wehub-resource-sync
542 lines
19 KiB
Python
542 lines
19 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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# Adapted from:
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# https://github.com/huggingface/transformers/blob/main/src/transformers/models/olmo_hybrid/modeling_olmo_hybrid.py
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# Copyright 2026 The vLLM team.
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#
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# This code combines OLMo2/OLMo3 attention with Gated DeltaNet linear attention
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# for the OLMo Hybrid architecture.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Inference-only OLMo Hybrid model compatible with HuggingFace weights."""
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from collections.abc import Iterable
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from functools import partial
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from itertools import islice
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import torch
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from torch import nn
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from vllm.compilation.decorators import support_torch_compile
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from vllm.config import (
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VllmConfig,
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)
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from vllm.distributed import (
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get_pp_group,
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get_tensor_model_parallel_rank,
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get_tensor_model_parallel_world_size,
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tensor_model_parallel_all_gather,
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)
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from vllm.distributed.utils import split_tensor_along_last_dim
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from vllm.logger import init_logger
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from vllm.model_executor.layers.activation import SiluAndMul
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from vllm.model_executor.layers.attention import Attention
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from vllm.model_executor.layers.layernorm import RMSNorm
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from vllm.model_executor.layers.linear import (
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MergedColumnParallelLinear,
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QKVParallelLinear,
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RowParallelLinear,
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)
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from vllm.model_executor.layers.logits_processor import LogitsProcessor
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from vllm.model_executor.layers.mamba.gdn.olmo_gdn_linear_attn import (
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OlmoHybridGatedDeltaNetAttention,
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)
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from vllm.model_executor.layers.mamba.mamba_utils import (
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MambaStateCopyFunc,
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MambaStateCopyFuncCalculator,
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MambaStateDtypeCalculator,
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MambaStateShapeCalculator,
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)
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from vllm.model_executor.layers.rotary_embedding import get_rope
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from vllm.model_executor.layers.vocab_parallel_embedding import (
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ParallelLMHead,
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VocabParallelEmbedding,
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)
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from vllm.model_executor.model_loader.weight_utils import (
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default_weight_loader,
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)
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from vllm.sequence import IntermediateTensors
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from .interfaces import HasInnerState, IsHybrid, SupportsLoRA, SupportsPP
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from .utils import (
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AutoWeightsLoader,
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extract_layer_index,
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is_pp_missing_parameter,
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make_empty_intermediate_tensors_factory,
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make_layers,
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maybe_prefix,
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)
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logger = init_logger(__name__)
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class OlmoHybridAttention(nn.Module):
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def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
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super().__init__()
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self.config = vllm_config.model_config.hf_config
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hidden_size = self.config.hidden_size
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self.tp_size = get_tensor_model_parallel_world_size()
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self.total_num_heads = self.config.num_attention_heads
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assert hidden_size % self.total_num_heads == 0
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assert self.total_num_heads % self.tp_size == 0
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self.num_heads = self.total_num_heads // self.tp_size
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self.total_num_kv_heads = (
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self.config.num_key_value_heads or self.total_num_heads
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)
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if self.total_num_kv_heads >= self.tp_size:
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assert self.total_num_kv_heads % self.tp_size == 0
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else:
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assert self.tp_size % self.total_num_kv_heads == 0
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self.num_kv_heads = max(1, self.total_num_kv_heads // self.tp_size)
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self.head_dim = hidden_size // self.total_num_heads
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self.q_size = self.num_heads * self.head_dim
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self.kv_size = self.num_kv_heads * self.head_dim
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self.max_position_embeddings = self.config.max_position_embeddings
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self.qkv_proj = QKVParallelLinear(
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hidden_size,
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self.head_dim,
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self.total_num_heads,
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self.total_num_kv_heads,
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bias=False,
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quant_config=vllm_config.quant_config,
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prefix=f"{prefix}.qkv_proj",
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)
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self.tp_rank = get_tensor_model_parallel_rank()
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self.k_norm = RMSNorm(
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self.total_num_kv_heads * self.head_dim,
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eps=self.config.rms_norm_eps,
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)
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self.q_norm = RMSNorm(
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self.config.hidden_size,
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eps=self.config.rms_norm_eps,
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)
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self.scaling = self.head_dim**-0.5
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self.attn = Attention(
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self.num_heads,
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self.head_dim,
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self.scaling,
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num_kv_heads=self.num_kv_heads,
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cache_config=vllm_config.cache_config,
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quant_config=vllm_config.quant_config,
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prefix=f"{prefix}.attn",
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)
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rope_parameters = getattr(self.config, "rope_parameters", None)
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self._use_rope = (rope_parameters is not None) and (
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rope_parameters["rope_theta"] is not None
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)
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if self._use_rope:
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self.rotary_emb = get_rope(
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self.head_dim,
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max_position=self.max_position_embeddings,
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rope_parameters=rope_parameters,
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)
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else:
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self.rotary_emb = None
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self.o_proj = RowParallelLinear(
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self.total_num_heads * self.head_dim,
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hidden_size,
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bias=False,
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quant_config=vllm_config.quant_config,
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prefix=f"{prefix}.o_proj",
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)
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def _apply_qk_norm(
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self, q: torch.Tensor, k: torch.Tensor
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) -> tuple[torch.Tensor, torch.Tensor]:
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if self.tp_size > 1:
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q = tensor_model_parallel_all_gather(q.contiguous())
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k = tensor_model_parallel_all_gather(k.contiguous())
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q = self.q_norm(q)
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k = self.k_norm(k)
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if self.tp_size > 1:
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splitter = partial(split_tensor_along_last_dim, num_partitions=self.tp_size)
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q = splitter(q)[self.tp_rank]
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k = splitter(k)[self.tp_rank]
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return q, k
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def forward(
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self,
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positions: torch.Tensor,
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hidden_states: torch.Tensor,
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) -> torch.Tensor:
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qkv, _ = self.qkv_proj(hidden_states)
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q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
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q, k = self._apply_qk_norm(q, k)
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if self._use_rope:
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q, k = self.rotary_emb(positions, q, k)
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attn_output = self.attn(q, k, v)
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output, _ = self.o_proj(attn_output)
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return output
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class OlmoHybridMLP(nn.Module):
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def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
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super().__init__()
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config = vllm_config.model_config.hf_config
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hidden_size = config.hidden_size
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intermediate_size = config.intermediate_size
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self.gate_up_proj = MergedColumnParallelLinear(
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hidden_size,
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[intermediate_size] * 2,
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bias=False,
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quant_config=vllm_config.quant_config,
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prefix=f"{prefix}.gate_up_proj",
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)
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self.act_fn = SiluAndMul()
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self.down_proj = RowParallelLinear(
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intermediate_size,
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hidden_size,
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bias=False,
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quant_config=vllm_config.quant_config,
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prefix=f"{prefix}.down_proj",
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)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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gate_up, _ = self.gate_up_proj(x)
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x = self.act_fn(gate_up)
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x, _ = self.down_proj(x)
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return x
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class OlmoHybridDecoderLayer(nn.Module):
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def __init__(self, *, vllm_config: VllmConfig, prefix: str = "") -> None:
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super().__init__()
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config = vllm_config.model_config.hf_config
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layer_idx = extract_layer_index(prefix)
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self.layer_type = config.layer_types[layer_idx]
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self.layer_idx = layer_idx
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if self.layer_type == "linear_attention":
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self.linear_attn = OlmoHybridGatedDeltaNetAttention(
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config,
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vllm_config,
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prefix=f"{prefix}.linear_attn",
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)
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self.input_layernorm = RMSNorm(
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config.hidden_size,
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eps=config.rms_norm_eps,
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)
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self.post_attention_layernorm = RMSNorm(
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config.hidden_size,
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eps=config.rms_norm_eps,
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)
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else:
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self.self_attn = OlmoHybridAttention(
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vllm_config=vllm_config,
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prefix=f"{prefix}.self_attn",
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)
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# Attention layers use these norm names
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self.post_attention_layernorm = RMSNorm(
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config.hidden_size,
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eps=config.rms_norm_eps,
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)
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self.post_feedforward_layernorm = RMSNorm(
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config.hidden_size,
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eps=config.rms_norm_eps,
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)
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self.mlp = OlmoHybridMLP(
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vllm_config=vllm_config,
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prefix=f"{prefix}.mlp",
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)
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def forward(
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self,
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positions: torch.Tensor,
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hidden_states: torch.Tensor,
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) -> torch.Tensor:
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if self.layer_type == "linear_attention":
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residual = hidden_states
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hidden_states = self.input_layernorm(hidden_states)
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attn_output = torch.empty_like(hidden_states)
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self.linear_attn(
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hidden_states=hidden_states,
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output=attn_output,
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)
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hidden_states = residual + attn_output
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residual = hidden_states
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hidden_states = self.post_attention_layernorm(hidden_states)
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hidden_states = self.mlp(hidden_states)
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hidden_states = residual + hidden_states
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else:
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residual = hidden_states
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hidden_states = self.self_attn(positions, hidden_states)
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hidden_states = self.post_attention_layernorm(hidden_states)
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hidden_states = residual + hidden_states
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residual = hidden_states
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hidden_states = self.mlp(hidden_states)
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hidden_states = self.post_feedforward_layernorm(hidden_states)
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hidden_states = residual + hidden_states
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return hidden_states
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@support_torch_compile
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class OlmoHybridModel(nn.Module):
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def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
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super().__init__()
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self.config = vllm_config.model_config.hf_config
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self.embed_tokens = VocabParallelEmbedding(
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self.config.vocab_size,
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self.config.hidden_size,
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prefix=f"{prefix}.embed_tokens",
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)
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self.start_layer, self.end_layer, self.layers = make_layers(
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self.config.num_hidden_layers,
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lambda prefix: OlmoHybridDecoderLayer(
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vllm_config=vllm_config, prefix=prefix
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),
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prefix=f"{prefix}.layers",
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)
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self.norm = RMSNorm(
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self.config.hidden_size,
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eps=self.config.rms_norm_eps,
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)
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self.make_empty_intermediate_tensors = make_empty_intermediate_tensors_factory(
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["hidden_states"], self.config.hidden_size
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)
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def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
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return self.embed_tokens(input_ids)
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def forward(
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self,
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input_ids: torch.Tensor,
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positions: torch.Tensor,
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intermediate_tensors: IntermediateTensors | None,
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inputs_embeds: torch.Tensor | None = None,
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) -> torch.Tensor | IntermediateTensors:
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if get_pp_group().is_first_rank:
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if inputs_embeds is not None:
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hidden_states = inputs_embeds
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else:
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hidden_states = self.embed_tokens(input_ids)
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else:
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assert intermediate_tensors is not None
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hidden_states = intermediate_tensors["hidden_states"]
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assert isinstance(hidden_states, torch.Tensor)
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for layer in islice(self.layers, self.start_layer, self.end_layer):
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hidden_states = layer(positions, hidden_states)
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if not get_pp_group().is_last_rank:
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return IntermediateTensors({"hidden_states": hidden_states})
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hidden_states = self.norm(hidden_states)
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return hidden_states
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def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
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stacked_params_mapping = [
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("qkv_proj", "q_proj", "q"),
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("qkv_proj", "k_proj", "k"),
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("qkv_proj", "v_proj", "v"),
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("gate_up_proj", "gate_proj", 0),
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("gate_up_proj", "up_proj", 1),
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]
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linear_attn_stacked_params_mapping = [
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("in_proj_qkvg", "q_proj", 0),
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("in_proj_qkvg", "k_proj", 1),
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("in_proj_qkvg", "v_proj", 2),
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("in_proj_qkvg", "g_proj", 3),
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("conv1d", "q_conv1d", 0),
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("conv1d", "k_conv1d", 1),
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("conv1d", "v_conv1d", 2),
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]
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params_dict = dict(self.named_parameters(remove_duplicate=False))
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loaded_params: set[str] = set()
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for name, loaded_weight in weights:
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if is_pp_missing_parameter(name, self):
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continue
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handled = False
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if "linear_attn" in name:
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for (
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param_name,
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weight_name,
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shard_id,
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) in linear_attn_stacked_params_mapping:
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if weight_name not in name:
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continue
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mapped_name = name.replace(weight_name, param_name)
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if mapped_name.endswith(".bias") and (
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mapped_name not in params_dict
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):
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continue
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if mapped_name not in params_dict:
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continue
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param = params_dict[mapped_name]
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weight_loader = param.weight_loader
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weight_loader(param, loaded_weight, shard_id)
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name = mapped_name
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handled = True
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break
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else:
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for param_name, weight_name, shard_id in stacked_params_mapping:
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if weight_name not in name:
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continue
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name = name.replace(weight_name, param_name)
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if name.endswith(".bias") and name not in params_dict:
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continue
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if name not in params_dict:
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continue
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param = params_dict[name]
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weight_loader = param.weight_loader
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weight_loader(param, loaded_weight, shard_id)
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handled = True
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break
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if not handled:
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if name.endswith(".bias") and name not in params_dict:
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continue
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if name not in params_dict:
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continue
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param = params_dict[name]
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weight_loader = getattr(param, "weight_loader", default_weight_loader)
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weight_loader(param, loaded_weight)
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loaded_params.add(name)
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return loaded_params
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class OlmoHybridForCausalLM(
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nn.Module, HasInnerState, SupportsPP, SupportsLoRA, IsHybrid
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):
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packed_modules_mapping = {
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"qkv_proj": ["q_proj", "k_proj", "v_proj"],
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"gate_up_proj": ["gate_proj", "up_proj"],
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"in_proj_qkvg": ["q_proj", "k_proj", "v_proj", "g_proj"],
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}
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def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
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super().__init__()
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config = vllm_config.model_config.hf_config
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self.config = config
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self.vllm_config = vllm_config
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self.model_config = vllm_config.model_config
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self.model = OlmoHybridModel(
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vllm_config=vllm_config, prefix=maybe_prefix(prefix, "model")
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)
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if config.tie_word_embeddings:
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self.lm_head = self.model.embed_tokens
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else:
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self.lm_head = ParallelLMHead(
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config.vocab_size,
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config.hidden_size,
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quant_config=vllm_config.quant_config,
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prefix=maybe_prefix(prefix, "lm_head"),
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)
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self.logits_processor = LogitsProcessor(config.vocab_size)
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self.make_empty_intermediate_tensors = (
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self.model.make_empty_intermediate_tensors
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)
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def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
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return self.model.embed_input_ids(input_ids)
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def forward(
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self,
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input_ids: torch.Tensor,
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positions: torch.Tensor,
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intermediate_tensors: IntermediateTensors | None = None,
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inputs_embeds: torch.Tensor | None = None,
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) -> torch.Tensor | IntermediateTensors:
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hidden_states = self.model(
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input_ids=input_ids,
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positions=positions,
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intermediate_tensors=intermediate_tensors,
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inputs_embeds=inputs_embeds,
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)
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return hidden_states
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def compute_logits(
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self,
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hidden_states: torch.Tensor,
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) -> torch.Tensor | None:
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logits = self.logits_processor(self.lm_head, hidden_states)
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return logits
|
|
|
|
@classmethod
|
|
def get_mamba_state_dtype_from_config(
|
|
cls,
|
|
vllm_config: "VllmConfig",
|
|
) -> tuple[torch.dtype, torch.dtype]:
|
|
return MambaStateDtypeCalculator.gated_delta_net_state_dtype(
|
|
vllm_config.model_config.dtype,
|
|
vllm_config.cache_config.mamba_cache_dtype,
|
|
vllm_config.cache_config.mamba_ssm_cache_dtype,
|
|
)
|
|
|
|
@classmethod
|
|
def get_mamba_state_shape_from_config(
|
|
cls, vllm_config: "VllmConfig"
|
|
) -> tuple[tuple[int, int], tuple[int, int]]:
|
|
parallel_config = vllm_config.parallel_config
|
|
hf_config = vllm_config.model_config.hf_config
|
|
tp_size = parallel_config.tensor_parallel_size
|
|
num_spec = (
|
|
vllm_config.speculative_config.num_speculative_tokens
|
|
if vllm_config.speculative_config
|
|
else 0
|
|
)
|
|
return MambaStateShapeCalculator.gated_delta_net_state_shape(
|
|
tp_size,
|
|
hf_config.linear_num_key_heads,
|
|
hf_config.linear_num_value_heads,
|
|
hf_config.linear_key_head_dim,
|
|
hf_config.linear_value_head_dim,
|
|
hf_config.linear_conv_kernel_dim,
|
|
num_spec,
|
|
)
|
|
|
|
@classmethod
|
|
def get_mamba_state_copy_func(cls) -> tuple[MambaStateCopyFunc, MambaStateCopyFunc]:
|
|
return MambaStateCopyFuncCalculator.gated_delta_net_state_copy_func()
|
|
|
|
def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]):
|
|
loader = AutoWeightsLoader(
|
|
self,
|
|
skip_prefixes=(
|
|
["lm_head.weight"] if self.config.tie_word_embeddings else None
|
|
),
|
|
)
|
|
return loader.load_weights(weights)
|