sampler.py

"""A layer that samples the next tokens from the model's outputs."""
from typing import Dict, List, Optional, Tuple

import torch
import torch.nn as nn

from vllm.model_executor.input_metadata import InputMetadata
from vllm.model_executor.parallel_utils.communication_op import (
    tensor_model_parallel_all_gather)
from vllm.sampling_params import SamplingParams, SamplingType
from vllm.sequence import (PromptLogprobs, SampleLogprobs, SamplerOutput,
                           SequenceData, SequenceGroupOutputs, SequenceOutputs)

_SAMPLING_EPS = 1e-5


class Sampler(nn.Module):
    """Samples the next tokens from the model's outputs.

    This layer does the following:
    1. Discard the hidden states that are not used for sampling (i.e., all
        tokens except the final one in each prompt).
    2. Compute the logits for the next tokens.
    3. Apply presence and frequency penalties.
    4. Apply temperature scaling.
    5. Apply top-p and top-k truncation.
    6. Sample the next tokens.
    Here, each sequence group within the batch can have different sampling
    parameters (e.g., sampling method, temperature, top-p, top-k, etc.).
    """

    def __init__(self, vocab_size: int) -> None:
        super().__init__()
        self.vocab_size = vocab_size

    def forward(
        self,
        embedding: torch.Tensor,
        hidden_states: torch.Tensor,
        input_metadata: InputMetadata,
        embedding_bias: Optional[torch.Tensor] = None,
    ) -> SamplerOutput:
        # Get the hidden states that we use for sampling.
        hidden_states = _prune_hidden_states(hidden_states, input_metadata)

        # Get the logits for the next tokens.
        logits = _get_logits(hidden_states, embedding, embedding_bias,
                             self.vocab_size)

        # Apply logits processors (if any).
        logits = _apply_logits_processors(logits, input_metadata)
        # Apply presence and frequency penalties.
        output_tokens = _get_output_tokens(input_metadata)
        assert len(output_tokens) == logits.shape[0]
        presence_penalties, frequency_penalties, repetition_penalties = (
            _get_penalties(input_metadata))
        assert len(presence_penalties) == logits.shape[0]
        assert len(frequency_penalties) == logits.shape[0]
        assert len(repetition_penalties) == logits.shape[0]
        logits = _apply_penalties(logits, output_tokens, presence_penalties,
                                  frequency_penalties, repetition_penalties)

        # Apply temperature scaling.
        temperatures = _get_temperatures(input_metadata)
        assert len(temperatures) == logits.shape[0]
        if any(t != 1.0 for t in temperatures):
            t = torch.tensor(temperatures,
                             dtype=logits.dtype,
                             device=logits.device)
            # Use in-place division to avoid creating a new tensor.
            logits.div_(t.unsqueeze(dim=1))

        # Apply top-p and top-k truncation.
        top_ps, top_ks = _get_top_p_top_k(input_metadata, self.vocab_size)
        assert len(top_ps) == len(top_ks) == logits.shape[0]
        do_top_p = any(p < 1.0 - _SAMPLING_EPS for p in top_ps)
        do_top_k = any(k != self.vocab_size for k in top_ks)
        if do_top_p or do_top_k:
            logits = _apply_top_p_top_k(logits, top_ps, top_ks)

        # We use float32 for probabilities and log probabilities.
        # Compute the probabilities.
        probs = torch.softmax(logits, dim=-1, dtype=torch.float)
        # Compute the log probabilities.
        # Use log_softmax to ensure numerical stability.
        logprobs = torch.log_softmax(logits, dim=-1, dtype=torch.float)

        # Sample the next tokens.
        sample_results = _sample(probs, logprobs, input_metadata)
        # Get the logprobs query results.
        prompt_logprobs, sample_logprobs = _get_logprobs(
            logprobs, input_metadata, sample_results)
        return _build_sampler_output(sample_results, input_metadata,
                                     prompt_logprobs, sample_logprobs)


def _get_logits(hidden_states: torch.Tensor, embedding: torch.Tensor,
                embedding_bias: Optional[torch.Tensor],
                vocab_size: int) -> torch.Tensor:
    # Get the logits for the next tokens.
    logits = torch.matmul(hidden_states, embedding.t())
    if embedding_bias is not None:
        logits += embedding_bias
    logits = tensor_model_parallel_all_gather(logits)
    # Remove paddings in vocab (if any).
    logits = logits[:, :vocab_size]
    return logits


def _prune_hidden_states(
    hidden_states: torch.Tensor,
    input_metadata: InputMetadata,
) -> torch.Tensor:
    hidden_states = hidden_states.view(-1, hidden_states.shape[-1])
    return hidden_states.index_select(0, input_metadata.selected_token_indices)


def _get_penalties(
    input_metadata: InputMetadata
) -> Tuple[List[float], List[float], List[float]]:
    # Collect the presence and frequency penalties.
    presence_penalties: List[float] = []
    frequency_penalties: List[float] = []
    repetition_penalties: List[float] = []
    for i, seq_group in enumerate(input_metadata.seq_groups):
        seq_ids, sampling_params = seq_group
        p = sampling_params.presence_penalty
        f = sampling_params.frequency_penalty
        r = sampling_params.repetition_penalty
        if (i < input_metadata.num_prompts
                and sampling_params.prompt_logprobs is not None):
            # NOTE: We do not apply presence and frequency penalties for the
            # prompt token positions where we don't sample new tokens.
            prompt_len = input_metadata.prompt_lens[i]
            presence_penalties += [0] * (prompt_len - 1)
            frequency_penalties += [0] * (prompt_len - 1)
            repetition_penalties += [1] * (prompt_len - 1)
        presence_penalties += [p] * len(seq_ids)
        frequency_penalties += [f] * len(seq_ids)
        repetition_penalties += [r] * len(seq_ids)
    return presence_penalties, frequency_penalties, repetition_penalties


def _get_output_tokens(input_metadata: InputMetadata) -> List[List[int]]:
    output_tokens: List[List[int]] = []
    for i, seq_group in enumerate(input_metadata.seq_groups):
        seq_ids, sampling_params = seq_group
        if (i < input_metadata.num_prompts
                and sampling_params.prompt_logprobs is not None):
            # NOTE: prompt token positions do not need output tokens to
            # compute penalties.
            prompt_len = input_metadata.prompt_lens[i]
            output_tokens.extend([] for _ in range(prompt_len - 1))
        for seq_id in seq_ids:
            seq_data = input_metadata.seq_data[seq_id]
            output_tokens.append(seq_data.output_token_ids)
    return output_tokens


def _apply_logits_processors(logits: torch.Tensor,
                             input_metadata: InputMetadata) -> torch.Tensor:
    logits_row_idx = 0
    found_logits_processors = False
    for seq_ids, sampling_params in input_metadata.seq_groups:
        logits_processors = sampling_params.logits_processors
        if logits_processors:
            found_logits_processors = True
            for seq_id in seq_ids:
                logits_row = logits[logits_row_idx]
                token_ids = input_metadata.seq_data[seq_id].output_token_ids
                for logits_processor in logits_processors:
                    logits_row = logits_processor(token_ids, logits_row)
                logits[logits_row_idx] = logits_row
                logits_row_idx += 1
        else:
            logits_row_idx += len(seq_ids)
    if found_logits_processors:
        assert logits_row_idx == logits.shape[0]
    return logits


def _apply_penalties(
    logits: torch.Tensor,
    output_tokens: List[List[int]],
    presence_penalties: List[float],
    frequency_penalties: List[float],
    repetition_penalties: List[float],
) -> torch.Tensor:
    num_seqs, vocab_size = logits.shape
    for i in range(num_seqs):
        if not output_tokens[i]:
            continue
        p = presence_penalties[i]
        f = frequency_penalties[i]
        r = repetition_penalties[i]
        if abs(p) < _SAMPLING_EPS and abs(f) < _SAMPLING_EPS and abs(
                r - 1.0) < _SAMPLING_EPS:
            continue
        break
    else:
        # Return early if all sequences have zero penalties.
        return logits

    max_output_len = max(len(tokens) for tokens in output_tokens)
    padded_output_tokens = [
        tokens + [vocab_size] * (max_output_len - len(tokens))
        for tokens in output_tokens
    ]
    output_tokens_tensor = torch.tensor(padded_output_tokens,
                                        dtype=torch.long,
                                        device=logits.device)

    # Compute the bin counts for the output tokens.
    # vocab_size + 1 for padding.
    bin_counts = torch.zeros((num_seqs, vocab_size + 1),
                             dtype=torch.long,
                             device=logits.device)
    bin_counts.scatter_add_(1, output_tokens_tensor,
                            torch.ones_like(output_tokens_tensor))
    bin_counts = bin_counts[:, :vocab_size]  # Remove the padding bin.
    mask = bin_counts > 0

    repetition_penalties = torch.tensor(repetition_penalties,
                                        dtype=logits.dtype,
                                        device=logits.device)
    frequency_penalties = torch.tensor(frequency_penalties,
                                       dtype=logits.dtype,
                                       device=logits.device)
    presence_penalties = torch.tensor(presence_penalties,
                                      dtype=logits.dtype,
                                      device=logits.device)

    repetition_penalties = repetition_penalties[:, None].repeat(1, vocab_size)
    repetition_penalties[~mask] = 1.0
    logits = torch.where(logits > 0, logits / repetition_penalties,
                         logits * repetition_penalties)

    # We follow the definition in OpenAI API.
    # Refer to https://platform.openai.com/docs/api-reference/parameter-details
    logits -= frequency_penalties.unsqueeze(dim=1) * bin_counts
    logits -= presence_penalties.unsqueeze(dim=1) * mask
    return logits


def _get_temperatures(input_metadata: InputMetadata) -> List[float]:
    # Collect the temperatures for the logits.
    temperatures: List[float] = []
    for i, seq_group in enumerate(input_metadata.seq_groups):
        seq_ids, sampling_params = seq_group
        temperature = sampling_params.temperature
        if temperature < _SAMPLING_EPS:
            # NOTE: Zero temperature means deterministic sampling
            # (i.e., greedy sampling or beam search).
            # Set the temperature to 1 to avoid division by zero.
            temperature = 1.0
        if (i < input_metadata.num_prompts
                and sampling_params.prompt_logprobs is not None):
            prompt_len = input_metadata.prompt_lens[i]
            temperatures += [temperature] * (prompt_len - 1)
        temperatures += [temperature] * len(seq_ids)
    return temperatures


def _get_top_p_top_k(
    input_metadata: InputMetadata,
    vocab_size: int,
) -> Tuple[List[float], List[int]]:
    top_ps: List[float] = []
    top_ks: List[int] = []
    for i, seq_group in enumerate(input_metadata.seq_groups):
        seq_ids, sampling_params = seq_group
        top_p = sampling_params.top_p
        # k should not be greater than the vocab size.
        top_k = min(sampling_params.top_k, vocab_size)
        # k=-1 means no truncation.
        top_k = vocab_size if top_k == -1 else top_k
        if (i < input_metadata.num_prompts
                and sampling_params.prompt_logprobs is not None):
            prompt_len = input_metadata.prompt_lens[i]
            top_ps += [top_p] * (prompt_len - 1)
            top_ks += [top_k] * (prompt_len - 1)
        top_ps += [top_p] * len(seq_ids)
        top_ks += [top_k] * len(seq_ids)
    return top_ps, top_ks


def _apply_top_p_top_k(
    logits: torch.Tensor,
    top_ps: List[float],
    top_ks: List[int],
) -> torch.Tensor:
    p = torch.tensor(top_ps, dtype=logits.dtype, device=logits.device)
    k = torch.tensor(top_ks, dtype=torch.int, device=logits.device)
    logits_sort, logits_idx = logits.sort(dim=-1, descending=True)

    # Apply top-p.
    probs_sort = logits_sort.softmax(dim=-1)
    probs_sum = probs_sort.cumsum(dim=-1)
    top_p_mask = (probs_sum - probs_sort) > p.unsqueeze(dim=1)
    logits_sort[top_p_mask] = -float("inf")

    # Apply top-k.
    # Create a mask for the top-k elements.
    top_k_mask = torch.arange(logits_idx.shape[-1], device=logits_idx.device)
    top_k_mask = top_k_mask.expand(logits_idx.shape[0], -1)
    top_k_mask = top_k_mask >= k.unsqueeze(dim=1)
    logits_sort[top_k_mask] = -float("inf")

    # Re-sort the probabilities.
    logits = torch.gather(logits_sort,
                          dim=-1,
                          index=torch.argsort(logits_idx, dim=-1))
    return logits


def _greedy_sample(
    selected_seq_groups: List[Tuple[List[int], SamplingParams]],
    logprobs: torch.Tensor,
) -> List[Tuple[List[int], List[int]]]:
    samples = torch.argmax(logprobs, dim=-1).cpu()
    sample_idx = 0
    results = []
    for seq_group in selected_seq_groups:
        seq_ids, _ = seq_group
        num_parent_seqs = len(seq_ids)
        assert num_parent_seqs == 1, (
            "Greedy sampling should have only one seq.")
        parent_ids = list(range(num_parent_seqs))
        next_token_ids = [samples[sample_idx].item()]
        results.append((next_token_ids, parent_ids))
        sample_idx += num_parent_seqs
    assert sample_idx == logprobs.size(0)
    return results


def _random_sample(
    selected_seq_groups: List[Tuple[List[int], SamplingParams]],
    is_prompts: List[bool],
    probs: torch.Tensor,
) -> List[Tuple[List[int], List[int]]]:
    # Find the maximum best_of value of the prompt phase requests.
    max_best_of = 1
    for seq_group, is_prompt in zip(selected_seq_groups, is_prompts):
        if is_prompt:
            seq_ids, sampling_params = seq_group
            max_best_of = max(max_best_of, sampling_params.best_of)
    random_samples = torch.multinomial(probs,
                                       num_samples=max_best_of,
                                       replacement=True).cpu()
    sample_idx = 0
    results = []
    for seq_group, is_prompt in zip(selected_seq_groups, is_prompts):
        seq_ids, sampling_params = seq_group
        num_parent_seqs = len(seq_ids)
        if is_prompt:
            # Prompt phase.
            assert num_parent_seqs == 1, (
                "Prompt input should have only one seq.")
            parent_ids = [0] * sampling_params.best_of
            next_token_ids = random_samples[
                sample_idx, :sampling_params.best_of].tolist()
        else:
            # Generation phase.
            parent_ids = list(range(num_parent_seqs))
            next_token_ids = random_samples[sample_idx:sample_idx +
                                            num_parent_seqs, 0].tolist()
        results.append((next_token_ids, parent_ids))
        sample_idx += num_parent_seqs
    assert sample_idx == probs.size(0)
    return results


def _beam_search_sample(
    selected_seq_groups: List[Tuple[List[int], SamplingParams]],
    is_prompts: List[bool],
    seq_data: Dict[int, SequenceData],
    logprobs: torch.Tensor,
) -> List[Tuple[List[int], List[int]]]:
    # We sample 2 * beam_width candidates to make sure that with high
    # probability we can get `beam_width` candidates in addition to
    # the finished sequences for the next iteration. See
    # https://github.com/tensorflow/tensor2tensor/blob/bafdc1b67730430d38d6ab802cbd51f9d053ba2e/tensor2tensor/utils/beam_search.py#L557-L563
    # for details. See also HF reference:
    # https://github.com/huggingface/transformers/blob/a4dd53d88e4852f023332d284ff07a01afcd5681/src/transformers/generation/utils.py#L3063-L3065
    #
    # NOTE: Beam search is not vectorized, so its speed can be slower than
    # other sampling methods.
    sample_idx = 0
    results = []
    for seq_group, is_prompt in zip(selected_seq_groups, is_prompts):
        seq_ids, sampling_params = seq_group
        num_parent_seqs = len(seq_ids)
        beam_width = sampling_params.best_of
        seq_group_logprobs = logprobs[sample_idx:sample_idx + num_parent_seqs]
        if is_prompt:
            # Prompt phase.
            assert num_parent_seqs == 1, (
                "Prompt input should have only one seq.")
            parent_ids = [0] * (2 * beam_width)
            _, next_token_ids = torch.topk(seq_group_logprobs[0],
                                           2 * beam_width)
            next_token_ids = next_token_ids.tolist()
        else:
            # Generation phase.
            cumulative_logprobs = [
                seq_data[seq_id].cumulative_logprob for seq_id in seq_ids
            ]
            cumulative_logprobs = torch.tensor(
                cumulative_logprobs,
                dtype=torch.float,
                device=seq_group_logprobs.device)
            seq_group_logprobs = (seq_group_logprobs +
                                  cumulative_logprobs.unsqueeze(dim=1))
            _, topk_ids = torch.topk(seq_group_logprobs.flatten(),
                                     2 * beam_width)
            topk_ids = topk_ids.tolist()
            vocab_size = seq_group_logprobs.size(-1)
            parent_ids = [i // vocab_size for i in topk_ids]
            next_token_ids = [i % vocab_size for i in topk_ids]
        results.append((next_token_ids, parent_ids))
        sample_idx += num_parent_seqs
    assert sample_idx == logprobs.size(0)
    return results


def _sample(
    probs: torch.Tensor,
    logprobs: torch.Tensor,
    input_metadata: InputMetadata,
) -> List[Tuple[List[int], List[int]]]:
    categorized_seq_group_ids = {t: [] for t in SamplingType}
    categorized_sample_indices = input_metadata.categorized_sample_indices
    for i, seq_group in enumerate(input_metadata.seq_groups):
        _, sampling_params = seq_group
        sampling_type = sampling_params.sampling_type
        categorized_seq_group_ids[sampling_type].append(i)

    sample_results_dict: Dict[int, Tuple[List[int], List[int]]] = {}
    for sampling_type in SamplingType:
        seq_group_ids = categorized_seq_group_ids[sampling_type]
        seq_groups = [input_metadata.seq_groups[i] for i in seq_group_ids]
        is_prompts = [i < input_metadata.num_prompts for i in seq_group_ids]
        sample_indices = categorized_sample_indices[sampling_type]
        num_tokens = len(sample_indices)
        if num_tokens == 0:
            continue
        if sampling_type == SamplingType.GREEDY:
            category_logprobs = logprobs[sample_indices]
            sample_results = _greedy_sample(seq_groups, category_logprobs)
        elif sampling_type == SamplingType.RANDOM:
            category_probs = probs[sample_indices]
            sample_results = _random_sample(seq_groups, is_prompts,
                                            category_probs)
        elif sampling_type == SamplingType.BEAM:
            category_logprobs = logprobs[sample_indices]
            sample_results = _beam_search_sample(seq_groups, is_prompts,
                                                 input_metadata.seq_data,
                                                 category_logprobs)
        else:
            raise ValueError(f"Unsupported sampling type: {sampling_type}")
        sample_results_dict.update(zip(seq_group_ids, sample_results))

    sample_results = [
        sample_results_dict[i] for i in range(len(input_metadata.seq_groups))
    ]
    return sample_results


def _get_logprobs(
    logprobs: torch.Tensor,
    input_metadata: InputMetadata,
    sample_results: List[Tuple[List[int], List[int]]],
) -> Tuple[List[Optional[List[Optional[Dict[int, float]]]]], List[List[Dict[
        int, float]]]]:
    # Prepare query indices
    batched_logprobs_query_seq_indices: List[int] = []
    batched_logprobs_query_token_indices: List[int] = []
    largest_num_logprobs = 0
    sample_idx = 0
    for i, (seq_group, sample_result) in enumerate(
            zip(input_metadata.seq_groups, sample_results)):
        seq_ids, sampling_params = seq_group
        next_token_ids, parent_ids = sample_result
        num_parent_seqs = len(seq_ids)
        if (i < input_metadata.num_prompts
                and sampling_params.prompt_logprobs is not None):
            largest_num_logprobs = max(largest_num_logprobs,
                                       sampling_params.prompt_logprobs)
            prompt_len = input_metadata.prompt_lens[i]
            prompt_tokens = input_metadata.seq_data[
                seq_ids[0]].prompt_token_ids
            batched_logprobs_query_seq_indices.extend(
                sample_idx + j for j in range(prompt_len - 1))
            batched_logprobs_query_token_indices.extend(
                token_id for token_id in prompt_tokens[1:])
            sample_idx += prompt_len - 1
        batched_logprobs_query_seq_indices.extend(
            [sample_idx + parent_id for parent_id in parent_ids])
        batched_logprobs_query_token_indices.extend(next_token_ids)
        if sampling_params.logprobs is not None:
            largest_num_logprobs = max(largest_num_logprobs,
                                       sampling_params.logprobs)
        sample_idx += num_parent_seqs
    assert sample_idx == logprobs.size(0)

    # Batched query for logprobs of selected token
    batched_logprobs_query_result = logprobs[[
        batched_logprobs_query_seq_indices,
        batched_logprobs_query_token_indices
    ]].cpu()

    # Batched query for logprobs of topk tokens
    if largest_num_logprobs > 0:
        top_logprobs, top_token_ids = torch.topk(logprobs,
                                                 largest_num_logprobs,
                                                 dim=-1)
        top_logprobs = top_logprobs.cpu()
        top_token_ids = top_token_ids.cpu()
    else:
        top_logprobs, top_token_ids = None, None

    # Gather results
    result_prompt_logprobs: List[Optional[PromptLogprobs]] = []
    result_sample_logprobs: List[SampleLogprobs] = []
    sample_idx = 0
    query_result_idx = 0
    for i, (seq_group, sample_result) in enumerate(
            zip(input_metadata.seq_groups, sample_results)):
        seq_ids, sampling_params = seq_group
        next_token_ids, parent_ids = sample_result

        # Prompt logprobs
        if (i < input_metadata.num_prompts
                and sampling_params.prompt_logprobs is not None):
            num_logprobs = sampling_params.prompt_logprobs
            prompt_len = input_metadata.prompt_lens[i]
            prompt_tokens = input_metadata.seq_data[
                seq_ids[0]].prompt_token_ids
            group_prompt_logprobs: PromptLogprobs = [None]
            for token_id in prompt_tokens[1:]:
                prompt_logprobs_dict = {
                    token_id:
                    batched_logprobs_query_result[query_result_idx].item()
                }
                if num_logprobs > 0:
                    prompt_logprobs_dict.update(
                        zip(top_token_ids[sample_idx, :num_logprobs].tolist(),
                            top_logprobs[sample_idx, :num_logprobs].tolist()))
                group_prompt_logprobs.append(prompt_logprobs_dict)
                sample_idx += 1
                query_result_idx += 1
            result_prompt_logprobs.append(group_prompt_logprobs)
        else:
            result_prompt_logprobs.append(None)

        # Sample logprobs
        num_logprobs = sampling_params.logprobs
        if num_logprobs is None:
            num_logprobs = 0
        group_sample_logprobs: SampleLogprobs = []
        for next_token_id, parent_id in zip(next_token_ids, parent_ids):
            sample_logprobs_dict = {
                next_token_id:
                batched_logprobs_query_result[query_result_idx].item()
            }
            query_result_idx += 1
            if num_logprobs > 0:
                sample_logprobs_dict.update(
                    zip(
                        top_token_ids[sample_idx +
                                      parent_id, :num_logprobs].tolist(),
                        top_logprobs[sample_idx +
                                     parent_id, :num_logprobs].tolist()))
            group_sample_logprobs.append(sample_logprobs_dict)
        result_sample_logprobs.append(group_sample_logprobs)
        sample_idx += len(seq_ids)

    return result_prompt_logprobs, result_sample_logprobs


def _build_sampler_output(
    sample_results: List[Tuple[List[int], List[int]]],
    input_metadata: InputMetadata,
    prompt_logprobs: List[Optional[PromptLogprobs]],
    sample_logprobs: List[SampleLogprobs],
) -> SamplerOutput:
    sampler_output = []
    for (seq_group, sample_result, group_prompt_logprobs,
         group_sample_logprobs) in zip(input_metadata.seq_groups,
                                       sample_results, prompt_logprobs,
                                       sample_logprobs):
        seq_ids, _ = seq_group
        next_token_ids, parent_ids = sample_result
        seq_outputs = []
        for parent_id, next_token_id, logprobs in zip(parent_ids,
                                                      next_token_ids,
                                                      group_sample_logprobs):
            seq_outputs.append(
                SequenceOutputs(seq_ids[parent_id], next_token_id, logprobs))
        sampler_output.append(
            SequenceGroupOutputs(seq_outputs, group_prompt_logprobs))
    return sampler_output