Fsdp wrap policy. _checkpoint. cuda Dec 12, 2023 · about 1 month ago. As an optional exercise, you are welcome to experiment with the code and replace. Should only be used for static Auto-wrapping submodules: instead of manually nested FSDP wrapping, one can also specify an auto_wrap_policy argument to automatically wrap the submodules with inner FSDP. If each process/rank within a node loads the Llama-70B model, it would require 70*4*8 GB ~ 2TB of CPU RAM, where 4 is the number of bytes per parameter and 8 is the Jan 10, 2024 · motivation: fine tuning llava-hf/llava-1. When using the default_auto_wrap_policy, a layer is wrapped in FSDP module if the number of parameters in that layer is more than the min_num_params . fully_sharded_data_parallel import (CPUOffload, BackwardPrefetch,) from torch. dev0 Based off 300d6a4 One additional patch pulled in to fix an (I think) unrelated issue jmif@2fe3989 Installing from jmif@2fe3989 will give you code I'm running Platform: Linux-6. In the code block below we demonstrate how to wrap our model when using FSDP. Running on an NVIDIA A100-SXM4–40GB with 8 GPUs, we are able to reach 2. fsdp_backward_prefetch_policy: [1] BACKWARD_PRE, [2] BACKWARD_POST, [3] NO_PREFETCH. 5-7b-hf with PEFT and accelerate + FSDP System has more than enough VRAM to fit the model and optimizer as I've done it before but tried switching to training with accelerate after running into issues like huggingface/peft#1142 Dec 20, 2022 · 🐛 Describe the bug. May 30, 2023 · compute_environment: LOCAL_MACHINE distributed_type: FSDP downcast_bf16: 'no' dynamo_config: dynamo_backend: NVFUSER fsdp_config: fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP fsdp_backward_prefetch_policy: BACKWARD_PRE fsdp_offload_params: true fsdp_sharding_strategy: 1 fsdp_state_dict_type: FULL_STATE_DICT fsdp_transformer_layer_cls_to_wrap: GPTNeoBlock machine_rank: 0 main_training_function FSDP Warning: When using FSDP, several parameter groups will be conflated into a single one due to nested module wrapping and parameter flattening. FSDP Warning: When using FSDP, it is efficient and recommended to call prepare for the model before creating the optimizer. 12. The following is the peak memory usage from FSDP with auto_wrap policy of MNIST training on a g4dn. The first thing you’d notice if you try this is that pdb may crash your program if you use it from inside a mpirun or torchrun launcher. Hi all! I am currently trying to wrap a model with a Transformer-like architecture in FSDP. gradient_checkpointing_enable () and wrap using accelerate. Mar 8, 2016 · Saved searches Use saved searches to filter your results more quickly Oct 15, 2023 · Please ensure that the gradient and the tensor have the same dtype """ import logging import math import os import time from pathlib import Path import functools from functools import partial import math from torch. The code for finetuning BERT-Large (330M) model on the GLUE MRPC task is the official Use activation_checkpointing_policy. The example below wraps model submodules having at Jun 28, 2022 · After import related FSDP module, I simply change my code [ref this link] to if args. 06%. Nov 6, 2023 · Hi, When wrapping a model like: fsdp_model = FullyShardedDataParallel( model(), fsdp_auto_wrap_policy=default_auto_wrap_policy, cpu_offload=CPUOffload(offload_params=True), ) Using summon_full_params(model) will unshard all parameters for all wrapped modules which will result in the full model in each RANK, causing OOM in case of a large model. Pass it to the FSDPStrategy object strategy = FSDPStrategy ( auto_wrap_policy = policy ) PyTorch provides several of these functional policies under torch. (Source: link) . I’m using the code as-is from the FSDP tutorial except for the following changes: I passed the custom auto_wrap policy to FSDP initialisation as Dec 4, 2023 · 当使用 default_auto_wrap_policy 时，如果该层的参数量超过 min_num_params ，则该层将被包装在一个 FSDP 模块中。官方有一个在 GLUE MRPC 任务上微调 BERT-Large (330M) 模型的示例代码，其完整地展示了如何正确使用 FSDP 功能，其中还包含了用于跟踪峰值内存使用情况的代码。 The main issue is due to _materialize_with_param_init_fn only consider modules _init_utils. Mixed precision is currently not supported with FSDP. Use ``activation_checkpointing_policy``. i. py --args with --args the ones given in the repro for finetuning the 65B model. FullyShardedDataParallel` but used when selecting the modules for which you want to enable activation checkpointing. Jan 3, 2024 · compute_environment: LOCAL_MACHINE distributed_type: FSDP downcast_bf16: ' no ' fsdp_config: fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP fsdp_backward_prefetch_policy: BACKWARD_PRE fsdp_forward_prefetch: true fsdp_offload_params: false fsdp_sharding_strategy: 1 fsdp_state_dict_type: FULL_STATE_DICT fsdp_sync_module_states: true fsdp Apr 26, 2023 · As you described, for use_orig_params=True, the summon_full_params (recurse=False) call will keep the root FSDP instance's parameters unsharded through the entire generate (), but that is also the behavior for a normal forward pass. This seems to be working as the model size per-GPU is shown as 22. auto import tqdm FSDP Activation checkpointing is shard aware meaning we need to apply it after wrapping the model with FSDP. to ( accelerator. 4 中定义了 auto_wrap_policy 并将其传递给 FSDP 包装器，在下面的示例中，my_auto_wrap_policy 定义了如果该层中的参数数量大于 100，则该层可以被 FSDP 包装或分片。如果该层参数小于100时，FSDP会将其与其他小层包裹在一起。 Sep 6, 2023 · An officially supported task in the examples folder (such as GLUE/SQuAD, ) System Info transformers version: 4. For example, what effect will auto_wrap_policy have, what is the meaning of its several input parameters. prepare ( fsdp_model ) optimizer = MyOpt () optimizer Aug 20, 2022 · For more details on partitioning and on the FSDP API please see both the basic and advanced FSDP tutorials, as well as this overview. Scaling up GPUs, by extending to multi nodes , we are expecting to have smaller shard on each and overall proportional memory reduction. Mar 22, 2023 · auto_wrap_policys may be simply passed in as an argument when wrapping a model with FSDP. if a model has gradient checkpointing, say pythia-6. gpu]) model = FSDP( model, fsdp_auto_wrap_policy=default_auto_wrap_policy, ) then I hit two problems: the fsdp model cannot run with model EMA. if model doesn't have gradient checkpointing, like mpt-7b, then i need to manually go into the model file and edit the model call directly to use the deepspeed activation checkpointing api in the model Mar 15, 2022 · Wrapping Decoder’s Linear Layers With FSDP. Apr 6, 2023 · To use FSDP (Fully-Sharded Data Parallel) with Fabric, create an FSDPStrategy object by specifying the auto-wrap policy and passing it as an argument to the Fabric class. Fabric helps to automatically place the model and tensors on the correct devices, enabling distributed training, mixed precision, and the ability to select the number of Sep 13, 2023 · Challenges with fine-tuning LLaMa 70B. activation_checkpointing_policy¶ (Union [Set [Type [Module]], Callable [[Module, bool, int], bool], ModuleWrapPolicy, None]) – Same as auto_wrap_policy parameter in torch. 基于这个参数可以实现 size-based wrap policy，例如官方实现的 size_based_auto_wrap_policy 。 FSDP 把 auto_wrap_policy 这个参数的配置权交给用户，扩展性固然是提升了，但是也无形的增加了 FSDP 的学习成本，比如 auto_wrap_policy 会起什么作用，它的几个入参的含义又是什么，刚 Nov 21, 2022 · To use FSDP, the submodules of a model need to be wrapped with the API to control when specific submodules are sharded or unsharded. FSDP will “all-gather” those layers on a Dec 12, 2023 · It can be tricky to use python debugger from a multi-rank setup. This introduces additional 使用fsdp_auto_wrap_policy可以在满足一定条件的时候自动新建一个FSDP unit, 例如超过设定的模型大小. FullyShardedDataParallel but used when selecting the modules for which you want to enable activation checkpointing Here, one main thing to note currently when using FSDP with PEFT is that use_orig_params needs to be False to realize GPU memory savings. 0. wrap import (size_based_auto_wrap_policy, enable_wrap, wrap,) import pandas as pd import numpy as np. _fsdp_wrap = True | False, that choice will be respected. use_peft else wrapping_policy, cpu_offload=CPUOffload(offload_params=True) if fsdp_config. You switched accounts on another tab or window. Nov 22, 2021 · The main API for manual wrapping we will support is enable_wrap () context manager which is passed the FSDP config, and a nonrecursive wrap () function which wraps if we are in the context manager. distributed: model = torch. distributed. 6M. Turned out, since there are some frozen parameters without gradients, I can not use gradient_clipping. fsdp_cpu_offload else None, mixed_precision=mixed_precision_policy if not fsdp_config. Mar 10, 2012 · Saved searches Use saved searches to filter your results more quickly Dec 4, 2022 · I’ve enabled FSDP using strategy='fsdp' flag with precision=16. 85%. 第二次第二个FSDP unit的进行all_gather, 以此类推. . Mar 24, 2023 · Parameter-Efficient Fine-Tuning (PEFT) methods enable efficient adaptation of pre-trained language models (PLMs) to various downstream applications without fine-tuning all the model's parameters. lr_scheduler import LambdaLR import datasets import torch import transformers import tokenizers from tqdm. Jul 5, 2023 · edited. activation_checkpointing_policy: Same as ``auto_wrap_policy`` parameter in:class:`torch. Jul 2, 2023 · Plain PyTorch (01_pytorch-vit. As far as I see right now, that should not increase memory usage comparatively. e. wrap import always_wrap_policy from torch. I use pytorch 2. 5B gpt2 model in a device1 or device2. Do you know why transformer_layer_cls_to_wrap can be automatically assigned to _no_split_module by default? 为此，在 2. After creating an instance of this class, users can pass it to the Accelerator class instantiation. Jun 11, 2022 · You signed in with another tab or window. Otherwise, you can choose a size-based wrapping policy where FSDP is applied to a layer if it exceeds a certain number of parameters. Two auto_wrap_policy callables worth noting are: size_based_auto_wrap_policy, transformer_auto_wrap_policy. Currently, I am using the transformer_auto_wrap_policy with Block being the Module to wrap. Dec 16, 2022 · In this case FSDP will simply wrap the whole model in a single FSDP unit. It can be observed that the peak memory usage on each device is smaller compared to FSDP without auto wrap policy applied, from ~75 MB to 66 MB. I train a 3. As these are very large LLMs, we want to leverage FSDP with CPU offloading to fit such large model training with only a tiny fraction of training params on consumer GPUs. py): Time elapsed 17. fsdp import MixedPrecision Jul 4, 2023 · We are running FSDP with a model using the size_based_auto wrap policy with A800 gpus. Oct 11, 2023 · fsdp_config: fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP fsdp_backward_prefetch_policy: BACKWARD_PRE fsdp_forward_prefetch: true fsdp_offload_params: true fsdp_sharding_strategy: 1 fsdp_state_dict_type: FULL_STATE_DICT fsdp_sync_module_states: true fsdp_transformer_layer_cls_to_wrap: LlamaDecoderLayer fsdp_use_orig_params: true Configure the policy policy = partial (size_based_auto_wrap_policy, min_num_params = 10000) # 3. parallel. fsdp. In this regard, PEFT methods only fine-tune a small number of (extra) model parameters Mar 9, 2016 · compute_environment: LOCAL_MACHINE distributed_type: FSDP downcast_bf16: 'no' fsdp_config: fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP fsdp_backward_prefetch_policy: BACKWARD_PRE fsdp_offload_params: false fsdp_sharding_strategy: 1 fsdp_state_dict_type: FULL_STATE_DICT fsdp_transformer_layer_cls_to_wrap: GPTJBlock machine_rank: 0 main_training_function: main mixed_precision: bf16 num Jul 15, 2023 · edited. On the contrary, as the number of GPU cards increases, we have not observed a decrease in VRAM usage. auto_wrap_policy Which is the way to specify how FSDP would partition the model, there is default support for transformer wrapping policy. Name is device1 and device2. 1 day ago · Hello, I need to implement FSDP in a model parallel setup. Reload to refresh your session. ignored_modules) accelerator = Accelerator (mixed_precision = args. checkpoint_wrapper import ( checkpoint_wrapper, CheckpointImpl, apply_activation_checkpointing_wrapper, ) class Model (torch. For more control, users can leverage the FullyShardedDataParallelPlugin wherein they can specify auto_wrap_policy, backward_prefetch and ignored_modules. size_based_auto_wrap_policy in torch_xla. size_based_auto_wrap_policy enables users to wrap submodules with a minimum number of parameters. fsdp_forward_prefetch: if True, then FSDP explicitly prefetches the next upcoming all-gather while executing in the forward pass. full_shard: # Model Sharding: ZeRO 3 from torch. There is a catch- it’s not too easy to attach the debugger on each rank, but it’s pretty easy to attach it to just one Nov 1, 2023 · from torch. Mar 4, 2022 · 🚀 The feature, motivation and pitch. Model wrapping: In order to minimize the transient GPU memory needs, users need to wrap a model in a nested fashion. Context: We have more and more situations where a large part of the model that's being trained is frozen. fsdp_min_num_params: minimum number of parameters when using fsdp_auto_wrap_policy=SIZE_BASED_WRAP. optim. Below is an excerpt from [8] detailing the importance of FSDP Auto Wrap Policy. Use activation_checkpointing_policy. However, since T5 is a transformer model, we are better served to leverage the transformer wrapper for this model. . However, I would like to also wrap the embedding and lm_head layers. PyTorch with Fabric (01-2_pytorch-fabric. 0 and fsdp train model. Users may feel puzzled when they get start with FSDP. 88 min Memory used: 26. 9b, then call model. 0-1012-gcp-x8 May 10, 2023 · For example: import torch from accelerate import Accelerator from torch. In order to minimize the transient GPU memory needs, users need to wrap a model in a nested fashion. I’m following the FSDP tutorial but am seeing an increase in GPU memory when moving to multiple GPUs rather than a decrease. mixed_precision, fsdp_plugin = fsdp_plugin) model = accelerator. 94 min Memory used: 26. fsdp import FullyShardedDataParallel as FSDP from torch. FSDP provides an auto-wrapping API (see the auto_wrap_policy argument) that can be used out of the box as well as several wrapping policies and the ability to write your own policy. Aug 7, 2023 · ### Information - [ ] The official example scripts - [ ] My own modified scripts ### Tasks - [ ] One of the scripts in the examples/ folder of Accelerate or an officially supported `no_trainer` script in the `examples` folder of the `transformers` repo (such as `run_no_trainer_glue. I want my encoder to run on a single GPU and the decoder to run on another GPU while harnessing the memory saving options, optimization options, and distributed training options that I get with FSDP. Hello, Currently I am trying to run qlora. Wanted to share a note for a future data scientist in trouble: I was trying LORA fine tuning of Mistral-7B using FSDP strategy and pytorch lighting trainer. org Jul 9, 2023 · Use fsdp_config instead FSDP Warning: When using FSDP, it is efficient and recommended to call prepare for the model before creating the optimizer. Dec 5, 2023 · How to wrap layers that have shared weights in FSDP. nn. FSDP is fully supported in fairseq via the following new arguments: Jan 18, 2024 · compute_environment: LOCAL_MACHINE distributed_type: FSDP downcast_bf16: 'no' fsdp_config: fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP fsdp_backward_prefetch_policy: BACKWARD_PRE fsdp_forward_prefetch: false fsdp_offload_params: false fsdp_sharding_strategy: 2 fsdp_state_dict_type: SHARDED_STATE_DICT fsdp_sync_module_states: false fsdp Jul 25, 2022 · import functools, os, torch from torch. import torch from torch import A wrapper for sharding Module parameters across data parallel workers. It used to get stuck at Step-1. as well as the ZeRO Stage 3 from DeepSpeed . Due to use_orig_params=False, the auto wrap policy for FSDP needs to change so that trainable and non-trainable parameters are wrapped separately. fsdp import FullyShardedDataParallel as FSDP from torch. I have a issue about FSDP: I have two devices and 8 gpu on each devices. py script with the 65B model on 2 A100 40GB GPUs with the script accelerate launch qlora. fsdp import ( FullyShardedDataParallel , ) accelerator = Accelerator ( bf16=True ) model = MyModel (). 34. Dec 7, 2022 · Hi there, I’m trying to decrease my model GPU memory footprint to train using high-resolution medical images as input. 2. sharding_strategy, device_id=torch. Fine-tuning large-scale PLMs is often prohibitively costly. junetou (junetou) April 3, 2023, 7:50am 1. 例如有100层的网络, 使用5个FSDP unit, 第一次第一个FSDP unit中的20层做all_gather操作, 前向传播, 并将其他的80层layer丢掉. Fortunately, this is fixable and you can use pdb almost like usual. prepare (model) elif args. provide a wrap_policies class with functions for common policies (such as # of parameters). 79 GB Test accuracy 95. I have a computer with 4 GPUs. Here are some few important areas to consider when you apply FSDP with its full power. 3 TFlops and 95% GPU memory utilization with a batch size of 14. Currently, FSDP does not recursively wrap submodules by default, which can result in some usability issues as all users will have to figure out a wrapping policy for their use case or manually annotate some models with wrap(). distributed. py`) - [ ] My own task or dataset (give details below) ### Reproduction fsdp param changed https://pytorch. It is also possible to shard individual layers separately and have an outer wrapper handle any leftover parameters. This is inspired by Xu et al. FullyShardedDataParallel but used when selecting the modules for which you want to enable activation checkpointing Accelerate will automatically wrap the model and create an optimizer for you in case of single model with a warning message. 84 GB Test accuracy 96. FSDP is faster than PyTorch DDP because the optimizer step is sharded, and the communication can be overlapped with the forward pass ; FSDP enables training 13B parameter models on 8 GPUs and 175B parameter models on 128 GPUs . wrap is an example of auto_wrap_policy callable, this policy wraps layers with the number of parameters larger than Aug 1, 2023 · FSDP gives users the right to configure the auto_wrap_policy parameter, which has indeed improved its flexibility, but it has also invisibly increased the learning cost of FSDP. FullyShardedDataParallel is commonly shorten to FSDP. Below you find a pseudo-code example of Jun 28, 2023 · @pacman100 I want to better understand the mechanism of FSDP's wrapping. nn. This is because parameter groups created before wrapping will have no meaning post wrapping due to parameter flattening of nested FSDP modules into 1D arrays (which can consume many layers). You should select fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP to wrap a Transformer layer and fsdp_transformer_layer_cls_to_wrap to specify which layer to wrap (for example BertLayer). We encountered three main challenges when trying to fine-tune LLaMa 70B with FSDP: FSDP wraps the model after loading the pre-trained model. algorithms. fsdp. wrap . DistributedDataParallel(model, device_ids=[args. However, below is the recommended way to prepare model and optimizer while using FSDP: Jul 6, 2023 · And the FSDP polices would work too: always_wrap_policy, lambda_auto_wrap_policy, size_based_auto_wrap_policy This pattern is convenient, not only because of the added expressiveness, but also because the policy can be exactly the same as the auto_wrap_policy which is often set together with activation_checkpointing (example on lit-gpt: https Jul 15, 2021 · The FSDP library in FairScale exposes the low-level options for many important aspects of large-scale training. pure_bf16 else None, sharding_strategy=fsdp_config. The code relies heavily on the official tutorials which should be referred to for details. Composer’s FSDP Auto Wrap Policy# To make auto-wrapping easier on users, Composer uses a custom auto wrap policy that wraps modules according to the following rules: If any module is attributed with module. py, but fsdp's __init__ always pass all ignored params init， so maybe we can pass module if user set ignored_module at the fist stage. as metioned above, th Apr 3, 2023 · About FSDP work problems. This allows FSDP to form each FSDP unit . You signed out in another tab or window. py) Time elapsed 17. device ) fsdp_model = FullyShardedDataParallel ( model ) fsdp_model = accelerator. FSDP Warning: When using FSDP, several parameter groups will be conflated into a single one due to nested module wrapping and parameter flattening. Make it easier to use policies for wrapping etc. I am running the following without a model parallel setup with no errors. from processing import col_drop from processing Oct 3, 2023 · model = FSDP( model, auto_wrap_policy= my_auto_wrapping_policy if train_config. Jan 9, 2023 · 위 코드에서 wrap policy란, FSDP에서 모델을 어떻게 나눌 것인가에 대한 policy로, PyTorch에서는 기본적으로 transformer 모델에 대한 policy를 지원하고 있기 Dec 13, 2022 · FULL_STATE_DICT, #cpu_offload=CPUOffload(offload_params=True), ignored_modules = model. I’ve also used the auto_wrap function in my lightning module (with a custom policy that reduces min number of params to wrap as my whole model contains 90m params only). In our script we are making use of that. xlarge AWS EC2 instance with 4 GPUs captured from PyTorch Profiler. nc im hq fj zb ba zu al lj ju