pytorch Errors

100 entries

Error Code Domain Category Fix Rate Confidence Tier
RuntimeError: torch.cuda.amp.autocast only works with CUDA — pytorch config_error 85% 88% ai_generated
RuntimeError: element 0 of tensors does not require grad and does not have a grad_fn — pytorch autograd_error 90% 91% ai_generated
RuntimeError: The size of tensor a must match the size of tensor b — pytorch shape_error 85% 88% ai_generated
RuntimeError: Error(s) in loading state_dict — pytorch io_error 82% 86% ai_generated
RuntimeError: torch.compile failed for model — pytorch runtime_error 78% 82% ai_generated
RuntimeError: Expected all tensors to be on the same device — pytorch device_error 85% 88% ai_generated
torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 2.00 GiB — pytorch memory_error 85% 92% ai_generated
RuntimeError: cuDNN error: CUDNN_STATUS_EXECUTION_FAILED when calling cudnnRNNBackwardData EXECUTION pytorch cuda_error 78% 85% ai_generated
TypeError: __getitem__ returned wrong type from Dataset — pytorch type_error 82% 86% ai_generated
RuntimeError: DataLoader worker (pid(s) 12345) exited unexpectedly: OSError: [Errno 9] Bad file descriptor — pytorch multiprocessing_error 80% 86% ai_generated
RuntimeError: DataLoader worker (pid 12345) is killed by signal: Killed — pytorch memory_error 82% 88% ai_generated
RuntimeError: DDP initialization failed: NCCL error — pytorch runtime_error 78% 82% ai_generated
RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cuda:0 and cpu — pytorch device_error 92% 90% ai_generated
RuntimeError: expected scalar type Float but found Half — pytorch type_error 85% 88% ai_generated
IndexError: index out of range in Embedding — pytorch runtime_error 85% 88% ai_generated
RuntimeError: FSDP parameter mismatch across ranks — pytorch runtime_error 78% 82% ai_generated
RuntimeError: Loss is NaN or Inf — pytorch training_error 75% 80% ai_generated
RuntimeError: register_hook callback raised exception — pytorch runtime_error 80% 84% ai_generated
RuntimeError: one of the variables needed for gradient computation has been modified by an inplace operation — pytorch autograd_error 85% 88% ai_generated
TracerWarning: Converting a tensor to a Python boolean — pytorch tracing_error 78% 82% ai_generated
Loss is nan at step N — pytorch training_error 75% 80% ai_generated
ValueError: learning rate scheduler step called at wrong time — pytorch config_error 82% 86% ai_generated
GPU memory increasing every iteration: memory leak — pytorch memory_error 78% 82% ai_generated
Inconsistent dropout/batchnorm behavior during inference — pytorch config_error 85% 88% ai_generated
Module is not registered as a submodule — pytorch config_error 82% 86% ai_generated
RuntimeError: Cannot re-initialize CUDA in forked subprocess — pytorch runtime_error 82% 86% ai_generated
RuntimeError: NCCL communicator was aborted on rank 2. Original reason for failure was: watchdog callback timed out — pytorch distributed_error 75% 85% ai_generated
RuntimeError: ONNX export failed for model — pytorch export_error 78% 82% ai_generated
ValueError: optimizer state dict mismatch — pytorch io_error 80% 84% ai_generated
RuntimeError: shared parameter found with different gradients — pytorch autograd_error 80% 84% ai_generated
RuntimeError: cannot pin memory: CUDA not available — pytorch config_error 85% 88% ai_generated
RuntimeError: quantization failed for module — pytorch conversion_error 78% 82% ai_generated
Results not reproducible: different outputs across runs — pytorch config_error 78% 82% ai_generated
RuntimeError: a leaf Variable that requires grad is being used in an in-place operation — pytorch autograd_error 82% 86% ai_generated
RuntimeError: Error(s) in loading state_dict: Missing key(s) in state_dict: "layer4.weight". Unexpected key(s): "module.layer4.weight" — pytorch serialization_error 90% 91% ai_generated
RuntimeError: Sizes of tensors must match except in dimension 1. Expected 64 but got 32 — pytorch shape_error 88% 90% ai_generated
RuntimeError: TorchScript failed to compile model — pytorch compilation_error 78% 82% ai_generated
torch.jit.frontend.UnsupportedNodeError: Set is not supported in TorchScript — pytorch compilation_error 80% 86% ai_generated
TypeError: transform compose received wrong input type — pytorch type_error 82% 86% ai_generated
RuntimeError: MPS backend out of memory (MPS backend does not support float64, falling back to float32) — pytorch runtime_error 82% 88% ai_generated
RuntimeError: torch.compile cache corrupted: invalid key hash for compiled module — pytorch runtime_error 90% 85% ai_generated
RuntimeError: [torch.distributed] Connection closed by peer: cannot connect to rank 2 at 10.0.0.5:29500 (timeout=30) — pytorch network_error 75% 87% ai_generated
RuntimeError: FX tracing failed: 'NoneType' object has no attribute 'shape' during symbolic tracing — pytorch runtime_error 78% 83% ai_generated
RuntimeError: DataLoader worker (pid 12345) pin_memory(): CUDA error: invalid device context — pytorch runtime_error 88% 86% ai_generated
RuntimeError: cuDNN error: CUDNN_STATUS_BAD_PARAM when calling cudnnSetRNNDescriptor_v8 CUDNN_STATUS_BAD_PARAM pytorch runtime_error 80% 85% ai_generated
RuntimeError: CUDA error: device-side assert triggered. Compile with TORCH_USE_CUDA_DSA to enable device-side assertions. CUDA_ERROR_ILLEGAL_ADDRESS pytorch runtime_error 85% 88% ai_generated
RuntimeError: torch.compile: function 'forward' failed with a graph break. Falling back to eager mode. Consider rewriting the function to avoid control flow or dynamic shapes. TORCH_COMPILE_GRAPH_BREAK pytorch module_error 85% 85% ai_generated
RuntimeError: Cannot re-initialize CUDA in forked subprocess. To use CUDA with multiprocessing, you must use the 'spawn' start method. CUDA_ERROR_NOT_INITIALIZED pytorch config_error 90% 90% ai_generated
RuntimeError: step() called before loss.backward(). Ensure you call loss.backward() before optimizer.step(). — pytorch runtime_error 95% 90% ai_generated
RuntimeError: MPS backend does not support float64. Falling back to float32. Please cast your tensors to float32 explicitly. MPS_ERROR_UNSUPPORTED_DTYPE pytorch type_error 90% 88% ai_generated
RuntimeError: DataLoader worker (pid 12345) received signal 11 (Segmentation fault). Possible causes: shared memory exhaustion or corrupted shared memory files in /dev/shm. SIGSEGV pytorch system_error 85% 87% ai_generated
RuntimeError: CUDA error: invalid device ordinal — pytorch runtime_error 85% 88% ai_generated
RuntimeError: DataLoader worker (pid 12345) received signal 11 (Segmentation fault). Possible causes: shared memory exhausted — pytorch system_error 75% 82% ai_generated
RuntimeError: NCCL communicator was aborted on rank 2. Original reason for failure was: watchdog callback timed out — pytorch network_error 70% 80% ai_generated
RuntimeError: TorchScript supports tracing only for tensors. This function cannot be traced because it uses dynamic control flow (if/else or loops) that depends on tensor data. — pytorch type_error 90% 90% ai_generated
RuntimeError: CUDA error: invalid argument — pytorch runtime_error 75% 85% ai_generated
RuntimeError: [torch.distributed] Barrier timeout after 600000 ms — pytorch network_error 80% 88% ai_generated
RuntimeError: Expected dtype float16 for half precision but got float32 — pytorch type_error 78% 82% ai_generated
RuntimeError: FSDP checkpoint loading failed: Unexpected key(s) in state_dict: "module._fsdp_wrapped_module.flat_param" — pytorch module_error 82% 87% ai_generated
RuntimeError: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the trace of this operation. — pytorch type_error 80% 84% ai_generated
RuntimeError: CUDA error: device-side assert triggered. Compile with TORCH_USE_CUDA_DSA to enable device-side assertions — pytorch assertion_error 90% 88% ai_generated
RuntimeError: TorchScript supports tracing only for tensors. This function cannot be traced because it uses dynamic control flow (e.g., if/else or loops). — pytorch runtime_error 90% 88% ai_generated
RuntimeError: cuDNN error: CUDNN_STATUS_NOT_INITIALIZED when calling cudnnCreate CUDNN_STATUS_NOT_INITIALIZED pytorch runtime_error 80% 87% ai_generated
RuntimeError: torch.compile: function 'forward' failed with a graph break. Falling back to eager mode. Consider rewriting the function to avoid Python control flow. — pytorch runtime_error 70% 88% ai_generated
ValueError: optimizer state dict mismatch: loaded state dict contains parameters that are not in the current optimizer. Expected keys: ['param_groups', 'state']. Got: ['param_groups', 'state', 'extra_key'] — pytorch data_error 80% 84% ai_generated
RuntimeError: CUDA error: invalid device function CUDA_ERROR_INVALID_DEVICE_FUNCTION pytorch runtime_error 80% 85% ai_generated
RuntimeError: Trying to backward through the graph a second time, but the saved intermediate values have already been freed. Specify retain_graph=True when calling backward the first time. — pytorch autograd_error 95% 88% ai_generated
RuntimeError: Given groups=1, weight of size [16, 3, 3, 3], expected input[1, 1, 32, 32] to have 3 channels, but got 1 channels instead — pytorch shape_error 95% 90% ai_generated
RuntimeError: Trying to backward through the graph a second time, but the buffers have already been freed. Specify retain_graph=True when calling backward the first time. — pytorch autograd_error 80% 85% ai_generated
RuntimeError: cuDNN version mismatch: PyTorch was compiled against cuDNN X but linked against cuDNN Y — pytorch build_error 85% 88% ai_generated
RuntimeError: parameter group's learning rate is not a float or a tensor of scalar type — pytorch config_error 75% 82% ai_generated
RuntimeError: DataLoader worker (pid 12345) received signal 9 (SIGKILL). Possible causes: out of memory or system limit reached. — pytorch resource_error 80% 85% ai_generated
RuntimeError: Attempting to deserialize object on a CUDA device but torch.cuda.is_available() is False. If you are running on a CPU-only machine, please use torch.load with map_location=torch.device('cpu') to map the storages to the CPU. — pytorch config_error 90% 87% ai_generated
RuntimeError: BucketView has wrong size: expected X, got Y. This could be due to mismatched model parameters across ranks. — pytorch runtime_error 78% 83% ai_generated
RuntimeError: CUDA error: unsupported image type cudaErrorUnsupportedImageType pytorch runtime_error 82% 85% ai_generated
RuntimeError: DataLoader worker (pid 12345) exited unexpectedly: Bus error (core dumped) — pytorch system_error 78% 83% ai_generated
RuntimeError: torch.compile: function 'forward' failed with a graph break. Falling back to eager mode. Consider rewriting the function to avoid unsupported operations. — pytorch runtime_error 75% 84% ai_generated
RuntimeError: NCCL communicator was aborted on rank 2. Original reason for failure was: watchdog callback timed out. — pytorch network_error 78% 86% ai_generated
RuntimeError: torch.compile: function 'forward' failed with a graph break. Falling back to eager mode. Consider rewriting the function to avoid dynamic control flow. — pytorch runtime_error 70% 84% ai_generated
RuntimeError: DataLoader worker (pid 12345) received signal 11 (Segmentation fault). Possible causes: shared memory exhaustion, insufficient shared memory, or too many workers. — pytorch resource_error 85% 87% ai_generated
RuntimeError: MPS backend does not support float64. Falling back to float32. Please cast your tensors to float32 explicitly — pytorch type_error 90% 87% ai_generated
RuntimeError: DataLoader worker (pid 12345) received signal 11 (Segmentation fault). Possible causes: shared memory exhausted or corrupted shared memory files. — pytorch resource_error 78% 82% ai_generated
RuntimeError: FSDP parameter mismatch across ranks. Expected parameter 'model.fc.weight' to have shape [100, 256] but got [100, 128] on rank 2. — pytorch config_error 82% 87% ai_generated
RuntimeError: Loss is NaN or Inf at step 500. Consider reducing learning rate, adding gradient clipping, or checking input data for outliers. — pytorch assertion_error 80% 90% ai_generated
RuntimeError: [torch.distributed] Barrier timeout after 600000 ms. NCCL communicator was aborted on rank 2. Original reason for failure was: watchdog callback timed out. NCCL_ERROR_TIMEOUT pytorch network_error 75% 86% ai_generated
RuntimeError: Loss is NaN or Inf at step 1000 — pytorch runtime_error 85% 85% ai_generated
RuntimeError: DataLoader worker (pid 12345) received signal 11 (Segmentation fault). Possible causes: shared memory exhausted or dataset memory corruption — pytorch system_error 85% 82% ai_generated
RuntimeError: FSDP parameter mismatch across ranks: expected parameter 'layer.0.weight' but got parameter 'module.layer.0.weight' — pytorch runtime_error 90% 86% ai_generated
RuntimeError: GradScaler unscale_() encountered None gradient. Ensure loss.backward() and optimizer.step() are called correctly. — pytorch runtime_error 88% 85% ai_generated
RuntimeError: [torch.distributed] Broadcast failed on rank 3: ncclInternalError: Internal error - unexpected event — pytorch network_error 75% 88% ai_generated
RuntimeError: TorchScript type mismatch: Expected Optional[Tensor] but got Tensor for argument 'mask' — pytorch type_error 90% 82% ai_generated
RuntimeError: DataLoader worker (pid 12345) timed out after 300 seconds waiting for data from queue. Consider increasing timeout or reducing batch size. — pytorch runtime_error 80% 86% ai_generated
RuntimeError: torch.compile: Recompilation limit reached (100 recompilations) for function 'forward'. Consider using dynamic=True or reducing tensor shape variability. — pytorch runtime_error 90% 84% ai_generated
RuntimeError: 1D target tensor expected, multi-target not supported — pytorch shape_error 90% 88% ai_generated
RuntimeError: [torch.distributed] AllReduce timed out in rank 0 (timeout: 600) 2 pytorch network_error 80% 82% ai_generated
RuntimeError: Tracing failed: Recursive trace of function 'forward' is not allowed — pytorch module_error 85% 87% ai_generated
RuntimeError: [Rank 0] NCCL communicator was aborted on rank 2. Original reason for failure was: AllReduce timed out after 600000 ms NCCL_TIMEOUT_600 pytorch network_error 75% 85% ai_generated
TypeError: pic should be PIL Image or ndarray. Got <class 'torch.Tensor'> — pytorch type_error 90% 88% ai_generated
RuntimeError: MPS backend is not available. Please install PyTorch with MPS support or use CPU/CUDA. MPS_NOT_AVAILABLE pytorch config_error 85% 90% ai_generated
RuntimeError: TracerWarning: Using a tensor as a Python bool might cause the trace to be incorrect. We can't record the control flow of this loop. TORCH_SCRIPT_TRACE_DYNAMIC_CF pytorch runtime_error 75% 83% ai_generated