797 lines
36 KiB
Python
797 lines
36 KiB
Python
# coding=utf-8
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# Copyright 2025 Jingze Shi and the HuggingFace Inc. team. All rights reserved.
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#
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# The Doge family of small language models is trained by SmallDoge Team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""PyTorch Doge model."""
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import math
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from typing import Callable, Optional, Union
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import torch
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import torch.nn.functional as F
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from torch import nn
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from ...activations import ACT2FN
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from ...cache_utils import Cache
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from ...configuration_utils import PretrainedConfig
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from ...integrations.flex_attention import compile_friendly_flex_attention
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from ...modeling_layers import GradientCheckpointingLayer
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from ...modeling_outputs import MoeCausalLMOutputWithPast, MoeModelOutputWithPast
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from ...modeling_rope_utils import rope_config_validation
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from ...modeling_utils import AttentionInterface
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from ...processing_utils import Unpack
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from ...utils import TransformersKwargs, is_torch_flex_attn_available
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from ...utils.generic import OutputRecorder
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from ..llama.modeling_llama import (
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LlamaForSequenceClassification,
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LlamaMLP,
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LlamaPreTrainedModel,
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LlamaRMSNorm,
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LlamaRotaryEmbedding,
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apply_rotary_pos_emb,
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eager_attention_forward,
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repeat_kv,
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)
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from ..mixtral.modeling_mixtral import MixtralForCausalLM, MixtralModel
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if is_torch_flex_attn_available():
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from torch.nn.attention.flex_attention import BlockMask
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class DogeConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`DogeModel`]. It is used to instantiate an Doge
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model according to the specified arguments, defining the model architecture like [SmallDoge/Doge-320M](https://huggingface.co/SmallDoge/Doge-320M).
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Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
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documentation from [`PretrainedConfig`] for more information.
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Args:
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vocab_size (`int`, *optional*, defaults to 32768):
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Vocabulary size of the Doge2 model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`DogeModel`]
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hidden_size (`int`, *optional*, defaults to 1024):
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Dimension of the hidden representations.
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intermediate_size (`int`, *optional*, defaults to 2048):
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Dimension of the MLP representations.
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num_hidden_layers (`int`, *optional*, defaults to 32):
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Number of hidden layers in the Transformer decoder.
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hidden_dropout (`float`, *optional*, defaults to 0.0):
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Dropout probability for each sequence transformation and state transformation module.
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hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
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The non-linear activation function (function or string) in the decoder.
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initializer_range (`float`, *optional*, defaults to 0.02):
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The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
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rms_norm_eps (`float`, *optional*, defaults to 1e-06):
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The epsilon used by the rms normalization layers.
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use_cache (`bool`, *optional*, defaults to `True`):
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Whether or not the model should return the last key/values attentions (not used by all models). Only
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relevant if `config.is_decoder=True`.
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tie_word_embeddings (`bool`, *optional*, defaults to `False`):
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Whether the model's input and output word embeddings should be tied.
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max_position_embeddings (`int`, *optional*, defaults to 2048):
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The maximum sequence length that this model might ever be used with.
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rope_theta (`float`, *optional*, defaults to 10000.0):
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The base period of the RoPE embeddings.
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rope_scaling (`Dict`, *optional*):
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Dictionary containing the scaling configuration for the RoPE embeddings.
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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.
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Doge family of small models use `{ 'rope_type': 'dynamic', 'factor': 4.0, 'original_max_position_embeddings': 2048 }` as the default value.
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Expected contents:
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`rope_type` (`str`):
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The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope', 'llama3'], with 'default' being the original RoPE implementation.
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`factor` (`float`, *optional*):
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Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings.
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In most scaling types, a `factor` of x will enable the model to handle sequences of length x * original maximum pre-trained length.
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`original_max_position_embeddings` (`int`, *optional*):
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Used with 'dynamic', 'longrope' and 'llama3'.
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The original max position embeddings used during pretraining.
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`attention_factor` (`float`, *optional*):
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Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention
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computation.
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If unspecified, it defaults to value recommended by the implementation, using the `factor` field to infer the suggested value.
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`beta_fast` (`float`, *optional*):
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Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear
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ramp function. If unspecified, it defaults to 32.
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`beta_slow` (`float`, *optional*):
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Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear
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ramp function. If unspecified, it defaults to 1.
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`short_factor` (`List[float]`, *optional*):
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Only used with 'longrope'. The scaling factor to be applied to short contexts (<`original_max_position_embeddings`).
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Must be a list of numbers with the same length as the hidden size divided by the number of attention heads divided by 2
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`long_factor` (`List[float]`, *optional*):
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Only used with 'longrope'. The scaling factor to be applied to long contexts (<`original_max_position_embeddings`).
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Must be a list of numbers with the same length as the hidden size divided by the number of attention heads divided by 2
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`low_freq_factor` (`float`, *optional*):
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Only used with 'llama3'. Scaling factor applied to low frequency components of the RoPE
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`high_freq_factor` (`float`, *optional*):
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Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE
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num_attention_heads (`int`, *optional*, defaults to 8):
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Number of attention heads for each attention layer in the Transformer decoder.
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num_key_value_heads (`int`, *optional*):
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This is the number of key_value heads that should be used to implement Grouped Query Attention.
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If `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
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`num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used.
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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.
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For more details checkout [this paper](https://arxiv.org/pdf/2305.13245.pdf).
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If it is not specified, will default to `num_attention_heads`.
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attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
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Whether to use a bias in the query, key, value and output projection layers during self-attention.
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attention_dropout (`float`, *optional*, defaults to 0.0):
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The dropout ratio for the attention probabilities.
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mlp_bias (`bool`, *optional*, defaults to `False`):
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Whether to use a bias in up_proj, down_proj and gate_proj layers in the MLP layers.
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sliding_window (`int`, *optional*):
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Sliding window attention window size. If not specified, will default to `None`.
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keep_window_size (`int`, *optional*, defaults to 2048):
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The window size of tokens that are not dynamically masked, and dynamic masking is only performed when the sequence length exceeds this value.
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is_moe (`bool`, *optional*, defaults to `False`):
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Whether to use the Cross Domain Mixture of Experts, if `True`, the MoE will inherit the MLP to initialize.
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num_experts (`int`, *optional*, defaults to 16384):
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Number of routed experts in the model. This is only used when `is_moe=True`.
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num_experts_per_tok (`int`, *optional*, defaults to 64):
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Number of selected experts to route per-token.
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norm_topk_prob (`bool`, *optional*, defaults to `False`):
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Whether to normalize the topk probabilities.
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output_router_logits (`bool`, *optional*, defaults to `False`):
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Whether or not the router logits should be returned by the model. Enabling this will also
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allow the model to output the auxiliary loss, including load balancing loss and router z-loss.
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router_aux_loss_coef (`float`, *optional*, defaults to 0.001):
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The aux loss factor for the total loss.
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```python
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>>> from transformers import DogeConfig, DogeModel
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>>> # Initializing a Doge-320M style configuration
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>>> configuration = DogeConfig()
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>>> # Initializing a model from the Doge-320M style configuration
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>>> model = DogeModel(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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```"""
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model_type = "doge"
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keys_to_ignore_at_inference = ["past_key_values"]
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# Default tensor parallel plan for base model `DogeModel`
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base_model_tp_plan = {
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"layers.*.self_attn.q_proj": "colwise",
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"layers.*.self_attn.k_proj": "colwise",
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"layers.*.self_attn.v_proj": "colwise",
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"layers.*.self_attn.dt_proj": "rowwise",
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"layers.*.self_attn.o_proj": "rowwise",
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"layers.*.input_layernorm.weight": "sequence_parallel",
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"layers.*.input_residual.weight": "sequence_parallel",
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"layers.*.post_attention_layernorm.weight": "sequence_parallel",
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"layers.*.post_attention_residual.weight": "sequence_parallel",
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"norm.weight": "sequence_parallel",
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"layers.*.mlp.gate_proj": "colwise",
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"layers.*.mlp.up_proj": "colwise",
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"layers.*.mlp.down_proj": "rowwise",
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"layers.*.mlp.router_gate": "colwise_rep",
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"layers.*.mlp.down_embed": "rowwise_rep",
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"layers.*.mlp.up_embed": "rowwise_rep",
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}
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base_model_pp_plan = {
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"embed_tokens": (["input_ids"], ["inputs_embeds"]),
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"layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
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"norm": (["hidden_states"], ["hidden_states"]),
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}
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def __init__(
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self,
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vocab_size=32768,
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hidden_size=1024,
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intermediate_size=2048,
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num_hidden_layers=32,
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hidden_dropout=0.0,
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hidden_act="silu",
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initializer_range=0.02,
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rms_norm_eps=1e-06,
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use_cache=True,
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tie_word_embeddings=False,
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max_position_embeddings=2048,
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rope_theta=10000.0,
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rope_scaling=None,
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num_attention_heads=8,
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num_key_value_heads=None,
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attention_bias=False,
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attention_dropout=0.0,
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mlp_bias=False,
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sliding_window=None,
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keep_window_size=2048,
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is_moe=False,
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num_experts=16384,
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num_experts_per_tok=64,
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norm_topk_prob=False,
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output_router_logits=False,
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router_aux_loss_coef=0.001,
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**kwargs,
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):
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self.vocab_size = vocab_size
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self.hidden_size = hidden_size
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self.intermediate_size = intermediate_size
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self.num_hidden_layers = num_hidden_layers
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self.hidden_dropout = hidden_dropout
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self.hidden_act = hidden_act
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self.initializer_range = initializer_range
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self.rms_norm_eps = rms_norm_eps
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self.use_cache = use_cache
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self.max_position_embeddings = max_position_embeddings
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self.rope_theta = rope_theta
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self.rope_scaling = rope_scaling
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self.num_attention_heads = num_attention_heads
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self.num_key_value_heads = num_key_value_heads
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self.attention_bias = attention_bias
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self.attention_dropout = attention_dropout
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self.mlp_bias = mlp_bias
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self.sliding_window = sliding_window
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self.keep_window_size = keep_window_size
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self.is_moe = is_moe
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self.num_experts = num_experts
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self.num_experts_per_tok = num_experts_per_tok
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self.norm_topk_prob = norm_topk_prob
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self.output_router_logits = output_router_logits
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self.router_aux_loss_coef = router_aux_loss_coef
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# Validate the correctness of rotary position embeddings parameters
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# BC: if there is a 'type' field, copy it it to 'rope_type'.
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if self.rope_scaling is not None and "type" in self.rope_scaling:
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self.rope_scaling["rope_type"] = self.rope_scaling["type"]
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rope_config_validation(self)
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# for backward compatibility
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if num_key_value_heads is None:
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self.num_key_value_heads = num_attention_heads
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super().__init__(
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tie_word_embeddings=tie_word_embeddings,
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**kwargs,
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)
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class DogeRMSNorm(LlamaRMSNorm):
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pass
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class DogeRotaryEmbedding(LlamaRotaryEmbedding):
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pass
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def flex_attention_forward(
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module: nn.Module,
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query: torch.Tensor,
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key: torch.Tensor,
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value: torch.Tensor,
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attention_mask: Union[torch.Tensor, "BlockMask"],
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scaling: Optional[float] = None,
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softcap: Optional[float] = None,
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head_mask: Optional[torch.Tensor] = None,
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**kwargs,
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) -> tuple[torch.Tensor, torch.Tensor]:
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block_mask = None
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causal_mask = None
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if isinstance(attention_mask, BlockMask):
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block_mask = attention_mask
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else:
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causal_mask = attention_mask
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if causal_mask is not None:
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causal_mask = causal_mask[:, :, :, : key.shape[-2]]
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def score_mod(score, batch_idx, head_idx, q_idx, kv_idx):
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if softcap is not None:
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score = softcap * torch.tanh(score / softcap)
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if causal_mask is not None:
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score = score + causal_mask[batch_idx][head_idx][q_idx][kv_idx]
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if head_mask is not None:
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score = score + head_mask[batch_idx][head_idx][0][0]
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return score
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attn_output, attention_weights = compile_friendly_flex_attention(
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query,
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key,
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value,
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score_mod=score_mod,
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block_mask=block_mask,
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enable_gqa=True,
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scale=scaling,
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# Last time checked on PyTorch == 2.5.1: Flex Attention always computes the lse regardless.
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# For simplification, we thus always return it as no additional computations are introduced.
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return_lse=True,
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)
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# lse is returned in float32
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attention_weights = attention_weights.to(value.dtype)
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attn_output = attn_output.transpose(1, 2).contiguous()
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return attn_output, attention_weights
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ALL_ATTENTION_FUNCTIONS = AttentionInterface()
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ALL_ATTENTION_FUNCTIONS["doge_flex_attention"] = flex_attention_forward
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class DogeAttention(nn.Module):
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def __init__(self, config: DogeConfig, layer_idx: Optional[int] = None):
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super().__init__()
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self.config = config
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self.layer_idx = layer_idx
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self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
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self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads
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self.scaling = self.head_dim**-0.5
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self.attention_dropout = config.attention_dropout
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self.keep_window_size = config.keep_window_size
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self.q_proj = nn.Linear(
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config.hidden_size, config.num_attention_heads * self.head_dim, bias=config.attention_bias
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)
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self.k_proj = nn.Linear(
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config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
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)
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self.v_proj = nn.Linear(
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config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
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)
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# dynamic mask for the QK^T attention weights matrix
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self.A = nn.Parameter(torch.zeros(config.num_key_value_heads))
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self.dt_proj = nn.Linear(
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config.num_key_value_heads * self.head_dim, config.num_key_value_heads, bias=config.attention_bias
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)
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self.o_proj = nn.Linear(
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config.num_attention_heads * self.head_dim, config.hidden_size, bias=config.attention_bias
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)
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self.q_norm = DogeRMSNorm(self.head_dim, eps=config.rms_norm_eps)
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self.k_norm = DogeRMSNorm(self.head_dim, eps=config.rms_norm_eps)
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def forward(
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self,
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hidden_states: torch.Tensor,
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position_embeddings: tuple[torch.Tensor, torch.Tensor],
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attention_mask: Optional[torch.Tensor] = None,
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past_key_value: Optional[Cache] = None,
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cache_position: Optional[torch.LongTensor] = None,
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**kwargs,
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) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
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input_shape = hidden_states.shape[:-1]
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hidden_shape = (*input_shape, -1, self.head_dim)
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query_states = self.q_norm(self.q_proj(hidden_states).view(hidden_shape)).transpose(1, 2)
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key_states = self.k_norm(self.k_proj(hidden_states).view(hidden_shape)).transpose(1, 2)
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value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2)
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cos, sin = position_embeddings
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query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
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if past_key_value is not None:
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# sin and cos are specific to RoPE models; cache_position needed for the static cache
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cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
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key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
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# calculate dynamic mask from value_states
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dt_states = self.dt_proj(
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value_states.transpose(1, 2).reshape(value_states.shape[0], value_states.shape[-2], -1)
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)
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dt_states = torch.exp(self.A * F.softplus(dt_states)).transpose(-1, -2)
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attn_mask = self.prepare_dynamic_mask(
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hidden_states=hidden_states,
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dt_states=dt_states,
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keep_window_size=self.keep_window_size,
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attention_mask=attention_mask,
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)
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attn_mask = repeat_kv(attn_mask, self.num_key_value_groups)
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attention_interface: Callable = eager_attention_forward
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if self.config._attn_implementation != "eager":
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attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
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attn_output, attn_weights = attention_interface(
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self,
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query_states,
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key_states,
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value_states,
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attention_mask=attn_mask,
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dropout=0.0 if not self.training else self.attention_dropout,
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scaling=self.scaling,
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**kwargs,
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)
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attn_output = attn_output.reshape(*input_shape, -1).contiguous()
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attn_output = self.o_proj(attn_output)
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return attn_output, attn_weights
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def prepare_dynamic_mask(
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self,
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hidden_states: torch.Tensor,
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dt_states: torch.Tensor,
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|
keep_window_size: int = 2048,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
):
|
|
"""
|
|
The core idea of DMA is to calculate the dynamic attention mask to mask the tokens that should be masked, so as to form sparse attention.
|
|
|
|
Combine `dt_states` 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.
|
|
dt_states (`torch.Tensor`): dt_states of shape `(batch_size, num_heads, key_sequence_length)`.
|
|
keep_window_size (`int`): The window size of tokens that are not dynamically masked, and dynamic masking is only performed when the sequence length exceeds this value.
|
|
attention_mask (`torch.Tensor`, *optional*): attention mask of shape `(batch_size, 1, query_sequence_length, key_sequence_length)`.
|
|
"""
|
|
min_dtype = torch.finfo(hidden_states.dtype).min
|
|
dtype = hidden_states.dtype
|
|
attn_mask = dt_states[:, :, None, :].expand(
|
|
-1, -1, hidden_states.shape[1], -1
|
|
) # [batch_size, num_heads, query_len, key_len]
|
|
if attention_mask is not None and not isinstance(attention_mask, BlockMask):
|
|
if attention_mask.dtype == torch.bool:
|
|
dtype = hidden_states.dtype
|
|
attention_mask = torch.where(
|
|
attention_mask, torch.tensor(0.0, device=attention_mask.device, dtype=dtype), min_dtype
|
|
)
|
|
attn_mask = attn_mask.masked_fill(attention_mask[:, :, :, : attn_mask.shape[-1]] != 0, min_dtype)
|
|
if attn_mask.shape[-1] > keep_window_size:
|
|
active_mask = torch.zeros_like(attn_mask, dtype=dtype, device=attn_mask.device)
|
|
topk_indices = torch.topk(attn_mask, keep_window_size, dim=-1, largest=True, sorted=False).indices
|
|
active_mask = active_mask.scatter(-1, topk_indices, 1.0)
|
|
attn_mask = attn_mask.masked_fill(active_mask == 0.0, min_dtype)
|
|
return attn_mask
|
|
|
|
|
|
class DogeMLP(LlamaMLP):
|
|
pass
|
|
|
|
|
|
class DogeCDMoE(nn.Module):
|
|
def __init__(self, config: DogeConfig):
|
|
super().__init__()
|
|
self.hidden_size = config.hidden_size
|
|
self.intermediate_size = config.intermediate_size
|
|
self.act_fn = ACT2FN[config.hidden_act]
|
|
|
|
self.num_experts = config.num_experts
|
|
self.num_keys = math.floor(math.sqrt(self.num_experts))
|
|
self.top_k = config.num_experts_per_tok
|
|
self.norm_topk_prob = config.norm_topk_prob
|
|
|
|
# shared expert
|
|
self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.mlp_bias)
|
|
self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.mlp_bias)
|
|
self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=config.mlp_bias)
|
|
|
|
# router gate for retrieval experts
|
|
self.router_gate = nn.Linear(self.hidden_size, self.num_keys * 2, bias=False)
|
|
|
|
# routed experts
|
|
self.down_embed = nn.Embedding(self.num_experts, self.hidden_size)
|
|
self.up_embed = nn.Embedding(self.num_experts, self.hidden_size)
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
**kwargs,
|
|
) -> torch.Tensor:
|
|
bsz, seq_len, _ = hidden_states.shape
|
|
|
|
# get routing logits with router gate
|
|
router_logits = self.router_gate(hidden_states).view(2, bsz * seq_len, -1)
|
|
|
|
# get experts with the highest routing logits
|
|
(scores_x, scores_y), (indices_x, indices_y) = router_logits.topk(self.num_keys, dim=-1)
|
|
all_scores = scores_x.unsqueeze(-1) + scores_y.unsqueeze(-2)
|
|
all_indices = indices_x.unsqueeze(-1) * self.num_keys + indices_y.unsqueeze(-2)
|
|
all_scores = all_scores.view(*all_scores.shape[:-2], -1)
|
|
all_indices = all_indices.view(*all_indices.shape[:-2], -1)
|
|
scores, position_indices = all_scores.topk(self.top_k, dim=-1)
|
|
indices = all_indices.gather(-1, position_indices)
|
|
routing_weights = F.softmax(scores, dim=-1)
|
|
if self.norm_topk_prob:
|
|
routing_weights /= routing_weights.sum(dim=-1, keepdim=True)
|
|
|
|
# mix routed experts states with shared expert states
|
|
down_embed = self.down_embed(indices)
|
|
up_embed = self.up_embed(indices)
|
|
experts_weights = torch.matmul(down_embed, hidden_states.view(bsz * seq_len, -1, 1)).view(bsz * seq_len, -1)
|
|
experts_weights = self.act_fn(experts_weights) * routing_weights
|
|
experts_states = torch.matmul(experts_weights.view(bsz * seq_len, 1, -1), up_embed).view(bsz, seq_len, -1)
|
|
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, router_logits
|
|
|
|
|
|
class DogeDecoderLayer(GradientCheckpointingLayer):
|
|
def __init__(self, config: DogeConfig, layer_idx: Optional[int] = None):
|
|
super().__init__()
|
|
self.hidden_dropout = config.hidden_dropout
|
|
|
|
self.input_layernorm = DogeRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
|
self.self_attn = DogeAttention(config=config, layer_idx=layer_idx)
|
|
self.input_residual = nn.Parameter(torch.ones(config.hidden_size))
|
|
|
|
self.post_attention_layernorm = DogeRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
|
self.mlp = DogeMLP(config) if not config.is_moe else DogeCDMoE(config)
|
|
self.post_attention_residual = nn.Parameter(torch.ones(config.hidden_size))
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
position_embeddings: tuple[torch.Tensor, torch.Tensor],
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
past_key_value: Optional[tuple[torch.Tensor]] = None,
|
|
use_cache: Optional[bool] = False,
|
|
cache_position: Optional[torch.LongTensor] = None,
|
|
**kwargs: Unpack[TransformersKwargs],
|
|
) -> tuple[torch.FloatTensor, Optional[tuple[torch.FloatTensor, torch.FloatTensor]]]:
|
|
# sequence transformation
|
|
residual = hidden_states
|
|
hidden_states = self.input_layernorm(hidden_states)
|
|
hidden_states, self_attn_weights = self.self_attn(
|
|
hidden_states=hidden_states,
|
|
position_embeddings=position_embeddings,
|
|
attention_mask=attention_mask,
|
|
position_ids=position_ids,
|
|
past_key_value=past_key_value,
|
|
use_cache=use_cache,
|
|
cache_position=cache_position,
|
|
**kwargs,
|
|
)
|
|
hidden_states = F.dropout(hidden_states, p=self.hidden_dropout, training=self.training)
|
|
hidden_states = self.input_residual * residual + hidden_states
|
|
|
|
# state transformation
|
|
residual = hidden_states
|
|
hidden_states = self.post_attention_layernorm(hidden_states)
|
|
hidden_states = self.mlp(hidden_states)
|
|
hidden_states = F.dropout(hidden_states, p=self.hidden_dropout, training=self.training)
|
|
hidden_states = self.post_attention_residual * residual + hidden_states
|
|
|
|
return hidden_states
|
|
|
|
|
|
class DogePreTrainedModel(LlamaPreTrainedModel):
|
|
_supports_flash_attn = False
|
|
_can_compile_fullgraph = False
|
|
_can_record_outputs = {
|
|
"router_logits": OutputRecorder(DogeCDMoE, index=1),
|
|
"hidden_states": DogeDecoderLayer,
|
|
"attentions": DogeAttention,
|
|
}
|
|
|
|
def _init_weights(self, module):
|
|
"""Initialize the weights"""
|
|
LlamaPreTrainedModel._init_weights(module)
|
|
if isinstance(module, DogeAttention):
|
|
if hasattr(module, "A"):
|
|
module.A.data.zero_()
|
|
elif isinstance(module, DogeDecoderLayer):
|
|
if hasattr(module, "input_residual"):
|
|
module.input_residual.data.fill_(1.0)
|
|
if hasattr(module, "post_attention_residual"):
|
|
module.post_attention_residual.data.fill_(1.0)
|
|
|
|
|
|
class DogeModel(MixtralModel):
|
|
pass
|
|
|
|
|
|
def load_balancing_loss_func(
|
|
gate_logits: Union[torch.Tensor, tuple[torch.Tensor], None],
|
|
num_experts: Optional[int] = None,
|
|
num_keys: Optional[int] = None,
|
|
top_k: int = 2,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
) -> Union[torch.Tensor, int]:
|
|
r"""
|
|
Computes auxiliary load balancing loss as in Switch Transformer - implemented in Pytorch.
|
|
|
|
See Switch Transformer (https://arxiv.org/abs/2101.03961) for more details. This function implements the loss
|
|
function presented in equations (4) - (6) of the paper. It aims at penalizing cases where the routing between
|
|
experts is too unbalanced.
|
|
|
|
Args:
|
|
gate_logits:
|
|
Logits from the `router_gate`, should be a tuple of model.config.num_hidden_layers tensors of
|
|
shape [2, batch_size * sequence_length, num_keys].
|
|
num_experts:
|
|
Number of experts
|
|
num_keys:
|
|
Number of keys
|
|
top_k:
|
|
The number of experts to route per-token, can be also interpreted as the `top-k` routing
|
|
parameter.
|
|
attention_mask (`torch.Tensor`, *optional*):
|
|
The attention_mask used in forward function
|
|
shape [batch_size X sequence_length] if not None.
|
|
|
|
Returns:
|
|
The auxiliary loss.
|
|
"""
|
|
if gate_logits is None or not isinstance(gate_logits, tuple):
|
|
return 0
|
|
|
|
compute_dtype = gate_logits[0].dtype
|
|
compute_device = gate_logits[0].device
|
|
all_expert_indices = []
|
|
all_routing_weights = []
|
|
|
|
for layer_gate_logits in gate_logits:
|
|
layer_gate_logits = layer_gate_logits.to(compute_device)
|
|
|
|
(scores_x, scores_y), (indices_x, indices_y) = layer_gate_logits.topk(num_keys, dim=-1)
|
|
|
|
all_scores = scores_x.unsqueeze(-1) + scores_y.unsqueeze(-2)
|
|
all_indices = indices_x.unsqueeze(-1) * num_keys + indices_y.unsqueeze(-2)
|
|
all_scores = all_scores.view(*all_scores.shape[:-2], -1)
|
|
all_indices = all_indices.view(*all_indices.shape[:-2], -1)
|
|
|
|
_, position_indices = all_scores.topk(top_k, dim=-1)
|
|
expert_indices = all_indices.gather(-1, position_indices)
|
|
|
|
routing_weights = F.softmax(all_scores, dim=-1)
|
|
|
|
all_expert_indices.append(expert_indices)
|
|
all_routing_weights.append(routing_weights)
|
|
all_expert_indices = torch.cat(all_expert_indices, dim=0)
|
|
all_routing_weights = torch.cat(all_routing_weights, dim=0)
|
|
|
|
if attention_mask is None:
|
|
# Compute the percentage of tokens routed to each experts
|
|
all_expert_indices = all_expert_indices.view(-1)
|
|
tokens_per_expert = torch.zeros(num_experts, dtype=compute_dtype, device=compute_device)
|
|
pad = torch.ones_like(all_expert_indices, dtype=compute_dtype, device=compute_device)
|
|
tokens_per_expert = tokens_per_expert.scatter_add_(0, all_expert_indices, pad) / all_expert_indices.shape[0]
|
|
|
|
# Compute the average probability of routing to these experts
|
|
router_prob_per_expert = torch.mean(all_routing_weights, dim=0)
|
|
else:
|
|
batch_size, sequence_length = attention_mask.shape
|
|
num_hidden_layers = len(gate_logits)
|
|
|
|
# Compute the mask that masks all padding tokens as 0 with the same shape of expert_mask
|
|
expert_attention_mask = (
|
|
attention_mask[None, :, :, None]
|
|
.expand((num_hidden_layers, batch_size, sequence_length, top_k))
|
|
.reshape(-1)
|
|
.to(compute_device)
|
|
)
|
|
all_expert_indices = all_expert_indices.view(-1)[expert_attention_mask.bool()]
|
|
|
|
# Compute the percentage of tokens routed to each experts
|
|
tokens_per_expert = torch.zeros(num_experts, dtype=compute_dtype, device=compute_device)
|
|
pad = torch.ones_like(all_expert_indices, dtype=compute_dtype, device=compute_device)
|
|
tokens_per_expert = tokens_per_expert.scatter_add_(0, all_expert_indices, pad) / torch.sum(
|
|
expert_attention_mask
|
|
)
|
|
|
|
# Compute the mask that masks all padding tokens as 0 with the same shape of tokens_per_expert
|
|
router_per_expert_attention_mask = (
|
|
attention_mask[None, :, :, None]
|
|
.expand((num_hidden_layers, batch_size, sequence_length, num_experts))
|
|
.reshape(-1, num_experts)
|
|
.to(compute_device)
|
|
)
|
|
|
|
# Compute the average probability of routing to these experts
|
|
router_prob_per_expert = torch.sum(all_routing_weights * router_per_expert_attention_mask, dim=0) / torch.sum(
|
|
router_per_expert_attention_mask, dim=0
|
|
)
|
|
|
|
overall_loss = torch.sum(tokens_per_expert * router_prob_per_expert)
|
|
return overall_loss * num_experts
|
|
|
|
|
|
class DogeForCausalLM(MixtralForCausalLM):
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.model = DogeModel(config)
|
|
self.num_experts = config.num_experts
|
|
|
|
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[list[torch.FloatTensor]] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
labels: Optional[torch.LongTensor] = None,
|
|
use_cache: Optional[bool] = None,
|
|
cache_position: Optional[torch.LongTensor] = None,
|
|
logits_to_keep: Union[int, torch.Tensor] = 0,
|
|
output_router_logits: Optional[bool] = None,
|
|
**kwargs: Unpack[TransformersKwargs],
|
|
) -> MoeCausalLMOutputWithPast:
|
|
r"""
|
|
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]`.
|
|
|
|
Example:
|
|
|
|
```python
|
|
>>> from transformers import AutoTokenizer, DogeForCausalLM
|
|
|
|
>>> model = DogeForCausalLM.from_pretrained("SmallDoge/Doge-320M")
|
|
>>> tokenizer = AutoTokenizer.from_pretrained("SmallDoge/Doge-320M")
|
|
|
|
>>> 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_router_logits = (
|
|
output_router_logits if output_router_logits is not None else self.config.output_router_logits
|
|
)
|
|
|
|
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
|
|
outputs: MoeModelOutputWithPast = 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,
|
|
cache_position=cache_position,
|
|
**kwargs,
|
|
)
|
|
|
|
hidden_states = outputs.last_hidden_state
|
|
# 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, labels, self.vocab_size, **kwargs)
|
|
|
|
aux_loss = None
|
|
if output_router_logits:
|
|
aux_loss = load_balancing_loss_func(
|
|
outputs.router_logits,
|
|
self.num_experts,
|
|
math.floor(math.sqrt(self.num_experts)),
|
|
self.num_experts_per_tok,
|
|
attention_mask,
|
|
)
|
|
if labels is not None:
|
|
loss += self.router_aux_loss_coef * aux_loss.to(loss.device) # make sure to reside in the same device
|
|
|
|
return MoeCausalLMOutputWithPast(
|
|
loss=loss,
|
|
aux_loss=aux_loss,
|
|
logits=logits,
|
|
past_key_values=outputs.past_key_values,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
router_logits=outputs.router_logits,
|
|
)
|
|
|
|
|
|
class DogeForSequenceClassification(LlamaForSequenceClassification):
|
|
pass
|
|
|
|
|
|
__all__ = [
|
|
"DogeConfig",
|
|
"DogeForCausalLM",
|
|
"DogeModel",
|
|
"DogePreTrainedModel",
|
|
"DogeForSequenceClassification",
|
|
]
|