mpt

MPT

This document explains how to build the MPT model using TensorRT-LLM and run on a single GPU and a single node with multiple GPUs.

• MPT
  • Overview
  • Support Matrix
    • MPT 7B
      • 1.1 Convert from HF Transformers in FP
      • 1.2 Convert from HF Transformers with weight-only quantization
      • 1.3 Convert from HF Transformers with SmoothQuant quantization
      • 1.4 Convert from HF Transformers with INT8 KV cache quantization
      • 1.5 AWQ weight-only quantization with AMMO
      • 1.6 FP8 Post-Training Quantization with AMMO
      • 1.6 Weight-only quantization with AMMO
      • 1.7 SmoothQuant and INT8 KV cache with AMMO
    • 2.1 Build TensorRT engine(s)
    • MPT 30B
      • 1. Convert weights from HF Transformers to TRTLLM format
      • 2. Build TensorRT engine(s)
      • 3. Run TRT engine to check if the build was correct
    • Replit Code V-1.5 3B
      • 1. Convert weights from HF Transformers to TRTLLM format
      • 2. Build TensorRT engine(s)
      • 3. Run TRT engine to check if the build was correct

Overview

The TensorRT-LLM MPT implementation can be found in tensorrt_llm/models/mpt/model.py. The TensorRT-LLM MPT example code is located in examples/mpt. 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

• FP16
• FP8 (with FP8 KV Cache)
• INT8 & INT4 Weight-Only
• INT8 Smooth Quant
• INT4 AWQ
• Tensor Parallel
• MHA, MQA & GQA
• STRONGLY TYPED

MPT 7B

Please install required packages first:

pip install -r requirements.txt

The convert_checkpoint.py script allows you to convert weights from HF Transformers format to TRTLLM checkpoints.

1.1 Convert from HF Transformers in FP

# Generate FP16 checkpoints.
python convert_checkpoint.py --model_dir mosaicml/mpt-7b --output_dir ./ckpts/mpt-7b/fp16/ --dtype float16

# Generate FP32 checkpoints with TP=4.
python convert_checkpoint.py --model_dir mosaicml/mpt-7b --output_dir ./ckpts/mpt-7b/fp32_tp4/ --dtype float32 --tp_size 4

1.2 Convert from HF Transformers with weight-only quantization

# Use int8 weight-only quantization.
python convert_checkpoint.py --model_dir mosaicml/mpt-7b --output_dir ./ckpts/mpt-7b/int8_wo/ --use_weight_only

# Use int4 weight-only quantization.
python convert_checkpoint.py --model_dir mosaicml/mpt-7b --output_dir ./ckpts/mpt-7b/int4_wo/ --use_weight_only --weight_only_precision int4

1.3 Convert from HF Transformers with SmoothQuant quantization

# Use int8 smoothquant (weight and activation) quantization.
python convert_checkpoint.py --model_dir mosaicml/mpt-7b --output_dir ./ckpts/mpt-7b/int8_sq/ --smoothquant 0.5

1.4 Convert from HF Transformers with INT8 KV cache quantization

# Use int8 kv cache quantization.
python convert_checkpoint.py --model_dir mosaicml/mpt-7b --output_dir ./ckpts/mpt-7b/fp16_int8kv/ --dtype float16 --calibrate_kv_cache

INT8-KV-cache can be used with SQ and Weight-only at the same time

We now introduce AMMO to do all quantization First make sure AMMO toolkit is installed (see examples/quantization/README.md)

1.5 AWQ weight-only quantization with AMMO

# INT4 AWQ quantization using AMMO.
python ../quantization/quantize.py --model_dir mosaicml/mpt-7b --output_dir ./ckpts/mpt-7b/int4_awq/ --qformat int4_awq

1.6 FP8 Post-Training Quantization with AMMO

# FP8 quantization using AMMO.
python ../quantization/quantize.py --model_dir mosaicml/mpt-7b --output_dir ./ckpts/mpt-7b/fp8/ --qformat fp8 --kv_cache_dtype fp8

1.6 Weight-only quantization with AMMO

# INT8 Weight-only quantization using AMMO with TP=2.
python ../quantization/quantize.py --model_dir mosaicml/mpt-7b --output_dir ./ckpts/mpt-7b/int8_wo/ --qformat int8_wo --tp_size 2

# INT4 Weight-only quantization using AMMO.
python ../quantization/quantize.py --model_dir mosaicml/mpt-7b --output_dir ./ckpts/mpt-7b/int4_wo/ --qformat int4_wo

1.7 SmoothQuant and INT8 KV cache with AMMO

# Use int4 awq quantization.
python ../quantization/quantize.py --model_dir mosaicml/mpt-7b --output_dir ./ckpts/mpt-7b/sq_int8kv/ --qformat int8_sq --kv_cache_dtype int8

INT8-KV-cache can also be used with Weight-only at the same time

2.1 Build TensorRT engine(s)

All of the checkpoint generated by convert_checkpoint.py or quantize.py (AMMO) can share the same building commands.

# Build a single-GPU float16 engine using TRTLLM checkpoints.
trtllm-build --checkpoint_dir=./ckpts/mpt-7b/fp16 \
             --max_batch_size 32 \
             --max_input_len 1024 \
             --max_output_len 512 \
             --gemm_plugin float16 \
             --workers 1 \
             --output_dir ./trt_engines/mpt-7b/fp16

MPT 30B

Same commands can be changed to convert MPT 30B to TRT LLM format. Below is an example to build MPT30B fp16 4-way tensor parallelized TRT engine

1. Convert weights from HF Transformers to TRTLLM format

The convert_checkpoint.py script allows you to convert weights from HF Transformers format to TRTLLM format.

python convert_checkpoint.py --model_dir mosaicml/mpt-30b --output_dir ./ckpts/mpt-30b/fp16_tp4/ --tp_szie 4 --dtype float16

2. Build TensorRT engine(s)

Examples of build invocations:

# Build 4-GPU MPT-30B float16 engines
trtllm-build --checkpoint_dir ./ckpts/mpt-30b/fp16_tp4 \
             --max_batch_size 32 \
             --max_input_len 1024 \
             --max_output_len 512 \
             --gemm_plugin float16 \
             --workers 4 \
             --output_dir ./trt_engines/mpt-30b/fp16_tp4

3. Run TRT engine to check if the build was correct

# Run 4-GPU MPT-30B TRT engine on a sample input prompt
mpirun -n 4 --allow-run-as-root \
    python ../run.py --max_output_len 10 \
                     --engine_dir ./trt_engines/mpt-30b/fp16/4-gpu/ \
                     --tokenizer_dir mosaicml/mpt-30b

Replit Code V-1.5 3B

Same commands can be changed to convert Replit Code V-1.5 3B to TRT LLM format. Below is an example to build Replit Code V-1.5 3B fp16 2-way tensor parallelized TRT engine.

1. Convert weights from HF Transformers to TRTLLM format

The convert_checkpoint.py script allows you to convert weights from HF Transformers format to TRTLLM format.

python convert_checkpoint.py --model_dir ./replit-code-v1_5-3b --output_dir ./ckpts/replit-code-v1_5-3b/bf16_tp2/ --tp_size 2 --dtype bfloat16

2. Build TensorRT engine(s)

Examples of build invocations:

# Build 2-GPU Replit Code V-1.5 3B bfloat16 engines
trtllm-build --checkpoint_dir ./ckpts/replit-code-v1_5-3b/bf16_tp2 \
             --max_batch_size 32 \
             --max_input_len 1024 \
             --max_output_len 512 \
             --gpt_attention_plugin bfloat16 \
             --gemm_plugin bfloat16 \
             --workers 2 \
             --output_dir ./trt_engines/replit-code-v1_5-3b/bf16_tp2

3. Run TRT engine to check if the build was correct

# Run 2-GPU Replit Code V-1.5 3B TRT engine on a sample input prompt
mpirun -n 2 --allow-run-as-root \
    python ../run.py --max_output_len 64 \
                     --input_text "def fibonacci" \
                     --engine_dir ./trt_engines/replit-code-v1_5-3b/bf16_tp2 \
                     --tokenizer_dir ./replit-code-v1_5-3b/

Here is the output of above command.

Input: "def fibonacci"
Output: "(n):
    if n == 0:
        return 0
    elif n == 1:
        return 1
    else:
        return fibonacci(n-1) + fibonacci(n-2)

print(fibonacci(10))"