import argparse
import time
import torch
import torch.distributed as dist
import transformers
import colossalai
from colossalai.inference import PPInferEngine
from colossalai.inference.pipeline.policies import LlamaModelInferPolicy
GIGABYTE = 1024**3
MEGABYTE = 1024 * 1024
colossalai.launch_from_torch()
def data_gen(batch_size: int = 4, seq_len: int = 512):
input_ids = torch.randint(10, 30000, (1, seq_len), dtype=torch.int32)
attention_mask = torch.ones((1, seq_len), dtype=torch.int32)
data = dict(input_ids=input_ids, attention_mask=attention_mask)
for k, v in data.items():
if torch.is_tensor(v) or "Tensor" in v.__class__.__name__:
new_shape = [1] * v.dim()
new_shape[0] = batch_size
data[k] = v.to("cuda").repeat(*new_shape)
return data
def print_details_info(timestamps, model_config, args, whole_end2end):
if dist.get_rank() == 0:
prefill = []
encoder = []
end2end = []
for timestamp in timestamps:
prefill.append(timestamp[1] - timestamp[0])
encoder.append(
sum(timestamp[i + 1] - timestamp[i] for i in range(1, len(timestamp) - 1)) / (len(timestamp) - 2)
)
end2end.append(timestamp[-1] - timestamp[0])
print(whole_end2end)
with open(
f"{args.log_path}/llama-{args.model}{args.dtype}_pp{args.pp_size}_{args.seq_len}_{args.new_length}_bsz{args.batch_size}_mbsz{args.mb_size}.log",
"w+",
) as f:
mb_avg_end2end = sum(end2end) / len(end2end)
mb_avg_latency = mb_avg_end2end / (args.new_length * args.mb_size)
whole_avg_latency = whole_end2end / (args.new_length * args.batch_size)
num_layers = getattr(model_config, "num_layers", model_config.num_hidden_layers)
num_parameters = num_layers * model_config.hidden_size * model_config.hidden_size * 12 / args.pp_size
if args.dtype in ["fp16", "bf16"]:
num_bytes = 2
else:
num_bytes = 4
f.write(
f"llama-{args.model}{args.dtype}_pp{args.pp_size}, input_len:{args.seq_len}, output_len:{args.new_length}, bsz:{args.batch_size}, mbsz:{args.mb_size}\n"
)
f.write("Average prefill time: {0:8.2f} ms\n".format(sum(prefill) / len(prefill) * 1000))
f.write("Average encode time: {0:8.2f} ms\n".format(sum(encoder) / len(encoder) * 1000))
f.write("Average micro batch end2end time: {0:8.2f} ms\n".format(mb_avg_end2end * 1000))
f.write("Average micro batch Per Token Latency: {0:8.2f} ms\n".format(mb_avg_latency * 1000))
f.write("Whole batch end2end time: {0:8.2f} ms\n".format(whole_end2end * 1000))
f.write("Whole batch Per Token Latency: {0:8.2f} ms\n".format(whole_avg_latency * 1000))
f.write("Throughput: {} tokens/s\n".format((1000 / (whole_avg_latency * 1000))))
f.write("flops: {0:8.2f} TFlops/s\n".format(1 / whole_avg_latency * num_parameters * num_bytes / 1e12))
f.write("----------------------------------------------------------\n")
if torch.cuda.is_available():
current_device = torch.cuda.current_device()
# free memory and the total available memory in bytes
global_free_memory, total_GPU_memory_occupied = torch.cuda.mem_get_info()
memory_allocated = torch.cuda.memory_allocated()
max_memory_allocated = torch.cuda.max_memory_allocated()
memory_reserved = torch.cuda.memory_reserved()
max_memory_reserved = torch.cuda.max_memory_reserved()
with open(
f"{args.log_path}/llama-{args.model}{args.dtype}_pp{args.pp_size}_{args.seq_len}_{args.new_length}_bsz{args.batch_size}_mbsz{args.mb_size}.log",
"a",
) as f:
f.write(
f"\nCurrently using GPU: {current_device}\n"
f"free memory : {global_free_memory / GIGABYTE:.4f} GB,\n"
f"total memory: {total_GPU_memory_occupied / GIGABYTE:.4f} GB,\n"
f"memory allocated: {memory_allocated / GIGABYTE:.4f} GB,\n"
f"Max CUDA memory allocated: {max_memory_allocated / GIGABYTE:.4f} GB,\n"
f"memory reserved/cached: {memory_reserved / GIGABYTE:.4f} GB,\n"
f"Max CUDA memory reserved/cached: {max_memory_reserved / GIGABYTE:.4f} GB,\n"
)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--model", default="toy", help="the size of model")
parser.add_argument("-b", "--batch_size", type=int, default=8, help="batch size")
parser.add_argument("-s", "--seq_len", type=int, default=8, help="sequence length")
parser.add_argument("--new_length", type=int, default=4, help="new tokens length")
parser.add_argument("--mb_size", type=int, default=1, help="micro_batch_size")
parser.add_argument("--pp_size", type=int, default=2, help="pipeline size")
parser.add_argument("--log_path", type=str, default="./log", help="where to store the benchmark log")
parser.add_argument("--dtype", type=str, default="fp16", help="data type")
args = parser.parse_args()
if args.model == "toy":
model = transformers.LlamaForCausalLM(transformers.LlamaConfig(num_hidden_layers=8))
elif args.model == "7b":
model = transformers.LlamaForCausalLM(transformers.AutoConfig.from_pretrained("decapoda-research/llama-7b-hf"))
elif args.model == "13b":
model = transformers.LlamaForCausalLM(transformers.AutoConfig.from_pretrained("decapoda-research/llama-13b-hf"))
else:
raise NotImplementedError
engine = PPInferEngine(
pp_size=args.pp_size,
dtype=args.dtype,
micro_batch_size=args.mb_size,
new_length=args.new_length,
model=model,
model_policy=LlamaModelInferPolicy(),
verbose=True,
max_batch_size=args.mb_size,
max_input_len=args.seq_len,
max_output_len=args.seq_len + args.new_length + 256,
)
data = data_gen(args.batch_size, args.seq_len)
torch.cuda.synchronize()
whole_end2end = time.time()
output, timestamps = engine.inference([data])
torch.cuda.synchronize()
whole_end2end = time.time() - whole_end2end
print_details_info(timestamps, model.config, args, whole_end2end)