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2449566 | 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 | import torch
def get_span_hidden_states(inputs, hidden_states, attentions, safe_idx, pooler_mask, attention_mask,
hidden_layer_fineturn, weight_pooling=True, span_weight=True, is_causal=False):
batch_size, seq_length = inputs['input_ids'].size()
batch_idxs = torch.arange(batch_size, device=inputs['input_ids'].device)[:, None, None]
# batch_size, max_seg, max_len_all = safe_idx.size()
# g_safe_idx = safe_idx.view(batch_size, max_seg // 4, -1)
# g_pooler_mask = pooler_mask.view(batch_size, max_seg // 4, -1)
mask_2d = attention_mask.unsqueeze(1) * attention_mask.unsqueeze(2) # (B, N, N)
mask_4d = mask_2d.unsqueeze(1)
g_safe_idx = safe_idx
g_pooler_mask = pooler_mask
hidden_state_pools = []
span_weights = []
for i in hidden_layer_fineturn:
# weights = attentions[i].sum(dim=(1, 2))
if is_causal:
weights = attentions[i-1].sum(dim=1)[:, -1].detach()
else:
weights = (attentions[i-1] * mask_4d).sum(dim=(1, 2)).detach()
weights = weights / weights.sum(-1, keepdim=True)
weights = weights.unsqueeze(-1)[batch_idxs, g_safe_idx] * g_pooler_mask.unsqueeze(-1)
gathered = hidden_states[i][batch_idxs, g_safe_idx] * g_pooler_mask.unsqueeze(-1)
gathered = gathered * weights
hidden_state_mean = gathered.sum(2) / weights.sum(2).clamp(min=1e-5)
hidden_state_pools.append(hidden_state_mean)
span_weights.append(weights.sum(2))
span_hidden_states = torch.stack(hidden_state_pools)
span_weights = torch.stack(span_weights)
return span_hidden_states, span_weights
def get_span_hidden_states_custom(inputs, hidden_states, attentions, safe_idx, pooler_mask, attention_mask,
hidden_layer_fineturn, weight_pooling=False, span_weight=False, is_causal=False):
batch_size, seq_length = inputs['input_ids'].size()
batch_idxs = torch.arange(batch_size, device=inputs['input_ids'].device)[:, None, None]
g_safe_idx = safe_idx
g_pooler_mask = pooler_mask
mask_2d = attention_mask.unsqueeze(1) * attention_mask.unsqueeze(2) # (B, N, N)
mask_4d = mask_2d.unsqueeze(1)
hidden_state_pools = []
span_weights = []
for i in hidden_layer_fineturn:
# weights = attentions[i].sum(dim=(1, 2))
if is_causal:
weights = attentions[i-1].sum(dim=1)[:, -1].detach()
else:
weights = (attentions[i-1] * mask_4d).sum(dim=(1, 2)).detach()
weights = weights / weights.sum(-1, keepdim=True)
weights = weights.unsqueeze(-1)[batch_idxs, g_safe_idx] * g_pooler_mask.unsqueeze(-1)
gathered = hidden_states[i][batch_idxs, g_safe_idx] * g_pooler_mask.unsqueeze(-1)
if weight_pooling:
gathered = gathered * weights
hidden_state_mean = gathered.sum(2) / weights.sum(2).clamp(min=1e-5)
else:
hidden_state_mean = gathered.sum(2) / g_pooler_mask.sum(2, keepdim=True).clamp(min=1e-5)
hidden_state_pools.append(hidden_state_mean)
if span_weight:
span_weights.append(weights.sum(2))
else:
# span_weights.append(g_pooler_mask.sum(2, keepdim=True).clamp(max=1.0))
span_weights.append(weights.sum(2) ** 1e-5)
span_hidden_states = torch.stack(hidden_state_pools)
span_weights = torch.stack(span_weights)
return span_hidden_states, span_weights
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