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# Copyright 2023-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import math
from typing import Optional, Union
import torch
import torch.nn as nn
import torch.nn.functional as F
from .config import TRANSFORMERS_MODEL_CONFIG
class _BaseAdaptedAttention(nn.Module):
"""Base module, which defines adaption prompts for multiple model types."""
def __init__(self, model_type: str, adapter_len: int, model, target_dtype=torch.float32):
"""
Initialize object.
Args:
model_type: The transformer model type. This is used to retrieve the right method to
compute query states.
adapter_len: The length of the adaption prompt to insert.
model: The original transformer attention module that is being wrapped.
"""
if isinstance(model, _BaseAdaptedAttention):
raise ValueError("Unable to stack multiple adaption prompts")
super().__init__()
self.model_type = model_type
self.model = model
self.adapter_len = adapter_len
# Assume all parameters of the attention model we are wrapping are on the same device.
device = next(model.parameters()).device
# Don't think this was specified in the paper, but we follow the official repo which used an Embedding
# which initializes the tokens with standard normal values.
# https://github.com/ZrrSkywalker/LLaMA-Adapter/blob/41c3546fe1997ab8a65809dc8d8f9252b19d9faf/llama/model.py#L234
# (bsz, adapter_len, hidden_size)
if hasattr(self.model, "hidden_size"):
# TODO: remove this clause after 2026-01-01
hidden_size = self.model.hidden_size
else: # changed in https://github.com/huggingface/transformers/pull/35235
hidden_size = self.model.config.hidden_size
if hasattr(self.model, "num_heads"):
# TODO: remove this clause after 2026-01-01
self.num_heads = self.model.num_heads
else: # changed in https://github.com/huggingface/transformers/pull/35235
self.num_heads = self.model.config.num_attention_heads
self.adaption_prompt = nn.Parameter(
torch.empty(1, adapter_len, hidden_size, device=device, dtype=target_dtype).normal_()
)
# Initialize the gate to 0 as this is "zero-init".
self.adaption_gate = nn.Parameter(torch.zeros(1, device=device, dtype=target_dtype))
class AdaptedAttentionGPT(_BaseAdaptedAttention):
"""This module wraps a GPT2Attention module and injects adaption prompts"""
def __init__(self, model_type, adapter_len, model):
target_dtype = (
model.c_proj.weight.dtype if model.c_proj.weight.dtype not in [torch.int8, torch.uint8] else torch.float32
)
super().__init__(model_type, adapter_len, model, target_dtype=target_dtype)
def forward(
self,
hidden_states: Optional[tuple[torch.FloatTensor]],
layer_past: Optional[tuple[torch.Tensor]] = None,
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
use_cache: Optional[bool] = False,
output_attentions: Optional[bool] = False,
**kwargs,
) -> tuple[Union[torch.Tensor, tuple[torch.Tensor]], ...]:
attn_outputs = self.model(
hidden_states=hidden_states,
attention_mask=attention_mask,
head_mask=head_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
use_cache=use_cache,
output_attentions=output_attentions,
**kwargs,
)
"""
Forward pass for the adapter which wraps the GPT2Attention module
"""
attn_output = attn_outputs[0]
add_outputs = attn_outputs[1:]
c_attn_layer = TRANSFORMERS_MODEL_CONFIG[self.model_type].k_proj_layer
bsz = attn_output.shape[0]
q_len = attn_output.shape[1]
embed_dim = attn_output.shape[2]
_, key, value = getattr(self.model, c_attn_layer)(self.adaption_prompt).split(embed_dim, dim=2)
adapter_k = (
key.view(1, self.adapter_len, self.num_heads, self.model.head_dim).repeat(bsz, 1, 1, 1).transpose(1, 2)
)
adapter_v = (
value.view(1, self.adapter_len, self.num_heads, self.model.head_dim).repeat(bsz, 1, 1, 1).transpose(1, 2)
)
# recompute query state since it is not returned by GPT2 forward
compute_query_states = TRANSFORMERS_MODEL_CONFIG[self.model_type].compute_query_states
query_states = compute_query_states(
self.model, hidden_states=hidden_states, encoder_hidden_states=encoder_hidden_states
)
previous_dtype = query_states.dtype
scores = torch.matmul(query_states, adapter_k.transpose(2, 3).to(previous_dtype)) / math.sqrt(
self.model.head_dim
)
# Upcast attention to fp32
# (bsz, num_heads, q_len, adapter_len)
scores = self.adaption_gate * F.softmax(scores, dim=-1, dtype=torch.float32).to(previous_dtype)
# (bsz, q_len, num_heads * head_dim)
adapter_output = torch.matmul(scores, adapter_v).transpose(1, 2).reshape(bsz, q_len, -1)
# Add adaption prompt output to original output.
hidden_state = attn_output + adapter_output
# Restore original dtype.
hidden_state = hidden_state.to(previous_dtype)
# add additional attention outputs (attention and cross attention)
output = (hidden_state,) + add_outputs
return output
class AdaptedAttention(_BaseAdaptedAttention):
"""This module wraps a LLamaAttention module and injects adaption prompts."""
def __init__(self, model_type, adapter_len, model):
target_dtype = (
model.q_proj.weight.dtype if model.q_proj.weight.dtype not in [torch.int8, torch.uint8] else torch.float32
)
super().__init__(model_type, adapter_len, model, target_dtype=target_dtype)
def forward(self, **kwargs):
"""
Forward pass for the adapter which wraps the original LlamaAttention module.
"Official" paper implementation:
https://github.com/ZrrSkywalker/LLaMA-Adapter/blob/41c3546fe1997ab8a65809dc8d8f9252b19d9faf/llama/model.py#L141
Args:
kwargs: See the original LlamaAttention module.
"""
if kwargs.get("output_attention", False):
raise NotImplementedError("output_attention is not currently supported.")
output, *_ = self.model(**kwargs)
bsz = output.shape[0]
q_len = output.shape[1]
embed_dim = output.shape[2]
k_proj_layer = TRANSFORMERS_MODEL_CONFIG[self.model_type].k_proj_layer
v_proj_layer = TRANSFORMERS_MODEL_CONFIG[self.model_type].v_proj_layer
o_proj_layer = TRANSFORMERS_MODEL_CONFIG[self.model_type].o_proj_layer
factor = (
self.model.k_proj.in_features // self.model.k_proj.out_features
) # Mistral has different input and output dimension for k_proj and v_proj layers
if k_proj_layer == v_proj_layer:
_, key, value = getattr(self.model, k_proj_layer)(self.adaption_prompt).split(embed_dim, dim=2)
else:
key = getattr(self.model, k_proj_layer)(self.adaption_prompt)
value = getattr(self.model, v_proj_layer)(self.adaption_prompt)
if hasattr(self.model, "num_heads"):
# TODO: remove this clause after 2026-01-01
num_heads = self.model.num_heads
else: # changed in https://github.com/huggingface/transformers/pull/35235
num_heads = self.model.config.num_attention_heads
# (bsz, num_key_value_heads, adapter_len, head_dim)
adapter_k = (
key.view(1, self.adapter_len, (num_heads // factor), self.model.head_dim)
.repeat(bsz, 1, 1, 1)
.transpose(1, 2)
)
adapter_v = (
value.view(1, self.adapter_len, (num_heads // factor), self.model.head_dim)
.repeat(bsz, 1, 1, 1)
.transpose(1, 2)
)
# Below is taken from https://github.com/huggingface/transformers/blob/e547458c43dfdbbb8f6a7757237e234c44e20a8f/src/transformers/models/mistral/modeling_mistral.py#L181
# (bsz, num_heads, adapter_len, head_dim)
adapter_k = torch.repeat_interleave(adapter_k, repeats=factor, dim=1)
adapter_v = torch.repeat_interleave(adapter_v, repeats=factor, dim=1)
# Recompute query states.
compute_query_states = TRANSFORMERS_MODEL_CONFIG[self.model_type].compute_query_states
# (bsz, num_heads, q_len, head_dim)
query_states = compute_query_states(model=self.model, **kwargs)
previous_dtype = query_states.dtype
# (bsz, num_heads, q_len, adapter_len)
scores = torch.matmul(query_states, adapter_k.transpose(2, 3).to(previous_dtype)) / math.sqrt(
self.model.head_dim
)
# Upcast attention to fp32
# (bsz, num_heads, q_len, adapter_len)
scores = self.adaption_gate * F.softmax(scores, dim=-1, dtype=torch.float32).to(previous_dtype)
# (bsz, q_len, num_heads * head_dim)
adapter_output = torch.matmul(scores, adapter_v).transpose(1, 2).reshape(bsz, q_len, -1)
# (bsz, q_len, hidden_size)
if o_proj_layer is not None:
adapter_output = getattr(self.model, o_proj_layer)(adapter_output)
# Add adaption prompt output to original output.
output = output + adapter_output
# Restore original dtype.
output = output.to(previous_dtype)
return output, *_
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