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Unverified Commit e7ebb662 authored by Isotr0py's avatar Isotr0py Committed by GitHub
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[Model] Remove transformers attention porting in VITs (#10414) 


Signed-off-by: default avatarIsotr0py <2037008807@qq.com>
parent 5be4e52b
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Showing with 139 additions and 102 deletions
vllm/model_executor/models/blip.py
View file @ e7ebb662
......@@ -4,10 +4,11 @@ from typing import Iterable, Optional, Set, Tuple, Union
import torch
import torch.nn as nn
import torch.nn.functional as F
from PIL import Image
from transformers import Blip2VisionConfig, BlipVisionConfig
from transformers.models.blip.modeling_blip import BlipAttention
from vllm.attention.selector import _Backend
from vllm.config import ModelConfig
from vllm.distributed import divide, get_tensor_model_parallel_world_size
from vllm.inputs import DecoderOnlyInputs, token_inputs
......@@ -21,11 +22,7 @@ from vllm.multimodal.utils import (cached_get_tokenizer,
repeat_and_pad_placeholder_tokens)
from vllm.sequence import SequenceData
try:
from xformers import ops as xops
USE_XFORMERS_OPS = True
except ImportError:
USE_XFORMERS_OPS = False
from .utils import get_vit_attn_backend
def get_blip_patch_grid_length(*, image_size: int, patch_size: int) -> int:
......@@ -168,7 +165,7 @@ class BlipVisionEmbeddings(nn.Module):
return embeddings
class BlipParallelAttention(nn.Module):
class BlipAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(
......@@ -208,6 +205,12 @@ class BlipParallelAttention(nn.Module):
self.tp_size = get_tensor_model_parallel_world_size()
self.num_heads_per_partition = divide(self.num_heads, self.tp_size)
# Detect attention implementation.
self.attn_backend = get_vit_attn_backend(support_fa=False)
if self.attn_backend not in {_Backend.TORCH_SDPA, _Backend.XFORMERS}:
raise RuntimeError(
f"BLIP does not support {self.attn_backend} backend now.")
def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
return tensor.view(bsz, seq_len, self.num_heads,
self.head_dim).transpose(1, 2).contiguous()
......@@ -231,11 +234,26 @@ class BlipParallelAttention(nn.Module):
self.num_heads_per_partition,
self.head_dim)
if self.attn_backend == _Backend.XFORMERS:
from xformers import ops as xops
out = xops.memory_efficient_attention_forward(query_states,
key_states,
value_states,
p=self.dropout,
scale=self.scale)
elif self.attn_backend == _Backend.TORCH_SDPA:
query_states, key_states, value_states = (x.transpose(1, 2)
for x in (query_states,
key_states,
value_states))
out = F.scaled_dot_product_attention(query_states,
key_states,
value_states,
dropout_p=self.dropout,
scale=self.scale)
out = out.transpose(1, 2)
out = out.view(bsz, tgt_len, -1)
attn_output, _ = self.projection(out)
......@@ -285,18 +303,11 @@ class BlipEncoderLayer(nn.Module):
super().__init__()
# fallback to sdpa attention if tp unavailable
num_heads = config.num_attention_heads
tp_size = get_tensor_model_parallel_world_size()
if USE_XFORMERS_OPS and num_heads % tp_size == 0:
self.self_attn = BlipParallelAttention(
self.self_attn = BlipAttention(
config,
quant_config=quant_config,
prefix=f"{prefix}.self_attn",
)
else:
# Blip doesn't have SDPA attention implemented in transformers
# use eager attention instead for cpu backend
self.self_attn = BlipAttention(config)
self.layer_norm1 = nn.LayerNorm(config.hidden_size,
eps=config.layer_norm_eps)
self.mlp = BlipMLP(config,
......@@ -374,11 +385,6 @@ class BlipVisionModel(nn.Module):
prefix: str = "",
) -> None:
super().__init__()
tp_size = get_tensor_model_parallel_world_size()
num_heads = config.num_attention_heads
self.shard_weight = USE_XFORMERS_OPS and num_heads % tp_size == 0
self.config = config
self.embeddings = BlipVisionEmbeddings(config)
......@@ -422,7 +428,7 @@ class BlipVisionModel(nn.Module):
("qkv_proj", "q_proj", "q"),
("qkv_proj", "k_proj", "k"),
("qkv_proj", "v_proj", "v"),
] if self.shard_weight else []
]
params_dict = dict(self.named_parameters())
loaded_params: Set[str] = set()
layer_count = len(self.encoder.layers)
......
vllm/model_executor/models/clip.py
View file @ e7ebb662
......@@ -5,10 +5,11 @@ from typing import Iterable, List, Optional, Set, Tuple, Union
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from PIL import Image
from transformers import CLIPVisionConfig
from transformers.models.clip.modeling_clip import CLIPSdpaAttention
from vllm.attention.selector import _Backend
from vllm.config import ModelConfig
from vllm.distributed import divide, get_tensor_model_parallel_world_size
from vllm.inputs import DecoderOnlyInputs, token_inputs
......@@ -23,11 +24,7 @@ from vllm.multimodal.utils import (cached_get_tokenizer,
repeat_and_pad_placeholder_tokens)
from vllm.sequence import SequenceData
try:
from xformers import ops as xops
USE_XFORMERS_OPS = True
except ImportError:
USE_XFORMERS_OPS = False
from .utils import get_vit_attn_backend
def get_clip_patch_grid_length(*, image_size: int, patch_size: int) -> int:
......@@ -197,7 +194,7 @@ class CLIPVisionEmbeddings(nn.Module):
return embeddings
class CLIPParallelAttention(nn.Module):
class CLIPAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(
......@@ -237,6 +234,12 @@ class CLIPParallelAttention(nn.Module):
self.tp_size = get_tensor_model_parallel_world_size()
self.num_heads_per_partition = divide(self.num_heads, self.tp_size)
# Detect attention implementation.
self.attn_backend = get_vit_attn_backend(support_fa=False)
if self.attn_backend not in {_Backend.TORCH_SDPA, _Backend.XFORMERS}:
raise RuntimeError(
f"CLIP does not support {self.attn_backend} backend now.")
def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
return tensor.view(bsz, seq_len, self.num_heads,
self.head_dim).transpose(1, 2).contiguous()
......@@ -261,11 +264,26 @@ class CLIPParallelAttention(nn.Module):
self.num_heads_per_partition,
self.head_dim)
if self.attn_backend == _Backend.XFORMERS:
from xformers import ops as xops
out = xops.memory_efficient_attention_forward(query_states,
key_states,
value_states,
p=self.dropout,
scale=self.scale)
elif self.attn_backend == _Backend.TORCH_SDPA:
query_states, key_states, value_states = (x.transpose(1, 2)
for x in (query_states,
key_states,
value_states))
out = F.scaled_dot_product_attention(query_states,
key_states,
value_states,
dropout_p=self.dropout,
scale=self.scale)
out = out.transpose(1, 2)
out = out.view(bsz, tgt_len, -1)
attn_output, _ = self.out_proj(out)
......@@ -311,17 +329,11 @@ class CLIPEncoderLayer(nn.Module):
prefix: str = "",
) -> None:
super().__init__()
num_heads = config.num_attention_heads
tp_size = get_tensor_model_parallel_world_size()
if USE_XFORMERS_OPS and num_heads % tp_size == 0:
self.self_attn = CLIPParallelAttention(
self.self_attn = CLIPAttention(
config,
quant_config=quant_config,
prefix=f"{prefix}.self_attn",
)
else:
self.self_attn = CLIPSdpaAttention(config)
self.layer_norm1 = nn.LayerNorm(config.hidden_size,
eps=config.layer_norm_eps)
self.mlp = CLIPMLP(config,
......@@ -461,11 +473,6 @@ class CLIPVisionModel(nn.Module):
prefix: str = "",
) -> None:
super().__init__()
tp_size = get_tensor_model_parallel_world_size()
num_heads = config.num_attention_heads
self.shard_weight = USE_XFORMERS_OPS and num_heads % tp_size == 0
self.vision_model = CLIPVisionTransformer(
config=config,
quant_config=quant_config,
......@@ -490,7 +497,7 @@ class CLIPVisionModel(nn.Module):
("qkv_proj", "q_proj", "q"),
("qkv_proj", "k_proj", "k"),
("qkv_proj", "v_proj", "v"),
] if self.shard_weight else []
]
params_dict = dict(self.named_parameters())
loaded_params: Set[str] = set()
layer_count = len(self.vision_model.encoder.layers)
......
vllm/model_executor/models/intern_vit.py
View file @ e7ebb662
......@@ -12,6 +12,7 @@ import torch.nn as nn
import torch.nn.functional as F
from transformers import PretrainedConfig
from vllm.attention.selector import _Backend
from vllm.distributed import (divide, get_tensor_model_parallel_rank,
get_tensor_model_parallel_world_size,
split_tensor_along_last_dim,
......@@ -24,11 +25,7 @@ from vllm.model_executor.layers.linear import (ColumnParallelLinear,
from vllm.model_executor.layers.quantization import QuantizationConfig
from vllm.model_executor.model_loader.weight_utils import default_weight_loader
try:
from xformers import ops as xops
USE_XFORMERS_OPS = True
except ImportError:
USE_XFORMERS_OPS = False
from .utils import get_vit_attn_backend
NORM2FN = {
'rms_norm': RMSNorm,
......@@ -186,6 +183,11 @@ class InternParallelAttention(nn.Module):
prefix=f"{prefix}.proj",
)
self.attn_backend = get_vit_attn_backend(support_fa=False)
if self.attn_backend not in {_Backend.TORCH_SDPA, _Backend.XFORMERS}:
raise RuntimeError(
f"InternViT does not support {self.attn_backend} backend now.")
def _apply_qk_norm(self, q: torch.Tensor, k: torch.Tensor):
if self.tp_size > 1:
q = tensor_model_parallel_all_gather(q.contiguous())
......@@ -211,11 +213,21 @@ class InternParallelAttention(nn.Module):
k = k.view(B, N, self.num_heads_per_partition, self.head_dim)
v = v.view(B, N, self.num_heads_per_partition, self.head_dim)
x = xops.memory_efficient_attention_forward(q, k, v, scale=self.scale)
x = x.view(B, N, -1)
if self.attn_backend == _Backend.XFORMERS:
from xformers import ops as xops
x, _ = self.proj(x)
return x
out = xops.memory_efficient_attention_forward(q,
k,
v,
scale=self.scale)
elif self.attn_backend == _Backend.TORCH_SDPA:
q, k, v = (x.transpose(1, 2) for x in (q, k, v))
out = F.scaled_dot_product_attention(q, k, v, scale=self.scale)
out = out.transpose(1, 2)
out = out.view(B, N, -1)
out, _ = self.proj(out)
return out
class InternSdpaAttention(nn.Module):
......@@ -362,7 +374,7 @@ class InternVisionEncoderLayer(nn.Module):
tp_size = get_tensor_model_parallel_world_size()
num_heads = config.num_attention_heads
if USE_XFORMERS_OPS and (num_heads + num_dummy_heads) % tp_size == 0:
if (num_heads + num_dummy_heads) % tp_size == 0:
return InternParallelAttention(config,
quant_config=quant_config,
num_dummy_heads=num_dummy_heads,
......
vllm/model_executor/models/molmo.py
View file @ e7ebb662
......@@ -187,7 +187,7 @@ class MultiHeadDotProductAttention(nn.Module):
)
# Detect attention implementation.
self.attn_backend: _Backend = get_vit_attn_backend()
self.attn_backend: _Backend = get_vit_attn_backend(support_fa=True)
if self.attn_backend not in {
_Backend.FLASH_ATTN, _Backend.TORCH_SDPA, _Backend.XFORMERS
}:
......
vllm/model_executor/models/qwen2_vl.py
View file @ e7ebb662
......@@ -260,7 +260,7 @@ class Qwen2VisionAttention(nn.Module):
prefix=f"{prefix}.proj")
# Detect attention implementation.
self.attn_backend: _Backend = get_vit_attn_backend()
self.attn_backend: _Backend = get_vit_attn_backend(support_fa=True)
if self.attn_backend not in {
_Backend.FLASH_ATTN, _Backend.TORCH_SDPA, _Backend.XFORMERS
}:
......
vllm/model_executor/models/siglip.py
View file @ e7ebb662
......@@ -6,11 +6,12 @@ from typing import Iterable, List, Optional, Set, Tuple, Union
import numpy as np
import torch
import torch.nn.functional as F
from PIL import Image
from torch import nn
from transformers import SiglipVisionConfig
from transformers.models.siglip.modeling_siglip import SiglipSdpaAttention
from vllm.attention.selector import _Backend
from vllm.config import ModelConfig
from vllm.distributed import divide, get_tensor_model_parallel_world_size
from vllm.inputs import DecoderOnlyInputs, token_inputs
......@@ -27,11 +28,7 @@ from vllm.multimodal.utils import (cached_get_tokenizer,
repeat_and_pad_placeholder_tokens)
from vllm.sequence import SequenceData
try:
from xformers import ops as xops
USE_XFORMERS_OPS = True
except ImportError:
USE_XFORMERS_OPS = False
from .utils import get_vit_attn_backend
def get_siglip_patch_grid_length(*, image_size: int, patch_size: int) -> int:
......@@ -254,7 +251,7 @@ class SiglipVisionEmbeddings(nn.Module):
return embeddings
class SiglipParallelAttention(nn.Module):
class SiglipAttention(nn.Module):
def __init__(
self,
......@@ -293,6 +290,11 @@ class SiglipParallelAttention(nn.Module):
self.tp_size = get_tensor_model_parallel_world_size()
self.num_heads_per_partition = divide(self.num_heads, self.tp_size)
self.attn_backend = get_vit_attn_backend(support_fa=False)
if self.attn_backend not in {_Backend.TORCH_SDPA, _Backend.XFORMERS}:
raise RuntimeError(
f"SIGLIP does not support {self.attn_backend} backend now.")
def forward(
self,
hidden_states: torch.Tensor,
......@@ -313,11 +315,26 @@ class SiglipParallelAttention(nn.Module):
self.num_heads_per_partition,
self.head_dim)
if self.attn_backend == _Backend.XFORMERS:
from xformers import ops as xops
out = xops.memory_efficient_attention_forward(query_states,
key_states,
value_states,
p=self.dropout,
scale=self.scale)
elif self.attn_backend == _Backend.TORCH_SDPA:
query_states, key_states, value_states = (x.transpose(1, 2)
for x in (query_states,
key_states,
value_states))
out = F.scaled_dot_product_attention(query_states,
key_states,
value_states,
dropout_p=self.dropout,
scale=self.scale)
out = out.transpose(1, 2)
out = out.view(batch_size, q_len, -1)
attn_output, _ = self.out_proj(out)
......@@ -372,17 +389,11 @@ class SiglipEncoderLayer(nn.Module):
self.embed_dim = config.hidden_size
num_heads = config.num_attention_heads
tp_size = get_tensor_model_parallel_world_size()
if USE_XFORMERS_OPS and num_heads % tp_size == 0:
self.self_attn = SiglipParallelAttention(
self.self_attn = SiglipAttention(
config,
quant_config=quant_config,
prefix=f"{prefix}.self_attn",
)
else:
self.self_attn = SiglipSdpaAttention(config)
self.layer_norm1 = nn.LayerNorm(self.embed_dim,
eps=config.layer_norm_eps)
self.mlp = SiglipMLP(
......@@ -569,10 +580,6 @@ class SiglipVisionModel(nn.Module):
) -> None:
super().__init__()
num_heads = config.num_attention_heads
tp_size = get_tensor_model_parallel_world_size()
self.shard_weight = USE_XFORMERS_OPS and num_heads % tp_size == 0
self.vision_model = SiglipVisionTransformer(
config,
quant_config,
......@@ -601,7 +608,7 @@ class SiglipVisionModel(nn.Module):
("qkv_proj", "q_proj", "q"),
("qkv_proj", "k_proj", "k"),
("qkv_proj", "v_proj", "v"),
] if self.shard_weight else []
]
params_dict = dict(self.named_parameters())
loaded_params: Set[str] = set()
layer_count = len(self.vision_model.encoder.layers)
......
vllm/model_executor/models/utils.py
View file @ e7ebb662
......@@ -587,7 +587,11 @@ class LLMWrapper(nn.Module):
return llm(*args, **kwargs)
def get_vit_attn_backend() -> _Backend:
def get_vit_attn_backend(support_fa: bool = False) -> _Backend:
"""
Get the available attention backend for Vision Transformer.
"""
# TODO(Isotr0py): Remove `support_fa` after support FA for all ViTs attn.
selected_backend: Optional[_Backend] = get_global_forced_attn_backend()
if selected_backend is None:
backend_by_env_var: Optional[str] = envs.VLLM_ATTENTION_BACKEND
......@@ -596,7 +600,7 @@ def get_vit_attn_backend() -> _Backend:
if selected_backend is None:
# For Volta and Turing GPUs, use xformers instead.
device_available = current_platform.has_device_capability(80)
if device_available:
if device_available and support_fa:
from transformers.utils import is_flash_attn_2_available
if is_flash_attn_2_available():
selected_backend = _Backend.FLASH_ATTN
......@@ -606,7 +610,8 @@ def get_vit_attn_backend() -> _Backend:
"so we use xformers backend instead. You can run "
"`pip install flash-attn` to use flash-attention backend.")
selected_backend = _Backend.XFORMERS
elif current_platform.is_cpu():
elif current_platform.is_cpu() or current_platform.is_rocm():
# ROCM doesn't support xformers
selected_backend = _Backend.TORCH_SDPA
else:
selected_backend = _Backend.XFORMERS
......
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