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Zero
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# Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT
# except for the third-party components listed below.
# Hunyuan 3D does not impose any additional limitations beyond what is outlined
# in the repsective licenses of these third-party components.
# Users must comply with all terms and conditions of original licenses of these third-party
# components and must ensure that the usage of the third party components adheres to
# all relevant laws and regulations.
# For avoidance of doubts, Hunyuan 3D means the large language models and
# their software and algorithms, including trained model weights, parameters (including
# optimizer states), machine-learning model code, inference-enabling code, training-enabling code,
# fine-tuning enabling code and other elements of the foregoing made publicly available
# by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT.
import os
import torch
import torch.nn.functional as F
scaled_dot_product_attention = F.scaled_dot_product_attention
if os.environ.get('CA_USE_SAGEATTN', '0') == '1':
try:
from sageattention import sageattn
except ImportError:
raise ImportError('Please install the package "sageattention" to use this USE_SAGEATTN.')
scaled_dot_product_attention = sageattn
class CrossAttentionProcessor:
def __call__(self, attn, q, k, v):
out = scaled_dot_product_attention(q, k, v)
return out
class FlashVDMCrossAttentionProcessor:
def __init__(self, topk=None):
self.topk = topk
def __call__(self, attn, q, k, v):
if k.shape[-2] == 3072:
topk = 1024
elif k.shape[-2] == 512:
topk = 256
else:
topk = k.shape[-2] // 3
if self.topk is True:
q1 = q[:, :, ::100, :]
sim = q1 @ k.transpose(-1, -2)
sim = torch.mean(sim, -2)
topk_ind = torch.topk(sim, dim=-1, k=topk).indices.squeeze(-2).unsqueeze(-1)
topk_ind = topk_ind.expand(-1, -1, -1, v.shape[-1])
v0 = torch.gather(v, dim=-2, index=topk_ind)
k0 = torch.gather(k, dim=-2, index=topk_ind)
out = scaled_dot_product_attention(q, k0, v0)
elif self.topk is False:
out = scaled_dot_product_attention(q, k, v)
else:
idx, counts = self.topk
start = 0
outs = []
for grid_coord, count in zip(idx, counts):
end = start + count
q_chunk = q[:, :, start:end, :]
k0, v0 = self.select_topkv(q_chunk, k, v, topk)
out = scaled_dot_product_attention(q_chunk, k0, v0)
outs.append(out)
start += count
out = torch.cat(outs, dim=-2)
self.topk = False
return out
def select_topkv(self, q_chunk, k, v, topk):
q1 = q_chunk[:, :, ::50, :]
sim = q1 @ k.transpose(-1, -2)
sim = torch.mean(sim, -2)
topk_ind = torch.topk(sim, dim=-1, k=topk).indices.squeeze(-2).unsqueeze(-1)
topk_ind = topk_ind.expand(-1, -1, -1, v.shape[-1])
v0 = torch.gather(v, dim=-2, index=topk_ind)
k0 = torch.gather(k, dim=-2, index=topk_ind)
return k0, v0
class FlashVDMTopMCrossAttentionProcessor(FlashVDMCrossAttentionProcessor):
def select_topkv(self, q_chunk, k, v, topk):
q1 = q_chunk[:, :, ::30, :]
sim = q1 @ k.transpose(-1, -2)
# sim = sim.to(torch.float32)
sim = sim.softmax(-1)
sim = torch.mean(sim, 1)
activated_token = torch.where(sim > 1e-6)[2]
index = torch.unique(activated_token, return_counts=True)[0].unsqueeze(0).unsqueeze(0).unsqueeze(-1)
index = index.expand(-1, v.shape[1], -1, v.shape[-1])
v0 = torch.gather(v, dim=-2, index=index)
k0 = torch.gather(k, dim=-2, index=index)
return k0, v0
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