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merged 5 commits into from
Nov 29, 2024
Merged

Benchmark intel xpu #1259

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bda74e9
support xpu
sunjiweiswift Nov 1, 2024
4f8173f
fix intel gpu peak mem
sunjiweiswift Nov 14, 2024
ca3614d
update benchmark for llama7b xpu
sunjiweiswift Nov 18, 2024
3791772
gupdate llama8b3.1 for intel GPU
sunjiweiswift Nov 18, 2024
c8f78eb
update readme
sunjiweiswift Nov 26, 2024
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46 changes: 29 additions & 17 deletions torchao/_models/llama/generate.py
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Original file line number Diff line number Diff line change
Expand Up @@ -20,12 +20,14 @@
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 @@ -415,10 +417,13 @@ def main(
prefill = torch.compile(prefill, fullgraph=True, dynamic=True)

if memory_profile:
if device != "cuda":
print("Memory profiling only works on CUDA")
else:
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)
else:
print("Memory profiling only works on CUDA or XPU devices")

aggregate_metrics = {
'tokens_per_sec': [],
}
Expand All @@ -428,6 +433,8 @@ def main(
if i==0:
if device == "cuda":
torch.cuda.reset_peak_memory_stats() # MKG
elif device == "xpu":
torch.xpu.reset_peak_memory_stats() # MKG
device_sync(device=device) # MKG
if i >= 0 and interactive:
prompt = input("What is your prompt? ")
Expand Down Expand Up @@ -495,24 +502,29 @@ def callback(x):
print(f"Bandwidth achieved: {model_size * tokens_sec:.02f} GB/s")

if memory_profile and i==0:
if device != "cuda":
print("Memory profiling only works on CUDA")
else:
if device == "cuda":
snapshot = torch.cuda.memory._snapshot()
with open(f"{memory_profile}.pickle", 'wb') as f:
from pickle import dump
dump(snapshot, f)
print(
f"\nmemory profile {memory_profile}.pickle saved, to convert that to a usable file, use",
"python pytorch/torch/cuda/_memory_viz.py trace_plot <pickle file> -o <desired output name>.html"
)
break

elif device == "xpu":
snapshot = torch.xpu.memory._snapshot()
else:
print("Memory profiling only works on CUDA or XPU devices")

with open(f"{memory_profile}.pickle", 'wb') as f:
from pickle import dump
dump(snapshot, f)
print(
f"\nmemory profile {memory_profile}.pickle saved, to convert that to a usable file, use",
"python pytorch/torch/cuda/_memory_viz.py trace_plot <pickle file> -o <desired output name>.html"
)
break
print("==========")

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
20 changes: 18 additions & 2 deletions torchao/quantization/README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -3,7 +3,7 @@ Typically quantization algorithms will have different schemes for how the activa

## Benchmarks
Benchmarks and evaluation are run on a machine with a single NVIDIA-A100-80GB GPU using the scripts for [generation](../_models/llama/generate.py) and [eval](../_models/llama/eval.py). Evaluation was done using the lm_eval library for tasks/data. The models used were meta-llama/Llama-2-7b-chat-hf and meta-llama/Meta-Llama-3-8B.

### CUDA backend
| Model | Technique | wikitext-perplexity | Tokens/Second | Memory Bandwidth (GB/s) | Peak Memory (GB) | Model Size (GB) |
| ----------- | ----------------------- | ------------------- | ------------- | ----------------------- | ---------------- | --------------- |
| Llama-2-7B | Base (bfloat16) | 12.212 | 107.38 | 1418.93 | 13.88 | 13.21 |
Expand All @@ -20,9 +20,16 @@ Benchmarks and evaluation are run on a machine with a single NVIDIA-A100-80GB GP
| | int4wo-64 | 8.316 | 180.80 | 763.33 | 6.88 | 4.22 |
| | int4wo-64-GPTQ | 7.921 | 180.80 | 763.33 | 6.88 | 4.22 |
| | autoquant-int4hqq | 8.110 | 188.41 | 800.58 | 7.14 | 4.25 |
### XPU backend
| Model | Technique | wikitext-perplexity | Tokens/Second | Memory Bandwidth (GB/s) | Peak Memory (GB) | Model Size (GB) |
| ----------- | ----------------------- | ------------------- | ------------- | ----------------------- | ---------------- | --------------- |
| Llama-2-7B | Base (bfloat16) | NA | 42.20 | 557.71 | 13.89 | 13.21 |
| | int8dq | NA | 9.87 | 65.35 | 14.60 | 6.62 |
| | int8wo | NA | 66.24 | 438.61 | 14.60 | 6.62


Benchmarks and evaluation for model meta-llama/Meta-Llama-3.1-8B are run on a machine with a single NVIDIA-H100 GPU using the scripts for [generation](../_models/llama/generate.py) and [eval](../_models/llama/eval.py). Evaluation was done using the lm_eval library for tasks/data.

### CUDA backend
| Model | Technique | wikitext-perplexity | Tokens/Second | Memory Bandwidth (GB/s) | Peak Memory (GB) | Model Size (GB) |
| ----------- | ----------------------- | ------------------- | ------------- | ----------------------- | ---------------- | --------------- |
| Llama-3.1-8B | Base (bfloat16) | 7.54 | 126.90 | 1904.75 | 16.75 | 15.01 |
Expand All @@ -31,6 +38,15 @@ Benchmarks and evaluation for model meta-llama/Meta-Llama-3.1-8B are run on a ma
| | float8wo | 7.60 | 178.46 | 1339.93 | 12.09 | 7.51 |
| | float8dq (PerTensor) | 7.62 | 116.40 | 873.58 | 11.14 | 7.51 |
| | float8dq (Per Row) | 7.61 | 154.63 | 1161.47 | 11.14 | 7.51 |
### XPU backend
| Model | Technique | wikitext-perplexity | Tokens/Second | Memory Bandwidth (GB/s) | Peak Memory (GB) | Model Size (GB) |
| ----------- | ----------------------- | ------------------- | ------------- | ----------------------- | ---------------- | --------------- |
| Llama-3-8.1B | Base (bfloat16) | 7.441 | 40.36 | 605.77 | 16.35 | 15.01 |
| | int8dq | 7.581 | 13.60 | 102.28 | 18.69 | 7.52 |
| | int8wo | 7.447 | 59.49 | 447.27 | 18.60 | 7.52


Benchmarks and evaluation for model meta-llama/Meta-Llama-3.1-8B are run on a machine with a single NVIDIA-H100 GPU or Intel-Max1100 using the scripts for [generation](../_models/llama/generate.py) and [eval](../_models/llama/eval.py). Evaluation was done using the lm_eval library for tasks/data.

note: Int8 dynamic quantization works best on compute bound models like [SAM](https://github.com/pytorch-labs/segment-anything-fast) whereas Llama with batchsize=1 tends to be memory bound, thus the rather low performance.

Expand Down
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