mirror of https://github.com/InternLM/InternLM
feat(solver/optimizer): add new file fsdp_optimizer.py
huangting4201
parent
6b7ca1c6b3
commit
f3f2511e74
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@ -1,10 +1,7 @@
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#!/usr/bin/env python
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# -*- encoding: utf-8 -*-
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from .hybrid_zero_optim import (
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FSDPadaptOptimizer,
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HybridZeroOptimizer,
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reload_zero_fp32_buff,
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)
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from .fsdp_optimizer import FSDPadaptOptimizer
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from .hybrid_zero_optim import HybridZeroOptimizer, reload_zero_fp32_buff
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__all__ = ["HybridZeroOptimizer", "FSDPadaptOptimizer", "reload_zero_fp32_buff"]
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__all__ = ["FSDPadaptOptimizer", "HybridZeroOptimizer", "reload_zero_fp32_buff"]
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@ -0,0 +1,46 @@
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#!/usr/bin/env python
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# -*- encoding: utf-8 -*-
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import torch
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from torch.optim import Optimizer
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class BaseOptimizer(Optimizer):
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"""
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Base Optimizer.
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"""
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def __init__(self, optim: Optimizer): # pylint: disable=W0231
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self.optim = optim
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@property
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def param_groups(self):
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return self.optim.param_groups
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@property
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def defaults(self):
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return self.optim.defaults
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def add_param_group(self, *args, **kwargs):
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return self.optim.add_param_group(*args, **kwargs)
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def step(self, *args, **kwargs):
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return self.optim.step(*args, **kwargs)
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def zero_grad(self, *args, **kwargs):
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self.optim.zero_grad(*args, **kwargs)
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def load_state_dict(self, *args, **kwargs):
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self.optim.load_state_dict(*args, **kwargs)
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def state_dict(self):
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return self.optim.state_dict()
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def backward(self, loss):
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loss.backward()
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def backward_by_grad(self, tensor, grad):
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torch.autograd.backward(tensors=tensor, grad_tensors=grad)
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def clip_grad_norm(self):
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pass
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@ -0,0 +1,220 @@
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#!/usr/bin/env python
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# -*- encoding: utf-8 -*-
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from torch.optim import Optimizer
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from internlm.core.context import Config, ParallelMode
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from internlm.core.context import global_context as gpc
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from internlm.solver.optimizer.utils import (
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DynamicGradScaler,
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reduce_tensor,
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release_param_grad,
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)
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from internlm.utils.logger import get_logger
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from .base_optimizer import BaseOptimizer
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from .utils import compute_norm
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logger = get_logger(__file__)
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class FSDPadaptOptimizer(BaseOptimizer):
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"""
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optimizer for Pytorch FSDP if 'use_fsdp' is True in config file
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reserve some necessary components of hybird-optim:
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grad_scaler;
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grad_clip and unscale;
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state_dict and load_state_dict
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"""
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def __init__(
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self,
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optimizer: Optimizer,
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grad_scal_cfg: Config = None,
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zero_cfg: Config = None,
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):
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super().__init__(optim=optimizer)
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# gradient scaler
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self.grad_scaler = DynamicGradScaler(
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initial_scale=grad_scal_cfg.fp16.initial_scale,
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min_scale=grad_scal_cfg.fp16.min_scale,
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growth_factor=grad_scal_cfg.growth_factor,
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backoff_factor=grad_scal_cfg.backoff_factor,
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growth_interval=grad_scal_cfg.fp16.growth_interval,
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hysteresis=grad_scal_cfg.hysteresis,
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max_scale=grad_scal_cfg.max_scale,
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)
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# clip gradient
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self._clip_grad_norm = zero_cfg.clip_grad_norm
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self.use_fsdp = gpc.config.parallel.use_fsdp
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# mark whether a module is part of TP or not
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# TODO: is_tensor_parallel_dict = dict()
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# fp16 and fp32 params
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# fp16 share mem space with model.FlatParam, fp32 share mem space with optim.param_group
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self._fp16_param_groups = dict()
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self._fp32_param_tensor_groups = dict()
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# init fp16 and fp32 params
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for group_idx, param_group in enumerate(self.optim.param_groups):
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group_params = param_group["params"]
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# fp16 FlatParam storage
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self._fp16_param_groups[group_idx] = group_params
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# create copy of fp32 weight
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fp32_tensor_param = [param.data.float().requires_grad_(True) for param in group_params]
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self._fp32_param_tensor_groups[group_idx] = fp32_tensor_param
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# replace
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param_group["params"] = fp32_tensor_param
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@property
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def loss_scale(self):
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return self.grad_scaler.scale
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def backward(self, loss, retain_graph=False):
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loss = self.loss_scale * loss
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loss.backward(retain_graph=retain_graph)
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def _compute_norm_with_fsdp_flatten(self, group_id):
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params = self._fp16_param_groups[group_id]
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gradients = [p.grad for p in params]
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norm_group = compute_norm(gradients=gradients, parameters=params, last_stage=True)
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return norm_group
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def zero_grad(self):
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for _, param_group in self._fp16_param_groups.items():
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for param in param_group:
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param.grad = None
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def step(self):
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# in case that fsdp-zero3 size is not equal to dp size
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# FSDP module will only reduce gradient within FSDP process group
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# so manually reduce grad is essential between two parallel FSDP process group
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for group_idx in range(len(self.param_groups)):
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params = self._fp16_param_groups[group_idx]
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for param in params:
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if param.requires_grad:
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reduce_tensor(tensor=param.grad, parallel_mode=ParallelMode.ZERO3_DP)
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# compute norm
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found_inf = False
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norm_groups = {}
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for group_idx in range(len(self.param_groups)):
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group_name = self.param_groups[group_idx]["name"] if "name" in self.param_groups[group_idx] else "default"
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group_name = f"{group_idx}_{group_name}"
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norm_group = self._compute_norm_with_fsdp_flatten(group_idx)
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if norm_group == -1:
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found_inf = True
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norm_groups[group_name] = norm_group
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loss_scale = float(self.loss_scale.item()) # backup
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self.grad_scaler.update(found_inf)
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if found_inf:
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if gpc.is_rank_for_log():
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logger.warning("Overflow occurs, please check it.")
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self.zero_grad()
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return False, norm_groups
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# get the global norm
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global_norm_groups = {}
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if self._clip_grad_norm > 0:
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for group_name, norm in norm_groups.items():
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global_norm_groups[group_name] = norm**0.5
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# create gradient for fp32 params
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for group_idx in range(len(self.param_groups)):
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dtype = self._fp32_param_tensor_groups[group_idx][0].dtype
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fp16_params = self._fp16_param_groups[group_idx]
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grad_fp32 = [p.grad.to(dtype) for p in fp16_params]
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device = self._fp32_param_tensor_groups[group_idx][0].device
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for p, g in zip(self._fp32_param_tensor_groups[group_idx], grad_fp32):
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p.grad = g.to(device)
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# unscale
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self._unscale_and_clip_grads(list(global_norm_groups.values()), loss_scale)
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self.optim.step()
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self.zero_grad()
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for group_idx in range(len(self._fp16_param_groups)):
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fp16_params = self._fp16_param_groups[group_idx]
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fp32_tensor_params = self._fp32_param_tensor_groups[group_idx]
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# release fp32 grad
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release_param_grad(fp32_tensor_params)
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# update fp16 param
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for p, q in zip(fp16_params, fp32_tensor_params):
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p.data.copy_(q)
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for group_name, global_norm in global_norm_groups.items():
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global_norm_groups[group_name] = global_norm / loss_scale
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return True, global_norm_groups
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def clip_grad_norm(self, model, max_norm):
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# will conduct in the step()
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pass
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#########################
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# utils from hybirdzero #
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#########################
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def _unscale_and_clip_grads(self, total_norm_groups, loss_scale):
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# compute combined scale factor for this group
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combined_scale_groups = []
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if self._clip_grad_norm > 0.0:
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# norm is in fact norm*scale
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for group_id, total_norm in enumerate(total_norm_groups):
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combined_scale_groups.append(loss_scale)
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clip = ((total_norm / loss_scale) + 1e-6) / self._clip_grad_norm
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if clip > 1.0:
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combined_scale_groups[group_id] = clip * loss_scale
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for group_id, grads in self._fp32_param_tensor_groups.items():
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for g in grads:
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g.grad.data.mul_(1.0 / combined_scale_groups[group_id])
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def state_dict(self):
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states = {}
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grad_scaler = self.grad_scaler.state_dict()
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states["grad_scaler"] = grad_scaler
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optim_states = self.optim.state_dict()
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states["base_optim_states"] = optim_states
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flat_fp32_weights = {}
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for group_idx, param in self._fp32_param_tensor_groups.items():
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flat_fp32_weights[group_idx] = param
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states["flat_fp32_weights"] = flat_fp32_weights
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return states
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def load_state_dict(self, states):
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assert "grad_scaler" in states, "Not found grad_scaler state!"
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grad_scaler = states["grad_scaler"]
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self.grad_scaler.load_state_dict(grad_scaler)
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optim_states = states["base_optim_states"]
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self.optim.load_state_dict(optim_states)
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# load fp32 optimizer weight
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flat_fp32_weights = states["flat_fp32_weights"]
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assert set(flat_fp32_weights.keys()) == set(self._fp32_param_tensor_groups)
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for group_idx, param in flat_fp32_weights.items():
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self_param = self._fp32_param_tensor_groups[group_idx]
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assert len(self_param) == len(
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param
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), f"The number of flat tensor is inconsistent, {len(self_param)} != {len(param)}"
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for p, q in zip(self_param, param):
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p.data.copy_(q.data)
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# load fp16 model weight
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for group_idx, param in flat_fp32_weights.items():
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fp16_param = self._fp16_param_groups[group_idx]
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fp32_param = self._fp32_param_tensor_groups[group_idx]
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for p, q in zip(fp16_param, fp32_param):
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p.data.copy_(q.data)
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@ -34,255 +34,13 @@ from internlm.utils.logger import get_logger
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from internlm.utils.megatron_timers import megatron_timer as timer
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from internlm.utils.timeout import llm_timeout
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from .base_optimizer import BaseOptimizer
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from .utils import compute_norm
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inf = math.inf
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logger = get_logger(__file__)
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class BaseOptimizer(Optimizer):
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"""
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Base Optimizer.
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"""
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def __init__(self, optim: Optimizer): # pylint: disable=W0231
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self.optim = optim
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@property
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def param_groups(self):
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return self.optim.param_groups
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@property
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def defaults(self):
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return self.optim.defaults
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def add_param_group(self, *args, **kwargs):
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return self.optim.add_param_group(*args, **kwargs)
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def step(self, *args, **kwargs):
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return self.optim.step(*args, **kwargs)
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def zero_grad(self, *args, **kwargs):
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self.optim.zero_grad(*args, **kwargs)
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def load_state_dict(self, *args, **kwargs):
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self.optim.load_state_dict(*args, **kwargs)
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def state_dict(self):
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return self.optim.state_dict()
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def backward(self, loss):
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loss.backward()
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def backward_by_grad(self, tensor, grad):
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torch.autograd.backward(tensors=tensor, grad_tensors=grad)
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def clip_grad_norm(self):
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pass
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class FSDPadaptOptimizer(BaseOptimizer):
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"""
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optimizer for Pytorch FSDP if 'use_fsdp' is True in config file
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reserve some necessary components of hybird-optim:
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grad_scaler;
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grad_clip and unscale;
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state_dict and load_state_dict
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"""
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def __init__(
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self,
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optimizer: Optimizer,
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grad_scal_cfg: Config = None,
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zero_cfg: Config = None,
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):
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super().__init__(optim=optimizer)
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# gradient scaler
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self.grad_scaler = DynamicGradScaler(
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initial_scale=grad_scal_cfg.fp16.initial_scale,
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min_scale=grad_scal_cfg.fp16.min_scale,
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growth_factor=grad_scal_cfg.growth_factor,
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backoff_factor=grad_scal_cfg.backoff_factor,
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growth_interval=grad_scal_cfg.fp16.growth_interval,
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hysteresis=grad_scal_cfg.hysteresis,
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max_scale=grad_scal_cfg.max_scale,
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)
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# clip gradient
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self._clip_grad_norm = zero_cfg.clip_grad_norm
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self.use_fsdp = gpc.config.parallel.use_fsdp
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# mark whether a module is part of TP or not
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# TODO: is_tensor_parallel_dict = dict()
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# fp16 and fp32 params
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# fp16 share mem space with model.FlatParam, fp32 share mem space with optim.param_group
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self._fp16_param_groups = dict()
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self._fp32_param_tensor_groups = dict()
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# init fp16 and fp32 params
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for group_idx, param_group in enumerate(self.optim.param_groups):
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group_params = param_group["params"]
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# fp16 FlatParam storage
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self._fp16_param_groups[group_idx] = group_params
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# create copy of fp32 weight
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fp32_tensor_param = [param.data.float().requires_grad_(True) for param in group_params]
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self._fp32_param_tensor_groups[group_idx] = fp32_tensor_param
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# replace
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param_group["params"] = fp32_tensor_param
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@property
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def loss_scale(self):
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return self.grad_scaler.scale
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def backward(self, loss, retain_graph=False):
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loss = self.loss_scale * loss
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loss.backward(retain_graph=retain_graph)
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def _compute_norm_with_fsdp_flatten(self, group_id):
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params = self._fp16_param_groups[group_id]
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gradients = [p.grad for p in params]
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norm_group = compute_norm(gradients=gradients, parameters=params, last_stage=True)
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return norm_group
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def zero_grad(self):
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for _, param_group in self._fp16_param_groups.items():
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for param in param_group:
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param.grad = None
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def step(self):
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# in case that fsdp-zero3 size is not equal to dp size
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# FSDP module will only reduce gradient within FSDP process group
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# so manually reduce grad is essential between two parallel FSDP process group
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for group_idx in range(len(self.param_groups)):
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params = self._fp16_param_groups[group_idx]
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for param in params:
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if param.requires_grad:
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reduce_tensor(tensor=param.grad, parallel_mode=ParallelMode.ZERO3_DP)
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# compute norm
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found_inf = False
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norm_groups = {}
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for group_idx in range(len(self.param_groups)):
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group_name = self.param_groups[group_idx]["name"] if "name" in self.param_groups[group_idx] else "default"
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group_name = f"{group_idx}_{group_name}"
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norm_group = self._compute_norm_with_fsdp_flatten(group_idx)
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if norm_group == -1:
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found_inf = True
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norm_groups[group_name] = norm_group
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loss_scale = float(self.loss_scale.item()) # backup
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self.grad_scaler.update(found_inf)
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if found_inf:
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if gpc.is_rank_for_log():
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logger.warning("Overflow occurs, please check it.")
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self.zero_grad()
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return False, norm_groups
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# get the global norm
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global_norm_groups = {}
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if self._clip_grad_norm > 0:
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for group_name, norm in norm_groups.items():
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global_norm_groups[group_name] = norm**0.5
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# create gradient for fp32 params
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for group_idx in range(len(self.param_groups)):
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dtype = self._fp32_param_tensor_groups[group_idx][0].dtype
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fp16_params = self._fp16_param_groups[group_idx]
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grad_fp32 = [p.grad.to(dtype) for p in fp16_params]
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device = self._fp32_param_tensor_groups[group_idx][0].device
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for p, g in zip(self._fp32_param_tensor_groups[group_idx], grad_fp32):
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p.grad = g.to(device)
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# unscale
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self._unscale_and_clip_grads(list(global_norm_groups.values()), loss_scale)
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self.optim.step()
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self.zero_grad()
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for group_idx in range(len(self._fp16_param_groups)):
|
||||
fp16_params = self._fp16_param_groups[group_idx]
|
||||
fp32_tensor_params = self._fp32_param_tensor_groups[group_idx]
|
||||
# release fp32 grad
|
||||
release_param_grad(fp32_tensor_params)
|
||||
# update fp16 param
|
||||
for p, q in zip(fp16_params, fp32_tensor_params):
|
||||
p.data.copy_(q)
|
||||
|
||||
for group_name, global_norm in global_norm_groups.items():
|
||||
global_norm_groups[group_name] = global_norm / loss_scale
|
||||
return True, global_norm_groups
|
||||
|
||||
def clip_grad_norm(self, model, max_norm):
|
||||
# will conduct in the step()
|
||||
pass
|
||||
|
||||
#########################
|
||||
# utils from hybirdzero #
|
||||
#########################
|
||||
|
||||
def _unscale_and_clip_grads(self, total_norm_groups, loss_scale):
|
||||
# compute combined scale factor for this group
|
||||
combined_scale_groups = []
|
||||
|
||||
if self._clip_grad_norm > 0.0:
|
||||
# norm is in fact norm*scale
|
||||
for group_id, total_norm in enumerate(total_norm_groups):
|
||||
combined_scale_groups.append(loss_scale)
|
||||
clip = ((total_norm / loss_scale) + 1e-6) / self._clip_grad_norm
|
||||
if clip > 1.0:
|
||||
combined_scale_groups[group_id] = clip * loss_scale
|
||||
|
||||
for group_id, grads in self._fp32_param_tensor_groups.items():
|
||||
for g in grads:
|
||||
g.grad.data.mul_(1.0 / combined_scale_groups[group_id])
|
||||
|
||||
def state_dict(self):
|
||||
states = {}
|
||||
grad_scaler = self.grad_scaler.state_dict()
|
||||
states["grad_scaler"] = grad_scaler
|
||||
optim_states = self.optim.state_dict()
|
||||
states["base_optim_states"] = optim_states
|
||||
|
||||
flat_fp32_weights = {}
|
||||
for group_idx, param in self._fp32_param_tensor_groups.items():
|
||||
flat_fp32_weights[group_idx] = param
|
||||
states["flat_fp32_weights"] = flat_fp32_weights
|
||||
|
||||
return states
|
||||
|
||||
def load_state_dict(self, states):
|
||||
assert "grad_scaler" in states, "Not found grad_scaler state!"
|
||||
grad_scaler = states["grad_scaler"]
|
||||
self.grad_scaler.load_state_dict(grad_scaler)
|
||||
optim_states = states["base_optim_states"]
|
||||
self.optim.load_state_dict(optim_states)
|
||||
|
||||
# load fp32 optimizer weight
|
||||
flat_fp32_weights = states["flat_fp32_weights"]
|
||||
assert set(flat_fp32_weights.keys()) == set(self._fp32_param_tensor_groups)
|
||||
for group_idx, param in flat_fp32_weights.items():
|
||||
self_param = self._fp32_param_tensor_groups[group_idx]
|
||||
assert len(self_param) == len(
|
||||
param
|
||||
), f"The number of flat tensor is inconsistent, {len(self_param)} != {len(param)}"
|
||||
for p, q in zip(self_param, param):
|
||||
p.data.copy_(q.data)
|
||||
|
||||
# load fp16 model weight
|
||||
for group_idx, param in flat_fp32_weights.items():
|
||||
fp16_param = self._fp16_param_groups[group_idx]
|
||||
fp32_param = self._fp32_param_tensor_groups[group_idx]
|
||||
for p, q in zip(fp16_param, fp32_param):
|
||||
p.data.copy_(q.data)
|
||||
|
||||
|
||||
class HybridZeroOptimizer(BaseOptimizer):
|
||||
"""
|
||||
Hybrid Zero Optimizer.
|
||||
|
|
|
|||
|
|
@ -100,6 +100,9 @@ def initialize_model():
|
|||
# state in the same dp group are all the same.
|
||||
set_mode(ParallelMode.DATA)
|
||||
|
||||
# if fsdp enabled, wrap the model
|
||||
model = wrap_FSDP_model(model)
|
||||
|
||||
return model
|
||||
|
||||
|
||||
|
|
|
|||
3
train.py
3
train.py
|
|
@ -111,9 +111,6 @@ def main(args):
|
|||
# initialize and resume train state
|
||||
train_state = TrainState(gpc.config, train_dl.batch_sampler)
|
||||
|
||||
# if fsdp enabled, wrap the model
|
||||
model = wrap_FSDP_model(model)
|
||||
|
||||
optimizer, beta2_scheduler, lr_scheduler = initialize_optimizer(model=model)
|
||||
|
||||
ckpt_manager = CheckpointManager(
|
||||
|
|
|
|||
Loading…
Reference in New Issue