272 lines
9.9 KiB
Python
272 lines
9.9 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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from collections import defaultdict
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from typing import TYPE_CHECKING, Optional
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import torch
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from vllm.config import ModelConfig, SchedulerConfig
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from vllm.model_executor.models.interfaces import MultiModalEmbeddings
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from vllm.model_executor.models.utils import extract_layer_index
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from vllm.multimodal.registry import MultiModalRegistry
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from vllm.v1.core.encoder_cache_manager import compute_encoder_budget
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from vllm.v1.kv_cache_interface import KVCacheGroupSpec
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if TYPE_CHECKING:
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from vllm.attention.layer import Attention
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class MultiModalBudget:
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"""Helper class to calculate budget information for multi-modal models."""
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def __init__(
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self,
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model_config: ModelConfig,
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scheduler_config: SchedulerConfig,
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mm_registry: MultiModalRegistry,
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*,
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max_model_len: int,
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max_num_reqs: int,
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) -> None:
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super().__init__()
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self.model_config = model_config
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self.scheduler_config = scheduler_config
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self.mm_registry = mm_registry
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encoder_compute_budget, encoder_cache_size = compute_encoder_budget(
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model_config=model_config,
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scheduler_config=scheduler_config,
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mm_registry=mm_registry,
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)
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self.max_num_encoder_input_tokens = encoder_compute_budget
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self.encoder_cache_size = encoder_cache_size
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self.max_model_len = max_model_len
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self.max_num_reqs = max_num_reqs
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self.mm_limits = mm_registry.get_mm_limits_per_prompt(model_config)
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max_items_per_prompt_by_modality = dict[str, int]()
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max_items_per_batch_by_modality = dict[str, int]()
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max_tokens_by_modality = mm_registry \
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.get_max_tokens_per_item_by_nonzero_modality(model_config)
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for modality, max_tokens in max_tokens_by_modality.items():
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(
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max_items_per_prompt,
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max_items_per_batch,
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) = self.get_max_items(modality, max_tokens)
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max_items_per_prompt_by_modality[modality] = max_items_per_prompt
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max_items_per_batch_by_modality[modality] = max_items_per_batch
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self.max_tokens_by_modality = max_tokens_by_modality
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self.max_items_per_prompt_by_modality = max_items_per_prompt_by_modality
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self.max_items_per_batch_by_modality = max_items_per_batch_by_modality
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def get_modality_with_max_tokens(self) -> tuple[str, int]:
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max_tokens_by_modality = self.max_tokens_by_modality
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modality, max_tokens = max(max_tokens_by_modality.items(),
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key=lambda item: item[1])
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return modality, max_tokens
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def get_encoder_budget(self) -> int:
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return min(self.max_num_encoder_input_tokens, self.encoder_cache_size)
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def get_max_items(
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self,
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modality: str,
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max_tokens_per_item: int,
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) -> tuple[int, int]:
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if max_tokens_per_item == 0:
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return 0, 0
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# Check how many items of this modality can be supported by
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# the encoder budget.
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encoder_budget = self.get_encoder_budget()
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# TODO: handle encoder-decoder models once we support them.
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if encoder_budget == 0:
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return 0, 0
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max_encoder_items_per_batch = encoder_budget // max_tokens_per_item
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# Check how many items of this modality can be supported by
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# the decoder budget.
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mm_limit = self.mm_limits[modality]
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max_items_per_prompt = max(
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1,
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min(mm_limit, self.max_model_len // max_tokens_per_item),
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)
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scheduler_config = self.scheduler_config
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max_num_reqs = self.max_num_reqs
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if not scheduler_config.enable_chunked_prefill:
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max_num_reqs = min(
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max_num_reqs,
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scheduler_config.max_num_batched_tokens // max_tokens_per_item,
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)
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max_decoder_items_per_batch = max_num_reqs * max_items_per_prompt
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max_items_per_batch = max(
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1,
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min(max_encoder_items_per_batch, max_decoder_items_per_batch),
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)
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return max_items_per_prompt, max_items_per_batch
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def sanity_check_mm_encoder_outputs(
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mm_embeddings: MultiModalEmbeddings,
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expected_num_items: int,
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) -> None:
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"""
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Perform sanity checks for the result of
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[`vllm.model_executor.models.SupportsMultiModal.get_multimodal_embeddings`][].
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"""
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assert isinstance(mm_embeddings, (list, tuple, torch.Tensor)), (
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"Expected multimodal embeddings to be a list/tuple of 2D tensors, "
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f"or a single 3D tensor, but got {type(mm_embeddings)} "
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"instead. This is most likely due to incorrect implementation "
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"of the model's `get_multimodal_embeddings` method.")
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assert len(mm_embeddings) == expected_num_items, (
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"Expected number of multimodal embeddings to match number of "
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f"input items: {expected_num_items}, but got {len(mm_embeddings)=} "
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"instead. This is most likely due to incorrect implementation "
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"of the model's `get_multimodal_embeddings` method.")
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assert all(e.ndim == 2 for e in mm_embeddings), (
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"Expected multimodal embeddings to be a sequence of 2D tensors, "
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f"but got tensors with shapes {[e.shape for e in mm_embeddings]} "
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"instead. This is most likely due to incorrect implementation "
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"of the model's `get_multimodal_embeddings` method.")
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def scatter_mm_placeholders(
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embeds: torch.Tensor,
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is_embed: Optional[torch.Tensor],
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) -> torch.Tensor:
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"""
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Scatter the multimodal embeddings into a contiguous tensor that represents
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the placeholder tokens.
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[`vllm.multimodal.processing.PromptUpdateDetails.is_embed`][].
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Args:
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embeds: The multimodal embeddings.
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Shape: `(num_embeds, embed_dim)`
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is_embed: A boolean mask indicating which positions in the placeholder
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tokens need to be filled with multimodal embeddings.
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Shape: `(num_placeholders, num_embeds)`
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"""
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if is_embed is None:
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return embeds
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placeholders = embeds.new_full(
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(is_embed.shape[0], embeds.shape[-1]),
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fill_value=torch.nan,
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)
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placeholders[is_embed] = embeds
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return placeholders
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def gather_mm_placeholders(
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placeholders: torch.Tensor,
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is_embed: Optional[torch.Tensor],
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) -> torch.Tensor:
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"""
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Reconstructs the embeddings from the placeholder tokens.
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This is the operation of [scatter_mm_placeholders][].
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"""
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if is_embed is None:
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return placeholders
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return placeholders[is_embed]
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def initialize_kv_cache_for_kv_sharing(
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shared_kv_cache_layers: dict[str, str],
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kv_cache_groups: list[KVCacheGroupSpec],
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kv_caches: dict[str, torch.Tensor],
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) -> None:
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"""
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Sets up KV cache sharing by reusing the allocated KV caches in `kv_caches`
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for layers that do not allocate its own KV cache, based on the mapping in
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`shared_kv_cache_layers`. Adds these layers to the corresponding KV cache
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group, which is needed to ensure that attention metadata is assigned later.
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Args:
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shared_kv_cache_layers: Layer pairings for cross-layer KV sharing.
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If an Attention layer `layer_name` is in the keys of this dict, it
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means this layer will perform attention using the keys and values
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from the KV cache of `shared_kv_cache_layers[layer_name]`.
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kv_cache_groups: The KV cache groups of the model.
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kv_caches: The allocated kv_caches with layer names as keys.
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Note that layers in shared_kv_cache_layers.keys() are not
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originally included as it only contains layers which have its own
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KV cache allocation.
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"""
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# Record index of KV cache group for each layer that allocates a KV cache.
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layer_to_kv_cache_group_idx: dict[str, int] = {}
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for i, kv_cache_group in enumerate(kv_cache_groups):
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for layer_name in kv_cache_group.layer_names:
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layer_to_kv_cache_group_idx[layer_name] = i
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for layer_name, target_layer_name in shared_kv_cache_layers.items():
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kv_caches[layer_name] = kv_caches[target_layer_name]
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group_idx = layer_to_kv_cache_group_idx[target_layer_name]
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kv_cache_groups[group_idx].layer_names.append(layer_name)
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def bind_kv_cache(
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kv_caches: dict[str, torch.Tensor],
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forward_context: dict[str, "Attention"],
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runner_kv_caches: list[torch.Tensor],
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) -> None:
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"""
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Bind the allocated KV cache to both ModelRunner and forward context so
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that the KV cache can be used in the forward pass.
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This function:
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1) Fills the ModelRunner's kv cache list (`runner_kv_caches`) with
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kv_caches.
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2) Associates each attention layer in the `forward_context` with its
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corresponding KV cache in kv_caches.
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Args:
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kv_caches: The allocated kv_caches with layer names as keys.
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forward_context: The global forward context containing all Attention
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layers with layer names as keys.
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runner_kv_caches: The kv_cache declared by ModelRunner.
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"""
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# Bind kv_caches to ModelRunner
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assert len(runner_kv_caches) == 0
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# Convert kv_caches dict to a list of tensors in the order of layer_index.
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index2name = defaultdict(list)
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for layer_name in kv_caches:
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index2name[extract_layer_index(layer_name)].append(layer_name)
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for layer_index in sorted(index2name.keys()):
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layer_names = index2name[layer_index]
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if len(layer_names) > 1:
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# One typical case is encoder-decoder model, e.g., bart.
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# The cross attention and self attention in the same decoder layer
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# has different layer_name but the same layer_index.
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raise NotImplementedError
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layer_name = layer_names[0]
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runner_kv_caches.append(kv_caches[layer_name])
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# Bind kv_caches to forward context
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for layer_name, kv_cache in kv_caches.items():
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# NOTE: Use list because of v0 PP virtual engine.
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forward_context[layer_name].kv_cache = [kv_cache]
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