calflops: a FLOPs and Params calculate tool for neural networks

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Introduction

This tool(calflops) is designed to compute the theoretical amount of FLOPs(floating-point operations)、MACs(multiply-add operations) and Parameters in all various neural networks, such as Linear、 CNN、 RNN、 GCN、Transformer(Bert、LlaMA etc Large Language Model)，even including any custom models via torch.nn.function.* as long as based on the Pytorch implementation. Meanwhile this tool supports the printing of FLOPS, Parameter calculation value and proportion of each submodule of the model, it is convient for users to understand the performance consumption of each part of the model.

Latest news, calflops has launched a tool on Huggingface Space, which is more convenient for computing FLOPS in the model of 🤗Huggingface Platform. Welcome to use it:https://huggingface.co/spaces/MrYXJ/calculate-model-flops

For LLM, this is probably the easiest tool to calculate FLOPs and it is very convenient for huggingface platform models. You can use calflops.calculate_flops_hf(model_name) by model_name which in huggingface models to calculate model FLOPs without downloading entire model weights locally.Notice this method requires the model to support the empty model being created for model inference in meta device.

from calflops import calculate_flops_hf

model_name = "meta-llama/Llama-2-7b"
access_token = "..." # your application token for using llama2
flops, macs, params = calculate_flops_hf(model_name=model_name, access_token=access_token) # default input shape: (1, 128)
print("%s FLOPs:%s  MACs:%s  Params:%s \n" %(model_name, flops, macs, params))

If model can't inference in meta device, you just need assign llm corresponding tokenizer to the parameter: transformers_tokenizer to pass in funcional of calflops.calculate_flops(), and it will automatically help you build the model input data whose size is input_shape. Alternatively, you also can pass in the input data of models which need multi data as input that you have constructed.

In addition, the implementation process of this package inspired by ptflops、deepspeed、hf accelerate libraries, Thanks for their great efforts, they are both very good work. Meanwhile this package also improves some aspects to calculate FLOPs based on them.

How to install

Install the latest version

From PyPI:

pip install --upgrade calflops

And you also can download latest calflops-*-py3-none-any.whl files from https://pypi.org/project/calflops/

pip install calflops-*-py3-none-any.whl

How to use calflops

Example

CNN Model

If model has only one input, you just need set the model input size by parameter input_shape , it can automatically generate random model input to complete the calculation:

from calflops import calculate_flops
from torchvision import models

model = models.alexnet()
batch_size = 1
input_shape = (batch_size, 3, 224, 224)
flops, macs, params = calculate_flops(model=model, 
                                      input_shape=input_shape,
                                      output_as_string=True,
                                      output_precision=4)
print("Alexnet FLOPs:%s   MACs:%s   Params:%s \n" %(flops, macs, params))
#Alexnet FLOPs:4.2892 GFLOPS   MACs:2.1426 GMACs   Params:61.1008 M 

If the model has multiple inputs, use the parameters args or kargs, as shown in the Transfomer Model below.

Calculate Huggingface Model By Model Name(Online)

No need to download the entire parameter weight of the model to the local, just by the model name can test any open source large model on the huggingface platform.

from calflops import calculate_flops_hf

batch_size, max_seq_length = 1, 128
model_name = "baichuan-inc/Baichuan-13B-Chat"

flops, macs, params = calculate_flops_hf(model_name=model_name, input_shape=(batch_size, max_seq_length))
print("%s FLOPs:%s  MACs:%s  Params:%s \n" %(model_name, flops, macs, params))

You can also use this model urls of huggingface platform to calculate it FLOPs.

from calflops import calculate_flops_hf

batch_size, max_seq_length = 1, 128
model_name = "https://huggingface.co/THUDM/glm-4-9b-chat" # THUDM/glm-4-9b-chat
flops, macs, params = calculate_flops_hf(model_name=model_name, input_shape=(batch_size, max_seq_length))
print("%s FLOPs:%s  MACs:%s  Params:%s \n" %(model_name, flops, macs, params))

------------------------------------- Calculate Flops Results -------------------------------------
Notations:
number of parameters (Params), number of multiply-accumulate operations(MACs),
number of floating-point operations (FLOPs), floating-point operations per second (FLOPS),
fwd FLOPs (model forward propagation FLOPs), bwd FLOPs (model backward propagation FLOPs),
default model backpropagation takes 2.00 times as much computation as forward propagation.

Total Training Params:                                                  9.4 B
fwd MACs:                                                               1.12 TMACs
fwd FLOPs:                                                              2.25 TFLOPS
fwd+bwd MACs:                                                           3.37 TMACs
fwd+bwd FLOPs:                                                          6.74 TFLOPS

-------------------------------- Detailed Calculated FLOPs Results --------------------------------
Each module caculated is listed after its name in the following order:
params, percentage of total params, MACs, percentage of total MACs, FLOPS, percentage of total FLOPs

Note: 1. A module can have torch.nn.module or torch.nn.functional to compute logits (e.g. CrossEntropyLoss).
 They are not counted as submodules in calflops and not to be printed out. However they make up the difference between a parent's MACs and the sum of its submodules'.
2. Number of floating-point operations is a theoretical estimation, thus FLOPS computed using that could be larger than the maximum system throughput.

ChatGLMForConditionalGeneration(
  9.4 B = 100% Params, 1.12 TMACs = 100% MACs, 2.25 TFLOPS = 50% FLOPs
  (transformer): ChatGLMModel(
    9.4 B = 100% Params, 1.12 TMACs = 100% MACs, 2.25 TFLOPS = 50% FLOPs
    (embedding): Embedding(
      620.76 M = 6.6% Params, 0 MACs = 0% MACs, 0 FLOPS = 0% FLOPs
      (word_embeddings): Embedding(620.76 M = 6.6% Params, 0 MACs = 0% MACs, 0 FLOPS = 0% FLOPs, 151552, 4096)
    )
    (rotary_pos_emb): RotaryEmbedding(0 = 0% Params, 0 MACs = 0% MACs, 0 FLOPS = 0% FLOPs)
    (encoder): GLMTransformer(
      8.16 B = 86.79% Params, 1.04 TMACs = 92.93% MACs, 2.09 TFLOPS = 46.46% FLOPs
      (layers): ModuleList(
        (0-39): 40 x GLMBlock(
          203.96 M = 2.17% Params, 26.11 GMACs = 2.32% MACs, 52.21 GFLOPS = 1.16% FLOPs
          (input_layernorm): RMSNorm(4.1 K = 0% Params, 0 MACs = 0% MACs, 0 FLOPS = 0% FLOPs)
          (self_attention): SelfAttention(
            35.66 M = 0.38% Params, 4.56 GMACs = 0.41% MACs, 9.13 GFLOPS = 0.2% FLOPs
            (query_key_value): Linear(18.88 M = 0.2% Params, 2.42 GMACs = 0.22% MACs, 4.83 GFLOPS = 0.11% FLOPs, in_features=4096, out_features=4608, bias=True)  
            (core_attention): CoreAttention(
              0 = 0% Params, 0 MACs = 0% MACs, 0 FLOPS = 0% FLOPs
              (attention_dropout): Dropout(0 = 0% Params, 0 MACs = 0% MACs, 0 FLOPS = 0% FLOPs, p=0.0, inplace=False)
            )
            (dense): Linear(16.78 M = 0.18% Params, 2.15 GMACs = 0.19% MACs, 4.29 GFLOPS = 0.1% FLOPs, in_features=4096, out_features=4096, bias=False)
          )
          (post_attention_layernorm): RMSNorm(4.1 K = 0% Params, 0 MACs = 0% MACs, 0 FLOPS = 0% FLOPs)
          (mlp): MLP(
            168.3 M = 1.79% Params, 21.54 GMACs = 1.92% MACs, 43.09 GFLOPS = 0.96% FLOPs
            (dense_h_to_4h): Linear(112.2 M = 1.19% Params, 14.36 GMACs = 1.28% MACs, 28.72 GFLOPS = 0.64% FLOPs, in_features=4096, out_features=27392, bias=False)
            (dense_4h_to_h): Linear(56.1 M = 0.6% Params, 7.18 GMACs = 0.64% MACs, 14.36 GFLOPS = 0.32% FLOPs, in_features=13696, out_features=4096, bias=False)
          )
        )
      )
      (final_layernorm): RMSNorm(4.1 K = 0% Params, 0 MACs = 0% MACs, 0 FLOPS = 0% FLOPs)
    )
    (output_layer): Linear(620.76 M = 6.6% Params, 79.46 GMACs = 7.07% MACs, 158.91 GFLOPS = 3.54% FLOPs, in_features=4096, out_features=151552, bias=False)
  )
)

There are some model uses that require an application first, and you only need to pass the application in through the access_token to calculate its FLOPs.

from calflops import calculate_flops_hf

batch_size, max_seq_length = 1, 128
model_name = "meta-llama/Llama-2-7b"
access_token = "" # your application for using llama2

flops, macs, params = calculate_flops_hf(model_name=model_name,
                                         access_token=access_token,
                                         input_shape=(batch_size, max_seq_length))
print("%s FLOPs:%s  MACs:%s  Params:%s \n" %(model_name, flops, macs, params))

Transformer Model (Local)

Compared to the CNN Model, Transformer Model if you want to use the parameter input_shape to make calflops automatically generating the input data, you should pass its corresponding tokenizer through the parameter transformer_tokenizer.

# Transformers Model, such as bert.
from calflops import calculate_flops
from transformers import AutoModel
from transformers import AutoTokenizer

batch_size, max_seq_length = 1, 128
model_name = "hfl/chinese-roberta-wwm-ext/"
model_save = "../pretrain_models/" + model_name
model = AutoModel.from_pretrained(model_save)
tokenizer = AutoTokenizer.from_pretrained(model_save)

flops, macs, params = calculate_flops(model=model, 
                                      input_shape=(batch_size,max_seq_length),
                                      transformer_tokenizer=tokenizer)
print("Bert(hfl/chinese-roberta-wwm-ext) FLOPs:%s   MACs:%s   Params:%s \n" %(flops, macs, params))
#Bert(hfl/chinese-roberta-wwm-ext) FLOPs:67.1 GFLOPS   MACs:33.52 GMACs   Params:102.27 M 

If you want to use your own generated specific data to calculate FLOPs, you can use parameter args or kwargs，and parameter input_shape can no longer be assigned to pass in this case. Here is an example that can be seen is inconvenient comparedt to use parametertransformer_tokenizer.

# Transformers Model, such as bert.
from calflops import calculate_flops
from transformers import AutoModel
from transformers import AutoTokenizer


batch_size, max_seq_length = 1, 128
model_name = "hfl/chinese-roberta-wwm-ext/"
model_save = "/code/yexiaoju/generate_tags/models/pretrain_models/" + model_name
model = AutoModel.from_pretrained(model_save)
tokenizer = AutoTokenizer.from_pretrained(model_save)

text = ""
inputs = tokenizer(text,
                   add_special_tokens=True, 
                   return_attention_mask=True,
                   padding=True,
                   truncation="longest_first",
                   max_length=max_seq_length)

if len(inputs["input_ids"]) < max_seq_length:
    apply_num = max_seq_length-len(inputs["input_ids"])
    inputs["input_ids"].extend([0]*apply_num)
    inputs["token_type_ids"].extend([0]*apply_num)
    inputs["attention_mask"].extend([0]*apply_num)

inputs["input_ids"] = torch.tensor([inputs["input_ids"]])
inputs["token_type_ids"] = torch.tensor([inputs["token_type_ids"]])
inputs["attention_mask"] = torch.tensor([inputs["attention_mask"]])

flops, macs, params = calculate_flops(model=model,
                                      kwargs = inputs,
                                      print_results=False)
print("Bert(hfl/chinese-roberta-wwm-ext) FLOPs:%s   MACs:%s   Params:%s \n" %(flops, macs, params))
#Bert(hfl/chinese-roberta-wwm-ext) FLOPs:22.36 GFLOPS   MACs:11.17 GMACs   Params:102.27 M 

Large Language Model

Online

from calflops import calculate_flops_hf

batch_size, max_seq_length = 1, 128
model_name = "meta-llama/Llama-2-7b"
access_token = "" # your application for using llama 

flops, macs, params = calculate_flops_hf(model_name=model_name,
                                         access_token=access_token,
                                         input_shape=(batch_size, max_seq_length))
print("%s FLOPs:%s  MACs:%s  Params:%s \n" %(model_name, flops, macs, params))

Local

Note here that the tokenizer must correspond to the llm model because llm tokenizer processes maybe are different.

``` python

Large Languase Model, such as llama2-7b.

from calflops import calculate_flops from transformers import LlamaTokenizer from transformers import LlamaForCausalLM

batch_size, max_seq_length = 1, 128 model_name = "llama2_hf_7B" mode