mirror of
https://git.datalinker.icu/vllm-project/vllm.git
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514 lines
20 KiB
Python
514 lines
20 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 copy import deepcopy
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from typing import TYPE_CHECKING
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import vllm.envs as envs
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from vllm.logger import init_logger
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from vllm.model_executor.models import ModelRegistry
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from vllm.utils import STR_DTYPE_TO_TORCH_DTYPE, cdiv, round_up
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from vllm.v1.kv_cache_interface import FullAttentionSpec, MambaSpec
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if TYPE_CHECKING:
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from vllm.config import VllmConfig
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logger = init_logger(__name__)
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class VerifyAndUpdateConfig:
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@staticmethod
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def verify_and_update_config(vllm_config: "VllmConfig") -> None:
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raise NotImplementedError
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class Gemma3TextModelConfig:
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@staticmethod
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def verify_and_update_config(vllm_config: "VllmConfig") -> None:
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hf_config = vllm_config.model_config.hf_config
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hf_config.is_causal = not hf_config.use_bidirectional_attention
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class GteNewModelConfig(VerifyAndUpdateConfig):
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@staticmethod
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def verify_and_update_config(vllm_config: "VllmConfig") -> None:
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config = vllm_config.model_config.hf_config
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assert config.__class__.__name__ == "NewConfig"
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assert config.hidden_act == "gelu"
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config.hidden_act = "geglu"
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head_dim = config.hidden_size // config.num_attention_heads
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config.rotary_kwargs = {
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"head_size": head_dim,
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"rotary_dim": getattr(config, "rotary_emb_dim", head_dim),
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"max_position": config.max_position_embeddings,
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"base": config.rope_theta,
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"rope_scaling": getattr(config, "rope_scaling", None),
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}
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class JambaForSequenceClassificationConfig(VerifyAndUpdateConfig):
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@staticmethod
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def verify_and_update_config(vllm_config: "VllmConfig") -> None:
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pooler_config = vllm_config.model_config.pooler_config
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if pooler_config.activation is None:
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pooler_config.activation = False
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class JinaRobertaModelConfig(VerifyAndUpdateConfig):
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@staticmethod
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def verify_and_update_config(vllm_config: "VllmConfig") -> None:
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model_config = vllm_config.model_config
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config = model_config.hf_config
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if config.position_embedding_type == "rotary":
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assert config.__class__.__name__ == "XLMRobertaFlashConfig"
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head_dim = config.hidden_size // config.num_attention_heads
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max_position = config.max_position_embeddings
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# Jina-embeddings-v3 has max_position_embeddings=8194, which will cause
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# out-of-bound index issue at RoPE for long prompts with torch.compile,
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# because it can't be divided by triton num_warps(default=4 or 8).
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# To deal with this, we increase max_position to multiple of n_warps,
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# so that triton kernel won't hit out-of-bound index in RoPE cache.
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if not model_config.enforce_eager:
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max_position = round_up(max_position, 8)
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config.rotary_kwargs = {
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"head_size": head_dim,
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"rotary_dim": getattr(config, "rotary_emb_dim", head_dim),
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"max_position": max_position,
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"base": getattr(config, "rope_theta", config.rotary_emb_base),
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"rope_scaling": getattr(config, "rope_scaling", None),
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}
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class NomicBertModelConfig(VerifyAndUpdateConfig):
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@staticmethod
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def verify_and_update_config(vllm_config: "VllmConfig") -> None:
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config = vllm_config.model_config.hf_config
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assert config.__class__.__name__ == "NomicBertConfig"
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assert config.activation_function in ["swiglu", "gelu"]
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config.position_embedding_type = getattr(
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config, "position_embedding_type", "rope"
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)
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if config.activation_function == "swiglu":
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config.hidden_act = "silu"
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else:
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config.hidden_act = config.activation_function
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assert config.mlp_fc1_bias == config.mlp_fc2_bias == config.qkv_proj_bias
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config.bias = config.qkv_proj_bias
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assert config.rotary_emb_scale_base is None
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assert not config.rotary_emb_interleaved
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config.layer_norm_eps = config.layer_norm_epsilon
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config.intermediate_size = config.n_inner
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config.hidden_size = config.n_embd
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config.num_hidden_layers = config.n_layer
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head_dim = config.hidden_size // config.num_attention_heads
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rotary_emb_dim = int(head_dim * config.rotary_emb_fraction)
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max_trained_positions = getattr(config, "max_trained_positions", 2048)
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config.rotary_kwargs = {
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"head_size": head_dim,
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"rotary_dim": rotary_emb_dim,
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"max_position": max_trained_positions,
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"base": getattr(config, "rope_theta", config.rotary_emb_base),
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"rope_scaling": getattr(config, "rope_scaling", None),
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}
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# we ignore config.rotary_scaling_factor so that for datasets shorter
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# than max_trained_positions 2048, the results are consistent
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# with SentenceTransformer.
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# The context extension uses vllm style rope_theta and rope_scaling.
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# See #17785 #18755
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if (
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not vllm_config.model_config.hf_overrides
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and vllm_config.model_config.original_max_model_len is None
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):
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# Default
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# Reset max_model_len to max_trained_positions.
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# nomic-embed-text-v2-moe the length is set to 512
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# by sentence_bert_config.json.
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max_model_len_before = vllm_config.model_config.max_model_len
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max_model_len = min(
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vllm_config.model_config.max_model_len, max_trained_positions
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)
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vllm_config.recalculate_max_model_len(max_model_len)
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logger.warning(
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"Nomic context extension is disabled. "
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"Changing max_model_len from %s to %s. "
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"To enable context extension, see: "
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"https://github.com/vllm-project/vllm/tree/main/examples/offline_inference/context_extension.html",
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max_model_len_before,
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vllm_config.model_config.max_model_len,
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)
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else:
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# We need to re-verify max_model_len to avoid lengths
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# greater than position_embedding.
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model_config = vllm_config.model_config
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hf_text_config = model_config.hf_text_config
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if isinstance(model_config.hf_overrides, dict):
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# hf_overrides_kw
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max_model_len = model_config.hf_overrides.get(
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"max_model_len", vllm_config.model_config.max_model_len
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)
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else:
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# hf_overrides_fn
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# This might be overridden by sentence_bert_config.json.
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max_model_len = vllm_config.model_config.max_model_len
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# reset hf_text_config for recalculate_max_model_len.
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if hasattr(hf_text_config, "max_model_len"):
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delattr(hf_text_config, "max_model_len")
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hf_text_config.max_position_embeddings = max_trained_positions
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hf_text_config.rope_scaling = config.rotary_kwargs["rope_scaling"]
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# The priority of sentence_bert_config.json is higher
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# than max_position_embeddings
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encoder_config = deepcopy(model_config.encoder_config)
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encoder_config.pop("max_seq_length", None)
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model_config.encoder_config = encoder_config
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vllm_config.recalculate_max_model_len(max_model_len)
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class Qwen2ForProcessRewardModelConfig(VerifyAndUpdateConfig):
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@staticmethod
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def verify_and_update_config(vllm_config: "VllmConfig") -> None:
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pooler_config = vllm_config.model_config.pooler_config
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if pooler_config.step_tag_id is None:
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pooler_config.step_tag_id = 151651
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class Qwen2ForRewardModelConfig(VerifyAndUpdateConfig):
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@staticmethod
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def verify_and_update_config(vllm_config: "VllmConfig") -> None:
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pooler_config = vllm_config.model_config.pooler_config
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if pooler_config.softmax is None:
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pooler_config.softmax = False
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class Qwen3ForSequenceClassificationConfig(VerifyAndUpdateConfig):
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@staticmethod
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def verify_and_update_config(vllm_config: "VllmConfig") -> None:
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config = vllm_config.model_config.hf_config
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is_original_qwen3_reranker = getattr(
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config, "is_original_qwen3_reranker", False
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)
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if not is_original_qwen3_reranker:
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return
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tokens = getattr(config, "classifier_from_token", None)
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assert tokens is not None and len(tokens) == 2, (
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"Try loading the original Qwen3 Reranker?, see: "
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"https://github.com/vllm-project/vllm/tree/main/examples/offline_inference/qwen3_reranker.py"
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)
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vllm_config.model_config.hf_config.method = "from_2_way_softmax"
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class JinaVLForSequenceClassificationConfig(VerifyAndUpdateConfig):
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@staticmethod
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def verify_and_update_config(vllm_config: "VllmConfig") -> None:
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config = vllm_config.model_config.hf_config
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config.num_labels = 1
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pooler_config = vllm_config.model_config.pooler_config
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if pooler_config.logit_bias is None:
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pooler_config.logit_bias = 2.65
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class SnowflakeGteNewModelConfig(VerifyAndUpdateConfig):
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@staticmethod
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def verify_and_update_config(vllm_config: "VllmConfig") -> None:
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config = vllm_config.model_config.hf_config
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assert config.__class__.__name__ == "GteConfig"
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assert config.hidden_act == "gelu"
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config.hidden_act = "geglu"
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head_dim = config.hidden_size // config.num_attention_heads
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config.rotary_kwargs = {
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"head_size": head_dim,
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"rotary_dim": getattr(config, "rotary_emb_dim", head_dim),
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"max_position": config.max_position_embeddings,
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"base": config.rope_theta,
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"rope_scaling": getattr(config, "rope_scaling", None),
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}
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class GptOssForCausalLMConfig(VerifyAndUpdateConfig):
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@staticmethod
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def verify_and_update_config(vllm_config: "VllmConfig") -> None:
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structured_outputs_config = vllm_config.structured_outputs_config
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if structured_outputs_config.reasoning_parser == "":
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structured_outputs_config.reasoning_parser = "openai_gptoss"
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# Increase the max capture size from 512 to 992 for performance.
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# NOTE(woosuk): This will increase the number of CUDA graphs
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# from 67 to 81.
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scheduler_config = vllm_config.scheduler_config
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if len(scheduler_config.cuda_graph_sizes) == 1:
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max_capture_size = scheduler_config.cuda_graph_sizes[0]
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# FIXME(woosuk): When using full cuda graph with FA3, the max
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# supported size is 992.
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if max_capture_size < 992:
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cuda_graph_sizes = [1, 2, 4]
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# Step size 8 for small batch sizes
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cuda_graph_sizes += [i for i in range(8, 256, 8)]
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# Step size 16 for larger batch sizes
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cuda_graph_sizes += [i for i in range(256, 993, 16)]
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scheduler_config.cuda_graph_sizes = cuda_graph_sizes
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logger.info(
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"Overriding max cuda graph capture size to %d for performance.", 992
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)
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class MambaModelConfig(VerifyAndUpdateConfig):
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@classmethod
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def verify_and_update_config(cls, vllm_config: "VllmConfig") -> None:
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"""
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Enable FULL_AND_PIECEWISE cuda graph mode by default (required
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to get good performance for mamba layers in V1).
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Args:
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vllm_config: vLLM Config
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"""
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if not envs.VLLM_USE_V1:
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return
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model_config = vllm_config.model_config
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cache_config = vllm_config.cache_config
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# Set mamba block size to max_model_len (this may get
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# override by prefix caching logic later)
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cache_config.mamba_block_size = model_config.max_model_len
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# TODO(@tdoublep) find a better way to do this than whitelist
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MAMBA2_MODELS = [
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"BambaForCausalLM",
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"FalconH1ForCausalLM",
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"GraniteMoeHybridForCausalLM",
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"Mamba2ForCausalLM",
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"NemotronHForCausalLM",
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"Zamba2ForCausalLM",
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]
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if cache_config.enable_prefix_caching:
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if model_config.architecture in MAMBA2_MODELS:
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logger.info(
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"Warning: Prefix caching is currently enabled. "
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"Its support for Mamba2 layers is experimental. "
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"Please report any issues you may observe."
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)
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else:
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logger.info(
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"Hybrid or mamba-based model detected without "
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"support for prefix caching: disabling."
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)
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cache_config.enable_prefix_caching = False
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# TODO(tdoublep): remove once cascade attention is supported
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logger.info(
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"Disabling cascade attention since it is not supported for hybrid models."
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)
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model_config.disable_cascade_attn = True
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class HybridAttentionMambaModelConfig(VerifyAndUpdateConfig):
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@classmethod
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def verify_and_update_config(cls, vllm_config: "VllmConfig") -> None:
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"""
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Ensure that page size of attention layers is greater than or
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equal to the mamba layers. If not, automatically set the attention
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block size to ensure that it is. If the attention page size is
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strictly greater than the mamba page size, we pad the mamba page size
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to make them equal.
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Args:
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vllm_config: vLLM Config
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"""
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if not envs.VLLM_USE_V1:
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return
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# Enable FULL_AND_PIECEWISE by default
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MambaModelConfig.verify_and_update_config(vllm_config)
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cache_config = vllm_config.cache_config
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model_config = vllm_config.model_config
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parallel_config = vllm_config.parallel_config
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if cache_config.cache_dtype == "auto":
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kv_cache_dtype = model_config.dtype
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else:
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kv_cache_dtype = STR_DTYPE_TO_TORCH_DTYPE[cache_config.cache_dtype]
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# get attention page size (for 1 token)
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attn_page_size_1_token = FullAttentionSpec(
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block_size=1,
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num_kv_heads=model_config.get_num_kv_heads(parallel_config),
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head_size=model_config.get_head_size(),
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dtype=kv_cache_dtype,
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).page_size_bytes
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model_cls, _ = ModelRegistry.resolve_model_cls(
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model_config.architecture,
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model_config=model_config,
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)
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# get mamba page size
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mamba_page_size = MambaSpec(
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shapes=model_cls.get_mamba_state_shape_from_config(vllm_config),
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dtypes=model_cls.get_mamba_state_dtype_from_config(vllm_config),
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block_size=model_config.max_model_len,
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).page_size_bytes
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# Model may be marked as is_hybrid
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# but mamba is skipped via config,
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# return directly
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if mamba_page_size == 0:
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return
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# Attention backend constraints:
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# - FlashAttention (FA) requires block size to be multiple of 16
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# - MLA (Multi-head Latent Attention) requires larger alignment:
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# * CUTLASS_MLA backend: 128-byte alignment
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# * Other MLA backends: 64-byte alignment
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if model_config.use_mla:
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use_cutlass_mla = envs.VLLM_ATTENTION_BACKEND == "CUTLASS_MLA"
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kernel_block_alignment_size = 128 if use_cutlass_mla else 64
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else:
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kernel_block_alignment_size = 16
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if cache_config.enable_prefix_caching:
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# With prefix caching, select attention block size to
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# optimize for mamba kernel performance
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# mamba SSD kernel uses a chunk_size, e.g. 256
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# Align the block to the kernel: use lowest multiple of chunk_size
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# of attention tokens that would fit mamba_page_size:
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# e.g. for mamba page size = 788kB
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# attn_1_token = 2kB -> fits ~394 tokens
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# then round up to a mulitple of 256 -> 512 tokens
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# End result:
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# attn_block_size = 512
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# mamba_block_size = 512 (aligned to a multiple of chunk_size)
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# TODO(tdoublep): this constraint can be relaxed fairly
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# easily by changing the way we layout chunks in the
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# mamba2 kernels.
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from math import gcd
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def lcm(a, b):
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return a * b // gcd(a, b)
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base_chunk_size = model_config.get_mamba_chunk_size()
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attn_tokens_per_mamba_state = cdiv(mamba_page_size, attn_page_size_1_token)
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chunk_size = lcm(base_chunk_size, kernel_block_alignment_size)
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attn_block_size = chunk_size * cdiv(attn_tokens_per_mamba_state, chunk_size)
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cache_config.mamba_block_size = attn_block_size
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else:
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# Without prefix caching, select minimum valid attention block size
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# to minimize mamba state padding
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# Calculate minimum attention block size that satisfies both:
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# 1. Backend alignment requirements (kernel_block_alignment_size)
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# 2. Mamba page size compatibility (attn_page_size >= mamba_page_size)
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attn_block_size = kernel_block_alignment_size * cdiv(
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mamba_page_size, kernel_block_alignment_size * attn_page_size_1_token
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)
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# override attention block size if either (a) the
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# user has not set it or (b) the user has set it
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# too small.
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if cache_config.block_size is None or cache_config.block_size < attn_block_size:
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cache_config.block_size = attn_block_size
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logger.info(
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"Setting attention block size to %d tokens "
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"to ensure that attention page size is >= mamba page size.",
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attn_block_size,
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)
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# compute new attention page size
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attn_page_size = cache_config.block_size * attn_page_size_1_token
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assert attn_page_size >= mamba_page_size
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if attn_page_size == mamba_page_size:
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# don't need to pad mamba page size
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return
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# pad mamba page size to exactly match attention
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if (
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cache_config.mamba_page_size_padded is None
|
|
or cache_config.mamba_page_size_padded != attn_page_size
|
|
):
|
|
cache_config.mamba_page_size_padded = attn_page_size
|
|
mamba_padding_pct = (
|
|
100 * (attn_page_size - mamba_page_size) / mamba_page_size
|
|
)
|
|
logger.info(
|
|
"Padding mamba page size by %.2f%% to ensure "
|
|
"that mamba page size and attention page size are "
|
|
"exactly equal.",
|
|
mamba_padding_pct,
|
|
)
|
|
|
|
|
|
class DeepseekV32ForCausalLM(VerifyAndUpdateConfig):
|
|
@classmethod
|
|
def verify_and_update_config(cls, vllm_config: "VllmConfig") -> None:
|
|
"""
|
|
Updated fp8 cache to custom "fp8_ds_mla" format for DeepSeekV32
|
|
"""
|
|
hf_config = vllm_config.model_config.hf_config
|
|
|
|
# Mirror the check in vllm/model_executor/models/deepseek_v2.py
|
|
is_v32 = hasattr(hf_config, "index_topk")
|
|
assert is_v32
|
|
|
|
# For DeepSeekV3.2, we use a custom fp8 format as default (i.e.
|
|
# "auto")
|
|
cache_config = vllm_config.cache_config
|
|
if cache_config.cache_dtype == "auto" or cache_config.cache_dtype.startswith(
|
|
"fp8"
|
|
):
|
|
cache_config.cache_dtype = "fp8_ds_mla"
|
|
logger.info("Using custom fp8 kv-cache format for DeepSeekV3.2")
|
|
if cache_config.cache_dtype == "bfloat16":
|
|
cache_config.cache_dtype = "auto"
|
|
logger.info("Using bfloat16 kv-cache for DeepSeekV3.2")
|
|
|
|
|
|
MODELS_CONFIG_MAP: dict[str, type[VerifyAndUpdateConfig]] = {
|
|
"GteModel": SnowflakeGteNewModelConfig,
|
|
"GteNewModel": GteNewModelConfig,
|
|
"GteNewForSequenceClassification": GteNewModelConfig,
|
|
"Gemma3TextModel": Gemma3TextModelConfig,
|
|
"NomicBertModel": NomicBertModelConfig,
|
|
"Qwen2ForProcessRewardModel": Qwen2ForProcessRewardModelConfig,
|
|
"Qwen2ForRewardModel": Qwen2ForRewardModelConfig,
|
|
"Qwen3ForSequenceClassification": Qwen3ForSequenceClassificationConfig,
|
|
"XLMRobertaModel": JinaRobertaModelConfig,
|
|
"JinaVLForRanking": JinaVLForSequenceClassificationConfig,
|
|
"JambaForSequenceClassification": JambaForSequenceClassificationConfig,
|
|
"GptOssForCausalLM": GptOssForCausalLMConfig,
|
|
"MambaForCausalLM": MambaModelConfig,
|
|
"Mamba2ForCausalLM": MambaModelConfig,
|
|
"FalconMambaForCausalLM": MambaModelConfig,
|
|
"DeepseekV32ForCausalLM": DeepseekV32ForCausalLM,
|
|
}
|