Skip to content

GitLab

"vscode:/vscode.git/clone" did not exist on "dbaecd0ca5e99da4e2cb4d86b0d6ef3fa2b1eba3"
Commit 80996d1d authored by Casper Hansen's avatar Casper Hansen
Browse files

Retire entry.py and create examples/eval.py

parent d3550fec
Show whitespace changes
Inline Side-by-side
Showing with 56 additions and 191 deletions
awq/entry.py deleted 100644 → 0
View file @ d3550fec
import os
import time
import torch
import argparse
from lm_eval import evaluator
from awq import AutoAWQForCausalLM
from awq.quantize.auto_clip import apply_clip
from awq.quantize.auto_scale import apply_scale
from awq.utils.lm_eval_adaptor import LMEvalAdaptor
from transformers import AutoTokenizer, GenerationConfig
def load_search_result_into_memory(model, search_path):
awq_results = torch.load(search_path, map_location="cpu")
apply_scale(model, awq_results["scale"])
apply_clip(model, awq_results["clip"])
def run_search(model_path, dump_path, quant_config):
"""
Step 1/2: Search the pile for an optimal scaling factor.
"""
# Load model
model = AutoAWQForCausalLM.from_pretrained(model_path)
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
# Quantize
model.quantize(tokenizer, quant_config=quant_config, run_search=True, run_quant=False)
# Save search results
model.save_quantized(dump_path)
# Save tokenizer
tokenizer.save_pretrained(dump_path)
def run_quant(model_path, search_path, dump_path, quant_config):
"""
Step 2/2: Use the search results to quantize model weights
"""
# Load model and search results
model = AutoAWQForCausalLM.from_pretrained(model_path)
load_search_result_into_memory(model.model, search_path)
# Run actual weight quantization
model.quantize(quant_config=quant_config, run_search=False, run_quant=True)
# Save quantized model
model.save_quantized(dump_path)
def run_eval(model_path, quant_file, device, tasks, task_batch_size, task_n_shot, task_use_pretrained):
"""
Post quantization: Evaluate perplexity on wikitext with EleutherAI Evaluation Harness
"""
# Load model
if task_use_pretrained:
model = AutoAWQForCausalLM.from_pretrained(model_path)
else:
model = AutoAWQForCausalLM.from_quantized(model_path, quant_file)
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
# Load adapter
lm_eval_model = LMEvalAdaptor(model_path, model, tokenizer, device, batch_size=task_batch_size)
# Evaluate perplexity of quantized model
results = evaluator.simple_evaluate(
model=lm_eval_model,
tasks=tasks.split(','),
batch_size=task_batch_size,
no_cache=True,
num_fewshot=task_n_shot,
)
print(evaluator.make_table(results))
@torch.inference_mode()
def run_speed(model_path, quant_file, device, n_generate=128, n_context=256, batch_size=1, disable_fused_layers=False):
def _timer(func):
start = time.time()
out = func()
return out, time.time() - start
def _warmup(device:str):
warm_up = torch.randn((4096,4096)).to(device)
torch.mm(warm_up,warm_up)
if quant_file:
fuse_layers = False if disable_fused_layers else True
model, load_time = _timer(lambda: AutoAWQForCausalLM.from_quantized(model_path, quant_file, fuse_layers=fuse_layers))
else:
model, load_time = _timer(lambda: AutoAWQForCausalLM.from_pretrained(model_path))
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
_warmup(device)
# Generate random inputs
n_context = n_context - n_generate
ids = torch.randint(0, tokenizer.vocab_size, (batch_size, n_context)).cuda()
# Context stage
_, context_time = _timer(lambda: model.generate(
ids,
generation_config=GenerationConfig(
max_new_tokens=0,
min_new_tokens=0,
use_cache=True
)
))
# Generation stage
_, generation_time = _timer(lambda: model.generate(
ids,
generation_config=GenerationConfig(
max_new_tokens=n_context,
min_new_tokens=n_context,
forced_eos_token_id=-100,
pad_token_id=tokenizer.pad_token_id,
eos_token_id=-100,
use_cache=True
)
))
# Prints
memory_used = torch.cuda.max_memory_allocated(device) / (1024 ** 2)
context_tokens_per_second = n_context / context_time * batch_size
context_ms_per_token = (context_time*1000) / n_context / batch_size
inference_tokens_per_second = n_generate / generation_time * batch_size
inference_ms_per_token = (generation_time*1000) / n_generate / batch_size
print(f"[=] Model summary: {model_path} [=]")
print(f"[*] Load time: {load_time:.2f} seconds")
print(f"[*] Context speed: {context_tokens_per_second:.2f} tokens/second ({context_ms_per_token:.2f} ms/token)")
print(f"[*] Generation speed: {inference_tokens_per_second:.2f} tokens/second ({inference_ms_per_token:.2f} ms/token)")
print(f"[*] VRAM: {memory_used:.2f} MB")
if __name__ == '__main__':
"""
- Run AWQ search and save result:
python -m awq.entry --entry_type search --model_path lmsys/vicuna-7b-v1.5 --search_path vicuna-7b-v1.5-awq
- Run AWQ to save the real quantized weights at the quant_path:
python -m awq.entry --entry_type quant --model_path lmsys/vicuna-7b-v1.5 --search_path vicuna-7b-v1.5-awq/awq_model_search_result.pt --quant_path vicuna-7b-v1.5-awq
- Run perplexity of quantized model:
python -m awq.entry --entry_type eval --model_path vicuna-7b-v1.5-awq --quant_file awq_model_w4_g128.pt
- Run perplexity unquantized FP16 model:
python -m awq.entry --entry_type eval --model_path lmsys/vicuna-7b-v1.5 --task_use_pretrained
- Run a speedtest to benchmark the quantized model:
python -m awq.entry --entry_type speed --model_path vicuna-7b-v1.5-awq --quant_file awq_model_w4_g128.pt --n_generate 128 --n_context 256
- Run a speedtest to benchmark the unquantized FP16 model:
python -m awq.entry --entry_type speed --model_path lmsys/vicuna-7b-v1.5 --n_generate 128 --n_context 256
"""
parser = argparse.ArgumentParser()
parser.add_argument('--entry_type', type=str, help='The type of task to run (search|quant|eval|speed)')
parser.add_argument('--model_path', type=str, help='Path to hf model')
parser.add_argument('--search_path', type=str, help='Path to save/load AWQ search results')
parser.add_argument('--quant_path', type=str, help='Path to save AWQ model to directory')
parser.add_argument('--quant_file', type=str, help='Path to quantized AWQ model file')
parser.add_argument('--device', type=str, default='cuda:0', help='Device to load model to')
parser.add_argument('--w_bit', type=int, default=4)
parser.add_argument('--q_group_size', type=int, default=128)
parser.add_argument('--tasks', type=str, default='wikitext', help='Tasks to evaluate. '
'Separate tasks by comma for multiple tasks.'
'https://github.com/EleutherAI/lm-evaluation-harness/blob/master/docs/task_table.md')
parser.add_argument("--task_use_pretrained", default=False, action='store_true',
help="Pass '--task_use_pretrained' to use a pretrained model running FP16")
parser.add_argument('--task_batch_size', type=int, default=1)
parser.add_argument('--task_n_shot', type=int, default=0)
parser.add_argument('--n_generate', type=int, default=128)
parser.add_argument('--n_context', type=int, default=256)
parser.add_argument('--batch_size', type=int, default=1)
parser.add_argument("--disable_fused_layers", default=False, action='store_true',
help="Pass '--disable_fused_layers' to disable fused layers")
args = parser.parse_args()
quant_config = { "zero_point": True, "q_group_size": args.q_group_size, "w_bit": args.w_bit }
if args.entry_type == 'search':
run_search(args.model_path, args.search_path, quant_config)
elif args.entry_type == 'quant':
run_quant(args.model_path, args.search_path, args.quant_path, quant_config)
elif args.entry_type == 'eval':
run_eval(args.model_path, args.quant_file, args.device,
args.tasks, args.task_batch_size, args.task_n_shot, args.task_use_pretrained)
elif args.entry_type == 'speed':
run_speed(args.model_path, args.quant_file, args.device, args.n_generate, args.n_context, args.batch_size, args.disable_fused_layers)
else:
raise Exception('--entry_type must be one of (search|quant|eval|speed)')
examples/eval.py 0 → 100644
View file @ 80996d1d
import argparse
from lm_eval import evaluator
from awq import AutoAWQForCausalLM
from transformers import AutoTokenizer
from awq.utils.lm_eval_adaptor import LMEvalAdaptor
def run_eval(model_path, quant_file, device, tasks, task_batch_size, task_n_shot, task_use_pretrained):
"""
Post quantization: Evaluate perplexity on wikitext with EleutherAI Evaluation Harness
"""
# Load model
if task_use_pretrained:
model = AutoAWQForCausalLM.from_pretrained(model_path)
else:
model = AutoAWQForCausalLM.from_quantized(model_path, quant_file)
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
# Load adapter
lm_eval_model = LMEvalAdaptor(model_path, model, tokenizer, device, batch_size=task_batch_size)
# Evaluate perplexity of quantized model
results = evaluator.simple_evaluate(
model=lm_eval_model,
tasks=tasks.split(','),
batch_size=task_batch_size,
no_cache=True,
num_fewshot=task_n_shot,
)
print(evaluator.make_table(results))
if __name__ == '__main__':
"""
- Run perplexity of quantized model:
python examples/eval.py --model_path vicuna-7b-v1.5-awq --quant_file awq_model_w4_g128.pt
- Run perplexity unquantized FP16 model:
python examples/eval.py --use_pretrained --model_path lmsys/vicuna-7b-v1.5
"""
parser = argparse.ArgumentParser()
parser.add_argument('--model_path', type=str, help='Path to hf model')
parser.add_argument('--quant_file', default='', type=str, help='Path to quantized AWQ model file')
parser.add_argument('--device', type=str, default='cuda:0', help='Device to load model to')
parser.add_argument("--use_pretrained", default=False, action='store_true',
help="Pass '--use_pretrained' to use a pretrained model running FP16")
parser.add_argument('--tasks', type=str, default='wikitext', help='Tasks to evaluate. '
'Separate tasks by comma for multiple tasks.'
'https://github.com/EleutherAI/lm-evaluation-harness/blob/master/docs/task_table.md')
parser.add_argument('--batch_size', type=int, default=1)
parser.add_argument('--n_shot', type=int, default=0)
args = parser.parse_args()
run_eval(args.model_path, args.quant_file, args.device,
args.tasks, args.batch_size, args.n_shot, args.use_pretrained)
\ No newline at end of file
Markdown is supported
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment