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Unify llama generate utils #3318

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55 changes: 17 additions & 38 deletions torchao/_models/llama/generate.py
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Original file line number Diff line number Diff line change
Expand Up @@ -40,35 +40,21 @@ def elapsed_time(self, other_event):
return abs(other_event.event_time - self.event_time) * 1000


def device_timer(device):
if "cuda" in device:
return torch.cuda.Event(enable_timing=True)
elif "xpu" in device:
return torch.xpu.Event(enable_timing=True)
def device_timer(device: str):
if device in ["cuda", "xpu"]:
return torch.Event(enable_timing=True)
elif ("cpu" in device) or ("mps" in device):
return HostEvent()
else:
print(f"device={device} is not yet suppported")


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")
def device_sync(device: str):
if torch.accelerator.is_available():
torch.accelerator.synchronize(device)


default_device = (
"cuda"
if torch.cuda.is_available()
else "xpu"
if torch.xpu.is_available()
else "cpu"
)
default_device = acc.type if (acc := torch.accelerator.current_accelerator(check_available=True)) else "cpu"

# support running without installing as a package
wd = Path(__file__).parent.parent.resolve()
Expand Down Expand Up @@ -160,10 +146,10 @@ def generate(
kv_cache_quantization: bool = False,
cache_size: Optional[int] = None,
linear_causal_mask: bool = False,
prefill_start_event: Optional[torch.cuda.Event] = None,
prefill_end_event: Optional[torch.cuda.Event] = None,
decode_start_event: Optional[torch.cuda.Event] = None,
decode_end_event: Optional[torch.cuda.Event] = None,
prefill_start_event: Optional[torch.Event] = None,
prefill_end_event: Optional[torch.Event] = None,
decode_start_event: Optional[torch.Event] = None,
decode_end_event: Optional[torch.Event] = None,
**sampling_kwargs,
) -> torch.Tensor:
"""
Expand Down Expand Up @@ -281,8 +267,8 @@ def main(
compile_prefill: bool = False,
profile: Optional[Path] = None,
memory_profile: Optional[Path] = None,
device=default_device,
precision=torch.bfloat16,
device: str = default_device,
precision = torch.bfloat16,
write_result: Optional[Path] = None,
output_json_path: Optional[Path] = None,
output_json_local: bool = False,
Expand Down Expand Up @@ -606,7 +592,7 @@ def ffn_or_attn_only(mod, fqn):
prepare_inputs_for_model,
False, # pad_calibration_inputs
model.config.vocab_size,
device="cuda",
device=device,
)
.record_inputs(
["wikitext"],
Expand All @@ -616,7 +602,7 @@ def ffn_or_attn_only(mod, fqn):
.values[0]
)
inputs = prepare_inputs_for_model(inputs)
with torch.device("cuda"):
with torch.device(device):
model.setup_caches(
max_batch_size=1, max_seq_length=calibration_seq_length
)
Expand Down Expand Up @@ -883,10 +869,7 @@ def ffn_or_attn_only(mod, fqn):

for i in range(start, num_samples):
if i == 0:
if device == "cuda":
torch.cuda.reset_peak_memory_stats() # MKG
elif device == "xpu":
torch.xpu.reset_peak_memory_stats() # MKG
torch.accelerator.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 @@ -1016,14 +999,10 @@ def callback(x):
torch.tensor(aggregate_metrics["decode_tokens_per_sec"])
).item()
bandwidth = model_size * tokpersec
mem = torch.cuda.max_memory_reserved() / 1e9
print(f"Average overall tokens/sec: {tokpersec:.2f}")
print(f"Average decode tokens/sec: {decode_tokpersec:.04f} s")
print(f"Average TTFT: {ttft:.04f} s")
if device == "cuda":
mem = torch.cuda.max_memory_reserved() / 1e9
elif device == "xpu":
mem = torch.xpu.max_memory_reserved() / 1e9
mem = torch.accelerator.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