e489ad7a21
- **Add SPDX license headers to python source files** - **Check for SPDX headers using pre-commit** commit 9d7ef44c3cfb72ca4c32e1c677d99259d10d4745 Author: Russell Bryant <rbryant@redhat.com> Date: Fri Jan 31 14:18:24 2025 -0500 Add SPDX license headers to python source files This commit adds SPDX license headers to python source files as recommended to the project by the Linux Foundation. These headers provide a concise way that is both human and machine readable for communicating license information for each source file. It helps avoid any ambiguity about the license of the code and can also be easily used by tools to help manage license compliance. The Linux Foundation runs license scans against the codebase to help ensure we are in compliance with the licenses of the code we use, including dependencies. Having these headers in place helps that tool do its job. More information can be found on the SPDX site: - https://spdx.dev/learn/handling-license-info/ Signed-off-by: Russell Bryant <rbryant@redhat.com> commit 5a1cf1cb3b80759131c73f6a9dddebccac039dea Author: Russell Bryant <rbryant@redhat.com> Date: Fri Jan 31 14:36:32 2025 -0500 Check for SPDX headers using pre-commit Signed-off-by: Russell Bryant <rbryant@redhat.com> --------- Signed-off-by: Russell Bryant <rbryant@redhat.com>
217 lines
7.1 KiB
Python
217 lines
7.1 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# adapted from https://github.com/deepseek-ai/DeepSeek-VL2/blob/faf18023f24b962b32d9f0a2d89e402a8d383a78/deepseek_vl2/models/modeling_deepseek_vl_v2.py#L115-L268
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from typing import Tuple
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from transformers.configuration_utils import PretrainedConfig
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class VisionEncoderConfig(PretrainedConfig):
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model_type: str = "vision"
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model_name: str = "vit_so400m_patch14_siglip_384.webli"
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image_size: int = 384
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patch_size: int = 16
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width: int = 1024
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layers: int = 24
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heads: int = 16
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mlp_ratio: int = 4
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global_pool: str = "map"
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ignore_head: bool = True
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class_token: bool = False
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num_classes: int = 0
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use_checkpoint: bool = False
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weight_init: str = "skip"
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deterministic: bool = False
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num_recomputing_layers: int = 0
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def __init__(self,
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model_name: str = "vit_so400m_patch14_siglip_384.webli",
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image_size: int = 384,
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patch_size: int = 16,
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width: int = 1024,
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layers: int = 24,
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heads: int = 16,
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mlp_ratio: int = 4,
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global_pool: str = "map",
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ignore_head: bool = True,
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class_token: bool = False,
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num_classes: int = 0,
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use_checkpoint: bool = False,
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**kwargs):
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self.model_name = model_name
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self.image_size = image_size
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self.patch_size = patch_size
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self.width = width
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self.layers = layers
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self.heads = heads
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self.mlp_ratio = mlp_ratio
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self.global_pool = global_pool
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self.ignore_head = ignore_head
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self.class_token = class_token
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self.num_classes = num_classes
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self.use_checkpoint = use_checkpoint
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super().__init__(**kwargs)
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class MlpProjectorConfig(PretrainedConfig):
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model_type = "mlp_projector"
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projector_type: str = "downsample_mlp_gelu"
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input_dim: int = 1152
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n_embed: int = 2048
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depth: int = 2
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mlp_ratio: int = 1
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downsample_ratio: int = 2
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token_pooling: bool = False
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def __init__(self,
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projector_type: str = "downsample_mlp_gelu",
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input_dim: int = 1152,
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n_embed: int = 2048,
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depth: int = 2,
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mlp_ratio: int = 1,
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downsample_ratio: int = 2,
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**kwargs):
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self.projector_type = projector_type
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self.input_dim = input_dim
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self.n_embed = n_embed
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self.depth = depth
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self.mlp_ratio = mlp_ratio
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self.downsample_ratio = downsample_ratio
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super().__init__(**kwargs)
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class DeepseekV2Config(PretrainedConfig):
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model_type = "deepseek_v2"
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keys_to_ignore_at_inference = ["past_key_values"]
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def __init__(
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self,
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vocab_size=102400,
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hidden_size=4096,
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intermediate_size=11008,
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moe_intermediate_size=1407,
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num_hidden_layers=30,
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num_attention_heads=32,
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num_key_value_heads=32,
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n_shared_experts=None,
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n_routed_experts=None,
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ep_size=1,
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routed_scaling_factor=1.0,
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kv_lora_rank=512,
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q_lora_rank=1536,
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qk_rope_head_dim=64,
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v_head_dim=128,
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qk_nope_head_dim=128,
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topk_method='gready',
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n_group=None,
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topk_group=None,
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num_experts_per_tok=None,
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moe_layer_freq=1,
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first_k_dense_replace=0,
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norm_topk_prob=False,
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scoring_func='softmax',
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aux_loss_alpha=0.001,
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seq_aux=True,
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hidden_act="silu",
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max_position_embeddings=2048,
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initializer_range=0.02,
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rms_norm_eps=1e-6,
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use_cache=True,
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pad_token_id=None,
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bos_token_id=100000,
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eos_token_id=100001,
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pretraining_tp=1,
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tie_word_embeddings=False,
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rope_theta=10000.0,
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rope_scaling=None,
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attention_bias=False,
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attention_dropout=0.0,
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use_mla=True,
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**kwargs,
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):
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self.vocab_size = vocab_size
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self.max_position_embeddings = max_position_embeddings
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self.hidden_size = hidden_size
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self.intermediate_size = intermediate_size
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self.moe_intermediate_size = moe_intermediate_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.n_shared_experts = n_shared_experts
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self.n_routed_experts = n_routed_experts
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self.ep_size = ep_size
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self.routed_scaling_factor = routed_scaling_factor
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self.kv_lora_rank = kv_lora_rank
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self.q_lora_rank = q_lora_rank
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self.qk_rope_head_dim = qk_rope_head_dim
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self.v_head_dim = v_head_dim
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self.qk_nope_head_dim = qk_nope_head_dim
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self.topk_method = topk_method
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self.n_group = n_group
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self.topk_group = topk_group
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self.num_experts_per_tok = num_experts_per_tok
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self.moe_layer_freq = moe_layer_freq
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self.first_k_dense_replace = first_k_dense_replace
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self.norm_topk_prob = norm_topk_prob
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self.scoring_func = scoring_func
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self.aux_loss_alpha = aux_loss_alpha
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self.seq_aux = seq_aux
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# for backward compatibility
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if num_key_value_heads is None:
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num_key_value_heads = num_attention_heads
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self.num_key_value_heads = num_key_value_heads
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self.hidden_act = hidden_act
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self.initializer_range = initializer_range
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self.rms_norm_eps = float(rms_norm_eps)
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self.pretraining_tp = pretraining_tp
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self.use_cache = use_cache
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self.rope_theta = rope_theta
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self.rope_scaling = rope_scaling
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self.attention_bias = attention_bias
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self.attention_dropout = attention_dropout
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self.use_mla = use_mla
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super().__init__(
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pad_token_id=pad_token_id,
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bos_token_id=bos_token_id,
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eos_token_id=eos_token_id,
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tie_word_embeddings=tie_word_embeddings,
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**kwargs,
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)
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class DeepseekVLV2Config(PretrainedConfig):
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model_type = "deepseek_vl_v2"
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vision_config: VisionEncoderConfig
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projector_config: MlpProjectorConfig
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tile_tag: str = "2D"
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global_view_pos: str = "head"
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candidate_resolutions: Tuple[Tuple[int, int]] = ((384, 384), )
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def __init__(self,
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tile_tag: str = "tile_tag",
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global_view_pos: str = "head",
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candidate_resolutions: Tuple[Tuple[int,
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int]] = ((384, 384), ),
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**kwargs):
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super().__init__(**kwargs)
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vision_config = kwargs.get("vision_config", {})
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self.vision_config = VisionEncoderConfig(**vision_config)
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projector_config = kwargs.get("projector_config", {})
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self.projector_config = MlpProjectorConfig(**projector_config)
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language_config = kwargs.get("language_config", {})
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self.text_config = DeepseekV2Config(**language_config)
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self.tile_tag = tile_tag
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self.global_view_pos = global_view_pos
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self.candidate_resolutions = candidate_resolutions
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self.vocab_size = self.text_config.vocab_size
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