Merge branch 'main' into titaiwang/add_mask_generation

Author: titaiwangms

_unittests/ut_export/test_issue_2025.py

@@ -0,0 +1,55 @@
+import unittest
+import numpy as np
+import torch
+from onnx_diagnostic.ext_test_case import ExtTestCase, requires_torch
+
+
+class TestIssues2025(ExtTestCase):
+    @requires_torch("2.8")
+    def test_issue_158786_qwen2vl(self):
+        # https://github.com/pytorch/pytorch/issues/158786
+        class Model(torch.nn.Module):
+            def __init__(self):
+                super().__init__()
+                self.spatial_merge_size = 2  # Default
+
+            def forward(self, a):
+                pos_ids = []
+                for t, h, w in a:
+                    t = t.item()
+                    h = h.item()
+                    w = w.item()
+                    torch._constrain_as_size(t)
+                    torch._constrain_as_size(h)
+                    torch._constrain_as_size(w)
+                    hpos_ids = torch.arange(h).unsqueeze(1).expand(-1, w)
+                    hpos_ids = hpos_ids.reshape(
+                        h // self.spatial_merge_size,
+                        self.spatial_merge_size,
+                        w // self.spatial_merge_size,
+                        self.spatial_merge_size,
+                    )
+                    hpos_ids = hpos_ids.permute(0, 2, 1, 3)
+                    hpos_ids = hpos_ids.flatten()
+
+                    wpos_ids = torch.arange(w).unsqueeze(0).expand(h, -1)
+                    wpos_ids = wpos_ids.reshape(
+                        h // self.spatial_merge_size,
+                        self.spatial_merge_size,
+                        w // self.spatial_merge_size,
+                        self.spatial_merge_size,
+                    )
+                    wpos_ids = wpos_ids.permute(0, 2, 1, 3)
+                    wpos_ids = wpos_ids.flatten()
+                    pos_ids.append(torch.stack([hpos_ids, wpos_ids], dim=-1).repeat(t, 1))
+                pos_ids = torch.cat(pos_ids, dim=0)
+                return pos_ids
+
+        model = Model()
+        inputs = torch.tensor(np.array([1, 98, 146]).reshape(1, 3))
+        ep = torch.export.export(model, (inputs,))
+        self.assertIn("torch.ops.aten.cat.default", str(ep))
+
+
+if __name__ == "__main__":
+    unittest.main(verbosity=2)

_unittests/ut_torch_export_patches/test_onnx_export_errors.py

@@ -22,7 +22,11 @@ class TestOnnxExportErrors(ExtTestCase):
     def test_pytree_flatten_mamba_cache(self):
         import torch
         import torch.utils._pytree as py_pytree
-        from transformers.cache_utils import MambaCache
+
+        try:
+            from transformers.models.mamba.modeling_mamba import MambaCache
+        except ImportError:
+            from transformers.cache_utils import MambaCache
 
         class _config:
             def __init__(self):

onnx_diagnostic/helpers/_log_helper.py

@@ -260,17 +260,17 @@ def open_dataframe(
     if isinstance(data, pandas.DataFrame):
         return data
     if isinstance(data, str):
-        df = pandas.read_csv(data)
+        df = pandas.read_csv(data, low_memory=False)
         df["RAWFILENAME"] = data
         return df
     if isinstance(data, tuple):
         if not data[-1]:
-            df = pandas.read_csv(data[2])
+            df = pandas.read_csv(data[2], low_memory=False)
             df["RAWFILENAME"] = data[2]
             return df
         zf = zipfile.ZipFile(data[-1])
         with zf.open(data[2]) as f:
-            df = pandas.read_csv(f)
+            df = pandas.read_csv(f, low_memory=False)
             df["RAWFILENAME"] = f"{data[-1]}/{data[2]}"
         zf.close()
         return df

onnx_diagnostic/helpers/cache_helper.py

@@ -4,6 +4,11 @@
 import transformers
 import transformers.cache_utils
 
+try:
+    from transformers.models.mamba.modeling_mamba import MambaCache
+except ImportError:
+    from transformers.cache_utils import MambaCache
+
 
 def flatten_unflatten_for_dynamic_shapes(
     obj: Any,
@@ -242,10 +247,8 @@ def make_encoder_decoder_cache(
     )
 
 
-def make_mamba_cache(
-    key_value_pairs: List[Tuple[torch.Tensor, torch.Tensor]],
-) -> transformers.cache_utils.MambaCache:
-    "Creates a :class:`transformers.cache_utils.MambaCache`."
+def make_mamba_cache(key_value_pairs: List[Tuple[torch.Tensor, torch.Tensor]]) -> MambaCache:
+    "Creates a ``MambaCache``."
     dtype = key_value_pairs[0][0].dtype
 
     class _config:
@@ -256,7 +259,7 @@ def __init__(self):
             self.num_hidden_layers = len(key_value_pairs)
             self.dtype = dtype
 
-    cache = transformers.cache_utils.MambaCache(
+    cache = MambaCache(
         _config(),
         max_batch_size=key_value_pairs[0][0].shape[0],
         device=key_value_pairs[0][0].device,
@@ -286,7 +289,7 @@ def __init__(self):
 
 def make_sliding_window_cache(
     key_value_pairs: List[Tuple[torch.Tensor, torch.Tensor]],
-) -> transformers.cache_utils.MambaCache:
+) -> transformers.cache_utils.SlidingWindowCache:
     "Creates a :class:`transformers.cache_utils.SlidingWindowCache`."
 
     class _config:

onnx_diagnostic/tasks/text_generation.py

@@ -1,6 +1,5 @@
 from typing import Any, Callable, Dict, Optional, Tuple, Union
 import torch
-import transformers
 from ..helpers.cache_helper import (
     make_dynamic_cache,
     make_mamba_cache,
@@ -95,9 +94,14 @@ def get_inputs(
     cache_length = "cache_length"  # torch.export.Dim("cache_length", min=1, max=4096)
 
     if config is not None and config.__class__.__name__ == "FalconMambaConfig":
+        try:
+            from transformers.models.mamba.modeling_mamba import MambaCache
+        except ImportError:
+            from transformers.cache_utils import MambaCache
+
         assert cls_cache in (
             "MambaCache",
-            transformers.cache_utils.MambaCache,
+            MambaCache,
         ), f"Unexpected value for cls_cache={cls_cache} and config={config}"
         seq_length_multiple = 8
         sequence_length = (

onnx_diagnostic/torch_export_patches/onnx_export_serialization.py

@@ -6,12 +6,16 @@
 import transformers
 from transformers.cache_utils import (
     DynamicCache,
-    MambaCache,
     EncoderDecoderCache,
     SlidingWindowCache,
     StaticCache,
 )
 
+try:
+    from transformers.models.mamba.modeling_mamba import MambaCache
+except ImportError:
+    from transformers.cache_utils import MambaCache
+
 from ..helpers import string_type
 from .serialization import _lower_name_with_
 

onnx_diagnostic/torch_export_patches/serialization/transformers_impl.py

@@ -3,11 +3,15 @@
 import transformers
 from transformers.cache_utils import (
     DynamicCache,
-    MambaCache,
     EncoderDecoderCache,
     SlidingWindowCache,
     StaticCache,
 )
+
+try:
+    from transformers.models.mamba.modeling_mamba import MambaCache
+except ImportError:
+    from transformers.cache_utils import MambaCache
 from transformers.modeling_outputs import BaseModelOutput
 from ...helpers.cache_helper import make_static_cache
 from . import make_serialization_function_for_dataclass