this repo has no description
Add DeepSeek-OCR-2 modeling files from HuggingFace hub (verbatim)
Copied from deepseek-ai/DeepSeek-OCR-2 commit aaa02f38.
No modifications -- patches for training compatibility follow in the next commit.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
+4527
src/deepseek_ocr2/__init__.py
src/deepseek_ocr2/__init__.py
This is a binary file and will not be displayed.
+210
src/deepseek_ocr2/configuration_deepseek_v2.py
+210
src/deepseek_ocr2/configuration_deepseek_v2.py
···
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from transformers.configuration_utils import PretrainedConfig
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from transformers.utils import logging
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logger = logging.get_logger(__name__)
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DEEPSEEK_PRETRAINED_CONFIG_ARCHIVE_MAP = {}
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class DeepseekV2Config(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`DeepseekV2Model`]. It is used to instantiate an DeepSeek
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model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
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defaults will yield a similar configuration to that of the DeepSeek-V2 with multi-latent attention.
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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 102400):
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Vocabulary size of the Deep model. Defines the number of different tokens that can be represented by the
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`inputs_ids` passed when calling [`DeepseekV2Model`]
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hidden_size (`int`, *optional*, defaults to 4096):
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Dimension of the hidden representations.
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intermediate_size (`int`, *optional*, defaults to 11008):
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Dimension of the MLP representations.
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moe_intermediate_size (`int`, *optional*, defaults to 1407):
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Dimension of the MoE 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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num_attention_heads (`int`, *optional*, defaults to 32):
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Number of attention heads for each attention layer in the Transformer decoder.
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n_shared_experts (`int`, *optional*, defaults to None):
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Number of shared experts, None means dense model.
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n_routed_experts (`int`, *optional*, defaults to None):
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Number of routed experts, None means dense model.
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routed_scaling_factor (`float`, *optional*, defaults to 1.0):
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Scaling factor or routed experts.
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topk_method (`str`, *optional*, defaults to `gready`):
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Topk method used in routed gate.
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n_group (`int`, *optional*, defaults to None):
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Number of groups for routed experts.
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topk_group (`int`, *optional*, defaults to None):
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Number of selected groups for each token(for each token, ensuring the selected experts is only within `topk_group` groups).
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num_experts_per_tok (`int`, *optional*, defaults to None):
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Number of selected experts, None means dense model.
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moe_layer_freq (`int`, *optional*, defaults to 1):
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The frequency of the MoE layer: one expert layer for every `moe_layer_freq - 1` dense layers.
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first_k_dense_replace (`int`, *optional*, defaults to 0):
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Number of dense layers in shallow layers(embed->dense->dense->...->dense->moe->moe...->lm_head).
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\--k dense layers--/
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norm_topk_prob (`bool`, *optional*, defaults to False):
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Whether to normalize the weights of the routed experts.
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scoring_func (`str`, *optional*, defaults to 'softmax'):
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Method of computing expert weights.
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aux_loss_alpha (`float`, *optional*, defaults to 0.001):
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Auxiliary loss weight coefficient.
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seq_aux = (`bool`, *optional*, defaults to True):
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Whether to compute the auxiliary loss for each individual sample.
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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. If
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`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. When
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converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
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by meanpooling all the original heads within that group. For more details checkout [this
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paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
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`num_attention_heads`.
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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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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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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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pad_token_id (`int`, *optional*):
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Padding token id.
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bos_token_id (`int`, *optional*, defaults to 1):
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Beginning of stream token id.
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eos_token_id (`int`, *optional*, defaults to 2):
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End of stream token id.
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pretraining_tp (`int`, *optional*, defaults to 1):
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Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this
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document](https://huggingface.co/docs/transformers/parallelism) to understand more about it. This value is
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necessary to ensure exact reproducibility of the pretraining results. Please refer to [this
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issue](https://github.com/pytorch/pytorch/issues/76232).
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tie_word_embeddings (`bool`, *optional*, defaults to `False`):
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Whether to tie weight embeddings
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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. Currently supports two scaling
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strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
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`{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
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`max_position_embeddings` to the expected new maximum.
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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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use_mla (`bool`, *optional*, defaults to `True`): Use multi-latent attention or multi-head attention. If True,
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the model will use multi-latent attention, otherwise, it will use multi-head attention.
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```python
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>>> from transformers import DeepseekV2Model, DeepseekV2Config
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>>> # Initializing a Deepseek-V2 style configuration
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>>> configuration = DeepseekV2Config()
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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 = "deepseek_v2"
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keys_to_ignore_at_inference = ["past_key_values"]
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def __init__(
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self,
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vocab_size=102400,
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hidden_size=4096,
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intermediate_size=11008,
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moe_intermediate_size = 1407,
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num_hidden_layers=30,
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num_attention_heads=32,
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num_key_value_heads=32,
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n_shared_experts = None,
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n_routed_experts = None,
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ep_size = 1,
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routed_scaling_factor = 1.0,
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kv_lora_rank = 512,
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q_lora_rank = 1536,
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qk_rope_head_dim = 64,
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v_head_dim = 128,
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qk_nope_head_dim = 128,
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topk_method = 'gready',
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n_group = None,
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topk_group = None,
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num_experts_per_tok = None,
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moe_layer_freq = 1,
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first_k_dense_replace = 0,
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norm_topk_prob = False,
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scoring_func = 'softmax',
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aux_loss_alpha = 0.001,
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seq_aux = True,
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hidden_act="silu",
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max_position_embeddings=2048,
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initializer_range=0.02,
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rms_norm_eps=1e-6,
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use_cache=True,
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pad_token_id=None,
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bos_token_id=100000,
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eos_token_id=100001,
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pretraining_tp=1,
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tie_word_embeddings=False,
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rope_theta=10000.0,
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rope_scaling=None,
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attention_bias=False,
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attention_dropout=0.0,
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use_mla=True,
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**kwargs,
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):
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self.vocab_size = vocab_size
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self.max_position_embeddings = max_position_embeddings
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self.hidden_size = hidden_size
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self.intermediate_size = intermediate_size
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self.moe_intermediate_size = moe_intermediate_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.n_shared_experts = n_shared_experts
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self.n_routed_experts = n_routed_experts
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self.ep_size = ep_size
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self.routed_scaling_factor = routed_scaling_factor
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self.kv_lora_rank = kv_lora_rank
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self.q_lora_rank = q_lora_rank
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self.qk_rope_head_dim = qk_rope_head_dim
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self.v_head_dim = v_head_dim
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self.qk_nope_head_dim = qk_nope_head_dim
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self.topk_method = topk_method
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self.n_group = n_group
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self.topk_group = topk_group
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self.num_experts_per_tok = num_experts_per_tok
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self.moe_layer_freq = moe_layer_freq
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self.first_k_dense_replace = first_k_dense_replace
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self.norm_topk_prob = norm_topk_prob
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self.scoring_func = scoring_func
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self.aux_loss_alpha = aux_loss_alpha
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self.seq_aux = seq_aux
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# for backward compatibility
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if num_key_value_heads is None:
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num_key_value_heads = num_attention_heads
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+
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self.num_key_value_heads = num_key_value_heads
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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 = float(rms_norm_eps)
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self.pretraining_tp = pretraining_tp
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self.use_cache = use_cache
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self.rope_theta = rope_theta
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self.rope_scaling = rope_scaling
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self.attention_bias = attention_bias
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self.attention_dropout = attention_dropout
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self.use_mla = use_mla
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super().__init__(
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pad_token_id=pad_token_id,
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bos_token_id=bos_token_id,
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eos_token_id=eos_token_id,
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tie_word_embeddings=tie_word_embeddings,
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**kwargs,
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)
+280
src/deepseek_ocr2/conversation.py
+280
src/deepseek_ocr2/conversation.py
···
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"""
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From https://github.com/lm-sys/FastChat/blob/main/fastchat/conversation.py
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"""
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import dataclasses
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from enum import IntEnum, auto
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from typing import Any, Dict, List
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class SeparatorStyle(IntEnum):
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"""Separator styles."""
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DeepSeek = auto()
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DeepSeekV2 = auto()
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PLAIN = auto()
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ALIGNMENT = auto()
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@dataclasses.dataclass
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class Conversation:
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"""A class that manages prompt templates and keeps all conversation history."""
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# The name of this template
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name: str
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# The template of the system prompt
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system_template: str = "{system_message}"
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# The system message
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system_message: str = ""
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# The names of two roles
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roles: List[str] = (("USER", "ASSISTANT"),)
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# All messages. Each item is (role, message).
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messages: List[List[str]] = ()
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# The number of few shot examples
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offset: int = 0
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# The separator style and configurations
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sep_style: SeparatorStyle = SeparatorStyle.DeepSeek
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sep: str = "\n"
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sep2: str = None
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# Stop criteria (the default one is EOS token)
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stop_str: str = None
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# Stops generation if meeting any token in this list
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stop_token_ids: List[int] = None
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def get_prompt(self) -> str:
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"""Get the prompt for generation."""
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system_prompt = self.system_template.format(system_message=self.system_message)
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if self.sep_style == SeparatorStyle.DeepSeek:
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seps = [self.sep, self.sep2]
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if system_prompt == "" or system_prompt is None:
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ret = ""
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else:
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ret = system_prompt + seps[0]
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for i, (role, message) in enumerate(self.messages):
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if message:
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ret += role + ": " + message + seps[i % 2]
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else:
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ret += role + ":"
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return ret
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elif self.sep_style == SeparatorStyle.DeepSeekV2:
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seps = [self.sep, self.sep2]
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if system_prompt == "" or system_prompt is None:
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ret = ""
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else:
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ret = system_prompt + seps[0]
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for i, (role, message) in enumerate(self.messages):
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if message:
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if role == "User":
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ret += "<｜sft▁begin｜>\n" + message + self.sep #<｜sft▁begin｜>User Input<｜sft▁end｜>\nResponse<｜end▁of▁sentence｜>
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else:
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ret += message + self.sep2
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else:
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ret = ret
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return ret
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+
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elif self.sep_style == SeparatorStyle.PLAIN:
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seps = [self.sep, self.sep2]
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ret = ""
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for i, (role, message) in enumerate(self.messages):
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if message:
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if type(message) is tuple:
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message, _, _ = message
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if i % 2 == 0:
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ret += message + seps[i % 2]
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else:
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ret += message + seps[i % 2]
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else:
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ret += ""
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return ret
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elif self.sep_style == SeparatorStyle.ALIGNMENT:
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seps = [self.sep, self.sep2]
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ret = ""
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for i, (role, message) in enumerate(self.messages):
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if message:
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if type(message) is tuple:
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message, _, _ = message
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if i % 2 == 0:
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ret += '<image>\n' + seps[i % 2]
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else:
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ret += message + seps[i % 2]
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else:
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ret += ""
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return ret
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else:
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raise ValueError(f"Invalid style: {self.sep_style}")
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def set_system_message(self, system_message: str):
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"""Set the system message."""
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self.system_message = system_message
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def append_message(self, role: str, message: str):
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"""Append a new message."""
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self.messages.append([role, message])
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def update_last_message(self, message: str):
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"""Update the last output.
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The last message is typically set to be None when constructing the prompt,
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so we need to update it in-place after getting the response from a model.
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"""
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self.messages[-1][1] = message
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def reset_message(self):
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"""Reset a new message."""
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self.messages = []
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+
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def to_gradio_chatbot(self):
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"""Convert the conversation to gradio chatbot format."""
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ret = []
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for i, (role, msg) in enumerate(self.messages[self.offset :]):
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if i % 2 == 0:
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ret.append([msg, None])
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else:
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ret[-1][-1] = msg
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return ret
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+
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def to_openai_api_messages(self):
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"""Convert the conversation to OpenAI chat completion format."""
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system_prompt = self.system_template.format(system_message=self.system_message)
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ret = [{"role": "system", "content": system_prompt}]
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+
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for i, (_, msg) in enumerate(self.messages[self.offset :]):
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if i % 2 == 0:
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ret.append({"role": "user", "content": msg})
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else:
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if msg is not None:
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ret.append({"role": "assistant", "content": msg})
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return ret
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+
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def copy(self):
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return Conversation(
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name=self.name,
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system_template=self.system_template,
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system_message=self.system_message,
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roles=self.roles,
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messages=[[x, y] for x, y in self.messages],
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offset=self.offset,
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sep_style=self.sep_style,
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sep=self.sep,
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+
sep2=self.sep2,
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stop_str=self.stop_str,
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stop_token_ids=self.stop_token_ids,
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)
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+
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def dict(self):
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return {
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"template_name": self.name,
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"system_message": self.system_message,
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"roles": self.roles,
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"messages": self.messages,
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"offset": self.offset,
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}
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+
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+
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# A global registry for all conversation templates
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conv_templates: Dict[str, Conversation] = {}
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+
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+
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def register_conv_template(template: Conversation, override: bool = False):
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"""Register a new conversation template."""
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if not override:
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assert template.name not in conv_templates, f"{template.name} has been registered."
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+
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conv_templates[template.name] = template
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+
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+
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def get_conv_template(name: str) -> Conversation:
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"""Get a conversation template."""
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return conv_templates[name].copy()
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+
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+
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+
register_conv_template(
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Conversation(
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name="deepseek",
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system_template="{system_message}",
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# system_message="You are a helpful assistant. Please answer truthfully and write out your "
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# "thinking step by step to be sure you get the right answer.",
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system_message="",
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roles=("<|User|>", "<|Assistant|>"),
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+
messages=(),
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+
offset=0,
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+
sep_style=SeparatorStyle.DeepSeek,
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+
sep="\n\n",
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+
sep2="<｜end▁of▁sentence｜>",
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+
stop_token_ids=[100001],
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stop_str=["User:", "<｜end▁of▁sentence｜>"]
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)
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)
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+
register_conv_template(
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+
Conversation(
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+
name="deepseekv2",
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+
system_template="{system_message}",
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+
# system_message="You are a helpful assistant. Please answer truthfully and write out your "
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# "thinking step by step to be sure you get the right answer.",
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+
system_message="",
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+
roles=("<｜User｜>", "<｜Assistant｜>"),
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+
messages=(),
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+
offset=0,
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+
sep_style=SeparatorStyle.DeepSeek,
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+
sep="",
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+
sep2="<｜end▁of▁sentence｜>",
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+
stop_token_ids=[100001],
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+
stop_str=["User:", "<｜end▁of▁sentence｜>"]
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)
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)
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+
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+
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+
register_conv_template(
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+
Conversation(
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+
name="plain",
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+
system_template="",
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+
system_message="",
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+
roles=("", ""),
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+
messages=(),
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+
offset=0,
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+
sep_style=SeparatorStyle.PLAIN,
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+
sep="",
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+
sep2="",
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+
stop_token_ids=[100001],
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stop_str=['</s>'],
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)
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)
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+
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+
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+
register_conv_template(
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+
Conversation(
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+
name="alignment",
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+
system_template="",
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+
system_message="",
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+
roles=("", ""),
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+
messages=(),
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+
offset=0,
252
+
sep_style=SeparatorStyle.ALIGNMENT,
253
+
sep="",
254
+
sep2="",
255
+
stop_token_ids=[100001],
256
+
stop_str=['</s>'],
257
+
)
258
+
)
259
+
260
+
261
+
if __name__ == "__main__":
262
+
print("deepseek template:")
263
+
conv = get_conv_template("deepseek")
264
+
conv.append_message(conv.roles[0], "Hello!")
265
+
conv.append_message(conv.roles[1], "Hi! This is Tony.")
266
+
conv.append_message(conv.roles[0], "Who are you?")
267
+
conv.append_message(conv.roles[1], "I am a helpful assistant.")
268
+
conv.append_message(conv.roles[0], "How are you?")
269
+
conv.append_message(conv.roles[1], None)
270
+
print(conv.get_prompt())
271
+
272
+
print("deepseekv2 template:")
273
+
conv = get_conv_template("deepseekv2")
274
+
conv.append_message(conv.roles[0], "Hello!")
275
+
conv.append_message(conv.roles[1], "Hi! This is Tony.")
276
+
conv.append_message(conv.roles[0], "Who are you?")
277
+
conv.append_message(conv.roles[1], "I am a helpful assistant.")
278
+
conv.append_message(conv.roles[0], "How are you?")
279
+
conv.append_message(conv.roles[1], None)
280
+
print(conv.get_prompt())
+1015
src/deepseek_ocr2/deepencoderv2.py
+1015
src/deepseek_ocr2/deepencoderv2.py
···
1
+
import torch.nn as nn
2
+
import torch
3
+
import torch.nn.functional as F
4
+
import copy
5
+
6
+
7
+
from typing import Optional, Tuple
8
+
9
+
# from megatron.model import LayerNorm
10
+
11
+
import transformers
12
+
13
+
14
+
from typing import Optional, Tuple, Type
15
+
from functools import partial
16
+
17
+
18
+
19
+
class MlpProjector(nn.Module):
20
+
21
+
def __init__(self, cfg):
22
+
23
+
super().__init__()
24
+
25
+
self.cfg = cfg
26
+
27
+
if cfg.projector_type == "identity":
28
+
modules = nn.Identity()
29
+
30
+
elif cfg.projector_type == "linear":
31
+
modules = nn.Linear(cfg.input_dim, cfg.n_embed)
32
+
33
+
elif cfg.projector_type == "mlp_gelu":
34
+
mlp_depth = cfg.get("depth", 1)
35
+
modules = [nn.Linear(cfg.input_dim, cfg.n_embed)]
36
+
for _ in range(1, mlp_depth):
37
+
modules.append(nn.GELU())
38
+
modules.append(nn.Linear(cfg.n_embed, cfg.n_embed))
39
+
modules = nn.Sequential(*modules)
40
+
41
+
elif cfg.projector_type == "normlayer_downsample_mlp_gelu":
42
+
mlp_depth = cfg.get("depth", 1)
43
+
mlp_ratio = cfg.get("mlp_ratio", 1)
44
+
modules = [
45
+
nn.LayerNorm(cfg.input_dim * cfg.downsample_ratio * cfg.downsample_ratio),
46
+
nn.Linear(cfg.input_dim * cfg.downsample_ratio * cfg.downsample_ratio, cfg.n_embed * mlp_ratio)
47
+
]
48
+
for _ in range(1, mlp_depth - 1):
49
+
modules.append(nn.GELU())
50
+
modules.append(nn.Linear(cfg.n_embed * mlp_ratio, cfg.n_embed * mlp_ratio))
51
+
modules.append(nn.GELU())
52
+
modules.append(nn.Linear(cfg.n_embed * mlp_ratio, cfg.n_embed))
53
+
modules = nn.Sequential(*modules)
54
+
55
+
elif cfg.projector_type == "downsample_mlp_gelu":
56
+
mlp_depth = cfg.get("depth", 1)
57
+
mlp_ratio = cfg.get("mlp_ratio", 1)
58
+
modules = [nn.Linear(cfg.input_dim * cfg.downsample_ratio * cfg.downsample_ratio, cfg.n_embed * mlp_ratio)]
59
+
for _ in range(1, mlp_depth - 1):
60
+
modules.append(nn.GELU())
61
+
modules.append(nn.Linear(cfg.n_embed * mlp_ratio, cfg.n_embed * mlp_ratio))
62
+
modules.append(nn.GELU())
63
+
modules.append(nn.Linear(cfg.n_embed * mlp_ratio, cfg.n_embed))
64
+
modules = nn.Sequential(*modules)
65
+
66
+
elif cfg.projector_type == "low_high_hybrid_split_mlp_gelu":
67
+
mlp_depth = cfg.get("depth", 1)
68
+
self.high_up_proj = nn.Linear(cfg.input_dim, cfg.n_embed // 2)
69
+
self.low_up_proj = nn.Linear(cfg.input_dim, cfg.n_embed // 2)
70
+
71
+
modules = []
72
+
for _ in range(1, mlp_depth):
73
+
modules.append(nn.GELU())
74
+
modules.append(nn.Linear(cfg.n_embed, cfg.n_embed))
75
+
modules = nn.Sequential(*modules)
76
+
77
+
elif cfg.projector_type == "hybrid_split_feature_mlp_gelu":
78
+
mlp_depth = cfg.get("depth", 1)
79
+
channel_div = cfg.get("channel_div", 0.5)
80
+
self.high_up_proj = nn.Linear(cfg.input_dim[0], int(cfg.n_embed * channel_div))
81
+
self.low_up_proj = nn.Linear(cfg.input_dim[1], cfg.n_embed - int(cfg.n_embed * channel_div))
82
+
83
+
modules = []
84
+
for _ in range(1, mlp_depth):
85
+
modules.append(nn.GELU())
86
+
modules.append(nn.Linear(cfg.n_embed, cfg.n_embed))
87
+
modules = nn.Sequential(*modules)
88
+
89
+
elif cfg.projector_type == "low_high_split_mlp_gelu":
90
+
mlp_depth = cfg.get("depth", 1)
91
+
modules = []
92
+
for _ in range(1, mlp_depth):
93
+
modules.append(nn.GELU())
94
+
modules.append(nn.Linear(cfg.n_embed // 2, cfg.n_embed // 2))
95
+
modules = nn.Sequential(*modules)
96
+
self.high_layers = nn.Sequential(*modules)
97
+
self.low_layers = copy.deepcopy(modules)
98
+
99
+
else:
100
+
raise ValueError(f"Unknown projector type: {cfg.projector_type}")
101
+
102
+
if cfg.get("token_pooling", False):
103
+
self.token_pooling_layer = nn.Linear(cfg.input_dim * 4, cfg.input_dim)
104
+
105
+
if cfg.get("conv_fusion_high_low_features", False):
106
+
self.fusion_layer = nn.Linear(cfg.input_dim, cfg.input_dim)
107
+
self.layers = modules
108
+
109
+
def forward(self, x):
110
+
if self.cfg.get("token_pooling", False):
111
+
batch_size, wxh, channels = x.shape
112
+
w = h = int(wxh**0.5)
113
+
x = x.view(batch_size, w, h, channels)
114
+
x = x.permute(0, 3, 1, 2)
115
+
# import ipdb; ipdb.set_trace()
116
+
patches = x.unfold(2, 2, 2).unfold(3, 2, 2)
117
+
batch_size, channels, h_patches, w_patches, _, _ = patches.size()
118
+
# 在通道维度上拼接
119
+
patches = patches.contiguous().view(batch_size, channels, h_patches * w_patches, -1)
120
+
121
+
# 通过线性层
122
+
patches = patches.permute(0, 2, 1, 3).contiguous()
123
+
patches = patches.view(batch_size, h_patches * w_patches, channels * 4)
124
+
125
+
x = self.token_pooling_layer(patches)
126
+
127
+
if self.cfg.get("conv_fusion_high_low_features", False):
128
+
x = self.fusion_layer(x[:, 0]) + x[:, 1]
129
+
130
+
if self.cfg.projector_type == 'low_high_hybrid_split_mlp_gelu':
131
+
high_x, low_x = x[0], x[1]
132
+
high_x = self.high_up_proj(high_x)
133
+
low_x = self.low_up_proj(low_x)
134
+
x = torch.concat([high_x, low_x], dim=-1)
135
+
136
+
if self.cfg.projector_type == 'hybrid_split_feature_mlp_gelu':
137
+
high_x = x[...,:self.cfg.input_dim[0]]
138
+
low_x = x[...,self.cfg.input_dim[0]:]
139
+
high_x = self.high_up_proj(high_x)
140
+
low_x = self.low_up_proj(low_x)
141
+
x = torch.concat([high_x, low_x], dim=-1)
142
+
143
+
if self.cfg.projector_type == 'low_high_split_mlp_gelu':
144
+
high_x, low_x = x[0], x[1]
145
+
high_x = self.high_layers(high_x)
146
+
low_x = self.low_layers(low_x)
147
+
x = torch.concat([high_x, low_x], dim=-1)
148
+
return x
149
+
150
+
if self.cfg.projector_type == 'downsample_mlp_gelu' or self.cfg.projector_type == 'normlayer_downsample_mlp_gelu':
151
+
bs, hw, input_dim = x.shape
152
+
h = w = int((hw) ** 0.5)
153
+
154
+
"""compute padding"""
155
+
if h % self.cfg.downsample_ratio:
156
+
pad = self.cfg.downsample_ratio - h % self.cfg.downsample_ratio
157
+
else:
158
+
pad = 0
159
+
x = x.reshape(bs, h, w, input_dim)
160
+
if pad > 0:
161
+
x = F.pad(x, (0, 0, 0, pad, 0, pad), "constant", 0)
162
+
163
+
"""4 to 1 concat"""
164
+
x = x.permute(0, 3, 1, 2) # B, C, H, W
165
+
x = F.unfold(x, kernel_size=self.cfg.downsample_ratio, stride=self.cfg.downsample_ratio, padding=0) # B, C*4, HW // 4
166
+
x = x.permute(0, 2, 1)
167
+
168
+
return self.layers(x)
169
+
170
+
@staticmethod
171
+
def get_flops_per_sample(cfg):
172
+
if cfg.projector_type == "linear":
173
+
fwd = 2 * cfg.input_dim * cfg.n_embed
174
+
175
+
elif "mlp_gelu" in cfg.projector_type :
176
+
mlp_depth = cfg.get("depth", 1)
177
+
downsample_ratio = cfg.get("downsample_ratio", 1)
178
+
input_dim = sum(cfg.input_dim) if isinstance(cfg.input_dim, list) else cfg.input_dim
179
+
input_dim = input_dim * downsample_ratio * downsample_ratio
180
+
fwd = 2 * input_dim * cfg.n_embed + (mlp_depth - 1) * 2 * cfg.n_embed * cfg.n_embed
181
+
else:
182
+
fwd = 0
183
+
184
+
return fwd * 3
185
+
186
+
187
+
#===================qwen2================================
188
+
189
+
class CustomQwen2Decoder(nn.Module):
190
+
"""
191
+
Qwen2 visual encoder
192
+
non-causal attention + causal attention
193
+
token_type_ids ：0=non-causal, 1=causal
194
+
"""
195
+
196
+
def __init__(
197
+
self,
198
+
decoder_layer: int = 24,
199
+
max_position_embeddings: int = 131072,
200
+
hidden_dimension: int = 896,
201
+
num_attention_heads: int = 14,
202
+
num_key_value_heads: int = 2,
203
+
intermediate_size: int = 4864,
204
+
vocab_size: int = 151936,
205
+
attn_implementation: str = "sdpa", # ⭐
206
+
rms_norm_eps: float = 1e-06,
207
+
rope_theta: float = 1000000.0,
208
+
attention_dropout: float = 0.0,
209
+
hidden_act: str = "silu",
210
+
initializer_range: float = 0.02,
211
+
):
212
+
super().__init__()
213
+
214
+
# attn_implementation check
215
+
if attn_implementation == "flash_attention_2":
216
+
raise ValueError(
217
+
"CustomQwen2Decoder do not support flash_attention_2，"
218
+
"new attention mask needs 'sdpa' or 'eager'"
219
+
)
220
+
221
+
# load
222
+
Qwen2Model = getattr(transformers.models.qwen2.modeling_qwen2, 'Qwen2Model')
223
+
Qwen2Config = getattr(transformers, 'Qwen2Config')
224
+
225
+
# config
226
+
config = Qwen2Config(
227
+
hidden_size=hidden_dimension,
228
+
num_hidden_layers=decoder_layer,
229
+
num_attention_heads=num_attention_heads,
230
+
num_key_value_heads=num_key_value_heads,
231
+
intermediate_size=intermediate_size,
232
+
max_position_embeddings=max_position_embeddings,
233
+
vocab_size=vocab_size,
234
+
rms_norm_eps=rms_norm_eps,
235
+
rope_theta=rope_theta,
236
+
attention_dropout=attention_dropout,
237
+
hidden_act=hidden_act,
238
+
initializer_range=initializer_range,
239
+
_attn_implementation=attn_implementation, # ⭐
240
+
)
241
+
242
+
#
243
+
self.model = self._create_custom_model(Qwen2Model, config)
244
+
245
+
del self.model.embed_tokens
246
+
247
+
def _create_custom_model(self, Qwen2Model, config):
248
+
""" Qwen2Model """
249
+
250
+
class CustomQwen2ModelInner(Qwen2Model):
251
+
252
+
253
+
def forward(
254
+
self,
255
+
input_ids=None,
256
+
attention_mask=None,
257
+
position_ids=None,
258
+
past_key_values=None,
259
+
inputs_embeds=None,
260
+
token_type_ids=None, # ⭐
261
+
use_cache=None,
262
+
output_attentions=None,
263
+
output_hidden_states=None,
264
+
return_dict=None,
265
+
cache_position=None,
266
+
):
267
+
# token_type_ids
268
+
self._current_token_type_ids = token_type_ids
269
+
270
+
outputs = super().forward(
271
+
input_ids=input_ids,
272
+
attention_mask=attention_mask,
273
+
position_ids=position_ids,
274
+
past_key_values=past_key_values,
275
+
inputs_embeds=inputs_embeds,
276
+
use_cache=use_cache,
277
+
output_attentions=output_attentions,
278
+
output_hidden_states=output_hidden_states,
279
+
return_dict=return_dict,
280
+
cache_position=cache_position,
281
+
)
282
+
283
+
return outputs
284
+
285
+
def _update_causal_mask(
286
+
self,
287
+
attention_mask,
288
+
input_tensor,
289
+
cache_position,
290
+
past_key_values,
291
+
output_attentions,
292
+
):
293
+
dtype, device = input_tensor.dtype, input_tensor.device
294
+
min_dtype = torch.finfo(dtype).min
295
+
batch_size, sequence_length = input_tensor.shape[0], input_tensor.shape[1]
296
+
297
+
token_type_ids = self._current_token_type_ids
298
+
299
+
# attention mask
300
+
causal_mask = self._create_custom_4d_mask(
301
+
sequence_length=sequence_length,
302
+
dtype=dtype,
303
+
device=device,
304
+
batch_size=batch_size,
305
+
token_type_ids=token_type_ids,
306
+
)
307
+
308
+
# padding mask
309
+
if attention_mask is not None and attention_mask.dim() == 2:
310
+
padding_mask = attention_mask[:, None, None, :].to(dtype=dtype)
311
+
padding_mask = (1.0 - padding_mask) * min_dtype
312
+
causal_mask = causal_mask + padding_mask
313
+
314
+
return causal_mask
315
+
316
+
def _create_custom_4d_mask(
317
+
self,
318
+
sequence_length,
319
+
dtype,
320
+
device,
321
+
batch_size,
322
+
token_type_ids,
323
+
):
324
+
min_dtype = torch.finfo(dtype).min
325
+
326
+
masks = []
327
+
for b in range(batch_size):
328
+
mask = torch.full(
329
+
(sequence_length, sequence_length),
330
+
fill_value=min_dtype,
331
+
dtype=dtype,
332
+
device=device
333
+
)
334
+
335
+
type_ids = token_type_ids[b]
336
+
337
+
image_positions = (type_ids == 0).nonzero(as_tuple=True)[0]
338
+
text_positions = (type_ids == 1).nonzero(as_tuple=True)[0]
339
+
340
+
# non-casual
341
+
if len(image_positions) > 0:
342
+
mask[image_positions[:, None], image_positions] = 0.0
343
+
344
+
# causal
345
+
for i, text_pos in enumerate(text_positions):
346
+
if len(image_positions) > 0:
347
+
mask[text_pos, image_positions] = 0.0
348
+
mask[text_pos, text_positions[:i+1]] = 0.0
349
+
350
+
masks.append(mask)
351
+
352
+
mask = torch.stack(masks, dim=0).unsqueeze(1)
353
+
return mask
354
+
355
+
return CustomQwen2ModelInner(config)
356
+
357
+
def forward(
358
+
self,
359
+
inputs_embeds,
360
+
token_type_ids,
361
+
attention_mask=None,
362
+
**kwargs
363
+
):
364
+
"""
365
+
Args:
366
+
inputs_embeds: [batch_size, seq_len, hidden_dim]
367
+
token_type_ids: [batch_size, seq_len], 0=non-causal, 1=causal
368
+
attention_mask: [batch_size, seq_len], optional
369
+
"""
370
+
return self.model(
371
+
inputs_embeds=inputs_embeds,
372
+
token_type_ids=token_type_ids,
373
+
attention_mask=attention_mask,
374
+
**kwargs
375
+
)
376
+
377
+
378
+
379
+
380
+
381
+
# batch_size = 2
382
+
# inputs_embeds = torch.randn(batch_size, 512, 896).cuda()
383
+
384
+
# inputs_embeds = torch.randn(batch_size, 512, 896).cuda()
385
+
# token_type_ids = torch.cat([
386
+
# torch.zeros(batch_size, 256, dtype=torch.long),
387
+
# torch.ones(batch_size, 256, dtype=torch.long),
388
+
# ], dim=1).cuda()
389
+
390
+
# # start = time.time()
391
+
# with torch.no_grad():
392
+
# outputs_sdpa = decoder_sdpa(inputs_embeds, token_type_ids)
393
+
# print(outputs_sdpa[0].shape)
394
+
# print(f"SDPA time: {time.time() - start:.4f}s")
395
+
396
+
397
+
398
+
class Qwen2Decoder2Encoder(nn.Module):
399
+
"""
400
+
Decoder based on Multilingual BART
401
+
Set the initial weights and configuration with a pretrained multilingual BART model,
402
+
and modify the detailed configurations as a Nougat decoder
403
+
"""
404
+
405
+
def __init__(
406
+
self,
407
+
decoder_layer: int,
408
+
hidden_dimension: int,
409
+
num_attention_heads: int,
410
+
num_key_value_heads: int,
411
+
intermediate_size: int,
412
+
max_query: int,
413
+
):
414
+
super().__init__()
415
+
416
+
self.model = CustomQwen2Decoder(
417
+
decoder_layer=decoder_layer,
418
+
hidden_dimension=hidden_dimension,
419
+
num_attention_heads=num_attention_heads,
420
+
num_key_value_heads=num_key_value_heads,
421
+
intermediate_size=intermediate_size,
422
+
attn_implementation="sdpa",
423
+
)
424
+
425
+
426
+
427
+
428
+
self.query_768 = nn.Embedding(144, hidden_dimension)
429
+
self.query_1024 = nn.Embedding(256, hidden_dimension)
430
+
431
+
432
+
# self.query_refixation = nn.Embedding(int(math.sqrt(max_query)), hidden_dimension)
433
+
434
+
435
+
def forward(self, x: torch.Tensor) -> torch.Tensor:
436
+
x = x.flatten(2).transpose(1, 2)
437
+
438
+
bs, n_query, _ = x.shape
439
+
440
+
if n_query == 144:
441
+
param_img = self.query_768.weight
442
+
elif n_query == 256:
443
+
param_img = self.query_1024.weight
444
+
445
+
batch_query_imgs = param_img.unsqueeze(0).expand(
446
+
bs, -1, -1
447
+
) # (batch_size, num_queries, hidden_size)
448
+
449
+
450
+
451
+
x_combined = torch.cat([x, batch_query_imgs], dim=1)
452
+
453
+
token_type_ids = torch.cat([
454
+
torch.zeros(bs, n_query, dtype=torch.long),
455
+
torch.ones(bs, n_query, dtype=torch.long),
456
+
], dim=1)
457
+
458
+
459
+
y = self.model(x_combined, token_type_ids)[0]
460
+
461
+
462
+
y = y[:, n_query:, :] # causal flow query
463
+
464
+
465
+
return y
466
+
467
+
468
+
def build_qwen2_decoder_as_encoder(
469
+
decoder_layer=24,
470
+
hidden_dimension=896,
471
+
num_attention_heads=14,
472
+
num_key_value_heads=2,
473
+
intermediate_size=4864,
474
+
max_query = 400,
475
+
checkpoint=None,
476
+
):
477
+
478
+
decoder_as_encoder = Qwen2Decoder2Encoder(
479
+
decoder_layer=decoder_layer,
480
+
hidden_dimension = hidden_dimension,
481
+
num_attention_heads = num_attention_heads,
482
+
num_key_value_heads = num_key_value_heads,
483
+
intermediate_size = intermediate_size,
484
+
max_query = max_query
485
+
)
486
+
487
+
488
+
489
+
490
+
if checkpoint is not None:
491
+
# with open(checkpoint, "rb") as f:
492
+
state_dict = torch.load(checkpoint)
493
+
494
+
decoder_as_encoder.load_state_dict(state_dict, strict=True)
495
+
# tob
496
+
print(checkpoint)
497
+
return decoder_as_encoder
498
+
499
+
500
+
501
+
502
+
#=========================Sam-Vary=================================
503
+
504
+
505
+
def get_abs_pos_sam(abs_pos, tgt_size):
506
+
507
+
dtype = abs_pos.dtype
508
+
509
+
src_size = abs_pos.size(1)
510
+
511
+
if src_size != tgt_size:
512
+
old_pos_embed = abs_pos.permute(0, 3, 1, 2)
513
+
old_pos_embed = old_pos_embed.to(torch.float32)
514
+
new_pos_embed = F.interpolate(
515
+
old_pos_embed,
516
+
size=(tgt_size, tgt_size),
517
+
mode='bicubic',
518
+
antialias=True,
519
+
align_corners=False,
520
+
).to(dtype)
521
+
new_pos_embed = new_pos_embed.permute(0, 2, 3, 1)
522
+
return new_pos_embed
523
+
else:
524
+
return abs_pos
525
+
526
+
527
+
528
+
529
+
class MLPBlock(nn.Module):
530
+
def __init__(
531
+
self,
532
+
embedding_dim: int,
533
+
mlp_dim: int,
534
+
act: Type[nn.Module] = nn.GELU,
535
+
) -> None:
536
+
super().__init__()
537
+
self.lin1 = nn.Linear(embedding_dim, mlp_dim)
538
+
self.lin2 = nn.Linear(mlp_dim, embedding_dim)
539
+
self.act = act()
540
+
541
+
def forward(self, x: torch.Tensor) -> torch.Tensor:
542
+
return self.lin2(self.act(self.lin1(x)))
543
+
544
+
545
+
# From https://github.com/facebookresearch/detectron2/blob/main/detectron2/layers/batch_norm.py # noqa
546
+
# Itself from https://github.com/facebookresearch/ConvNeXt/blob/d1fa8f6fef0a165b27399986cc2bdacc92777e40/models/convnext.py#L119 # noqa
547
+
class LayerNorm2d(nn.Module):
548
+
def __init__(self, num_channels: int, eps: float = 1e-6) -> None:
549
+
super().__init__()
550
+
self.weight = nn.Parameter(torch.ones(num_channels))
551
+
self.bias = nn.Parameter(torch.zeros(num_channels))
552
+
self.eps = eps
553
+
554
+
def forward(self, x: torch.Tensor) -> torch.Tensor:
555
+
u = x.mean(1, keepdim=True)
556
+
s = (x - u).pow(2).mean(1, keepdim=True)
557
+
x = (x - u) / torch.sqrt(s + self.eps)
558
+
x = self.weight[:, None, None] * x + self.bias[:, None, None]
559
+
return x
560
+
561
+
562
+
# This class and its supporting functions below lightly adapted from the ViTDet backbone available at: https://github.com/facebookresearch/detectron2/blob/main/detectron2/modeling/backbone/vit.py # noqa
563
+
class ImageEncoderViT(nn.Module):
564
+
def __init__(
565
+
self,
566
+
img_size: int = 1024,
567
+
patch_size: int = 16,
568
+
in_chans: int = 3,
569
+
embed_dim: int = 768,
570
+
depth: int = 12,
571
+
num_heads: int = 12,
572
+
mlp_ratio: float = 4.0,
573
+
out_chans: int = 256,
574
+
qkv_bias: bool = True,
575
+
norm_layer: Type[nn.Module] = nn.LayerNorm,
576
+
act_layer: Type[nn.Module] = nn.GELU,
577
+
use_abs_pos: bool = True,
578
+
use_rel_pos: bool = False,
579
+
rel_pos_zero_init: bool = True,
580
+
window_size: int = 0,
581
+
global_attn_indexes: Tuple[int, ...] = (),
582
+
) -> None:
583
+
"""
584
+
Args:
585
+
img_size (int): Input image size.
586
+
patch_size (int): Patch size.
587
+
in_chans (int): Number of input image channels.
588
+
embed_dim (int): Patch embedding dimension.
589
+
depth (int): Depth of ViT.
590
+
num_heads (int): Number of attention heads in each ViT block.
591
+
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
592
+
qkv_bias (bool): If True, add a learnable bias to query, key, value.
593
+
norm_layer (nn.Module): Normalization layer.
594
+
act_layer (nn.Module): Activation layer.
595
+
use_abs_pos (bool): If True, use absolute positional embeddings.
596
+
use_rel_pos (bool): If True, add relative positional embeddings to the attention map.
597
+
rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.
598
+
window_size (int): Window size for window attention blocks.
599
+
global_attn_indexes (list): Indexes for blocks using global attention.
600
+
"""
601
+
super().__init__()
602
+
self.img_size = img_size
603
+
604
+
self.patch_embed = PatchEmbed(
605
+
kernel_size=(patch_size, patch_size),
606
+
stride=(patch_size, patch_size),
607
+
in_chans=in_chans,
608
+
embed_dim=embed_dim,
609
+
)
610
+
611
+
self.pos_embed: Optional[nn.Parameter] = None
612
+
if use_abs_pos:
613
+
# Initialize absolute positional embedding with pretrain image size.
614
+
self.pos_embed = nn.Parameter(
615
+
torch.zeros(1, img_size // patch_size, img_size // patch_size, embed_dim)
616
+
)
617
+
618
+
self.blocks = nn.ModuleList()
619
+
for i in range(depth):
620
+
block = Block(
621
+
dim=embed_dim,
622
+
num_heads=num_heads,
623
+
mlp_ratio=mlp_ratio,
624
+
qkv_bias=qkv_bias,
625
+
norm_layer=norm_layer,
626
+
act_layer=act_layer,
627
+
use_rel_pos=use_rel_pos,
628
+
rel_pos_zero_init=rel_pos_zero_init,
629
+
window_size=window_size if i not in global_attn_indexes else 0,
630
+
input_size=(img_size // patch_size, img_size // patch_size),
631
+
)
632
+
self.blocks.append(block)
633
+
634
+
self.neck = nn.Sequential(
635
+
nn.Conv2d(
636
+
embed_dim,
637
+
out_chans,
638
+
kernel_size=1,
639
+
bias=False,
640
+
),
641
+
LayerNorm2d(out_chans),
642
+
nn.Conv2d(
643
+
out_chans,
644
+
out_chans,
645
+
kernel_size=3,
646
+
padding=1,
647
+
bias=False,
648
+
),
649
+
LayerNorm2d(out_chans),
650
+
)
651
+
652
+
self.net_2 = nn.Conv2d(256, 512, kernel_size=3, stride=2, padding=1, bias=False)
653
+
self.net_3 = nn.Conv2d(512, 896, kernel_size=3, stride=2, padding=1, bias=False)
654
+
655
+
def forward(self, x: torch.Tensor) -> torch.Tensor:
656
+
x = self.patch_embed(x)
657
+
if self.pos_embed is not None:
658
+
# x = x + self.pos_embed
659
+
x = x + get_abs_pos_sam(self.pos_embed, x.size(1))
660
+
661
+
for blk in self.blocks:
662
+
x = blk(x)
663
+
664
+
x = self.neck(x.permute(0, 3, 1, 2))
665
+
x2 = self.net_2(x)
666
+
x3 = self.net_3(x2.clone())
667
+
668
+
return x3
669
+
670
+
671
+
class Block(nn.Module):
672
+
"""Transformer blocks with support of window attention and residual propagation blocks"""
673
+
674
+
def __init__(
675
+
self,
676
+
dim: int,
677
+
num_heads: int,
678
+
mlp_ratio: float = 4.0,
679
+
qkv_bias: bool = True,
680
+
norm_layer: Type[nn.Module] = nn.LayerNorm,
681
+
act_layer: Type[nn.Module] = nn.GELU,
682
+
use_rel_pos: bool = False,
683
+
rel_pos_zero_init: bool = True,
684
+
window_size: int = 0,
685
+
input_size: Optional[Tuple[int, int]] = None,
686
+
) -> None:
687
+
"""
688
+
Args:
689
+
dim (int): Number of input channels.
690
+
num_heads (int): Number of attention heads in each ViT block.
691
+
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
692
+
qkv_bias (bool): If True, add a learnable bias to query, key, value.
693
+
norm_layer (nn.Module): Normalization layer.
694
+
act_layer (nn.Module): Activation layer.
695
+
use_rel_pos (bool): If True, add relative positional embeddings to the attention map.
696
+
rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.
697
+
window_size (int): Window size for window attention blocks. If it equals 0, then
698
+
use global attention.
699
+
input_size (tuple(int, int) or None): Input resolution for calculating the relative
700
+
positional parameter size.
701
+
"""
702
+
super().__init__()
703
+
self.norm1 = norm_layer(dim)
704
+
self.attn = Attention(
705
+
dim,
706
+
num_heads=num_heads,
707
+
qkv_bias=qkv_bias,
708
+
use_rel_pos=use_rel_pos,
709
+
rel_pos_zero_init=rel_pos_zero_init,
710
+
input_size=input_size if window_size == 0 else (window_size, window_size),
711
+
)
712
+
713
+
self.norm2 = norm_layer(dim)
714
+
self.mlp = MLPBlock(embedding_dim=dim, mlp_dim=int(dim * mlp_ratio), act=act_layer)
715
+
716
+
self.window_size = window_size
717
+
718
+
def forward(self, x: torch.Tensor) -> torch.Tensor:
719
+
shortcut = x
720
+
x = self.norm1(x)
721
+
# Window partition
722
+
if self.window_size > 0:
723
+
H, W = x.shape[1], x.shape[2]
724
+
x, pad_hw = window_partition(x, self.window_size)
725
+
726
+
x = self.attn(x)
727
+
# Reverse window partition
728
+
if self.window_size > 0:
729
+
x = window_unpartition(x, self.window_size, pad_hw, (H, W))
730
+
731
+
x = shortcut + x
732
+
x = x + self.mlp(self.norm2(x))
733
+
734
+
return x
735
+
736
+
737
+
class Attention(nn.Module):
738
+
"""Multi-head Attention block with relative position embeddings."""
739
+
740
+
def __init__(
741
+
self,
742
+
dim: int,
743
+
num_heads: int = 8,
744
+
qkv_bias: bool = True,
745
+
use_rel_pos: bool = False,
746
+
rel_pos_zero_init: bool = True,
747
+
input_size: Optional[Tuple[int, int]] = None,
748
+
) -> None:
749
+
"""
750
+
Args:
751
+
dim (int): Number of input channels.
752
+
num_heads (int): Number of attention heads.
753
+
qkv_bias (bool): If True, add a learnable bias to query, key, value.
754
+
rel_pos (bool): If True, add relative positional embeddings to the attention map.
755
+
rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.
756
+
input_size (tuple(int, int) or None): Input resolution for calculating the relative
757
+
positional parameter size.
758
+
"""
759
+
super().__init__()
760
+
self.num_heads = num_heads
761
+
head_dim = dim // num_heads
762
+
self.scale = head_dim**-0.5
763
+
764
+
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
765
+
self.proj = nn.Linear(dim, dim)
766
+
767
+
self.use_rel_pos = use_rel_pos
768
+
if self.use_rel_pos:
769
+
assert (
770
+
input_size is not None
771
+
), "Input size must be provided if using relative positional encoding."
772
+
# initialize relative positional embeddings
773
+
self.rel_pos_h = nn.Parameter(torch.zeros(2 * input_size[0] - 1, head_dim))
774
+
self.rel_pos_w = nn.Parameter(torch.zeros(2 * input_size[1] - 1, head_dim))
775
+
776
+
def forward(self, x: torch.Tensor) -> torch.Tensor:
777
+
B, H, W, _ = x.shape
778
+
# qkv with shape (3, B, nHead, H * W, C)
779
+
qkv = self.qkv(x).reshape(B, H * W, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
780
+
# q, k, v with shape (B * nHead, H * W, C)
781
+
q, k, v = qkv.reshape(3, B * self.num_heads, H * W, -1).unbind(0)
782
+
783
+
rel_h, rel_w = None, None
784
+
if self.use_rel_pos:
785
+
rel_h, rel_w = add_decomposed_rel_pos(q, self.rel_pos_h, self.rel_pos_w, (H, W), (H, W))
786
+
787
+
q = q.view(B, self.num_heads, H * W, -1)
788
+
k = k.view(B, self.num_heads, H * W, -1)
789
+
v = v.view(B, self.num_heads, H * W, -1)
790
+
791
+
if self.use_rel_pos:
792
+
rel_h = rel_h.view(B, self.num_heads, rel_h.size(1), rel_h.size(2), rel_h.size(3))
793
+
rel_w = rel_w.view(B, self.num_heads, rel_w.size(1), rel_w.size(2), rel_w.size(3))
794
+
attn_bias = (rel_h + rel_w).view(B, self.num_heads, rel_h.size(2), rel_h.size(3) * rel_w.size(4))
795
+
x = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=attn_bias)
796
+
# x = _attention_rel_h_rel_w(q, k, v, rel_h, rel_w)
797
+
else:
798
+
x = torch.nn.functional.scaled_dot_product_attention(q, k, v)
799
+
800
+
x = x.view(B, self.num_heads, H, W, -1).permute(0, 2, 3, 1, 4).reshape(B, H, W, -1)
801
+
802
+
x = self.proj(x)
803
+
804
+
return x
805
+
806
+
807
+
def window_partition(x: torch.Tensor, window_size: int) -> Tuple[torch.Tensor, Tuple[int, int]]:
808
+
"""
809
+
Partition into non-overlapping windows with padding if needed.
810
+
Args:
811
+
x (tensor): input tokens with [B, H, W, C].
812
+
window_size (int): window size.
813
+
814
+
Returns:
815
+
windows: windows after partition with [B * num_windows, window_size, window_size, C].
816
+
(Hp, Wp): padded height and width before partition
817
+
"""
818
+
B, H, W, C = x.shape
819
+
820
+
pad_h = (window_size - H % window_size) % window_size
821
+
pad_w = (window_size - W % window_size) % window_size
822
+
if pad_h > 0 or pad_w > 0:
823
+
x = F.pad(x, (0, 0, 0, pad_w, 0, pad_h))
824
+
Hp, Wp = H + pad_h, W + pad_w
825
+
826
+
x = x.view(B, Hp // window_size, window_size, Wp // window_size, window_size, C)
827
+
windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
828
+
return windows, (Hp, Wp)
829
+
830
+
831
+
def window_unpartition(
832
+
windows: torch.Tensor, window_size: int, pad_hw: Tuple[int, int], hw: Tuple[int, int]
833
+
) -> torch.Tensor:
834
+
"""
835
+
Window unpartition into original sequences and removing padding.
836
+
Args:
837
+
windows (tensor): input tokens with [B * num_windows, window_size, window_size, C].
838
+
window_size (int): window size.
839
+
pad_hw (Tuple): padded height and width (Hp, Wp).
840
+
hw (Tuple): original height and width (H, W) before padding.
841
+
842
+
Returns:
843
+
x: unpartitioned sequences with [B, H, W, C].
844
+
"""
845
+
Hp, Wp = pad_hw
846
+
H, W = hw
847
+
B = windows.shape[0] // (Hp * Wp // window_size // window_size)
848
+
x = windows.view(B, Hp // window_size, Wp // window_size, window_size, window_size, -1)
849
+
x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, Hp, Wp, -1)
850
+
851
+
if Hp > H or Wp > W:
852
+
x = x[:, :H, :W, :].contiguous()
853
+
return x
854
+
855
+
856
+
def get_rel_pos(q_size: int, k_size: int, rel_pos: torch.Tensor) -> torch.Tensor:
857
+
"""
858
+
Get relative positional embeddings according to the relative positions of
859
+
query and key sizes.
860
+
Args:
861
+
q_size (int): size of query q.
862
+
k_size (int): size of key k.
863
+
rel_pos (Tensor): relative position embeddings (L, C).
864
+
865
+
Returns:
866
+
Extracted positional embeddings according to relative positions.
867
+
"""
868
+
max_rel_dist = int(2 * max(q_size, k_size) - 1)
869
+
# Interpolate rel pos if needed.
870
+
if rel_pos.shape[0] != max_rel_dist:
871
+
# Interpolate rel pos.
872
+
dtype = rel_pos.dtype
873
+
rel_pos = rel_pos.to(torch.float32)
874
+
rel_pos_resized = F.interpolate(
875
+
rel_pos.reshape(1, rel_pos.shape[0], -1).permute(0, 2, 1),
876
+
size=max_rel_dist,
877
+
mode="linear",
878
+
).to(dtype)
879
+
rel_pos_resized = rel_pos_resized.reshape(-1, max_rel_dist).permute(1, 0)
880
+
else:
881
+
rel_pos_resized = rel_pos
882
+
883
+
# Scale the coords with short length if shapes for q and k are different.
884
+
q_coords = torch.arange(q_size, device=rel_pos.device)[:, None] * max(k_size / q_size, 1.0)
885
+
k_coords = torch.arange(k_size, device=rel_pos.device)[None, :] * max(q_size / k_size, 1.0)
886
+
relative_coords = (q_coords - k_coords) + (k_size - 1) * max(q_size / k_size, 1.0)
887
+
888
+
return rel_pos_resized[relative_coords.long()]
889
+
890
+
891
+
def add_decomposed_rel_pos(
892
+
q: torch.Tensor,
893
+
rel_pos_h: torch.Tensor,
894
+
rel_pos_w: torch.Tensor,
895
+
q_size: Tuple[int, int],
896
+
k_size: Tuple[int, int],
897
+
) -> torch.Tensor:
898
+
"""
899
+
Calculate decomposed Relative Positional Embeddings from :paper:`mvitv2`.
900
+
https://github.com/facebookresearch/mvit/blob/19786631e330df9f3622e5402b4a419a263a2c80/mvit/models/attention.py # noqa B950
901
+
Args:
902
+
q (Tensor): query q in the attention layer with shape (B, q_h * q_w, C).
903
+
rel_pos_h (Tensor): relative position embeddings (Lh, C) for height axis.
904
+
rel_pos_w (Tensor): relative position embeddings (Lw, C) for width axis.
905
+
q_size (Tuple): spatial sequence size of query q with (q_h, q_w).
906
+
k_size (Tuple): spatial sequence size of key k with (k_h, k_w).
907
+
908
+
Returns:
909
+
attn (Tensor): attention map with added relative positional embeddings.
910
+
"""
911
+
q_h, q_w = q_size
912
+
k_h, k_w = k_size
913
+
Rh = get_rel_pos(q_h, k_h, rel_pos_h)
914
+
Rw = get_rel_pos(q_w, k_w, rel_pos_w)
915
+
916
+
B, _, dim = q.shape
917
+
r_q = q.reshape(B, q_h, q_w, dim)
918
+
rel_h = torch.einsum("bhwc,hkc->bhwk", r_q, Rh)
919
+
rel_w = torch.einsum("bhwc,wkc->bhwk", r_q, Rw)
920
+
rel_h = rel_h.unsqueeze(-1)
921
+
rel_w = rel_w.unsqueeze(-2)
922
+
rel_h = rel_h.reshape(B, q_h * q_w, k_h, 1)
923
+
rel_w = rel_w.reshape(B, q_h * q_w, 1, k_w)
924
+
925
+
return rel_h, rel_w
926
+
927
+
928
+
class PatchEmbed(nn.Module):
929
+
"""
930
+
Image to Patch Embedding.
931
+
"""
932
+
933
+
def __init__(
934
+
self,
935
+
kernel_size: Tuple[int, int] = (16, 16),
936
+
stride: Tuple[int, int] = (16, 16),
937
+
padding: Tuple[int, int] = (0, 0),
938
+
in_chans: int = 3,
939
+
embed_dim: int = 768,
940
+
) -> None:
941
+
"""
942
+
Args:
943
+
kernel_size (Tuple): kernel size of the projection layer.
944
+
stride (Tuple): stride of the projection layer.
945
+
padding (Tuple): padding size of the projection layer.
946
+
in_chans (int): Number of input image channels.
947
+
embed_dim (int): Patch embedding dimension.
948
+
"""
949
+
super().__init__()
950
+
951
+
self.proj = nn.Conv2d(
952
+
in_chans, embed_dim, kernel_size=kernel_size, stride=stride, padding=padding
953
+
)
954
+
955
+
def forward(self, x: torch.Tensor) -> torch.Tensor:
956
+
x = self.proj(x)
957
+
# B C H W -> B H W C
958
+
x = x.permute(0, 2, 3, 1)
959
+
return x
960
+
961
+
962
+
def build_sam_vit_b(checkpoint=None):
963
+
return _build_sam(
964
+
encoder_embed_dim=768,
965
+
encoder_depth=12,
966
+
encoder_num_heads=12,
967
+
encoder_global_attn_indexes=[2, 5, 8, 11],
968
+
checkpoint=checkpoint,
969
+
)
970
+
971
+
def build_sam_fast_vit_b(checkpoint=None, compile_mode='max-autotune', dtype=torch.bfloat16):
972
+
image_encoder = build_sam_vit_b(checkpoint).eval().to(dtype)
973
+
# sam = _apply_eval_dtype_sam(sam, dtype)
974
+
image_encoder = torch.compile(image_encoder, mode=compile_mode)
975
+
return image_encoder
976
+
977
+
978
+
def _build_sam(
979
+
encoder_embed_dim,
980
+
encoder_depth,
981
+
encoder_num_heads,
982
+
encoder_global_attn_indexes,
983
+
checkpoint=None,
984
+
):
985
+
prompt_embed_dim = 256
986
+
image_size = 1024
987
+
vit_patch_size = 16
988
+
image_embedding_size = image_size // vit_patch_size
989
+
image_encoder=ImageEncoderViT(
990
+
depth=encoder_depth,
991
+
embed_dim=encoder_embed_dim,
992
+
img_size=image_size,
993
+
mlp_ratio=4,
994
+
norm_layer=partial(torch.nn.LayerNorm, eps=1e-6),
995
+
num_heads=encoder_num_heads,
996
+
patch_size=vit_patch_size,
997
+
qkv_bias=True,
998
+
use_rel_pos=True,
999
+
global_attn_indexes=encoder_global_attn_indexes,
1000
+
window_size=14,
1001
+
out_chans=prompt_embed_dim,
1002
+
)
1003
+
image_encoder.eval()
1004
+
if checkpoint is not None:
1005
+
# with open(checkpoint, "rb") as f:
1006
+
state_dict = torch.load(checkpoint)
1007
+
# print(state_dict.keys())
1008
+
# for key in state_dict:
1009
+
# image_encoder.load_state_dict({k[14:]: v for k, v in state_dict.items() if 'image_encoder' in k}, strict=False)
1010
+
# ocr-anyting
1011
+
# image_encoder.load_state_dict(state_dict, strict=True)
1012
+
# tob
1013
+
image_encoder.load_state_dict({k[30:]: v for k, v in state_dict.items() if 'vision_tower_high' in k}, strict=True)
1014
+
print(checkpoint)
1015
+
return image_encoder
+1030
src/deepseek_ocr2/modeling_deepseekocr2.py
+1030
src/deepseek_ocr2/modeling_deepseekocr2.py
···
1
+
from .modeling_deepseekv2 import DeepseekV2Model, DeepseekV2ForCausalLM
2
+
from .configuration_deepseek_v2 import DeepseekV2Config
3
+
from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
4
+
from typing import List, Optional, Tuple, Union
5
+
from transformers.cache_utils import Cache
6
+
import requests
7
+
from PIL import Image, ImageOps, ImageDraw, ImageFont
8
+
from io import BytesIO
9
+
import torch
10
+
import torch.nn as nn
11
+
from torch.nn import CrossEntropyLoss
12
+
from torchvision import transforms
13
+
# from torchvision.transforms.functional import InterpolationMode
14
+
import os
15
+
from .deepencoderv2 import build_sam_vit_b, build_qwen2_decoder_as_encoder, MlpProjector
16
+
from addict import Dict
17
+
from transformers import TextStreamer
18
+
from .conversation import get_conv_template
19
+
from abc import ABC
20
+
import math
21
+
import re
22
+
from tqdm import tqdm
23
+
import numpy as np
24
+
# import time
25
+
26
+
27
+
28
+
def load_image(image_path):
29
+
30
+
try:
31
+
image = Image.open(image_path)
32
+
33
+
corrected_image = ImageOps.exif_transpose(image)
34
+
35
+
return corrected_image
36
+
37
+
except Exception as e:
38
+
print(f"error: {e}")
39
+
try:
40
+
return Image.open(image_path)
41
+
except:
42
+
return None
43
+
44
+
45
+
def re_match(text):
46
+
pattern = r'(<\|ref\|>(.*?)<\|/ref\|><\|det\|>(.*?)<\|/det\|>)'
47
+
matches = re.findall(pattern, text, re.DOTALL)
48
+
49
+
# pattern1 = r'<\|ref\|>.*?<\|/ref\|>\n'
50
+
# new_text1 = re.sub(pattern1, '', text, flags=re.DOTALL)
51
+
52
+
mathes_image = []
53
+
mathes_other = []
54
+
for a_match in matches:
55
+
if '<|ref|>image<|/ref|>' in a_match[0]:
56
+
mathes_image.append(a_match[0])
57
+
else:
58
+
mathes_other.append(a_match[0])
59
+
return matches, mathes_image, mathes_other
60
+
61
+
62
+
def extract_coordinates_and_label(ref_text, image_width, image_height):
63
+
64
+
try:
65
+
label_type = ref_text[1]
66
+
cor_list = eval(ref_text[2])
67
+
except Exception as e:
68
+
print(e)
69
+
return None
70
+
71
+
return (label_type, cor_list)
72
+
73
+
74
+
def draw_bounding_boxes(image, refs, ouput_path):
75
+
76
+
image_width, image_height = image.size
77
+
78
+
img_draw = image.copy()
79
+
draw = ImageDraw.Draw(img_draw)
80
+
81
+
overlay = Image.new('RGBA', img_draw.size, (0, 0, 0, 0))
82
+
draw2 = ImageDraw.Draw(overlay)
83
+
84
+
# try:
85
+
# except IOError:
86
+
# try:
87
+
# font = ImageFont.truetype("DejaVuSans.ttf", 20)
88
+
# except IOError:
89
+
font = ImageFont.load_default()
90
+
91
+
img_idx = 0
92
+
93
+
for i, ref in enumerate(refs):
94
+
try:
95
+
result = extract_coordinates_and_label(ref, image_width, image_height)
96
+
if result:
97
+
label_type, points_list = result
98
+
99
+
color = (np.random.randint(0, 200), np.random.randint(0, 200), np.random.randint(0, 255))
100
+
101
+
color_a = color + (20, )
102
+
for points in points_list:
103
+
x1, y1, x2, y2 = points
104
+
105
+
x1 = int(x1 / 999 * image_width)
106
+
y1 = int(y1 / 999 * image_height)
107
+
108
+
x2 = int(x2 / 999 * image_width)
109
+
y2 = int(y2 / 999 * image_height)
110
+
111
+
if label_type == 'image':
112
+
try:
113
+
cropped = image.crop((x1, y1, x2, y2))
114
+
cropped.save(f"{ouput_path}/images/{img_idx}.jpg")
115
+
except Exception as e:
116
+
print(e)
117
+
pass
118
+
img_idx += 1
119
+
120
+
try:
121
+
if label_type == 'title':
122
+
draw.rectangle([x1, y1, x2, y2], outline=color, width=4)
123
+
draw2.rectangle([x1, y1, x2, y2], fill=color_a, outline=(0, 0, 0, 0), width=1)
124
+
else:
125
+
draw.rectangle([x1, y1, x2, y2], outline=color, width=2)
126
+
draw2.rectangle([x1, y1, x2, y2], fill=color_a, outline=(0, 0, 0, 0), width=1)
127
+
text_x = x1
128
+
text_y = max(0, y1 - 15)
129
+
130
+
131
+
text_bbox = draw.textbbox((0, 0), label_type, font=font)
132
+
text_width = text_bbox[2] - text_bbox[0]
133
+
text_height = text_bbox[3] - text_bbox[1]
134
+
draw.rectangle([text_x, text_y, text_x + text_width, text_y + text_height],
135
+
fill=(255, 255, 255, 30))
136
+
137
+
draw.text((text_x, text_y), label_type, font=font, fill=color)
138
+
except:
139
+
pass
140
+
except:
141
+
continue
142
+
img_draw.paste(overlay, (0, 0), overlay)
143
+
return img_draw
144
+
145
+
146
+
def process_image_with_refs(image, ref_texts, output_path):
147
+
148
+
result_image = draw_bounding_boxes(image, ref_texts, output_path)
149
+
150
+
return result_image
151
+
152
+
153
+
154
+
155
+
156
+
def find_closest_aspect_ratio(aspect_ratio, target_ratios, width, height, image_size):
157
+
best_ratio_diff = float('inf')
158
+
best_ratio = (1, 1)
159
+
area = width * height
160
+
for ratio in target_ratios:
161
+
target_aspect_ratio = ratio[0] / ratio[1]
162
+
ratio_diff = abs(aspect_ratio - target_aspect_ratio)
163
+
if ratio_diff < best_ratio_diff:
164
+
best_ratio_diff = ratio_diff
165
+
best_ratio = ratio
166
+
elif ratio_diff == best_ratio_diff:
167
+
if area > 0.5 * image_size * image_size * ratio[0] * ratio[1]:
168
+
best_ratio = ratio
169
+
# print(f'width: {width}, height: {height}, best_ratio: {best_ratio}')
170
+
return best_ratio
171
+
172
+
173
+
def dynamic_preprocess(image, min_num=2, max_num=6, image_size=768, use_thumbnail=False):
174
+
orig_width, orig_height = image.size
175
+
aspect_ratio = orig_width / orig_height
176
+
177
+
# calculate the existing image aspect ratio
178
+
target_ratios = set(
179
+
(i, j) for n in range(min_num, max_num + 1) for i in range(1, n + 1) for j in range(1, n + 1) if
180
+
i * j <= max_num and i * j >= min_num)
181
+
# print(target_ratios)
182
+
target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1])
183
+
184
+
# find the closest aspect ratio to the target
185
+
target_aspect_ratio = find_closest_aspect_ratio(
186
+
aspect_ratio, target_ratios, orig_width, orig_height, image_size)
187
+
188
+
# print(target_aspect_ratio)
189
+
# calculate the target width and height
190
+
target_width = image_size * target_aspect_ratio[0]
191
+
target_height = image_size * target_aspect_ratio[1]
192
+
blocks = target_aspect_ratio[0] * target_aspect_ratio[1]
193
+
194
+
# resize the image
195
+
resized_img = image.resize((target_width, target_height))
196
+
processed_images = []
197
+
for i in range(blocks):
198
+
box = (
199
+
(i % (target_width // image_size)) * image_size,
200
+
(i // (target_width // image_size)) * image_size,
201
+
((i % (target_width // image_size)) + 1) * image_size,
202
+
((i // (target_width // image_size)) + 1) * image_size
203
+
)
204
+
# split the image
205
+
split_img = resized_img.crop(box)
206
+
processed_images.append(split_img)
207
+
assert len(processed_images) == blocks
208
+
if use_thumbnail and len(processed_images) != 1:
209
+
thumbnail_img = image.resize((image_size, image_size))
210
+
processed_images.append(thumbnail_img)
211
+
return processed_images, target_aspect_ratio
212
+
213
+
214
+
215
+
def normalize_transform(mean, std):
216
+
if mean is None and std is None:
217
+
transform = None
218
+
elif mean is None and std is not None:
219
+
mean = [0.] * len(std)
220
+
transform = transforms.Normalize(mean=mean, std=std)
221
+
elif mean is not None and std is None:
222
+
std = [1.] * len(mean)
223
+
transform = transforms.Normalize(mean=mean, std=std)
224
+
else:
225
+
transform = transforms.Normalize(mean=mean, std=std)
226
+
227
+
return transform
228
+
229
+
230
+
231
+
def format_messages(
232
+
conversations: List[Dict[str, str]],
233
+
sft_format: str = "deepseek",
234
+
system_prompt: str = "",
235
+
):
236
+
"""
237
+
Applies the SFT template to conversation.
238
+
239
+
Args:
240
+
conversations (List[Dict]): A List of messages.
241
+
sft_format (str, optional): The format of the SFT template to use. Defaults to "deepseek".
242
+
system_prompt (str, optional): The system prompt to use in the SFT template. Defaults to "".
243
+
244
+
Returns:
245
+
sft_prompt (str): The formatted text.
246
+
"""
247
+
248
+
conv = get_conv_template(sft_format)
249
+
conv.set_system_message(system_prompt)
250
+
for message in conversations:
251
+
conv.append_message(message["role"], message["content"].strip())
252
+
sft_prompt = conv.get_prompt().strip()
253
+
254
+
return sft_prompt
255
+
256
+
257
+
def text_encode(tokenizer, text: str, bos: bool = True, eos: bool = False):
258
+
t = tokenizer.encode(text, add_special_tokens=False)
259
+
bos_id = 0
260
+
eos_id = 1
261
+
if bos:
262
+
t = [bos_id] + t
263
+
if eos:
264
+
t = t + [eos_id]
265
+
266
+
return t
267
+
268
+
def load_pil_images(conversations: List[Dict[str, str]]) -> List[Image.Image]:
269
+
"""
270
+
271
+
Args:
272
+
conversations (List[Dict[str, str]]): the conversations with a list of messages. An example is :
273
+
[
274
+
{
275
+
"role": "User",
276
+
"content": "<image_placeholder>\nExtract all information from this image and convert them into markdown format.",
277
+
"images": ["./examples/table_datasets.png"]
278
+
},
279
+
{"role": "Assistant", "content": ""},
280
+
]
281
+
282
+
Returns:
283
+
pil_images (List[PIL.Image.Image]): the list of PIL images.
284
+
285
+
"""
286
+
287
+
pil_images = []
288
+
289
+
for message in conversations:
290
+
if "images" not in message:
291
+
continue
292
+
293
+
for image_path in message["images"]:
294
+
# print('----------------')
295
+
# print(image_path)
296
+
# print('----------------')
297
+
# exit()
298
+
299
+
# pil_img = Image.open(image_path)
300
+
pil_img = load_image(image_path)
301
+
pil_img = pil_img.convert("RGB")
302
+
pil_images.append(pil_img)
303
+
304
+
return pil_images
305
+
306
+
307
+
class BaseTransform(ABC):
308
+
309
+
def set_rng(self, *args, **kwargs):
310
+
pass
311
+
312
+
def __call__(self, *args, **kwargs) -> torch.Tensor:
313
+
pass
314
+
315
+
@property
316
+
def default_shape(self):
317
+
raise NotImplementedError
318
+
319
+
320
+
class BasicImageTransform(BaseTransform):
321
+
def __init__(
322
+
self,
323
+
mean: Optional[Tuple[float, float, float]] = (0.5, 0.5, 0.5),
324
+
std: Optional[Tuple[float, float, float]] = (0.5, 0.5, 0.5),
325
+
normalize: bool = True
326
+
):
327
+
self.mean = mean
328
+
self.std = std
329
+
330
+
transform_pipelines = [
331
+
transforms.ToTensor()
332
+
]
333
+
334
+
normalize = normalize_transform(mean, std) if normalize else nn.Identity()
335
+
if normalize is not None:
336
+
transform_pipelines.append(normalize)
337
+
338
+
self.transform = transforms.Compose(transform_pipelines)
339
+
340
+
def __call__(self, x):
341
+
x = self.transform(x)
342
+
return x
343
+
344
+
class NoEOSTextStreamer(TextStreamer):
345
+
def on_finalized_text(self, text: str, stream_end: bool = False):
346
+
347
+
eos_text = self.tokenizer.decode([self.tokenizer.eos_token_id], skip_special_tokens=False)
348
+
text = text.replace(eos_text, "\n")
349
+
print(text, flush=True, end="")
350
+
351
+
352
+
class DeepseekOCR2Config(DeepseekV2Config):
353
+
model_type = "DeepseekOCR2"
354
+
355
+
class DeepseekOCR2Model(DeepseekV2Model):
356
+
config_class = DeepseekOCR2Config
357
+
358
+
def __init__(self, config: DeepseekV2Config):
359
+
super(DeepseekOCR2Model, self).__init__(config)
360
+
361
+
self.sam_model = build_sam_vit_b()
362
+
self.qwen2_model = build_qwen2_decoder_as_encoder()
363
+
# self.conv_2 = nn.Conv2d(in_channels=1024, out_channels=2048, kernel_size=2, stride=2)
364
+
n_embed = 1280
365
+
self.projector = MlpProjector(Dict(projector_type="linear", input_dim=896, n_embed=n_embed))
366
+
embed_std = 1 / torch.sqrt(torch.tensor(n_embed, dtype=torch.float32))
367
+
# self.image_newline = nn.Parameter(torch.randn(n_embed) * embed_std)
368
+
self.view_seperator = nn.Parameter(torch.randn(n_embed) * embed_std)
369
+
370
+
371
+
372
+
373
+
def forward(
374
+
self,
375
+
input_ids: torch.LongTensor = None,
376
+
attention_mask: Optional[torch.Tensor] = None,
377
+
position_ids: Optional[torch.LongTensor] = None,
378
+
past_key_values: Optional[List[torch.FloatTensor]] = None,
379
+
inputs_embeds: Optional[torch.FloatTensor] = None,
380
+
use_cache: Optional[bool] = None,
381
+
output_attentions: Optional[bool] = None,
382
+
output_hidden_states: Optional[bool] = None,
383
+
images: Optional[torch.FloatTensor] = None,
384
+
images_seq_mask: Optional[torch.FloatTensor] = None,
385
+
images_spatial_crop: Optional[torch.FloatTensor] = None,
386
+
return_dict: Optional[bool] = None,
387
+
) -> Union[Tuple, BaseModelOutputWithPast]:
388
+
389
+
390
+
391
+
392
+
if inputs_embeds is None:
393
+
# inputs_embeds = self.embed_tokens(input_ids)
394
+
# inputs_embeds = self.embed_tokens(input_ids)
395
+
inputs_embeds = self.get_input_embeddings()(input_ids)
396
+
397
+
398
+
399
+
sam_model = getattr(self, 'sam_model', None)
400
+
# sam_model = self.sam_model
401
+
qwen2_model = getattr(self, 'qwen2_model', None)
402
+
403
+
404
+
405
+
if sam_model is not None and (input_ids.shape[1] != 1 or self.training) and torch.sum(images[0][1]).item() != 0:
406
+
407
+
idx = 0
408
+
409
+
# sam_model = torch.jit.script(sam_model)
410
+
411
+
# start_time = time.time()
412
+
for image, crop_shape in zip(images, images_spatial_crop):
413
+
images_in_this_batch = []
414
+
415
+
patches = image[0]
416
+
image_ori = image[1]
417
+
418
+
with torch.no_grad():
419
+
# with torch.inference_mode():
420
+
421
+
if torch.sum(patches).item() != 0:
422
+
# P, C, H, W = patches.shape
423
+
crop_flag = 1
424
+
local_features_1 = sam_model(patches)
425
+
426
+
local_features_2 = qwen2_model(local_features_1)
427
+
# vit_time = time.time()
428
+
local_features = local_features_2
429
+
local_features = self.projector(local_features)
430
+
431
+
432
+
global_features_1 = sam_model(image_ori)
433
+
global_features_2 = qwen2_model(global_features_1)
434
+
global_features = global_features_2
435
+
global_features = self.projector(global_features)
436
+
437
+
print('=====================')
438
+
print('BASE: ', global_features.shape)
439
+
print('PATCHES: ', local_features.shape)
440
+
print('=====================')
441
+
442
+
_, hw, n_dim = global_features.shape
443
+
# h = w = int(hw ** 0.5)
444
+
445
+
_2, hw2, n_dim2 = local_features.shape
446
+
# h2 = w2 = int(hw2 ** 0.5)
447
+
448
+
449
+
global_features = global_features.view(-1, n_dim)
450
+
451
+
452
+
local_features = local_features.view(-1, n_dim2)
453
+
454
+
global_local_features = torch.cat([local_features, global_features, self.view_seperator[None, :]], dim=0)
455
+
456
+
# end_time = time.time()
457
+
458
+
# print('sam: ', sam_time - start_time)
459
+
# print('vit: ', vit_time - sam_time)
460
+
# print('all: ', end_time - start_time)
461
+
462
+
# exit()
463
+
464
+
else:
465
+
global_features_1 = sam_model(image_ori)
466
+
global_features_2 = qwen2_model(global_features_1)
467
+
global_features = global_features_2
468
+
global_features = self.projector(global_features)
469
+
print('=====================')
470
+
print('BASE: ', global_features.shape)
471
+
print('NO PATCHES')
472
+
print('=====================')
473
+
_, hw, n_dim = global_features.shape
474
+
# h = w = int(hw ** 0.5)
475
+
476
+
477
+
# global_features = global_features.view(h, w, n_dim)
478
+
479
+
# global_features = torch.cat(
480
+
# [global_features, self.image_newline[None, None, :].expand(h, 1, n_dim)], dim=1
481
+
# )
482
+
483
+
global_features = global_features.view(-1, n_dim)
484
+
485
+
global_local_features = torch.cat([global_features, self.view_seperator[None, :]], dim=0)
486
+
487
+
images_in_this_batch.append(global_local_features)
488
+
489
+
490
+
# print(inputs_embeds.shape)
491
+
492
+
if images_in_this_batch:
493
+
images_in_this_batch = torch.cat(images_in_this_batch, dim=0)
494
+
# exit()
495
+
496
+
inputs_embeds[idx].masked_scatter_(images_seq_mask[idx].unsqueeze(-1).cuda(), images_in_this_batch)
497
+
498
+
idx += 1
499
+
500
+
501
+
return super(DeepseekOCR2Model, self).forward(
502
+
input_ids=None, attention_mask=attention_mask, past_key_values=past_key_values,
503
+
inputs_embeds=inputs_embeds, use_cache=use_cache, position_ids = position_ids,
504
+
output_attentions=output_attentions, output_hidden_states=output_hidden_states,
505
+
return_dict=return_dict
506
+
)
507
+
508
+
509
+
class DeepseekOCR2ForCausalLM(DeepseekV2ForCausalLM):
510
+
511
+
config_class = DeepseekOCR2Config
512
+
# supports_gradient_checkpointing = True
513
+
514
+
def __init__(self, config):
515
+
super(DeepseekV2ForCausalLM, self).__init__(config)
516
+
self.model = DeepseekOCR2Model(config)
517
+
518
+
self.vocab_size = config.vocab_size
519
+
520
+
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
521
+
522
+
# self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
523
+
524
+
# Initialize weights and apply final processing
525
+
self.post_init()
526
+
527
+
def get_model(self):
528
+
return self.model
529
+
530
+
531
+
def forward(
532
+
self,
533
+
input_ids: torch.LongTensor = None,
534
+
attention_mask: Optional[torch.Tensor] = None,
535
+
position_ids: Optional[torch.LongTensor] = None,
536
+
past_key_values: Optional[List[torch.FloatTensor]] = None,
537
+
inputs_embeds: Optional[torch.FloatTensor] = None,
538
+
labels: Optional[torch.LongTensor] = None,
539
+
use_cache: Optional[bool] = None,
540
+
output_attentions: Optional[bool] = None,
541
+
output_hidden_states: Optional[bool] = None,
542
+
images: Optional[torch.FloatTensor] = None,
543
+
images_seq_mask: Optional[torch.FloatTensor] = None,
544
+
images_spatial_crop: Optional[torch.FloatTensor] = None,
545
+
return_dict: Optional[bool] = None,
546
+
547
+
) -> Union[Tuple, CausalLMOutputWithPast]:
548
+
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
549
+
output_hidden_states = (
550
+
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
551
+
)
552
+
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
553
+
554
+
555
+
556
+
outputs = self.model(
557
+
input_ids=input_ids,
558
+
past_key_values=past_key_values,
559
+
attention_mask=attention_mask,
560
+
position_ids=position_ids,
561
+
inputs_embeds=inputs_embeds,
562
+
use_cache=use_cache,
563
+
output_attentions=output_attentions,
564
+
output_hidden_states=output_hidden_states,
565
+
images=images,
566
+
images_seq_mask = images_seq_mask,
567
+
images_spatial_crop = images_spatial_crop,
568
+
return_dict=return_dict
569
+
570
+
)
571
+
572
+
573
+
574
+
# print(transformer_outputs)
575
+
576
+
hidden_states = outputs[0]
577
+
logits = self.lm_head(hidden_states)
578
+
logits = logits.float()
579
+
580
+
# logits
581
+
582
+
loss = None
583
+
if labels is not None:
584
+
# Shift so that tokens < n predict n
585
+
shift_logits = logits[..., :-1, :].contiguous()
586
+
shift_labels = labels[..., 1:].contiguous()
587
+
# Flatten the tokens
588
+
loss_fct = CrossEntropyLoss()
589
+
shift_logits = shift_logits.view(-1, self.config.vocab_size)
590
+
shift_labels = shift_labels.view(-1)
591
+
# Enable model parallelism
592
+
shift_labels = shift_labels.to(shift_logits.device)
593
+
loss = loss_fct(shift_logits, shift_labels)
594
+
595
+
if not return_dict:
596
+
output = (logits,) + outputs[1:]
597
+
return (loss,) + output if loss is not None else output
598
+
599
+
return CausalLMOutputWithPast(
600
+
loss=loss,
601
+
logits=logits,
602
+
past_key_values=outputs.past_key_values,
603
+
hidden_states=outputs.hidden_states,
604
+
attentions=outputs.attentions,
605
+
)
606
+
607
+
608
+
def prepare_inputs_for_generation(
609
+
self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
610
+
):
611
+
# Omit tokens covered by past_key_values
612
+
past_length = 0
613
+
if past_key_values is not None:
614
+
if isinstance(past_key_values, Cache):
615
+
cache_length = past_key_values.get_seq_length()
616
+
past_length = past_key_values.seen_tokens
617
+
max_cache_length = past_key_values.get_max_length()
618
+
else:
619
+
cache_length = past_length = past_key_values[0][0].shape[2]
620
+
max_cache_length = None
621
+
622
+
# Keep only the unprocessed tokens:
623
+
# 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where
624
+
# some of the inputs are exclusively passed as part of the cache (e.g. when passing input_embeds as
625
+
# input)
626
+
if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]:
627
+
input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :]
628
+
# 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard
629
+
# input_ids based on the past_length.
630
+
elif past_length < input_ids.shape[1]:
631
+
input_ids = input_ids[:, past_length:]
632
+
# 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens.
633
+
634
+
# If we are about to go beyond the maximum cache length, we need to crop the input attention mask.
635
+
if (
636
+
max_cache_length is not None
637
+
and attention_mask is not None
638
+
and cache_length + input_ids.shape[1] > max_cache_length
639
+
):
640
+
attention_mask = attention_mask[:, -max_cache_length:]
641
+
642
+
position_ids = kwargs.get("position_ids", None)
643
+
if attention_mask is not None and position_ids is None:
644
+
# create position_ids on the fly for batch generation
645
+
position_ids = attention_mask.long().cumsum(-1) - 1
646
+
position_ids.masked_fill_(attention_mask == 0, 1)
647
+
if past_key_values:
648
+
position_ids = position_ids[:, -input_ids.shape[1] :]
649
+
650
+
# if self.generation_config.cache_implementation == "static":
651
+
# # generation with static cache
652
+
# cache_position = kwargs.get("cache_position", None)
653
+
# if cache_position is None:
654
+
# past_length = 0
655
+
# else:
656
+
# past_length = cache_position[-1] + 1
657
+
# input_ids = input_ids[:, past_length:]
658
+
# position_ids = position_ids[:, past_length:]
659
+
660
+
# TODO @gante we should only keep a `cache_position` in generate, and do +=1.
661
+
# same goes for position ids. Could also help with continued generation.
662
+
cache_position = torch.arange(past_length, past_length + position_ids.shape[-1], device=position_ids.device)
663
+
664
+
# if `inputs_embeds` are passed, we only want to use them in the 1st generation step
665
+
if inputs_embeds is not None and past_key_values is None:
666
+
model_inputs = {"inputs_embeds": inputs_embeds}
667
+
else:
668
+
model_inputs = {"input_ids": input_ids}
669
+
670
+
model_inputs.update(
671
+
{
672
+
"position_ids": position_ids,
673
+
"past_key_values": past_key_values,
674
+
"use_cache": kwargs.get("use_cache"),
675
+
"attention_mask": attention_mask,
676
+
"images": kwargs.get("images", None),
677
+
"images_seq_mask": kwargs.get("images_seq_mask", None),
678
+
"images_spatial_crop": kwargs.get("images_spatial_crop", None),
679
+
}
680
+
)
681
+
return model_inputs
682
+
683
+
684
+
def disable_torch_init(self):
685
+
"""
686
+
Disable the redundant torch default initialization to accelerate model creation.
687
+
"""
688
+
import torch
689
+
setattr(torch.nn.Linear, "reset_parameters", lambda self: None)
690
+
setattr(torch.nn.LayerNorm, "reset_parameters", lambda self: None)
691
+
692
+
693
+
694
+
def infer(self, tokenizer, prompt='', image_file='', output_path = '', base_size=1024, image_size=640, crop_mode=True, test_compress=False, save_results=False, eval_mode=False):
695
+
self.disable_torch_init()
696
+
697
+
os.makedirs(output_path, exist_ok=True)
698
+
os.makedirs(f'{output_path}/images', exist_ok=True)
699
+
700
+
if prompt and image_file:
701
+
conversation = [
702
+
{
703
+
"role": "<|User|>",
704
+
# "content": "<image>\n<|grounding|>Given the layout of the image. ",
705
+
"content": f'{prompt}',
706
+
# "content": "君不见黄河之水天上来的下一句是什么？",
707
+
# "content": "<image>\nFree OCR. ",
708
+
# "content": "<image>\nParse the figure. ",
709
+
# "content": "<image>\nExtract the text in the image. ",
710
+
"images": [f'{image_file}'],
711
+
},
712
+
{"role": "<|Assistant|>", "content": ""},
713
+
]
714
+
715
+
elif prompt:
716
+
conversation = [
717
+
{
718
+
"role": "<|User|>",
719
+
# "content": "<image>\n<|grounding|>Given the layout of the image. ",
720
+
"content": f'{prompt}',
721
+
# "content": "君不见黄河之水天上来的下一句是什么？",
722
+
# "content": "<image>\nFree OCR. ",
723
+
# "content": "<image>\nParse the figure. ",
724
+
# "content": "<image>\nExtract the text in the image. ",
725
+
# "images": [f'{image_file}'],
726
+
},
727
+
{"role": "<|Assistant|>", "content": ""},
728
+
]
729
+
else:
730
+
assert False, f'prompt is none!'
731
+
732
+
prompt = format_messages(conversations=conversation, sft_format='plain', system_prompt='')
733
+
734
+
patch_size = 16
735
+
downsample_ratio = 4
736
+
images = load_pil_images(conversation)
737
+
738
+
valid_img_tokens = 0
739
+
ratio = 1
740
+
741
+
image_draw = images[0].copy()
742
+
743
+
w,h = image_draw.size
744
+
# print(w, h)
745
+
ratio = 1 - ((max(w, h) - min(w, h)) / (max(w, h)))
746
+
747
+
748
+
image_transform=BasicImageTransform(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), normalize=True)
749
+
images_seq_mask = []
750
+
751
+
image_token = '<image>'
752
+
image_token_id = 128815
753
+
text_splits = prompt.split(image_token)
754
+
755
+
images_list, images_crop_list, images_seq_mask = [], [], []
756
+
tokenized_str = []
757
+
images_spatial_crop = []
758
+
for text_sep, image in zip(text_splits, images):
759
+
760
+
tokenized_sep = text_encode(tokenizer, text_sep, bos=False, eos=False)
761
+
tokenized_str += tokenized_sep
762
+
images_seq_mask += [False] * len(tokenized_sep)
763
+
764
+
if crop_mode:
765
+
766
+
if image.size[0] <= 768 and image.size[1] <= 768:
767
+
crop_ratio = [1, 1]
768
+
769
+
else:
770
+
if crop_mode:
771
+
# best_width, best_height = select_best_resolution(image.size, self.candidate_resolutions)
772
+
images_crop_raw, crop_ratio = dynamic_preprocess(image)
773
+
else:
774
+
# best_width, best_height = self.image_size, self.image_size
775
+
crop_ratio = [1, 1]
776
+
777
+
"""process the global view"""
778
+
# image = image.resize((base_size, base_size))
779
+
global_view = ImageOps.pad(image, (base_size, base_size),
780
+
color=tuple(int(x * 255) for x in image_transform.mean))
781
+
782
+
if base_size == 1024:
783
+
valid_img_tokens += int(256 * ratio)
784
+
elif base_size == 1280:
785
+
valid_img_tokens += int(400 * ratio)
786
+
# elif base_size == 640:
787
+
# valid_img_tokens += int(100 * ratio)
788
+
789
+
790
+
791
+
792
+
793
+
images_list.append(image_transform(global_view).to(torch.bfloat16))
794
+
795
+
# global_view_tensor = image_transform(global_view).to(torch.bfloat16)
796
+
797
+
width_crop_num, height_crop_num = crop_ratio
798
+
799
+
images_spatial_crop.append([width_crop_num, height_crop_num])
800
+
801
+
802
+
if width_crop_num > 1 or height_crop_num > 1:
803
+
"""process the local views"""
804
+
805
+
for i in range(len(images_crop_raw)):
806
+
images_crop_list.append(image_transform(images_crop_raw[i]).to(torch.bfloat16))
807
+
808
+
if image_size == 768:
809
+
valid_img_tokens += len(images_crop_list) * 144
810
+
811
+
num_queries = math.ceil((image_size // patch_size) / downsample_ratio)
812
+
num_queries_base = math.ceil((base_size // patch_size) / downsample_ratio)
813
+
814
+
815
+
816
+
"""add image tokens"""
817
+
818
+
819
+
820
+
tokenized_image = ([image_token_id] * num_queries_base) * num_queries_base
821
+
tokenized_image += [image_token_id]
822
+
if width_crop_num > 1 or height_crop_num > 1:
823
+
tokenized_image += ([image_token_id] * (num_queries * width_crop_num)) * (
824
+
num_queries * height_crop_num)
825
+
tokenized_str += tokenized_image
826
+
images_seq_mask += [True] * len(tokenized_image)
827
+
# num_image_tokens.append(len(tokenized_image))
828
+
829
+
else:
830
+
# best_width, best_height = self.image_size, self.image_size
831
+
# print(image.size, (best_width, best_height)) # check the select_best_resolutions func
832
+
833
+
"""process the global view"""
834
+
if image_size <= 768:
835
+
print('directly resize')
836
+
image = image.resize((image_size, image_size))
837
+
# else:
838
+
global_view = ImageOps.pad(image, (image_size, image_size),
839
+
color=tuple(int(x * 255) for x in image_transform.mean))
840
+
images_list.append(image_transform(global_view).to(torch.bfloat16))
841
+
842
+
if base_size == 1024:
843
+
valid_img_tokens += int(256 * ratio)
844
+
elif base_size == 1280:
845
+
valid_img_tokens += int(400 * ratio)
846
+
elif base_size == 640:
847
+
valid_img_tokens += int(100 * 1)
848
+
elif base_size == 512:
849
+
valid_img_tokens += int(64 * 1)
850
+
elif base_size == 768:
851
+
valid_img_tokens += int(144 * 1)
852
+
853
+
width_crop_num, height_crop_num = 1, 1
854
+
855
+
images_spatial_crop.append([width_crop_num, height_crop_num])
856
+
857
+
858
+
"""add image tokens"""
859
+
num_queries = math.ceil((image_size // patch_size) / downsample_ratio)
860
+
861
+
tokenized_image = ([image_token_id] * num_queries) * num_queries
862
+
tokenized_image += [image_token_id]
863
+
# tokenized_image += ([self.image_token_id] * (num_queries * width_crop_num) + [self.image_token_id]) * (
864
+
# num_queries * height_crop_num)
865
+
tokenized_str += tokenized_image
866
+
images_seq_mask += [True] * len(tokenized_image)
867
+
# num_image_tokens.append(len(tokenized_image))
868
+
869
+
870
+
"""process the last text split"""
871
+
tokenized_sep = text_encode(tokenizer, text_splits[-1], bos=False, eos=False)
872
+
tokenized_str += tokenized_sep
873
+
images_seq_mask += [False] * len(tokenized_sep)
874
+
875
+
"""add the bos tokens"""
876
+
bos_id = 0
877
+
tokenized_str = [bos_id] + tokenized_str
878
+
images_seq_mask = [False] + images_seq_mask
879
+
880
+
881
+
882
+
input_ids = torch.LongTensor(tokenized_str)
883
+
884
+
885
+
886
+
887
+
images_seq_mask = torch.tensor(images_seq_mask, dtype=torch.bool)
888
+
889
+
890
+
if len(images_list) == 0:
891
+
images_ori = torch.zeros((1, 3, image_size, image_size))
892
+
images_spatial_crop = torch.zeros((1, 2), dtype=torch.long)
893
+
images_crop = torch.zeros((1, 3, base_size, base_size))
894
+
895
+
else:
896
+
images_ori = torch.stack(images_list, dim=0)
897
+
images_spatial_crop = torch.tensor(images_spatial_crop, dtype=torch.long)
898
+
if images_crop_list:
899
+
images_crop = torch.stack(images_crop_list, dim=0)
900
+
else:
901
+
images_crop = torch.zeros((1, 3, base_size, base_size))
902
+
903
+
904
+
905
+
if not eval_mode:
906
+
streamer = NoEOSTextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=False)
907
+
with torch.autocast("cuda", dtype=torch.bfloat16):
908
+
with torch.no_grad():
909
+
output_ids = self.generate(
910
+
input_ids.unsqueeze(0).cuda(),
911
+
images=[(images_crop.cuda(), images_ori.cuda())],
912
+
images_seq_mask = images_seq_mask.unsqueeze(0).cuda(),
913
+
images_spatial_crop = images_spatial_crop,
914
+
# do_sample=False,
915
+
# num_beams = 1,
916
+
temperature=0.0,
917
+
eos_token_id=tokenizer.eos_token_id,
918
+
streamer=streamer,
919
+
max_new_tokens=8192,
920
+
no_repeat_ngram_size = 20,
921
+
use_cache = True
922
+
)
923
+
924
+
else:
925
+
with torch.autocast("cuda", dtype=torch.bfloat16):
926
+
with torch.no_grad():
927
+
output_ids = self.generate(
928
+
input_ids.unsqueeze(0).cuda(),
929
+
images=[(images_crop.cuda(), images_ori.cuda())],
930
+
images_seq_mask = images_seq_mask.unsqueeze(0).cuda(),
931
+
images_spatial_crop = images_spatial_crop,
932
+
# do_sample=False,
933
+
# num_beams = 1,
934
+
temperature=0.0,
935
+
eos_token_id=tokenizer.eos_token_id,
936
+
max_new_tokens=8192,
937
+
no_repeat_ngram_size = 35,
938
+
use_cache = True
939
+
)
940
+
941
+
942
+
if '<image>' in conversation[0]['content'] and eval_mode:
943
+
outputs = tokenizer.decode(output_ids[0, input_ids.unsqueeze(0).cuda().shape[1]:])
944
+
stop_str = '<｜end▁of▁sentence｜>'
945
+
if outputs.endswith(stop_str):
946
+
outputs = outputs[:-len(stop_str)]
947
+
# re_match
948
+
outputs = outputs.strip()
949
+
950
+
return outputs
951
+
952
+
if '<image>' in conversation[0]['content'] and test_compress:
953
+
outputs = tokenizer.decode(output_ids[0, input_ids.unsqueeze(0).cuda().shape[1]:])
954
+
pure_texts_outputs_token_length = len(text_encode(tokenizer, outputs, bos=False, eos=False))
955
+
print('='*50)
956
+
print('image size: ', (w, h))
957
+
print('valid image tokens: ', int(valid_img_tokens))
958
+
print('output texts tokens (valid): ', pure_texts_outputs_token_length)
959
+
print('compression ratio: ', round(pure_texts_outputs_token_length/valid_img_tokens, 2))
960
+
print('='*50)
961
+
962
+
963
+
if '<image>' in conversation[0]['content'] and save_results:
964
+
outputs = tokenizer.decode(output_ids[0, input_ids.unsqueeze(0).cuda().shape[1]:])
965
+
stop_str = '<｜end▁of▁sentence｜>'
966
+
967
+
print('='*15 + 'save results:' + '='*15)
968
+
969
+
# # # # conv.messages[-1][-1] = outputs
970
+
if outputs.endswith(stop_str):
971
+
outputs = outputs[:-len(stop_str)]
972
+
outputs = outputs.strip()
973
+
974
+
matches_ref, matches_images, mathes_other = re_match(outputs)
975
+
# print(matches_ref)
976
+
result = process_image_with_refs(image_draw, matches_ref, output_path)
977
+
978
+
979
+
for idx, a_match_image in enumerate(tqdm(matches_images, desc="image")):
980
+
outputs = outputs.replace(a_match_image, ' + '.jpg)\n')
981
+
982
+
for idx, a_match_other in enumerate(tqdm(mathes_other, desc="other")):
983
+
outputs = outputs.replace(a_match_other, '').replace('\\coloneqq', ':=').replace('\\eqqcolon', '=:')
984
+
985
+
986
+
# if 'structural formula' in conversation[0]['content']:
987
+
# outputs = '<smiles>' + outputs + '</smiles>'
988
+
with open(f'{output_path}/result.mmd', 'w', encoding = 'utf-8') as afile:
989
+
afile.write(outputs)
990
+
991
+
if 'line_type' in outputs:
992
+
import matplotlib.pyplot as plt
993
+
lines = eval(outputs)['Line']['line']
994
+
995
+
line_type = eval(outputs)['Line']['line_type']
996
+
# print(lines)
997
+
998
+
endpoints = eval(outputs)['Line']['line_endpoint']
999
+
1000
+
fig, ax = plt.subplots(figsize=(3,3), dpi=200)
1001
+
ax.set_xlim(-15, 15)
1002
+
ax.set_ylim(-15, 15)
1003
+
1004
+
for idx, line in enumerate(lines):
1005
+
try:
1006
+
p0 = eval(line.split(' -- ')[0])
1007
+
p1 = eval(line.split(' -- ')[-1])
1008
+
1009
+
if line_type[idx] == '--':
1010
+
ax.plot([p0[0], p1[0]], [p0[1], p1[1]], linewidth=0.8, color='k')
1011
+
else:
1012
+
ax.plot([p0[0], p1[0]], [p0[1], p1[1]], linewidth = 0.8, color = 'k')
1013
+
1014
+
ax.scatter(p0[0], p0[1], s=5, color = 'k')
1015
+
ax.scatter(p1[0], p1[1], s=5, color = 'k')
1016
+
except:
1017
+
pass
1018
+
1019
+
for endpoint in endpoints:
1020
+
1021
+
label = endpoint.split(': ')[0]
1022
+
(x, y) = eval(endpoint.split(': ')[1])
1023
+
ax.annotate(label, (x, y), xytext=(1, 1), textcoords='offset points',
1024
+
fontsize=5, fontweight='light')
1025
+
1026
+
1027
+
plt.savefig(f'{output_path}/geo.jpg')
1028
+
plt.close()
1029
+
1030
+
result.save(f"{output_path}/result_with_boxes.jpg")
+1992
src/deepseek_ocr2/modeling_deepseekv2.py
+1992
src/deepseek_ocr2/modeling_deepseekv2.py
···
1
+
# coding=utf-8
2
+
# Copyright 2023 DeepSeek-AI and The HuggingFace Inc. team. All rights reserved.
3
+
#
4
+
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
5
+
# and OPT implementations in this library. It has been modified from its
6
+
# original forms to accommodate minor architectural differences compared
7
+
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
8
+
#
9
+
# Licensed under the Apache License, Version 2.0 (the "License");
10
+
# you may not use this file except in compliance with the License.
11
+
# You may obtain a copy of the License at
12
+
#
13
+
# http://www.apache.org/licenses/LICENSE-2.0
14
+
#
15
+
# Unless required by applicable law or agreed to in writing, software
16
+
# distributed under the License is distributed on an "AS IS" BASIS,
17
+
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
18
+
# See the License for the specific language governing permissions and
19
+
# limitations under the License.
20
+
""" PyTorch DeepSeek model and compatible with both DeepSeekV2 and DeepSeekV3"""
21
+
import math
22
+
import warnings
23
+
from typing import List, Optional, Tuple, Union
24
+
import numpy as np
25
+
26
+
import torch
27
+
import torch.nn.functional as F
28
+
import torch.utils.checkpoint
29
+
import torch.distributed as dist
30
+
from einops import repeat
31
+
from torch import nn
32
+
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
33
+
34
+
from transformers.activations import ACT2FN
35
+
from transformers.cache_utils import Cache, DynamicCache
36
+
from transformers.modeling_attn_mask_utils import _prepare_4d_causal_attention_mask
37
+
from transformers.models.llama.modeling_llama import (
38
+
LlamaAttention,
39
+
LlamaFlashAttention2
40
+
)
41
+
from transformers.modeling_outputs import (
42
+
BaseModelOutputWithPast,
43
+
CausalLMOutputWithPast,
44
+
SequenceClassifierOutputWithPast,
45
+
)
46
+
from transformers.modeling_utils import PreTrainedModel
47
+
from transformers.pytorch_utils import (
48
+
ALL_LAYERNORM_LAYERS,
49
+
is_torch_greater_or_equal_than_1_13,
50
+
)
51
+
from transformers.utils import (
52
+
add_start_docstrings,
53
+
add_start_docstrings_to_model_forward,
54
+
is_flash_attn_2_available,
55
+
is_flash_attn_greater_or_equal_2_10,
56
+
logging,
57
+
replace_return_docstrings,
58
+
)
59
+
from transformers.utils.import_utils import is_torch_fx_available
60
+
61
+
from .configuration_deepseek_v2 import DeepseekV2Config
62
+
63
+
if is_flash_attn_2_available():
64
+
from flash_attn import flash_attn_func, flash_attn_varlen_func
65
+
from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa
66
+
67
+
# This makes `_prepare_4d_causal_attention_mask` a leaf function in the FX graph.
68
+
# It means that the function will not be traced through and simply appear as a node in the graph.
69
+
if is_torch_fx_available():
70
+
if not is_torch_greater_or_equal_than_1_13:
71
+
import torch.fx
72
+
73
+
_prepare_4d_causal_attention_mask = torch.fx.wrap(_prepare_4d_causal_attention_mask)
74
+
75
+
logger = logging.get_logger(__name__)
76
+
77
+
_CONFIG_FOR_DOC = "DeepseekV2Config"
78
+
79
+
80
+
def _get_unpad_data(attention_mask):
81
+
seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
82
+
indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
83
+
max_seqlen_in_batch = seqlens_in_batch.max().item()
84
+
cu_seqlens = F.pad(
85
+
torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.torch.int32), (1, 0)
86
+
)
87
+
return (
88
+
indices,
89
+
cu_seqlens,
90
+
max_seqlen_in_batch,
91
+
)
92
+
93
+
94
+
class DeepseekV2RMSNorm(nn.Module):
95
+
def __init__(self, hidden_size, eps=1e-6):
96
+
"""
97
+
DeepseekV2RMSNorm is equivalent to T5LayerNorm
98
+
"""
99
+
super().__init__()
100
+
self.weight = nn.Parameter(torch.ones(hidden_size))
101
+
self.variance_epsilon = eps
102
+
103
+
def forward(self, hidden_states):
104
+
input_dtype = hidden_states.dtype
105
+
hidden_states = hidden_states.to(torch.float32)
106
+
variance = hidden_states.pow(2).mean(-1, keepdim=True)
107
+
hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
108
+
return self.weight * hidden_states.to(input_dtype)
109
+
110
+
111
+
ALL_LAYERNORM_LAYERS.append(DeepseekV2RMSNorm)
112
+
113
+
114
+
115
+
116
+
class DeepseekV2RotaryEmbedding(nn.Module):
117
+
def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
118
+
super().__init__()
119
+
120
+
self.dim = dim
121
+
self.max_position_embeddings = max_position_embeddings
122
+
self.base = base
123
+
inv_freq = 1.0 / (
124
+
self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim)
125
+
)
126
+
self.register_buffer("inv_freq", inv_freq, persistent=False)
127
+
128
+
# Build here to make `torch.jit.trace` work.
129
+
self._set_cos_sin_cache(
130
+
seq_len=max_position_embeddings,
131
+
device=self.inv_freq.device,
132
+
dtype=torch.get_default_dtype(),
133
+
)
134
+
self.max_seq_len_cached = None
135
+
136
+
def _set_cos_sin_cache(self, seq_len, device, dtype):
137
+
self.max_seq_len_cached = seq_len
138
+
t = torch.arange(
139
+
self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype
140
+
)
141
+
142
+
freqs = torch.outer(t, self.inv_freq.to(t.device))
143
+
# Different from paper, but it uses a different permutation in order to obtain the same calculation
144
+
emb = torch.cat((freqs, freqs), dim=-1)
145
+
self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
146
+
self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
147
+
148
+
def forward(self, x, seq_len=None):
149
+
# x: [bs, num_attention_heads, seq_len, head_size]
150
+
if self.max_seq_len_cached is None or seq_len > self.max_seq_len_cached:
151
+
self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)
152
+
153
+
return (
154
+
self.cos_cached[:seq_len].to(dtype=x.dtype),
155
+
self.sin_cached[:seq_len].to(dtype=x.dtype),
156
+
)
157
+
158
+
159
+
# Copied from transformers.models.llama.modeling_llama.LlamaLinearScalingRotaryEmbedding with Llama->DeepseekV2
160
+
class DeepseekV2LinearScalingRotaryEmbedding(DeepseekV2RotaryEmbedding):
161
+
"""DeepseekV2RotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""
162
+
163
+
def __init__(
164
+
self,
165
+
dim,
166
+
max_position_embeddings=2048,
167
+
base=10000,
168
+
device=None,
169
+
scaling_factor=1.0,
170
+
):
171
+
self.scaling_factor = scaling_factor
172
+
super().__init__(dim, max_position_embeddings, base, device)
173
+
174
+
def _set_cos_sin_cache(self, seq_len, device, dtype):
175
+
self.max_seq_len_cached = seq_len
176
+
t = torch.arange(
177
+
self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype
178
+
)
179
+
t = t / self.scaling_factor
180
+
181
+
freqs = torch.outer(t, self.inv_freq)
182
+
# Different from paper, but it uses a different permutation in order to obtain the same calculation
183
+
emb = torch.cat((freqs, freqs), dim=-1)
184
+
self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
185
+
self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
186
+
187
+
188
+
# Copied from transformers.models.llama.modeling_llama.LlamaDynamicNTKScalingRotaryEmbedding with Llama->DeepseekV2
189
+
class DeepseekV2DynamicNTKScalingRotaryEmbedding(DeepseekV2RotaryEmbedding):
190
+
"""DeepseekV2RotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla"""
191
+
192
+
def __init__(
193
+
self,
194
+
dim,
195
+
max_position_embeddings=2048,
196
+
base=10000,
197
+
device=None,
198
+
scaling_factor=1.0,
199
+
):
200
+
self.scaling_factor = scaling_factor
201
+
super().__init__(dim, max_position_embeddings, base, device)
202
+
203
+
def _set_cos_sin_cache(self, seq_len, device, dtype):
204
+
self.max_seq_len_cached = seq_len
205
+
206
+
if seq_len > self.max_position_embeddings:
207
+
base = self.base * (
208
+
(self.scaling_factor * seq_len / self.max_position_embeddings)
209
+
- (self.scaling_factor - 1)
210
+
) ** (self.dim / (self.dim - 2))
211
+
inv_freq = 1.0 / (
212
+
base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim)
213
+
)
214
+
self.register_buffer("inv_freq", inv_freq, persistent=False)
215
+
216
+
t = torch.arange(
217
+
self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype
218
+
)
219
+
220
+
freqs = torch.outer(t, self.inv_freq)
221
+
# Different from paper, but it uses a different permutation in order to obtain the same calculation
222
+
emb = torch.cat((freqs, freqs), dim=-1)
223
+
self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
224
+
self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
225
+
226
+
227
+
# Inverse dim formula to find dim based on number of rotations
228
+
def yarn_find_correction_dim(
229
+
num_rotations, dim, base=10000, max_position_embeddings=2048
230
+
):
231
+
return (dim * math.log(max_position_embeddings / (num_rotations * 2 * math.pi))) / (
232
+
2 * math.log(base)
233
+
)
234
+
235
+
236
+
# Find dim range bounds based on rotations
237
+
def yarn_find_correction_range(
238
+
low_rot, high_rot, dim, base=10000, max_position_embeddings=2048
239
+
):
240
+
low = math.floor(
241
+
yarn_find_correction_dim(low_rot, dim, base, max_position_embeddings)
242
+
)
243
+
high = math.ceil(
244
+
yarn_find_correction_dim(high_rot, dim, base, max_position_embeddings)
245
+
)
246
+
return max(low, 0), min(high, dim - 1) # Clamp values just in case
247
+
248
+
249
+
def yarn_get_mscale(scale=1, mscale=1):
250
+
if scale <= 1:
251
+
return 1.0
252
+
return 0.1 * mscale * math.log(scale) + 1.0
253
+
254
+
255
+
def yarn_linear_ramp_mask(min, max, dim):
256
+
if min == max:
257
+
max += 0.001 # Prevent singularity
258
+
259
+
linear_func = (torch.arange(dim, dtype=torch.float32) - min) / (max - min)
260
+
ramp_func = torch.clamp(linear_func, 0, 1)
261
+
return ramp_func
262
+
263
+
264
+
class DeepseekV2YarnRotaryEmbedding(DeepseekV2RotaryEmbedding):
265
+
266
+
def __init__(
267
+
self,
268
+
dim,
269
+
max_position_embeddings=2048,
270
+
base=10000,
271
+
device=None,
272
+
scaling_factor=1.0,
273
+
original_max_position_embeddings=4096,
274
+
beta_fast=32,
275
+
beta_slow=1,
276
+
mscale=1,
277
+
mscale_all_dim=0,
278
+
):
279
+
self.scaling_factor = scaling_factor
280
+
self.original_max_position_embeddings = original_max_position_embeddings
281
+
self.beta_fast = beta_fast
282
+
self.beta_slow = beta_slow
283
+
self.mscale = mscale
284
+
self.mscale_all_dim = mscale_all_dim
285
+
super().__init__(dim, max_position_embeddings, base, device)
286
+
287
+
def _set_cos_sin_cache(self, seq_len, device, dtype):
288
+
self.max_seq_len_cached = seq_len
289
+
dim = self.dim
290
+
291
+
freq_extra = 1.0 / (
292
+
self.base
293
+
** (torch.arange(0, dim, 2, dtype=torch.float32, device=device) / dim)
294
+
)
295
+
freq_inter = 1.0 / (
296
+
self.scaling_factor
297
+
* self.base
298
+
** (torch.arange(0, dim, 2, dtype=torch.float32, device=device) / dim)
299
+
)
300
+
301
+
low, high = yarn_find_correction_range(
302
+
self.beta_fast,
303
+
self.beta_slow,
304
+
dim,
305
+
self.base,
306
+
self.original_max_position_embeddings,
307
+
)
308
+
inv_freq_mask = 1.0 - yarn_linear_ramp_mask(low, high, dim // 2).to(
309
+
device=device, dtype=torch.float32
310
+
)
311
+
inv_freq = freq_inter * (1 - inv_freq_mask) + freq_extra * inv_freq_mask
312
+
self.register_buffer("inv_freq", inv_freq, persistent=False)
313
+
314
+
t = torch.arange(seq_len, device=device, dtype=torch.float32)
315
+
316
+
freqs = torch.outer(t, inv_freq)
317
+
318
+
_mscale = float(
319
+
yarn_get_mscale(self.scaling_factor, self.mscale)
320
+
/ yarn_get_mscale(self.scaling_factor, self.mscale_all_dim)
321
+
)
322
+
323
+
emb = torch.cat((freqs, freqs), dim=-1)
324
+
self.register_buffer(
325
+
"cos_cached", (emb.cos() * _mscale).to(dtype), persistent=False
326
+
)
327
+
self.register_buffer(
328
+
"sin_cached", (emb.sin() * _mscale).to(dtype), persistent=False
329
+
)
330
+
331
+
332
+
# Copied from transformers.models.llama.modeling_llama.rotate_half
333
+
def rotate_half(x):
334
+
"""Rotates half the hidden dims of the input."""
335
+
x1 = x[..., : x.shape[-1] // 2]
336
+
x2 = x[..., x.shape[-1] // 2 :]
337
+
return torch.cat((-x2, x1), dim=-1)
338
+
339
+
340
+
# Copied from transformers.models.llama.modeling_llama.apply_rotary_pos_emb
341
+
def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1):
342
+
"""Applies Rotary Position Embedding to the query and key tensors.
343
+
344
+
Args:
345
+
q (`torch.Tensor`): The query tensor.
346
+
k (`torch.Tensor`): The key tensor.
347
+
cos (`torch.Tensor`): The cosine part of the rotary embedding.
348
+
sin (`torch.Tensor`): The sine part of the rotary embedding.
349
+
position_ids (`torch.Tensor`):
350
+
The position indices of the tokens corresponding to the query and key tensors. For example, this can be
351
+
used to pass offsetted position ids when working with a KV-cache.
352
+
unsqueeze_dim (`int`, *optional*, defaults to 1):
353
+
The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
354
+
sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
355
+
that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
356
+
k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
357
+
cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
358
+
the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
359
+
Returns:
360
+
`tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
361
+
"""
362
+
cos = cos[position_ids].unsqueeze(unsqueeze_dim)
363
+
sin = sin[position_ids].unsqueeze(unsqueeze_dim)
364
+
365
+
366
+
# print()
367
+
368
+
b, h, s, d = q.shape
369
+
q = q.view(b, h, s, d // 2, 2).transpose(4, 3).reshape(b, h, s, d)
370
+
371
+
b, h, s, d = k.shape
372
+
k = k.view(b, h, s, d // 2, 2).transpose(4, 3).reshape(b, h, s, d)
373
+
374
+
q_embed = (q * cos) + (rotate_half(q) * sin)
375
+
k_embed = (k * cos) + (rotate_half(k) * sin)
376
+
377
+
378
+
return q_embed, k_embed
379
+
380
+
381
+
class DeepseekV2MLP(nn.Module):
382
+
def __init__(self, config, hidden_size=None, intermediate_size=None):
383
+
super().__init__()
384
+
self.config = config
385
+
self.hidden_size = config.hidden_size if hidden_size is None else hidden_size
386
+
self.intermediate_size = (
387
+
config.intermediate_size if intermediate_size is None else intermediate_size
388
+
)
389
+
390
+
self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
391
+
self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
392
+
self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
393
+
self.act_fn = ACT2FN[config.hidden_act]
394
+
395
+
def forward(self, x):
396
+
down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
397
+
return down_proj
398
+
399
+
400
+
class MoEGate(nn.Module):
401
+
def __init__(self, config):
402
+
super().__init__()
403
+
self.config = config
404
+
self.top_k = config.num_experts_per_tok
405
+
self.n_routed_experts = config.n_routed_experts
406
+
self.routed_scaling_factor = config.routed_scaling_factor
407
+
self.scoring_func = config.scoring_func
408
+
self.alpha = config.aux_loss_alpha
409
+
self.seq_aux = config.seq_aux
410
+
self.topk_method = config.topk_method
411
+
self.n_group = config.n_group
412
+
self.topk_group = config.topk_group
413
+
414
+
# topk selection algorithm
415
+
self.norm_topk_prob = config.norm_topk_prob
416
+
self.gating_dim = config.hidden_size
417
+
self.weight = nn.Parameter(
418
+
torch.empty((self.n_routed_experts, self.gating_dim))
419
+
)
420
+
if self.topk_method == "noaux_tc":
421
+
self.e_score_correction_bias = nn.Parameter(
422
+
torch.empty((self.n_routed_experts))
423
+
)
424
+
self.reset_parameters()
425
+
426
+
def reset_parameters(self) -> None:
427
+
import torch.nn.init as init
428
+
429
+
init.kaiming_uniform_(self.weight, a=math.sqrt(5))
430
+
431
+
def forward(self, hidden_states):
432
+
bsz, seq_len, h = hidden_states.shape
433
+
### compute gating score
434
+
hidden_states = hidden_states.view(-1, h)
435
+
logits = F.linear(
436
+
hidden_states.type(torch.float32), self.weight.type(torch.float32), None
437
+
)
438
+
if self.scoring_func == "softmax":
439
+
scores = logits.softmax(dim=-1, dtype=torch.float32)
440
+
elif self.scoring_func == "sigmoid":
441
+
scores = logits.sigmoid()
442
+
else:
443
+
raise NotImplementedError(
444
+
f"insupportable scoring function for MoE gating: {self.scoring_func}"
445
+
)
446
+
447
+
### select top-k experts
448
+
if self.topk_method == "greedy":
449
+
topk_weight, topk_idx = torch.topk(
450
+
scores, k=self.top_k, dim=-1, sorted=False
451
+
)
452
+
elif self.topk_method == "group_limited_greedy":
453
+
group_scores = (
454
+
scores.view(bsz * seq_len, self.n_group, -1).max(dim=-1).values
455
+
) # [n, n_group]
456
+
group_idx = torch.topk(
457
+
group_scores, k=self.topk_group, dim=-1, sorted=False
458
+
)[
459
+
1
460
+
] # [n, top_k_group]
461
+
group_mask = torch.zeros_like(group_scores) # [n, n_group]
462
+
group_mask.scatter_(1, group_idx, 1) # [n, n_group]
463
+
score_mask = (
464
+
group_mask.unsqueeze(-1)
465
+
.expand(
466
+
bsz * seq_len, self.n_group, self.n_routed_experts // self.n_group
467
+
)
468
+
.reshape(bsz * seq_len, -1)
469
+
) # [n, e]
470
+
tmp_scores = scores.masked_fill(~score_mask.bool(), 0.0) # [n, e]
471
+
topk_weight, topk_idx = torch.topk(
472
+
tmp_scores, k=self.top_k, dim=-1, sorted=False
473
+
)
474
+
elif self.topk_method == "noaux_tc":
475
+
assert not self.training
476
+
scores_for_choice = scores.view(bsz * seq_len, -1) + self.e_score_correction_bias.unsqueeze(0)
477
+
group_scores = (
478
+
scores_for_choice.view(bsz * seq_len, self.n_group, -1).topk(2, dim=-1)[0].sum(dim = -1)
479
+
) # [n, n_group]
480
+
group_idx = torch.topk(
481
+
group_scores, k=self.topk_group, dim=-1, sorted=False
482
+
)[
483
+
1
484
+
] # [n, top_k_group]
485
+
group_mask = torch.zeros_like(group_scores) # [n, n_group]
486
+
group_mask.scatter_(1, group_idx, 1) # [n, n_group]
487
+
score_mask = (
488
+
group_mask.unsqueeze(-1)
489
+
.expand(
490
+
bsz * seq_len, self.n_group, self.n_routed_experts // self.n_group
491
+
)
492
+
.reshape(bsz * seq_len, -1)
493
+
) # [n, e]
494
+
tmp_scores = scores_for_choice.masked_fill(~score_mask.bool(), 0.0) # [n, e]
495
+
_, topk_idx = torch.topk(
496
+
tmp_scores, k=self.top_k, dim=-1, sorted=False
497
+
)
498
+
topk_weight = scores.gather(1, topk_idx)
499
+
500
+
### norm gate to sum 1
501
+
if self.top_k > 1 and self.norm_topk_prob:
502
+
denominator = topk_weight.sum(dim=-1, keepdim=True) + 1e-20
503
+
topk_weight = topk_weight / denominator * self.routed_scaling_factor
504
+
else:
505
+
topk_weight = topk_weight * self.routed_scaling_factor
506
+
### expert-level computation auxiliary loss
507
+
if self.training and self.alpha > 0.0:
508
+
scores_for_aux = scores
509
+
aux_topk = self.top_k
510
+
# always compute aux loss based on the naive greedy topk method
511
+
topk_idx_for_aux_loss = topk_idx.view(bsz, -1)
512
+
if self.seq_aux:
513
+
scores_for_seq_aux = scores_for_aux.view(bsz, seq_len, -1)
514
+
ce = torch.zeros(
515
+
bsz, self.n_routed_experts, device=hidden_states.device
516
+
)
517
+
ce.scatter_add_(
518
+
1,
519
+
topk_idx_for_aux_loss,
520
+
torch.ones(bsz, seq_len * aux_topk, device=hidden_states.device),
521
+
).div_(seq_len * aux_topk / self.n_routed_experts)
522
+
aux_loss = (ce * scores_for_seq_aux.mean(dim=1)).sum(
523
+
dim=1
524
+
).mean() * self.alpha
525
+
else:
526
+
mask_ce = F.one_hot(
527
+
topk_idx_for_aux_loss.view(-1), num_classes=self.n_routed_experts
528
+
)
529
+
ce = mask_ce.float().mean(0)
530
+
Pi = scores_for_aux.mean(0)
531
+
fi = ce * self.n_routed_experts
532
+
aux_loss = (Pi * fi).sum() * self.alpha
533
+
else:
534
+
aux_loss = None
535
+
return topk_idx, topk_weight, aux_loss
536
+
537
+
538
+
class AddAuxiliaryLoss(torch.autograd.Function):
539
+
"""
540
+
The trick function of adding auxiliary (aux) loss,
541
+
which includes the gradient of the aux loss during backpropagation.
542
+
"""
543
+
544
+
@staticmethod
545
+
def forward(ctx, x, loss):
546
+
assert loss.numel() == 1
547
+
ctx.dtype = loss.dtype
548
+
ctx.required_aux_loss = loss.requires_grad
549
+
return x
550
+
551
+
@staticmethod
552
+
def backward(ctx, grad_output):
553
+
grad_loss = None
554
+
if ctx.required_aux_loss:
555
+
grad_loss = torch.ones(1, dtype=ctx.dtype, device=grad_output.device)
556
+
return grad_output, grad_loss
557
+
558
+
559
+
class DeepseekV2MoE(nn.Module):
560
+
"""
561
+
A mixed expert module containing shared experts.
562
+
"""
563
+
564
+
def __init__(self, config):
565
+
super().__init__()
566
+
self.config = config
567
+
self.num_experts_per_tok = config.num_experts_per_tok
568
+
569
+
if hasattr(config, "ep_size") and config.ep_size > 1:
570
+
assert config.ep_size == dist.get_world_size()
571
+
self.ep_size = config.ep_size
572
+
self.experts_per_rank = config.n_routed_experts // config.ep_size
573
+
self.ep_rank = dist.get_rank()
574
+
self.experts = nn.ModuleList(
575
+
[
576
+
(
577
+
DeepseekV2MLP(
578
+
config, intermediate_size=config.moe_intermediate_size
579
+
)
580
+
if i >= self.ep_rank * self.experts_per_rank
581
+
and i < (self.ep_rank + 1) * self.experts_per_rank
582
+
else None
583
+
)
584
+
for i in range(config.n_routed_experts)
585
+
]
586
+
)
587
+
else:
588
+
self.ep_size = 1
589
+
self.experts_per_rank = config.n_routed_experts
590
+
self.ep_rank = 0
591
+
self.experts = nn.ModuleList(
592
+
[
593
+
DeepseekV2MLP(
594
+
config, intermediate_size=config.moe_intermediate_size
595
+
)
596
+
for i in range(config.n_routed_experts)
597
+
]
598
+
)
599
+
self.gate = MoEGate(config)
600
+
if config.n_shared_experts is not None:
601
+
intermediate_size = config.moe_intermediate_size * config.n_shared_experts
602
+
self.shared_experts = DeepseekV2MLP(
603
+
config=config, intermediate_size=intermediate_size
604
+
)
605
+
606
+
def forward(self, hidden_states):
607
+
identity = hidden_states
608
+
orig_shape = hidden_states.shape
609
+
topk_idx, topk_weight, aux_loss = self.gate(hidden_states)
610
+
hidden_states = hidden_states.view(-1, hidden_states.shape[-1])
611
+
flat_topk_idx = topk_idx.view(-1)
612
+
if self.training:
613
+
hidden_states = hidden_states.repeat_interleave(
614
+
self.num_experts_per_tok, dim=0
615
+
)
616
+
y = torch.empty_like(hidden_states)
617
+
for i, expert in enumerate(self.experts):
618
+
y[flat_topk_idx == i] = expert(hidden_states[flat_topk_idx == i])
619
+
y = (y.view(*topk_weight.shape, -1) * topk_weight.unsqueeze(-1)).sum(dim=1)
620
+
y = y.to(hidden_states.dtype).view(*orig_shape)
621
+
y = AddAuxiliaryLoss.apply(y, aux_loss)
622
+
else:
623
+
y = self.moe_infer(hidden_states, topk_idx, topk_weight).view(*orig_shape)
624
+
if self.config.n_shared_experts is not None:
625
+
y = y + self.shared_experts(identity)
626
+
return y
627
+
628
+
@torch.no_grad()
629
+
def moe_infer(self, x, topk_ids, topk_weight):
630
+
cnts = topk_ids.new_zeros((topk_ids.shape[0], len(self.experts)))
631
+
cnts.scatter_(1, topk_ids, 1)
632
+
tokens_per_expert = cnts.sum(dim=0)
633
+
idxs = topk_ids.view(-1).argsort()
634
+
sorted_tokens = x[idxs // topk_ids.shape[1]]
635
+
sorted_tokens_shape = sorted_tokens.shape
636
+
if self.ep_size > 1:
637
+
tokens_per_ep_rank = tokens_per_expert.view(self.ep_size, -1).sum(dim=1)
638
+
tokens_per_expert_group = tokens_per_expert.new_empty(
639
+
tokens_per_expert.shape[0]
640
+
)
641
+
dist.all_to_all_single(tokens_per_expert_group, tokens_per_expert)
642
+
output_splits = (
643
+
tokens_per_expert_group.view(self.ep_size, -1)
644
+
.sum(1)
645
+
.cpu()
646
+
.numpy()
647
+
.tolist()
648
+
)
649
+
gathered_tokens = sorted_tokens.new_empty(
650
+
tokens_per_expert_group.sum(dim=0).cpu().item(), sorted_tokens.shape[1]
651
+
)
652
+
input_split_sizes = tokens_per_ep_rank.cpu().numpy().tolist()
653
+
dist.all_to_all(
654
+
list(gathered_tokens.split(output_splits)),
655
+
list(sorted_tokens.split(input_split_sizes)),
656
+
)
657
+
tokens_per_expert_post_gather = tokens_per_expert_group.view(
658
+
self.ep_size, self.experts_per_rank
659
+
).sum(dim=0)
660
+
gatherd_idxs = np.zeros(shape=(gathered_tokens.shape[0],), dtype=np.int32)
661
+
s = 0
662
+
for i, k in enumerate(tokens_per_expert_group.cpu().numpy()):
663
+
gatherd_idxs[s : s + k] = i % self.experts_per_rank
664
+
s += k
665
+
gatherd_idxs = gatherd_idxs.argsort()
666
+
sorted_tokens = gathered_tokens[gatherd_idxs]
667
+
tokens_per_expert = tokens_per_expert_post_gather
668
+
tokens_per_expert = tokens_per_expert.cpu().numpy()
669
+
670
+
outputs = []
671
+
start_idx = 0
672
+
for i, num_tokens in enumerate(tokens_per_expert):
673
+
end_idx = start_idx + num_tokens
674
+
if num_tokens == 0:
675
+
continue
676
+
expert = self.experts[i + self.ep_rank * self.experts_per_rank]
677
+
tokens_for_this_expert = sorted_tokens[start_idx:end_idx]
678
+
expert_out = expert(tokens_for_this_expert)
679
+
outputs.append(expert_out)
680
+
start_idx = end_idx
681
+
682
+
outs = torch.cat(outputs, dim=0) if len(outputs) else sorted_tokens.new_empty(0)
683
+
if self.ep_size > 1:
684
+
new_x = torch.empty_like(outs)
685
+
new_x[gatherd_idxs] = outs
686
+
gathered_tokens = new_x.new_empty(*sorted_tokens_shape)
687
+
dist.all_to_all(
688
+
list(gathered_tokens.split(input_split_sizes)),
689
+
list(new_x.split(output_splits)),
690
+
)
691
+
outs = gathered_tokens
692
+
693
+
new_x = torch.empty_like(outs)
694
+
new_x[idxs] = outs
695
+
final_out = (
696
+
new_x.view(*topk_ids.shape, -1)
697
+
.type(topk_weight.dtype)
698
+
.mul_(topk_weight.unsqueeze(dim=-1))
699
+
.sum(dim=1)
700
+
.type(new_x.dtype)
701
+
)
702
+
return final_out
703
+
704
+
705
+
# Copied from transformers.models.llama.modeling_llama.repeat_kv
706
+
def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
707
+
"""
708
+
This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
709
+
num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
710
+
"""
711
+
batch, num_key_value_heads, slen, head_dim = hidden_states.shape
712
+
if n_rep == 1:
713
+
return hidden_states
714
+
hidden_states = hidden_states[:, :, None, :, :].expand(
715
+
batch, num_key_value_heads, n_rep, slen, head_dim
716
+
)
717
+
return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
718
+
719
+
720
+
# Copied from transformers.models.llama.modeling_llama.LlamaAttention with Llama->DeepseekV2
721
+
class DeepseekV2Attention(nn.Module):
722
+
"""Multi-headed attention from 'Attention Is All You Need' paper"""
723
+
724
+
def __init__(self, config: DeepseekV2Config, layer_idx: Optional[int] = None):
725
+
super().__init__()
726
+
self.config = config
727
+
self.layer_idx = layer_idx
728
+
if layer_idx is None:
729
+
logger.warning_once(
730
+
f"Instantiating {self.__class__.__name__} without passing `layer_idx` is not recommended and will "
731
+
"to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` "
732
+
"when creating this class."
733
+
)
734
+
735
+
self.attention_dropout = config.attention_dropout
736
+
self.hidden_size = config.hidden_size
737
+
self.num_heads = config.num_attention_heads
738
+
739
+
self.max_position_embeddings = config.max_position_embeddings
740
+
self.rope_theta = config.rope_theta
741
+
self.q_lora_rank = config.q_lora_rank
742
+
self.qk_rope_head_dim = config.qk_rope_head_dim
743
+
self.kv_lora_rank = config.kv_lora_rank
744
+
self.v_head_dim = config.v_head_dim
745
+
self.qk_nope_head_dim = config.qk_nope_head_dim
746
+
self.q_head_dim = config.qk_nope_head_dim + config.qk_rope_head_dim
747
+
748
+
self.is_causal = True
749
+
750
+
if self.q_lora_rank is None:
751
+
self.q_proj = nn.Linear(
752
+
self.hidden_size, self.num_heads * self.q_head_dim, bias=False
753
+
)
754
+
else:
755
+
self.q_a_proj = nn.Linear(
756
+
self.hidden_size, config.q_lora_rank, bias=config.attention_bias
757
+
)
758
+
self.q_a_layernorm = DeepseekV2RMSNorm(config.q_lora_rank)
759
+
self.q_b_proj = nn.Linear(
760
+
config.q_lora_rank, self.num_heads * self.q_head_dim, bias=False
761
+
)
762
+
# config.kv_lora_rank + config.qk_rope_head_dim,
763
+
self.kv_a_proj_with_mqa = nn.Linear(
764
+
self.hidden_size,
765
+
config.kv_lora_rank + config.qk_rope_head_dim,
766
+
bias=config.attention_bias,
767
+
)
768
+
self.kv_a_layernorm = DeepseekV2RMSNorm(config.kv_lora_rank)
769
+
self.kv_b_proj = nn.Linear(
770
+
config.kv_lora_rank,
771
+
self.num_heads
772
+
* (self.q_head_dim - self.qk_rope_head_dim + self.v_head_dim),
773
+
bias=False,
774
+
)
775
+
776
+
self.o_proj = nn.Linear(
777
+
self.num_heads * self.v_head_dim,
778
+
self.hidden_size,
779
+
bias=config.attention_bias,
780
+
)
781
+
self._init_rope()
782
+
783
+
self.softmax_scale = self.q_head_dim ** (-0.5)
784
+
if self.config.rope_scaling is not None:
785
+
mscale_all_dim = self.config.rope_scaling.get("mscale_all_dim", 0)
786
+
scaling_factor = self.config.rope_scaling["factor"]
787
+
if mscale_all_dim:
788
+
mscale = yarn_get_mscale(scaling_factor, mscale_all_dim)
789
+
self.softmax_scale = self.softmax_scale * mscale * mscale
790
+
791
+
def _init_rope(self):
792
+
if self.config.rope_scaling is None:
793
+
self.rotary_emb = DeepseekV2RotaryEmbedding(
794
+
self.qk_rope_head_dim,
795
+
max_position_embeddings=self.max_position_embeddings,
796
+
base=self.rope_theta,
797
+
)
798
+
# self.rotary_emb = DeepseekV2LinearScalingRotaryEmbedding(
799
+
# self.qk_rope_head_dim,
800
+
# max_position_embeddings=self.max_position_embeddings,
801
+
# scaling_factor=scaling_factor,
802
+
# base=self.rope_theta,
803
+
# )
804
+
else:
805
+
scaling_type = self.config.rope_scaling["type"]
806
+
scaling_factor = self.config.rope_scaling["factor"]
807
+
if scaling_type == "linear":
808
+
self.rotary_emb = DeepseekV2LinearScalingRotaryEmbedding(
809
+
self.qk_rope_head_dim,
810
+
max_position_embeddings=self.max_position_embeddings,
811
+
scaling_factor=scaling_factor,
812
+
base=self.rope_theta,
813
+
)
814
+
elif scaling_type == "dynamic":
815
+
self.rotary_emb = DeepseekV2DynamicNTKScalingRotaryEmbedding(
816
+
self.qk_rope_head_dim,
817
+
max_position_embeddings=self.max_position_embeddings,
818
+
scaling_factor=scaling_factor,
819
+
base=self.rope_theta,
820
+
)
821
+
elif scaling_type == "yarn":
822
+
kwargs = {
823
+
key: self.config.rope_scaling[key]
824
+
for key in [
825
+
"original_max_position_embeddings",
826
+
"beta_fast",
827
+
"beta_slow",
828
+
"mscale",
829
+
"mscale_all_dim",
830
+
]
831
+
if key in self.config.rope_scaling
832
+
}
833
+
self.rotary_emb = DeepseekV2YarnRotaryEmbedding(
834
+
self.qk_rope_head_dim,
835
+
max_position_embeddings=self.max_position_embeddings,
836
+
scaling_factor=scaling_factor,
837
+
base=self.rope_theta,
838
+
**kwargs,
839
+
)
840
+
else:
841
+
raise ValueError(f"Unknown RoPE scaling type {scaling_type}")
842
+
843
+
def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
844
+
return (
845
+
tensor.view(bsz, seq_len, self.num_heads, self.v_head_dim)
846
+
.transpose(1, 2)
847
+
.contiguous()
848
+
)
849
+
850
+
def forward(
851
+
self,
852
+
hidden_states: torch.Tensor,
853
+
attention_mask: Optional[torch.Tensor] = None,
854
+
position_ids: Optional[torch.LongTensor] = None,
855
+
past_key_value: Optional[Cache] = None,
856
+
output_attentions: bool = False,
857
+
use_cache: bool = False,
858
+
**kwargs,
859
+
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
860
+
if "padding_mask" in kwargs:
861
+
warnings.warn(
862
+
"Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
863
+
)
864
+
bsz, q_len, _ = hidden_states.size()
865
+
866
+
if self.q_lora_rank is None:
867
+
q = self.q_proj(hidden_states)
868
+
else:
869
+
q = self.q_b_proj(self.q_a_layernorm(self.q_a_proj(hidden_states)))
870
+
q = q.view(bsz, q_len, self.num_heads, self.q_head_dim).transpose(1, 2)
871
+
872
+
873
+
q_nope, q_pe = torch.split(
874
+
q, [self.qk_nope_head_dim, self.qk_rope_head_dim], dim=-1
875
+
)
876
+
877
+
compressed_kv = self.kv_a_proj_with_mqa(hidden_states)
878
+
compressed_kv, k_pe = torch.split(
879
+
compressed_kv, [self.kv_lora_rank, self.qk_rope_head_dim], dim=-1
880
+
)
881
+
compressed_kv = self.kv_a_layernorm(compressed_kv)
882
+
k_pe = k_pe.view(bsz, q_len, 1, self.qk_rope_head_dim).transpose(1, 2)
883
+
884
+
kv_seq_len = k_pe.shape[-2]
885
+
if past_key_value is not None:
886
+
if self.layer_idx is None:
887
+
raise ValueError(
888
+
f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} "
889
+
"for auto-regressive decoding with k/v caching, please make sure to initialize the attention class "
890
+
"with a layer index."
891
+
)
892
+
kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
893
+
894
+
cos, sin = self.rotary_emb(q_pe, seq_len=kv_seq_len)
895
+
q_pe, k_pe = apply_rotary_pos_emb(q_pe, k_pe, cos, sin, position_ids)
896
+
897
+
if past_key_value is not None:
898
+
cache_kwargs = {"sin": sin, "cos": cos} # Specific to RoPE models
899
+
compressed_kv = compressed_kv.unsqueeze(1)
900
+
k_pe, compressed_kv = past_key_value.update(k_pe, compressed_kv, self.layer_idx, cache_kwargs)
901
+
compressed_kv = compressed_kv.squeeze(1)
902
+
903
+
kv_b_proj = self.kv_b_proj.weight.view(self.num_heads, -1, self.kv_lora_rank)
904
+
q_absorb = kv_b_proj[:, :self.qk_nope_head_dim, :]
905
+
out_absorb = kv_b_proj[:, self.qk_nope_head_dim:, :]
906
+
907
+
q_nope = torch.matmul(q_nope, q_absorb)
908
+
attn_weights = (torch.matmul(q_pe, k_pe.mT) +
909
+
torch.matmul(q_nope, compressed_kv.unsqueeze(-3).mT)) * self.softmax_scale
910
+
if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
911
+
raise ValueError(
912
+
f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
913
+
f" {attn_weights.size()}"
914
+
)
915
+
assert attention_mask is not None
916
+
if attention_mask is not None:
917
+
if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
918
+
raise ValueError(
919
+
f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
920
+
)
921
+
attn_weights = attn_weights + attention_mask
922
+
923
+
# upcast attention to fp32
924
+
attn_weights = nn.functional.softmax(
925
+
attn_weights, dim=-1, dtype=torch.float32
926
+
).to(q_pe.dtype)
927
+
attn_weights = nn.functional.dropout(
928
+
attn_weights, p=self.attention_dropout, training=self.training
929
+
)
930
+
attn_output = torch.einsum('bhql,blc->bhqc', attn_weights, compressed_kv)
931
+
932
+
attn_output = torch.matmul(attn_output, out_absorb.mT)
933
+
934
+
if attn_output.size() != (bsz, self.num_heads, q_len, self.v_head_dim):
935
+
raise ValueError(
936
+
f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.v_head_dim)}, but is"
937
+
f" {attn_output.size()}"
938
+
)
939
+
940
+
attn_output = attn_output.transpose(1, 2).contiguous()
941
+
942
+
attn_output = attn_output.reshape(bsz, q_len, self.num_heads * self.v_head_dim)
943
+
944
+
attn_output = self.o_proj(attn_output)
945
+
946
+
if not output_attentions:
947
+
attn_weights = None
948
+
949
+
return attn_output, attn_weights, past_key_value
950
+
951
+
952
+
# Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2 with Llama->DeepseekV2
953
+
class DeepseekV2FlashAttention2(DeepseekV2Attention):
954
+
"""
955
+
DeepseekV2 flash attention module. This module inherits from `DeepseekV2Attention` as the weights of the module stays
956
+
untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
957
+
flash attention and deal with padding tokens in case the input contains any of them.
958
+
"""
959
+
960
+
def __init__(self, *args, **kwargs):
961
+
super().__init__(*args, **kwargs)
962
+
963
+
# TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
964
+
# flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
965
+
# Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left).
966
+
self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()
967
+
968
+
def forward(
969
+
self,
970
+
hidden_states: torch.Tensor,
971
+
attention_mask: Optional[torch.LongTensor] = None,
972
+
position_ids: Optional[torch.LongTensor] = None,
973
+
past_key_value: Optional[Cache] = None,
974
+
output_attentions: bool = False,
975
+
use_cache: bool = False,
976
+
**kwargs,
977
+
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
978
+
# DeepseekV2FlashAttention2 attention does not support output_attentions
979
+
if "padding_mask" in kwargs:
980
+
warnings.warn(
981
+
"Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
982
+
)
983
+
984
+
# overwrite attention_mask with padding_mask
985
+
attention_mask = kwargs.pop("padding_mask")
986
+
987
+
output_attentions = False
988
+
989
+
bsz, q_len, _ = hidden_states.size()
990
+
991
+
if self.q_lora_rank is None:
992
+
q = self.q_proj(hidden_states)
993
+
else:
994
+
q = self.q_b_proj(self.q_a_layernorm(self.q_a_proj(hidden_states)))
995
+
q = q.view(bsz, q_len, self.num_heads, self.q_head_dim).transpose(1, 2)
996
+
q_nope, q_pe = torch.split(
997
+
q, [self.qk_nope_head_dim, self.qk_rope_head_dim], dim=-1
998
+
)
999
+
1000
+
# Flash attention requires the input to have the shape
1001
+
# batch_size x seq_length x head_dim x hidden_dim
1002
+
# therefore we just need to keep the original shape
1003
+
compressed_kv = self.kv_a_proj_with_mqa(hidden_states)
1004
+
compressed_kv, k_pe = torch.split(
1005
+
compressed_kv, [self.kv_lora_rank, self.qk_rope_head_dim], dim=-1
1006
+
)
1007
+
k_pe = k_pe.view(bsz, q_len, 1, self.qk_rope_head_dim).transpose(1, 2)
1008
+
kv = (
1009
+
self.kv_b_proj(self.kv_a_layernorm(compressed_kv))
1010
+
.view(bsz, q_len, self.num_heads, self.qk_nope_head_dim + self.v_head_dim)
1011
+
.transpose(1, 2)
1012
+
)
1013
+
1014
+
k_nope, value_states = torch.split(
1015
+
kv, [self.qk_nope_head_dim, self.v_head_dim], dim=-1
1016
+
)
1017
+
kv_seq_len = value_states.shape[-2]
1018
+
1019
+
kv_seq_len = value_states.shape[-2]
1020
+
if past_key_value is not None:
1021
+
kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
1022
+
1023
+
cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
1024
+
q_pe, k_pe = apply_rotary_pos_emb(q_pe, k_pe, cos, sin, position_ids)
1025
+
1026
+
query_states = k_pe.new_empty(bsz, self.num_heads, q_len, self.q_head_dim)
1027
+
query_states[:, :, :, : self.qk_nope_head_dim] = q_nope
1028
+
query_states[:, :, :, self.qk_nope_head_dim :] = q_pe
1029
+
1030
+
key_states = k_pe.new_empty(bsz, self.num_heads, q_len, self.q_head_dim)
1031
+
key_states[:, :, :, : self.qk_nope_head_dim] = k_nope
1032
+
key_states[:, :, :, self.qk_nope_head_dim :] = k_pe
1033
+
1034
+
if self.q_head_dim != self.v_head_dim:
1035
+
value_states = F.pad(value_states, [0, self.q_head_dim - self.v_head_dim])
1036
+
1037
+
# TODO: support compressed_kv for kv_cache (instead of key_states, value_states) in flash_attention version
1038
+
if past_key_value is not None:
1039
+
cache_kwargs = {"sin": sin, "cos": cos} # Specific to RoPE models
1040
+
key_states, value_states = past_key_value.update(
1041
+
key_states, value_states, self.layer_idx, cache_kwargs
1042
+
)
1043
+
1044
+
# TODO: These transpose are quite inefficient but Flash Attention requires the layout [batch_size, sequence_length, num_heads, head_dim]. We would need to refactor the KV cache
1045
+
# to be able to avoid many of these transpose/reshape/view.
1046
+
query_states = query_states.transpose(1, 2)
1047
+
key_states = key_states.transpose(1, 2)
1048
+
value_states = value_states.transpose(1, 2)
1049
+
1050
+
dropout_rate = self.attention_dropout if self.training else 0.0
1051
+
1052
+
# In PEFT, usually we cast the layer norms in float32 for training stability reasons
1053
+
# therefore the input hidden states gets silently casted in float32. Hence, we need
1054
+
# cast them back in the correct dtype just to be sure everything works as expected.
1055
+
# This might slowdown training & inference so it is recommended to not cast the LayerNorms
1056
+
# in fp32. (DeepseekV2RMSNorm handles it correctly)
1057
+
1058
+
input_dtype = query_states.dtype
1059
+
if input_dtype == torch.float32:
1060
+
# Handle the case where the model is quantized
1061
+
if hasattr(self.config, "_pre_quantization_dtype"):
1062
+
target_dtype = self.config._pre_quantization_dtype
1063
+
elif torch.is_autocast_enabled():
1064
+
target_dtype = torch.get_autocast_gpu_dtype()
1065
+
else:
1066
+
target_dtype = (
1067
+
self.q_proj.weight.dtype
1068
+
if self.q_lora_rank is None
1069
+
else self.q_a_proj.weight.dtype
1070
+
)
1071
+
1072
+
logger.warning_once(
1073
+
f"The input hidden states seems to be silently casted in float32, this might be related to"
1074
+
f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
1075
+
f" {target_dtype}."
1076
+
)
1077
+
1078
+
query_states = query_states.to(target_dtype)
1079
+
key_states = key_states.to(target_dtype)
1080
+
value_states = value_states.to(target_dtype)
1081
+
1082
+
attn_output = self._flash_attention_forward(
1083
+
query_states,
1084
+
key_states,
1085
+
value_states,
1086
+
attention_mask,
1087
+
q_len,
1088
+
dropout=dropout_rate,
1089
+
softmax_scale=self.softmax_scale,
1090
+
)
1091
+
if self.q_head_dim != self.v_head_dim:
1092
+
attn_output = attn_output[:, :, :, : self.v_head_dim]
1093
+
1094
+
attn_output = attn_output.reshape(
1095
+
bsz, q_len, self.num_heads * self.v_head_dim
1096
+
).contiguous()
1097
+
attn_output = self.o_proj(attn_output)
1098
+
1099
+
if not output_attentions:
1100
+
attn_weights = None
1101
+
1102
+
return attn_output, attn_weights, past_key_value
1103
+
1104
+
def _flash_attention_forward(
1105
+
self,
1106
+
query_states,
1107
+
key_states,
1108
+
value_states,
1109
+
attention_mask,
1110
+
query_length,
1111
+
dropout=0.0,
1112
+
softmax_scale=None,
1113
+
):
1114
+
"""
1115
+
Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token
1116
+
first unpad the input, then computes the attention scores and pad the final attention scores.
1117
+
1118
+
Args:
1119
+
query_states (`torch.Tensor`):
1120
+
Input query states to be passed to Flash Attention API
1121
+
key_states (`torch.Tensor`):
1122
+
Input key states to be passed to Flash Attention API
1123
+
value_states (`torch.Tensor`):
1124
+
Input value states to be passed to Flash Attention API
1125
+
attention_mask (`torch.Tensor`):
1126
+
The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the
1127
+
position of padding tokens and 1 for the position of non-padding tokens.
1128
+
dropout (`int`, *optional*):
1129
+
Attention dropout
1130
+
softmax_scale (`float`, *optional*):
1131
+
The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)
1132
+
"""
1133
+
if not self._flash_attn_uses_top_left_mask:
1134
+
causal = self.is_causal
1135
+
else:
1136
+
# TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1. For details, please see the comment in DeepseekV2FlashAttention2 __init__.
1137
+
causal = self.is_causal and query_length != 1
1138
+
1139
+
# Contains at least one padding token in the sequence
1140
+
if attention_mask is not None:
1141
+
batch_size = query_states.shape[0]
1142
+
(
1143
+
query_states,
1144
+
key_states,
1145
+
value_states,
1146
+
indices_q,
1147
+
cu_seq_lens,
1148
+
max_seq_lens,
1149
+
) = self._upad_input(
1150
+
query_states, key_states, value_states, attention_mask, query_length
1151
+
)
1152
+
1153
+
cu_seqlens_q, cu_seqlens_k = cu_seq_lens
1154
+
max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens
1155
+
1156
+
attn_output_unpad = flash_attn_varlen_func(
1157
+
query_states,
1158
+
key_states,
1159
+
value_states,
1160
+
cu_seqlens_q=cu_seqlens_q,
1161
+
cu_seqlens_k=cu_seqlens_k,
1162
+
max_seqlen_q=max_seqlen_in_batch_q,
1163
+
max_seqlen_k=max_seqlen_in_batch_k,
1164
+
dropout_p=dropout,
1165
+
softmax_scale=softmax_scale,
1166
+
causal=causal,
1167
+
)
1168
+
1169
+
attn_output = pad_input(
1170
+
attn_output_unpad, indices_q, batch_size, query_length
1171
+
)
1172
+
else:
1173
+
attn_output = flash_attn_func(
1174
+
query_states,
1175
+
key_states,
1176
+
value_states,
1177
+
dropout,
1178
+
softmax_scale=softmax_scale,
1179
+
causal=causal,
1180
+
)
1181
+
1182
+
return attn_output
1183
+
1184
+
def _upad_input(
1185
+
self, query_layer, key_layer, value_layer, attention_mask, query_length
1186
+
):
1187
+
indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask)
1188
+
batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape
1189
+
1190
+
key_layer = index_first_axis(
1191
+
key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim),
1192
+
indices_k,
1193
+
)
1194
+
value_layer = index_first_axis(
1195
+
value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim),
1196
+
indices_k,
1197
+
)
1198
+
if query_length == kv_seq_len:
1199
+
query_layer = index_first_axis(
1200
+
query_layer.reshape(batch_size * kv_seq_len, self.num_heads, head_dim),
1201
+
indices_k,
1202
+
)
1203
+
cu_seqlens_q = cu_seqlens_k
1204
+
max_seqlen_in_batch_q = max_seqlen_in_batch_k
1205
+
indices_q = indices_k
1206
+
elif query_length == 1:
1207
+
max_seqlen_in_batch_q = 1
1208
+
cu_seqlens_q = torch.arange(
1209
+
batch_size + 1, dtype=torch.int32, device=query_layer.device
1210
+
) # There is a memcpy here, that is very bad.
1211
+
indices_q = cu_seqlens_q[:-1]
1212
+
query_layer = query_layer.squeeze(1)
1213
+
else:
1214
+
# The -q_len: slice assumes left padding.
1215
+
attention_mask = attention_mask[:, -query_length:]
1216
+
query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(
1217
+
query_layer, attention_mask
1218
+
)
1219
+
1220
+
return (
1221
+
query_layer,
1222
+
key_layer,
1223
+
value_layer,
1224
+
indices_q,
1225
+
(cu_seqlens_q, cu_seqlens_k),
1226
+
(max_seqlen_in_batch_q, max_seqlen_in_batch_k),
1227
+
)
1228
+
1229
+
1230
+
ATTENTION_CLASSES = {
1231
+
"eager": DeepseekV2Attention,
1232
+
"flash_attention_2": DeepseekV2FlashAttention2,
1233
+
1234
+
"mla_eager": DeepseekV2Attention,
1235
+
"mla_flash_attention_2": DeepseekV2FlashAttention2,
1236
+
1237
+
"mha_eager": LlamaAttention,
1238
+
"mha_flash_attention_2": LlamaFlashAttention2
1239
+
}
1240
+
1241
+
1242
+
class DeepseekV2DecoderLayer(nn.Module):
1243
+
def __init__(self, config: DeepseekV2Config, layer_idx: int):
1244
+
super().__init__()
1245
+
self.hidden_size = config.hidden_size
1246
+
1247
+
1248
+
if config.use_mla:
1249
+
attn_implementation = "mla_" + config._attn_implementation
1250
+
else:
1251
+
attn_implementation = "mha_" + config._attn_implementation
1252
+
1253
+
self.self_attn = ATTENTION_CLASSES[attn_implementation](
1254
+
config=config, layer_idx=layer_idx
1255
+
)
1256
+
1257
+
self.mlp = (
1258
+
DeepseekV2MoE(config)
1259
+
if (
1260
+
config.n_routed_experts is not None
1261
+
and layer_idx >= config.first_k_dense_replace
1262
+
and layer_idx % config.moe_layer_freq == 0
1263
+
)
1264
+
else DeepseekV2MLP(config)
1265
+
)
1266
+
self.input_layernorm = DeepseekV2RMSNorm(
1267
+
config.hidden_size, eps=config.rms_norm_eps
1268
+
)
1269
+
self.post_attention_layernorm = DeepseekV2RMSNorm(
1270
+
config.hidden_size, eps=config.rms_norm_eps
1271
+
)
1272
+
1273
+
def forward(
1274
+
self,
1275
+
hidden_states: torch.Tensor,
1276
+
attention_mask: Optional[torch.Tensor] = None,
1277
+
position_ids: Optional[torch.LongTensor] = None,
1278
+
past_key_value: Optional[Tuple[torch.Tensor]] = None,
1279
+
output_attentions: Optional[bool] = False,
1280
+
use_cache: Optional[bool] = False,
1281
+
**kwargs,
1282
+
) -> Tuple[
1283
+
torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]
1284
+
]:
1285
+
"""
1286
+
Args:
1287
+
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
1288
+
attention_mask (`torch.FloatTensor`, *optional*):
1289
+
attention mask of size `(batch_size, sequence_length)` if flash attention is used or `(batch_size, 1,
1290
+
query_sequence_length, key_sequence_length)` if default attention is used.
1291
+
output_attentions (`bool`, *optional*):
1292
+
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
1293
+
returned tensors for more detail.
1294
+
use_cache (`bool`, *optional*):
1295
+
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
1296
+
(see `past_key_values`).
1297
+
past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
1298
+
"""
1299
+
if "padding_mask" in kwargs:
1300
+
warnings.warn(
1301
+
"Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
1302
+
)
1303
+
residual = hidden_states
1304
+
1305
+
hidden_states = self.input_layernorm(hidden_states)
1306
+
1307
+
# Self Attention
1308
+
hidden_states, self_attn_weights, present_key_value = self.self_attn(
1309
+
hidden_states=hidden_states,
1310
+
attention_mask=attention_mask,
1311
+
position_ids=position_ids,
1312
+
past_key_value=past_key_value,
1313
+
output_attentions=output_attentions,
1314
+
use_cache=use_cache,
1315
+
**kwargs,
1316
+
)
1317
+
hidden_states = residual + hidden_states
1318
+
1319
+
# Fully Connected
1320
+
residual = hidden_states
1321
+
hidden_states = self.post_attention_layernorm(hidden_states)
1322
+
hidden_states = self.mlp(hidden_states)
1323
+
hidden_states = residual + hidden_states
1324
+
1325
+
outputs = (hidden_states,)
1326
+
1327
+
if output_attentions:
1328
+
outputs += (self_attn_weights,)
1329
+
1330
+
if use_cache:
1331
+
outputs += (present_key_value,)
1332
+
1333
+
return outputs
1334
+
1335
+
1336
+
DeepseekV2_START_DOCSTRING = r"""
1337
+
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
1338
+
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
1339
+
etc.)
1340
+
1341
+
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
1342
+
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
1343
+
and behavior.
1344
+
1345
+
Parameters:
1346
+
config ([`DeepseekV2Config`]):
1347
+
Model configuration class with all the parameters of the model. Initializing with a config file does not
1348
+
load the weights associated with the model, only the configuration. Check out the
1349
+
[`~PreTrainedModel.from_pretrained`] method to load the model weights.
1350
+
"""
1351
+
1352
+
1353
+
@add_start_docstrings(
1354
+
"The bare DeepseekV2 Model outputting raw hidden-states without any specific head on top.",
1355
+
DeepseekV2_START_DOCSTRING,
1356
+
)
1357
+
class DeepseekV2PreTrainedModel(PreTrainedModel):
1358
+
config_class = DeepseekV2Config
1359
+
base_model_prefix = "model"
1360
+
supports_gradient_checkpointing = True
1361
+
_no_split_modules = ["DeepseekV2DecoderLayer"]
1362
+
_skip_keys_device_placement = "past_key_values"
1363
+
_supports_flash_attn_2 = True
1364
+
_supports_cache_class = True
1365
+
1366
+
def _init_weights(self, module):
1367
+
std = self.config.initializer_range
1368
+
if isinstance(module, nn.Linear):
1369
+
module.weight.data.normal_(mean=0.0, std=std)
1370
+
if module.bias is not None:
1371
+
module.bias.data.zero_()
1372
+
elif isinstance(module, nn.Embedding):
1373
+
module.weight.data.normal_(mean=0.0, std=std)
1374
+
if module.padding_idx is not None:
1375
+
module.weight.data[module.padding_idx].zero_()
1376
+
1377
+
1378
+
DeepseekV2_INPUTS_DOCSTRING = r"""
1379
+
Args:
1380
+
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
1381
+
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
1382
+
it.
1383
+
1384
+
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
1385
+
[`PreTrainedTokenizer.__call__`] for details.
1386
+
1387
+
[What are input IDs?](../glossary#input-ids)
1388
+
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
1389
+
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
1390
+
1391
+
- 1 for tokens that are **not masked**,
1392
+
- 0 for tokens that are **masked**.
1393
+
1394
+
[What are attention masks?](../glossary#attention-mask)
1395
+
1396
+
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
1397
+
[`PreTrainedTokenizer.__call__`] for details.
1398
+
1399
+
If `past_key_values` is used, optionally only the last `input_ids` have to be input (see
1400
+
`past_key_values`).
1401
+
1402
+
If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
1403
+
and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
1404
+
information on the default strategy.
1405
+
1406
+
- 1 indicates the head is **not masked**,
1407
+
- 0 indicates the head is **masked**.
1408
+
position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
1409
+
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
1410
+
config.n_positions - 1]`.
1411
+
1412
+
[What are position IDs?](../glossary#position-ids)
1413
+
past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*):
1414
+
Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
1415
+
blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`
1416
+
returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.
1417
+
1418
+
Two formats are allowed:
1419
+
- a [`~cache_utils.Cache`] instance;
1420
+
- Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
1421
+
shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy
1422
+
cache format.
1423
+
1424
+
The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the
1425
+
legacy cache format will be returned.
1426
+
1427
+
If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
1428
+
have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids`
1429
+
of shape `(batch_size, sequence_length)`.
1430
+
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
1431
+
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
1432
+
is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
1433
+
model's internal embedding lookup matrix.
1434
+
use_cache (`bool`, *optional*):
1435
+
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
1436
+
`past_key_values`).
1437
+
output_attentions (`bool`, *optional*):
1438
+
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
1439
+
tensors for more detail.
1440
+
output_hidden_states (`bool`, *optional*):
1441
+
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
1442
+
more detail.
1443
+
return_dict (`bool`, *optional*):
1444
+
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
1445
+
"""
1446
+
1447
+
1448
+
@add_start_docstrings(
1449
+
"The bare DeepseekV2 Model outputting raw hidden-states without any specific head on top.",
1450
+
DeepseekV2_START_DOCSTRING,
1451
+
)
1452
+
class DeepseekV2Model(DeepseekV2PreTrainedModel):
1453
+
"""
1454
+
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`DeepseekV2DecoderLayer`]
1455
+
1456
+
Args:
1457
+
config: DeepseekV2Config
1458
+
"""
1459
+
1460
+
def __init__(self, config: DeepseekV2Config):
1461
+
super().__init__(config)
1462
+
self.padding_idx = config.pad_token_id
1463
+
self.vocab_size = config.vocab_size
1464
+
1465
+
self.embed_tokens = nn.Embedding(
1466
+
config.vocab_size, config.hidden_size, self.padding_idx
1467
+
)
1468
+
self.layers = nn.ModuleList(
1469
+
[
1470
+
DeepseekV2DecoderLayer(config, layer_idx)
1471
+
for layer_idx in range(config.num_hidden_layers)
1472
+
]
1473
+
)
1474
+
# print(config._attn_implementation)
1475
+
self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2"
1476
+
self.norm = DeepseekV2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
1477
+
1478
+
self.gradient_checkpointing = False
1479
+
# Initialize weights and apply final processing
1480
+
self.post_init()
1481
+
1482
+
def get_input_embeddings(self):
1483
+
return self.embed_tokens
1484
+
1485
+
def set_input_embeddings(self, value):
1486
+
self.embed_tokens = value
1487
+
1488
+
@add_start_docstrings_to_model_forward(DeepseekV2_INPUTS_DOCSTRING)
1489
+
def forward(
1490
+
self,
1491
+
input_ids: torch.LongTensor = None,
1492
+
attention_mask: Optional[torch.Tensor] = None,
1493
+
position_ids: Optional[torch.LongTensor] = None,
1494
+
past_key_values: Optional[List[torch.FloatTensor]] = None,
1495
+
inputs_embeds: Optional[torch.FloatTensor] = None,
1496
+
use_cache: Optional[bool] = None,
1497
+
output_attentions: Optional[bool] = None,
1498
+
output_hidden_states: Optional[bool] = None,
1499
+
return_dict: Optional[bool] = None,
1500
+
cache_position: Optional[torch.LongTensor] = None
1501
+
) -> Union[Tuple, BaseModelOutputWithPast]:
1502
+
output_attentions = (
1503
+
output_attentions
1504
+
if output_attentions is not None
1505
+
else self.config.output_attentions
1506
+
)
1507
+
output_hidden_states = (
1508
+
output_hidden_states
1509
+
if output_hidden_states is not None
1510
+
else self.config.output_hidden_states
1511
+
)
1512
+
use_cache = use_cache if use_cache is not None else self.config.use_cache
1513
+
1514
+
return_dict = (
1515
+
return_dict if return_dict is not None else self.config.use_return_dict
1516
+
)
1517
+
1518
+
# retrieve input_ids and inputs_embeds
1519
+
if input_ids is not None and inputs_embeds is not None:
1520
+
raise ValueError(
1521
+
"You cannot specify both input_ids and inputs_embeds at the same time"
1522
+
)
1523
+
elif input_ids is not None:
1524
+
batch_size, seq_length = input_ids.shape[:2]
1525
+
elif inputs_embeds is not None:
1526
+
batch_size, seq_length = inputs_embeds.shape[:2]
1527
+
else:
1528
+
raise ValueError("You have to specify either input_ids or inputs_embeds")
1529
+
1530
+
if self.gradient_checkpointing and self.training:
1531
+
if use_cache:
1532
+
logger.warning_once(
1533
+
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`transformers."
1534
+
)
1535
+
use_cache = False
1536
+
1537
+
past_key_values_length = 0
1538
+
if use_cache:
1539
+
use_legacy_cache = not isinstance(past_key_values, Cache)
1540
+
if use_legacy_cache:
1541
+
past_key_values = DynamicCache.from_legacy_cache(past_key_values)
1542
+
past_key_values_length = past_key_values.get_usable_length(seq_length)
1543
+
1544
+
if position_ids is None:
1545
+
device = input_ids.device if input_ids is not None else inputs_embeds.device
1546
+
position_ids = torch.arange(
1547
+
past_key_values_length,
1548
+
seq_length + past_key_values_length,
1549
+
dtype=torch.long,
1550
+
device=device,
1551
+
)
1552
+
position_ids = position_ids.unsqueeze(0)
1553
+
1554
+
if inputs_embeds is None:
1555
+
inputs_embeds = self.embed_tokens(input_ids)
1556
+
1557
+
if self._use_flash_attention_2:
1558
+
# 2d mask is passed through the layers
1559
+
attention_mask = (
1560
+
attention_mask
1561
+
if (attention_mask is not None and 0 in attention_mask)
1562
+
else None
1563
+
)
1564
+
else:
1565
+
# 4d mask is passed through the layers
1566
+
attention_mask = _prepare_4d_causal_attention_mask(
1567
+
attention_mask,
1568
+
(batch_size, seq_length),
1569
+
inputs_embeds,
1570
+
past_key_values_length,
1571
+
)
1572
+
1573
+
# embed positions
1574
+
hidden_states = inputs_embeds
1575
+
1576
+
# decoder layers
1577
+
all_hidden_states = () if output_hidden_states else None
1578
+
all_self_attns = () if output_attentions else None
1579
+
next_decoder_cache = None
1580
+
1581
+
for decoder_layer in self.layers:
1582
+
if output_hidden_states:
1583
+
all_hidden_states += (hidden_states,)
1584
+
1585
+
if self.gradient_checkpointing and self.training:
1586
+
layer_outputs = self._gradient_checkpointing_func(
1587
+
decoder_layer.__call__,
1588
+
hidden_states,
1589
+
attention_mask,
1590
+
position_ids,
1591
+
past_key_values,
1592
+
output_attentions,
1593
+
use_cache,
1594
+
)
1595
+
else:
1596
+
layer_outputs = decoder_layer(
1597
+
hidden_states,
1598
+
attention_mask=attention_mask,
1599
+
position_ids=position_ids,
1600
+
past_key_value=past_key_values,
1601
+
output_attentions=output_attentions,
1602
+
use_cache=use_cache,
1603
+
)
1604
+
1605
+
hidden_states = layer_outputs[0]
1606
+
1607
+
if use_cache:
1608
+
next_decoder_cache = layer_outputs[2 if output_attentions else 1]
1609
+
1610
+
if output_attentions:
1611
+
all_self_attns += (layer_outputs[1],)
1612
+
1613
+
hidden_states = self.norm(hidden_states)
1614
+
1615
+
# add hidden states from the last decoder layer
1616
+
if output_hidden_states:
1617
+
all_hidden_states += (hidden_states,)
1618
+
1619
+
next_cache = None
1620
+
if use_cache:
1621
+
next_cache = (
1622
+
next_decoder_cache.to_legacy_cache()
1623
+
if use_legacy_cache
1624
+
else next_decoder_cache
1625
+
)
1626
+
if not return_dict:
1627
+
return tuple(
1628
+
v
1629
+
for v in [hidden_states, next_cache, all_hidden_states, all_self_attns]
1630
+
if v is not None
1631
+
)
1632
+
return BaseModelOutputWithPast(
1633
+
last_hidden_state=hidden_states,
1634
+
past_key_values=next_cache,
1635
+
hidden_states=all_hidden_states,
1636
+
attentions=all_self_attns,
1637
+
)
1638
+
1639
+
1640
+
class DeepseekV2ForCausalLM(DeepseekV2PreTrainedModel):
1641
+
_tied_weights_keys = ["lm_head.weight"]
1642
+
1643
+
def __init__(self, config):
1644
+
super().__init__(config)
1645
+
self.model = DeepseekV2Model(config)
1646
+
self.vocab_size = config.vocab_size
1647
+
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
1648
+
1649
+
# Initialize weights and apply final processing
1650
+
self.post_init()
1651
+
1652
+
def get_input_embeddings(self):
1653
+
return self.model.embed_tokens
1654
+
1655
+
def set_input_embeddings(self, value):
1656
+
self.model.embed_tokens = value
1657
+
1658
+
def get_output_embeddings(self):
1659
+
return self.lm_head
1660
+
1661
+
def set_output_embeddings(self, new_embeddings):
1662
+
self.lm_head = new_embeddings
1663
+
1664
+
def set_decoder(self, decoder):
1665
+
self.model = decoder
1666
+
1667
+
def get_decoder(self):
1668
+
return self.model
1669
+
1670
+
@add_start_docstrings_to_model_forward(DeepseekV2_INPUTS_DOCSTRING)
1671
+
@replace_return_docstrings(
1672
+
output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC
1673
+
)
1674
+
def forward(
1675
+
self,
1676
+
input_ids: torch.LongTensor = None,
1677
+
attention_mask: Optional[torch.Tensor] = None,
1678
+
position_ids: Optional[torch.LongTensor] = None,
1679
+
past_key_values: Optional[List[torch.FloatTensor]] = None,
1680
+
inputs_embeds: Optional[torch.FloatTensor] = None,
1681
+
labels: Optional[torch.LongTensor] = None,
1682
+
use_cache: Optional[bool] = None,
1683
+
output_attentions: Optional[bool] = None,
1684
+
output_hidden_states: Optional[bool] = None,
1685
+
return_dict: Optional[bool] = None,
1686
+
cache_position: Optional[torch.LongTensor] = None
1687
+
) -> Union[Tuple, CausalLMOutputWithPast]:
1688
+
r"""
1689
+
Args:
1690
+
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
1691
+
Labels for computing the masked language modeling loss. Indices should either be in `[0, transformers.,
1692
+
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
1693
+
(masked), the loss is only computed for the tokens with labels in `[0, transformers., config.vocab_size]`.
1694
+
1695
+
Returns:
1696
+
1697
+
Example:
1698
+
1699
+
```python
1700
+
>>> from transformers import AutoTokenizer, DeepseekV2ForCausalLM
1701
+
1702
+
>>> model = DeepseekV2ForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
1703
+
>>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
1704
+
1705
+
>>> prompt = "Hey, are you conscious? Can you talk to me?"
1706
+
>>> inputs = tokenizer(prompt, return_tensors="pt")
1707
+
1708
+
>>> # Generate
1709
+
>>> generate_ids = model.generate(inputs.input_ids, max_length=30)
1710
+
>>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
1711
+
"Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
1712
+
```"""
1713
+
output_attentions = (
1714
+
output_attentions
1715
+
if output_attentions is not None
1716
+
else self.config.output_attentions
1717
+
)
1718
+
output_hidden_states = (
1719
+
output_hidden_states
1720
+
if output_hidden_states is not None
1721
+
else self.config.output_hidden_states
1722
+
)
1723
+
return_dict = (
1724
+
return_dict if return_dict is not None else self.config.use_return_dict
1725
+
)
1726
+
1727
+
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
1728
+
outputs = self.model(
1729
+
input_ids=input_ids,
1730
+
attention_mask=attention_mask,
1731
+
position_ids=position_ids,
1732
+
past_key_values=past_key_values,
1733
+
inputs_embeds=inputs_embeds,
1734
+
use_cache=use_cache,
1735
+
output_attentions=output_attentions,
1736
+
output_hidden_states=output_hidden_states,
1737
+
return_dict=return_dict,
1738
+
cache_position=cache_position
1739
+
)
1740
+
1741
+
hidden_states = outputs[0]
1742
+
logits = self.lm_head(hidden_states)
1743
+
logits = logits.float()
1744
+
1745
+
loss = None
1746
+
if labels is not None:
1747
+
# Shift so that tokens < n predict n
1748
+
shift_logits = logits[..., :-1, :].contiguous()
1749
+
shift_labels = labels[..., 1:].contiguous()
1750
+
# Flatten the tokens
1751
+
loss_fct = CrossEntropyLoss()
1752
+
shift_logits = shift_logits.view(-1, self.config.vocab_size)
1753
+
shift_labels = shift_labels.view(-1)
1754
+
# Enable model parallelism
1755
+
shift_labels = shift_labels.to(shift_logits.device)
1756
+
loss = loss_fct(shift_logits, shift_labels)
1757
+
1758
+
if not return_dict:
1759
+
output = (logits,) + outputs[1:]
1760
+
return (loss,) + output if loss is not None else output
1761
+
1762
+
return CausalLMOutputWithPast(
1763
+
loss=loss,
1764
+
logits=logits,
1765
+
past_key_values=outputs.past_key_values,
1766
+
hidden_states=outputs.hidden_states,
1767
+
attentions=outputs.attentions,
1768
+
)
1769
+
1770
+
def prepare_inputs_for_generation(
1771
+
self,
1772
+
input_ids,
1773
+
past_key_values=None,
1774
+
attention_mask=None,
1775
+
inputs_embeds=None,
1776
+
**kwargs,
1777
+
):
1778
+
past_length = 0
1779
+
if past_key_values is not None:
1780
+
if isinstance(past_key_values, Cache):
1781
+
cache_length = past_key_values.get_seq_length()
1782
+
past_length = past_key_values.seen_tokens
1783
+
max_cache_length = past_key_values.get_max_length()
1784
+
else:
1785
+
cache_length = past_length = past_key_values[0][0].shape[2]
1786
+
max_cache_length = None
1787
+
1788
+
# Keep only the unprocessed tokens:
1789
+
# 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where
1790
+
# some of the inputs are exclusively passed as part of the cache (e.g. when passing input_embeds as
1791
+
# input)
1792
+
if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]:
1793
+
input_ids = input_ids[:, -(attention_mask.shape[1] - past_length):]
1794
+
# 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard
1795
+
# input_ids based on the past_length.
1796
+
elif past_length < input_ids.shape[1]:
1797
+
input_ids = input_ids[:, past_length:]
1798
+
# 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens.
1799
+
1800
+
# If we are about to go beyond the maximum cache length, we need to crop the input attention mask.
1801
+
if (
1802
+
max_cache_length is not None
1803
+
and attention_mask is not None
1804
+
and cache_length + input_ids.shape[1] > max_cache_length
1805
+
):
1806
+
attention_mask = attention_mask[:, -max_cache_length:]
1807
+
1808
+
position_ids = kwargs.get("position_ids", None)
1809
+
if attention_mask is not None and position_ids is None:
1810
+
# create position_ids on the fly for batch generation
1811
+
position_ids = attention_mask.long().cumsum(-1) - 1
1812
+
position_ids.masked_fill_(attention_mask == 0, 1)
1813
+
if past_key_values:
1814
+
position_ids = position_ids[:, -input_ids.shape[1]:]
1815
+
1816
+
if self.generation_config.cache_implementation == "static":
1817
+
# generation with static cache
1818
+
cache_position = kwargs.get("cache_position", None)
1819
+
if cache_position is None:
1820
+
past_length = 0
1821
+
else:
1822
+
past_length = cache_position[-1] + 1
1823
+
input_ids = input_ids[:, past_length:]
1824
+
position_ids = position_ids[:, past_length:]
1825
+
1826
+
# TODO @gante we should only keep a `cache_position` in generate, and do +=1.
1827
+
# same goes for position ids. Could also help with continued generation.
1828
+
cache_position = torch.arange(past_length, past_length + position_ids.shape[-1], device=position_ids.device)
1829
+
1830
+
# if `inputs_embeds` are passed, we only want to use them in the 1st generation step
1831
+
if inputs_embeds is not None and past_key_values is None:
1832
+
model_inputs = {"inputs_embeds": inputs_embeds}
1833
+
else:
1834
+
# The `contiguous()` here is necessary to have a static stride during decoding. torchdynamo otherwise
1835
+
# recompiles graphs as the stride of the inputs is a guard. Ref: https://github.com/huggingface/transformers/pull/29114
1836
+
# TODO: use `next_tokens` directly instead.
1837
+
model_inputs = {"input_ids": input_ids.contiguous()}
1838
+
1839
+
model_inputs.update(
1840
+
{
1841
+
"position_ids": position_ids.contiguous(),
1842
+
"cache_position": cache_position,
1843
+
"past_key_values": past_key_values,
1844
+
"use_cache": kwargs.get("use_cache"),
1845
+
"attention_mask": attention_mask,
1846
+
}
1847
+
)
1848
+
return model_inputs
1849
+
1850
+
@staticmethod
1851
+
def _reorder_cache(past_key_values, beam_idx):
1852
+
reordered_past = ()
1853
+
for layer_past in past_key_values:
1854
+
reordered_past += (
1855
+
tuple(
1856
+
past_state.index_select(0, beam_idx.to(past_state.device))
1857
+
for past_state in layer_past
1858
+
),
1859
+
)
1860
+
return reordered_past
1861
+
1862
+
1863
+
@add_start_docstrings(
1864
+
"""
1865
+
The DeepseekV2 Model transformer with a sequence classification head on top (linear layer).
1866
+
1867
+
[`DeepseekV2ForSequenceClassification`] uses the last token in order to do the classification, as other causal models
1868
+
(e.g. GPT-2) do.
1869
+
1870
+
Since it does classification on the last token, it requires to know the position of the last token. If a
1871
+
`pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
1872
+
no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
1873
+
padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
1874
+
each row of the batch).
1875
+
""",
1876
+
DeepseekV2_START_DOCSTRING,
1877
+
)
1878
+
class DeepseekV2ForSequenceClassification(DeepseekV2PreTrainedModel):
1879
+
def __init__(self, config):
1880
+
super().__init__(config)
1881
+
self.num_labels = config.num_labels
1882
+
self.model = DeepseekV2Model(config)
1883
+
self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
1884
+
1885
+
# Initialize weights and apply final processing
1886
+
self.post_init()
1887
+
1888
+
def get_input_embeddings(self):
1889
+
return self.model.embed_tokens
1890
+
1891
+
def set_input_embeddings(self, value):
1892
+
self.model.embed_tokens = value
1893
+
1894
+
@add_start_docstrings_to_model_forward(DeepseekV2_INPUTS_DOCSTRING)
1895
+
def forward(
1896
+
self,
1897
+
input_ids: torch.LongTensor = None,
1898
+
attention_mask: Optional[torch.Tensor] = None,
1899
+
position_ids: Optional[torch.LongTensor] = None,
1900
+
past_key_values: Optional[List[torch.FloatTensor]] = None,
1901
+
inputs_embeds: Optional[torch.FloatTensor] = None,
1902
+
labels: Optional[torch.LongTensor] = None,
1903
+
use_cache: Optional[bool] = None,
1904
+
output_attentions: Optional[bool] = None,
1905
+
output_hidden_states: Optional[bool] = None,
1906
+
return_dict: Optional[bool] = None,
1907
+
) -> Union[Tuple, SequenceClassifierOutputWithPast]:
1908
+
r"""
1909
+
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
1910
+
Labels for computing the sequence classification/regression loss. Indices should be in `[0, transformers.,
1911
+
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
1912
+
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
1913
+
"""
1914
+
return_dict = (
1915
+
return_dict if return_dict is not None else self.config.use_return_dict
1916
+
)
1917
+
1918
+
transformer_outputs = self.model(
1919
+
input_ids,
1920
+
attention_mask=attention_mask,
1921
+
position_ids=position_ids,
1922
+
past_key_values=past_key_values,
1923
+
inputs_embeds=inputs_embeds,
1924
+
use_cache=use_cache,
1925
+
output_attentions=output_attentions,
1926
+
output_hidden_states=output_hidden_states,
1927
+
return_dict=return_dict,
1928
+
)
1929
+
hidden_states = transformer_outputs[0]
1930
+
logits = self.score(hidden_states)
1931
+
1932
+
if input_ids is not None:
1933
+
batch_size = input_ids.shape[0]
1934
+
else:
1935
+
batch_size = inputs_embeds.shape[0]
1936
+
1937
+
if self.config.pad_token_id is None and batch_size != 1:
1938
+
raise ValueError(
1939
+
"Cannot handle batch sizes > 1 if no padding token is defined."
1940
+
)
1941
+
if self.config.pad_token_id is None:
1942
+
sequence_lengths = -1
1943
+
else:
1944
+
if input_ids is not None:
1945
+
sequence_lengths = (
1946
+
torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1
1947
+
).to(logits.device)
1948
+
else:
1949
+
sequence_lengths = -1
1950
+
1951
+
pooled_logits = logits[
1952
+
torch.arange(batch_size, device=logits.device), sequence_lengths
1953
+
]
1954
+
1955
+
loss = None
1956
+
if labels is not None:
1957
+
labels = labels.to(logits.device)
1958
+
if self.config.problem_type is None:
1959
+
if self.num_labels == 1:
1960
+
self.config.problem_type = "regression"
1961
+
elif self.num_labels > 1 and (
1962
+
labels.dtype == torch.long or labels.dtype == torch.int
1963
+
):
1964
+
self.config.problem_type = "single_label_classification"
1965
+
else:
1966
+
self.config.problem_type = "multi_label_classification"
1967
+
1968
+
if self.config.problem_type == "regression":
1969
+
loss_fct = MSELoss()
1970
+
if self.num_labels == 1:
1971
+
loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
1972
+
else:
1973
+
loss = loss_fct(pooled_logits, labels)
1974
+
elif self.config.problem_type == "single_label_classification":
1975
+
loss_fct = CrossEntropyLoss()
1976
+
loss = loss_fct(
1977
+
pooled_logits.view(-1, self.num_labels), labels.view(-1)
1978
+
)
1979
+
elif self.config.problem_type == "multi_label_classification":
1980
+
loss_fct = BCEWithLogitsLoss()
1981
+
loss = loss_fct(pooled_logits, labels)
1982
+
if not return_dict:
1983
+
output = (pooled_logits,) + transformer_outputs[1:]
1984
+
return ((loss,) + output) if loss is not None else output
1985
+
1986
+
return SequenceClassifierOutputWithPast(
1987
+
loss=loss,
1988
+
logits=pooled_logits,
1989
+
past_key_values=transformer_outputs.past_key_values,
1990
+
hidden_states=transformer_outputs.hidden_states,
1991
+
attentions=transformer_outputs.attentions,
1992
+
)