mamba_ssm/utils/generation.py

# Copyright (c) 2023, Albert Gu, Tri Dao.
import gc
import time
from collections import namedtuple
from dataclasses import dataclass, field
from functools import partial
from typing import Callable, Optional, Sequence, Union

import torch
import torch.nn.functional as F
from einops import rearrange, repeat
from torch import Tensor
from torch.profiler import ProfilerActivity, profile, record_function
from transformers.generation import GreedySearchDecoderOnlyOutput, SampleDecoderOnlyOutput, TextStreamer


@dataclass
class InferenceParams:
    """Inference parameters that are passed to the main model in order
    to efficienly calculate and store the context during inference."""

    max_seqlen: int
    max_batch_size: int
    seqlen_offset: int = 0
    batch_size_offset: int = 0
    key_value_memory_dict: dict = field(default_factory=dict)
    lengths_per_sample: Optional[Tensor] = None

    def reset(self, max_seqlen, max_batch_size):
        self.max_seqlen = max_seqlen
        self.max_batch_size = max_batch_size
        self.seqlen_offset = 0
        if self.lengths_per_sample is not None:
            self.lengths_per_sample.zero_()


def modify_logits_for_min_p_filtering(logits, min_p):
    """Set the logits for none min_p values to -inf. Done in-place."""
    if min_p <= 0.0 or min_p >= 1.0:
        return
    indices_to_remove = logits < min_p
    logits.masked_fill_(indices_to_remove, float("-Inf"))
# https://github.com/NVIDIA/Megatron-LM/blob/0bb597b42c53355a567aba2a1357cc34b9d99ddd/megatron/text_generation/sampling.py
# https://github.com/huggingface/transformers/blob/a44985b41cfa2de48a5e1de7f1f93b7483da25d1/src/transformers/generation/logits_process.py#L231
def modify_logits_for_top_k_filtering(logits, top_k):
    """Set the logits for none top-k values to -inf. Done in-place."""
    indices_to_remove = logits < torch.topk(logits, top_k)[0][..., -1, None]
    logits.masked_fill_(indices_to_remove, float("-Inf"))


# https://github.com/NVIDIA/Megatron-LM/blob/0bb597b42c53355a567aba2a1357cc34b9d99ddd/megatron/text_generation/sampling.py
# https://github.com/huggingface/transformers/blob/a44985b41cfa2de48a5e1de7f1f93b7483da25d1/src/transformers/generation/logits_process.py#L170
def modify_logits_for_top_p_filtering(logits, top_p):
    """Set the logits for none top-p values to -inf. Done in-place."""
    if top_p <= 0.0 or top_p >= 1.0:
        return
    # First sort and calculate cumulative sum of probabilities.
    sorted_logits, sorted_indices = torch.sort(logits, descending=False)
    cumulative_probs = sorted_logits.softmax(dim=-1).cumsum(dim=-1)
    # Remove tokens with cumulative top_p above the threshold (token with 0 are kept)
    sorted_indices_to_remove = cumulative_probs <= (1 - top_p)
    # scatter sorted tensors to original indexing
    indices_to_remove = sorted_indices_to_remove.scatter(
        1, sorted_indices, sorted_indices_to_remove
    )
    logits.masked_fill_(indices_to_remove, float("-inf"))


def modify_logit_for_repetition_penalty(logits, prev_output_tokens, repetition_penalty=1.0):
    """Apply repetition penalty. See https://arxiv.org/abs/1909.05858
    logits: (batch_size, vocab_size)
    prev_output_tokens: (batch_size, seq_len)
    """
    if repetition_penalty == 1.0:
        return logits
    score = torch.gather(logits, 1, prev_output_tokens)
    # if score < 0 then repetition penalty has to be multiplied to reduce the previous token probability
    score = torch.where(score < 0, score * repetition_penalty, score / repetition_penalty)
    logits.scatter_(1, prev_output_tokens, score)
    return logits


def sample(logits, top_k=1, top_p=0.0, min_p=0.0, temperature=1.0):
    """Sample from top-k logits.
    Arguments:
        logits: Tensor of shape (batch_size, vocab_size)
    """
    if top_k == 1:  # Short-circuit for greedy decoding
        return logits.argmax(dim=-1)
    else:
        if top_p > 0.0:
            assert top_p <= 1.0, "top-p should be in (0, 1]."
        if top_k > 0:
            top_k = min(top_k, logits.size(-1))  # Safety check
            logits_top, indices = torch.topk(logits, top_k, dim=-1)
            if temperature != 1.0:
                logits_top /= temperature
            modify_logits_for_top_p_filtering(logits_top, top_p)
            return indices[
                torch.arange(indices.shape[0], device=indices.device),
                torch.multinomial(torch.softmax(logits_top, dim=-1), num_samples=1).squeeze(dim=-1),
            ]
        else:
            if min_p > 0.0:
                logits_top = logits.clone()
                max_prob = logits_top[..., 0].item()
                min_prob = max_prob * min_p
                modify_logits_for_min_p_filtering(logits_top, min_p)
                if temperature != 1.0:
                    logits_top /= temperature
                return torch.multinomial(torch.softmax(logits_top, dim=-1), num_samples=1).squeeze(dim=-1)
            # Clone so that when we modify for top_p we don't change the original logits
            logits_top = logits / temperature if temperature != 1.0 else logits.clone()
            modify_logits_for_top_p_filtering(logits_top, top_p)
            return torch.multinomial(torch.softmax(logits_top, dim=-1), num_samples=1).squeeze(
                dim=-1
            )


@torch.inference_mode()
def decode(
    input_ids,
    model,
    max_length,
    top_k=1,
    top_p=0.0,
    min_p=0.0,
    temperature=1.0,
    repetition_penalty=1.0,
    eos_token_id=None,
    teacher_outputs=None,
    vocab_size=None,
    cg=False,
    enable_timing=False,
    streamer: Optional[TextStreamer] = None
):
    """Decoding, either greedy or with top-k or top-p sampling.
    If top-k = 0, don't limit the number of candidates (pure sampling).
    Top-k and top-p can be used together. If top_k > 0 and top_p > 0, then top-k is applied first,
    then top-p.
    We assume that all sequences in the same batch have the same length.

    Arguments:
        input_ids: (batch, seq_len)
        max_length: int
        teacher_outputs (optional): (batch, seq_len). If provided, instead of sampling from the
            logits, the next token is taken from the teacher_outputs. Useful for testing.
    Returns: GreedySearchDecoderOnlyOutput or SampleDecoderOnlyOutput, with the following fields:
        sequences: (batch, max_length)
        scores: tuples of (batch, vocab_size)
    """
    if streamer is not None:
        streamer.put(input_ids.cpu())

    batch_size, seqlen_og = input_ids.shape
    teacher_output_len = teacher_outputs.shape[1] if teacher_outputs is not None else 0
    if cg:
        if not hasattr(model, "_decoding_cache"):
            model._decoding_cache = None
        model._decoding_cache = update_graph_cache(
            model,
            model._decoding_cache,
            batch_size,
            seqlen_og,
            max_length,
        )
        inference_params = model._decoding_cache.inference_params
        inference_params.reset(max_length, batch_size)
    else:
        inference_params = InferenceParams(max_seqlen=max_length, max_batch_size=batch_size)

    def get_logits(input_ids, inference_params):
        decoding = inference_params.seqlen_offset > 0
        if decoding:
            position_ids = torch.full(
                (batch_size, 1),
                inference_params.seqlen_offset,
                dtype=torch.long,
                device=input_ids.device,
            )
        else:
            position_ids = None
        if not cg or not decoding:
            logits = model(
                input_ids,
                position_ids=position_ids,
                inference_params=inference_params,
                num_last_tokens=1,
            ).logits.squeeze(dim=1)
        else:
            logits = model._decoding_cache.run(
                input_ids, position_ids, inference_params.seqlen_offset
            ).squeeze(dim=1)
        return logits[..., :vocab_size] if vocab_size is not None else logits

    def sample_tokens(logits, inference_params):
        if teacher_outputs is None or teacher_output_len <= inference_params.seqlen_offset:
            token = sample(logits, top_k=top_k, top_p=top_p, min_p=min_p, temperature=temperature)
        else:
            token = teacher_outputs[:, inference_params.seqlen_offset]
        # return rearrange(token, "b -> b 1")
        return token.unsqueeze(1)

    def should_stop(current_token, inference_params):
        if inference_params.seqlen_offset == 0:
            return False
        if eos_token_id is not None and (current_token == eos_token_id).all():
            return True
        if inference_params.seqlen_offset >= max_length - 1:
            return True
        return False

    start = torch.cuda.Event(enable_timing=enable_timing)
    end = torch.cuda.Event(enable_timing=enable_timing)

    if enable_timing:
        start.record()
    scores, sequences = [], [input_ids]
    sequences_cat = input_ids
    while not should_stop(sequences[-1], inference_params):
        scores.append(get_logits(sequences[-1], inference_params))
        inference_params.seqlen_offset += sequences[-1].shape[1]
        if repetition_penalty == 1.0:
            sampled_tokens = sample_tokens(scores[-1], inference_params)
        else:
            logits = modify_logit_for_repetition_penalty(
                scores[-1].clone(), sequences_cat, repetition_penalty
            )
            sampled_tokens = sample_tokens(logits, inference_params)
            sequences_cat = torch.cat([sequences_cat, sampled_tokens], dim=1)
        sequences.append(sampled_tokens)
        if streamer is not None:
            streamer.put(sampled_tokens.cpu())
    if streamer is not None:
        streamer.end()
    if enable_timing:
        end.record()
        torch.cuda.synchronize()
        print(f"Prompt processing + decoding time: {(start.elapsed_time(end)):.0f}ms")
    output_cls = GreedySearchDecoderOnlyOutput if top_k == 1 else SampleDecoderOnlyOutput
    return output_cls(sequences=torch.cat(sequences, dim=1), scores=tuple(scores))


class GenerationMixin:
    def allocate_inference_cache(self, batch_size, max_seqlen, dtype=None, **kwargs):
        raise NotImplementedError

    def generate(
        self,
        input_ids,
        max_length,
        top_k=1,
        top_p=0.0,
        min_p=0.0,
        temperature=1.0,
        return_dict_in_generate=False,
        output_scores=False,
        **kwargs,
    ):
        output = decode(
            input_ids, self, max_length, top_k=top_k, top_p=top_p, min_p = min_p, temperature=temperature, **kwargs
        )
        if not output_scores:
            output.scores = None
        return output if return_dict_in_generate else output.sequences


@dataclass
class DecodingCGCache:
    max_batch_size: int = 0
    max_seqlen: int = 0
    device = None
    dtype = None
    callables: dict = field(default_factory=dict)
    mempool = None
    inference_params: Optional[InferenceParams] = None
    run: Optional[Callable] = None


@torch.inference_mode()
def update_graph_cache(
    model,
    cache,
    batch_size,
    seqlen_og,
    max_seqlen,
    decoding_seqlens=(1,),
    dtype=None,
    n_warmups=2,
):
    if cache is None:
        cache = DecodingCGCache()
    param_example = next(iter(model.parameters()))
    device = param_example.device
    if dtype is None:
        dtype = param_example.dtype
    if (
        (device, dtype) != (cache.device, cache.dtype)
        or batch_size > cache.max_batch_size
        or max_seqlen > cache.max_seqlen
    ):  # Invalidate the cache
        cache.callables = {}
        cache.mempool = None
        cache.inference_params = None
        gc.collect()
        cache.device, cache.dtype = device, dtype
        cache.max_batch_size, cache.max_seqlen = batch_size, max_seqlen
        assert hasattr(model, "allocate_inference_cache"), "CUDA graph decoding requires that the model has a method allocate_inference_cache"
        inf_cache = model.allocate_inference_cache(batch_size, max_seqlen, dtype)
        lengths_per_sample = torch.full((batch_size,), seqlen_og, dtype=torch.int32, device=device)
        cache.inference_params = InferenceParams(
            max_seqlen=max_seqlen,
            max_batch_size=batch_size,
            seqlen_offset=seqlen_og,
            key_value_memory_dict=inf_cache,
            lengths_per_sample=lengths_per_sample,
        )
        cache.mempool = torch.cuda.graphs.graph_pool_handle()
    for decoding_seqlen in decoding_seqlens:
        if (batch_size, decoding_seqlen) not in cache.callables:
            cache.callables[batch_size, decoding_seqlen] = capture_graph(
                model,
                cache.inference_params,
                batch_size,
                max_seqlen,
                decoding_seqlen=decoding_seqlen,
                mempool=cache.mempool,
                n_warmups=n_warmups,
            )

    def dispatch(input_ids, position_ids, seqlen):
        batch_size, decoding_seqlen = input_ids.shape[:2]
        return cache.callables[batch_size, decoding_seqlen](input_ids, position_ids, seqlen)

    cache.run = dispatch
    cache.inference_params.seqlen_offset = 0  # Reset so it's not confusing
    return cache


def capture_graph(
    model, inference_params, batch_size, max_seqlen, decoding_seqlen=1, mempool=None, n_warmups=2
):
    device = next(iter(model.parameters())).device
    input_ids = torch.full((batch_size, decoding_seqlen), 0, dtype=torch.long, device=device)
    position_ids = torch.full((batch_size, decoding_seqlen), 0, dtype=torch.long, device=device)
    seqlen_offset_og = inference_params.seqlen_offset
    inference_params.seqlen_offset = max_seqlen - decoding_seqlen
    inference_params.lengths_per_sample[:] = inference_params.seqlen_offset

    # Warmup before capture
    s = torch.cuda.Stream()
    s.wait_stream(torch.cuda.current_stream())
    with torch.cuda.stream(s):
        for _ in range(n_warmups):
            logits = model(
                input_ids,
                position_ids=position_ids,
                inference_params=inference_params,
                num_last_tokens=decoding_seqlen,
            ).logits
        s.synchronize()
        # This might be needed for correctness if we run with NCCL_GRAPH_MIXING_SUPPORT=0,
        # which requires that graph launch and non-captured launch to not overlap (I think,
        # that's how I interpret the documentation). I'm not sure if this is required.
        if torch.distributed.is_initialized():
            torch.distributed.barrier()
    torch.cuda.current_stream().wait_stream(s)
    # Captures the graph
    # To allow capture, automatically sets a side stream as the current stream in the context
    graph = torch.cuda.CUDAGraph()
    with torch.cuda.graph(graph, pool=mempool):
        logits = model(
            input_ids,
            position_ids=position_ids,
            inference_params=inference_params,
            num_last_tokens=decoding_seqlen,
        ).logits

    def run(new_input_ids, new_position_ids, seqlen):
        inference_params.lengths_per_sample[:] = seqlen
        input_ids.copy_(new_input_ids)
        position_ids.copy_(new_position_ids)
        graph.replay()
        return logits.clone()

    inference_params.seqlen_offset = seqlen_offset_og
    return run