Repository: huggingface/transformers
Pull request: Several fixes for Gemma3n

# What does this PR do?



Changed files:
filename: src/transformers/models/gemma3n/modeling_gemma3n.py
patch: @@ -1135,9 +1135,17 @@ def correct(self, predictions: torch.Tensor, activated: torch.Tensor) -> torch.T
         corrected += predictions  # add the original input
         return corrected.contiguous().type_as(activated)
 
+    def forward(self, corrected: torch.Tensor) -> torch.Tensor:
+        """
+        This is only defined as the `forward` so that accelerate hooks can move correctly `correct_output_scale`
+        (which is a nn.Parameter, not a Module) between devices when offloading. It is otherwise only used in
+        `scale_corrected_output`
+        """
+        return (corrected.type_as(self.correct_output_scale) * self.correct_output_scale).type_as(corrected)
+
     def scale_corrected_output(self, corrected: torch.Tensor) -> torch.Tensor:
         """Scales the provided 3D tensor of shape [batch_size, num_tokens, hidden_size]."""
-        return (corrected.type_as(self.correct_output_scale) * self.correct_output_scale).type_as(corrected)
+        return self.forward(corrected)
 
 
 class Gemma3nTextRotaryEmbedding(nn.Module):
@@ -1290,7 +1298,7 @@ def __init__(self, config: Gemma3nTextConfig, layer_idx: int):
         self.v_norm = Gemma3nRMSNorm(dim=config.head_dim, eps=config.rms_norm_eps, with_scale=False)
 
         first_kv_shared_layer_idx = self.config.num_hidden_layers - self.config.num_kv_shared_layers
-        self.is_kv_shared_layer = layer_idx >= first_kv_shared_layer_idx
+        self.is_kv_shared_layer = layer_idx >= first_kv_shared_layer_idx > 0
         # Find the index of the last sliding or full layer before sharing starts (or None if no sharing)
         layer_type = config.layer_types[layer_idx]
         self.kv_shared_layer_index = (
@@ -1319,21 +1327,22 @@ def forward(
         query_states = query_states.transpose(1, 2)
 
         if self.is_kv_shared_layer and self.kv_shared_layer_index is not None and past_key_value is not None:
-            # HybridCache has complex slicing when layer_type == "sliding_attention" that impact Shared KV Cache.
+            # Device of past layer may be different from current one
+            indices = cache_position.to(past_key_value.key_cache[self.kv_shared_layer_index].device)
+            # In this case we need special handling of the slice as the layer is of fixed small size (for full layers, we never go beyond)
             if isinstance(past_key_value, HybridCache) and self.is_sliding:
                 max_length = past_key_value.sliding_window
-                if cache_position.shape[0] > max_length:
-                    # If in the prefill phase for a "sliding_attention" layer and the prefill is larger than the cache,
-                    # slice into the entire cache.
-                    indices = slice(0, max_length)
-                else:
-                    # If prefill fits or generating for a "sliding_attention" layer, clamp to max_cache_len - 1
-                    indices = cache_position.clamp(min=0, max=max_length - 1)
-            else:
-                indices = cache_position
+                indices = (
+                    slice(0, max_length)
+                    if cache_position.shape[0] > max_length
+                    else cache_position.clamp(min=0, max=max_length - 1)
+                )
 
-            key_states = past_key_value.key_cache[self.kv_shared_layer_index][:, :, indices]
-            value_states = past_key_value.value_cache[self.kv_shared_layer_index][:, :, indices]
+            # Device of past layer may be different from current one
+            key_states = past_key_value.key_cache[self.kv_shared_layer_index][:, :, indices].to(query_states.device)
+            value_states = past_key_value.value_cache[self.kv_shared_layer_index][:, :, indices].to(
+                query_states.device
+            )
         else:
             key_states = self.k_proj(hidden_states).view(hidden_shape)
             key_states = self.k_norm(key_states)
@@ -1447,10 +1456,9 @@ def forward(
         attn_ffw_laurel_gated = attn_laurel + attn_ffw_norm
         corrected_predictions = self.altup.correct(predictions, attn_ffw_laurel_gated)
 
-        first_prediction = corrected_predictions[self.config.altup_active_idx]
-        first_prediction_clone = first_prediction.clone()
+        first_prediction = corrected_predictions[self.config.altup_active_idx].clone()
         if self.config.altup_correct_scale:
-            first_prediction = self.altup.scale_corrected_output(first_prediction_clone)
+            first_prediction = self.altup.scale_corrected_output(first_prediction)
 
         # per_layer_input_gate adapted from jax.numpy.einsum("btd,dp->btp", ...)
         first_prediction = self.per_layer_input_gate(first_prediction)
@@ -1475,7 +1483,7 @@ class Gemma3nPreTrainedModel(PreTrainedModel):
     config_class = Gemma3nConfig
     base_model_prefix = ""
     supports_gradient_checkpointing = True
-    _no_split_modules = ["Gemma3nDecoderLayer"]
+    _no_split_modules = ["Gemma3nTextDecoderLayer"]
     _skip_keys_device_placement = ["past_key_values"]
     _supports_flash_attn_3 = True
     _supports_flash_attn_2 = True
@@ -1656,18 +1664,17 @@ def forward(
         position_embeddings_local = self.rotary_emb_local(hidden_states_0, position_ids)
 
         # Expand hidden_states to support per-layer inputs
-        target_magnitude: torch.Tensor = torch.mean(hidden_states_0**2, dim=-1, keepdim=True) ** 0.5
-        epsilon_tensor = torch.tensor(torch.finfo().min)
+        target_magnitude = torch.mean(hidden_states_0**2, dim=-1, keepdim=True) ** 0.5
+        epsilon_tensor = torch.tensor(1e-5)
 
         temp_hidden_states = [hidden_states_0]
         for i in range(1, self.config.altup_num_inputs):
             # altup_proj adapted from jax.numpy.einsum("btp,pd->btd", ...)
-            altup_proj: torch.Tensor = self.altup_projections[i - 1](hidden_states_0)
-            current_hidden_state = altup_proj.type(hidden_states_0.dtype)
-            new_magnitude = torch.mean(current_hidden_state**2, dim=-1, keepdim=True) ** 0.5
-            current_hidden_state = current_hidden_state * (
-                target_magnitude / torch.maximum(new_magnitude, epsilon_tensor)
-            )
+            altup_proj = self.altup_projections[i - 1](hidden_states_0)
+            current_hidden_state = altup_proj.to(dtype=hidden_states_0.dtype, device=target_magnitude.device)
+            new_magnitude = torch.mean(current_hidden_state**2, dim=-1, keepdim=True)
+            new_magnitude = torch.sqrt(torch.maximum(new_magnitude, epsilon_tensor.to(target_magnitude.device)))
+            current_hidden_state = current_hidden_state * target_magnitude / new_magnitude
             temp_hidden_states.append(current_hidden_state)
 
         hidden_states = torch.stack(temp_hidden_states, dim=0)  # [num_altup_inputs, batch, seq_len, hidden_size]
@@ -1685,9 +1692,9 @@ def forward(
 
             layer_outputs = decoder_layer(
                 hidden_states,
-                position_embeddings_global=position_embeddings_global,
-                position_embeddings_local=position_embeddings_local,
-                per_layer_input=per_layer_input,
+                position_embeddings_global,
+                position_embeddings_local,
+                per_layer_input,
                 attention_mask=causal_mask,
                 position_ids=position_ids,
                 past_key_value=past_key_values,
@@ -1712,11 +1719,10 @@ def forward(
         for i in range(1, self.config.altup_num_inputs):
             # altup_unembed_projections adapted from jax.numpy.einsum("btp,pd->btd", ...)
             altup_unemb_proj: torch.Tensor = self.altup_unembed_projections[i - 1](hidden_states[i])
-            current_hidden_state = altup_unemb_proj.type(hidden_states_0.dtype)
-            new_magnitude = torch.mean(current_hidden_state**2, dim=-1, keepdim=True) ** 0.5
-            current_hidden_state = current_hidden_state * (
-                target_magnitude / torch.maximum(new_magnitude, epsilon_tensor)
-            )
+            current_hidden_state = altup_unemb_proj.to(dtype=hidden_states_0.dtype, device=target_magnitude.device)
+            new_magnitude = torch.mean(current_hidden_state**2, dim=-1, keepdim=True)
+            new_magnitude = torch.sqrt(torch.maximum(new_magnitude, epsilon_tensor.to(target_magnitude.device)))
+            current_hidden_state = current_hidden_state * target_magnitude / new_magnitude
             temp_hidden_states.append(current_hidden_state)
 
         hidden_states = torch.stack(temp_hidden_states)
@@ -1743,7 +1749,9 @@ def project_per_layer_inputs(
         per_layer_inputs: Optional[torch.Tensor] = None,
     ) -> torch.Tensor:
         per_layer_projection: torch.Tensor = self.per_layer_model_projection(inputs_embeds)
-        per_layer_projection *= self.per_layer_projection_scale.type(inputs_embeds.dtype)
+        per_layer_projection *= self.per_layer_projection_scale.to(
+            dtype=inputs_embeds.dtype, device=per_layer_projection.device
+        )
         per_layer_projection = per_layer_projection.reshape(
             *inputs_embeds.shape[:-1],
             self.config.num_hidden_layers,
@@ -1758,7 +1766,9 @@ def project_per_layer_inputs(
             # per-layer inputs are sometimes padded with zeros, slice the relevant embeddings.
             per_layer_inputs = per_layer_inputs[..., : self.config.num_hidden_layers, :]
 
-        return (per_layer_projection + per_layer_inputs) * self.per_layer_input_scale.type(inputs_embeds.dtype)
+        return (per_layer_projection + per_layer_inputs) * self.per_layer_input_scale.to(
+            dtype=inputs_embeds.dtype, device=per_layer_projection.device
+        )
 
 
 @auto_docstring(custom_intro="The base Gemma 3n language model with a language modeling head.")

filename: src/transformers/models/gemma3n/modular_gemma3n.py
patch: @@ -1685,9 +1685,17 @@ def correct(self, predictions: torch.Tensor, activated: torch.Tensor) -> torch.T
         corrected += predictions  # add the original input
         return corrected.contiguous().type_as(activated)
 
+    def forward(self, corrected: torch.Tensor) -> torch.Tensor:
+        """
+        This is only defined as the `forward` so that accelerate hooks can move correctly `correct_output_scale`
+        (which is a nn.Parameter, not a Module) between devices when offloading. It is otherwise only used in
+        `scale_corrected_output`
+        """
+        return (corrected.type_as(self.correct_output_scale) * self.correct_output_scale).type_as(corrected)
+
     def scale_corrected_output(self, corrected: torch.Tensor) -> torch.Tensor:
         """Scales the provided 3D tensor of shape [batch_size, num_tokens, hidden_size]."""
-        return (corrected.type_as(self.correct_output_scale) * self.correct_output_scale).type_as(corrected)
+        return self.forward(corrected)
 
 
 class Gemma3nTextRotaryEmbedding(Gemma2RotaryEmbedding):
@@ -1732,7 +1740,7 @@ def __init__(self, config: Gemma3nTextConfig, layer_idx: int):
         self.v_norm = Gemma3nRMSNorm(dim=config.head_dim, eps=config.rms_norm_eps, with_scale=False)
 
         first_kv_shared_layer_idx = self.config.num_hidden_layers - self.config.num_kv_shared_layers
-        self.is_kv_shared_layer = layer_idx >= first_kv_shared_layer_idx
+        self.is_kv_shared_layer = layer_idx >= first_kv_shared_layer_idx > 0
         # Find the index of the last sliding or full layer before sharing starts (or None if no sharing)
         layer_type = config.layer_types[layer_idx]
         self.kv_shared_layer_index = (
@@ -1761,21 +1769,22 @@ def forward(
         query_states = query_states.transpose(1, 2)
 
         if self.is_kv_shared_layer and self.kv_shared_layer_index is not None and past_key_value is not None:
-            # HybridCache has complex slicing when layer_type == "sliding_attention" that impact Shared KV Cache.
+            # Device of past layer may be different from current one
+            indices = cache_position.to(past_key_value.key_cache[self.kv_shared_layer_index].device)
+            # In this case we need special handling of the slice as the layer is of fixed small size (for full layers, we never go beyond)
             if isinstance(past_key_value, HybridCache) and self.is_sliding:
                 max_length = past_key_value.sliding_window
-                if cache_position.shape[0] > max_length:
-                    # If in the prefill phase for a "sliding_attention" layer and the prefill is larger than the cache,
-                    # slice into the entire cache.
-                    indices = slice(0, max_length)
-                else:
-                    # If prefill fits or generating for a "sliding_attention" layer, clamp to max_cache_len - 1
-                    indices = cache_position.clamp(min=0, max=max_length - 1)
-            else:
-                indices = cache_position
+                indices = (
+                    slice(0, max_length)
+                    if cache_position.shape[0] > max_length
+                    else cache_position.clamp(min=0, max=max_length - 1)
+                )
 
-            key_states = past_key_value.key_cache[self.kv_shared_layer_index][:, :, indices]
-            value_states = past_key_value.value_cache[self.kv_shared_layer_index][:, :, indices]
+            # Device of past layer may be different from current one
+            key_states = past_key_value.key_cache[self.kv_shared_layer_index][:, :, indices].to(query_states.device)
+            value_states = past_key_value.value_cache[self.kv_shared_layer_index][:, :, indices].to(
+                query_states.device
+            )
         else:
             key_states = self.k_proj(hidden_states).view(hidden_shape)
             key_states = self.k_norm(key_states)
@@ -1880,10 +1889,9 @@ def forward(
         attn_ffw_laurel_gated = attn_laurel + attn_ffw_norm
         corrected_predictions = self.altup.correct(predictions, attn_ffw_laurel_gated)
 
-        first_prediction = corrected_predictions[self.config.altup_active_idx]
-        first_prediction_clone = first_prediction.clone()
+        first_prediction = corrected_predictions[self.config.altup_active_idx].clone()
         if self.config.altup_correct_scale:
-            first_prediction = self.altup.scale_corrected_output(first_prediction_clone)
+            first_prediction = self.altup.scale_corrected_output(first_prediction)
 
         # per_layer_input_gate adapted from jax.numpy.einsum("btd,dp->btp", ...)
         first_prediction = self.per_layer_input_gate(first_prediction)
@@ -1906,7 +1914,7 @@ def forward(
 class Gemma3nPreTrainedModel(Gemma2PreTrainedModel):
     config_class = Gemma3nConfig
     base_model_prefix = ""
-    _no_split_modules = ["Gemma3nDecoderLayer"]
+    _no_split_modules = ["Gemma3nTextDecoderLayer"]
 
     def _init_weights(self, module):
         # important: this ported version of Gemma2 isn't meant for training from scratch - only
@@ -1995,7 +2003,9 @@ def project_per_layer_inputs(
         per_layer_inputs: Optional[torch.Tensor] = None,
     ) -> torch.Tensor:
         per_layer_projection: torch.Tensor = self.per_layer_model_projection(inputs_embeds)
-        per_layer_projection *= self.per_layer_projection_scale.type(inputs_embeds.dtype)
+        per_layer_projection *= self.per_layer_projection_scale.to(
+            dtype=inputs_embeds.dtype, device=per_layer_projection.device
+        )
         per_layer_projection = per_layer_projection.reshape(
             *inputs_embeds.shape[:-1],
             self.config.num_hidden_layers,
@@ -2010,7 +2020,9 @@ def project_per_layer_inputs(
             # per-layer inputs are sometimes padded with zeros, slice the relevant embeddings.
             per_layer_inputs = per_layer_inputs[..., : self.config.num_hidden_layers, :]
 
-        return (per_layer_projection + per_layer_inputs) * self.per_layer_input_scale.type(inputs_embeds.dtype)
+        return (per_layer_projection + per_layer_inputs) * self.per_layer_input_scale.to(
+            dtype=inputs_embeds.dtype, device=per_layer_projection.device
+        )
 
     @can_return_tuple
     @auto_docstring
@@ -2091,18 +2103,17 @@ def forward(
         position_embeddings_local = self.rotary_emb_local(hidden_states_0, position_ids)
 
         # Expand hidden_states to support per-layer inputs
-        target_magnitude: torch.Tensor = torch.mean(hidden_states_0**2, dim=-1, keepdim=True) ** 0.5
-        epsilon_tensor = torch.tensor(torch.finfo().min)
+        target_magnitude = torch.mean(hidden_states_0**2, dim=-1, keepdim=True) ** 0.5
+        epsilon_tensor = torch.tensor(1e-5)
 
         temp_hidden_states = [hidden_states_0]
         for i in range(1, self.config.altup_num_inputs):
             # altup_proj adapted from jax.numpy.einsum("btp,pd->btd", ...)
-            altup_proj: torch.Tensor = self.altup_projections[i - 1](hidden_states_0)
-            current_hidden_state = altup_proj.type(hidden_states_0.dtype)
-            new_magnitude = torch.mean(current_hidden_state**2, dim=-1, keepdim=True) ** 0.5
-            current_hidden_state = current_hidden_state * (
-                target_magnitude / torch.maximum(new_magnitude, epsilon_tensor)
-            )
+            altup_proj = self.altup_projections[i - 1](hidden_states_0)
+            current_hidden_state = altup_proj.to(dtype=hidden_states_0.dtype, device=target_magnitude.device)
+            new_magnitude = torch.mean(current_hidden_state**2, dim=-1, keepdim=True)
+            new_magnitude = torch.sqrt(torch.maximum(new_magnitude, epsilon_tensor.to(target_magnitude.device)))
+            current_hidden_state = current_hidden_state * target_magnitude / new_magnitude
             temp_hidden_states.append(current_hidden_state)
 
         hidden_states = torch.stack(temp_hidden_states, dim=0)  # [num_altup_inputs, batch, seq_len, hidden_size]
@@ -2120,9 +2131,9 @@ def forward(
 
             layer_outputs = decoder_layer(
                 hidden_states,
-                position_embeddings_global=position_embeddings_global,
-                position_embeddings_local=position_embeddings_local,
-                per_layer_input=per_layer_input,
+                position_embeddings_global,
+                position_embeddings_local,
+                per_layer_input,
                 attention_mask=causal_mask,
                 position_ids=position_ids,
                 past_key_value=past_key_values,
@@ -2147,11 +2158,10 @@ def forward(
         for i in range(1, self.config.altup_num_inputs):
             # altup_unembed_projections adapted from jax.numpy.einsum("btp,pd->btd", ...)
             altup_unemb_proj: torch.Tensor = self.altup_unembed_projections[i - 1](hidden_states[i])
-            current_hidden_state = altup_unemb_proj.type(hidden_states_0.dtype)
-            new_magnitude = torch.mean(current_hidden_state**2, dim=-1, keepdim=True) ** 0.5
-            current_hidden_state = current_hidden_state * (
-                target_magnitude / torch.maximum(new_magnitude, epsilon_tensor)
-            )
+            current_hidden_state = altup_unemb_proj.to(dtype=hidden_states_0.dtype, device=target_magnitude.device)
+            new_magnitude = torch.mean(current_hidden_state**2, dim=-1, keepdim=True)
+            new_magnitude = torch.sqrt(torch.maximum(new_magnitude, epsilon_tensor.to(target_magnitude.device)))
+            current_hidden_state = current_hidden_state * target_magnitude / new_magnitude
             temp_hidden_states.append(current_hidden_state)
 
         hidden_states = torch.stack(temp_hidden_states)

filename: src/transformers/testing_utils.py
patch: @@ -1642,7 +1642,6 @@ def set_model_tester_for_less_flaky_test(test_case):
         "AriaVisionText2TextModelTester",
         "GPTNeoModelTester",
         "DPTModelTester",
-        "Gemma3nTextModelTester",  # cannot have a single layer combined with the cache sharing config attrs in the tester
     ]
     if test_case.model_tester.__class__.__name__ in exceptional_classes:
         target_num_hidden_layers = None

filename: tests/models/gemma3n/test_modeling_gemma3n.py
patch: @@ -39,13 +39,20 @@
     require_read_token,
     require_torch,
     require_torch_gpu,
+    require_torch_sdpa,
     slow,
     torch_device,
 )
 
 from ...generation.test_utils import GenerationTesterMixin
 from ...test_configuration_common import ConfigTester
-from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
+from ...test_modeling_common import (
+    TEST_EAGER_MATCHES_SDPA_INFERENCE_PARAMETERIZATION,
+    ModelTesterMixin,
+    _test_eager_matches_sdpa_inference,
+    floats_tensor,
+    ids_tensor,
+)
 from ..gemma.test_modeling_gemma import GemmaModelTester
 
 
@@ -256,6 +263,7 @@ def __init__(
         vocab_size=99,
         vocab_size_per_layer_input=99,
         hidden_size=16,
+        hidden_size_per_layer_input=16,
         num_hidden_layers=4,  # override to correctly test sharing cache pattern
         num_kv_shared_layers=2,  # important to override
         layer_types=[
@@ -291,6 +299,7 @@ def __init__(
         self.vocab_size = vocab_size
         self.vocab_size_per_layer_input = vocab_size_per_layer_input
         self.hidden_size = hidden_size
+        self.hidden_size_per_layer_input = hidden_size_per_layer_input
         self.num_hidden_layers = num_hidden_layers
         self.num_kv_shared_layers = num_kv_shared_layers
         self.layer_types = layer_types
@@ -317,7 +326,6 @@ def __init__(
         for_causal_lm_class = Gemma3nForCausalLM
 
 
-@unittest.skip("Skipped for now!")
 @require_torch
 class Gemma3nTextModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
     all_model_classes = (Gemma3nTextModel, Gemma3nForCausalLM) if is_torch_available() else ()
@@ -365,6 +373,64 @@ def _check_hidden_states_for_generate(
                 [expected_shape] * len(iter_hidden_states),
             )
 
+    @parameterized.expand(TEST_EAGER_MATCHES_SDPA_INFERENCE_PARAMETERIZATION)
+    @require_torch_sdpa
+    def test_eager_matches_sdpa_inference(
+        self,
+        name,
+        torch_dtype,
+        padding_side,
+        use_attention_mask,
+        output_attentions,
+        enable_kernels,
+    ):
+        "We need to relax a bit the `atols` for fp32 here due to the altup projections"
+        atols = {
+            ("cpu", False, torch.float32): 1e-3,  # this was relaxed
+            ("cpu", False, torch.float16): 5e-3,
+            ("cpu", False, torch.bfloat16): 1e-2,
+            ("cpu", True, torch.float32): 1e-3,  # this was relaxed
+            ("cpu", True, torch.float16): 5e-3,
+            ("cpu", True, torch.bfloat16): 1e-2,
+            ("cuda", False, torch.float32): 1e-3,  # this was relaxed
+            ("cuda", False, torch.bfloat16): 1e-2,
+            ("cuda", False, torch.float16): 5e-3,
+            ("cuda", True, torch.float32): 1e-3,  # this was relaxed
+            ("cuda", True, torch.bfloat16): 1e-2,
+            ("cuda", True, torch.float16): 5e-3,
+        }
+        _test_eager_matches_sdpa_inference(
+            self, name, torch_dtype, padding_side, use_attention_mask, output_attentions, enable_kernels, atols=atols
+        )
+
+    @pytest.mark.generate
+    @unittest.skip(
+        "Gemma3n has a special shape for hidden states (due to per-layer projs) which is not compatible with contrastive decoding"
+    )
+    def test_contrastive_generate(self):
+        pass
+
+    @pytest.mark.generate
+    @unittest.skip(
+        "Gemma3n has a special shape for hidden states (due to per-layer projs) which is not compatible with contrastive decoding"
+    )
+    def test_contrastive_generate_dict_outputs_use_cache(self):
+        pass
+
+    @pytest.mark.generate
+    @unittest.skip(
+        "Gemma3n has a special shape for hidden states (due to per-layer projs) which is not compatible with contrastive decoding"
+    )
+    def test_contrastive_generate_low_memory(self):
+        pass
+
+    @pytest.mark.generate
+    @unittest.skip(
+        "Gemma3n has a special shape for hidden states (due to per-layer projs) which is not compatible with dola decoding"
+    )
+    def test_dola_decoding_sample(self):
+        pass
+
 
 class Gemma3nVision2TextModelTester:
     text_config = {"activation_sparsity_pattern": None}

filename: tests/test_modeling_common.py
patch: @@ -156,6 +156,334 @@
 ] + [("fp32_pad_left_output_attentions", "fp32", "left", True, True, False)]
 
 
+def _test_eager_matches_sdpa_inference(
+    self,
+    name,
+    torch_dtype,
+    padding_side,
+    use_attention_mask,
+    output_attentions,
+    enable_kernels,
+    atols=None,
+    rtols=None,
+):
+    """
+    This test is written as a regular function to be able to overload it easily with different tolerances.
+    Otherwise, `paramterezie.expand` prevents it as it removes the original function from the namespace.
+    """
+    # TODO: we shouldn't need to do this skip, i.e. the test would be composable from the model tester. CLIP-like
+    # models have a custom mixin, which we detect to skip this test.
+    if any(".CLIPModelTesterMixin" in str(base) for base in self.__class__.__bases__):
+        self.skipTest(reason="CLIP-like models have a different `test_eager_matches_sdpa_inference`")
+
+    if not self.has_attentions:
+        self.skipTest(reason="Model architecture does not support attentions")
+
+    if not self.all_model_classes[0]._supports_sdpa:
+        self.skipTest(f"{self.all_model_classes[0].__name__} does not support SDPA")
+
+    # convert shorthand name to torch.dtype
+    if torch_dtype == "fp16":
+        torch_dtype = torch.float16
+    elif torch_dtype == "bf16":
+        torch_dtype = torch.bfloat16
+    elif torch_dtype == "fp32":
+        torch_dtype = torch.float32
+
+    if not is_torch_fp16_available_on_device(torch_device) and torch_dtype == torch.float16:
+        self.skipTest(f"float16 not supported on {torch_device} (on the specific device currently used)")
+
+    if not is_torch_bf16_available_on_device(torch_device) and torch_dtype == torch.bfloat16:
+        self.skipTest(
+            f"bfloat16 not supported on {torch_device} (on the specific device currently used, e.g. Nvidia T4 GPU)"
+        )
+
+    # Dictionary of tolerances for eager <> sdpa tests. Key = (device, sdpa_kernels_enabled, dtype)
+    if atols is None:
+        atols = {
+            ("cpu", False, torch.float32): 1e-6,
+            ("cpu", False, torch.float16): 5e-3,
+            ("cpu", False, torch.bfloat16): 1e-2,
+            ("cpu", True, torch.float32): 1e-6,
+            ("cpu", True, torch.float16): 5e-3,
+            ("cpu", True, torch.bfloat16): 1e-2,
+            ("cuda", False, torch.float32): 1e-6,
+            ("cuda", False, torch.bfloat16): 1e-2,
+            ("cuda", False, torch.float16): 5e-3,
+            ("cuda", True, torch.float32): 1e-6,
+            ("cuda", True, torch.bfloat16): 1e-2,
+            ("cuda", True, torch.float16): 5e-3,
+        }
+    if rtols is None:
+        rtols = {
+            ("cpu", False, torch.float32): 1e-4,
+            ("cpu", False, torch.float16): 5e-3,
+            ("cpu", False, torch.bfloat16): 1e-2,
+            ("cpu", True, torch.float32): 1e-4,
+            ("cpu", True, torch.float16): 5e-3,
+            ("cpu", True, torch.bfloat16): 1e-2,
+            ("cuda", False, torch.float32): 1e-4,
+            ("cuda", False, torch.bfloat16): 1e-2,
+            ("cuda", False, torch.float16): 5e-3,
+            ("cuda", True, torch.float32): 1e-4,
+            ("cuda", True, torch.bfloat16): 3e-2,  # (different from others)
+            ("cuda", True, torch.float16): 5e-3,
+        }
+
+    set_model_tester_for_less_flaky_test(self)
+
+    for model_class in self.all_model_classes:
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+        set_config_for_less_flaky_test(config)
+        model = model_class(config)
+        # TODO: standardize the interfaces for musicgen models, see other todo in this test
+        if model.__class__.__name__ == "MusicgenMelodyForConditionalGeneration":
+            is_encoder_decoder = True
+        else:
+            is_encoder_decoder = model.config.is_encoder_decoder
+
+        with tempfile.TemporaryDirectory() as tmpdirname:
+            model.save_pretrained(tmpdirname)
+            model_from_pretrained_kwargs = {
+                "pretrained_model_name_or_path": tmpdirname,
+                "torch_dtype": torch_dtype,
+            }
+
+            if hasattr(config, "use_mask_token") or "use_mask_token" in inspect.signature(model.__init__).parameters:
+                model_from_pretrained_kwargs["use_mask_token"] = True
+
+            # TODO: remove this try/except, models should have a shared API
+            try:
+                model_sdpa = model_class.from_pretrained(**model_from_pretrained_kwargs, attn_implementation="sdpa")
+            except ValueError:
+                model_sdpa = model_class.from_pretrained(**model_from_pretrained_kwargs)
+            model_sdpa = model_sdpa.eval().to(torch_device, dtype=torch_dtype)
+
+            model_eager = model_class.from_pretrained(**model_from_pretrained_kwargs, attn_implementation="eager")
+            model_eager = model_eager.eval().to(torch_device, dtype=torch_dtype)
+
+        set_model_for_less_flaky_test(model_eager)
+        set_model_for_less_flaky_test(model_sdpa)
+
+        can_output_attn = "output_attentions" in inspect.signature(model_sdpa.forward).parameters
+        if not (self.has_attentions and can_output_attn) and output_attentions:
+            self.skipTest(reason="Model does not support output_attentions")
+
+        # TODO: if we can also check with `batch_size=1` without being flaky?
+        for batch_size in [7]:
+            # musicgen decoder models; TODO: find better abstraction
+            if (
+                model.__class__.__name__.startswith("Musicgen")
+                and hasattr(self.model_tester, "num_codebooks")
+                and not hasattr(model_eager, "text_encoder")
+            ):
+                input_data_batch_size = batch_size * self.model_tester.num_codebooks
+            else:
+                input_data_batch_size = batch_size
+
+            processed_inputs = {}
+            processed_inputs[model.main_input_name] = inputs_dict[model.main_input_name]
+
+            for key in getattr(self, "additional_model_inputs", []):
+                # Some models don't have all `additional_model_inputs`, especially when we
+                # craft cases to test model in different settings
+                if key in inputs_dict:
+                    processed_inputs[key] = inputs_dict[key]
+
+            for key, value in processed_inputs.items():
+                if torch.is_floating_point(value):
+                    value = value.to(torch_dtype)
+
+                # extend value to have at least `input_data_batch_size` elements
+                if value.shape[0] < input_data_batch_size:
+                    size = (input_data_batch_size - value.shape[0], *value.shape[1:])
+                    if torch.is_floating_point(value):
+                        extension = torch.rand(size=size, dtype=value.dtype, device=torch_device)
+                    else:
+                        extension = torch.randint(high=5, size=size, dtype=value.dtype, device=torch_device)
+                    value = torch.cat((value, extension), dim=0).to(torch_device)
+
+                processed_inputs[key] = value[:input_data_batch_size]
+
+            if not use_attention_mask:
+                dummy_attention_mask = None
+            else:
+                dummy_attention_mask = inputs_dict.get("attention_mask", None)
+                if dummy_attention_mask is None:
+                    if is_encoder_decoder:
+                        seqlen = inputs_dict.get("decoder_input_ids", processed_inputs[model.main_input_name]).shape[
+                            -1
+                        ]
+                    else:
+                        seqlen = processed_inputs[model.main_input_name].shape[-1]
+                    dummy_attention_mask = torch.ones(batch_size, seqlen).to(torch.int64).to(torch_device)
+
+                # extend dummy_attention_mask to have at least `batch_size` elements
+                if dummy_attention_mask.shape[0] < batch_size:
+                    size = (batch_size - dummy_attention_mask.shape[0], *dummy_attention_mask.shape[1:])
+                    extension = torch.ones(size=size, dtype=dummy_attention_mask.dtype, device=torch_device)
+                    dummy_attention_mask = torch.cat((dummy_attention_mask, extension), dim=0)
+
+                dummy_attention_mask = dummy_attention_mask[:batch_size].to(torch_device)
+
+                dummy_attention_mask[:] = 1
+                if padding_side == "left":
+                    dummy_attention_mask[-1, :2] = 0
+                    dummy_attention_mask[-1, 2:] = 1
+                elif padding_side == "right":
+                    dummy_attention_mask[-1, -2:] = 0
+                    dummy_attention_mask[-1, :-2] = 1
+
+            if is_encoder_decoder:
+                # musicgen encoder-decoder models; TODO: find better abstraction
+                if model.__class__.__name__.startswith("Musicgen") and hasattr(self.model_tester, "num_codebooks"):
+                    input_data_batch_size = batch_size * self.model_tester.num_codebooks
+                else:
+                    input_data_batch_size = batch_size
+
+                decoder_input_ids = inputs_dict.get("decoder_input_ids", processed_inputs[model.main_input_name])
+                decoder_input_ids = decoder_input_ids[:input_data_batch_size]
+                if decoder_input_ids.shape[0] != input_data_batch_size:
+                    extension = torch.ones(
+                        input_data_batch_size - decoder_input_ids.shape[0],
+                        *decoder_input_ids.shape[1:],
+                        dtype=decoder_input_ids.dtype,
+                        device=torch_device,
+                    )
+                    decoder_input_ids = torch.cat((decoder_input_ids, extension), dim=0)
+                    decoder_input_ids = decoder_input_ids.to(torch_device)
+
+                # TODO: never an `attention_mask` arg here?
+                processed_inputs.update(
+                    {
+                        "decoder_input_ids": decoder_input_ids,
+                        "decoder_attention_mask": dummy_attention_mask,
+                        "output_hidden_states": True,
+                    }
+                )
+            else:
+                processed_inputs.update(
+                    {
+                        "output_hidden_states": True,
+                    }
+                )
+
+                # Otherwise fails for e.g. WhisperEncoderModel
+                if "attention_mask" in inspect.signature(model_eager.forward).parameters:
+                    processed_inputs["attention_mask"] = dummy_attention_mask
+
+                if self.has_attentions and "output_attentions" in inspect.signature(model_sdpa.forward).parameters:
+                    processed_inputs["output_attentions"] = output_attentions
+            if "bool_masked_pos" in inspect.signature(model_eager.forward).parameters:
+                dummy_mask = torch.ones((self.model_tester.num_masks,))
+
+                # In case of additional token (like class) we define a custom `mask_length`
+                if hasattr(self.model_tester, "mask_length"):
+                    mask_length = self.model_tester.mask_length - dummy_mask.size(0)
+                else:
+                    mask_length = self.model_tester.seq_length - dummy_mask.size(0)
+                dummy_mask = torch.cat([dummy_mask, torch.zeros(mask_length)])
+                dummy_bool_masked_pos = dummy_mask.expand(batch_size, -1).bool()
+                processed_inputs["bool_masked_pos"] = dummy_bool_masked_pos.to(torch_device)
+
+            if "noise" in inspect.signature(model_eager.forward).parameters:
+                np.random.seed(2)
+                num_patches = int((self.model_tester.image_size // self.model_tester.patch_size) ** 2)
+                noise = np.random.uniform(size=(batch_size, num_patches))
+                processed_inputs["noise"] = torch.from_numpy(noise)
+
+            # TODO: test gradients as well (& for FA2 as well!)
+            with torch.no_grad():
+                with sdpa_kernel(
+                    enable_flash=enable_kernels,
+                    enable_math=True,
+                    enable_mem_efficient=enable_kernels,
+                ):
+                    prepared_inputs = self._prepare_for_class(processed_inputs, model_class)
+                    prepared_inputs = {
+                        k: v.to(torch_device) if isinstance(v, torch.Tensor) else v for k, v in prepared_inputs.items()
+                    }
+                    outputs_eager = model_eager(**prepared_inputs)
+                    outputs_sdpa = model_sdpa(**prepared_inputs)
+
+            if "logits_per_text" in outputs_eager:
+                key = "logits_per_text"
+            elif "vision_hidden_states" in outputs_eager:
+                key = "vision_hidden_states"
+            elif "audio_values" in outputs_eager:
+                key = "audio_values"
+            elif "decoder_hidden_states" in outputs_eager:
+                key = "decoder_hidden_states"
+            elif "logits" in outputs_eager and "Classification" in model_class.__name__:
+                key = "logits"
+            elif "language_model_outputs" in outputs_eager and "blip" in model_class.__name__.lower():
+                outputs_eager = outputs_eager["language_model_outputs"]
+                outputs_sdpa = outputs_sdpa["language_model_outputs"]
+                key = "hidden_states" if "hidden_states" in outputs_eager else "decoder_hidden_states"
+            else:
+                key = "hidden_states"
+
+            # TODO: rename logits -> hidden_states
+            logits_eager = outputs_eager[key]
+            logits_sdpa = outputs_sdpa[key]
+
+            if key in ["vision_hidden_states", "decoder_hidden_states", "hidden_states"]:
+                logits_eager = logits_eager[-1]
+                logits_sdpa = logits_sdpa[-1]
+
+            if key == "logits_per_text":
+                nan_mask = torch.isnan(logits_eager)
+                logits_eager[nan_mask] = 0
+                logits_sdpa[nan_mask] = 0
+
+            if torch_device in ["cpu", "cuda"]:
+                atol = atols[torch_device, enable_kernels, torch_dtype]
+                rtol = rtols[torch_device, enable_kernels, torch_dtype]
+            elif torch_device == "hpu":
+                atol = atols["cuda", enable_kernels, torch_dtype]
+                rtol = rtols["cuda", enable_kernels, torch_dtype]
+            elif torch_device == "xpu":
+                # As of PyTorch 2.5 XPU backend supports only torch.nn.attention.SDPBackend.MATH
+                # which is implemented on PyTorch level using aten operators and is
+                # device agnostic with respect to implementation of each aten operator.
+                atol = atols["cuda", False, torch_dtype]
+                rtol = rtols["cuda", False, torch_dtype]
+            else:
+                atol = 1e-7
+                rtol = 1e-4
+
+            # Masked tokens output slightly deviates - we don't mind that.
+            if use_attention_mask:
+                _logits_sdpa = torch.zeros_like(input=logits_sdpa)
+                _logits_eager = torch.zeros_like(input=logits_eager)
+
+                _logits_sdpa[:-1] = logits_sdpa[:-1]
+                _logits_eager[:-1] = logits_eager[:-1]
+
+                if padding_side == "left":
+                    _logits_sdpa[-1:, 2:] = logits_sdpa[-1:, 2:]
+                    _logits_eager[-1:, 2:] = logits_eager[-1:, 2:]
+
+                elif padding_side == "right":
+                    _logits_sdpa[-1:, 2:] = logits_sdpa[-1:, :-2]
+                    _logits_eager[-1:, 2:] = logits_eager[-1:, :-2]
+
+                logits_sdpa = _logits_sdpa
+                logits_eager = _logits_eager
+
+            results = [
+                torch.allclose(_logits_sdpa, _logits_eager, atol=atol, rtol=rtol)
+                for (_logits_sdpa, _logits_eager) in zip(logits_sdpa, logits_eager)
+            ]
+            # If 80% batch elements have matched results, it's fine
+            if np.mean(results) < 0.8:
+                mean_relative_diff = ((logits_sdpa - logits_eager).abs() / (logits_eager.abs() + 1e-12)).mean()
+                raise ValueError(
+                    f"mean relative difference for {key}: {mean_relative_diff:.3e}, torch atol = {atol}, torch rtol = "
+                    f"{rtol}"
+                )
+
+
 def _config_zero_init(config):
     configs_no_init = copy.deepcopy(config)
     for key in configs_no_init.__dict__.keys():
@@ -3405,321 +3733,9 @@ def test_sdpa_can_dispatch_composite_models(self):
     def test_eager_matches_sdpa_inference(
         self, name, torch_dtype, padding_side, use_attention_mask, output_attentions, enable_kernels
     ):
-        # TODO: we shouldn't need to do this skip, i.e. the test would be composable from the model tester. CLIP-like
-        # models have a custom mixin, which we detect to skip this test.
-        if any(".CLIPModelTesterMixin" in str(base) for base in self.__class__.__bases__):
-            self.skipTest(reason="CLIP-like models have a different `test_eager_matches_sdpa_inference`")
-
-        if not self.has_attentions:
-            self.skipTest(reason="Model architecture does not support attentions")
-
-        if not self.all_model_classes[0]._supports_sdpa:
-            self.skipTest(f"{self.all_model_classes[0].__name__} does not support SDPA")
-
-        # convert shorthand name to torch.dtype
-        if torch_dtype == "fp16":
-            torch_dtype = torch.float16
-        elif torch_dtype == "bf16":
-            torch_dtype = torch.bfloat16
-        elif torch_dtype == "fp32":
-            torch_dtype = torch.float32
-
-        if not is_torch_fp16_available_on_device(torch_device) and torch_dtype == torch.float16:
-            self.skipTest(f"float16 not supported on {torch_device} (on the specific device currently used)")
-
-        if not is_torch_bf16_available_on_device(torch_device) and torch_dtype == torch.bfloat16:
-            self.skipTest(
-                f"bfloat16 not supported on {torch_device} (on the specific device currently used, e.g. Nvidia T4 GPU)"
-            )
-
-        # Dictionary of tolerances for eager <> sdpa tests. Key = (device, sdpa_kernels_enabled, dtype)
-        atols = {
-            ("cpu", False, torch.float32): 1e-6,
-            ("cpu", False, torch.float16): 5e-3,
-            ("cpu", False, torch.bfloat16): 1e-2,
-            ("cpu", True, torch.float32): 1e-6,
-            ("cpu", True, torch.float16): 5e-3,
-            ("cpu", True, torch.bfloat16): 1e-2,
-            ("cuda", False, torch.float32): 1e-6,
-            ("cuda", False, torch.bfloat16): 1e-2,
-            ("cuda", False, torch.float16): 5e-3,
-            ("cuda", True, torch.float32): 1e-6,
-            ("cuda", True, torch.bfloat16): 1e-2,
-            ("cuda", True, torch.float16): 5e-3,
-        }
-        rtols = {
-            ("cpu", False, torch.float32): 1e-4,
-            ("cpu", False, torch.float16): 5e-3,
-            ("cpu", False, torch.bfloat16): 1e-2,
-            ("cpu", True, torch.float32): 1e-4,
-            ("cpu", True, torch.float16): 5e-3,
-            ("cpu", True, torch.bfloat16): 1e-2,
-            ("cuda", False, torch.float32): 1e-4,
-            ("cuda", False, torch.bfloat16): 1e-2,
-            ("cuda", False, torch.float16): 5e-3,
-            ("cuda", True, torch.float32): 1e-4,
-            ("cuda", True, torch.bfloat16): 3e-2,  # (different from others)
-            ("cuda", True, torch.float16): 5e-3,
-        }
-
-        set_model_tester_for_less_flaky_test(self)
-
-        for model_class in self.all_model_classes:
-            config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
-            set_config_for_less_flaky_test(config)
-            model = model_class(config)
-            # TODO: standardize the interfaces for musicgen models, see other todo in this test
-            if model.__class__.__name__ == "MusicgenMelodyForConditionalGeneration":
-                is_encoder_decoder = True
-            else:
-                is_encoder_decoder = model.config.is_encoder_decoder
-
-            with tempfile.TemporaryDirectory() as tmpdirname:
-                model.save_pretrained(tmpdirname)
-                model_from_pretrained_kwargs = {
-                    "pretrained_model_name_or_path": tmpdirname,
-                    "torch_dtype": torch_dtype,
-                }
-
-                if (
-                    hasattr(config, "use_mask_token")
-                    or "use_mask_token" in inspect.signature(model.__init__).parameters
-                ):
-                    model_from_pretrained_kwargs["use_mask_token"] = True
-
-                # TODO: remove this try/except, models should have a shared API
-                try:
-                    model_sdpa = model_class.from_pretrained(
-                        **model_from_pretrained_kwargs, attn_implementation="sdpa"
-                    )
-                except ValueError:
-                    model_sdpa = model_class.from_pretrained(**model_from_pretrained_kwargs)
-                model_sdpa = model_sdpa.eval().to(torch_device, dtype=torch_dtype)
-
-                model_eager = model_class.from_pretrained(**model_from_pretrained_kwargs, attn_implementation="eager")
-                model_eager = model_eager.eval().to(torch_device, dtype=torch_dtype)
-
-            set_model_for_less_flaky_test(model_eager)
-            set_model_for_less_flaky_test(model_sdpa)
-
-            can_output_attn = "output_attentions" in inspect.signature(model_sdpa.forward).parameters
-            if not (self.has_attentions and can_output_attn) and output_attentions:
-                self.skipTest(reason="Model does not support output_attentions")
-
-            # TODO: if we can also check with `batch_size=1` without being flaky?
-            for batch_size in [7]:
-                # musicgen decoder models; TODO: find better abstraction
-                if (
-                    model.__class__.__name__.startswith("Musicgen")
-                    and hasattr(self.model_tester, "num_codebooks")
-                    and not hasattr(model_eager, "text_encoder")
-                ):
-                    input_data_batch_size = batch_size * self.model_tester.num_codebooks
-                else:
-                    input_data_batch_size = batch_size
-
-                processed_inputs = {}
-                processed_inputs[model.main_input_name] = inputs_dict[model.main_input_name]
-
-                for key in getattr(self, "additional_model_inputs", []):
-                    # Some models don't have all `additional_model_inputs`, especially when we
-                    # craft cases to test model in different settings
-                    if key in inputs_dict:
-                        processed_inputs[key] = inputs_dict[key]
-
-                for key, value in processed_inputs.items():
-                    if torch.is_floating_point(value):
-                        value = value.to(torch_dtype)
-
-                    # extend value to have at least `input_data_batch_size` elements
-                    if value.shape[0] < input_data_batch_size:
-                        size = (input_data_batch_size - value.shape[0], *value.shape[1:])
-                        if torch.is_floating_point(value):
-                            extension = torch.rand(size=size, dtype=value.dtype, device=torch_device)
-                        else:
-                            extension = torch.randint(high=5, size=size, dtype=value.dtype, device=torch_device)
-                        value = torch.cat((value, extension), dim=0).to(torch_device)
-
-                    processed_inputs[key] = value[:input_data_batch_size]
-
-                if not use_attention_mask:
-                    dummy_attention_mask = None
-                else:
-                    dummy_attention_mask = inputs_dict.get("attention_mask", None)
-                    if dummy_attention_mask is None:
-                        if is_encoder_decoder:
-                            seqlen = inputs_dict.get(
-                                "decoder_input_ids", processed_inputs[model.main_input_name]
-                            ).shape[-1]
-                        else:
-                            seqlen = processed_inputs[model.main_input_name].shape[-1]
-                        dummy_attention_mask = torch.ones(batch_size, seqlen).to(torch.int64).to(torch_device)
-
-                    # extend dummy_attention_mask to have at least `batch_size` elements
-                    if dummy_attention_mask.shape[0] < batch_size:
-                        size = (batch_size - dummy_attention_mask.shape[0], *dummy_attention_mask.shape[1:])
-                        extension = torch.ones(size=size, dtype=dummy_attention_mask.dtype, device=torch_device)
-                        dummy_attention_mask = torch.cat((dummy_attention_mask, extension), dim=0)
-
-                    dummy_attention_mask = dummy_attention_mask[:batch_size].to(torch_device)
-
-                    dummy_attention_mask[:] = 1
-                    if padding_side == "left":
-                        dummy_attention_mask[-1, :2] = 0
-                        dummy_attention_mask[-1, 2:] = 1
-                    elif padding_side == "right":
-                        dummy_attention_mask[-1, -2:] = 0
-                        dummy_attention_mask[-1, :-2] = 1
-
-                if is_encoder_decoder:
-                    # musicgen encoder-decoder models; TODO: find better abstraction
-                    if model.__class__.__name__.startswith("Musicgen") and hasattr(self.model_tester, "num_codebooks"):
-                        input_data_batch_size = batch_size * self.model_tester.num_codebooks
-                    else:
-                        input_data_batch_size = batch_size
-
-                    decoder_input_ids = inputs_dict.get("decoder_input_ids", processed_inputs[model.main_input_name])
-                    decoder_input_ids = decoder_input_ids[:input_data_batch_size]
-                    if decoder_input_ids.shape[0] != input_data_batch_size:
-                        extension = torch.ones(
-                            input_data_batch_size - decoder_input_ids.shape[0],
-                            *decoder_input_ids.shape[1:],
-                            dtype=decoder_input_ids.dtype,
-                            device=torch_device,
-                        )
-                        decoder_input_ids = torch.cat((decoder_input_ids, extension), dim=0)
-                        decoder_input_ids = decoder_input_ids.to(torch_device)
-
-                    # TODO: never an `attention_mask` arg here?
-                    processed_inputs.update(
-                        {
-                            "decoder_input_ids": decoder_input_ids,
-                            "decoder_attention_mask": dummy_attention_mask,
-                            "output_hidden_states": True,
-                        }
-                    )
-                else:
-                    processed_inputs.update(
-                        {
-                            "output_hidden_states": True,
-                        }
-                    )
-
-                    # Otherwise fails for e.g. WhisperEncoderModel
-                    if "attention_mask" in inspect.signature(model_eager.forward).parameters:
-                        processed_inputs["attention_mask"] = dummy_attention_mask
-
-                    if self.has_attentions and "output_attentions" in inspect.signature(model_sdpa.forward).parameters:
-                        processed_inputs["output_attentions"] = output_attentions
-                if "bool_masked_pos" in inspect.signature(model_eager.forward).parameters:
-                    dummy_mask = torch.ones((self.model_tester.num_masks,))
-
-                    # In case of additional token (like class) we define a custom `mask_length`
-                    if hasattr(self.model_tester, "mask_length"):
-                        mask_length = self.model_tester.mask_length - dummy_mask.size(0)
-                    else:
-                        mask_length = self.model_tester.seq_length - dummy_mask.size(0)
-                    dummy_mask = torch.cat([dummy_mask, torch.zeros(mask_length)])
-                    dummy_bool_masked_pos = dummy_mask.expand(batch_size, -1).bool()
-                    processed_inputs["bool_masked_pos"] = dummy_bool_masked_pos.to(torch_device)
-
-                if "noise" in inspect.signature(model_eager.forward).parameters:
-                    np.random.seed(2)
-                    num_patches = int((self.model_tester.image_size // self.model_tester.patch_size) ** 2)
-                    noise = np.random.uniform(size=(batch_size, num_patches))
-                    processed_inputs["noise"] = torch.from_numpy(noise)
-
-                # TODO: test gradients as well (& for FA2 as well!)
-                with torch.no_grad():
-                    with sdpa_kernel(
-                        enable_flash=enable_kernels,
-                        enable_math=True,
-                        enable_mem_efficient=enable_kernels,
-                    ):
-                        prepared_inputs = self._prepare_for_class(processed_inputs, model_class)
-                        prepared_inputs = {
-                            k: v.to(torch_device) if isinstance(v, torch.Tensor) else v
-                            for k, v in prepared_inputs.items()
-                        }
-                        outputs_eager = model_eager(**prepared_inputs)
-                        outputs_sdpa = model_sdpa(**prepared_inputs)
-
-                if "logits_per_text" in outputs_eager:
-                    key = "logits_per_text"
-                elif "vision_hidden_states" in outputs_eager:
-                    key = "vision_hidden_states"
-                elif "audio_values" in outputs_eager:
-                    key = "audio_values"
-                elif "decoder_hidden_states" in outputs_eager:
-                    key = "decoder_hidden_states"
-                elif "logits" in outputs_eager and "Classification" in model_class.__name__:
-                    key = "logits"
-                elif "language_model_outputs" in outputs_eager and "blip" in model_class.__name__.lower():
-                    outputs_eager = outputs_eager["language_model_outputs"]
-                    outputs_sdpa = outputs_sdpa["language_model_outputs"]
-                    key = "hidden_states" if "hidden_states" in outputs_eager else "decoder_hidden_states"
-                else:
-                    key = "hidden_states"
-
-                # TODO: rename logits -> hidden_states
-                logits_eager = outputs_eager[key]
-                logits_sdpa = outputs_sdpa[key]
-
-                if key in ["vision_hidden_states", "decoder_hidden_states", "hidden_states"]:
-                    logits_eager = logits_eager[-1]
-                    logits_sdpa = logits_sdpa[-1]
-
-                if key == "logits_per_text":
-                    nan_mask = torch.isnan(logits_eager)
-                    logits_eager[nan_mask] = 0
-                    logits_sdpa[nan_mask] = 0
-
-                if torch_device in ["cpu", "cuda"]:
-                    atol = atols[torch_device, enable_kernels, torch_dtype]
-                    rtol = rtols[torch_device, enable_kernels, torch_dtype]
-                elif torch_device == "hpu":
-                    atol = atols["cuda", enable_kernels, torch_dtype]
-                    rtol = rtols["cuda", enable_kernels, torch_dtype]
-                elif torch_device == "xpu":
-                    # As of PyTorch 2.5 XPU backend supports only torch.nn.attention.SDPBackend.MATH
-                    # which is implemented on PyTorch level using aten operators and is
-                    # device agnostic with respect to implementation of each aten operator.
-                    atol = atols["cuda", False, torch_dtype]
-                    rtol = rtols["cuda", False, torch_dtype]
-                else:
-                    atol = 1e-7
-                    rtol = 1e-4
-
-                # Masked tokens output slightly deviates - we don't mind that.
-                if use_attention_mask:
-                    _logits_sdpa = torch.zeros_like(input=logits_sdpa)
-                    _logits_eager = torch.zeros_like(input=logits_eager)
-
-                    _logits_sdpa[:-1] = logits_sdpa[:-1]
-                    _logits_eager[:-1] = logits_eager[:-1]
-
-                    if padding_side == "left":
-                        _logits_sdpa[-1:, 2:] = logits_sdpa[-1:, 2:]
-                        _logits_eager[-1:, 2:] = logits_eager[-1:, 2:]
-
-                    elif padding_side == "right":
-                        _logits_sdpa[-1:, 2:] = logits_sdpa[-1:, :-2]
-                        _logits_eager[-1:, 2:] = logits_eager[-1:, :-2]
-
-                    logits_sdpa = _logits_sdpa
-                    logits_eager = _logits_eager
-
-                results = [
-                    torch.allclose(_logits_sdpa, _logits_eager, atol=atol, rtol=rtol)
-                    for (_logits_sdpa, _logits_eager) in zip(logits_sdpa, logits_eager)
-                ]
-                # If 80% batch elements have matched results, it's fine
-                if np.mean(results) < 0.8:
-                    mean_relative_diff = ((logits_sdpa - logits_eager).abs() / (logits_eager.abs() + 1e-12)).mean()
-                    raise ValueError(
-                        f"mean relative difference for {key}: {mean_relative_diff:.3e}, torch atol = {atol}, torch rtol = "
-                        f"{rtol}"
-                    )
+        _test_eager_matches_sdpa_inference(
+            self, name, torch_dtype, padding_side, use_attention_mask, output_attentions, enable_kernels
+        )
 
     @require_torch_sdpa
     @require_torch_accelerator