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
from .beam_utils import BeamHypotheses

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

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