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# Please first install awq/kernels
# then directly run CUDA_VISIBLE_DEVICES=0 python benchmark.py
import argparse
import torch
import time
import numpy as np
from transformers import AutoTokenizer, AutoModelForCausalLM, AutoConfig, modeling_utils
import tinychat.utils.constants
from tinychat.utils.load_quant import load_awq_model
from awq.quantize.quantizer import real_quantize_model_weight
from tinychat.utils.tune import (
tune_all_wqlinears,
device_warmup,
tune_llava_patch_embedding,
)
from tinychat.modules import make_quant_norm, make_quant_attn, make_fused_mlp
def skip(*args, **kwargs):
pass
def main():
parser = argparse.ArgumentParser()
parser.add_argument(
"--model_type", type=str, default="LLaMa", help="type of the model"
)
parser.add_argument(
"--model_path",
type=str,
default="/data/llm/checkpoints/vicuna-hf/vicuna-7b",
help="path to the model",
)
parser.add_argument("--q_group_size", type=int, default=128)
parser.add_argument(
"--verbose",
default=False,
action="store_true",
help="Wheter to print more information.",
)
parser.add_argument(
"--max_seq_len",
type=int,
default=8192,
help="maximum sequence length for kv cache",
)
parser.add_argument(
"--max_batch_size", type=int, default=1, help="maximum batch size for kv cache"
)
parser.add_argument(
"--flash_attn",
action="store_true",
help="whether to use flash attention",
)
parser.add_argument(
"--chunk_prefilling",
action="store_true",
help="If used, in context stage, the history tokens will not be recalculated, greatly speeding up the calculation",
)
parser.add_argument(
"--context_length",
type=list,
nargs="+",
help="The length of input. And if chunk_prefilling used, this serves as the length of tokens from history rounds.",
)
parser.add_argument(
"--question_length",
type=list,
nargs="+",
help="The length of new input. Only useful and necessary when benchmarking chunk_prefilling method",
)
parser.add_argument(
"--precision", type=str, default="W4A16", help="compute precision"
)
args = parser.parse_args()
# some checks
assert (args.question_length is not None and args.chunk_prefilling) or (
not args.chunk_prefilling
), "If you want to benchmark chunk prefilling, you need specify the question length and context length"
assert args.precision in ["W4A16", "W16A16"], "We only support W4A16/W16A16 now"
token_num = 256
# We support fixing a certain kind of length
if args.chunk_prefilling:
if len(args.context_length) == 1 and len(args.question_length) > 1:
args.context_length = [
args.context_length[0] for _ in range(len(args.question_length))
]
elif len(args.question_length) == 1 and len(args.context_length) > 1:
args.question_length = [
args.question_length[0] for _ in range(len(args.context_length))
]
elif len(args.question_length) != len(args.context_length):
raise ValueError(
"The number of items in the question_length and context_length is expected to be either one or equal!"
)
tinychat.utils.constants.max_batch_size = args.max_batch_size
tinychat.utils.constants.max_seq_len = args.max_seq_len
from tinychat.models import FalconForCausalLM, LlamaForCausalLM, MPTForCausalLM
from tinychat.models.vila_llama import VilaLlamaForCausalLM
modeling_utils._init_weights = False
torch.nn.init.kaiming_uniform_ = skip
torch.nn.init.kaiming_normal_ = skip
torch.nn.init.uniform_ = skip
torch.nn.init.normal_ = skip
device = "cuda:0"
model_type_dict = {
"llama": LlamaForCausalLM,
"falcon": FalconForCausalLM,
"mpt": MPTForCausalLM,
}
config = AutoConfig.from_pretrained(args.model_path, trust_remote_code=True)
assert args.model_type.lower() in [
"llama",
"falcon",
"mpt",
"vila",
], "We only support llama & falcon & mpt & vila now"
if "vila" in args.model_type.lower():
model = VilaLlamaForCausalLM(config).half()
print(model)
if args.precision in ["W4A16"]:
real_quantize_model_weight(
model.llm,
w_bit=4,
q_config=dict(q_group_size=args.q_group_size, zero_point=True),
init_only=True,
)
make_quant_attn(model.llm, device, args.flash_attn)
make_quant_norm(model.llm)
make_fused_mlp(model.llm)
model = model.to(device)
device_warmup(device)
tune_llava_patch_embedding(model.get_vision_tower(), device=device)
if not args.chunk_prefilling:
image_num = [
int(int("".join(i)) * 1 / 196) for i in args.context_length
] # consider about three thirds of the history tokens are images
if sum(image_num) > 0:
image_tensor = 2 * torch.rand((max(image_num), 3, 384, 384)) - 1
image_tensor = image_tensor.half().to(device)
else:
image_tensor = None
print("huggingface ckpt loaded")
# warming up
input_ids = [1 for _ in range(2048)]
inputs = torch.as_tensor([input_ids], device=device)
out = model(
inputs, start_pos=0, chunk_prefilling=args.chunk_prefilling
) # warmup
if not args.chunk_prefilling:
for i, context_length in enumerate(args.context_length):
context_length = int("".join(context_length))
time_lis = []
if image_num[i]:
images = image_tensor[0 : image_num[i], :, :, :]
input_ids = [-200 for _ in range(image_num[i])] + [
1 for _ in range(context_length - 196 * image_num[i])
]
else:
images = None
input_ids = [1 for _ in range(context_length)]
print("-" * 80)
print(
"Context length: {} with {} pictures".format(
context_length, image_num[i]
)
)
with torch.inference_mode():
for i in range(10): # Run ten times and get the average value
start_pos = 0
torch.cuda.synchronize()
t_st = time.time()
inputs = torch.as_tensor([input_ids], device=device)
out = model(
inputs,
start_pos=start_pos,
chunk_prefilling=args.chunk_prefilling,
images=images,
)
start_pos += inputs.shape[1]
torch.cuda.synchronize()
t_ed = time.time()
token = out[:, -1].max(1)[1].unsqueeze(1)
time_lis.append(t_ed - t_st)
if args.verbose:
print(i, t_ed - t_st)
print(f"Time To First Token: {np.mean(time_lis):.5f} s.")
print("-" * 80)
else:
for i, (context_length, question_length) in enumerate(
zip(args.context_length, args.question_length)
):
context_length = int("".join(context_length))
question_length = int("".join(question_length))
input_ids_old = [1 for _ in range(context_length)]
images = None
input_ids_new = [1 for _ in range(question_length)]
time_lis = []
print("-" * 80)
print(
"History length: {} ; Question length: {}".format(
context_length, question_length
)
)
with torch.inference_mode():
for i in range(10): # Run ten times and get the average value
# history rounds
start_pos = 0
if context_length > question_length:
inputs = torch.as_tensor([input_ids_old], device=device)
out = model(
inputs,
start_pos=start_pos,
chunk_prefilling=args.chunk_prefilling,
images=None,
)
start_pos += context_length
# the present round
torch.cuda.synchronize()
t_st = time.time()
inputs = torch.as_tensor([input_ids_new], device=device)
out = model(
inputs,
start_pos=start_pos,
chunk_prefilling=args.chunk_prefilling,
)
start_pos += inputs.shape[1]
torch.cuda.synchronize()
t_ed = time.time()
token = out[:, -1].max(1)[1].unsqueeze(1)
time_lis.append(t_ed - t_st)
if args.verbose:
print(i, t_ed - t_st)
print(
f"Time To First Token of this round: {np.mean(time_lis):.5f} s."
)
print("-" * 80)
else:
model = model_type_dict[args.model_type.lower()](config).half()
if args.precision in ["W4A16"]:
real_quantize_model_weight(
model,
w_bit=4,
q_config=dict(q_group_size=args.q_group_size, zero_point=True),
init_only=True,
)
model = model.to(device)
if args.precision in ["W4A16"]:
# tune_all_wqlinears(model)
make_quant_attn(model, device, args.flash_attn)
make_quant_norm(model)
make_fused_mlp(model)
device_warmup(device)
print("huggingface ckpt loaded")
# warming up
input_ids = [1 for _ in range(2048)]
inputs = torch.as_tensor([input_ids], device=device)
out = model(
inputs,
start_pos=0,
chunk_prefilling=args.chunk_prefilling,
quant=args.precision in ["W4A16"],
) # warmup
if not args.chunk_prefilling:
for context_length in args.context_length:
context_length = int("".join(context_length))
input_ids = [1 for _ in range(context_length)]
time_lis = []
print("-" * 80)
print("Context length: {}".format(context_length))
with torch.inference_mode():
for i in range(10): # Run ten times and get the average value
start_pos = 0
torch.cuda.synchronize()
t_st = time.time()
inputs = torch.as_tensor([input_ids], device=device)
out = model(
inputs,
start_pos=start_pos,
chunk_prefilling=args.chunk_prefilling,
quant=args.precision in ["W4A16"],
)
start_pos += inputs.shape[1]
torch.cuda.synchronize()
t_ed = time.time()
token = torch.argmax(out, keepdim=True)[0]
time_lis.append(t_ed - t_st)
if args.verbose:
print(i, t_ed - t_st)
print(f"Time To First Token: {np.mean(time_lis):.5f} s.")
# decoing throughput
time_lis = []
start_pos = context_length
torch.cuda.synchronize()
t_st = time.time()
for i in range(token_num):
token = model(
token,
start_pos=start_pos,
chunk_prefilling=args.chunk_prefilling,
quant=args.precision in ["W4A16"],
)
start_pos += 1
token = torch.argmax(token, keepdim=True)[0]
torch.cuda.synchronize()
t_ed = time.time()
time_lis.append(t_ed - t_st)
print(
f"Decoding throughput: {token_num/sum(time_lis):.5f} token/s."
)
print("-" * 80)
else:
for context_length, question_length in zip(
args.context_length, args.question_length
):
context_length = int("".join(context_length))
question_length = int("".join(question_length))
input_ids_old = [1 for _ in range(context_length)]
input_ids_new = [1 for _ in range(question_length)]
time_lis = []
print("-" * 80)
print(
"History length: {} ; Question length: {}".format(
context_length, question_length
)
)
with torch.inference_mode():
for i in range(10): # Run ten times and get the average value
# history rounds
start_pos = 0
if context_length > question_length:
inputs = torch.as_tensor([input_ids_old], device=device)
out = model(
inputs,
start_pos=start_pos,
chunk_prefilling=args.chunk_prefilling,
quant=args.precision in ["W4A16"],
)
start_pos += inputs.shape[1]
# the present round
torch.cuda.synchronize()
t_st = time.time()
inputs = torch.as_tensor([input_ids_new], device=device)
out = model(
inputs,
start_pos=start_pos,
chunk_prefilling=args.chunk_prefilling,
quant=args.precision in ["W4A16"],
)
start_pos += inputs.shape[1]
torch.cuda.synchronize()
t_ed = time.time()
token = out[:, -1].max(1)[1].unsqueeze(1)
time_lis.append(t_ed - t_st)
if args.verbose:
print(i, t_ed - t_st)
print(
f"Time To First Token of this round: {np.mean(time_lis):.5f} s."
)
print("-" * 80)
if __name__ == "__main__":
main()
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