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101 lines
3.8 KiB
101 lines
3.8 KiB
import argparse
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import os
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import time
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import torch
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from transformers import BloomForCausalLM, BloomTokenizerFast
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import colossalai
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from colossalai.inference.tensor_parallel.engine import TPInferEngine
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from colossalai.logging import disable_existing_loggers
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from colossalai.shardformer import ShardConfig
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from colossalai.testing import clear_cache_before_run, rerun_if_address_is_in_use, spawn
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os.environ['TRANSFORMERS_NO_ADVISORY_WARNINGS'] = 'true'
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def print_perf_stats(latency_set, config, bs, warmup=3):
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# trim warmup queries
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latency_set = list(latency_set)
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latency_set = latency_set[warmup:]
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count = len(latency_set)
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if count > 0:
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latency_set.sort()
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avg = sum(latency_set) / count
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num_layers = getattr(config, "num_layers", config.num_hidden_layers)
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num_parameters = num_layers * config.hidden_size * config.hidden_size * 12
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num_bytes = 2 # float16
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print("Avg Per Token Latency: {0:8.2f} ms".format(avg * 1000))
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print("Avg BW: {0:8.2f} GB/s".format(1 / avg * num_parameters * num_bytes / 1e9))
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print("Avg flops: {0:8.2f} TFlops/s".format(1 / avg * num_parameters * num_bytes * bs / 1e12))
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print("Avg Throughput: tokens/s: {}".format((1000 / (avg * 1000)) * bs))
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def bench_bloom(args):
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model_path = args.path
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max_batch_size = args.batch_size
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max_input_len = args.input_len
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max_output_len = args.output_len
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tokenizer = BloomTokenizerFast.from_pretrained(model_path)
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tokenizer.pad_token = tokenizer.eos_token
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model = BloomForCausalLM.from_pretrained(model_path, pad_token_id=tokenizer.eos_token_id)
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model = model.half()
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# init TPInferEngine and shard the original model
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# To benchmark torch original, comment out the line of optimizing model
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shard_config = ShardConfig(enable_tensor_parallelism=True if args.tp_size > 1 else False, inference_only=True)
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infer_engine = TPInferEngine(model, shard_config, max_batch_size, max_input_len, max_output_len)
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# prepare data for generation
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generate_kwargs = dict(max_new_tokens=max_output_len, do_sample=False)
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input_tokens = {
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"input_ids": torch.randint(10, 1000, (max_batch_size, max_input_len)),
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"attention_mask": torch.ones((max_batch_size, max_input_len))
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}
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for t in input_tokens:
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if torch.is_tensor(input_tokens[t]):
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input_tokens[t] = input_tokens[t].to(torch.cuda.current_device())
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print(f" input_tokens[{t}].shape: {input_tokens[t].shape}")
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iters = 10
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times = []
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for i in range(iters):
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torch.cuda.synchronize()
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start = time.time()
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outputs = infer_engine.generate(input_tokens, **generate_kwargs)
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torch.cuda.synchronize()
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end = time.time()
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out_len = outputs.shape[1]
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print(f" iter {i}: out len {str(out_len)}, generation time {str(end - start)} s")
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times.append((end - start) / (out_len - max_input_len))
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print_perf_stats(times, model.config, max_batch_size)
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def check_bloom(rank, world_size, port, args):
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disable_existing_loggers()
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colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
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bench_bloom(args)
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@rerun_if_address_is_in_use()
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@clear_cache_before_run()
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def test_bloom(args):
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spawn(check_bloom, args.tp_size, args=args)
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument('-p', '--path', type=str, help='Model path', required=True)
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parser.add_argument('-tp', '--tp_size', type=int, default=1, help='Tensor parallel size')
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parser.add_argument('-b', '--batch_size', type=int, default=16, help='Maximum batch size')
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parser.add_argument('--input_len', type=int, default=1024, help='Maximum input length')
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parser.add_argument('--output_len', type=int, default=128, help='Maximum output length')
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args = parser.parse_args()
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test_bloom(args)
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