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init v0.11.0rc0

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This commit is contained in:
luopingyi
2025-10-14 10:38:28 +08:00
parent 67afd0ea78
commit 66dc16f966
278 changed files with 28130 additions and 11708 deletions
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25
vllm_ascend/lora/punica_wrapper/lora_ops.py → vllm_ascend/lora/lora_ops.py
View File

@@ -21,7 +21,7 @@ def bgmv_shrink(inputs: torch.Tensor,
output_tensor: torch.Tensor,
lora_indices_tensor: torch.Tensor,
scaling: float = 1.0):
return torch.ops._C.bgmv_shrink(
return torch.ops._C_ascend.bgmv_shrink(
inputs,
lora_a_weights,
lora_indices_tensor,
@@ -35,7 +35,7 @@ def bgmv_expand(inputs: torch.Tensor,
output_tensor: torch.Tensor,
lora_indices_tensor: torch.Tensor,
add_inputs: bool = True):
return torch.ops._C.bgmv_expand(
return torch.ops._C_ascend.bgmv_expand(
inputs,
lora_b_weights,
lora_indices_tensor,
@@ -52,9 +52,9 @@ def bgmv_expand_slice(inputs: torch.Tensor,
slice_offset: int,
slice_size: int,
add_inputs: bool = True):
return torch.ops._C.bgmv_expand(inputs, lora_b_weights,
lora_indices_tensor, output_tensor,
slice_offset, slice_size)
return torch.ops._C_ascend.bgmv_expand(inputs, lora_b_weights,
lora_indices_tensor, output_tensor,
slice_offset, slice_size)
def sgmv_shrink(
@@ -69,9 +69,9 @@ def sgmv_shrink(
token_nums: int,
scaling: float,
):
return torch.ops._C.sgmv_shrink(inputs, lora_a_weights,
lora_indices_tensor, seq_len_tensor,
output_tensor, scaling)
return torch.ops._C_ascend.sgmv_shrink(inputs, lora_a_weights,
lora_indices_tensor, seq_len_tensor,
output_tensor, scaling)
def sgmv_expand(inputs: torch.Tensor,
@@ -84,7 +84,7 @@ def sgmv_expand(inputs: torch.Tensor,
max_seq_length: int,
token_nums: int,
add_inputs: bool = False):
return torch.ops._C.sgmv_expand(
return torch.ops._C_ascend.sgmv_expand(
inputs,
lora_b_weights,
lora_indices_tensor,
@@ -107,6 +107,7 @@ def sgmv_expand_slice(inputs: torch.Tensor,
slice_offset: int,
slice_size: int,
add_inputs: bool = False):
return torch.ops._C.sgmv_expand(inputs, lora_b_weights,
lora_indices_tensor, seq_len_tensor,
output_tensor, slice_offset, slice_size)
return torch.ops._C_ascend.sgmv_expand(inputs, lora_b_weights,
lora_indices_tensor, seq_len_tensor,
output_tensor, slice_offset,
slice_size)

26
vllm_ascend/lora/punica_wrapper/punica_npu.py → vllm_ascend/lora/punica_npu.py
View File

@@ -11,12 +11,14 @@ if is_310p():
bgmv_shrink, sgmv_expand,
sgmv_expand_slice, sgmv_shrink)
else:
from vllm_ascend.lora.punica_wrapper.lora_ops import (
bgmv_expand, bgmv_expand_slice, bgmv_shrink, sgmv_expand,
sgmv_expand_slice, sgmv_shrink)
from vllm_ascend.lora.lora_ops import (bgmv_expand, bgmv_expand_slice,
bgmv_shrink, sgmv_expand,
sgmv_expand_slice, sgmv_shrink)
from vllm.lora.punica_wrapper.punica_base import PunicaWrapperBase
from vllm_ascend.lora.utils import refresh_all_lora_classes
# The platforms that are compatible with the PyTorch-native implementation can
# inherit this class
@@ -31,6 +33,7 @@ class PunicaWrapperNPU(PunicaWrapperBase):
device: Union[torch.device, str], **kwargs):
PunicaWrapperBase.__init__(self, max_num_batched_tokens, max_batches,
device)
refresh_all_lora_classes()
def _shrink_prefill(
self,
@@ -338,13 +341,7 @@ class PunicaWrapperNPU(PunicaWrapperBase):
y_org = y
y = y.view(-1, y.shape[-1])
x = x.view(-1, x.shape[-1])
if lora_a_stacked.dim() == 2:
lora_a_stacked = lora_a_stacked.unsqueeze(0)
if lora_b_stacked.dim() == 2:
lora_b_stacked = lora_b_stacked.unsqueeze(0)
r = lora_a_stacked.size(-1)
r = lora_b_stacked.size(-1)
if buffer is None:
buffer = torch.zeros((x.size(0), r),
@@ -352,13 +349,8 @@ class PunicaWrapperNPU(PunicaWrapperBase):
device=x.device)
indices = self.sampler_indices
if indices.max() >= lora_a_stacked.size(0):
indices = torch.clamp(indices, 0, lora_a_stacked.size(0) - 1)
lora_a_reshaped = lora_a_stacked.transpose(1, 2)
lora_b_reshaped = lora_b_stacked.transpose(1, 2)
bgmv_shrink(x, lora_a_reshaped, buffer, indices, scale)
bgmv_expand(buffer, lora_b_reshaped, y, indices, add_inputs=True)
bgmv_shrink(x, lora_a_stacked, buffer, indices, scale)
bgmv_expand(buffer, lora_b_stacked, y, indices, add_inputs=True)
y = y.view_as(y_org)

0
vllm_ascend/lora/punica_wrapper/__init__.py
View File

110
vllm_ascend/lora/utils.py Normal file
View File

@@ -0,0 +1,110 @@
from typing import Optional
import vllm
from torch import nn
from transformers import PretrainedConfig
from vllm.config import LoRAConfig
from vllm.lora.layers import (ColumnParallelLinearWithLoRA,
MergedColumnParallelLinearWithLoRA,
MergedQKVParallelLinearWithLoRA,
QKVParallelLinearWithLoRA,
RowParallelLinearWithLoRA,
VocabParallelEmbeddingWithLoRA)
from vllm.lora.layers.utils import _not_fully_sharded_can_replace
from vllm_ascend.ops.linear import (AscendColumnParallelLinear,
AscendMergedColumnParallelLinear,
AscendQKVParallelLinear,
AscendRowParallelLinear)
from vllm_ascend.ops.vocab_parallel_embedding import \
AscendVocabParallelEmbedding
class AscendColumnParallelLinearWithLoRA(ColumnParallelLinearWithLoRA):
@classmethod
def can_replace_layer(
cls,
source_layer: nn.Module,
lora_config: LoRAConfig,
packed_modules_list: list,
model_config: Optional[PretrainedConfig],
) -> bool:
return type(source_layer) is AscendColumnParallelLinear
class AscendMergedColumnParallelLinearWithLoRA(
MergedColumnParallelLinearWithLoRA):
@classmethod
def can_replace_layer(
cls,
source_layer: nn.Module,
lora_config: LoRAConfig,
packed_modules_list: list,
model_config: Optional[PretrainedConfig],
) -> bool:
return type(source_layer) is AscendMergedColumnParallelLinear
class AscendRowParallelLinearWithLoRA(RowParallelLinearWithLoRA):
@classmethod
def can_replace_layer(
cls,
source_layer: nn.Module,
lora_config: LoRAConfig,
packed_modules_list: list,
model_config: Optional[PretrainedConfig],
) -> bool:
return type(source_layer) is AscendRowParallelLinear
class AscendVocabParallelEmbeddingWithLoRA(VocabParallelEmbeddingWithLoRA):
@classmethod
def can_replace_layer(
cls,
source_layer: nn.Module,
lora_config: LoRAConfig,
packed_modules_list: list,
model_config: Optional[PretrainedConfig],
) -> bool:
return type(source_layer) is AscendVocabParallelEmbedding
class AscendQKVParallelLinearWithLoRA(QKVParallelLinearWithLoRA):
@classmethod
@_not_fully_sharded_can_replace
def can_replace_layer(cls, source_layer: nn.Module,
lora_config: LoRAConfig, packed_modules_list: list,
model_config: Optional[PretrainedConfig]) -> bool:
return type(source_layer) is AscendQKVParallelLinear and len(
packed_modules_list) == 1
class AscendMergedQKVParallelLinearWithLoRA(MergedQKVParallelLinearWithLoRA):
@classmethod
@_not_fully_sharded_can_replace
def can_replace_layer(
cls,
source_layer: nn.Module,
lora_config: LoRAConfig,
packed_modules_list: list,
model_config: Optional[PretrainedConfig],
) -> bool:
return (type(source_layer) is AscendQKVParallelLinear
and len(packed_modules_list) == 3)
def refresh_all_lora_classes():
vllm.lora.utils._all_lora_classes.add(AscendColumnParallelLinearWithLoRA)
vllm.lora.utils._all_lora_classes.add(
AscendMergedColumnParallelLinearWithLoRA)
vllm.lora.utils._all_lora_classes.add(AscendRowParallelLinearWithLoRA)
vllm.lora.utils._all_lora_classes.add(AscendVocabParallelEmbeddingWithLoRA)
vllm.lora.utils._all_lora_classes.add(AscendQKVParallelLinearWithLoRA)
vllm.lora.utils._all_lora_classes.add(
AscendMergedQKVParallelLinearWithLoRA)