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eval.py
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eval.py
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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torchao
from pathlib import Path
from typing import List, Optional
from generate import (
_load_model,
device_sync,
)
from torchao.quantization import (
quantize_,
int4_weight_only,
int8_weight_only,
int8_dynamic_activation_int8_weight,
fpx_weight_only,
uintx_weight_only,
float8_weight_only,
float8_dynamic_activation_float8_weight,
)
from torchao._models.llama.model import prepare_inputs_for_model
from torchao.quantization import PerRow, PerTensor
from tokenizer import get_tokenizer
import time
from torchao.utils import TORCH_VERSION_AT_LEAST_2_5, unwrap_tensor_subclass
def run_evaluation(
checkpoint_path: Path,
tasks: List[str],
limit: Optional[int] = None,
device = "cuda",
precision = torch.bfloat16,
quantization: Optional[str] = None,
compile=False,
max_length=None,
calibration_tasks: Optional[List[str]] = None,
calibration_limit: Optional[int] = None,
calibration_seq_length: Optional[int] = None,
pad_calibration_inputs: Optional[bool] = False,
):
"""Runs the evaluation of a model using LM Eval."""
print(
f"\nEvaluating model {checkpoint_path} on tasks: {tasks}, limit: {limit}, device: {device}, precision: {precision}, "
+f"quantization: {quantization}, compile: {compile}, max_length: {max_length}, calibration_tasks: {calibration_tasks}, "
+f"calibration_seq_length: {calibration_seq_length}, pad_calibration_inputs: {pad_calibration_inputs}\n"
)
torchao.quantization.utils.recommended_inductor_config_setter()
assert checkpoint_path.is_file(), checkpoint_path
tokenizer_path = checkpoint_path.parent / "tokenizer.model"
assert tokenizer_path.is_file(), str(tokenizer_path)
# Load Model and Tokenizer
print("Loading model ...")
t0 = time.time()
model = _load_model(checkpoint_path, "cpu", precision)
if max_length is None:
max_length = model.config.block_size
device_sync(device=device) # MKG
print(f"Time to load model: {time.time() - t0:.02f} seconds")
tokenizer = get_tokenizer(tokenizer_path, checkpoint_path)
if quantization:
if "spinquant" in quantization:
from torchao.prototype.spinquant import apply_spinquant
apply_spinquant(model)
if "int8wo" in quantization:
quantize_(model, int8_weight_only())
if "int8dq" in quantization:
quantize_(model, int8_dynamic_activation_int8_weight())
if "fp6" in quantization:
quantize_(model, fpx_weight_only(3, 2))
if "int4wo" in quantization and not "gptq" in quantization:
if "hqq" in quantization:
use_hqq = True
else:
use_hqq = False
groupsize=int(quantization.split("-")[1])
assert groupsize in [32,64,128,256], f"int4wo groupsize needs to be one of [32,64,128,256] but got {groupsize}"
quantize_(model.to(device), int4_weight_only(group_size=groupsize, use_hqq=use_hqq))
if "uintx" in quantization:
# uintx-nbits-groupsize
# "uintx-2-64"
if "hqq" in quantization:
use_hqq = True
else:
use_hqq = False
_quant_args = quantization.split("-")
nbits = int(_quant_args[1])
_NBITS_TO_DTYPE = {1: torch.uint1, 2: torch.uint2, 3: torch.uint3, 4: torch.uint4, 5: torch.uint5, 6: torch.uint6, 7: torch.uint7, 8: torch.uint8}
dtype = _NBITS_TO_DTYPE[nbits]
group_size = int(_quant_args[2])
quantize_(model, uintx_weight_only(dtype, group_size, use_hqq=use_hqq))
if "marlin" in quantization:
from torchao.dtypes import MarlinSparseLayout
quantize_(model, int4_weight_only(layout=MarlinSparseLayout()))
if "int4wo" in quantization and "gptq" in quantization:
# avoid circular imports
from torchao._models._eval import MultiTensorInputRecorder
from torchao.quantization.GPTQ_MT import Int4WeightOnlyGPTQQuantizer
groupsize=int(quantization.split("-")[-2])
assert groupsize in [32,64,128,256], f"int4wo groupsize needs to be one of [32,64,128,256] but got {groupsize}"
assert precision==torch.bfloat16, f"{quantization} requires precision or bfloat16 but got {precision}"
assert "cuda" in device, "int4 gptq quantization only works on cuda"
inputs = MultiTensorInputRecorder(
tokenizer,
calibration_seq_length,
prepare_inputs_for_model,
pad_calibration_inputs,
model.config.vocab_size,
device="cpu"
).record_inputs(
calibration_tasks,
calibration_limit,
).get_inputs()
quantizer = Int4WeightOnlyGPTQQuantizer(group_size=groupsize, device=device)
model.setup_caches(max_batch_size=1, max_seq_length=calibration_seq_length)
model = quantizer.quantize(model, inputs).to(device)
else:
if not TORCH_VERSION_AT_LEAST_2_5:
unwrap_tensor_subclass(model)
if "float8wo" in quantization:
quantize_(model, float8_weight_only())
if "float8dq" in quantization:
granularity = str(quantization.split("-")[-1])
if granularity=="tensor":
granularity = PerTensor()
elif granularity=="row":
granularity = PerRow()
else:
if granularity=="float8dq":
granularity = PerTensor()
else:
raise ValueError(f"Unknown granularity {granularity}")
quantize_(model, float8_dynamic_activation_float8_weight(granularity=granularity))
if "autoround" in quantization:
from torchao.prototype.autoround.autoround_llm import quantize_model_with_autoround_
from transformers import AutoTokenizer
from torchao._models.llama.model import TransformerBlock
_tokenizer = AutoTokenizer.from_pretrained(checkpoint_path.parent)
# parse args from quantization string:
# autoround-<model_device>-<quant_lm_head>-<iters>-<groupsize>-<batch_size>-<seqlen>-<nsamples>-<grad_acc_steps>-<c>
_quant_args = quantization.split("-")
_default_quant_args = [False, 200, 128, 8, 2048, 128, 1, 0]
_model_devie = _quant_args[1] if len(_quant_args) > 1 else device
_quant_args = _quant_args[2:]
(
quant_lm_head,
iters,
groupsize,
batch_size,
seqlen,
nsamples,
grad_acc_steps,
compile_optimization_process,
) = [int(x) for x in _quant_args] + _default_quant_args[len(_quant_args) :]
model = model.to(_model_devie)
print(
(
f"Quantizing model with autoround(iters={iters}, groupsize={groupsize}, "
f"quant_lm_head={quant_lm_head}, batch_size={batch_size}, seqlen={seqlen}, nsamples={nsamples}, "
f"gradient_accumulate_steps={grad_acc_steps}, "
f"compile_optimization_process={compile_optimization_process})"
)
)
with torch.device(_model_devie):
model.setup_caches(
max_batch_size=batch_size, max_seq_length=seqlen, training=True
)
if quant_lm_head:
is_target_module = (
lambda mod, fqn: isinstance(mod, TransformerBlock)
or "output" in fqn
)
else:
is_target_module = lambda mod, fqn: isinstance(mod, TransformerBlock)
quantize_model_with_autoround_(
model=model,
tokenizer=_tokenizer,
is_target_module=is_target_module,
bits=4,
seqlen=seqlen,
batch_size=batch_size,
iters=iters,
nsamples=nsamples,
gradient_accumulate_steps=grad_acc_steps,
compile_optimization_process=compile_optimization_process == 1,
)
model.to(device)
model.reset_caches()
if "codebook" in quantization:
from torchao.prototype.quantization.codebook import codebook_weight_only
model.to(device)
quantize_(model, codebook_weight_only(dtype=torch.uint4, scale_block_size=64))
if compile:
model = torch.compile(model, mode="max-autotune", fullgraph=True)
with torch.no_grad():
print("Running evaluation ...")
# avoid circular imports
from torchao._models._eval import TransformerEvalWrapper
TransformerEvalWrapper(
model=model.to(device),
tokenizer=tokenizer,
max_seq_length=max_length,
input_prep_func=prepare_inputs_for_model,
device=device,
).run_eval(
tasks=tasks,
limit=limit,
)
if __name__ == '__main__':
import argparse
parser = argparse.ArgumentParser(description='Run HF Model Evaluation')
parser.add_argument('--checkpoint_path', type=Path, default=Path("../../../checkpoints/meta-llama/Llama-2-7b-chat-hf/model.pth"), help='Model checkpoint path.')
parser.add_argument('--tasks', nargs='+', type=str, default=["wikitext"], help='List of lm-eluther tasks to evaluate usage: --tasks task1 task2')
parser.add_argument('--limit', type=int, default=None, help='Number of eval samples to evaluate')
parser.add_argument('--precision', type=lambda x: getattr(torch, x.split(".")[-1]), default=torch.bfloat16, help='dtype precision to use')
parser.add_argument('--device', type=str, default="cuda", help='Device to use for evaluation')
parser.add_argument(
"-q",
"--quantization",
type=str,
help=(
"Which quantization techniques to apply: int8dq, int8wo, fp6, int4wo-<groupsize>, "
"int4wo-<groupsize>-gptq, autoquant, autoquant-int4, int4wo-<groupsize>-hqq, "
"uintx-<nbits>-<groupsize>, uintx-<nbits>-<groupsize>-hqq, sparse-marlin, spinquant, "
"autoround-<model_device>-<quant_lm_head>-<iters>-<groupsize>-<batch_size>-<seqlen>-<nsamples>-<grad_acc_steps>-<c>, "
"float8wo, float8dq, float8saq"
),
)
parser.add_argument('--compile', action='store_true', help='Whether to compile the model.')
parser.add_argument('--max_length', type=int, default=None, help='Length of text to process at one time')
parser.add_argument('--calibration_tasks', type=str, nargs='+', default=['wikitext'], help='tasks to do gptq calibration on, if doing gptq')
parser.add_argument('--calibration_limit', type=int, default=1000, help='number of samples to use for gptq calibration')
parser.add_argument('--calibration_seq_length', type=int, default=100, help='length of sequences to use for gptq calibration')
parser.add_argument('--pad_calibration_inputs', type=bool, default=False, help='pads sequences shorter than calibration_seq_length to that length, yielding more calibration inputs but running much slower')
args = parser.parse_args()
run_evaluation(
args.checkpoint_path,
args.tasks,
args.limit,
args.device,
args.precision,
args.quantization,
args.compile,
args.max_length,
args.calibration_tasks,
args.calibration_limit,
args.calibration_seq_length,
args.pad_calibration_inputs,
)