update cal_mfu.py

Former-commit-id: 66ec36522c9bf8dfffc1065202362801875a104d
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-.- 2024-09-07 23:21:35 +08:00
parent 4ee9efbd98
commit ab1775cd95

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# coding=utf-8
# Copyright 2024 the LlamaFactory team.
#
# 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
import torch
from transformers import AutoConfig
import fire
def model_flops_counter(
batch_size: int,
seqlen: int,
model_config: dict,
is_backward: bool = True,
is_recompute: bool = False,
is_flashattn: bool = False,
) -> float:
"""
calculate the FLOPs of model per iteration
"""
hidden_size = model_config.hidden_size
num_attention_heads = model_config.num_attention_heads
num_key_value_heads = model_config.num_key_value_heads
vocab_size = model_config.vocab_size
intermediate_size = model_config.intermediate_size
num_hidden_layers = model_config.num_hidden_layers
"""
B: batch_size
S: seqlen
L: num_hidden_layers
H: hidden_size
V: vocab_size
I: intermediate_size
"""
### MLP calculation
per_mlp_calculation = 2 * hidden_size * intermediate_size
mlp_calculation_per_layer = per_mlp_calculation * 3
mlp_calculation = batch_size * seqlen * mlp_calculation_per_layer * num_hidden_layers
### Attention calculation
Q_calculation = 2 * hidden_size * hidden_size
O_calculation = 2 * hidden_size * hidden_size
K_calculation = 2 * hidden_size * hidden_size * num_key_value_heads / num_attention_heads
V_calculation = 2 * hidden_size * hidden_size * num_key_value_heads / num_attention_heads
QKVO_calculation = Q_calculation + O_calculation + K_calculation + V_calculation # 8H^2 / coe
self_attn_calculation = seqlen * hidden_size * 2 * 2 # (4 * S * H)
attention_calculation = batch_size * seqlen * num_hidden_layers * (QKVO_calculation + self_attn_calculation) # BSL(8H^2/coe + 4S * H)
#Embedding and LMhead calculation
embedding_calculation = hidden_size * vocab_size
lmhead_calculation = hidden_size * vocab_size
IO_calculation = 3 * batch_size * seqlen * (embedding_calculation + lmhead_calculation) # 2 *(1+2)BSHV
E = attention_calculation + mlp_calculation
coefficient = 3
fix_term = 0
if(is_recompute):
coefficient = 4
if(is_flashattn):
fix_term = batch_size *seqlen * self_attn_calculation
total_calculation = coefficient * E + IO_calculation + fix_term
return total_calculation
def hardware_flops_counter(
seconds: float, # seconds used in given iterations
num_gpus: int = 1,
) -> float:
if "A100" in torch.cuda.get_device_name():
return 312 * 1e12 * seconds * num_gpus
elif "V100" in torch.cuda.get_device_name():
return 125 * 1e12 * seconds * num_gpus
def compute_mfu(
batch_size: int,
seqlen: int,
model_config: dict,
num_iter: int,
seconds: float,
num_gpus: int = 1,
) -> float:
"""
compute MFU given model configuration, training config and training information
"""
percentage = (num_iter * model_flops_counter(batch_size,seqlen,model_config)) / hardware_flops_counter(seconds, num_gpus)
print(f"MFU : {percentage* 100:.2f}%")
return percentage
# User input
### model_name
model_name = "meta-llama/Meta-Llama-3-8B-Instruct"
### training config
batch_size = 8
seqlen = 1*1024
num_gpus = 1
### training information
num_iter = 225
seconds = 605 # time used in {num_iter} iterations
model_config = AutoConfig.from_pretrained(model_name)
if __name__ == "__main__":
fire.Fire(
compute_mfu(
batch_size=batch_size,
seqlen=seqlen,
model_config=model_config,
num_iter=num_iter,
seconds=seconds,
num_gpus=num_gpus
)
)