Add qat
Longfang Zhao
parent
512eed0b65
commit
63a1bcc82f
|
|
@ -0,0 +1,177 @@
|
|||
"""
|
||||
W8A8 QAT fake-quantization utilities (pure PyTorch, STE).
|
||||
|
||||
- WeightFakeQuant: per-output-channel symmetric int8 on Linear weights.
|
||||
- ActFakeQuant: per-tensor symmetric int8 on activations, with EMA observer
|
||||
and a warmup window (collect stats only, no quant in forward).
|
||||
- QATLinear: drop-in nn.Linear replacement that fake-quantizes its input
|
||||
activation and its weight on every forward.
|
||||
- prepare_qat: in-place swap every nn.Linear in a model with QATLinear,
|
||||
skipping any module whose qualified name matches `skip_patterns`
|
||||
(default: lm_head, MoE router `mlp.gate`).
|
||||
|
||||
The STE used here zeros gradients for values that saturate (clip-aware STE),
|
||||
which is more stable than plain pass-through for low-bit training.
|
||||
"""
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
|
||||
|
||||
class _FakeQuantize(torch.autograd.Function):
|
||||
"""round(x/scale + zp) -> clamp -> dequant. Backward: STE inside clip, 0 outside."""
|
||||
|
||||
@staticmethod
|
||||
def forward(ctx, x, scale, zero_point, qmin, qmax):
|
||||
q = torch.round(x / scale + zero_point)
|
||||
q_clamped = q.clamp(qmin, qmax)
|
||||
ctx.save_for_backward((q >= qmin) & (q <= qmax))
|
||||
return (q_clamped - zero_point) * scale
|
||||
|
||||
@staticmethod
|
||||
def backward(ctx, grad_output):
|
||||
(mask,) = ctx.saved_tensors
|
||||
return grad_output * mask.to(grad_output.dtype), None, None, None, None
|
||||
|
||||
|
||||
def fake_quantize(x, scale, zero_point, qmin, qmax):
|
||||
return _FakeQuantize.apply(x, scale, zero_point, qmin, qmax)
|
||||
|
||||
|
||||
class WeightFakeQuant(nn.Module):
|
||||
"""Per-output-channel symmetric int8 fake-quant for Linear weight [out, in]."""
|
||||
|
||||
def __init__(self, bits: int = 8):
|
||||
super().__init__()
|
||||
self.bits = bits
|
||||
self.qmax = (1 << (bits - 1)) - 1
|
||||
self.qmin = -(1 << (bits - 1))
|
||||
|
||||
def forward(self, w: torch.Tensor) -> torch.Tensor:
|
||||
max_abs = w.detach().abs().amax(dim=1, keepdim=True).clamp_min(1e-8)
|
||||
scale = (max_abs / self.qmax).to(w.dtype)
|
||||
zp = torch.zeros_like(scale)
|
||||
return fake_quantize(w, scale, zp, self.qmin, self.qmax)
|
||||
|
||||
|
||||
class ActFakeQuant(nn.Module):
|
||||
"""Per-tensor symmetric int8 fake-quant with EMA min/max observer + warmup."""
|
||||
|
||||
def __init__(self, bits: int = 8, momentum: float = 0.99, observer_steps: int = 200):
|
||||
super().__init__()
|
||||
self.bits = bits
|
||||
self.qmax = (1 << (bits - 1)) - 1
|
||||
self.qmin = -(1 << (bits - 1))
|
||||
self.momentum = momentum
|
||||
self.observer_steps = observer_steps
|
||||
self.register_buffer("running_max", torch.zeros(1))
|
||||
self.register_buffer("step", torch.zeros(1, dtype=torch.long))
|
||||
self.enabled = True
|
||||
|
||||
def extra_repr(self) -> str:
|
||||
return f"bits={self.bits}, observer_steps={self.observer_steps}, momentum={self.momentum}"
|
||||
|
||||
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
||||
if not self.enabled:
|
||||
return x
|
||||
if self.training:
|
||||
cur_max = x.detach().abs().amax().to(self.running_max.dtype)
|
||||
if self.step.item() == 0:
|
||||
self.running_max.copy_(cur_max.unsqueeze(0))
|
||||
else:
|
||||
self.running_max.mul_(self.momentum).add_(cur_max.unsqueeze(0) * (1.0 - self.momentum))
|
||||
self.step += 1
|
||||
# Warmup: collect stats only; passing x through unquantized lets the
|
||||
# observer stabilize before fake-quant starts perturbing gradients.
|
||||
if self.step.item() < self.observer_steps:
|
||||
return x
|
||||
max_abs = self.running_max.clamp_min(1e-8)
|
||||
scale = (max_abs / self.qmax).to(x.dtype)
|
||||
zp = torch.zeros_like(scale)
|
||||
return fake_quantize(x, scale, zp, self.qmin, self.qmax)
|
||||
|
||||
|
||||
class QATLinear(nn.Linear):
|
||||
"""nn.Linear with fake-quant on input activation and on weight."""
|
||||
|
||||
def __init__(self, in_features, out_features, bias=True, *, w_bits=8, a_bits=8,
|
||||
a_momentum=0.99, a_observer_steps=200):
|
||||
super().__init__(in_features, out_features, bias=bias)
|
||||
self.weight_fq = WeightFakeQuant(bits=w_bits)
|
||||
self.act_fq = ActFakeQuant(bits=a_bits, momentum=a_momentum, observer_steps=a_observer_steps)
|
||||
|
||||
@classmethod
|
||||
def from_float(cls, linear: nn.Linear, **kw) -> "QATLinear":
|
||||
qm = cls(linear.in_features, linear.out_features, bias=linear.bias is not None, **kw)
|
||||
# Share existing tensors so optimizer state stays attached after swap.
|
||||
qm.weight = linear.weight
|
||||
if linear.bias is not None:
|
||||
qm.bias = linear.bias
|
||||
return qm
|
||||
|
||||
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
||||
x_q = self.act_fq(x)
|
||||
w_q = self.weight_fq(self.weight)
|
||||
return F.linear(x_q, w_q, self.bias)
|
||||
|
||||
|
||||
DEFAULT_SKIP_PATTERNS = ("lm_head", "mlp.gate")
|
||||
|
||||
|
||||
def _matches_skip(full_name: str, pattern: str) -> bool:
|
||||
"""Pattern matches at a dot boundary, so 'mlp.gate' does NOT skip 'mlp.gate_proj'."""
|
||||
return full_name == pattern or full_name.endswith("." + pattern)
|
||||
|
||||
|
||||
def prepare_qat(model: nn.Module, w_bits: int = 8, a_bits: int = 8,
|
||||
skip_patterns=DEFAULT_SKIP_PATTERNS,
|
||||
a_momentum: float = 0.99, a_observer_steps: int = 200) -> int:
|
||||
"""Swap every nn.Linear in `model` with QATLinear in place.
|
||||
|
||||
A Linear is skipped iff its qualified name equals a skip pattern or ends
|
||||
with `'.' + pattern` (segment-aware, not substring). Defaults skip the LM
|
||||
head (precision-sensitive, often tied to embeddings) and the MoE router
|
||||
`mlp.gate` (small, routing-sensitive) without touching the dense
|
||||
FeedForward's `mlp.gate_proj`.
|
||||
|
||||
Returns the number of linears replaced.
|
||||
"""
|
||||
replaced = 0
|
||||
for parent_name, parent in model.named_modules():
|
||||
for child_name, child in list(parent.named_children()):
|
||||
if not isinstance(child, nn.Linear) or isinstance(child, QATLinear):
|
||||
continue
|
||||
full_name = f"{parent_name}.{child_name}" if parent_name else child_name
|
||||
if any(_matches_skip(full_name, p) for p in skip_patterns):
|
||||
continue
|
||||
new_mod = QATLinear.from_float(
|
||||
child, w_bits=w_bits, a_bits=a_bits,
|
||||
a_momentum=a_momentum, a_observer_steps=a_observer_steps,
|
||||
)
|
||||
# Move buffers to weight's device. dtype on buffers stays fp32 on purpose.
|
||||
new_mod.act_fq.running_max = new_mod.act_fq.running_max.to(child.weight.device)
|
||||
new_mod.act_fq.step = new_mod.act_fq.step.to(child.weight.device)
|
||||
setattr(parent, child_name, new_mod)
|
||||
replaced += 1
|
||||
return replaced
|
||||
|
||||
|
||||
def set_act_quant_enabled(model: nn.Module, enabled: bool) -> None:
|
||||
"""Toggle activation fake-quant (e.g. disable for eval-on-fp comparison)."""
|
||||
for m in model.modules():
|
||||
if isinstance(m, ActFakeQuant):
|
||||
m.enabled = enabled
|
||||
|
||||
|
||||
@torch.no_grad()
|
||||
def bake_quantized_weights(model: nn.Module) -> None:
|
||||
"""Replace each QATLinear's weight with its fake-quantized value, in place.
|
||||
|
||||
After this, the model can run as plain QATLinears without further weight
|
||||
updates and the saved fp weights already reflect the int8 grid; useful for
|
||||
final eval / export.
|
||||
"""
|
||||
for m in model.modules():
|
||||
if isinstance(m, QATLinear):
|
||||
m.weight.copy_(m.weight_fq(m.weight))
|
||||
|
|
@ -0,0 +1,221 @@
|
|||
import os
|
||||
import sys
|
||||
|
||||
__package__ = "trainer"
|
||||
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
|
||||
|
||||
import datasets # noqa: F401 # Windows pyarrow/torch DLL conflict workaround (issue #771)
|
||||
import argparse
|
||||
import time
|
||||
import warnings
|
||||
import torch
|
||||
import torch.distributed as dist
|
||||
from contextlib import nullcontext
|
||||
from torch import optim
|
||||
from torch.nn.parallel import DistributedDataParallel
|
||||
from torch.utils.data import DataLoader, DistributedSampler
|
||||
from model.model_minimind import MiniMindConfig
|
||||
from model.quant import prepare_qat, DEFAULT_SKIP_PATTERNS
|
||||
from dataset.lm_dataset import SFTDataset
|
||||
from trainer.trainer_utils import (
|
||||
get_lr, Logger, is_main_process, lm_checkpoint,
|
||||
init_distributed_mode, setup_seed, init_model, SkipBatchSampler,
|
||||
)
|
||||
|
||||
warnings.filterwarnings('ignore')
|
||||
|
||||
|
||||
def train_epoch(epoch, loader, iters, start_step=0, wandb=None):
|
||||
start_time = time.time()
|
||||
last_step = start_step
|
||||
for step, (input_ids, labels) in enumerate(loader, start=start_step + 1):
|
||||
input_ids = input_ids.to(args.device)
|
||||
labels = labels.to(args.device)
|
||||
last_step = step
|
||||
lr = get_lr(epoch * iters + step, args.epochs * iters, args.learning_rate)
|
||||
for param_group in optimizer.param_groups:
|
||||
param_group['lr'] = lr
|
||||
|
||||
with autocast_ctx:
|
||||
res = model(input_ids, labels=labels)
|
||||
loss = res.loss + res.aux_loss
|
||||
loss = loss / args.accumulation_steps
|
||||
|
||||
scaler.scale(loss).backward()
|
||||
|
||||
if step % args.accumulation_steps == 0:
|
||||
scaler.unscale_(optimizer)
|
||||
torch.nn.utils.clip_grad_norm_(model.parameters(), args.grad_clip)
|
||||
scaler.step(optimizer)
|
||||
scaler.update()
|
||||
optimizer.zero_grad(set_to_none=True)
|
||||
|
||||
if step % args.log_interval == 0 or step == iters:
|
||||
spend_time = time.time() - start_time
|
||||
current_loss = loss.item() * args.accumulation_steps
|
||||
current_aux_loss = res.aux_loss.item() if res.aux_loss is not None else 0.0
|
||||
current_logits_loss = current_loss - current_aux_loss
|
||||
current_lr = optimizer.param_groups[-1]['lr']
|
||||
eta_min = spend_time / max(step - start_step, 1) * (iters - step) // 60
|
||||
Logger(
|
||||
f'Epoch:[{epoch + 1}/{args.epochs}]({step}/{iters}), '
|
||||
f'loss: {current_loss:.4f}, logits_loss: {current_logits_loss:.4f}, '
|
||||
f'aux_loss: {current_aux_loss:.4f}, lr: {current_lr:.8f}, '
|
||||
f'epoch_time: {eta_min:.1f}min'
|
||||
)
|
||||
if wandb:
|
||||
wandb.log({
|
||||
"loss": current_loss, "logits_loss": current_logits_loss,
|
||||
"aux_loss": current_aux_loss, "learning_rate": current_lr,
|
||||
"epoch_time": eta_min,
|
||||
})
|
||||
|
||||
if (step % args.save_interval == 0 or step == iters) and is_main_process():
|
||||
model.eval()
|
||||
moe_suffix = '_moe' if lm_config.use_moe else ''
|
||||
ckp = f'{args.save_dir}/{args.save_weight}_{lm_config.hidden_size}{moe_suffix}.pth'
|
||||
raw_model = model.module if isinstance(model, DistributedDataParallel) else model
|
||||
raw_model = getattr(raw_model, '_orig_mod', raw_model)
|
||||
state_dict = raw_model.state_dict()
|
||||
torch.save({k: v.half().cpu() for k, v in state_dict.items()}, ckp)
|
||||
lm_checkpoint(
|
||||
lm_config, weight=args.save_weight, model=model, optimizer=optimizer,
|
||||
epoch=epoch, step=step, wandb=wandb, save_dir='../checkpoints', scaler=scaler,
|
||||
)
|
||||
model.train()
|
||||
del state_dict
|
||||
|
||||
del input_ids, labels, res, loss
|
||||
|
||||
if last_step > start_step and last_step % args.accumulation_steps != 0:
|
||||
scaler.unscale_(optimizer)
|
||||
torch.nn.utils.clip_grad_norm_(model.parameters(), args.grad_clip)
|
||||
scaler.step(optimizer)
|
||||
scaler.update()
|
||||
optimizer.zero_grad(set_to_none=True)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
parser = argparse.ArgumentParser(description="MiniMind QAT (W8A8 fake-quant fine-tune)")
|
||||
parser.add_argument("--save_dir", type=str, default="../out")
|
||||
parser.add_argument('--save_weight', default='qat', type=str)
|
||||
parser.add_argument("--epochs", type=int, default=1, help="QAT 通常 1~2 epoch 即可")
|
||||
parser.add_argument("--batch_size", type=int, default=16)
|
||||
parser.add_argument("--learning_rate", type=float, default=2e-6,
|
||||
help="QAT 用比 SFT 更小的 lr（默认 1/5），防止打乱已收敛的权重分布")
|
||||
parser.add_argument("--device", type=str, default="cuda:0" if torch.cuda.is_available() else "cpu")
|
||||
parser.add_argument("--dtype", type=str, default="bfloat16")
|
||||
parser.add_argument("--num_workers", type=int, default=8)
|
||||
parser.add_argument("--accumulation_steps", type=int, default=1)
|
||||
parser.add_argument("--grad_clip", type=float, default=1.0)
|
||||
parser.add_argument("--log_interval", type=int, default=100)
|
||||
parser.add_argument("--save_interval", type=int, default=1000)
|
||||
parser.add_argument('--hidden_size', default=768, type=int)
|
||||
parser.add_argument('--num_hidden_layers', default=8, type=int)
|
||||
parser.add_argument('--max_seq_len', default=768, type=int)
|
||||
parser.add_argument('--use_moe', default=0, type=int, choices=[0, 1])
|
||||
parser.add_argument("--data_path", type=str, default="../dataset/sft_t2t_mini.jsonl")
|
||||
parser.add_argument('--from_weight', default='full_sft', type=str,
|
||||
help="QAT 起点权重前缀（默认基于 full_sft 微调）")
|
||||
parser.add_argument('--from_resume', default=0, type=int, choices=[0, 1])
|
||||
parser.add_argument("--use_wandb", action="store_true")
|
||||
parser.add_argument("--wandb_project", type=str, default="MiniMind-QAT")
|
||||
parser.add_argument("--use_compile", default=0, type=int, choices=[0, 1])
|
||||
|
||||
# ---- QAT-specific ----
|
||||
parser.add_argument("--w_bits", type=int, default=8, help="weight 量化位宽")
|
||||
parser.add_argument("--a_bits", type=int, default=8, help="activation 量化位宽")
|
||||
parser.add_argument("--quant_observer_steps", type=int, default=200,
|
||||
help="activation observer 预热步数（仅采集 EMA，不做 fake-quant）")
|
||||
parser.add_argument("--quant_skip", type=str, default=",".join(DEFAULT_SKIP_PATTERNS),
|
||||
help="名称中包含这些子串的 Linear 跳过量化（逗号分隔）"
|
||||
"；默认跳过 lm_head 和 MoE 路由 mlp.gate")
|
||||
args = parser.parse_args()
|
||||
|
||||
# 1. dist + seed
|
||||
local_rank = init_distributed_mode()
|
||||
if dist.is_initialized():
|
||||
args.device = f"cuda:{local_rank}"
|
||||
setup_seed(42 + (dist.get_rank() if dist.is_initialized() else 0))
|
||||
|
||||
# 2. config + checkpoint probe
|
||||
os.makedirs(args.save_dir, exist_ok=True)
|
||||
lm_config = MiniMindConfig(
|
||||
hidden_size=args.hidden_size, num_hidden_layers=args.num_hidden_layers,
|
||||
use_moe=bool(args.use_moe),
|
||||
)
|
||||
ckp_data = lm_checkpoint(
|
||||
lm_config, weight=args.save_weight, save_dir='../checkpoints',
|
||||
) if args.from_resume == 1 else None
|
||||
|
||||
# 3. autocast
|
||||
device_type = "cuda" if "cuda" in args.device else "cpu"
|
||||
dtype = torch.bfloat16 if args.dtype == "bfloat16" else torch.float16
|
||||
autocast_ctx = nullcontext() if device_type == "cpu" else torch.cuda.amp.autocast(dtype=dtype)
|
||||
|
||||
# 4. wandb
|
||||
wandb = None
|
||||
if args.use_wandb and is_main_process():
|
||||
import swanlab as wandb
|
||||
wandb_id = ckp_data.get('wandb_id') if ckp_data else None
|
||||
resume = 'must' if wandb_id else None
|
||||
wandb_run_name = (
|
||||
f"MiniMind-QAT-W{args.w_bits}A{args.a_bits}-Epoch-{args.epochs}-"
|
||||
f"BS-{args.batch_size}-LR-{args.learning_rate}"
|
||||
)
|
||||
wandb.init(project=args.wandb_project, name=wandb_run_name, id=wandb_id, resume=resume)
|
||||
|
||||
# 5. model: load fp weights -> wrap with QATLinear -> dataset/optimizer
|
||||
if args.from_weight == 'none':
|
||||
raise SystemExit("--from_weight=none is not supported for QAT; load a pretrained/SFT ckpt.")
|
||||
model, tokenizer = init_model(lm_config, args.from_weight, device=args.device)
|
||||
|
||||
skip_patterns = tuple(s.strip() for s in args.quant_skip.split(',') if s.strip())
|
||||
replaced = prepare_qat(
|
||||
model, w_bits=args.w_bits, a_bits=args.a_bits,
|
||||
skip_patterns=skip_patterns, a_observer_steps=args.quant_observer_steps,
|
||||
)
|
||||
Logger(f'QAT: wrapped {replaced} Linear modules '
|
||||
f'(W{args.w_bits}A{args.a_bits}, skip={skip_patterns}, '
|
||||
f'observer_warmup={args.quant_observer_steps} steps)')
|
||||
|
||||
train_ds = SFTDataset(args.data_path, tokenizer, max_length=args.max_seq_len)
|
||||
train_sampler = DistributedSampler(train_ds) if dist.is_initialized() else None
|
||||
scaler = torch.cuda.amp.GradScaler(enabled=(args.dtype == 'float16'))
|
||||
optimizer = optim.AdamW(model.parameters(), lr=args.learning_rate)
|
||||
|
||||
# 6. resume
|
||||
start_epoch, start_step = 0, 0
|
||||
if ckp_data:
|
||||
model.load_state_dict(ckp_data['model']) # state dict already contains QAT buffers
|
||||
optimizer.load_state_dict(ckp_data['optimizer'])
|
||||
scaler.load_state_dict(ckp_data['scaler'])
|
||||
start_epoch = ckp_data['epoch']
|
||||
start_step = ckp_data.get('step', 0)
|
||||
|
||||
# 7. compile + DDP
|
||||
if args.use_compile == 1:
|
||||
model = torch.compile(model)
|
||||
Logger('torch.compile enabled')
|
||||
if dist.is_initialized():
|
||||
model = DistributedDataParallel(model, device_ids=[local_rank])
|
||||
|
||||
# 8. train
|
||||
for epoch in range(start_epoch, args.epochs):
|
||||
train_sampler and train_sampler.set_epoch(epoch)
|
||||
setup_seed(42 + epoch)
|
||||
indices = torch.randperm(len(train_ds)).tolist()
|
||||
skip = start_step if (epoch == start_epoch and start_step > 0) else 0
|
||||
batch_sampler = SkipBatchSampler(train_sampler or indices, args.batch_size, skip)
|
||||
loader = DataLoader(train_ds, batch_sampler=batch_sampler,
|
||||
num_workers=args.num_workers, pin_memory=True)
|
||||
if skip > 0:
|
||||
Logger(f'Epoch [{epoch + 1}/{args.epochs}]: 跳过前{start_step}个step，从step {start_step + 1}开始')
|
||||
train_epoch(epoch, loader, len(loader) + skip, start_step, wandb)
|
||||
else:
|
||||
train_epoch(epoch, loader, len(loader), 0, wandb)
|
||||
|
||||
# 9. cleanup
|
||||
if dist.is_initialized():
|
||||
dist.barrier()
|
||||
dist.destroy_process_group()
|
||||
Loading…
Reference in New Issue