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4e2a1b3 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 | import torch, os, inspect
from einops import rearrange, repeat
try:
import flash_attn_interface
FLASH_ATTN_3_AVAILABLE = True
except ModuleNotFoundError:
FLASH_ATTN_3_AVAILABLE = False
try:
import flash_attn
FLASH_ATTN_2_AVAILABLE = True
except ModuleNotFoundError:
FLASH_ATTN_2_AVAILABLE = False
try:
from sageattention import sageattn
SAGE_ATTN_AVAILABLE = True
except ModuleNotFoundError:
SAGE_ATTN_AVAILABLE = False
try:
import xformers.ops as xops
XFORMERS_AVAILABLE = True
except ModuleNotFoundError:
XFORMERS_AVAILABLE = False
try:
if "enable_gqa" in inspect.signature(torch.nn.functional.scaled_dot_product_attention).parameters:
TORCH_SUPPORT_GQA = True
else:
TORCH_SUPPORT_GQA = False
except:
TORCH_SUPPORT_GQA = False
def initialize_attention_priority():
if os.environ.get('DIFFSYNTH_ATTENTION_IMPLEMENTATION') is not None:
return os.environ.get('DIFFSYNTH_ATTENTION_IMPLEMENTATION').lower()
elif FLASH_ATTN_3_AVAILABLE:
return "flash_attention_3"
elif FLASH_ATTN_2_AVAILABLE:
return "flash_attention_2"
elif SAGE_ATTN_AVAILABLE:
return "sage_attention"
elif XFORMERS_AVAILABLE:
return "xformers"
else:
return "torch"
ATTENTION_IMPLEMENTATION = initialize_attention_priority()
def rearrange_qkv(q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, q_pattern="b n s d", k_pattern="b n s d", v_pattern="b n s d", required_in_pattern="b n s d", dims=None):
dims = {} if dims is None else dims
if q_pattern != required_in_pattern:
q = rearrange(q, f"{q_pattern} -> {required_in_pattern}", **dims)
if k_pattern != required_in_pattern:
k = rearrange(k, f"{k_pattern} -> {required_in_pattern}", **dims)
if v_pattern != required_in_pattern:
v = rearrange(v, f"{v_pattern} -> {required_in_pattern}", **dims)
return q, k, v
def rearrange_out(out: torch.Tensor, out_pattern="b n s d", required_out_pattern="b n s d", dims=None):
dims = {} if dims is None else dims
if out_pattern != required_out_pattern:
out = rearrange(out, f"{required_out_pattern} -> {out_pattern}", **dims)
return out
def torch_sdpa(q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, q_pattern="b n s d", k_pattern="b n s d", v_pattern="b n s d", out_pattern="b n s d", dims=None, attn_mask=None, scale=None, is_causal=False):
required_in_pattern, required_out_pattern= "b n s d", "b n s d"
q, k, v = rearrange_qkv(q, k, v, q_pattern, k_pattern, v_pattern, required_in_pattern, dims)
if q.shape[1] != k.shape[1] or q.shape[1] != v.shape[1]:
# Grouped Query Attention
if TORCH_SUPPORT_GQA:
out = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask, scale=scale, is_causal=is_causal, enable_gqa=True)
else:
# In low-version torch, `enable_gqa` is not supported.
k = repeat(k, "b n s d -> b (n m) s d", m=q.shape[1]//k.shape[1])
v = repeat(v, "b n s d -> b (n m) s d", m=q.shape[1]//v.shape[1])
out = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask, scale=scale, is_causal=is_causal)
else:
out = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask, scale=scale, is_causal=is_causal)
out = rearrange_out(out, out_pattern, required_out_pattern, dims)
return out
def torch_sdpa_sliding_window(q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, sliding_window: int, q_pattern="b n s d", k_pattern="b n s d", v_pattern="b n s d", out_pattern="b n s d", dims=None, scale=None):
required_in_pattern, required_out_pattern = "b n s d", "b n s d"
q, k, v = rearrange_qkv(q, k, v, q_pattern, k_pattern, v_pattern, required_in_pattern, dims)
B, N, S, D = q.shape
W = sliding_window
chunk_size = W
num_chunks = (S + chunk_size - 1) // chunk_size
output = torch.empty_like(q)
dtype = q.dtype
device = q.device
min_val = torch.finfo(dtype).min
for i in range(num_chunks):
q_start = i * chunk_size
q_end = min(q_start + chunk_size, S)
actual_chunk_size = q_end - q_start
kv_start = max(0, q_start - W)
kv_end = min(S, q_end + W)
q_chunk = q[:, :, q_start:q_end, :]
k_chunk = k[:, :, kv_start:kv_end, :]
v_chunk = v[:, :, kv_start:kv_end, :]
q_indices = torch.arange(q_start, q_end, device=device)
k_indices = torch.arange(kv_start, kv_end, device=device)
diff = q_indices.unsqueeze(1) - k_indices.unsqueeze(0)
valid = diff.abs() <= W
local_mask = torch.zeros(actual_chunk_size, kv_end - kv_start, dtype=dtype, device=device)
local_mask.masked_fill_(~valid, min_val)
local_mask = local_mask.unsqueeze(0).unsqueeze(0)
out_chunk = torch_sdpa(
q_chunk, k_chunk, v_chunk, attn_mask=local_mask, scale=scale
)
output[:, :, q_start:q_end, :] = out_chunk
output = rearrange_out(output, out_pattern, required_out_pattern, dims)
return output
def flash_attention_3(q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, q_pattern="b n s d", k_pattern="b n s d", v_pattern="b n s d", out_pattern="b n s d", dims=None, scale=None, is_causal=False, window_size=None):
required_in_pattern, required_out_pattern= "b s n d", "b s n d"
q, k, v = rearrange_qkv(q, k, v, q_pattern, k_pattern, v_pattern, required_in_pattern, dims)
window_size = (window_size, window_size) if window_size is not None else (-1, -1)
out = flash_attn_interface.flash_attn_func(q, k, v, softmax_scale=scale, window_size=window_size)
if isinstance(out, tuple):
out = out[0]
out = rearrange_out(out, out_pattern, required_out_pattern, dims)
return out
def flash_attention_2(q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, q_pattern="b n s d", k_pattern="b n s d", v_pattern="b n s d", out_pattern="b n s d", dims=None, scale=None, is_causal=False, window_size=None):
required_in_pattern, required_out_pattern= "b s n d", "b s n d"
q, k, v = rearrange_qkv(q, k, v, q_pattern, k_pattern, v_pattern, required_in_pattern, dims)
window_size = (window_size, window_size) if window_size is not None else (-1, -1)
out = flash_attn.flash_attn_func(q, k, v, softmax_scale=scale, causal=is_causal, window_size=window_size)
out = rearrange_out(out, out_pattern, required_out_pattern, dims)
return out
def sage_attention(q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, q_pattern="b n s d", k_pattern="b n s d", v_pattern="b n s d", out_pattern="b n s d", dims=None, scale=None):
required_in_pattern, required_out_pattern= "b n s d", "b n s d"
q, k, v = rearrange_qkv(q, k, v, q_pattern, k_pattern, v_pattern, required_in_pattern, dims)
out = sageattn(q, k, v, sm_scale=scale)
out = rearrange_out(out, out_pattern, required_out_pattern, dims)
return out
def xformers_attention(q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, q_pattern="b n s d", k_pattern="b n s d", v_pattern="b n s d", out_pattern="b n s d", dims=None, scale=None):
required_in_pattern, required_out_pattern= "b s n d", "b s n d"
q, k, v = rearrange_qkv(q, k, v, q_pattern, k_pattern, v_pattern, required_in_pattern, dims)
out = xops.memory_efficient_attention(q, k, v, scale=scale)
out = rearrange_out(out, out_pattern, required_out_pattern, dims)
return out
def attention_forward(q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, q_pattern="b n s d", k_pattern="b n s d", v_pattern="b n s d", out_pattern="b n s d", dims=None, attn_mask=None, scale=None, is_causal=False, compatibility_mode=False, window_size=None):
if compatibility_mode or (attn_mask is not None) or ATTENTION_IMPLEMENTATION == "torch":
if window_size is None:
return torch_sdpa(q, k, v, q_pattern, k_pattern, v_pattern, out_pattern, dims, attn_mask=attn_mask, scale=scale, is_causal=is_causal)
else:
# Sliding Window Attention is not compatible with `is_causal` and `attn_mask`.
assert is_causal == False and attn_mask is None
return torch_sdpa_sliding_window(q, k, v, window_size, q_pattern, k_pattern, v_pattern, out_pattern, dims, scale=scale)
elif ATTENTION_IMPLEMENTATION == "flash_attention_3":
return flash_attention_3(q, k, v, q_pattern, k_pattern, v_pattern, out_pattern, dims, scale=scale, is_causal=is_causal, window_size=window_size)
elif ATTENTION_IMPLEMENTATION == "flash_attention_2":
return flash_attention_2(q, k, v, q_pattern, k_pattern, v_pattern, out_pattern, dims, scale=scale, is_causal=is_causal, window_size=window_size)
elif ATTENTION_IMPLEMENTATION == "sage_attention":
if window_size is not None or is_causal: return attention_forward(q, k, v, q_pattern, k_pattern, v_pattern, out_pattern, dims, attn_mask, scale, is_causal, compatibility_mode=True, window_size=window_size)
return sage_attention(q, k, v, q_pattern, k_pattern, v_pattern, out_pattern, dims, scale=scale)
elif ATTENTION_IMPLEMENTATION == "xformers":
if window_size is not None or is_causal: return attention_forward(q, k, v, q_pattern, k_pattern, v_pattern, out_pattern, dims, attn_mask, scale, is_causal, compatibility_mode=True, window_size=window_size)
return xformers_attention(q, k, v, q_pattern, k_pattern, v_pattern, out_pattern, dims, scale=scale)
else:
raise NotImplementedError("No available attention implementation.")
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