Source examples/other/fp8.
FP8 KV Cache#
This utility extracts the KV cache scaling factors from a quantized HF (Hugging Face) model. The extracted scaling factors are saved to a JSON file, which can later be used by vLLM (variable-length language model) during runtime. This tool is particularly useful when the KV cache data type is FP8 and is intended for use on ROCm (AMD GPU) platforms.
Prerequisites#
Python 3.x
PyTorch
NumPy
Hugging Face Transformers
Hugging Face Hub
AMMO
Before incorporating the FP8 datatype for inference workloads, you must adhere to the following steps:
Install all necessary prerequisites and dependencies.
Convert HF model into a quantized HF model.
Extract KV Cache Scaling Factors from quantized HF model.
Load KV Cache Scaling Factors into VLLM.
2. Convert HF model into a quantized HF model.#
Note: The following steps are adapted from the TensorRT-LLM repository.
quantize.py (examples/other/fp8/quantizer/quantize.py) uses the quantization toolkit (AMMO) to calibrate the PyTorch models and export TensorRT-LLM checkpoints. Each TensorRT-LLM checkpoint contains a config file (in .json format) and one or several rank weight files (in .safetensors format).
The detailed quantization toolkit (AMMO) conversion guide for FP8 can be found at examples/other/fp8/quantizer/README.md.
3. Extract KV Cache Scaling Factors from quantized HF model.#
extract_scales.py (examples/other/fp8/extract_scales.py) can be utilized to extract the KV cache scaling factors from your quantized HF model, however at the moment, this tool exclusively supports Llama 2 models. It is also important to note the following:
File Structure: The utility operates under the assumption that all parameters, including KV cache scaling factors, corresponding to a particular Tensor Parallelism (TP) rank are stored in a single file. These files must adhere to a specific naming convention where the TP rank is immediately identified after a specific keyword (e.g., “rank”) in the filename.
TP Decomposition: The utility assumes consistency between the TP decomposition employed by the quantizer tool and that used by vLLM.
AMMO Compatibility: Currently, the generated KV cache scaling factors for AMMO remain uniform across all TP ranks.
# prerequisites:
# - Quantized HF LLaMa 2 model
python3 examples/other/fp8/extract_scales.py --help
Usage: extract_scales.py [-h] --quantized_model QUANTIZED_MODEL [--load_format {auto,safetensors,npz,pt}] [--output_dir OUTPUT_DIR] [--output_name OUTPUT_NAME] [--tp_size TP_SIZE]
KV Scale Extraction Example
optional arguments:
--quantized_model: Specify either the local path to, or name of, a quantized HF model. It is expected that the quantization format is FP8_E4M3, for use on ROCm (AMD GPU).
Optional arguments:
--cache_dir: Specify a cache directory to use in the event of a HF model download. (Default: None)
--load_format: Specify the format of the model's tensor files containing the KV cache scaling factors. (Choices: auto, safetensors, npz, pt; Default: auto)
--revision: Specify the model's revision number. (Default: None)
--output_dir: Specify the output directory. By default the KV cache scaling factors will be saved in the model directory. (Default: None)
--output_name: Specify the output filename. (Default: kv_cache_scales.json)
--tp_size: Specify the tensor-parallel (TP) size that the quantized model should correspond to. If specified, during KV cache scaling factor extraction the observed TP size will be checked against this and an error will be raised if there is a mismatch. (Default: None)
Example:
python3 examples/other/fp8/extract_scales.py --quantized_model <QUANTIZED_MODEL_DIR> --tp_size <TENSOR_PARALLEL_SIZE> --output_dir <PATH_TO_OUTPUT_DIR>
4. Load KV Cache Scaling Factors into VLLM.#
This script evaluates the inference throughput of language models using various backends such as vLLM. It measures the time taken to process a given number of prompts and generate sequences for each prompt. The recently generated KV cache scaling factors are now integrated into the benchmarking process and allow for KV cache scaling factors to be utilized for FP8.
# prerequisites:
# - LLaMa 2 kv_cache_scales.json file
python3 benchmarks/benchmark_throughput.py --help
usage: benchmark_throughput.py [-h] [--backend {vllm,hf,mii}] [--dataset DATASET] [--input-len INPUT_LEN] [--output-len OUTPUT_LEN] [--model MODEL]
[--tokenizer TOKENIZER] [--quantization {awq,gptq,None}] [--tensor-parallel-size TENSOR_PARALLEL_SIZE] [--n N]
[--use-beam-search] [--num-prompts NUM_PROMPTS] [--seed SEED] [--hf-max-batch-size HF_MAX_BATCH_SIZE] [--trust-remote-code]
[--max-model-len MAX_MODEL_LEN] [--dtype {auto,half,float16,bfloat16,float,float32}] [--enforce-eager] [--kv-cache-dtype {auto,fp8}]
[--quantization-param-path KV_CACHE_quantization_param_path]
Benchmark Throughput Example
optional arguments:
-h, --help show this help message and exit
--backend {vllm,hf,mii}
--dataset DATASET Path to the dataset.
--input-len INPUT_LEN Input prompt length for each request
--output-len OUTPUT_LEN Output length for each request. Overrides the output length from the dataset.
--model MODEL
--tokenizer TOKENIZER
--quantization {awq,gptq,None}, -q {awq,gptq,None}
--tensor-parallel-size TENSOR_PARALLEL_SIZE, -tp TENSOR_PARALLEL_SIZE
--n N Number of generated sequences per prompt.
--use-beam-search
--num-prompts NUM_PROMPTS Number of prompts to process.
--seed SEED
--hf-max-batch-size HF_MAX_BATCH_SIZE Maximum batch size for HF backend.
--trust-remote-code trust remote code from huggingface
--max-model-len MAX_MODEL_LEN Maximum length of a sequence (including prompt and output). If None, will be derived from the model.
--dtype {auto,half,float16,bfloat16,float,float32} data type for model weights and activations. The "auto" option will use FP16 precision for FP32 and FP16 models, and BF16 precision for BF16 models.
--enforce-eager enforce eager execution
--kv-cache-dtype {auto,fp8} Data type for kv cache storage. If "auto", will use model data type. FP8_E5M2 (without scaling) is only supported on cuda version greater than 11.8. On ROCm (AMD GPU), FP8_E4M3 is instead supported ```for common inference criteria.
--quantization-param-path QUANT_PARAM_JSON Path to the JSON file containing the KV cache scaling factors. This should generally be supplied, when KV cache dtype is FP8. Otherwise, KV cache scaling factors default to 1.0, which may cause accuracy issues. FP8_E5M2 (without scaling) is only supported on cuda version greater than 11.8. On ROCm (AMD GPU), FP8_E4M3 is instead supported for common inference criteria.
Example:
python3 benchmarks/benchmark_throughput.py --input-len <INPUT_LEN> --output-len <OUTPUT_LEN> -tp <TENSOR_PARALLEL_SIZE> --kv-cache-dtype fp8 --quantization-param-path <path/to/kv_cache_scales.json> --model <path-to-llama2>
Example materials#
extract_scales.py
import argparse
import glob
import json
import os
from typing import Any, Callable, Dict, List, Optional, Tuple
import numpy as np
import torch
from safetensors.torch import safe_open
from vllm.model_executor.layers.quantization.schema import QuantParamSchema
# Adapted from vllm/model_executor/model_loader/weight_utils.py
# The main differences are that we add the NPZ format and simplify
# its functionality drastically for our purposes (e.g. we assume that
# the quantized model exists locally and there is no need to download it)
def _prepare_hf_weights(
quantized_model_dir: str,
load_format: str = "auto",
fall_back_to_pt: bool = True,
) -> Tuple[List[str], bool]:
if not os.path.isdir(quantized_model_dir):
raise FileNotFoundError(
f"The quantized model directory `{quantized_model_dir}` "
"does not exist.")
use_safetensors = False
# Some quantized models use .pt files for storing the weights.
if load_format == "auto":
allow_patterns = ["*.safetensors", "*.bin"]
elif load_format == "safetensors":
use_safetensors = True
allow_patterns = ["*.safetensors"]
elif load_format == "pt":
allow_patterns = ["*.pt"]
elif load_format == "npz":
allow_patterns = ["*.npz"]
else:
raise ValueError(f"Unknown load_format: {load_format}")
if fall_back_to_pt:
allow_patterns += ["*.pt"]
hf_weights_files: List[str] = []
for pattern in allow_patterns:
hf_weights_files += glob.glob(
os.path.join(quantized_model_dir, pattern))
if len(hf_weights_files) > 0:
if pattern == "*.safetensors":
use_safetensors = True
break
if not use_safetensors:
# Exclude files that are not needed for inference.
# https://github.com/huggingface/transformers/blob/v4.34.0/src/transformers/trainer.py#L227-L233
blacklist = [
"training_args.bin",
"optimizer.bin",
"optimizer.pt",
"scheduler.pt",
"scaler.pt",
]
hf_weights_files = [
f for f in hf_weights_files
if not any(f.endswith(x) for x in blacklist)
]
if len(hf_weights_files) == 0:
raise RuntimeError(
f"Cannot find any model weights with `{quantized_model_dir}`")
return hf_weights_files, use_safetensors
# Adapted from vllm/model_executor/model_loader/weight_utils.py
def _hf_tensorfile_iterator(filename: str, load_format: str,
use_safetensors: bool):
if load_format == "npz":
assert not use_safetensors
with np.load(filename) as data:
for name in data.files:
param = torch.from_numpy(data[name])
yield name, param
elif use_safetensors:
with safe_open(filename, framework="pt") as f:
for name in f.keys(): # NOQA: SIM118
param = f.get_tensor(name)
yield name, param
else:
state = torch.load(filename, map_location="cpu")
for name, param in state.items():
yield name, param
del state
torch.cuda.empty_cache()
def _kv_scales_extractor(
hf_tensor_files: List[str],
use_safetensors: bool,
rank_keyword: str = "rank",
expected_tp_size: Optional[int] = None) -> Dict[int, Dict[int, float]]:
"""
Given a list of files containing tensor data, attempt to extract KV cache
scales from these files. Intended as a helper function taking in the output
from _prepare_hf_weights.
Args:
rank_keyword Matches the number immediately after this keyword in the
tensor filename to determine the TP rank corresponding
to said tensor file
expected_tp_size If specified, the TP size of the tensor files is checked
against this and an error is raised if they don't match.
Returns a dictionary mapping TP ranks to their relevant KV cache scales.
The per-rank scales are themselves represented as a dictionary of layer
indices to the respective per-layer scale.
"""
for char in rank_keyword:
assert not char.isdecimal(
), f"Rank keyword {rank_keyword} contains a numeric character!"
rank_scales_map: Dict[int, Dict[int, float]] = {}
for tensor_file in hf_tensor_files:
try:
rank_idx = tensor_file.find(rank_keyword)
if rank_idx != -1:
start_idx = rank_idx + len(rank_keyword)
stop_idx = start_idx
while stop_idx < len(
tensor_file) and tensor_file[stop_idx].isdecimal():
stop_idx += 1
if stop_idx == start_idx:
raise RuntimeError("Did not find rank # in filename.")
rank = int(tensor_file[start_idx:stop_idx])
elif len(hf_tensor_files) == 1:
# Since there is only one tensor file, we can assume
# that it's intended for TP rank 0
rank = 0
else:
raise RuntimeError(
f"Filename does not contain '{rank_keyword}'.")
except RuntimeError:
print("Unable to determine TP rank "
f"corresponding to file '{tensor_file}'")
raise
if rank not in rank_scales_map:
layer_scales_map: Dict[int, float] = {}
rank_scales_map[rank] = layer_scales_map
else:
raise RuntimeError(
f"Tensor file '{tensor_file}' shares TP rank {rank} "
"with another tensor file.")
module_delimiter = ":" if args.load_format == "npz" else "."
for name, param in _hf_tensorfile_iterator(tensor_file,
args.load_format,
use_safetensors):
if "kv_cache_scaling_factor" in name:
nums = [
int(s) for s in name.split(module_delimiter)
if s.isdecimal()
]
assert len(
nums) == 1, f"Could not determine layer idx for {name}"
layer_idx = nums[0]
assert layer_idx not in layer_scales_map, f"Duplicate scaling"\
f" factor corresponding to layer {layer_idx}"
try:
layer_scales_map[layer_idx] = param.item()
except RuntimeError:
print(
"This utility supports only per-tensor scalar scales "
f"for now. The tensor\n {name} = {param} \nis an "
"invalid scale factor.")
raise
if all(
len(layer_scales_map) == 0
for layer_scales_map in rank_scales_map.values()):
# Note: this is true even if the rank_scales_map is empty
print("WARNING: No KV cache scale factors found. No output saved.")
return None
empirical_tp_world_size = max(rank_scales_map.keys()) + 1
if expected_tp_size is not None:
assert expected_tp_size == empirical_tp_world_size, \
f"User expected TP world size = {expected_tp_size} " \
"from model but tool is expecting TP world size = " \
f"{empirical_tp_world_size} from model instead."
for i in range(empirical_tp_world_size):
assert i in rank_scales_map, "Expected TP world size = "\
f"{empirical_tp_world_size} but did not find KV " \
f"cache scaling factors for TP rank {i}"
print(f"Found TP world size = {empirical_tp_world_size} "
"when extracting KV cache scales!")
return rank_scales_map
def _metadata_extractor(quantized_model_dir: str,
metadata_extract_fns: \
Dict[str, Callable[[Dict[str, Any]], Any]]) \
-> Dict[str, Any]:
"""
Given a directory containing quantized model files, this function
aims to extract metadata from the JSON files within this directory.
Each JSON file is expected to represent a dictionary in JSON
format (referred to as a "JSON-dictionary"). Metadata extraction is
defined by a dictionary called metadata_extract_fns, where each
metadata field name is mapped to an extraction function.
These extraction functions are designed to take a JSON-dictionary
as their only argument and return the corresponding metadata.
While extraction functions are permitted to raise exceptions, they
should only raise a KeyError or ValueError if the metadata field
cannot be extracted from the current JSON-dictionary, yet there's
a possibility of finding it in another JSON-dictionary.
The function returns a dictionary that maps metadata fields to
their extracted data. The keys of this dictionary correspond exactly
to those in metadata_extract_fns. If any fields fail to be extracted,
their corresponding values are set to None, and a warning is printed.
"""
if not os.path.isdir(quantized_model_dir):
raise FileNotFoundError(
f"The quantized model directory `{quantized_model_dir}` "
"does not exist.")
metadata_files = glob.glob(os.path.join(quantized_model_dir, "*.json"))
result: Dict[str, Any] = {}
for file in metadata_files:
with open(file) as f:
try:
metadata = json.load(f)
except json.JSONDecodeError:
print(f"Could not parse `{file}` as a valid metadata file,"
" skipping it.")
continue
if not isinstance(metadata, dict):
print(f"The file `{file}` does not correspond to a "
"JSON-serialized dictionary, skipping it.")
continue
for metadata_name, extract_fn in metadata_extract_fns.items():
try:
metadata_info = extract_fn(metadata)
if metadata_name not in result:
result[metadata_name] = metadata_info
elif metadata_info != result[metadata_name]:
raise RuntimeError(
"Metadata mismatch! Originally found "
f"{metadata_name} = {result[metadata_name]} but "
f"now found {metadata_name} = {metadata_info} in "
f"`{file}`")
except KeyError:
# It is possible that a given file does not contain some
# of our selected metadata as it could be located in some
# other metadata file.
# 'EFINAE': extract_fn failure is not an error.
pass
except ValueError:
# See above.
pass
# Warn if we cannot find any of the requested metadata
for metadata_name in metadata_extract_fns:
if metadata_name not in result:
print("WARNING: Unable to find requested metadata field "
f"`{metadata_name}`, setting it to None.")
result[metadata_name] = None
return result
def main(args):
metadata_extract_fns = {
"model_type": lambda json_dict: json_dict["layers"][0]["decoder_type"],
"tp_size": lambda json_dict: int(json_dict["tensor_parallel"]),
"model_dtype": lambda json_dict: json_dict["dtype"]
}
recovered_metadata = _metadata_extractor(args.quantized_model,
metadata_extract_fns)
if args.tp_size is not None:
metadata_tp_size = recovered_metadata["tp_size"]
if metadata_tp_size is not None:
assert args.tp_size == metadata_tp_size, \
f"User expected TP world size = {args.tp_size} " \
f"but found TP world size = {metadata_tp_size} from metadata!"
expected_tp_size = args.tp_size or recovered_metadata["tp_size"]
rank_keyword = "rank"
hf_tensor_files, use_safetensors = _prepare_hf_weights(
args.quantized_model, args.load_format)
rank_scales_map = _kv_scales_extractor(hf_tensor_files, use_safetensors,
rank_keyword, expected_tp_size)
# Postprocess: formatting to the current schema. Consider pulling it
# out into a dedicated function should it ever become more complicated.
rank_scales_map = {
rank: {k: scale[k]
for k in sorted(scale.keys())}
for rank, scale in rank_scales_map.items()
}
# TODO: Expand this with activation and weights scaling factors when
# they are used in the future
schema = QuantParamSchema(
model_type=recovered_metadata["model_type"],
kv_cache={
"dtype": ("float8_e4m3fn" if len(rank_scales_map) > 0 else
recovered_metadata["model_dtype"]),
"scaling_factor":
rank_scales_map
},
)
if args.output_dir is None:
output_file = os.path.join(args.quantized_model, args.output_name)
else:
if not os.path.isdir(args.output_dir):
os.makedirs(args.output_dir, exist_ok=True)
output_file = os.path.join(args.output_dir, args.output_name)
with open(output_file, 'w') as f:
f.write(schema.model_dump_json(indent=4))
print(f"Completed! KV cache scaling factors saved to {output_file}")
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description="This simple utility extracts the "
"KV cache scaling factors from a quantized HF model "
"and saves them to a JSON file compatible with later "
"use by vLLM (pass this file to the appropriate "
"runtime typically using the argument "
"--quantization-param-path <filename>). This is only used "
"if the KV cache dtype is FP8 and on ROCm (AMD GPU).")
parser.add_argument(
"--quantized-model",
help="Specify the directory containing a single quantized HF model. "
"It is expected that the quantization format is FP8_E4M3, for use "
"on ROCm (AMD GPU).",
required=True)
parser.add_argument(
"--load_format",
help="Optionally specify the format of the model's tensor files "
"containing the KV cache scaling factors.",
choices=["auto", "safetensors", "npz", "pt"],
default="auto")
parser.add_argument(
"--output-dir",
help="Optionally specify the output directory. By default the "
"KV cache scaling factors will be saved in the model directory, "
"however you can override this behavior here.",
default=None)
parser.add_argument(
"--output-name",
help="Optionally specify the output filename.",
# TODO: Change this once additional scaling factors are enabled
default="kv_cache_scales.json")
parser.add_argument(
"--tp-size",
help="Optionally specify the tensor-parallel (TP) size that the "
"quantized model should correspond to. If specified, during KV "
"cache scaling factor extraction the observed TP size will be "
"checked against this and an error will be raised if there is "
"a mismatch. If not specified, the quantized model's expected "
"TP size is instead inferred from the largest TP rank observed. "
"The expected TP size is cross-checked against the TP ranks "
"observed in the quantized model and an error is raised if any "
"discrepancies are found.",
default=None,
type=int)
args = parser.parse_args()
main(args)
quantizer/quantize.py
# SPDX-FileCopyrightText: Copyright (c) 2022-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # noqa: E501
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Adapted from examples/quantization/hf_ptq.py
"""
import argparse
import copy
import json
import random
import time
import ammo.torch.quantization as atq
import numpy as np
import torch
from ammo.torch.export import export_model_config
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForCausalLM, AutoTokenizer
RAND_SEED = 1234
MAX_SEQ_LEN = 2048
EMPTY_CFG = {
"quant_cfg": {
"*weight_quantizer": {
"enable": False,
},
"*input_quantizer": {
"enable": False
},
"*lm_head*": {
"enable": False
},
"*output_layer*": {
"enable": False
},
"default": {
"enable": False
},
},
"algorithm": "max",
}
KV_CACHE_CFG = {
"*.query_key_value.output_quantizer": {
"num_bits": 8,
"axis": None,
"enable": True
},
"*.Wqkv.output_quantizer": {
"num_bits": 8,
"axis": None,
"enable": True
},
"*.W_pack.output_quantizer": {
"num_bits": 8,
"axis": None,
"enable": True
},
"*.c_attn.output_quantizer": {
"num_bits": 8,
"axis": None,
"enable": True
},
"*.k_proj.output_quantizer": {
"num_bits": 8,
"axis": None,
"enable": True
},
"*.v_proj.output_quantizer": {
"num_bits": 8,
"axis": None,
"enable": True
},
}
QUANT_CFG_CHOICES = {
"int8_sq": atq.INT8_SMOOTHQUANT_CFG,
"fp8": atq.FP8_DEFAULT_CFG,
"int4_awq": atq.INT4_AWQ_CFG,
"w4a8_awq": atq.W4A8_AWQ_BETA_CFG,
"int8_wo": EMPTY_CFG,
"int4_wo": EMPTY_CFG,
"full_prec": EMPTY_CFG,
}
MODEL_NAME_PATTERN_MAP = {
"GPT2": "gpt2",
"Xverse": "llama",
"Llama": "llama",
"Mistral": "llama",
"GPTJ": "gptj",
"FalconForCausalLM": "falcon",
"RWForCausalLM": "falcon",
"baichuan": "baichuan",
"MPT": "mpt",
"Bloom": "bloom",
"ChatGLM": "chatglm",
"QWen": "qwen",
}
def get_tokenizer(ckpt_path, max_seq_len=MAX_SEQ_LEN, model_type=None):
print(f"Initializing tokenizer from {ckpt_path}")
tokenizer = AutoTokenizer.from_pretrained(
ckpt_path,
model_max_length=max_seq_len,
padding_side="left",
trust_remote_code=True,
)
if model_type and model_type == "qwen":
# qwen use token id 151643 as pad and eos tokens
tokenizer.pad_token = tokenizer.convert_ids_to_tokens(151643)
tokenizer.eos_token = tokenizer.convert_ids_to_tokens(151643)
# can't set attribute 'pad_token' for "<unk>"
if tokenizer.pad_token != "<unk>":
tokenizer.pad_token = tokenizer.eos_token
if tokenizer.pad_token is None:
tokenizer.pad_token = tokenizer.eos_token
assert (tokenizer.pad_token
is not None), f"Pad token for {model_type} cannot be set!"
return tokenizer
def get_model(ckpt_path, dtype="fp16", device="cuda"):
print(f"Initializing model from {ckpt_path}")
if dtype == "bf16" or dtype == "bfloat16":
dtype = torch.bfloat16
elif dtype == "fp16" or dtype == "float16":
dtype = torch.float16
elif dtype == "fp32" or dtype == "float32":
dtype = torch.float32
else:
raise NotImplementedError(f"Unknown dtype {dtype}")
# model_kwargs = {"torch_dtype": dtype}
model_kwargs = {"torch_dtype": "auto"}
model = AutoModelForCausalLM.from_pretrained(ckpt_path,
device_map="auto",
**model_kwargs,
trust_remote_code=True)
model.eval()
model_dtype = next(model.parameters()).dtype
if dtype != model_dtype:
print("[TensorRT-LLM][WARNING] The manually set model data type is "
f"{dtype}, but the data type of the HuggingFace model is "
f"{model_dtype}.")
return model
def get_model_type(model):
for k, v in MODEL_NAME_PATTERN_MAP.items():
if k.lower() in type(model).__name__.lower():
return v
return None
def get_calib_dataloader(data="cnn_dailymail",
tokenizer=None,
batch_size=1,
calib_size=512,
block_size=512,
device=None):
print("Loading calibration dataset")
if data == "pileval":
dataset = load_dataset(
"json",
data_files="https://the-eye.eu/public/AI/pile/val.jsonl.zst",
split="train")
dataset = dataset["text"][:calib_size]
elif data == "cnn_dailymail":
dataset = load_dataset("cnn_dailymail", name="3.0.0", split="train")
dataset = dataset["article"][:calib_size]
else:
raise NotImplementedError
batch_encoded = tokenizer.batch_encode_plus(dataset,
return_tensors="pt",
padding="max_length",
truncation=True,
max_length=block_size)
if device:
batch_encoded = batch_encoded.to(device)
batch_encoded = batch_encoded["input_ids"]
calib_dataloader = DataLoader(batch_encoded,
batch_size=batch_size,
shuffle=False)
return calib_dataloader
def quantize_model(model, quant_cfg, calib_dataloader=None):
def calibrate_loop():
if calib_dataloader is None:
return
"""Adjusts weights and scaling factors based on selected algorithms."""
for idx, data in enumerate(calib_dataloader):
print(f"Calibrating batch {idx}")
model(data)
print("Starting quantization...")
start_time = time.time()
atq.quantize(model, quant_cfg, forward_loop=calibrate_loop)
end_time = time.time()
print("Quantization done. Total time used: {:.2f} s.".format(end_time -
start_time))
return model
def main(args):
if not torch.cuda.is_available():
raise OSError("GPU is required for inference.")
random.seed(RAND_SEED)
np.random.seed(RAND_SEED)
model = get_model(args.model_dir, args.dtype, args.device)
model_type = get_model_type(model)
tokenizer = get_tokenizer(args.model_dir, model_type=model_type)
if args.qformat in ["full_prec", "int8_wo", "int4_wo"
] and args.kv_cache_dtype is None:
print(f"No quantization applied, export {args.dtype} model")
else:
if "awq" in args.qformat:
if args.calib_size > 32:
print("AWQ calibration could take longer with calib_size = "
f"{args.calib_size}, Using calib_size=32 instead")
args.calib_size = 32
print("\nAWQ calibration could take longer than other calibration "
"methods. Please increase the batch size to speed up the "
"calibration process. Batch size can be set by adding the "
"argument --batch_size <batch_size> to the command line.\n")
calib_dataloader = get_calib_dataloader(
tokenizer=tokenizer,
batch_size=args.batch_size,
calib_size=args.calib_size,
device=args.device,
)
if args.qformat in QUANT_CFG_CHOICES:
quant_cfg = QUANT_CFG_CHOICES[args.qformat]
else:
raise ValueError(
f"Unsupported quantization format: {args.qformat}")
if "awq" in args.qformat:
quant_cfg = copy.deepcopy(QUANT_CFG_CHOICES[args.qformat])
weight_quantizer = quant_cfg["quant_cfg"][
"*weight_quantizer"] # type: ignore
if isinstance(weight_quantizer, list):
weight_quantizer = weight_quantizer[0]
weight_quantizer["block_sizes"][-1] = args.awq_block_size
if args.kv_cache_dtype is not None:
if args.kv_cache_dtype == "fp8":
for value in KV_CACHE_CFG.values():
value.update({"num_bits": (4, 3)}) # type: ignore
quant_cfg["quant_cfg"].update(KV_CACHE_CFG) # type: ignore
print(quant_cfg)
model = quantize_model(model, quant_cfg, calib_dataloader)
with torch.inference_mode():
if model_type is None:
print(f"Unknown model type {type(model).__name__}. Continue "
"exporting...")
model_type = f"unknown:{type(model).__name__}"
export_path = args.output_dir
start_time = time.time()
if args.qformat == "int4_awq" and model_type == "qwen":
torch.save(model.state_dict(), export_path)
else:
export_npz = (model_type not in [
'gptj', 'falcon', 'chatglm', 'mpt', 'llama', 'baichuan'
])
# export safetensors
export_model_config(
model,
model_type,
getattr(torch, args.dtype),
export_dir=export_path,
inference_tensor_parallel=args.tp_size,
inference_pipeline_parallel=args.pp_size,
# export_tensorrt_llm_config=(not export_npz),
export_tensorrt_llm_config=False,
export_npz=export_npz)
# Workaround for wo quantization
if args.qformat in ["int8_wo", "int4_wo", "full_prec"]:
with open(f"{export_path}/config.json") as f:
tensorrt_llm_config = json.load(f)
if args.qformat == "int8_wo":
tensorrt_llm_config["quantization"]["quant_algo"] = 'W8A16'
elif args.qformat == "int4_wo":
tensorrt_llm_config["quantization"]["quant_algo"] = 'W4A16'
else:
tensorrt_llm_config["quantization"]["quant_algo"] = None
with open(f"{export_path}/config.json", "w") as f:
json.dump(tensorrt_llm_config, f, indent=4)
end_time = time.time()
print("Quantized model exported to {} \nTotal time used {:.2f} s.".
format(export_path, end_time - start_time))
if __name__ == "__main__":
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--model-dir",
help="Specify where the HuggingFace model is",
required=True)
parser.add_argument("--device", default="cuda")
parser.add_argument("--dtype", help="Model data type.", default="float16")
parser.add_argument(
"--qformat",
help="Quantization format.",
default="full_prec",
choices=[
"fp8", "int8_sq", "int4_awq", "w4a8_awq", "int8_wo", "int4_wo",
"full_prec"
],
)
parser.add_argument("--batch-size",
help="Batch size for calibration.",
type=int,
default=1)
parser.add_argument("--calib-size",
help="Number of samples for calibration.",
type=int,
default=512)
parser.add_argument("--output-dir", default="exported_model")
parser.add_argument("--tp-size", type=int, default=1)
parser.add_argument("--pp-size", type=int, default=1)
parser.add_argument("--awq-block-size", type=int, default=128)
parser.add_argument("--kv-cache-dtype",
help="KV Cache dtype.",
default=None,
choices=["int8", "fp8", None])
args = parser.parse_args()
main(args)
quantizer/README.md
Quantizer Utilities
quantize.py: NVIDIA Quantization utilities using TensorRT-Model-Optimizer, ported
from TensorRT-LLM: examples/quantization/quantize.py
Prerequisite
AMMO (AlgorithMic Model Optimization) Installation: nvidia-ammo 0.7.1 or later
pip install --no-cache-dir --extra-index-url https://pypi.nvidia.com nvidia-ammo
AMMO Download (code and docs)
https://developer.nvidia.com/downloads/assets/cuda/files/nvidia-ammo/nvidia_ammo-0.5.0.tar.gz
https://developer.nvidia.com/downloads/assets/cuda/files/nvidia-ammo/nvidia_ammo-0.7.1.tar.gz
Usage
Run on H100 system for speed if FP8; number of GPUs depends on the model size
Example: quantize Llama2-7b model from HF to FP8 with FP8 KV Cache:
python quantize.py --model-dir ./ll2-7b --dtype float16 --qformat fp8 --kv-cache-dtype fp8 --output-dir ./ll2_7b_fp8 --calib-size 512 --tp-size 1
Outputs: model structure, quantized model & parameters (with scaling factors) are in JSON and Safetensors (npz is generated only for the reference)
# ll ./ll2_7b_fp8/
total 19998244
drwxr-xr-x 2 root root 4096 Feb 7 01:08 ./
drwxrwxr-x 8 1060 1061 4096 Feb 7 01:08 ../
-rw-r--r-- 1 root root 176411 Feb 7 01:08 llama_tp1.json
-rw-r--r-- 1 root root 13477087480 Feb 7 01:09 llama_tp1_rank0.npz
-rw-r--r-- 1 root root 7000893272 Feb 7 01:08 rank0.safetensors
#