mirror of https://github.com/hpcaitech/ColossalAI
130 lines
5.0 KiB
Python
130 lines
5.0 KiB
Python
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 torch.profiler import ProfilerActivity, profile, record_function
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from transformers import LlamaForCausalLM, LlamaTokenizer
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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 init_to_get_rotary(self, base=10000):
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self.config.head_dim_ = self.config.hidden_size // self.config.num_attention_heads
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if not hasattr(self.config, "rope_scaling"):
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rope_scaling_factor = 1.0
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else:
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rope_scaling_factor = self.config.rope_scaling.factor if self.config.rope_scaling is not None else 1.0
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if hasattr(self.config, "max_sequence_length"):
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max_seq_len = self.config.max_sequence_length
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elif hasattr(self.config, "max_position_embeddings"):
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max_seq_len = self.config.max_position_embeddings * rope_scaling_factor
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else:
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max_seq_len = 2048 * rope_scaling_factor
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base = float(base)
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inv_freq = 1.0 / (
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base ** (torch.arange(0, self.config.head_dim_, 2, device="cpu", dtype=torch.float32) / self.config.head_dim_)
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)
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t = torch.arange(max_seq_len + 1024 * 64, device="cpu", dtype=torch.float32) / rope_scaling_factor
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freqs = torch.outer(t, inv_freq)
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self._cos_cached = torch.cos(freqs).to(torch.float16).cuda()
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self._sin_cached = torch.sin(freqs).to(torch.float16).cuda()
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return
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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
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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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def run_llama_test(args):
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llama_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 = LlamaTokenizer.from_pretrained(llama_model_path)
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tokenizer.pad_token_id = tokenizer.unk_token_id
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model = LlamaForCausalLM.from_pretrained(llama_model_path, pad_token_id=tokenizer.eos_token_id)
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init_to_get_rotary(model.model, base=10000)
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model = model.half()
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model_config = model.config
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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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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(1, 1000, (max_batch_size, max_input_len), device="cuda"),
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"attention_mask": torch.ones((max_batch_size, max_input_len), device="cuda"),
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}
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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("generation time {} s".format(str(end - start)))
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times.append((end - start) / (out_len - max_input_len))
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print("outputs, ", len(outputs))
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print_perf_stats(times, model_config, max_batch_size)
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with profile(activities=[ProfilerActivity.CPU, ProfilerActivity.CUDA], record_shapes=True) as prof:
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with record_function("model_inference"):
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torch.cuda.synchronize()
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outputs = infer_engine.generate(input_tokens, **generate_kwargs)
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torch.cuda.synchronize()
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print(prof.key_averages().table(sort_by="cuda_time_total", row_limit=10))
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def check_llama(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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run_llama_test(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_llama(args):
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spawn(check_llama, 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_llama(args)
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