remove mamba

Author: xadupre

_unittests/ut_helpers/test_cache_helper.py

@@ -149,23 +149,6 @@ def test_unflatten_flatten_encoder_decoder_cache(self):
                 self.string_type(c2, with_shape=True),
             )
 
-    @requires_transformers("4.51")  # the structure changes
-    def test_make_mamba_cache(self):
-        cache = make_mamba_cache(
-            [
-                (torch.rand((4, 4, 4)), torch.rand((4, 4, 4))),
-                (torch.rand((4, 4, 4)), torch.rand((4, 4, 4))),
-                (torch.rand((4, 4, 4)), torch.rand((4, 4, 4))),
-            ]
-        )
-        text = self.string_type(cache, with_shape=True)
-        self.assertEqual(
-            "MambaCache(conv_states=#3[T1s4x4x4,T1s4x4x4,T1s4x4x4], "
-            "ssm_states=#3[T1s4x4x4,T1s4x4x4,T1s4x4x4])",
-            text,
-        )
-        self.assertEqual(0, max_diff(cache, cache)["abs"])
-
     @unittest.skipIf(
         not make_sliding_window_cache, "SlidingWindowCache removed in transformers>=5"
     )

_unittests/ut_helpers/test_torch_helper.py

@@ -4,7 +4,7 @@
 import onnx
 import torch
 import transformers
-from onnx_diagnostic.ext_test_case import ExtTestCase, hide_stdout, requires_torch
+from onnx_diagnostic.ext_test_case import ExtTestCase, hide_stdout
 from onnx_diagnostic.helpers import max_diff, string_type
 from onnx_diagnostic.helpers.torch_helper import (
     dummy_llm,
@@ -22,7 +22,6 @@
 from onnx_diagnostic.helpers.cache_helper import (
     make_dynamic_cache,
     make_encoder_decoder_cache,
-    make_mamba_cache,
     make_sliding_window_cache,
     CacheKeyValue,
 )
@@ -313,24 +312,6 @@ def test_torch_deepcopy_cache_dce(self):
         self.assertEqual(hash1, hash2)
         self.assertGreater(torch_tensor_size(cc), 1)
 
-    @requires_torch("4.50")
-    def test_torch_deepcopy_mamba_cache(self):
-        cache = make_mamba_cache(
-            [
-                (torch.rand((4, 4, 4)), torch.rand((4, 4, 4))),
-                (torch.rand((4, 4, 4)), torch.rand((4, 4, 4))),
-                (torch.rand((4, 4, 4)), torch.rand((4, 4, 4))),
-            ]
-        )
-        at = torch_deepcopy(cache)
-        self.assertEqual(type(cache), type(at))
-        self.assertEqual(max_diff(cache, at)["abs"], 0)
-        hash1 = string_type(at, with_shape=True, with_min_max=True)
-        cache.conv_states[0] += 1000
-        hash2 = string_type(at, with_shape=True, with_min_max=True)
-        self.assertEqual(hash1, hash2)
-        self.assertGreater(torch_tensor_size(cache), 1)
-
     def test_torch_deepcopy_base_model_outputs(self):
         bo = transformers.modeling_outputs.BaseModelOutput(
             last_hidden_state=torch.rand((4, 4, 4))

_unittests/ut_tasks/test_tasks.py

@@ -1,7 +1,7 @@
 import os
 import unittest
 import torch
-from onnx_diagnostic.ext_test_case import ExtTestCase, hide_stdout, has_transformers
+from onnx_diagnostic.ext_test_case import ExtTestCase, hide_stdout
 from onnx_diagnostic.helpers.torch_helper import to_any, torch_deepcopy
 from onnx_diagnostic.torch_models.hghub.model_inputs import get_untrained_model_with_inputs
 from onnx_diagnostic.torch_export_patches import torch_export_patches
@@ -257,22 +257,6 @@ def test_sentence_similary(self):
                 model, (), kwargs=inputs, dynamic_shapes=use_dyn_not_str(ds), strict=False
             )
 
-    @hide_stdout()
-    def test_falcon_mamba_dev(self):
-        mid = "tiiuae/falcon-mamba-tiny-dev"
-        data = get_untrained_model_with_inputs(mid, verbose=1, add_second_input=True)
-        self.assertEqual(data["task"], "text-generation")
-        model, inputs, ds = data["model"], data["inputs"], data["dynamic_shapes"]
-        model(**inputs)
-        model(**data["inputs2"])
-        self.assertIn((data["size"], data["n_weights"]), [(274958336, 68739584)])
-        if not has_transformers("5.3.99"):
-            raise unittest.SkipTest("The model has control flow.")
-        with torch_export_patches(patch_transformers=True, verbose=10, stop_if_static=1):
-            torch.export.export(
-                model, (), kwargs=inputs, dynamic_shapes=use_dyn_not_str(ds), strict=False
-            )
-
 
 if __name__ == "__main__":
     unittest.main(verbosity=2)

_unittests/ut_tasks/try_tasks.py

@@ -683,80 +683,6 @@ def mean_pooling(model_output, attention_mask):
         print("Sentence embeddings:")
         print(sentence_embeddings)
 
-    @never_test()
-    def test_falcon_mamba_dev(self):
-        # clear&&NEVERTEST=1 python _unittests/ut_tasks/try_tasks.py -k falcon_mamba_dev
-        # https://huggingface.co/tiiuae/falcon-mamba-tiny-dev
-
-        from transformers import AutoTokenizer
-        import transformers
-        import torch
-
-        model = "tiiuae/falcon-mamba-tiny-dev"
-
-        tokenizer = AutoTokenizer.from_pretrained(model)
-        pipeline = transformers.pipeline(
-            "text-generation",
-            model=model,
-            tokenizer=tokenizer,
-            dtype=torch.bfloat16,
-            trust_remote_code=True,
-            device_map="auto",
-        )
-        print()
-        with steal_forward(pipeline.model):
-            sequences = pipeline(
-                "Girafatron is obsessed with giraffes, "
-                "the most glorious animal on the face of this Earth. "
-                "Giraftron believes all other animals are irrelevant "
-                "when compared to the glorious majesty of the giraffe."
-                "\nDaniel: Hello, Girafatron!\nGirafatron:",
-                max_length=200,
-                do_sample=True,
-                top_k=10,
-                num_return_sequences=1,
-                eos_token_id=tokenizer.eos_token_id,
-            )
-        for seq in sequences:
-            print(f"Result: {seq['generated_text']}")
-
-    @never_test()
-    def test_falcon_mamba_7b(self):
-        # clear&&NEVERTEST=1 python _unittests/ut_tasks/try_tasks.py -k falcon_mamba_7b
-        # https://huggingface.co/tiiuae/falcon-mamba-7b
-
-        from transformers import AutoTokenizer
-        import transformers
-        import torch
-
-        model = "tiiuae/falcon-mamba-7b"
-
-        tokenizer = AutoTokenizer.from_pretrained(model)
-        pipeline = transformers.pipeline(
-            "text-generation",
-            model=model,
-            tokenizer=tokenizer,
-            dtype=torch.bfloat16,
-            trust_remote_code=True,
-            device_map="auto",
-        )
-        print()
-        with steal_forward(pipeline.model):
-            sequences = pipeline(
-                "Girafatron is obsessed with giraffes, "
-                "the most glorious animal on the face of this Earth. "
-                "Giraftron believes all other animals are irrelevant "
-                "when compared to the glorious majesty of the giraffe."
-                "\nDaniel: Hello, Girafatron!\nGirafatron:",
-                max_length=200,
-                do_sample=True,
-                top_k=10,
-                num_return_sequences=1,
-                eos_token_id=tokenizer.eos_token_id,
-            )
-        for seq in sequences:
-            print(f"Result: {seq['generated_text']}")
-
     @never_test()
     def test_object_detection(self):
         # clear&&NEVERTEST=1 python _unittests/ut_tasks/try_tasks.py -k object_

_unittests/ut_torch_export_patches/test_onnx_export_errors.py

@@ -1,128 +1,8 @@
 import unittest
-from onnx_diagnostic.ext_test_case import (
-    ExtTestCase,
-    requires_torch,
-    requires_transformers,
-    skipif_ci_windows,
-    ignore_warnings,
-    hide_stdout,
-)
-from onnx_diagnostic.helpers import string_type
-from onnx_diagnostic.torch_export_patches.onnx_export_errors import (
-    torch_export_patches,
-)
+from onnx_diagnostic.ext_test_case import ExtTestCase, ignore_warnings
 
 
 class TestOnnxExportErrors(ExtTestCase):
-    @requires_transformers("4.49.999")
-    @skipif_ci_windows("not working on Windows")
-    @ignore_warnings(UserWarning)
-    @hide_stdout()
-    def test_pytree_flatten_mamba_cache(self):
-        import torch
-        import torch.utils._pytree as py_pytree
-
-        try:
-            from transformers.models.mamba.modeling_mamba import MambaCache
-        except ImportError:
-            from transformers.cache_utils import MambaCache
-
-        class _config:
-            def __init__(self):
-                self.intermediate_size = 8
-                self.state_size = 16
-                self.conv_kernel = 32
-                self.num_hidden_layers = 64
-                self.dtype = torch.float16
-
-        cache = MambaCache(_config(), max_batch_size=1, device="cpu")
-
-        with torch_export_patches(verbose=1):
-            values, spec = py_pytree.tree_flatten(cache)
-            cache2 = py_pytree.tree_unflatten(values, spec)
-            self.assertEqual(cache.max_batch_size, cache2.max_batch_size)
-            self.assertEqual(cache.intermediate_size, cache2.intermediate_size)
-            self.assertEqual(cache.ssm_state_size, cache2.ssm_state_size)
-            self.assertEqual(cache.conv_kernel_size, cache2.conv_kernel_size)
-            self.assertEqualArrayAny(cache.conv_states, cache2.conv_states)
-            self.assertEqualArrayAny(cache.ssm_states, cache2.ssm_states)
-
-    @requires_transformers("4.50")
-    @requires_torch("2.7")
-    @skipif_ci_windows("not working on Windows")
-    @ignore_warnings(UserWarning)
-    @hide_stdout()
-    def test_exportable_mamba_cache(self):
-        import torch
-        from transformers.models.mamba.modeling_mamba import MambaCache
-
-        class _config:
-            def __init__(self):
-                self.intermediate_size = 8
-                self.state_size = 16
-                self.conv_kernel = 32
-                self.num_hidden_layers = 64
-                self.dtype = torch.float16
-
-        class Model(torch.nn.Module):
-            def forward(self, x: torch.Tensor, cache: MambaCache):
-                x1 = cache.ssm_states[0] + x
-                x2 = cache.conv_states[0][:, :, ::2] + x1
-                return x2
-
-        cache = MambaCache(_config(), max_batch_size=1, device="cpu")
-        # MambaCache was updated in 4.50
-        self.assertEqual(
-            "MambaCache(conv_states=#64[T10r3,...], ssm_states=#64[T10r3,...])",
-            string_type(cache),
-        )
-        x = torch.ones(2, 8, 16).to(torch.float16)
-        model = Model()
-        model(x, cache)
-
-        with torch_export_patches(verbose=1, patch_transformers=True):
-            cache = MambaCache(_config(), max_batch_size=1, device="cpu")
-            torch.export.export(Model(), (x, cache))
-
-    @requires_transformers("4.49.999")
-    @skipif_ci_windows("not working on Windows")
-    @ignore_warnings(UserWarning)
-    def test_exportable_mamba_cache_dynamic(self):
-        import torch
-        from transformers.models.mamba.modeling_mamba import MambaCache
-
-        class _config:
-            def __init__(self):
-                self.intermediate_size = 8
-                self.state_size = 16
-                self.conv_kernel = 32
-                self.num_hidden_layers = 2
-                self.dtype = torch.float16
-
-        class Model(torch.nn.Module):
-            def forward(self, x: torch.Tensor, cache: MambaCache):
-                x1 = cache.ssm_states[0] + x
-                x2 = cache.conv_states[0][:, :, ::2] + x1
-                return x2
-
-        cache = MambaCache(_config(), max_batch_size=1, device="cpu")
-        self.assertEqual(
-            string_type(cache),
-            "MambaCache(conv_states=#2[T10r3,T10r3], ssm_states=#2[T10r3,T10r3])",
-        )
-        x = torch.ones(2, 8, 16).to(torch.float16)
-        model = Model()
-        model(x, cache)
-        DYN = torch.export.Dim.DYNAMIC
-
-        with torch_export_patches(patch_transformers=True):
-            cache = MambaCache(_config(), max_batch_size=2, device="cpu")
-            torch.export.export(
-                Model(),
-                (x, cache),
-                dynamic_shapes=({0: DYN}, [[{0: DYN}, {0: DYN}], [{0: DYN}, {0: DYN}]]),
-            )
-
     @ignore_warnings(UserWarning)
     def test_exportable_dynamic_shapes_constraints(self):
         import torch

onnx_diagnostic/export/shape_helper.py

@@ -46,7 +46,6 @@ def all_dynamic_shapes_from_inputs(inputs: Any, dim_prefix: Any = "d") -> Any:
         from onnx_diagnostic.helpers.cache_helper import (
             make_dynamic_cache,
             make_encoder_decoder_cache,
-            make_mamba_cache,
             make_static_cache,
         )
         from onnx_diagnostic.export.shape_helper import all_dynamic_shapes_from_inputs
@@ -84,13 +83,6 @@ def all_dynamic_shapes_from_inputs(inputs: Any, dim_prefix: Any = "d") -> Any:
                 ],
                 max_cache_len=15,
             ),
-            make_mamba_cache(
-                [
-                    (torch.rand((4, 4, 4)), torch.rand((4, 4, 4))),
-                    (torch.rand((4, 4, 4)), torch.rand((4, 4, 4))),
-                    (torch.rand((4, 4, 4)), torch.rand((4, 4, 4))),
-                ]
-            ),
         ]
 
         with torch_export_patches(patch_transformers=True):