0%| | 0/234 [00:00<?, ?it/s]
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
Cell In[7], line 2
1 model.reset() # make sure you always reset the model before training a new vector
----> 2 control_vector = ControlVector.train(
3 model,
4 tokenizer,
5 dataset,
6 )
File /notebooks/code/repeng/notebooks/../repeng/extract.py:34, in ControlVector.train(cls, model, tokenizer, dataset, **kwargs)
26 @classmethod
27 def train(
28 cls,
(...)
32 **kwargs,
33 ) -> "ControlVector":
---> 34 dirs = read_representations(
35 model,
36 tokenizer,
37 dataset,
38 **kwargs,
39 )
40 return cls(model_type=model.config.model_type, directions=dirs)
File /notebooks/code/repeng/notebooks/../repeng/extract.py:139, in read_representations(model, tokenizer, inputs, hidden_layers, batch_size)
136 # the order is [positive, negative, positive, negative, ...]
137 train_strs = [s for ex in inputs for s in (ex.positive, ex.negative)]
--> 139 layer_hiddens = batched_get_hiddens(
140 model, tokenizer, train_strs, hidden_layers, batch_size
141 )
143 # get differences between (positive, negative) pairs
144 relative_layer_hiddens = {}
File /notebooks/code/repeng/notebooks/../repeng/extract.py:208, in batched_get_hiddens(model, tokenizer, inputs, hidden_layers, batch_size)
206 with torch.no_grad():
207 for batch in tqdm.tqdm(batched_inputs):
--> 208 out = model(
209 **tokenizer(batch, padding=True, return_tensors="pt").to(model.device),
210 output_hidden_states=True,
211 )
212 for layer in hidden_layers:
213 # if not indexing from end, account for embedding hiddens
214 hidden_idx = layer + 1 if layer >= 0 else layer
File /notebooks/code/repeng/notebooks/../repeng/control.py:123, in ControlModel.__call__(self, *args, **kwargs)
122 def __call__(self, *args, **kwargs):
--> 123 return self.model(*args, **kwargs)
File /usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py:1194, in Module._call_impl(self, *input, **kwargs)
1190 # If we don't have any hooks, we want to skip the rest of the logic in
1191 # this function, and just call forward.
1192 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
1193 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1194 return forward_call(*input, **kwargs)
1195 # Do not call functions when jit is used
1196 full_backward_hooks, non_full_backward_hooks = [], []
File /usr/local/lib/python3.10/dist-packages/transformers/models/mistral/modeling_mistral.py:1157, in MistralForCausalLM.forward(self, input_ids, attention_mask, position_ids, past_key_values, inputs_embeds, labels, use_cache, output_attentions, output_hidden_states, return_dict)
1154 return_dict = return_dict if return_dict is not None else self.config.use_return_dict
1156 # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
-> 1157 outputs = self.model(
1158 input_ids=input_ids,
1159 attention_mask=attention_mask,
1160 position_ids=position_ids,
1161 past_key_values=past_key_values,
1162 inputs_embeds=inputs_embeds,
1163 use_cache=use_cache,
1164 output_attentions=output_attentions,
1165 output_hidden_states=output_hidden_states,
1166 return_dict=return_dict,
1167 )
1169 hidden_states = outputs[0]
1170 logits = self.lm_head(hidden_states)
File /usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py:1194, in Module._call_impl(self, *input, **kwargs)
1190 # If we don't have any hooks, we want to skip the rest of the logic in
1191 # this function, and just call forward.
1192 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
1193 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1194 return forward_call(*input, **kwargs)
1195 # Do not call functions when jit is used
1196 full_backward_hooks, non_full_backward_hooks = [], []
File /usr/local/lib/python3.10/dist-packages/transformers/models/mistral/modeling_mistral.py:1042, in MistralModel.forward(self, input_ids, attention_mask, position_ids, past_key_values, inputs_embeds, use_cache, output_attentions, output_hidden_states, return_dict)
1032 layer_outputs = self._gradient_checkpointing_func(
1033 decoder_layer.__call__,
1034 hidden_states,
(...)
1039 use_cache,
1040 )
1041 else:
-> 1042 layer_outputs = decoder_layer(
1043 hidden_states,
1044 attention_mask=attention_mask,
1045 position_ids=position_ids,
1046 past_key_value=past_key_values,
1047 output_attentions=output_attentions,
1048 use_cache=use_cache,
1049 )
1051 hidden_states = layer_outputs[0]
1053 if use_cache:
File /usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py:1194, in Module._call_impl(self, *input, **kwargs)
1190 # If we don't have any hooks, we want to skip the rest of the logic in
1191 # this function, and just call forward.
1192 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
1193 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1194 return forward_call(*input, **kwargs)
1195 # Do not call functions when jit is used
1196 full_backward_hooks, non_full_backward_hooks = [], []
File /usr/local/lib/python3.10/dist-packages/transformers/models/mistral/modeling_mistral.py:757, in MistralDecoderLayer.forward(self, hidden_states, attention_mask, position_ids, past_key_value, output_attentions, use_cache, **kwargs)
754 hidden_states = self.input_layernorm(hidden_states)
756 # Self Attention
--> 757 hidden_states, self_attn_weights, present_key_value = self.self_attn(
758 hidden_states=hidden_states,
759 attention_mask=attention_mask,
760 position_ids=position_ids,
761 past_key_value=past_key_value,
762 output_attentions=output_attentions,
763 use_cache=use_cache,
764 )
765 hidden_states = residual + hidden_states
767 # Fully Connected
File /usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py:1194, in Module._call_impl(self, *input, **kwargs)
1190 # If we don't have any hooks, we want to skip the rest of the logic in
1191 # this function, and just call forward.
1192 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
1193 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1194 return forward_call(*input, **kwargs)
1195 # Do not call functions when jit is used
1196 full_backward_hooks, non_full_backward_hooks = [], []
File /usr/local/lib/python3.10/dist-packages/transformers/models/mistral/modeling_mistral.py:257, in MistralAttention.forward(self, hidden_states, attention_mask, position_ids, past_key_value, output_attentions, use_cache, **kwargs)
252 warnings.warn(
253 "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
254 )
255 bsz, q_len, _ = hidden_states.size()
--> 257 query_states = self.q_proj(hidden_states)
258 key_states = self.k_proj(hidden_states)
259 value_states = self.v_proj(hidden_states)
File /usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py:1194, in Module._call_impl(self, *input, **kwargs)
1190 # If we don't have any hooks, we want to skip the rest of the logic in
1191 # this function, and just call forward.
1192 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
1193 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1194 return forward_call(*input, **kwargs)
1195 # Do not call functions when jit is used
1196 full_backward_hooks, non_full_backward_hooks = [], []
File /usr/local/lib/python3.10/dist-packages/torch/nn/modules/linear.py:114, in Linear.forward(self, input)
113 def forward(self, input: Tensor) -> Tensor:
--> 114 return F.linear(input, self.weight, self.bias)
RuntimeError: "addmm_impl_cpu_" not implemented for 'Half'
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent