Update modeling_opt.py

modeling_opt.py

@@ -3,6 +3,7 @@
 #
 # 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
@@ -315,183 +316,10 @@ class OPTAttention(nn.Module):
         return attn_output, attn_weights_reshaped, past_key_value
 
 
-class OPTOutEffHop(OPTAttention):
-    """Multi-headed attention from 'Attention Is All You Need' paper"""
-
-    def __init__(
-        self,
-        config: OPTConfig,
-        is_decoder: bool = False,
-        **kwargs,
-    ):
-        super().__init__()
-        self.config = config
-        self.embed_dim = config.hidden_size
-        self.num_heads = config.num_attention_heads
-        self.dropout = config.attention_dropout
-        self.enable_bias = config.enable_bias
-        self.attention= softmax_1
-        self.head_dim = self.embed_dim // self.num_heads
-        self.is_causal = True
-
-        if (self.head_dim * self.num_heads) != self.embed_dim:
-            raise ValueError(
-                f'''embed_dim must be divisible by num_heads (got `embed_dim`: {
-                    self.embed_dim}'''
-                f" and `num_heads`: {self.num_heads})."
-            )
-        self.scaling = self.head_dim**-0.5
-        self.is_decoder = is_decoder
-
-        self.k_proj = nn.Linear(
-            self.embed_dim, self.embed_dim, bias=self.enable_bias)
-        self.v_proj = nn.Linear(
-            self.embed_dim, self.embed_dim, bias=self.enable_bias)
-        self.q_proj = nn.Linear(
-            self.embed_dim, self.embed_dim, bias=self.enable_bias)
-        self.out_proj = nn.Linear(
-            self.embed_dim, self.embed_dim, bias=self.enable_bias)
-
-    def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
-        return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        key_value_states: Optional[torch.Tensor] = None,
-        past_key_value: Optional[Tuple[torch.Tensor]] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        layer_head_mask: Optional[torch.Tensor] = None,
-        output_attentions: bool = False,
-    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
-        """Input shape: Batch x Time x Channel"""
-
-        # if key_value_states are provided this layer is used as a cross-attention layer
-        # for the decoder
-        is_cross_attention = key_value_states is not None
-
-        bsz, tgt_len, _ = hidden_states.size()
-
-        # get query proj
-        query_states = self.q_proj(hidden_states) * self.scaling
-        # get key, value proj
-        if is_cross_attention and past_key_value is not None:
-            # reuse k,v, cross_attentions
-            key_states = past_key_value[0]
-            value_states = past_key_value[1]
-        elif is_cross_attention:
-            # cross_attentions
-            key_states = self._shape(self.k_proj(key_value_states), -1, bsz)
-            value_states = self._shape(self.v_proj(key_value_states), -1, bsz)
-        elif past_key_value is not None:
-            # reuse k, v, self_attention
-            key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
-            value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
-            key_states = torch.cat([past_key_value[0], key_states], dim=2)
-            value_states = torch.cat([past_key_value[1], value_states], dim=2)
-        else:
-            # self_attention
-            key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
-            value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
-
-        if self.is_decoder:
-            # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.
-            # Further calls to cross_attention layer can then reuse all cross-attention
-            # key/value_states (first "if" case)
-            # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of
-            # all previous decoder key/value_states. Further calls to uni-directional self-attention
-            # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
-            # if encoder bi-directional self-attention `past_key_value` is always `None`
-            past_key_value = (key_states, value_states)
-
-        proj_shape = (bsz * self.num_heads, -1, self.head_dim)
-        query_states = self._shape(
-            query_states, tgt_len, bsz).view(*proj_shape)
-        key_states = key_states.view(*proj_shape)
-        value_states = value_states.view(*proj_shape)
-
-        src_len = key_states.size(1)
-        attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))
-
-        if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len):
-            raise ValueError(
-                f'''Attention weights should be of size {
-                    (bsz * self.num_heads, tgt_len, src_len)}, but is"
-                f" {attn_weights.size()}'''
-            )
 
-        if attention_mask is not None:
-            if attention_mask.size() != (bsz, 1, tgt_len, src_len):
-                raise ValueError(
-                    f'''Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {
-                        attention_mask.size()}'''
-                )
-            attn_weights = attn_weights.view(
-                bsz, self.num_heads, tgt_len, src_len) + attention_mask
-            attn_weights = torch.max(
-                attn_weights, torch.tensor(torch.finfo(
-                    attn_weights.dtype).min, device=attn_weights.device)
-            )
-            attn_weights = attn_weights.view(
-                bsz * self.num_heads, tgt_len, src_len)
-
-        # upcast to fp32 if the weights are in fp16. Please see https://github.com/huggingface/transformers/pull/17437
-        if attn_weights.dtype == torch.float16:
-            attn_weights = softmax_1(
-                attn_weights, dim=-1, dtype=torch.float32).to(torch.float16)
-        else:
-            attn_weights = softmax_1(attn_weights, dim=-1)
-
-        if layer_head_mask is not None:
-            if layer_head_mask.size() != (self.num_heads,):
-                raise ValueError(
-                    f'''Head mask for a single layer should be of size {
-                        (self.num_heads,)}, but is'''
-                    f" {layer_head_mask.size()}"
-                )
-            attn_weights = layer_head_mask.view(
-                1, -1, 1, 1) * attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
-            attn_weights = attn_weights.view(
-                bsz * self.num_heads, tgt_len, src_len)
-
-        if output_attentions:
-            # this operation is a bit awkward, but it's required to
-            # make sure that attn_weights keeps its gradient.
-            # In order to do so, attn_weights have to be reshaped
-            # twice and have to be reused in the following
-            attn_weights_reshaped = attn_weights.view(
-                bsz, self.num_heads, tgt_len, src_len)
-            attn_weights = attn_weights_reshaped.view(
-                bsz * self.num_heads, tgt_len, src_len)
-        else:
-            attn_weights_reshaped = None
-
-        attn_probs = nn.functional.dropout(
-            attn_weights, p=self.dropout, training=self.training)
-
-        attn_output = torch.bmm(attn_probs, value_states)
-
-        if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim):
-            raise ValueError(
-                f'''`attn_output` should be of size {
-                    (bsz, self.num_heads, tgt_len, self.head_dim)}, but is'''
-                f" {attn_output.size()}"
-            )
-
-        attn_output = attn_output.view(
-            bsz, self.num_heads, tgt_len, self.head_dim)
-        attn_output = attn_output.transpose(1, 2)
-
-        # Use the `embed_dim` from the config (stored in the class) rather than `hidden_state` because `attn_output` can be
-        # partitioned aross GPUs when using tensor-parallelism.
-        attn_output = attn_output.reshape(bsz, tgt_len, self.embed_dim)
-
-        attn_output = self.out_proj(attn_output)
-
-        return attn_output, attn_weights_reshaped, past_key_value
 
 
-class OptFlashAttention2(OPTOutEffHop):
+class OptFlashAttention2(OPTAttention):
     """
     OPT flash attention module. This module inherits from `OPTAttention` as the weights of the module stays untouched.
     The only required change would be on the forward pass where it needs to correctly call the public API of flash