From e718bd4f4c4258c2783c2b86c4b38e120d7f450e Mon Sep 17 00:00:00 2001 From: xxl <505279206@qq.com> Date: Fri, 14 Mar 2025 14:56:33 +0800 Subject: [PATCH] first commit --- README.md | 143 +- config.json | 43 + configuration_doge.py | 228 + generation_config.json | 7 + model.safetensors | 3 + modeling_doge.py | 1198 + special_tokens_map.json | 23 + tokenizer.json | 164208 +++++++++++++++++++++++++++++++++++++ tokenizer_config.json | 2066 + 9 files changed, 167917 insertions(+), 2 deletions(-) create mode 100644 config.json create mode 100644 configuration_doge.py create mode 100644 generation_config.json create mode 100644 model.safetensors create mode 100644 modeling_doge.py create mode 100644 special_tokens_map.json create mode 100644 tokenizer.json create mode 100644 tokenizer_config.json 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** + +
+ SmallDoge +
+
+
+ + Discord + + + arXiv + + + GitHub + + + License + +
+ +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**: +[Visualize in Weights & Biases](https://wandb.ai/loser_cheems/huggingface/runs/0jht5dro) + +**DPO**: +[Visualize in Weights & Biases](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": 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To call a function, please respond with JSON for a function call.\" }}\n {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n {{- \"Do not use variables.\\n\\n\" }}\n {%- for t in tools %}\n {{- t | tojson(indent=4) }}\n {{- \"\\n\\n\" }}\n {%- endfor %}\n{%- endif %}\n{{- system_message }}\n{{- \"<|end_of_text|>\\n\\n\" }}\n\n{#- Custom tools are passed in a user message with some extra guidance #}\n{%- if tools_in_user_message and not tools is none %}\n {#- Extract the first user message so we can plug it in here #}\n {%- if messages | length != 0 %}\n {%- set first_user_message = messages[0]['content']|trim %}\n {%- set messages = messages[1:] %}\n {%- else %}\n {{- raise_exception(\"Cannot put tools in the first user message when there's no first user message!\") }}\n{%- endif %}\n {{- '<|start_header_id|>user<|end_header_id|>\\n' -}}\n {{- \"Given the following functions, please respond with a JSON for a function call \" }}\n {{- \"with its proper arguments that best answers the given prompt.\\n\\n\" }}\n {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n {{- \"Do not use variables.\\n\\n\" }}\n {%- for t in tools %}\n {{- t | tojson(indent=4) }}\n {{- \"\\n\\n\" }}\n {%- endfor %}\n {{- first_user_message + \"<|end_of_text|>\\n\\n\"}}\n{%- endif %}\n\n{%- for message in messages %}\n {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}\n {{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\\n'+ message['content'] | trim + '<|end_of_text|>\\n\\n' }}\n {%- elif 'tool_calls' in message %}\n {%- if not message.tool_calls|length == 1 %}\n {{- raise_exception(\"This model only supports single tool-calls at once!\") }}\n {%- endif %}\n {%- set tool_call = message.tool_calls[0].function %}\n {%- if builtin_tools is defined and tool_call.name in builtin_tools %}\n {{- '<|start_header_id|>assistant<|end_header_id|>\\n' -}}\n {{- \"<|python_tag|>\" + tool_call.name + \".call(\" }}\n {%- for arg_name, arg_val in tool_call.arguments | items %}\n {{- arg_name + '=\"' + arg_val + '\"' }}\n {%- if not loop.last %}\n {{- \", \" }}\n {%- endif %}\n {%- endfor %}\n {{- \")\" }}\n {%- else %}\n {{- '<|start_header_id|>assistant<|end_header_id|>\\n' -}}\n {{- '{\"name\": \"' + tool_call.name + '\", ' }}\n {{- '\"parameters\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- \"}\" }}\n {%- endif %}\n {%- if builtin_tools is defined %}\n {#- This means we're in ipython mode #}\n {{- \"<|end_of_text|>\\n\\n\" }}\n {%- else %}\n {{- \"<|end_of_text|>\\n\\n\" }}\n {%- endif %}\n {%- elif message.role == \"tool\" or message.role == \"ipython\" %}\n {{- \"<|start_header_id|>ipython<|end_header_id|>\\n\" }}\n {%- if message.content is mapping or message.content is iterable %}\n {{- message.content | tojson }}\n {%- else %}\n {{- message.content }}\n {%- endif %}\n {{- \"<|end_of_text|>\\n\\n\" }}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|start_header_id|>assistant<|end_header_id|>\\n' }}\n{%- endif %}\n", + "clean_up_tokenization_spaces": true, + "eos_token": "<|end_of_text|>", + "extra_special_tokens": {}, + "model_input_names": [ + "input_ids", + "attention_mask" + ], + "model_max_length": 131072, + "pad_token": "<|finetune_right_pad_id|>", + "padding_side": "left", + "tokenizer_class": "PreTrainedTokenizerFast", + "unk_token": null +}