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[userbenchmark] Update Torch-TRT latency report #2006

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[userbenchmark] Update Torch-TRT latency report #2006

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21 changes: 6 additions & 15 deletions userbenchmark/torch_trt/run.py
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Original file line number Diff line number Diff line change
Expand Up @@ -141,24 +141,17 @@ def run_one_step(
# Run inference for the specified number of iterations
for _ in range(num_iter):
torch.cuda.synchronize()
start_event = torch.cuda.Event(enable_timing=True)
end_event = torch.cuda.Event(enable_timing=True)

# Collect time_ns() instead of time() which does not provide better precision than 1
# second according to https://docs.python.org/3/library/time.html#time.time.
t0 = time.time_ns()
start_event.record()
func()
end_event.record()
torch.cuda.synchronize()
t1 = time.time_ns()
result_summary.append(
(start_event.elapsed_time(end_event), (t1 - t0) / 1_000_000)
)
result_summary.append((t1 - t0) / 1_000_000)

# Get median times for GPU and CPU Walltime
gpu_time = np.median([x[0] for x in result_summary])
cpu_walltime = np.median([x[1] for x in result_summary])
latency = np.median(result_summary)

# Differentiate model attribute access based on input type
if isinstance(model, ModelTask):
Expand All @@ -173,18 +166,16 @@ def run_one_step(
precision = getattr(model, "precision", None)

if num_batches is not None:
median_gpu_time_per_batch = gpu_time / num_batches
median_cpu_walltime_per_batch = cpu_walltime / num_batches
median_latency_per_batch = latency / num_batches
else:
median_gpu_time_per_batch = gpu_time
median_cpu_walltime_per_batch = cpu_walltime
median_latency_per_batch = -1.0

# Store metrics as dictionary
metrics = {
f"{name}.bs_{batch_size}.precision_{precision}."
+ f"ir_{selected_ir}.median_gpu_time_ms_per_batch": median_gpu_time_per_batch,
+ f"ir_{selected_ir}.median_latency_ms": latency,
f"{name}.bs_{batch_size}.precision_{precision}."
+ f"ir_{selected_ir}.median_cpu_walltime_ms_per_batch": median_cpu_walltime_per_batch,
+ f"ir_{selected_ir}.median_latency_ms_per_batch": median_latency_per_batch,
}

return metrics
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