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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 json
import os
import fire
import torch
import torch.distributed as dist
from transformers import AutoConfig
from llamafactory.train.tuner import run_exp
BASE = 2 # gemm (add + mul)
def compute_model_flops(
model_name_or_path: str,
total_batch_size: int,
seq_length: int,
include_backward: bool = True,
include_recompute: bool = False,
include_flashattn: bool = False,
) -> int:
r"""
Calculates the FLOPs of model per forward/backward pass.
"""
config = AutoConfig.from_pretrained(model_name_or_path)
hidden_size = getattr(config, "hidden_size", None)
vocab_size = getattr(config, "vocab_size", None)
intermediate_size = getattr(config, "intermediate_size", None)
num_attention_heads = getattr(config, "num_attention_heads", None)
num_key_value_heads = getattr(config, "num_key_value_heads", None)
num_hidden_layers = getattr(config, "num_hidden_layers", None)
tie_word_embeddings = getattr(config, "tie_word_embeddings", False)
# mlp module
mlp_flops_per_token = 3 * BASE * hidden_size * intermediate_size # up, gate, down
mlp_flops = total_batch_size * seq_length * num_hidden_layers * mlp_flops_per_token
# attn projector module
q_flops_per_token = BASE * hidden_size * hidden_size
o_flops_per_token = BASE * hidden_size * hidden_size
k_flops_per_token = BASE * hidden_size * hidden_size * num_key_value_heads // num_attention_heads
v_flops_per_token = BASE * hidden_size * hidden_size * num_key_value_heads // num_attention_heads
attn_proj_flops_per_token = q_flops_per_token + o_flops_per_token + k_flops_per_token + v_flops_per_token
attn_proj_flops = total_batch_size * seq_length * num_hidden_layers * attn_proj_flops_per_token
# attn sdpa module
sdpa_flops_per_layer = 2 * BASE * hidden_size * seq_length * seq_length # (q * k^T) * v
sdpa_flops = total_batch_size * num_hidden_layers * sdpa_flops_per_layer
# embedding module
embedding_flops_per_token = hidden_size * vocab_size
embedding_flops = total_batch_size * seq_length * embedding_flops_per_token
if tie_word_embeddings is False:
embedding_flops *= 2
non_embedding_flops = mlp_flops + attn_proj_flops + sdpa_flops
non_embedding_coeff, embedding_coeff = 1, 1
if include_backward:
non_embedding_coeff += 2
embedding_coeff += 2
if include_recompute:
non_embedding_coeff += 1
total_flops = non_embedding_coeff * non_embedding_flops + embedding_coeff * embedding_flops
if include_flashattn:
total_flops += sdpa_flops
return total_flops
def compute_device_flops(world_size: int) -> float:
r"""
Calculates the FLOPs of the device capability per second.
"""
device_name = torch.cuda.get_device_name()
if "H100" in device_name or "H800" in device_name:
return 989 * 1e12 * world_size
elif "A100" in device_name or "A800" in device_name:
return 312 * 1e12 * world_size
elif "V100" in device_name:
return 125 * 1e12 * world_size
elif "4090" in device_name:
return 98 * 1e12 * world_size
else:
raise NotImplementedError("Device not supported: {}.".format(device_name))
def calculate_mfu(
model_name_or_path: str,
batch_size: int = 1,
seq_length: int = 1024,
num_steps: int = 100,
finetuning_type: str = "lora",
flash_attn: str = "auto",
deepspeed_stage: int = 0,
disable_gc: bool = False,
liger_kernel: bool = False,
unsloth_gc: bool = False,
) -> float:
r"""
Calculates MFU for given model and hyper-params.
Usage: python cal_mfu.py --model_name_or_path path_to_model --batch_size 1 --seq_length 1024
"""
args = {
"model_name_or_path": model_name_or_path,
"flash_attn": flash_attn,
"disable_gradient_checkpointing": disable_gc,
"enable_liger_kernel": liger_kernel,
"use_unsloth_gc": unsloth_gc,
"stage": "pt",
"do_train": True,
"finetuning_type": finetuning_type,
"dataset": "c4_demo",
"cutoff_len": seq_length,
"output_dir": os.path.join("saves", "test_mfu"),
"logging_strategy": "no",
"save_strategy": "no",
"save_only_model": True,
"overwrite_output_dir": True,
"per_device_train_batch_size": batch_size,
"max_steps": num_steps,
"bf16": True,
}
if deepspeed_stage in [2, 3]:
args["deepspeed"] = "examples/deepspeed/ds_z{}_config.json".format(deepspeed_stage)
run_exp(args)
with open(os.path.join("saves", "test_mfu", "all_results.json"), "r", encoding="utf-8") as f:
result = json.load(f)
if dist.is_initialized():
world_size = dist.get_world_size()
else:
world_size = 1
total_batch_size = batch_size * world_size
mfu_value = (
result["train_steps_per_second"]
* compute_model_flops(model_name_or_path, total_batch_size, seq_length)
/ compute_device_flops(world_size)
)
print("MFU: {:.2f}%".format(mfu_value * 100))
if __name__ == "__main__":
fire.Fire(calculate_mfu)
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