3、如果在使用deepspeed的stage 3 的过程中出现了backward pass is invalid for module in evaluation mode" with deepspeed stage 3 这个是因为训练过程中出现了model.eval(),或者model中出现了self.eval() 4、RuntimeError: Input tensor data type is not supported for NCCL process group 解释: 这个错误通常...
if self.fp16_enabled() and not get_accelerator().is_fp16_supported(): Copy link Contributor tjruwaseDec 21, 2023 Can you please move this into_do_sanity_check()? Sorry, something went wrong. Copy link ContributorAuthor nelyahuDec 24, 2023 ...
1.fp16: 这部分配置与半精度浮点数(16位浮点数)计算相关。它有助于加快训练速度,同时减少内存使用。 enabled: 是否启用半精度浮点数。 autocast: 是否自动将数据类型转换为半精度。 loss_scale: 损失缩放值,用于防止半精度下的数值下溢。 loss_scale_window: 调整损失缩放值的窗口大小。 initial_scale_power: 损...
最近在跑chatglm2的sft的时候出现了下面的错误,我的运行方式是bf16, deepspeed zero3,因为担心fp16会有很多的nan. File "/home/suser/.conda/envs/llm/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1538, in _call_impl return func(*args, **kwargs) File "/home/suser/.conda/en...
I assume this may be an issue on the DeepSpeed side so happy to open an issue there as well if needed. Expected behavior Shouldn't we create abfloat16optimizer instead offp16? I am just usingtorch.optim.Adam
This needs to be on a local storage of a node (not on a shared storage)' ) parser.add_argument( "--model_name_or_path", type=str, help= "Path to pretrained model or model identifier from huggingface.co/models.", required=True, ) parser.add_argument( "--per_device_train_batch_...
parameters, MII supports INT8 Inference with ZeroQuant. Using this feature not only reduces the memory footprint and the number of GPUs required for inference but also increases the inference throughput by supporting larger batch sizes and using INT8 compute, thus lowering cost compared to FP16....
(VNNI). Its bfloat16 performance is 1024 operations per cycle compared to its 32-bit floating point (FP32) performance of 64 operations per cycle. Therefore, Intel AMX can significantly speed up deep learning applications when int8 or bfloat16 datatype is used for matrix multiplication or ...
--fp16 save Conv's weight/bias in half_float data type --benchmarkModel Do NOT save big size data, such as Conv's weight,BN's gamma,beta,mean and variance etc. Only used to test the cost of the model --bizCode arg MNN Model Flag, ex: MNN ...
{'weight_quantization': {'shared_parameters': {'enabled': False, 'quantizer_kernel': False, 'schedule_offset': 0, 'quantize_groups': 1, 'quantize_verbose': False, 'quantization_type': 'symmetric', 'quantize_weight_in_forward': False, 'rounding': 'nearest', 'fp16_mixed_quantize': ...