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Merge pull request #9 from hpcaitech/feature/activation_reuse
evaluation
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import torch | ||
import argparse | ||
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from model.pipeline_gpt1d import GPT2_small_pipeline_1D, GPT2_exlarge_pipeline_1D, GPT3_pipeline_1D | ||
from energon.engine import InferenceEngine | ||
from energon.logging import get_dist_logger | ||
from energon.core import global_context as gpc | ||
from energon.context import ParallelMode | ||
from energon.utils import get_timers | ||
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MODEL_CLASSES = { | ||
"gpt2_small": GPT2_small_pipeline_1D, | ||
"gpt2_exlarge": GPT2_exlarge_pipeline_1D, | ||
"gpt3": GPT3_pipeline_1D, | ||
} | ||
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def main(): | ||
parser = argparse.ArgumentParser() | ||
parser.add_argument("--tensor_para_size", type=int, default=1, help="Tensor Parallel Size") | ||
parser.add_argument("--pipe_para_size", type=int, default=1, help="Pipeline Parallel Size") | ||
parser.add_argument("--iteration", type=int, default=10, help="Pipeline Parallel Size") | ||
parser.add_argument("--fp16", action="store_true", help="Whether to use 16-bit precision instead of 32-bit") | ||
parser.add_argument("--model_name", default=None, type=str, required=True, help="Shortcut name selected in the list: " + ", ".join(MODEL_CLASSES.keys()),) | ||
args = parser.parse_args() | ||
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dtype=torch.float | ||
if args.fp16: | ||
dtype=torch.half | ||
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config = {'num_chunks':1, 'checkpoint':False, 'dtype':dtype, 'embed_split_hidden':False} | ||
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input_ids = torch.randint(1, 10, (1, 2048), dtype=torch.int64) | ||
attention_mask = torch.randint(0, 1, (1, 1, 2048), dtype=torch.int64) | ||
hidden_states = None | ||
sample = dict(hidden_states=hidden_states, input_ids=input_ids, attention_mask=attention_mask) | ||
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engine = InferenceEngine(GPT3_pipeline_1D, config, sample, pp_init_size = args.pipe_para_size, tp_init_size = args.tensor_para_size, dtype = torch.half) | ||
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# prof = torch.profiler.profile( | ||
# schedule=torch.profiler.schedule(wait=1, | ||
# warmup=1, | ||
# active=2, | ||
# repeat=1), | ||
# on_trace_ready=torch.profiler.tensorboard_trace_handler('./log/gpt3_pp{}tp{}'.format(pp, tp)), | ||
# profile_memory=True, | ||
# record_shapes=True, | ||
# with_stack=True) | ||
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# prof.start() | ||
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output = engine.run() | ||
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timer = get_timers() | ||
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torch.distributed.barrier() | ||
timer('evaluate-time').start() | ||
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for i in range(args.iteration): | ||
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# torch.distributed.barrier() | ||
timer('latency-time').start() | ||
output = engine.run() | ||
# torch.distributed.barrier() | ||
timer('latency-time').stop() | ||
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# prof.step() | ||
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# prof.stop() | ||
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torch.distributed.barrier() | ||
timer('evaluate-time').stop() | ||
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logger = get_dist_logger() | ||
evaluate_elapsed = timer('evaluate-time').elapsed() | ||
latency_elapsed = timer('latency-time').elapsed() | ||
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logger.info(f'Throughput, ' | ||
f'Pipeline Rank/ Tensor Rank: {pp}/{gpc.get_world_size(ParallelMode.PARALLEL_1D)},' | ||
f'Time: {itr/evaluate_elapsed}') | ||
logger.info(f'Latency, ' | ||
f'Pipeline Rank/ Tensor Rank: {pp}/{gpc.get_world_size(ParallelMode.PARALLEL_1D)},' | ||
f'Time: {latency_elapsed/itr}') | ||
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logger.info(f'max memory allocated, ' | ||
f'Pipeline Rank/ Tensor Rank: {pp}/{gpc.get_world_size(ParallelMode.PARALLEL_1D)},' | ||
f'memory: {torch.cuda.max_memory_allocated()/1e9} GB') | ||
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# if output is not None: | ||
# print(output.shape) | ||
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# print(engine._model.model) | ||
# engine.switch(2,2) | ||
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# for i in range(10): | ||
# output = engine.run() | ||
# if output is not None: | ||
# print(output.shape) | ||
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if __name__ == "__main__": | ||
main() |