Spaces:
Running
on
Zero
Running
on
Zero
File size: 3,776 Bytes
1d117d0 8e4b5fb 1d117d0 8e4b5fb 1d117d0 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 |
try :
import xformers
except ImportError:
pass
import torch
def attention_xformers(
q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, heads: int, mask=None, skip_reshape=False
) -> torch.Tensor:
"""#### Make an attention call using xformers. Fastest attention implementation.
#### Args:
- `q` (torch.Tensor): The query tensor.
- `k` (torch.Tensor): The key tensor, must have the same shape as `q`.
- `v` (torch.Tensor): The value tensor, must have the same shape as `q`.
- `heads` (int): The number of heads, must be a divisor of the hidden dimension.
- `mask` (torch.Tensor, optional): The mask tensor. Defaults to `None`.
#### Returns:
- `torch.Tensor`: The output tensor.
"""
b, _, dim_head = q.shape
dim_head //= heads
q, k, v = map(
lambda t: t.unsqueeze(3)
.reshape(b, -1, heads, dim_head)
.permute(0, 2, 1, 3)
.reshape(b * heads, -1, dim_head)
.contiguous(),
(q, k, v),
)
out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=mask)
out = (
out.unsqueeze(0)
.reshape(b, heads, -1, dim_head)
.permute(0, 2, 1, 3)
.reshape(b, -1, heads * dim_head)
)
return out
def attention_pytorch(
q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, heads: int, mask=None, skip_reshape=False
) -> torch.Tensor:
"""#### Make an attention call using PyTorch.
#### Args:
- `q` (torch.Tensor): The query tensor.
- `k` (torch.Tensor): The key tensor, must have the same shape as `q.
- `v` (torch.Tensor): The value tensor, must have the same shape as `q.
- `heads` (int): The number of heads, must be a divisor of the hidden dimension.
- `mask` (torch.Tensor, optional): The mask tensor. Defaults to `None`.
#### Returns:
- `torch.Tensor`: The output tensor.
"""
b, _, dim_head = q.shape
dim_head //= heads
q, k, v = map(
lambda t: t.view(b, -1, heads, dim_head).transpose(1, 2),
(q, k, v),
)
out = torch.nn.functional.scaled_dot_product_attention(
q, k, v, attn_mask=mask, dropout_p=0.0, is_causal=False
)
out = out.transpose(1, 2).reshape(b, -1, heads * dim_head)
return out
def xformers_attention(
q: torch.Tensor, k: torch.Tensor, v: torch.Tensor
) -> torch.Tensor:
"""#### Compute attention using xformers.
#### Args:
- `q` (torch.Tensor): The query tensor.
- `k` (torch.Tensor): The key tensor, must have the same shape as `q`.
- `v` (torch.Tensor): The value tensor, must have the same shape as `q`.
Returns:
- `torch.Tensor`: The output tensor.
"""
B, C, H, W = q.shape
q, k, v = map(
lambda t: t.view(B, C, -1).transpose(1, 2).contiguous(),
(q, k, v),
)
out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None)
out = out.transpose(1, 2).reshape(B, C, H, W)
return out
def pytorch_attention(
q: torch.Tensor, k: torch.Tensor, v: torch.Tensor
) -> torch.Tensor:
"""#### Compute attention using PyTorch.
#### Args:
- `q` (torch.Tensor): The query tensor.
- `k` (torch.Tensor): The key tensor, must have the same shape as `q.
- `v` (torch.Tensor): The value tensor, must have the same shape as `q.
#### Returns:
- `torch.Tensor`: The output tensor.
"""
B, C, H, W = q.shape
q, k, v = map(
lambda t: t.view(B, 1, C, -1).transpose(2, 3).contiguous(),
(q, k, v),
)
out = torch.nn.functional.scaled_dot_product_attention(
q, k, v, attn_mask=None, dropout_p=0.0, is_causal=False
)
out = out.transpose(2, 3).reshape(B, C, H, W)
return out
|