fix rebase

src/client/remote_model.py

@@ -151,55 +151,6 @@ class DistributedBloomModel(BloomModel):
         )
 
 
-class DistributedBloomPrefix(DistributedBloomModel):
-    """DistributedBloomModel with prefix tokens for prompt tuning"""
-
-    def __init__(self, config):
-        super().__init__(config)
-        assert config.num_prefix_tokens > 0, "The number of prefix tokens must be > 0"
-        self.prefix_length = config.num_prefix_tokens
-
-        self.prompt_embeddings = nn.Embedding(self.prefix_length, config.hidden_size)
-        self.prefix_tokens = torch.arange(self.prefix_length).long()
-
-    def get_prompt(self, batch_size):
-        prefix_tokens = self.prefix_tokens.unsqueeze(0).expand(batch_size, -1)
-        prefix_tokens = prefix_tokens.to(self.word_embeddings.weight.device)
-        prompts = self.prompt_embeddings(prefix_tokens)
-        return prompts
-
-    def forward(
-        self,
-        input_ids: Optional[torch.LongTensor] = None,
-        inputs_embeds: Optional[torch.Tensor] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        **kwargs,
-    ):
-        assert (
-            input_ids is None or inputs_embeds is None
-        ), "You cannot specify both input_ids and inputs_embeds at the same time"
-        assert input_ids is not None or inputs_embeds is not None, "You must specify either input_ids or inputs_embeds"
-
-        if inputs_embeds is None:
-            inputs_embeds = self.word_embeddings(input_ids)
-
-        batch_size = inputs_embeds.shape[0]
-
-        if attention_mask is not None:
-            prefix_attention_mask = torch.ones(batch_size, self.prefix_length, device=attention_mask.device)
-            attention_mask = torch.cat((prefix_attention_mask, attention_mask), dim=1)
-
-        prompts = self.get_prompt(batch_size)
-        inputs_embeds = torch.cat([prompts, inputs_embeds], dim=1)
-
-        transformer_outputs = super().forward(inputs_embeds=inputs_embeds, attention_mask=attention_mask, **kwargs)
-
-        # Remove prefix
-        last_hidden_state = transformer_outputs[0][:, self.prefix_length :]
-        transformer_outputs["last_hidden_state"] = last_hidden_state
-        return transformer_outputs
-
-
 class DistributedBloomForCausalLM(RemoteGenerationMixin, BloomForCausalLM):
     """DistributedBloomForCausalLM, but all transformer layers are hosted by the swarm"""