Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

Benchmark intel xpu #1259

Open
wants to merge 4 commits into
base: main
Choose a base branch
from
+19 −5
Open

Benchmark intel xpu #1259

Changes from all commits
Commits
Show all changes
4 commits
Select commit Hold shift + click to select a range
7407ce9
support xpu
sunjiweiswift Nov 1, 2024
c683c52
Merge branch 'pytorch:main' into benchmark_intel_xpu
sunjiweiswift Nov 7, 2024
d769d90
support xpu
sunjiweiswift Nov 11, 2024
657ff7e
fix intel gpu peak mem
sunjiweiswift Nov 14, 2024
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
24 changes: 19 additions & 5 deletions torchao/_models/llama/generate.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -19,12 +19,14 @@
def device_sync(device):
if "cuda" in device:
torch.cuda.synchronize(device)
elif "xpu" in device:
torch.xpu.synchronize(device)
elif ("cpu" in device) or ("mps" in device):
pass
else:
print(f"device={device} is not yet suppported")

default_device = 'cuda' if torch.cuda.is_available() else 'cpu'
default_device = 'cuda' if torch.cuda.is_available() else 'xpu' if torch.xpu.is_available() else 'cpu'

# support running without installing as a package
wd = Path(__file__).parent.parent.resolve()
Expand Down Expand Up @@ -333,15 +335,21 @@ def main(
prefill = torch.compile(prefill, fullgraph=True, dynamic=True)

if memory_profile:
torch.cuda.memory._record_memory_history(True,trace_alloc_max_entries=250000, trace_alloc_record_context=True)
if device == "cuda":
torch.cuda.memory._record_memory_history(True,trace_alloc_max_entries=250000, trace_alloc_record_context=True)
elif device == "xpu":
torch.xpu.memory._record_memory_history(True,trace_alloc_max_entries=250000, trace_alloc_record_context=True)
aggregate_metrics = {
'tokens_per_sec': [],
}
start = -1 if compile else 0

for i in range(start, num_samples):
if i==0:
torch.cuda.reset_peak_memory_stats()
if device == "cuda":
torch.cuda.reset_peak_memory_stats()
elif device == "xpu":
torch.xpu.reset_peak_memory_stats()
device_sync(device=device) # MKG
if i >= 0 and interactive:
prompt = input("What is your prompt? ")
Expand Down Expand Up @@ -409,7 +417,10 @@ def callback(x):
print(f"Bandwidth achieved: {model_size * tokens_sec:.02f} GB/s")

if memory_profile and i==0:
snapshot = torch.cuda.memory._snapshot()
if device == "cuda":
snapshot = torch.cuda.memory._snapshot()
elif device == "xpu":
snapshot = torch.xpu.memory._snapshot()
with open(f"{memory_profile}.pickle", 'wb') as f:
from pickle import dump
dump(snapshot, f)
Expand All @@ -423,7 +434,10 @@ def callback(x):

tokpersec = torch.mean(torch.tensor(aggregate_metrics['tokens_per_sec'])).item()
bandwidth = model_size * tokpersec
mem = torch.cuda.max_memory_reserved() /1e9
if device == "cuda":
mem = torch.cuda.max_memory_reserved() /1e9
elif device == "xpu":
mem = torch.xpu.max_memory_reserved() /1e9
print(f"Average tokens/sec: {tokpersec:.2f}")
if batch_size > 1:
print(f"Average tokens/sec including batches {batch_size*tokpersec:.2f}")
Expand Down
Loading