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53c10a4 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 | # Copyright 2024 Bytedance Ltd. and/or its affiliates
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Utilities to create common models
"""
from functools import lru_cache
from typing import Optional, Tuple
import torch
import torch.distributed as dist
from torch import nn
@lru_cache
def is_rank0() -> int:
return (not dist.is_initialized()) or (dist.get_rank() == 0)
def print_gpu_memory_usage(prefix: str = "GPU memory usage") -> None:
"""Report the current GPU VRAM usage."""
if is_rank0():
free_mem, total_mem = torch.cuda.mem_get_info()
print(f"{prefix}: {(total_mem - free_mem) / (1024**3):.2f} GB / {total_mem / (1024**3):.2f} GB.")
def _get_model_size(model: nn.Module, scale: str = "auto") -> Tuple[float, str]:
"""Compute the model size."""
n_params = sum(p.numel() for p in model.parameters())
if scale == "auto":
if n_params > 1e9:
scale = "B"
elif n_params > 1e6:
scale = "M"
elif n_params > 1e3:
scale = "K"
else:
scale = ""
if scale == "B":
n_params = n_params / 1e9
elif scale == "M":
n_params = n_params / 1e6
elif scale == "K":
n_params = n_params / 1e3
elif scale == "":
pass
else:
raise NotImplementedError(f"Unknown scale {scale}.")
return n_params, scale
def print_model_size(model: nn.Module, name: Optional[str] = None) -> None:
"""Print the model size."""
if is_rank0():
n_params, scale = _get_model_size(model, scale="auto")
if name is None:
name = model.__class__.__name__
print(f"{name} contains {n_params:.2f}{scale} parameters.")
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