# Update run.py shim: add flash_attn_varlen_qkvpacked_func # (PTv3Object uses this for variable-length attention in skin model) path = '/root/UniRig/run.py' with open(path) as f: src = f.read() # Replace old shim with updated one that includes varlen func old_marker = "# Inject into a fake flash_attn module" new_shim = ''' import torch import torch.nn as nn import torch.nn.functional as F class _FlashMHACompat(nn.Module): """ Drop-in for flash_attn.modules.mha.MHA. Matches flash_attn weight layout (Wq, Wkv, out_proj) so checkpoints load cleanly. Uses torch SDPA for computation. """ def __init__(self, embed_dim, num_heads, cross_attn=False, **kwargs): super().__init__() self.embed_dim = embed_dim self.num_heads = num_heads self.head_dim = embed_dim // num_heads self.cross_attn = cross_attn self.Wq = nn.Linear(embed_dim, embed_dim, bias=True) self.Wkv = nn.Linear(embed_dim, 2 * embed_dim, bias=True) self.out_proj = nn.Linear(embed_dim, embed_dim, bias=True) def forward(self, x, x_kv=None): B, Sq, D = x.shape q = self.Wq(x) src = x_kv if (self.cross_attn and x_kv is not None) else x kv = self.Wkv(src) k, v = kv.chunk(2, dim=-1) Skv = src.shape[1] def _reshape(t, s): return t.view(B, s, self.num_heads, self.head_dim).transpose(1, 2) q, k, v = _reshape(q, Sq), _reshape(k, Skv), _reshape(v, Skv) out = F.scaled_dot_product_attention(q, k, v) out = out.transpose(1, 2).contiguous().view(B, Sq, D) return self.out_proj(out) def _flash_attn_varlen_qkvpacked_func(qkv, cu_seqlens, max_seqlen, dropout_p=0., softmax_scale=None, **kwargs): """ Drop-in for flash_attn.flash_attn_varlen_qkvpacked_func using torch SDPA. qkv: (total_tokens, 3, num_heads, head_dim) [float16] cu_seqlens: (batch+1,) cumulative sequence lengths Returns: (total_tokens, num_heads, head_dim) """ orig_dtype = qkv.dtype qkv = qkv.float() total, _, H, D = qkv.shape q, k, v = qkv.unbind(1) # each: (total, H, D) scale = softmax_scale if softmax_scale is not None else (D ** -0.5) outputs = [] batch_size = cu_seqlens.shape[0] - 1 for i in range(batch_size): s, e = int(cu_seqlens[i]), int(cu_seqlens[i + 1]) qi = q[s:e].unsqueeze(0).transpose(1, 2) # (1, H, L, D) ki = k[s:e].unsqueeze(0).transpose(1, 2) vi = v[s:e].unsqueeze(0).transpose(1, 2) dp = dropout_p if torch.is_grad_enabled() else 0. out = F.scaled_dot_product_attention(qi, ki, vi, dropout_p=dp, scale=scale) outputs.append(out.transpose(1, 2).squeeze(0)) # (L, H, D) result = torch.cat(outputs, dim=0) # (total, H, D) return result.to(orig_dtype) # Inject into a fake flash_attn module ''' # Find and replace the old shim marker if old_marker in src: # Find start of shim (everything before the marker is safe_globals + existing code) shim_start = src.find('\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F') if shim_start == -1: shim_start = src.find('class _FlashMHACompat') inject_end = src.find('sys.modules["flash_attn.modules.mha"] = _fa_mha_mha\n') + len('sys.modules["flash_attn.modules.mha"] = _fa_mha_mha\n') before = src[:shim_start] after = src[inject_end:] inject_block = new_shim + '''import sys, types _fa = types.ModuleType("flash_attn") _fa.flash_attn_varlen_qkvpacked_func = _flash_attn_varlen_qkvpacked_func _fa_mha = types.ModuleType("flash_attn.modules") _fa_mha_mha = types.ModuleType("flash_attn.modules.mha") _fa_mha_mha.MHA = _FlashMHACompat sys.modules["flash_attn"] = _fa sys.modules["flash_attn.modules"] = _fa_mha sys.modules["flash_attn.modules.mha"] = _fa_mha_mha ''' src = before + inject_block + after else: # Fresh inject at top inject_block = new_shim + '''import sys, types _fa = types.ModuleType("flash_attn") _fa.flash_attn_varlen_qkvpacked_func = _flash_attn_varlen_qkvpacked_func _fa_mha = types.ModuleType("flash_attn.modules") _fa_mha_mha = types.ModuleType("flash_attn.modules.mha") _fa_mha_mha.MHA = _FlashMHACompat sys.modules["flash_attn"] = _fa sys.modules["flash_attn.modules"] = _fa_mha sys.modules["flash_attn.modules.mha"] = _fa_mha_mha ''' src = inject_block + src with open(path, 'w') as f: f.write(src) print('run.py patched: flash_attn_varlen_qkvpacked_func added to shim')