Delete RoCBertLayerBetterTransformer

Signed-off-by: Shogo Hida <[email protected]>

optimum/bettertransformer/models/__init__.py

@@ -21,7 +21,6 @@
     DistilBertLayerBetterTransformer,
     FSMTEncoderLayerBetterTransformer,
     MBartEncoderLayerBetterTransformer,
-    RoCBertLayerBetterTransformer,
     ViltLayerBetterTransformer,
     ViTLayerBetterTransformer,
     Wav2Vec2EncoderLayerBetterTransformer,

optimum/bettertransformer/models/encoder_models.py

@@ -1192,111 +1192,3 @@ def _get_activation_function(self, config: "PretrainedConfig"):
             return config.vision_config.hidden_act
         else:
             return config.hidden_act
-
-
-class RoCBertLayerBetterTransformer(BetterTransformerBaseLayer):
-    def __init__(self, rocbert_layer, config):
-        r"""
-        A simple conversion of the RoCBERT layer to its `BetterTransformer` implementation.
-
-        Args:
-            rocbert_layer (`torch.nn.Module`):
-                The original RoCBERT Layer where the weights needs to be retrieved.
-        """
-        super().__init__(config)
-        # In_proj layer
-        self.in_proj_weight = nn.Parameter(
-            torch.cat(
-                [
-                    rocbert_layer.attention.self.query.weight,
-                    rocbert_layer.attention.self.key.weight,
-                    rocbert_layer.attention.self.value.weight,
-                ]
-            )
-        )
-        self.in_proj_bias = nn.Parameter(
-            torch.cat(
-                [
-                    rocbert_layer.attention.self.query.bias,
-                    rocbert_layer.attention.self.key.bias,
-                    rocbert_layer.attention.self.value.bias,
-                ]
-            )
-        )
-
-        # Out proj layer
-        self.out_proj_weight = rocbert_layer.attention.output.dense.weight
-        self.out_proj_bias = rocbert_layer.attention.output.dense.bias
-
-        # Linear layer 1
-        self.linear1_weight = rocbert_layer.intermediate.dense.weight
-        self.linear1_bias = rocbert_layer.intermediate.dense.bias
-
-        # Linear layer 2
-        self.linear2_weight = rocbert_layer.output.dense.weight
-        self.linear2_bias = rocbert_layer.output.dense.bias
-
-        # Layer norm 1
-        self.norm1_eps = rocbert_layer.attention.output.LayerNorm.eps
-        self.norm1_weight = rocbert_layer.attention.output.LayerNorm.weight
-        self.norm1_bias = rocbert_layer.attention.output.LayerNorm.bias
-
-        # Layer norm 2
-        self.norm2_eps = rocbert_layer.output.LayerNorm.eps
-        self.norm2_weight = rocbert_layer.output.LayerNorm.weight
-        self.norm2_bias = rocbert_layer.output.LayerNorm.bias
-
-        # Model hyper parameters
-        self.num_heads = rocbert_layer.attention.self.num_attention_heads
-        self.embed_dim = rocbert_layer.attention.self.all_head_size
-
-        # Last step: set the last layer to `False` -> this will be set to `True` when converting the model
-        self.is_last_layer = False
-
-        self.validate_bettertransformer()
-
-    def forward(self, hidden_states, attention_mask, *_):
-        r"""
-        This is just a wrapper around the forward function proposed in:
-        https://github.com/huggingface/transformers/pull/19553
-        """
-        super().forward_checker()
-
-        if hidden_states.is_nested:
-            attention_mask = None
-
-        if attention_mask is not None:
-            # attention mask comes in with values 0 and -inf. we convert to torch.nn.TransformerEncoder style bool mask
-            # 0->false->keep this token -inf->true->mask this token
-            attention_mask = attention_mask.bool()
-            attention_mask = torch.reshape(attention_mask, (attention_mask.shape[0], attention_mask.shape[-1]))
-            seqlen = attention_mask.shape[1]
-            lengths = torch.sum(~attention_mask, 1)
-            if not all([l == seqlen for l in lengths]):
-                hidden_states = torch._nested_tensor_from_mask(hidden_states, ~attention_mask)
-            attention_mask = None
-
-        hidden_states = torch._transformer_encoder_layer_fwd(
-            hidden_states,
-            self.embed_dim,
-            self.num_heads,
-            self.in_proj_weight,
-            self.in_proj_bias,
-            self.out_proj_weight,
-            self.out_proj_bias,
-            self.use_gelu,
-            self.norm_first,
-            self.norm1_eps,
-            self.norm1_weight,
-            self.norm1_bias,
-            self.norm2_weight,
-            self.norm2_bias,
-            self.linear1_weight,
-            self.linear1_bias,
-            self.linear2_weight,
-            self.linear2_bias,
-            attention_mask,
-        )
-        if hidden_states.is_nested and self.is_last_layer:
-            hidden_states = hidden_states.to_padded_tensor(0.0)
-        return (hidden_states,)