optimize qwen2 memory usage again (#11520)

python/llm/src/ipex_llm/transformers/models/qwen2.py

@@ -73,43 +73,6 @@ def qwen2_model_forward(
     output_attentions: Optional[bool] = None,
     output_hidden_states: Optional[bool] = None,
     return_dict: Optional[bool] = None,
-):
-    use_cache = use_cache if use_cache is not None else self.config.use_cache
-    input = input_ids if input_ids is not None else inputs_embeds
-    use_quantize_kv = (
-        self.config.hidden_size != 3584     # disable quantize kv in specific model
-        and use_quantize_kv_cache(self.layers[0].mlp.up_proj, input)
-    )
-    if use_cache:
-        if use_quantize_kv and not isinstance(past_key_values, DynamicFp8Cache):
-            past_key_values = DynamicFp8Cache.from_legacy_cache(past_key_values)
-        if not use_quantize_kv and not isinstance(past_key_values, DynamicNormalCache):
-            past_key_values = DynamicNormalCache.from_legacy_cache(past_key_values)
-    return qwen2_model_forward_internal(
-        self=self,
-        input_ids=input_ids,
-        attention_mask=attention_mask,
-        position_ids=position_ids,
-        past_key_values=past_key_values,
-        inputs_embeds=inputs_embeds,
-        use_cache=use_cache,
-        output_attentions=output_attentions,
-        output_hidden_states=output_hidden_states,
-        return_dict=return_dict,
-    )
-
-
-def qwen2_model_forward_internal(
-    self,
-    input_ids: torch.LongTensor = None,
-    attention_mask: Optional[torch.Tensor] = None,
-    position_ids: Optional[torch.LongTensor] = None,
-    past_key_values: Optional[List[torch.FloatTensor]] = None,
-    inputs_embeds: Optional[torch.FloatTensor] = None,
-    use_cache: Optional[bool] = None,
-    output_attentions: Optional[bool] = None,
-    output_hidden_states: Optional[bool] = None,
-    return_dict: Optional[bool] = None,
 ) -> Union[Tuple, BaseModelOutputWithPast]:
     output_attentions = output_attentions if output_attentions is not None else \
         self.config.output_attentions
@@ -144,11 +107,21 @@ def qwen2_model_forward_internal(
 
     past_key_values_length = 0
 
+    # ipex-llm changes start
+    # IPEX-LLM OPT: kv cache and quantize kv cache
+    inputs = input_ids if input_ids is not None else inputs_embeds
+    use_quantize_kv = (
+        self.config.hidden_size != 3584     # disable quantize kv in specific model
+        and use_quantize_kv_cache(self.layers[0].mlp.up_proj, inputs)
+    )
+
     if use_cache:
-        use_legacy_cache = not isinstance(past_key_values, Cache)
-        if use_legacy_cache:
-            past_key_values = DynamicCache.from_legacy_cache(past_key_values)
+        if use_quantize_kv and not isinstance(past_key_values, DynamicFp8Cache):
+            past_key_values = DynamicFp8Cache.from_legacy_cache(past_key_values)
+        if not use_quantize_kv and not isinstance(past_key_values, DynamicNormalCache):
+            past_key_values = DynamicNormalCache.from_legacy_cache(past_key_values)
         past_key_values_length = past_key_values.get_usable_length(seq_length)
+    # ipex-llm changes end
 
     if position_ids is None:
         device = input_ids.device if input_ids is not None else inputs_embeds.device
@@ -176,7 +149,15 @@ def qwen2_model_forward_internal(
                 "the input. "
             )
 
-    if self._attn_implementation == "flash_attention_2":
+    # ipex-llm changes start: don't generate `attention_mask` in specific cases
+    if seq_length == 1 or batch_size == 1 and use_sdp_causal(
+        seq_length, seq_length + past_key_values_length,
+        self.config.hidden_size // self.config.num_attention_heads,
+        inputs_embeds, self.training
+    ):
+        attention_mask = None
+    # ipex-llm changes end
+    elif self._attn_implementation == "flash_attention_2":
         # 2d mask is passed through the layers
         attention_mask = attention_mask if (attention_mask is not None and
                                             0 in attention_mask) else None
@@ -251,10 +232,11 @@ def qwen2_model_forward_internal(
     if output_hidden_states:
         all_hidden_states += (hidden_states,)
 
+    # ipex-llm changes start: remove `to_legacy_cache`
     next_cache = None
     if use_cache:
-        next_cache = next_decoder_cache.to_legacy_cache() if use_legacy_cache else \
-            next_decoder_cache
+        next_cache = next_decoder_cache
+    # ipex-llm changes end
 
     if not return_dict:
         return tuple(v for v in [hidden_states, next_cache,