Fix mistral generate for long prompt / response (#27548)

* Fix mistral generate for long prompt / response

* Add unit test

* fix linter

* fix linter

* fix test

* add assisted generation test for mistral and load the model in 4 bit + fa2

src/transformers/models/mistral/modeling_mistral.py

@@ -364,7 +364,7 @@ class MistralFlashAttention2(MistralAttention):
         if past_key_value is not None:
             # Activate slicing cache only if the config has a value `sliding_windows` attribute
             if getattr(self.config, "sliding_window", None) is not None and kv_seq_len > self.config.sliding_window:
-                slicing_tokens = kv_seq_len - self.config.sliding_window
+                slicing_tokens = 1 - self.config.sliding_window
 
                 past_key = past_key_value[0]
                 past_value = past_key_value[1]

tests/models/mistral/test_modeling_mistral.py

@@ -24,6 +24,7 @@ import pytest
 from transformers import AutoTokenizer, MistralConfig, is_torch_available
 from transformers.testing_utils import (
     backend_empty_cache,
+    require_bitsandbytes,
     require_flash_attn,
     require_torch,
     require_torch_gpu,
@@ -494,3 +495,32 @@ class MistralIntegrationTest(unittest.TestCase):
         del model
         backend_empty_cache(torch_device)
         gc.collect()
+
+    @require_bitsandbytes
+    @slow
+    @require_flash_attn
+    def test_model_7b_long_prompt(self):
+        EXPECTED_OUTPUT_TOKEN_IDS = [306, 338]
+        # An input with 4097 tokens that is above the size of the sliding window
+        input_ids = [1] + [306, 338] * 2048
+        model = MistralForCausalLM.from_pretrained(
+            "mistralai/Mistral-7B-v0.1",
+            device_map="auto",
+            load_in_4bit=True,
+            use_flash_attention_2=True,
+        )
+        input_ids = torch.tensor([input_ids]).to(model.model.embed_tokens.weight.device)
+        generated_ids = model.generate(input_ids, max_new_tokens=4, temperature=0)
+        self.assertEqual(EXPECTED_OUTPUT_TOKEN_IDS, generated_ids[0][-2:].tolist())
+
+        # Assisted generation
+        assistant_model = model
+        assistant_model.generation_config.num_assistant_tokens = 2
+        assistant_model.generation_config.num_assistant_tokens_schedule = "constant"
+        generated_ids = model.generate(input_ids, max_new_tokens=4, temperature=0)
+        self.assertEqual(EXPECTED_OUTPUT_TOKEN_IDS, generated_ids[0][-2:].tolist())
+
+        del assistant_model
+        del model
+        backend_empty_cache(torch_device)
+        gc.collect()