Refien model code.

chatglm/modeling_chatglm.py

@@ -93,11 +93,11 @@ class CoreAttention(torch.nn.Module):
 
         self.attention_dropout = torch.nn.Dropout(config.attention_dropout)
 
-    def forward(self, query_layer, key_layer, value_layer, attention_mask):
+    def forward(self, query_layer, key_layer, value_layer):
         query_layer, key_layer, value_layer = [
             k.permute(1, 2, 0, 3) for k in [query_layer, key_layer, value_layer]
         ]
-        if attention_mask is None and query_layer.shape[2] == key_layer.shape[2]:
+        if query_layer.shape[2] == key_layer.shape[2]:
             context_layer = torch.nn.functional.scaled_dot_product_attention(
                 query_layer, key_layer, value_layer, is_causal=True
             )
@@ -170,7 +170,7 @@ class SelfAttention(torch.nn.Module):
         x_out2 = x_out2.flatten(3)
         return torch.cat((x_out2, x_pass), dim=-1)
 
-    def forward(self, hidden_states, attention_mask, rotary_pos_emb, kv_cache=None):
+    def forward(self, hidden_states, rotary_pos_emb, kv_cache=None):
         # hidden_states: [sq, b, h]
         # Attention heads [sq, b, h] --> [sq, b, (np * 3 * hn)]
         mixed_x_layer = self.query_key_value(hidden_states)
@@ -250,9 +250,7 @@ class SelfAttention(torch.nn.Module):
         # ==================================
         # core attention computation
         # ==================================
-        context_layer = self.core_attention(
-            query_layer, key_layer, value_layer, attention_mask
-        )
+        context_layer = self.core_attention(query_layer, key_layer, value_layer)
         # =================
         # Output. [sq, b, h]
         # =================
@@ -344,13 +342,13 @@ class GLMBlock(torch.nn.Module):
         # MLP
         self.mlp = MLP(config, device=device)
 
-    def forward(self, hidden_states, attention_mask, rotary_pos_emb, kv_cache=None):
+    def forward(self, hidden_states, rotary_pos_emb, kv_cache=None):
         # hidden_states: [s, b, h]
         # Layer norm at the beginning of the transformer layer.
         layernorm_output = self.input_layernorm(hidden_states)
         # Self attention.
         attention_output, kv_cache = self.self_attention(
-            layernorm_output, attention_mask, rotary_pos_emb, kv_cache=kv_cache
+            layernorm_output, rotary_pos_emb, kv_cache=kv_cache
         )
         residual = hidden_states
 
@@ -394,9 +392,7 @@ class GLMTransformer(torch.nn.Module):
     def forward(
         self,
         hidden_states,
-        attention_mask,
         rotary_pos_emb,
-        kv_caches=None,
         use_cache: Optional[bool] = True,
     ):
         kv_caches = [None for _ in range(self.num_layers)]
@@ -405,7 +401,7 @@ class GLMTransformer(torch.nn.Module):
         for index in range(self.num_layers):
             layer = self.layers[index]
             hidden_states, kv_cache = layer(
-                hidden_states, attention_mask, rotary_pos_emb, kv_cache=kv_caches[index]
+                hidden_states, rotary_pos_emb, kv_cache=kv_caches[index]
             )
             if use_cache:
                 presents = presents + (kv_cache,)
@@ -476,13 +472,8 @@ class ChatGLMModel(nn.Module):
         self,
         input_ids,
         position_ids: Optional[torch.Tensor] = None,
-        attention_mask: Optional[torch.BoolTensor] = None,
-        full_attention_mask: Optional[torch.BoolTensor] = None,
-        past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
-        inputs_embeds: Optional[torch.Tensor] = None,
         use_cache: Optional[bool] = None,
         output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
         return_last_logit: Optional[bool] = False,
     ):
         output_hidden_states = (
@@ -491,30 +482,18 @@ class ChatGLMModel(nn.Module):
             else self.config.output_hidden_states
         )
         use_cache = use_cache if use_cache is not None else self.config.use_cache
-        return_dict = (
-            return_dict if return_dict is not None else self.config.use_return_dict
-        )
-
         batch_size, seq_length = input_ids.shape
-
-        if inputs_embeds is None:
         inputs_embeds = self.embedding(input_ids)
 
         # Rotary positional embeddings
         rotary_pos_emb = self.rotary_pos_emb(self.seq_length)
-
         # show.DumpTensorToImage(rotary_pos_emb[:, :, 0], "rotary_pos_emb.png", scale=0.1)
 
-        if position_ids is not None:
         rotary_pos_emb = rotary_pos_emb[position_ids]
-        else:
-            rotary_pos_emb = rotary_pos_emb[None, :seq_length]
         rotary_pos_emb = rotary_pos_emb.transpose(0, 1).contiguous()
         hidden_states = self.encoder(
             inputs_embeds,
-            full_attention_mask,
             rotary_pos_emb=rotary_pos_emb,
-            kv_caches=past_key_values,
             use_cache=use_cache,
         )
         if return_last_logit:
@@ -741,7 +720,6 @@ class ChatGLMForConditionalGeneration(nn.Module):
             use_cache = use_cache if use_cache is not None else self.config.use_cache
             model_inputs = {
                 "input_ids": input_ids_in,
-                "past_key_values": None,
                 "position_ids": position_ids_in,
                 "return_last_logit": True,
                 "use_cache": use_cache,
@@ -749,7 +727,6 @@ class ChatGLMForConditionalGeneration(nn.Module):
 
             logits = self.transformer(
                 **model_inputs,
-                return_dict=True,
                 output_hidden_states=output_hidden_states,
             )
             next_token_logits = logits[:, -1, :]