Description
I am trying to quantize a model and I am running this on a 4090. Since many of the available quantization benchmarks are done on higher gpus, I am trying to establish a baseline perfromance gain I can expect from quantization.
I tried the tutorial at torchao_demo on a gpu and it worked great. My model has similar kind of transformer layers with q, k, v projections but I am not able to see the same kind of performance with a large chunk of aten::_copy() operations in profile log.
To debug, I wanted to benchmark on a single linear layer as the majority of modified layers seem to be of this type. But I am not able to see any performance gain in this experiment of mine. I would appreciate if I can get more context into the specific layers that gets optimized by torchao.
'''
https://github.com/ethanshenley/PyTorch-Conference-Recipes/blob/main/torchao_demo.ipynb
'''
import gc
import psutil
import torch
import torch.nn as nn
import time
from torchao.quantization import quantize_, int8_weight_only,float8_weight_only
device = "cuda:0"
def get_memory_usage():
return psutil.Process().memory_info().rss / 1024 / 1024 # in MB
def run_inference(model, inputs, num_runs=10):
start_time = time.time()
for i in range(num_runs):
with torch.no_grad():
outputs = model(inputs[i].squeeze())
torch.cuda.synchronize(device)
end_time = time.time()
return (end_time - start_time) / num_runs
# Load model and tokenizer
bsz = 16
n_runs = 100
for sz in range(1024, 20480, 1024):
print('====================================================')
print(f"Running with linear layer of size {sz}...")
model = nn.Linear(sz, sz).to(device)
inputs = torch.randn(n_runs, bsz, sz).to(device)
print("\nRunning baseline model...")
baseline_memory = get_memory_usage()
baseline_time = run_inference(model, inputs, n_runs)
print(f"Baseline - Time: {baseline_time:.4f}s, Memory: {baseline_memory:.2f}MB")
print("\nRunning int8 weight-only quantized model...")
model_int8 = nn.Linear(sz, sz).to(device)
quantize_(model_int8, int8_weight_only())
int8_memory = get_memory_usage()
int8_time = run_inference(model_int8, inputs, n_runs)
print(f"Int8 Weight-Only - Time: {int8_time:.4f}s, Memory: {int8_memory:.2f}MB")
print("\nRunning fp8 weight-only quantized model...")
model_fp8 = nn.Linear(sz, sz).to(device)
quantize_(model_fp8, float8_weight_only())
fp8_memory = get_memory_usage()
fp8_time = run_inference(model, inputs, n_runs)
print(f"fp8 Weight-Only - Time: {fp8_time:.4f}s, Memory: {fp8_memory:.2f}MB")
print("\nPerformance Improvements:")
print(f"Int8 weight-only speedup: {baseline_time / int8_time:.2f}x")
print(f"Int8 weight-only memory reduction: {baseline_memory / int8_memory:.2f}x")
print(f"fp8 weight-only speedup: {baseline_time / fp8_time:.2f}x")
print(f"fp8 weight-only memory reduction: {baseline_memory / fp8_memory:.2f}x")
del model, model_int8, model_fp8, inputs
gc.collect()
torch.cuda.empty_cache()
torch.cuda.synchronize(device)
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