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# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import importlib
from collections.abc import Collection
from dataclasses import dataclass, field
from typing import List
import torch
from fairseq.dataclass import FairseqDataclass
from fairseq.optim import FairseqOptimizer, register_optimizer
from omegaconf import II, DictConfig
try:
import deepspeed
has_deepspeed = True
except ImportError as e:
has_deepspeed = False
def _get_cpu_adam():
try:
from deepspeed.ops.op_builder import CPUAdamBuilder
return CPUAdamBuilder().load()
except ImportError:
# fbcode
from deepspeed.ops.adam import DeepSpeedCPUAdam as ds_opt_adam
return ds_opt_adam
@dataclass
class FairseqCPUAdamConfig(FairseqDataclass):
adam_betas: str = field(
default="(0.9, 0.999)", metadata={"help": "betas for Adam optimizer"}
)
adam_eps: float = field(
default=1e-8, metadata={"help": "epsilon for Adam optimizer"}
)
weight_decay: float = field(default=0.0, metadata={"help": "weight decay"})
fp16_adam_stats: bool = field(
default=False, metadata={"help": "use FP16 stats (with automatic scaling)"}
)
# TODO common vars below in parent
lr: List[float] = II("optimization.lr")
@register_optimizer("cpu_adam", dataclass=FairseqCPUAdamConfig)
class FairseqCPUAdam(FairseqOptimizer):
"""Adam optimizer for fairseq, optimized for CPU tensors.
Important note: this optimizer corresponds to the "AdamW" variant of
Adam in its weight decay behavior. As such, it is most closely
analogous to torch.optim.AdamW from PyTorch.
"""
def __init__(self, cfg: DictConfig, params):
super().__init__(cfg)
self._optimizer = CPUAdam(params, **self.optimizer_config)
@property
def optimizer_config(self):
"""
Return a kwarg dictionary that will be used to override optimizer
args stored in checkpoints. This allows us to load a checkpoint and
resume training using a different set of optimizer args, e.g., with a
different learning rate.
"""
return {
"lr": self.cfg.lr[0]
if isinstance(self.cfg.lr, Collection)
else self.cfg.lr,
"betas": eval(self.cfg.adam_betas),
"eps": self.cfg.adam_eps,
"weight_decay": self.cfg.weight_decay,
"use_fp16_stats": self.cfg.fp16_adam_stats,
}
class CPUAdam(torch.optim.Optimizer):
optimizer_id = 0
def __init__(
self,
params,
lr=1e-3,
bias_correction=True,
betas=(0.9, 0.999),
eps=1e-8,
weight_decay=0,
use_fp16_stats=False,
):
defaults = {
"lr": lr,
"bias_correction": bias_correction,
"betas": betas,
"eps": eps,
"weight_decay": weight_decay,
}
super().__init__(params, defaults)
self.use_fp16_stats = use_fp16_stats
self.FLOAT16_MAX = 65504.0
if not has_deepspeed:
raise ImportError("Please install DeepSpeed: pip install deepspeed")
self.opt_id = CPUAdam.optimizer_id
CPUAdam.optimizer_id = CPUAdam.optimizer_id + 1
self.ds_opt_adam = _get_cpu_adam()
adamw_mode = True
self.ds_opt_adam.create_adam(
self.opt_id, lr, betas[0], betas[1], eps, weight_decay, adamw_mode
)
@property
def supports_memory_efficient_fp16(self):
return True
@property
def supports_flat_params(self):
return True
@torch.no_grad()
def step(self, closure=None):
loss = None
if closure is not None:
with torch.enable_grad():
loss = closure()
torch.cuda.synchronize()
for group_id, group in enumerate(self.param_groups):
for param_id, p in enumerate(group["params"]):
if p.grad is None:
continue
state = self.state[p]
if len(state) == 0:
state["step"] = 0
dtype = torch.float16 if self.use_fp16_stats else p.data.dtype
# gradient momentums
state["exp_avg"] = torch.zeros_like(
p.data, dtype=dtype, device="cpu"
)
# gradient variances
state["exp_avg_sq"] = torch.zeros_like(
p.data, dtype=dtype, device="cpu"
)
if self.use_fp16_stats:
assert torch.is_floating_point(p.data)
state["exp_avg_scale"] = 1.0
state["exp_avg_sq_scale"] = 1.0
exp_avg, exp_avg_sq = state["exp_avg"], state["exp_avg_sq"]
p_data_bak = p.data # backup of the original data pointer
p.data = p.data.to(dtype=torch.float32, device="cpu")
p.grad.data = p.grad.data.to(dtype=torch.float32, device="cpu")
if self.use_fp16_stats:
exp_avg = exp_avg.float() * state["exp_avg_scale"]
exp_avg_sq = exp_avg_sq.float() * state["exp_avg_sq_scale"]
state["step"] += 1
beta1, beta2 = group["betas"]
self.ds_opt_adam.adam_update(
self.opt_id,
state["step"],
group["lr"],
beta1,
beta2,
group["eps"],
group["weight_decay"],
group["bias_correction"],
p.data,
p.grad.data,
exp_avg,
exp_avg_sq,
)
if p_data_bak.data_ptr() != p.data.data_ptr():
p_data_bak.copy_(p.data)
p.data = p_data_bak
if self.use_fp16_stats:
def inf_norm(t):
return torch.norm(t, float("inf"))
# from github.com/openai/jukebox/blob/master/jukebox/utils/fp16.py
state["exp_avg_scale"], state["exp_avg_sq_scale"] = (
1e-8 + inf_norm(exp_avg) / self.FLOAT16_MAX,
1e-8 + inf_norm(exp_avg_sq) / self.FLOAT16_MAX,
)
state["exp_avg"], state["exp_avg_sq"] = (
(exp_avg / state["exp_avg_scale"]).half(),
(exp_avg_sq / state["exp_avg_sq_scale"]).half(),
)
return loss
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