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#!/usr/bin/env python3
from .promptops import *
from .aux import CmdlineArgs, log
from .data import get_data_loader
from .trainllm import env_stuff, load_model, load_tokenizer
import sys
import torch
import json
import torch.distributed as dist
from accelerate import Accelerator
from datetime import datetime
"""
This currently assumes the batch size to be 1. With larger batches the padding tokens went
into the decoder. Right-padding as a solution?
"""
def llm_generate(model, tokenizer, tok_batch, debug=False, max_len=2000):
tok_batch['input_ids'] = tok_batch['input_ids'].to(model.device)
tok_batch['attention_mask'] = tok_batch['attention_mask'].to(model.device)
start_time = datetime.now()
if debug:
log(f"Tokenized input: {tok_batch['input_ids']}")
raw_output_toks = model.generate(**tok_batch, tokenizer=tokenizer,
do_sample=False, num_beams=4, max_length=max_len, top_p=None, temperature=None,
eos_token_id=[tokenizer.eos_token_id,
tokenizer.convert_tokens_to_ids("<|reserved_special_token_14|>")])
#clean_output_toks = remove_prompt_from_output(tok_batch['attention_mask'], raw_output_toks, filler_id)
assert len(raw_output_toks) == 1, "Only batch size=1 supported %-("
gen_idx = len(tok_batch['attention_mask'][0])
if debug:
log(f"Full tokenized output: {raw_output_toks[0]}")
log(f"Full tokens: {tokenizer.convert_ids_to_tokens(raw_output_toks[0])}")
full_out = tokenizer.batch_decode([raw_output_toks[0]], skip_special_tokens=True)
log(f"Full text: {full_out[0]}")
clean_output_toks = raw_output_toks[0][gen_idx:]
clean_outputs = tokenizer.batch_decode([clean_output_toks], skip_special_tokens=True)
if debug:
log(f"Pruned tokenized output: {clean_output_toks}")
log(f"Pruned tokens: {tokenizer.convert_ids_to_tokens(clean_output_toks)}")
log(f"Cleaned output: {clean_outputs[0]}")
end_time = datetime.now()
log(f"This took: {end_time - start_time}")
return clean_outputs
def reassemble_multi(list_of_lists):
result = []
for gen_idx in range(len(list_of_lists[0])):
for i in range(len(list_of_lists)):
if gen_idx < len(list_of_lists[i]):
result.append(list_of_lists[i][gen_idx])
return result
def predict(model, tokenizer, data_loader, accel, multi=False, debug=False, max_len=2000):
outs_final = []
with torch.no_grad():
for idx, batch in enumerate(data_loader):
if idx % accel.num_processes == accel.process_index:
start_time = datetime.now()
outputs = llm_generate(model, tokenizer, batch, debug=debug, max_len=max_len)
end_time = datetime.now()
log(f"Generated for {idx} in proc {accel.process_index} in {end_time - start_time}")
outs_final += outputs
if multi:
accel.wait_for_everyone()
rank0_buffer = [None] * accel.num_processes if accel.is_main_process else None
dist.gather_object(outs_final, rank0_buffer, dst=0)
if accel.is_main_process:
outs_final = reassemble_multi(rank0_buffer)
else:
outs_final = None
return outs_final
def _cmdline_args():
inputs = sys.argv[1:]
description = """Predict output for an input via prompting"""
pos_args = ["mdl_id"]
#post-process the arguments
args = CmdlineArgs(description, pos_args, input_args=inputs,
kw_arg_dict={"debug": False,
"input_file": "none",
"output_file": "none",
"multiproc": False,
"max_len": 2000,
"prompt_format": PF_ALPACA})
if args.input_file == "none":
args.input_file = None
if args.output_file == "none":
args.output_file = None
log(f"Launched as {args}")
return args
def save_all(outputs, args, acc):
if acc.is_main_process:
if args.output_file is None:
log("Writing to STDOUT")
out_fh = sys.stdout
else:
out_fh = open(args.output_file, "w")
if args.prompt_format in {PF_RAW, PF_RAWLINES}:
for line in outputs:
out_fh.write(line + "\n")
else:
json.dump(outputs, out_fh)
def and_i_called_this_function_do_main_too():
args = _cmdline_args()
if args.multiproc:
env_stuff()
acc = Accelerator()
device = acc.device
log(f"Device: {device}.", accelerator=acc)
if not args.multiproc and not acc.is_main_process:
log("Not launched in multi-processing mode, exiting non-main process.")
sys.exit(0)
tokenizer = load_tokenizer(args.mdl_id, acc)
data_loader = get_data_loader(args.input_file, args.prompt_format, tokenizer, debug=args.debug)
model = load_model(args.mdl_id, device, acc, attention="eager")
model.eval()
log(f"Device: {model.device}.", accelerator=acc)
log("Model loaded, starting to generate")
outputs = predict(model, tokenizer, data_loader, acc, multi=args.multiproc, debug=args.debug, max_len=args.max_len)
save_all(outputs, args, acc)
log("Done")
if __name__ == "__main__":
and_i_called_this_function_do_main_too()
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