsx async abort: Not affected Versions of relevant libraries: [pip3] numpy==1.24.1 [pip3] pytorch-triton==2.1.0+46672772b4 [pip3] torch==2.1.0.dev20230404+cu118 [pip3] torchaudio==2.1.0.dev20230404+cu118 [pip3] torchvision==0.16.0.dev20230404+cu118 [conda] numpy 1.24.1 pypi_0 pypi [conda] pytorch-triton 2.1.0+46672772b4 pypi_0 pypi [conda] torch 2.1.0.dev20230404+cu118 pypi_0 pypi [conda] torchaudio 2.1.0.dev20230404+cu118 pypi_0 pypi [conda] torchvision 0.16.0.dev20230404+cu118 pypi_0 pypi ``` cc @soumith @voznesenskym @penguinwu @anijain2305 @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @Xia-Weiwen @wenzhe-nrv @jiayisunx @peterbell10 @desertfire
2
2,911
98,955
Please verify 1.14.0 ONNX release candidate on TestPyPI
module: onnx, triaged
### 🚀 The feature, motivation and pitch Hi ONNX partner, We have released TestPyPI packages of ONNX 1.14.0: https://test.pypi.org/project/onnx/1.14.0rc1/ (ONNX 1.14.0rc1 is the latest version number for testing now). Please verify it and let us know about any problems. Thank you for your help! ### Alternatives _No response_ ### Additional context _No response_
3
2,912
98,948
behaviour of `torch.tensor()` changes after editing `Tensor.__getitem__`
triaged, module: python frontend
### 🐛 Describe the bug Bit of a weird one, not sure if this is something interesting but just in case: ```python import torch torch.tensor([torch.tensor(0)]) # works fine torch.Tensor.__getitem__ = None torch.tensor([torch.tensor(0)]) # fails ``` For some reason the second `torch.tensor([torch.tensor(0)])` fails, in particular due to the changing of `__getitem__`. Error message is ```TypeError: len() of a 0-d tensor``` ### Versions Collecting environment information... PyTorch version: 2.0.0+cu118 Is debug build: False CUDA used to build PyTorch: 11.8 ROCM used to build PyTorch: N/A OS: Ubuntu 20.04.5 LTS (x86_64) GCC version: (Ubuntu 9.4.0-1ubuntu1~20.04.1) 9.4.0 Clang version: 10.0.0-4ubuntu1 CMake version: version 3.25.2 Libc version: glibc-2.31 Python version: 3.9.16 (main, Dec 7 2022, 01:11:51) [GCC 9.4.0] (64-bit runtime) Python platform: Linux-5.10.147+-x86_64-with-glibc2.31 Is CUDA available: False CUDA runtime version: 11.8.89 CUDA_MODULE_LOADING set to: N/A GPU models and configuration: Could not collect Nvidia driver version: Could not collect cuDNN version: Probably one of the following: /usr/lib/x86_64-linux-gnu/libcudnn.so.8.7.0 /usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.7.0 /usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.7.0 /usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.7.0 /usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.7.0 /usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.7.0 /usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.7.0 HIP runtime version: N/A MIOpen runtime version: N/A Is XNNPACK available: True CPU: Architecture: x86_64 CPU op-mode(s): 32-bit, 64-bit Byte Order: Little Endian Address sizes: 46 bits physical, 48 bits virtual CPU(s): 2 On-line CPU(s) list: 0,1 Thread(s) per core: 2 Core(s) per socket: 1 Socket(s): 1 NUMA node(s): 1 Vendor ID: GenuineIntel CPU family: 6 Model: 79 Model name: Intel(R) Xeon(R) CPU @ 2.20GHz Stepping: 0 CPU MHz: 2199.998 BogoMIPS: 4399.99 Hypervisor vendor: KVM Virtualization type: full L1d cache: 32 KiB L1i cache: 32 KiB L2 cache: 256 KiB L3 cache: 55 MiB NUMA node0 CPU(s): 0,1 Vulnerability Itlb multihit: Not affected Vulnerability L1tf: Mitigation; PTE Inversion Vulnerability Mds: Vulnerable; SMT Host state unknown Vulnerability Meltdown: Vulnerable Vulnerability Mmio stale data: Vulnerable Vulnerability Retbleed: Vulnerable Vulnerability Spec store bypass: Vulnerable Vulnerability Spectre v1: Vulnerable: __user pointer sanitization and usercopy barriers only; no swapgs barriers Vulnerability Spectre v2: Vulnerable, IBPB: disabled, STIBP: disabled, PBRSB-eIBRS: Not affected Vulnerability Srbds: Not affected Vulnerability Tsx async abort: Vulnerable Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch invpcid_single ssbd ibrs ibpb stibp fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm rdseed adx smap xsaveopt arat md_clear arch_capabilities Versions of relevant libraries: [pip3] numpy==1.22.4 [pip3] torch==2.0.0+cu118 [pip3] torchaudio==2.0.1+cu118 [pip3] torchdata==0.6.0 [pip3] torchsummary==1.5.1 [pip3] torchtext==0.15.1 [pip3] torchvision==0.15.1+cu118 [pip3] triton==2.0.0 [conda] Could not collect cc @albanD
9
2,913
98,947
Add `torch.cat` support for torch native sparse tensors. (Need for PyG)
module: sparse, feature, triaged
### 🚀 The feature, motivation and pitch Up until recently PyG required torch-sparse which provided functionality for sparse tensor math for GNNs. PyG 2.3+ has the goal of dropping torch-sparse and other legacy torch-* packages that PyG used to require. However, for dropping torch-sparse, we are relying on upstream pytorch native sparse tensors. One required functionality of these tensors is the ability to `torch.cat` them together. Currently this is not supported: This issue tracks the failure w/ reproduction steps: https://github.com/pytorch/pytorch/issues/98861 ### Alternatives _No response_ ### Additional context _No response_ cc @alexsamardzic @nikitaved @pearu @cpuhrsch @amjames @bhosmer
10
2,914
98,942
[torch.fx] Upgrade on node info
triaged, module: fx
### 🚀 The feature, motivation and pitch Torch.fx is a very useful tool capable of analysing and understanding deep models, but from an analysis perspective it would be interesting to know the dimensions of the tensors that have passed through the nodes. In order to understand where there might be memory space problems, for example. What I'd like to see is for each node to have two additional arguments, input_size and output_size so that this information can be easily retrieved during an analysis using torch.fx I've see something in torch.passes.shape_prop that seems to analyze tensors but I don't see anything on how to use it. And same for the proxy, I see that it have some useful information in but no way to use it. Maybe the solution already exist but I prefer suggest if it doesn't ### Alternatives _No response_ ### Additional context _No response_ cc @ezyang @SherlockNoMad @soumith @EikanWang @jgong5 @wenzhe-nrv
2
2,915
98,939
torch.dist with minus norm returns tensor(0.), while with -inf can return result
module: distributions, triaged
### 🐛 Describe the bug # with minus norm ``` import torch arg_1_tensor = torch.rand([4], dtype=torch.float32) arg_1 = arg_1_tensor.clone() print(arg_1) arg_2_tensor = torch.rand([4], dtype=torch.float32) arg_2 = arg_2_tensor.clone() print(arg_2) arg_3 = -100 res = torch.dist(input=arg_1,other=arg_2,p=arg_3,) print(res) ``` ``` tensor([0.3692, 0.1006, 0.4169, 0.5297]) tensor([0.4667, 0.3731, 0.2566, 0.8941]) tensor(0.) ``` # with -inf norm ``` import torch inf = float('inf') arg_1_tensor = torch.rand([4], dtype=torch.float32) arg_1 = arg_1_tensor.clone() print(arg_1) arg_2_tensor = torch.rand([4], dtype=torch.float32) arg_2 = arg_2_tensor.clone() print(arg_2) arg_3 = -inf res = torch.dist(input=arg_1,other=arg_2,p=arg_3,) print(res) ``` ``` tensor([0.2863, 0.5415, 0.4990, 0.6137]) tensor([0.1516, 0.4867, 0.1853, 0.8488]) tensor(0.0548) ``` I find this happens when norm is less than -40. ### Versions pytorch version: 2.0.0 cuda : 118 cc @fritzo @neerajprad @alicanb @nikitaved
2
2,916
98,937
TracingContext.get().frame_summary_stack doesn't produce full stack trace
triaged, module: dynamo
### 🐛 Describe the bug If you are here because an error message, comment on the issue to describe how you were affected, so we can help prioritize this issue. TracingContext.get().frame_summary_stack doesn't report full backtraces; you'll only get frames from inside the region of code that dynamo traced through. In principle, we could also collect the regular backtrace from before callback entry and report that too. @Chillee notes that it is good not to give too much information, and indeed the stack trace before calling into the model is probably not that useful. But if we have graph breaks inside the model, we may have lost useful context. Maybe only want the partial stack trace up to torch.compile? Not going to do it unless someone shouts. ### Versions master cc @soumith @voznesenskym @penguinwu @anijain2305 @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @Xia-Weiwen @wenzhe-nrv @jiayisunx @desertfire
0
2,917
98,929
torch.sparse_csr_tensor() stops gradients
module: sparse, triaged
### 🐛 Describe the bug I would expect the following code to produce a gradient w.r.t. `a`, but instead it stops the gradient flow at `torch.sparse_csr_tensor()`. ```python import torch a = torch.randn(3, requires_grad=True) b = torch.sparse_csr_tensor( torch.tensor([0, 2, 2, 3]), torch.tensor([0, 1, 2]), a, ) print(b.grad_fn) torch.sum(b).backward() ``` Accordingly, this is the output: ``` None Traceback (most recent call last): File "/path/csr_grad.py", line 58, in <module> main_csr() File "/path/csr_grad.py", line 36, in main_csr torch.sum(b).backward() File "/venv/lib64/python3.11/site-packages/torch/_tensor.py", line 487, in backward torch.autograd.backward( File "/venv/lib64/python3.11/site-packages/torch/autograd/__init__.py", line 200, in backward Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass RuntimeError: element 0 of tensors does not require grad and does not have a grad_fn ``` The same code works for COO tensors, but I need the performance benefits of CSR tensors. ### Versions ``` Collecting environment information... PyTorch version: 2.0.0+cu117 Is debug build: False CUDA used to build PyTorch: 11.7 ROCM used to build PyTorch: N/A OS: Fedora Linux 37 (KDE Plasma) (x86_64) GCC version: (GCC) 12.2.1 20221121 (Red Hat 12.2.1-4) Clang version: Could not collect CMake version: version 3.26.1 Libc version: glibc-2.36 Python version: 3.11.2 (main, Feb 8 2023, 00:00:00) [GCC 12.2.1 20221121 (Red Hat 12.2.1-4)] (64-bit runtime) Python platform: Linux-6.2.7-200.fc37.x86_64-x86_64-with-glibc2.36 Is CUDA available: True CUDA runtime version: Could not collect CUDA_MODULE_LOADING set to: LAZY GPU models and configuration: GPU 0: NVIDIA GeForce RTX 2070 SUPER Nvidia driver version: 530.30.02 cuDNN version: Probably one of the following: /usr/lib64/libcudnn.so.8.8.1 /usr/lib64/libcudnn_adv_infer.so.8.8.1 /usr/lib64/libcudnn_adv_train.so.8.8.1 /usr/lib64/libcudnn_cnn_infer.so.8.8.1 /usr/lib64/libcudnn_cnn_train.so.8.8.1 /usr/lib64/libcudnn_ops_infer.so.8.8.1 /usr/lib64/libcudnn_ops_train.so.8.8.1 HIP runtime version: N/A MIOpen runtime version: N/A Is XNNPACK available: True CPU: Architecture: x86_64 CPU op-mode(s): 32-bit, 64-bit Address sizes: 43 bits physical, 48 bits virtual Byte Order: Little Endian CPU(s): 16 On-line CPU(s) list: 0-15 Vendor ID: AuthenticAMD Model name: AMD Ryzen 7 2700 Eight-Core Processor CPU family: 23 Model: 8 Thread(s) per core: 2 Core(s) per socket: 8 Socket(s): 1 Stepping: 2 Frequency boost: enabled CPU(s) scaling MHz: 67% CPU max MHz: 3200.0000 CPU min MHz: 1550.0000 BogoMIPS: 6387.18 Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl nonstop_tsc cpuid extd_apicid aperfmperf rapl pni pclmulqdq monitor ssse3 fma cx16 sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb hw_pstate ssbd ibpb vmmcall fsgsbase bmi1 avx2 smep bmi2 rdseed adx smap clflushopt sha_ni xsaveopt xsavec xgetbv1 clzero irperf xsaveerptr arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif overflow_recov succor smca sev sev_es Virtualization: AMD-V L1d cache: 256 KiB (8 instances) L1i cache: 512 KiB (8 instances) L2 cache: 4 MiB (8 instances) L3 cache: 16 MiB (2 instances) NUMA node(s): 1 NUMA node0 CPU(s): 0-15 Vulnerability Itlb multihit: Not affected Vulnerability L1tf: Not affected Vulnerability Mds: Not affected Vulnerability Meltdown: Not affected Vulnerability Mmio stale data: Not affected Vulnerability Retbleed: Mitigation; untrained return thunk; SMT vulnerable Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization Vulnerability Spectre v2: Mitigation; Retpolines, IBPB conditional, STIBP disabled, RSB filling, PBRSB-eIBRS Not affected Vulnerability Srbds: Not affected Vulnerability Tsx async abort: Not affected Versions of relevant libraries: [pip3] mypy-extensions==1.0.0 [pip3] numpy==1.24.2 [pip3] torch==2.0.0 [pip3] triton==2.0.0 [conda] Could not collect ``` cc @alexsamardzic @nikitaved @pearu @cpuhrsch @amjames @bhosmer
6
2,918
98,928
Changing module attributes doesn't retrigger compilation
high priority, triaged, bug, oncall: pt2, module: dynamo
### 🐛 Describe the bug When compiling a method that has an execution flow that depends on some module attribute value, changing this attribute value outside of the method, doesn't retrigger compilation. However, replacing the module attribute with `self.training` or a global variable behaves as expected and recompile when the value changes. The documentation seems to say that mutating attributes should be fully supported: https://pytorch.org/get-started/pytorch-2.0/#reading-and-updating-attributes Repro script: ```python import torch from torch import nn import torch._dynamo import logging torch._dynamo.config.log_level = logging.DEBUG torch._dynamo.config.verbose = True def check(module, attr: str) -> None: inp = torch.ones(1) compiled_value = m(inp) eager_value = m._orig_mod(inp) prefix = "✅" if (compiled_value == eager_value).all().item() else "❌" print(f"{prefix} {attr}={getattr(m._orig_mod, attr)}: compiled={compiled_value}, eager: {eager_value}") print("=== Foo attribute test ===") class MyModuleFoo(nn.Module): foo: bool def __init__(self): super().__init__() self.foo = True def forward(self, x: torch.Tensor) -> torch.Tensor: if self.foo: return x * 123 else: return x * 0 m = torch.compile(MyModuleFoo()) check(m, "foo") m._orig_mod.foo = False check(m, "foo") print("=== Training attribute test ===") class MyModuleTraining(nn.Module): def forward(self, x: torch.Tensor) -> torch.Tensor: if self.training: return x * 123 else: return x * 0 m = torch.compile(MyModuleTraining()) check(m, "training") m._orig_mod.training = False check(m, "training") ``` Output: ```console === Foo attribute test === [2023-04-12 11:40:57,882] torch._dynamo.eval_frame: [DEBUG] skipping __init__ /Volumes/Data/bin/miniconda3/envs/torch2/lib/python3.8/contextlib.py [2023-04-12 11:40:57,882] torch._dynamo.eval_frame: [DEBUG] skipping __enter__ /Volumes/Data/bin/miniconda3/envs/torch2/lib/python3.8/contextlib.py [2023-04-12 11:40:57,883] torch._dynamo.eval_frame: [DEBUG] skipping __init__ /Volumes/Data/bin/miniconda3/envs/torch2/lib/python3.8/contextlib.py [2023-04-12 11:40:57,883] torch._dynamo.eval_frame: [DEBUG] skipping __enter__ /Volumes/Data/bin/miniconda3/envs/torch2/lib/python3.8/contextlib.py [2023-04-12 11:40:57,883] torch._dynamo.eval_frame: [DEBUG] skipping enable_dynamic /Volumes/Data/bin/miniconda3/envs/torch2/lib/python3.8/site-packages/torch/_dynamo/eval_frame.py [2023-04-12 11:40:57,899] torch._dynamo.symbolic_convert: [INFO] Step 1: torchdynamo start tracing forward [2023-04-12 11:40:57,899] torch._dynamo.symbolic_convert: [DEBUG] TRACE starts_line test_compile_repro.py:27 [2023-04-12 11:40:57,899] torch._dynamo.symbolic_convert: [DEBUG] TRACE LOAD_FAST self [] [2023-04-12 11:40:57,899] torch._dynamo.symbolic_convert: [DEBUG] TRACE LOAD_ATTR foo [NNModuleVariable()] [2023-04-12 11:40:57,900] torch._dynamo.symbolic_convert: [DEBUG] TRACE POP_JUMP_IF_FALSE 14 [ConstantVariable(bool)] [2023-04-12 11:40:57,900] torch._dynamo.symbolic_convert: [DEBUG] TRACE starts_line test_compile_repro.py:28 [2023-04-12 11:40:57,900] torch._dynamo.symbolic_convert: [DEBUG] TRACE LOAD_FAST x [] [2023-04-12 11:40:57,900] torch._dynamo.symbolic_convert: [DEBUG] TRACE LOAD_CONST 123 [TensorVariable()] [2023-04-12 11:40:57,901] torch._dynamo.symbolic_convert: [DEBUG] TRACE BINARY_MULTIPLY None [TensorVariable(), ConstantVariable(int)] [2023-04-12 11:40:57,909] torch._dynamo.symbolic_convert: [DEBUG] TRACE RETURN_VALUE None [TensorVariable()] [2023-04-12 11:40:57,909] torch._dynamo.symbolic_convert: [INFO] Step 1: torchdynamo done tracing forward (RETURN_VALUE) [2023-04-12 11:40:57,909] torch._dynamo.symbolic_convert: [DEBUG] RETURN_VALUE triggered compile [2023-04-12 11:40:57,909] torch._dynamo.output_graph: [DEBUG] COMPILING GRAPH due to GraphCompileReason(reason='return_value', user_stack=[<FrameSummary file test_compile_repro.py, line 28 in forward>]) [2023-04-12 11:40:57,910] torch._dynamo.output_graph: [INFO] Step 2: calling compiler function debug_wrapper [2023-04-12 11:40:59,805] torch._inductor.compile_fx: [INFO] Step 3: torchinductor compiling FORWARDS graph 0 [2023-04-12 11:40:59,973] torch._inductor.compile_fx: [INFO] Step 3: torchinductor done compiling FORWARDS graph 0 [2023-04-12 11:40:59,975] torch._dynamo.output_graph: [INFO] Step 2: done compiler function debug_wrapper [2023-04-12 11:40:59,980] torch._dynamo.eval_frame: [DEBUG] skipping _fn /Volumes/Data/bin/miniconda3/envs/torch2/lib/python3.8/site-packages/torch/_dynamo/eval_frame.py [2023-04-12 11:40:59,980] torch._dynamo.eval_frame: [DEBUG] skipping nothing /Volumes/Data/bin/miniconda3/envs/torch2/lib/python3.8/site-packages/torch/_dynamo/eval_frame.py [2023-04-12 11:40:59,981] torch._dynamo.eval_frame: [DEBUG] skipping __exit__ /Volumes/Data/bin/miniconda3/envs/torch2/lib/python3.8/contextlib.py [2023-04-12 11:40:59,981] torch._dynamo.eval_frame: [DEBUG] skipping __exit__ /Volumes/Data/bin/miniconda3/envs/torch2/lib/python3.8/contextlib.py ✅ foo=True: compiled=tensor([123.]), eager: tensor([123.]) ❌ foo=False: compiled=tensor([123.]), eager: tensor([0.]) === Training attribute test === [2023-04-12 11:41:00,039] torch._dynamo.symbolic_convert: [INFO] Step 1: torchdynamo start tracing forward [2023-04-12 11:41:00,039] torch._dynamo.symbolic_convert: [DEBUG] TRACE starts_line test_compile_repro.py:42 [2023-04-12 11:41:00,039] torch._dynamo.symbolic_convert: [DEBUG] TRACE LOAD_FAST self [] [2023-04-12 11:41:00,039] torch._dynamo.symbolic_convert: [DEBUG] TRACE LOAD_ATTR training [NNModuleVariable()] [2023-04-12 11:41:00,040] torch._dynamo.symbolic_convert: [DEBUG] TRACE POP_JUMP_IF_FALSE 14 [ConstantVariable(bool)] [2023-04-12 11:41:00,040] torch._dynamo.symbolic_convert: [DEBUG] TRACE starts_line test_compile_repro.py:43 [2023-04-12 11:41:00,040] torch._dynamo.symbolic_convert: [DEBUG] TRACE LOAD_FAST x [] [2023-04-12 11:41:00,040] torch._dynamo.symbolic_convert: [DEBUG] TRACE LOAD_CONST 123 [TensorVariable()] [2023-04-12 11:41:00,041] torch._dynamo.symbolic_convert: [DEBUG] TRACE BINARY_MULTIPLY None [TensorVariable(), ConstantVariable(int)] [2023-04-12 11:41:00,042] torch._dynamo.symbolic_convert: [DEBUG] TRACE RETURN_VALUE None [TensorVariable()] [2023-04-12 11:41:00,043] torch._dynamo.symbolic_convert: [INFO] Step 1: torchdynamo done tracing forward (RETURN_VALUE) [2023-04-12 11:41:00,043] torch._dynamo.symbolic_convert: [DEBUG] RETURN_VALUE triggered compile [2023-04-12 11:41:00,043] torch._dynamo.output_graph: [DEBUG] COMPILING GRAPH due to GraphCompileReason(reason='return_value', user_stack=[<FrameSummary file test_compile_repro.py, line 43 in forward>]) [2023-04-12 11:41:00,044] torch._dynamo.output_graph: [INFO] Step 2: calling compiler function debug_wrapper [2023-04-12 11:41:00,053] torch._inductor.compile_fx: [INFO] Step 3: torchinductor compiling FORWARDS graph 1 [2023-04-12 11:41:00,061] torch._inductor.compile_fx: [INFO] Step 3: torchinductor done compiling FORWARDS graph 1 [2023-04-12 11:41:00,061] torch._dynamo.output_graph: [INFO] Step 2: done compiler function debug_wrapper ✅ training=True: compiled=tensor([123.]), eager: tensor([123.]) [2023-04-12 11:41:00,064] torch._dynamo.symbolic_convert: [INFO] Step 1: torchdynamo start tracing forward [2023-04-12 11:41:00,064] torch._dynamo.symbolic_convert: [DEBUG] TRACE starts_line test_compile_repro.py:42 [2023-04-12 11:41:00,064] torch._dynamo.symbolic_convert: [DEBUG] TRACE LOAD_FAST self [] [2023-04-12 11:41:00,064] torch._dynamo.symbolic_convert: [DEBUG] TRACE LOAD_ATTR training [NNModuleVariable()] [2023-04-12 11:41:00,064] torch._dynamo.symbolic_convert: [DEBUG] TRACE POP_JUMP_IF_FALSE 14 [ConstantVariable(bool)] [2023-04-12 11:41:00,064] torch._dynamo.symbolic_convert: [DEBUG] TRACE starts_line test_compile_repro.py:45 [2023-04-12 11:41:00,064] torch._dynamo.symbolic_convert: [DEBUG] TRACE LOAD_FAST x [] [2023-04-12 11:41:00,065] torch._dynamo.symbolic_convert: [DEBUG] TRACE LOAD_CONST 0 [TensorVariable()] [2023-04-12 11:41:00,065] torch._dynamo.symbolic_convert: [DEBUG] TRACE BINARY_MULTIPLY None [TensorVariable(), ConstantVariable(int)] [2023-04-12 11:41:00,065] torch._dynamo.symbolic_convert: [DEBUG] TRACE RETURN_VALUE None [TensorVariable()] [2023-04-12 11:41:00,065] torch._dynamo.symbolic_convert: [INFO] Step 1: torchdynamo done tracing forward (RETURN_VALUE) [2023-04-12 11:41:00,066] torch._dynamo.symbolic_convert: [DEBUG] RETURN_VALUE triggered compile [2023-04-12 11:41:00,066] torch._dynamo.output_graph: [DEBUG] COMPILING GRAPH due to GraphCompileReason(reason='return_value', user_stack=[<FrameSummary file test_compile_repro.py, line 45 in forward>]) [2023-04-12 11:41:00,066] torch._dynamo.output_graph: [INFO] Step 2: calling compiler function debug_wrapper [2023-04-12 11:41:00,073] torch._inductor.compile_fx: [INFO] Step 3: torchinductor compiling FORWARDS graph 2 [2023-04-12 11:41:00,082] torch._inductor.compile_fx: [INFO] Step 3: torchinductor done compiling FORWARDS graph 2 [2023-04-12 11:41:00,082] torch._dynamo.output_graph: [INFO] Step 2: done compiler function debug_wrapper ✅ training=False: compiled=tensor([0.]), eager: tensor([0.]) ``` ### Error logs _No response_ ### Minified repro _No response_ ### Versions PyTorch version: 2.0.0 Is debug build: False CUDA used to build PyTorch: None ROCM used to build PyTorch: N/A OS: macOS 13.3 (x86_64) GCC version: Could not collect Clang version: 14.0.3 (clang-1403.0.22.8) CMake version: Could not collect Libc version: N/A Python version: 3.8.13 (default, Mar 28 2022, 06:16:26) [Clang 12.0.0 ] (64-bit runtime) Python platform: macOS-10.16-x86_64-i386-64bit Is CUDA available: False CUDA runtime version: No CUDA CUDA_MODULE_LOADING set to: N/A GPU models and configuration: No CUDA Nvidia driver version: No CUDA cuDNN version: No CUDA HIP runtime version: N/A MIOpen runtime version: N/A Is XNNPACK available: True CPU: Intel(R) Core(TM) i7-8750H CPU @ 2.20GHz Versions of relevant libraries: [pip3] flake8==3.9.2 [pip3] flake8-junit-report==2.1.0 [pip3] mypy==0.812 [pip3] mypy-extensions==0.4.3 [pip3] numpy==1.23.3 [pip3] torch==2.0.0 [pip3] torchvision==0.15.1 [conda] numpy 1.23.3 pypi_0 pypi [conda] torch 2.0.0 pypi_0 pypi [conda] torchvision 0.15.1 pypi_0 pypi cc @ezyang @gchanan @zou3519 @soumith @msaroufim @wconstab @ngimel @bdhirsh @voznesenskym @penguinwu @anijain2305 @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @Xia-Weiwen @wenzhe-nrv @jiayisunx @desertfire
9
2,919
98,926
add gradscaler on CPU
module: cpu, open source, module: half, module: amp (automated mixed precision), ciflow/trunk, ciflow/periodic
Just test gradscaler on CPU cc @jgong5 @mingfeima @XiaobingSuper @sanchitintel @ashokei @jingxu10 @mcarilli @ptrblck @leslie-fang-intel
3
2,920
98,925
Request for deterministic support for reflection_pad2d_backward_cuda
module: cuda, triaged, enhancement, module: padding
### 🚀 The feature, motivation and pitch Currently, the `reflection_pad2d_backward_cuda` operation used in neural network training cannot be computed deterministically on the GPU. This causes issues for users who have set `torch.use_deterministic_algorithms(True)` and require deterministic behavior in their training process. I would like to request that deterministic support be added for `reflection_pad2d_backward_cuda` so that users can perform neural network training deterministically on the GPU. Thank you for your consideration. ### Alternatives _No response_ ### Additional context _No response_ cc @ngimel
1
2,921
98,924
Integrate open device privateuse1 customized method registration
triaged, module: backend
### 🚀 The feature, motivation and pitch Currently, if user registers the privateuse1 backend, the user may need to perform the following operations: 1. Call `torch._register_device_module` to register the device module. 2. Call `torch.serialization.register_package` to register the `_tag` and `_deserialize` methods customized by privateuse1 backend. There may be more examples You need to register multiple places in the patch of privateuse1. ### Alternatives Unified registration entry. Users only need to implement the corresponding method. Such as [#98920](https://github.com/pytorch/pytorch/pull/98920) ### Additional context _No response_
1
2,922
98,921
Unable to load MultiStepLR with torch.load(weights_only=True)
module: serialization, triaged
### 🐛 Describe the bug `MultiStepLR.state_dict()` contains an instance of `collections.Counter`, but `collections.Counter` is not included in the safelist of weights_only_unpickler. So, errors occur when loading checkpoints depending on the class of the LR scheduler. reproduction code ```python import torch model = torch.nn.Linear(4, 4) optimizer = torch.optim.Adam(model.parameters()) scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, [10, 20]) # print(scheduler.state_dict()) torch.save({ "model_state_dict": model.state_dict(), "optimizer_state_dict": optimizer.state_dict(), "scheduler_state_dict": scheduler.state_dict(), }, "./checkpoint.pth") print("SAVE") torch.load("./checkpoint.pth", weights_only=True) print("LOAD") ``` output ``` SAVE Traceback (most recent call last): File "/home/nagadomi/dev/nunif/tmp/weights_only/bug_multistep.py", line 16, in <module> torch.load("./checkpoint.pth", weights_only=True) File "/home/nagadomi/dev/nunif/.venv/lib/python3.10/site-packages/torch/serialization.py", line 808, in load raise pickle.UnpicklingError(UNSAFE_MESSAGE + str(e)) from None _pickle.UnpicklingError: Weights only load failed. Re-running `torch.load` with `weights_only` set to `False` will likely succeed, but it can result in arbitrary code execution.Do it only if you get the file from a trusted source. WeightsUnpickler error: Unsupported class collections.Counter ``` `scheduler.state_dict()` ``` {'milestones': Counter({10: 1, 20: 1}), 'gamma': 0.1, 'base_lrs': [0.001], 'last_epoch': 0, 'verbose': False, '_step_count': 1, '_get_lr_called_within_step': False, '_last_lr': [0.001]} ``` ### Versions PyTorch version: 2.0.0+cu117 Is debug build: False CUDA used to build PyTorch: 11.7 ROCM used to build PyTorch: N/A OS: Ubuntu 22.04.2 LTS (x86_64) GCC version: (Ubuntu 11.3.0-1ubuntu1~22.04) 11.3.0 Clang version: Could not collect CMake version: version 3.26.0 Libc version: glibc-2.35 Python version: 3.10.6 (main, Mar 10 2023, 10:55:28) [GCC 11.3.0] (64-bit runtime) Python platform: Linux-5.19.0-38-generic-x86_64-with-glibc2.35 Is CUDA available: True CUDA runtime version: 11.8.89 CUDA_MODULE_LOADING set to: LAZY GPU models and configuration: GPU 0: NVIDIA GeForce RTX 3070 Ti Nvidia driver version: 525.105.17 cuDNN version: Probably one of the following: /usr/lib/x86_64-linux-gnu/libcudnn.so.8.8.1 /usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.8.1 /usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.8.1 /usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.8.1 /usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.8.1 /usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.8.1 /usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.8.1 HIP runtime version: N/A MIOpen runtime version: N/A Is XNNPACK available: True Versions of relevant libraries: [pip3] numpy==1.24.2 [pip3] perlin-numpy==0.0.0 [pip3] torch==2.0.0 [pip3] torchaudio==0.13.1 [pip3] torchdata==0.6.0 [pip3] torchtext==0.15.1 [pip3] torchvision==0.15.1 [conda] Could not collect cc @mruberry
0
2,923
98,917
Change module to module_ in torch/csrc/api/include/torch/python.h
module: build, triaged
### 🚀 The feature, motivation and pitch Until 2023.04.12 on master branch, 'module' is used as a variable name in torch/csrc/api/include/torch/python.h, however, 'module' has became a keyword since C++20, consider replace module to module_? ### Alternatives _No response_ ### Additional context _No response_ cc @malfet @seemethere
2
2,924
98,907
Move template code to header
module: cpp, triaged
### 🐛 Describe the bug The template code https://github.com/pytorch/pytorch/blob/39fd7f945f292bea7af411946f75417966470359/torch/csrc/api/src/nn/modules/batchnorm.cpp#L17-L34 appears in the `.cpp` file. It will make extending from `BatchNormImplBase` using libtorch troublesome. Unless copying the above quoted code into users own source code, the linker will not able to find implementations of `pretty_print` ### Versions latest master cc @jbschlosser
0
2,925
98,904
Test failure: TestCommonCPU.test_python_ref__refs_abs_cpu_complex32
module: tests, triaged
### 🐛 Describe the bug When I execute the following test case on s390x, I got the failure. ``` % python test/test_ops.py TestCommonCPU.test_python_ref__refs_abs_cpu_complex32 ... ---------------------------------------------------------------------- Ran 1 test in 3.065s FAILED (unexpected successes=1) ``` When I executed the same test on x86, it passed. ``` $ python test/test_ops.py TestCommonCPU.test_python_ref__refs_abs_cpu_complex32 ... x ---------------------------------------------------------------------- Ran 1 test in 0.920s OK (expected failures=1) ``` So, this test suite expects that one of tests is generates different results. I am not sure why this test case expects to generate different results so far. ### Versions ``` Collecting environment information... PyTorch version: 2.1.0a0+gite3df6a7 Is debug build: True CUDA used to build PyTorch: None ROCM used to build PyTorch: N/A OS: Ubuntu 22.04.1 LTS (s390x) GCC version: (Ubuntu 11.3.0-1ubuntu1~22.04) 11.3.0 Clang version: Could not collect CMake version: version 3.22.1 Libc version: glibc-2.35 Python version: 3.10.6 (main, Nov 14 2022, 16:10:14) [GCC 11.3.0] (64-bit runtime) Python platform: Linux-5.4.0-144-generic-s390x-with-glibc2.35 Is CUDA available: False CUDA runtime version: No CUDA CUDA_MODULE_LOADING set to: N/A GPU models and configuration: No CUDA Nvidia driver version: No CUDA cuDNN version: No CUDA HIP runtime version: N/A MIOpen runtime version: N/A Is XNNPACK available: False Versions of relevant libraries: [pip3] numpy==1.24.2 [pip3] torch==2.1.0a0+gite3df6a7 [conda] Could not collect ``` cc @mruberry
1
2,926
98,888
Changes to TorchScript autodiff changing default behavior are no longer accepted
triage review, oncall: jit
TorchScript support is very limited currently, and changes to autodiff can (and did!) lead to very hard to diagnose bugs. To prevent these bugs, and in view of TorchScript not being actively developed, we no longer accept PRs that expand or change functionality of autodiff. If it's absolutely necessary to change autodiff for your backend, the changes should be behind a config flag and not enabled by default. To reviewers: please don't accept PRs modifying TS autodiff. cc @EikanWang @jgong5 @wenzhe-nrv @sanchitintel
1
2,927
98,882
[PT2] AOTAutograd de-dups but skips de-dup guards for DDP
triaged, oncall: pt2
Run DDP with a shared buffer (different TorchDynamo `Source`): <details> <summary> Repro Script </summary> ``` """ torchrun --standalone --nproc_per_node=1 test/dup_repro.py TORCH_LOGS=aot,dynamo torchrun --standalone --nproc_per_node=1 test/dup_repro.py """ import os import torch import torch.distributed as dist import torch.nn as nn from torch.distributed.fsdp import FullyShardedDataParallel as FSDP from torch.nn.parallel import DistributedDataParallel as DDP USE_FSDP = False print(f"USE_FSDP={USE_FSDP}") os.environ["TORCHDYNAMO_PRINT_GUARDS"] = "1" class BufModule(nn.Module): def __init__(self) -> None: super().__init__() self.register_buffer( "_buf", torch.randn((3,), requires_grad=False, device="cuda") ) def forward(self, x: torch.Tensor) -> torch.Tensor: return x + self._buf class Model(nn.Module): def __init__(self) -> None: super().__init__() # Define a parameter since DDP requires at least one self._param = nn.Parameter(torch.randn((1,), device="cuda")) self._buf_module = BufModule() # Use same tensor but with different source self.register_buffer("_buf", self._buf_module._buf) def forward(self, x: torch.Tensor) -> torch.Tensor: z = x + self._buf z = self._buf_module(z) z += self._param return z dist.init_process_group(backend="nccl") gpu_id = int(os.environ["LOCAL_RANK"]) device = f"cuda:{gpu_id}" torch.cuda.set_device(device) model = Model() if USE_FSDP: model = FSDP(model, use_orig_params=True) else: model = DDP(model, device_ids=[dist.get_rank()]) model = torch.compile(model) if USE_FSDP: assert model._buf is model._buf_module._buf else: assert model.module._buf is model.module._buf_module._buf inp = torch.randn((2, 3), device="cuda") model(inp) ``` </details> DDP forward graph: ``` ====== Forward graph 0 ====== <eval_with_key>.7 class GraphModule(torch.nn.Module): def forward(self, primals_1: f32[1], primals_2: f32[3], primals_3: f32[2, 3]): # File: test/dup_repro.py:39, code: z = x + self._buf add: f32[2, 3] = torch.ops.aten.add.Tensor(primals_3, primals_2); primals_3 = None # File: test/dup_repro.py:27, code: return x + self._buf add_1: f32[2, 3] = torch.ops.aten.add.Tensor(add, primals_2); add = primals_2 = None # File: test/dup_repro.py:41, code: z += self._param add_2: f32[2, 3] = torch.ops.aten.add.Tensor(add_1, primals_1); add_1 = primals_1 = None return [add_2] ``` It looks like `model._buf` and `model._buf_module._buf` got de-duplicated since the forward graph only has `primals_2` as the size `[3]` tensor (`primals_1` is `model._param` and `primals_3` is the input tensor). However, there is no de-dup guard: ``` GUARDS ___guarded_code.valid and ___check_obj_id(L['self'], 140390721572928) and L['self'].training == True and not ___are_deterministic_algorithms_enabled() and ___check_type_id(G['__import_torch_dot_nn_dot_modules_dot_module']._global_forward_hooks, 93969908371680) and set(G['__import_torch_dot_nn_dot_modules_dot_module']._global_forward_hooks.keys()) == set() and ___check_type_id(G['__import_torch_dot_nn_dot_modules_dot_module']._global_backward_hooks, 93969908371680) and set(G['__import_torch_dot_nn_dot_modules_dot_module']._global_backward_hooks.keys()) == set() and ___check_type_id(G['__import_torch_dot_nn_dot_modules_dot_module']._global_forward_pre_hooks, 93969908371680) and set(G['__import_torch_dot_nn_dot_modules_dot_module']._global_forward_pre_hooks.keys()) == set() and ___check_type_id(G['__import_torch_dot_nn_dot_modules_dot_module']._global_backward_pre_hooks, 93969908371680) and set(G['__import_torch_dot_nn_dot_modules_dot_module']._global_backward_pre_hooks.keys()) == set() and ___check_tensors(L['x']) ``` I was expecting a guard like: ``` L['self']._buf is L['self']._buf_module._buf ``` Let me know if I am not understanding correctly. cc @ezyang @soumith @msaroufim @wconstab @ngimel @bdhirsh
11
2,928
98,872
Expand component configurable logging system to C++
module: logging, triaged
The component configurable logging system introduced for #94788 needs to be expanded to control logs generated from C++ Some of the requirements for this are discussed here: * https://github.com/pytorch/pytorch/issues/94788#issuecomment-1502519872 * https://github.com/pytorch/pytorch/issues/94788#issuecomment-1503989249 List of requirements: * Logs generated in C++ should get piped out to the Python logging system. * C++ should have access to all the logging component and artifact types available in Python. * From Python user's perspective, whether a log came from C++ or Python should be opaque.
4
2,929
98,871
Document the user-facing API for the component-level logging system
module: docs, triaged
Document the component-level logging system that was added for #94788 cc @svekars @carljparker
0
2,930
98,864
Support SPDA on non-CUDA backends
oncall: transformer/mha
### 🚀 The feature, motivation and pitch Currently, [SPDA](https://pytorch.org/docs/stable/generated/torch.nn.functional.scaled_dot_product_attention.html#torch.nn.functional.scaled_dot_product_attention) supports CUDA backend as outlined in the documentation tutorial. Requesting the op to become available for other backends, specifically, `torch_xla`. CC @JackCaoG @wconstab cc @jbschlosser @bhosmer @cpuhrsch @erichan1
2
2,931
98,863
Problem with instalation torch2 on a100+cu12.1
oncall: binaries, module: cuda, triaged
### 🐛 Describe the bug I am trying to install torch2 with cu118 on cu121 machine Using pip install --upgrade torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118 Trying import torch print(torch.__version__) torch.zeros(2).cuda(0) And got this: CUDA call failed lazily at initialization with error: device >= 0 && device < num_gpus INTERNAL ASSERT FAILED at "../aten/src/ATen/cuda/CUDAContext.cpp":50, please report a bug to PyTorch. CUDA call was originally invoked at: [' File "/usr/lib/python3.8/runpy.py", line 194, in _run_module_as_main\n return _run_code(code, main_globals, None,\n', ' File "/usr/lib/python3.8/runpy.py", line 87, in _run_code\n exec(code, run_globals)\n', ' File "/home/fsuser/.local/lib/python3.8/site-packages/ipykernel_launcher.py", line 17, in <module>\n app.launch_new_instance()\n', ' File "/home/fsuser/.local/lib/python3.8/site-packages/traitlets/config/application.py", line 1043, in launch_instance\n app.start()\n', ' File "/home/fsuser/.local/lib/python3.8/site-packages/ipykernel/kernelapp.py", line 725, in start\n self.io_loop.start()\n', ' File "/home/fsuser/.local/lib/python3.8/site-packages/tornado/platform/asyncio.py", line 215, in start\n self.asyncio_loop.run_forever()\n', ' File "/usr/lib/python3.8/asyncio/base_events.py", line 570, in run_forever\n self._run_once()\n', ' File "/usr/lib/python3.8/asyncio/base_events.py", line 1859, in _run_once\n handle._run()\n', ' File "/usr/lib/python3.8/asyncio/events.py", line 81, in _run\n self._context.run(self._callback, *self._args)\n', ' File "/home/fsuser/.local/lib/python3.8/site-packages/ipykernel/kernelbase.py", line 513, in dispatch_queue\n await self.process_one()\n', ' File "/home/fsuser/.local/lib/python3.8/site-packages/ipykernel/kernelbase.py", line 502, in process_one\n await dispatch(*args)\n', ' File "/home/fsuser/.local/lib/python3.8/site-packages/ipykernel/kernelbase.py", line 409, in dispatch_shell\n await result\n', ' File "/home/fsuser/.local/lib/python3.8/site-packages/ipykernel/kernelbase.py", line 729, in execute_request\n reply_content = await reply_content\n', ' File "/home/fsuser/.local/lib/python3.8/site-packages/ipykernel/ipkernel.py", line 422, in do_execute\n res = shell.run_cell(\n', ' File "/home/fsuser/.local/lib/python3.8/site-packages/ipykernel/zmqshell.py", line 540, in run_cell\n return super().run_cell(*args, **kwargs)\n', ' File "/home/fsuser/.local/lib/python3.8/site-packages/IPython/core/interactiveshell.py", line 3006, in run_cell\n result = self._run_cell(\n', ' File "/home/fsuser/.local/lib/python3.8/site-packages/IPython/core/interactiveshell.py", line 3061, in _run_cell\n result = runner(coro)\n', ' File "/home/fsuser/.local/lib/python3.8/site-packages/IPython/core/async_helpers.py", line 129, in _pseudo_sync_runner\n coro.send(None)\n', ' File "/home/fsuser/.local/lib/python3.8/site-packages/IPython/core/interactiveshell.py", line 3266, in run_cell_async\n has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n', ' File "/home/fsuser/.local/lib/python3.8/site-packages/IPython/core/interactiveshell.py", line 3445, in run_ast_nodes\n if await self.run_code(code, result, async_=asy):\n', ' File "/home/fsuser/.local/lib/python3.8/site-packages/IPython/core/interactiveshell.py", line 3505, in run_code\n exec(code_obj, self.user_global_ns, self.user_ns)\n', ' File "/tmp/ipykernel_15415/129772968.py", line 1, in <module>\n import torch\n', ' File "<frozen importlib._bootstrap>", line 991, in _find_and_load\n', ' File "<frozen importlib._bootstrap>", line 975, in _find_and_load_unlocked\n', ' File "<frozen importlib._bootstrap>", line 671, in _load_unlocked\n', ' File "<frozen importlib._bootstrap_external>", line 848, in exec_module\n', ' File "<frozen importlib._bootstrap>", line 219, in _call_with_frames_removed\n', ' File "/home/fsuser/.local/lib/python3.8/site-packages/torch/__init__.py", line 1146, in <module>\n _C._initExtension(manager_path())\n', ' File "<frozen importlib._bootstrap>", line 991, in _find_and_load\n', ' File "<frozen importlib._bootstrap>", line 975, in _find_and_load_unlocked\n', ' File "<frozen importlib._bootstrap>", line 671, in _load_unlocked\n', ' File "<frozen importlib._bootstrap_external>", line 848, in exec_module\n', ' File "<frozen importlib._bootstrap>", line 219, in _call_with_frames_removed\n', ' File "/home/fsuser/.local/lib/python3.8/site-packages/torch/cuda/__init__.py", line 197, in <module>\n _lazy_call(_check_capability)\n', ' File "/home/fsuser/.local/lib/python3.8/site-packages/torch/cuda/__init__.py", line 195, in _lazy_call\n _queued_calls.append((callable, traceback.format_stack()))\n'] ### Versions 2.0.0+cu118 cc @seemethere @malfet @ngimel
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Sparse Tensor not working for `torch.cat`
module: sparse, triaged
### 🐛 Describe the bug cc: @rusty1s these examples use `collate` which uses cat on torch native sparse tensors: https://github.com/pyg-team/pytorch_geometric/blob/master/examples/egc.py https://github.com/pyg-team/pytorch_geometric/blob/master/examples/gcn2_ppi.py https://github.com/pyg-team/pytorch_geometric/blob/master/examples/multi_gpu/distributed_batching.py repro: `cd /opt/pyg; pip uninstall -y torch-geometric torch-scatter torch-sparse torch-spline-conv torch-cluster; rm -rf pytorch_geometric; git clone -b fix-for-collate https://github.com/pyg-team/pytorch_geometric.git; cd /opt/pyg/pytorch_geometric; pip install .; python3 examples/egc.py; python3 examples/gcn2_ppi.py` error: ``` Traceback (most recent call last): File "examples/gcn2_ppi.py", line 93, in <module> loss = train() File "examples/gcn2_ppi.py", line 67, in train for data in train_loader: File "/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py", line 635, in __next__ data = self._next_data() File "/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py", line 679, in _next_data data = self._dataset_fetcher.fetch(index) # may raise StopIteration File "/usr/local/lib/python3.8/dist-packages/torch/utils/data/_utils/fetch.py", line 61, in fetch return self.collate_fn(data) File "/usr/local/lib/python3.8/dist-packages/torch_geometric/loader/dataloader.py", line 20, in __call__ return Batch.from_data_list(batch, self.follow_batch, File "/usr/local/lib/python3.8/dist-packages/torch_geometric/data/batch.py", line 76, in from_data_list batch, slice_dict, inc_dict = collate( File "/usr/local/lib/python3.8/dist-packages/torch_geometric/data/collate.py", line 85, in collate value, slices, incs = _collate(attr, values, data_list, stores, File "/usr/local/lib/python3.8/dist-packages/torch_geometric/data/collate.py", line 178, in _collate value = torch.cat(values, dim=cat_dim) RuntimeError: Sparse CSR tensors do not have is_contiguous ``` ### Versions root@979d4b259838:/opt/pyg/pytorch_geometric# python collect_env.py Collecting environment information... PyTorch version: 2.0.0a0+1767026 Is debug build: False CUDA used to build PyTorch: 12.1 ROCM used to build PyTorch: N/A OS: Ubuntu 20.04.5 LTS (x86_64) GCC version: (Ubuntu 9.4.0-1ubuntu1~20.04.1) 9.4.0 Clang version: Could not collect CMake version: version 3.24.1 Libc version: glibc-2.31 Python version: 3.8.10 (default, Nov 14 2022, 12:59:47) [GCC 9.4.0] (64-bit runtime) Python platform: Linux-4.15.0-142-generic-x86_64-with-glibc2.29 Is CUDA available: True CUDA runtime version: 12.1.66 CUDA_MODULE_LOADING set to: LAZY GPU models and configuration: GPU 0: NVIDIA A40 GPU 1: NVIDIA A40 GPU 2: NVIDIA A40 GPU 3: NVIDIA A40 Nvidia driver version: 515.65.01 cuDNN version: Probably one of the following: /usr/lib/x86_64-linux-gnu/libcudnn.so.8.8.1 /usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.8.1 /usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.8.1 /usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.8.1 /usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.8.1 /usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.8.1 /usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.8.1 HIP runtime version: N/A MIOpen runtime version: N/A Is XNNPACK available: True CPU: Architecture: x86_64 CPU op-mode(s): 32-bit, 64-bit Byte Order: Little Endian Address sizes: 43 bits physical, 48 bits virtual CPU(s): 32 On-line CPU(s) list: 0-31 Thread(s) per core: 2 Core(s) per socket: 16 Socket(s): 1 NUMA node(s): 1 Vendor ID: AuthenticAMD CPU family: 23 Model: 49 Model name: AMD EPYC 7282 16-Core Processor Stepping: 0 Frequency boost: enabled CPU MHz: 1493.554 CPU max MHz: 2800.0000 CPU min MHz: 1500.0000 BogoMIPS: 5589.38 Virtualization: AMD-V L1d cache: 512 KiB L1i cache: 512 KiB L2 cache: 8 MiB L3 cache: 64 MiB NUMA node0 CPU(s): 0-31 Vulnerability Itlb multihit: Not affected Vulnerability L1tf: Not affected Vulnerability Mds: Not affected Vulnerability Meltdown: Not affected Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization Vulnerability Spectre v2: Mitigation; Full AMD retpoline, IBPB conditional, IBRS_FW, STIBP conditional, RSB filling Vulnerability Srbds: Not affected Vulnerability Tsx async abort: Not affected Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid extd_apicid aperfmperf pni pclmulqdq monitor ssse3 fma cx16 sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw ibs skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb cat_l3 cdp_l3 hw_pstate sme ssbd ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 cqm rdt_a rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local clzero irperf xsaveerptr arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif umip rdpid overflow_recov succor smca Versions of relevant libraries: [pip3] flake8==5.0.3 [pip3] mypy-extensions==1.0.0 [pip3] numpy==1.22.3 [pip3] pytorch-quantization==2.1.2 [pip3] torch==2.0.0a0+1767026 [pip3] torch_geometric==2.4.0 [pip3] torch-tensorrt==1.4.0.dev0 [pip3] torchmetrics==0.9.3 [pip3] torchtext==0.13.0a0+fae8e8c [pip3] torchvision==0.15.0a0 [pip3] triton==2.0.0 [pip3] tritonclient==2.29.0 [conda] Could not collect cc @alexsamardzic @nikitaved @pearu @cpuhrsch @amjames @bhosmer
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Sharded Grad Scaler Issue Tracker
oncall: distributed, triaged, module: amp (automated mixed precision), module: fsdp
I recently unintentionally discovered that there exists a [sharded_grad_scaler.py](https://github.com/pytorch/pytorch/blob/4584851da5cad7f2e5f9fd5ed2245f3a06f8359e/torch/distributed/fsdp/sharded_grad_scaler.py#L4) which derives a lot from our [amp/grad_scaler.py](https://github.com/pytorch/pytorch/blob/e64ddd1ab9d46cfc921c19269969ffc5cd7d6f6c/torch/cuda/amp/grad_scaler.py#L195). - [ ] A lot of the duplication seems unnecessary. ShardedGradScaler should reuse/call as much of GradScaler as possible instead copying/pasting. This way, ShardedGradScaler can be automatically enrolled in GradScaler bug fixes/improvements. - [ ] It is uncertain whether ShardedGradScaler supports all that current GradScaler supports in a consistent way, such as being able to call unscale separate from step like in the grad clipping use case: https://pytorch.org/docs/stable/amp.html#torch.cuda.amp.GradScaler.unscale_ - [ ] ShardedGradScaler should be documented as a public API once it's ready. It would be good to link to it from the existing GradScaler page. cc @mrshenli @pritamdamania87 @zhaojuanmao @satgera @rohan-varma @gqchen @aazzolini @osalpekar @jiayisuse @H-Huang @kwen2501 @awgu @mcarilli @ptrblck @leslie-fang-intel @jgong5
0
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[PT2] Some errors with `cond` and `torch.compile`
triaged, oncall: pt2, module: functorch
I am not sure if these are intended to be supported use cases, but as a part of https://github.com/pytorch/pytorch/pull/98775, I experimented with `cond()`. This is not blocking any use case. ``` import torch from functorch.experimental.control_flow import cond class Module(torch.nn.Module): def __init__(self): super().__init__() self.linear = torch.nn.Linear(3, 3) def forward(self, pred, x): def true_fn(val): return self.linear(val) * torch.tensor(2) def false_fn(val): return self.linear(val) * torch.tensor(-1) return cond(pred, true_fn, false_fn, [x]) mod = Module() mod = torch.compile(mod) x = torch.randn([3, 3]) pred = torch.tensor(x[0][0].item() < 0) real_result = mod.forward(pred, x) ``` raises ``` File "/fsx/users/andgu/work/pytorch/torch/_ops.py", line 236, in dispatch assert final_key in self.py_kernels, f"{dispatch_key} -> {final_key}" torch._dynamo.exc.BackendCompilerFailed: backend='debug_wrapper' raised: AssertionError: DispatchKey.Functionalize -> DispatchKey.Functionalize ``` <details> <summary> Full traceback </summary> ``` Traceback (most recent call last): File "dynamo/test_cond.py", line 23, in <module> real_result = mod.forward(pred, x) File "/fsx/users/andgu/work/pytorch/torch/_dynamo/eval_frame.py", line 118, in forward return self.dynamo_ctx(self._orig_mod.forward)(*args, **kwargs) File "/fsx/users/andgu/work/pytorch/torch/_dynamo/eval_frame.py", line 247, in _fn return fn(*args, **kwargs) File "/fsx/users/andgu/work/pytorch/torch/_dynamo/eval_frame.py", line 394, in catch_errors return callback(frame, cache_size, hooks) File "/fsx/users/andgu/work/pytorch/torch/_dynamo/convert_frame.py", line 453, in _convert_frame result = inner_convert(frame, cache_size, hooks) File "/fsx/users/andgu/work/pytorch/torch/_dynamo/convert_frame.py", line 113, in _fn return fn(*args, **kwargs) File "/fsx/users/andgu/work/pytorch/torch/_dynamo/convert_frame.py", line 296, in _convert_frame_assert return _compile( File "/fsx/users/andgu/work/pytorch/torch/_dynamo/utils.py", line 169, in time_wrapper r = func(*args, **kwargs) File "/fsx/users/andgu/work/pytorch/torch/_dynamo/convert_frame.py", line 361, in _compile out_code = transform_code_object(code, transform) File "/fsx/users/andgu/work/pytorch/torch/_dynamo/bytecode_transformation.py", line 683, in transform_code_object transformations(instructions, code_options) File "/fsx/users/andgu/work/pytorch/torch/_dynamo/convert_frame.py", line 348, in transform tracer.run() File "/fsx/users/andgu/work/pytorch/torch/_dynamo/symbolic_convert.py", line 1892, in run super().run() File "/fsx/users/andgu/work/pytorch/torch/_dynamo/symbolic_convert.py", line 611, in run and self.step() File "/fsx/users/andgu/work/pytorch/torch/_dynamo/symbolic_convert.py", line 571, in step getattr(self, inst.opname)(inst) File "/fsx/users/andgu/work/pytorch/torch/_dynamo/symbolic_convert.py", line 1979, in RETURN_VALUE self.output.compile_subgraph( File "/fsx/users/andgu/work/pytorch/torch/_dynamo/output_graph.py", line 630, in compile_subgraph self.compile_and_call_fx_graph(tx, list(reversed(stack_values)), root) File "/fsx/users/andgu/work/pytorch/torch/_dynamo/output_graph.py", line 700, in compile_and_call_fx_graph compiled_fn = self.call_user_compiler(gm) File "/fsx/users/andgu/work/pytorch/torch/_dynamo/utils.py", line 169, in time_wrapper r = func(*args, **kwargs) File "/fsx/users/andgu/work/pytorch/torch/_dynamo/output_graph.py", line 782, in call_user_compiler raise BackendCompilerFailed(self.compiler_fn, e).with_traceback( File "/fsx/users/andgu/work/pytorch/torch/_dynamo/output_graph.py", line 778, in call_user_compiler compiled_fn = compiler_fn(gm, self.fake_example_inputs()) File "/fsx/users/andgu/work/pytorch/torch/_dynamo/debug_utils.py", line 1098, in debug_wrapper compiled_gm = compiler_fn(gm, example_inputs) File "/fsx/users/andgu/work/pytorch/torch/__init__.py", line 1530, in __call__ return compile_fx(model_, inputs_, config_patches=self.config) File "/fsx/users/andgu/work/pytorch/torch/_inductor/compile_fx.py", line 722, in compile_fx return aot_autograd( File "/fsx/users/andgu/work/pytorch/torch/_dynamo/backends/common.py", line 62, in compiler_fn cg = aot_module_simplified(gm, example_inputs, **kwargs) File "/fsx/users/andgu/work/pytorch/torch/_functorch/aot_autograd.py", line 3093, in aot_module_simplified compiled_fn = create_aot_dispatcher_function( File "/fsx/users/andgu/work/pytorch/torch/_dynamo/utils.py", line 169, in time_wrapper r = func(*args, **kwargs) File "/fsx/users/andgu/work/pytorch/torch/_functorch/aot_autograd.py", line 2712, in create_aot_dispatcher_function fw_metadata = run_functionalized_fw_and_collect_metadata( File "/fsx/users/andgu/work/pytorch/torch/_functorch/aot_autograd.py", line 686, in inner flat_f_outs = f(*flat_f_args) File "/fsx/users/andgu/work/pytorch/torch/_functorch/aot_autograd.py", line 3017, in functional_call out = Interpreter(mod).run(*args[params_len:], **kwargs) File "/fsx/users/andgu/work/pytorch/torch/fx/interpreter.py", line 137, in run self.env[node] = self.run_node(node) File "/fsx/users/andgu/work/pytorch/torch/fx/interpreter.py", line 179, in run_node return getattr(self, n.op)(n.target, args, kwargs) File "/fsx/users/andgu/work/pytorch/torch/fx/interpreter.py", line 251, in call_function return target(*args, **kwargs) File "/fsx/users/andgu/work/pytorch/torch/_ops.py", line 247, in __call__ return torch.overrides.handle_torch_function( File "/fsx/users/andgu/work/pytorch/torch/overrides.py", line 1538, in handle_torch_function result = mode.__torch_function__(public_api, types, args, kwargs) File "/fsx/users/andgu/work/pytorch/torch/_inductor/overrides.py", line 33, in __torch_function__ return replace_fn(func)(*args, **kwargs) File "/fsx/users/andgu/work/pytorch/torch/_ops.py", line 252, in __call__ return self.dispatch(dispatch_key_set.highestPriorityTypeId(), *args, **kwargs) File "/fsx/users/andgu/work/pytorch/torch/_ops.py", line 242, in dispatch return kernel(*args, **kwargs) File "/fsx/users/andgu/work/pytorch/functorch/experimental/_cond.py", line 122, in cond_autograd return cond(pred, true_fn, false_fn, *operands) File "/fsx/users/andgu/work/pytorch/torch/_ops.py", line 252, in __call__ return self.dispatch(dispatch_key_set.highestPriorityTypeId(), *args, **kwargs) File "/fsx/users/andgu/work/pytorch/torch/_ops.py", line 236, in dispatch assert final_key in self.py_kernels, f"{dispatch_key} -> {final_key}" torch._dynamo.exc.BackendCompilerFailed: backend='debug_wrapper' raised: AssertionError: DispatchKey.Functionalize -> DispatchKey.Functionalize While executing %cond : [#users=1] = call_function[target=torch.ops.cond](args = (%l_pred_, %cond_true_0, %cond_false_0, [%l_x_]), kwargs = {}) Original traceback: File "dynamo/test_cond.py", line 17, in forward return cond(pred, true_fn, false_fn, [x]) ``` </details> ``` import torch from functorch.experimental.control_flow import cond x = torch.randn((3,)) def f1(x1, x2): return x1 + x2 def f2(x1, x2): return x1 * x2 @torch.compile() def f(z): return cond(z, f1, f2, [x, x]) f(torch.tensor(True)) ``` raises the same error: ``` File "/fsx/users/andgu/work/pytorch/torch/_ops.py", line 236, in dispatch assert final_key in self.py_kernels, f"{dispatch_key} -> {final_key}" torch._dynamo.exc.BackendCompilerFailed: backend='debug_wrapper' raised: AssertionError: DispatchKey.Functionalize -> DispatchKey.Functionalize While executing %cond : [#users=1] = call_function[target=torch.ops.cond](args = (%l_z_, %cond_true_0, %cond_false_0, [%g_x_, %g_x_]), kwargs = {}) Original traceback: File "dynamo/test_cond.py", line 18, in f return cond(z, f1, f2, [x, x]) ``` cc @ezyang @soumith @msaroufim @wconstab @ngimel @bdhirsh @zou3519 @Chillee @samdow @kshitij12345 @janeyx99
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PyTorch's packaged libgomp causes significant performance penalties on CPU when used together with other Python packages
module: build, triaged, module: multithreading
### 🐛 Describe the bug PyTorch's PYPI packages come with their own `libgomp-SOMEHASH.so` packaged. Other packages like SciKit Learn do the same. The problem is, that depending on the order of loading your Python modules, the PyTorch OpenMP might be initialized with only a single thread. This can be easily seen by running (I removed all non-related output): ```json # python3 -m threadpoolctl -i torch sklearn [ { "user_api": "openmp", "internal_api": "openmp", "prefix": "libgomp", "filepath": "/.../python3.8/site-packages/torch/lib/libgomp-a34b3233.so.1", "version": null, "num_threads": 12 # PyTorch 12 Threads }, { "user_api": "openmp", "internal_api": "openmp", "prefix": "libgomp", "filepath": "/.../python3.8/site-packages/scikit_learn.libs/libgomp-a34b3233.so.1.0.0", "version": null, "num_threads": 1 # SKlearn 1 Thread } ] ``` and: ```json # python3 -m threadpoolctl -i sklearn torch [ { "user_api": "openmp", "internal_api": "openmp", "prefix": "libgomp", "filepath": "/.../python3.8/site-packages/scikit_learn.libs/libgomp-a34b3233.so.1.0.0", "version": null, "num_threads": 24 # SKlearn 24 Threads }, { "user_api": "openmp", "internal_api": "openmp", "prefix": "libgomp", "filepath": "/.../python3.8/site-packages/torch/lib/libgomp-a34b3233.so.1", "version": null, "num_threads": 1 # PyTorch 1 Thread } ] ``` In the first case, PyTorch gets all threads, in the second case, SciKit Learn gets all threads. This minimal example shows the effect on the performance: ```python import sklearn # remove or swap with 2nd line import torch import torchvision from time import perf_counter_ns as timer model = torchvision.models.resnet50() model.eval() data = torch.rand(64, 3, 224, 224) start = timer() with torch.no_grad(): for i in range(5): model(data) end = timer() print(f'Total: {(end-start)/1000000.0}ms') ``` Result without `import sklearn` or by swapping the two import lines: `Total: 5020.870435ms` And with `import sklearn`: `Total: 27399.992653ms` Even if we would manually set the number of threads correctly, it still would have a performance penalty when switching between PyTorch and SKlearn, as the thread pools need to be swapped. My current workaround is to remove all `libgomp-*.so` within my Python user site and replace them with symlinks to the system's `libgomp.so`. This causes that Sklearn and Pytorch use the same thread pool, which in my opinion is the desired behavior. Another solution would be to compile PyTorch from source. I'm not sure why PyTorch is shipping it's own `libgomp`. I'm guessing it's for compatibility reasons on older systems, that don't have `libgomp` or an outdated/incompatible version. However, the current approach causes significant downsides when using PyTorch with other packages or user applications, that are linked against the system's `libgomp`. So far I identified `onnxruntime-openmp` and `scikit-learn` that do the same, but I assume there are many more. I came up with multiple solutions: 1. A hacky solution would be to ensure that all packages use the identical `libgomp-SOMEHASH.so.SO_VERSION`, e.g., SKlearn and onnxruntime use `libgomp-a34b3233.so.1.0.0` while PyTorch uses `libgomp-a34b3233.so.1`. This works as `libdl` only checks the file name. But that does not solve the fundamental problem of shipping your own `libgomp`, and still would have the problem when the user include own libraries linked against system `libgomp`. 2. A proper solution would be to do something like the [intel-openmp](https://pypi.org/project/intel-openmp/) package, that provides a centralized way of accessing the libraries and then can be easily taken up by multiple python packages without conflicts. Here, PyTorch, SKlearn, etc. could just have this package as common requirement, and load all the same library. As this is a cross project issue, I'm not sure what the best way is to coordinate with the other projects. This issue is related to: #44282, #19764 ### Versions ``` Collecting environment information... PyTorch version: 2.0.0+cu117 Is debug build: False CUDA used to build PyTorch: 11.7 ROCM used to build PyTorch: N/A OS: CentOS Linux 7 (Core) (x86_64) GCC version: (GCC) 10.3.0 Clang version: Could not collect CMake version: version 3.26.3 Libc version: glibc-2.17 Python version: 3.8.16 (default, Mar 17 2023, 07:42:34) [GCC 10.2.1 20210130 (Red Hat 10.2.1-11)] (64-bit runtime) Python platform: Linux-3.10.0-1160.76.1.el7.x86_64-x86_64-with-glibc2.17 Is CUDA available: False CUDA runtime version: 11.4.120 GPU models and configuration: Could not collect Nvidia driver version: Could not collect cuDNN version: Could not collect HIP runtime version: N/A MIOpen runtime version: N/A Is XNNPACK available: True Versions of relevant libraries: [pip3] numpy==1.23.5 [pip3] pytorch-lightning==2.0.1 [pip3] torch==2.0.0 [pip3] torchmetrics==0.11.4 [pip3] torchvision==0.15.1 [conda] Could not collect ``` cc @malfet @seemethere
7
2,936
98,835
[PT2.0] empty output shape causes Segmentation fault
triaged, bug, oncall: pt2, module: aotdispatch, module: inductor
### 🐛 Describe the bug If output tensor is initialized with torch.empty(0) and then passed through the torch.compile then there is an segfault observed n allocating tensor with invalid size Use below sample code to reproduce the issue: ``` import torch def fn(x, y): torch.abs(x, out=y) x = torch.rand((8, 8)) y = torch.empty(0) compiled_fn = torch.compile(fn) compiled_fn(x, y) print(y) ``` ### Error logs ``` Internal Error: Received signal - Segmentation fault dmesg: read kernel buffer failed: Operation not permitted Fatal Python error: Segmentation fault Thread 0x00007fa475c0c700 (most recent call first): File "/usr/lib/python3.8/concurrent/futures/thread.py", line 78 in _worker File "/usr/lib/python3.8/threading.py", line 870 in run File "/usr/lib/python3.8/threading.py", line 932 in _bootstrap_inner File "/usr/lib/python3.8/threading.py", line 890 in _bootstrap Thread 0x00007fa47540b700 (most recent call first): File "/usr/lib/python3.8/selectors.py", line 415 in select File "/usr/lib/python3.8/multiprocessing/connection.py", line 931 in wait File "/usr/lib/python3.8/concurrent/futures/process.py", line 362 in _queue_management_worker File "/usr/lib/python3.8/threading.py", line 870 in run File "/usr/lib/python3.8/threading.py", line 932 in _bootstrap_inner File "/usr/lib/python3.8/threading.py", line 890 in _bootstrap Thread 0x00007fa41ca8d700 (most recent call first): File "/usr/lib/python3.8/threading.py", line 306 in wait File "/usr/lib/python3.8/threading.py", line 558 in wait File "/usr/local/lib/python3.8/dist-packages/tqdm/_monitor.py", line 60 in run File "/usr/lib/python3.8/threading.py", line 932 in _bootstrap_inner File "/usr/lib/python3.8/threading.py", line 890 in _bootstrap Current thread 0x00007fa4a6fd5dc0 (most recent call first): File "/tmp/torchinductor_root/u4/cu4v5vey6e3iafqgfidpy6wayatqg6bhyuqbkarylgvhc2uvflyj.py", line 49 in call File "/usr/local/lib/python3.8/dist-packages/torch/_functorch/aot_autograd.py", line 1247 in call_func_with_args File "/usr/local/lib/python3.8/dist-packages/torch/_functorch/aot_autograd.py", line 1898 in runtime_wrapper File "/usr/local/lib/python3.8/dist-packages/torch/_functorch/aot_autograd.py", line 1222 in g File "/usr/local/lib/python3.8/dist-packages/torch/_functorch/aot_autograd.py", line 2819 in forward File "/usr/local/lib/python3.8/dist-packages/torch/_dynamo/eval_frame.py", line 209 in _fn File "out_abs.py", line 17 in forward File "/usr/local/lib/python3.8/dist-packages/torch/_dynamo/eval_frame.py", line 209 in _fn File "/usr/local/lib/python3.8/dist-packages/torch/_dynamo/eval_frame.py", line 82 in forward File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 1501 in _call_impl File "out_abs.py", line 25 in run_on_device File "out_abs.py", line 32 in <module> Segmentation fault (core dumped) ``` ### Minified repro ``` import torch def fn(x, y): torch.abs(x, out=y) x = torch.rand((8, 8)) y = torch.empty(0) compiled_fn = torch.compile(fn) compiled_fn(x, y) print(y) ``` ### Versions Name: torch Version: 2.0.0 Summary: Tensors and Dynamic neural networks in Python with strong GPU acceleration Home-page: https://pytorch.org/ Author: PyTorch Team Author-email: packages@pytorch.org License: BSD-3 Location: /home/jthakur/.pt_2_0/lib/python3.8/site-packages Requires: filelock, jinja2, networkx, nvidia-cublas-cu11, nvidia-cuda-cupti-cu11, nvidia-cuda-nvrtc-cu11, nvidia-cuda-runtime-cu11, nvidia-cudnn-cu11, nvidia-cufft-cu11, nvidia-curand-cu11, nvidia-cusolver-cu11, nvidia-cusparse-cu11, nvidia-nccl-cu11, nvidia-nvtx-cu11, sympy, triton, typing-extensions Required-by: torchaudio, torchvision, triton cc @ezyang @gchanan @zou3519 @soumith @msaroufim @wconstab @ngimel @bdhirsh @voznesenskym @penguinwu @anijain2305 @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @Xia-Weiwen @wenzhe-nrv @jiayisunx @peterbell10 @desertfire
5
2,937
98,827
[functorch] vmap_hessian_fc - fails under torch.compile
triaged, oncall: pt2, module: functorch
### 🐛 Describe the bug Running vmap_hessian_fc from torchbench/userbench with following patch ```patch diff --git a/userbenchmark/functorch/vmap_hessian_fc.py b/userbenchmark/functorch/vmap_hessian_fc.py index ebfe7cf3..6ae546c0 100644 --- a/userbenchmark/functorch/vmap_hessian_fc.py +++ b/userbenchmark/functorch/vmap_hessian_fc.py @@ -1,6 +1,6 @@ import torch import torch.nn as nn from functorch import vmap, jacfwd, jacrev from .util import BenchmarkCase # batched hessians of fully connected layers is a popular quantity @@ -8,12 +8,25 @@ from .util import BenchmarkCase # This test case is from https://github.com/pytorch/functorch/issues/989 # We haven't been able to get the full model yet, so, this test case # is going into the functorch userbenchmark instead of torchbenchmark. + +from torch._dynamo import allow_in_graph +from functools import wraps + +def traceable(f): + f = allow_in_graph(f) + + @wraps(f) + def wrapper(*args, **kwargs): + return f(*args, **kwargs) + + return wrapper + class VmapHessianFC(BenchmarkCase): def __init__(self): - device = 'cuda' + device = 'cpu' D1 = 2 # x, y D2 = 3 # u, v, p - B = 10000 + B = 10 x = torch.randn(B, D1).to(device) model = nn.Sequential( @@ -43,9 +56,12 @@ class VmapHessianFC(BenchmarkCase): out = self.model(x) return out, out - hessian, pred = vmap( + fn = vmap( jacfwd(jacrev(predict, argnums=0, has_aux=True), argnums=0, has_aux=True), in_dims=0, - )( + ) + + fn = torch.compile(traceable(fn)) + hessian, pred = fn( self.x ) ``` Leads to failure: ``` RuntimeError: Failed running call_function <function VmapHessianFC.run.<locals>.predict at 0x7fa75ef8f040>(*(FakeTensor(FakeTensor(..., device='meta', size=(10, 2)), cpu),), **{}): InferenceMode::is_enabled() && self.is_inference() INTERNAL ASSERT FAILED at "/home/kshiteej/Pytorch/pytorch_functorch/aten/src/ATen/native/VariableMethodStubs.cpp":67, please report a bug to PyTorch. Expected this method to only be reached in inference mode and when all the inputs are inference tensors. You should NOT call this method directly as native::_fw_primal. Please use the dispatcher, i.e., at::_fw_primal. Please file an issue if you come across this error otherwise. (scroll up for backtrace) ``` ### Versions master cc @ezyang @soumith @msaroufim @wconstab @ngimel @bdhirsh @zou3519 @Chillee @samdow @janeyx99
1
2,938
98,825
[functorch] functorch_maml_omniglot - fails under torch.compile
triaged, oncall: pt2, module: functorch
### 🐛 Describe the bug Running `functorch_maml_omniglot` from `torchbench` with following patch ```patch diff --git a/torchbenchmark/models/functorch_maml_omniglot/__init__.py b/torchbenchmark/models/functorch_maml_omniglot/__init__.py index faf16d73..430eaadf 100644 --- a/torchbenchmark/models/functorch_maml_omniglot/__init__.py +++ b/torchbenchmark/models/functorch_maml_omniglot/__init__.py @@ -10,6 +10,17 @@ from typing import Tuple from ...util.model import BenchmarkModel from torchbenchmark.tasks import OTHER +from torch._dynamo import allow_in_graph +from functools import wraps + +def traceable(f): + f = allow_in_graph(f) + + @wraps(f) + def wrapper(*args, **kwargs): + return f(*args, **kwargs) + + return wrapper def loss_for_task(net, n_inner_iter, x_spt, y_spt, x_qry, y_qry): params, buffers, fnet = net @@ -66,7 +77,7 @@ class Model(BenchmarkModel): self.model = net root = str(Path(__file__).parent.parent) - self.meta_inputs = torch.load(f'{root}/maml_omniglot/batch.pt') + self.meta_inputs = torch.load(f'{root}/functorch_maml_omniglot/batch.pt') self.meta_inputs = tuple([torch.from_numpy(i).to(self.device) for i in self.meta_inputs]) self.example_inputs = (self.meta_inputs[0][0],) @@ -90,7 +101,9 @@ class Model(BenchmarkModel): # In parallel, trains one model per task. There is a support (x, y) # for each task and a query (x, y) for each task. compute_loss_for_task = functools.partial(loss_for_task, net, n_inner_iter) - qry_losses, qry_accs = vmap(compute_loss_for_task)(x_spt, y_spt, x_qry, y_qry) + fn = vmap(compute_loss_for_task) + fn = torch.compile(traceable(fn)) + qry_losses, qry_accs = fn(x_spt, y_spt, x_qry, y_qry) # Compute the maml loss by summing together the returned losses. qry_losses.sum().backward() ``` Leads to failure: ``` Exception: Please convert all Tensors to FakeTensors first or instantiate FakeTensorMode with 'allow_non_fake_inputs'. Found in aten.convolution.default(*(FakeTensor(FakeTensor(..., device='meta', size=(160, 1, 28, 28)), cpu), ``` ### Versions master cc @ezyang @soumith @msaroufim @wconstab @ngimel @bdhirsh @zou3519 @Chillee @samdow @janeyx99
3
2,939
98,822
[functorch] torch.compile - functorch transforms Interaction
triaged, oncall: pt2, module: functorch
### 🐛 Describe the bug This is an umbrella issue for all the issues related to functorch - torch.compile interaction. Currently, functorch transforms can be compiled under torch.compile with undocumented API. However, there are still a few issues which require resolution. Example of compiling transform ```python import torch from torch._dynamo import allow_in_graph from functools import wraps def traceable(f): f = allow_in_graph(f) @wraps(f) def wrapper(*args, **kwargs): return f(*args, **kwargs) return wrapper def fn(x): return vmap(torch.sin)(x) opt_fn = torch.compile(traceable(fn)) opt_fn(torch.randn(3, 3)) ``` torchbench issues: - [ ] https://github.com/pytorch/pytorch/issues/98825 - [ ] https://github.com/pytorch/pytorch/issues/98827 user reported issues: - [x] https://github.com/pytorch/pytorch/issues/97425 - [ ] https://github.com/pytorch/pytorch/issues/100105 - [ ] https://github.com/pytorch/pytorch/issues/100075 ### Versions master cc @ezyang @soumith @msaroufim @wconstab @ngimel @bdhirsh @zou3519 @Chillee @samdow @janeyx99
0
2,940
98,817
[FSDP] summon_full_params with_grad=True CPU offload can crash
oncall: distributed, triaged, module: fsdp
### 🐛 Describe the bug It can crash if the grads are not None (i.e. optim.zero_grad(set_to_none=False) is called, or grad is not zeroed at all): ``` 527 work = group._allgather_base(output_tensor, input_tensor) 528 RuntimeError: Tensors must be CUDA and dense ``` ### Versions main cc @mrshenli @pritamdamania87 @zhaojuanmao @satgera @gqchen @aazzolini @osalpekar @jiayisuse @H-Huang @kwen2501 @awgu
0
2,941
98,816
File-level retry enhancements
triaged, module: devx
### 🐛 Describe the bug Here is the list of several potential enhancements on how file-level retry https://github.com/pytorch/pytorch/pull/97506 works. The goal is to start the discussion to see which makes sense and keep track of their progress: * [x] Correctly resume from the last running test when timed out. Here is an example of `distributed/_tensor/test_dtensor_ops` failure at https://github.com/pytorch/pytorch/actions/runs/4662747010/jobs/8253593870. The test timed out flakily at the first run and was retried accordingly `Command took >30min, retrying (retries left=1)`. However, when the retry kicked in, it started again from the beginning because the test timed out, not failing `stepwise: no previously failed tests, not skipping`. Thus, the second try also timed out. It makes sense to start from the last running test here instead. * [ ] Ensure that each flaky test is retried once. Take an example when a test fails, the retry logic will run the test again starting at the failed test. The number of remaining retry would decrease from 1 to 0 (no more retry). Assuming that the failed test is indeed flaky and it now passes, the test file will continue. However, the edge case here is that if there is yet another flaky test further down the list, there is no retry left to handle it. A possible solution is to only decrease the number of retries if the same flaky test fails again. Here is an example https://github.com/pytorch/pytorch/actions/runs/4660804309/jobs/8249750328 in which the first flaky test `TestForeachCUDA.test_binary_op__foreach_clamp_max_is_fastpath_True_cuda_float32` was retried successfully while the second flaky test `TestForeachCUDA.test_binary_op__foreach_mul_is_fastpath_True_cuda_bfloat16` was out of luck. Other issues: * [ ] Integrate with ONNX tests https://github.com/pytorch/pytorch/issues/98626 ### Versions PyTorch CI cc @ZainRizvi @kit1980 @clee2000
0
2,942
98,814
autocast does not work properly on embedding module
triaged, module: amp (automated mixed precision)
### 🐛 Describe the bug Hello, I'm not sure whether it is intended, but autocast seems not working on embedding module. below is the link of a colab notebook that reproduce the issue https://colab.research.google.com/drive/1EoHFFH5CXvkwExQeyvsFIqMV9JqjMRfI?usp=sharing ``` import torch embeddings = torch.nn.Embedding(3, 128).cuda() keys = torch.tensor([0,1,2]).cuda() with torch.autocast(device_type='cuda', dtype=torch.float16): print(embeddings(keys).dtype) ``` the processed datatype is still float32 instead of float 16. I'm not sure whether this is supposed to be the case, but this causes the "index put requires the source and destination dtypes match" error in my code when I use amp training. Thanks ### Versions Collecting environment information... PyTorch version: 2.0.0+cu118 Is debug build: False CUDA used to build PyTorch: 11.8 ROCM used to build PyTorch: N/A OS: Ubuntu 20.04.5 LTS (x86_64) GCC version: (Ubuntu 9.4.0-1ubuntu1~20.04.1) 9.4.0 Clang version: 10.0.0-4ubuntu1 CMake version: version 3.25.2 Libc version: glibc-2.31 Python version: 3.9.16 (main, Dec 7 2022, 01:11:51) [GCC 9.4.0] (64-bit runtime) Python platform: Linux-5.10.147+-x86_64-with-glibc2.31 Is CUDA available: True CUDA runtime version: 11.8.89 CUDA_MODULE_LOADING set to: LAZY GPU models and configuration: GPU 0: Tesla T4 Nvidia driver version: 525.85.12 cuDNN version: Probably one of the following: /usr/lib/x86_64-linux-gnu/libcudnn.so.8.7.0 /usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.7.0 /usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.7.0 /usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.7.0 /usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.7.0 /usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.7.0 /usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.7.0 HIP runtime version: N/A MIOpen runtime version: N/A Is XNNPACK available: True CPU: Architecture: x86_64 CPU op-mode(s): 32-bit, 64-bit Byte Order: Little Endian Address sizes: 46 bits physical, 48 bits virtual CPU(s): 2 On-line CPU(s) list: 0,1 Thread(s) per core: 2 Core(s) per socket: 1 Socket(s): 1 NUMA node(s): 1 Vendor ID: GenuineIntel CPU family: 6 Model: 85 Model name: Intel(R) Xeon(R) CPU @ 2.00GHz Stepping: 3 CPU MHz: 2000.150 BogoMIPS: 4000.30 Hypervisor vendor: KVM Virtualization type: full L1d cache: 32 KiB L1i cache: 32 KiB L2 cache: 1 MiB L3 cache: 38.5 MiB NUMA node0 CPU(s): 0,1 Vulnerability Itlb multihit: Not affected Vulnerability L1tf: Mitigation; PTE Inversion Vulnerability Mds: Vulnerable; SMT Host state unknown Vulnerability Meltdown: Vulnerable Vulnerability Mmio stale data: Vulnerable Vulnerability Retbleed: Vulnerable Vulnerability Spec store bypass: Vulnerable Vulnerability Spectre v1: Vulnerable: __user pointer sanitization and usercopy barriers only; no swapgs barriers Vulnerability Spectre v2: Vulnerable, IBPB: disabled, STIBP: disabled, PBRSB-eIBRS: Not affected Vulnerability Srbds: Not affected Vulnerability Tsx async abort: Vulnerable Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch invpcid_single ssbd ibrs ibpb stibp fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm mpx avx512f avx512dq rdseed adx smap clflushopt clwb avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves arat md_clear arch_capabilities Versions of relevant libraries: [pip3] numpy==1.22.4 [pip3] torch==2.0.0+cu118 [pip3] torchaudio==2.0.1+cu118 [pip3] torchdata==0.6.0 [pip3] torchsummary==1.5.1 [pip3] torchtext==0.15.1 [pip3] torchvision==0.15.1+cu118 [pip3] triton==2.0.0 [conda] Could not collect cc @mcarilli @ptrblck @leslie-fang-intel @jgong5
2
2,943
98,808
[FSDP] move up the first all gather
oncall: distributed, triaged, module: fsdp
### 🚀 The feature, motivation and pitch The first allgather in FSDP is currently launched before computation for layer 1 needs to begin, but it can actually begin much sooner: 1. Potentially overlap with `_to_kwargs` data movement 2. API for advanced users to kick off this all gather even outside of model forward pass, to overlap with other work in their training loop. The API could look as follows: ``` def gather_first_fsdp_layer_params(self: FullyShardedDataParallel): handle = self._root_handle handle.unshard() # kicks off the allgather on the unshard stream, but does not block ``` And can be used as follows in an example training loop: ``` while has_next(dataloader): batch = next(dataloader) # kick off FSPD allgather model.dense.gather_first_fsdp_layer_params() # overlap with next data transfer + sparse part of model dataloader.prefetch() model.sparse() model.dense() ``` ### Alternatives _No response_ ### Additional context _No response_ cc @mrshenli @pritamdamania87 @zhaojuanmao @satgera @gqchen @aazzolini @osalpekar @jiayisuse @H-Huang @kwen2501 @awgu
0
2,944
98,805
Discrepancy of supported Python versions between Get Started page and index of pre-built binaries for PIP installation
module: docs, triaged
### 📚 The doc issue Sorry if this is not directly under contents under https://pytorch.org/docs/stable/index.html but for the page for "Get Started" https://pytorch.org/get-started/locally/ In the page description of required Python versions, it shows "Currently, PyTorch on Windows only supports Python 3.7-3.9; Python 2.x is not supported." However, if we choose pip as the package manager to install Pytorch, the index of pre-built binaries of package `torch` is actually not containing `py37`. For example, under https://download.pytorch.org/whl/nightly/torch/, there is no item contains field `py37`. By the way, Python 3.10 and 3.11 seems actually supported. If we try the command suggested by the page with Python 3.7, in my case `pip3 install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu118`, the installation is not going to work. The error message is: ``` ERROR: Could not find a version that satisfies the requirement torch (from versions: none) ERROR: No matching distribution found for torch ``` ### Suggest a potential alternative/fix We may change the page description to match the actual support. Maybe the supported versions should be 3.8-3.11. cc @svekars @carljparker
0
2,945
98,792
DataLoader doesn't accept non-cpu device for loading.
module: dataloader, triaged
### 🐛 Describe the bug Not sure if this is intentional but a DataLoader does not accept a non-cpu device despite tensors living somewhere else. [Example of a few months of a big issue that allows you to pass in `cuda` Generator to the dataloader.](https://discuss.pytorch.org/t/runtimeerror-expected-a-cuda-device-type-for-generator-but-found-cpu/161463) ```python from torch.utils.data import DataLoader, TensorDataset, RandomSampler device= torch.device("cuda") x, y = torch.tensor([1,2,3], device=device), torch.tensor([1,2,3], device=device) dataset = TensorDataset(x,y) next(iter(DataLoader(dataset, generator=torch.Generator(device)))) # RuntimeError: Expected a 'cpu' device type for generator but found 'cuda' ``` ### Versions Collecting environment information... PyTorch version: 2.0.0 Is debug build: False CUDA used to build PyTorch: None ROCM used to build PyTorch: N/A OS: macOS 13.0 (x86_64) GCC version: Could not collect Clang version: 14.0.0 (clang-1400.0.29.102) CMake version: Could not collect Libc version: N/A Python version: 3.11.0 | packaged by conda-forge | (main, Jan 15 2023, 05:44:48) [Clang 14.0.6 ] (64-bit runtime) Python platform: macOS-13.0-x86_64-i386-64bit Is CUDA available: False CUDA runtime version: No CUDA CUDA_MODULE_LOADING set to: N/A GPU models and configuration: No CUDA Nvidia driver version: No CUDA cuDNN version: No CUDA HIP runtime version: N/A MIOpen runtime version: N/A Is XNNPACK available: True CPU: Apple M1 Pro Versions of relevant libraries: [pip3] mypy-extensions==1.0.0 [pip3] numpy==1.24.2 [pip3] torch==2.0.0 [pip3] torchvision==0.15.1 [conda] mkl 2022.2.1 h44ed08c_16952 conda-forge [conda] numpy 1.24.2 py311ha9d2c9f_0 conda-forge [conda] torch 2.0.0 pypi_0 pypi [conda] torchvision 0.15.1 pypi_0 pypi And for google colab: Collecting environment information... PyTorch version: 2.0.0+cu117 Is debug build: False CUDA used to build PyTorch: 11.7 ROCM used to build PyTorch: N/A OS: Ubuntu 20.04.5 LTS (x86_64) GCC version: (Ubuntu 9.4.0-1ubuntu1~20.04.1) 9.4.0 Clang version: 10.0.0-4ubuntu1 CMake version: version 3.26.3 Libc version: glibc-2.31 Python version: 3.11.3 (main, Apr 5 2023, 14:15:06) [GCC 9.4.0] (64-bit runtime) Python platform: Linux-5.10.147+-x86_64-with-glibc2.31 Is CUDA available: True CUDA runtime version: 11.8.89 CUDA_MODULE_LOADING set to: LAZY GPU models and configuration: GPU 0: Tesla T4 Nvidia driver version: 525.85.12 cuDNN version: Probably one of the following: /usr/lib/x86_64-linux-gnu/libcudnn.so.8.7.0 /usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.7.0 /usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.7.0 /usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.7.0 /usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.7.0 /usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.7.0 /usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.7.0 HIP runtime version: N/A MIOpen runtime version: N/A Is XNNPACK available: True CPU: Architecture: x86_64 CPU op-mode(s): 32-bit, 64-bit Byte Order: Little Endian Address sizes: 46 bits physical, 48 bits virtual CPU(s): 2 On-line CPU(s) list: 0,1 Thread(s) per core: 2 Core(s) per socket: 1 Socket(s): 1 NUMA node(s): 1 Vendor ID: GenuineIntel CPU family: 6 Model: 79 Model name: Intel(R) Xeon(R) CPU @ 2.20GHz Stepping: 0 CPU MHz: 2199.998 BogoMIPS: 4399.99 Hypervisor vendor: KVM Virtualization type: full L1d cache: 32 KiB L1i cache: 32 KiB L2 cache: 256 KiB L3 cache: 55 MiB NUMA node0 CPU(s): 0,1 Vulnerability Itlb multihit: Not affected Vulnerability L1tf: Mitigation; PTE Inversion Vulnerability Mds: Vulnerable; SMT Host state unknown Vulnerability Meltdown: Vulnerable Vulnerability Mmio stale data: Vulnerable Vulnerability Retbleed: Vulnerable Vulnerability Spec store bypass: Vulnerable Vulnerability Spectre v1: Vulnerable: __user pointer sanitization and usercopy barriers only; no swapgs barriers Vulnerability Spectre v2: Vulnerable, IBPB: disabled, STIBP: disabled, PBRSB-eIBRS: Not affected Vulnerability Srbds: Not affected Vulnerability Tsx async abort: Vulnerable Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch invpcid_single ssbd ibrs ibpb stibp fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm rdseed adx smap xsaveopt arat md_clear arch_capabilities Versions of relevant libraries: [pip3] numpy==1.24.2 [pip3] torch==2.0.0 [pip3] torchvision==0.15.1 [pip3] triton==2.0.0 [conda] Could not collect cc @SsnL @VitalyFedyunin @ejguan @NivekT @dzhulgakov
2
2,946
98,768
[SPMD] DistCompiler graph optimization improvement
oncall: distributed, triaged
### 🚀 The feature, motivation and pitch The graph optimization for DistCompiler is enabled via the stack of https://github.com/pytorch/pytorch/pull/98182. Many feedbacks and experiences are provided during the development and code review of the stack. This issue is used to track the issues and TODOs. ### List of TODOs 1. Instead of having another layer of Module, IterGraphModule should serve the purpose of lowering Inductor. Context: https://github.com/pytorch/pytorch/pull/98182/files#r1158786964 2. `graph_optimization_pass` should support `run_before` argument. 3. `graph_optimization_pass` should support support multiple runs of graph optimization. 4. Ensure that `_optimized_func` of `graph_optimization_pass` does not have conflict. 5. Graph optimization passes should support symbolic shape. Context: https://github.com/pytorch/pytorch/pull/98285/files#r1161194034 cc @mrshenli @pritamdamania87 @zhaojuanmao @satgera @rohan-varma @gqchen @aazzolini @osalpekar @jiayisuse @H-Huang @kwen2501 @awgu
0
2,947
98,728
[triton hash update] update the pinned triton hash
open source, ciflow/trunk, topic: not user facing, ciflow/inductor
This PR is auto-generated nightly by [this action](https://github.com/pytorch/pytorch/blob/master/.github/workflows/_update-commit-hash.yml). Update the pinned triton hash.
99
2,948
98,727
Pytorch member variable not working after converting to onnx format
module: onnx, triaged
### 🐛 Describe the bug Hi team, we're now investigating the export to onnx feature and we found that some update logic in the original pytorch model is not working in the converted onnx model. The pytorch result kept updating as expected but the onnx result stays the same. ``` # onnx (stays the same) [array([[ 0.09353793, -0.06549314, -0.17803375, 0.07057121, -0.07197426, -0.00245702, 0.09384082, -0.07102646, 0.00091066, -0.012063 ]], dtype=float32)] [array([[ 0.09353793, -0.06549314, -0.17803375, 0.07057121, -0.07197426, -0.00245702, 0.09384082, -0.07102646, 0.00091066, -0.012063 ]], dtype=float32)] [array([[ 0.09353793, -0.06549314, -0.17803375, 0.07057121, -0.07197426, -0.00245702, 0.09384082, -0.07102646, 0.00091066, -0.012063 ]], dtype=float32)] # pytorch result (keep updating) tensor([[ 0.1028, -0.0641, -0.1713, 0.0673, -0.0882, -0.0108, 0.1027, -0.0583, 0.0012, -0.0174]], grad_fn=<DifferentiableGraphBackward>) tensor([[ 0.0977, -0.0628, -0.1801, 0.0675, -0.0858, -0.0092, 0.1020, -0.0584, 0.0034, -0.0185]], grad_fn=<DifferentiableGraphBackward>) tensor([[ 0.0987, -0.0620, -0.1770, 0.0681, -0.0860, -0.0084, 0.1019, -0.0604, 0.0033, -0.0192]], grad_fn=<DifferentiableGraphBackward>) ``` How to deal with such scenario that we need to update some member variable like self.last_hidden ? ### Reproduce code ``` import torch.nn as nn import torch class RNN(nn.Module): # you can also accept arguments in your model constructor def __init__(self, data_size, hidden_size, output_size): super(RNN, self).__init__() self.last_hidden = torch.zeros(1, hidden_size) input_size = data_size + hidden_size self.i2h = nn.Linear(input_size, hidden_size) self.h2o = nn.Linear(hidden_size, output_size) def forward(self, data): input = torch.cat((data, self.last_hidden), 1) hidden = self.i2h(input) output = self.h2o(hidden) self.last_hidden = hidden return output data_size = 50 hidden_size = 20 output_size = 10 rnn_model = RNN(data_size, hidden_size, output_size) rnn_model.eval() data = torch.zeros(1, data_size) last_hidden = torch.zeros(1, hidden_size) torch.jit.save(torch.jit.script(rnn_model), 'rnn_model.pt') pytorch_rnn_model = torch.load('rnn_model.pt') torch.onnx.export( pytorch_rnn_model, data, 'rnn_model.onnx', opset_version=15, input_names=('input',), output_names=('output',), dynamic_axes={ 'input': {0: 'batch', 1: 'sequence'}, 'output': {0: 'batch', 1: 'sequence'}, }, training=torch.onnx.TrainingMode.EVAL, do_constant_folding=False, verbose=True, keep_initializers_as_inputs=True, ) import onnxruntime rnn_onnx_model = onnxruntime.InferenceSession('rnn_model.onnx', providers=['CPUExecutionProvider']) input_name = rnn_onnx_model.get_inputs()[0].name output_name = rnn_onnx_model.get_outputs()[0].name print(rnn_onnx_model.run([output_name], {input_name: data.numpy()})) print(rnn_onnx_model.run([output_name], {input_name: data.numpy()})) print(rnn_onnx_model.run([output_name], {input_name: data.numpy()})) print(pytorch_rnn_model.forward(data)) print(pytorch_rnn_model.forward(data)) print(pytorch_rnn_model.forward(data)) ``` ### Versions ``` Collecting environment information... PyTorch version: 2.0.0 Is debug build: False CUDA used to build PyTorch: None ROCM used to build PyTorch: N/A OS: macOS 13.1 (x86_64) GCC version: Could not collect Clang version: 14.0.0 (clang-1400.0.29.202) CMake version: Could not collect Libc version: N/A Python version: 3.8.16 (default, Mar 1 2023, 21:19:10) [Clang 14.0.6 ] (64-bit runtime) Python platform: macOS-10.16-x86_64-i386-64bit Is CUDA available: False CUDA runtime version: No CUDA CUDA_MODULE_LOADING set to: N/A GPU models and configuration: No CUDA Nvidia driver version: No CUDA cuDNN version: No CUDA HIP runtime version: N/A MIOpen runtime version: N/A Is XNNPACK available: True CPU: Apple M1 Max Versions of relevant libraries: [pip3] numpy==1.23.5 [pip3] torch==2.0.0 [pip3] torchvision==0.15.1 [conda] blas 1.0 mkl https://repo.anaconda.com/pkgs/main [conda] mkl 2021.4.0 hecd8cb5_637 https://repo.anaconda.com/pkgs/main [conda] mkl-service 2.4.0 py38h9ed2024_0 https://repo.anaconda.com/pkgs/main [conda] mkl_fft 1.3.1 py38h4ab4a9b_0 https://repo.anaconda.com/pkgs/main [conda] mkl_random 1.2.2 py38hb2f4e1b_0 https://repo.anaconda.com/pkgs/main [conda] numpy 1.23.5 py38he696674_0 https://repo.anaconda.com/pkgs/main [conda] numpy-base 1.23.5 py38h9cd3388_0 https://repo.anaconda.com/pkgs/main [conda] torch 2.0.0 pypi_0 pypi [conda] torchvision 0.15.1 pypi_0 pypi ```
11
2,949
98,724
Conflict between ``torch.func`` transformations and ``torch.jit.trace``
triaged, module: functorch
### 🐛 Describe the bug ```python @torch.func.grad @partial(torch.jit.trace, example_inputs=torch.ones([3])) def f(a): return torch.sum(a) f(torch.ones([3])) ``` the above code works as expected, while ```python @partial(torch.jit.trace, example_inputs=torch.ones([3])) @torch.func.grad def f(a): return torch.sum(a) f(torch.ones([3])) ``` raise runtimeerror as ```bash --------------------------------------------------------------------------- RuntimeError Traceback (most recent call last) /var/folders/_3/7wt5f7ss5tq2_bfcwr3gj9780000gn/T/ipykernel_92193/3316297889.py in <module> 1 @partial(torch.jit.trace, example_inputs=torch.ones([3])) 2 @torch.func.grad ----> 3 def f(a): 4 return torch.sum(a) ~/opt/anaconda3/envs/tf27/lib/python3.8/site-packages/torch/jit/_trace.py in trace(func, example_inputs, optimize, check_trace, check_inputs, check_tolerance, strict, _force_outplace, _module_class, _compilation_unit, example_kwarg_inputs, _store_inputs) 857 ) 858 else: --> 859 traced = torch._C._create_function_from_trace( 860 name, 861 func, ~/opt/anaconda3/envs/tf27/lib/python3.8/site-packages/torch/_functorch/eager_transforms.py in wrapper(*args, **kwargs) 1378 @wraps(func) 1379 def wrapper(*args, **kwargs): -> 1380 results = grad_and_value(func, argnums, has_aux=has_aux)(*args, **kwargs) 1381 if has_aux: 1382 grad, (_, aux) = results ~/opt/anaconda3/envs/tf27/lib/python3.8/site-packages/torch/_functorch/vmap.py in fn(*args, **kwargs) 37 def fn(*args, **kwargs): 38 with torch.autograd.graph.disable_saved_tensors_hooks(message): ---> 39 return f(*args, **kwargs) 40 return fn 41 ~/opt/anaconda3/envs/tf27/lib/python3.8/site-packages/torch/_functorch/eager_transforms.py in wrapper(*args, **kwargs) 1265 # NB: need create_graph so that backward pass isn't run in no_grad mode 1266 flat_outputs = _as_tuple(output) -> 1267 flat_grad_input = _autograd_grad(flat_outputs, flat_diff_args, create_graph=True) 1268 grad_input = tree_unflatten(flat_grad_input, spec) 1269 ~/opt/anaconda3/envs/tf27/lib/python3.8/site-packages/torch/_functorch/eager_transforms.py in _autograd_grad(outputs, inputs, grad_outputs, retain_graph, create_graph) 111 if len(diff_outputs) == 0: 112 return tuple(torch.zeros_like(inp) for inp in inputs) --> 113 grad_inputs = torch.autograd.grad(diff_outputs, inputs, grad_outputs, 114 retain_graph=retain_graph, 115 create_graph=create_graph, ~/opt/anaconda3/envs/tf27/lib/python3.8/site-packages/torch/autograd/__init__.py in grad(outputs, inputs, grad_outputs, retain_graph, create_graph, only_inputs, allow_unused, is_grads_batched) 286 287 grad_outputs_ = _tensor_or_tensors_to_tuple(grad_outputs, len(t_outputs)) --> 288 grad_outputs_ = _make_grads(t_outputs, grad_outputs_, is_grads_batched=is_grads_batched) 289 290 if retain_graph is None: ~/opt/anaconda3/envs/tf27/lib/python3.8/site-packages/torch/autograd/__init__.py in _make_grads(outputs, grads, is_grads_batched) 85 elif grad is None: 86 if out.requires_grad: ---> 87 if out.numel() != 1: 88 raise RuntimeError("grad can be implicitly created only for scalar outputs") 89 new_grads.append(torch.ones_like(out, memory_format=torch.preserve_format)) RuntimeError: Cannot access data pointer of Tensor that doesn't have storage ``` ### Versions ```bash Collecting environment information... PyTorch version: 2.0.0 Is debug build: False CUDA used to build PyTorch: None ROCM used to build PyTorch: N/A OS: macOS 11.3.1 (x86_64) GCC version: Could not collect Clang version: 12.0.5 (clang-1205.0.22.9) CMake version: Could not collect Libc version: N/A Python version: 3.8.0 (default, Nov 6 2019, 15:49:01) [Clang 4.0.1 (tags/RELEASE_401/final)] (64-bit runtime) Python platform: macOS-10.16-x86_64-i386-64bit Is CUDA available: False CUDA runtime version: No CUDA CUDA_MODULE_LOADING set to: N/A GPU models and configuration: No CUDA Nvidia driver version: No CUDA cuDNN version: No CUDA HIP runtime version: N/A MIOpen runtime version: N/A Is XNNPACK available: True CPU: Intel(R) Core(TM) i7-9750H CPU @ 2.60GHz Versions of relevant libraries: [pip3] flake8==4.0.1 [pip3] mypy==0.982 [pip3] mypy-extensions==0.4.3 [pip3] numpy==1.23.5 [pip3] torch==2.0.0 [pip3] torchvision==0.11.1 [conda] Could not collect ``` ## Possible related issues: https://github.com/pytorch/pytorch/issues/96041 cc @zou3519 @Chillee @samdow @soumith @kshitij12345 @janeyx99
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98,707
Ubuntu 22.04 LTS issue <built-in function load_binary> returned NULL without setting an exception
module: rocm, triaged, oncall: pt2
### 🐛 Describe the bug Greetings, I was directed to this repository as I am encountering an issue with PyTorch. Specifically, I am experiencing an error with loading triton when attempting to run the software with stable diffusion and dreambooth addon on a newly installed KUbuntu 22.04 operating system on an AMD CPU with an AMD GPU (6950XT) and rocm 5.4.2 (installed via official deb package) Regrettably, I do not possess a high level of proficiency in Python, and as such, I can only describe the steps that I have taken on a freshly installed machine. The system is set up by cloning the repository https://github.com/AUTOMATIC1111/stable-diffusion-webui with torch rocm via `pip install torch torchvision --extra-index-url https://download.pytorch.org/whl/rocm5.4.2` in venv as well as dreambooth extension. No further modifications are done. In the training of the model it stops with the following error message: ``` Steps: 0%| | 0/600 [00:00<?, ?it/s]Traceback (most recent call last): File "/home/krim/GIT/stable-diffusion-webui/extensions/sd_dreambooth_extension/dreambooth/ui_functions.py", line 727, in start_training result = main(class_gen_method=class_gen_method) File "/home/krim/GIT/stable-diffusion-webui/extensions/sd_dreambooth_extension/dreambooth/train_dreambooth.py", line 1371, in main return inner_loop() File "/home/krim/GIT/stable-diffusion-webui/extensions/sd_dreambooth_extension/dreambooth/memory.py", line 119, in decorator return function(batch_size, grad_size, prof, *args, **kwargs) File "/home/krim/GIT/stable-diffusion-webui/extensions/sd_dreambooth_extension/dreambooth/train_dreambooth.py", line 1169, in inner_loop noise_pred = unet( File "/home/krim/GIT/stable-diffusion-webui/venv/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl return forward_call(*args, **kwargs) File "/home/krim/GIT/stable-diffusion-webui/venv/lib/python3.10/site-packages/accelerate/utils/operations.py", line 495, in __call__ return convert_to_fp32(self.model_forward(*args, **kwargs)) File "/home/krim/GIT/stable-diffusion-webui/venv/lib/python3.10/site-packages/torch/amp/autocast_mode.py", line 14, in decorate_autocast return func(*args, **kwargs) File "/home/krim/GIT/stable-diffusion-webui/venv/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py", line 82, in forward return self.dynamo_ctx(self._orig_mod.forward)(*args, **kwargs) File "/home/krim/GIT/stable-diffusion-webui/venv/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py", line 209, in _fn return fn(*args, **kwargs) File "/home/krim/GIT/stable-diffusion-webui/venv/lib/python3.10/site-packages/diffusers/models/unet_2d_condition.py", line 556, in forward t_emb = self.time_proj(timesteps) File "/home/krim/GIT/stable-diffusion-webui/venv/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl return forward_call(*args, **kwargs) File "/home/krim/GIT/stable-diffusion-webui/venv/lib/python3.10/site-packages/diffusers/models/embeddings.py", line 222, in forward def forward(self, timesteps): File "/home/krim/GIT/stable-diffusion-webui/venv/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py", line 209, in _fn return fn(*args, **kwargs) File "/home/krim/GIT/stable-diffusion-webui/venv/lib/python3.10/site-packages/torch/_functorch/aot_autograd.py", line 2819, in forward return compiled_fn(full_args) File "/home/krim/GIT/stable-diffusion-webui/venv/lib/python3.10/site-packages/torch/_functorch/aot_autograd.py", line 1222, in g return f(*args) File "/home/krim/GIT/stable-diffusion-webui/venv/lib/python3.10/site-packages/torch/_functorch/aot_autograd.py", line 1898, in runtime_wrapper all_outs = call_func_with_args( File "/home/krim/GIT/stable-diffusion-webui/venv/lib/python3.10/site-packages/torch/_functorch/aot_autograd.py", line 1247, in call_func_with_args out = normalize_as_list(f(args)) File "/home/krim/GIT/stable-diffusion-webui/venv/lib/python3.10/site-packages/torch/_inductor/compile_fx.py", line 248, in run return model(new_inputs) File "/home/krim/GIT/stable-diffusion-webui/venv/lib/python3.10/site-packages/torch/_inductor/compile_fx.py", line 265, in run compiled_fn = cudagraphify_impl(model, new_inputs, static_input_idxs) File "/home/krim/GIT/stable-diffusion-webui/venv/lib/python3.10/site-packages/torch/_inductor/compile_fx.py", line 320, in cudagraphify_impl model(list(static_inputs)) File "/tmp/torchinductor_krim/xq/cxqqlwutwzuplluoktmemj63w363iojzltjm3s3avxbjioaget6s.py", line 113, in call triton__0.run(arg0_1, buf0, buf1, 160, grid=grid(160), stream=stream0) File "/home/krim/GIT/stable-diffusion-webui/venv/lib/python3.10/site-packages/torch/_inductor/triton_ops/autotune.py", line 190, in run result = launcher( File "<string>", line 6, in launcher File "/home/krim/GIT/stable-diffusion-webui/venv/lib/python3.10/site-packages/triton/compiler.py", line 1944, in __getattribute__ self._init_handles() File "/home/krim/GIT/stable-diffusion-webui/venv/lib/python3.10/site-packages/triton/compiler.py", line 1930, in _init_handles mod, func, n_regs, n_spills = hip_utils.load_binary(self.metadata["name"], self.asm["hsaco_path"], self.shared, device) SystemError: <built-in function load_binary> returned NULL without setting an exception ``` See https://github.com/d8ahazard/sd_dreambooth_extension/issues/1174 On Reddit, I stumbled upon only one other individual who reported facing the same issue. Unfortunately, they did not provide any insights on how to even begin debugging this issue. Are there any suggestions or ideas that anyone could offer how to start debugging it? ### Versions ``` Collecting environment information... /home/krim/GIT/stable-diffusion-webui/venv/lib/python3.10/site-packages/torch/cuda/__init__.py:546: UserWarning: Can't initialize NVML warnings.warn("Can't initialize NVML") PyTorch version: 2.0.0+rocm5.4.2 Is debug build: False CUDA used to build PyTorch: N/A ROCM used to build PyTorch: 5.4.22803-474e8620 OS: Ubuntu 22.04.2 LTS (x86_64) GCC version: (Ubuntu 11.3.0-1ubuntu1~22.04) 11.3.0 Clang version: Could not collect CMake version: version 3.26.1 Libc version: glibc-2.35 Python version: 3.10.6 (main, Mar 10 2023, 10:55:28) [GCC 11.3.0] (64-bit runtime) Python platform: Linux-6.2.10-060210-generic-x86_64-with-glibc2.35 Is CUDA available: True CUDA runtime version: Could not collect CUDA_MODULE_LOADING set to: LAZY GPU models and configuration: AMD Radeon RX 6950 XT Nvidia driver version: Could not collect cuDNN version: Could not collect HIP runtime version: 5.4.22803 MIOpen runtime version: 2.19.0 Is XNNPACK available: True CPU: Architecture: x86_64 CPU op-mode(s): 32-bit, 64-bit Address sizes: 48 bits physical, 48 bits virtual Byte Order: Little Endian CPU(s): 24 On-line CPU(s) list: 0-23 Vendor ID: AuthenticAMD Model name: AMD Ryzen 9 5900X 12-Core Processor CPU family: 25 Model: 33 Thread(s) per core: 2 Core(s) per socket: 12 Socket(s): 1 Stepping: 0 Frequency boost: enabled CPU max MHz: 4950,1948 CPU min MHz: 2200,0000 BogoMIPS: 7386.11 Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl nonstop_tsc cpuid extd_apicid aperfmperf rapl pni pclmulqdq monitor ssse3 fma cx16 sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw ibs skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb cat_l3 cdp_l3 hw_pstate ssbd mba ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local clzero irperf xsaveerptr rdpru wbnoinvd arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif v_spec_ctrl umip pku ospke vaes vpclmulqdq rdpid overflow_recov succor smca fsrm Virtualization: AMD-V L1d cache: 384 KiB (12 instances) L1i cache: 384 KiB (12 instances) L2 cache: 6 MiB (12 instances) L3 cache: 64 MiB (2 instances) NUMA node(s): 1 NUMA node0 CPU(s): 0-23 Vulnerability Itlb multihit: Not affected Vulnerability L1tf: Not affected Vulnerability Mds: Not affected Vulnerability Meltdown: Not affected Vulnerability Mmio stale data: Not affected Vulnerability Retbleed: Not affected Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization Vulnerability Spectre v2: Mitigation; Retpolines, IBPB conditional, IBRS_FW, STIBP always-on, RSB filling, PBRSB-eIBRS Not affected Vulnerability Srbds: Not affected Vulnerability Tsx async abort: Not affected Versions of relevant libraries: [pip3] lion-pytorch==0.0.7 [pip3] numpy==1.23.3 [pip3] open-clip-torch==2.7.0 [pip3] pytorch-lightning==1.9.4 [pip3] pytorch-triton-rocm==2.0.1 [pip3] torch==2.0.0+rocm5.4.2 [pip3] torchdiffeq==0.2.3 [pip3] torchmetrics==0.11.4 [pip3] torchsde==0.2.5 [pip3] torchvision==0.15.1+rocm5.4.2 [conda] Could not collect ``` cc @jeffdaily @sunway513 @jithunnair-amd @pruthvistony @ROCmSupport @ezyang @soumith @msaroufim @wconstab @ngimel @bdhirsh @voznesenskym @penguinwu @anijain2305 @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @Xia-Weiwen @wenzhe-nrv @jiayisunx @peterbell10 @desertfire
13
2,951
98,695
Torchscript: Name Mangling prevents Type Refinement
oncall: jit
### 🐛 Describe the bug Name mangling in Torchscript prevents type refinement. See the following script for an example: ```python from typing import Tuple import torch from torch import nn from torch import Tensor class A(nn.Module): def __init__(self, s: int): super().__init__() self.linear = nn.Linear(int(s**2), int(s**2)) def forward(self, x: Tensor): return x class Sequence(nn.Module): def __init__(self, n_modules: int): super().__init__() self.mods = nn.ModuleList([]) for i in range(n_modules): self.mods.append(A(i + 1)) def forward(self, x): tmp : Tuple[A] = (self.mods[0], self.mods[1]) for mod in self.mods: assert isinstance(mod, A) x = mod(x) return x if __name__ == "__main__": seq = Sequence(3) seqs = torch.jit.script(seq) ``` The script fails with the following error: ``` Traceback (most recent call last): File "problem.py", line 34, in <module> seqs = torch.jit.script(seq) File "/home/schuetze/.local/lib/python3.8/site-packages/torch/jit/_script.py", line 1284, in script return torch.jit._recursive.create_script_module( File "/home/schuetze/.local/lib/python3.8/site-packages/torch/jit/_recursive.py", line 480, in create_script_module return create_script_module_impl(nn_module, concrete_type, stubs_fn) File "/home/schuetze/.local/lib/python3.8/site-packages/torch/jit/_recursive.py", line 546, in create_script_module_impl create_methods_and_properties_from_stubs(concrete_type, method_stubs, property_stubs) File "/home/schuetze/.local/lib/python3.8/site-packages/torch/jit/_recursive.py", line 397, in create_meth ods_and_properties_from_stubs concrete_type._create_methods_and_properties(property_defs, property_rcbs, method_defs, method_rcbs, method_defaults) RuntimeError: Variable 'tmp' is annotated with type Tuple[__torch__.A] but is being assigned to a value of type Tuple[__torch__.A, __torch__.___torch_mangle_1.A]: File "problem.py", line 26 def forward(self, x): tmp : Tuple[A] = (self.mods[0], self.mods[1]) ~~~ <--- HERE for mod in self.mods: assert isinstance(mod, A) ``` The types of the different elements are mangled names, and therefore I cannot do type refinment. Is there a way for me around this problem? _For context (in case it matters):_ I need to use type refinement because I need to help the compiler with some overload resolution. In the real example, there's not just class A, but also class B and I need to tell the compiler that some element of a nn.ModuleList have the same type and what that type is. ### Versions Collecting environment information... PyTorch version: 2.0.0+cu117 Is debug build: False CUDA used to build PyTorch: 11.7 ROCM used to build PyTorch: N/A OS: Ubuntu 20.04.6 LTS (x86_64) GCC version: (Ubuntu 9.4.0-1ubuntu1~20.04.1) 9.4.0 Clang version: Could not collect CMake version: version 3.18.2 Libc version: glibc-2.31 Python version: 3.8.10 (default, Mar 13 2023, 10:26:41) [GCC 9.4.0] (64-bit runtime) Python platform: Linux-5.15.0-67-generic-x86_64-with-glibc2.29 Is CUDA available: False CUDA runtime version: No CUDA CUDA_MODULE_LOADING set to: N/A GPU models and configuration: No CUDA Nvidia driver version: No CUDA cuDNN version: No CUDA HIP runtime version: N/A MIOpen runtime version: N/A Is XNNPACK available: True CPU: Architecture: x86_64 CPU op-mode(s): 32-bit, 64-bit Byte Order: Little Endian Address sizes: 39 bits physical, 48 bits virtual CPU(s): 4 On-line CPU(s) list: 0-3 Thread(s) per core: 2 Core(s) per socket: 2 Socket(s): 1 NUMA node(s): 1 Vendor ID: GenuineIntel CPU family: 6 Model: 78 Model name: Intel(R) Core(TM) i7-6600U CPU @ 2.60GHz Stepping: 3 CPU MHz: 2800.000 CPU max MHz: 3400,0000 CPU min MHz: 400,0000 BogoMIPS: 5599.85 Virtualization: VT-x L1d cache: 64 KiB L1i cache: 64 KiB L2 cache: 512 KiB L3 cache: 4 MiB NUMA node0 CPU(s): 0-3 Vulnerability Itlb multihit: KVM: Mitigation: VMX disabled Vulnerability L1tf: Mitigation; PTE Inversion; VMX conditional cache flushes, SMT vulnerable Vulnerability Mds: Mitigation; Clear CPU buffers; SMT vulnerable Vulnerability Meltdown: Mitigation; PTI Vulnerability Mmio stale data: Mitigation; Clear CPU buffers; SMT vulnerable Vulnerability Retbleed: Mitigation; IBRS Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization Vulnerability Spectre v2: Mitigation; IBRS, IBPB conditional, RSB filling, PBRSB-eIBRS Not affected Vulnerability Srbds: Mitigation; Microcode Vulnerability Tsx async abort: Mitigation; TSX disabled Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb invpcid_single pti ssbd ibrs ibpb stibp tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid mpx rdseed adx smap clflushopt intel_pt xsaveopt xsavec xgetbv1 xsaves dtherm ida arat pln pts hwp hwp_notify hwp_act_window hwp_epp md_clear flush_l1d arch_capabilities Versions of relevant libraries: [pip3] flake8==5.0.4 [pip3] functorch==1.13.1 [pip3] mypy==0.982 [pip3] mypy-extensions==0.4.3 [pip3] numpy==1.24.2 [pip3] numpy-financial==1.0.0 [pip3] onnx2torch==1.5.6 [pip3] pytorch-lightning==1.5.4 [pip3] pytorch3d==0.7.3 [pip3] torch==2.0.0 [pip3] torch-tb-profiler==0.4.0 [pip3] torchdata==0.6.0 [pip3] torchmetrics==0.9.3 [pip3] torchtext==0.15.1 [pip3] torchvision==0.15.1 [pip3] triton==2.0.0 [pip3] tritonclient==2.27.0 [conda] Could not collect cc @EikanWang @jgong5 @wenzhe-nrv @sanchitintel
0
2,952
98,690
Linking ResNeXt PyTorch Hub in Pipeline docs
oncall: distributed
### 📚 The doc issue Mention ResNeXt model at the first without link reference to PyTorch Hub. see https://pytorch.org/docs/stable/pipeline.html#skip-connections The main reason to show differentiation between ResNet and ResNeXt model. Apart from ResNet topic is excluded in docs of Pipeline. Since ResNeXt is next generation of well known more popular ResNet. Introducing ResNeXt at first is a necessity. ### Suggest a potential alternative/fix Provide link to PyTorch Hub ResNeXt in Pipeline docs. cc @mrshenli @pritamdamania87 @zhaojuanmao @satgera @rohan-varma @gqchen @aazzolini @osalpekar @jiayisuse @H-Huang @kwen2501 @awgu
0
2,953
98,678
DISABLED test_gradgrad_nn_GroupNorm_cuda_float64 (__main__.TestModuleCUDA)
module: nn, triaged, skipped
Platforms: linux This test was disabled because it is failing on master ([recent examples](http://torch-ci.com/failure/test_modules.py%3A%3ATestModuleCUDA%3A%3Atest_gradgrad_nn_GroupNorm_cuda_float64)). This test is failing on slow gradcheck, pending a fix https://github.com/pytorch/pytorch/pull/98424#issuecomment-1499858018 cc @albanD @mruberry @jbschlosser @walterddr @mikaylagawarecki
4
2,954
98,677
DISABLED test_grad_nn_GroupNorm_cuda_float64 (__main__.TestModuleCUDA)
module: nn, triaged, skipped
Platforms: linux This test was disabled because it is failing on master ([recent examples](http://torch-ci.com/failure/test_modules.py%3A%3ATestModuleCUDA%3A%3Atest_grad_nn_GroupNorm_cuda_float64)). This test is failing on slow gradcheck, pending a fix https://github.com/pytorch/pytorch/pull/98424#issuecomment-1499858018 cc @albanD @mruberry @jbschlosser @walterddr @mikaylagawarecki
1
2,955
98,675
torch.matmul with batched CSR matrix
module: sparse, triaged
### 🐛 Describe the bug ```python import torch device = torch.device("cuda:0") a = torch.tensor([ [ [1.0, 0.0], [2.0, 1.0] ], [ [0.1, 0.1], [0.0, 2.0], ] ]).to_sparse_csr().to(device) b = torch.randn(2, 2).to(device) print(torch.matmul(a, b)) ``` This leads to `RuntimeError: Sparse CSR tensors do not have strides`, so seems like this is not implemented, yet. However, it is unclear to me why this is a problem with striding. ``` Traceback (most recent call last): File "/<path>/sparse_missing_support.py", line 24, in <module> main() File "/<path>/sparse_missing_support.py", line 19, in main print(torch.matmul(a, b.to_sparse_csr())) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ RuntimeError: Sparse CSR tensors do not have strides ``` ### Versions ``` Collecting environment information... PyTorch version: 2.0.0+cu117 Is debug build: False CUDA used to build PyTorch: 11.7 ROCM used to build PyTorch: N/A OS: Fedora Linux 37 (KDE Plasma) (x86_64) GCC version: (GCC) 12.2.1 20221121 (Red Hat 12.2.1-4) Clang version: Could not collect CMake version: version 3.26.1 Libc version: glibc-2.36 Python version: 3.11.2 (main, Feb 8 2023, 00:00:00) [GCC 12.2.1 20221121 (Red Hat 12.2.1-4)] (64-bit runtime) Python platform: Linux-6.2.7-200.fc37.x86_64-x86_64-with-glibc2.36 Is CUDA available: True CUDA runtime version: Could not collect CUDA_MODULE_LOADING set to: LAZY GPU models and configuration: GPU 0: NVIDIA GeForce RTX 2070 SUPER Nvidia driver version: 530.30.02 cuDNN version: Probably one of the following: /usr/lib64/libcudnn.so.8.8.1 /usr/lib64/libcudnn_adv_infer.so.8.8.1 /usr/lib64/libcudnn_adv_train.so.8.8.1 /usr/lib64/libcudnn_cnn_infer.so.8.8.1 /usr/lib64/libcudnn_cnn_train.so.8.8.1 /usr/lib64/libcudnn_ops_infer.so.8.8.1 /usr/lib64/libcudnn_ops_train.so.8.8.1 HIP runtime version: N/A MIOpen runtime version: N/A Is XNNPACK available: True CPU: Architecture: x86_64 CPU op-mode(s): 32-bit, 64-bit Address sizes: 43 bits physical, 48 bits virtual Byte Order: Little Endian CPU(s): 16 On-line CPU(s) list: 0-15 Vendor ID: AuthenticAMD Model name: AMD Ryzen 7 2700 Eight-Core Processor CPU family: 23 Model: 8 Thread(s) per core: 2 Core(s) per socket: 8 Socket(s): 1 Stepping: 2 Frequency boost: enabled CPU(s) scaling MHz: 67% CPU max MHz: 3200.0000 CPU min MHz: 1550.0000 BogoMIPS: 6387.18 Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl nonstop_tsc cpuid extd_apicid aperfmperf rapl pni pclmulqdq monitor ssse3 fma cx16 sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb hw_pstate ssbd ibpb vmmcall fsgsbase bmi1 avx2 smep bmi2 rdseed adx smap clflushopt sha_ni xsaveopt xsavec xgetbv1 clzero irperf xsaveerptr arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif overflow_recov succor smca sev sev_es Virtualization: AMD-V L1d cache: 256 KiB (8 instances) L1i cache: 512 KiB (8 instances) L2 cache: 4 MiB (8 instances) L3 cache: 16 MiB (2 instances) NUMA node(s): 1 NUMA node0 CPU(s): 0-15 Vulnerability Itlb multihit: Not affected Vulnerability L1tf: Not affected Vulnerability Mds: Not affected Vulnerability Meltdown: Not affected Vulnerability Mmio stale data: Not affected Vulnerability Retbleed: Mitigation; untrained return thunk; SMT vulnerable Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization Vulnerability Spectre v2: Mitigation; Retpolines, IBPB conditional, STIBP disabled, RSB filling, PBRSB-eIBRS Not affected Vulnerability Srbds: Not affected Vulnerability Tsx async abort: Not affected Versions of relevant libraries: [pip3] mypy-extensions==1.0.0 [pip3] numpy==1.24.2 [pip3] torch==2.0.0 [pip3] triton==2.0.0 [conda] Could not collect ``` cc @alexsamardzic @nikitaved @pearu @cpuhrsch @amjames @bhosmer
7
2,956
98,673
[ux] Non-blocking tensor constructors
triaged, enhancement, has workaround, module: tensor creation
### 🚀 The feature, motivation and pitch It appears that regular `torch.tensor`/`torch.as_tensor` constructors incur blocking behavior when placing the result in CUDA memory: https://github.com/pytorch/vision/issues/7504 https://github.com/pytorch/vision/pull/7506 So @nlgranger had to replace `torch.tensor(..., device = my_cuda_device)` by `torch.tensor(...).to(device = my_cuda_device, non_blocking = True)` I think, it would be more idiomatic/clean to allow `non_blocking` argument directly on torch.tensor/torch.as_tensor ### Alternatives _No response_ ### Additional context _No response_ cc @gchanan @mruberry
0
2,957
98,668
Cannot use `checkpoint_sequential` with `torch.compile`
triaged, oncall: pt2
## Issue description I have a number of classes that derive directly from `nn.Sequential`, when I `torch.compile` models containing these classes and attempt to use them in conjunction with `checkpoint_sequential` execution immediately aborts with a TypeError exception (@ line 352 in torch/utils/checkpoint.py): ``` segment_size = len(functions) // segments TypeError: object of type 'OptimizedModule' has no len() ``` I could imagine that perhaps the two are not meant to interoperate, but it's not clear whether that's the case. I have seen this with both the stable 2.0.0 CUDA 11.7 release as well as the nightly CUDA 11.8 release (system info below) ## Code example I have managed to reproduce the issue in a minimal way with this code snippet: ```python import torch import torch.nn as nn import torch.utils.checkpoint as ckpt import collections class Sequence(nn.Sequential): def __init__(self) -> None: builder = collections.OrderedDict() builder['linear_1'] = nn.Linear(32, 32) builder['linear_2'] = nn.Linear(32, 32) super(Sequence, self).__init__(builder) m = Sequence() n = torch.compile(m) x = torch.randn(32) y = ckpt.checkpoint_sequential(n, segments=2, input=x, use_reentrant=False) ``` ## System Info ``` Collecting environment information... PyTorch version: 2.1.0.dev20230406+cu118 Is debug build: False CUDA used to build PyTorch: 11.8 ROCM used to build PyTorch: N/A OS: Arch Linux (x86_64) GCC version: (GCC) 12.2.1 20230201 Clang version: 15.0.7 CMake version: Could not collect Libc version: glibc-2.37 Python version: 3.10.10 (main, Mar 5 2023, 22:26:53) [GCC 12.2.1 20230201] (64-bit runtime) Python platform: Linux-6.2.9-arch1-1-x86_64-with-glibc2.37 Is CUDA available: True CUDA runtime version: Could not collect CUDA_MODULE_LOADING set to: LAZY GPU models and configuration: GPU 0: NVIDIA GeForce RTX 3090 Nvidia driver version: 530.41.03 cuDNN version: Could not collect HIP runtime version: N/A MIOpen runtime version: N/A Is XNNPACK available: True CPU: Architecture: x86_64 CPU op-mode(s): 32-bit, 64-bit Address sizes: 39 bits physical, 48 bits virtual Byte Order: Little Endian CPU(s): 12 On-line CPU(s) list: 0-11 Vendor ID: GenuineIntel Model name: 12th Gen Intel(R) Core(TM) i5-12400F CPU family: 6 Model: 151 Thread(s) per core: 2 Core(s) per socket: 6 Socket(s): 1 Stepping: 5 CPU(s) scaling MHz: 35% CPU max MHz: 5600.0000 CPU min MHz: 800.0000 BogoMIPS: 4993.00 Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf tsc_known_freq pni pclmulqdq dtes64 monitor ds_cpl vmx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l2 invpcid_single cdp_l2 ssbd ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid rdt_a rdseed adx smap clflushopt clwb intel_pt sha_ni xsaveopt xsavec xgetbv1 xsaves split_lock_detect avx_vnni dtherm ida arat pln pts hwp hwp_notify hwp_act_window hwp_epp hwp_pkg_req hfi umip pku ospke waitpkg gfni vaes vpclmulqdq rdpid movdiri movdir64b fsrm md_clear serialize arch_lbr flush_l1d arch_capabilities Virtualization: VT-x L1d cache: 288 KiB (6 instances) L1i cache: 192 KiB (6 instances) L2 cache: 7.5 MiB (6 instances) L3 cache: 18 MiB (1 instance) NUMA node(s): 1 NUMA node0 CPU(s): 0-11 Vulnerability Itlb multihit: Not affected Vulnerability L1tf: Not affected Vulnerability Mds: Not affected Vulnerability Meltdown: Not affected Vulnerability Mmio stale data: Not affected Vulnerability Retbleed: Not affected Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization Vulnerability Spectre v2: Mitigation; Enhanced IBRS, IBPB conditional, RSB filling, PBRSB-eIBRS SW sequence Vulnerability Srbds: Not affected Vulnerability Tsx async abort: Not affected Versions of relevant libraries: [pip3] numpy==1.24.1 [pip3] pytorch-triton==2.1.0+46672772b4 [pip3] torch==2.1.0.dev20230406+cu118 [pip3] torchaudio==2.1.0.dev20230406+cu118 [pip3] torchdata==0.7.0.dev20230406 [pip3] torchvision==0.16.0.dev20230406+cu118 [conda] Could not collect ``` cc @ezyang @soumith @msaroufim @wconstab @ngimel @bdhirsh
2
2,958
98,618
DISABLED test_transpose_with_norm (__main__.CPUReproTests)
triaged, skipped, module: inductor
Platforms: linux This test was disabled because it is failing on master ([recent examples](http://torch-ci.com/failure/inductor%2Ftest_cpu_repro.py%3A%3ACPUReproTests%3A%3Atest_transpose_with_norm)). After https://github.com/pytorch/pytorch/pull/97841 to re-enable it, this test has been failing consistently, i.e. https://hud.pytorch.org/pytorch/pytorch/commit/46d765c15e702e2e2bc64b2948fba1f8845c4cda cc @soumith @voznesenskym @penguinwu @anijain2305 @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @Xia-Weiwen @wenzhe-nrv @jiayisunx @peterbell10 @desertfire
1
2,959
98,617
Add test/distributed/test_c10d_mpi.py
oncall: distributed, triaged
> We have one config where MPI is available and we build the wheels with MPI support. https://github.com/pytorch/pytorch/pull/98545#issuecomment-1500440581 We don't have any tests for MPI as we do for gloo (test_c10d_gloo) or nccl (test_c10d_nccl). As @malfet mentions since we have support in CI for mpi, we should at least create a basic test_c10d_mpi to perform basic distributed operations (e.g. init_process_group) to prevent any future regressions. cc @mrshenli @pritamdamania87 @zhaojuanmao @satgera @rohan-varma @gqchen @aazzolini @osalpekar @jiayisuse @kwen2501 @awgu
0
2,960
98,600
Wrong illustration in README.md
module: docs, triaged
### 📚 The doc issue The last frame of the illustration is wrong: ![wrong illustration](https://github.com/pytorch/pytorch/raw/390c51bf87a5e9958f0190c0226493dc4dcdd61e/docs/source/_static/img/dynamic_graph.gif) ### Suggest a potential alternative/fix The correct illustration should look like this: ![correct illustration](https://user-images.githubusercontent.com/8487114/230635413-925f0858-7166-4557-bb46-484be4b8f1f2.png) cc @svekars @carljparker
1
2,961
98,587
Cannot use AT_CUDA_DRIVER_CHECK from user code
module: build, module: cpp-extensions, module: internals, module: cuda, triaged
### 🐛 Describe the bug In my C++ extension that links against PyTorch I cannot use the AT_CUDA_DRIVER_CHECK macro to check the return code of a call to the CUDA driver API, because that macro makes use of the at::cuda::NVRTC struct which is defined in the ATen/cuda/nvrtc_stub/ATenNVRTC.h header, which doesn't get installed/shipped by PyTorch! ### Versions I'm using PyTorch 2.0.0 installed from the official conda channels (build py3.9_cuda11.7_cudnn8.5.0_0). cc @malfet @seemethere @zou3519 @ezyang @bhosmer @smessmer @ljk53 @bdhirsh @ngimel
2
2,962
98,566
`F.interpolate` and `F.grid_sample` - documentation error and bug
module: docs, triaged
### 🐛 Describe the bug `F.interpolate` and `F.grid_sample` are closely related as documented [here](https://pytorch.org/docs/stable/generated/torch.nn.functional.interpolate.html) and [here](https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html#torch.nn.functional.grid_sample) respectively. I ran into two separate issues with them: 1. The documentation is wrong and states the exact opposite behavior/use case for the `align_corners` flag of the function, 2. In the volumetric case, there is a bug that causes resolution-independent sampling with trilinear interpolation not to work at all. To demonstrate both, I created minimal reproducible test cases that should be ready to use or adapt if wanted: ``` #!/usr/bin/env python3 import unittest import torch from torch.nn import functional as F class TestGridSampleAndInterpolate(unittest.TestCase): """ Reproducer for grid sample and interpolation behavior. """ def test_2d(self): """ Demonstrate functionality in the 2D case. Everything works correctly (as far as I can tell), but the behavior is opposite to the documentation as stated on the website. """ torch.random.manual_seed(42) # We create a tensor with minimal size to demonstrate. # It must be a 4D tensor (N, C, H, W) since we need at least two # spatial dimensions for the `grid_sample` function later on. test_tensor = torch.rand((1, 1, 2, 2)) # We use the interpolate function as documented. For this behavior it does # not matter whether we use the `scale_factor` or `size` parameters since # we aim for a non-fractional upscaling anyways. # First, we demonstrate that the described behavior in the documentation that # states that this causes sampling coordinates to be "referring to the corner # points of the input’s corner pixels, making the sampling more resolution # agnostic". It should make it completely resolution agnostic... test_tensor_scaled = F.interpolate( test_tensor, scale_factor=2.0, mode="bilinear", align_corners=False ) # Now we generate 100 random sample point coordinates. # They have to be scaled into the target coordinate system ranging from -1 to 1. coords_xy = torch.rand(100, 2) * 2.0 - 1.0 # We now sample the grid in both resolution using the `grid_sample` function # as described in the documentation. sample_results_original = F.grid_sample( test_tensor.expand((100, 1, 2, 2)), coords_xy[:, None, None, :], mode="bilinear", align_corners=False, ) # We do the same with the upscaled grid. sample_results_scaled = F.grid_sample( test_tensor_scaled.expand((100, 1, 4, 4)), coords_xy[:, None, None, :], mode="bilinear", align_corners=False, ) # And the results _should_ match - but they don't by a large margin. self.assertGreater( (sample_results_original - sample_results_scaled).abs().max(), 1e-1 ) # However, if we do the exact opposite of what's stated in the documentation # and use `align_corners=True` everything works as expected. test_tensor_scaled = F.interpolate( test_tensor, scale_factor=2.0, mode="bilinear", align_corners=True ) sample_results_original = F.grid_sample( test_tensor.expand((100, 1, 2, 2)), coords_xy[:, None, None, :], mode="bilinear", align_corners=True, ) # We do the same with the upscaled grid. sample_results_scaled = F.grid_sample( test_tensor_scaled.expand((100, 1, 4, 4)), coords_xy[:, None, None, :], mode="bilinear", align_corners=True, ) self.assertTrue(torch.allclose(sample_results_original, sample_results_scaled)) def test_3d(self): """ Demonstrate functionality in the 3D case. Now comes the really interesting part: this seems not to work in the 3D case at all (except potentially in the degenerate case where the volume is composed of equal slices, making it bilinear interpolation - I have not tested that sufficiently much, though). This is very important for correct volume upsampling. """ torch.random.manual_seed(42) # We create a tensor with minimal size to demonstrate. # It must be a 5D tensor (N, C, H, W, D) since we need at least three # spatial dimensions for the `grid_sample` function later on. test_tensor = torch.rand((1, 1, 2, 2, 2)) # We again generate 100 random sample point coordinates. # They have to be scaled into the target coordinate system ranging from -1 to 1. coords_xyz = torch.rand(100, 3) * 2.0 - 1.0 # We use the interpolate function as documented. For this behavior it does # not matter whether we use the `scale_factor` or `size` parameters since # we aim for a non-fractional upscaling anyways. # Here, we look at both modes (`align_corners=True` and `align_corners=False`) # and show that they both do not work as intended and advertised. for align_corners in [True, False]: test_tensor_scaled = F.interpolate( test_tensor, scale_factor=2.0, mode="trilinear", align_corners=align_corners, ) # We now sample the grid in both resolution using the `grid_sample` function # as described in the documentation. sample_results_original = F.grid_sample( test_tensor.expand((100, 1, 2, 2, 2)), coords_xyz[:, None, None, None, :], # Using 'bilinear' here since the documentation mentions that # it is the correct mode for actually trilinear interpolation # in the volumetric case. mode="bilinear", align_corners=align_corners, ) # We do the same with the upscaled grid. sample_results_scaled = F.grid_sample( test_tensor_scaled.expand((100, 1, 4, 4, 4)), coords_xyz[:, None, None, None, :], mode="bilinear", align_corners=False, ) # And the results _should_ match at least in one of the two cases - here # they never do. self.assertGreater( (sample_results_original - sample_results_scaled).abs().max(), 1e-1 ) if __name__ == "__main__": unittest.main() ``` All these tests are consistently passing for me on PyTorch 1.13.1 (since I currently swapped conditions for the tests to show the _undesired_ instead of the desired behavior). As shown in the 2D case, there is no reason that these interpolations / sampling operations should not be exact, the maximum error in 2D using `align_corners=True` usually has errors <1e-8. **Is this an edge case?** Of course I did not start out trying this on 2x2(x2) sized volumes or images, I just found it to be a fairly minimal reproducer, but the same effects occur on larger images/volumes just the same and across the entire image/volume. It would be great if you could have a look at this! ### Versions PyTorch version: 1.13.1+cu117 Is debug build: False CUDA used to build PyTorch: 11.7 ROCM used to build PyTorch: N/A OS: Ubuntu 22.04.1 LTS (x86_64) GCC version: (Ubuntu 11.3.0-1ubuntu1~22.04) 11.3.0 Clang version: Could not collect CMake version: version 3.25.2 Libc version: glibc-2.35 Python version: 3.10.9 (main, Dec 16 2022, 10:01:32) [GCC 11.3.0] (64-bit runtime) Python platform: Linux-5.15.90.1-microsoft-standard-WSL2-x86_64-with-glibc2.35 Is CUDA available: True CUDA runtime version: 11.7.99 CUDA_MODULE_LOADING set to: LAZY GPU models and configuration: GPU 0: NVIDIA RTX A6000 Nvidia driver version: 527.27 cuDNN version: Could not collect HIP runtime version: N/A MIOpen runtime version: N/A Is XNNPACK available: True CPU: Architecture: x86_64 CPU op-mode(s): 32-bit, 64-bit Address sizes: 48 bits physical, 48 bits virtual Byte Order: Little Endian CPU(s): 64 On-line CPU(s) list: 0-63 Vendor ID: AuthenticAMD Model name: AMD Ryzen Threadripper PRO 3995WX 64-Cores CPU family: 23 Model: 49 Thread(s) per core: 2 Core(s) per socket: 32 Socket(s): 1 Stepping: 0 BogoMIPS: 5389.87 Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl tsc_reliable nonstop_tsc cpuid extd_apicid pni pclmulqdq ssse3 fma cx16 sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm cmp_legacy svm cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw topoext perfctr_core ssbd ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 xsaves clzero xsaveerptr arat npt nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold v_vmsave_vmload umip rdpid Virtualization: AMD-V Hypervisor vendor: Microsoft Virtualization type: full L1d cache: 1 MiB (32 instances) L1i cache: 1 MiB (32 instances) L2 cache: 16 MiB (32 instances) L3 cache: 16 MiB (1 instance) Vulnerability Itlb multihit: Not affected Vulnerability L1tf: Not affected Vulnerability Mds: Not affected Vulnerability Meltdown: Not affected Vulnerability Mmio stale data: Not affected Vulnerability Retbleed: Mitigation; untrained return thunk; SMT enabled with STIBP protection Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization Vulnerability Spectre v2: Mitigation; Retpolines, IBPB conditional, STIBP always-on, RSB filling, PBRSB-eIBRS Not affected Vulnerability Srbds: Not affected Vulnerability Tsx async abort: Not affected Versions of relevant libraries: [pip3] flake8==6.0.0 [pip3] mypy-extensions==1.0.0 [pip3] numpy==1.23.5 [pip3] pytorch-lightning==1.9.4 [pip3] torch==1.13.1 [pip3] torchaudio==0.13.1 [pip3] torchmetrics==0.11.4 [pip3] torchvision==0.14.1 [conda] Could not collect cc @svekars @carljparker
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98,561
Tracker - Failing models in the torch.compile dashboard
triaged, oncall: pt2
# Torchbench models - [ ] @bdhirsh - Inductor(default) fails for `hf_Longformer` - Repro at https://github.com/pytorch/pytorch/issues/100067 Cmd - `python benchmarks/dynamo/torchbench.py --accuracy --training --amp --backend inductor --disable-cudagraphs --device cuda --total-partitions 3 --partition-id 1 --only=hf_Longformer` Possible fix - https://github.com/pytorch/pytorch/pull/100115 - [x] @wconstab - https://github.com/pytorch/pytorch/issues/103385 - [x] @williamwen42 - `hf_T5_base` is failing - [x] @yanboliang - detectron2_maskrcnn - They dont show up in dashboard because they are skipped - https://github.com/pytorch/pytorch/issues/99665 # TIMM models - [x] @eellison - Inductor (w/ cudagraphs) OOM for `cait_m36_384` # Huggingface models - [ ] (**Up for Grabs**) - Inductor (default) accuracy failure with `AlbertForQuestionAnswering` - Can't repro on AWS machine Next steps - Repro this on GCP machine, check the offending tensors, increase tolerance if needed. - [x] @eellison - Inductor (w/ cudagraphs) OOM for `DebertaV2ForQuestionAnswering` ## Dynamic shapes (NOT POPULATED YET) - [ ] @ezyang - Inductor (dynamic) w/eval fails for `hf_BigBird` Cmd - `python benchmarks/dynamo/torchbench.py --accuracy --inference --amp --backend inductor --dynamic-shapes --dynamic-batch-only --disable-cudagraphs --device cuda --only=hf_BigBird` ~~~ 2023-04-24T18:32:37.8937866Z expr = pexpr(V.graph.sizevars.simplify(self.shape)) 2023-04-24T18:32:37.8938395Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/sympy/printing/printer.py", line 292, in doprint 2023-04-24T18:32:37.8938757Z return self._str(self._print(expr)) 2023-04-24T18:32:37.8939235Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/sympy/printing/printer.py", line 331, in _print 2023-04-24T18:32:37.8939711Z return printmethod(expr, **kwargs) 2023-04-24T18:32:37.8940289Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_inductor/codegen/common.py", line 191, in _print_Pow 2023-04-24T18:32:37.8940643Z assert exp.is_integer 2023-04-24T18:32:37.8941042Z torch._dynamo.exc.BackendCompilerFailed: backend='inductor' raised: 2023-04-24T18:32:37.8941357Z AssertionError: ~~~ - [ ] @bdhirsh - Inductor(default) w/ training fails Cmd - `python benchmarks/dynamo/torchbench.py --accuracy --training --amp --backend inductor --disable-cudagraphs --device cuda --total-partitions 3 --partition-id 1 --only=hf_BigBird` Error ~~~ 2023-04-24T19:31:30.2966005Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/big_bird/modeling_big_bird.py", line 567, in bigbird_block_sparse_attention 2023-04-24T19:31:30.2966675Z np.random.seed(seed) 2023-04-24T19:31:30.2967733Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/big_bird/modeling_big_bird.py", line 569, in <resume in bigbird_block_sparse_attention> 2023-04-24T19:31:30.2968585Z rand_attn = [ 2023-04-24T19:31:30.2969590Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/big_bird/modeling_big_bird.py", line 591, in <resume in bigbird_block_sparse_attention> 2023-04-24T19:31:30.2970293Z rand_attn = np.stack(rand_attn, axis=0) 2023-04-24T19:31:30.2971020Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/big_bird/modeling_big_bird.py", line 592, in <resume in bigbird_block_sparse_attention> 2023-04-24T19:31:30.2971769Z rand_attn = torch.tensor(rand_attn, device=query_layer.device, dtype=torch.long) 2023-04-24T19:31:30.2972585Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/big_bird/modeling_big_bird.py", line 629, in <resume in bigbird_block_sparse_attention> 2023-04-24T19:31:30.2972999Z first_context_layer.unsqueeze_(2) 2023-04-24T19:31:30.2974281Z RuntimeError: Output 0 of CompiledFunctionBackward is a view and is being modified inplace. This view was created inside a custom Function (or because an input was returned as-is) and the autograd logic to handle view+inplace would override the custom backward associated with the custom Function, leading to incorrect gradients. This behavior is forbidden. You can fix this by cloning the output of the custom Function. ~~~ - [ ] @anijain2305 + @ezyang - Inductor (w/ dynamic) fails for `convit_base` for sympy error ~~~ 2023-04-24T18:14:43.2692442Z cuda train convit_base WARNING:common:fp64 golden ref were not generated for convit_base. Setting accuracy check to cosine 2023-04-24T18:14:58.0197194Z [2023-04-24 18:14:58,017] torch.fx.experimental.symbolic_shapes: [WARNING] 13.0: RecursionError in sympy.solve(floor(s0**0.5) - 14, s0) 2023-04-24T18:15:01.5186560Z ERROR:common:backend='inductor' raised: 2023-04-24T18:15:01.5187148Z CppCompileError: C++ compile error 2023-04-24T18:15:01.5187414Z 2023-04-24T18:15:01.5187542Z Command: 2023-04-24T18:15:01.5190876Z g++ /tmp/torchinductor_jenkins/za/czarprmgsfhybui3toxkkq3vz6vil3qetzl7exfphbvz2kjjt4tr.cpp -shared -fPIC -Wall -std=c++17 -Wno-unused-variable -I/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/include -I/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/include/TH -I/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/include/THC -I/opt/conda/envs/py_3.10/include/python3.10 -lgomp -O3 -ffast-math -fno-finite-math-only -march=native -fopenmp -D C10_USING_CUSTOM_GENERATED_MACROS -o /tmp/torchinductor_jenkins/za/czarprmgsfhybui3toxkkq3vz6vil3qetzl7exfphbvz2kjjt4tr.so 2023-04-24T18:15:01.5192575Z 2023-04-24T18:15:01.5192698Z Output: 2023-04-24T18:15:01.5193959Z /tmp/torchinductor_jenkins/za/czarprmgsfhybui3toxkkq3vz6vil3qetzl7exfphbvz2kjjt4tr.cpp: In function ‘void kernel(float*, long int*, long int*, long int)’: 2023-04-24T18:15:01.5195123Z /tmp/torchinductor_jenkins/za/czarprmgsfhybui3toxkkq3vz6vil3qetzl7exfphbvz2kjjt4tr.cpp:42:72: error: invalid operands of types ‘long int’ and ‘double’ to binary ‘operator%’ 2023-04-24T18:15:01.5196985Z auto tmp0 = out_ptr1[static_cast<long>((((i0 / 1L) % (std::floor(std::sqrt(ks0))))*(std::floor(std::sqrt(ks0)))) + ((i1 / 1L) % (std::floor(std::sqrt(ks0)))))]; 2023-04-24T18:15:01.5197557Z ~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2023-04-24T18:15:01.5198643Z /tmp/torchinductor_jenkins/za/czarprmgsfhybui3toxkkq3vz6vil3qetzl7exfphbvz2kjjt4tr.cpp:42:147: error: invalid operands of types ‘long int’ and ‘double’ to binary ‘operator%’ 2023-04-24T18:15:01.5202118Z auto tmp0 = out_ptr1[static_cast<long>((((i0 / 1L) % (std::floor(std::sqrt(ks0))))*(std::floor(std::sqrt(ks0)))) + ((i1 / 1L) % (std::floor(std::sqrt(ks0)))))]; ~~~ Next steps * @anijain2305 - Use this opportunity to test minifier with dynamic shapes * @ezyang to fix/assign the owner to fix the issue Completed ------- - [x] Inductor (default) accuracy flakiness for `sebotnet33ts_256` - Fixed by @anijain2305 https://github.com/pytorch/pytorch/pull/99851 - [x] Inductor (default) w/eval fails for `hf_BigBird` - Fixed by @anijain2305 cc @ezyang @soumith @msaroufim @wconstab @ngimel @bdhirsh
1
2,964
98,557
torch.jit.script codegen warning with cuda and vmap
oncall: jit, triaged, module: functorch
### 🐛 Describe the bug I'm getting the following warning which hints at suboptimal speed, and doesn't look like it should happen at any point. ```... site-packages\torch\_functorch\vmap.py:619: UserWarning: FALLBACK path has been taken inside: torch::jit::fuser::cuda::runCudaFusionGroup. This is an indication that codegen Failed for some reason. To debug try disable codegen fallback path via setting the env variable `export PYTORCH_NVFUSER_DISABLE=fallback` (Triggered internally at C:\cb\pytorch_1000000000000\work\third_party\nvfuser\csrc\manager.cpp:340.) batched_outputs = func(*batched_inputs, **kwargs)``` This can be reproduced with the following code: ```import torch from torch import vmap import torch.jit @torch.jit.script def test(params): x0 = params[0] y = torch.arange(0, 64, dtype=torch.float32, device=params.device) return torch.cos(x0)*y params = torch.zeros((200, 4), dtype=torch.float32, device='cuda') torch.vmap(test, chunk_size=100)(params) ``` It seems to occur only when erf is passed an array instead of scalar. I tested this both on pytorch 2.0 and on the nightly build (version is collected below), and on windows and ubuntu (see collected environments) ### Versions [windows_env.txt](https://github.com/pytorch/pytorch/files/11175006/windows_env.txt) [linux_env.txt](https://github.com/pytorch/pytorch/files/11175007/linux_env.txt) cc @EikanWang @jgong5 @wenzhe-nrv @sanchitintel @zou3519 @Chillee @samdow @soumith @kshitij12345 @janeyx99
3
2,965
98,542
Training runs 50% slower when using 2 GPUs comparing to 1
oncall: distributed
### 🐛 Describe the bug Hi, I'm trying to train an InternImage model and I'm running into a weird issue using either Torchrun or torch.distributed.launch I'm using the PyTorch Nvidia docker 22.04 with CUDA 11.6.2 and PyTorch 1.12. When I'm training the model using "python train.py ..." the model runs well, however, when I try to take advantage of my 2 GPUs, I"m running into problems. I can see that both my GPUs are running since their temperature is going up and their memory usage is going up, but while training the model with 1 GPU takes me 2.5 days, using the 2 GPUs, the model ETA is > 3.5 days. I don't care that much about the time, but the main problem is that the training crashes after 1000 iterations, when it's saving the first checkpoint. None of these issues is observed when I use 1 GPU. I'll appreciate your help troubleshooting this problem. I'm using WSL2, with Nvidia docker 22.04, and I have two RTX3090 GPUs This is the error I'm getting: 2023-04-06 21:18:27,943 - mmseg - INFO - Saving checkpoint at 1000 iterations WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 19682 closing signal SIGTERM ERROR:torch.distributed.elastic.multiprocessing.api:failed (exitcode: -9) local_rank: 0 (pid: 19681) of binary: /opt/conda/bin/python Traceback (most recent call last): File "/opt/conda/bin/torchrun", line 33, in <module> sys.exit(load_entry_point('torch==1.12.0a0+bd13bc6', 'console_scripts', 'torchrun')()) File "/opt/conda/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/errors/__init__.py", line 345, in wrapper return f(*args, **kwargs) File "/opt/conda/lib/python3.8/site-packages/torch/distributed/run.py", line 761, in main run(args) File "/opt/conda/lib/python3.8/site-packages/torch/distributed/run.py", line 752, in run elastic_launch( File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 131, in __call__ return launch_agent(self._config, self._entrypoint, list(args)) File "/opt/conda/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 245, in launch_agent raise ChildFailedError( torch.distributed.elastic.multiprocessing.errors.ChildFailedError: ====================================================== train.py FAILED ------------------------------------------------------ Failures: <NO_OTHER_FAILURES> ------------------------------------------------------ Root Cause (first observed failure): [0]: time : 2023-04-06_21:18:35 host : f56b97aeeef1 rank : 0 (local_rank: 0) exitcode : -9 (pid: 19681) error_file: <N/A> traceback : Signal 9 (SIGKILL) received by PID 19681 ====================================================== root@f56b97aeeef1:/workspace/InternImage/segmentation# /opt/conda/lib/python3.8/multiprocessing/resource_tracker.py:216: UserWarning: resource_tracker: There appear to be 38 leaked semaphore objects to clean up at shutdown warnings.warn('resource_tracker: There appear to be %d ' /opt/conda/lib/python3.8/multiprocessing/resource_tracker.py:216: UserWarning: resource_tracker: There appear to be 38 leaked semaphore objects to clean up at shutdown warnings.warn('resource_tracker: There appear to be %d ' ### Versions Collecting environment information... PyTorch version: 1.12.0a0+bd13bc6 Is debug build: False CUDA used to build PyTorch: 11.6 ROCM used to build PyTorch: N/A OS: Ubuntu 20.04.4 LTS (x86_64) GCC version: (Ubuntu 9.4.0-1ubuntu1~20.04.1) 9.4.0 Clang version: Could not collect CMake version: version 3.22.3 Libc version: glibc-2.31 Python version: 3.8.13 | packaged by conda-forge | (default, Mar 25 2022, 06:04:10) [GCC 10.3.0] (64-bit runtime) Python platform: Linux-5.15.90.1-microsoft-standard-WSL2-x86_64-with-glibc2.10 Is CUDA available: True CUDA runtime version: 11.6.124 CUDA_MODULE_LOADING set to: GPU models and configuration: GPU 0: NVIDIA GeForce RTX 3090 GPU 1: NVIDIA GeForce RTX 3090 Nvidia driver version: 531.18 cuDNN version: Probably one of the following: /usr/lib/x86_64-linux-gnu/libcudnn.so.8.4.0 /usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.4.0 /usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.4.0 /usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.4.0 /usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.4.0 /usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.4.0 /usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.4.0 HIP runtime version: N/A MIOpen runtime version: N/A Is XNNPACK available: True CPU: Architecture: x86_64 CPU op-mode(s): 32-bit, 64-bit Byte Order: Little Endian Address sizes: 46 bits physical, 48 bits virtual CPU(s): 20 On-line CPU(s) list: 0-19 Thread(s) per core: 2 Core(s) per socket: 10 Socket(s): 1 Vendor ID: GenuineIntel CPU family: 6 Model: 85 Model name: Intel(R) Core(TM) i9-10900X CPU @ 3.70GHz Stepping: 7 CPU MHz: 3695.996 BogoMIPS: 7391.99 Hypervisor vendor: Microsoft Virtualization type: full L1d cache: 320 KiB L1i cache: 320 KiB L2 cache: 10 MiB L3 cache: 19.3 MiB Vulnerability Itlb multihit: KVM: Mitigation: VMX unsupported Vulnerability L1tf: Not affected Vulnerability Mds: Not affected Vulnerability Meltdown: Not affected Vulnerability Mmio stale data: Vulnerable: Clear CPU buffers attempted, no microcode; SMT Host state unknown Vulnerability Retbleed: Mitigation; Enhanced IBRS Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization Vulnerability Spectre v2: Mitigation; Enhanced IBRS, IBPB conditional, RSB filling, PBRSB-eIBRS SW sequence Vulnerability Srbds: Not affected Vulnerability Tsx async abort: Mitigation; TSX disabled Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc arch_perfmon rep_good nopl xtopology tsc_reliable nonstop_tsc cpuid pni pclmulqdq ssse3 fma cx16 pdcm pcid sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch invpcid_single ssbd ibrs ibpb stibp ibrs_enhanced fsgsbase bmi1 avx2 smep bmi2 erms invpcid avx512f avx512dq rdseed adx smap clflushopt clwb avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves avx512_vnni flush_l1d arch_capabilities Versions of relevant libraries: [pip3] numpy==1.22.3 [pip3] pytorch-quantization==2.1.2 [pip3] torch==1.12.0a0+bd13bc6 [pip3] torch-tensorrt==1.1.0a0 [pip3] torchtext==0.13.0a0 [pip3] torchvision==0.13.0a0 [conda] magma-cuda110 2.5.2 5 local [conda] mkl 2019.5 281 conda-forge [conda] mkl-include 2019.5 281 conda-forge [conda] numpy 1.22.3 py38h1d589f8_2 conda-forge [conda] pytorch-quantization 2.1.2 pypi_0 pypi [conda] torch 1.12.0a0+bd13bc6 pypi_0 pypi [conda] torch-tensorrt 1.1.0a0 pypi_0 pypi [conda] torchtext 0.13.0a0 pypi_0 pypi [conda] torchvision 0.13.0a0 pypi_0 pypi cc @mrshenli @pritamdamania87 @zhaojuanmao @satgera @rohan-varma @gqchen @aazzolini @osalpekar @jiayisuse @H-Huang @kwen2501 @awgu
0
2,966
98,541
Memory corruption using torch.ops.* to access re-registered operator
module: internals, triaged
### 🐛 Describe the bug We register an operator (foo::sum), call torch.ops.foo.sum, de-register it (by deleting the Library object), re-register it, and then call torch.ops.foo.sum again. This causes memory corruption (and leads to segfaults). Repro: Run the following under an asan build. ```python class TestTesting(TestCase): def test_AAA(self) -> None: from torch.library import Library my_lib1 = Library("foo", "DEF") my_lib1.define("sum(Tensor self) -> Tensor") @torch.library.impl(my_lib1, "sum", "CPU") def my_sum(*args, **kwargs): return args[0] x = torch.tensor([1, 2]) self.assertEqual(torch.ops.foo.sum(x), x) import sys assert sys.getrefcount(my_lib1) == 2 del my_lib1 my_lib1 = Library("foo", "DEF") my_lib1.define("sum(Tensor self) -> Tensor") @torch.library.impl(my_lib1, "sum", "CPU") def my_sum(*args, **kwargs): return args[0] x = torch.tensor([1, 2]) self.assertEqual(torch.ops.foo.sum(x), x) ``` Likely related to https://github.com/pytorch/pytorch/issues/98537, unsure if same exact issue. ### Versions master cc @ezyang @bhosmer @smessmer @ljk53 @bdhirsh
0
2,967
98,537
Segfault when using torch.ops.* to access de-registered op
module: crash, triaged, module: dispatch, module: library
### 🐛 Describe the bug We register an operator (foo::sum), call torch.ops.foo.sum, de-register it (by deleting the Library object), and then call torch.ops.foo.sum again. This segfaults. ```python import torch from torch.library import Library my_lib1 = Library("foo", "DEF") my_lib1.define("sum(Tensor self) -> Tensor") @torch.library.impl(my_lib1, "sum", "CPU") def my_sum(*args, **kwargs): return args[0] * 2 x = torch.tensor([1, 2]) torch.ops.foo.sum(x) import sys assert sys.getrefcount(my_lib1) == 2 del my_lib1 x = torch.tensor([1, 2]) torch.ops.foo.sum(x) ``` It should not segfault ### Versions master cc @anjali411
3
2,968
98,533
Dynamo compiled graph gets overwritten by eager in a data dependent branch when False branch is empty
triaged, oncall: pt2
### 🐛 Describe the bug I have encountered an unexpected dynamo capture behavior that is related to a data dependent branch. While the result of the code execution is correct, the way it executes is unexpected -- an already compiled graph got silently dropped and fall back to eager if an empty false branch is run. It is best illustrated using an example. The example is a simplified version of running HuggingFace Whisper model. ``` import torch import torch._dynamo as dynamo global_dict = { 3: 3 } def f(x, y): # begin graph 0 x_len = x.shape[-1] update_idx = global_dict.get(x_len, None) # end graph 0 if update_idx is not None: # begin graph 1 y[update_idx] += 1 # end graph 1 return y class DebugCompiler: def __init__(self): self.count = 0 def __call__(self, gm, example_inputs): id = self.count print(f'compiling graph {id}') gm.graph.print_tabular() def run(*args, **kwargs): print(f'running graph {id}') return gm.forward(*args, **kwargs) self.count += 1 return run f = dynamo.optimize(DebugCompiler(), dynamic=True)(f) def test(x_sizes): y = torch.zeros(5) for size_of_x in x_sizes: x = torch.tensor(range(size_of_x)) y = f(x, y) print(y) # Expected output: # ``` # compiling graph 0 # running graph 0 # compiling graph 1 # running graph 1 # running graph 0 # running graph 1 # ``` test([3, 3]) # Expected output: # ``` # running graph 0 # running graph 1 # running graph 0 # running graph 0 # running graph 1 # running graph 0 # running graph 1 # running graph 0 # running graph 1 # ``` # Actual output: # ``` # running graph 0 # running graph 1 # running graph 0 # running graph 0 # running graph 0 # running graph 0 # ``` test([3, 4, 3, 3, 3]) ``` There are two subgraphs in `f`, `graph0` checks the shape of `x` and compare against a dictionary. `graph1` updates `y`. The execution of `graph1` depends on the output of the check of `graph1`. The unexpected behavior is that once the "False" branch in `f` is triggered, `graph1` is never used. The code for updating `y` silently runs in eager mode. This is proven in the 2nd test case where for the first `3`, graph 1 is used, then the input `4` triggers the False branch, and afterwards, even running with `3` does not trigger the compiled graph anymore. The output `y` for each run is still correct however, which means, the code for updating y is running in eager mode instead of using `graph1`. On the other hand if there is tensor computation in the False branch this problem does not happen. ``` def f(x, y): # begin graph 0 x_len = x.shape[-1] update_idx = global_dict.get(x_len, None) # end graph 0 if update_idx is not None: # begin graph 1 y[update_idx] += 1 # end graph 1 else: # begin graph 2 y[update_idx] -= 1 # end graph 2 return y ``` ### Versions torch==2.1.0.dev20230331+cu117 cc @ezyang @soumith @msaroufim @wconstab @ngimel @bdhirsh
8
2,969
98,515
torch.cond should work with expressions involving SymInt
triaged, module: functorch
### 🐛 Describe the bug If x.size(0) is an unbacked symint (because x came from nonzero), it is useful to say `torch.cond(x.size(0) > 5, do_true, do_false)`. I believe @tugsbayasgalan will indirectly finish this off with https://github.com/pytorch/pytorch/pull/98453 because with symbool, we can then do `scalar_to_tensor(x.size(0) > 5)` and then pass that as a tensor argument to torch.cond. ### Versions master cc @zou3519 @Chillee @samdow @soumith @kshitij12345 @janeyx99
1
2,970
98,503
Power VSX vectorization support disabled
module: build, triaged, module: regression, module: POWER
### 🐛 Describe the bug During cmake cleanup VSX vectorization support for power was disabled as well: https://github.com/pytorch/pytorch/commit/847dbb8684f9d9bbf59cae629d07bff3ede0c4a2#diff-12e8125164bbfc7556b1781a8ed516e333cc0bf058acb7197f7415be44606c72L1729 ZVECTOR support for s390x was removed as well, but it's not part of this issue, it's already being worked on separately. For ZVECTOR it takes more than just 1 cmake line to properly restore vectorization support, thus I assume it might be also the case for VSX. ### Versions pytorch master branch cc @malfet @seemethere
2
2,971
98,499
`torch.nn.utils.rnn.unpad_sequence` modifies arguments in-place
module: docs, module: rnn, triaged
### 🐛 Describe the bug `torch.nn.utils.rnn.unpad_sequence` has the unexpected side-effect of transposing the input-tensor in-place. This should be either documented or, even better, fixed so that the function call does not modify input data. ```python import torch x = torch.randn(4,2) # x.shape == (4, 2) torch.nn.utils.rnn.unpad_sequence(x, lengths=torch.tensor([4, 4])) # x.shape == (2, 4) print(x.shape) ``` Yields `torch.Size([2, 4])` The problem is the `x.transpose_(0, 1)` when `batch_first=False` [in this line of code](https://github.com/pytorch/pytorch/blob/master/torch/nn/utils/rnn.py#L440). ### Versions PyTorch version: 1.13.1+cu117 Is debug build: False CUDA used to build PyTorch: 11.7 ROCM used to build PyTorch: N/A OS: Ubuntu 20.04.6 LTS (x86_64) GCC version: (Ubuntu 9.4.0-1ubuntu1~20.04.1) 9.4.0 Clang version: 10.0.0-4ubuntu1 CMake version: Could not collect Libc version: glibc-2.31 Python version: 3.9.16 (main, Mar 8 2023, 14:00:05) [GCC 11.2.0] (64-bit runtime) Python platform: Linux-5.15.0-69-generic-x86_64-with-glibc2.31 Is CUDA available: False CUDA runtime version: No CUDA CUDA_MODULE_LOADING set to: N/A GPU models and configuration: No CUDA Nvidia driver version: No CUDA cuDNN version: No CUDA HIP runtime version: N/A MIOpen runtime version: N/A Is XNNPACK available: True CPU: Architecture: x86_64 CPU op-mode(s): 32-bit, 64-bit Byte Order: Little Endian Address sizes: 39 bits physical, 48 bits virtual CPU(s): 8 On-line CPU(s) list: 0-7 Thread(s) per core: 2 Core(s) per socket: 4 Socket(s): 1 NUMA node(s): 1 Vendor ID: GenuineIntel CPU family: 6 Model: 142 Model name: Intel(R) Core(TM) i5-8350U CPU @ 1.70GHz Stepping: 10 CPU MHz: 1900.000 CPU max MHz: 3600.0000 CPU min MHz: 400.0000 BogoMIPS: 3799.90 Virtualization: VT-x L1d cache: 128 KiB L1i cache: 128 KiB L2 cache: 1 MiB L3 cache: 6 MiB NUMA node0 CPU(s): 0-7 Vulnerability Itlb multihit: KVM: Mitigation: VMX disabled Vulnerability L1tf: Mitigation; PTE Inversion; VMX conditional cache flushes, SMT vulnerable Vulnerability Mds: Mitigation; Clear CPU buffers; SMT vulnerable Vulnerability Meltdown: Mitigation; PTI Vulnerability Mmio stale data: Mitigation; Clear CPU buffers; SMT vulnerable Vulnerability Retbleed: Mitigation; IBRS Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization Vulnerability Spectre v2: Mitigation; IBRS, IBPB conditional, RSB filling, PBRSB-eIBRS Not affected Vulnerability Srbds: Mitigation; Microcode Vulnerability Tsx async abort: Mitigation; TSX disabled Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb invpcid_single pti ssbd ibrs ibpb stibp tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust sgx bmi1 avx2 smep bmi2 erms invpcid mpx rdseed adx smap clflushopt intel_pt xsaveopt xsavec xgetbv1 xsaves dtherm ida arat pln pts hwp hwp_notify hwp_act_window hwp_epp md_clear flush_l1d arch_capabilities Versions of relevant libraries: [pip3] mypy==0.990 [pip3] mypy-extensions==1.0.0 [pip3] numpy==1.23.5 [pip3] torch==1.13.1 [conda] numpy 1.23.5 pypi_0 pypi [conda] torch 1.13.1 pypi_0 pypi cc @svekars @carljparker @zou3519
0
2,972
98,498
Higher order derivatives not working when setting compute device to `torch.device("mps")`
module: autograd, triaged, module: mps
### 🐛 Describe the bug I have a code example that performs fast updates, similar to what we have in MAML, to a perform inner updates and then computes the meta-loss to backpropagate through the optimization trajectory. When computing gradients in the fast updates, we can set `create_graph=True` to enable second-order derivatives when call backward on the meta-loss. When using `torch.device("mps")`, it throws an error that `derivative for aten:linear is not implemented`. It works fine when you set `create_graph=False` in the inner updates but then it wouldn't compute the higher order derivatives. I don't get the error when using `torch.device("cpu")` and `torch.device("cuda")`. Here is the code to reproduce the error: ```python device = torch.device("mps") model = nn.Sequential(nn.Linear(10, 5), nn.ReLU(), nn.Linear(5, 1)) model.to(device) # Initial fast parameters fast_params_0 = {n: deepcopy(p) for (n, p) in model.named_parameters()} # First inner update x = torch.randn(10, 10, device=device) y = torch.randn(10, 1, device=device) logits_0 = torch.func.functional_call(model, fast_params_0, x) loss = nn.MSELoss()(logits_0, y) grads_0 = torch.autograd.grad(loss, fast_params_0.values(), create_graph=True, retain_graph=True) # Compute fast parameters after the first inner update fast_params_1 = {n: p - 0.1 * g for ((n, p), g) in zip(fast_params_0.items(), list(grads_0))} # Compute meta-loss and backprop through the optimization trajectory x = torch.randn(10, 10, device=device) y = torch.randn(10, 1, device=device) logits_1 = torch.func.functional_call(model, fast_params_1, x) met_loss = nn.MSELoss()(logits_1, y) met_loss.backward() ``` And, the error I get: ``` RuntimeError: derivative for aten::linear_backward is not implemented ``` *I get the same error for any layer type. ### Versions ``` [pip3] numpy==1.23.5 [pip3] pytorchcv==0.0.67 [pip3] torch==2.0.0 [pip3] torchaudio==2.0.0 [pip3] torchmetrics==0.11.4 [pip3] torchvision==0.15.0 [conda] numpy 1.23.5 py39h1398885_0 [conda] numpy-base 1.23.5 py39h90707a3_0 [conda] pytorch 2.0.0 py3.9_0 pytorch [conda] pytorchcv 0.0.67 pypi_0 pypi [conda] torchaudio 2.0.0 py39_cpu pytorch [conda] torchmetrics 0.11.4 pypi_0 pypi [conda] torchvision 0.15.0 py39_cpu pytorch ``` cc @ezyang @albanD @zou3519 @gqchen @pearu @nikitaved @soulitzer @Lezcano @Varal7 @kulinseth @malfet @DenisVieriu97 @razarmehr @abhudev
9
2,973
98,497
[onnx]Unsupported: ONNX export of convolution for kernel of unknown shape
module: onnx, triaged
### 🐛 Describe the bug I encounter this error when converting a pytorch model to onnx. I am trying to convolve with specific weights and in groups. I narrowed down the piece of code creating the problem shown below. ```python import torch class Filter(nn.Module): def __init__(self): super().__init__() self.resample_filter = torch.rand(4,4) def forward(self, x): x = torch.nn.functional.pad(x, [1, 1, 1, 1]) # If this line is commented out, it works. weight = self.resample_filter[None, None].repeat([x.shape[1] , 1] + [1] * self.resample_filter.ndim) x = torch.nn.functional.conv2d(input=x, padding=1, weight=weight, groups=x.shape[1] ) return x x = torch.rand((1, 3, 256, 256)) f = Filter() y = f(x) torch.onnx.export(f, x, "test-filter.onnx", opset_version=15) ``` Observed results - error message: ``` Traceback (most recent call last): File "/home/soham/miniconda3/envs/rotation3d/lib/python3.10/runpy.py", line 196, in _run_module_as_main return _run_code(code, main_globals, None, File "/home/soham/miniconda3/envs/rotation3d/lib/python3.10/runpy.py", line 86, in _run_code exec(code, run_globals) File "/home/soham/.vscode/extensions/ms-python.python-2023.6.0/pythonFiles/lib/python/debugpy/adapter/../../debugpy/launcher/../../debugpy/__main__.py", line 39, in <module> cli.main() File "/home/soham/.vscode/extensions/ms-python.python-2023.6.0/pythonFiles/lib/python/debugpy/adapter/../../debugpy/launcher/../../debugpy/../debugpy/server/cli.py", line 430, in main run() File "/home/soham/.vscode/extensions/ms-python.python-2023.6.0/pythonFiles/lib/python/debugpy/adapter/../../debugpy/launcher/../../debugpy/../debugpy/server/cli.py", line 284, in run_file runpy.run_path(target, run_name="__main__") File "/home/soham/.vscode/extensions/ms-python.python-2023.6.0/pythonFiles/lib/python/debugpy/_vendored/pydevd/_pydevd_bundle/pydevd_runpy.py", line 321, in run_path return _run_module_code(code, init_globals, run_name, File "/home/soham/.vscode/extensions/ms-python.python-2023.6.0/pythonFiles/lib/python/debugpy/_vendored/pydevd/_pydevd_bundle/pydevd_runpy.py", line 135, in _run_module_code _run_code(code, mod_globals, init_globals, File "/home/soham/.vscode/extensions/ms-python.python-2023.6.0/pythonFiles/lib/python/debugpy/_vendored/pydevd/_pydevd_bundle/pydevd_runpy.py", line 124, in _run_code exec(code, run_globals) File "/home/soham/casablanca/rotation3d/test_modules.py", line 173, in <module> torch.onnx.export(f, x, "test-filter.onnx", opset_version=15) File "/home/soham/miniconda3/envs/rotation3d/lib/python3.10/site-packages/torch/onnx/__init__.py", line 305, in export return utils.export(model, args, f, export_params, verbose, training, File "/home/soham/miniconda3/envs/rotation3d/lib/python3.10/site-packages/torch/onnx/utils.py", line 118, in export _export(model, args, f, export_params, verbose, training, input_names, output_names, File "/home/soham/miniconda3/envs/rotation3d/lib/python3.10/site-packages/torch/onnx/utils.py", line 719, in _export _model_to_graph(model, args, verbose, input_names, File "/home/soham/miniconda3/envs/rotation3d/lib/python3.10/site-packages/torch/onnx/utils.py", line 503, in _model_to_graph graph = _optimize_graph(graph, operator_export_type, File "/home/soham/miniconda3/envs/rotation3d/lib/python3.10/site-packages/torch/onnx/utils.py", line 232, in _optimize_graph graph = torch._C._jit_pass_onnx(graph, operator_export_type) File "/home/soham/miniconda3/envs/rotation3d/lib/python3.10/site-packages/torch/onnx/__init__.py", line 354, in _run_symbolic_function return utils._run_symbolic_function(*args, **kwargs) File "/home/soham/miniconda3/envs/rotation3d/lib/python3.10/site-packages/torch/onnx/utils.py", line 1061, in _run_symbolic_function return symbolic_fn(g, *inputs, **attrs) File "/home/soham/miniconda3/envs/rotation3d/lib/python3.10/site-packages/torch/onnx/symbolic_helper.py", line 172, in wrapper return fn(g, *args, **kwargs) File "/home/soham/miniconda3/envs/rotation3d/lib/python3.10/site-packages/torch/onnx/symbolic_opset9.py", line 1301, in _convolution raise RuntimeError("Unsupported: ONNX export of convolution for kernel " RuntimeError: Unsupported: ONNX export of convolution for kernel of unknown shape. ``` ### Versions PyTorch version: 1.11.0 Is debug build: False CUDA used to build PyTorch: 11.3 ROCM used to build PyTorch: N/A OS: Ubuntu 22.04.2 LTS (x86_64) GCC version: (Ubuntu 11.3.0-1ubuntu1~22.04) 11.3.0 Clang version: Could not collect CMake version: Could not collect Libc version: glibc-2.35 Python version: 3.10.9 (main, Jan 11 2023, 15:21:40) [GCC 11.2.0] (64-bit runtime) Python platform: Linux-5.19.0-38-generic-x86_64-with-glibc2.35 Is CUDA available: True CUDA runtime version: Could not collect CUDA_MODULE_LOADING set to: GPU models and configuration: GPU 0: NVIDIA GeForce RTX 3080 Ti Laptop GPU Nvidia driver version: 515.65.01 cuDNN version: Could not collect HIP runtime version: N/A MIOpen runtime version: N/A Is XNNPACK available: True CPU: Architecture: x86_64 CPU op-mode(s): 32-bit, 64-bit Address sizes: 39 bits physical, 48 bits virtual Byte Order: Little Endian CPU(s): 20 On-line CPU(s) list: 0-19 Vendor ID: GenuineIntel Model name: 12th Gen Intel(R) Core(TM) i7-12700H CPU family: 6 Model: 154 Thread(s) per core: 2 Core(s) per socket: 14 Socket(s): 1 Stepping: 3 CPU max MHz: 4700,0000 CPU min MHz: 400,0000 BogoMIPS: 5376.00 Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf tsc_known_freq pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb invpcid_single ssbd ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid rdseed adx smap clflushopt clwb intel_pt sha_ni xsaveopt xsavec xgetbv1 xsaves split_lock_detect avx_vnni dtherm ida arat pln pts hwp hwp_notify hwp_act_window hwp_epp hwp_pkg_req hfi umip pku ospke waitpkg gfni vaes vpclmulqdq rdpid movdiri movdir64b fsrm md_clear serialize arch_lbr ibt flush_l1d arch_capabilities Virtualization: VT-x L1d cache: 544 KiB (14 instances) L1i cache: 704 KiB (14 instances) L2 cache: 11,5 MiB (8 instances) L3 cache: 24 MiB (1 instance) NUMA node(s): 1 NUMA node0 CPU(s): 0-19 Vulnerability Itlb multihit: Not affected Vulnerability L1tf: Not affected Vulnerability Mds: Not affected Vulnerability Meltdown: Not affected Vulnerability Mmio stale data: Not affected Vulnerability Retbleed: Not affected Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization Vulnerability Spectre v2: Mitigation; Enhanced IBRS, IBPB conditional, RSB filling, PBRSB-eIBRS SW sequence Vulnerability Srbds: Not affected Vulnerability Tsx async abort: Not affected Versions of relevant libraries: [pip3] numpy==1.23.5 [pip3] torch==1.11.0 [pip3] torch-fidelity==0.3.0 [pip3] torchmetrics==0.11.3 [pip3] torchvision==0.12.0 [conda] blas 1.0 mkl [conda] cudatoolkit 11.3.1 h2bc3f7f_2 [conda] ffmpeg 4.3 hf484d3e_0 pytorch [conda] mkl 2021.4.0 h06a4308_640 [conda] mkl-service 2.4.0 py310h7f8727e_0 [conda] mkl_fft 1.3.1 py310hd6ae3a3_0 [conda] mkl_random 1.2.2 py310h00e6091_0 [conda] numpy 1.23.5 py310hd5efca6_0 [conda] numpy-base 1.23.5 py310h8e6c178_0 [conda] pytorch 1.11.0 py3.10_cuda11.3_cudnn8.2.0_0 pytorch [conda] pytorch-mutex 1.0 cuda pytorch [conda] torch-fidelity 0.3.0 pypi_0 pypi [conda] torchmetrics 0.11.3 pypi_0 pypi [conda] torchvision 0.12.0 py310_cu113 pytorch
6
2,974
98,495
Strided to batch BSR/BSC conversion fails when the number of zeros per block varies while the number of blocks per patch is constant
module: sparse, triaged
## Issue description As in the title. ## Code example ```python >>> torch.tensor([[[1, 2]], [[3, 4]]]).to_sparse_bsr((1, 1)) tensor(crow_indices=tensor([[0, 2], [0, 2]]), col_indices=tensor([[0, 1], [0, 1]]), values=tensor([[[[1]], [[2]]], [[[3]], [[4]]]]), size=(2, 1, 2), nnz=2, layout=torch.sparse_bsr) >>> torch.tensor([[[1, 2]], [[0, 4]]]).to_sparse_bsr((1, 1)) Traceback (most recent call last): File "<stdin>", line 1, in <module> RuntimeError: Expect the same number of specified elements per batch. >>> torch.tensor([[[1, 0]], [[0, 4]]]).to_sparse_bsr((1, 1)) tensor(crow_indices=tensor([[0, 1], [0, 1]]), col_indices=tensor([[0], [1]]), values=tensor([[[[1]]], [[[4]]]]), size=(2, 1, 2), nnz=1, layout=torch.sparse_bsr) ``` Notice that in the failing conversion example, the number of zeros in the first block is 0 and in the second block it is 1. Apparently, the check logic in https://github.com/pytorch/pytorch/blob/ccc27bc361f2fa5043534b8f898922ffd0ca9340/aten/src/ATen/native/TensorConversions.cpp#L95-L98 is flawed for BSR and BSC conversion cases. ## System Info - PyTorch version: master cc @alexsamardzic @nikitaved @cpuhrsch @amjames @bhosmer
3
2,975
98,487
torch.fx.GraphModule inside custom backend has `training` attribute always set to `True` regardless of the user settings
triaged, oncall: pt2
### 🐛 Describe the bug Calling `eval()` and `train()` on either the original `torch.nn.Module` or `OptimizedModule` returned by `torch.compile` has no effect on the `training` attribute of `torch.fx.GraphModule` that is passed to custom backend function. ### Error logs _No response_ ### Minified repro ``` import torch def my_custom_backend(gm, example_inputs): print(gm.training) return gm.forward class MockModule(torch.nn.Module): def __init__(self): super().__init__() def forward(self, x): if self.training: return x + 2 else: return x + 3 mod = MockModule() optimized_mod = torch.compile(mod, backend=my_custom_backend) mod.eval() optimized_mod.eval() print(optimized_mod(torch.zeros(10))) print(optimized_mod(torch.zeros(10))) mod.train() optimized_mod.train() print(optimized_mod(torch.zeros(10))) print(optimized_mod(torch.zeros(10))) mod.eval() optimized_mod.eval() print(optimized_mod(torch.zeros(10))) print(optimized_mod(torch.zeros(10))) ``` Result: ``` True tensor([3., 3., 3., 3., 3., 3., 3., 3., 3., 3.]) tensor([3., 3., 3., 3., 3., 3., 3., 3., 3., 3.]) True tensor([2., 2., 2., 2., 2., 2., 2., 2., 2., 2.]) tensor([2., 2., 2., 2., 2., 2., 2., 2., 2., 2.]) tensor([3., 3., 3., 3., 3., 3., 3., 3., 3., 3.]) tensor([3., 3., 3., 3., 3., 3., 3., 3., 3., 3.]) ``` ### Versions Torch 2.0 cc @ezyang @soumith @msaroufim @wconstab @ngimel @bdhirsh
0
2,976
98,486
Options are not forwarded to the custom backend
triaged, oncall: pt2
### 🐛 Describe the bug Based on the description inside the [torch. compile](https://pytorch.org/docs/stable/generated/torch.compile.html) options are passed to the backend. Unfortunately, they are only passed to the inductor backend. Currently, the backend function has the following contract `(gm: torch.fx.GraphModule, example_inputs: List[torch.Tensor]) -> Callable` and there is no way to get options from the registered backend function. Am I missing something? Is there a way to get options from the custom backend function? If not, is it possible to add that possibility or update the description in the documentation? ### Error logs _No response_ ### Minified repro #torch version 2.0 ### Versions #torch version 2.0 cc @ezyang @soumith @msaroufim @wconstab @ngimel @bdhirsh
0
2,977
98,484
Improvements to FSDP debugability
oncall: distributed, triaged, module: fsdp
### 🚀 The feature, motivation and pitch There are a couple pain points which make FSDP harder to debug: - In some cases, post backward hooks don't fire resulting in layers not getting gradients, resulting in training convergence issues. We could add logging in a debug mode for this, but need to think of a more comprehensive solution to identify this issue. - On lazy init, we should iterate through the original params and check that shared params are in the same FSDP instance. ### Alternatives _No response_ ### Additional context _No response_ cc @mrshenli @pritamdamania87 @zhaojuanmao @satgera @gqchen @aazzolini @osalpekar @jiayisuse @H-Huang @kwen2501 @awgu
0
2,978
98,481
Bring CudaPluggableAllocator to feature parity with the Native Allocator
module: internals, module: cuda, triaged, module: CUDACachingAllocator
### 🚀 The feature, motivation and pitch I have tried a few times to add Unified Memory support to Pytorch, so as to leverage as many resources of my computer as possible while running training and inference alike, but to no avail; so I abandoned my fork somewhat. After I hear about the pluggable Allocator mechanism, I tried it and [RAPIDS rmm](https://github.com/AUTOMATIC1111/stable-diffusion-webui) with [stable diffusion WebUI](https://github.com/AUTOMATIC1111/stable-diffusion-webui) , but it gave errors such as "CudaPluggableAllocator does not yet support CacheInfo", and thus prohibiting my ability to run operations requiring more than 5.5GB of memory efficiently; thus, I would like to request that CudaPluggableAllocator get all features which the native allocator has. ### Alternatives Implement Cuda and ROCm Unified Memory support, and provide users an easy way to use it, like how one can switch between the native allocator and Cudamallocasync with an environment variable. ### Additional context _No response_ cc @ezyang @bhosmer @smessmer @ljk53 @bdhirsh @ngimel
6
2,979
98,467
tacotron2 times out
triaged, oncall: pt2, module: inductor
Repro: ``` python benchmarks/dynamo/torchbench.py --accuracy --inference --amp --backend inductor --disable-cudagraphs --device cuda --only tacotron2 ``` ctrl+c gives this stack information, which looks like a problem in the fuser heuristic, ``` File "/fsx/users/binbao/conda/envs/release/lib/python3.10/site-packages/torch/_inductor/scheduler.py", line 636, in __init__ self.fuse_nodes() File "/fsx/users/binbao/conda/envs/release/lib/python3.10/site-packages/torch/_inductor/scheduler.py", line 817, in fuse_nodes self.fuse_nodes_once() File "/fsx/users/binbao/conda/envs/release/lib/python3.10/site-packages/torch/_inductor/scheduler.py", line 833, in fuse_nodes_once if self.can_fuse(node1, node2) and not self.will_fusion_create_cycle( File "/fsx/users/binbao/conda/envs/release/lib/python3.10/site-packages/torch/_inductor/scheduler.py", line 907, in will_fusion_create_cycle return any(check(self.name_to_fused_node[n]) for n in combined_predecessors) File "/fsx/users/binbao/conda/envs/release/lib/python3.10/site-packages/torch/_inductor/scheduler.py", line 907, in <genexpr> return any(check(self.name_to_fused_node[n]) for n in combined_predecessors) File "/fsx/users/binbao/conda/envs/release/lib/python3.10/site-packages/torch/_inductor/scheduler.py", line 896, in check return bool(combined_names & node.recursive_predecessors) or any( KeyboardInterrupt ``` cc @ezyang @soumith @msaroufim @wconstab @ngimel @bdhirsh @voznesenskym @penguinwu @anijain2305 @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @Xia-Weiwen @wenzhe-nrv @jiayisunx @peterbell10
1
2,980
98,465
Need better error message when a merge cancelled because of timeout
module: ci, triaged
As suggested by @malfet, the error message needs improvements. See this as an example: https://github.com/pytorch/pytorch/pull/98201#issuecomment-1497216298 The merge was cancelled because MacOS jobs time-outed - see https://github.com/pytorch/pytorch/issues/98362 The error message on the PR is just "The merge job was canceled. If you believe this is a mistake,then you can re trigger it through pytorch-bot", which is not very informative. Should be something like this instead: {timeout//60} hours have passed, but {len(jobs_pending} job(s) are still running, first few of them are ...) cc @seemethere @malfet @pytorch/pytorch-dev-infra
0
2,981
98,459
Fail to pass test HAVE_XXX_REGEX while building pytorch
module: build, triaged
### 🐛 Describe the bug Dear PyTorch team, # Subject I am not able to build from the `master` branch due to regex test has failed ``` -- Performing Test HAVE_STD_REGEX -- Performing Test HAVE_STD_REGEX -- Performing Test HAVE_STD_REGEX -- compiled but failed to run -- Performing Test HAVE_GNU_POSIX_REGEX -- Performing Test HAVE_GNU_POSIX_REGEX -- Performing Test HAVE_GNU_POSIX_REGEX -- failed to compile -- Performing Test HAVE_POSIX_REGEX -- Performing Test HAVE_POSIX_REGEX -- Performing Test HAVE_POSIX_REGEX -- compiled but failed to run CMake Error at third_party/benchmark/CMakeLists.txt:304 (message): Failed to determine the source files for the regular expression backend ``` # Environment OS: Linux Fedora 37 CC: clang CXX: clang++ LLVM: version 12 # Understanding Those test `HAVE_XXXX_REGEX` are performed for several third parties: - benchmark - onnx - protobuf - QNNPACK - XNNPACK Those `HAVE_XXXX_REGEX` are defined by the cmake function `cxx_feature_check` in `pytorch/third_party/benchmark/cmake/CXXFeatureCheck.cmake` line ~ 20 source code of the function: [here](https://github.com/google/benchmark/blob/main/cmake/CXXFeatureCheck.cmake) To my understanding, the function will try to i) compile ii) run the code from those files ``` pytorch/third_party/benchmark/cmake/gnu_posix_regex.cpp pytorch/third_party/benchmark/cmake/posix_regex.cpp pytorch/third_party/benchmark/cmake/std_regex.cpp pytorch/third_party/onnx/third_party/benchmark/cmake/gnu_posix_regex.cpp pytorch/third_party/onnx/third_party/benchmark/cmake/posix_regex.cpp pytorch/third_party/onnx/third_party/benchmark/cmake/std_regex.cpp pytorch/third_party/onnx-tensorrt/third_party/onnx/third_party/benchmark/cmake/gnu_posix_regex.cpp pytorch/third_party/onnx-tensorrt/third_party/onnx/third_party/benchmark/cmake/posix_regex.cpp pytorch/third_party/onnx-tensorrt/third_party/onnx/third_party/benchmark/cmake/std_regex.cpp pytorch/third_party/protobuf/third_party/benchmark/cmake/gnu_posix_regex.cpp pytorch/third_party/protobuf/third_party/benchmark/cmake/posix_regex.cpp pytorch/third_party/protobuf/third_party/benchmark/cmake/std_regex.cpp ``` So I tried to build those files with clang++ and run them ```bash $ for f in $(find pytorch/ -name '*regex*.cpp');do echo "Test ==> $(grep -Po '(?<=pytorch/third_party/)[[:alnum:]\-]+' <<< $f) ---- $(basename $f)"; clang++ $f; ./a.out; echo $?; rm -f ./a.out; done Test ==> benchmark ---- gnu_posix_regex.cpp pytorch/third_party/benchmark/cmake/gnu_posix_regex.cpp:1:10: fatal error: 'gnuregex.h' file not found #include <gnuregex.h> ^~~~~~~~~~~~ 1 error generated. bash: ./a.out: Aucun fichier ou dossier de ce type 127 Test ==> benchmark ---- posix_regex.cpp 0 Test ==> benchmark ---- std_regex.cpp 0 Test ==> onnx ---- gnu_posix_regex.cpp pytorch/third_party/onnx/third_party/benchmark/cmake/gnu_posix_regex.cpp:1:10: fatal error: 'gnuregex.h' file not found #include <gnuregex.h> ^~~~~~~~~~~~ 1 error generated. bash: ./a.out: Aucun fichier ou dossier de ce type 127 Test ==> onnx ---- posix_regex.cpp 0 Test ==> onnx ---- std_regex.cpp 0 Test ==> onnx-tensorrt ---- gnu_posix_regex.cpp pytorch/third_party/onnx-tensorrt/third_party/onnx/third_party/benchmark/cmake/gnu_posix_regex.cpp:1:10: fatal error: 'gnuregex.h' file not found #include <gnuregex.h> ^~~~~~~~~~~~ 1 error generated. bash: ./a.out: Aucun fichier ou dossier de ce type 127 Test ==> onnx-tensorrt ---- posix_regex.cpp 0 Test ==> onnx-tensorrt ---- std_regex.cpp 0 Test ==> protobuf ---- gnu_posix_regex.cpp pytorch/third_party/protobuf/third_party/benchmark/cmake/gnu_posix_regex.cpp:1:10: fatal error: 'gnuregex.h' file not found #include <gnuregex.h> ^~~~~~~~~~~~ 1 error generated. bash: ./a.out: Aucun fichier ou dossier de ce type 127 Test ==> protobuf ---- posix_regex.cpp 0 Test ==> protobuf ---- std_regex.cpp 0 ``` So this homemade test highlight that `posix_regex` and `std_regex` exit with success. Why Cmake report that do not run ? # how to reproduce the error ``` $ podman run -it --rm --name f37 fedora:37 bash # dnf install -y rocm-comgr-devel rocm-device-libs boost-devel cmake blis-devel libstdc++-devel python3-setuptools python3-pyyaml ninja-build git # git clone https://github.com/pytorch/pytorch # cd pytorch # export CC="clang" # export CXX="clang++" # export LDSHARED="clang --shared" # export LDFLAGS="-stdlib=libstdc++" # export CFLAGS="-fsanitize=address -fno-sanitize-recover=all -shared-libasan -pthread" # export CXX_FLAGS="-shared-libasan -pthread" # export CPLUS_INCLUDE_PATH="/usr/include/c++/12/:${CPLUS_INCLUDE_PATH}" # export ASAN_SYMBOLIZER_PATH=/usr/bin/llvm-symbolizer # USE_CUDA=0 USE_OPENMP=0 BUILD_CAFFE2_OPS=0 USE_DISTRIBUTED=0 DEBUG=1 \ python setup.py develop ... -- Performing Test HAVE_STD_REGEX -- Performing Test HAVE_STD_REGEX -- Performing Test HAVE_STD_REGEX -- compiled but failed to run -- Performing Test HAVE_GNU_POSIX_REGEX -- Performing Test HAVE_GNU_POSIX_REGEX -- Performing Test HAVE_GNU_POSIX_REGEX -- failed to compile -- Performing Test HAVE_POSIX_REGEX -- Performing Test HAVE_POSIX_REGEX -- Performing Test HAVE_POSIX_REGEX -- compiled but failed to run CMake Error at third_party/benchmark/CMakeLists.txt:304 (message): Failed to determine the source files for the regular expression backend ``` Thanks for your help ### Versions ``` $ python collect_env.py Collecting environment information... PyTorch version: N/A Is debug build: N/A CUDA used to build PyTorch: N/A ROCM used to build PyTorch: N/A OS: Fedora Linux 37 (Workstation Edition) (x86_64) GCC version: (GCC) 12.2.1 20221121 (Red Hat 12.2.1-4) Clang version: 15.0.7 (Fedora 15.0.7-1.fc37) CMake version: version 3.26.1 Libc version: glibc-2.36 Python version: 3.11.2 (main, Feb 8 2023, 00:00:00) [GCC 12.2.1 20221121 (Red Hat 12.2.1-4)] (64-bit runtime) Python platform: Linux-6.1.14-200.fc37.x86_64-x86_64-with-glibc2.36 Is CUDA available: N/A CUDA runtime version: Could not collect CUDA_MODULE_LOADING set to: N/A GPU models and configuration: Could not collect Nvidia driver version: Could not collect cuDNN version: Could not collect HIP runtime version: N/A MIOpen runtime version: N/A Is XNNPACK available: N/A CPU: Architecture : x86_64 Mode(s) opératoire(s) des processeurs : 32-bit, 64-bit Tailles des adresses: 48 bits physical, 48 bits virtual Boutisme : Little Endian Processeur(s) : 12 Liste de processeur(s) en ligne : 0-11 Identifiant constructeur : AuthenticAMD Nom de modèle : AMD Ryzen 5 5600X 6-Core Processor Famille de processeur : 25 Modèle : 33 Thread(s) par cœur : 2 Cœur(s) par socket : 6 Socket(s) : 1 Révision : 0 Accroissement de fréquence : activé multiplication des MHz du/des CPU(s) : 80% Vitesse maximale du processeur en MHz : 4650,2920 Vitesse minimale du processeur en MHz : 2200,0000 BogoMIPS : 7399,98 Drapeaux : fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl nonstop_tsc cpuid extd_apicid aperfmperf rapl pni pclmulqdq monitor ssse3 fma cx16 sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw ibs skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb cat_l3 cdp_l3 hw_pstate ssbd mba ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local clzero irperf xsaveerptr rdpru wbnoinvd arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif v_spec_ctrl umip pku ospke vaes vpclmulqdq rdpid overflow_recov succor smca fsrm Virtualisation : AMD-V Cache L1d : 192 KiB (6 instances) Cache L1i : 192 KiB (6 instances) Cache L2 : 3 MiB (6 instances) Cache L3 : 32 MiB (1 instance) Nœud(s) NUMA : 1 Nœud NUMA 0 de processeur(s) : 0-11 Vulnérabilité Itlb multihit : Not affected Vulnérabilité L1tf : Not affected Vulnérabilité Mds : Not affected Vulnérabilité Meltdown : Not affected Vulnérabilité Mmio stale data : Not affected Vulnérabilité Retbleed : Not affected Vulnérabilité Spec store bypass : Mitigation; Speculative Store Bypass disabled via prctl Vulnérabilité Spectre v1 : Mitigation; usercopy/swapgs barriers and __user pointer sanitization Vulnérabilité Spectre v2 : Mitigation; Retpolines, IBPB conditional, IBRS_FW, STIBP always-on, RSB filling, PBRSB-eIBRS Not affected Vulnérabilité Srbds : Not affected Vulnérabilité Tsx async abort : Not affected Versions of relevant libraries: [pip3] numpy==1.24.2 [conda] Could not collect ``` cc @malfet @seemethere
1
2,982
98,456
README could use link to governance
high priority, module: docs, triaged
### 📚 The doc issue README.md does not have link to governance https://github.com/pytorch/pytorch/blob/master/docs/source/community/governance.rst ~~indicate different governance structure, different maintainers, etc..~~ ~~### Suggest a potential alternative/fix~~ ### Suggest updating the readme to point to the governance documentation ~~.. modify the current readme contents to be a thank you for Emeritus efforts section.~~ updated issue to reflect the readme maintainer list is now in sync .. and could benefit from a link to governance structure docs cc @ezyang @gchanan @zou3519 @svekars @carljparker
3
2,983
98,441
Torch Compile is slightly slower than eager mode.
triaged, oncall: pt2
### 🐛 Describe the bug When running some models on Torch, I have noticed that the torch.compile mode is slightly slower than the eager mode. It may or may not be related to this issue : https://github.com/pytorch/pytorch/issues/98102 one example is : microsoft-deberta-base To reproduce: go to this folder transformers/examples/pytorch/language-modeling/ and run: eager mode: `python run_mlm.py --model_name_or_path microsoft/deberta-v3-base --dataset_name wikitext --dataset_config_name wikitext-2-raw-v1 --num_train_epochs 1 --per_device_train_batch_size 1 --per_device_eval_batch_size 1 --do_train --do_eval --overwrite_output_dir --output_dir ./outputs/ --seed 1137 --fp16 --report_to none --max_train_samples 1000 ` torch.compile: `python run_mlm.py --model_name_or_path microsoft/deberta-v3-base --dataset_name wikitext --dataset_config_name wikitext-2-raw-v1 --num_train_epochs 1 --per_device_train_batch_size 1 --per_device_eval_batch_size 1 --do_train --do_eval --overwrite_output_dir --output_dir ./outputs/ --seed 1137 --fp16 --report_to none --max_train_samples 1000 --torch_compile` results : <html> <body> <!--StartFragment--> Metric | Eager | TorchCompile -- | -- | -- Avg of 2nd half | 72.44162 ms | 102.73143 ms Train loss | 5.995 | 5.9397 Train runtime | 0:03:09.17 | 0:04:38.75 Train samples | 1000 | 1000 Train samples per second | 5.286 | 3.587 Train steps per second | 5.286 | 3.587 Eval accuracy | 0.3637 | 0.3657 Eval loss | 4.8822 | 4.8525 Eval runtime | 0:00:10.11 | 0:00:32.71 Eval samples | 230 | 230 Eval samples per second | 22.746 | 7.031 Eval steps per second | 22.746 | 7.031 Perplexity | 131.92 | 128.0628 <!--EndFragment--> </body> </html> Ran on a Single Tesla V100 16GB GPU. ### Versions [conda] numpy 1.24.1 pypi_0 pypi [conda] pytorch-triton 2.1.0+46672772b4 pypi_0 pypi [conda] torch 2.1.0.dev20230404+cu117 pypi_0 pypi [conda] torch-ort 1.14.0 pypi_0 pypi [conda] torchaudio 2.0.0.dev20230313+cu117 pypi_0 pypi [conda] torchvision 0.15.0.dev20230313+cu117 pypi_0 pypi [conda] triton 2.0.0 pypi_0 pypi cc @ezyang @soumith @msaroufim @wconstab @ngimel @bdhirsh
5
2,984
98,434
assert callable(unaltered_fn)
high priority, triaged, oncall: pt2
### 🐛 Describe the bug This is a bug generated from https://github.com/pytorch/pytorch/issues/97078 To reproduce, check out transformers and patch (I tested on a515d0a77c769954ac2f0151a2a99c04d8d6cf95) ``` diff --git a/src/transformers/trainer.py b/src/transformers/trainer.py index 2eb081af7..886df74c1 100755 --- a/src/transformers/trainer.py +++ b/src/transformers/trainer.py @@ -1572,8 +1572,7 @@ class Trainer: # torch.compile() needs to be called after wrapping the model with FSDP or DDP # to ensure that it accounts for the graph breaks required by those wrappers - if self.args.torch_compile: - model = torch.compile(model, backend=self.args.torch_compile_backend, mode=self.args.torch_compile_mode) + model = torch.compile(model, backend=self.args.torch_compile_backend, mode=self.args.torch_compile_mode, dynamic=True) return model ``` Then, run ``` pytest tests/trainer/test_trainer.py --tb=native -k test_adafactor_lr_none ``` It fails with ``` ______________________________________________ TrainerIntegrationPrerunTest.test_adafactor_lr_none ______________________________________________ Traceback (most recent call last): File "/data/users/ezyang/a/transformers/tests/trainer/test_trainer.py", line 465, in setUp trainer.train() File "/data/users/ezyang/a/transformers/src/transformers/trainer.py", line 1658, in train return inner_training_loop( File "/data/users/ezyang/a/transformers/src/transformers/trainer.py", line 1745, in _inner_training_loop model = self._wrap_model(self.model_wrapped) File "/data/users/ezyang/a/transformers/src/transformers/trainer.py", line 1575, in _wrap_model model = torch.compile(model, backend=self.args.torch_compile_backend, mode=self.args.torch_compile_mode, dynamic=True) File "/data/users/ezyang/a/pytorch/torch/__init__.py", line 1600, in compile return torch._dynamo.optimize(backend=backend, nopython=fullgraph, dynamic=dynamic, disable=disable)(model) File "/data/users/ezyang/a/pytorch/torch/_dynamo/eval_frame.py", line 499, in optimize return _optimize_catch_errors( File "/data/users/ezyang/a/pytorch/torch/_dynamo/eval_frame.py", line 401, in _optimize_catch_errors return OptimizeContext( File "/data/users/ezyang/a/pytorch/torch/_dynamo/eval_frame.py", line 330, in __init__ compiler_fn = innermost_fn(callback) File "/data/users/ezyang/a/pytorch/torch/_dynamo/eval_frame.py", line 149, in innermost_fn assert callable(unaltered_fn) AssertionError ``` It's possible that HF is misusing the torch.compile API (there's some sort of repeated wrapping going on, it seems like), but even if that's true, it shouldn't assert error. cc @gchanan @zou3519 @soumith @msaroufim @wconstab @ngimel @bdhirsh @stas00 ### Versions master
2
2,985
98,422
[FX] Symbolic trace over `torch.Tensor.${fn}` APIs
oncall: fx
### 🐛 Describe the bug `torch.fx.symbolic_trace` seems to not support usages like `torch.Tensor.xxx`. ```python import torch def f(x): return torch.Tensor.flatten(x) # return x.flatten() # works # return torch.Tensor.flip(x, dims=[0]) # fails too torch.fx.symbolic_trace(f) # fails # torch.compile(f) # works ``` While such usages are incommon, I wonder if it is possible to support such styles as at least they are used in unit tests (e.g., `test_binary_ufuncs.py`)? Or may is there a way to magically regard `torch.Tensor.${fn}(x, *args)` as `x.${fn}(*args)`? ### Versions <details><summary><i>Environments :: Click to expand.</i></summary> <div> ```python """ Collecting environment information... PyTorch version: 2.1.0.dev20230403+cu118 Is debug build: False CUDA used to build PyTorch: 11.8 ROCM used to build PyTorch: N/A OS: Ubuntu 20.04.6 LTS (x86_64) GCC version: (Ubuntu 9.4.0-1ubuntu1~20.04.1) 9.4.0 Clang version: 10.0.0-4ubuntu1 CMake version: version 3.22.2 Libc version: glibc-2.31 Python version: 3.8.13 (default, Mar 28 2022, 11:38:47) [GCC 7.5.0] (64-bit runtime) Python platform: Linux-5.15.0-67-generic-x86_64-with-glibc2.17 Is CUDA available: True CUDA runtime version: 12.1.66 CUDA_MODULE_LOADING set to: LAZY GPU models and configuration: GPU 0: NVIDIA GeForce RTX 2080 Ti Nvidia driver version: 530.30.02 cuDNN version: Probably one of the following: /usr/lib/x86_64-linux-gnu/libcudnn.so.8.8.1 /usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.8.1 /usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.8.1 /usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.8.1 /usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.8.1 /usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.8.1 /usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.8.1 HIP runtime version: N/A MIOpen runtime version: N/A Is XNNPACK available: True CPU: Architecture: x86_64 CPU op-mode(s): 32-bit, 64-bit Byte Order: Little Endian Address sizes: 39 bits physical, 48 bits virtual CPU(s): 16 On-line CPU(s) list: 0-15 Thread(s) per core: 2 Core(s) per socket: 8 Socket(s): 1 NUMA node(s): 1 Vendor ID: GenuineIntel CPU family: 6 Model: 165 Model name: Intel(R) Core(TM) i7-10700K CPU @ 3.80GHz Stepping: 5 CPU MHz: 3800.000 CPU max MHz: 5100.0000 CPU min MHz: 800.0000 BogoMIPS: 7599.80 L1d cache: 256 KiB L1i cache: 256 KiB L2 cache: 2 MiB L3 cache: 16 MiB NUMA node0 CPU(s): 0-15 Vulnerability Itlb multihit: KVM: Mitigation: VMX unsupported Vulnerability L1tf: Not affected Vulnerability Mds: Not affected Vulnerability Meltdown: Not affected Vulnerability Mmio stale data: Mitigation; Clear CPU buffers; SMT vulnerable Vulnerability Retbleed: Mitigation; Enhanced IBRS Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization Vulnerability Spectre v2: Mitigation; Enhanced IBRS, IBPB conditional, RSB filling, PBRSB-eIBRS SW sequence Vulnerability Srbds: Mitigation; Microcode Vulnerability Tsx async abort: Not affected Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 monitor ds_cpl smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb invpcid_single ssbd ibrs ibpb stibp ibrs_enhanced fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid mpx rdseed adx smap clflushopt intel_pt xsaveopt xsavec xgetbv1 xsaves dtherm ida arat pln pts hwp hwp_notify hwp_act_window hwp_epp pku ospke md_clear flush_l1d arch_capabilities Versions of relevant libraries: [pip3] mypy==0.812 [pip3] mypy-extensions==0.4.3 [pip3] numpy==1.22.3 [pip3] pytorch-triton==2.1.0+46672772b4 [pip3] torch==2.1.0.dev20230403+cu118 [pip3] torchaudio==2.1.0.dev20230403+cu118 [pip3] torchvision==0.16.0.dev20230403+cu118 [conda] numpy 1.22.3 pypi_0 pypi [conda] pytorch-triton 2.1.0+46672772b4 pypi_0 pypi [conda] torch 2.1.0.dev20230403+cu118 pypi_0 pypi [conda] torchaudio 2.1.0.dev20230403+cu118 pypi_0 pypi [conda] torchvision 0.16.0.dev20230403+cu118 pypi_0 pypi """ ``` </div> </details> cc @ezyang @SherlockNoMad @EikanWang @jgong5 @wenzhe-nrv
0
2,986
98,419
Support backward hook optimizers in FSDP
oncall: distributed, triaged, module: fsdp
### 🚀 The feature, motivation and pitch I'm currently optimizing the [Lightning reference implementation of LLaMA](https://github.com/Lightning-AI/lit-llama) (7B), although the following will be generally applicable to any LLM with high memory pressure. The default configuration (24GB model sharded across 4x40GB A100s) is just on the cusp of being able to run (Between weights, 2 AdamW states, and gradients for the shard **logical** GPU memory caps out around 27GB, although I don't think that captures comms buffers) and the profile shows clear signs of allocator thrashing. After a bit of hacking I came up with this monstrosity: ``` def add_optimizer_hooks( model, optimizers: Dict[torch.nn.Parameter, torch.optim.Optimizer], # Per-parameter optimizers ): """Ugly FSDP analog to torch.distributed.optim._apply_optimizer_in_backward FSDP changes acc_grad every step, so we need to apply this before *each* `backward()` call, unlike the normal recipe where we only apply it once. """ param_handles = torch.distributed.fsdp._traversal_utils._get_fsdp_handles(model) assert set(model.parameters()) == {i.flat_param for i in param_handles} == set(optimizers.keys()) # We need to use the post backward stream so updates apply gradients are accumulated stream = torch.distributed.fsdp._common_utils._get_module_fsdp_state(model)._streams["post_backward"] for h in param_handles: # We're going to call this early, so if we don't override to a no-op FSDP proper will call it again and assert fail. h.prepare_gradient_for_optim = lambda: None p = h.flat_param assert hasattr(p, "_post_backward_hook_state") fsdp_acc_grad, _ = p._post_backward_hook_state def _opt_hook(optimizer, p, h, *_unused): assert p._post_backward_called with torch.cuda.stream(stream): # Use the class to get at `prepare_gradient_for_optim` h.__class__.prepare_gradient_for_optim(h) assert p.grad is not None optimizer.step() optimizer.zero_grad(set_to_none=True) # Cool that this is now the default assert p.grad is None fsdp_acc_grad.register_hook(functools.partial(_opt_hook, optimizers[p], p, h)) ``` <img width="350" alt="Screenshot 2023-04-05 at 8 43 23 AM" src="https://user-images.githubusercontent.com/13089297/230133300-0865ec63-45e5-416c-aa66-091c16c3ef3e.png"> <img width="339" alt="Screenshot 2023-04-05 at 8 43 47 AM" src="https://user-images.githubusercontent.com/13089297/230133388-ab6303bb-a2fa-4c22-baff-a1f075bf5a68.png"> More importantly, that's enough to get out of the high contention regime and decreases the step time close to an order of magnitude. But given how much FSDP internal state I had to crack open to get things running (and I'm sure I missed plenty...) it's really only suitable as a PoC. CC @rohan-varma @albanD @zdevito ### Alternatives I know there's been more general discussion of creating optimizers on the fly so there might be a better alternative to the big list of single Tensor optimizers. ### Additional context _No response_ cc @mrshenli @pritamdamania87 @zhaojuanmao @satgera @rohan-varma @gqchen @aazzolini @osalpekar @jiayisuse @H-Huang @kwen2501 @awgu
4
2,987
98,416
Backwards graph is labeled incorrectly when dynamic=True
triaged, oncall: pt2, module: dynamic shapes
### 🐛 Describe the bug Run `TORCH_COMPILE_DEBUG=1 python tt.py` with ``` #!/usr/bin/env python3 import time import torch import torch._dynamo as dynamo import torchvision.models as models model = models.alexnet() optimizer = torch.optim.SGD(model.parameters(), lr=0.1) compiled_model = torch.compile(model, dynamic=True) x = torch.randn(16, 3, 224, 224) optimizer.zero_grad() epoches=10 count = [] for epoch in range(epoches): start = time.time() #out = model(x) out = compiled_model(x) out.sum().backward() optimizer.step() end = time.time() count.append(end - start) print(f"Epoch {epoch}/{epoches} time: {end - start}") print(f"Epoch avg time: {sum(count)/len(count)}") ``` but really any training script will work. Inspect the inductor directory: ``` $ ls torch_compile_debug/run_2023_04_05_07_17_04_323873-pid_27994/torchinductor/ aot_model___0_debug.log model__0_forward_1.0 model__0_inference_2.1 ``` The backwards graph is incorrectly reported as an inference graph. cc @soumith @msaroufim @wconstab @ngimel @bdhirsh @Chillee ### Versions master
2
2,988
98,414
PyTorch 1.12, high failure rate for test_optim/test_nadam
module: optimizer, triaged
### 🐛 Describe the bug Similar to https://github.com/pytorch/pytorch/issues/63079 but this is for test_nadam under test_optim instead. I've done some initial investigations on other nodes (8xT4,4xV100,4xA100) and other CUDA &/ PyTorch versions. I'll try to collect what worked and what didn't in separate comments. If I remember correctly it is only for combination PyTorch-1.12.x CUDA-11.7.0 on A40s I've observed the test failure. ## To Reproduce 1. Build PyTorch 1.12.1 from source with GCC 11.3.0 with CUDA 11.7.0 2. Run `python test_optim.py -k test_nadam` ensuing error message: ``` /dev/shm/PyTorch/1.12.1/foss-2022a-CUDA-11.7.0/pytorch-v1.12.1/build/lib.linux-x86_64-cpython-310/torch/testing/_internal/common_cuda.py:19: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead. CUDA11OrLater = torch.version.cuda and LooseVersion(torch.version.cuda) >= "11.0" /apps/Arch/software/Python/3.10.4-GCCcore-11.3.0/lib/python3.10/site-packages/setuptools/_distutils/version.py:351: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead. other = LooseVersion(other) F ====================================================================== FAIL: test_nadam (__main__.TestOptim) ---------------------------------------------------------------------- Traceback (most recent call last): File "/cephyr/NOBACKUP/priv/c3-staff/vikren/build/EasyBuild/PyTorch/test_optim.py", line 671, in test_nadam self._test_basic_cases( File "/cephyr/NOBACKUP/priv/c3-staff/vikren/build/EasyBuild/PyTorch/test_optim.py", line 259, in _test_basic_cases self._test_state_dict( File "/cephyr/NOBACKUP/priv/c3-staff/vikren/build/EasyBuild/PyTorch/test_optim.py", line 241, in _test_state_dict self.assertEqual(bias, bias_cuda) File "/dev/shm/PyTorch/1.12.1/foss-2022a-CUDA-11.7.0/pytorch-v1.12.1/build/lib.linux-x86_64-cpython-310/torch/testing/_internal/common_utils.py", line 2219, in assertEqual assert_equal( File "/dev/shm/PyTorch/1.12.1/foss-2022a-CUDA-11.7.0/pytorch-v1.12.1/build/lib.linux-x86_64-cpython-310/torch/testing/_comparison.py", line 1095, in assert_equal raise error_metas[0].to_error(msg) AssertionError: Tensor-likes are not close! Mismatched elements: 1 / 10 (10.0%) Greatest absolute difference: 1.609325408935547e-05 at index (1,) (up to 1e-05 allowed) Greatest relative difference: 1.477008233390933e-05 at index (1,) (up to 1.3e-06 allowed) ---------------------------------------------------------------------- Ran 1 test in 5.343s FAILED (failures=1) ``` ### Versions ``` Collecting environment information... PyTorch version: 1.12.1 Is debug build: False CUDA used to build PyTorch: 11.7 ROCM used to build PyTorch: N/A OS: Rocky Linux 8.6 (Green Obsidian) (x86_64) GCC version: (GCC) 11.3.0 Clang version: Could not collect CMake version: version 3.23.1 Libc version: glibc-2.28 Python version: 3.10.4 (main, Aug 14 2022, 22:57:54) [GCC 11.3.0] (64-bit runtime) Python platform: Linux-4.18.0-372.32.1.el8_6.x86_64-x86_64-with-glibc2.28 Is CUDA available: True CUDA runtime version: 11.7.64 GPU models and configuration: GPU 0: NVIDIA A40 GPU 1: NVIDIA A40 GPU 2: NVIDIA A40 GPU 3: NVIDIA A40 Nvidia driver version: 520.61.05 cuDNN version: Could not collect HIP runtime version: N/A MIOpen runtime version: N/A Is XNNPACK available: True Versions of relevant libraries: [pip3] numpy==1.22.3 [conda] Could not collect ``` cc @vincentqb @jbschlosser @albanD @janeyx99
13
2,989
98,413
TORCH_COMPILE_DEBUG and TORCH_LOGS interact badly
triaged, oncall: pt2
### 🐛 Describe the bug Here's an example log: https://gist.github.com/ezyang/6e2904c8ecbd863eefcbee7456ada544 for this run: ``` TORCH_COMPILE_DEBUG=1 PYTHONUNBUFFERED=1 WANDB_DISABLED=true TORCH_LOGS=dynamo,inductor,guards CUDA_VISIBLE_DEVICES=3 PYTHONPATH=src pp python examples/pytorch/translation/run_translation.py --model_name_or_path t5-base --do_train --source_lang en --target_lang de --source_prefix 'translate English to German: ' --dataset_name stas/wmt14-en-de-pre-processed --output_dir /tmp/tst-translation --num_train_epochs 1 --per_device_train_batch_size=1 --max_train_samples 1000 --overwrite_output_dir --seed 1137 --per_device_eval_batch_size 1 --fp16 --torch_compile 2>&1 | tee comp.log ``` Some things to note: 1. Despite not asking for it, I'm still getting inductor DEBUG logs printed to stderr: ``` [2023-04-05 06:43:18,087] torch._inductor.codegen.triton.__schedule: [DEBUG] Schedule: ``` My intention for TORCH_COMPILE_DEBUG was to get the directory dump; I think it shouldn't interact with console output 2. It keeps repeatedly printing this: ``` 04/05/2023 06:43:27 - WARNING - torch._logging._internal - Using TORCH_LOGS environment variable for log settings, ignoring call to set_logs 04/05/2023 06:43:27 - WARNING - torch._logging._internal - Using TORCH_LOGS environment variable for log settings, ignoring call to set_logs 04/05/2023 06:43:27 - WARNING - torch._logging._internal - Using TORCH_LOGS environment variable for log settings, ignoring call to set_logs ``` ### Versions master cc @soumith @msaroufim @wconstab @ngimel @bdhirsh
2
2,990
98,409
`torch.Tensor.layout` is not documented
module: docs, triaged, module: python frontend
### 🐛 Describe the bug This looks like a public function, so it should have been documented, but it is not: ``` % python -c "import torch;print(torch.Tensor.layout.__doc__)" None ``` I wonder if this intentional. If not, we should add the documentation. ### Versions 2.0/nightly cc @svekars @carljparker @albanD
1
2,991
98,406
Contribute to the privateuse1 backend.
module: internals, triaged, module: backend
### 🚀 The feature, motivation and pitch This issue is used to discuss how to improve the PrivateUse1 backend to facilitate third-party manufacturers to access Pytorch. With the popularity of pytorch and the evolution of computing acceleration hardware, the strong coupling between pytorch and cuda has become a serious problem. So the completeness of PrivateUse1 is what third-party hardware manufacturers need. After all, we can't add more enumerated types to DeviceType unless we are a big company like Apple or Intel (just kidding🫡). For each feature, I will summarize it into this issue. Please join us, thank you. ### Alternatives _No response_ ### Additional context _No response_ cc @ezyang @bhosmer @smessmer @ljk53 @bdhirsh
8
2,992
98,386
[PTD][Checkpoint] Enable single_file_per_rank for fsspec storage read/write
oncall: distributed, triaged
### 🚀 The feature, motivation and pitch With our current setup, single_file_per_rank is not supported for fsspec StorageWriter and StorageReader. This means we can only write single file per tensor/blob, which will significantly affect our performance. We need to support single file per rank in fsspec and add the option back. ### Alternatives _No response_ ### Additional context _No response_ cc @mrshenli @pritamdamania87 @zhaojuanmao @satgera @rohan-varma @gqchen @aazzolini @osalpekar @jiayisuse @H-Huang @kwen2501 @awgu
0
2,993
98,361
pip doesn't install the right version of pytorch when torchtext is involved
oncall: binaries
### 🐛 Describe the bug I encountered some weird installation problems while installing the nightly version. ```bash pip3 install --force-reinstall --pre torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/nightly/cu118 ``` This is fine. ```bash pip3 install --force-reinstall --pre torch torchtext torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/nightly/cu118 ``` This is going to install torch2.0.0 instead of the nightly version. I’m sure it worked a few days ago. ### Versions PyTorch version: 2.1.0.dev20230404+cu118 Is debug build: False CUDA used to build PyTorch: 11.8 ROCM used to build PyTorch: N/A CMake version: version 3.26.1 Libc version: glibc-2.31 Versions of relevant libraries: [pip3] torch==2.1.0.dev20230404+cu118 [pip3] torchaudio==2.1.0.dev20230404+cu118 [pip3] torchdata==0.6.0 [pip3] torchtext==0.15.1 [pip3] torchvision==0.16.0.dev20230404+cu118 [pip3] triton==2.1.0 cc @seemethere @malfet
6
2,994
98,355
Intermittent failure of mobilenet_v3_large
triaged, module: flaky-tests, oncall: pt2
Repro: ``` python benchmarks/dynamo/torchbench.py --training --accuracy --device cuda --inductor --amp --only mobilenet_v3_large ``` See more details at https://github.com/pytorch/pytorch/pull/98314 cc @ezyang @soumith @msaroufim @wconstab @ngimel @bdhirsh
0
2,995
98,338
[functorch] [vmap] tests fail when `_set_vmap_fallback_enabled(False)`.
triaged, module: functorch
### 🐛 Describe the bug With the following patch ```patch diff --git a/test/functorch/test_vmap.py b/test/functorch/test_vmap.py index a00612fdf5..f63651d9d4 100644 --- a/test/functorch/test_vmap.py +++ b/test/functorch/test_vmap.py @@ -58,6 +58,8 @@ from torch._functorch.make_functional import functional_init_with_buffers from torch.testing._internal.autograd_function_db import autograd_function_db from torch._functorch.vmap import restore_vmap +torch._C._functorch._set_vmap_fallback_enabled(False) + FALLBACK_REGEX = 'There is a performance drop' ``` A lot of tests fail <details> <summary> Failed Tests </summary> ``` ============================================================================ short test summary info ============================================================================ FAILED test/functorch/test_vmap.py::TestVmapAPI::test_fallback_does_not_warn_by_default - RuntimeError: aten::_test_functorch_fallback hit the vmap fallback which is currentl... FAILED test/functorch/test_vmap.py::TestVmapAPI::test_fallback_warning - RuntimeError: aten::_test_functorch_fallback hit the vmap fallback which is currently disabled FAILED test/functorch/test_vmap.py::TestVmapAPI::test_fallback_zero_dim - AssertionError: "The fallback path does not support vmap over dims of size 0" does not match "aten::... FAILED test/functorch/test_vmap.py::TestVmapAPI::test_inplace_fallback_nary_same_levels - RuntimeError: aten::atan2_ hit the vmap fallback which is currently disabled FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_linalg_eigh_cpu - RuntimeError: aten::linalg_eigh hit the vmap fallback which is currently disabled FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive__segment_reduce_lengths_cpu_float32 - RuntimeError: aten::segment_reduce hit the vmap fal... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive__segment_reduce_offsets_cpu_float32 - RuntimeError: aten::segment_reduce hit the vmap fal... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive__upsample_bilinear2d_aa_cpu_float32 - RuntimeError: aten::_upsample_bilinear2d_aa.vec hit... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_abs_cpu_float32 - RuntimeError: aten::absolute_ hit the vmap fallback which is currently ... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_acos_cpu_float32 - RuntimeError: aten::arccos_ hit the vmap fallback which is currently d... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_acosh_cpu_float32 - RuntimeError: aten::arccosh_ hit the vmap fallback which is currently... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_addbmm_cpu_float32 - RuntimeError: aten::addbmm_ hit the vmap fallback which is currently... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_addmm_cpu_float32 - RuntimeError: aten::addmm_ hit the vmap fallback which is currently d... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_addmm_decomposed_cpu_float32 - RuntimeError: aten::addmm_ hit the vmap fallback which is ... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_addmv_cpu_float32 - RuntimeError: aten::addmv_ hit the vmap fallback which is currently d... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_addr_cpu_float32 - RuntimeError: aten::addr_ hit the vmap fallback which is currently dis... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_argwhere_cpu_float32 - RuntimeError: aten::argwhere hit the vmap fallback which is curren... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_asin_cpu_float32 - RuntimeError: aten::arcsin_ hit the vmap fallback which is currently d... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_asinh_cpu_float32 - RuntimeError: aten::arcsinh_ hit the vmap fallback which is currently... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_atan2_cpu_float32 - RuntimeError: aten::atan2_ hit the vmap fallback which is currently d... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_atan_cpu_float32 - RuntimeError: aten::arctan_ hit the vmap fallback which is currently d... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_atanh_cpu_float32 - RuntimeError: aten::arctanh_ hit the vmap fallback which is currently... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_baddbmm_cpu_float32 - RuntimeError: aten::baddbmm_ hit the vmap fallback which is current... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_bincount_cpu_int64 - RuntimeError: aten::bincount hit the vmap fallback which is currentl... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_bucketize_cpu_float32 - RuntimeError: aten::bucketize.Tensor hit the vmap fallback which ... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_chalf_cpu_float32 - RuntimeError: aten::chalf hit the vmap fallback which is currently di... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_conj_physical_cpu_float32 - RuntimeError: aten::conj_physical_ hit the vmap fallback whic... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_count_nonzero_cpu_float32 - RuntimeError: aten::count_nonzero hit the vmap fallback which... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_cumprod_cpu_float32 - RuntimeError: aten::cumprod_ hit the vmap fallback which is current... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_cumsum_cpu_float32 - RuntimeError: aten::cumsum_ hit the vmap fallback which is currently... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_diagflat_cpu_float32 - RuntimeError: aten::diagflat hit the vmap fallback which is curren... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_div_floor_rounding_cpu_float32 - RuntimeError: aten::div_.Tensor_mode hit the vmap fallba... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_div_trunc_rounding_cpu_float32 - RuntimeError: aten::div_.Tensor_mode hit the vmap fallba... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_fft_ihfft2_cpu_float32 - RuntimeError: aten::fft_ihfft2 hit the vmap fallback which is cu... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_fft_ihfftn_cpu_float32 - RuntimeError: aten::fft_ihfftn hit the vmap fallback which is cu... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_fill_cpu_float32 - RuntimeError: aten::fill.Scalar hit the vmap fallback which is current... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_floor_divide_cpu_float32 - RuntimeError: aten::floor_divide_.Tensor hit the vmap fallback... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_fmod_cpu_float32 - RuntimeError: aten::fmod_.Tensor hit the vmap fallback which is curren... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_gcd_cpu_int64 - RuntimeError: aten::gcd_ hit the vmap fallback which is currently disabled FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_heaviside_cpu_float32 - RuntimeError: aten::heaviside_ hit the vmap fallback which is cur... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_histc_cpu_float32 - RuntimeError: aten::histc hit the vmap fallback which is currently di... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_histogram_cpu_float32 - RuntimeError: aten::histogram.bin_ct hit the vmap fallback which ... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_hypot_cpu_float32 - RuntimeError: aten::hypot_ hit the vmap fallback which is currently d... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_igamma_cpu_float32 - RuntimeError: aten::igamma_ hit the vmap fallback which is currently... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_igammac_cpu_float32 - RuntimeError: aten::igammac_ hit the vmap fallback which is current... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_index_add_cpu_float32 - RuntimeError: aten::index_add_ hit the vmap fallback which is cur... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_index_copy_cpu_float32 - RuntimeError: aten::index_copy_ hit the vmap fallback which is c... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_index_reduce_cpu_float32 - RuntimeError: aten::index_reduce hit the vmap fallback which i... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_isclose_cpu_float32 - RuntimeError: aten::isclose hit the vmap fallback which is currentl... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_isin_cpu_float32 - RuntimeError: aten::isin.Tensor_Tensor hit the vmap fallback which is ... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_istft_cpu_complex64 - RuntimeError: aten::istft hit the vmap fallback which is currently ... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_lcm_cpu_int64 - RuntimeError: aten::lcm_ hit the vmap fallback which is currently disabled FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_ldexp_cpu_float32 - RuntimeError: aten::ldexp_ hit the vmap fallback which is currently d... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_lerp_cpu_float32 - RuntimeError: aten::lerp_.Scalar hit the vmap fallback which is curren... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_linalg_ldl_solve_cpu_float32 - RuntimeError: aten::linalg_ldl_solve hit the vmap fallback... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_linalg_lu_cpu_float32 - RuntimeError: aten::linalg_lu hit the vmap fallback which is curr... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_linalg_tensorsolve_cpu_float32 - RuntimeError: aten::linalg_tensorsolve hit the vmap fall... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_lu_solve_cpu_float32 - RuntimeError: aten::lu_solve hit the vmap fallback which is curren... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_lu_unpack_cpu_float32 - RuntimeError: aten::lu_unpack hit the vmap fallback which is curr... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_masked_fill_cpu_float32 - RuntimeError: aten::masked_fill.Tensor hit the vmap fallback wh... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_masked_scatter_cpu_float32 - RuntimeError: aten::masked_scatter hit the vmap fallback whi... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_matrix_exp_cpu_float32 - RuntimeError: aten::matrix_exp hit the vmap fallback which is cu... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_nanquantile_cpu_float32 - RuntimeError: aten::nanquantile.scalar hit the vmap fallback wh... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_native_dropout_backward_cpu_float32 - RuntimeError: aten::native_dropout_backward hit the... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_neg_cpu_float32 - RuntimeError: aten::negative_ hit the vmap fallback which is currently ... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_nextafter_cpu_float32 - RuntimeError: aten::nextafter_ hit the vmap fallback which is cur... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_nn_functional_bilinear_cpu_float32 - RuntimeError: aten::bilinear hit the vmap fallback w... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_nn_functional_ctc_loss_cpu_float32 - RuntimeError: aten::ctc_loss.Tensor hit the vmap fal... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_nn_functional_huber_loss_cpu_float32 - RuntimeError: aten::huber_loss hit the vmap fallba... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_nn_functional_kl_div_cpu_float32 - RuntimeError: aten::kl_div hit the vmap fallback which... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_nn_functional_margin_ranking_loss_cpu_float32 - RuntimeError: aten::margin_ranking_loss h... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_nn_functional_max_pool1d_cpu_float32 - RuntimeError: aten::max_pool1d hit the vmap fallba... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_nn_functional_max_pool3d_cpu_float32 - RuntimeError: aten::max_pool3d_with_indices hit th... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_nn_functional_max_unpool1d_cpu_float32 - RuntimeError: aten::max_unpool2d hit the vmap fa... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_nn_functional_max_unpool1d_grad_cpu_float32 - RuntimeError: aten::max_unpool2d hit the vm... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_nn_functional_max_unpool2d_cpu_float32 - RuntimeError: aten::max_unpool2d hit the vmap fa... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_nn_functional_max_unpool2d_grad_cpu_float32 - RuntimeError: aten::max_unpool2d hit the vm... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_nn_functional_max_unpool3d_cpu_float32 - RuntimeError: aten::max_unpool3d hit the vmap fa... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_nn_functional_max_unpool3d_grad_cpu_float32 - RuntimeError: aten::max_unpool3d hit the vm... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_nn_functional_multi_margin_loss_cpu_float32 - RuntimeError: aten::multi_margin_loss hit t... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_nn_functional_multilabel_margin_loss_cpu_float32 - RuntimeError: aten::multilabel_margin_... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_nn_functional_pdist_cpu_float32 - RuntimeError: aten::pdist hit the vmap fallback which i... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_nn_functional_smooth_l1_loss_cpu_float32 - RuntimeError: aten::smooth_l1_loss hit the vma... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_nn_functional_soft_margin_loss_cpu_float32 - RuntimeError: aten::soft_margin_loss hit the... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_nn_functional_triplet_margin_loss_cpu_float32 - RuntimeError: aten::triplet_margin_loss h... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_ormqr_cpu_float32 - RuntimeError: aten::ormqr hit the vmap fallback which is currently di... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_polygamma_polygamma_n_0_cpu_float32 - RuntimeError: aten::polygamma_ hit the vmap fallbac... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_polygamma_polygamma_n_1_cpu_float32 - RuntimeError: aten::polygamma_ hit the vmap fallbac... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_polygamma_polygamma_n_2_cpu_float32 - RuntimeError: aten::polygamma_ hit the vmap fallbac... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_polygamma_polygamma_n_3_cpu_float32 - RuntimeError: aten::polygamma_ hit the vmap fallbac... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_polygamma_polygamma_n_4_cpu_float32 - RuntimeError: aten::polygamma_ hit the vmap fallbac... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_pow_cpu_float32 - RuntimeError: aten::pow_.Tensor hit the vmap fallback which is currentl... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_put_cpu_float32 - RuntimeError: aten::put hit the vmap fallback which is currently disabled FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_quantile_cpu_float32 - RuntimeError: aten::quantile.scalar hit the vmap fallback which is... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_remainder_cpu_float32 - RuntimeError: aten::remainder_.Tensor hit the vmap fallback which... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_renorm_cpu_float32 - RuntimeError: aten::renorm hit the vmap fallback which is currently ... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_scatter_add_cpu_float32 - RuntimeError: aten::scatter_add_ hit the vmap fallback which is... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_scatter_cpu_float32 - RuntimeError: aten::scatter_.src hit the vmap fallback which is cur... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_scatter_reduce_amax_cpu_float32 - RuntimeError: aten::scatter_reduce.two hit the vmap fal... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_scatter_reduce_amin_cpu_float32 - RuntimeError: aten::scatter_reduce.two hit the vmap fal... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_scatter_reduce_mean_cpu_float32 - RuntimeError: aten::scatter_reduce.two hit the vmap fal... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_scatter_reduce_prod_cpu_float32 - RuntimeError: aten::scatter_reduce.two hit the vmap fal... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_scatter_reduce_sum_cpu_float32 - RuntimeError: aten::scatter_reduce.two hit the vmap fall... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_special_airy_ai_cpu_float32 - RuntimeError: aten::special_airy_ai hit the vmap fallback w... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_special_bessel_j0_cpu_float32 - RuntimeError: aten::special_bessel_j0 hit the vmap fallba... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_special_bessel_j1_cpu_float32 - RuntimeError: aten::special_bessel_j1 hit the vmap fallba... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_special_bessel_y0_cpu_float32 - RuntimeError: aten::special_bessel_y0 hit the vmap fallba... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_special_bessel_y1_cpu_float32 - RuntimeError: aten::special_bessel_y1 hit the vmap fallba... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_special_chebyshev_polynomial_t_cpu_float32 - RuntimeError: aten::special_chebyshev_polyno... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_special_chebyshev_polynomial_u_cpu_float32 - RuntimeError: aten::special_chebyshev_polyno... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_special_hermite_polynomial_h_cpu_float32 - RuntimeError: aten::special_hermite_polynomial... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_special_hermite_polynomial_he_cpu_float32 - RuntimeError: aten::special_hermite_polynomia... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_special_laguerre_polynomial_l_cpu_float32 - RuntimeError: aten::special_laguerre_polynomi... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_special_log_ndtr_cpu_float32 - RuntimeError: aten::special_log_ndtr hit the vmap fallback... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_special_modified_bessel_i0_cpu_float32 - RuntimeError: aten::special_modified_bessel_i0 h... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_special_modified_bessel_i1_cpu_float32 - RuntimeError: aten::special_modified_bessel_i1 h... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_special_modified_bessel_k0_cpu_float32 - RuntimeError: aten::special_modified_bessel_k0 h... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_special_modified_bessel_k1_cpu_float32 - RuntimeError: aten::special_modified_bessel_k1 h... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_special_scaled_modified_bessel_k0_cpu_float32 - RuntimeError: aten::special_scaled_modifi... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_special_scaled_modified_bessel_k1_cpu_float32 - RuntimeError: aten::special_scaled_modifi... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_special_spherical_bessel_j0_cpu_float32 - RuntimeError: aten::special_spherical_bessel_j0... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_square_cpu_float32 - RuntimeError: aten::square_ hit the vmap fallback which is currently... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_stft_cpu_float32 - RuntimeError: aten::stft hit the vmap fallback which is currently disa... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_sub_cpu_float32 - RuntimeError: aten::subtract_.Tensor hit the vmap fallback which is cur... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_take_cpu_float32 - RuntimeError: aten::take hit the vmap fallback which is currently disa... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_trunc_cpu_float32 - RuntimeError: aten::fix_ hit the vmap fallback which is currently dis... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_xlogy_cpu_float32 - RuntimeError: aten::xlogy_.Tensor hit the vmap fallback which is curr... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_linalg_failure_1D_input_linalg_eigh_cpu_float32 - AssertionError: "dimension" does not match "aten::... FAILED test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_linalg_failure_1D_input_linalg_lu_cpu_float32 - AssertionError: "dimension" does not match "aten::li... ==================================================== 129 failed, 1581 passed, 77 skipped, 226 xfailed in 1513.32s (0:25:13) =================================================== ``` </details> More context: https://github.com/pytorch/pytorch/pull/98328#discussion_r1157628714 ### Versions master cc @zou3519 @Chillee @samdow @soumith @janeyx99
0
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[cpu] Fix div with rounding_mode="floor" when division overflows
module: cpu, open source, release notes: python_frontend, topic: bug fixes
Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom): * __->__ #98330 * #98329 Fixes #77742 Sleef_fmod returns NaN when the division overflows, but we should be returning inf here. So lets just use the direct division result whenever it's nonfinite. cc @jgong5 @mingfeima @XiaobingSuper @sanchitintel @ashokei @jingxu10
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"We don't have an op for aten::bitwise_and but it isn't a special case." when exporting NMS operation as ONNX.
oncall: jit, module: onnx
### 🐛 Describe the bug Hi! I'm trying to use a detection model from ultralytics in ONNX format, but realized it does not have Non Max Suppression. I checked if I could use Pytorch to easily generate the corresponding post-procession ONNX, but it fails with a "please report a bug to PyTorch" message, so here I am :-) ```python import torch from torch import nn from ultralytics.yolo.utils.ops import non_max_suppression from ultralytics.yolo.v8.detect import DetectionPredictor class PostProcessingModule(nn.Module, DetectionPredictor): def forward(self, yolo_results, iou_threshold, score_threshold): return non_max_suppression( yolo_results, iou_threshold, score_threshold ) if __name__ == '__main__': yolo_results = torch.rand([1, 14, 1000]).type(torch.float32) iou_threshold = 0.5 score_threshold = 0.5 t_model = PostProcessingModule() torch.onnx.export( t_model, (yolo_results, iou_threshold, score_threshold), "NMS_after.onnx", input_names=["yolo_results", "iou_threshold", "score_threshold"], output_names=["yolo_results_filtered"], ) ``` Here's the full error message: > RuntimeError : 0 INTERNAL ASSERT FAILED at "/opt/conda/conda-bld/pytorch_1678402412426/work/torch/csrc/jit/ir/alias_analysis.cpp":615, please report a bug to PyTorch. We don't have an op for aten::bitwise_and but it isn't a special case. Argument types: Tensor, bool, > > Candidates: > aten::bitwise_and.Tensor(Tensor self, Tensor other) -> Tensor > aten::bitwise_and.Scalar(Tensor self, Scalar other) -> Tensor > aten::bitwise_and.Scalar_Tensor(Scalar self, Tensor other) -> Tensor > aten::bitwise_and.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) > aten::bitwise_and.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) > aten::bitwise_and.Scalar_Tensor_out(Scalar self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) ### Versions Collecting environment information... PyTorch version: 2.0.0 Is debug build: False CUDA used to build PyTorch: 11.8 ROCM used to build PyTorch: N/A OS: Ubuntu 22.04.2 LTS (x86_64) GCC version: (Ubuntu 11.3.0-1ubuntu1~22.04) 11.3.0 Clang version: Could not collect CMake version: Could not collect Libc version: glibc-2.35 Python version: 3.9.16 | packaged by conda-forge | (main, Feb 1 2023, 21:39:03) [GCC 11.3.0] (64-bit runtime) Python platform: Linux-5.15.0-69-generic-x86_64-with-glibc2.35 Is CUDA available: True CUDA runtime version: 11.5.119 CUDA_MODULE_LOADING set to: LAZY GPU models and configuration: GPU 0: NVIDIA GeForce RTX 2060 Nvidia driver version: 510.108.03 cuDNN version: Could not collect HIP runtime version: N/A MIOpen runtime version: N/A Is XNNPACK available: True CPU: Architecture : x86_64 Mode(s) opératoire(s) des processeurs : 32-bit, 64-bit Address sizes: 39 bits physical, 48 bits virtual Boutisme : Little Endian Processeur(s) : 12 Liste de processeur(s) en ligne : 0-11 Identifiant constructeur : GenuineIntel Nom de modèle : Intel(R) Core(TM) i7-9750H CPU @ 2.60GHz Famille de processeur : 6 Modèle : 158 Thread(s) par cœur : 2 Cœur(s) par socket : 6 Socket(s) : 1 Révision : 10 Vitesse maximale du processeur en MHz : 4500,0000 Vitesse minimale du processeur en MHz : 800,0000 BogoMIPS : 5199.98 Drapaux : fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 monitor ds_cpl vmx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb invpcid_single pti ssbd ibrs ibpb stibp tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid mpx rdseed adx smap clflushopt intel_pt xsaveopt xsavec xgetbv1 xsaves dtherm ida arat pln pts hwp hwp_notify hwp_act_window hwp_epp md_clear flush_l1d arch_capabilities Virtualisation : VT-x Cache L1d : 192 KiB (6 instances) Cache L1i : 192 KiB (6 instances) Cache L2 : 1,5 MiB (6 instances) Cache L3 : 12 MiB (1 instance) Nœud(s) NUMA : 1 Nœud NUMA 0 de processeur(s) : 0-11 Vulnerability Itlb multihit: KVM: Mitigation: Split huge pages Vulnerability L1tf: Mitigation; PTE Inversion; VMX conditional cache flushes, SMT vulnerable Vulnerability Mds: Mitigation; Clear CPU buffers; SMT vulnerable Vulnerability Meltdown: Mitigation; PTI Vulnerability Mmio stale data: Mitigation; Clear CPU buffers; SMT vulnerable Vulnerability Retbleed: Mitigation; IBRS Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization Vulnerability Spectre v2: Mitigation; IBRS, IBPB conditional, RSB filling, PBRSB-eIBRS Not affected Vulnerability Srbds: Mitigation; Microcode Vulnerability Tsx async abort: Not affected Versions of relevant libraries: [pip3] flake8==6.0.0 [pip3] mypy-extensions==1.0.0 [pip3] numpy==1.24.2 [pip3] numpydoc==1.5.0 [pip3] pytorch-lightning==2.0.0 [pip3] torch==2.0.0 [pip3] torchmetrics==0.11.4 [pip3] torchvision==0.15.0 [pip3] triton==2.0.0 [conda] blas 1.0 mkl conda-forge [conda] mkl 2023.0.0 h6d00ec8_25399 [conda] numpy 1.24.2 py39h7360e5f_0 conda-forge [conda] numpydoc 1.5.0 pyhd8ed1ab_0 conda-forge [conda] pytorch 2.0.0 py3.9_cuda11.8_cudnn8.7.0_0 pytorch [conda] pytorch-cuda 11.8 h7e8668a_3 pytorch [conda] pytorch-lightning 2.0.0 pyhd8ed1ab_1 conda-forge [conda] pytorch-mutex 1.0 cuda pytorch [conda] torchmetrics 0.11.4 pyhd8ed1ab_0 conda-forge [conda] torchtriton 2.0.0 py39 pytorch [conda] torchvision 0.15.0 py39_cu118 pytorch cc @EikanWang @jgong5 @wenzhe-nrv @sanchitintel
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Make BetterTransformer implementation non-blocking
oncall: transformer/mha
### 🚀 The feature, motivation and pitch I am using `optimum` integration for `BetterTransformer` with AMP. Here is what I get without `BetterTransformer`: ![Screenshot from 2023-04-04 12-46-46](https://user-images.githubusercontent.com/56936206/229759565-cf3162dd-13d1-4b11-bbc9-e7d6782ec315.png) ![Screenshot from 2023-04-04 12-44-00](https://user-images.githubusercontent.com/56936206/229759632-9a423006-9bff-446b-ba4c-e95c657e7f5b.png) The key point here is `_process_doc_contents` is a CPU-intensive preprocessing function and `repad` is GPU-blocking. Notice how we seemingly spend no time in `transformers` code. Now after I introduce `BetterTransformer` I get the following picture: ![Screenshot from 2023-04-04 12-46-50](https://user-images.githubusercontent.com/56936206/229761459-04c8d070-229d-4162-9d4f-dc1331531370.png) ![Screenshot from 2023-04-04 12-51-58](https://user-images.githubusercontent.com/56936206/229761625-a8a1ff00-05b3-493e-8efa-f0a1fdf95fc3.png) Suddenly, we start spending a lot of time in `transformers` code, which tells me some operation is GPU-blocking there. Furthermore, my guess is confirmed by the drop in GPU saturation and increase in total time by ~30s (the time needed to preprocess the inputs). Why is this issue not in `transformers` repo? As far as I can tell from the profiler's output, the execution was blocked by this function: ![image](https://user-images.githubusercontent.com/56936206/229764685-1f02c589-eaa8-402a-852a-b0bfd7b7bec3.png) **Software:** Ubuntu 18.04.6 LTS ``` torch==2.0.0 transformers==4.27.4 optimum==1.7.3 ``` **Hardware:** NVIDIA GeForce RTX 2060 ``` +-----------------------------------------------------------------------------+ | NVIDIA-SMI 510.108.03 Driver Version: 510.108.03 CUDA Version: 11.6 | |-------------------------------+----------------------+----------------------+ | GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC | | Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. | | | | MIG M. | |===============================+======================+======================| | 0 NVIDIA GeForce ... Off | 00000000:01:00.0 Off | N/A | | 32% 38C P8 9W / 160W | 5MiB / 6144MiB | 0% Default | | | | N/A | +-------------------------------+----------------------+----------------------+ +-----------------------------------------------------------------------------+ | Processes: | | GPU GI CI PID Type Process name GPU Memory | | ID ID Usage | |=============================================================================| | 0 N/A N/A 2541 G /usr/lib/xorg/Xorg 4MiB | +-----------------------------------------------------------------------------+ ``` Sorry for not providing a reproducible example. I will work on it later since for now I am not sure how to implement it. ### Alternatives _No response_ ### Additional context _No response_ cc @jbschlosser @bhosmer @cpuhrsch @erichan1
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When I use the DDP model, I use a custom loss function, when the batch size changes during training, the process will be stuck.
oncall: distributed, triaged
### 🐛 Describe the bug For some reasons, I need to discard part of the data in the collate_fn of the dataloader, which makes my batch size change. My program gets stuck in the loss function when the batch size is changed. It doesn't report an error, it just keeps stuck there and never proceeds to the next step. You can run the code below and you will get output similar to mine. You can observe that when the batch size changes, the 0 rank will be stuck in the sentence `p_dist, n_dist = self.compute_triplet_dist(dist, p_mask, n_mask)` Because 0 rank outputs loss 1 but not loss 2. Since it doesn't report an error, I don't know what happened. I tried to use debug to trace, but I am not familiar with multi-process debugging. Every time I reach the stuck position, debug doesn't work anymore. ```python import os import numpy as np import torch import torch.nn.functional as F from torch import nn, Tensor, optim, distributed from torch.cuda.amp import autocast, GradScaler from torch.nn import Conv2d, LeakyReLU, MaxPool2d from torch.nn.parallel import DistributedDataParallel as DDP import torch.multiprocessing as mp from typing import Dict, Tuple class ToyMoulde(nn.Module): def __init__(self) -> None: super().__init__() self.layer1 = nn.Sequential( Conv2d(1, 32, (5, 5), padding=2, bias=False), LeakyReLU(inplace=True), MaxPool2d(kernel_size=2, stride=2), Conv2d(32, 64, (3, 3), padding=1, bias=False), LeakyReLU(inplace=True), MaxPool2d(kernel_size=2, stride=2), Conv2d(64, 128, (3, 3), padding=1, bias=False), LeakyReLU(inplace=True), Conv2d(128, 128, (3, 3), padding=1, bias=False), LeakyReLU(inplace=True), ) self.linear1 = nn.Linear(128, 1024) def forward(self, input_): x = self.layer1(input_) x = torch.max(x, dim=-1)[0] x = torch.max(x, dim=-1, keepdim=True)[0] x = x.permute(0, 2, 1).contiguous() x = self.linear1(x) x = x.permute(0, 2, 1).contiguous() return x def parallel_network(network: nn.Module) -> DDP: distributed.barrier() network_ = torch.nn.SyncBatchNorm.convert_sync_batchnorm(network) network_ = DDP( network_, device_ids=[distributed.get_rank()], output_device=distributed.get_rank(), ) return network_ def generateInput(batch_size): x = torch.rand(batch_size, 1, 64, 64) y = torch.rand(batch_size) * 8 return (x, y) def main(local_rank): if distributed.is_nccl_available(): backend = "nccl" else: backend = "gloo" print(f"backend is {backend}") distributed.init_process_group( backend=backend, init_method="env://", world_size=torch.cuda.device_count(), rank=local_rank, ) distributed.barrier() DistInfo.init() torch.cuda.set_device(local_rank) device = torch.cuda.current_device() network = ToyMoulde() network.to(device) network = parallel_network(network) scaler = GradScaler(enabled=True) optimizer = optim.Adam(network.parameters(), lr=1.0e-4) Loss = TripletLoss(0.2) for train_iter in range(10): distributed.barrier() print("------------------------", end="") if train_iter < 1: samples, lables = generateInput(64) else: samples, lables = generateInput(64 - local_rank * 5) samples = samples.to(device) lables = lables.to(device).long() with autocast(enabled=True): predict = network(samples) print(f"\n{DistInfo.local_rank} rank, {train_iter} iter\n", end="") distributed.barrier() loss = Loss(predict, lables) scaler.scale(loss).backward() scaler.step(optimizer) scaler.update() if __name__ == "__main__": os.environ["MASTER_ADDR"] = "127.0.0.1" os.environ["MASTER_PORT"] = "8888" mp.spawn( main, nprocs=torch.cuda.device_count(), ) class TripletLoss(nn.Module): def __init__(self, margin): super().__init__() self.dist_info = DistInfo() self.margin = margin self.metric_func = euclidean def forward(self, feature_: Tensor, label_: Tensor) -> Tuple[Tensor, Dict]: feature = cat_all_gather(feature_) label = cat_all_gather(label_) feature = feature.permute(2, 0, 1).contiguous() dist: Tensor = self.metric_func(feature, feature) p_mask, n_mask = self.get_mask(label) print(f"{DistInfo.local_rank} rank, loss 1") p_dist, n_dist = self.compute_triplet_dist(dist, p_mask, n_mask) print(f"{DistInfo.local_rank} rank, loss 2") triplet_loss = F.relu(p_dist - n_dist + self.margin) batch_loss, loss_num = self.compute_batch_loss(triplet_loss) return batch_loss.mean() @staticmethod def get_mask(label: Tensor) -> Tuple[torch.BoolTensor, torch.BoolTensor]: row_label: Tensor = label.unsqueeze(0) col_label: Tensor = label.unsqueeze(1) p_mask: torch.BoolTensor = row_label == col_label n_mask: torch.BoolTensor = ~p_mask return p_mask, n_mask @staticmethod def compute_triplet_dist( dist: Tensor, p_mask: torch.BoolTensor, n_mask: torch.BoolTensor ): pmask: Tensor = p_mask.byte() nmask: Tensor = n_mask.byte() pmask = pmask.fill_diagonal_(0) pmask = pmask.unsqueeze(2) nmask = nmask.unsqueeze(1) triplet = pmask * nmask a_idx, p_idx, n_idx = torch.where(triplet) p_dist = dist[:, a_idx, p_idx] n_dist = dist[:, a_idx, n_idx] return p_dist, n_dist @staticmethod def compute_batch_loss(triplet_loss): eps = 1.0e-9 loss_sum = triplet_loss.sum(-1) loss_num = (triplet_loss != 0).sum(-1).float() batch_loss = loss_sum / (loss_num + eps) batch_loss[loss_num == 0] = 0 return batch_loss, loss_num def cat_all_gather(input_: torch.Tensor): if not isinstance(input_, torch.Tensor): raise TypeError(f"input must is a torch.Tensor, but input is {type(input_)}") if DistInfo.world_size == 1: return input_ gather_list = [torch.empty_like(input_) for _ in range(DistInfo.world_size)] distributed.all_gather(gather_list, input_) gather_list[DistInfo.local_rank] = input_ output = torch.cat(gather_list, 0).contiguous() return output class DistInfo: is_parallel = False local_rank = 0 world_size = 1 def __init__(self): if distributed.is_initialized(): DistInfo.is_parallel = True DistInfo.local_rank = distributed.get_rank() DistInfo.world_size = distributed.get_world_size() @classmethod def init(cls): if distributed.is_initialized(): cls.is_parallel = True cls.local_rank = distributed.get_rank() cls.world_size = distributed.get_world_size() def euclidean(probe: torch.Tensor, gallery: torch.Tensor) -> torch.Tensor: x2 = torch.sum(probe**2, -1).unsqueeze(-1) y2 = torch.sum(gallery**2, -1).unsqueeze(-2) inner = probe.matmul(gallery.transpose(-1, -2)) dist = x2 + y2 - 2 * inner dist = torch.sqrt(F.relu(dist)) return dist ``` ``` backend is nccl backend is nccl ------------------------------------------------ 0 rank, 0 iter 1 rank, 0 iter 1 rank, loss 1 0 rank, loss 1 1 rank, loss 2 0 rank, loss 2 ------------------------------------------------ 0 rank, 1 iter 1 rank, 1 iter 0 rank, loss 1 1 rank, loss 1 1 rank, loss 2 ``` ### Versions ``` Collecting environment information... PyTorch version: 1.13.0+cu117 Is debug build: False CUDA used to build PyTorch: 11.7 ROCM used to build PyTorch: N/A OS: Ubuntu 22.04.2 LTS (x86_64) GCC version: (Ubuntu 11.3.0-1ubuntu1~22.04) 11.3.0 Clang version: Could not collect CMake version: Could not collect Libc version: glibc-2.35 Python version: 3.10.8 (main, Nov 4 2022, 13:48:29) [GCC 11.2.0] (64-bit runtime) Python platform: Linux-5.15.0-67-generic-x86_64-with-glibc2.35 Is CUDA available: True CUDA runtime version: Could not collect CUDA_MODULE_LOADING set to: LAZY GPU models and configuration: GPU 0: NVIDIA GeForce RTX 3090 GPU 1: NVIDIA GeForce RTX 3090 Nvidia driver version: 525.89.02 cuDNN version: Could not collect HIP runtime version: N/A MIOpen runtime version: N/A Is XNNPACK available: True CPU: Architecture: x86_64 CPU op-mode(s): 32-bit, 64-bit Address sizes: 46 bits physical, 48 bits virtual Byte Order: Little Endian CPU(s): 20 On-line CPU(s) list: 0-19 Vendor ID: GenuineIntel Model name: Intel(R) Xeon(R) Silver 4210R CPU @ 2.40GHz CPU family: 6 Model: 85 Thread(s) per core: 1 Core(s) per socket: 10 Socket(s): 2 Stepping: 7 CPU max MHz: 3200.0000 CPU min MHz: 1000.0000 BogoMIPS: 4800.00 Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 cdp_l3 invpcid_single intel_ppin ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm mpx rdt_a avx512f avx512dq rdseed adx smap clflushopt clwb intel_pt avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm ida arat pln pts hwp hwp_act_window hwp_epp hwp_pkg_req pku ospke avx512_vnni md_clear flush_l1d arch_capabilities Virtualization: VT-x L1d cache: 640 KiB (20 instances) L1i cache: 640 KiB (20 instances) L2 cache: 20 MiB (20 instances) L3 cache: 27.5 MiB (2 instances) NUMA node(s): 2 NUMA node0 CPU(s): 0-9 NUMA node1 CPU(s): 10-19 Vulnerability Itlb multihit: KVM: Mitigation: VMX disabled Vulnerability L1tf: Not affected Vulnerability Mds: Not affected Vulnerability Meltdown: Not affected Vulnerability Mmio stale data: Mitigation; Clear CPU buffers; SMT disabled Vulnerability Retbleed: Mitigation; Enhanced IBRS Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization Vulnerability Spectre v2: Mitigation; Enhanced IBRS, IBPB conditional, RSB filling, PBRSB-eIBRS SW sequence Vulnerability Srbds: Not affected Vulnerability Tsx async abort: Mitigation; TSX disabled Versions of relevant libraries: [pip3] mypy-extensions==0.4.3 [pip3] numpy==1.23.4 [pip3] torch==1.13.0+cu117 [pip3] torchaudio==0.13.0+cu117 [pip3] torchvision==0.14.0+cu117 [conda] numpy 1.23.4 pypi_0 pypi [conda] torch 1.13.0+cu117 pypi_0 pypi [conda] torchaudio 0.13.0+cu117 pypi_0 pypi [conda] torchvision 0.14.0+cu117 pypi_0 pypi ``` cc @mrshenli @pritamdamania87 @zhaojuanmao @satgera @rohan-varma @gqchen @aazzolini @osalpekar @jiayisuse @H-Huang @kwen2501 @awgu
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[Inductor] [CPU] Huggingface model BartForCausalLM & MBartForCausalLM & OPTForCausalLM & PLBartForCausalLM performance regression > 10% on 2023-04-02 nightly release
triaged, oncall: pt2, module: inductor, module: cpu inductor
### 🐛 Describe the bug Compare with the 2023-03-29, there is a performance regression on huggingface model**BartForCausalLM & MBartForCausalLM & OPTForCausalLM & PLBartForCausalLM** on [TorchInductor CPU Performance Dashboard](https://github.com/pytorch/pytorch/issues/93531#issuecomment-1495275117) on 2023-04-02 as bellow: | | 2023-04-02 | | | | 2023-03-29 | | | | Result Comp | | | | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | | model | batch_size | speedup | inductor | eager | batch_size | speedup | inductor | eager | speedup ratio | eager ratio | inductor ratio | | BartForCausalLM | 1 | 0.8682 | 3.6489689 | 3.168034799 | 1 | 1.0814 | 2.9063651 | 3.142943219 | 0.8 | 0.99 | 0.8 | MBartForCausalLM | 1 | 0.8722 | 3.6418691 | 3.176438229 | 1 | 1.08 | 2.9201856 | 3.153800448 | 0.81 | 0.99 | 0.8 | OPTForCausalLM | 1 | 0.7925 | 7.1054617 | 5.631078397 | 1 | 1.1534 | 4.8903451 | 5.640524038 | 0.69 | 1 | 0.69 | PLBartForCausalLM | 1 | 0.8884 | 1.4350074 | 1.274860574 | 1 | 1.1012 | 1.1629979 | 1.280693287 | 0.81 | 1 | 0.81 2023-04-02 nightly release SW information: SW | Nightly commit | Master/Main commit -- | -- | -- Pytorch|[5775e1c1](https://github.com/pytorch/pytorch/commit/5775e1c1)|[7fcff01](https://github.com/pytorch/pytorch/commit/7fcff01) Torchbench|/|[83a316df](https://github.com/pytorch/benchmark/commit/83a316df) torchaudio|[375e751](https://github.com/pytorch/audio/commit/375e751)|[a8f4e97](https://github.com/pytorch/audio/commit/a8f4e97) torchtext|[9749082](https://github.com/pytorch/text/commit/9749082)| [46e7eef](https://github.com/pytorch/text/commit/46e7eef) torchvision|[8d15ca7](https://github.com/pytorch/vision/commit/8d15ca7)|[98c5815](https://github.com/pytorch/vision/commit/98c5815) torchdata|[b3048d5](https://github.com/pytorch/data/commit/b3048d5)|[f1283eb](https://github.com/pytorch/data/commit/f1283eb) dynamo_benchmarks|[1238ae3](https://github.com/pytorch/pytorch/commit/1238ae3)|/ 2023-03-29 nightly release SW information: SW | Nightly commit | Master/Main commit -- | -- | -- Pytorch|[f1f0a4f](https://github.com/pytorch/pytorch/commit/f1f0a4f)|[91166ef](https://github.com/pytorch/pytorch/commit/91166ef) Torchbench|/|[83a316df](https://github.com/pytorch/benchmark/commit/83a316df) torchaudio|[375e751](https://github.com/pytorch/audio/commit/375e751)|[a8f4e97](https://github.com/pytorch/audio/commit/a8f4e97) torchtext|[9749082](https://github.com/pytorch/text/commit/9749082)| [46e7eef](https://github.com/pytorch/text/commit/46e7eef) torchvision|[8d15ca7](https://github.com/pytorch/vision/commit/8d15ca7)|[98c5815](https://github.com/pytorch/vision/commit/98c5815) torchdata|[b3048d5](https://github.com/pytorch/data/commit/b3048d5)|[f1283eb](https://github.com/pytorch/data/commit/f1283eb) dynamo_benchmarks|[1238ae3](https://github.com/pytorch/pytorch/commit/1238ae3)|/ Graph dump by cosim: ### Versions Minified repro: ``` python -m torch.backends.xeon.run_cpu --core_list 0 --ncores_per_instance 1 benchmarks/dynamo/huggingface.py --performance --float32 -dcpu -n50 --inductor --no-skip --dashboard --only BartForCausalLM --cold_start_latency --batch_size 1 --threads 1 ``` cc @ezyang @soumith @msaroufim @wconstab @ngimel @bdhirsh @voznesenskym @penguinwu @anijain2305 @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @Xia-Weiwen @wenzhe-nrv @jiayisunx @peterbell10 @desertfire
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