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136 lines
4.4 KiB
136 lines
4.4 KiB
# Copyright 2025 The rednote-hilab team and the HuggingFace Inc. team. All rights reserved.
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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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import torch
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from ...modeling_outputs import CausalLMOutputWithPast
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from ...processing_utils import Unpack
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from ...utils import logging
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from ..deepseek_v3.modeling_deepseek_v3 import (
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DeepseekV3DecoderLayer,
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DeepseekV3MLP,
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DeepseekV3MoE,
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DeepseekV3PreTrainedModel,
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DeepseekV3TopkRouter,
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)
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from ..qwen3.modeling_qwen3 import (
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Qwen3Attention,
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Qwen3ForCausalLM,
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Qwen3Model,
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Qwen3RMSNorm,
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Qwen3RotaryEmbedding,
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TransformersKwargs,
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)
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from .configuration_dots1 import Dots1Config
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logger = logging.get_logger(__name__)
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class Dots1RMSNorm(Qwen3RMSNorm):
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pass
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class Dots1RotaryEmbedding(Qwen3RotaryEmbedding):
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pass
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class Dots1Attention(Qwen3Attention):
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pass
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class Dots1MLP(DeepseekV3MLP):
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pass
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class Dots1TopkRouter(DeepseekV3TopkRouter):
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pass
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class Dots1MoE(DeepseekV3MoE):
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def route_tokens_to_experts(self, router_logits):
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router_logits = router_logits.sigmoid() # main diff with deepseekv3
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router_logits_for_choice = router_logits + self.gate.e_score_correction_bias
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group_scores = (
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router_logits_for_choice.view(-1, self.n_group, self.n_routed_experts // self.n_group)
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.topk(2, dim=-1)[0]
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.sum(dim=-1)
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)
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group_idx = torch.topk(group_scores, k=self.topk_group, dim=-1, sorted=False)[1]
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group_mask = torch.zeros_like(group_scores)
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group_mask.scatter_(1, group_idx, 1)
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score_mask = (
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group_mask.unsqueeze(-1)
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.expand(-1, self.n_group, self.n_routed_experts // self.n_group)
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.reshape(-1, self.n_routed_experts)
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)
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scores_for_choice = router_logits_for_choice.masked_fill(~score_mask.bool(), 0.0)
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topk_indices = torch.topk(scores_for_choice, k=self.top_k, dim=-1, sorted=False)[1]
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topk_weights = router_logits.gather(1, topk_indices)
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if self.norm_topk_prob:
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denominator = topk_weights.sum(dim=-1, keepdim=True) + 1e-20
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topk_weights /= denominator
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topk_weights = topk_weights * self.routed_scaling_factor
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return topk_indices, topk_weights
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class Dots1DecoderLayer(DeepseekV3DecoderLayer):
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def __init__(self, config: Dots1Config, layer_idx: int):
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super().__init__(config, layer_idx)
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self.attention_type = config.layer_types[layer_idx]
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class Dots1PreTrainedModel(DeepseekV3PreTrainedModel):
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pass
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class Dots1Model(Qwen3Model):
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pass
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class Dots1ForCausalLM(Qwen3ForCausalLM):
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def forward(
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self,
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**super_kwargs: Unpack[TransformersKwargs],
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) -> CausalLMOutputWithPast:
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r"""
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labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
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Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
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config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
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(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
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Example:
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```python
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>>> from transformers import AutoTokenizer, Dots1ForCausalLM
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>>> model = Dots1ForCausalLM.from_pretrained("rednote-hilab/dots1.llm1.inst")
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>>> tokenizer = AutoTokenizer.from_pretrained("rednote-hilab/dots1.llm1.inst")
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>>> prompt = "Hey, are you conscious? Can you talk to me?"
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>>> inputs = tokenizer(prompt, return_tensors="pt")
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>>> # Generate
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>>> generate_ids = model.generate(inputs.input_ids, max_length=30)
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>>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
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"Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
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```"""
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return super().forward(**super_kwargs)
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__all__ = [
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"Dots1PreTrainedModel",
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"Dots1Model",
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"Dots1ForCausalLM",
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]
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