[firred] bring fired aed to wenet (#2680)

* add att for fired

* merge main

* fix conflict

* merge main

* fix conflict

* convert work

* add missing files

* precision match!

* fix typo

wenet/firered/attention.py

@@ -0,0 +1,186 @@
+# Copyright (c) 2025 Wenet Community. authors: Mddct(Dinghao Zhou)
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import math
+from typing import Optional, Tuple, Union
+
+import torch
+from wenet.transformer.attention import (T_CACHE,
+                                         RelPositionMultiHeadedAttention)
+from wenet.transformer.embedding import PositionalEncoding
+
+
+class FireRedRelPositionalEncoding(PositionalEncoding):
+
+    def __init__(self, d_model: int, dropout_rate: float, max_len: int = 5000):
+
+        super().__init__(d_model, dropout_rate, max_len)
+        pe_positive = torch.zeros(max_len, d_model, requires_grad=False)
+        pe_negative = torch.zeros(max_len, d_model, requires_grad=False)
+        position = torch.arange(0, max_len).unsqueeze(1).float()
+        div_term = torch.exp(
+            torch.arange(0, d_model, 2).float() *
+            -(torch.log(torch.tensor(10000.0)).item() / d_model))
+        pe_positive[:, 0::2] = torch.sin(position * div_term)
+        pe_positive[:, 1::2] = torch.cos(position * div_term)
+        pe_negative[:, 0::2] = torch.sin(-1 * position * div_term)
+        pe_negative[:, 1::2] = torch.cos(-1 * position * div_term)
+
+        pe_positive = torch.flip(pe_positive, [0]).unsqueeze(0)
+        pe_negative = pe_negative[1:].unsqueeze(0)
+        pe = torch.cat([pe_positive, pe_negative], dim=1)
+        self.register_buffer('pe', pe)
+
+    def position_encoding(self,
+                          offset: Union[int, torch.Tensor],
+                          size: int,
+                          apply_dropout: bool = True) -> torch.Tensor:
+
+        raise NotImplementedError('firedasr not support streaming pos encding')
+
+    def forward(self, x, offset=None):
+        Tmax, T = self.pe.size(1), x.size(1)
+        pos_emb = self.pe[:, Tmax // 2 - T + 1:Tmax // 2 + T].clone().detach()
+        return self.dropout(x), self.dropout(pos_emb)
+
+
+class FiredRelPositionMultiHeadedAttention(RelPositionMultiHeadedAttention):
+    """Multi-Head Attention layer with relative position encoding.
+    Paper: https://arxiv.org/abs/1901.02860
+    Args:
+        n_head (int): The number of heads.
+        n_feat (int): The number of features.
+        dropout_rate (float): Dropout rate.
+    """
+
+    def __init__(self,
+                 n_head: int,
+                 n_feat: int,
+                 dropout_rate: float,
+                 query_bias: bool = True,
+                 key_bias: bool = True,
+                 value_bias: bool = True,
+                 use_sdpa: bool = False,
+                 n_kv_head: Optional[int] = None,
+                 head_dim: Optional[int] = None):
+        """Construct an RelPositionMultiHeadedAttention object."""
+        super().__init__(n_head, n_feat, dropout_rate, query_bias, key_bias,
+                         value_bias, use_sdpa, n_kv_head, head_dim)
+
+        self.layer_norm_q = torch.nn.LayerNorm(n_feat)
+        self.layer_norm_k = torch.nn.LayerNorm(n_feat)
+        self.layer_norm_v = torch.nn.LayerNorm(n_feat)
+
+    def rel_shift(self, x):
+        """Compute relative positinal encoding.
+        Args:
+            x (torch.Tensor): Input tensor (batch, time, size).
+        Returns:
+            torch.Tensor: Output tensor.
+        """
+
+        zero_pad = torch.zeros((x.size()[0], x.size()[1], x.size()[2], 1),
+                               device=x.device,
+                               dtype=x.dtype)
+        x_padded = torch.cat([zero_pad, x], dim=-1)
+
+        x_padded = x_padded.view(x.size()[0],
+                                 x.size()[1],
+                                 x.size(3) + 1, x.size(2))
+        x = x_padded[:, :, 1:].view_as(x)
+        x = x[:, :, :, :x.size(-1) // 2 + 1]
+
+        return x
+
+    def forward_qkv(
+        self, query: torch.Tensor, key: torch.Tensor, value: torch.Tensor
+    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        query = self.layer_norm_q(query)
+        key = self.layer_norm_k(key)
+        value = self.layer_norm_v(value)
+        return super().forward_qkv(query, key, value)
+
+    def forward(
+        self,
+        query: torch.Tensor,
+        key: torch.Tensor,
+        value: torch.Tensor,
+        mask: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool),
+        pos_emb: torch.Tensor = torch.empty(0),
+        cache: T_CACHE = (torch.zeros((0, 0, 0, 0)), torch.zeros((0, 0, 0, 0)))
+    ) -> Tuple[torch.Tensor, T_CACHE]:
+        """Compute 'Scaled Dot Product Attention' with rel. positional encoding.
+        Args:
+            query (torch.Tensor): Query tensor (#batch, time1, size).
+            key (torch.Tensor): Key tensor (#batch, time2, size).
+            value (torch.Tensor): Value tensor (#batch, time2, size).
+            mask (torch.Tensor): Mask tensor (#batch, 1, time2) or
+                (#batch, time1, time2), (0, 0, 0) means fake mask.
+            pos_emb (torch.Tensor): Positional embedding tensor
+                (#batch, time2, size).
+            cache (torch.Tensor): Cache tensor (1, head, cache_t, d_k * 2),
+                where `cache_t == chunk_size * num_decoding_left_chunks`
+                and `head * d_k == size`
+        Returns:
+            torch.Tensor: Output tensor (#batch, time1, d_model).
+            torch.Tensor: Cache tensor (1, head, cache_t + time1, d_k * 2)
+                where `cache_t == chunk_size * num_decoding_left_chunks`
+                and `head * d_k == size`
+        """
+        q, k, v = self.forward_qkv(query, key, value)
+        q = q.transpose(1, 2)  # (batch, time1, head, d_k)
+        k, v, new_cache = self._update_kv_and_cache(k, v, cache)
+
+        n_batch_pos = pos_emb.size(0)
+        p = self.linear_pos(pos_emb).view(n_batch_pos, -1, self.h, self.d_k)
+        p = p.transpose(1, 2)  # (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 matrix b and matrix d
+        # (batch, head, time1, time2)
+        matrix_bd = torch.matmul(q_with_bias_v, p.transpose(-2, -1))
+        matrix_bd = self.rel_shift(matrix_bd)
+        if not self.use_sdpa:
+            # 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))
+
+            scores = (matrix_ac + matrix_bd) / math.sqrt(
+                self.d_k)  # (batch, head, time1, time2)
+
+            return self.forward_attention(v, scores, mask), new_cache
+        else:
+            # NOTE(Mddct): we need mask bias, not boolean mask
+            assert mask.dtype != torch.bool
+            mask = mask.unsqueeze(1)
+            # matrix_bd as a mask bias
+            mask = (matrix_bd + mask) / math.sqrt(self.d_k)
+            output = torch.nn.functional.scaled_dot_product_attention(
+                q_with_bias_u,
+                k,
+                v,
+                attn_mask=mask,
+                dropout_p=self.dropout_rate,
+                scale=1 / math.sqrt(self.d_k),
+            )
+            output = (output.transpose(1, 2).contiguous().view(
+                query.size(0), -1,
+                self.h * self.d_k))  # (batch, time1, d_model)
+            return self.linear_out(output), new_cache