generation.py

# Copyright (c) 2023, Tri Dao.
# Adapted from https://github.com/NVIDIA/Megatron-LM/blob/0bb597b42c53355a567aba2a1357cc34b9d99ddd/megatron/text_generation/forward_step.py#L31
import gc
import time
from collections import namedtuple
from dataclasses import dataclass, field
from typing import Callable, Optional, Sequence, Union

import torch
from einops import rearrange
from torch import Tensor
from torch.profiler import ProfilerActivity, profile, record_function
from transformers.generation import GreedySearchDecoderOnlyOutput, SampleDecoderOnlyOutput


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

    max_sequence_len: int
    max_batch_size: int
    sequence_len_offset: int = 0
    batch_size_offset: int = 0
    key_value_memory_dict: dict = field(default_factory=dict)
    fused_ft_kernel: bool = False
    lengths_per_sample: Optional[Tensor] = None


# 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."""
    if top_p <= 0.0:
        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 = logits.masked_fill(indices_to_remove, float("-inf"))


def sample(logits, top_k=1, top_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)
            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:
            logits_top = logits / temperature
            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
            )


def decode(
    input_ids,
    model,
    max_length,
    top_k=1,
    top_p=0.0,
    temperature=1.0,
    eos_token_id=None,
    teacher_outputs=None,
    vocab_size=None,
    tensor_parallel=1,
    fused_ft_kernel=False,
    cg=False,
    timing=False,
):
    """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)
    """
    batch_size, seqlen_og = input_ids.shape
    teacher_output_len = teacher_outputs.shape[1] if teacher_outputs is not None else 0
    if cg:
        assert fused_ft_kernel
        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,
            tensor_parallel=tensor_parallel,
        )
        inference_params = model._decoding_cache.inference_params
        inference_params.max_sequence_len = max_length
        inference_params.max_batch_size = batch_size
        inference_params.sequence_len_offset = 0
    else:
        inference_params = InferenceParams(
            max_sequence_len=max_length, max_batch_size=batch_size, fused_ft_kernel=fused_ft_kernel
        )

    def logits_forward_fn(input_ids, position_ids, inference_params):
        if not cg:
            return model(
                input_ids,
                position_ids=position_ids,
                inference_params=inference_params,
                last_token_only=True,
            ).logits
        else:
            return model._decoding_cache.run(
                input_ids, position_ids, inference_params.sequence_len_offset
            ).clone()

    logits_postprocess_fn = (
        lambda logits: logits[..., :vocab_size] if vocab_size is not None else logits
    )

    scores = []
    with torch.inference_mode():
        if timing:
            if tensor_parallel > 1:
                torch.distributed.barrier()
            torch.cuda.synchronize()
            start = time.time()
        logits = model(input_ids, inference_params=inference_params, last_token_only=True).logits
        logits = logits_postprocess_fn(logits)
        scores.append(logits if not cg else logits.clone())
        if teacher_outputs is None or teacher_output_len <= seqlen_og:
            next_token = sample(logits, top_k=top_k, top_p=top_p, temperature=temperature)
        else:
            next_token = teacher_outputs[:, seqlen_og]
        sequences = [next_token]
        inference_params.sequence_len_offset = seqlen_og
        while True:
            position_ids = torch.full(
                (batch_size, 1),
                inference_params.sequence_len_offset,
                dtype=torch.long,
                device=input_ids.device,
            )
            logits = logits_postprocess_fn(logits_forward_fn(
                rearrange(next_token, "b -> b 1"), position_ids, inference_params
            ))
            scores.append(logits)
            if (
                teacher_outputs is None
                or teacher_output_len <= inference_params.sequence_len_offset + 1
            ):
                next_token = sample(logits, top_k=top_k, top_p=top_p, temperature=temperature)
            else:
                next_token = teacher_outputs[:, inference_params.sequence_len_offset + 1]
            sequences.append(next_token)
            inference_params.sequence_len_offset += 1
            if eos_token_id is not None and (next_token == eos_token_id).all():
                break
            if inference_params.sequence_len_offset >= max_length - 1:
                break
        if timing:
            if tensor_parallel > 1:
                torch.distributed.barrier()
            torch.cuda.synchronize()
            print(f"Prompt processing + decoding time: {(time.time() - start) * 1000:.0f}ms")
    output_cls = GreedySearchDecoderOnlyOutput if top_k == 1 else SampleDecoderOnlyOutput
    return output_cls(
        sequences=torch.cat([input_ids, torch.stack(sequences, dim=1)], 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,
        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, temperature=temperature, **kwargs
        )
        if not output_scores:
            output.scores = None
        return output if return_dict_in_generate else output.sequences


def allocate_inference_cache(
    max_batch_size,
    max_seqlen,
    nheads,
    headdim,
    layers: Union[int, Sequence],
    device,
    dtype=torch.float16,
):
    assert dtype in [torch.float16, torch.bfloat16, torch.float32]
    packsize = 4 if dtype == torch.float32 else 8
    assert headdim % packsize == 0
    k_cache_shape = (max_batch_size, nheads, headdim // packsize, max_seqlen, packsize)
    v_cache_shape = (max_batch_size, nheads, max_seqlen, headdim)
    if isinstance(layers, int):
        layers = range(layers)
    return {
        i: (
            torch.empty(k_cache_shape, device=device, dtype=dtype),
            torch.empty(v_cache_shape, device=device, dtype=dtype),
        )
        for i in layers
    }


def seqlen_to_seqlen_type(seqlen: int) -> int:
    """Convert sequence length to a seqlen_type.
    This is used to determine which cuda graph to use.
    Arguments:
        seqlen: int
    """
    return 0 if seqlen < 32 else (1 if seqlen < 2048 else 2)


def seqlen_type_to_max_seqlen(seqlen_type: int) -> int:
    assert seqlen_type in [0, 1, 2]
    return 32 if seqlen_type == 0 else (2048 if seqlen_type == 1 else 2**32)


@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, tensor_parallel=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
        if hasattr(model, "allocate_inference_cache"):
            inf_cache = model.allocate_inference_cache(batch_size, max_seqlen, dtype)
        else:
            headdim = getattr(
                model.config,
                "head_dim",
                model.config.hidden_size // model.config.num_attention_heads,
            )
            inf_cache = allocate_inference_cache(
                batch_size,
                max_seqlen,
                model.config.num_attention_heads // tensor_parallel,
                headdim,
                model.config.num_hidden_layers,
                device,
                dtype,
            )
        lengths_per_sample = torch.full((batch_size,), seqlen_og, dtype=torch.int32, device=device)
        cache.inference_params = InferenceParams(
            max_sequence_len=max_seqlen,
            max_batch_size=batch_size,
            sequence_len_offset=seqlen_og,
            key_value_memory_dict=inf_cache,
            fused_ft_kernel=True,
            lengths_per_sample=lengths_per_sample,
        )
        cache.mempool = torch.cuda.graphs.graph_pool_handle()
    for s_type in range(seqlen_to_seqlen_type(seqlen_og), seqlen_to_seqlen_type(max_seqlen) + 1):
        if (batch_size, s_type) not in cache.callables:
            max_seqlen_ = min(max(seqlen_og, seqlen_type_to_max_seqlen(s_type)), max_seqlen)
            cache.callables[batch_size, s_type] = capture_graph(
                model,
                cache.inference_params,
                batch_size,
                max_seqlen_,
                mempool=cache.mempool,
                n_warmups=n_warmups,
            )

    def dispatch(input_ids, position_ids, seqlen):
        batch_size = input_ids.shape[0]
        return cache.callables[batch_size, seqlen_to_seqlen_type(seqlen)](
            input_ids, position_ids, seqlen
        )

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


def capture_graph(model, inference_params, batch_size, max_seqlen, mempool=None, n_warmups=2):
    device = next(iter(model.parameters())).device
    input_ids = torch.full((batch_size, 1), 0, dtype=torch.long, device=device)
    position_ids = torch.full((batch_size, 1), 0, dtype=torch.long, device=device)
    sequence_len_offset_og = inference_params.sequence_len_offset
    # TD [2023-04-14]: important for correctness of the FT's attention kernel, as seqlen_cpu is
    # used to determine the size of smem. Hence seqlen_cpu must be >= lengths_per_sample.
    inference_params.sequence_len_offset = max_seqlen - 1
    inference_params.lengths_per_sample[:] = max_seqlen - 1

    # 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,
                last_token_only=True,
            ).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,
            last_token_only=True,
        ).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

    inference_params.sequence_len_offset = sequence_len_offset_og
    return run