generate_samples.py

# coding=utf-8
# Copyright (c) 2019, NVIDIA CORPORATION.  All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

"""Sample Generate GPT2"""

import os
import random
import numpy as np
import torch
import torch.nn.functional as F
import argparse
import time
from arguments import get_args
from utils import Timers
from utils import load_checkpoint_model
from data_utils.tokenization_gpt2 import GPT2Tokenizer
from configure_data import configure_data
import mpu

from fp16 import FP16_Module
from model import GPT2Model
from model import DistributedDataParallel as DDP
from utils import print_rank_0

USE_TORCH_DDP = False

def get_masks_and_position_ids(data,
                               eod_token,
                               reset_position_ids,
                               reset_attention_mask):
    # Extract batch size and sequence length.
    batch_size, seq_length = data.size()

    # Attention mask (lower triangular).
    if reset_attention_mask:
        att_mask_batch = batch_size
    else:
        att_mask_batch = 1
    attention_mask = torch.tril(torch.ones(
        (att_mask_batch, seq_length, seq_length), device=data.device)).view(
            att_mask_batch, 1, seq_length, seq_length)

    # Loss mask.
    loss_mask = torch.ones(data.size(), dtype=torch.float, device=data.device)
    loss_mask[data == eod_token] = 0.0

    # Position ids.
    position_ids = torch.arange(seq_length, dtype=torch.long,
                                device=data.device)
    position_ids = position_ids.unsqueeze(0).expand_as(data)
    # We need to clone as the ids will be modifed based on batch index.
    if reset_position_ids:
        position_ids = position_ids.clone()

    if reset_position_ids or reset_attention_mask:
        # Loop through the batches:
        for b in range(batch_size):

            # Find indecies where EOD token is.
            eod_index = position_ids[b, data[b] == eod_token]
            # Detach indecies from positions if going to modify positions.
            if reset_position_ids:
                eod_index = eod_index.clone()

            # Loop through EOD indecies:
            prev_index = 0
            for j in range(eod_index.size()[0]):
                i = eod_index[j]
                # Mask attention loss.
                if reset_attention_mask:
                    attention_mask[b, 0, (i+1):, :(i+1)] = 0
                # Reset positions.
                if reset_position_ids:
                    position_ids[b, (i+1):] -= (i + 1 - prev_index)
                    prev_index = i + 1

    return attention_mask, loss_mask, position_ids


def initialize_distributed(args):
    """Initialize torch.distributed."""

    # Manually set the device ids.
    device = args.rank % torch.cuda.device_count()
    if args.local_rank is not None:
        device = args.local_rank
    torch.cuda.set_device(device)
    # Call the init process
    init_method = 'tcp://'
    master_ip = os.getenv('MASTER_ADDR', 'localhost')
    master_port = os.getenv('MASTER_PORT', '6000')
    #init_method += master_ip + ':' + master_port
    init_method += master_ip + ':' + '12580'
    torch.distributed.init_process_group(
        backend=args.distributed_backend,
        world_size=args.world_size, rank=args.rank,
        init_method=init_method)

    # Set the model-parallel / data-parallel communicators.
    mpu.initialize_model_parallel(args.model_parallel_size)

def set_random_seed(seed):
    """Set random seed for reproducability."""

    if seed is not None and seed > 0:
        random.seed(seed)
        np.random.seed(seed)
        torch.manual_seed(seed)
        mpu.model_parallel_cuda_manual_seed(seed)

def get_batch(context_tokens, device, args):
    tokens = context_tokens
    tokens = tokens.view(args.batch_size, -1).contiguous()
    tokens = tokens.to(device)

    # Get the masks and postition ids.
    attention_mask, loss_mask, position_ids = get_masks_and_position_ids(
        tokens,
        args.eod_token,
        args.reset_position_ids,
        args.reset_attention_mask)

    return tokens, attention_mask, position_ids

def top_k_logits(logits, top_k=0, top_p=0.0, filter_value=-float('Inf')):
    # This function has been mostly taken from huggingface conversational ai code at
    # https://medium.com/huggingface/how-to-build-a-state-of-the-art-conversational-ai-with-transfer-learning-2d818ac26313

    if top_k > 0:
        # Remove all tokens with a probability less than the last token of the top-k
        indices_to_remove = logits < torch.topk(logits, top_k)[0][..., -1, None]
        logits[indices_to_remove] = filter_value
        
    if top_p > 0.0:
        #convert to 1D
        logits=logits.view(logits.size()[1]).contiguous()
        sorted_logits, sorted_indices = torch.sort(logits, descending=True)
        cumulative_probs = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1)

        # Remove tokens with cumulative probability above the threshold
        sorted_indices_to_remove = cumulative_probs > top_p
        # Shift the indices to the right to keep also the first token above the threshold
        sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone()
        sorted_indices_to_remove[..., 0] = 0
        indices_to_remove = sorted_indices[sorted_indices_to_remove]
        logits[indices_to_remove] = filter_value
        #going back to 2D
        logits=logits.view(1, -1).contiguous()
	
    return logits


def generate_samples(model, tokenizer, args, device):
    
    context_count=0
    model.eval()
    with torch.no_grad():
        while True:
            torch.distributed.barrier(group=mpu.get_model_parallel_group())
            terminate_runs=0

            if mpu.get_model_parallel_rank() == 0:
                if args.input_text:
                    raw_text = open(args.input_text).read().strip()
                else:
                    raw_text = input("\nContext prompt (stop to exit) >>> ")
                    while not raw_text:
                        print('Prompt should not be empty!')
                        raw_text = input("\nContext prompt (stop to exit) >>> ")
           
                if "stop" in raw_text:
                    terminate_runs = 1
                else:
                    #context_tokens = tokenizer.EncodeAsIds(raw_text).tokenization
                    context_tokens = tokenizer.encode(raw_text)
                    context_length = len(context_tokens)

                    if context_length >=args.seq_length//2:
                        print("\nContext length", context_length, \
                            "\nPlease give smaller context (half of the sequence length)!")
                        continue
            else:
                #context_tokens = tokenizer.EncodeAsIds("EMPTY TEXT").tokenization
                context_tokens = tokenizer.encode("空文本")
                context_length = len(context_tokens)
            
            terminate_runs_tensor = torch.cuda.LongTensor([terminate_runs])
            torch.distributed.broadcast(terminate_runs_tensor, mpu.get_model_parallel_src_rank(), group=mpu.get_model_parallel_group())
            terminate_runs = terminate_runs_tensor[0].item()

            if terminate_runs == 1:
                return

            pad_id = tokenizer.encoder['<pad>']
            args.eod_token = tokenizer.encoder['<eod>']
            if context_length < args.seq_length:
                context_tokens.extend([pad_id] * (args.seq_length - context_length))

            context_tokens_tensor = torch.cuda.LongTensor(context_tokens)
            context_length_tensor = torch.cuda.LongTensor([context_length])

            torch.distributed.broadcast(context_length_tensor, mpu.get_model_parallel_src_rank(), group=mpu.get_model_parallel_group())
            torch.distributed.broadcast(context_tokens_tensor, mpu.get_model_parallel_src_rank(), group=mpu.get_model_parallel_group())

            context_length = context_length_tensor[0].item()
            tokens, attention_mask, position_ids=get_batch(context_tokens_tensor, device, args)

            start_time = time.time()

            counter = 0
            org_context_length = context_length

            past_key_values = None
            while counter < (org_context_length + args.out_seq_length):
                if counter == 0:
                    logits, past_key_values = model(tokens[:, :context_length], position_ids[:, :context_length], attention_mask[:, :, :context_length, :context_length], past_key_values=past_key_values, use_cache=True)
                    logits = logits[:, context_length - 1, :]
                else:
                    logits, past_key_values = model(tokens[:, context_length - 1 : context_length], position_ids[:, context_length - 1 : context_length], attention_mask[:, :, context_length - 1, :context_length], past_key_values=past_key_values, use_cache=True)
                    logits = logits[:, 0, :]
                if args.fp16: 
                    past_key_values = [x.half() for x in past_key_values]
                else:
                    past_key_values = [x for x in past_key_values]
                logits = top_k_logits(logits, top_k=args.top_k, top_p=args.top_p)            
                log_probs = F.softmax(logits/args.temperature, dim=-1)
                prev = torch.multinomial(log_probs, num_samples=1)
                tokens[0, context_length] = prev[0] 
                torch.distributed.broadcast(tokens, mpu.get_model_parallel_src_rank(), group=mpu.get_model_parallel_group())
                context_length += 1
                counter += 1

                output_tokens_list = tokens.view(-1).contiguous()
                decode_tokens = tokenizer.decode(output_tokens_list.tolist())
                token_end = decode_tokens.find("<eod>")


                if mpu.get_model_parallel_rank() == 0 and (counter % 16 == 0 or token_end != -1):
                   os.system('clear')
                   print("\nTaken time {:.2f}\n".format(time.time() - start_time), flush=True)
                   print("\nContext:", raw_text, flush=True)
                   trim_decode_tokens = decode_tokens[len(raw_text):decode_tokens.find("<eod>")]
                   print("\nCPM:", trim_decode_tokens, flush=True)
                if token_end != -1:
                   #print(token_end)
                   break
                
            if mpu.get_model_parallel_rank() == 0:
                os.system('clear')
                print("\nTaken time {:.2f}\n".format(time.time() - start_time), flush=True)
                print("\nContext:", raw_text, flush=True)
                output_tokens_list = tokens.view(-1).contiguous()
                decode_tokens = tokenizer.decode(output_tokens_list.tolist())
                trim_decode_tokens = decode_tokens[len(raw_text):decode_tokens.find("<eod>")]
                print("\nCPM:", trim_decode_tokens, flush=True)
                #print(token_end)
            raw_text = None

            torch.distributed.barrier(group=mpu.get_model_parallel_group())
            context_count += 1

            if args.input_text:
                break

def prepare_tokenizer(args):

    tokenizer_args = {
        'tokenizer_type': args.tokenizer_type,
        'corpus': None,
        'model_path': args.tokenizer_path,
        'vocab_size': args.vocab_size,
        'model_type': args.tokenizer_model_type,
        'cache_dir': args.cache_dir}
    tokenizer = make_tokenizer(**tokenizer_args)

    args.tokenizer_num_tokens = tokenizer.num_tokens
    args.tokenizer_num_type_tokens = tokenizer.num_type_tokens
    args.eod_token = tokenizer.get_command('eos').Id

    after = tokenizer.num_tokens
    while after % mpu.get_model_parallel_world_size() != 0:
        after += 1

    args.vocab_size = after
    print("prepare tokenizer done", flush=True)

    return tokenizer

def get_model(args):
    """Build the model."""

    print_rank_0('building CPM model ...')
    model = GPT2Model(num_layers=args.num_layers,
                      vocab_size=args.vocab_size,
                      hidden_size=args.hidden_size,
                      num_attention_heads=args.num_attention_heads,
                      embedding_dropout_prob=args.hidden_dropout,
                      attention_dropout_prob=args.attention_dropout,
                      output_dropout_prob=args.hidden_dropout,
                      max_sequence_length=args.max_position_embeddings,
                      checkpoint_activations=args.checkpoint_activations,
                      checkpoint_num_layers=args.checkpoint_num_layers,
                      parallel_output=args.parallel_output)

    if mpu.get_data_parallel_rank() == 0:
        print(' > number of parameters on model parallel rank {}: {}'.format(
            mpu.get_model_parallel_rank(),
            sum([p.nelement() for p in model.parameters()])), flush=True)

    # GPU allocation.
    model.cuda(torch.cuda.current_device())

    # Fp16 conversion.
    if args.fp16:
        model = FP16_Module(model)

    # Wrap model for distributed training.
    if USE_TORCH_DDP:
        i = torch.cuda.current_device()
        model = DDP(model, device_ids=[i], output_device=i,
                    process_group=mpu.get_data_parallel_group())
    else:
        model = DDP(model)

    return model


def setup_model(args):
    """Setup model."""

    model = get_model(args)

    args.iteration = load_checkpoint_model(model, args)

    return model

def main():
    """Main training program."""

    print('Generate Samples')

    # Disable CuDNN.
    torch.backends.cudnn.enabled = False

    # Timer.
    timers = Timers()

    # Arguments.
    args = get_args()

    # Pytorch distributed.
    initialize_distributed(args)

    # Random seeds for reproducability.
    set_random_seed(args.seed)

    #get the tokenizer
    tokenizer = GPT2Tokenizer(os.path.join(args.tokenizer_path, 'vocab.json'), os.path.join(args.tokenizer_path, 'chinese_vocab.model'))

    # Model
    args.parallel_output = False
    model = setup_model(args)

    #setting default batch size to 1
    args.batch_size = 1

    #generate samples
    generate_samples(model, tokenizer, args, torch.cuda.current_device())
    

if __name__ == "__main__":
    main()