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dist_batch_and_latency_inference_w_httpclient.py
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dist_batch_and_latency_inference_w_httpclient.py
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import os
import time
from time import sleep
import argparse
from loguru import logger
from pipeline_parallel.dist_pp_utils import get_pp_inference_module
from utils.dist_args_utils import *
from utils.dist_inference_utils import *
from comm.comm_utils import *
from coordinator.http_coordinate_client import get_coordinator_client, init_coordinator_client, alias_to_model_name
from coordinator.coordinator_client import LocalCoordinatorClient # TODO: merge two coor clients
from task_datasets.inference_data import get_request_processor
import traceback
def update_setting(args, pipeline, query):
# update pipline
pipeline.echo_prompt = query.get('echo', False)
pipeline.top_k_per_token = query.get('logprobs', 0)
pipeline.generate_seq_length = query.get('max_tokens', 1)
pipeline.num_completions = query.get('n', 1)
pipeline.stop = query.get('stop', None)
pipeline.temperature = query.get('temperature', 0)
pipeline.top_p = query.get('top_p', 1.0)
# in latency scenario, batch size is 1
pipeline.batch_size = 1
pipeline.seq_num = 1
pipeline.token_micro_batch_size = 1
pipeline.token_micro_batch_num = 1
pipeline.micro_batch_size = 1
print("<update_setting> generate_seq_length:", pipeline.generate_seq_length)
# update args
args.top_p = pipeline.top_p
args.temperature = pipeline.temperature
pipeline.change_buffer_size()
if hasattr(pipeline, 'update_processors'):
pipeline.update_processors(args)
def to_result(
outputs, tokenizer, top_k_per_token, echo_prompt,
):
i = 0
n_pads = 0 # in latency inference, #pad should be 0
item = {
'choices': [],
}
for i_ret, output_dict in enumerate(outputs):
choice = {
"text": (tokenizer.decode(output_dict['token_ids'][i][n_pads:]) if 'token_ids' in output_dict else ''),
"index": i_ret,
"logprobs": {
"tokens": (tokenizer.convert_ids_to_tokens(output_dict['token_ids'][i][n_pads:] if 'token_ids' in output_dict else [])),
"token_logprobs": (output_dict['token_logprobs'][i][n_pads:].tolist() if 'token_logprobs' in output_dict else []),
"top_logprobs": ([
{
tokenizer.convert_ids_to_tokens(topk_id.item()): top_logprob.item() for topk_id, top_logprob in zip(topk_ids, top_logprobs)
} \
for topk_ids, top_logprobs in zip(
output_dict['topk_ids'][i][n_pads:],
output_dict['topk_logprobs'][i][n_pads:]
)
] if top_k_per_token > 0 else None),
"text_offset": [],
},
"finish_reason": "length",
}
if echo_prompt:
if len(choice['logprobs']['token_logprobs']) > 0:
choice['logprobs']['token_logprobs'][0] = None
if choice['logprobs']['top_logprobs'] is not None:
choice['logprobs']['top_logprobs'][0] = None
item['choices'].append(choice)
return item
def main():
parser = argparse.ArgumentParser(description='Inference Runner with coordinator.')
add_device_arguments(parser)
add_torch_distributed_inference_w_euler_coordinator_arguments(parser)
add_inference_arguments(parser)
add_inference_details_arguments(parser)
add_global_coordinator_arguments(parser)
parser.add_argument('--seed', type=int, default=1, metavar='S',
help='random seed (default: 1)')
parser.add_argument('--job_id', type=str, default='-', metavar='S',
help='DB ID')
parser.add_argument('--profiling', type=str, default='tidy_profiling', metavar='S',
help='enable which profiling? default: tidy mode')
parser.add_argument('--trace-postfix', type=str, default='default', metavar='S',
help='postfix of the tracing file name.')
parser.add_argument('--net-interface', type=str, default='default', metavar='S',
help='network interface name.')
args = parser.parse_args()
print_arguments(args)
# torch.manual_seed(args.seed)
if args.use_cuda:
assert (torch.cuda.is_available())
device = torch.device('cuda', args.cuda_id)
else:
device = torch.device('cpu')
print("Print working directory:", args.working_directory)
model_name_abbr = args.model_name.split('/')[-1]
print("model name abbr: ", model_name_abbr)
print("model name: ", alias_to_model_name(model_name_abbr))
init_coordinator_client(args, alias_to_model_name(model_name_abbr))
coord_client = get_coordinator_client()
pipe = None
try:
res = coord_client.notify_inference_join(args.net_interface)
prime_ip = res['prime_ip']
rank = res['rank']
port = res['nccl_port']
print("<====Coordinator assigned prime-IP:", prime_ip, " and my assigned rank", rank, "====>")
init_inference_communicators_with_coordinator(args, prime_ip, rank, port=port)
if get_pipeline_parallel_rank() == 0:
coord_client.update_status("running", returned_payload={'state': 'initialized'})
input_path = coord_client.load_input_job_from_dfs(args.job_id, return_path=True)
request_processor = get_request_processor(args, infer_data=input_path)
request_processor.set_arguments(args)
pipe = get_pp_inference_module(args, device, rank=rank, be_coordinated=False)
print(f"Inference pipeline loading model <{model_name_abbr}> is done!")
if get_pipeline_parallel_rank() == 0:
coord_client.update_status("running", returned_payload={'state': 'model_loaded'})
if args.profiling == 'no-profiling':
_ = distributed_inference_mask_iter(args, pipe, device, request_processor, client=coord_client)
else:
prefix = './trace_json/inference_' + args.pp_mode
trace_file = prefix + get_inference_arguments_str(args, rank=rank) + '_' + args.profiling + '_' + \
args.trace_postfix + '.json'
if args.profiling == 'tidy_profiling':
_ = distributed_inference_mask_iter(args, pipe, device, request_processor, client=coord_client)
pipe.export_profiling_result(filename=trace_file)
else:
print("No recognized profiler?")
assert False
if get_pipeline_parallel_rank() == get_pipeline_parallel_world_size()-1:
coord_client.update_status("finished", returned_payload={'result': request_processor.data})
except Exception as e:
print('Exception in batch inference:', e)
coord_client.update_status("failed", returned_payload={'message': str(e)})
try:
local_cord_client = LocalCoordinatorClient(
working_directory="/nfs/iiscratch-zhang.inf.ethz.ch/export/zhang/export/fm/new/working_dir/",
coordinator_url="https://coordinator.shift.ml/eth",
)
tokenizer = get_tokenizer(args)
begin_time = time.time()
max_time = 3600
while True:
now = time.time()
if now - begin_time > max_time:
logger.info("Reaching max time. Exit interactive mode.")
break
else:
logger.info(f"{now - begin_time} seconds remaining for interactive mode.")
# TODO: please check here
instructions = local_cord_client.fetch_instructions(alias_to_model_name(model_name_abbr), rank)
last_instruction = instructions[-1]
if last_instruction["message"] == "break":
logger.info("Received stop instruction.")
break
elif last_instruction["message"] == "continue":
logger.info("Received keep instruction.")
sleep(10)
elif last_instruction["message"] == "run":
for instruction in [x for x in instructions if x["message"] == "run"]:
job_id = None
try:
logger.info("Instruction:")
logger.info(str(instruction))
# TODO: we assume len(payload) is 1, right?
query = instruction['payload']['payload'][0]
prompt = query['prompt']
job_id = instruction['payload']['id']
job_status = instruction['payload']['status']
if job_status != "submitted":
continue
# set input length
seq_length = tokenizer(
prompt, return_tensors='pt', padding=True, truncation=False
)['input_ids'].size(1)
seq_length = min(seq_length, 2048 - query.get('max_tokens', 1)) # 2048 is hardcoded.
logger.info(f"Set input length to {seq_length}.")
tokenizer.model_max_length = seq_length
pipe.input_seq_length = seq_length
# update hyperparameters and buffers
logger.info(f"Update settings.")
update_setting(args, pipe, query)
# get inputs
inputs = tokenizer(prompt, return_tensors='pt', padding='max_length', truncation=True, )
input_ids = inputs['input_ids'].long().to(device)
attention_mask = inputs['attention_mask'].long().to(device)
# run inference
logger.info(f"Start Inference.")
output_ids_list = []
pipe.inference_batch(input_ids, output_ids_list, attention_mask=attention_mask)
if get_pipeline_parallel_rank() == pipe.pipeline_group_size - 1:
result = to_result(output_ids_list, tokenizer, pipe.top_k_per_token, pipe.echo_prompt)
return_payload = {
'request': query,
'result': result,
}
# TODO: please check if return_payload is correct.
local_cord_client.update_status(
job_id,
"finished",
returned_payload=return_payload
)
except Exception as e:
error = traceback.format_exc()
local_cord_client.update_status(
job_id,
"failed",
returned_payload={"message": error}
)
print(error)
sleep(10)
except Exception as e:
print('Exception in latency inference:', e)
if __name__ == '__main__':
main()