Fix Cache.max_cache_len max value for Hybrid models (#39737)

* fix gemma

* fix min

* fix quant init issue

* fix gemma 3n

* skip quant cache test

* fix modular

* new test for Gemma

* include cyril change

---------

Co-authored-by: Cyril Vallez <cyril.vallez@gmail.com>

tests/models/gemma3/test_modeling_gemma3.py

@@ -151,6 +151,52 @@ class Gemma3ModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase
     def test_sdpa_padding_matches_padding_free_with_position_ids(self):
         pass
 
+    def test_generation_beyond_sliding_window_tiny_model(self):
+        """Test generation with a tiny randomly initialised model whose input length is larger than the `sliding_window`.
+        The model is configured with both `full_attention` and `sliding_attention` layers to make sure the hybrid cache
+        and mask slicing logic is covered.
+        """
+        config = Gemma3TextConfig.from_pretrained("hf-internal-testing/tiny-random-Gemma3ForCausalLM")
+        config.attn_implementation = "eager"
+        config.layer_types = ["full_attention", "sliding_attention"]
+        config.sliding_window = 8
+        config.max_position_embeddings = 128
+        model = AutoModelForCausalLM.from_pretrained(
+            "hf-internal-testing/tiny-random-Gemma3ForCausalLM", config=config
+        ).to(torch_device)
+
+        input_len = 10
+        input_ids = torch.tensor(
+            [
+                [42300, 241087, 255445, 81315, 193760, 184471, 67719, 98191, 210651, 124725],
+                [102294, 205314, 226646, 62020, 60245, 68025, 251839, 114053, 4695, 175511],
+            ],
+            device=torch_device,
+        )
+        attention_mask = torch.ones_like(input_ids).to(torch_device)
+        with torch.no_grad():
+            _ = model.generate(
+                input_ids,
+                attention_mask=attention_mask,
+                max_new_tokens=1,
+                do_sample=False,
+                use_cache=True,
+                cache_implementation="hybrid",
+            )
+            # 2 generations are needed to trigger https://github.com/huggingface/transformers/issues/39711
+            # Since it requires model._cache to have been previously initialized
+            output = model.generate(
+                input_ids,
+                attention_mask=attention_mask,
+                max_new_tokens=5,
+                do_sample=False,
+                use_cache=True,
+                cache_implementation="hybrid",
+            )
+        generated_sequences = output[:, input_len:].cpu()
+        EXPECTED_OUTPUT = torch.tensor([[90109, 90109, 90109, 83191, 83191], [246901, 69832, 69832, 69832, 62288]])
+        torch.testing.assert_close(generated_sequences, EXPECTED_OUTPUT)
+
 
 class Gemma3Vision2TextModelTester:
     def __init__(

tests/models/gemma3n/test_modeling_gemma3n.py

@@ -431,6 +431,11 @@ class Gemma3nTextModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.Tes
     def test_dola_decoding_sample(self):
         pass
 
+    @pytest.mark.generate
+    @unittest.skip("Gemma3n does not support QuantizedCache as it performs cache manipulation in the forward pass")
+    def test_generate_with_quant_cache(self):
+        pass
+
 
 class Gemma3nVision2TextModelTester:
     text_config = {"activation_sparsity_pattern": None}