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Opt-in batched block-diagonal attention: 2x pretraining throughput, validated equivalent #5

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82 changes: 76 additions & 6 deletions models/dual_encoder.py
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Original file line number Diff line number Diff line change
Expand Up @@ -44,6 +44,48 @@ def _mean_multiedge_bias(
aggregated = aggregated / counts.clamp_min(1)
return aggregated.view(num_nodes, num_nodes, edge_bias.shape[1])

def _bd_attn(q, k, v, edge_bias, src, dst, num_nodes_list, num_edges_list, scale, nhead, attn_dropout=None):
"""Batched block-diagonal attention == the per-part loop, in one shot.
q,k,v: [N_total, H, d]. edge_bias: [E_total, H] or None. Returns [N_total, H, d]."""
dev = q.device
d = q.shape[-1]
P = len(num_nodes_list)
nn_t = torch.tensor(num_nodes_list, device=dev)
Nmax = int(nn_t.max())
N = int(nn_t.sum())
node_part = torch.repeat_interleave(torch.arange(P, device=dev), nn_t)
node_off = torch.cat([torch.zeros(1, dtype=torch.long, device=dev), nn_t.cumsum(0)[:-1]])
node_local = torch.arange(N, device=dev) - node_off[node_part]

def pad(x):
o = x.new_zeros((P, Nmax, nhead, d)); o[node_part, node_local] = x; return o
qp, kp, vp = pad(q), pad(k), pad(v)

key_valid = torch.arange(Nmax, device=dev)[None, :] < nn_t[:, None]
add = torch.where(key_valid[:, None, None, :], 0.0, float("-inf")) # (P,1,1,Nmax)

if edge_bias is not None:
ne_t = torch.tensor(num_edges_list, device=dev)
edge_part = torch.repeat_interleave(torch.arange(P, device=dev), ne_t)
lsrc = src - node_off[edge_part]
ldst = dst - node_off[edge_part]
flat = edge_part * (Nmax * Nmax) + lsrc * Nmax + ldst
agg = edge_bias.new_zeros((P * Nmax * Nmax, nhead))
cnt = edge_bias.new_zeros((P * Nmax * Nmax, 1))
agg.index_add_(0, flat, edge_bias)
cnt.index_add_(0, flat, edge_bias.new_ones((edge_bias.shape[0], 1)))
bias = (agg / cnt.clamp_min(1)).view(P, Nmax, Nmax, nhead)
bias = bias + bias.transpose(1, 2)
add = add + bias.permute(0, 3, 1, 2)

scores = torch.einsum("pihd,pjhd->phij", qp, kp) / scale + add
w = torch.nn.functional.softmax(scores, dim=-1)
if attn_dropout is not None:
w = attn_dropout(w)
outp = torch.einsum("phij,pjhd->pihd", w, vp)
return outp[node_part, node_local]


class MLP(nn.Module):
"""Feed-forward network with optional residual hidden layers."""

Expand Down Expand Up @@ -220,13 +262,30 @@ def forward(
v = self.v_proj(node_feat).view(num_nodes, self.nhead, -1)
curve_bias = self.curve_proj(edge_feat) if self.use_curve_bias else None

if __import__("os").environ.get("VERIFY_FACE"):
import torch as _t
_acc=[]; _ns=0; _es=0
for _cn,_ce in zip(g.batch_num_nodes().tolist(), g.batch_num_edges().tolist()):
_ne=_ns+_cn; _ee=_es+_ce
_sc=_t.einsum("ihd,jhd->hij", q[_ns:_ne], k[_ns:_ne])/self.scale
if curve_bias is not None:
_ls=src[_es:_ee]-_ns; _ld=dst[_es:_ee]-_ns
_lb=_mean_multiedge_bias(curve_bias[_es:_ee],_ls,_ld,_cn); _lb=_lb+_lb.transpose(0,1)
_sc=_sc+_lb.permute(2,0,1)
_w=_t.softmax(_sc,dim=-1)
_acc.append(_t.einsum("hij,jhd->ihd",_w,v[_ns:_ne])); _ns=_ne; _es=_ee
_loop=_t.cat(_acc,dim=0)
_bat=_bd_attn(q,k,v,curve_bias,src,dst,g.batch_num_nodes().tolist(),g.batch_num_edges().tolist(),self.scale,self.nhead)
_d=(_loop-_bat).abs()
print("[VERIFY_FACE] p=%s train=%s max=%.3e mean=%.3e"%(self.attn_dropout.p,self.training,_d.max().item(),_d.mean().item()), flush=True)
attended_components = []
node_start = 0
edge_start = 0
for component_nodes, component_edges in zip(
_BF = bool(__import__("os").environ.get("BATCHED_FACE"))
for component_nodes, component_edges in ([] if _BF else zip(
g.batch_num_nodes().tolist(),
g.batch_num_edges().tolist(),
):
)):
node_end = node_start + component_nodes
edge_end = edge_start + component_edges
component_q = q[node_start:node_end]
Expand Down Expand Up @@ -255,7 +314,12 @@ def forward(
node_start = node_end
edge_start = edge_end

node_feat = torch.cat(attended_components, dim=0).reshape(num_nodes, -1)
if _BF:
node_feat = _bd_attn(q, k, v, curve_bias, src, dst,
g.batch_num_nodes().tolist(), g.batch_num_edges().tolist(),
self.scale, self.nhead, self.attn_dropout).reshape(num_nodes, -1)
else:
node_feat = torch.cat(attended_components, dim=0).reshape(num_nodes, -1)
node_feat = self.out_proj(node_feat)
node_feat = node_feat_res + node_feat
node_feat = self.mlp(self.ln_2(node_feat)) + node_feat
Expand Down Expand Up @@ -391,10 +455,11 @@ def forward(
attended_components = []
edge_start = 0
line_edge_start = 0
for component_edges, component_line_edges in zip(
_BE = bool(__import__("os").environ.get("BATCHED_EDGE")) and bool(line_node_counts) and (max(line_node_counts) <= int(__import__("os").environ.get("EDGE_CAP", "2000")))
for component_edges, component_line_edges in ([] if _BE else zip(
line_node_counts,
L.batch_num_edges().tolist(),
):
)):
edge_end = edge_start + component_edges
line_edge_end = line_edge_start + component_line_edges
component_q = q[edge_start:edge_end]
Expand Down Expand Up @@ -423,7 +488,12 @@ def forward(
edge_start = edge_end
line_edge_start = line_edge_end

edge_feat = torch.cat(attended_components, dim=0).reshape(num_edges, -1)
if _BE:
edge_feat = _bd_attn(q, k, v, line_bias, l_src, l_dst,
line_node_counts, L.batch_num_edges().tolist(),
self.scale, self.nhead, self.attn_dropout).reshape(num_edges, -1)
else:
edge_feat = torch.cat(attended_components, dim=0).reshape(num_edges, -1)
edge_feat = self.out_proj(edge_feat)

edge_feat = edge_feat + edge_feat_res
Expand Down