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(config={}) 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)