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.gitignore	.gitignore
README.md	README.md
convert_checkpoint.py	convert_checkpoint.py
requirements.txt	requirements.txt
tokenization_chatglm.py	tokenization_chatglm.py
visualize.py	visualize.py

ChatGLM

This document explains how to build the ChatGLM-6B, ChatGLM2-6B, ChatGLM2-6B-32k, ChatGLM3-6B, ChatGLM3-6B-Base, ChatGLM3-6B-32k models using TensorRT-LLM and run on a single GPU, a single node with multiple GPUs or multiple nodes with multiple GPUs.

ChatGLM

Overview

The TensorRT-LLM ChatGLM implementation can be found in tensorrt_llm/models/chatglm/model.py. The TensorRT-LLM ChatGLM example code is located in examples/chatglm. There is one main file:

convert_checkpoint.py to convert a checkpoint from the HuggingFace (HF) Transformers format to the TensorRT-LLM format.

In addition, there are two shared files in the parent folder examples for inference and evaluation:

../run.py to run the inference on an input text;
../summarize.py to summarize the articles in the cnn_dailymail dataset.

Support Matrix

Model Name	FP16	FMHA	WO	SQ	AWQ	FP8	TP	ST	C++	benchmark	IFB
chatglm_6b	Y		Y				Y	Y	Y	Y	Y
chatglm2_6b	Y	Y	Y	Y	Y	Y	Y	Y	Y	Y	Y
chatglm2_6b_32k	Y	Y	Y		Y	Y	Y	Y	Y	Y	Y
chatglm3_6b	Y	Y	Y	Y	Y	Y	Y	Y	Y	Y	Y
chatglm3_6b_base	Y	Y	Y	Y	Y	Y	Y	Y	Y	Y	Y
chatglm3_6b_32k	Y	Y	Y	Y	Y	Y	Y	Y	Y	Y	Y
glm_10b	Y	Y	Y				Y	Y

Model Name: the name of the model, the same as the name on HuggingFace
FMHA: Fused MultiHead Attention (see introduction below)
WO: Weight Only Quantization (int8 / int4)
SQ: Smooth Quantization (int8)
AWQ: Activation Aware Weight Quantization (int4)
FP8: FP8 Quantization
TP: Tensor Parallel
PP: Pipeline Parallel
ST: Strongly Typed
C++: C++ Runtime
benchmark: benchmark by python / C++ Runtime
IFB: In-flight Batching (see introduction below)

Model comparison

Name	nL	nAH	nKH	nHW	nH	nF	nMSL	nV	bP2D	bBQKV	bBDense	Comments
chatglm_6b	28	32	32	128	4096	16384	2048	130528	Y	Y	Y
chatglm2_6b	28	32	2	128	4096	13696	32768	65024	N	Y	N	Multi_query_attention, RMSNorm rather than LayerNorm in chatglm_6b
chatglm2_6b_32k	28	32	2	128	4096	13696	32768	65024	N	Y	N	RoPE base=160000 rather than 10000 in chatglm2_6b
chatglm3_6b	28	32	2	128	4096	13696	8192	65024	N	Y	N	Different in preprocess and postprocess than chatglm2_6b
chatglm3_6b_base	28	32	2	128	4096	13696	32768	65024	N	Y	N
chatglm3_6b_32k	28	32	2	128	4096	13696	32768	65024	N	Y	N	RoPE base=500000 rather than 10000 in chatglm3_6b
glm_10b	48	64	32	64	4096	16384	1024	50304	Y	Y	Y

nL: number of layers
nAH: number of attention heads
nKH: number of kv heads (less than nAH if multi_query_attention is used)
nHW: head width
nH: hidden size
nF: FFN hidden size
nMSL: max sequence length (input + output)
nV: vocabulary size
bP2D: use position_encoding_2d (Y: Yes, N: No)
bBQKV: use bias for QKV multiplication in self-attention
bBDense: use bias for Dense multiplication in self-attention

Tokenizer and special tokens comparison

Name	Tokenizer	bos	eos	pad	cls	startofpiece	endofpiece	mask	smask	gmask
chatglm_6b	ChatGLMTokenizer	130004	130005	3		130004	130005	130000		130001
chatglm2_6b	ChatGLMTokenizer	1	2	0
chatglm2_6b_32k	ChatGLMTokenizer	1	2	0
chatglm3_6b	ChatGLMTokenizer	1	2	0				130000
chatglm2_6b_base	ChatGLMTokenizer	1	2	0				130000
chatglm2_6b_32k	ChatGLMTokenizer	1	2	0				130000
glm_10b	GLMGPT2Tokenizer	50257	50256	50256	50259	50257	50258	50260	50264	50263

Usage

The next section describe how to build the engine and run the inference demo.

1. Download repo and weights from HuggingFace Transformers

pip install -r requirements.txt
apt-get update
apt-get install git-lfs
rm -rf chatglm*

# clone one or more models we want to build
git clone https://huggingface.co/THUDM/chatglm-6b       chatglm_6b
git clone https://huggingface.co/THUDM/chatglm2-6b      chatglm2_6b
git clone https://huggingface.co/THUDM/chatglm2-6b-32k  chatglm2_6b_32k
git clone https://huggingface.co/THUDM/chatglm3-6b      chatglm3_6b
git clone https://huggingface.co/THUDM/chatglm3-6b-base chatglm3_6b_base
git clone https://huggingface.co/THUDM/chatglm3-6b-32k  chatglm3_6b_32k
git clone https://huggingface.co/THUDM/glm-10b          glm_10b

# replace tokenization file if using transformers-4.36.1 for model ChatGLM-6B (this might be needless in the future)
cp chatglm_6b/tokenization_chatglm.py chatglm_6b/tokenization_chatglm.py-backup
cp tokenization_chatglm.py chatglm_6b

2. Convert weights from HF Transformers to TensorRT-LLM format

The convert_checkpoint.py script converts HF weights to TensorRT-LLM checkpoints. The number of checkpoint files (in .safetensors format) is same to the number of GPUs used to run inference.

# ChatGLM3-6B: single gpu, dtype float16
python3 convert_checkpoint.py --model_dir chatglm3_6b --output_dir trt_ckpt/chatglm3_6b/fp16/1-gpu

# ChatGLM3-6B: 2-way tensor parallelism
python3 convert_checkpoint.py --model_dir chatglm3_6b --tp_size 2 --output_dir trt_ckpt/chatglm3_6b/fp16/2-gpu

# Chatglm2-6B: single gpu, dtype float16
python3 convert_checkpoint.py --model_dir chatglm2_6b --output_dir trt_ckpt/chatglm2_6b/fp16/1-gpu

# Chatglm-6B: single gpu, dtype float16
python3 convert_checkpoint.py --model_dir chatglm_6b --output_dir trt_ckpt/chatglm_6b/fp16/1-gpu

# GLM-10B: single gpu, dtype float16
python3 convert_checkpoint.py --model_dir glm_10b --output_dir trt_ckpt/glm_10b/fp16/1-gpu

3. Build TensorRT engine(s)

The trtllm-build command builds TensorRT-LLM engines from TensorRT-LLM checkpoints. The number of engine files is also same to the number of GPUs used to run inference.

Normally, the trtllm-build command only requires a single GPU, but you can enable parallel building by passing the number of GPUs to the --workers argument.

Using ChatGLM2-6B-32K / ChatGLM3-6B-32K models, we need to guarantee max_batch_size * max_beam_width * (max_input_len + max_output_len) <= 78398 = 2^31 / (13696 * 2) due to constrain of TensorRT. For example, we will fail to build engine while using default max_batch_size (8) and adding arguments --max_beam_width=4 --max_input_len=20000 --max_output_len=100.

# ChatGLM3-6B: single-gpu engine
trtllm-build --checkpoint_dir trt_ckpt/chatglm3_6b/fp16/1-gpu \
        --gemm_plugin float16 \
        --output_dir trt_engines/chatglm3_6b/fp16/1-gpu

# ChatGLM3-6B: 2-way tensor parallelism
trtllm-build --checkpoint_dir trt_ckpt/chatglm3_6b/fp16/2-gpu \
        --gemm_plugin float16 \
        --output_dir trt_engines/chatglm3_6b/fp16/2-gpu

# ChatGLM2-6B: single-gpu engine with dtype float16, GPT Attention plugin, Gemm plugin
trtllm-build --checkpoint_dir trt_ckpt/chatglm2_6b/fp16/1-gpu \
        --gemm_plugin float16 \
        --output_dir trt_engines/chatglm2_6b/fp16/1-gpu

# ChatGLM-6B: single-gpu engine with dtype float16, GPT Attention plugin, Gemm plugin
trtllm-build --checkpoint_dir trt_ckpt/chatglm_6b/fp16/1-gpu \
        --gemm_plugin float16 \
        --output_dir trt_engines/chatglm_6b/fp16/1-gpu

# GLM-10B: single-gpu engine with dtype float16, GPT Attention plugin, Gemm plugin
trtllm-build --checkpoint_dir trt_ckpt/glm_10b/fp16/1-gpu \
        --gemm_plugin float16 \
        --output_dir trt_engines/glm_10b/fp16/1-gpu

If the engines are run successfully, you will see output like (ChatGLM3-6B as the example):

......
[01/26/2024-02:40:36] [TRT] [I] Engine generation completed in 136.52 seconds.
[01/26/2024-02:40:36] [TRT] [I] [MemUsageStats] Peak memory usage of TRT CPU/GPU memory allocators: CPU 1016 MiB, GPU 11909 MiB
[01/26/2024-02:40:36] [TRT] [I] [MemUsageChange] TensorRT-managed allocation in building engine: CPU +0, GPU +11909, now: CPU 0, GPU 11909 (MiB)
[01/26/2024-02:40:40] [TRT] [I] [MemUsageStats] Peak memory usage during Engine building and serialization: CPU: 29706 MiB
[01/26/2024-02:40:40] [TRT-LLM] [I] Total time of building Unnamed Network 0: 00:02:20
[01/26/2024-02:40:42] [TRT-LLM] [I] Serializing engine to trt_engines/chatglm3_6b/fp16/1-gpu/rank0.engine...
[01/26/2024-02:42:29] [TRT-LLM] [I] Engine serialized. Total time: 00:01:47
[01/26/2024-02:42:30] [TRT-LLM] [I] Total time of building all engines: 00:05:19

Enable plugins

Use --gpt_attention_plugin <DataType> to configure GPT Attention plugin (default as float16)
Use --gemm_plugin <DataType> to configure GEMM plugin (default as float16)
Use --context_fmha enable or --context_fmha_fp32_acc enable to enable FMHA kernels, which can provide better performance and low GPU memory occupation.
- --gpt_attention_plugin float16 must be used when using FMHA.
- --context_fmha enable uses FP16 accumulator, which might cause low accuracy. In this case, --context_fmha_fp32_acc enable should be used to protect accuracy at a cost of small performance drop.

In-flight batching

The engine(s) must be built accordingly if in-flight batching in C++ runtime will be used.
Use --gpt_attention_plugin float16, --paged_kv_cache enable, --remove_input_padding enable to build engine(s) supporting In-flight Batching.
- It is possible to use --gpt_attention_plugin float32 In-flight Batching.
- The size of the block in paged KV cache can be controlled additionally by using --tokens_per_block=N.

4. Run inference

Single node, single GPU

# Run the default engine of ChatGLM3-6B on single GPU, other model name is available if built.
python3 ../run.py --input_text "What's new between ChatGLM3-6B and ChatGLM2-6B?" \
        --max_output_len 50 \
        --tokenizer_dir chatglm3_6b \
        --engine_dir trt_engines/chatglm3_6b/fp16/1-gpu

# Run the default engine of ChatGLM3-6B on single GPU, using streaming output, other model name is available if built.
python3 ../run.py --input_text "What's new between ChatGLM3-6B and ChatGLM2-6B?" \
        --max_output_len 50 \
        --tokenizer_dir chatglm3_6b \
        --engine_dir trt_engines/chatglm3_6b/fp16/1-gpu \
        --streaming

# Run the default engine of GLM3-10B on single GPU, other model name is available if built.
# Token "[MASK]" or "[sMASK]" or "[gMASK]" must be included in the prompt as the original model commanded.
python3 ../run.py --input_text "Peking University is [MASK] than Tsinghua University." \
        --max_output_len 50 \
        --tokenizer_dir glm_10b \
        --engine_dir trt_engines/glm_10b/fp16/1-gpu

Single node, multi GPU

# Run the Tensor Parallel 2 engine of ChatGLM3-6B on two GPU, other model name is available if built.
mpirun -n 2 \
    python ../run.py --input_text "What's new between ChatGLM3-6B and ChatGLM2-6B?" \
        --max_output_len 50 \
        --tokenizer_dir chatglm3_6b \
        --engine_dir trt_engines/chatglm3_6b/fp16/2-gpu

--allow-run-as-root might be needed if using mpirun as root.

If the engines are run successfully, you will see output like (ChatGLM3-6B as the example):

......
Input [Text 0]: "[gMASK]sop What's new between ChatGLM3-6B and ChatGLM2-6B?"
Output [Text 0 Beam 0]: "There is no new information provided in the official documentation, but I found some differences in the code. The ChatGLM3-6B has an additional parameter called 'config', which is not present in ChatGLM2-6B. Additionally"

5. Run summarization task

# Run the summarization of ChatGLM3-6B task, other model name is available if built.
python3 ../summarize.py --test_trt_llm \
        --hf_model_dir chatglm3_6b \
        --engine_dir trt_engines/chatglm3_6b/fp16/1-gpu

If the engines are run successfully, you will see output like (ChatGLM3-6B as the example):

......
[01/26/2024-02:51:56] [TRT-LLM] [I] TensorRT-LLM (total latency: 12.688004493713379 sec)
[01/26/2024-02:51:56] [TRT-LLM] [I] TensorRT-LLM (total output tokens: 1390)
[01/26/2024-02:51:56] [TRT-LLM] [I] TensorRT-LLM (tokens per second: 109.5522941128145)
[01/26/2024-02:51:56] [TRT-LLM] [I] TensorRT-LLM beam 0 result
[01/26/2024-02:51:56] [TRT-LLM] [I]   rouge1 : 23.926583062537716
[01/26/2024-02:51:56] [TRT-LLM] [I]   rouge2 : 6.945058457209619
[01/26/2024-02:51:56] [TRT-LLM] [I]   rougeL : 17.89273173719794
[01/26/2024-02:51:56] [TRT-LLM] [I]   rougeLsum : 21.22686350784501

Weight Only quantization

Use --use_weight_only to enable INT8-Weight-Only quantization, this will significantly lower the latency and memory footprint. Furthermore, use --weight_only_precision int8 or --weight_only_precision int4 to configure the data type of the weights.

# ChatGLM3-6B: single gpu, int8 weight only quantization
python3 convert_checkpoint.py --model_dir chatglm3_6b \
        --use_weight_only \
        --weight_only_precision int8 \
        --output_dir trt_ckpt/chatglm3_6b/int8_wo/1-gpu

# ChatGLM3-6B: single-gpu engine with int8 weight only quantization, GPT Attention plugin, Gemm plugin
trtllm-build --checkpoint_dir trt_ckpt/chatglm3_6b/int8_wo/1-gpu \
        --gemm_plugin float16 \
        --output_dir trt_engines/chatglm3_6b/int8_wo/1-gpu

# Run inference.
python3 ../run.py --input_text "What's new between ChatGLM3-6B and ChatGLM2-6B?" \
        --max_output_len 50 \
        --tokenizer_dir chatglm3_6b \
        --engine_dir trt_engines/chatglm3_6b/int8_wo/1-gpu

Smooth Quantization (SQ)

Use --smoothquant to enable smooth quantization.

# ChatGLM3-6B: single gpu, int8 smooth quantization
python3 convert_checkpoint.py --model_dir chatglm3_6b \
        --smoothquant 0.5 \
        --per_channel \
        --per_token \
        --output_dir trt_ckpt/chatglm3_6b/sq/1-gpu

# ChatGLM3-6B: single-gpu engine with int8 smooth quantization, GPT Attention plugin, Gemm plugin
trtllm-build --checkpoint_dir trt_ckpt/chatglm3_6b/sq/1-gpu \
        --gemm_plugin float16 \
        --output_dir trt_engines/chatglm3_6b/sq/1-gpu

# Run inference.
python3 ../run.py --input_text "What's new between ChatGLM3-6B and ChatGLM2-6B?" \
        --max_output_len 50 \
        --tokenizer_dir chatglm3_6b \
        --engine_dir trt_engines/chatglm3_6b/sq/1-gpu

Activation-aware Weight Quantization (AWQ)

The ../quantization/quantize.py script can be used to quantize the models and export TensorRT-LLM checkpoints.

# ChatGLM3-6B: single gpu, int4 awq quantization
python ../quantization/quantize.py --model_dir chatglm3_6b \
        --dtype float16 \
        --qformat int4_awq \
        --output_dir trt_ckpt/chatglm3_6b/int4_awq/1-gpu

# ChatGLM3-6B: single-gpu engine with int4 awq quantization, GPT Attention plugin, Gemm plugin
trtllm-build --checkpoint_dir trt_ckpt/chatglm3_6b/int4_awq/1-gpu \
        --gemm_plugin float16 \
        --output_dir trt_engines/chatglm3_6b/int4_awq/1-gpu

# Run inference.
python3 ../run.py --input_text "What's new between ChatGLM3-6B and ChatGLM2-6B?" \
        --max_output_len 50 \
        --tokenizer_dir chatglm3_6b \
        --engine_dir trt_engines/chatglm3_6b/int4_awq/1-gpu

FP8 Quantization

The ../quantization/quantize.py script can be used to quantize the models and export TensorRT-LLM checkpoints.

# ChatGLM3-6B: single gpu, fp8 quantization
python ../quantization/quantize.py --model_dir chatglm3_6b \
        --dtype float16 \
        --qformat fp8 \
        --kv_cache_dtype fp8 \
        --output_dir trt_ckpt/chatglm3_6b/fp8/1-gpu

# ChatGLM3-6B: single-gpu engine with fp8 quantization, GPT Attention plugin, Gemm plugin
trtllm-build --checkpoint_dir trt_ckpt/chatglm3_6b/fp8/1-gpu \
        --gemm_plugin float16 \
        --strongly_typed \
        --output_dir trt_engines/chatglm3_6b/fp8/1-gpu

# Run inference.
python3 ../run.py --input_text "What's new between ChatGLM3-6B and ChatGLM2-6B?" \
        --max_output_len 50 \
        --tokenizer_dir chatglm3_6b \
        --engine_dir trt_engines/chatglm3_6b/fp8/1-gpu

Benchmark

The TensorRT-LLM ChatGLM benchmark is located in benchmarks/

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README.md

ChatGLM

Overview

Support Matrix

Model comparison

Tokenizer and special tokens comparison

Usage

1. Download repo and weights from HuggingFace Transformers

2. Convert weights from HF Transformers to TensorRT-LLM format

3. Build TensorRT engine(s)

Enable plugins

In-flight batching

4. Run inference

Single node, single GPU

Single node, multi GPU

5. Run summarization task

Weight Only quantization

Smooth Quantization (SQ)

Activation-aware Weight Quantization (AWQ)

FP8 Quantization

Benchmark

Files

chatglm

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chatglm

Folders and files

parent directory

README.md

ChatGLM

Overview

Support Matrix

Model comparison

Tokenizer and special tokens comparison

Usage

1. Download repo and weights from HuggingFace Transformers

2. Convert weights from HF Transformers to TensorRT-LLM format

3. Build TensorRT engine(s)

Enable plugins

In-flight batching

4. Run inference

Single node, single GPU

Single node, multi GPU

5. Run summarization task

Weight Only quantization

Smooth Quantization (SQ)

Activation-aware Weight Quantization (AWQ)

FP8 Quantization

Benchmark