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GPT

Table Of Contents

Model architecture

Fig. 1 Flowchart of GPT model.

Introduction

GPT model is a variant of Decoding model. GPT model does not require the results from encoder and the cross multi-head attention, and use GeLU as the activation. However, OpenAI shows that using very giant model and lots of training data can significantly improve the capacity of GPT model in their paper. However, it is impossible to put such model into a single GPU. For example, the largest model, GPT-3, has 175 billion parameters, which takes about 350GBs under half data type. Therefore, multi-gpus, even multi-nodes, is necessary.

In FasterTransformer 4.0, we propose the multi-gpu inference library to run GPT-3. FasterTransformer supports Tensor Parallel and Layer Parallel in the same time and provides the api of cpp, TensorFlow/PyTorch op and triton backend. In cpp and PyTorch op, users can use MPI to run multiple gpus on multiple nodes. For example, using 4 dgx-1 V100 nodes (16 GBs memory per GPU) to run the GPT-3 model. To be convenient on serving, we also provide the triton backend. However, this backend only supports single nodes, multi-gpus currently. For TensorFlow op, FasterTransformer only supports single gpu now.

The arguments, inputs, and outputs of GPT:

  • Arguments:
    1. Maximum batch size (B)
    2. Maximum sequence length (S)
    3. Top k value (K)
    4. Top p value (P)
    5. Head number (H)
    6. Size per head (N)
    7. Number of decoder layers
    8. Start id of the vocabulary
    9. End id of the vocabulary
    10. Vocab size (V)
    11. Tensor parallel size
    12. Layer parallel size
  • Inputs:
    1. The table for embedding lookup. The shape is [ V, H x N ].
    2. The weights of all parameters.
    3. Position encoding table. The shape is [ S, H x N ].
    4. Inputs contexts. The shape is [ b, s ], where b <= B, s <= S.
  • Outputs:
    1. The output ids. The shape is [b, S ].

Setup

Requirements

  • CMake >= 3.8 for Tensorflow, CMake >= 3.13 for PyTorch
  • CUDA 10.1 or newer version
  • Python 3 is recommended because some features are not supported in python 2
  • Tensorflow 1.13 or 1.14 or 1.15
  • PyTorch >= 1.5.0

Recommend use nvcr image like nvcr.io/nvidia/tensorflow:20.12-tf1-py3 or nvcr.io/nvidia/pytorch:20.12-py3.

How to use

Prepare

  • Install required tools
pip install -r ../requirement.txt

To run the GPT on c, users need to convert the checkpoint of TensorFlow or PyTorch to binary files, and then load by FasterTransformer c api. Unfortunately, there is no published large model. So, users are only able to verify the correctness by smaller model. Currently, FasterTransformer provides two kinds of samples. First one is using the checkpoint of OpenAI GPT-2 model (which is trained by TensorFlow); Another choice is using the checkpoint of Megatron (which is trained by pytorch).

  • Download vocab and merge table

They can be used in both OpenAI GPT-2 and Megatron.

wget https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-vocab.json -P models
wget https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-merges.txt -P models
  • Downlaod openai-gpt model and convert

To convert the OpenAI GPT model to binary, FasterTransformer provides a tool sample/tensorflow/utils/openai_gpt_ckpt_convert.py to convert the checkpoint.

python tensorflow/utils/download_gpt2_model.py <model_name>
e.g. python tensorflow/utils/download_gpt2_model.py 124M
python ../sample/tensorflow/utils/openai_gpt_ckpt_convert.py -o models/openai-gpt-models/c-model/124m/ -i models/124M/model.ckpt -g 1 # convert 124M model with 1 TP mode
python ../sample/tensorflow/utils/openai_gpt_ckpt_convert.py -o models/openai-gpt-models/c-model/124m/ -i models/124M/model.ckpt -g 4 # convert 124M model with 4 TP mode

In the repo of OpenAI, they provide many models, including 124M, 355M, 774M and 1558M

  • Download megatron model and convert

To convert the Megatron GPT model to binary, FasterTransformer provides a tool sample/pytorch/utils/megatron_ckpt_convert.py to convert the checkpoint.

wget --content-disposition https://api.ngc.nvidia.com/v2/models/nvidia/megatron_lm_345m/versions/v0.0/zip -O megatron_lm_345m_v0.0.zip
mkdir -p models/megatron-models/345m
unzip megatron_lm_345m_v0.0.zip -d models/megatron-models/345m
git clone https://github.com/NVIDIA/Megatron-LM.git
python ../sample/pytorch/utils/megatron_ckpt_convert.py -head_num 16 -i ./models/megatron-models/345m/release/ -o ./models/megatron-models/c-model/345m/ -t_g 1 -i_g 1
python ../sample/pytorch/utils/megatron_ckpt_convert.py -head_num 16 -i ./models/megatron-models/345m/release/ -o ./models/megatron-models/c-model/345m/ -t_g 1 -i_g 8

where t_g means the number GPUs of TP during training, and i_g means the number of GPUs for TP during inference.

Note that there are different checkpoint version of Megatron. The version of the checkpoint above is 0. If users have trained a model by themselves, the default version of latest Megatron is 3. To convert the checkpoint with version 3, please add -checkpoint_version 3.

Run GPT

  1. Run GPT under on C++ with multiple gpu

    1.1 Generate the decoding_gemm_config.in file.

    ./bin/gpt_gemm <local_batch_size> <context_local_batch_size> <head_number> <size_per_head> <vocab_size> <start_len> <tensor_para_size> <is_fp16>
E.g., ./bin/gpt_gemm 8 8 12 64 50257 32 1 1

    Here, local_batch_size can be set as batch_size if users do not use the layer parallelism. If users use layer parallelism, we recommand to set local_batch_size to be smaller than batch_size to hide the bubble. But this requires larger batch_size. context_local_batch_size is used for computing the k/v cache of input. Similar to local_batch_size, users can use batch_size directly if you don't use layer parallelism, and setting to be smaller than batch_size when you use layer parallelism.

    1.2 Run GPT on C++

    Users can see the details of arguments in sample/cpp/gpt_config.ini. It controls the model path, model size, tensor parallelism size, and some hyper-parameters.

    ./bin/gpt_sample

    then use following script to convert the token ids to sentence.

    python ../sample/pytorch/utils/convert_gpt_token.py --vocab_file=./models/gpt2-vocab.json  --bpe_file=./models/gpt2-merges.txt

    By setting the is_half of gpt_config.ini to 1, users can run gpt model under fp16.

    1.3 Run with tensor parallelism (TP), layer parallelism (LP) and pipeline parallelism (PP)

    Users can use tensor_para_size and layer_para_size in gpt_config.ini to control the size of model parallel. Besides, in the layer parallelism, we can use pipeline parallelism to reduce the bubbles. We can set the layer_para_batch_size to determine the real batch size for each forward. For example, if the total batch size is 4, and layer_para_batch_size is 1, then we will split the total batch into 4 parts, and each time we only use 1 batch size. Users can set them in the gpt_config.ini.

    Note that we split the definition of LP and PP here, but we often combine them to hide the cost of bubble.

    mpirun -n 8 ./bin/gpt_sample
python ../sample/pytorch/utils/convert_gpt_token.py --vocab_file=./models/gpt2-vocab.json  --bpe_file=./models/gpt2-merges.txt

    1.4 Run gpt on multi-nodes

    Since the c sample codes use the MPI to communicate, it can extend to multi-nodes easily, except that users need to setup some network environment to communicate between multi-nodes. The following scripts are an example to show how to run multi-nodes inference on slurm.

    srun -N2 -n2 -t 600 --pty bash # Assume we get 2 nodes: prm-dgx-09 and prm-dgx-10
srun -N2 -n2 docker pull nvcr.io/nvidia/tensorflow:20.07-tf1-py3 

srun -N2 -n2  nvidia-docker run -itd --rm --privileged --network=host --pid=host --cap-add=IPC_LOCK --device=/dev/infiniband -v $PWD:$PWD -w $PWD --name ft-test nvcr.io/nvidia/tensorflow:20.12-tf1-py3 /bin/bash

srun -N2 -n2  nvidia-docker exec -i --env SLURM_NTASKS --env SLURM_NODEID --env SLURM_PROCID --env SLURM_STEP_NODELIST --env SLURMD_NODENAME --privileged ft-test bash -c "mkdir /root/.ssh && cp $PWD/ssh/* /root/.ssh && chmod 700 /root/.ssh && chmod 640 /root/.ssh/authorized_keys2 && chmod 400 /root/.ssh/id_rsa && apt-get update && apt-get install ssh -y && mkdir /run/sshd/ && /usr/sbin/sshd -p 11068 && nvidia-smi -lgc 1530"

nvidia-docker exec -ti ft-test bash 
cd FasterTransformer/build
mpirun --allow-run-as-root -np 2 -H prm-dgx-09:1,prm-dgx-10:1 -mca plm_rsh_args "-p 11068" ./bin/gpt_sample
srun -N2 -n2 docker stop ft-test

  2. Run GPT on PyTorch

    Basically, gpt_sample.py includes the example how to declare a model, load a ckeckpoint, and forward context inputs and get generated outputs in Pytorch.

    For generating outputs based on context inputs, create a text file including the context inputs (line by line) and set --sample_file_input to the text file path. (By default, the script will generate outputs without context inputs.) Set --sample_file_output to write the outputs to a file. Use --fp_16 to run in FP16.

    Run with -h to see more settings.

    python ./pytorch/gpt_sample.py -h

    2.1 Run GPT with TP and PP on single node (NVIDIA DGX A100)

    # No parallelism (tensor_para_size=1, layer_para_size=1)
mpirun -n 1 --allow-run-as-root python ./pytorch/gpt_sample.py

# TP (tensor_para_size=8, layer_para_size=1)
mpirun -n 8 --allow-run-as-root python ./pytorch/gpt_sample.py --tensor_para_size=8 --layer_para_size=1 --ckpt_path="/workspace/fastertransformer/models/megatron-models/c-model/345m/8-gpu"

# LP (tensor_para_size=1, layer_para_size=8)
mpirun -n 8 --allow-run-as-root python ./pytorch/gpt_sample.py --tensor_para_size=1 --layer_para_size=8 --ckpt_path="/workspace/fastertransformer/models/megatron-models/c-model/345m/1-gpu"

# TP and LP (tensor_para_size=4, layer_para_size=2)
mpirun -n 8 --allow-run-as-root python ./pytorch/gpt_sample.py --tensor_para_size=4 --layer_para_size=2 --ckpt_path="/workspace/fastertransformer/models/megatron-models/c-model/345m/4-gpu"

    For PP, set --layer_para_batch_size so that batch_size >= layer_para_batch_size.

    2.2 Run GPT with TP and PP on single-node/multi-node (NVIDIA SuperPOD)

    Set up in interactive mode

    cd <FasterTransformer root path>
srun -N1 --mpi=pmix --ntasks-per-node=8 --container-image nvcr.io/nvidia/pytorch:20.12-py3 --container-mounts <FasterTransformer root path>:/workspace/fastertransformer --container-workdir /workspace/fastertransformer --pty bash

mkdir build && cd build
cmake -DSM=80 -DCMAKE_BUILD_TYPE=Release -DBUILD_PYT=ON .. && make -j12

    Run on singe-node

    • tensor_para_size=8, layer_para_size=1
    srun -N1 --mpi=pmix --ntasks-per-node=8 --container-image nvcr.io/nvidia/pytorch:20.12-py3 --container-mounts <FasterTransformer root path>:/workspace/fastertransformer --container-workdir /workspace/fastertransformer/build python ./pytorch/gpt_sample.py --tensor_para_size=8 --layer_para_size=1 --ckpt_path="/workspace/fastertransformer/models/megatron-models/c-model/345m/8-gpu"

    Run on multi-node

    • tensor_para_size=8, layer_para_size=2
    srun -N2 --mpi=pmix --ntasks-per-node=8 --container-image nvcr.io/nvidia/pytorch:20.12-py3 --container-mounts <FasterTransformer root path>:/workspace/fastertransformer --container-workdir /workspace/fastertransformer/build python ./pytorch/gpt_sample.py --tensor_para_size=8 --layer_para_size=2 --ckpt_path="/workspace/fastertransformer/models/megatron-models/c-model/345m/8-gpu"

  3. Run GPT on tensorflow

    Note that the tensorflow op only supports single gpu.

    ./bin/gpt_gemm 4 4 12 64 50257 1 1 1
python tensorflow/gpt_sample.py --batch_size=4 \
                                --length=32 \
                                --top_k=4 \
                                --top_p=0.6 \
                                --data_type=fp16

gpt with triton backend

Details are in transformer_backend

Performance

Hardware settings:

  • 8xA100-80GBs (with mclk 1593MHz, pclk 1410MHz) with AMD EPYC 7742 64-Core Processor

We demonstrate the inference time of Megatron and FasterTransformer on Triton, and show the speedup of FasterTransformer compare to Megatron. In the experiments of encoder, we updated the following parameters:

  • head_num = 96
  • size_per_head = 128
  • num_layers = 48 for GPT-89B model, 96 for GPT-175B model
  • data_type = FP16
  • vocab_size = 51200
  • top_p = 0.9
  • tensor parallel size = 8

Perofrmance of GPT-89B

Batch_size Input Seqlen Output Seqlen Megatron
Latency (ms)		 FT
Latency (ms)		 FT
Speedup
1 128 8 342.86 279.44 1.23
2 128 8 369.43 280.24 1.32
4 128 8 540.97 317.71 1.70
8 128 8 912.46 377.50 2.42
12 128 8 1263.39 445.46 2.84
16 128 8 1663.39 524.80 3.17
20 128 8 1991.16 575.83 3.46
32 128 8 3086.85 786.57 3.92
1 512 32 1244.81 887.52 1.40
2 512 32 1357.54 940.11 1.44
4 512 32 1970.08 1133.22 1.74
8 512 32 3341.66 1415.02 2.36
16 512 32 6090.07 1952.2 3.12

Performance of GPT-175B

Batch_size Input Seqlen Output Seqlen Megatron
Latency (ms)		 FT
Latency (ms)		 FT
Speedup
1 128 8 660.38 488.86 1.35
2 128 8 687.34 509.47 1.35
4 128 8 1004.88 629.64 1.60
8 128 8 1705.07 749.86 2.27
12 128 8 2365.02 886.24 2.67
16 128 8 3111.57 1037.47 3.00
20 128 8 3723.73 1135.72 3.28
32 128 8 5778.72 1547.44 3.73
1 512 32 2384.78 1719.96 1.39
2 512 32 2503.24 1830.56 1.37
4 512 32 3658.65 2092.56 1.75
8 512 32 6238.79 2629.97 2.37
16 512 32 11409.53 3706.23 3.08