advanced_usage.md

Advanced Usage

One-click scripts

Argument description of run.py (supported MatMul combinations):

Argument	Description
model	Directory containing model file or model id: String
--weight_dtype	Data type of quantized weight: int4/int3/int2/int5/int6/int7/int1/int8/fp8(=fp8_e4m3)/fp8_e5m2/fp4(=fp4e2m1)/nf4 (default int4)
--alg	Quantization algorithm: sym/asym (default sym)
--group_size	Group size: Int, 16/32/64/128/-1 (per channel) (default: 32)
--scale_dtype	Data type of scales: fp32/bf16/fp8 (default fp32)
--compute_dtype	Data type of Gemm computation: int8/bf16/fp16/fp32 (default: fp32)
--use_ggml	Enable ggml for quantization and inference
-p / --prompt	Prompt to start generation with: String (default: empty)
-f / --file	Path to a text file containing the prompt (for large prompts)
-n / --n_predict	Number of tokens to predict: Int (default: -1, -1 = infinity)
-t / --threads	Number of threads to use during computation: Int (default: 56)
-b / --batch_size_truncate	Batch size for prompt processing: Int (default: 512)
-c / --ctx_size	Size of the prompt context: Int (default: 512, can not be larger than specific model's context window length)
-s / --seed	NG seed: Int (default: -1, use random seed for < 0)
--repeat_penalty	Penalize repeat sequence of tokens: Float (default: 1.1, 1.0 = disabled)
--color	Colorise output to distinguish prompt and user input from generations
--keep	Number of tokens to keep from the initial prompt: Int (default: 0, -1 = all)
--shift-roped-k	Use ring-buffer and thus do not re-computing after reaching ctx_size (default: False)
--token	Access token ID for models that require it (e.g: LLaMa2, etc..)

1. Conversion and Quantization

Neural Speed assumes the compatible model format as llama.cpp and ggml. You can also convert the model by following the below steps:

# convert the model directly use model id in Hugging Face. (recommended)
python scripts/convert.py --outtype f32 --outfile ne-f32.bin EleutherAI/gpt-j-6b

# or you can download fp32 model (e.g., LLAMA2) from Hugging Face at first, then convert the pytorch model to ggml format.
git clone https://huggingface.co/meta-llama/Llama-2-7b-chat-hf
python scripts/convert.py --outtype f32 --outfile ne-f32.bin model_path

# To convert model with PEFT(Parameter-Efficient Fine-Tuning) adapter, you need to merge the PEFT adapter into the model first, use below command to merge the PEFT adapter and save the merged model, afterwards you can use 'scripts/convert.py' just like above mentioned.
python scripts/load_peft_and_merge.py --model_name_or_path meta-llama/Llama-2-7b-hf --peft_name_or_path dfurman/llama-2-7b-instruct-peft --save_path ./Llama-2-7b-hf-instruct-peft

# quantize weights of fp32 ggml bin
# model_name: llama, llama2, mpt, falcon, gptj, starcoder, dolly
# optimized INT4 model with group size 128 (recommended)
python scripts/quantize.py --model_name llama2 --model_file ne-f32.bin --out_file ne-q4_j.bin --weight_dtype int4 --group_size 128 --compute_dtype int8

# Alternatively you could run ggml q4_0 format like following
python scripts/quantize.py --model_name llama2 --model_file ne-f32.bin --out_file ne-q4_0.bin --weight_dtype int4 --use_ggml
# optimized INT4 model with group size 32
python scripts/quantize.py --model_name llama2 --model_file ne-f32.bin --out_file ne-q4_j.bin --weight_dtype int4 --group_size 32 --compute_dtype int8

Argument description of quantize.py (supported MatMul combinations):

Argument	Description
--model_file	Path to the fp32 model: String
--out_file	Path to the quantized model: String
--build_dir	Path to the build file: String
--config	Path to the configuration file: String (default: "")
--nthread	Number of threads to use: Int (default: 1)
--weight_dtype	Data type of quantized weight: int4/int3/int2/int5/int6/int7/int1/int8/fp8(=fp8_e4m3)/fp8_e5m2/fp4(=fp4_e2m1)/nf4 (default: int4)
--alg	Quantization algorithm to use: sym/asym (default: sym)
--group_size	Group size: Int 16/32/64/128/-1 (per channel) (default: 32)
--scale_dtype	Data type of scales: bf16/fp32/fp8 (default: fp32)
--compute_dtype	Data type of Gemm computation: int8/bf16/fp16/fp32 (default: fp32)
--use_ggml	Enable ggml for quantization and inference

Supported Matrix Multiplication Data Types Combinations

Our Neural Speed supports INT4 / INT3 / INT2 / INT5 / INT6 / INT7 / INT1 / INT8 / FP8 (E4M3, E5M2) / FP4 (E2M1) / NF4 weight-only quantization and FP32 / FP16 / BF16 / INT8 computation forward matmul on the Intel platforms. Here are the all supported data types combinations for matmul operations (quantization and forward).

This table will be updated frequently due to active development. For details you can refer to BesTLA

Weight dtype	Compute dtype (default value)	Scale dtype (default value)	Quantization scheme (default value)
FP32	FP32	NA	NA
INT8	INT8 / BF16 / FP16 / FP32 (FP32)	BF16 / FP32 (FP32)	sym / asym (sym)
INT4	INT8 / BF16 / FP16 / FP32 (FP32)	BF16 / FP32 (FP32)	sym / asym (sym)
INT3	INT8 / BF16 / FP16 / FP32 (FP32)	BF16 / FP32 (FP32)	sym / asym (sym)
INT2	INT8 / BF16 / FP16 / FP32 (FP32)	BF16 / FP32 (FP32)	sym / asym (sym)
INT5	INT8 / BF16 / FP16 / FP32 (FP32)	BF16 / FP32 (FP32)	sym / asym (sym)
INT6	INT8 / BF16 / FP16 / FP32 (FP32)	BF16 / FP32 (FP32)	sym / asym (sym)
INT7	INT8 / BF16 / FP16 / FP32 (FP32)	BF16 / FP32 (FP32)	sym / asym (sym)
INT1	INT8 / BF16 / FP16 / FP32 (FP32)	BF16 / FP32 (FP32)	sym / asym (sym)
FP8 (E4M3, E5M2)	BF16 / FP16 / FP32 (FP32)	FP8 (FP8)	sym (sym)
FP4 (E2M1)	BF16 / FP16 / FP32 (FP32)	BF16 / FP32 (FP32)	sym (sym)
NF4	BF16 / FP16 / FP32 (FP32)	BF16 / FP32 (FP32)	sym (sym)

2. Inference

# recommend to use numactl to bind cores in Intel cpus for better performance
# if you use different core numbers, please also  change -t arg value
# please type prompt about codes when run `StarCoder`, for example, -p "def fibonnaci(".

#Linux and WSL
numactl -m 0 -C 0-<physic_cores-1> python scripts/inference.py --model_name llama -m ne-q4_j.bin -c 512 -b 1024 -n 256 -t <physic_cores> --color -p "She opened the door and see"

# if you want to generate fixed outputs, please set --seed arg, for example:
numactl -m 0 -C 0-<physic_cores-1> python scripts/inference.py --model_name llama -m ne-q4_j.bin -c 512 -b 1024 -n 256 -t <physic_cores> --color -p "She opened the door and see" --seed 12

# if you want to reduce repeated generated texts, please set --repeat_penalty (value > 1.0, default = 1.0), for example:
numactl -m 0 -C 0-<physic_cores-1> python scripts/inference.py --model_name llama -m ne-q4_j.bin -c 512 -b 1024 -n 256 -t <physic_cores> --color -p "She opened the door and see" --repeat_penalty 1.2

#Windows
#Recommend to build and run our project in WSL to get a better and stable performance
python scripts/inference.py --model_name llama -m ne-q4_j.bin -c 512 -b 1024 -n 256 -t <physic_cores|P-cores> --color -p "She opened the door and see"

Argument description of inference.py:

Argument	Description
--model_name	Model name: String
-m / --model	Path to the executed model: String
--build_dir	Path to the build file: String
-p / --prompt	Prompt to start generation with: String (default: empty)
-f / --file	Path to a text file containing the prompt (for large prompts)
-n / --n_predict	Number of tokens to predict: Int (default: -1, -1 = infinity)
-t / --threads	Number of threads to use during computation: Int (default: 56)
-b / --batch_size	Batch size for prompt processing: Int (default: 512)
-c / --ctx_size	Size of the prompt context: Int (default: 512, can not be larger than specific model's context window length)
-s / --seed	NG seed: Int (default: -1, use random seed for < 0)
--repeat_penalty	Penalize repeat sequence of tokens: Float (default: 1.1, 1.0 = disabled)
--color	Colorise output to distinguish prompt and user input from generations
--keep	Number of tokens to keep from the initial prompt: Int (default: 0, -1 = all)
--shift-roped-k	Use ring-buffer and thus do not re-computing after reaching ctx_size (default: False)
--glm_tokenizer	The path of the chatglm tokenizer: String (default: THUDM/chatglm-6b)
--memory-f32 --memory-f16 --memory-auto	Data type of kv memory (default to auto); If set to auto, the runtime will try with bestla flash attn managed format (currently requires GCC11+ & AMX) and fall back to fp16 if failed