BUG? transformers version '5.12.0' gemma-4 generate DynamicSlidingWindowLayer
Author: NickyDark1
Pull request: no


Hi, I'm generating text with MTP but I'm getting the DynamicSlidingWindowLayer error.
If I disable MTP, it generates the text normally (# assistant_model=assistant_model,)
transformers version '5.12.0'
torch version 
CODE: '2.11.0+cu128'
GPU: RTX 6000 Ada

```py

'''
import transformers, torch
transformers.__version__
'5.12.0'

torch.__version__
'2.11.0+cu128'
'''

from transformers import (
    AutoProcessor,
    AutoModelForMultimodalLM,
    AutoModelForCausalLM,
    StoppingCriteria,
    StoppingCriteriaList,
    DynamicCache,
)
import torch

MODEL_ID     = "google/gemma-4-E2B-it"
ASSISTANT_ID = MODEL_ID + "-assistant"

# ── CONFIGURACIÓN ──
GPU_GEMMA   = "34GiB"    # tope de VRAM para el modelo principal
GPU_MTP     = "5GiB"    # tope para el assistant (drafter)
CPU_OFFLOAD = "16GiB"   # cuánta RAM puede usar como desborde

processor = AutoProcessor.from_pretrained(MODEL_ID)
model = AutoModelForMultimodalLM.from_pretrained(
    MODEL_ID, dtype="auto", 
    device_map="auto",
    attn_implementation="sdpa",
    # max_memory={0: GPU_GEMMA, "cpu": CPU_OFFLOAD},
    local_files_only=True
)

assistant_model = AutoModelForCausalLM.from_pretrained(
            ASSISTANT_ID, dtype=torch.bfloat16, 
            device_map="auto",
            attn_implementation="sdpa",
            # max_memory={0: GPU_GEMMA, "cpu": CPU_OFFLOAD},
            local_files_only=True
)

from PIL import Image
from transformers import TextStreamer

# Prompt - add image before text
messages = [
    {
        "role": "user", "content": [
            {"type": "image", "url": "https://raw.githubusercontent.com/google-gemma/cookbook/refs/heads/main/apps/sample-data/GoldenGate.png"},
            {"type": "image", "url": "https://acroadtrip.blob.core.windows.net/catalogo-imagenes/m/RT_V_f2e006163df5464487412d51c7f0cca1.webp"},
            {"type": "text", "text": "que observas en las 2 imagenes"}
        ]
    }
]


# Process input
inputs = processor.apply_chat_template(
    messages,
    tokenize=True,
    return_dict=True,
    return_tensors="pt",
    add_generation_prompt=True,
    processor_kwargs={"max_soft_tokens": 280},
    enable_thinking=True,
).to(model.device)
input_len = inputs["input_ids"].shape[-1]

streamer = TextStreamer(processor, skip_prompt=True)

# Generate output
outputs = model.generate(**inputs,
                        max_new_tokens=1994,
                         assistant_model=assistant_model,
                         temperature=0.1,
                         top_p=0.95,
                         top_k=64,
                         use_cache=True, # True False                  
                         streamer=streamer

                         )
```

ERROR:
```py

Failed to load /usr/local/lib/python3.11/dist-packages/torchao/_C_cutlass_90a.abi3.so: Could not load this library: /usr/local/lib/python3.11/dist-packages/torchao/_C_cutlass_90a.abi3.so
Failed to load /usr/local/lib/python3.11/dist-packages/torchao/_C_mxfp8.cpython-310-x86_64-linux-gnu.so: Could not load this library: /usr/local/lib/python3.11/dist-packages/torchao/_C_mxfp8.cpython-310-x86_64-linux-gnu.so
Loading weights: 100%
 1951/1951 [00:01<00:00, 1398.39it/s]
Loading weights: 100%
 50/50 [00:00<00:00, 2364.37it/s]
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
File <timed exec>:87

File /usr/local/lib/python3.11/dist-packages/torch/utils/_contextlib.py:124, in context_decorator.<locals>.decorate_context(*args, **kwargs)
    120 @functools.wraps(func)
    121 def decorate_context(*args, **kwargs):
    122     # pyrefly: ignore [bad-context-manager]
    123     with ctx_factory():
--> 124         return func(*args, **kwargs)

File /usr/local/lib/python3.11/dist-packages/transformers/generation/utils.py:2584, in GenerationMixin.generate(self, inputs, generation_config, logits_processor, stopping_criteria, prefix_allowed_tokens_fn, synced_gpus, assistant_model, streamer, negative_prompt_ids, negative_prompt_attention_mask, custom_generate, **kwargs)
   2581 model_kwargs["use_cache"] = generation_config.use_cache
   2583 # 9. Call generation mode
-> 2584 result = decoding_method(
   2585     self,
   2586     input_ids,
   2587     logits_processor=prepared_logits_processor,
   2588     stopping_criteria=prepared_stopping_criteria,
   2589     generation_config=generation_config,
   2590     **generation_mode_kwargs,
   2591     **model_kwargs,
   2592 )
   2594 return result

File /usr/local/lib/python3.11/dist-packages/transformers/generation/utils.py:3686, in GenerationMixin._assisted_decoding(self, input_ids, logits_processor, stopping_criteria, generation_config, synced_gpus, streamer, inputs_tensor, assistant_model, assistant_tokenizer, tokenizer, **model_kwargs)
   3683 new_cur_len = input_ids.shape[1]
   3685 # 4.2. Discard past key values relative to unused assistant tokens
-> 3686 outputs.past_key_values.crop(new_cur_len - 1)
   3688 # 5. Update the candidate generation strategy if needed
   3689 candidate_generator.update_candidate_strategy(input_ids, new_logits, n_matches)

File /usr/local/lib/python3.11/dist-packages/transformers/cache_utils.py:1350, in Cache.crop(self, max_length)
   1348 """Crop the cache to the given length"""
   1349 for layer_idx in range(len(self.layers)):
-> 1350     self.layers[layer_idx].crop(max_length)

File /usr/local/lib/python3.11/dist-packages/transformers/cache_utils.py:279, in DynamicSlidingWindowLayer.crop(self, max_length)
    274 """
    275 Crop the past key values up to a new `max_length` in terms of tokens. `max_length` can also be
    276 negative to remove `max_length` tokens.
    277 """
    278 if self.get_seq_length() >= self.sliding_window:
--> 279     raise ValueError(
    280         "Cannot `crop` a `DynamicSlidingWindowLayer` after it has seen more tokens than its"
    281         "sliding window (otherwise some states are lost)"
    282     )
    283 super().crop(max_length)
    284 self.cumulative_length = self.keys.shape[-2]

ValueError: Cannot `crop` a `DynamicSlidingWindowLayer` after it has seen more tokens than itssliding window (otherwise some states are lost)
```

Comments:
* This code works correctly model 'google/gemma-4-12B-it' MTP active

```py
%%time

'''
import transformers, torch
transformers.__version__
'5.12.0'

torch.__version__
'2.11.0+cu128'
'''

from transformers import (
    AutoProcessor,
    AutoModelForMultimodalLM,
    AutoModelForCausalLM,
    # StoppingCriteria,
    # StoppingCriteriaList,
    # DynamicCache,
)

from PIL import Image
from transformers import TextStreamer
import torch

# MODEL_ID     = "google/gemma-4-E2B-it"
# MODEL_ID = "google/gemma-4-E2B-it-qat-w4a16-ct"
MODEL_ID = "google/gemma-4-12B-it"
ASSISTANT_ID = MODEL_ID + "-assistant"

# ── CONFIGURACIÓN ──
GPU_GEMMA   = "34GiB"    # tope de VRAM para el modelo principal
GPU_MTP     = "5GiB"    # tope para el assistant (drafter)
CPU_OFFLOAD = "16GiB"   # cuánta RAM puede usar como desborde

processor = AutoProcessor.from_pretrained(MODEL_ID)
model = AutoModelForMultimodalLM.from_pretrained(
    MODEL_ID, 
    dtype="auto", 
    device_map="auto",
    attn_implementation="sdpa",
    # max_memory={0: GPU_GEMMA, "cpu": CPU_OFFLOAD},
    local_files_only=True
)

assistant_model = AutoModelForCausalLM.from_pretrained(
            ASSISTANT_ID, dtype=torch.bfloat16, 
            device_map="auto",
            attn_implementation="sdpa",
            # max_memory={0: GPU_GEMMA, "cpu": CPU_OFFLOAD},
            local_files_only=True
)


# Prompt - add image before text
messages = [
    {
        "role": "user", "content": [
            {"type": "image", "url": "https://raw.githubusercontent.com/google-gemma/cookbook/refs/heads/main/apps/sample-data/GoldenGate.png"},
            {"type": "image", "url": "https://acroadtrip.blob.core.windows.net/catalogo-imagenes/m/RT_V_f2e006163df5464487412d51c7f0cca1.webp"},
            {"type": "text", "text": "que observas en las 2 imagenes"}
        ]
    }
]


# Process input
inputs = processor.apply_chat_template(
    messages,
    tokenize=True,
    return_dict=True,
    return_tensors="pt",
    add_generation_prompt=True,
    processor_kwargs={"max_soft_tokens": 280}, # 70, 140, 280, 560, and 1120
    enable_thinking=True,
).to(model.device)
input_len = inputs["input_ids"].shape[-1]

streamer = TextStreamer(processor, skip_prompt=True)

# Generate output
outputs = model.generate(**inputs,
                        max_new_tokens=1994,
                         assistant_model=assistant_model,
                         temperature=0.1,
                         top_p=0.95,
                         top_k=64,
                         use_cache=True, # True False                  
                         streamer=streamer

                         )
```

Good find! @NickyDark1 

Looks like a regression from #46600.

It dropped the `dynamic_full guard` in `_prepare_cache_for_generation` (generation/utils.py), so config is now always passed to DynamicCache. 

Assisted/speculative decoding uses dynamic_full to get croppable full layers, but now builds DynamicSlidingWindowLayers instead and crop() raises past the sliding window. The diffusion_gemma copy still has the guard.