diff --git a/README.md b/README.md
index e6dc6b4..a339d37 100644
--- a/README.md
+++ b/README.md
@@ -1,3 +1,142 @@
-# Doge-160M-Instruct
+---
+library_name: transformers
+license: apache-2.0
+datasets:
+- HuggingFaceTB/smoltalk
+- HuggingFaceH4/ultrafeedback_binarized
+base_model:
+- SmallDoge/Doge-160M
+language:
+- en
+pipeline_tag: question-answering
+tags:
+- trl
+- sft
+- dpo
+- doge
+---
-Doge-160M-Instruct
\ No newline at end of file
+
+# **Doge 160M Instruct**
+
+<div align="center">
+ <img src="https://huggingface.co/spaces/SmallDoge/README/resolve/main/org_icon.png" width="100%" alt="SmallDoge" />
+</div>
+<hr>
+<div align="center">
+ <a href="https://discord.gg/P2yYH95N" target="_blank" style="margin: 2px;">
+ <img alt="Discord" src="https://img.shields.io/badge/Discord-Small%20Doges-7289da?logo=discord&logoColor=white&color=7289da" style="display: inline-block; vertical-align: middle;"/>
+ </a>
+ <a href="https://arxiv.org/abs/2412.11834" target="_blank" style="margin: 2px;">
+ <img alt="arXiv" src="https://img.shields.io/static/v1?label=arXiv&message=2412.11834&color=B31B1B&logo=arXiv" style="display: inline-block; vertical-align: middle;"/>
+ </a>
+ <a href="https://github.com/SmallDoges/small-doge" target="_blank" style="margin: 2px;">
+ <img alt="GitHub" src="https://img.shields.io/badge/GitHub-SmallDoge-181717?logo=github" style="display: inline-block; vertical-align: middle;"/>
+ </a>
+ <a href="https://github.com/SmallDoges/small-doge/blob/main/LICENSE" style="margin: 2px;">
+ <img alt="License" src="https://img.shields.io/badge/License-Apache--2.0-blue.svg" style="display: inline-block; vertical-align: middle;"/>
+ </a>
+</div>
+
+Doge uses Dynamic Mask Attention as sequence transformation and can use Multi-Layer Perceptron or Cross Domain Mixture of Experts as state transformation. Dynamic Mask Attention allows the Transformer to use self-attention during training and state space during inference, and Cross Domain Mixture of Experts can directly inherit the weights of Multi-Layer Perceptron for further training. This model is trained by [SmallDoge](https://huggingface.co/SmallDoge) community, for detailed algorithm and model architecture, please refer to [Wonderful Matrices](https://arxiv.org/abs/2412.11834), all training details and code are publicly available on the [small-doge](https://github.com/SmallDoges/small-doge) repository.
+
+
+## Uses
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig, TextStreamer
+
+tokenizer = AutoTokenizer.from_pretrained("SmallDoge/Doge-160M-Instruct")
+model = AutoModelForCausalLM.from_pretrained("SmallDoge/Doge-160M-Instruct", trust_remote_code=True)
+
+generation_config = GenerationConfig(
+ max_new_tokens=100,
+ use_cache=True,
+ do_sample=True,
+ temperature=0.8,
+ top_p=0.9,
+ repetition_penalty=1.0
+)
+steamer = TextStreamer(
+ tokenizer=tokenizer,
+ skip_prompt=True
+)
+
+prompt = "Hi, how are you doing today?"
+conversation = [
+ {"role": "user", "content": prompt}
+]
+inputs = tokenizer.apply_chat_template(
+ conversation=conversation,
+ tokenize=True,
+ return_tensors="pt",
+)
+
+outputs = model.generate(
+ inputs,
+ tokenizer=tokenizer,
+ generation_config=generation_config,
+ streamer=steamer
+)
+```
+
+
+## Model Details
+
+We build the Doge-Instruct by first SFT on [SmolTalk](https://huggingface.co/datasets/HuggingFaceTB/smoltalk) and then DPO on [UltraFeedback Binarized](https://huggingface.co/datasets/HuggingFaceH4/ultrafeedback_binarized).
+
+> TODO: The larger model is under training and will be uploaded soon.
+
+**SFT**:
+| Model | Training Data | Epochs | Content Length | LR | Batch Size | Precision |
+|---|---|---|---|---|---|---|
+| [Doge-20M-Instruct-SFT](https://huggingface.co/SmallDoge/Doge-20M-Instruct-SFT) | [HuggingFaceTB/smoltalk](https://huggingface.co/datasets/HuggingFaceTB/smoltalk) | 2 | 2048 | 8e-4 | 0.25M | bfloat16 |
+| [Doge-60M-Instruct-SFT](https://huggingface.co/SmallDoge/Doge-60M-Instruct-SFT) | [HuggingFaceTB/smoltalk](https://huggingface.co/datasets/HuggingFaceTB/smoltalk) | 2 | 2048 | 6e-4 | 0.25M | bfloat16 |
+| [Doge-160M-Instruct-SFT](https://huggingface.co/SmallDoge/Doge-160M-Instruct-SFT) | [HuggingFaceTB/smoltalk](https://huggingface.co/datasets/HuggingFaceTB/smoltalk) | 2 | 2048 | 4e-4 | 0.25M | bfloat16 |
+
+**DPO**:
+| Model | Training Data | Epochs | Content Length | LR | Batch Size | Precision |
+|---|---|---|---|---|---|---|
+| [Doge-20M-Instruct](https://huggingface.co/SmallDoge/Doge-20M-Instruct) | [HuggingFaceH4/ultrafeedback_binarized](https://huggingface.co/datasets/HuggingFaceH4/ultrafeedback_binarized) | 2 | 1024 | 8e-5 | 0.125M | bfloat16 |
+| [Doge-60M-Instruct](https://huggingface.co/SmallDoge/Doge-60M-Instruct) | [HuggingFaceH4/ultrafeedback_binarized](https://huggingface.co/datasets/HuggingFaceH4/ultrafeedback_binarized) | 2 | 1024 | 6e-5 | 0.125M | bfloat16 |
+| [Doge-160M-Instruct](https://huggingface.co/SmallDoge/Doge-160M-Instruct) | [HuggingFaceH4/ultrafeedback_binarized](https://huggingface.co/datasets/HuggingFaceH4/ultrafeedback_binarized) | 2 | 1024 | 4e-5 | 0.125M | bfloat16 |
+
+
+**Evaluation**:
+
+| Model | IFEval (Prompt Strict Acc) | MMLU | BBH | ARC | PIQA | HellaSwag | tokens / s on i7-11 CPU |
+|---|---|---|---|---|---|---|---|
+| [Doge-20M-Instruct](https://huggingface.co/SmallDoge/Doge-20M-Instruct) | 7.3 | 26.3 | 18.3 | 29.2 | 57.8 | 27.8 | 142 |
+| [Doge-60M-Instruct](https://huggingface.co/SmallDoge/Doge-60M-Instruct) | 7.4 | 27.5 | 27.7 | 37.5 | 61.4 | 32.1 | 62 |
+| [SmolLM-135M-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM-135M-Instruct) | 12.2 | 28.3 | 25.2 | 33.9 | 64.0 | 38.9 | 30 |
+| [Doge-160M-Instruct](https://huggingface.co/SmallDoge/Doge-160M-Instruct) | 16.8 | 29.7 | 29.1 | 42.8 | 64.1 | 37.1 | 28 |
+
+
+**Procedure**:
+
+**SFT**:
+[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/loser_cheems/huggingface/runs/0jht5dro)
+
+**DPO**:
+[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/loser_cheems/huggingface/runs/m5onn07v)
+
+
+**Environment**:
+- Image: nvcr.io/nvidia/pytorch:24.12-py3
+- Hardware: 1x NVIDIA RTX 4090
+- Software: Transformers, TRL
+
+
+## Citation
+
+```bibtex
+@misc{shi2024wonderfulmatrices,
+ title={Wonderful Matrices: Combining for a More Efficient and Effective Foundation Model Architecture},
+ author={Jingze Shi and Bingheng Wu},
+ year={2024},
+ eprint={2412.11834},
+ archivePrefix={arXiv},
+ primaryClass={cs.LG},
+ url={https://arxiv.org/abs/2412.11834},
+}
+```
\ No newline at end of file
diff --git a/config.json b/config.json
new file mode 100644
index 0000000..5a9e39f
--- /dev/null
+++ b/config.json
@@ -0,0 +1,43 @@
+{
+ "_name_or_path": "SmallDoge/Doge-160M-Instruct",
+ "architectures": [
+ "DogeForCausalLM"
+ ],
+ "attention_dropout": 0.0,
+ "auto_map": {
+ "AutoConfig": "configuration_doge.DogeConfig",
+ "AutoModelForCausalLM": "modeling_doge.DogeForCausalLM"
+ },
+ "bos_token_id": 0,
+ "dynamic_mask_ratio": 0.0,
+ "eos_token_id": 1,
+ "expert_retrieval_size": 64,
+ "hidden_act": "silu",
+ "hidden_bias": false,
+ "hidden_dropout": 0.0,
+ "hidden_size": 768,
+ "initializer_range": 0.02,
+ "intermediate_size": 1536,
+ "is_moe": false,
+ "max_position_embeddings": 2048,
+ "model_type": "doge",
+ "num_attention_heads": 6,
+ "num_cdmoe_experts": 16348,
+ "num_cdmoe_experts_per_head": 8,
+ "num_cdmoe_heads": 4,
+ "num_hidden_layers": 24,
+ "num_key_value_heads": 3,
+ "pad_token_id": 2,
+ "rms_norm_eps": 1e-06,
+ "rope_scaling": {
+ "factor": 4.0,
+ "original_max_position_embeddings": 2048,
+ "rope_type": "dynamic"
+ },
+ "rope_theta": 10000.0,
+ "tie_word_embeddings": true,
+ "torch_dtype": "float32",
+ "transformers_version": "4.48.3",
+ "use_cache": true,
+ "vocab_size": 32768
+}
diff --git a/configuration_doge.py b/configuration_doge.py
new file mode 100644
index 0000000..a57b932
--- /dev/null
+++ b/configuration_doge.py
@@ -0,0 +1,228 @@
+# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# This file was automatically generated from src/transformers/models/doge/modular_doge.py.
+# Do NOT edit this file manually as any edits will be overwritten by the generation of
+# the file from the modular. If any change should be done, please apply the change to the
+# modular_doge.py file directly. One of our CI enforces this.
+# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# coding=utf-8
+# Copyright 2024 Jingze Shi and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on the Wonderful Matrices paper implementation.
+# The Doge family of small language models is trained by Jingze Shi.
+#
+# 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
+
+
+class DogeConfig(PretrainedConfig):
+ r"""
+ This is the configuration class to store the configuration of a [`DogeModel`]. It is used to instantiate an Doge
+ model according to the specified arguments, defining the model architecture like [SmallDoge/Doge-20M](https://huggingface.co/SmallDoge/Doge-20M).
+
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+ documentation from [`PretrainedConfig`] for more information.
+
+ Args:
+ vocab_size (`int`, *optional*, defaults to 32768):
+ Vocabulary size of the Doge model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`DogeModel`]
+ hidden_size (`int`, *optional*, defaults to 1024):
+ Dimension of the hidden representations.
+ intermediate_size (`int`, *optional*, defaults to 2048):
+ Dimension of the MLP representations.
+ num_hidden_layers (`int`, *optional*, defaults to 32):
+ Number of hidden layers in the Transformer decoder.
+ hidden_bias (`bool`, *optional*, defaults to `False`):
+ Whether to use bias in the hidden layers.
+ hidden_dropout (`float`, *optional*, defaults to 0.0):
+ Dropout probability for each sequence transformation and state transformation module.
+ hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
+ The non-linear activation function (function or string) in the decoder.
+ initializer_range (`float`, *optional*, defaults to 0.02):
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+ rms_norm_eps (`float`, *optional*, defaults to 1e-06):
+ The epsilon used by the rms normalization layers.
+ use_cache (`bool`, *optional*, defaults to `True`):
+ Whether or not the model should return the last key/values attentions (not used by all models). Only
+ relevant if `config.is_decoder=True`.
+ bos_token_id (`int`, *optional*, defaults to 0):
+ Beginning of stream token id.
+ eos_token_id (`int`, *optional*, defaults to 1):
+ End of stream token id.
+ pad_token_id (`int`, *optional*, defaults to 2):
+ Padding token id.
+ tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+ Whether to tie weight embeddings
+ max_position_embeddings (`int`, *optional*, defaults to 2048):
+ The maximum sequence length that this model might ever be used with.
+ rope_theta (`float`, *optional*, defaults to 10000.0):
+ The base period of the RoPE embeddings.
+ rope_scaling (`Dict`, *optional*):
+ Dictionary containing the scaling configuration for the RoPE embeddings.
+ NOTE: if you apply new rope type and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value accordingly.
+ Doge family of small models use `{ 'rope_type': 'dynamic', 'factor': 4.0, 'original_max_position_embeddings': 2048 }` as the default value.
+ Expected contents:
+ `rope_type` (`str`):
+ The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope', 'llama3'], with 'default' being the original RoPE implementation.
+ `factor` (`float`, *optional*):
+ Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings.
+ In most scaling types, a `factor` of x will enable the model to handle sequences of length x * original maximum pre-trained length.
+ `original_max_position_embeddings` (`int`, *optional*):
+ Used with 'dynamic', 'longrope' and 'llama3'.
+ The original max position embeddings used during pretraining.
+ `attention_factor` (`float`, *optional*):
+ Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention
+ computation.
+ If unspecified, it defaults to value recommended by the implementation, using the `factor` field to infer the suggested value.
+ `beta_fast` (`float`, *optional*):
+ Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear
+ ramp function. If unspecified, it defaults to 32.
+ `beta_slow` (`float`, *optional*):
+ Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear
+ ramp function. If unspecified, it defaults to 1.
+ `short_factor` (`List[float]`, *optional*):
+ Only used with 'longrope'. The scaling factor to be applied to short contexts (<`original_max_position_embeddings`).
+ Must be a list of numbers with the same length as the hidden size divided by the number of attention heads divided by 2
+ `long_factor` (`List[float]`, *optional*):
+ Only used with 'longrope'. The scaling factor to be applied to long contexts (<`original_max_position_embeddings`).
+ Must be a list of numbers with the same length as the hidden size divided by the number of attention heads divided by 2
+ `low_freq_factor` (`float`, *optional*):
+ Only used with 'llama3'. Scaling factor applied to low frequency components of the RoPE
+ `high_freq_factor` (`float`, *optional*):
+ Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE
+ num_attention_heads (`int`, *optional*, defaults to 8):
+ Number of attention heads for each attention layer in the Transformer decoder.
+ num_key_value_heads (`int`, *optional*):
+ This is the number of key_value heads that should be used to implement Grouped Query Attention.
+ If `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
+ `num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used.
+ When converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed by meanpooling all the original heads within that group.
+ For more details checkout [this paper](https://arxiv.org/pdf/2305.13245.pdf).
+ If it is not specified, will default to `num_attention_heads`.
+ attention_dropout (`float`, *optional*, defaults to 0.0):
+ The dropout ratio for the attention probabilities.
+ dynamic_mask_ratio (`float`, *optional*, defaults to 0.0):
+ The ratio to control the proportion of the dynamic mask filled with the minimum value. For more details checkout [this paper](https://arxiv.org/pdf/2412.11834).
+ is_moe (`bool`, *optional*, defaults to `False`):
+ Whether to use the Cross Domain Mixture of Experts, if `True`, the MoE will inherit the MLP to initialize. For more details checkout [this paper](https://arxiv.org/pdf/2412.11834).
+ num_cdmoe_experts (`int`, *optional*, defaults to 16348):
+ Number of Experts for the Cross Domain Mixture of Experts.
+ num_cdmoe_heads (`int`, *optional*, defaults to 4):
+ Number of retrieval heads, used to mix multi-head experts.
+ num_cdmoe_experts_per_head (`int`, *optional*, defaults to 8):
+ Number of Experts per retrieval head, used to mix multi-head experts.
+ expert_retrieval_size (`int`, *optional*, defaults to 64):
+ Dimension of the Expert retrieval states for calculating the dot product of query and key to determine the expert index.
+
+ ```python
+ >>> from transformers import DogeConfig, DogeModel
+
+ >>> # Initializing a Doge-320M style configuration
+ >>> configuration = DogeConfig()
+
+ >>> # Initializing a model from the Doge-320M style configuration
+ >>> model = DogeModel(configuration)
+
+ >>> # Accessing the model configuration
+ >>> configuration = model.config
+ ```"""
+
+ model_type = "doge"
+ keys_to_ignore_at_inference = ["past_key_values"]
+ # Default tensor parallel plan for base model `DogeModel`
+ 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.dt_proj": "rowwise",
+ "layers.*.self_attn.o_proj": "rowwise",
+ "layers.*.mlp.gate_proj": "colwise",
+ "layers.*.mlp.up_proj": "colwise",
+ "layers.*.mlp.down_proj": "rowwise",
+ }
+
+ def __init__(
+ self,
+ vocab_size=32768,
+ hidden_size=1024,
+ intermediate_size=2048,
+ num_hidden_layers=32,
+ hidden_bias=False,
+ hidden_dropout=0.0,
+ hidden_act="silu",
+ initializer_range=0.02,
+ rms_norm_eps=1e-06,
+ use_cache=True,
+ bos_token_id=0,
+ eos_token_id=1,
+ pad_token_id=2,
+ tie_word_embeddings=False,
+ max_position_embeddings=2048,
+ rope_theta=10000.0,
+ rope_scaling=None,
+ num_attention_heads=8,
+ num_key_value_heads=None,
+ attention_dropout=0.0,
+ dynamic_mask_ratio=0.0,
+ is_moe=False,
+ num_cdmoe_experts=16348,
+ num_cdmoe_heads=4,
+ num_cdmoe_experts_per_head=8,
+ expert_retrieval_size=64,
+ **kwargs,
+ ):
+ self.vocab_size = vocab_size
+ self.hidden_size = hidden_size
+ self.intermediate_size = intermediate_size
+ self.num_hidden_layers = num_hidden_layers
+
+ self.hidden_bias = hidden_bias
+ self.hidden_dropout = hidden_dropout
+ self.hidden_act = hidden_act
+ self.initializer_range = initializer_range
+ self.rms_norm_eps = rms_norm_eps
+ self.use_cache = use_cache
+
+ self.max_position_embeddings = max_position_embeddings
+ self.rope_theta = rope_theta
+ self.rope_scaling = rope_scaling
+ self.num_attention_heads = num_attention_heads
+ self.num_key_value_heads = num_key_value_heads
+ self.attention_dropout = attention_dropout
+ self.dynamic_mask_ratio = dynamic_mask_ratio
+ self.is_moe = is_moe
+ self.num_cdmoe_experts = num_cdmoe_experts
+ self.num_cdmoe_heads = num_cdmoe_heads
+ self.num_cdmoe_experts_per_head = num_cdmoe_experts_per_head
+ self.expert_retrieval_size = expert_retrieval_size
+
+ # Validate the correctness of rotary position embeddings parameters
+ # BC: if there is a 'type' field, copy it it to 'rope_type'.
+ if self.rope_scaling is not None and "type" in self.rope_scaling:
+ self.rope_scaling["rope_type"] = self.rope_scaling["type"]
+ rope_config_validation(self)
+
+ # for backward compatibility
+ if num_key_value_heads is None:
+ self.num_key_value_heads = num_attention_heads
+
+ super().__init__(
+ bos_token_id=bos_token_id,
+ eos_token_id=eos_token_id,
+ pad_token_id=pad_token_id,
+ tie_word_embeddings=tie_word_embeddings,
+ **kwargs,
+ )
+
+
+__all__ = ["DogeConfig"]
diff --git a/generation_config.json b/generation_config.json
new file mode 100644
index 0000000..4430b72
--- /dev/null
+++ b/generation_config.json
@@ -0,0 +1,7 @@
+{
+ "_from_model_config": true,
+ "bos_token_id": 0,
+ "eos_token_id": 1,
+ "pad_token_id": 2,
+ "transformers_version": "4.48.3"
+}
diff --git a/model.safetensors b/model.safetensors
new file mode 100644
index 0000000..19f8ce1
--- /dev/null
+++ b/model.safetensors
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5ca3abe1683a8fac3b1f2749ad16a75299cb8c4f0fbe24a0b4d085e67a6fb014
+size 610825528
diff --git a/modeling_doge.py b/modeling_doge.py
new file mode 100644
index 0000000..3de42d2
--- /dev/null
+++ b/modeling_doge.py
@@ -0,0 +1,1198 @@
+# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# This file was automatically generated from src/transformers/models/doge/modular_doge.py.
+# Do NOT edit this file manually as any edits will be overwritten by the generation of
+# the file from the modular. If any change should be done, please apply the change to the
+# modular_doge.py file directly. One of our CI enforces this.
+# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# coding=utf-8
+# Copyright 2024 Jingze Shi and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on the Wonderful Matrices paper implementation.
+# The Doge family of small language models is trained by Jingze Shi.
+#
+# 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.
+
+import math
+from typing import Callable, List, Optional, Tuple, Union
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+
+from transformers.activations import ACT2FN
+from transformers.cache_utils import Cache, DynamicCache, StaticCache
+from transformers.generation import GenerationMixin
+from transformers.modeling_attn_mask_utils import AttentionMaskConverter
+from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutputWithPast
+from transformers.modeling_rope_utils import ROPE_INIT_FUNCTIONS
+from transformers.modeling_utils import PreTrainedModel
+from transformers.processing_utils import Unpack
+from transformers.utils import (
+ LossKwargs,
+ add_start_docstrings,
+ add_start_docstrings_to_model_forward,
+ is_torch_flex_attn_available,
+ logging,
+ replace_return_docstrings,
+)
+from .configuration_doge import DogeConfig
+
+if is_torch_flex_attn_available():
+ from torch.nn.attention.flex_attention import flex_attention
+
+logger = logging.get_logger(__name__)
+
+_CONFIG_FOR_DOC = "DogeConfig"
+
+
+class DogeRMSNorm(nn.Module):
+ def __init__(self, hidden_size, eps=1e-6):
+ """
+ DogeRMSNorm is equivalent to T5LayerNorm
+ """
+ super().__init__()
+ self.weight = nn.Parameter(torch.ones(hidden_size))
+ self.variance_epsilon = eps
+
+ def forward(self, hidden_states):
+ input_dtype = hidden_states.dtype
+ hidden_states = hidden_states.to(torch.float32)
+ variance = hidden_states.pow(2).mean(-1, keepdim=True)
+ hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
+ return self.weight * hidden_states.to(input_dtype)
+
+ def extra_repr(self):
+ return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"
+
+
+class DogeResidual(nn.Module):
+ def __init__(self, hidden_size):
+ super().__init__()
+ self.weight = nn.Parameter(torch.ones(hidden_size))
+
+ def forward(self, residual_states, hidden_states):
+ return self.weight * residual_states + hidden_states
+
+ def extra_repr(self):
+ return f"{tuple(self.weight.shape)}"
+
+
+class DogeRotaryEmbedding(nn.Module):
+ def __init__(self, config: DogeConfig, device=None):
+ super().__init__()
+ # BC: "rope_type" was originally "type"
+ if hasattr(config, "rope_scaling") and config.rope_scaling is not None:
+ self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type"))
+ else:
+ self.rope_type = "default"
+ self.max_seq_len_cached = config.max_position_embeddings
+ self.original_max_seq_len = config.max_position_embeddings
+
+ self.config = config
+ self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
+
+ inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device)
+ self.register_buffer("inv_freq", inv_freq, persistent=False)
+ self.original_inv_freq = self.inv_freq
+
+ def _dynamic_frequency_update(self, position_ids, device):
+ """
+ dynamic RoPE layers should recompute `inv_freq` in the following situations:
+ 1 - growing beyond the cached sequence length (allow scaling)
+ 2 - the current sequence length is in the original scale (avoid losing precision with small sequences)
+ """
+ seq_len = torch.max(position_ids) + 1
+ if seq_len > self.max_seq_len_cached: # growth
+ inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device, seq_len=seq_len)
+ self.register_buffer("inv_freq", inv_freq, persistent=False) # TODO joao: may break with compilation
+ self.max_seq_len_cached = seq_len
+
+ if seq_len < self.original_max_seq_len and self.max_seq_len_cached > self.original_max_seq_len: # reset
+ # This .to() is needed if the model has been moved to a device after being initialized (because
+ # the buffer is automatically moved, but not the original copy)
+ self.original_inv_freq = self.original_inv_freq.to(device)
+ self.register_buffer("inv_freq", self.original_inv_freq, persistent=False)
+ self.max_seq_len_cached = self.original_max_seq_len
+
+ @torch.no_grad()
+ def forward(self, x, position_ids):
+ if "dynamic" in self.rope_type:
+ self._dynamic_frequency_update(position_ids, device=x.device)
+
+ # Core RoPE block
+ inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)
+ position_ids_expanded = position_ids[:, None, :].float()
+ # Force float32 (see https://github.com/huggingface/transformers/pull/29285)
+ device_type = x.device.type
+ device_type = device_type if isinstance(device_type, str) and device_type != "mps" else "cpu"
+ with torch.autocast(device_type=device_type, enabled=False):
+ freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+ emb = torch.cat((freqs, freqs), dim=-1)
+ cos = emb.cos()
+ sin = emb.sin()
+
+ # Advanced RoPE types (e.g. yarn) apply a post-processing scaling factor, equivalent to scaling attention
+ cos = cos * self.attention_scaling
+ sin = sin * self.attention_scaling
+
+ return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
+
+
+def rotate_half(x):
+ """Rotates half the hidden dims of the input."""
+ x1 = x[..., : x.shape[-1] // 2]
+ x2 = x[..., x.shape[-1] // 2 :]
+ return torch.cat((-x2, x1), dim=-1)
+
+
+def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
+ """Applies Rotary Position Embedding to the query and key tensors.
+
+ Args:
+ q (`torch.Tensor`): The query tensor.
+ k (`torch.Tensor`): The key tensor.
+ cos (`torch.Tensor`): The cosine part of the rotary embedding.
+ sin (`torch.Tensor`): The sine part of the rotary embedding.
+ position_ids (`torch.Tensor`, *optional*):
+ Deprecated and unused.
+ unsqueeze_dim (`int`, *optional*, defaults to 1):
+ The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
+ sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
+ that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
+ k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
+ cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
+ the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
+ Returns:
+ `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
+ """
+ cos = cos.unsqueeze(unsqueeze_dim)
+ sin = sin.unsqueeze(unsqueeze_dim)
+ q_embed = (q * cos) + (rotate_half(q) * sin)
+ k_embed = (k * cos) + (rotate_half(k) * sin)
+ return q_embed, k_embed
+
+
+def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
+ """
+ This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
+ num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
+ """
+ batch, num_key_value_heads, slen, head_dim = hidden_states.shape
+ if n_rep == 1:
+ return hidden_states
+ hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
+ return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
+
+
+def eager_attention_forward(
+ module: nn.Module,
+ query: torch.Tensor,
+ key: torch.Tensor,
+ value: torch.Tensor,
+ attention_mask: Optional[torch.Tensor],
+ scaling: float,
+ dropout: float = 0.0,
+ **kwargs,
+) -> Tuple[torch.Tensor, torch.Tensor]:
+ key_states = repeat_kv(key, module.num_key_value_groups)
+ value_states = repeat_kv(value, module.num_key_value_groups)
+
+ attn_weights = torch.matmul(query, key_states.transpose(-1, -2)) * scaling
+ if attention_mask is not None:
+ causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
+ attn_weights = attn_weights + causal_mask
+
+ attn_weights = F.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
+ attn_weights = F.dropout(attn_weights, p=dropout, training=module.training)
+ attn_output = torch.matmul(attn_weights, value_states)
+ attn_output = attn_output.transpose(1, 2).contiguous()
+
+ return attn_output, attn_weights
+
+
+def sdpa_attention_forward(
+ module: nn.Module,
+ query: torch.Tensor,
+ key: torch.Tensor,
+ value: torch.Tensor,
+ attention_mask: Optional[torch.Tensor],
+ dropout: float = 0.0,
+ scaling: Optional[float] = None,
+ is_causal: Optional[bool] = None,
+ **kwargs,
+) -> Tuple[torch.Tensor, None]:
+ key = repeat_kv(key, module.num_key_value_groups)
+ value = repeat_kv(value, module.num_key_value_groups)
+
+ causal_mask = attention_mask
+ if attention_mask is not None:
+ causal_mask = causal_mask[:, :, :, : key.shape[-2]]
+
+ # SDPA with memory-efficient backend is bugged with non-contiguous inputs and custom attn_mask for some torch versions
+ # Reference: https://github.com/pytorch/pytorch/issues/112577.
+ query = query.contiguous()
+ key = key.contiguous()
+ value = value.contiguous()
+
+ # We dispatch to SDPA's Flash Attention or Efficient kernels via this `is_causal` if statement instead of an inline conditional assignment
+ # in SDPA to support both torch.compile's dynamic shapes and full graph options. An inline conditional prevents dynamic shapes from compiling.
+ if is_causal is None:
+ is_causal = causal_mask is None and query.shape[2] > 1
+
+ # Shapes (e.g. query.shape[2]) are tensors during jit tracing, resulting in `is_causal` being a tensor.
+ # We convert it to a bool for the SDPA kernel that only accepts bools.
+ if torch.jit.is_tracing() and isinstance(is_causal, torch.Tensor):
+ is_causal = is_causal.item()
+
+ # NOTE: As of pytorch 2.5.1, SDPA backward pass of cuDNN is still incorrect, so we disable cuDNN SDPA (see https://github.com/pytorch/pytorch/issues/138581)
+ torch.backends.cuda.enable_cudnn_sdp(False)
+ attn_output = F.scaled_dot_product_attention(
+ query=query,
+ key=key,
+ value=value,
+ attn_mask=causal_mask,
+ dropout_p=dropout,
+ scale=scaling,
+ is_causal=is_causal,
+ )
+ attn_output = attn_output.transpose(1, 2).contiguous()
+
+ return attn_output, None
+
+
+def flex_attention_forward(
+ module: nn.Module,
+ query: torch.Tensor,
+ key: torch.Tensor,
+ value: torch.Tensor,
+ attention_mask: Optional[torch.Tensor],
+ scaling: Optional[float] = None,
+ is_causal: Optional[bool] = None,
+ softcap: Optional[float] = None,
+ head_mask: Optional[torch.Tensor] = None,
+ **kwargs,
+) -> Tuple[torch.Tensor, torch.Tensor]:
+ causal_mask = attention_mask
+ if attention_mask is not None:
+ causal_mask = causal_mask[:, :, :, : key.shape[-2]]
+
+ if is_causal is None:
+ is_causal = causal_mask is None and query.shape[2] > 1
+
+ def causal_mod(score, batch, head, q_idx, kv_idx):
+ if softcap is not None:
+ score = softcap * torch.tanh(score / softcap)
+ if causal_mask is not None:
+ score = score + causal_mask[batch][0][q_idx][kv_idx]
+ if head_mask is not None:
+ score = score + head_mask[batch][head][0][0]
+ return score
+
+ def dynamic_mod(score, batch, head, q_idx, kv_idx):
+ if softcap is not None:
+ score = softcap * torch.tanh(score / softcap)
+ if causal_mask is not None:
+ score = score + causal_mask[batch][head][q_idx][kv_idx]
+ if head_mask is not None:
+ score = score + head_mask[batch][head][0][0]
+ return score
+
+ # TODO: flex_attention: As of pytorch 2.5.1, captured buffers that require grad are not yet supported.
+ # NOTE: So we only use flex_attention in inference mode.
+ mask_mod = causal_mod if is_causal or module.training else dynamic_mod
+
+ attn_output, attention_weights = flex_attention(
+ query=query,
+ key=key,
+ value=value,
+ score_mod=mask_mod,
+ enable_gqa=True,
+ scale=scaling,
+ # Last time checked on PyTorch == 2.5.1: Flex Attention always computes the lse regardless.
+ # For simplification, we thus always return it as no additional computations are introduced.
+ return_lse=True,
+ )
+ # lse is returned in float32
+ attention_weights = attention_weights.to(value.dtype)
+ attn_output = attn_output.transpose(1, 2).contiguous()
+
+ return attn_output, attention_weights
+
+
+ALL_ATTENTION_FUNCTIONS = {
+ "eager": eager_attention_forward,
+ "sdpa": sdpa_attention_forward,
+ "flex_attention": flex_attention_forward,
+}
+
+
+class DogeDynamicMaskAttention(nn.Module):
+ """Dynamic Mask Attention from 'Wonderful Matrices' paper."""
+
+ def __init__(self, config: DogeConfig, layer_idx: Optional[int] = None):
+ super().__init__()
+ self.config = config
+ self.layer_idx = layer_idx
+ self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
+ self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads
+ self.scaling = self.head_dim**-0.5
+ self.attention_dropout = config.attention_dropout
+ self.dynamic_mask_ratio = config.dynamic_mask_ratio
+
+ self.q_proj = nn.Linear(
+ config.hidden_size, config.num_attention_heads * self.head_dim, bias=config.hidden_bias
+ )
+ self.k_proj = nn.Linear(
+ config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.hidden_bias
+ )
+ self.v_proj = nn.Linear(
+ config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.hidden_bias
+ )
+ # dynamic mask for the QK^T attention weights matrix
+ self.A = nn.Parameter(torch.zeros(config.num_attention_heads))
+ self.dt_proj = nn.Linear(
+ config.num_key_value_heads * self.head_dim, config.num_attention_heads, bias=config.hidden_bias
+ )
+ self.o_proj = nn.Linear(
+ config.num_attention_heads * self.head_dim, config.hidden_size, bias=config.hidden_bias
+ )
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ position_embeddings: Tuple[torch.Tensor, torch.Tensor],
+ attention_mask: Optional[torch.Tensor] = None,
+ past_key_value: Optional[Cache] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ **kwargs,
+ ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+ input_shape = hidden_states.shape[:-1]
+ hidden_shape = (*input_shape, -1, self.head_dim)
+
+ query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2)
+ key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2)
+ value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2)
+
+ cos, sin = position_embeddings
+ query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
+
+ if past_key_value is not None:
+ # sin and cos are specific to RoPE models; cache_position needed for the static cache
+ cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
+ key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+ # calculate dynamic mask from value_states
+ dt_states = self.dt_proj(
+ value_states.transpose(1, 2).reshape(value_states.shape[0], value_states.shape[-2], -1)
+ )
+ dynamic_mask = torch.exp(self.A * F.softplus(dt_states)).transpose(-1, -2)
+ attn_mask = self.prepare_dynamic_mask(
+ hidden_states=hidden_states,
+ dynamic_mask=dynamic_mask,
+ dynamic_mask_ratio=self.dynamic_mask_ratio,
+ attention_mask=attention_mask,
+ )
+
+ attention_interface: Callable = eager_attention_forward
+ if self.config._attn_implementation != "eager":
+ if self.config._attn_implementation == "sdpa" and kwargs.get("output_attentions", False):
+ logger.warning_once(
+ "`torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to "
+ 'eager attention. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
+ )
+ else:
+ attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
+
+ attn_output, attn_weights = attention_interface(
+ self,
+ query_states,
+ key_states,
+ value_states,
+ attention_mask=attn_mask,
+ dropout=0.0 if not self.training else self.attention_dropout,
+ scaling=self.scaling,
+ **kwargs,
+ )
+
+ attn_output = attn_output.reshape(*input_shape, -1).contiguous()
+ attn_output = self.o_proj(attn_output)
+ return attn_output, attn_weights
+
+ def prepare_dynamic_mask(
+ self,
+ hidden_states: torch.Tensor,
+ dynamic_mask: torch.Tensor,
+ dynamic_mask_ratio: float = 0.0,
+ attention_mask: Optional[torch.Tensor] = None,
+ ):
+ """
+ Combine `dynamic_mask` with `attention_mask` to generate the final `attn_mask`.
+
+ Args:
+ hidden_states (`torch.Tensor`): The input hidden_states, used to determine the minimum value of the current input precision.
+ dynamic_mask (`torch.Tensor`): dynamic mask of shape `(batch_size, num_heads, key_sequence_length)`.
+ dynamic_mask_ratio (`float`, *optional*): Ratio from 0.0 to 1.0 used to control the proportion of the dynamic mask filled with the minimum value.
+ attention_mask (`torch.Tensor`, *optional*): attention mask of shape `(batch_size, 1, query_sequence_length, key_sequence_length)`.
+ """
+ attn_mask = None
+ if dynamic_mask is not None:
+ attn_mask = dynamic_mask[:, :, None, :]
+ if 0.0 < dynamic_mask_ratio < 1.0:
+ min_type = torch.finfo(hidden_states.dtype).min
+ num_dynamic_mask = int(attn_mask.shape[-1] * dynamic_mask_ratio)
+ if num_dynamic_mask > 0:
+ rate_value = torch.kthvalue(attn_mask, num_dynamic_mask, dim=-1, keepdim=True).values
+ attn_mask = attn_mask.masked_fill(attn_mask < rate_value, min_type)
+ if attention_mask is not None:
+ attn_mask = attn_mask + attention_mask[:, :, :, : attn_mask.shape[-1]]
+ else:
+ attn_mask = attention_mask
+
+ return attn_mask
+
+
+class DogeMLP(nn.Module):
+ def __init__(self, config: DogeConfig):
+ super().__init__()
+ self.hidden_dim = config.hidden_size
+ self.intermediate_dim = config.intermediate_size
+ self.act_fn = ACT2FN[config.hidden_act]
+
+ self.gate_proj = nn.Linear(self.hidden_dim, self.intermediate_dim, bias=config.hidden_bias)
+ self.up_proj = nn.Linear(self.hidden_dim, self.intermediate_dim, bias=config.hidden_bias)
+ self.down_proj = nn.Linear(self.intermediate_dim, self.hidden_dim, bias=config.hidden_bias)
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ **kwargs,
+ ) -> torch.Tensor:
+ hidden_states = self.down_proj(self.act_fn(self.gate_proj(hidden_states)) * self.up_proj(hidden_states))
+ return hidden_states
+
+
+class DogeCDMoE(DogeMLP):
+ """Cross Domain Mixture of Experts from 'Wonderful Matrices' paper."""
+
+ def __init__(self, config: DogeConfig):
+ super().__init__(config)
+ self.hidden_dim = config.hidden_size
+ self.act_fn = ACT2FN[config.hidden_act]
+
+ self.expert_retrieval_dim = config.expert_retrieval_size
+ self.num_cdmoe_experts = config.num_cdmoe_experts
+ self.num_cdmoe_heads = config.num_cdmoe_heads
+ self.num_cdmoe_experts_per_head = config.num_cdmoe_experts_per_head
+ self.num_keys = int(math.sqrt(self.num_cdmoe_experts))
+
+ # queries and keys for retrieval experts
+ self.queries_proj = nn.Linear(self.hidden_dim, self.num_cdmoe_heads * self.expert_retrieval_dim, bias=False)
+ self.keys = nn.Parameter(torch.zeros(self.num_cdmoe_heads, self.expert_retrieval_dim, self.num_keys))
+
+ # experts
+ self.down_embed = nn.Embedding(self.num_cdmoe_experts, self.hidden_dim)
+ self.up_embed = nn.Embedding(self.num_cdmoe_experts, self.hidden_dim)
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ **kwargs,
+ ) -> torch.Tensor:
+ bsz, seq_len, _ = hidden_states.shape
+
+ # get routing weights with queries and keys
+ queries = self.queries_proj(hidden_states)
+ queries = queries.view(2, self.num_cdmoe_heads, bsz * seq_len, -1)
+ keys = self.keys.view(2, self.num_cdmoe_heads, -1, self.num_keys)
+ routing_weights = torch.matmul(queries, keys)
+ routing_weights = routing_weights.transpose(-2, -3).view(2, bsz, seq_len, self.num_cdmoe_heads, self.num_keys)
+
+ # get experts with the highest routing weights
+ (scores_x, scores_y), (indices_x, indices_y) = routing_weights.topk(self.num_cdmoe_experts_per_head, dim=-1)
+ all_scores = scores_x.unsqueeze(-1) + scores_y.unsqueeze(-2)
+ all_scores = all_scores.view(*scores_x.shape[:-1], -1)
+ all_indices = (indices_x.unsqueeze(-1) * self.num_keys) + indices_y.unsqueeze(-2)
+ all_indices = all_indices.view(*indices_x.shape[:-1], -1)
+ scores, pk_indices = all_scores.topk(self.num_cdmoe_experts_per_head, dim=-1)
+ indices = all_indices.gather(-1, pk_indices)
+ down_embed = self.down_embed(indices)
+ up_embed = self.up_embed(indices)
+
+ # mix experts states with cross domain states
+ experts_weights = torch.sum(hidden_states[:, :, None, None, :] * down_embed, dim=-1)
+ experts_weights = self.act_fn(experts_weights) * scores.softmax(dim=-1)
+ experts_states = torch.sum(experts_weights[:, :, :, :, None] * up_embed, dim=(-2, -3))
+ hidden_states = self.down_proj(self.act_fn(self.gate_proj(hidden_states)) * self.up_proj(hidden_states))
+ hidden_states = hidden_states + experts_states
+ return hidden_states
+
+
+class DogeDecoderLayer(nn.Module):
+ def __init__(self, config: DogeConfig, layer_idx: Optional[int] = None):
+ super().__init__()
+ self.hidden_dropout = config.hidden_dropout
+
+ self.pre_layernorm = DogeRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+ self.self_attn = DogeDynamicMaskAttention(config=config, layer_idx=layer_idx)
+ self.pre_residual = DogeResidual(config.hidden_size)
+
+ self.post_layernorm = DogeRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+ self.feed_forward = DogeMLP(config) if not config.is_moe else DogeCDMoE(config)
+ self.post_residual = DogeResidual(config.hidden_size)
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_value: Optional[Cache] = None,
+ output_attentions: Optional[bool] = False,
+ use_cache: Optional[bool] = False,
+ cache_position: Optional[torch.LongTensor] = None,
+ position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, # necessary, but kept here for BC
+ **kwargs,
+ ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
+ # sequence transformation
+ residual = hidden_states
+ hidden_states = self.pre_layernorm(hidden_states)
+ hidden_states, self_attn_weights = self.self_attn(
+ hidden_states=hidden_states,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_value=past_key_value,
+ output_attentions=output_attentions,
+ use_cache=use_cache,
+ cache_position=cache_position,
+ position_embeddings=position_embeddings,
+ **kwargs,
+ )
+ self_attn_weights = None
+ hidden_states = F.dropout(hidden_states, p=self.hidden_dropout, training=self.training)
+ hidden_states = self.pre_residual(residual, hidden_states)
+
+ # state transformation
+ residual = hidden_states
+ hidden_states = self.post_layernorm(hidden_states)
+ hidden_states = self.feed_forward(hidden_states)
+ hidden_states = F.dropout(hidden_states, p=self.hidden_dropout, training=self.training)
+ hidden_states = self.post_residual(residual, hidden_states)
+
+ outputs = (hidden_states,)
+ if output_attentions:
+ outputs += (self_attn_weights,)
+
+ return outputs
+
+
+DOGE_START_DOCSTRING = r"""
+ This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
+ library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
+ etc.)
+
+ This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
+ Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
+ and behavior.
+
+ Parameters:
+ config ([`DogeConfig`]):
+ Model configuration class with all the parameters of the model. Initializing with a config file does not
+ load the weights associated with the model, only the configuration. Check out the
+ [`~PreTrainedModel.from_pretrained`] method to load the model weights.
+"""
+
+
+@add_start_docstrings(
+ "The bare Doge Model outputting raw hidden-states without any specific head on top.",
+ DOGE_START_DOCSTRING,
+)
+class DogePreTrainedModel(PreTrainedModel):
+ config_class = DogeConfig
+ base_model_prefix = "model"
+ supports_gradient_checkpointing = True
+ _no_split_modules = ["DogeDecoderLayer"]
+ _skip_keys_device_placement = ["past_key_values"]
+ _supports_sdpa = True
+ # _supports_flex_attn = True # TODO: enable this when flex_attention is fully supported
+ _supports_cache_class = True
+ _supports_quantized_cache = True
+ _supports_static_cache = True
+
+ def _init_weights(self, module):
+ std = self.config.initializer_range
+ if isinstance(module, (nn.Linear)):
+ module.weight.data.normal_(mean=0.0, std=std)
+ if module.bias is not None:
+ module.bias.data.zero_()
+ elif isinstance(module, nn.Embedding):
+ module.weight.data.normal_(mean=0.0, std=std)
+ if module.padding_idx is not None:
+ module.weight.data[module.padding_idx].zero_()
+
+
+DOGE_INPUTS_DOCSTRING = r"""
+ Args:
+ input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+ Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
+ it.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+
+ [What are input IDs?](../glossary#input-ids)
+ attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+ - 1 for tokens that are **not masked**,
+ - 0 for tokens that are **masked**.
+
+ [What are attention masks?](../glossary#attention-mask)
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+
+ If `past_key_values` is used, optionally only the last `input_ids` have to be input (see
+ `past_key_values`).
+
+ If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
+ and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
+ information on the default strategy.
+
+ - 1 indicates the head is **not masked**,
+ - 0 indicates the head is **masked**.
+ position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
+ config.n_positions - 1]`.
+
+ [What are position IDs?](../glossary#position-ids)
+ past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*):
+ Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
+ blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`
+ returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.
+
+ Two formats are allowed:
+ - a [`~cache_utils.Cache`] instance, see our
+ [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache);
+ - Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
+ shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy
+ cache format.
+
+ The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the
+ legacy cache format will be returned.
+
+ If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
+ have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids`
+ of shape `(batch_size, sequence_length)`.
+ inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+ Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
+ is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
+ model's internal embedding lookup matrix.
+ use_cache (`bool`, *optional*):
+ If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
+ `past_key_values`).
+ output_attentions (`bool`, *optional*):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
+ tensors for more detail.
+ output_hidden_states (`bool`, *optional*):
+ Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+ more detail.
+ return_dict (`bool`, *optional*):
+ Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+ cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
+ Indices depicting the position of the input sequence tokens in the sequence. Contrarily to `position_ids`,
+ this tensor is not affected by padding. It is used to update the cache in the correct position and to infer
+ the complete sequence length.
+"""
+
+
+@add_start_docstrings(
+ "The bare Doge Model outputting raw hidden-states without any specific head on top.",
+ DOGE_START_DOCSTRING,
+)
+class DogeModel(DogePreTrainedModel):
+ """
+ Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`DogeDecoderLayer`]
+
+ Args:
+ config: DogeConfig
+ """
+
+ def __init__(self, config: DogeConfig):
+ super().__init__(config)
+ self.config = config
+ self.padding_idx = config.pad_token_id
+ self.vocab_size = config.vocab_size
+
+ self.word_embed = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
+ self.rotary_emb = DogeRotaryEmbedding(config)
+ self.layers = nn.ModuleList(
+ [DogeDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
+ )
+ self.final_layernorm = DogeRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+ self.gradient_checkpointing = False
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def get_input_embeddings(self):
+ return self.word_embed
+
+ def set_input_embeddings(self, value):
+ self.word_embed = value
+
+ @add_start_docstrings_to_model_forward(DOGE_INPUTS_DOCSTRING)
+ def forward(
+ self,
+ input_ids: torch.LongTensor = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ **kwargs,
+ ) -> Union[Tuple, BaseModelOutputWithPast]:
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ use_cache = use_cache if use_cache is not None else self.config.use_cache
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ if (input_ids is None) ^ (inputs_embeds is not None):
+ raise ValueError("You cannot specify both input_ids and inputs_embeds")
+
+ if self.gradient_checkpointing and self.training and use_cache:
+ logger.warning_once(
+ "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
+ )
+ use_cache = False
+
+ if inputs_embeds is None:
+ inputs_embeds = self.word_embed(input_ids)
+
+ if use_cache and past_key_values is None:
+ past_key_values = DynamicCache()
+
+ if cache_position is None:
+ past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+ cache_position = torch.arange(
+ past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
+ )
+
+ if position_ids is None:
+ position_ids = cache_position.unsqueeze(0)
+
+ causal_mask = self._update_causal_mask(
+ attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions
+ )
+
+ hidden_states = inputs_embeds
+
+ # create position embeddings to be shared across the decoder layers
+ position_embeddings = self.rotary_emb(hidden_states, position_ids)
+
+ # decoder layers
+ all_hidden_states = () if output_hidden_states else None
+ all_self_attns = () if output_attentions else None
+
+ for decoder_layer in self.layers[: self.config.num_hidden_layers]:
+ if output_hidden_states:
+ all_hidden_states += (hidden_states,)
+
+ if self.gradient_checkpointing and self.training:
+ layer_outputs = self._gradient_checkpointing_func(
+ decoder_layer.__call__,
+ hidden_states,
+ causal_mask,
+ position_ids,
+ past_key_values,
+ output_attentions,
+ use_cache,
+ cache_position,
+ position_embeddings,
+ )
+ else:
+ layer_outputs = decoder_layer(
+ hidden_states,
+ attention_mask=causal_mask,
+ position_ids=position_ids,
+ past_key_value=past_key_values,
+ output_attentions=output_attentions,
+ use_cache=use_cache,
+ cache_position=cache_position,
+ position_embeddings=position_embeddings,
+ **kwargs,
+ )
+
+ hidden_states = layer_outputs[0]
+
+ if output_attentions:
+ all_self_attns += (layer_outputs[1],)
+
+ hidden_states = self.final_layernorm(hidden_states)
+
+ # add hidden states from the last decoder layer
+ if output_hidden_states:
+ all_hidden_states += (hidden_states,)
+
+ output = BaseModelOutputWithPast(
+ last_hidden_state=hidden_states,
+ past_key_values=past_key_values if use_cache else None,
+ hidden_states=all_hidden_states,
+ attentions=all_self_attns,
+ )
+ return output if return_dict else output.to_tuple()
+
+ def _update_causal_mask(
+ self,
+ attention_mask: torch.Tensor,
+ input_tensor: torch.Tensor,
+ cache_position: torch.Tensor,
+ past_key_values: Cache,
+ output_attentions: bool,
+ ):
+ # We have to provide attention_mask for dynamic mask computation
+ past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+ using_static_cache = isinstance(past_key_values, StaticCache)
+
+ dtype, device = input_tensor.dtype, input_tensor.device
+ sequence_length = input_tensor.shape[1]
+ if using_static_cache:
+ target_length = past_key_values.get_max_cache_shape()
+ else:
+ target_length = (
+ attention_mask.shape[-1]
+ if isinstance(attention_mask, torch.Tensor)
+ else past_seen_tokens + sequence_length + 1
+ )
+
+ # In case the provided `attention` mask is 2D, we generate a causal mask here (4D).
+ causal_mask = self._prepare_4d_causal_attention_mask_with_cache_position(
+ attention_mask,
+ sequence_length=sequence_length,
+ target_length=target_length,
+ dtype=dtype,
+ device=device,
+ cache_position=cache_position,
+ batch_size=input_tensor.shape[0],
+ )
+
+ if (
+ self.config._attn_implementation == "sdpa"
+ and attention_mask is not None
+ and attention_mask.device.type in ["cuda", "xpu"]
+ and not output_attentions
+ ):
+ # Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when
+ # using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path.
+ # Details: https://github.com/pytorch/pytorch/issues/110213
+ min_dtype = torch.finfo(dtype).min
+ causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype)
+
+ return causal_mask
+
+ @staticmethod
+ def _prepare_4d_causal_attention_mask_with_cache_position(
+ attention_mask: torch.Tensor,
+ sequence_length: int,
+ target_length: int,
+ dtype: torch.dtype,
+ device: torch.device,
+ cache_position: torch.Tensor,
+ batch_size: int,
+ **kwargs,
+ ):
+ """
+ Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape
+ `(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing.
+
+ Args:
+ attention_mask (`torch.Tensor`):
+ A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape
+ `(batch_size, 1, query_length, key_value_length)`.
+ sequence_length (`int`):
+ The sequence length being processed.
+ target_length (`int`):
+ The target length: when generating with static cache, the mask should be as long as the static cache,
+ to account for the 0 padding, the part of the cache that is not filled yet.
+ dtype (`torch.dtype`):
+ The dtype to use for the 4D attention mask.
+ device (`torch.device`):
+ The device to plcae the 4D attention mask on.
+ cache_position (`torch.Tensor`):
+ Indices depicting the position of the input sequence tokens in the sequence.
+ batch_size (`torch.Tensor`):
+ Batch size.
+ """
+ if attention_mask is not None and attention_mask.dim() == 4:
+ # In this case we assume that the mask comes already in inverted form and requires no inversion or slicing.
+ causal_mask = attention_mask
+ else:
+ min_dtype = torch.finfo(dtype).min
+ causal_mask = torch.full(
+ (sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=device
+ )
+ if sequence_length != 1:
+ causal_mask = torch.triu(causal_mask, diagonal=1)
+ causal_mask *= torch.arange(target_length, device=device) > cache_position.reshape(-1, 1)
+ causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1)
+ if attention_mask is not None:
+ causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit
+ mask_length = attention_mask.shape[-1]
+ padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :]
+ padding_mask = padding_mask == 0
+ causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
+ padding_mask, min_dtype
+ )
+
+ return causal_mask
+
+
+class DogeForCausalLM(DogePreTrainedModel, GenerationMixin):
+ _tied_weights_keys = ["lm_head.weight"]
+ _tp_plan = {"lm_head": "colwise_rep"}
+
+ def __init__(self, config: DogeConfig):
+ super().__init__(config)
+ self.config = config
+ self.model = DogeModel(config)
+ self.vocab_size = config.vocab_size
+ self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def get_input_embeddings(self):
+ return self.model.word_embed
+
+ def set_input_embeddings(self, value):
+ self.model.word_embed = value
+
+ def get_output_embeddings(self):
+ return self.lm_head
+
+ def set_output_embeddings(self, new_embeddings):
+ self.lm_head = new_embeddings
+
+ def get_decoder(self):
+ return self.model
+
+ def set_decoder(self, decoder):
+ self.model = decoder
+
+ @add_start_docstrings_to_model_forward(DOGE_INPUTS_DOCSTRING)
+ @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
+ def forward(
+ self,
+ input_ids: torch.LongTensor = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ labels: Optional[torch.LongTensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ logits_to_keep: Union[int, torch.Tensor] = 0,
+ **kwargs: Unpack[LossKwargs],
+ ) -> Union[Tuple, CausalLMOutputWithPast]:
+ r"""
+ Args:
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+ config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+ (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+ logits_to_keep (`int`, *optional*):
+ If an `int`, compute logits for the last `logits_to_keep` tokens. If `0`, calculate logits for all
+ `input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that
+ token can save memory, which becomes pretty significant for long sequences or large vocabulary size.
+ If a `torch.Tensor`, must be 1D corresponding to the indices to keep in the sequence length dimension.
+ This is useful when using packed tensor format (single dimension for batch and sequence length).
+
+ Returns:
+
+ Example:
+
+ ```python
+ >>> from transformers import AutoTokenizer, AutoModelForCausalLM
+
+ >>> model = AutoModelForCausalLM.from_pretrained("SmallDoge/Doge-20M")
+ >>> tokenizer = AutoTokenizer.from_pretrained("SmallDoge/Doge-20M")
+
+ >>> prompt = "Hey, are you conscious? Can you talk to me?"
+ >>> inputs = tokenizer(prompt, return_tensors="pt")
+
+ >>> # Generate
+ >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
+ >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+ "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
+ ```"""
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ # decoder output consists of (dec_features, layer_state, dec_hidden, dec_attn)
+ outputs = self.model(
+ input_ids=input_ids,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ inputs_embeds=inputs_embeds,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ cache_position=cache_position,
+ **kwargs,
+ )
+
+ hidden_states = outputs[0]
+ # only compute necessary logits, and do not upcast them to float if we are not computing the loss
+ slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
+ logits = self.lm_head(hidden_states[:, slice_indices, :])
+
+ loss = None
+ if labels is not None:
+ loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.vocab_size, **kwargs)
+
+ if not return_dict:
+ output = (logits,) + outputs[1:]
+ return (loss,) + output if loss is not None else output
+
+ return CausalLMOutputWithPast(
+ loss=loss,
+ logits=logits,
+ past_key_values=outputs.past_key_values,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+
+@add_start_docstrings(
+ """
+ The Doge Model transformer with a sequence classification head on top (linear layer).
+
+ [`DogeForSequenceClassification`] uses the last token in order to do the classification, as other causal models
+ (e.g. GPT-2) do.
+
+ Since it does classification on the last token, it requires to know the position of the last token. If a
+ `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
+ no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
+ padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
+ each row of the batch).
+ """,
+ DOGE_START_DOCSTRING,
+)
+class DogeForSequenceClassification(DogePreTrainedModel):
+ def __init__(self, config: DogeConfig):
+ super().__init__(config)
+ self.num_labels = config.num_labels
+
+ self.model = DogeModel(config)
+ self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
+ self.config = config
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def get_input_embeddings(self):
+ return self.model.word_embed
+
+ def set_input_embeddings(self, value):
+ self.model.word_embed = value
+
+ @add_start_docstrings_to_model_forward(DOGE_INPUTS_DOCSTRING)
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ labels: Optional[torch.LongTensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[Tuple, SequenceClassifierOutputWithPast]:
+ r"""
+ labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+ Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+ config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+ `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+ """
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ transformer_outputs = self.model(
+ input_ids,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ inputs_embeds=inputs_embeds,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+ hidden_states = transformer_outputs[0]
+ logits = self.score(hidden_states)
+
+ if input_ids is not None:
+ batch_size = input_ids.shape[0]
+ else:
+ batch_size = inputs_embeds.shape[0]
+
+ if self.config.pad_token_id is None and batch_size != 1:
+ raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
+ if self.config.pad_token_id is None:
+ last_non_pad_token = -1
+ elif input_ids is not None:
+ # To handle both left- and right- padding, we take the rightmost token that is not equal to pad_token_id
+ non_pad_mask = (input_ids != self.config.pad_token_id).to(logits.device, torch.int32)
+ token_indices = torch.arange(input_ids.shape[-1], device=logits.device)
+ last_non_pad_token = (token_indices * non_pad_mask).argmax(-1)
+ else:
+ last_non_pad_token = -1
+ logger.warning_once(
+ f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be "
+ "unexpected if using padding tokens in conjunction with `inputs_embeds.`"
+ )
+
+ pooled_logits = logits[torch.arange(batch_size, device=logits.device), last_non_pad_token]
+
+ loss = None
+ if labels is not None:
+ loss = self.loss_function(logits=logits, labels=labels, pooled_logits=pooled_logits, config=self.config)
+
+ if not return_dict:
+ output = (pooled_logits,) + transformer_outputs[1:]
+ return ((loss,) + output) if loss is not None else output
+
+ return SequenceClassifierOutputWithPast(
+ loss=loss,
+ logits=pooled_logits,
+ past_key_values=transformer_outputs.past_key_values,
+ hidden_states=transformer_outputs.hidden_states,
+ attentions=transformer_outputs.attentions,
+ )
+
+
+__all__ = ["DogeForCausalLM", "DogeModel", "DogePreTrainedModel", "DogeForSequenceClassification"]
diff --git a/special_tokens_map.json b/special_tokens_map.json
new file mode 100644
index 0000000..68b10c7
--- /dev/null
+++ b/special_tokens_map.json
@@ -0,0 +1,23 @@
+{
+ "bos_token": {
+ "content": "<|begin_of_text|>",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false
+ },
+ "eos_token": {
+ "content": "<|end_of_text|>",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false
+ },
+ "pad_token": {
+ "content": "<|finetune_right_pad_id|>",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false
+ }
+}
diff --git a/tokenizer.json b/tokenizer.json
new file mode 100644
index 0000000..355c7de
--- /dev/null
+++ b/tokenizer.json
@@ -0,0 +1,164208 @@
+{
+ "version": "1.0",
+ "truncation": null,
+ "padding": null,
+ "added_tokens": [
+ {
+ "id": 0,
+ "content": "<|begin_of_text|>",
+ "single_word": false,
+ "lstrip": false,
+ "rstrip": false,
+ "normalized": false,
+ "special": true
+ },
+ {
+ "id": 1,
+ "content": "<|end_of_text|>",
+ "single_word": false,
+ "lstrip": false,
+ "rstrip": false,
+ "normalized": false,
+ "special": true
+ },
+ {
+ "id": 2,
+ "content": "<|finetune_right_pad_id|>",
+ "single_word": false,
+ "lstrip": false,
+ "rstrip": false,
+ "normalized": false,
+ "special": true
+ },
+ {
+ "id": 3,
+ "content": "<|start_header_id|>",
+ "single_word": false,
+ "lstrip": false,
+ "rstrip": false,
+ "normalized": false,
+ "special": true
+ },
+ {
+ "id": 4,
+ "content": "<|end_header_id|>",
+ "single_word": false,
+ "lstrip": false,
+ "rstrip": false,
+ "normalized": false,
+ "special": true
+ },
+ {
+ "id": 5,
+ "content": "<|eom_id|>",
+ "single_word": false,
+ "lstrip": false,
+ "rstrip": false,
+ "normalized": false,
+ "special": true
+ },
+ {
+ "id": 6,
+ "content": "<|eot_id|>",
+ "single_word": false,
+ "lstrip": false,
+ "rstrip": false,
+ "normalized": false,
+ "special": true
+ },
+ {
+ "id": 7,
+ "content": "<|python_tag|>",
+ "single_word": false,
+ "lstrip": false,
+ "rstrip": false,
+ "normalized": false,
+ "special": true
+ },
+ {
+ "id": 8,
+ "content": "<|begin_of_thought|>",
+ "single_word": false,
+ "lstrip": false,
+ "rstrip": false,
+ "normalized": false,
+ "special": true
+ },
+ {
+ "id": 9,
+ "content": "<|end_of_thought|>",
+ "single_word": false,
+ "lstrip": false,
+ "rstrip": false,
+ "normalized": false,
+ "special": true
+ },
+ {
+ "id": 10,
+ "content": "<|begin_of_solution|>",
+ "single_word": false,
+ "lstrip": false,
+ "rstrip": false,
+ "normalized": false,
+ "special": true
+ },
+ {
+ "id": 11,
+ "content": "<|end_of_solution|>",
+ "single_word": false,
+ "lstrip": false,
+ "rstrip": false,
+ "normalized": false,
+ "special": true
+ },
+ {
+ "id": 12,
+ "content": "<|reserved_special_token_0|>",
+ "single_word": false,
+ "lstrip": false,
+ "rstrip": false,
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+ },
+ {
+ "id": 13,
+ "content": "<|reserved_special_token_1|>",
+ "single_word": false,
+ "lstrip": false,
+ "rstrip": false,
+ "normalized": false,
+ "special": true
+ },
+ {
+ "id": 14,
+ "content": "<|reserved_special_token_2|>",
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+ "content": "<|reserved_special_token_3|>",
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