AttributeError: 'MultiheadAttention' object has no attribute 'requires_grad' #111279
Comments
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issue related in |
janeyx99
added
the
oncall: distributed
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If that helps, I don't use Lightning but huggingface accelerate, and I have the same issue with multi GPU training. It works fine with torch 2.0.1, but I get this error with 2.1.0 and 2.1.1. You mentioned it works with strategy="auto" but I'm not sure what that means in Lightning. No information on the docs. Does that still allow parallel training? Could you find a workaround so far? |
I guess "auto" means automatic choose one strategy (parallel training or not) according to the number of GPU specified. So it allows parallel training when the number of GPUs is greater than 1. I don't a workaround yet. |
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Have you tried the same exact setup but downgrading to torch==2.0.1? (and downgrade accordingly torchvision, triton, or whatever else you need) |
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Yes. Torch==2.0.1 works fine. |
quancs
changed the title
janeyx99
added
high priority
module: regression
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Marking as hi-pri due to it being a regression, cc @wconstab |
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As a workaround/unblock, you can workaround by setting: (tested locally) |
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This patch also fixes the error for me. I'm not sure if it handles the DDPOptimizer's bucketing logic properly though: |
yf225
added
triaged
This PR fixes Issue #111279. While #111279 reported the issue with `MultiheadAttention`, a minimal reproduction would be: ```python class ToyModel(nn.Module): def __init__(self,): super().__init__() self.linear = nn.Linear(128, 10) def forward(self, x: torch.Tensor) -> torch.Tensor: return self.linear.forward(x) # Error # return self.linear(x) # OK ``` Dynamo treats `self.linear(x)` as `call_module` while treating `self.linear.forward(x)` as a [`get_attr` and a `call_method`](https://github.com/pytorch/pytorch/blob/main/torch/_dynamo/variables/nn_module.py#L358-L378). However, existing DDPOptimizer assumes, for a `get_attr` node, `getattr(gm, node.target)` gives a tensor with the `requires_grad` attribute. Existing DDPOptimizer also does not support `call_method` nodes. This PR adds support for `call_method` and check on `get_attr`. It also checks if a module's parameters have been added to a bucket to support multiple method calls from the same module. Pull Request resolved: #121771 Approved by: https://github.com/yf225
馃悰 Describe the bug
Cannot train compiled model using
ddp, but single gpu training is OK.CMD:
code (
code.py):The error reported:
Versions
Collecting environment information...
PyTorch version: 2.1.1
Is debug build: False
CUDA used to build PyTorch: 12.1
ROCM used to build PyTorch: N/A
OS: Ubuntu 22.04.3 LTS (x86_64)
GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
Clang version: Could not collect
CMake version: version 3.27.9
Libc version: glibc-2.35
Python version: 3.10.13 (main, Sep 11 2023, 13:44:35) [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-5.15.0-87-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 A100-SXM4-80GB
GPU 1: NVIDIA A100-SXM4-80GB
GPU 2: NVIDIA A100-SXM4-80GB
GPU 3: NVIDIA A100-SXM4-80GB
GPU 4: NVIDIA A100-SXM4-80GB
GPU 5: NVIDIA A100-SXM4-80GB
GPU 6: NVIDIA A100-SXM4-80GB
GPU 7: NVIDIA A100-SXM4-80GB
Nvidia driver version: 535.129.03
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, 57 bits virtual
Byte Order: Little Endian
CPU(s): 128
On-line CPU(s) list: 0-127
Vendor ID: GenuineIntel
Model name: Intel(R) Xeon(R) Platinum 8358 CPU @ 2.60GHz
CPU family: 6
Model: 106
Thread(s) per core: 2
Core(s) per socket: 32
Socket(s): 2
Stepping: 6
CPU max MHz: 3400.0000
CPU min MHz: 800.0000
BogoMIPS: 5200.00
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 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 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 rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb intel_pt avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local split_lock_detect wbnoinvd dtherm ida arat pln pts avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq la57 rdpid fsrm md_clear pconfig flush_l1d arch_capabilities
Virtualization: VT-x
L1d cache: 3 MiB (64 instances)
L1i cache: 2 MiB (64 instances)
L2 cache: 80 MiB (64 instances)
L3 cache: 96 MiB (2 instances)
NUMA node(s): 2
NUMA node0 CPU(s): 0-31,64-95
NUMA node1 CPU(s): 32-63,96-127
Vulnerability Gather data sampling: Mitigation; Microcode
Vulnerability Itlb multihit: Not affected
Vulnerability L1tf: Not affected
Vulnerability Mds: Not affected
Vulnerability Meltdown: Not affected
Vulnerability Mmio stale data: Mitigation; Clear CPU buffers; SMT vulnerable
Vulnerability Retbleed: Not affected
Vulnerability Spec rstack overflow: 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; 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] mypy==1.6.1
[pip3] mypy-extensions==1.0.0
[pip3] numpy==1.23.0
[pip3] pytorch-lightning==2.1.2
[pip3] pytorch-ranger==0.1.1
[pip3] torch==2.1.1
[pip3] torch-complex==0.4.3
[pip3] torch-optimizer==0.1.0
[pip3] torch-stoi==0.1.2
[pip3] torch-tb-profiler==0.4.3
[pip3] torchaudio==2.1.1
[pip3] torchmetrics==1.2.1
[pip3] torchtnt==0.2.1
[pip3] torchvision==0.16.1
[pip3] triton==2.1.0
[conda] blas 1.0 mkl
[conda] ffmpeg 4.3 hf484d3e_0 pytorch
[conda] libjpeg-turbo 2.0.0 h9bf148f_0 pytorch
[conda] mkl 2023.1.0 h213fc3f_46343
[conda] mkl-service 2.4.0 py310h5eee18b_1
[conda] mkl_fft 1.3.8 py310h5eee18b_0
[conda] mkl_random 1.2.4 py310hdb19cb5_0
[conda] numpy 1.23.0 pypi_0 pypi
[conda] numpy-base 1.26.2 py310hb5e798b_0
[conda] pytorch 2.1.1 py3.10_cuda12.1_cudnn8.9.2_0 pytorch
[conda] pytorch-cuda 12.1 ha16c6d3_5 pytorch
[conda] pytorch-lightning 2.1.2 pypi_0 pypi
[conda] pytorch-mutex 1.0 cuda pytorch
[conda] pytorch-ranger 0.1.1 pypi_0 pypi
[conda] torch-complex 0.4.3 pypi_0 pypi
[conda] torch-optimizer 0.1.0 pypi_0 pypi
[conda] torch-stoi 0.1.2 pypi_0 pypi
[conda] torch-tb-profiler 0.4.3 pypi_0 pypi
[conda] torchaudio 2.1.1 py310_cu121 pytorch
[conda] torchmetrics 1.2.1 pypi_0 pypi
[conda] torchtnt 0.2.1 pypi_0 pypi
[conda] torchtriton 2.1.0 py310 pytorch
[conda] torchvision 0.16.1 py310_cu121 pytorch
cc @ezyang @gchanan @zou3519 @kadeng @mrshenli @pritamdamania87 @zhaojuanmao @satgera @rohan-varma @gqchen @aazzolini @osalpekar @jiayisuse @H-Huang @kwen2501 @awgu @penguinwu @fegin @XilunWu @wanchaol @fduwjj @wz337 @tianyu-l @wconstab @yf225 @msaroufim @bdhirsh @anijain2305
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