Update multiheadAttention.py

ppocr/modeling/backbones/multiheadAttention.py

@@ -9,214 +9,6 @@ from paddle.nn.initializer import XavierNormal as xavier_normal_
 zeros_ = constant_(value=0.)
 ones_ = constant_(value=1.)
 
-class MultiheadAttention(nn.Layer):
-    r"""Allows the model to jointly attend to information
-    from different representation subspaces.
-    See reference: Attention Is All You Need
-
-    .. math::
-        \text{MultiHead}(Q, K, V) = \text{Concat}(head_1,\dots,head_h)W^O
-        \text{where} head_i = \text{Attention}(QW_i^Q, KW_i^K, VW_i^V)
-
-    Args:
-        embed_dim: total dimension of the model
-        num_heads: parallel attention layers, or heads
-
-    Examples::
-
-        >>> multihead_attn = nn.MultiheadAttention(embed_dim, num_heads)
-        >>> attn_output, attn_output_weights = multihead_attn(query, key, value)
-    """
-
-    def __init__(self, embed_dim, num_heads, dropout=0., bias=True, add_bias_kv=False, add_zero_attn=False):
-        super(MultiheadAttention, self).__init__()
-        self.embed_dim = embed_dim
-        self.num_heads = num_heads
-        self.dropout = dropout
-        self.head_dim = embed_dim // num_heads
-        assert self.head_dim * num_heads == self.embed_dim, "embed_dim must be divisible by num_heads"
-        self.scaling = self.head_dim ** -0.5
-
-        self.out_proj = Linear(embed_dim, embed_dim, bias_attr=bias)
-
-        if add_bias_kv:
-            self.bias_k = self.create_parameter(
-            shape=(1, 1, embed_dim), default_initializer=zeros_)
-            self.add_parameter("bias_k", self.bias_k)
-            self.bias_v = self.create_parameter(
-            shape=(1, 1, embed_dim), default_initializer=zeros_)
-            self.add_parameter("bias_v", self.bias_v)
-        else:
-            self.bias_k = self.bias_v = None
-
-        self.add_zero_attn = add_zero_attn
-
-        self._reset_parameters()
-
-        self.conv1 = paddle.nn.Conv2D(in_channels=embed_dim, out_channels=embed_dim, kernel_size=(1, 1))
-        self.conv2 = paddle.nn.Conv2D(in_channels=embed_dim, out_channels=embed_dim * 2, kernel_size=(1, 1))
-        self.conv3 = paddle.nn.Conv2D(in_channels=embed_dim, out_channels=embed_dim * 3, kernel_size=(1, 1))
-
-    def _reset_parameters(self):
-
-
-        xavier_uniform_(self.out_proj.weight)
-        if self.bias_k is not None:
-            xavier_normal_(self.bias_k)
-        if self.bias_v is not None:
-            xavier_normal_(self.bias_v)
-
-    def forward(self, query, key, value, key_padding_mask=None, incremental_state=None,
-                need_weights=True, static_kv=False, attn_mask=None, qkv_ = [False,False,False]):
-        """
-        Inputs of forward function
-            query: [target length, batch size, embed dim]
-            key: [sequence length, batch size, embed dim]
-            value: [sequence length, batch size, embed dim]
-            key_padding_mask: if True, mask padding based on batch size
-            incremental_state: if provided, previous time steps are cashed
-            need_weights: output attn_output_weights
-            static_kv: key and value are static
-
-        Outputs of forward function
-            attn_output: [target length, batch size, embed dim]
-            attn_output_weights: [batch size, target length, sequence length]
-        """
-        qkv_same = qkv_[0]
-        kv_same = qkv_[1]
-
-        tgt_len, bsz, embed_dim = query.shape
-        assert embed_dim == self.embed_dim
-        assert list(query.shape) == [tgt_len, bsz, embed_dim]
-        assert key.shape == value.shape
-
-        if qkv_same:
-            # self-attention
-            q, k, v = self._in_proj_qkv(query)
-        elif kv_same:
-            # encoder-decoder attention
-            q = self._in_proj_q(query)
-            if key is None:
-                assert value is None
-                k = v = None
-            else:
-                k, v = self._in_proj_kv(key)
-        else:
-            q = self._in_proj_q(query)
-            k = self._in_proj_k(key)
-            v = self._in_proj_v(value)
-        q *= self.scaling
-
-        if self.bias_k is not None:
-            assert self.bias_v is not None
-            self.bias_k = paddle.concat([self.bias_k for i in range(bsz)],axis=1)
-            self.bias_v = paddle.concat([self.bias_v for i in range(bsz)],axis=1)
-            k = paddle.concat([k, self.bias_k])
-            v = paddle.concat([v, self.bias_v])
-            if attn_mask is not None:
-                attn_mask = paddle.concat([attn_mask, paddle.zeros([attn_mask.shape[0], 1],dtype=attn_mask.dtype)], axis=1)
-            if key_padding_mask is not None:
-                key_padding_mask = paddle.concat(
-                    [key_padding_mask,paddle.zeros([key_padding_mask.shape[0], 1],dtype=key_padding_mask.dtype)], axis=1)
-
-        q = q.reshape([tgt_len, bsz * self.num_heads, self.head_dim]).transpose([1, 0, 2])
-        if k is not None:
-            k = k.reshape([-1, bsz * self.num_heads, self.head_dim]).transpose([1, 0, 2])
-        if v is not None:
-            v = v.reshape([-1, bsz * self.num_heads, self.head_dim]).transpose([1, 0, 2])
-
- 
-
-        src_len = k.shape[1]
-
-        if key_padding_mask is not None:
-            assert key_padding_mask.shape[0] == bsz
-            assert key_padding_mask.shape[1] == src_len
-
-        if self.add_zero_attn:
-            src_len += 1
-            k = paddle.concat([k, paddle.zeros((k.shape[0], 1) + k.shape[2:],dtype=k.dtype)], axis=1)
-            v = paddle.concat([v, paddle.zeros((v.shape[0], 1) + v.shape[2:],dtype=v.dtype)], axis=1)
-            if attn_mask is not None:
-                attn_mask = paddle.concat([attn_mask, paddle.zeros([attn_mask.shape[0], 1],dtype=attn_mask.dtype)], axis=1)
-            if key_padding_mask is not None:
-                key_padding_mask = paddle.concat(
-                    [key_padding_mask, paddle.zeros([key_padding_mask.shape[0], 1],dtype=key_padding_mask.dtype)], axis=1)
-        attn_output_weights = paddle.bmm(q, k.transpose([0,2,1]))
-        assert list(attn_output_weights.shape) == [bsz * self.num_heads, tgt_len, src_len]
-
-        if attn_mask is not None:
-            attn_mask = attn_mask.unsqueeze(0)
-            attn_output_weights += attn_mask
-        if key_padding_mask is not None:
-            attn_output_weights = attn_output_weights.reshape([bsz, self.num_heads, tgt_len, src_len])
-            key = key_padding_mask.unsqueeze(1).unsqueeze(2).astype('float32')
-            y = paddle.full(shape=key.shape, dtype='float32', fill_value='-inf')
-            y = paddle.where(key==0.,key, y)
-            attn_output_weights += y
-            attn_output_weights = attn_output_weights.reshape([bsz*self.num_heads, tgt_len, src_len])
-
-        attn_output_weights = F.softmax(
-            attn_output_weights.astype('float32'), axis=-1,
-            dtype=paddle.float32 if attn_output_weights.dtype == paddle.float16 else attn_output_weights.dtype)
-        attn_output_weights = F.dropout(attn_output_weights, p=self.dropout, training=self.training)
-
-        attn_output = paddle.bmm(attn_output_weights, v)
-        assert list(attn_output.shape) == [bsz * self.num_heads, tgt_len, self.head_dim]
-        attn_output = attn_output.transpose([1, 0,2]).reshape([tgt_len, bsz, embed_dim])
-        attn_output = self.out_proj(attn_output)
-        if need_weights:
-            # average attention weights over heads
-            attn_output_weights = attn_output_weights.reshape([bsz, self.num_heads, tgt_len, src_len])
-            attn_output_weights = attn_output_weights.sum(axis=1) / self.num_heads
-        else:
-            attn_output_weights = None
-
-        return attn_output, attn_output_weights
-
-    def _in_proj_qkv(self, query):
-        query = query.transpose([1, 2, 0])
-        query = paddle.unsqueeze(query, axis=2)
-        res = self.conv3(query)
-        res = paddle.squeeze(res, axis=2)
-        res = res.transpose([2, 0, 1])
-        return res.chunk(3, axis=-1)
-
-    def _in_proj_kv(self, key):
-        key = key.transpose([1, 2, 0])
-        key = paddle.unsqueeze(key, axis=2)
-        res = self.conv2(key)
-        res = paddle.squeeze(res, axis=2)
-        res = res.transpose([2, 0, 1])
-        return res.chunk(2, axis=-1)
-
-    def _in_proj_q(self, query):
-        query = query.transpose([1, 2, 0])
-        query = paddle.unsqueeze(query, axis=2)
-        res = self.conv1(query)
-        res = paddle.squeeze(res, axis=2)
-        res = res.transpose([2, 0, 1])
-        return res
-
-    def _in_proj_k(self, key):
-        
-        key = key.transpose([1, 2, 0])
-        key = paddle.unsqueeze(key, axis=2)
-        res = self.conv1(key)
-        res = paddle.squeeze(res, axis=2)
-        res = res.transpose([2, 0, 1])
-        return res
-
-    def _in_proj_v(self, value):
-        
-        value = value.transpose([1,2,0])#(1, 2, 0)
-        value = paddle.unsqueeze(value, axis=2)
-        res = self.conv1(value)
-        res = paddle.squeeze(res, axis=2)
-        res = res.transpose([2, 0, 1])
-        return res
-
-
 
 class MultiheadAttentionOptim(nn.Layer):
     r"""Allows the model to jointly attend to information
@@ -362,4 +154,4 @@ class MultiheadAttentionOptim(nn.Layer):
         res = self.conv3(value)
         res = paddle.squeeze(res, axis=2)
         res = res.transpose([2, 0, 1])
-        return res
+        return res
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