create T5MultiheadAttention module (#1825)

Author: pmabbo13

torchtext/prototype/t5/modules.py

@@ -0,0 +1,52 @@
+# /* Portions Copyright (c) Meta Platforms, Inc. and affiliates.
+# 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.
+
+# Parts of code are originally from
+# https://github.com/huggingface/transformers/blob/8581a798c0a48fca07b29ce2ca2ef55adcae8c7e/src/transformers/models/t5/modeling_t5.py
+# */
+
+import torch
+import torch.nn as nn
+
+
+class T5MultiheadAttention(nn.MultiheadAttention):
+    def __init__(
+        self,
+        embed_dim,
+        num_heads,
+        is_decoder=False,
+        dropout=0.0,
+        bias=False,
+        kdim=None,
+        vdim=None,
+        device=None,
+        dtype=None,
+    ) -> None:
+        r"""
+        Args:
+            embed_dim: Total dimension of the model.
+            num_heads: Parallel attention heads.
+            is_decoder: Whether or not multihead attention is being performed on a decoder layer. Default: `False`
+            dropout: Probability of an element to be zeroed. Default: 0.0
+            bias: If specified, adds bias to input / output projection layers. Default: `False`.
+            kdim: Total number of features for keys. Default: `None` (uses `kdim=embed_dim`).
+            vdim: Total number of features for values. Default: `None` (uses `vdim=embed_dim`).
+        """
+        super().__init__(embed_dim, num_heads, dropout, bias, False, False, kdim, vdim, True, device, dtype)
+        factory_kwargs = {"device": device, "dtype": dtype}
+        self.is_decoder = is_decoder
+        self.q_proj_weight = nn.Parameter(torch.empty((embed_dim, embed_dim), **factory_kwargs))
+        self.k_proj_weight = nn.Parameter(torch.empty((embed_dim, self.kdim), **factory_kwargs))
+        self.v_proj_weight = nn.Parameter(torch.empty((embed_dim, self.vdim), **factory_kwargs))
+        self.register_parameter("in_proj_weight", None)
+
+    def forward():
+        pass