Fix ONNX exports for Optimum compatible models (#31311)

* fixed models

* format with bumped ruff version on my local

* fix copies

* add tracing checks

* format

* Update src/transformers/utils/generic.py

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* format

* style fix

* Update modeling_mobilevit.py

* add docstring and change name

* Update __init__.py

* Update __init__.py

---------

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

src/transformers/models/clap/modeling_clap.py

@@ -37,6 +37,7 @@
     add_start_docstrings_to_model_forward,
     logging,
     replace_return_docstrings,
+    torch_int,
 )
 from .configuration_clap import ClapAudioConfig, ClapConfig, ClapTextConfig
 
@@ -590,8 +591,10 @@ def __init__(self, config, dim, input_resolution, num_heads, shift_size=0):
     def set_shift_and_window_size(self, input_resolution):
         if min(input_resolution) <= self.window_size:
             # if window size is larger than input resolution, we don't partition windows
-            self.shift_size = 0
-            self.window_size = min(input_resolution)
+            self.shift_size = torch_int(0)
+            self.window_size = (
+                torch.min(torch.tensor(input_resolution)) if torch.jit.is_tracing() else min(input_resolution)
+            )
 
     def get_attn_mask(self, height, width, dtype, device):
         if self.shift_size > 0:

src/transformers/models/donut/modeling_donut_swin.py

@@ -35,6 +35,7 @@
     add_start_docstrings,
     add_start_docstrings_to_model_forward,
     logging,
+    torch_int,
 )
 from .configuration_donut_swin import DonutSwinConfig
 
@@ -562,8 +563,10 @@ def __init__(self, config, dim, input_resolution, num_heads, shift_size=0):
     def set_shift_and_window_size(self, input_resolution):
         if min(input_resolution) <= self.window_size:
             # if window size is larger than input resolution, we don't partition windows
-            self.shift_size = 0
-            self.window_size = min(input_resolution)
+            self.shift_size = torch_int(0)
+            self.window_size = (
+                torch.min(torch.tensor(input_resolution)) if torch.jit.is_tracing() else min(input_resolution)
+            )
 
     def get_attn_mask(self, height, width, dtype, device):
         if self.shift_size > 0:

src/transformers/models/dpt/modeling_dpt.py

@@ -39,7 +39,7 @@
 from ...modeling_outputs import BaseModelOutput, DepthEstimatorOutput, SemanticSegmenterOutput
 from ...modeling_utils import PreTrainedModel
 from ...pytorch_utils import find_pruneable_heads_and_indices, prune_linear_layer
-from ...utils import ModelOutput, logging
+from ...utils import ModelOutput, logging, torch_int
 from ...utils.backbone_utils import load_backbone
 from .configuration_dpt import DPTConfig
 
@@ -226,7 +226,7 @@ def _resize_pos_embed(self, posemb, grid_size_height, grid_size_width, start_ind
         posemb_tok = posemb[:, :start_index]
         posemb_grid = posemb[0, start_index:]
 
-        old_grid_size = int(math.sqrt(len(posemb_grid)))
+        old_grid_size = torch_int(posemb_grid.size(0) ** 0.5)
 
         posemb_grid = posemb_grid.reshape(1, old_grid_size, old_grid_size, -1).permute(0, 3, 1, 2)
         posemb_grid = nn.functional.interpolate(posemb_grid, size=(grid_size_height, grid_size_width), mode="bilinear")

src/transformers/models/imagegpt/modeling_imagegpt.py

@@ -33,7 +33,13 @@
 )
 from ...modeling_utils import PreTrainedModel
 from ...pytorch_utils import Conv1D, find_pruneable_heads_and_indices, prune_conv1d_layer
-from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
+from ...utils import (
+    add_start_docstrings,
+    add_start_docstrings_to_model_forward,
+    logging,
+    replace_return_docstrings,
+    torch_float,
+)
 from .configuration_imagegpt import ImageGPTConfig
 
 
@@ -229,7 +235,7 @@ def _attn(self, query, key, value, attention_mask=None, head_mask=None):
         attn_weights = torch.matmul(query, key.transpose(-1, -2))
 
         if self.scale_attn_weights:
-            attn_weights = attn_weights / (float(value.size(-1)) ** 0.5)
+            attn_weights = attn_weights / torch_float(value.size(-1) ** 0.5)
 
         # Layer-wise attention scaling
         if self.scale_attn_by_inverse_layer_idx:

src/transformers/models/layoutlmv3/modeling_layoutlmv3.py

@@ -33,7 +33,13 @@
 )
 from ...modeling_utils import PreTrainedModel
 from ...pytorch_utils import apply_chunking_to_forward
-from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
+from ...utils import (
+    add_start_docstrings,
+    add_start_docstrings_to_model_forward,
+    logging,
+    replace_return_docstrings,
+    torch_int,
+)
 from .configuration_layoutlmv3 import LayoutLMv3Config
 
 
@@ -910,8 +916,8 @@ def forward(
         patch_height = patch_width = None
         if pixel_values is not None:
             patch_height, patch_width = (
-                int(pixel_values.shape[2] / self.config.patch_size),
-                int(pixel_values.shape[3] / self.config.patch_size),
+                torch_int(pixel_values.shape[2] / self.config.patch_size),
+                torch_int(pixel_values.shape[3] / self.config.patch_size),
             )
             visual_embeddings = self.forward_image(pixel_values)
             visual_attention_mask = torch.ones(

src/transformers/models/mobilevit/modeling_mobilevit.py

@@ -39,6 +39,7 @@
     add_start_docstrings_to_model_forward,
     logging,
     replace_return_docstrings,
+    torch_int,
 )
 from .configuration_mobilevit import MobileViTConfig
 
@@ -437,8 +438,16 @@ def unfolding(self, features: torch.Tensor) -> Tuple[torch.Tensor, Dict]:
 
         batch_size, channels, orig_height, orig_width = features.shape
 
-        new_height = int(math.ceil(orig_height / patch_height) * patch_height)
-        new_width = int(math.ceil(orig_width / patch_width) * patch_width)
+        new_height = (
+            torch_int(torch.ceil(orig_height / patch_height) * patch_height)
+            if torch.jit.is_tracing()
+            else int(math.ceil(orig_height / patch_height) * patch_height)
+        )
+        new_width = (
+            torch_int(torch.ceil(orig_width / patch_width) * patch_width)
+            if torch.jit.is_tracing()
+            else int(math.ceil(orig_width / patch_width) * patch_width)
+        )
 
         interpolate = False
         if new_width != orig_width or new_height != orig_height:

src/transformers/models/sam/modeling_sam.py

@@ -15,7 +15,6 @@
 """PyTorch SAM model."""
 
 import collections
-import math
 from dataclasses import dataclass
 from typing import Dict, List, Optional, Tuple, Union
 
@@ -232,7 +231,7 @@ def forward(self, query: Tensor, key: Tensor, value: Tensor, attention_similarit
         # SamAttention
         _, _, _, c_per_head = query.shape
         attn = query @ key.permute(0, 1, 3, 2)  # batch_size * point_batch_size  x N_heads x N_tokens x N_tokens
-        attn = attn / math.sqrt(c_per_head)
+        attn = attn / (c_per_head**0.5)
         attn = torch.softmax(attn, dim=-1)
 
         if attention_similarity is not None:

src/transformers/models/swin/modeling_swin.py

@@ -36,6 +36,7 @@
     add_start_docstrings_to_model_forward,
     logging,
     replace_return_docstrings,
+    torch_int,
 )
 from ...utils.backbone_utils import BackboneMixin
 from .configuration_swin import SwinConfig
@@ -639,8 +640,10 @@ def __init__(self, config, dim, input_resolution, num_heads, shift_size=0):
     def set_shift_and_window_size(self, input_resolution):
         if min(input_resolution) <= self.window_size:
             # if window size is larger than input resolution, we don't partition windows
-            self.shift_size = 0
-            self.window_size = min(input_resolution)
+            self.shift_size = torch_int(0)
+            self.window_size = (
+                torch.min(torch.tensor(input_resolution)) if torch.jit.is_tracing() else min(input_resolution)
+            )
 
     def get_attn_mask(self, height, width, dtype, device):
         if self.shift_size > 0:

src/transformers/utils/__init__.py

@@ -60,6 +60,8 @@
     tensor_size,
     to_numpy,
     to_py_obj,
+    torch_float,
+    torch_int,
     transpose,
     working_or_temp_dir,
 )

src/transformers/utils/generic.py

@@ -753,6 +753,30 @@ def infer_framework(model_class):
         raise TypeError(f"Could not infer framework from class {model_class}.")
 
 
+def torch_int(x):
+    """
+    Casts an input to a torch int64 tensor if we are in a tracing context, otherwise to a Python int.
+    """
+    if not is_torch_available():
+        return int(x)
+
+    import torch
+
+    return x.to(torch.int64) if torch.jit.is_tracing() else int(x)
+
+
+def torch_float(x):
+    """
+    Casts an input to a torch float32 tensor if we are in a tracing context, otherwise to a Python float.
+    """
+    if not is_torch_available():
+        return int(x)
+
+    import torch
+
+    return x.to(torch.float32) if torch.jit.is_tracing() else int(x)
+
+
 def filter_out_non_signature_kwargs(extra: Optional[list] = None):
     """
     Decorator to filter out named arguments that are not in the function signature.