Refine model.
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84938e565e
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@ -392,19 +392,15 @@ class GLMTransformer(torch.nn.Module):
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def forward(
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self,
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hidden_states,
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rotary_pos_emb,
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use_cache: Optional[bool] = True,
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rotary_pos_emb
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):
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kv_caches = [None for _ in range(self.num_layers)]
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presents = () if use_cache else None
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for index in range(self.num_layers):
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layer = self.layers[index]
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hidden_states, kv_cache = layer(
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hidden_states, rotary_pos_emb, kv_cache=kv_caches[index]
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)
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if use_cache:
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presents = presents + (kv_cache,)
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hidden_states = self.final_layernorm(hidden_states)
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return hidden_states
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@ -472,7 +468,6 @@ class ChatGLMModel(nn.Module):
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self,
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input_ids,
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position_ids: Optional[torch.Tensor] = None,
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use_cache: Optional[bool] = None,
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output_hidden_states: Optional[bool] = None,
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return_last_logit: Optional[bool] = False,
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):
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@ -481,7 +476,6 @@ class ChatGLMModel(nn.Module):
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if output_hidden_states is not None
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else self.config.output_hidden_states
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)
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use_cache = use_cache if use_cache is not None else self.config.use_cache
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batch_size, seq_length = input_ids.shape
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inputs_embeds = self.embedding(input_ids)
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@ -493,8 +487,7 @@ class ChatGLMModel(nn.Module):
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rotary_pos_emb = rotary_pos_emb.transpose(0, 1).contiguous()
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hidden_states = self.encoder(
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inputs_embeds,
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rotary_pos_emb=rotary_pos_emb,
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use_cache=use_cache,
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rotary_pos_emb=rotary_pos_emb
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)
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if return_last_logit:
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hidden_states = hidden_states[-1:]
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@ -683,8 +676,7 @@ class ChatGLMForConditionalGeneration(nn.Module):
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input_ids,
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pad_token_id=generation_config.pad_token_id,
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eos_token_id=generation_config.eos_token_id,
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output_hidden_states=generation_config.output_hidden_states,
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use_cache=generation_config.use_cache,
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output_hidden_states=generation_config.output_hidden_states
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)
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outputs = outputs.tolist()[0][len(inputs["input_ids"][0]) : -1]
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@ -697,8 +689,7 @@ class ChatGLMForConditionalGeneration(nn.Module):
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input_ids: torch.LongTensor,
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pad_token_id: Optional[int] = None,
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eos_token_id: Optional[Union[int, List[int]]] = None,
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output_hidden_states: Optional[bool] = None,
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use_cache: Optional[bool] = None,
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output_hidden_states: Optional[bool] = None
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):
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if isinstance(eos_token_id, int):
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eos_token_id = [eos_token_id]
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@ -717,12 +708,10 @@ class ChatGLMForConditionalGeneration(nn.Module):
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.unsqueeze(0)
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.repeat(batch_size, 1)
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)
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use_cache = use_cache if use_cache is not None else self.config.use_cache
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model_inputs = {
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"input_ids": input_ids_in,
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"position_ids": position_ids_in,
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"return_last_logit": True,
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"use_cache": use_cache,
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"return_last_logit": True
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}
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logits = self.transformer(
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