Fix low memory generation example issue in transformers 4.36 (#10702)

* update cache in low memory generate

* update

python/llm/example/GPU/PyTorch-Models/Model/llama2/low_memory_generate.py

@@ -28,6 +28,7 @@ import torch.nn as nn
 from accelerate import init_empty_weights
 from safetensors.torch import load_file, save_file
 from tqdm.auto import tqdm
+import transformers
 from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer
 from transformers.generation import GenerationMixin
 from transformers.modeling_outputs import CausalLMOutputWithPast
@@ -247,6 +248,12 @@ class LowMemoryLlama(GenerationMixin):
                 *args,
                 **kwargs,
         ):
+        from packaging import version
+        trans_version = transformers.__version__
+        if version.parse(trans_version) >= version.parse("4.36.0"):
+            transformers_4_36 = True
+        else:
+            transformers_4_36 = False
 
         # Reinit model and clean memory
         del self.model
@@ -257,18 +264,23 @@ class LowMemoryLlama(GenerationMixin):
         # Send batch to device
         inputs = input_ids.to(self.device)
 
+        current_shape = inputs.shape[1]
         # Set up kv cache
-        kv_cache = {}
+        if transformers_4_36:
-        if past_key_values is None:
+            from transformers.cache_utils import Cache, DynamicCache
-            past_key_values = {}
+            use_legacy_cache = not isinstance(past_key_values, Cache)
+            if use_legacy_cache:
+                past_key_values = DynamicCache.from_legacy_cache(past_key_values)
+            pre_shape = past_key_values.get_usable_length(current_shape)
+        else:
+            if past_key_values is not None:
+                pre_shape = past_key_values[0][0].size(2)
+            else:
+                pre_shape = 0
+                past_key_values = [None] * len(self.model.model.layers)
 
         with torch.inference_mode():
             # Generate attention mask and position ids
-            current_shape = inputs.shape[1]
-            if past_key_values.get(self.layer_names[1], None):
-                pre_shape = past_key_values[self.layer_names[1]][0].size(2)
-            else:
-                pre_shape = 0
             pos = self.position_ids[:, pre_shape : current_shape + pre_shape]
             attn = self.attention_mask[:, :, -current_shape:, - current_shape - pre_shape:]
 
@@ -282,9 +294,14 @@ class LowMemoryLlama(GenerationMixin):
                 if layer_name in ("model.embed_tokens", "model.norm", "lm_head"):
                     inputs = layer(inputs)
                 else:
-                    inputs, new_kv_cache = layer(inputs, use_cache=True, past_key_value=past_key_values.get(layer_name, None),
+                    decoder_layer_index = int(layer_name.split('.')[-1])
+                    past_key_value = past_key_values if transformers_4_36 else past_key_values[decoder_layer_index]
+                    inputs, new_kv_cache = layer(inputs, use_cache=True, past_key_value=past_key_value,
                                                  position_ids=pos, attention_mask=attn)
-                    kv_cache[layer_name] = new_kv_cache
+                    if transformers_4_36:
+                        past_key_values = new_kv_cache
+                    else:
+                        past_key_values[decoder_layer_index] = new_kv_cache
 
                 # Delete weight before moving to('meta')
                 for module in layer.modules():
@@ -296,7 +313,7 @@ class LowMemoryLlama(GenerationMixin):
 
         result = CausalLMOutputWithPast(
             logits=inputs.detach(),
-            past_key_values=kv_cache,
+            past_key_values=past_key_values,
         )
         return result