wehub-resource-sync
68 lines
2.1 KiB
Python
68 lines
2.1 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 __future__ import annotations
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from collections.abc import Iterable
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import torch
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import torch.nn as nn
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from vllm.config import VllmConfig
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from vllm.model_executor.models.qwen3 import Qwen3Model
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from vllm.model_executor.models.utils import AutoWeightsLoader, maybe_prefix
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from vllm.sequence import IntermediateTensors
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WeightItem = tuple[str, torch.Tensor]
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class VoyageQwen3BidirectionalEmbedModel(nn.Module):
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"""
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Qwen3Model + Voyage embedding head + bidirectional attention.
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Checkpoint conventions (HF):
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- MLP: gate_proj + up_proj (unfused)
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- Attn: q_proj + k_proj + v_proj (unfused)
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- Linear head: linear.weight
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- Weights prefixed with "model." (e.g., model.layers.0...)
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vLLM Qwen3Model expects:
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- mlp.gate_up_proj (fused)
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- self_attn.qkv_proj (fused)
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- No "model." prefix
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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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self.config = vllm_config.model_config.hf_config
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self.model = Qwen3Model(
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vllm_config=vllm_config, prefix=maybe_prefix(prefix, "model")
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)
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# Embedding head (hidden_size -> num_labels, bias=False)
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self.linear = nn.Linear(
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self.config.hidden_size,
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self.config.num_labels,
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bias=False,
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)
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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 | None,
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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:
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out = self.model(input_ids, positions, intermediate_tensors, inputs_embeds)
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return self.linear(out)
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def load_weights(self, weights: Iterable[WeightItem]) -> set[str]:
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loader = AutoWeightsLoader(self)
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return loader.load_weights(weights)
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