ColossalAI/colossalai/amp/naive_amp/mixed_precision_optimizer.py

215 lines
7.8 KiB
Python

from typing import Dict, List, Tuple
import torch
from torch import Tensor, inf
from torch.nn import Module, Parameter
from torch.optim import Optimizer
from colossalai.interface import OptimizerWrapper
from .mixed_precision_mixin import BF16MixedPrecisionMixin, FP16MixedPrecisionMixin
class NaiveFP16MixedPrecisionMixin(FP16MixedPrecisionMixin):
def __init__(
self,
working_params: List[Parameter],
initial_scale: float = 2**16,
min_scale: float = 1,
growth_factor: float = 2,
backoff_factor: float = 0.5,
growth_interval: int = 1000,
hysteresis: int = 2,
max_scale: float = 2**32,
) -> None:
super().__init__(
initial_scale, min_scale, growth_factor, backoff_factor, growth_interval, hysteresis, max_scale
)
self.params = working_params
def check_local_overflow(self) -> bool:
for p in self.params:
if p.grad is not None and not torch.isfinite(p.grad).all():
return True
return False
class MixedPrecisionOptimizer(OptimizerWrapper):
def __init__(
self,
optim: Optimizer,
precision: str = "fp16",
initial_scale: float = 2**16,
min_scale: float = 1,
growth_factor: float = 2,
backoff_factor: float = 0.5,
growth_interval: int = 1000,
hysteresis: int = 2,
max_scale: float = 2**32,
max_norm: float = 0.0,
):
super().__init__(optim)
if precision == "fp16":
working_params = []
for group in self.optim.param_groups:
for p in group["params"]:
working_params.append(p)
self.mixed_precision = NaiveFP16MixedPrecisionMixin(
working_params,
initial_scale=initial_scale,
min_scale=min_scale,
growth_factor=growth_factor,
backoff_factor=backoff_factor,
growth_interval=growth_interval,
hysteresis=hysteresis,
max_scale=max_scale,
)
elif precision == "bf16":
self.mixed_precision = BF16MixedPrecisionMixin()
else:
raise ValueError(f"Unsupported precision: {precision}")
self.max_norm = max_norm
self.working_to_master_map: Dict[Parameter, Tensor] = {}
self.master_to_working_map: Dict[Tensor, Parameter] = {}
# create master weights
for group in self.optim.param_groups:
master_params = []
for p in group["params"]:
if p.requires_grad:
master_p = p
if p.dtype != torch.float:
master_p = p.detach().float()
self.working_to_master_map[p] = master_p
self.master_to_working_map[master_p] = p
master_params.append(master_p)
group["params"] = master_params
def backward(self, loss: Tensor, *args, **kwargs):
loss = self.mixed_precision.pre_backward(loss)
loss.backward(*args, **kwargs)
def backward_by_grad(self, tensor: Tensor, grad: Tensor):
grad = self.mixed_precision.pre_backward_by_grad(tensor, grad)
tensor.backward(grad)
def zero_grad(self, *args, **kwargs):
for p in self.working_to_master_map.keys():
p.grad = None
self.mixed_precision.pre_zero_grad()
return super().zero_grad(*args, **kwargs)
def _unscale_and_clip_grads(self, total_norm: float) -> None:
"""
Unscale and clip gradients before performing the optimization step.
Args:
total_norm (float): The computed total gradient norm.
Returns:
None
"""
div_scale = 1.0
# If mixed-precision training is used, get the gradient division scale from the mixed-precision handler.
if self.mixed_precision is not None:
div_scale = self.mixed_precision.get_grad_div_scale()
if self.max_norm > 0.0:
# Calculate the scaling factor for gradient clipping
# The gradient norm is scaled by 'div_scale' and then clipped to 'max_norm'
clip = ((total_norm / div_scale) + 1e-6) / self.max_norm
# If the clip factor exceeds 1, adjust 'div_scale' accordingly to ensure clipping
if clip > 1:
div_scale = clip * div_scale
# Apply the scaling factor to gradients
for group in self.param_groups:
for p in group["params"]:
if p.grad is None:
continue
p.grad.data.mul_(1.0 / div_scale)
def _compute_grad_norm(self, param_gradient_pairs: List[Tuple[Tensor]], norm_type: int = 2) -> int:
r"""
Compute and return the gradient norm for gradient clipping.
Args:
param_gradient_pairs (List[Tuple[Tensor]]): List of (parameter, gradient) pairs; gradients are used for norm calculation.
norm_type (int, optional): Type of the norm used (e.g., 2 for L2 norm). Defaults to 2.
Returns:
float: The total norm of the given gradients.
"""
if len(param_gradient_pairs) == 0:
return 0.0
# gradients used for norm calculation.
gradients = [grad for param, grad in param_gradient_pairs]
if norm_type == inf:
total_norm = max(grad.data.abs().max() for grad in gradients)
else:
total_norm_exponentiated = 0.0
for grad in gradients:
total_norm_exponentiated += grad.data.double().norm(norm_type) ** norm_type
total_norm = total_norm_exponentiated ** (1.0 / norm_type)
return total_norm
def step(self, *args, **kwargs):
if self.mixed_precision.should_skip_step():
self.zero_grad()
return
# prepare grads
for group in self.optim.param_groups:
for p in group["params"]:
working_param = self.master_to_working_map[p]
if p is working_param:
continue
if working_param.grad is not None:
p.grad = working_param.grad.data.float()
working_param.grad = None
# gradient unscale and clip.
if self.max_norm <= 0:
# no need to compute gradient norm.
total_norm = 0.0
else:
# compute the total norm.
param_gradient_pairs = [
(self.master_to_working_map[p], p.grad)
for group in self.param_groups
for p in group["params"]
if p.grad is not None
]
total_norm = self._compute_grad_norm(param_gradient_pairs)
self._unscale_and_clip_grads(total_norm)
self.optim.step(*args, **kwargs)
# update working params
for group in self.optim.param_groups:
for p in group["params"]:
working_param = self.master_to_working_map[p]
if p is working_param:
continue
working_param.data.copy_(p.data)
def update_master_params(self, model: Module):
# Update master params from working params
with torch.no_grad():
for p in model.parameters():
if (p is None) or (p not in self.working_to_master_map):
continue
master_param = self.working_to_master_map[p]
master_param.data.copy_(p.data)
def get_working_to_master_map(self) -> Dict[int, torch.Tensor]:
return {id(working_p): master_p for working_p, master_p in self.working_to_master_map.items()}
def get_master_to_working_map(self) -> Dict[int, torch.Tensor]:
return {id(master_p): working_p for master_p, working_p in self.master_to_working_map.items()}