Comments (6)
This works for me on main.
- What error message are you getting?
- could you try again with a pytorch nightly?
also cc @guilhermeleobas
from pytorch.
I found a similar question link
from pytorch.
This works for me on main.
- What error message are you getting?
- could you try again with a pytorch nightly?
also cc @guilhermeleobas
Thank you. I'll try pytorch nightly again later today.
Unsupported: Unsupported: meta converter nyi with fake tensor propagation.
from user code:
File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/external_utils.py", line 17, in inner
return fn(*args, **kwargs)
Set TORCH_LOGS="+dynamo" and TORCHDYNAMO_VERBOSE=1 for more information
You can suppress this exception and fall back to eager by setting:
import torch._dynamo
torch._dynamo.config.suppress_errors = True
if I use torch._dynamo.config.suppress_errors
Unsupported: Unsupported: meta converter nyi with fake tensor propagation.
from pytorch.
torch nightly s also not working properly @guilhermeleobas
**Error** Unsupported: Unsupported: meta converter nyi with fake tensor propagation.
**System Information**
Collecting environment information...
PyTorch version: 2.4.0.dev20240401+cpu
Is debug build: False
CUDA used to build PyTorch: Could not collect
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: 14.0.0-1ubuntu1.1
CMake version: version 3.27.9
Libc version: glibc-2.35
Python version: 3.10.12 (main, Nov 20 2023, 15:14:05) [GCC 11.4.0] (64-bit runtime)
Python platform: Linux-6.1.58+-x86_64-with-glibc2.35
Is CUDA available: False
CUDA runtime version: 12.2.140
CUDA_MODULE_LOADING set to: N/A
GPU models and configuration: GPU 0: Tesla T4
Nvidia driver version: 535.104.05
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.8.9.6
/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.9.6
/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.9.6
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.9.6
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.9.6
/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.9.6
/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.9.6
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): 2
On-line CPU(s) list: 0,1
Vendor ID: GenuineIntel
Model name: Intel(R) Xeon(R) CPU @ 2.20GHz
CPU family: 6
Model: 79
Thread(s) per core: 2
Core(s) per socket: 1
Socket(s): 1
Stepping: 0
BogoMIPS: 4399.99
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
Hypervisor vendor: KVM
Virtualization type: full
L1d cache: 32 KiB (1 instance)
L1i cache: 32 KiB (1 instance)
L2 cache: 256 KiB (1 instance)
L3 cache: 55 MiB (1 instance)
NUMA node(s): 1
NUMA node0 CPU(s): 0,1
Vulnerability Gather data sampling: Not affected
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 rstack overflow: Not affected
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
Versions of relevant libraries:
[pip3] numpy==1.25.2
[pip3] torch==2.4.0.dev20240401+cpu
[pip3] torchaudio==2.2.0.dev20240402+cpu
[pip3] torchdata==0.7.1
[pip3] torchsummary==1.5.1
[pip3] torchtext==0.17.1
[pip3] torchvision==0.19.0.dev20240402+cpu
[pip3] triton==2.2.0
from pytorch.
em... the code produces correct result for me:
tensor([ 0.5000, 1.0000, 6.0000, 8.0000, 10.0000])
Do you mind running the repro again with TORCH_LOGS=+dynamo
and sharing the full error message?
from pytorch.
em... the code produces correct result for me:
tensor([ 0.5000, 1.0000, 6.0000, 8.0000, 10.0000])
Do you mind running the repro again with
TORCH_LOGS=+dynamo
and sharing the full error message?
thank you very much
from pytorch.
Related Issues (20)
- KINETO_USE_DAEMON causing issues
- `torch.compile` and complex numbers HOT 2
- Support dynamo tracing weakref obj
- Migrate multiple/custom runner labels before deprecation
- torch._inductor.config.max_autotune_gemm_backends = "TRITON" crashes with Convolution layer
- ☂️ `torch.compile` generates slower code for LLMs than eager on ARM platform (M1/AARCH64)
- [ARM] `Vectorized<half>::loadu(x, 8)` yields slow code if `-fno-unsafe-math-optimizations` are used HOT 3
- [FSDP2] _sharded_param_data is sitll on meta while sharded_param moved to cuda after calling initialize_parameters() HOT 2
- [Distributed Checkpoint] When loading FSDP sharded checkpointing each rank needs all the checkpointing files HOT 1
- [DTensor][Tensor Parallel] transformer test numerical issue when `dtype=torch.float32`
- Improve oneDNN memory alloction performance for pytorch Windows HOT 1
- torch.compile error: Attempting to broadcast a dimension of length 2 at -1 HOT 2
- DefaultCPUAllocator: not enough memory in matrix multiplication broadcasting HOT 2
- 'MultiHeadAttention.attention' is being compiled since it was called from 'MultiHeadAttention.forward'
- Support third-party devices emit an range for each autograd operator
- ONNX export success, but Load model from onnx failed
- Excution difference between Mac and Linux HOT 6
- dynamo test (test_model_output.py) failing on cpu devices because of cuda hardcoding for the device HOT 1
- torch C10 half type not compatible with nvidia __half atomicAdd HOT 1
- RFC: Integrate Arm Compute Library (ACL) into PyTorch as a submodule and add build support in setup.py
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from pytorch.