rhyTdZddlmZmZddlZddlmZddlmZddl m Z ddl m Z dd l mZdd lmZd d lmZd d lmZmZmZmZmZmZmZddlmZej:eZGddeZ GddeZ!GddeZ"GddeZ#GddeZ$GddeZ%gdZ&y)zPyTorch BitNet model.)CallableOptionalN)Cache)FlashAttentionKwargs)CausalLMOutputWithPast)ALL_ATTENTION_FUNCTIONS)Unpack)logging)GemmaMLP)LlamaAttentionLlamaDecoderLayerLlamaForCausalLM LlamaModel LlamaRMSNormapply_rotary_pos_embeager_attention_forward) BitNetConfigc eZdZy) BitNetRMSNormN__name__ __module__ __qualname__|/var/www/html/ai-insurance-compliance-backend/venv/lib/python3.12/site-packages/transformers/models/bitnet/modular_bitnet.pyrr*rrc*eZdZdeffd ZdZxZS) BitNetMLPconfigcpt||t|j|j|_yN)eps)super__init__rintermediate_size rms_norm_eps ffn_sub_norm)selfr# __class__s rr(zBitNetMLP.__init__/s+  )&*B*BH[H[\rc |j|j|j|j||j |z}|S)N) down_projr+act_fn gate_projup_proj)r,xr/s rforwardzBitNetMLP.forward3sFNN4#4#4T[[PQAR5SVZVbVbcdVe5e#fg r)rrrrr(r4 __classcell__r-s@rr"r".s]|]rr"c2eZdZdedeffd Z d dejdeejejfde ejde e de ejd e e d eeje eje eejffd ZxZS) BitNetAttentionr# layer_idxcrt|||t|j|j|_yr%)r'r(r hidden_sizer* attn_sub_norm)r,r#r9r-s rr(zBitNetAttention.__init__9s- +*6+=+=6CVCVWr hidden_statesposition_embeddingsattention_maskpast_key_valuecache_positionkwargsreturnc V|jdd}g|d|j}|j|j|j dd} |j |j|j dd} |j |j|j dd} |\} } t| | | | \} } |'| | |d}|j| | |j|\} } t}|jjdk7rt|jj}||| | | |f|jsdn |j|j d|\}}|j"g|dj%}|j'|}|j)|}||fS)Nrr )sincosrAeagerg)dropoutscaling)shapehead_dimq_projview transposek_projv_projrupdater9rr#_attn_implementationr trainingattention_dropoutrJreshape contiguousr<o_proj)r,r=r>r?r@rArB input_shape hidden_shape query_states key_states value_statesrGrF cache_kwargsattention_interface attn_output attn_weightss rr4zBitNetAttention.forward=s$))#2. 88b8$--8 {{=166|DNNqRST [[/44\BLLQPQR {{=166|DNNqRST &S#7 jRUWZ#[ j  %#&snUL'5'<'+)!. +) ! +) !!1!12 +)-.+) u||Xell3XeELL>Q5RR S+)rr8c eZdZy)BitNetDecoderLayerNrrrrrhrhkr rrhc eZdZy) BitNetModelNrrrrrjrjor rrjc2eZdZdgZdZdZdeffd ZxZS)BitNetForCausalLMzlm_head.weightNrCc "t|di|S)a$ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the masked language modeling loss. Indices should either be in `[0, transformers., config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, transformers., config.vocab_size]`. Example: ```python >>> from transformers import AutoTokenizer, BitNetForCausalLM >>> model = BitNetForCausalLM.from_pretrained("microsoft/bitnet-b1.58-2B-4T") >>> tokenizer = AutoTokenizer.from_pretrained("microsoft/bitnet-b1.58-2B-4T") >>> prompt = f'<|begin_of_text|>User: Hey, are you conscious? Can you talk to me?<|eot_id|>Assistant: ' >>> inputs = tokenizer(prompt, return_tensors="pt") >>> # Generate >>> generate_ids = model.generate(inputs.input_ids, max_length=100) >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] "User: Hey, are you conscious? Can you talk to me?Assistant: No, I'm not conscious. I'm an artificial intelligence designed to assist with information and tasks. How can I help you today?" ```r)r'r4)r, super_kwargsr-s rr4zBitNetForCausalLM.forwardxs4w...r) rrr_tied_weights_keys_tp_plan_pp_planrr4r5r6s@rrlrlss**+HH/ //rrl)rlrjBitNetPreTrainedModel)'__doc__typingrrrc cache_utilsrmodeling_flash_attention_utilsrmodeling_outputsrmodeling_utilsr processing_utilsr utilsr gemma.modeling_gemmar llama.modeling_llamarrrrrrrconfiguration_bitnetr get_loggerrloggerrr"r8rhrjrl__all__rrrrs% B65&+/   H % L 0)n0)f *  * /(/D r