generation.py

# coding=utf-8
# Copyright (c) 2020, 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.

"""Generation utilities."""

import torch
import torch.nn.functional as F

from megatron import get_args, get_tokenizer, mpu
from megatron.utils import get_ltor_masks_and_position_ids
from .communication import (
    copy_from_last_to_first_pipeline_stage,
    broadcast_from_last_pipeline_stage,
    broadcast_from_last_to_first_pipeline_stage)
from .forward_step import ForwardStep
from .sampling import sample

def score_and_return_on_first_stage(model, tokens, lengths):
    """Function for just scoring.
    Arguments:
        model: no interleaving is supported.
        tokens: prompt tokens extended to be of size [b, max_prompt_length]
        lengths: original prompt length, size: [b]
    Note: Outside of model, other parameters only need to be available on
          rank 0.
    Outputs: 
        output_log_probs: log probability of the selected tokens. size: [b, s]
    """

    args = get_args()

    batch_size = tokens.size(0)
    max_prompt_length = lengths.max().item()
    assert max_prompt_length == tokens.size(1)
    max_sequence_length = min(max_prompt_length, args.max_position_embeddings)

    # forward step.
    forward_step = ForwardStep(model, batch_size, max_sequence_length)

    # ===================
    # Pre-allocate memory
    # ===================

    # Log probability of the sequence (prompt + generated tokens).
    output_log_probs = None
    output_log_probs_size = (batch_size, max_sequence_length - 1)
    
    if mpu.is_pipeline_last_stage():
        output_log_probs = torch.empty(output_log_probs_size,
                                       dtype=torch.float32,
                                       device=torch.cuda.current_device())
    
    # =============
    # Run infernece
    # =============
    with torch.no_grad():
        attention_mask, position_ids = _build_attention_mask_and_position_ids(tokens)
        
        # logits will be meanigful only in the last pipeline stage.
        logits = forward_step(tokens, position_ids, attention_mask)

        if mpu.is_pipeline_last_stage():
            # Always the last stage should have an output.
            assert logits is not None
            log_probs = F.log_softmax(logits, dim=2)
            
            # Pick the tokens that we need to get the log
            # probabilities for. Note that next input token is
            # the token which we selected in the current logits,
            # so shift by 1.
            indices = torch.unsqueeze(tokens[:, 1:], 2)
            output_log_probs = torch.gather(log_probs, 2, indices).squeeze(2)
    
    # ======================================
    # Broadcast to the first pipeline stage.
    # ======================================
    output_log_probs = broadcast_from_last_to_first_pipeline_stage(
        output_log_probs_size, torch.float32, output_log_probs)
    
    return tokens, lengths, output_log_probs

def generate_tokens_probs_and_return_on_first_stage(
        model, tokens, lengths,
        return_output_log_probs=False,
        top_k=0, top_p=0.0,
        temperature=1.0,
        use_eod_token_for_early_termination=True,
        stop_on_double_eol=False,
        stop_on_eol=False
        ):
    """Main token generation function.
    Arguments:
        model: no interleaving is supported.
        tokens: prompt tokens extended to be of size [b, max-sequence-length]
        lengths: original prompt length, size: [b]
        return_output_log_probs: flag to calculate the log probability of
            the generated tokens. Note that the log probability is the one
            from the original logit.
        top_k, top_p: top-k and top-p sampling parameters.
            Note that top-k = 1 is gready. Also, these paramters are
            exclusive meaning that:
                if top-k > 0 then we expect top-p=0.
                if top-p > 0 then we check for top-k=0.
        temperature: sampling temperature.
        use_eod_token_for_early_termination: if True, do early termination if
            all the sequences have reached this token.
    Note: Outside of model, other parameters only need to be available on
          rank 0.
    Outputs: Note that is size is adjusted to a lower value than
             max-sequence-length if generation is terminated early.
        tokens: prompt and generated tokens. size: [b, :]
        generated_sequence_lengths: total length (including prompt) of
            the generated sequence. size: [b]
        output_log_probs: log probability of the selected tokens. size: [b, s]
    """

    args = get_args()
    tokenizer = get_tokenizer()

    batch_size = tokens.size(0)
    min_prompt_length = lengths.min().item()
    max_sequence_length = tokens.size(1)
    max_sequence_length = min(max_sequence_length, args.max_position_embeddings)
    
    # If the context is too big, this happens
    if min_prompt_length >= max_sequence_length:
        raise ValueError("context length + tokens_to_generate too large")

    # forward step.
    forward_step = ForwardStep(model, batch_size, max_sequence_length)

    # Added termination_id to support the case that we want to terminate the
    # generation once that id is generated.
    if hasattr(args, 'eos_id'):
        termination_id = args.eos_id
    else:
        termination_id = tokenizer.eod

    # ===================
    # Pre-allocate memory
    # ===================

    # Log probability of the sequence (prompt + generated tokens).
    output_log_probs = None
    output_log_probs_size = (batch_size, max_sequence_length - 1)
    # Lengths of generated seuquence including including prompts.
    generated_sequence_lengths = None
    if mpu.is_pipeline_last_stage():
        if return_output_log_probs:
            output_log_probs = torch.empty(output_log_probs_size,
                                           dtype=torch.float32,
                                           device=torch.cuda.current_device())
        generated_sequence_lengths = torch.ones(
                batch_size, dtype=torch.int64,
                device=torch.cuda.current_device()) * max_sequence_length
    
    # Whether we have reached a termination id.
    is_generation_done = torch.zeros(batch_size, dtype=torch.uint8,
                                     device=torch.cuda.current_device())

    # =============
    # Run infernece
    # =============

    with torch.no_grad():
        attention_mask, position_ids = _build_attention_mask_and_position_ids(
            tokens)
        prev_context_length = 0
        for context_length in range(min_prompt_length, max_sequence_length):

            # Pick the slice that we need to pass through the network.
            tokens2use = tokens[:, prev_context_length:context_length]
            positions2use = position_ids[:, prev_context_length:context_length]
            attention_mask2use = attention_mask[
                ..., prev_context_length:context_length, :context_length]

            # logits will be meanigful only in the last pipeline stage.
            logits = forward_step(tokens2use, positions2use, attention_mask2use)

            if mpu.is_pipeline_last_stage():
                # Always the last stage should have an output.
                assert logits is not None

                # Sample.
                last_token_logits = logits[:, -1, :]
                new_sample = sample(last_token_logits,
                                    top_k=top_k,
                                    top_p=top_p,
                                    temperature=temperature,
                                    vocab_size=tokenizer.vocab_size)
                
                # If a prompt length is smaller or equal th current context
                # length, it means we have started generating tokens
                started = lengths <= context_length
                # Update the tokens.
                tokens[started, context_length] = new_sample[started]

                # Calculate the log probabilities.
                if return_output_log_probs:
                    log_probs = F.log_softmax(logits, dim=2)
                    if return_output_log_probs:
                        # Pick the tokens that we need to get the log
                        # probabilities for. Note that next input token is
                        # the token which we selected in the current logits,
                        # so shift by 1.
                        indices = torch.unsqueeze(
                            tokens[
                                :,
                                (prev_context_length + 1):(context_length + 1)],
                            2)
                        output_log_probs[:,
                                         prev_context_length:context_length] = \
                            torch.gather(log_probs, 2, indices).squeeze(2)

            # Update the tokens on the first stage so the next input to
            # the network is correct.
            copy_from_last_to_first_pipeline_stage(batch_size, torch.int64,
                                                   tokens[:, context_length])

            # Update the context length for the next token generation.
            prev_context_length = context_length

            # Check if all the sequences have hit the termination_id.
            done = None
            if mpu.is_pipeline_last_stage():
                # TODO(rprenger) These stopping methods are tokenizer dependent
                # instead tokenization should be in the inference loop so stop sequences can be used
                if stop_on_double_eol:
                    hit_double_eol = (new_sample == 628).byte() & started.byte()
                    hit_two_eols = (new_sample == 198).byte() & (tokens[:, context_length-1] == 198).byte() & started.byte()
                    done_token = hit_double_eol | hit_two_eols
                elif stop_on_eol:
                    hit_double_eol = (new_sample == 628).byte() & started.byte()
                    hit_eol = (new_sample == 198).byte() & started.byte()
                    done_token = hit_double_eol | hit_eol
                else: 
                    done_token = (new_sample == termination_id).byte() & \
                        started.byte()
                
                just_finished = (done_token & ~is_generation_done).bool()
                generated_sequence_lengths[just_finished.view(-1)] = \
                    context_length + 1
                is_generation_done = is_generation_done | done_token
                done = torch.all(is_generation_done)
            done = broadcast_from_last_pipeline_stage(1, torch.uint8,
                                                      tensor=done)
            if use_eod_token_for_early_termination and done:
                break
            
    # ===================================================
    # Update the length of based on max generated length.
    # ===================================================

    tokens = tokens[:, :(context_length + 1)]
    if mpu.is_pipeline_last_stage():
        if return_output_log_probs:
            output_log_probs = output_log_probs[:, :context_length]

    # ======================================
    # Broadcast to the first pipeline stage.
    # ======================================

    generated_sequence_lengths = broadcast_from_last_to_first_pipeline_stage(
        batch_size, torch.int64, generated_sequence_lengths)
    if return_output_log_probs:
        output_log_probs_size = (batch_size, context_length)
        output_log_probs = broadcast_from_last_to_first_pipeline_stage(
            output_log_probs_size, torch.float32, output_log_probs)

    return tokens, generated_sequence_lengths, output_log_probs

## from huggingface beam search
class BeamHypotheses(object):
    def __init__(self, num_beams, length_penalty=1.0, early_stopping=False):
        """
        Initialize n-best list of hypotheses.
        """
        self.length_penalty = length_penalty
        self.early_stopping = early_stopping
        self.num_beams = num_beams
        self.beams = []
        self.worst_score = 1e9

    def __len__(self):
        """
        Number of hypotheses in the list.
        """
        return len(self.beams)

    def add(self, hyp, sum_logprobs, length):
        """
        Add a new hypothesis to the list.
        """
        score = sum_logprobs / length ** self.length_penalty
        if len(self) < self.num_beams or score > self.worst_score:
            self.beams.append((score, hyp))
            if len(self) > self.num_beams:
                sorted_scores = sorted([(s, idx) for idx, (s, _) in enumerate(self.beams)])
                del self.beams[sorted_scores[0][1]]
                self.worst_score = sorted_scores[1][0]
            else:
                self.worst_score = min(score, self.worst_score)

    def is_done(self, best_sum_logprobs, cur_len):
        """
        If there are enough hypotheses and that none of the hypotheses being generated
        can become better than the worst one in the heap, then we are done with this sentence.
        """

        if len(self) < self.num_beams:
            return False
        elif self.early_stopping:
            return True
        else:
            cur_score = best_sum_logprobs / cur_len ** self.length_penalty
            ret = self.worst_score >= cur_score
            return ret

def beam_search_and_return_on_first_stage(model, tokens, lengths, beam_size, stop_token, num_return_gen, length_penalty):
    args = get_args()
    tokenizer = get_tokenizer()

    batch_size = tokens.size(0)
    assert(batch_size == 1)
    prompt_length = lengths.item()
    final_sequence_length = tokens.size(1)
    final_sequence_length = min(final_sequence_length, args.max_position_embeddings)
    
    # If the context is too big, this happens
    if prompt_length >= final_sequence_length:
        raise ValueError("context length + tokens_to_generate too large")

    # forward step.
    forward_step = ForwardStep(model, beam_size, final_sequence_length)

    beam_hyp = BeamHypotheses(beam_size, length_penalty)
    done = False
    scores = torch.zeros(beam_size,
                         dtype=torch.float32,
                         device=torch.cuda.current_device()).unsqueeze(1)
    # =============
    # Run infernece
    # =============
    with torch.no_grad():
        tokens = tokens.repeat(beam_size, 1)
        attention_mask, position_ids = _build_attention_mask_and_position_ids(tokens)
        prev_context_length = 0
        for context_length in range(prompt_length, final_sequence_length):

            # Pick the slice that we need to pass through the network.
            tokens2use = tokens[:, prev_context_length:context_length]
            positions2use = position_ids[:, prev_context_length:context_length]
            attention_mask2use = attention_mask[
                ..., prev_context_length:context_length, :context_length]

            # logits will be meanigful only in the last pipeline stage.
            logits = forward_step(tokens2use, positions2use, attention_mask2use)

            if mpu.is_pipeline_last_stage():
                vocab_size = logits.size(2)
                log_probs = F.log_softmax(logits, dim=2)
                new_scores = log_probs[:, -1, :] + scores

                if context_length == prompt_length:  # if this is the first one
                    sorted_scores, indices = torch.sort(new_scores[0,:], descending=True)
                else:
                    sorted_scores, indices = torch.sort(new_scores.view(-1), descending=True)

                best_beam_ids = torch.div(indices[: 2 * beam_size], vocab_size).trunc().long()
                best_words = indices[:2 * beam_size] % vocab_size
                best_scores = sorted_scores[: 2 * beam_size]

                next_beams = []
                for beam_token_rank, (token_id, beam_score, beam_id) in enumerate(
                    zip(best_words, best_scores, best_beam_ids)
                ):
                    if token_id.item() == stop_token:
                        # if beam_token does not belong to top num_beams tokens, it should not be added
                        is_beam_token_worse_than_top_num_beams = beam_token_rank >= beam_size
                        if is_beam_token_worse_than_top_num_beams:
                            continue
                        beam_hyp.add(
                            tokens[beam_id].clone(),
                            beam_score,
                            context_length + 1 - prompt_length
                        )
                    else:
                        # add next predicted token since it is not eos_token
                        next_beams.append((token_id, beam_score, beam_id))

                    if len(next_beams) == beam_size:
                        break

                if beam_hyp.is_done(best_scores.max().item(), context_length + 1 - prompt_length):
                    done = True
                    break
                
                best_batches = tokens.new([item[2] for item in next_beams])
                tokens = tokens[best_batches,:]
                tokens[:, context_length] = tokens.new([item[0] for item in next_beams])
                scores = scores.new([item[1] for item in next_beams]).unsqueeze(1)
            
                # set inference key values to make it consistent with best beam index
                forward_step.inference_params.swap_key_value_dict(best_batches)

            # Update the tokens on the first stage so the next input to
            # the network is correct.
            copy_from_last_to_first_pipeline_stage(batch_size, torch.int64,
                                                   tokens[:, context_length])

            # Update the context length for the next token generation.
            prev_context_length = context_length
    
        copy_from_last_to_first_pipeline_stage(scores.size(0), torch.float32,
                                               scores[:,0])

        # if cannot find stop token, add open beams to hyps
        if not done:
            for beam_id in range(beam_size):
                beam_hyp.add(tokens[beam_id].clone(), scores[beam_id], context_length + 1 - prompt_length)

        # rank based on scores
        sorted_hyps = sorted(beam_hyp.beams, key=lambda x: x[0], reverse=True)
        num_return_gen = min(num_return_gen, len(sorted_hyps))
        scores = [sorted_hyps[i][0] for i in range(num_return_gen)]
        tokens = [sorted_hyps[i][1] for i in range(num_return_gen)]
        scores = torch.stack(scores, dim=0)
        tokens = torch.stack(tokens, dim=0)

    return tokens, scores


def _build_attention_mask_and_position_ids(tokens):
    """Build the attention mask and postition ids for the input tokens."""

    # Since we are not interested in loss-mask and reset attention/position
    # is also False, eod_token is not used so it is safe to set it to None.
    attention_mask, _, position_ids = get_ltor_masks_and_position_ids(
        data=tokens,
        eod_token=None,
        reset_position_ids=False,
        reset_attention_mask=False,
        eod_mask_loss=False)

    return attention_mask, position_ids