Sync from v0.13

vllm/transformers_utils/configs/midashenglm.py

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+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# Copyright 2025 Horizon team, Xiaomi MiLM Plus.
+# Copyright 2024 The Qwen team.
+# Copyright 2023 The vLLM team.
+# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from transformers import PretrainedConfig
+from transformers.models.qwen2_5_omni.configuration_qwen2_5_omni import (
+    Qwen2_5OmniTextConfig,
+)
+
+
+class DashengConfig(PretrainedConfig):
+    model_type = "midashenglm_dasheng_encoder"
+
+    def __init__(
+        self,
+        embed_dim: int = 768,
+        outputdim: int = 527,
+        patch_size: int | tuple[int, int] = 16,
+        patch_stride: int | tuple[int, int] = 16,
+        input_channels: int = 1,
+        target_length: int = 1012,
+        depth: int = 12,
+        num_heads: int = 12,
+        mlp_ratio: float = 4.0,
+        qkv_bias: bool = True,
+        init_values: float | None = None,
+        drop_rate: float = 0.0,
+        attn_drop_rate: float = 0.0,
+        f_min: float = 0.0,
+        f_max: float = 8000.0,
+        center: bool = True,
+        win_length: int = 512,
+        hop_length: int = 160,
+        sample_rate: int = 16000,
+        n_fft: int = 512,
+        n_mels: int = 64,
+        **kwargs,
+    ):
+        self.embed_dim = embed_dim
+        self.outputdim = outputdim
+        self.patch_size = patch_size
+        self.patch_stride = patch_stride
+        self.input_channels = input_channels
+        self.target_length = target_length
+        self.depth = depth
+        self.num_heads = num_heads
+        self.mlp_ratio = mlp_ratio
+        self.qkv_bias = qkv_bias
+        self.init_values = init_values
+        self.drop_rate = drop_rate
+        self.attn_drop_rate = attn_drop_rate
+        self.f_min = f_min
+        self.f_max = f_max
+        self.center = center
+        self.win_length = win_length
+        self.hop_length = hop_length
+        self.sample_rate = sample_rate
+        self.n_fft = n_fft
+        self.n_mels = n_mels
+        super().__init__(**kwargs)
+
+
+class MiDashengLMConfig(PretrainedConfig):
+    model_type = "midashenglm"
+
+    def __init__(
+        self,
+        audio_encoder_config: dict | None = None,
+        subsample_factor: int = 5,
+        text_config: dict | None = None,
+        audio_token_id: int | None = None,
+        **kwargs,
+    ):
+        self.audio_encoder_config = DashengConfig(**(audio_encoder_config or {}))
+        self.subsample_factor = subsample_factor
+        self.text_config = (
+            Qwen2_5OmniTextConfig(**text_config)
+            if text_config
+            else Qwen2_5OmniTextConfig()
+        )
+        self.text_config.rope_parameters = None  # uses_mrope is false
+        self.audio_token_id = audio_token_id
+        super().__init__(**kwargs)