aphrodite-engine/aphrodite/modeling/models/utils.py

from typing import (Dict, Iterable, List, Literal, Optional, Protocol, Tuple,
                    Union, overload)

import torch
import torch.nn as nn
from torch.func import functional_call
from transformers import PretrainedConfig

from aphrodite.common.config import (CacheConfig, LoRAConfig, MultiModalConfig,
                                     SchedulerConfig)
from aphrodite.common.sequence import IntermediateTensors
from aphrodite.common.utils import is_pin_memory_available, progress_bar
from aphrodite.modeling.model_loader.loader import build_model
from aphrodite.modeling.models import ModelRegistry
from aphrodite.multimodal.base import NestedTensors
from aphrodite.quantization import QuantizationConfig


def filter_weights(weights: Iterable[Tuple[str, torch.Tensor]], prefix: str):
    """
    Helper function to load weights for inner aphrodite models.

    See also:
        :ref:`init_aphrodite_registered_model`
    """
    weights_list = list(weights)
    for name, loaded_weight in progress_bar(weights_list,
                                desc="Loading modules..."):
        name = name.split(".")
        if prefix == name.pop(0):
            name = ".".join(name)
            yield name, loaded_weight


def init_aphrodite_registered_model(
    hf_config: PretrainedConfig,
    cache_config: Optional[CacheConfig],
    quant_config: Optional[QuantizationConfig],
    *,
    lora_config: Optional[LoRAConfig] = None,
    multimodal_config: Optional[MultiModalConfig] = None,
    scheduler_config: Optional[SchedulerConfig] = None,
) -> nn.Module:
    """
    Helper function to initialize an inner model registered to aphrodite,
    based on the arguments passed to the outer aphrodite model.
    """
    model_class, _ = ModelRegistry.resolve_model_cls(hf_config.architectures)

    return build_model(
        model_class,
        hf_config,
        cache_config,
        quant_config,
        lora_config=lora_config,
        multimodal_config=multimodal_config,
        scheduler_config=scheduler_config,
    )


@overload
def flatten_bn(x: torch.Tensor) -> torch.Tensor:
    ...

@overload
def flatten_bn(x: List[torch.Tensor]) -> List[torch.Tensor]:
    ...

@overload
def flatten_bn(
    x: Union[List[torch.Tensor], torch.Tensor],
    *,
    concat: Literal[True],
) -> torch.Tensor:
    ...

def flatten_bn(
    x: Union[List[torch.Tensor], torch.Tensor],
    *,
    concat: bool = False,
) -> Union[List[torch.Tensor], torch.Tensor]:
    """
    Flatten the ``B`` and ``N`` dimensions of batched multimodal inputs.
    The input tensor should have shape ``(B, N, ...)```.
    """
    if isinstance(x, torch.Tensor):
        return x.flatten(0, 1)
    if concat:
        return torch.cat(x)
    return [x_n for x_b in x for x_n in x_b]


def _flatten_embeddings(embeddings: NestedTensors) -> torch.Tensor:
    """
    Recursively flattens and concatenates NestedTensors on all but the last
    dimension.
    """

    if isinstance(embeddings, torch.Tensor):
        # Flatten all but the last dimension.
        return embeddings.flatten(0, -2)

    return torch.cat(tuple(_flatten_embeddings(t) for t in embeddings))

def _embedding_count_expression(embeddings: NestedTensors) -> str:
    """
    Constructs a debugging representation of the number of embeddings in the
    NestedTensors.
    """
    if isinstance(embeddings, torch.Tensor):
        return " x ".join([str(dim) for dim in embeddings.shape[:-1]])
    return " + ".join(
        _embedding_count_expression(inner) for inner in embeddings)


def merge_multimodal_embeddings(input_ids: torch.Tensor,
                                inputs_embeds: torch.Tensor,
                                multimodal_embeddings: NestedTensors,
                                placeholder_token_id: int) -> torch.Tensor:
    """
    Merge ``multimodal_embeddings`` into ``inputs_embeds`` by overwriting the
    positions in ``inputs_embeds`` corresponding to placeholder tokens in
    ``input_ids``.
    Note:
        This updates ``inputs_embeds`` in place.
    """
    mask = (input_ids == placeholder_token_id)
    num_expected_tokens = mask.sum().item()
    assert isinstance(num_expected_tokens, int)

    flattened = _flatten_embeddings(multimodal_embeddings)
    if flattened.shape[0] != num_expected_tokens:
        expr = _embedding_count_expression(multimodal_embeddings)
        raise ValueError(
            f"Attempted to assign {expr} = {flattened.shape[0]} "
            f"multimodal tokens to {num_expected_tokens} placeholders")

    inputs_embeds[mask] = flattened
    return inputs_embeds


class LayerFn(Protocol):

    def __call__(
        self,
        prefix="",
    ) -> torch.nn.Module:
        ...


class PPMissingLayer(torch.nn.Identity):
    """
    A placeholder layer for missing layers in a pipeline parallel model.
    """

    def __init__(self, *args, **kwargs):
        super().__init__()


_CPU_OFFLOAD_BYTES = 0
_CPU_OFFLOAD_MAX_BYTES = 0


def set_cpu_offload_max_bytes(max_bytes: int) -> None:
    global _CPU_OFFLOAD_MAX_BYTES, _CPU_OFFLOAD_BYTES
    _CPU_OFFLOAD_BYTES = 0
    _CPU_OFFLOAD_MAX_BYTES = max_bytes


def maybe_offload_to_cpu(module: torch.nn.Module) -> torch.nn.Module:
    device = next(module.parameters()).device

    if device == torch.device("cpu"):
        return module

    global _CPU_OFFLOAD_MAX_BYTES, _CPU_OFFLOAD_BYTES
    if _CPU_OFFLOAD_BYTES >= _CPU_OFFLOAD_MAX_BYTES:
        return module

    pin_memory = is_pin_memory_available()

    # offload parameters to CPU
    # use pin_memory if possible, which helps cudagraph capture speed
    offloaded_parameters = False
    for p in module.parameters():
        if _CPU_OFFLOAD_BYTES >= _CPU_OFFLOAD_MAX_BYTES:
            # we use per-parameter offloading
            # one module might have some parameters offloaded and some not
            break

        # `torch.empty_like` does not support `pin_memory` argument
        cpu_data = torch.empty_strided(size=p.data.size(),
                                       stride=p.data.stride(),
                                       dtype=p.data.dtype,
                                       layout=p.data.layout,
                                       device='cpu',
                                       pin_memory=pin_memory)
        cpu_data.copy_(p.data)
        p.data = cpu_data
        _CPU_OFFLOAD_BYTES += p.data.numel() * p.data.element_size()
        offloaded_parameters = True

    if offloaded_parameters:
        original_forward = module.forward

        def forward(*args, **kwargs):
            module.forward = original_forward
            device_state = {
                # here we blindly call `to(device)`
                # if the parameter is already on the device, it will be a no-op
                k: v.to(device, non_blocking=True)
                for k, v in module.state_dict().items()
            }
            output = functional_call(module,
                                     device_state,
                                     args=args,
                                     kwargs=kwargs)
            module.forward = forward
            return output

        module.forward = forward

    return module


def make_layers(
    num_hidden_layers: int,
    layer_fn: LayerFn,
    prefix: str,
) -> Tuple[int, int, torch.nn.ModuleList]:
    """Make a list of layers with the given layer function, taking
    pipeline parallelism into account.
    """
    from aphrodite.distributed.parallel_state import get_pp_group
    from aphrodite.distributed.utils import get_pp_indices
    start_layer, end_layer = get_pp_indices(num_hidden_layers,
                                            get_pp_group().rank_in_group,
                                            get_pp_group().world_size)
    modules = torch.nn.ModuleList(
        [PPMissingLayer() for _ in range(start_layer)] + [
            maybe_offload_to_cpu(layer_fn(prefix=f"{prefix}.{idx}"))
            for idx in range(start_layer, end_layer)
        ] + [PPMissingLayer() for _ in range(end_layer, num_hidden_layers)])
    return start_layer, end_layer, modules


# NOTE: don't use lru_cache here because it can prevent garbage collection
_model_to_pp_missing_layer_names: Dict[int, List[str]] = {}


def get_pp_missing_layer_names(model: torch.nn.Module) -> List[str]:
    """Get the names of the missing layers in a pipeline parallel model."""
    model_id = id(model)
    if model_id in _model_to_pp_missing_layer_names:
        return _model_to_pp_missing_layer_names[model_id]

    missing_layer_names = []
    for name, module in model.named_modules():
        if isinstance(module, PPMissingLayer):
            # NOTE: the trailing dot is used to match the prefix of the layer.
            # without the dot, we could match a layer that is not missing,
            # e.g., 'encoder.layer.1' would match 'encoder.layer.11'
            missing_layer_names.append(name + '.')
    _model_to_pp_missing_layer_names[model_id] = missing_layer_names

    return missing_layer_names


def is_pp_missing_parameter(name: str, model: torch.nn.Module) -> bool:
    """Check if a parameter is missing in a pipeline parallel model."""
    for missing_layer_name in get_pp_missing_layer_names(model):
        if name.startswith(missing_layer_name):
            return True
    return False


def make_empty_intermediate_tensors_factory(keys: List[str], hidden_size: int):

    def make_empty_intermediate_tensors(
            batch_size: int, dtype: torch.dtype,
            device: torch.device) -> IntermediateTensors:
        return IntermediateTensors({
            key: torch.zeros((batch_size, hidden_size),
                             dtype=dtype,
                             device=device)
            for key in keys
        })

    return make_empty_intermediate_tensors