153 lines
5.6 KiB
Python
153 lines
5.6 KiB
Python
# coding=utf-8
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# Copyright 2025 The BitNet Team and The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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"""PyTorch BitNet model."""
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from typing import Callable, Optional
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import torch
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from ...cache_utils import Cache
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from ...modeling_flash_attention_utils import FlashAttentionKwargs
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from ...modeling_outputs import CausalLMOutputWithPast
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from ...modeling_utils import ALL_ATTENTION_FUNCTIONS
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from ...processing_utils import Unpack
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from ...utils import logging
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from ..gemma.modeling_gemma import GemmaMLP
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from ..llama.modeling_llama import (
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LlamaAttention,
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LlamaDecoderLayer,
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LlamaForCausalLM,
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LlamaModel,
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LlamaRMSNorm,
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apply_rotary_pos_emb,
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eager_attention_forward,
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)
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from .configuration_bitnet import BitNetConfig
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logger = logging.get_logger(__name__)
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class BitNetRMSNorm(LlamaRMSNorm):
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pass
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class BitNetMLP(GemmaMLP):
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def __init__(self, config: BitNetConfig):
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super().__init__(config)
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self.ffn_sub_norm = BitNetRMSNorm(config.intermediate_size, eps=config.rms_norm_eps)
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def forward(self, x):
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down_proj = self.down_proj(self.ffn_sub_norm(self.act_fn(self.gate_proj(x)) * self.up_proj(x)))
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return down_proj
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class BitNetAttention(LlamaAttention):
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def __init__(self, config: BitNetConfig, layer_idx: int):
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super().__init__(config, layer_idx)
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self.attn_sub_norm = BitNetRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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def forward(
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self,
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hidden_states: torch.Tensor,
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position_embeddings: tuple[torch.Tensor, torch.Tensor],
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attention_mask: Optional[torch.Tensor],
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past_key_value: Optional[Cache] = None,
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cache_position: Optional[torch.LongTensor] = None,
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**kwargs: Unpack[FlashAttentionKwargs],
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) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
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input_shape = hidden_states.shape[:-1]
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hidden_shape = (*input_shape, -1, self.head_dim)
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query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2)
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key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2)
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value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2)
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cos, sin = position_embeddings
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query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
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if past_key_value is not None:
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# sin and cos are specific to RoPE models; cache_position needed for the static cache
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cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
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key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
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attention_interface: Callable = eager_attention_forward
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if self.config._attn_implementation != "eager":
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attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
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attn_output, attn_weights = attention_interface(
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self,
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query_states,
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key_states,
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value_states,
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attention_mask,
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dropout=0.0 if not self.training else self.attention_dropout,
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scaling=self.scaling,
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**kwargs,
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)
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attn_output = attn_output.reshape(*input_shape, -1).contiguous()
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attn_output = self.attn_sub_norm(attn_output) # diff with Llama
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attn_output = self.o_proj(attn_output)
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return attn_output, attn_weights
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class BitNetDecoderLayer(LlamaDecoderLayer):
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pass
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class BitNetModel(LlamaModel):
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pass
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class BitNetForCausalLM(LlamaForCausalLM):
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_tied_weights_keys = ["lm_head.weight"]
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_tp_plan = None
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_pp_plan = None
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def forward(
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self,
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**super_kwargs,
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) -> CausalLMOutputWithPast:
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r"""
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labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
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Labels for computing the masked language modeling loss. Indices should either be in `[0, transformers.,
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config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
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(masked), the loss is only computed for the tokens with labels in `[0, transformers., config.vocab_size]`.
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Example:
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```python
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>>> from transformers import AutoTokenizer, BitNetForCausalLM
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>>> model = BitNetForCausalLM.from_pretrained("microsoft/bitnet-b1.58-2B-4T")
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>>> tokenizer = AutoTokenizer.from_pretrained("microsoft/bitnet-b1.58-2B-4T")
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>>> prompt = f'<|begin_of_text|>User: Hey, are you conscious? Can you talk to me?<|eot_id|>Assistant: '
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>>> inputs = tokenizer(prompt, return_tensors="pt")
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>>> # Generate
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>>> generate_ids = model.generate(inputs.input_ids, max_length=100)
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>>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
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"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?"
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```"""
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return super().forward(**super_kwargs)
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__all__ = [
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"BitNetForCausalLM",
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"BitNetModel",
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"BitNetPreTrainedModel", # noqa: F822
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]
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