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support qwen3.5 moe padding_free #226

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Merged
tpx818 merged 3 commits into from
Jun 23, 2026
+199 −120
Merged

support qwen3.5 moe padding_free #226

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9c4ca46
support qwen35 moe gdn sp
meichangsu1 Jun 10, 2026
8a2f835
support gdn padding-free & fix sp unit test
meichangsu1 Jun 11, 2026
485f23a
Merge remote-tracking branch 'origin/main' into qwe35_moe
meichangsu1 Jun 16, 2026
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3 changes: 2 additions & 1 deletion src/twinkle/model/transformers/strategy/sequence_parallel/linear_attention_sp.py
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Original file line number Diff line number Diff line change
Expand Up @@ -181,7 +181,8 @@ def _run_forward(
sequence_parallel_context=None,
) -> torch.Tensor:
using_torch_fallback = _ensure_linear_attention_kernels(mod)
from transformers.models.qwen3_5.modeling_qwen3_5 import apply_mask_to_padding_states
modeling_module = import_module(mod.__class__.__module__)
apply_mask_to_padding_states = getattr(modeling_module, 'apply_mask_to_padding_states')
Comment thread
meichangsu1 marked this conversation as resolved.

local_attention_mask = attention_mask
if torch.is_tensor(attention_mask) and attention_mask.dim() == 2:
Expand Down
220 changes: 124 additions & 96 deletions src/twinkle/patch/gdn_padding_free.py
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Original file line number Diff line number Diff line change
@@ -1,5 +1,6 @@
import torch
import transformers
from importlib import import_module
from packaging.version import Version
from transformers.utils.import_utils import is_flash_linear_attention_available
from typing import Optional
Expand All @@ -12,16 +13,35 @@ def _is_qwen35_model(hf_config) -> bool:
return 'qwen3_5' in getattr(hf_config, 'model_type', '')


def _find_qwen35_classes(module: Optional[torch.nn.Module], hf_config, enable_sp: bool):
def _iter_qwen35_class_pairs():
class_specs = (
(
'transformers.models.qwen3_5.modeling_qwen3_5',
'Qwen3_5DecoderLayer',
'Qwen3_5GatedDeltaNet',
),
(
'transformers.models.qwen3_5_moe.modeling_qwen3_5_moe',
'Qwen3_5MoeDecoderLayer',
'Qwen3_5MoeGatedDeltaNet',
),
)
for module_name, decoder_class_name, gdn_class_name in class_specs:
try:
modeling_module = import_module(module_name)
yield getattr(modeling_module, decoder_class_name), getattr(modeling_module, gdn_class_name)
except Exception:
continue


def _find_qwen35_class_pairs(module: Optional[torch.nn.Module], hf_config, enable_sp: bool):
if module is None or enable_sp or not _is_qwen35_model(hf_config):
return None, None
try:
from transformers.models.qwen3_5.modeling_qwen3_5 import Qwen3_5DecoderLayer, Qwen3_5GatedDeltaNet
except Exception:
return None, None
if any(isinstance(submodule, Qwen3_5GatedDeltaNet) for submodule in module.modules()):
return Qwen3_5DecoderLayer, Qwen3_5GatedDeltaNet
return None, None
return ()
class_pairs = []
for decoder_layer_cls, gated_delta_net_cls in _iter_qwen35_class_pairs():
if any(isinstance(submodule, gated_delta_net_cls) for submodule in module.modules()):
class_pairs.append((decoder_layer_cls, gated_delta_net_cls))
return tuple(class_pairs)


def _get_flash_linear_attention_kernels():
Expand Down Expand Up @@ -84,104 +104,112 @@ class GatedDeltaNetPaddingFreePatch(Patch):

def __call__(self, module, *args, **kwargs):
del args
Qwen3_5DecoderLayer, Qwen3_5GatedDeltaNet = _find_qwen35_classes(
qwen35_class_pairs = _find_qwen35_class_pairs(
module,
kwargs.get('hf_config'),
bool(kwargs.get('enable_sp', False)),
)
if Qwen3_5DecoderLayer is None or Qwen3_5GatedDeltaNet is None:
return
if getattr(Qwen3_5GatedDeltaNet, '_twinkle_sp_linear_patched', False):
if not qwen35_class_pairs:
return
module._twinkle_gdn_padding_free_patched = True

if not getattr(Qwen3_5DecoderLayer, '_twinkle_padding_free_cu_seqlens_patched', False):
origin_decoder_forward = Qwen3_5DecoderLayer.forward

def decoder_forward(
layer,
hidden_states: torch.Tensor,
position_embeddings: tuple[torch.Tensor, torch.Tensor],
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
past_key_values=None,
cache_position: Optional[torch.Tensor] = None,
**extra_kwargs,
):
if getattr(layer, 'layer_type', None) != 'linear_attention':
return call_with_supported_kwargs(
origin_decoder_forward,
layer,
for decoder_layer_cls, gated_delta_net_cls in qwen35_class_pairs:
if getattr(gated_delta_net_cls, '_twinkle_sp_linear_patched', False):
continue

if not getattr(decoder_layer_cls, '_twinkle_padding_free_cu_seqlens_patched', False):
origin_decoder_forward = decoder_layer_cls.forward

def decoder_forward(
layer,
hidden_states: torch.Tensor,
position_embeddings: tuple[torch.Tensor, torch.Tensor],
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
past_key_values=None,
cache_position: Optional[torch.Tensor] = None,
_origin_decoder_forward=origin_decoder_forward,
**extra_kwargs,
):
if getattr(layer, 'layer_type', None) != 'linear_attention':
return call_with_supported_kwargs(
_origin_decoder_forward,
layer,
hidden_states=hidden_states,
position_embeddings=position_embeddings,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
cache_position=cache_position,
**extra_kwargs,
)
cu_seq_lens_q = extra_kwargs.pop('cu_seq_lens_q', None)
extra_kwargs.pop('cu_seq_lens_k', None)
extra_kwargs.pop('max_length_q', None)
extra_kwargs.pop('max_length_k', None)

residual = hidden_states
hidden_states = layer.input_layernorm(hidden_states)
hidden_states = layer.linear_attn(
hidden_states=hidden_states,
position_embeddings=position_embeddings,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
cache_position=cache_position,
**extra_kwargs,
)
cu_seq_lens_q = extra_kwargs.pop('cu_seq_lens_q', None)
extra_kwargs.pop('cu_seq_lens_k', None)
extra_kwargs.pop('max_length_q', None)
extra_kwargs.pop('max_length_k', None)

residual = hidden_states
hidden_states = layer.input_layernorm(hidden_states)
hidden_states = layer.linear_attn(
hidden_states=hidden_states,
cache_params=past_key_values,
cache_position=cache_position,
attention_mask=attention_mask,
cu_seq_lens_q=cu_seq_lens_q,
)
hidden_states = residual + hidden_states

residual = hidden_states
hidden_states = layer.post_attention_layernorm(hidden_states)
hidden_states = layer.mlp(hidden_states)
hidden_states = residual + hidden_states
return hidden_states

Qwen3_5DecoderLayer.forward = decoder_forward
Qwen3_5DecoderLayer._twinkle_padding_free_cu_seqlens_patched = True

if not getattr(Qwen3_5GatedDeltaNet, '_twinkle_padding_free_gdn_patched', False):
origin_forward = Qwen3_5GatedDeltaNet.forward
patch_chunk_rule = _needs_chunk_gated_delta_rule_cu_seqlens_patch()

def forward(
mod,
hidden_states: torch.Tensor,
cache_params=None,
cache_position=None,
attention_mask: Optional[torch.Tensor] = None,
cu_seq_lens_q: Optional[torch.Tensor] = None,
**extra_kwargs,
):
if cu_seq_lens_q is None:
return call_with_supported_kwargs(
origin_forward,
mod,
hidden_states,
cache_params=cache_params,
cache_params=past_key_values,
cache_position=cache_position,
attention_mask=attention_mask,
cu_seq_lens_q=cu_seq_lens_q,
**extra_kwargs,
)
return _patch_gdn_kernels_for_cu_seqlens(
hidden_states = residual + hidden_states

residual = hidden_states
hidden_states = layer.post_attention_layernorm(hidden_states)
hidden_states = layer.mlp(hidden_states)
if isinstance(hidden_states, tuple):
hidden_states, _ = hidden_states
hidden_states = residual + hidden_states
return hidden_states
Comment thread
meichangsu1 marked this conversation as resolved.

decoder_layer_cls.forward = decoder_forward
decoder_layer_cls._twinkle_padding_free_cu_seqlens_patched = True

if not getattr(gated_delta_net_cls, '_twinkle_padding_free_gdn_patched', False):
origin_forward = gated_delta_net_cls.forward
patch_chunk_rule = _needs_chunk_gated_delta_rule_cu_seqlens_patch()

def forward(
mod,
cu_seqlens=cu_seq_lens_q,
patch_chunk_rule=patch_chunk_rule,
origin_forward=origin_forward,
forward_args=(hidden_states, ),
forward_kwargs={
'cache_params': cache_params,
'cache_position': cache_position,
'attention_mask': attention_mask,
'cu_seq_lens_q': cu_seq_lens_q,
**extra_kwargs,
},
)
hidden_states: torch.Tensor,
cache_params=None,
cache_position=None,
attention_mask: Optional[torch.Tensor] = None,
cu_seq_lens_q: Optional[torch.Tensor] = None,
_origin_forward=origin_forward,
_patch_chunk_rule=patch_chunk_rule,
**extra_kwargs,
):
if cu_seq_lens_q is None:
return call_with_supported_kwargs(
_origin_forward,
mod,
hidden_states,
cache_params=cache_params,
cache_position=cache_position,
attention_mask=attention_mask,
**extra_kwargs,
)
return _patch_gdn_kernels_for_cu_seqlens(
mod,
cu_seqlens=cu_seq_lens_q,
patch_chunk_rule=_patch_chunk_rule,
origin_forward=_origin_forward,
forward_args=(hidden_states, ),
forward_kwargs={
'cache_params': cache_params,
'cache_position': cache_position,
'attention_mask': attention_mask,
'cu_seq_lens_q': cu_seq_lens_q,
**extra_kwargs,
},
)

Qwen3_5GatedDeltaNet.forward = forward
Qwen3_5GatedDeltaNet._twinkle_padding_free_gdn_patched = True
gated_delta_net_cls.forward = forward
gated_delta_net_cls._twinkle_padding_free_gdn_patched = True
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