diff --git a/examples/utils/generator.py b/examples/utils/generator.py
index 439af507d..96e34fa78 100644
--- a/examples/utils/generator.py
+++ b/examples/utils/generator.py
@@ -240,7 +240,6 @@ def generate(
 
         generated_tokens = inputs["input_ids"].numpy() if use_io_binding else inputs["input_ids"].copy()
         batch_size, prompt_length = generated_tokens.shape
-        valid_prompt_len = int(inputs["attention_mask_2d"].sum(axis=-1).max())
         has_eos = np.zeros(batch_size, dtype=bool)
 
         # buffers to keep numpy copy of model inputs, don't want to keep going back and forth between OrtValue and numpy
@@ -249,6 +248,7 @@ def generate(
                 inputs.pop("attention_mask_2d").numpy() if use_io_binding else inputs.pop("attention_mask_2d")
             )
         }
+        valid_prompt_len = int(np_buffers["attention_mask"].sum(axis=-1).max())
         if self.use_position_ids:
             np_buffers["position_ids"] = (
                 np_buffers["attention_mask"]
@@ -267,6 +267,8 @@ def generate(
                 session = self.sessions["iterator"]
                 io_binding = session.io_binding() if use_io_binding else None
 
+            print(np_buffers["attention_mask"])
+
             if use_io_binding:
                 if idx < 2:
                     # need to bind logits twice, once for prompt processing and once for token generation
@@ -383,6 +385,13 @@ def generate(
                 # GQA, or static during token generation
                 inputs["attention_mask"].update_inplace(attention_mask)
 
+            first_cache = outputs[1]
+            if use_io_binding:
+                first_cache = first_cache.numpy()
+            print(first_cache[:, 0, :, 0])
+            if idx == 2:
+                sdcd
+
             # update cache
             cache.update(outputs[1:])
         if use_io_binding:
@@ -436,6 +445,8 @@ def get_initial_inputs(
         attention_mask = encodings_dict["attention_mask"]
         if not self.extended_attention_mask:
             attention_mask.astype(self.input_info["attention_mask"]["dtype"])
+        # print(input_ids)
+        # print(attention_mask)
 
         cache = self.get_fresh_cache(
             batch_size,
@@ -450,7 +461,10 @@ def get_initial_inputs(
                 [attention_mask, np.zeros((batch_size, cache.max_cache_len - prompt_length), dtype=np.int32)], 1
             )
 
-        inputs = {"input_ids": input_ids, "attention_mask": attention_mask, "attention_mask_2d": attention_mask}
+        inputs = {"input_ids": input_ids, "attention_mask": attention_mask, "attention_mask_2d": attention_mask.copy()}
+        if isinstance(cache, GQASharedCache) and self.prompt_len:
+            # prompt processing needs to attend to the whole prompt+padding
+            inputs["attention_mask"][:, :prompt_length] = 1
         if self.extended_attention_mask:
             replace_with_extended_mask(inputs, "causal", -1000)
             inputs["attention_mask"] = inputs["attention_mask"].astype(self.input_info["attention_mask"]["dtype"])