I try to train ACTOR model with default deepspeed architecture in LLaMA 7B model.
However, when my batch size is 1, the code is OK. It would crash with more than 2 batch size.
Its Error report:
/root/InstructUIE/run_llama/nebullvm/apps/accelerate/chatllama/chatllama/llama_model.py:29 │ │ 3 in forward │ │ │ │ 290 │ │ │ │ 291 │ │ bsz, seqlen, _ = x.shape │ │ 292 │ │ print(x.shape) │ │ ❱ 293 │ │ xq, xk, xv = self.wq(x), self.wk(x), self.wv(x) │ │ 294 │ │ │ │ 295 │ │ xq = xq.view(bsz, seqlen, self.n_local_heads, self.head_dim) │ │ 296 │ │ xk = xk.view(bsz, seqlen, self.n_local_heads, self.head_dim) │ │ │ │ /opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py:1194 in _call_impl │ │ │ │ 1191 │ │ # this function, and just call forward. │ │ 1192 │ │ if not (self._backward_hooks or self._forward_hooks or self._forward_pre_ho │ │ 1193 │ │ │ │ or _global_forward_hooks or _global_forward_pre_hooks): │ │ ❱ 1194 │ │ │ return forward_call(*input, **kwargs) │ │ 1195 │ │ # Do not call functions when jit is used │ │ 1196 │ │ full_backward_hooks, non_full_backward_hooks = [], [] │ │ 1197 │ │ if self._backward_hooks or _global_backward_hooks: │ │ │ │ /opt/conda/lib/python3.7/site-packages/torch/nn/modules/linear.py:114 in forward │ │ │ │ 111 │ │ │ init.uniform_(self.bias, -bound, bound) │ │ 112 │ │ │ 113 │ def forward(self, input: Tensor) -> Tensor: │ │ ❱ 114 │ │ return F.linear(input, self.weight, self.bias) │ │ 115 │ │ │ 116 │ def extra_repr(self) -> str: │ │ 117 │ │ return 'in_features={}, out_features={}, bias={}'.format( │ ╰────────────────────────────────────────────────────────────────────────────────────────────╯ RuntimeError: CUDA error: CUBLAS_STATUS_EXECUTION_FAILED when callingcublasGemmEx( handle,
opa, opb, m, n, k, &falpha, a, CUDA_R_16BF, lda, b, CUDA_R_16BF, ldb, &fbeta, c, CUDA_R_16BF,
ldc, CUDA_R_32F, CUBLAS_GEMM_DFALT_TENSOR_OP) terminate called after throwing an instance of 'c10::Error' what(): CUDA error: device-side assert triggered CUDA kernel errors might be asynchronously reported at some other API call,so the stacktrace below might be incorrect. For debugging consider passing CUDA_LAUNCH_BLOCKING=1.
I checked the tensor shape and linear layer, its OK, but why?
Here is my deepspeed config file
{ "train_micro_batch_size_per_gpu": 2, "gradient_accumulation_steps": 64, "bfloat16": { "enabled": true }, "optimizer": { "type": "AdamW", "params": { "lr": 0.0001, "betas": [0.9, 0.999], "eps": 1e-8, "weight_decay": 0.1 } }, "zero_optimization": { "stage": 2, "offload_optimizer": { "device": "cpu", "pin_memory": true }, "offload_param": { "device": "cpu", "pin_memory": true }, "allgather_partitions": true, "allgather_bucket_size": 2e8, "overlap_comm": true, "reduce_scatter": true, "reduce_bucket_size": 2e8, "contiguous_gradients": true } }
My GPU are A100PCIe *8, here is my environment:
`
(base) root@61e731354b65:~# nvcc --version
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2022 NVIDIA Corporation
Built on Tue_May__3_18:49:52_PDT_2022
Cuda compilation tools, release 11.7, V11.7.64
Build cuda_11.7.r11.7/compiler.31294372_0
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 18.04.6 LTS (x86_64)
GCC version: (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0
Clang version: Could not collect
CMake version: Could not collect
Libc version: glibc-2.17
I try to train ACTOR model with default deepspeed architecture in LLaMA 7B model. However, when my batch size is 1, the code is OK. It would crash with more than 2 batch size. Its Error report:
/root/InstructUIE/run_llama/nebullvm/apps/accelerate/chatllama/chatllama/llama_model.py:29 │ │ 3 in forward │ │ │ │ 290 │ │ │ │ 291 │ │ bsz, seqlen, _ = x.shape │ │ 292 │ │ print(x.shape) │ │ ❱ 293 │ │ xq, xk, xv = self.wq(x), self.wk(x), self.wv(x) │ │ 294 │ │ │ │ 295 │ │ xq = xq.view(bsz, seqlen, self.n_local_heads, self.head_dim) │ │ 296 │ │ xk = xk.view(bsz, seqlen, self.n_local_heads, self.head_dim) │ │ │ │ /opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py:1194 in _call_impl │ │ │ │ 1191 │ │ # this function, and just call forward. │ │ 1192 │ │ if not (self._backward_hooks or self._forward_hooks or self._forward_pre_ho │ │ 1193 │ │ │ │ or _global_forward_hooks or _global_forward_pre_hooks): │ │ ❱ 1194 │ │ │ return forward_call(*input, **kwargs) │ │ 1195 │ │ # Do not call functions when jit is used │ │ 1196 │ │ full_backward_hooks, non_full_backward_hooks = [], [] │ │ 1197 │ │ if self._backward_hooks or _global_backward_hooks: │ │ │ │ /opt/conda/lib/python3.7/site-packages/torch/nn/modules/linear.py:114 in forward │ │ │ │ 111 │ │ │ init.uniform_(self.bias, -bound, bound) │ │ 112 │ │ │ 113 │ def forward(self, input: Tensor) -> Tensor: │ │ ❱ 114 │ │ return F.linear(input, self.weight, self.bias) │ │ 115 │ │ │ 116 │ def extra_repr(self) -> str: │ │ 117 │ │ return 'in_features={}, out_features={}, bias={}'.format( │ ╰────────────────────────────────────────────────────────────────────────────────────────────╯ RuntimeError: CUDA error: CUBLAS_STATUS_EXECUTION_FAILED when callingcublasGemmEx( handle, opa, opb, m, n, k, &falpha, a, CUDA_R_16BF, lda, b, CUDA_R_16BF, ldb, &fbeta, c, CUDA_R_16BF, ldc, CUDA_R_32F, CUBLAS_GEMM_DFALT_TENSOR_OP)terminate called after throwing an instance of 'c10::Error' what(): CUDA error: device-side assert triggered CUDA kernel errors might be asynchronously reported at some other API call,so the stacktrace below might be incorrect. For debugging consider passing CUDA_LAUNCH_BLOCKING=1.I checked the tensor shape and linear layer, its OK, but why? Here is my deepspeed config file{ "train_micro_batch_size_per_gpu": 2, "gradient_accumulation_steps": 64, "bfloat16": { "enabled": true }, "optimizer": { "type": "AdamW", "params": { "lr": 0.0001, "betas": [0.9, 0.999], "eps": 1e-8, "weight_decay": 0.1 } }, "zero_optimization": { "stage": 2, "offload_optimizer": { "device": "cpu", "pin_memory": true }, "offload_param": { "device": "cpu", "pin_memory": true }, "allgather_partitions": true, "allgather_bucket_size": 2e8, "overlap_comm": true, "reduce_scatter": true, "reduce_bucket_size": 2e8, "contiguous_gradients": true } }My GPU are A100PCIe *8, here is my environment: ` (base) root@61e731354b65:~# nvcc --version nvcc: NVIDIA (R) Cuda compiler driver Copyright (c) 2005-2022 NVIDIA Corporation Built on Tue_May__3_18:49:52_PDT_2022 Cuda compilation tools, release 11.7, V11.7.64 Build cuda_11.7.r11.7/compiler.31294372_0
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 18.04.6 LTS (x86_64) GCC version: (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0 Clang version: Could not collect CMake version: Could not collect Libc version: glibc-2.17
Python version: 3.7.13 (default, Mar 29 2022, 02:18:16) [GCC 7.5.0] (64-bit runtime) Python platform: Linux-3.10.0-1160.71.1.el7.x86_64-x86_64-with-debian-buster-sid Is CUDA available: True CUDA runtime version: 11.7.64 CUDA_MODULE_LOADING set to: LAZY GPU models and configuration: GPU 0: NVIDIA A100 80GB PCIe GPU 1: NVIDIA A100 80GB PCIe GPU 2: NVIDIA A100 80GB PCIe GPU 3: NVIDIA A100 80GB PCIe GPU 4: NVIDIA A100 80GB PCIe GPU 5: NVIDIA A100 80GB PCIe GPU 6: NVIDIA A100 80GB PCIe GPU 7: NVIDIA A100 80GB PCIe
Nvidia driver version: 510.54 cuDNN version: Probably one of the following: /usr/lib/x86_64-linux-gnu/libcudnn.so.8.2.0 /usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.2.0 /usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.2.0 /usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.2.0 /usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.2.0 /usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.2.0 /usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.2.0 /usr/local/cuda-11.7/targets/x86_64-linux/lib/libcudnn.so.8 /usr/local/cuda-11.7/targets/x86_64-linux/lib/libcudnn_adv_infer.so.8 /usr/local/cuda-11.7/targets/x86_64-linux/lib/libcudnn_adv_train.so.8 /usr/local/cuda-11.7/targets/x86_64-linux/lib/libcudnn_cnn_infer.so.8 /usr/local/cuda-11.7/targets/x86_64-linux/lib/libcudnn_cnn_train.so.8 /usr/local/cuda-11.7/targets/x86_64-linux/lib/libcudnn_ops_infer.so.8 /usr/local/cuda-11.7/targets/x86_64-linux/lib/libcudnn_ops_train.so.8 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 CPU(s): 72 On-line CPU(s) list: 0-71 Thread(s) per core: 2 Core(s) per socket: 18 Socket(s): 2 NUMA node(s): 2 Vendor ID: GenuineIntel CPU family: 6 Model: 85 Model name: Intel(R) Xeon(R) Gold 6154 CPU @ 3.00GHz Stepping: 4 CPU MHz: 1199.890 CPU max MHz: 3700.0000 CPU min MHz: 1200.0000 BogoMIPS: 6000.00 Virtualization: VT-x L1d cache: 32K L1i cache: 32K L2 cache: 1024K L3 cache: 25344K NUMA node0 CPU(s): 0-17,36-53 NUMA node1 CPU(s): 18-35,54-71 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 aperfmperf eagerfpu 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 epb cat_l3 cdp_l3 invpcid_single intel_ppin intel_pt ssbd mba ibrs ibpb stibp tpr_shadow vnmi flexpriority ept vpid fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm cqm mpx rdt_a avx512f avx512dq rdseed adx smap clflushopt clwb avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 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 md_clear spec_ctrl intel_stibp flush_l1d arch_capabilities
Versions of relevant libraries: [pip3] numpy==1.21.5 [pip3] torch==1.13.1+cu117 [pip3] torchaudio==0.13.1+cu117 [pip3] torchelastic==0.2.0 [pip3] torchtext==0.13.0 [pip3] torchvision==0.14.1+cu117 [conda] blas 1.0 mkl [conda] cudatoolkit 11.3.1 ha36c431_9 nvidia [conda] ffmpeg 4.3 hf484d3e_0 pytorch [conda] mkl 2021.4.0 h06a4308_640 [conda] mkl-service 2.4.0 py37h7f8727e_0 [conda] mkl_fft 1.3.1 py37hd3c417c_0 [conda] mkl_random 1.2.2 py37h51133e4_0 [conda] numpy 1.21.5 py37he7a7128_2 [conda] numpy-base 1.21.5 py37hf524024_2 [conda] pytorch-mutex 1.0 cuda pytorch [conda] torch 1.13.1+cu117 pypi_0 pypi [conda] torchaudio 0.13.1+cu117 pypi_0 pypi [conda] torchelastic 0.2.0 pypi_0 pypi [conda] torchtext 0.13.0 py37 pytorch [conda] torchvision 0.14.1+cu117 pypi_0 pypi` What happened?