Making large AI models cheaper, faster and more accessible
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def print_perf_stats(latency_set, config, bs, warmup=3):
# trim warmup queries
latency_set = list(latency_set)
latency_set = latency_set[warmup:]
count = len(latency_set)
if count > 0:
latency_set.sort()
avg = sum(latency_set) / count
num_layers = (
getattr(config, "num_layers") if hasattr(config, "num_layers") else getattr(config, "num_hidden_layers")
)
num_parameters = num_layers * config.hidden_size * config.hidden_size * 12
num_bytes = 2 # float16
print("Avg Per Token Latency: {0:8.2f} ms".format(avg * 1000))
print("Avg BW: {0:8.2f} GB/s".format(1 / avg * num_parameters * num_bytes / 1e9))
print("Avg flops: {0:8.2f} TFlops/s".format(1 / avg * num_parameters * num_bytes * bs / 1e12))
print("Avg Throughput: tokens/s: {}".format((1000 / (avg * 1000)) * bs))