phi

Phi

This document explains how to build the Phi model using TensorRT-LLM and run on a single GPU.

Overview

The TensorRT-LLM Phi implementation can be found in tensorrt_llm/models/phi/model.py. The TensorRT-LLM Phi example code is located in examples/phi. There is one 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
• BF16
• Tensor Parallel

Usage

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

python ./convert_checkpoint.py --model_dir "microsoft/phi-2" --output_dir ./phi-2-checkpoint --dtype float16

2. Build TensorRT engine(s)

TensorRT-LLM builds TensorRT engine(s) using a HF checkpoint. If no checkpoint directory is specified, TensorRT-LLM will build engine(s) using dummy weights.

Examples of build invocations:

# Build a float16 engine using a single GPU and HF weights.
# Enable several TensorRT-LLM plugins to increase runtime performance. It also helps with build time.
# workers == tp_size
trtllm-build \
    --checkpoint_dir ./phi-2-checkpoint \
    --output_dir ./phi-2-engine \
    --gemm_plugin float16 \
    --max_batch_size 8 \
    --max_input_len 1024 \
    --max_output_len 1024 \
    --workers 1

Fused MultiHead Attention (FMHA)

You can enable the FMHA kernels for phi by adding --context_fmha enable to the invocation of trtllm-build. Note that it is disabled by default because of possible accuracy issues due to the use of Flash Attention.

If you find that the default fp16 accumulation (--context_fmha enable) cannot meet the requirement, you can try to enable fp32 accumulation by adding --context_fmha_fp32_acc enable. However, it is expected to see performance drop.

Note --context_fmha enable / --context_fmha_fp32_acc enable has to be used together with --gpt_attention_plugin float16.

3. Summarization using the Phi model

The following section describes how to run a TensorRT-LLM Phi model to summarize the articles from the cnn_dailymail dataset. For each summary, the script can compute the ROUGE scores and use the ROUGE-1 score to validate the implementation. The script can also perform the same summarization using the HF Phi model.

As previously explained, the first step is to build the TensorRT engine as described above using HF weights. You also have to install the requirements:

pip install -r requirements.txt

The summarization can be done using the ../summarize.py script as follows:

# Run the summarization task using a TensorRT-LLM model and a single GPU.
python3 ../summarize.py --engine_dir ./phi-2-engine \
                        --hf_model_dir "microsoft/phi-2" \
                        --batch_size 1 \
                        --test_trt_llm \
                        --test_hf \
                        --data_type fp16 \
                        --check_accuracy \
                        --tensorrt_llm_rouge1_threshold=20

# Run the summarization task using a TensorRT-LLM model and 2-way tensor parallelism.
mpirun -n 2 --allow-run-as-root                             \
python3 ../summarize.py --engine_dir ./phi-2-engine-tp2  \
                        --hf_model_dir "microsoft/phi-2"    \
                        --batch_size 1                      \
                        --test_hf                           \
                        --test_trt_llm                      \
                        --data_type fp16                    \
                        --check_accuracy                    \
                        --tensorrt_llm_rouge1_threshold 20