From 95cf2646fa6dae67bf53354f4ed5e81780d8fee9 Mon Sep 17 00:00:00 2001
From: zhifu gao <zhifu.gzf@alibaba-inc.com>
Date: 星期一, 11 三月 2024 14:43:08 +0800
Subject: [PATCH] onnx (#1460)
---
funasr/models/transformer/attention.py | 100 ++++++++++++++++++++++++++++++++++++++++++++++++++
1 files changed, 100 insertions(+), 0 deletions(-)
diff --git a/funasr/models/transformer/attention.py b/funasr/models/transformer/attention.py
index f09d642..695023d 100644
--- a/funasr/models/transformer/attention.py
+++ b/funasr/models/transformer/attention.py
@@ -118,6 +118,106 @@
return self.forward_attention(v, scores, mask)
+class MultiHeadedAttentionExport(nn.Module):
+ def __init__(self, model):
+ super().__init__()
+ self.d_k = model.d_k
+ self.h = model.h
+ self.linear_q = model.linear_q
+ self.linear_k = model.linear_k
+ self.linear_v = model.linear_v
+ self.linear_out = model.linear_out
+ self.attn = None
+ self.all_head_size = self.h * self.d_k
+
+ def forward(self, query, key, value, mask):
+ q, k, v = self.forward_qkv(query, key, value)
+ scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(self.d_k)
+ return self.forward_attention(v, scores, mask)
+
+ def transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor:
+ new_x_shape = x.size()[:-1] + (self.h, self.d_k)
+ x = x.view(new_x_shape)
+ return x.permute(0, 2, 1, 3)
+
+ def forward_qkv(self, query, key, value):
+ q = self.linear_q(query)
+ k = self.linear_k(key)
+ v = self.linear_v(value)
+ q = self.transpose_for_scores(q)
+ k = self.transpose_for_scores(k)
+ v = self.transpose_for_scores(v)
+ return q, k, v
+
+ def forward_attention(self, value, scores, mask):
+ scores = scores + mask
+
+ self.attn = torch.softmax(scores, dim=-1)
+ context_layer = torch.matmul(self.attn, value) # (batch, head, time1, d_k)
+
+ context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
+ new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
+ context_layer = context_layer.view(new_context_layer_shape)
+ return self.linear_out(context_layer) # (batch, time1, d_model)
+
+
+class RelPosMultiHeadedAttentionExport(MultiHeadedAttentionExport):
+ def __init__(self, model):
+ super().__init__(model)
+ self.linear_pos = model.linear_pos
+ self.pos_bias_u = model.pos_bias_u
+ self.pos_bias_v = model.pos_bias_v
+
+ def forward(self, query, key, value, pos_emb, mask):
+ q, k, v = self.forward_qkv(query, key, value)
+ q = q.transpose(1, 2) # (batch, time1, head, d_k)
+
+ p = self.transpose_for_scores(self.linear_pos(pos_emb)) # (batch, head, time1, d_k)
+
+ # (batch, head, time1, d_k)
+ q_with_bias_u = (q + self.pos_bias_u).transpose(1, 2)
+ # (batch, head, time1, d_k)
+ q_with_bias_v = (q + self.pos_bias_v).transpose(1, 2)
+
+ # compute attention score
+ # first compute matrix a and matrix c
+ # as described in https://arxiv.org/abs/1901.02860 Section 3.3
+ # (batch, head, time1, time2)
+ matrix_ac = torch.matmul(q_with_bias_u, k.transpose(-2, -1))
+
+ # compute matrix b and matrix d
+ # (batch, head, time1, time1)
+ matrix_bd = torch.matmul(q_with_bias_v, p.transpose(-2, -1))
+ matrix_bd = self.rel_shift(matrix_bd)
+
+ scores = (matrix_ac + matrix_bd) / math.sqrt(
+ self.d_k
+ ) # (batch, head, time1, time2)
+
+ return self.forward_attention(v, scores, mask)
+
+ def rel_shift(self, x):
+ zero_pad = torch.zeros((*x.size()[:3], 1), device=x.device, dtype=x.dtype)
+ x_padded = torch.cat([zero_pad, x], dim=-1)
+
+ x_padded = x_padded.view(*x.size()[:2], x.size(3) + 1, x.size(2))
+ x = x_padded[:, :, 1:].view_as(x)[
+ :, :, :, : x.size(-1) // 2 + 1
+ ] # only keep the positions from 0 to time2
+ return x
+
+ def forward_attention(self, value, scores, mask):
+ scores = scores + mask
+
+ self.attn = torch.softmax(scores, dim=-1)
+ context_layer = torch.matmul(self.attn, value) # (batch, head, time1, d_k)
+
+ context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
+ new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
+ context_layer = context_layer.view(new_context_layer_shape)
+ return self.linear_out(context_layer) # (batch, time1, d_model)
+
+
class LegacyRelPositionMultiHeadedAttention(MultiHeadedAttention):
"""Multi-Head Attention layer with relative position encoding (old version).
--
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