- Introduction
- Supported Parameters
- Get Start with FP8 Quantization
- Optimum-habana LLM example
- VLLM example
Float point 8 (FP8) is a promising data type for low precision quantization which provides a data distribution that is completely different from INT8 and it's shown as below.
Intel Gaudi2, also known as HPU, provides this data type capability for low precision quantization, which includes E4M3 and E5M2. For more information about these two data type, please refer to link.
Intel Neural Compressor provides general quantization APIs to leverage HPU FP8 capability. with simple with lower memory usage and lower compute cost, 8 bit model
| Attribute | Description | Values |
|---|---|---|
| fp8_config | The target data type of FP8 quantization. | E4M3 (default) - As Fig. 2 E5M2 - As Fig. 1. |
| hp_dtype | The high precision data type of non-FP8 operators. | bf16 (default) - torch.bfloat16 fp16 - torch.float16. fp32 - torch.float32. |
| observer | The observer to measure the statistics. | maxabs (default), saves all tensors to files. |
| allowlist | List of nn.Module names or types to quantize. When setting an empty list, all the supported modules will be quantized by default. See Supported Modules. Not setting the list at all is not recommended as it will set the allowlist to these modules only: torch.nn.Linear, torch.nn.Conv2d, and BMM. | Default = {'names': [], 'types': FP8_WHITE_LIST} |
| blocklist | List of nn.Module names or types not to quantize. Defaults to empty list, so you may omit it from the config file. | Default = {'names': [], 'types': ()} |
| mode | The mode, measure or quantize, to run HQT with. | MEASURE - Measure statistics of all modules and emit the results to dump_stats_path. QUANTIZE - Quantize and run the model according to the provided measurements. AUTO (default) - Select from [MEASURE, QUANTIZE] automatically. |
| dump_stats_path | The path to save and load the measurements. The path is created up until the level before last "/". The string after the last / will be used as prefix to all the measurement files that will be created. | Default = "./hqt_output/measure" |
| scale_method | The method for calculating the scale from the measurement. | - unit_scale - Always use scale of 1. - hw_aligned_single_scale - Always use scale that's aligned to the corresponding HW accelerated scale. - maxabs_hw (default) - Scale is calculated to stretch/compress the maxabs measurement to the full-scale of FP8 and then aligned to the corresponding HW accelerated scale. - maxabs_pow2 - Scale is calculated to stretch/compress the maxabs measurement to the full-scale of FP8 and then rounded to the power of 2. - maxabs_hw_opt_weight - Scale of model params (weights) is chosen as the scale that provides minimal mean-square-error between quantized and non-quantized weights, from all possible HW accelerated scales. Scale of activations is calculated the same as maxabs_hw. - act_maxabs_pow2_weights_pcs_opt_pow2 - Scale of model params (weights) is calculated per-channel of the params tensor. The scale per-channel is calculated the same as maxabs_hw_opt_weight. Scale of activations is calculated the same as maxabs_pow2. - act_maxabs_hw_weights_pcs_maxabs_pow2 - Scale of model params (weights) is calculated per-channel of the params tensor. The scale per-channel is calculated the same as maxabs_pow2. Scale of activations is calculated the same as maxabs_hw. |
| measure_exclude | If this attribute is not defined, the default is OUTPUT. Since most models do not require measuring output tensors, you can exclude it to speed up the measurement process. | NONE - All tensors are measured. OUTPUT (default) - Excludes measurement of output tensors. |
Demo Usage
Computer vision example
Optimum is an extension of Transformers that provides a set of performance optimization tools to train and run models on targeted hardware with maximum efficiency.
Optimum-habana is the interface between the Transformers, Diffusers libraries and Intel Gaudi AI Accelerators (HPU). It provides higher performance based on modified modeling files, and utilizes Intel Neural Compressor for FP8 quantization internally, running-with-fp8
Refer to optimum-habana, install-the-library-and-get-example-scripts
Option to install from source,
$ git clone https://github.com/huggingface/optimum-habana
$ cd optimum-habana && git checkout v1.14.0 (change the version)
$ pip install -e .
$ pip install git+https://github.com/HabanaAI/[email protected]
$ cd examples/text-generation
$ pip install -r requirements.txt
$ pip install -r requirements_lm_eval.txt (Option)
optimum-habana/examples/text-generation/utils.py
initialize_model() -> setup_model() -> setup_quantization() -> FP8Config/prepare()/convert()
Introduction: kv-cache-quantization in huggingface transformers
BF16 KVCache Code -> Modeling_all_models.py -> KVCache()
FP8 KVCache code trace with neural compressor support, for example Llama models,
optimum-habana/optimum/habana/transformers/models/llama/modeling_llama.py
GaudiLlamaForCausalLM() -> self.model()
GaudiLlamaModel() -> forward() -> decoder_layer() -> GaudiLlamaDecoderLayer() forward() -> pre_attn() -> pre_attn_forward() -> self.k_cache.update
neural_compressor/torch/algorithms/fp8_quant/_quant_common/helper_modules.py
PatchedKVCache() -> update()
PatchedModuleFusedSDPA()
Models list which support FP8 KV Cache,
microsoft/Phi-3-mini-4k-instruct
bigcode/starcoder2-3b
Qwen/Qwen2.5-7B-Instruct|
meta-llama/Llama-3.2-3B-Instruct
tiiuae/falcon-7b-instruct
mistralai/Mixtral-8x7B-Instruct-v0.1
EleutherAI/gpt-j-6b
mistralai/Mistral-Nemo-Instruct-2407
...
Refer to here.
Change "--model_name_or_path" to be your model like
"meta-llama/Llama-3.1-8B-Instruct",
"Qwen/Qwen2.5-7B-Instruct", or
"mistralai/Mixtral-8x7B-Instruct-v0.1" and so on.
"--use_kv_cache" is to enable FP8 KV cache.
Add "--profiling_warmup_steps 5 --profiling_steps 2 --profiling_record_shapes" as args in the end of commandline of run_generation.py.
Refer to torch.profiler.ProfilerActivity.HPU.
"lm_eval.tasks", "lm_eval.evaluator", "lm_eval" are installed from the above requirements_lm_eval.txt. The tasks can be set and the default is ["hellaswag", "lambada_openai", "piqa", "winogrande"], more info
Llama-2-7b-hf |
fp8 & fp8 KVCache | bf16 w/ bf16 KVCache |
|---|---|---|
| hellaswag | 0.5691097390957977 | 0.5704043019318861 |
| lambada_openai | 0.7360760721909567 | 0.7372404424607025 |
| piqa | 0.7850924918389554 | 0.7818280739934712 |
| winogrande | 0.6929755327545383 | 0.6929755327545383 |
Qwen2.5-7B-Instruct |
fp8 & fp8 KVCache | bf16 w/ bf16 KVCache |
|---|---|---|
| hellaswag | 0.2539334793865764 | 0.2539334793865764 |
| lambada_openai | 0.0 | 0.0 |
| piqa | 0.5391730141458106 | 0.5391730141458106 |
| winogrande | 0.4956590370955012 | 0.4956590370955012 |
Llama-3.1-8B-Instruct |
fp8 & fp8 KVCache | bf16 w/ bf16 KVCache |
|---|---|---|
| hellaswag | 0.5934076877116112 | 0.5975901214897431 |
| lambada_openai | 0.7230739375121289 | 0.7255967397632447 |
| piqa | 0.7932535364526659 | 0.8030467899891186 |
| winogrande | 0.7434885556432518 | 0.7371744277821626 |
Mixtral-8x7B-Instruct-v0.1 |
fp8 & fp8 KVCache | bf16 w/ bf16 KVCache |
|---|---|---|
| hellaswag | 0.25323640709022105 | 0.25323640709022105 |
| lambada_openai | 0.0 | 0.0 |
| piqa | 0.528835690968444 | 0.528835690968444 |
| winogrande | 0.4956590370955012 | 0.4956590370955012 |
Refer to Habana vllm-fork to install.
Option to install vllm-hpu-extension, neural_compressor and vllm from the source,
$ git clone https://github.com/HabanaAI/vllm-fork.git
$ cd vllm-fork
$ pip install -r requirements-hpu.txt
$ python setup.py develop --user
## Check
$ pip list |grep vllm
vllm 0.6.3.dev1122+g2f43ebf5.d20241121.gaudi118 /home/fengding/vllm-fork
vllm-hpu-extension 0.1
## Validation
$ VLLM_SKIP_WARMUP=true python3 examples/offline_inference.py
......
Prompt: 'Hello, my name is', Generated text: ' Kelly and I have a job to do.\nI need someone to come over'
Prompt: 'The president of the United States is', Generated text: ' facing a sharp criticism of his handling of the coronavirus pandemic, including'
Prompt: 'The capital of France is', Generated text: ' the capital of the Socialist Party of France (SPF), with its state-'
Prompt: 'The future of AI is', Generated text: " in what's coming, not what's coming.\nI don't know what"
Refer to vllm-hpu-extension->calibration
$ git clone https://github.com/HabanaAI/vllm-hpu-extension
$ cd vllm-hpu-extension/calibration
# For Llama-3.1.8B-Instruct
$ ./calibrate_model.sh -m meta-llama/Llama-3.1-8B-Instruct -d /home/fengding/processed-data.pkl -o ./output_llama3.1.8b.Instruct -b 128 -t 1 -l 128
## Generate scale factors in ./output_llama3.1.8b.Instruct
$ cd vllm-fork/
$ PT_HPU_ENABLE_LAZY_COLLECTIVES=true \
PT_HPU_WEIGHT_SHARING=0 \
VLLM_CONTIGUOUS_PA=true \
VLLM_SKIP_WARMUP=true \
QUANT_CONFIG=output_llama3.1.8b.Instruct/maxabs_quant_g2.json \
python3 -m vllm.entrypoints.openai.api_server \
--model meta-llama/Llama-3.1-8B-Instruct \
--port 8080 \
--gpu-memory-utilization 0.9 \
--tensor-parallel-size 1 \
--disable-log-requests \
--block-size 128 \
--quantization inc \
--kv-cache-dtype fp8_inc \
--device hpu \
--weights-load-device cpu \
--dtype bfloat16 \
--num_scheduler_steps 16 2>&1 > vllm_serving.log &
Refer to vllm-fork->README_GAUDI.md for more details.
$ curl --noproxy "*" http://localhost:8080/v1/completions -H "Content-Type: application/json" -d '{"model": "meta-llama/Llama-3.1-8B-Instruct", "prompt": "San Francisco is a", "max_tokens": 100}'
python benchmarks/benchmark_serving.py \
--backend vllm \
--model meta-llama/Llama-3.1-8B-Instruct \
--dataset-name sonnet \
--dataset-path benchmarks/sonnet.txt \
--request-rate 128 \
--num-prompts 128 \
--port 8080 \
--sonnet-input-len 128 \
--sonnet-output-len 128 \
--sonnet-prefix-len 100
Code trace
vllm-fork/vllm/attention/backends/hpu_attn.py
from vllm_hpu_extension.utils import Matmul, Softmax, VLLMKVCache HPUAttentionImpl() -> self.k_cache() / self.v_cache()
neural_compressor/torch/algorithms/fp8_quant/_quant_common/helper_modules.py
PatchedVLLMKVCache()
neural_compressor/torch/algorithms/fp8_quant/common.py
"VLLMKVCache": ModuleInfo("kv_cache", PatchedVLLMKVCache)