tocmo0nlord/axolotl
1
0
Fork 0
Code Issues Pull Requests Actions 3 Packages Projects Releases Wiki Activity

feat: add lolcats with fixed typed

Browse Source
This commit is contained in:
NanoCode012
2025-02-03 22:38:19 +07:00
parent 158330ab60
commit 311d6eb5da
13 changed files with 3741 additions and 0 deletions
Download Patch File Download Diff File Expand all files Collapse all files

201
src/axolotl/integrations/lolcats/LICENSE Normal file
View File

@@ -0,0 +1,201 @@
Apache License
Version 2.0, January 2004
http://www.apache.org/licenses/
TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
1. Definitions.
"License" shall mean the terms and conditions for use, reproduction,
and distribution as defined by Sections 1 through 9 of this document.
"Licensor" shall mean the copyright owner or entity authorized by
the copyright owner that is granting the License.
"Legal Entity" shall mean the union of the acting entity and all
other entities that control, are controlled by, or are under common
control with that entity. For the purposes of this definition,
"control" means (i) the power, direct or indirect, to cause the
direction or management of such entity, whether by contract or
otherwise, or (ii) ownership of fifty percent (50%) or more of the
outstanding shares, or (iii) beneficial ownership of such entity.
"You" (or "Your") shall mean an individual or Legal Entity
exercising permissions granted by this License.
"Source" form shall mean the preferred form for making modifications,
including but not limited to software source code, documentation
source, and configuration files.
"Object" form shall mean any form resulting from mechanical
transformation or translation of a Source form, including but
not limited to compiled object code, generated documentation,
and conversions to other media types.
"Work" shall mean the work of authorship, whether in Source or
Object form, made available under the License, as indicated by a
copyright notice that is included in or attached to the work
(an example is provided in the Appendix below).
"Derivative Works" shall mean any work, whether in Source or Object
form, that is based on (or derived from) the Work and for which the
editorial revisions, annotations, elaborations, or other modifications
represent, as a whole, an original work of authorship. For the purposes
of this License, Derivative Works shall not include works that remain
separable from, or merely link (or bind by name) to the interfaces of,
the Work and Derivative Works thereof.
"Contribution" shall mean any work of authorship, including
the original version of the Work and any modifications or additions
to that Work or Derivative Works thereof, that is intentionally
submitted to Licensor for inclusion in the Work by the copyright owner
or by an individual or Legal Entity authorized to submit on behalf of
the copyright owner. For the purposes of this definition, "submitted"
means any form of electronic, verbal, or written communication sent
to the Licensor or its representatives, including but not limited to
communication on electronic mailing lists, source code control systems,
and issue tracking systems that are managed by, or on behalf of, the
Licensor for the purpose of discussing and improving the Work, but
excluding communication that is conspicuously marked or otherwise
designated in writing by the copyright owner as "Not a Contribution."
"Contributor" shall mean Licensor and any individual or Legal Entity
on behalf of whom a Contribution has been received by Licensor and
subsequently incorporated within the Work.
2. Grant of Copyright License. Subject to the terms and conditions of
this License, each Contributor hereby grants to You a perpetual,
worldwide, non-exclusive, no-charge, royalty-free, irrevocable
copyright license to reproduce, prepare Derivative Works of,
publicly display, publicly perform, sublicense, and distribute the
Work and such Derivative Works in Source or Object form.
3. Grant of Patent License. Subject to the terms and conditions of
this License, each Contributor hereby grants to You a perpetual,
worldwide, non-exclusive, no-charge, royalty-free, irrevocable
(except as stated in this section) patent license to make, have made,
use, offer to sell, sell, import, and otherwise transfer the Work,
where such license applies only to those patent claims licensable
by such Contributor that are necessarily infringed by their
Contribution(s) alone or by combination of their Contribution(s)
with the Work to which such Contribution(s) was submitted. If You
institute patent litigation against any entity (including a
cross-claim or counterclaim in a lawsuit) alleging that the Work
or a Contribution incorporated within the Work constitutes direct
or contributory patent infringement, then any patent licenses
granted to You under this License for that Work shall terminate
as of the date such litigation is filed.
4. Redistribution. You may reproduce and distribute copies of the
Work or Derivative Works thereof in any medium, with or without
modifications, and in Source or Object form, provided that You
meet the following conditions:
(a) You must give any other recipients of the Work or
Derivative Works a copy of this License; and
(b) You must cause any modified files to carry prominent notices
stating that You changed the files; and
(c) You must retain, in the Source form of any Derivative Works
that You distribute, all copyright, patent, trademark, and
attribution notices from the Source form of the Work,
excluding those notices that do not pertain to any part of
the Derivative Works; and
(d) If the Work includes a "NOTICE" text file as part of its
distribution, then any Derivative Works that You distribute must
include a readable copy of the attribution notices contained
within such NOTICE file, excluding those notices that do not
pertain to any part of the Derivative Works, in at least one
of the following places: within a NOTICE text file distributed
as part of the Derivative Works; within the Source form or
documentation, if provided along with the Derivative Works; or,
within a display generated by the Derivative Works, if and
wherever such third-party notices normally appear. The contents
of the NOTICE file are for informational purposes only and
do not modify the License. You may add Your own attribution
notices within Derivative Works that You distribute, alongside
or as an addendum to the NOTICE text from the Work, provided
that such additional attribution notices cannot be construed
as modifying the License.
You may add Your own copyright statement to Your modifications and
may provide additional or different license terms and conditions
for use, reproduction, or distribution of Your modifications, or
for any such Derivative Works as a whole, provided Your use,
reproduction, and distribution of the Work otherwise complies with
the conditions stated in this License.
5. Submission of Contributions. Unless You explicitly state otherwise,
any Contribution intentionally submitted for inclusion in the Work
by You to the Licensor shall be under the terms and conditions of
this License, without any additional terms or conditions.
Notwithstanding the above, nothing herein shall supersede or modify
the terms of any separate license agreement you may have executed
with Licensor regarding such Contributions.
6. Trademarks. This License does not grant permission to use the trade
names, trademarks, service marks, or product names of the Licensor,
except as required for reasonable and customary use in describing the
origin of the Work and reproducing the content of the NOTICE file.
7. Disclaimer of Warranty. Unless required by applicable law or
agreed to in writing, Licensor provides the Work (and each
Contributor provides its Contributions) on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
implied, including, without limitation, any warranties or conditions
of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
PARTICULAR PURPOSE. You are solely responsible for determining the
appropriateness of using or redistributing the Work and assume any
risks associated with Your exercise of permissions under this License.
8. Limitation of Liability. In no event and under no legal theory,
whether in tort (including negligence), contract, or otherwise,
unless required by applicable law (such as deliberate and grossly
negligent acts) or agreed to in writing, shall any Contributor be
liable to You for damages, including any direct, indirect, special,
incidental, or consequential damages of any character arising as a
result of this License or out of the use or inability to use the
Work (including but not limited to damages for loss of goodwill,
work stoppage, computer failure or malfunction, or any and all
other commercial damages or losses), even if such Contributor
has been advised of the possibility of such damages.
9. Accepting Warranty or Additional Liability. While redistributing
the Work or Derivative Works thereof, You may choose to offer,
and charge a fee for, acceptance of support, warranty, indemnity,
or other liability obligations and/or rights consistent with this
License. However, in accepting such obligations, You may act only
on Your own behalf and on Your sole responsibility, not on behalf
of any other Contributor, and only if You agree to indemnify,
defend, and hold each Contributor harmless for any liability
incurred by, or claims asserted against, such Contributor by reason
of your accepting any such warranty or additional liability.
END OF TERMS AND CONDITIONS
APPENDIX: How to apply the Apache License to your work.
To apply the Apache License to your work, attach the following
boilerplate notice, with the fields enclosed by brackets "[]"
replaced with your own identifying information. (Don't include
the brackets!) The text should be enclosed in the appropriate
comment syntax for the file format. We also recommend that a
file or class name and description of purpose be included on the
same "printed page" as the copyright notice for easier
identification within third-party archives.
Copyright [yyyy] [name of copyright owner]
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.

20
src/axolotl/integrations/lolcats/linear_attention/__init__.py Normal file
View File

@@ -0,0 +1,20 @@
"""
Linear and linear attention + sliding window classes
"""
from .linear_attention import LinearAttentionState, LolcatsLinearAttention
from .linear_window_attention_sw import (
LinearAttentionSlidingWindowCache,
LolcatsSlidingWindowAttention,
)
from .linear_window_attention_sw_long import LolcatsSlidingWindowLongAttention
from .linear_window_attention_tk import (
LinearAttentionTKWindowCache,
LolcatsTKWindowAttention,
)
from .linear_window_attention_tk_gen import (
LinearAttentionTKWindowGenerationCache,
LolcatsWindowAttentionTKGen,
)
# Experimental chunk linear attentions
from .linear_window_attention_tk_long import LolcatsTKWindowLongAttention

561
src/axolotl/integrations/lolcats/linear_attention/linear_attention.py Normal file
View File

@@ -0,0 +1,561 @@
"""
Linear attention classes
"""
import copy
from typing import Any, List, Optional, Tuple
import torch
import torch.nn as nn
from transformers.cache_utils import Cache
# Causal linear attention dot product CUDA kernel from fast-transformers
try:
from csrc import causal_dot_product as fast_causal_dot_product
except ImportError:
fast_causal_dot_product = None
from ..model.feature_map import init_feature_map, init_learned_kernel
from ..model.rotary import apply_rotary_pos_emb, get_rotary_embeddings
from .utils import repeat_kv
# -------------------
# Attention functions
# -------------------
def causal_dot_product(q: torch.Tensor, k: torch.Tensor, v: torch.Tensor):
"""
Causal linear attention dot product
- If available, use CUDA kernel from fast-transformers
"""
if fast_causal_dot_product is None:
kv = torch.einsum("bhlf,bhld->bhlfd", k, v)
return torch.einsum("bhlf,bhlfd->bhld", q, kv.cumsum(dim=2))
return fast_causal_dot_product(q, k, v)
def linear_attention(
q: torch.Tensor,
k: torch.Tensor,
v: torch.Tensor,
fp32_attention: bool = False,
eps: float = 1e-12,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
"""
Compute linear attention with CUDA kernel implementation from fast-transformers
- https://github.com/idiap/fast-transformers
- Assume q, k are shape (batch_size, num_heads, seq_len, feature_dim);
v is shape (b, h, l, head_dim)
"""
dtype = q.dtype
# Causal mask already applied
y = causal_dot_product(
q.contiguous().to(dtype=torch.float32),
k.contiguous().to(dtype=torch.float32),
v.contiguous().to(dtype=torch.float32),
)
if fp32_attention:
y = (
y
/ (
torch.einsum("bhld,bhld->bhl", q.float(), k.float().cumsum(dim=2)) + eps
)[..., None]
).to(dtype=dtype)
else:
y = y.to(dtype=dtype)
k = k.float().cumsum(dim=2).to(dtype=dtype)
y = y / (torch.einsum("bhld,bhld->bhl", q, k) + eps)[..., None]
return y, None, None
def softmax_attention(
q: torch.Tensor,
k: torch.Tensor,
v: Optional[torch.Tensor] = None,
causal: bool = True,
fp32_attention: bool = True,
):
"""
Standard softmax attention; only compute outputs if v is not None
-> Assume q, k, v are shape (batch_size, num_heads, seq_len, head_dim)
"""
y = None
a = torch.einsum("bhmd,bhnd->bhmn", q, k) * (k.shape[-1] ** -0.5)
if causal: # Apply causal mask
m, n = a.shape[-2:]
causal_mask = torch.ones((m, n), device=a.device, dtype=torch.bool).triu(
n - m + 1
)
a = a.masked_fill(causal_mask, -torch.finfo(a.dtype).max)
if fp32_attention:
a = torch.softmax(a, dim=-1, dtype=torch.float32).to(q.dtype)
else:
a = torch.softmax(a, dim=-1)
if v is not None:
y = torch.einsum("bhmn,bhnd->bhmd", a, v)
return y, a, None
def quadratic_attention(
q: torch.Tensor,
k: torch.Tensor,
v: Optional[torch.Tensor] = None,
causal: bool = True,
fp32_attention: bool = False,
eps: float = 1e-12,
):
"""
Compute attention with feature maps by instantiating L x L matrix of attention weights
-> Use for attention distillation
-> Assume q, k are shape (batch_size, num_heads, seq_len, feature_dim); v is shape (b, h, l, head_dim)
"""
y = None
dtype = q.dtype
if fp32_attention:
q, k = q.float(), k.float()
a = torch.einsum("bhmd,bhnd->bhmn", q, k) # note we don't scale, tho we could
if causal: # Apply causal mask
m, n = a.shape[-2:]
causal_mask = torch.ones((m, n), device=a.device, dtype=torch.bool).triu(
n - m + 1
)
a = a.masked_fill(causal_mask, 0)
# Normalize to compute attention
a = a / (a.sum(dim=-1, keepdim=True) + eps)
a = a.to(dtype=dtype) if fp32_attention else a
if torch.isnan(a).sum() > 0:
breakpoint()
if v is not None:
y = torch.einsum("bhmn,bhnd->bhmd", a, v)
return y, a, None
# ---------------------
# Attention layer class
# ---------------------
class LolcatsLinearAttention(nn.Module):
"""
LoLCATs attention implementation initialized from a
`LlamaAttention` or `MistralAttention` object (base_attn)
Most of the arguments are directly tied to argparse args
- For now we don't support padding.
"""
def __init__(
self,
base_attn: nn.Module, # like LlamaAttention
feature_map: str,
feature_map_kwargs: dict,
layer_idx: Optional[int] = None,
max_layer_idx: Optional[int] = None,
learned_kernel: Optional[str] = None,
learned_kernel_kwargs: Optional[dict] = None,
tie_qk_kernels: Optional[bool] = False,
rotary_config: Optional[dict] = None,
train_attention: Optional[bool] = False,
remove_base_attn: bool = True,
attention_type: Optional[str] = "lolcats_llama",
mask_value: int = 0,
eps: float = 1e-12,
fp32_attention: bool = False,
track_state_grads: bool = False,
rank: Optional[int] = 0,
**kwargs,
) -> None:
super().__init__()
self.base_config = getattr(base_attn, "config", None)
if self.base_config is not None:
self.base_config = self.base_config.to_dict()
self.attention_type = attention_type
self.mask_value = mask_value
self.eps = eps
self.layer_idx = layer_idx if layer_idx is not None else base_attn.layer_idx
self.max_layer_idx = max_layer_idx
self.tie_qk_kernels = tie_qk_kernels
self.train_attention = train_attention
self.base_inference = False
self.fp32_attention = fp32_attention
self.track_state_grads = track_state_grads
if rank == 0: # multi-gpu
if fp32_attention and layer_idx == 0:
print(f"-> fp32_attention is {fp32_attention}")
if layer_idx == 0 and feature_map_kwargs is not None:
for k, v in feature_map_kwargs.items():
print(f"-> {k}: {v}")
if layer_idx == 0 and learned_kernel_kwargs is not None:
for k, v in learned_kernel_kwargs.items():
print(f"-> {k}: {v}")
self.remove_base_attn = remove_base_attn
# Rotary embeddings (patch for Llama 3.1, Transformer v4.43.0)
self.rotary_config = rotary_config
# if isinstance(self.rotary_config, DictDefault):
# self.rotary_config = OmegaConf.to_container(self.rotary_config)
self.rotary_emb = None
if self.base_config is not None and self.rotary_config is None:
self.rotary_emb = base_attn.rotary_emb
self.init_weights_(base_attn, remove_base_attn)
self.init_feature_map_(
feature_map, feature_map_kwargs, learned_kernel, learned_kernel_kwargs
)
def init_feature_map_(
self,
feature_map: str,
feature_map_kwargs: dict,
learned_kernel: Optional[str] = None,
learned_kernel_kwargs: Optional[dict] = None,
):
"""
Initialize MLP-based feature map
"""
self.fmap_gqa = False # Turn True if specified below
if learned_kernel is not None and learned_kernel_kwargs is not None:
# Ensure dict
learned_kernel_kwargs = {k: v for k, v in learned_kernel_kwargs.items()}
learned_kernel_kwargs["num_heads"] = self.num_heads
learned_kernel_kwargs["head_dim"] = self.head_dim
learned_kernel_kwargs["dtype"] = self.q_proj.weight.dtype
learned_kernel_kwargs["device"] = self.q_proj.weight.device
# Create MLP
mlp_learned_kernel = init_learned_kernel(
learned_kernel, **learned_kernel_kwargs
)
# Add "activation"; see src.models.feature_map.py
self.feature_map_q = init_feature_map(
name=feature_map, mlp=mlp_learned_kernel, **feature_map_kwargs
)
if self.tie_qk_kernels: # tie mlp weights for query and key feature maps
self.feature_map_k = self.feature_map_q
else:
self.feature_map_k = copy.deepcopy(self.feature_map_q)
def init_weights_(self, base_attn: nn.Module, remove_base_attn: bool = True):
"""
Initialize module layers, weights, positional dependencies, etc.
from original softmax attention layer (base_attn)
"""
# Make other attributes accessible
self.attention_dropout = 0 # We don't use dropout
self.hidden_size = base_attn.hidden_size
self.num_heads = base_attn.num_heads
self.head_dim = base_attn.head_dim
self.num_key_value_heads = base_attn.num_key_value_heads
self.num_key_value_groups = base_attn.num_key_value_groups
self.q_shape = [self.num_heads, self.head_dim]
self.k_shape = [self.num_key_value_heads, self.head_dim]
self.v_shape = [self.num_key_value_heads, self.head_dim]
device = base_attn.q_proj.weight.device
# Rotary embeddings
if self.rotary_emb is None:
self.max_position_embeddings = base_attn.max_position_embeddings
scaling_factor = getattr(base_attn.rotary_emb, "scaling_factor", 1.0)
if self.rotary_config is None:
self.rotary_emb = get_rotary_embeddings(
rope_scaling_type=None,
head_dim=self.head_dim,
max_position_embeddings=self.max_position_embeddings, # base_attn.rotary_emb.max_position_embeddings,
rope_theta=base_attn.rotary_emb.base,
rope_scaling_factor=scaling_factor, # base_attn.rotary_emb.scaling_factor,
device=device,
)
else:
if "device" not in self.rotary_config:
self.rotary_config["device"] = device
self.rotary_emb = get_rotary_embeddings(**self.rotary_config)
# Copy original model projection layers
self.q_proj = base_attn.q_proj
self.k_proj = base_attn.k_proj
self.v_proj = base_attn.v_proj
self.o_proj = base_attn.o_proj
try: # If wanting to use FA2 for ground-truth inference
self._flash_attn_uses_top_left_mask = (
base_attn._flash_attn_uses_top_left_mask
)
except AttributeError:
pass
if self.remove_base_attn or remove_base_attn:
del base_attn # We don't need to keep these around
else:
self.base_attn = base_attn # For some training runs helpful to just call
def process_qkv(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Any] = None,
): # "legacy" cache approach
"""
Compute queries, keys, and values
"""
b, l, _ = hidden_states.size()
q = self.q_proj(hidden_states)
k = self.k_proj(hidden_states)
v = self.v_proj(hidden_states)
kv_seq_len = k.shape[-2]
# Shape is (batch_size, seq_len, num_heads, head_dim)
q = q.view(b, l, *self.q_shape).transpose(1, 2)
k = k.view(b, l, *self.k_shape).transpose(1, 2)
v = v.view(b, l, *self.v_shape).transpose(1, 2)
if (
past_key_value is not None
): # and k.shape[2] > q.shape[2]: # e.g., when generating
past_key_value.window_size = getattr(
self, "decode_window_size", None
) # self.decode_window_size
if isinstance(
past_key_value, Cache
): # In Transformers v4.36+ this is a DynamicCache object
kv_seq_len += past_key_value.get_usable_length(
kv_seq_len, self.layer_idx
)
else:
kv_seq_len += past_key_value[0].shape[-2]
# Apply rotary embeddings and repeat for GQA
if position_ids is not None and kv_seq_len <= position_ids[0, -1]:
kv_seq_len = position_ids[0, -1] + 1 # hack for adjusting position ids
if self.rotary_emb is None:
raise ValueError("Rotary embeddings not initialized")
try: # As in Transformers v4.36
cos, sin = self.rotary_emb(k, seq_len=kv_seq_len)
q, k = apply_rotary_pos_emb(q, k, cos, sin, position_ids)
except TypeError: # As in Transformers v4.39+
cos, sin = self.rotary_emb(v, position_ids)
q, k = apply_rotary_pos_emb(q, k, cos, sin)
k = repeat_kv(k, self.num_key_value_groups)
v = repeat_kv(v, self.num_key_value_groups)
return q, k, v, kv_seq_len
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Any] = None, # "legacy" cache approach
output_attentions: bool = False,
use_cache: bool = False,
**kwargs,
):
"""
Forward pass modified from transformers.models.mistral.modeling_mistral (v4.36)
- Consistent with HuggingFace Transformers for easy use with their pretrained models
"""
b, l, _ = hidden_states.size()
q, k, v, kv_seq_len = self.process_qkv(
hidden_states, attention_mask, position_ids, past_key_value
)
if self.base_inference:
with torch.no_grad():
# 1. Compute "ground-truth" attention output and weights
y_true, _, _ = softmax_attention(q, k, v, causal=True)
y_true = (
y_true.transpose(1, 2).contiguous().view(b, l, self.hidden_size)
)
y_true = self.o_proj(y_true)
attn_weights = (None, None)
elif self.train_attention: # Distilling / learning attentions
# Note for now we assume no padding when distilling; attention masks only enforce causality
assert (
output_attentions is True
), f"When training feature maps, output_attentions should be True but is {output_attentions}"
with torch.no_grad():
# 1. Compute "ground-truth" attention output and weights
_y_true, attn_true, _ = softmax_attention(q, k, v, causal=True)
y_true = (
_y_true.transpose(1, 2).contiguous().view(b, l, self.hidden_size)
)
y_true = self.o_proj(y_true)
# 2. Compute "predicted" attention (just weights)
q, k = self.feature_map_q.q_map(q), self.feature_map_k.k_map(k)
y_pred, attn_pred, _ = quadratic_attention(q, k, v, causal=True)
attn_weights = ( # type: ignore
(attn_pred, attn_true),
(y_pred, _y_true),
) # Save both attention weights so we can supervise.
else: # Finetuning
q, k = self.feature_map_q(q), self.feature_map_k(k)
# Apply prefill mask
if attention_mask is not None and q.shape[2] > 1:
if len(attention_mask.shape) == 4:
lin_attn_mask = (attention_mask == 0)[:, :1, -1, :l][
..., None
] # b, 1, k_len, 1
else:
lin_attn_mask = attention_mask[:, None, :, None] # b, 1, k_len, 1
k = k.masked_fill(~lin_attn_mask, 0)
if past_key_value is not None: # Initialize states
if len(past_key_value.kv_states) == self.layer_idx:
b, h, _, f = k.shape
past_key_value.kv_states.append(
torch.zeros(
b, h, f, self.head_dim, dtype=q.dtype, device=q.device
)
)
past_key_value.k_states.append(
torch.zeros(b, h, 1, f, dtype=q.dtype, device=q.device)
)
# Generating
if q.shape[2] == 1 and kv_seq_len > 1 and past_key_value is not None:
assert use_cache is True
kv_state, k_state = past_key_value.update(
k, v, self.layer_idx, accumulate_in_fp32=self.fp32_attention
)
if self.fp32_attention:
q = q.float()
y_true = (
torch.einsum("bhlf,bhfd->bhld", q, kv_state.float())
/ torch.einsum("bhlf,bhlf->bhl", q, k_state.float())[
..., None
]
).to(dtype=k.dtype)
else:
y_true = (
torch.einsum("bhlf,bhfd->bhld", q, kv_state)
/ torch.einsum("bhlf,bhlf->bhl", q, k_state)[..., None]
)
else:
kv_state = past_key_value.kv_states[self.layer_idx]
k_state = past_key_value.k_states[self.layer_idx]
y_true, _, _ = linear_attention(
q, k, v, self.fp32_attention, self.eps
) # Ordinarily the states are ignored
past_key_value.update(
k.detach(),
v.detach(),
self.layer_idx,
accumulate_in_fp32=self.fp32_attention,
)
# doing some unnecessary recomputation here
else:
y_true, _, _ = linear_attention(q, k, v, self.fp32_attention, self.eps)
# Concatenate heads and apply output projection
y_true = y_true.transpose(1, 2).contiguous().view(b, l, self.hidden_size)
y_true = self.o_proj(y_true)
attn_weights = None
return y_true, attn_weights, past_key_value
class LinearAttentionState(Cache):
"""
Handle the KV and K states for linear attention
- Adopts HF Transformers `past_key_values` convention
- Inherits from `Cache` class
- Modified from transformers.cache_utils.DynamicCache (v4.36)
"""
def __init__(self) -> None:
self._seen_tokens = 0 # should be `self.seen_tokens` in Transformers v4.36
self._seen_tokens_by_layer: List[int] = []
self.kv_states: List[torch.Tensor] = []
self.k_states: List[torch.Tensor] = []
def get_seq_length(self, layer_idx: Optional[int] = 0) -> int:
"""
Returns the sequence length of the cached states. A layer index can be optionally passed.
"""
if layer_idx is None:
raise ValueError("Layer index must not be None")
if len(self._seen_tokens_by_layer) <= layer_idx: # Initializing kv and k states
self._seen_tokens_by_layer.append(0)
return self._seen_tokens_by_layer[layer_idx]
def get_max_length(self) -> Optional[int]:
"""
Returns the maximum sequence length of the cached states. DynamicCache does not have a maximum length.
"""
return None
def get_usable_length(
self, new_seq_length: int, layer_idx: Optional[int] = 0
) -> int:
"""Given the sequence length of the new inputs, returns the usable length of the cache."""
# Cache without size limit -> all cache is usable
# Cache with size limit -> if the length cache plus the length of the new inputs is larger the maximum cache
# length, we will need to evict part of the cache (and thus not all cache is usable)
max_length = self.get_max_length()
previous_seq_length = self.get_seq_length(layer_idx)
if max_length is not None and previous_seq_length + new_seq_length > max_length:
return max_length - new_seq_length
return previous_seq_length
def update(
self,
key_states: torch.Tensor,
value_states: torch.Tensor,
layer_idx: Optional[int] = None,
cache_kwargs: Optional[Any] = None,
accumulate_in_fp32: bool = True,
**kwargs,
) -> Tuple[torch.Tensor, torch.Tensor]:
if layer_idx is None:
raise ValueError("Layer index must not be None")
with torch.no_grad():
if layer_idx == 0:
self._seen_tokens += key_states.shape[-2]
dtype = key_states.dtype
if accumulate_in_fp32:
key_states, value_states = key_states.float(), value_states.float()
kv_state = torch.einsum(
"bhlf,bhld->bhfd", key_states, value_states
).detach()
k_state = key_states.sum(
dim=-2, keepdim=True
).detach() # b, h, 1, f; note the 1
# Update the cache
if len(self.k_states) <= layer_idx: # Initializing kv and k states
print(
"if len(self.k_states) <= layer_idx: # Initializing kv and k states"
)
self.kv_states.append(kv_state.to(dtype))
self.k_states.append(k_state.to(dtype))
else:
kv_state = (self.kv_states[layer_idx].to(kv_state.dtype) + kv_state).to(
dtype
)
k_state = (self.k_states[layer_idx].to(kv_state.dtype) + k_state).to(
dtype
)
self.kv_states[layer_idx] = kv_state
self.k_states[layer_idx] = k_state
self._seen_tokens_by_layer[layer_idx] += key_states.shape[-2]
return self.kv_states[layer_idx], self.k_states[layer_idx]
def to_legacy_cache(self):
"""Hack, but just return self"""
return self
def reorder_cache(self, beam_idx: torch.LongTensor):
"""
Reorders the cache for beam search, given the selected beam indices.
-> Copied from transformers/src/transformers/cache_utils.py
"""
raise NotImplementedError(
"Reordering cache not implemented for LinearAttentionState"
)

460
src/axolotl/integrations/lolcats/linear_attention/linear_window_attention_sw.py Normal file
View File

@@ -0,0 +1,460 @@
"""
Subquadratic attention combining sliding window and linear attentions
- Using "standard" sliding windows
- Didactically computes outputs with n^2 attention weights for now
- Copied + adapted from linear_window_attention_tk.py for single-file reference
For each layer:
- We first compute (softmax) attention over sliding windows
- We then compute standard linear attention to "fill in" the earlier parts
- We combine to model the entire sequence
"""
from typing import Any, Callable, List, Optional, Tuple
import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers.cache_utils import Cache
from .linear_attention import (
LinearAttentionState,
LolcatsLinearAttention,
softmax_attention,
)
# ----------------------
# Sliding window helpers
# ----------------------
def get_masks(
window_size: int, q_len: int, k_len: int, device: torch.device
) -> tuple[torch.Tensor, torch.Tensor]:
"""
Return masks for softmax and linear attention terms
-> 1 is include, 0 is ignore
"""
causal_mask = torch.ones((q_len, k_len), device=device, dtype=torch.int).tril(
k_len - q_len
)
linear_mask = torch.ones((q_len, k_len), device=device, dtype=torch.int).tril(
k_len - q_len - window_size
)
window_mask = causal_mask - linear_mask
# Return softmax mask (window), linear attention mask
# -> shapes broadcast over (b, h, q_len, k_len)
return window_mask[None, None, ...], linear_mask[None, None, ...]
def hybrid_attention_quadratic(
q: torch.Tensor,
k: torch.Tensor,
f_q: torch.Tensor,
f_k: torch.Tensor,
v: torch.Tensor,
window_factor: torch.Tensor,
linear_factor: torch.Tensor,
window_size: int,
kv_state: Optional[torch.Tensor] = None,
k_state: Optional[torch.Tensor] = None,
eps: float = 1e-12,
mask_value: float = -1e8,
):
"""
Hybrid attention combining sliding window and linear attentions
"""
mask_window, mask_linear = get_masks(
window_size, q.shape[-2], k.shape[-2], q.device
)
# 1. Sliding window (softmax attention)
a_sm = torch.einsum("bhmd,bhnd->bhmn", q.float(), k.float()) * (k.shape[-1] ** -0.5)
a_sm = a_sm.masked_fill(~mask_window.bool(), mask_value)
# torch.softmax(a_sm, dim=-1), but we account for the max when combining
a_sm_max = torch.amax(a_sm, dim=-1, keepdim=True)
a_sm = window_factor * torch.exp(a_sm - a_sm_max)
sum_sm = a_sm.sum(dim=-1, keepdim=True)
# 2. Under window (linear attention)
a_ln = torch.einsum("bhmd,bhnd->bhmn", f_q.float(), f_k.float())
a_ln = linear_factor * a_ln.masked_fill(~mask_linear.bool(), 0)
sum_ln = a_ln.sum(dim=-1, keepdim=True)
# 3. Combine
a = ((a_sm + a_ln) / (sum_sm + sum_ln)).to(q.dtype) # Save attention weights
# Allow outputs to also depend on prior kv_state and k_state
y = torch.einsum("bhmn,bhnd->bhmd", a_sm + a_ln, v.float())
if (
kv_state is not None and k_state is not None
): # Combine with prior kv_state and k_state
y += linear_factor * torch.einsum(
"bhld,bhdf->bhlf", f_q.float(), kv_state.float()
)
sum_ln += (
linear_factor
* torch.einsum("bhld,bhnd->bhl", f_q.float(), k_state.float())[..., None]
)
y = (y / (sum_sm + sum_ln)).to(q.dtype)
return y, a # attention weights only for the last chunk
# ---------------------
# Attention layer class
# ---------------------
class LolcatsSlidingWindowAttention(LolcatsLinearAttention):
"""
Lolcats attention combining sliding window and linear attention
"""
def __init__(
self,
window_size: int = 64,
decode_window_size: Optional[int] = None,
affine_attention_factors: bool = False,
init_window_factor: float = 0,
train_window_factor: bool = True,
state_grad_enabled: bool = False,
**kwargs,
):
self.window_size = window_size
self.decode_window_size = (
decode_window_size if decode_window_size is not None else window_size
)
self.window_kwargs = {"dimension": 2, "size": window_size, "step": 1}
super().__init__(**kwargs)
self.attention_type = kwargs["attention_type"] # 'hedgehog_llama_window_sw'
# Determine how we compute attentions
self.quadratic_attention = hybrid_attention_quadratic
self.attention_type = kwargs[
"attention_type"
] # 'hedgehog_long_llama_window_sw'
# Learnable factor for combining attentions
self.affine_attention_factors = affine_attention_factors
device, dtype = self.q_proj.weight.device, self.q_proj.weight.dtype
if train_window_factor:
self.window_factors = nn.Parameter(
init_window_factor
* torch.ones(1, self.num_heads, 1, 1, device=device, dtype=dtype)
)
else:
self.register_buffer(
"window_factors",
init_window_factor
* torch.ones(1, self.num_heads, 1, 1, device=device, dtype=dtype),
)
# Whether we use original flash attention 2 inference (use during attention transfer)
self.base_inference = False
self.state_grad_enabled = state_grad_enabled
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Cache] = None,
output_attentions: bool = False,
use_cache: bool = False,
**kwargs,
):
"""
Forward pass with the option to compute attention weights multiple ways
if self.train_attention is True
-> Consistent with HuggingFace Transformers for easy use with their pretrained models
"""
b, l, _ = hidden_states.size()
q, k, v, kv_seq_len = self.process_qkv(
hidden_states, attention_mask, position_ids, past_key_value
)
f_q, f_k = self.feature_map_q(q), self.feature_map_k(
k
) # Have to do after repeat for grouped-query attn if we use same fmap
if self.train_attention:
# 1. Compute "ground-truth" attention output and weights
with torch.no_grad():
_y_true, a_true = softmax_attention(q, k, v)[:2]
y_true = (
_y_true.transpose(1, 2).contiguous().view(b, l, self.hidden_size)
)
y_true = self.o_proj(y_true)
# 2. Compute "predicted" attention outputs
# compute attn weights under sliding window
window_factors = F.sigmoid(self.window_factors)
linear_factors = 1 - window_factors if self.affine_attention_factors else 1
y_pred, a_pred = self.quadratic_attention(
q,
k,
f_q,
f_k,
v,
window_factors,
linear_factors,
window_size=self.window_size,
)
attn_weights = ((a_pred, a_true), (y_pred, _y_true))
else:
attn_weights = None
# attention_mask = None # For now this is always True
if past_key_value is None: # Regular training
window_factors = F.sigmoid(self.window_factors)
linear_factors = (
1 - window_factors if self.affine_attention_factors else 1
)
y_true, a_pred = self.quadratic_attention(
q,
k,
f_q,
f_k,
v,
window_factors,
linear_factors,
window_size=self.window_size,
)
attn_weights = a_pred
else:
past_key_value.window_size = self.decode_window_size
if (
f_q.shape[2] == 1 and kv_seq_len > 1 and not self.training
): # Generating
assert use_cache is True
_kv = past_key_value.update_for_decoding(
k, v, self.layer_idx, self.feature_map_k, dtype=q.dtype
)
k_cache, v_cache, f_kv_state, f_k_state = _kv
# Sliding window + linear attention decode
window_factors = F.sigmoid(self.window_factors)
linear_factors = (
1 - window_factors if self.affine_attention_factors else 1
)
# Softmax attention terms
a_sm = torch.einsum(
"bhmd,bhnd->bhmn", q.float(), k_cache.float()
) * (k.shape[-1] ** -0.5)
a_sm_max = torch.amax(a_sm, dim=-1, keepdim=True)
a_sm = window_factors * torch.exp(a_sm - a_sm_max)
sum_sm = a_sm.sum(dim=-1, keepdim=True)
# Combine with linear attention terms
y_true = torch.einsum(
"bhmn,bhnd->bhmd", a_sm, v_cache.float()
) + linear_factors * torch.einsum(
"bhlf,bhfd->bhld", f_q.float(), f_kv_state.float()
)
sum_ln = (
linear_factors
* torch.einsum(
"bhlf,bhnf->bhl", f_q.float(), f_k_state.float()
)[..., None]
)
y_true = (y_true / (sum_sm + sum_ln)).to(q.dtype)
else: # Stateful training
try:
kv_state = past_key_value.kv_states[self.layer_idx]
k_state = past_key_value.k_states[self.layer_idx]
except IndexError:
kv_state, k_state = None, None
window_factors = F.sigmoid(self.window_factors)
linear_factors = (
1 - window_factors if self.affine_attention_factors else 1
)
y_true, _ = self.quadratic_attention(
q,
k,
f_q,
f_k,
v,
window_factors,
linear_factors,
window_size=self.window_size,
kv_state=kv_state,
k_state=k_state,
)
# Save and update KV cache and states
# past_key_value.update(k, v.detach(), self.layer_idx,
# fmap_key_states=f_k.detach(),
# accumulate_in_fp32=True)
past_key_value.update(
k,
v,
self.layer_idx,
fmap_key_states=f_k,
accumulate_in_fp32=True,
)
# Concatenate heads and apply output projection
y_true = y_true.transpose(1, 2).contiguous().view(b, l, self.hidden_size)
y_true = self.o_proj(y_true)
return y_true, attn_weights, past_key_value
class LinearAttentionSlidingWindowCache(LinearAttentionState):
"""
Class for `past_key_values`
-> Alternative to KV cache; here we only maintain a "KV state" and "K state"
-> Modified from transformers.cache_utils.DynamicCache (v4.36)
"""
def __init__(self, window_size: int = 64) -> None:
super().__init__()
self._seen_tokens = 0 # should be `self.seen_tokens` in Transformers v4.36
self._seen_tokens_by_layer: List[int] = []
self.kv_states: List[torch.Tensor] = []
self.k_states: List[torch.Tensor] = []
# Account for sliding windows
self.decode_kv_states: List[torch.Tensor] = []
self.decode_k_states: List[torch.Tensor] = []
self.k_cache: List[torch.Tensor] = []
self.v_cache: List[torch.Tensor] = []
self.window_size = window_size
def update(
self,
key_states: torch.Tensor,
value_states: torch.Tensor,
layer_idx: Optional[int] = None,
cache_kwargs: Optional[Any] = None,
accumulate_in_fp32: bool = False,
fmap_key_states: Optional[torch.Tensor] = None, # should not be None
grad_enabled: bool = False,
**kwargs,
) -> Tuple[torch.Tensor, torch.Tensor]:
"""
Update KV, K states; and KV cache during training
- For decoding, use `self.decode_kv_states` to keep track of KV states
up to sliding window terms
- For (chunked) training, use `self.kv_states` to keep track of KV states
up to end of sequence
- Likewise for `self.decode_k_states` and `self.k_states`
"""
if fmap_key_states is None:
raise ValueError("fmap_key_states must not be None")
if layer_idx is None:
raise ValueError("Layer index must not be None")
with torch.set_grad_enabled(grad_enabled):
if layer_idx == 0:
self._seen_tokens += key_states.shape[-2]
dtype = key_states.dtype
if accumulate_in_fp32:
# key_states = key_states.float()
fmap_key_states = fmap_key_states.float()
value_states = value_states.float()
# Decoding KV state (KV terms up to last window_size)
decode_kv_state = torch.einsum(
"bhlf,bhld->bhfd",
fmap_key_states[:, :, : -self.window_size],
value_states[:, :, : -self.window_size],
)
# KV state
kv_state = decode_kv_state + torch.einsum(
"bhlf,bhld->bhfd",
fmap_key_states[:, :, -self.window_size :],
value_states[:, :, -self.window_size :],
)
# shape is b, h, 1, f; note the 1
decode_k_state = fmap_key_states[:, :, : -self.window_size].sum(
dim=-2, keepdim=True
)
k_state = decode_k_state + fmap_key_states[:, :, -self.window_size :].sum(
dim=-2, keepdim=True
)
# Update the cache
if len(self.k_states) <= layer_idx: # Initializing kv and k states
self.kv_states.append(kv_state.to(dtype))
self.k_states.append(k_state.to(dtype))
self.decode_kv_states.append(decode_kv_state.to(dtype))
self.decode_k_states.append(decode_k_state.to(dtype))
self.k_cache.append(key_states[:, :, -self.window_size :, :])
self.v_cache.append(
value_states[:, :, -self.window_size :, :].to(dtype)
)
# self._seen_tokens_by_layer[layer_idx].append(key_states.shape[-2])
else:
# Update kv and k states recurrently
kv_state = (self.kv_states[layer_idx].to(kv_state.dtype) + kv_state).to(
dtype
)
k_state = (self.k_states[layer_idx].to(kv_state.dtype) + k_state).to(
dtype
)
self.kv_states[layer_idx] = kv_state
self.k_states[layer_idx] = k_state
decode_kv_state = (
self.decode_kv_states[layer_idx].to(kv_state.dtype)
+ decode_kv_state
).to(dtype)
decode_k_state = (
self.decode_k_states[layer_idx].to(kv_state.dtype) + decode_k_state
).to(dtype)
self.decode_kv_states[layer_idx] = decode_kv_state
self.decode_k_states[layer_idx] = decode_k_state
self.k_cache[layer_idx] = key_states[:, :, -self.window_size :, :]
self.v_cache[layer_idx] = value_states[:, :, -self.window_size :, :]
self._seen_tokens_by_layer[layer_idx] += key_states.shape[-2]
return self.kv_states[layer_idx], self.k_states[layer_idx]
def update_for_decoding(
self,
keys: torch.Tensor,
values: torch.Tensor,
layer_idx: int,
feature_map_k: Callable,
dtype: torch.dtype,
):
"""
Update the decoding KV and K states, and KV cache, during decodeing
"""
with torch.no_grad():
k_cache = self.k_cache[layer_idx]
v_cache = self.v_cache[layer_idx]
if k_cache.shape[-2] < self.window_size: # build window-size cache
self.k_cache[layer_idx] = torch.cat([k_cache, keys], dim=-2)
self.v_cache[layer_idx] = torch.cat([v_cache, values], dim=-2)
else:
# MZ 6/3: handle short inputs; zero-out padding when initial k.shape[2] < self.window_size
# if k_cache[:, :, :1, :].sum() == 0: # heuristic for zeroing out padding in cache
# f_k_state = torch.zeros(k_cache[:, :, :1, :].shape, dtype=dtype, device=k_cache.device)
# else:
# f_k_state = feature_map_k(k_cache[:, :, :1, :])
# -> MZ (later): above only relevant if we zero-pad in our hybrid attention computation
k_state = feature_map_k(k_cache[:, :, :1, :])
v_state = v_cache[:, :, :1, :]
kv_state = torch.einsum(
"bhlf,bhld->bhfd", k_state.float(), v_state.float()
).to(
dtype
) # b, h, f, d
self.decode_kv_states[layer_idx] += kv_state
self.decode_k_states[layer_idx] += k_state
self.k_cache[layer_idx] = torch.cat(
[k_cache[:, :, 1:, :], keys], dim=-2
)
self.v_cache[layer_idx] = torch.cat(
[v_cache[:, :, 1:, :], values], dim=-2
)
if layer_idx == 0:
self._seen_tokens += keys.shape[-2]
self._seen_tokens_by_layer[layer_idx] += keys.shape[-2]
return (
self.k_cache[layer_idx],
self.v_cache[layer_idx],
self.decode_kv_states[layer_idx],
self.decode_k_states[layer_idx],
)

687
src/axolotl/integrations/lolcats/linear_attention/linear_window_attention_sw_linear.py Normal file
View File

@@ -0,0 +1,687 @@
"""
Subquadratic attention combining sliding window and linear attentions
- Using "standard" sliding windows
- Didactically computes outputs with n^2 attention weights for now
- Copied + adapted from linear_window_attention_tk.py for single-file reference
For each layer:
- We first compute (softmax) attention over sliding windows
- We then compute standard linear attention to "fill in" the earlier parts
- We combine to model the entire sequence
"""
import logging
from typing import Any, Callable, List, Optional, Tuple
import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers.cache_utils import Cache
try:
from transformers.modeling_flash_attention_utils import _flash_attention_forward
except ModuleNotFoundError:
_flash_attention_forward = None # Transformers v4.36
from ..model.rotary import apply_rotary_pos_emb
# Causal linear attention dot product CUDA kernel from fast-transformers
from .linear_attention import (
LinearAttentionState,
LolcatsLinearAttention,
causal_dot_product,
)
LOG = logging.getLogger(
"axolotl.integrations.lolcats.linear_attention.linear_window_attention_sw_long"
)
# ----------------------
# Sliding window helpers
# ----------------------
def get_masks(
window_size: int, q_len: int, k_len: int, device: torch.device
) -> tuple[torch.Tensor, torch.Tensor]:
"""
Return masks for softmax and linear attention terms
-> 1 is include, 0 is ignore
"""
causal_mask = torch.ones((q_len, k_len), device=device, dtype=torch.int).tril(
max(k_len - q_len, 0)
)
linear_mask = torch.ones((q_len, k_len), device=device, dtype=torch.int).tril(
max(k_len - q_len, 0) - window_size
)
window_mask = causal_mask - linear_mask
# Return softmax mask (window), linear attention mask
# -> shapes broadcast over (b, h, q_len, k_len)
return window_mask[None, None, ...], linear_mask[None, None, ...]
def hybrid_attention_quadratic(
q: torch.Tensor,
k: torch.Tensor,
f_q: torch.Tensor,
f_k: torch.Tensor,
v: torch.Tensor,
window_factor: torch.Tensor,
linear_factor: torch.Tensor,
window_size: int,
kv_state: Optional[torch.Tensor] = None,
k_state: Optional[torch.Tensor] = None,
eps: float = 1e-12,
mask_value: float = -1e8,
):
"""
Hybrid attention combining sliding window and linear attentions
"""
mask_window, mask_linear = get_masks(
window_size, q.shape[-2], k.shape[-2], q.device
)
# 1. Sliding window (softmax attention)
a_sm = torch.einsum("bhmd,bhnd->bhmn", q.float(), k.float()) * (k.shape[-1] ** -0.5)
a_sm = a_sm.masked_fill(~mask_window.bool(), mask_value)
# torch.softmax(a_sm, dim=-1), but we account for the max when combining
a_sm_max = torch.amax(a_sm, dim=-1, keepdim=True)
a_sm = window_factor * torch.exp(a_sm - a_sm_max)
sum_sm = a_sm.sum(dim=-1, keepdim=True)
# 2. Under window (linear attention)
a_ln = torch.einsum("bhmd,bhnd->bhmn", f_q.float(), f_k.float())
a_ln = linear_factor * a_ln.masked_fill(~mask_linear.bool(), 0)
sum_ln = a_ln.sum(dim=-1, keepdim=True)
# 3. Combine
a = ((a_sm + a_ln) / (sum_sm + sum_ln)).to(q.dtype) # Save attention weights
# Allow outputs to also depend on prior kv_state and k_state
y = torch.einsum("bhmn,bhnd->bhmd", a_sm + a_ln, v.float())
if (
kv_state is not None and k_state is not None
): # Combine with prior kv_state and k_state
y += linear_factor * torch.einsum(
"bhld,bhdf->bhlf", f_q.float(), kv_state.float()
)
sum_ln += (
linear_factor
* torch.einsum("bhld,bhnd->bhl", f_q.float(), k_state.float())[..., None]
)
y = (y / (sum_sm + sum_ln)).to(q.dtype)
return y, a # attention weights only for the last chunk
# ------------------------------
# Hybrid window attention linear
# ------------------------------
def under_window_linear_attention(
f_q: torch.Tensor,
f_k: torch.Tensor,
v: torch.Tensor,
window_size: int,
linear_factor: torch.Tensor,
eps: float = 1e-12,
):
"""Compute hybrid window attention dot product with linear complexity in q_len"""
dtype = f_q.dtype
w = window_size
f_k = F.pad(f_k, (0, 0, w, 0), value=0)[:, :, :-w, :]
v = F.pad(v, (0, 0, w, 0), value=0)[:, :, :-w, :]
qkv = linear_factor * causal_dot_product(
f_q.contiguous().to(dtype=torch.float32),
f_k.contiguous().to(dtype=torch.float32),
v.contiguous().to(dtype=torch.float32),
).to(dtype=dtype)
sum_f_k = f_k.float().cumsum(dim=2).to(dtype=dtype)
sum_qk = linear_factor * torch.einsum("bhld,bhld->bhl", f_q, sum_f_k)[..., None]
sum_qk[sum_qk == 0] += eps
return qkv, sum_qk
def sliding_window_softmax_attention(
q: torch.Tensor,
k: torch.Tensor,
v: torch.Tensor,
window_size: int,
window_factor: torch.Tensor,
mask_value: float = -1e8,
):
"""
Compute sliding window softmax attention without materializing
O(seq_len^2) attention weights
"""
d = q.shape[-1]
# Compute windows for keys
window_kwargs = {"dimension": 2, "size": window_size, "step": 1}
k = F.pad(k, (0, 0, window_size - 1, 0), value=0).unfold(**window_kwargs)
v = F.pad(v, (0, 0, window_size - 1, 0), value=0).unfold(**window_kwargs)
# Compute windowed_softmax(qk); causal in its construction
a_sm = torch.einsum("bhld,bhldw->bhlw", q, k) * (d**-0.5)
a_sm[a_sm == 0] = -torch.finfo(
q.dtype
).max # heuristic for zeroing out padding above
a_sm_max = torch.amax(a_sm, dim=-1, keepdim=True)
a_sm = window_factor * torch.exp(a_sm - a_sm_max)
sum_sm = a_sm.sum(dim=-1, keepdim=True)
return torch.einsum("bhlw,bhldw->bhld", a_sm, v), sum_sm
# return torch.einsum('bhlw,bhldw->bhld', torch.softmax(qk, dim=-1), v)
def hybrid_attention_linear(
q: torch.Tensor,
k: torch.Tensor,
f_q: torch.Tensor,
f_k: torch.Tensor,
v: torch.Tensor,
window_factor: Optional[torch.Tensor] = None,
linear_factor: Optional[torch.Tensor] = None,
window_size: int = 64,
kv_state: Optional[torch.Tensor] = None,
k_state: Optional[torch.Tensor] = None,
eps: float = 1e-12,
mask_value: float = -1e8,
):
"""
Alternative hybrid attention combining sliding window and linear attentions
-> Uses O(n) memory if n is sequence length by padding and unfolding windows
"""
# window_kwargs = {"dimension": 2, "size": window_size, "step": 1}
if window_factor is None:
raise ValueError("window_factor must be provided")
if linear_factor is None:
raise ValueError("linear_factor must be provided")
# 1. Sliding window (softmax attention)
with torch.no_grad():
qkv_sm, sum_qk_sm = sliding_window_softmax_attention(
q, k, v, window_size, window_factor, mask_value
)
# 2. Under window (linear attention)
qkv_ln, sum_qk_ln = under_window_linear_attention(
f_q, f_k, v, window_size, linear_factor, eps
)
# 3. Combine
y = (qkv_sm + qkv_ln) / (sum_qk_sm + sum_qk_ln)
return y, None
# ---------------------
# Attention layer class
# ---------------------
class LolcatsLinearSlidingWindowAttention(LolcatsLinearAttention):
"""
Lolcats attention combining sliding window and linear attention
"""
def __init__(
self,
window_size: int = 64,
decode_window_size: Optional[int] = None,
affine_attention_factors: bool = False,
init_window_factor: float = 0,
train_window_factor: bool = True,
state_grad_enabled: bool = False,
**kwargs,
):
self.window_size = window_size
self.decode_window_size = (
decode_window_size if decode_window_size is not None else window_size
)
self.window_kwargs = {"dimension": 2, "size": window_size, "step": 1}
super().__init__(**kwargs)
# Determine how we compute attentions
self.linear_attention = hybrid_attention_linear
self.attention_type = "lolcats_llama_window_sw"
# Learnable factor for combining attentions
self.affine_attention_factors = affine_attention_factors
device, dtype = self.q_proj.weight.device, self.q_proj.weight.dtype
if train_window_factor:
self.window_factors = nn.Parameter(
init_window_factor
* torch.ones(1, self.num_heads, 1, 1, device=device, dtype=dtype)
)
else:
self.register_buffer(
"window_factors",
init_window_factor
* torch.ones(1, self.num_heads, 1, 1, device=device, dtype=dtype),
)
# Whether we use original flash attention 2 inference (use during attention transfer)
self.base_inference = False
self.state_grad_enabled = state_grad_enabled
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Cache] = None,
output_attentions: bool = False,
use_cache: bool = False,
**kwargs,
):
"""
Forward pass with the option to compute attention weights multiple ways
if self.train_attention is True
-> Consistent with HuggingFace Transformers for easy use with their pretrained models
"""
b, l, _ = hidden_states.size()
if self.train_attention and self.base_inference:
with torch.no_grad():
_y_true = flash_attention_2(
self, # self.base_attn,
hidden_states=hidden_states,
attention_mask=None,
position_ids=position_ids,
past_key_value=None,
output_attentions=False,
use_cache=False,
)[0]
# _y_true.shape is (batch_size, seq_len, num_heads, head_dim)
y_true = _y_true.reshape(b, l, -1).contiguous()
y_true = self.o_proj(y_true)
# layer_io = (hidden_states, _y_true) # hack
layer_io = (hidden_states.cpu(), _y_true.cpu()) # hack
return y_true, layer_io, None
else:
q, k, v, kv_seq_len = self.process_qkv(
hidden_states, attention_mask, position_ids, past_key_value
)
f_q, f_k = self.feature_map_q(q), self.feature_map_k(
k
) # Have to do after repeat for grouped-query attn if we use same fmap
attn_weights = None
# attention_mask = None # For now this is always True
if past_key_value is None: # Regular training
window_factors = F.sigmoid(self.window_factors)
linear_factors = (
1 - window_factors if self.affine_attention_factors else 1
)
y_true, a_pred = self.linear_attention(
q,
k,
f_q,
f_k,
v,
window_factors,
linear_factors,
window_size=self.window_size,
)
attn_weights = a_pred
else:
past_key_value.window_size = self.decode_window_size
if (
f_q.shape[2] == 1 and kv_seq_len > 1 and not self.training
): # Generating
assert use_cache is True
_kv = past_key_value.update_for_decoding(
k, v, self.layer_idx, self.feature_map_k, dtype=q.dtype
)
k_cache, v_cache, f_kv_state, f_k_state = _kv
# Sliding window + linear attention decode
window_factors = F.sigmoid(self.window_factors)
linear_factors = (
1 - window_factors if self.affine_attention_factors else 1
)
# Softmax attention terms
a_sm = torch.einsum(
"bhmd,bhnd->bhmn", q.float(), k_cache.float()
) * (k.shape[-1] ** -0.5)
a_sm_max = torch.amax(a_sm, dim=-1, keepdim=True)
a_sm = window_factors * torch.exp(a_sm - a_sm_max)
sum_sm = a_sm.sum(dim=-1, keepdim=True)
# Combine with linear attention terms
y_true = torch.einsum(
"bhmn,bhnd->bhmd", a_sm, v_cache.float()
) + linear_factors * torch.einsum(
"bhlf,bhfd->bhld", f_q.float(), f_kv_state.float()
)
sum_ln = (
linear_factors
* torch.einsum(
"bhlf,bhnf->bhl", f_q.float(), f_k_state.float()
)[..., None]
)
y_true = (y_true / (sum_sm + sum_ln)).to(q.dtype)
else: # Stateful training
try:
kv_state = past_key_value.kv_states[self.layer_idx]
k_state = past_key_value.k_states[self.layer_idx]
except IndexError:
kv_state, k_state = None, None
window_factors = F.sigmoid(self.window_factors)
linear_factors = (
1 - window_factors if self.affine_attention_factors else 1
)
y_true, _ = self.linear_attention(
q,
k,
f_q,
f_k,
v,
window_factors,
linear_factors,
window_size=self.window_size,
kv_state=kv_state,
k_state=k_state,
)
# Save and update KV cache and states
# past_key_value.update(k, v.detach(), self.layer_idx,
# fmap_key_states=f_k.detach(),
# accumulate_in_fp32=True)
past_key_value.update(
k,
v,
self.layer_idx,
fmap_key_states=f_k,
accumulate_in_fp32=True,
)
# Concatenate heads and apply output projection
_y_true = y_true.transpose(1, 2).contiguous()
y_true = self.o_proj(_y_true.view(b, l, self.hidden_size))
if self.train_attention:
attn_weights = _y_true # flash_attn outputs are shape (b, l, h, d)
return y_true, attn_weights, past_key_value
class LinearAttentionSlidingWindowCache(LinearAttentionState):
"""
Class for `past_key_values`
-> Alternative to KV cache; here we only maintain a "KV state" and "K state"
-> Modified from transformers.cache_utils.DynamicCache (v4.36)
"""
def __init__(self, window_size: int = 64) -> None:
super().__init__()
self._seen_tokens = 0 # should be `self.seen_tokens` in Transformers v4.36
self._seen_tokens_by_layer: List[int] = []
self.kv_states: List[torch.Tensor] = []
self.k_states: List[torch.Tensor] = []
# Account for sliding windows
self.decode_kv_states: List[torch.Tensor] = []
self.decode_k_states: List[torch.Tensor] = []
self.k_cache: List[torch.Tensor] = []
self.v_cache: List[torch.Tensor] = []
self.window_size = window_size
def update(
self,
key_states: torch.Tensor,
value_states: torch.Tensor,
layer_idx: Optional[int] = None,
cache_kwargs: Optional[Any] = None,
accumulate_in_fp32: bool = False,
fmap_key_states: Optional[torch.Tensor] = None, # should not be None
grad_enabled: bool = False,
**kwargs,
) -> Tuple[torch.Tensor, torch.Tensor]:
"""
Update KV, K states; and KV cache during training
- For decoding, use `self.decode_kv_states` to keep track of KV states
up to sliding window terms
- For (chunked) training, use `self.kv_states` to keep track of KV states
up to end of sequence
- Likewise for `self.decode_k_states` and `self.k_states`
"""
if fmap_key_states is None:
raise ValueError("fmap_key_states must not be None")
if layer_idx is None:
raise ValueError("Layer index must not be None")
with torch.set_grad_enabled(grad_enabled):
if layer_idx == 0:
self._seen_tokens += key_states.shape[-2]
dtype = key_states.dtype
if accumulate_in_fp32:
# key_states = key_states.float()
fmap_key_states = fmap_key_states.float()
value_states = value_states.float()
# Decoding KV state (KV terms up to last window_size)
decode_kv_state = torch.einsum(
"bhlf,bhld->bhfd",
fmap_key_states[:, :, : -self.window_size],
value_states[:, :, : -self.window_size],
)
# KV state
kv_state = decode_kv_state + torch.einsum(
"bhlf,bhld->bhfd",
fmap_key_states[:, :, -self.window_size :],
value_states[:, :, -self.window_size :],
)
# shape is b, h, 1, f; note the 1
decode_k_state = fmap_key_states[:, :, : -self.window_size].sum(
dim=-2, keepdim=True
)
k_state = decode_k_state + fmap_key_states[:, :, -self.window_size :].sum(
dim=-2, keepdim=True
)
# Update the cache
if len(self.k_states) <= layer_idx: # Initializing kv and k states
self.kv_states.append(kv_state.to(dtype))
self.k_states.append(k_state.to(dtype))
self.decode_kv_states.append(decode_kv_state.to(dtype))
self.decode_k_states.append(decode_k_state.to(dtype))
self.k_cache.append(key_states[:, :, -self.window_size :, :])
self.v_cache.append(
value_states[:, :, -self.window_size :, :].to(dtype)
)
# self._seen_tokens_by_layer[layer_idx].append(key_states.shape[-2])
else:
# Update kv and k states recurrently
kv_state = (self.kv_states[layer_idx].to(kv_state.dtype) + kv_state).to(
dtype
)
k_state = (self.k_states[layer_idx].to(kv_state.dtype) + k_state).to(
dtype
)
self.kv_states[layer_idx] = kv_state
self.k_states[layer_idx] = k_state
decode_kv_state = (
self.decode_kv_states[layer_idx].to(kv_state.dtype)
+ decode_kv_state
).to(dtype)
decode_k_state = (
self.decode_k_states[layer_idx].to(kv_state.dtype) + decode_k_state
).to(dtype)
self.decode_kv_states[layer_idx] = decode_kv_state
self.decode_k_states[layer_idx] = decode_k_state
self.k_cache[layer_idx] = key_states[:, :, -self.window_size :, :]
self.v_cache[layer_idx] = value_states[:, :, -self.window_size :, :]
self._seen_tokens_by_layer[layer_idx] += key_states.shape[-2]
return self.kv_states[layer_idx], self.k_states[layer_idx]
def update_for_decoding(
self,
keys: torch.Tensor,
values: torch.Tensor,
layer_idx: int,
feature_map_k: Callable,
dtype: torch.dtype,
):
"""
Update the decoding KV and K states, and KV cache, during decodeing
"""
with torch.no_grad():
k_cache = self.k_cache[layer_idx]
v_cache = self.v_cache[layer_idx]
if k_cache.shape[-2] < self.window_size: # build window-size cache
self.k_cache[layer_idx] = torch.cat([k_cache, keys], dim=-2)
self.v_cache[layer_idx] = torch.cat([v_cache, values], dim=-2)
else:
# MZ 6/3: handle short inputs; zero-out padding when initial k.shape[2] < self.window_size
# if k_cache[:, :, :1, :].sum() == 0: # heuristic for zeroing out padding in cache
# f_k_state = torch.zeros(k_cache[:, :, :1, :].shape, dtype=dtype, device=k_cache.device)
# else:
# f_k_state = feature_map_k(k_cache[:, :, :1, :])
# -> MZ (later): above only relevant if we zero-pad in our hybrid attention computation
k_state = feature_map_k(k_cache[:, :, :1, :])
v_state = v_cache[:, :, :1, :]
kv_state = torch.einsum(
"bhlf,bhld->bhfd", k_state.float(), v_state.float()
).to(
dtype
) # b, h, f, d
self.decode_kv_states[layer_idx] += kv_state
self.decode_k_states[layer_idx] += k_state
self.k_cache[layer_idx] = torch.cat(
[k_cache[:, :, 1:, :], keys], dim=-2
)
self.v_cache[layer_idx] = torch.cat(
[v_cache[:, :, 1:, :], values], dim=-2
)
if layer_idx == 0:
self._seen_tokens += keys.shape[-2]
self._seen_tokens_by_layer[layer_idx] += keys.shape[-2]
return (
self.k_cache[layer_idx],
self.v_cache[layer_idx],
self.decode_kv_states[layer_idx],
self.decode_k_states[layer_idx],
)
# -----------------
# Flash Attention 2
# -----------------
def flash_attention_2(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Cache] = None,
output_attentions: bool = False,
use_cache: bool = False,
cache_position: Optional[torch.LongTensor] = None,
):
"""
Wrapper for LlamaFlashAttention2
Copied and modified from HF Transformers v4.36 and v4.43 implementations
- (4.43) https://github.com/huggingface/transformers/blob/868d36d29ec132deeaaf8571b25b6a1b911d0145/src/transformers/models/llama/modeling_llama.py#L402
- (4.36) https://github.com/huggingface/transformers/blob/a7cab3c283312b8d4de5df3bbe719971e24f4281/src/transformers/models/llama/modeling_llama.py#L456
"""
bsz, q_len, _ = hidden_states.size()
query_states = self.q_proj(hidden_states)
key_states = self.k_proj(hidden_states)
value_states = self.v_proj(hidden_states)
# Flash attention requires the input to have the shape
# batch_size x seq_length x head_dim x hidden_dim
# therefore we just need to keep the original shape
query_states = query_states.view(
bsz, q_len, self.num_heads, self.head_dim
).transpose(1, 2)
key_states = key_states.view(
bsz, q_len, self.num_key_value_heads, self.head_dim
).transpose(1, 2)
value_states = value_states.view(
bsz, q_len, self.num_key_value_heads, self.head_dim
).transpose(1, 2)
try: # As in Transformers v4.36
kv_seq_len = key_states.shape[-2]
if past_key_value is not None:
kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
cos, sin = self.rotary_emb(key_states, seq_len=kv_seq_len)
query_states, key_states = apply_rotary_pos_emb(
query_states, key_states, cos, sin, position_ids
)
except Exception: # As in Transformers v4.39
cos, sin = self.rotary_emb(key_states, position_ids)
query_states, key_states = apply_rotary_pos_emb(
query_states, key_states, cos, sin
)
if past_key_value is not None:
# sin and cos are specific to RoPE models; cache_position needed for the static cache
cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
key_states, value_states = past_key_value.update(
key_states, value_states, self.layer_idx, cache_kwargs
)
# 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
# to be able to avoid many of these transpose/reshape/view.
query_states = query_states.transpose(1, 2)
key_states = key_states.transpose(1, 2)
value_states = value_states.transpose(1, 2)
dropout_rate = self.attention_dropout if self.training else 0.0
# In PEFT, usually we cast the layer norms in float32 for training stability reasons
# therefore the input hidden states gets silently casted in float32. Hence, we need
# cast them back in the correct dtype just to be sure everything works as expected.
# This might slowdown training & inference so it is recommended to not cast the LayerNorms
# in fp32. (LlamaRMSNorm handles it correctly)
input_dtype = query_states.dtype
if input_dtype == torch.float32:
if torch.is_autocast_enabled():
target_dtype = torch.get_autocast_gpu_dtype()
# Handle the case where the model is quantized
elif hasattr(self.config, "_pre_quantization_dtype"):
target_dtype = self.config._pre_quantization_dtype
else:
target_dtype = self.q_proj.weight.dtype
LOG.debug(
f"The input hidden states seems to be silently casted in float32, this might be related to"
f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
f" {target_dtype}."
)
query_states = query_states.to(target_dtype)
key_states = key_states.to(target_dtype)
value_states = value_states.to(target_dtype)
if getattr(self, "_flash_attention_forward", False):
attn_output = self._flash_attention_forward(
query_states,
key_states,
value_states,
attention_mask,
q_len,
dropout=dropout_rate,
is_causal=True,
)
else:
attn_output = _flash_attention_forward(
query_states,
key_states,
value_states,
attention_mask,
q_len,
dropout=0, # dropout_rate,
sliding_window=getattr(self, "sliding_window", None),
use_top_left_mask=self._flash_attn_uses_top_left_mask,
is_causal=True,
)
return attn_output, past_key_value

24
src/axolotl/integrations/lolcats/linear_attention/linear_window_attention_sw_long.py Normal file
View File

@@ -0,0 +1,24 @@
"""
LoLCATs attention combining sliding window and linear attentions
- Using standard sliding window arrangement
- Training over long sequences with fixed memory with recurrent view
- During attention transfer, use Flash Attention to compute softmax attention outputs
For each layer:
- We first compute (softmax) attention over sliding windows
- We then compute standard linear attention to "fill in" the earlier parts
- We combine to model the entire sequence
"""
from .linear_window_attention_sw import hybrid_attention_quadratic
from .linear_window_attention_tk_long import LolcatsTKWindowLongAttention
class LolcatsSlidingWindowLongAttention(LolcatsTKWindowLongAttention):
"""
Lolcats attention combining sliding window and linear attention
"""
def __init__(self, remove_base_attn=True, **kwargs):
# keep self.base_attn for Flash Attention inference
super().__init__(remove_base_attn=True, **kwargs)
self.quadratic_attention = hybrid_attention_quadratic

466
src/axolotl/integrations/lolcats/linear_attention/linear_window_attention_tk.py Normal file
View File

@@ -0,0 +1,466 @@
"""
Subquadratic attention combining sliding window and linear attentions
- Using the TK "terracing" arrangement
For each layer:
- We first compute (softmax) attention over sliding windows
- We then compute standard linear attention to "fill in" the earlier parts
- We combine to model the entire sequence
"""
import math
from typing import Any, Callable, List, Optional, Tuple
import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers.cache_utils import Cache
from .linear_attention import (
LinearAttentionState,
LolcatsLinearAttention,
softmax_attention,
)
# ----------------------
# Sliding window helpers
# ----------------------
def get_masks(
window_size: int, q_len: int, k_len: int, device: torch.device
) -> tuple[torch.Tensor, torch.Tensor]:
"""
Return masks for softmax and linear attention terms
-> 1 is include, 0 is ignore
"""
win_len = window_size
m = math.ceil(max(q_len, k_len) / window_size)
# Creates an n x n mask where n = window_size^2
mask = torch.block_diag(
*[
torch.ones(
(win_len, win_len),
)
]
* m
)
mask += torch.roll(mask, -win_len, -1) # this adds the terracing
if mask.shape[0] > q_len:
mask = mask[-q_len:]
if mask.shape[1] > k_len:
mask = mask[:, -k_len:]
# Return softmax mask (window), linear attention mask
mask = mask[None, None, ...] # b, h, q_len, k_len
return (
torch.tril(mask).to(device=device, dtype=torch.int),
torch.tril(1 - mask).to(device=device, dtype=torch.int),
)
def hybrid_attention_quadratic(
q: torch.Tensor,
k: torch.Tensor,
f_q: torch.Tensor,
f_k: torch.Tensor,
v: torch.Tensor,
window_factor: torch.Tensor,
linear_factor: torch.Tensor,
window_size: int,
kv_state: Optional[torch.Tensor] = None,
k_state: Optional[torch.Tensor] = None,
eps: float = 1e-12,
mask_value: float = -1e8,
):
"""
Hybrid attention combining sliding window and linear attentions
"""
mask_window, mask_linear = get_masks(
window_size, q.shape[-2], k.shape[-2], q.device
)
# 1. Sliding window (softmax attention)
a_sm = torch.einsum("bhmd,bhnd->bhmn", q.float(), k.float()) * (k.shape[-1] ** -0.5)
a_sm = a_sm.masked_fill(~mask_window.bool(), mask_value)
# torch.softmax(a_sm, dim=-1), but we account for the max when combining
a_sm_max = torch.amax(a_sm, dim=-1, keepdim=True)
a_sm = window_factor * torch.exp(a_sm - a_sm_max)
sum_sm = a_sm.sum(dim=-1, keepdim=True)
# 2. Under window (linear attention)
a_ln = torch.einsum("bhmd,bhnd->bhmn", f_q.float(), f_k.float())
a_ln = linear_factor * a_ln.masked_fill(~mask_linear.bool(), 0)
sum_ln = a_ln.sum(dim=-1, keepdim=True)
# 3. Combine
a = ((a_sm + a_ln) / (sum_sm + sum_ln)).to(q.dtype) # Save attention weights
# Allow outputs to also depend on prior kv_state and k_state
y = torch.einsum("bhmn,bhnd->bhmd", a_sm + a_ln, v.float())
if (
kv_state is not None and k_state is not None
): # Combine with prior kv_state and k_state
y += linear_factor * torch.einsum(
"bhld,bhdf->bhlf", f_q.float(), kv_state.float()
)
sum_ln += (
linear_factor
* torch.einsum("bhld,bhnd->bhl", f_q.float(), k_state.float())[..., None]
)
y = (y / (sum_sm + sum_ln)).to(q.dtype)
return y, a # attention weights only for the last chunk
# ---------------------
# Attention layer class
# ---------------------
class LolcatsTKWindowAttention(LolcatsLinearAttention):
"""
Lolcats attention combining sliding window and linear attention
"""
def __init__(
self,
window_size: int = 64,
decode_window_size: Optional[int] = None,
affine_attention_factors: bool = False,
init_window_factor: float = 0,
train_window_factor: bool = True,
state_grad_enabled: bool = False,
**kwargs,
):
self.window_size = window_size
self.decode_window_size = (
decode_window_size if decode_window_size is not None else window_size
)
self.window_kwargs = {"dimension": 2, "size": window_size, "step": 1}
super().__init__(**kwargs)
self.attention_type = kwargs["attention_type"] # 'hedgehog_llama_window_tk'
# Determine how we compute attentions
self.quadratic_attention = hybrid_attention_quadratic
self.attention_type = kwargs[
"attention_type"
] # 'hedgehog_long_llama_window_tk'
# Learnable factor for combining attentions
self.affine_attention_factors = affine_attention_factors
device, dtype = self.q_proj.weight.device, self.q_proj.weight.dtype
if train_window_factor:
self.window_factors = nn.Parameter(
init_window_factor
* torch.ones(1, self.num_heads, 1, 1, device=device, dtype=dtype)
)
else:
self.register_buffer(
"window_factors",
init_window_factor
* torch.ones(1, self.num_heads, 1, 1, device=device, dtype=dtype),
)
# Whether we use original flash attention 2 inference (use during attention transfer)
self.base_inference = False
self.state_grad_enabled = state_grad_enabled
self.window_factor = self.window_factors # legacy naming support
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Cache] = None,
output_attentions: bool = False,
use_cache: bool = False,
**kwargs,
):
"""
Forward pass with the option to compute attention weights multiple ways
if self.train_attention is True
-> Consistent with HuggingFace Transformers for easy use with their pretrained models
"""
b, l, _ = hidden_states.size()
q, k, v, kv_seq_len = self.process_qkv(
hidden_states, attention_mask, position_ids, past_key_value
)
f_q, f_k = self.feature_map_q(q), self.feature_map_k(
k
) # Have to do after repeat for grouped-query attn if we use same fmap
if self.train_attention:
# 1. Compute "ground-truth" attention output and weights
with torch.no_grad():
_y_true, a_true = softmax_attention(q, k, v)[:2]
y_true = (
_y_true.transpose(1, 2).contiguous().view(b, l, self.hidden_size)
)
y_true = self.o_proj(y_true)
# 2. Compute "predicted" attention outputs
# compute attn weights under sliding window
window_factors = F.sigmoid(self.window_factors)
linear_factors = 1 - window_factors if self.affine_attention_factors else 1
y_pred, a_pred = self.quadratic_attention(
q,
k,
f_q,
f_k,
v,
window_factors,
linear_factors,
window_size=self.window_size,
)
attn_weights = ((a_pred, a_true), (y_pred, _y_true))
else:
attn_weights = None
# attention_mask = None # For now this is always True
if past_key_value is None: # Regular training
window_factors = F.sigmoid(self.window_factors)
linear_factors = (
1 - window_factors if self.affine_attention_factors else 1
)
y_true, a_pred = self.quadratic_attention(
q,
k,
f_q,
f_k,
v,
window_factors,
linear_factors,
window_size=self.window_size,
)
attn_weights = a_pred
else:
past_key_value.window_size = self.decode_window_size
if (
f_q.shape[2] == 1 and kv_seq_len > 1 and not self.training
): # Generating
assert use_cache is True
_kv = past_key_value.update_for_decoding(
k, v, self.layer_idx, self.feature_map_k, dtype=q.dtype
)
k_cache, v_cache, f_kv_state, f_k_state = _kv
# Sliding window + linear attention decode
window_factors = F.sigmoid(self.window_factors)
linear_factors = (
1 - window_factors if self.affine_attention_factors else 1
)
# Softmax attention terms
a_sm = torch.einsum(
"bhmd,bhnd->bhmn", q.float(), k_cache.float()
) * (k.shape[-1] ** -0.5)
a_sm_max = torch.amax(a_sm, dim=-1, keepdim=True)
a_sm = window_factors * torch.exp(a_sm - a_sm_max)
sum_sm = a_sm.sum(dim=-1, keepdim=True)
# Combine with linear attention terms
y_true = torch.einsum(
"bhmn,bhnd->bhmd", a_sm, v_cache.float()
) + linear_factors * torch.einsum(
"bhlf,bhfd->bhld", f_q.float(), f_kv_state.float()
)
sum_ln = (
linear_factors
* torch.einsum(
"bhld,bhnd->bhl", f_q.float(), f_k_state.float()
)[..., None]
)
y_true = (y_true / (sum_sm + sum_ln)).to(q.dtype)
else: # Stateful training
try:
kv_state = past_key_value.kv_states[self.layer_idx]
k_state = past_key_value.k_states[self.layer_idx]
except IndexError:
kv_state, k_state = None, None
window_factors = F.sigmoid(self.window_factors)
linear_factors = (
1 - window_factors if self.affine_attention_factors else 1
)
y_true, _ = self.quadratic_attention(
q,
k,
f_q,
f_k,
v,
window_factors,
linear_factors,
window_size=self.window_size,
kv_state=kv_state,
k_state=k_state,
)
# Save and update KV cache and states
# past_key_value.update(k, v.detach(), self.layer_idx,
# fmap_key_states=f_k.detach(),
# accumulate_in_fp32=True)
past_key_value.update(
k,
v,
self.layer_idx,
fmap_key_states=f_k,
accumulate_in_fp32=True,
)
# Concatenate heads and apply output projection
y_true = y_true.transpose(1, 2).contiguous().view(b, l, self.hidden_size)
y_true = self.o_proj(y_true)
return y_true, attn_weights, past_key_value
class LinearAttentionTKWindowCache(LinearAttentionState):
"""
Class for `past_key_values`
-> Alternative to KV cache; here we only maintain a "KV state" and "K state"
-> Modified from transformers.cache_utils.DynamicCache (v4.36)
"""
def __init__(self, window_size: int = 64) -> None:
super().__init__()
self._seen_tokens = 0 # should be `self.seen_tokens` in Transformers v4.36
self._seen_tokens_by_layer: List[int] = []
self.kv_states: List[torch.Tensor] = []
self.k_states: List[torch.Tensor] = []
# Account for sliding windows
self.decode_kv_states: List[torch.Tensor] = []
self.decode_k_states: List[torch.Tensor] = []
self.k_cache: List[torch.Tensor] = []
self.v_cache: List[torch.Tensor] = []
self.window_size = window_size
def update(
self,
key_states: torch.Tensor,
value_states: torch.Tensor,
layer_idx: Optional[int] = None,
cache_kwargs: Optional[Any] = None,
accumulate_in_fp32: bool = False,
fmap_key_states: Optional[torch.Tensor] = None, # should not be None
grad_enabled: bool = False,
**kwargs,
) -> Tuple[torch.Tensor, torch.Tensor]:
"""
Update KV, K states; and KV cache during training
- For decoding, use `self.decode_kv_states` to keep track of KV states
up to sliding window terms
- For (chunked) training, use `self.kv_states` to keep track of KV states
up to end of sequence
- Likewise for `self.decode_k_states` and `self.k_states`
"""
if fmap_key_states is None:
raise ValueError("fmap_key_states should not be None")
if layer_idx is None:
raise ValueError("layer_idx should not be None")
with torch.set_grad_enabled(grad_enabled):
if layer_idx == 0:
self._seen_tokens += key_states.shape[-2]
dtype = key_states.dtype
if accumulate_in_fp32:
# key_states = key_states.float()
fmap_key_states = fmap_key_states.float()
value_states = value_states.float()
# Decoding KV state (KV terms up to last window_size)
decode_kv_state = torch.einsum(
"bhlf,bhld->bhfd",
fmap_key_states[:, :, : -self.window_size],
value_states[:, :, : -self.window_size],
)
# KV state
kv_state = decode_kv_state + torch.einsum(
"bhlf,bhld->bhfd",
fmap_key_states[:, :, -self.window_size :],
value_states[:, :, -self.window_size :],
)
# shape is b, h, 1, f; note the 1
decode_k_state = fmap_key_states[:, :, : -self.window_size].sum(
dim=-2, keepdim=True
)
k_state = decode_k_state + fmap_key_states[:, :, -self.window_size :].sum(
dim=-2, keepdim=True
)
# Update the cache
if len(self.k_states) <= layer_idx: # Initializing kv and k states
self.kv_states.append(kv_state.to(dtype))
self.k_states.append(k_state.to(dtype))
self.decode_kv_states.append(decode_kv_state.to(dtype))
self.decode_k_states.append(decode_k_state.to(dtype))
self.k_cache.append(key_states[:, :, -self.window_size :, :])
self.v_cache.append(
value_states[:, :, -self.window_size :, :].to(dtype)
)
# self._seen_tokens_by_layer[layer_idx].append(key_states.shape[-2])
else:
# Update kv and k states recurrently
kv_state = (self.kv_states[layer_idx].to(kv_state.dtype) + kv_state).to(
dtype
)
k_state = (self.k_states[layer_idx].to(kv_state.dtype) + k_state).to(
dtype
)
self.kv_states[layer_idx] = kv_state
self.k_states[layer_idx] = k_state
decode_kv_state = (
self.decode_kv_states[layer_idx].to(kv_state.dtype)
+ decode_kv_state
).to(dtype)
decode_k_state = (
self.decode_k_states[layer_idx].to(kv_state.dtype) + decode_k_state
).to(dtype)
self.decode_kv_states[layer_idx] = decode_kv_state
self.decode_k_states[layer_idx] = decode_k_state
self.k_cache[layer_idx] = key_states[:, :, -self.window_size :, :]
self.v_cache[layer_idx] = value_states[:, :, -self.window_size :, :]
self._seen_tokens_by_layer[layer_idx] += key_states.shape[-2]
return self.kv_states[layer_idx], self.k_states[layer_idx]
def update_for_decoding(
self,
keys: torch.Tensor,
values: torch.Tensor,
layer_idx: int,
feature_map_k: Callable,
dtype: torch.dtype,
):
"""
Update the decoding KV and K states, and KV cache, during decodeing
"""
with torch.no_grad():
k_cache = self.k_cache[layer_idx]
v_cache = self.v_cache[layer_idx]
if k_cache.shape[-2] < self.window_size: # build window-size cache
self.k_cache[layer_idx] = torch.cat([k_cache, keys], dim=-2)
self.v_cache[layer_idx] = torch.cat([v_cache, values], dim=-2)
else:
k_state = feature_map_k(k_cache[:, :, :1, :])
v_state = v_cache[:, :, :1, :]
kv_state = torch.einsum(
"bhlf,bhld->bhfd", k_state.float(), v_state.float()
).to(
dtype
) # b, h, f, d
self.decode_kv_states[layer_idx] += kv_state
self.decode_k_states[layer_idx] += k_state
self.k_cache[layer_idx] = torch.cat(
[k_cache[:, :, 1:, :], keys], dim=-2
)
self.v_cache[layer_idx] = torch.cat(
[v_cache[:, :, 1:, :], values], dim=-2
)
if layer_idx == 0:
self._seen_tokens += keys.shape[-2]
self._seen_tokens_by_layer[layer_idx] += keys.shape[-2]
return (
self.k_cache[layer_idx],
self.v_cache[layer_idx],
self.decode_kv_states[layer_idx],
self.decode_k_states[layer_idx],
)

221
src/axolotl/integrations/lolcats/linear_attention/linear_window_attention_tk_gen.py Normal file
View File

@@ -0,0 +1,221 @@
"""
LoLCATs + ThunderKittens linear attention + sliding window for generation
"""
import logging
from typing import Any, Callable, List, Optional
import torch
import torch.nn.functional as F
from .linear_attention import LinearAttentionState
from .linear_window_attention_tk_long import LolcatsTKWindowLongAttention
LOG = logging.getLogger(
"axolotl.integrations.lolcats.linear_attention.linear_attention_tk_gen"
)
try:
from thunderkittens import hedgehog as tk_window_hedgehog_attention
LOG.debug("Successfully imported ThunderKittens for TK window attention")
except ImportError:
LOG.debug("Failed to import ThunderKittens for TK window attention")
class LolcatsWindowAttentionTKGen(LolcatsTKWindowLongAttention):
def __init__(self, *args, window_size: int = 64, **kwargs):
super().__init__(*args, **kwargs)
self.train_attention = False
self.base_inference = False
self.window_size = 64 # hard-coded support for TK kernel
self.decode_window_size = 64
b, h, l, d = 1, 32, 8192, 128
self.y_true = torch.zeros(b, h, l, d, dtype=torch.bfloat16, device="cuda")
self.kv_state = torch.zeros(b, h, d, d, dtype=torch.float32, device="cuda")
self.k_state = torch.zeros(b, h, d, dtype=torch.float32, device="cuda")
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Any] = None, # “legacy” cache approach
output_attentions: bool = False,
use_cache: bool = False,
**kwargs,
):
"""
Forward pass with the option to compute attention weights multiple ways
if self.train_attention is True
-> Consistent with HuggingFace Transformers for easy use with their pretrained models
"""
b, l, _ = hidden_states.size()
assert (
past_key_value is not None
), "past_key_value must be provided for generation"
assert (
self.train_attention is False
), "train_attention is not supported for generation"
assert (
self.base_inference is False
), "base_inference is not supported for generation"
assert use_cache is True, "use_cache must be True for generation"
past_key_value.window_size = self.decode_window_size
q, k, v, kv_seq_len = self.process_qkv(
hidden_states, attention_mask, position_ids, past_key_value
)
if q.shape[2] == 1 and kv_seq_len > 1: # Generating after prefill
f_q = self.feature_map_q(q)
_kv = past_key_value.update_for_decoding(
k, v, self.layer_idx, self.feature_map_k
)
k_cache, v_cache, kv_state, k_state = _kv
# Sliding window + linear attention decode
window_factors = F.sigmoid(self.window_factors)
linear_factors = 1 - window_factors if self.affine_attention_factors else 1
# Softmax attention terms
a_sm = torch.einsum("bhmd,bhnd->bhmn", q.float(), k_cache.float()) * (
k.shape[-1] ** -0.5
)
a_sm_max = torch.amax(a_sm, dim=-1, keepdim=True)
a_sm = window_factors * torch.exp(a_sm - a_sm_max)
sum_sm = a_sm.sum(dim=-1, keepdim=True)
# Combine with linear attention terms
y_true = torch.einsum(
"bhmn,bhnd->bhmd", a_sm, v_cache.float()
) + linear_factors * torch.einsum(
"bhld,bhdf->bhlf", f_q.float(), kv_state.float()
)
sum_ln = (
linear_factors
* torch.einsum("bhld,bhnd->bhl", f_q.float(), k_state.float())[
..., None
]
)
self.y_true = (y_true / (sum_sm + sum_ln)).to(q.dtype)
else: # Process prefill
# Use TK-implemented linear + terrace window attention
b, h, l, d = q.shape
device = q.device
# tk.hedgehog arguments
# y_true = torch.zeros(b, h, l, d, dtype=torch.bfloat16, device=device)
# kv_state = torch.zeros(b, h, d, d, dtype=torch.float32, device=device)
# k_state = torch.zeros(b, h, d, dtype=torch.float32, device=device)
betas = F.sigmoid(self.window_factors[0, :, 0, 0].to(dtype=torch.float32))
alphas = (
1 - betas
if self.affine_attention_factors
else torch.ones(betas.shape, dtype=torch.float32, device=device)
)
q_map = self.feature_map_q.mlp.layer
k_map = self.feature_map_k.mlp.layer
# Saves outputs to y_pred, k_state, kv_state, where we fuse:
# 1. f_q, f_k = self.feature_map_q(q), self.feature_map_k(k)
# 2. y_pred = attention(q, k, f_q, f_k, v) # b, h, l, d
# 3. kv_state = torch.einsum(‘bhlf,bhld->bhfd’,
# f_k[:, :, :-self.window_size],
# v[:, :, :-self.window_size]) # b, h, f, d
# 4. k_state = f_k[:, :, :-self.window_size].sum(dim=-2) # b, h, d
tk_window_hedgehog_attention(
q.contiguous(),
k.contiguous(),
v.contiguous(),
self.y_true,
self.k_state,
self.kv_state,
q_map,
k_map,
alphas,
betas,
)
past_key_value.update_with_kv(
self.kv_state, self.k_state.unsqueeze(-2), k, v, self.layer_idx
)
# Concatenate heads and apply output projection
y_true = self.y_true.transpose(1, 2).contiguous().view(b, l, self.hidden_size)
y_true = self.o_proj(y_true)
return y_true, None, past_key_value
class LinearAttentionTKWindowGenerationCache(LinearAttentionState):
"""
Class for `past_key_values`
-> Alternative to KV cache; here we only maintain a “KV state” and “K state”
-> Modified from transformers.cache_utils.DynamicCache (v4.36)
"""
def __init__(self, window_size: int = 64) -> None:
super().__init__()
self._seen_tokens = 0 # should be `self.seen_tokens` in Transformers v4.36
self._seen_tokens_by_layer: List[int] = []
self.window_size = window_size
self.decode_kv_states: List[torch.Tensor] = []
self.decode_k_states: List[torch.Tensor] = []
self.k_cache: List[torch.Tensor] = []
self.v_cache: List[torch.Tensor] = []
def update_with_kv(
self,
kv_state: torch.Tensor,
k_state: torch.Tensor,
k: torch.Tensor,
v: torch.Tensor,
layer_idx: int,
):
"""
Update the cache with new KV and K states
"""
if layer_idx == 0:
self._seen_tokens += k.shape[2]
self._seen_tokens_by_layer.append(k.shape[2])
# Initialize KV and K states
if len(self.decode_k_states) <= layer_idx:
self.decode_kv_states.append(kv_state)
self.decode_k_states.append(k_state)
else: # Update KV and K states
self.decode_kv_states[layer_idx] = (
self.decode_kv_states[layer_idx] + kv_state
)
self.decode_k_states[layer_idx] = self.decode_k_states[layer_idx] + k_state
self.k_cache.append(k[:, :, -self.window_size :, :])
self.v_cache.append(v[:, :, -self.window_size :, :])
def update_for_decoding(
self, k: torch.Tensor, v: torch.Tensor, layer_idx: int, feature_map_k: Callable
):
"""
Update the cache for decoding
"""
k_cache = self.k_cache[layer_idx]
v_cache = self.v_cache[layer_idx]
k_state = feature_map_k(k_cache[:, :, :1, :])
v_state = v_cache[:, :, :1, :]
kv_state = torch.einsum("bhlf,bhld->bhfd", k_state.float(), v_state.float()).to(
k.dtype
)
self.decode_kv_states[layer_idx] += kv_state
self.decode_k_states[layer_idx] += k_state
self.k_cache[layer_idx] = torch.cat([k_cache[:, :, 1:, :], k], dim=-2)
self.v_cache[layer_idx] = torch.cat([v_cache[:, :, 1:, :], v], dim=-2)
if layer_idx == 0:
self._seen_tokens += k.shape[-2]
self._seen_tokens_by_layer[layer_idx] += k.shape[-2]
return (
self.k_cache[layer_idx],
self.v_cache[layer_idx],
self.decode_kv_states[layer_idx],
self.decode_k_states[layer_idx],
)

305
src/axolotl/integrations/lolcats/linear_attention/linear_window_attention_tk_long.py Normal file
View File

@@ -0,0 +1,305 @@
"""
LoLCATs attention combining sliding window and linear attentions
- Using the TK "terracing" arrangement
- Training over long sequences with fixed memory with recurrent view
- During attention transfer, use Flash Attention to compute softmax attention outputs
For each layer:
- We first compute (softmax) attention over sliding windows
- We then compute standard linear attention to "fill in" the earlier parts
- We combine to model the entire sequence
"""
import logging
from typing import Optional
import torch
import torch.nn.functional as F
from transformers.cache_utils import Cache
try:
from transformers.modeling_flash_attention_utils import _flash_attention_forward
except ModuleNotFoundError:
_flash_attention_forward = None # Transformers v4.36
from ..model.rotary import apply_rotary_pos_emb
from .linear_attention import softmax_attention
from .linear_window_attention_tk import LolcatsTKWindowAttention
LOG = logging.getLogger(
"axolotl.integrations.lolcats.linear_attention.linear_window_attention_tk_long"
)
class LolcatsTKWindowLongAttention(LolcatsTKWindowAttention):
"""
Lolcats attention combining sliding window and linear attention
"""
def __init__(self, remove_base_attn=True, **kwargs):
# keep self.base_attn for Flash Attention inference
super().__init__(remove_base_attn=True, **kwargs)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Cache] = None,
output_attentions: bool = False,
use_cache: bool = False,
**kwargs,
):
"""
Forward pass with the option to compute attention weights multiple ways
if self.train_attention is True
-> Consistent with HuggingFace Transformers for easy use with their pretrained models
"""
b, l, _ = hidden_states.size()
if self.train_attention and self.base_inference:
with torch.no_grad():
# LOG.debug(hidden_states.shape)
_y_true = flash_attention_2(
self, # self.base_attn,
hidden_states=hidden_states,
attention_mask=None,
position_ids=position_ids,
past_key_value=None,
output_attentions=False,
# output_hidden_states=False,
use_cache=False,
)[0]
# _y_true.shape is (batch_size, seq_len, num_heads, head_dim)
y_true = _y_true.reshape(b, l, -1).contiguous()
y_true = self.o_proj(y_true)
layer_io = (hidden_states, _y_true) # hack
# layer_io = (hidden_states.cpu(), _y_true.cpu()) # hack
return y_true, layer_io, None
q, k, v, kv_seq_len = self.process_qkv(
hidden_states, attention_mask, position_ids, past_key_value
)
f_q, f_k = self.feature_map_q(q), self.feature_map_k(k)
# attention_mask = None # For now this is always True
if past_key_value is None: # Regular training
window_factors = F.sigmoid(self.window_factors)
linear_factors = 1 - window_factors if self.affine_attention_factors else 1
y_pred, a_pred = self.quadratic_attention(
q,
k,
f_q,
f_k,
v,
window_factors,
linear_factors,
window_size=self.window_size,
)
else:
past_key_value.window_size = self.decode_window_size
if f_q.shape[2] == 1 and kv_seq_len > 1 and not self.training: # Generating
assert use_cache is True
_kv = past_key_value.update_for_decoding(
k, v, self.layer_idx, self.feature_map_k, dtype=q.dtype
)
k_cache, v_cache, f_kv_state, f_k_state = _kv
# Sliding window + linear attention decode
window_factors = F.sigmoid(self.window_factors)
linear_factors = (
1 - window_factors if self.affine_attention_factors else 1
)
a_sm = torch.einsum("bhmd,bhnd->bhmn", q.float(), k_cache.float()) * (
k.shape[-1] ** -0.5
)
# a_sm = torch.softmax(a_sm, dim=-1)
a_sm_max = torch.amax(a_sm, dim=-1, keepdim=True)
a_sm = window_factors * torch.exp(a_sm - a_sm_max)
sum_sm = a_sm.sum(dim=-1, keepdim=True)
y_pred = torch.einsum(
"bhmn,bhnd->bhmd", a_sm, v_cache.float()
) + linear_factors * torch.einsum(
"bhlf,bhfd->bhld", f_q.float(), f_kv_state.float()
)
sum_ln = (
linear_factors
* torch.einsum("bhlf,bhnf->bhl", f_q.float(), f_k_state.float())[
..., None
]
)
y_pred = (y_pred / (sum_sm + sum_ln)).to(q.dtype)
else: # Stateful training
if (
self.state_grad_enabled
and self.layer_idx == 0
and position_ids is not None
):
LOG.debug(
f"\n position_ids: [{position_ids[0, 0]}, {position_ids[0, -1]}]"
)
LOG.debug(
f"q.shape: {q.shape}, k.shape: {k.shape}, v.shape: {v.shape}"
)
try:
kv_state = past_key_value.kv_states[self.layer_idx]
k_state = past_key_value.k_states[self.layer_idx]
except IndexError:
kv_state, k_state = None, None
window_factors = F.sigmoid(self.window_factors)
linear_factors = (
1 - window_factors if self.affine_attention_factors else 1
)
y_pred, a_pred = self.quadratic_attention(
q,
k,
f_q,
f_k,
v,
window_factors,
linear_factors,
window_size=self.window_size,
kv_state=kv_state,
k_state=k_state,
)
# Save and update KV cache and states
# past_key_value.update(k, v.detach(), self.layer_idx,
# fmap_key_states=f_k.detach(),
# accumulate_in_fp32=True)
past_key_value.update(
k, v, self.layer_idx, fmap_key_states=f_k, accumulate_in_fp32=True
)
# Concatenate heads and apply output projection
_y_pred = y_pred.transpose(1, 2).contiguous()
y_pred = self.o_proj(_y_pred.view(b, l, self.hidden_size))
if self.train_attention:
with torch.no_grad():
a_true = softmax_attention(q, k, None, causal=True)[1]
attn_weights = (_y_pred, (a_pred, a_true))
else:
attn_weights = _y_pred # flash_attn outputs are shape (b, l, h, d)
return y_pred, attn_weights, past_key_value
# -----------------
# Flash Attention 2
# -----------------
def flash_attention_2(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Cache] = None,
output_attentions: bool = False,
use_cache: bool = False,
cache_position: Optional[torch.LongTensor] = None,
):
"""
Wrapper for LlamaFlashAttention2
Copied and modified from HF Transformers v4.36 and v4.43 implementations
- (4.43) https://github.com/huggingface/transformers/blob/868d36d29ec132deeaaf8571b25b6a1b911d0145/src/transformers/models/llama/modeling_llama.py#L402
- (4.36) https://github.com/huggingface/transformers/blob/a7cab3c283312b8d4de5df3bbe719971e24f4281/src/transformers/models/llama/modeling_llama.py#L456
"""
bsz, q_len, _ = hidden_states.size()
query_states = self.q_proj(hidden_states)
key_states = self.k_proj(hidden_states)
value_states = self.v_proj(hidden_states)
# Flash attention requires the input to have the shape
# batch_size x seq_length x head_dim x hidden_dim
# therefore we just need to keep the original shape
query_states = query_states.view(
bsz, q_len, self.num_heads, self.head_dim
).transpose(1, 2)
key_states = key_states.view(
bsz, q_len, self.num_key_value_heads, self.head_dim
).transpose(1, 2)
value_states = value_states.view(
bsz, q_len, self.num_key_value_heads, self.head_dim
).transpose(1, 2)
try: # As in Transformers v4.36
kv_seq_len = key_states.shape[-2]
if past_key_value is not None:
kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
cos, sin = self.rotary_emb(key_states, seq_len=kv_seq_len)
query_states, key_states = apply_rotary_pos_emb(
query_states, key_states, cos, sin, position_ids
)
except Exception: # As in Transformers v4.39
cos, sin = self.rotary_emb(key_states, position_ids)
query_states, key_states = apply_rotary_pos_emb(
query_states, key_states, cos, sin
)
if past_key_value is not None:
# sin and cos are specific to RoPE models; cache_position needed for the static cache
cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
key_states, value_states = past_key_value.update(
key_states, value_states, self.layer_idx, cache_kwargs
)
# 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
# to be able to avoid many of these transpose/reshape/view.
query_states = query_states.transpose(1, 2)
key_states = key_states.transpose(1, 2)
value_states = value_states.transpose(1, 2)
dropout_rate = self.attention_dropout if self.training else 0.0
# In PEFT, usually we cast the layer norms in float32 for training stability reasons
# therefore the input hidden states gets silently casted in float32. Hence, we need
# cast them back in the correct dtype just to be sure everything works as expected.
# This might slowdown training & inference so it is recommended to not cast the LayerNorms
# in fp32. (LlamaRMSNorm handles it correctly)
input_dtype = query_states.dtype
if input_dtype == torch.float32:
if torch.is_autocast_enabled():
target_dtype = torch.get_autocast_gpu_dtype()
# Handle the case where the model is quantized
elif hasattr(self.config, "_pre_quantization_dtype"):
target_dtype = self.config._pre_quantization_dtype
else:
target_dtype = self.q_proj.weight.dtype
LOG.debug(
f"The input hidden states seems to be silently casted in float32, this might be related to"
f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
f" {target_dtype}."
)
query_states = query_states.to(target_dtype)
key_states = key_states.to(target_dtype)
value_states = value_states.to(target_dtype)
if getattr(self, "_flash_attention_forward", False):
attn_output = self._flash_attention_forward(
query_states,
key_states,
value_states,
attention_mask,
q_len,
dropout=dropout_rate,
is_causal=True,
)
else:
attn_output = _flash_attention_forward(
query_states,
key_states,
value_states,
attention_mask,
q_len,
dropout=0, # dropout_rate,
sliding_window=getattr(self, "sliding_window", None),
use_top_left_mask=self._flash_attn_uses_top_left_mask,
is_causal=True,
)
return attn_output, past_key_value

34
src/axolotl/integrations/lolcats/linear_attention/utils.py Normal file
View File

@@ -0,0 +1,34 @@
"""
Shared attention helpers
"""
import torch
# Copied from transformers.models.mistral.modeling_mistral (llama.modeling_llama at v4.36)
def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
"""
This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep).
The hidden states go from:
(batch, num_key_value_heads, seqlen, head_dim) to
(batch, num_attention_heads, seqlen, head_dim)
"""
batch, num_key_value_heads, slen, head_dim = hidden_states.shape
if n_rep == 1:
return hidden_states
hidden_states = hidden_states[:, :, None, :, :].expand(
batch, num_key_value_heads, n_rep, slen, head_dim
)
return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
def mask_attention(
qk_dot: torch.Tensor, attn_mask: torch.Tensor, mask_value: float = -10000
) -> torch.Tensor:
"""
Apply attention mask (e.g., for padding)
"""
if len(attn_mask.shape) == 4: # attn_mask either (b, h, l, d) or (b, l)
return qk_dot.masked_fill(~attn_mask.bool(), mask_value)
else:
return qk_dot.masked_fill(~attn_mask[:, None, None, :].bool(), mask_value)

222
src/axolotl/integrations/lolcats/linearize_attention.py Normal file
View File

@@ -0,0 +1,222 @@
"""
Convert attention to linear attention
Adapted from: https://github.com/HazyResearch/lolcats/blob/main/src/model/convert_model.py
@misc{zhang2024lolcatslowranklinearizinglarge,
title={LoLCATs: On Low-Rank Linearizing of Large Language Models},
author={Michael Zhang and Simran Arora and Rahul Chalamala and Alan Wu and Benjamin Spector and Aaryan Singhal and Krithik Ramesh and Christopher Ré},
year={2024},
eLOG.info={2410.10254},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2410.10254},
}
"""
import logging
from functools import partial
from typing import Any
import torch.nn as nn
from tqdm import tqdm
from axolotl.utils.dict import DictDefault
LOG = logging.getLogger("axolotl.integrations.lolcats.linearize_attention")
def convert_attention(
model: nn.Module,
attention_config: DictDefault,
train_attention: bool = False,
remove_base_attn: bool = True,
):
"""
Call to convert all attention layers
"""
softmax_attns = []
if "softmax_attentions" in attention_config:
softmax_attns = attention_config["softmax_attentions"]
if attention_config.attention_type != "softmax":
layers = traverse_layers(model)
for layer_idx, layer in enumerate(
tqdm(layers, desc="Converting attentions...")
):
if layer_idx not in softmax_attns:
layer.self_attn = convert_llama_attention(
layer,
attention_config,
layers,
train_attention,
remove_base_attn,
)
layer.self_attn.converted = True
else: # Freeze any preserved softmax attention layers
for p in layer.parameters():
p.requires_grad = False
else:
LOG.info(
f"-> attention_config.attention_type is {attention_config.attention_type}; not converting attentions"
)
return model
def toggle_attention(llama_model: nn.Module, train: bool = False):
"""
Make attentions trainable if train is True
-> Set train_attention = False when finetuning
"""
for layer in traverse_layers(llama_model):
layer.self_attn.train_attention = train
return llama_model
def remove_base_attention(llama_model: nn.Module):
"""
Remove teacher attention after distillation (if we keep it)
"""
for layer in traverse_layers(llama_model):
if getattr(layer.self_attn, "base_attn", False):
del layer.self_attn.base_attn
return llama_model
def traverse_layers(model: nn.Module, verbose: bool = False):
"""
Return list of model layers
"""
try:
layers = model.model.layers
if verbose:
LOG.info("-> Loading from model.model.layers")
except AttributeError as e: # if base model
if verbose:
LOG.info(e)
try:
layers = model.layers
if verbose:
LOG.info("-> Loading from model.layers")
except AttributeError as e1: # If we make a PEFT model
if verbose:
LOG.info(e1)
layers = model.base_model.model.model.layers
if verbose:
LOG.info("-> Loading from model.base_model.model.model.layers")
return layers
def convert_llama_attention(
layer: nn.Module,
attention_config: DictDefault,
layers: list[nn.Module], # list of layers
train_attention: bool = False,
remove_base_attn: bool = True,
):
"""
Converts a single layer's attention layer as specified by attention_config
"""
return get_attention(**attention_config)(
base_attn=layer.self_attn,
layer_idx=layer.self_attn.layer_idx, # Transformers v4.36
max_layer_idx=len(layers) - 1,
train_attention=train_attention,
remove_base_attn=remove_base_attn,
)
def get_attention(attention_type: str, **kwargs):
"""
Get the linear attention class; either purely linear or linear with sliding window
-> 'linear' == 'lolcats_llama'
-> 'linear and sliding_window' == 'lolcats_llama_window_*'
"""
kwargs["attention_type"] = attention_type
if attention_type == "lolcats_llama":
from .linear_attention import LolcatsLinearAttention
return partial(LolcatsLinearAttention, **kwargs)
elif attention_type == "lolcats_llama_window_tk":
from .linear_attention import LolcatsTKWindowAttention
return partial(LolcatsTKWindowAttention, **kwargs)
elif attention_type == "lolcats_llama_window_sw":
from .linear_attention import LolcatsSlidingWindowAttention
return partial(LolcatsSlidingWindowAttention, **kwargs)
elif attention_type == "lolcats_llama_window_sw_linear":
from .linear_attention.linear_window_attention_sw_linear import (
LolcatsLinearSlidingWindowAttention,
)
return partial(LolcatsLinearSlidingWindowAttention, **kwargs)
# Experimental chunked linear attentions below
elif attention_type == "lolcats_long_llama_window_tk":
from .linear_attention import LolcatsTKWindowLongAttention
return partial(LolcatsTKWindowLongAttention, **kwargs)
elif attention_type == "lolcats_long_llama_window_sw":
from .linear_attention import LolcatsSlidingWindowLongAttention
return partial(LolcatsSlidingWindowLongAttention, **kwargs)
# TK generation build (requires Thunderkittens)
elif attention_type == "lolcats_llama_window_tk_gen":
from .linear_attention import LolcatsWindowAttentionTKGen
return partial(LolcatsWindowAttentionTKGen, **kwargs)
else:
LOG.info(f"-> attention_type {attention_type} not handled... returning None")
return None
def get_attention_cache(attention_type: str, past_key_values: Any = None):
"""
Determine how we store past keys and values when generating
"""
if attention_type is None:
return past_key_values
# LOG.info(f'Returning attention cache based on attention_type == {attention_type}')
elif "lolcats_llama_window_tk_gen" in attention_type:
from .linear_attention import LinearAttentionTKWindowGenerationCache
return LinearAttentionTKWindowGenerationCache()
elif "llama_window_tk" in attention_type:
from .linear_attention import LinearAttentionTKWindowCache
return LinearAttentionTKWindowCache()
elif "llama_window_sw" in attention_type:
from .linear_attention import LinearAttentionSlidingWindowCache
return LinearAttentionSlidingWindowCache()
elif "llama_window_sw_linear" in attention_type:
from .linear_attention import LinearAttentionSlidingWindowCache
return LinearAttentionSlidingWindowCache()
# TK generation build (requires Thunderkittens)
elif attention_type == "lolcats_llama_window_tk_gen":
from .linear_attention.linear_window_attention_tk_gen import (
LinearAttentionTKWindowGenerationCache,
)
return LinearAttentionTKWindowGenerationCache()
elif "softmax" in attention_type:
return past_key_values
else:
from .linear_attention import LinearAttentionState
return LinearAttentionState()

336
src/axolotl/integrations/lolcats/model/feature_map.py Normal file
View File

@@ -0,0 +1,336 @@
"""
Learnable linear attention feature map classes and functions
"""
from typing import Optional
import torch
import torch.nn as nn
import torch.nn.functional as F
def init_feature_map(name: str, mlp: nn.Module, **kwargs):
"""
Initialize feature map final activation for linear attention
"""
return FeatureMap(activation_name=name, mlp=mlp, **kwargs)
def init_feature_map_act(name: str, fullspace: bool = True, **kwargs):
"""
Initialize feature map final activation for linear attention
"""
if name == "softmax_dim" and fullspace:
return SoftmaxDim(**kwargs)
elif name == "softmax_dim" and not fullspace:
return SoftmaxDimHalfspace(**kwargs)
elif name == "exp_dim" and fullspace:
return Exp(**kwargs)
elif name == "exp_dim" and not fullspace:
return ExpHalfspace(**kwargs)
elif name == "pos_elu":
return PosELU(**kwargs)
elif name == "relu":
return ReLU(**kwargs)
else:
raise NotImplementedError
def init_learned_kernel(name: str, **kwargs):
"""
Initialize feature map MLP for linear attention
"""
if name == "untied_head_einsum":
return FeatureMapMLP(**kwargs)
elif name == "untied_head_adapter":
return FeatureMapAdapter(**kwargs)
else:
raise NotImplementedError
class FeatureMap(nn.Module):
"""
Final 'activation' of feature map. Can probably be combined with
`FeatureMapMLP` below
Full feature map is like f(xW + b)
-> This is the `f` part
"""
def __init__(
self,
activation_name: str,
head_dim_idx: int = -1,
eps: float = 1e-12,
mlp: Optional[nn.Module] = None,
fullspace: bool = True,
):
super().__init__()
self.head_dim_idx = head_dim_idx
self.eps = eps
self.mlp = mlp if mlp is not None else nn.Identity()
self.activation = init_feature_map_act(activation_name, fullspace, eps=eps)
def forward(self, x: torch.Tensor, *mlp_args, **mlp_kwargs):
"""
Assume x.shape is (batch_size, n_heads, seq_len, head_dim)
"""
return self.activation(self.mlp(x, *mlp_args, **mlp_kwargs), x)
def q_map(self, *args, **kwargs):
"""
Use for inference in case q and k feature maps differ
"""
return self.forward(*args, **kwargs)
def k_map(self, *args, **kwargs):
"""
Use for inference in case q and k feature maps differ
"""
return self.forward(*args, **kwargs)
# -----------------------
# Feature map activations
# -----------------------
class FeatureMapAct(nn.Module):
"""
Base class for feature map activations
"""
def __init__(self, eps: float = 1e-12):
super().__init__()
self.eps = eps
def forward(self, x: torch.Tensor, *args, **kwargs):
"""
x.shape is (batch_size, n_heads, seq_len, head_dim)
"""
return x
class PosELU(FeatureMapAct):
"""
1 + ELU activation as in https://arxiv.org/abs/2006.16236
"""
def forward(self, x: torch.Tensor, *args, **kwargs):
return (1 + F.elu(x)).clamp(min=self.eps)
class ReLU(FeatureMapAct):
"""
ReLU activation as in https://arxiv.org/abs/2103.13076
"""
def forward(self, x: torch.Tensor, *args, **kwargs):
return F.relu(x).clamp(min=self.eps)
class SoftmaxDim(FeatureMapAct):
"""
Softmax activation as in https://arxiv.org/abs/2402.04347
"""
def forward(self, x: torch.Tensor, *args, **kwargs):
return torch.cat(
[torch.softmax(x, dim=-1), torch.softmax(-x, dim=-1)], dim=-1
).clamp(min=self.eps)
class SoftmaxDimHalfspace(FeatureMapAct):
"""
Softmax activation as in https://arxiv.org/abs/2402.04347
"""
def forward(self, x: torch.Tensor, *args, **kwargs):
return torch.softmax(x, dim=-1).clamp(min=self.eps)
class Exp(FeatureMapAct):
"""
Exp activation as in https://arxiv.org/abs/2402.04347
"""
def forward(self, x: torch.Tensor, *args, **kwargs):
x_max = torch.amax(x, dim=-1, keepdim=True)
x_min = torch.amin(x, dim=-1, keepdim=True)
return torch.cat([torch.exp(x - x_max), torch.exp(-x + x_min)], dim=-1).clamp(
min=self.eps
)
class ExpHalfspace(FeatureMapAct):
"""
Exp activation as in https://arxiv.org/abs/2402.04347
"""
def forward(self, x: torch.Tensor, *args, **kwargs):
x_max = torch.amax(x, dim=-1, keepdim=True)
return torch.exp(x - x_max).clamp(min=self.eps)
# ----------------
# Feature map MLPs
# ----------------
class FeatureMapMLP(nn.Module):
"""
Learnable MLP in feature map.
Full feature map is like f(xW + b)
-> This is the `W` and (optional) `b` part
"""
def __init__(
self,
num_heads: int,
head_dim: int, # input dim
feature_dim: int, # output dim
dtype: torch.dtype,
device: torch.device,
skip_connection: bool = False,
bias: bool = False,
zero_init: bool = False,
normal_init: bool = False,
):
super().__init__()
self.num_heads = num_heads
self.head_dim = head_dim
self.feature_dim = feature_dim
self.dtype = dtype
self.device = device
self.skip_connection = skip_connection
self.bias = bias
self.zero_init = zero_init
self.normal_init = normal_init
self.init_weights_()
if self.zero_init: # Zero-out weights or set as identity post-initialization
self.zero_init_with_skip_() if self.skip_connection else self.zero_init_()
if self.normal_init:
with torch.no_grad():
nn.init.normal_(self.layer)
if self.skip_connection:
assertion_fail = f"If self.skip_connection we need self.head_dim == self.feature_dim but self.head_dim is {self.head_dim} != self.feature_dim is {self.feature_dim}"
assert self.head_dim == self.feature_dim, assertion_fail
def init_weights_(self):
"""
Initialize (W)eights and (b)iases
"""
self.layer = nn.Parameter(
torch.zeros(
(self.num_heads, self.head_dim, self.feature_dim),
dtype=self.dtype,
device=self.device,
)
)
nn.init.kaiming_uniform_(self.layer)
if self.bias:
self.bias = nn.Parameter(
torch.zeros(
(1, self.num_heads, 1, 1), # self.feature_dim),
dtype=self.dtype,
device=self.device,
)
)
nn.init.kaiming_uniform_(self.bias)
else:
self.bias = 0.0 # hack
def zero_init_with_skip_(self):
"""
Initialize weights to zero matrix if skip connection
"""
with torch.no_grad():
nn.init.zeros_(self.layer)
def zero_init_(self):
"""
Initialize weights to identity matrix if no skip connection
"""
with torch.no_grad():
for i in range(self.layer.shape[0]):
try:
nn.init.eye_(self.layer[i])
except RuntimeError:
with torch.no_grad():
dtype = self.layer[i].dtype
weight = torch.eye(
*self.layer[i].shape,
requires_grad=self.layer[i].requires_grad,
device=self.layer[i].device,
)
self.layer[i] = weight.to(dtype=dtype)
def forward(self, x: torch.Tensor):
"""
Assume x.shape is (batch_size, num_heads, seq_len, head_dim)
"""
_x = torch.einsum("hdf,bhld->bhlf", self.layer, x) + self.bias
return x + _x if self.skip_connection else _x
class FeatureMapAdapter(FeatureMapMLP):
"""
Learnable Feature map with bottleneck adapter
as in https://arxiv.org/abs/1902.00751
We don't use but could be fun to try
"""
def __init__(self, hidden_dim: int, *args, **kwargs):
kwargs["skip_connection"] = True
kwargs["bias"] = True
kwargs["zero_init"] = True
self.hidden_dim = hidden_dim
super().__init__(*args, **kwargs)
def init_weights_(self):
"""
Initialize (W)eights and (b)iases
"""
kwargs = {"dtype": self.dtype, "device": self.device}
self.layer0 = nn.Parameter(
torch.zeros((self.num_heads, self.head_dim, self.hidden_dim), **kwargs)
)
self.layer1 = nn.Parameter(
torch.zeros((self.num_heads, self.hidden_dim, self.feature_dim), **kwargs)
)
nn.init.kaiming_uniform_(self.layer0)
nn.init.kaiming_uniform_(self.layer1)
self.bias0 = nn.Parameter(
torch.zeros((1, self.num_heads, 1, self.hidden_dim), **kwargs)
)
self.bias1 = nn.Parameter(
torch.zeros((1, self.num_heads, 1, self.feature_dim), **kwargs)
)
nn.init.kaiming_uniform_(self.bias0)
nn.init.kaiming_uniform_(self.bias1)
def zero_init_with_skip_(self):
with torch.no_grad():
nn.init.zeros_(self.layer0)
nn.init.zeros_(self.layer1)
nn.init.zeros_(self.bias0)
nn.init.zeros_(self.bias1)
def zero_init_(self):
raise NotImplementedError
def forward(self, x: torch.Tensor):
"""
Assume x.shape is (batch_size, num_heads, seq_len, head_dim)
-> Down-project, apply nonlinearity, up-project; add skip connection
"""
_x = torch.einsum("hde,bhld->bhle", self.layer0, x) + self.bias0
_x = F.relu(_x)
_x = torch.einsum("hef,bhle->bhlf", self.layer1, _x) + self.bias1
return x + _x if self.skip_connection else _x

204
src/axolotl/integrations/lolcats/model/rotary.py Normal file
View File

@@ -0,0 +1,204 @@
# coding=utf-8
# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
#
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
# and OPT implementations in this library. It has been modified from its
# original forms to accommodate minor architectural differences compared
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
#
# 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.
"""
Rotary embeddings. Same as usual for Transformer models.
Note these are modified from HF Transformers v4.36, from:
- transformers/models/llama/modeling_llama.py or transformers/models/mistral/modeling_mistral.py
- i.e., https://github.com/huggingface/transformers/blob/a7cab3c283312b8d4de5df3bbe719971e24f4281/src/transformers/models/llama/modeling_llama.py#L123
"""
from typing import Optional
import torch
import torch.nn as nn
def get_rotary_embeddings(
rope_scaling_type: Optional[str] = None,
head_dim: int = 128,
max_position_embeddings: int = 4096,
rope_theta: float = 10000.0,
rope_scaling_factor: float = 1.0,
device: Optional[torch.device] = None,
) -> nn.Module:
"""Return rotary embedding object"""
if rope_scaling_type is None:
return RotaryEmbedding(
head_dim,
max_position_embeddings=max_position_embeddings,
base=rope_theta,
device=device,
)
elif rope_scaling_type == "linear":
return LinearScalingRotaryEmbedding(
head_dim,
max_position_embeddings=max_position_embeddings,
scaling_factor=rope_scaling_factor,
base=rope_theta,
device=device,
)
elif rope_scaling_type == "dynamic":
return DynamicNTKScalingRotaryEmbedding(
head_dim,
max_position_embeddings=max_position_embeddings,
scaling_factor=rope_scaling_factor,
base=rope_theta,
device=device,
)
else:
raise NotImplementedError(
f'Sorry rope_scaling_type == "{rope_scaling_type}" not implemented.'
)
# Copied from transformers.models.mistral.modeling_mistral (llama.modeling_llama at v4.36)
def rotate_half(x):
"""Rotates half the hidden dims of the input."""
x1 = x[..., : x.shape[-1] // 2]
x2 = x[..., x.shape[-1] // 2 :]
return torch.cat((-x2, x1), dim=-1)
# Copied from transformers.models.mistral.modeling_mistral (llama.modeling_llama at v4.36)
def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
"""Applies Rotary Position Embedding to the query and key tensors."""
if position_ids is not None:
cos, sin = cos[position_ids], sin[position_ids]
cos = cos.unsqueeze(unsqueeze_dim)
sin = sin.unsqueeze(unsqueeze_dim)
q_embed = (q * cos) + (rotate_half(q) * sin)
k_embed = (k * cos) + (rotate_half(k) * sin)
return q_embed, k_embed
# Modified from transformers.models.mistral.modeling_mistral (llama.modeling_llama at v4.36)
class RotaryEmbedding(nn.Module):
"""Original Rotary Embeddings from RoFormer https://arxiv.org/abs/2104.09864"""
def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
super().__init__()
self.dim = dim
self.max_position_embeddings = max_position_embeddings
self.base = base
inv_freq = 1.0 / (
self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim)
)
self.register_buffer("inv_freq", inv_freq, persistent=False)
# Build here to make `torch.jit.trace` work.
self._set_cos_sin_cache(
seq_len=max_position_embeddings,
device=self.inv_freq.device,
dtype=torch.get_default_dtype(),
)
def _set_cos_sin_cache(self, seq_len, device, dtype):
self.max_seq_len_cached = seq_len
t = torch.arange(
self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype
)
freqs = torch.outer(t, self.inv_freq)
# Different from paper, but it uses a different permutation in order to obtain the same calculation
emb = torch.cat((freqs, freqs), dim=-1)
self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
def forward(self, x, seq_len=None):
"""
Compute rotary embeddings
"""
# x: [bs, num_attention_heads, seq_len, head_size]
if seq_len > self.max_seq_len_cached:
self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)
return (
self.cos_cached[:seq_len].to(dtype=x.dtype),
self.sin_cached[:seq_len].to(dtype=x.dtype),
)
# Copied from transformers/models/llama/modeling_llama.py at v4.36
class LinearScalingRotaryEmbedding(RotaryEmbedding):
"""RotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""
def __init__(
self,
dim,
max_position_embeddings=2048,
base=10000,
device=None,
scaling_factor=1.0,
):
self.scaling_factor = scaling_factor
super().__init__(dim, max_position_embeddings, base, device)
def _set_cos_sin_cache(self, seq_len, device, dtype):
self.max_seq_len_cached = seq_len
t = torch.arange(
self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype
)
t = t / self.scaling_factor
freqs = torch.outer(t, self.inv_freq)
# Different from paper, but it uses a different permutation in order to obtain the same calculation
emb = torch.cat((freqs, freqs), dim=-1)
self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
# Copied from transformers/models/llama/modeling_llama.py at v4.36
class DynamicNTKScalingRotaryEmbedding(RotaryEmbedding):
"""RotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla"""
def __init__(
self,
dim,
max_position_embeddings=2048,
base=10000,
device=None,
scaling_factor=1.0,
):
self.scaling_factor = scaling_factor
super().__init__(dim, max_position_embeddings, base, device)
def _set_cos_sin_cache(self, seq_len, device, dtype):
self.max_seq_len_cached = seq_len
if seq_len > self.max_position_embeddings:
base = self.base * (
(self.scaling_factor * seq_len / self.max_position_embeddings)
- (self.scaling_factor - 1)
) ** (self.dim / (self.dim - 2))
inv_freq = 1.0 / (
base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim)
)
self.register_buffer("inv_freq", inv_freq, persistent=False)
t = torch.arange(
self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype
)
freqs = torch.outer(t, self.inv_freq)
# Different from paper, but it uses a different permutation in order to obtain the same calculation
emb = torch.cat((freqs, freqs), dim=-1)
self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)