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199 lines
8.1 KiB
Markdown
199 lines
8.1 KiB
Markdown
# BLOOM
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This document shows how to build and run a BLOOM model in TensorRT-LLM on both single GPU, single node multi-GPU and multi-node multi-GPU.
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## Overview
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The TensorRT-LLM BLOOM implementation can be found in [tensorrt_llm/models/bloom/model.py](../../tensorrt_llm/models/bloom/model.py). The TensorRT-LLM BLOOM example code is located in [`examples/bloom`](./). There are three main files in that folder::
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* [`build.py`](./build.py) to build the [TensorRT](https://developer.nvidia.com/tensorrt) engine(s) needed to run the BLOOM model,
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* [`run.py`](./run.py) to run the inference on an input text,
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* [`summarize.py`](./summarize.py) to summarize the articles in the [cnn_dailymail](https://huggingface.co/datasets/cnn_dailymail) dataset using the model.
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## Support Matrix
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* FP16
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* INT8 & INT4 Weight-Only
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* INT8 KV CACHE
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* Smooth Quant
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* Tensor Parallel
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## Usage
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The TensorRT-LLM BLOOM example code locates at [examples/bloom](./). It takes HF weights as input, and builds the corresponding TensorRT engines. The number of TensorRT engines depends on the number of GPUs used to run inference.
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### Build TensorRT engine(s)
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Need to prepare the HF BLOOM checkpoint first by following the guides here https://huggingface.co/docs/transformers/main/en/model_doc/bloom.
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e.g. To install BLOOM-560M
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```bash
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# Setup git-lfs
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git lfs install
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rm -rf ./bloom/560M
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mkdir -p ./bloom/560M && git clone https://huggingface.co/bigscience/bloom-560m ./bloom/560M
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```
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TensorRT-LLM BLOOM builds TensorRT engine(s) from HF checkpoint. If no checkpoint directory is specified, TensorRT-LLM will build engine(s) with dummy weights.
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Normally `build.py` only requires single GPU, but if you've already got all the GPUs needed for inference, you could enable parallel building to make the engine building process faster by adding `--parallel_build` argument. Please note that currently `parallel_build` feature only supports single node.
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Here're some examples:
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```bash
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# Build a single-GPU float16 engine from HF weights.
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# Try use_gemm_plugin to prevent accuracy issue. TODO check this holds for BLOOM
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# Single GPU on BLOOM 560M
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python build.py --model_dir ./bloom/560M/ \
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--dtype float16 \
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--use_gemm_plugin float16 \
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--use_gpt_attention_plugin float16 \
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--output_dir ./bloom/560M/trt_engines/fp16/1-gpu/
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# Build the BLOOM 560M using a single GPU and apply INT8 weight-only quantization.
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python build.py --model_dir ./bloom/560M/ \
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--dtype float16 \
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--use_gemm_plugin float16 \
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--use_gpt_attention_plugin float16 \
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--use_weight_only \
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--output_dir ./bloom/560M/trt_engines/int8_weight_only/1-gpu/
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# Use 2-way tensor parallelism on BLOOM 560M
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python build.py --model_dir ./bloom/560M/ \
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--dtype float16 \
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--use_gemm_plugin float16 \
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--use_gpt_attention_plugin float16 \
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--output_dir ./bloom/560M/trt_engines/fp16/2-gpu/ \
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--world_size 2
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# Use 8-way tensor parallelism on BLOOM 176B
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# Currently, TensorRT does not support tensors with more than 2^31-1 elements,
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# so we have to shard the embedding table to multi-GPUs.
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# sharding embedding table in the vocab dimension (the lookup plugin is optional)
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python build.py --model_dir ./bloom/176B/ \
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--dtype float16 \
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--use_gemm_plugin float16 \
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--use_gpt_attention_plugin float16 \
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--output_dir ./bloom/176B/trt_engines/fp16/8-gpu/ \
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--world_size 8 \
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--use_parallel_embedding \
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--embedding_sharding_dim 0 \
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--use_lookup_plugin float16
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# sharding embedding table in the hidden dimension
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python build.py --model_dir ./bloom/176B/ \
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--dtype float16 \
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--use_gemm_plugin float16 \
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--use_gpt_attention_plugin float16 \
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--output_dir ./bloom/176B/trt_engines/fp16/8-gpu/ \
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--world_size 8 \
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--use_parallel_embedding \
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--embedding_sharding_dim 1
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# share embedding table between embedding() and lm_head() layers
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# To reduce the generated engine size, we has to use gemm and lookup plugin (--use_gemm_plugin --use_lookup_plugin) and must shard the embedding table in the vocab dimension.
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python build.py --model_dir ./bloom/176B/ \
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--dtype float16 \
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--use_gemm_plugin float16 \
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--use_gpt_attention_plugin float16 \
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--output_dir ./bloom/176B/trt_engines/fp16/8-gpu/ \
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--world_size 8 \
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--use_parallel_embedding \
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--embedding_sharding_dim 0 \
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--use_lookup_plugin float16 \
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--use_embedding_sharing
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```
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#### INT8 weight only + INT8 KV cache
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For INT8 KV cache, [`hf_bloom_convert.py`](./hf_bloom_convert.py) features a
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`--calibrate-kv-cache, -kv` option. Setting `-kv` will calibrate the model,
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and then export the scaling factors needed for INT8 KV cache inference.
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Example:
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```bash
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python3 hf_bloom_convert.py -i bloom/560M -o ./c-model/bloom/int8_kv_cache/560M --calibrate-kv-cache -t float16
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```
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[`build.py`](./build.py) add new options for the support of INT8 KV cache.
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`--int8_kv_cache` is the command-line option to enable INT8 KV cache.
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In addition, it could be combined with INT8 weight-only quantization, as follows:
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Examples of INT8 weight-only quantization + INT8 KV cache
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```bash
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# Build model with both INT8 weight-only and INT8 KV cache enabled
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python build.py --bin_model_dir=./c-model/bloom/int8_kv_cache/560M/1-gpu \
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--dtype float16 \
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--use_gpt_attention_plugin float16 \
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--use_gemm_plugin float16 \
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--use_layernorm_plugin \
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--int8_kv_cache \
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--use_weight_only
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```
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#### SmoothQuant
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Unlike the FP16 build where the HF weights are processed and loaded into the TensorRT-LLM directly, the SmoothQuant needs to load INT8 weights which should be pre-processed before building an engine.
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Example:
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```bash
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python3 hf_bloom_convert.py -i bloom/560M -o ./c-model/bloom-smooth/560M --smoothquant 0.5 --tensor-parallelism 1 --storage-type float16
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```
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[`build.py`](./build.py) add new options for the support of INT8 inference of SmoothQuant models.
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`--use_smooth_quant` is the starting point of INT8 inference. By default, it
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will run the model in the _per-tensor_ mode.
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Then, you can add any combination of `--per-token` and `--per-channel` to get the corresponding behaviors.
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Examples of build invocations:
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```bash
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# Build model for SmoothQuant in the _per_tensor_ mode.
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python3 build.py --bin_model_dir=./c-model/bloom-smooth/560M/1-gpu \
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--use_smooth_quant --use_gpt_attention_plugin float16
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# Build model for SmoothQuant in the _per_token_ + _per_channel_ mode
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python3 build.py --bin_model_dir=./c-model/bloom-smooth/560M/1-gpu \
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--use_smooth_quant --use_gpt_attention_plugin float16 \
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--per_token \
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--per_channel
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```
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Note that GPT attention plugin is required to be enabled for SmoothQuant for now.
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Note we use `--bin_model_dir` instead of `--model_dir` since SmoothQuant model needs INT8 weights and various scales from the binary files.
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### 4. Run
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```bash
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python summarize.py --test_trt_llm \
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--hf_model_location ./bloom/560M/ \
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--data_type fp16 \
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--engine_dir ./bloom/560M/trt_engines/fp16/1-gpu/
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python summarize.py --test_trt_llm \
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--hf_model_location ./bloom/560M/ \
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--data_type fp16 \
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--engine_dir ./bloom/560M/trt_engines/int8_weight_only/1-gpu/
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mpirun -n 2 --allow-run-as-root \
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python summarize.py --test_trt_llm \
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--hf_model_location ./bloom/560M/ \
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--data_type fp16 \
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--engine_dir ./bloom/560M/trt_engines/fp16/2-gpu/
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mpirun -n 8 --allow-run-as-root \
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python summarize.py --test_trt_llm \
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--hf_model_location ./bloom/176B/ \
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--data_type fp16 \
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--engine_dir ./bloom/176B/trt_engines/fp16/8-gpu/
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```
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