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import argparse
import time
import torch
from accelerate import Accelerator
from torch.cuda import max_memory_allocated, memory_allocated
from torch.optim import AdamW
from tqdm import trange
from transformers import AutoConfig, AutoModelForCausalLM, PretrainedConfig
from transformers.optimization import get_cosine_schedule_with_warmup
import fla
classes = [getattr(fla.models, i) for i in fla.models.__all__]
configs = {i.model_type: i() for i in classes if issubclass(i, PretrainedConfig)}
def sizeof_fmt(num, suffix='B'):
for unit in ('', 'Ki', 'Mi', 'Gi', 'Ti', 'Pi', 'Ei', 'Zi'):
if abs(num) < 1024.0:
return f'{num:.2f}{unit}{suffix}'
num /= 1024.0
return f'{num:.2f}Yi{suffix}'
def prepare_inputs(
batch_size: int,
seq_len: int,
context_len: int,
varlen: bool,
vocab_size: int,
device: torch.device,
):
if varlen:
tokens = torch.randint(high=vocab_size, size=(1, batch_size * seq_len), device=device)
cu_seqlens = torch.cat([
torch.tensor([0]),
torch.randperm(batch_size * seq_len - 16)[:torch.randint(8, 64, size=(1,))] + 16,
torch.tensor([batch_size * seq_len]),
], 0).sort()[0].to(dtype=torch.int32, device=device)
if context_len is not None:
cu_seqlens = torch.cat(
[torch.arange(i, j, context_len) for i, j in zip(cu_seqlens[:-1].tolist(), cu_seqlens[1:].tolist(), strict=False)] +
[torch.tensor([len(tokens[0])])],
).to(dtype=torch.int32, device=device)
else:
tokens = torch.randint(high=vocab_size, size=(batch_size, seq_len), device=device)
cu_seqlens = None
return tokens, cu_seqlens
def profile(
name: str,
batch_size: int = 8,
seq_len: int = 2048,
context_len: int = 2048,
varlen: bool = False,
warmup_steps: int = 16,
steps: int = 32,
total_steps: int = 1024,
lr: float = 3e-4,
betas: tuple[float] = (0.9, 0.95),
weight_decay: float = 0.1,
dtype: torch.dtype | None = torch.bfloat16,
mixed_precision: str = 'bf16',
compile: bool = False,
):
device = torch.device('cuda')
config = configs[name] if name in configs else AutoConfig.from_pretrained(name)
model = AutoModelForCausalLM.from_config(config).cuda().to(dtype)
if compile:
print("Compiling the model")
model = torch.compile(model)
num_parameters = model.num_parameters()
print(f"Initializing {name} model from the config:\n{config}\n{model}")
print(f"Number of parameters in total: {num_parameters} ({sizeof_fmt(num_parameters)})")
print(f"Allocated memory after initialization: {sizeof_fmt(memory_allocated(device))}")
accelerator = Accelerator(mixed_precision=mixed_precision)
optimizer = AdamW(
model.parameters(),
lr=lr,
betas=betas,
weight_decay=weight_decay,
fused=True,
)
scheduler = get_cosine_schedule_with_warmup(optimizer, 0, total_steps)
bar = trange(warmup_steps)
model, optimizer, scheduler = accelerator.prepare(model, optimizer, scheduler)
torch.cuda.synchronize(device)
for _ in bar:
# forward pass
tokens, cu_seqlens = prepare_inputs(
batch_size=batch_size,
seq_len=seq_len,
context_len=context_len,
varlen=varlen,
vocab_size=config.vocab_size,
device=device,
)
outputs = model(tokens, labels=tokens, cu_seqlens=cu_seqlens)
# backward pass
accelerator.backward(outputs.loss)
optimizer.step()
scheduler.step()
optimizer.zero_grad()
bar.set_description_str(f"Max memory allocated: {sizeof_fmt(max_memory_allocated(device))}")
start, total_tokens = time.time(), 0
bar = trange(steps)
torch.cuda.synchronize(device)
for _ in bar:
# forward pass
tokens, cu_seqlens = prepare_inputs(
batch_size=batch_size,
seq_len=seq_len,
context_len=context_len,
varlen=varlen,
vocab_size=config.vocab_size,
device=device,
)
outputs = model(tokens, labels=tokens, cu_seqlens=cu_seqlens)
# backward pass
accelerator.backward(outputs.loss)
optimizer.step()
optimizer.zero_grad()
total_tokens += batch_size * seq_len
torch.cuda.synchronize(device)
duration = time.time() - start
bar.set_description_str(f"Thoughput: {total_tokens / duration:10.2f} tokens/s")
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--name", default='retnet')
parser.add_argument("--batch_size", default=8, type=int)
parser.add_argument("--seq_len", default=2048, type=int)
parser.add_argument("--context_len", default=None, type=int)
parser.add_argument("--varlen", action='store_true')
parser.add_argument("--warmup_steps", default=64, type=int)
parser.add_argument("--steps", default=256, type=int)
parser.add_argument("--compile", action='store_true')
args = parser.parse_args()
profile(
name=args.name,
batch_size=args.batch_size,
seq_len=args.seq_len,
context_len=args.context_len,
varlen=args.varlen,
warmup_steps=args.warmup_steps,
steps=args.steps,
compile=args.compile,
)
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