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# Copyright 2025 HuggingFace Inc. team. All rights reserved.
#
# 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.configuration_utils import PretrainedConfig
from transformers.modeling_rope_utils import rope_config_validation
from transformers import WhisperConfig
class InternS1ProTextConfig(PretrainedConfig):
model_type = "interns1_pro_text"
base_config_key = "text_config"
keys_to_ignore_at_inference = ["past_key_values"]
base_model_tp_plan = {
"layers.*.self_attn.q_proj": "colwise",
"layers.*.self_attn.k_proj": "colwise",
"layers.*.self_attn.v_proj": "colwise",
"layers.*.self_attn.o_proj": "rowwise",
"layers.*.mlp.experts.*.gate_proj": "colwise",
"layers.*.mlp.experts.*.up_proj": "colwise",
"layers.*.mlp.experts.*.down_proj": "rowwise",
"layers.*.mlp.gate_proj": "colwise",
"layers.*.mlp.up_proj": "colwise",
"layers.*.mlp.down_proj": "rowwise",
}
base_model_pp_plan = {
"embed_tokens": (["input_ids"], ["inputs_embeds"]),
"layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
"norm": (["hidden_states"], ["hidden_states"]),
}
def __init__(
self,
vocab_size=151936,
hidden_size=2048,
intermediate_size=5632,
num_hidden_layers=24,
num_attention_heads=16,
num_key_value_heads=16,
hidden_act="silu",
max_position_embeddings=128000,
initializer_range=0.02,
rms_norm_eps=1e-6,
use_cache=True,
tie_word_embeddings=False,
rope_theta=5000000.0,
attention_bias=False,
attention_dropout=0.0,
decoder_sparse_step=1,
moe_intermediate_size=1408,
num_experts_per_tok=4,
num_experts=60,
norm_topk_prob=True,
router_aux_loss_coef=0.001,
mlp_only_layers=None,
rope_scaling=None,
head_dim=None,
**kwargs,
):
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
# for backward compatibility
if num_key_value_heads is None:
num_key_value_heads = num_attention_heads
self.num_key_value_heads = num_key_value_heads
self.hidden_act = hidden_act
self.initializer_range = initializer_range
self.rms_norm_eps = rms_norm_eps
self.use_cache = use_cache
self.rope_theta = rope_theta
self.attention_bias = attention_bias
self.attention_dropout = attention_dropout
self.rope_scaling = rope_scaling
self.head_dim = head_dim or hidden_size // num_attention_heads
rope_config_validation(self, ignore_keys={"fope_init_factor", "fope_sep_head", "num_inv_freq"})
# MoE arguments
self.decoder_sparse_step = decoder_sparse_step
self.moe_intermediate_size = moe_intermediate_size
self.num_experts_per_tok = num_experts_per_tok
self.num_experts = num_experts
self.norm_topk_prob = norm_topk_prob
self.router_aux_loss_coef = router_aux_loss_coef
self.mlp_only_layers = [] if mlp_only_layers is None else mlp_only_layers
super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs)
class InternS1ProVisionConfig(PretrainedConfig):
model_type = "interns1_pro_vision"
base_config_key = "vision_config"
def __init__(
self,
depth=27,
hidden_size=1152,
hidden_act="gelu_pytorch_tanh",
intermediate_size=4304,
num_heads=16,
in_channels=3,
patch_size=16,
spatial_merge_size=2,
temporal_patch_size=2,
out_hidden_size=3584,
num_position_embeddings=2304,
initializer_range=0.02,
**kwargs,
):
super().__init__(**kwargs)
self.depth = depth
self.hidden_size = hidden_size
self.hidden_act = hidden_act
self.intermediate_size = intermediate_size
self.num_heads = num_heads
self.in_channels = in_channels
self.patch_size = patch_size
self.spatial_merge_size = spatial_merge_size
self.temporal_patch_size = temporal_patch_size
self.out_hidden_size = out_hidden_size
self.num_position_embeddings = num_position_embeddings
self.initializer_range = initializer_range
class InternS1ProTimeSeriesConfig(WhisperConfig):
model_type = "interns1_pro_time_series"
base_config_key = "ts_config"
def __init__(
self,
ts_adapt_in_dim: int=256,
ts_adapt_out_dim: int=1024,
ts_hidden_dim: int=1024,
ts_cnn_channels: list[int]=[1, 32, 64, 128, 128],
ts_cnn_kernel_sizes: list[int]=[3, 5, 5, 5],
ts_cnn_strides: list[int]=[2, 4, 4, 5],
ts_cnn_paddings: list[int]=[1, 2, 2, 2],
ts_concat_subsampling_in_channels: int=128,
ts_concat_subsampling_concat_size: int=2,
use_flash_attn: bool=False,
**kwargs
):
super().__init__(**kwargs)
self.ts_cnn_channels = ts_cnn_channels
self.ts_cnn_kernel_sizes = ts_cnn_kernel_sizes
self.ts_cnn_strides = ts_cnn_strides
self.ts_cnn_paddings = ts_cnn_paddings
self.ts_concat_subsampling_in_channels = ts_concat_subsampling_in_channels
self.ts_concat_subsampling_concat_size = ts_concat_subsampling_concat_size
self.ts_adapt_in_dim = ts_adapt_in_dim
self.ts_adapt_out_dim = ts_adapt_out_dim
self.ts_hidden_dim = ts_hidden_dim
self.use_flash_attn = use_flash_attn
assert self.ts_adapt_out_dim == self.ts_hidden_dim, "ts_adapt_out_dim should be equal to ts_hidden_dim"
assert self.ts_concat_subsampling_in_channels == self.ts_cnn_channels[-1], "ts_concat_subsampling_in_channels should be equal to the out_channel of the last cnn layer"
class InternS1ProConfig(PretrainedConfig):
model_type = "interns1_pro"
sub_configs = {"vision_config": InternS1ProVisionConfig, "text_config": InternS1ProTextConfig, 'ts_config':InternS1ProTimeSeriesConfig}
keys_to_ignore_at_inference = ["past_key_values"]
def __init__(
self,
text_config=None,
vision_config=None,
ts_config=None,
image_token_id=151655,
video_token_id=151656,
vision_start_token_id=151652,
vision_end_token_id=151653,
ts_token_id=151685,
ts_start_id=151683,
ts_end_id=151684,
tie_word_embeddings=False,
**kwargs,
):
if isinstance(vision_config, dict):
self.vision_config = self.sub_configs["vision_config"](**vision_config)
elif vision_config is None:
self.vision_config = self.sub_configs["vision_config"]()
if isinstance(text_config, dict):
self.text_config = self.sub_configs["text_config"](**text_config)
elif text_config is None:
self.text_config = self.sub_configs["text_config"]()
if isinstance(ts_config, dict):
self.ts_config = self.sub_configs["ts_config"](**ts_config)
elif ts_config is None:
self.ts_config = self.sub_configs["ts_config"]()
self.image_token_id = image_token_id
self.video_token_id = video_token_id
self.vision_start_token_id = vision_start_token_id
self.vision_end_token_id = vision_end_token_id
self.ts_token_id = ts_token_id
self.ts_start_id = ts_start_id
self.ts_end_id = ts_end_id
super().__init__(**kwargs, tie_word_embeddings=tie_word_embeddings)
__all__ = ["InternS1ProConfig", "InternS1ProTextConfig", "InternS1ProVisionConfig", "InternS1ProTimeSeriesConfig"]
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