v4.40.2

setup.py

@@ -429,7 +429,7 @@ install_requires = [
 
 setup(
     name="transformers",
-    version="4.40.0",  # expected format is one of x.y.z.dev0, or x.y.z.rc1 or x.y.z (no to dashes, yes to dots)
+    version="4.40.2",  # expected format is one of x.y.z.dev0, or x.y.z.rc1 or x.y.z (no to dashes, yes to dots)
     author="The Hugging Face team (past and future) with the help of all our contributors (https://github.com/huggingface/transformers/graphs/contributors)",
     author_email="transformers@huggingface.co",
     description="State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow",

src/transformers/__init__.py

@@ -18,7 +18,7 @@
 # to defer the actual importing for when the objects are requested. This way `import transformers` provides the names
 # in the namespace without actually importing anything (and especially none of the backends).
 
-__version__ = "4.40.0"
+__version__ = "4.40.2"
 
 from typing import TYPE_CHECKING
 

src/transformers/generation/stopping_criteria.py

@@ -146,7 +146,18 @@ class EosTokenCriteria(StoppingCriteria):
 
     @add_start_docstrings(STOPPING_CRITERIA_INPUTS_DOCSTRING)
     def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> torch.BoolTensor:
-        is_done = torch.isin(input_ids[:, -1], self.eos_token_id.to(input_ids.device))
+        if input_ids.device.type == "mps":
+            # https://github.com/pytorch/pytorch/issues/77764#issuecomment-2067838075
+            is_done = (
+                input_ids[:, -1]
+                .tile(self.eos_token_id.shape[0], 1)
+                .eq(self.eos_token_id.unsqueeze(1).to(input_ids.device))
+                .sum(dim=0)
+                .bool()
+                .squeeze()
+            )
+        else:
+            is_done = torch.isin(input_ids[:, -1], self.eos_token_id.to(input_ids.device))
         return is_done
 
 

src/transformers/models/cohere/modeling_cohere.py

@@ -1021,8 +1021,11 @@ class CohereModel(CoherePreTrainedModel):
             causal_mask = causal_mask.clone()  # copy to contiguous memory for in-place edit
             if attention_mask.dim() == 2:
                 mask_length = attention_mask.shape[-1]
-                padding_mask = causal_mask[..., :mask_length].eq(0.0) * attention_mask[:, None, None, :].eq(0.0)
-                causal_mask[..., :mask_length] = causal_mask[..., :mask_length].masked_fill(padding_mask, min_dtype)
+                padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :]
+                padding_mask = padding_mask == 0
+                causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
+                    padding_mask, min_dtype
+                )
             elif attention_mask.dim() == 4:
                 # backwards compatibility: we allow passing a 4D attention mask shorter than the input length with
                 # cache. In that case, the 4D attention mask attends to the newest tokens only.

src/transformers/models/dbrx/modeling_dbrx.py

@@ -1253,8 +1253,11 @@ class DbrxModel(DbrxPreTrainedModel):
             causal_mask = causal_mask.clone()  # copy to contiguous memory for in-place edit
             if attention_mask.dim() == 2:
                 mask_length = attention_mask.shape[-1]
-                padding_mask = causal_mask[..., :mask_length].eq(0.0) * attention_mask[:, None, None, :].eq(0.0)
-                causal_mask[..., :mask_length] = causal_mask[..., :mask_length].masked_fill(padding_mask, min_dtype)
+                padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :]
+                padding_mask = padding_mask == 0
+                causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
+                    padding_mask, min_dtype
+                )
             elif attention_mask.dim() == 4:
                 # backwards compatibility: we allow passing a 4D attention mask shorter than the input length with
                 # cache. In that case, the 4D attention mask attends to the newest tokens only.

src/transformers/models/gemma/modeling_gemma.py

@@ -1007,8 +1007,11 @@ class GemmaModel(GemmaPreTrainedModel):
             causal_mask = causal_mask.clone()  # copy to contiguous memory for in-place edit
             if attention_mask.dim() == 2:
                 mask_length = attention_mask.shape[-1]
-                padding_mask = causal_mask[..., :mask_length].eq(0.0) * attention_mask[:, None, None, :].eq(0.0)
-                causal_mask[..., :mask_length] = causal_mask[..., :mask_length].masked_fill(padding_mask, min_dtype)
+                padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :]
+                padding_mask = padding_mask == 0
+                causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
+                    padding_mask, min_dtype
+                )
             elif attention_mask.dim() == 4:
                 # backwards compatibility: we allow passing a 4D attention mask shorter than the input length with
                 # cache. In that case, the 4D attention mask attends to the newest tokens only.

src/transformers/models/llama/modeling_llama.py

@@ -1099,8 +1099,11 @@ class LlamaModel(LlamaPreTrainedModel):
             causal_mask = causal_mask.clone()  # copy to contiguous memory for in-place edit
             if attention_mask.dim() == 2:
                 mask_length = attention_mask.shape[-1]
-                padding_mask = causal_mask[..., :mask_length].eq(0.0) * attention_mask[:, None, None, :].eq(0.0)
-                causal_mask[..., :mask_length] = causal_mask[..., :mask_length].masked_fill(padding_mask, min_dtype)
+                padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :]
+                padding_mask = padding_mask == 0
+                causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
+                    padding_mask, min_dtype
+                )
             elif attention_mask.dim() == 4:
                 # backwards compatibility: we allow passing a 4D attention mask shorter than the input length with
                 # cache. In that case, the 4D attention mask attends to the newest tokens only.

src/transformers/models/olmo/modeling_olmo.py

@@ -1082,8 +1082,11 @@ class OlmoModel(OlmoPreTrainedModel):
             causal_mask = causal_mask.clone()  # copy to contiguous memory for in-place edit
             if attention_mask.dim() == 2:
                 mask_length = attention_mask.shape[-1]
-                padding_mask = causal_mask[..., :mask_length].eq(0.0) * attention_mask[:, None, None, :].eq(0.0)
-                causal_mask[..., :mask_length] = causal_mask[..., :mask_length].masked_fill(padding_mask, min_dtype)
+                padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :]
+                padding_mask = padding_mask == 0
+                causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
+                    padding_mask, min_dtype
+                )
             elif attention_mask.dim() == 4:
                 # backwards compatibility: we allow passing a 4D attention mask shorter than the input length with
                 # cache. In that case, the 4D attention mask attends to the newest tokens only.

src/transformers/training_args.py

@@ -84,12 +84,12 @@ if is_torch_neuroncore_available(check_device=False):
     if os.environ.get("TORCHELASTIC_RUN_ID"):
         if is_optimum_neuron_available():
             logger.info(
-                "Make sure that you are performing the training with the TrainiumTrainer from optimum[neuron], this "
+                "Make sure that you are performing the training with the NeuronTrainer from optimum[neuron], this "
                 "will fail otherwise."
             )
         else:
             logger.warning(
-                "Please use the TrainiumTrainer from optimum[neuron] instead of the Transformers library to perform "
+                "Please use the NeuronTrainer from optimum[neuron] instead of the Transformers library to perform "
                 "training on AWS Trainium instances. More information here: "
                 "https://github.com/huggingface/optimum-neuron"
             )

src/transformers/utils/fx.py

@@ -15,22 +15,28 @@
 
 import builtins
 import collections
+import contextlib
 import functools
 import inspect
 import math
 import operator
 import os
 import random
+import sys
 import warnings
-from typing import Any, Callable, Dict, List, Optional, Type, Union
+from typing import Any, Callable, Dict, List, Literal, Optional, Tuple, Type, Union
 
 import torch
+import torch.utils._pytree as pytree
 from torch import nn
-from torch.fx import Graph, GraphModule, Proxy, Tracer
+from torch.fx import Graph, GraphModule, Node, Proxy, Tracer
 from torch.fx._compatibility import compatibility
+from torch.fx._symbolic_trace import is_fx_tracing
 from torch.fx.proxy import ParameterProxy
 
-from .. import PretrainedConfig, PreTrainedModel, logging
+from .. import logging
+from ..cache_utils import Cache, DynamicCache, SinkCache, StaticCache
+from ..modeling_utils import PretrainedConfig, PreTrainedModel
 from ..models.auto import get_values
 from ..models.auto.modeling_auto import (
     MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES,
@@ -55,7 +61,7 @@ from ..models.auto.modeling_auto import (
     MODEL_MAPPING_NAMES,
 )
 from ..pytorch_utils import is_torch_greater_or_equal_than_2_0
-from ..utils import (
+from .import_utils import (
     ENV_VARS_TRUE_VALUES,
     TORCH_FX_REQUIRED_VERSION,
     get_torch_version,
@@ -192,6 +198,8 @@ _SPECIAL_SUPPORTED_MODELS = [
 ]
 _SUPPORTED_MODELS = tuple(sorted(set(_REGULAR_SUPPORTED_MODELS + _SPECIAL_SUPPORTED_MODELS)))
 
+_CURRENT_TRACER = None
+
 
 def torch_nn_embedding(self, input):
     return torch.empty(*input.shape, self.weight.shape[-1], device="meta", dtype=self.weight.dtype)
@@ -701,6 +709,92 @@ class MetaDeviceAttribute(HFAttribute):
     pass
 
 
+class HFCacheProxy(HFProxy):
+    """
+    Proxy that represents an instance of `transformers.cache_utils.Cache`.
+    """
+
+    @property
+    def __class__(self):
+        return ProxyableCache
+
+
+def create_wrapper(
+    function: Callable,
+    op_type: Union[Literal["call_function"], Literal["call_method"], Literal["get_attr"]],
+    proxy_factory_fn: Optional[Callable[[Node], Proxy]] = None,
+) -> Callable:
+    @functools.wraps(function)
+    def wrapper(*args, **kwargs):
+        if not is_fx_tracing():
+            return function(*args, **kwargs)
+
+        found_proxies = []
+
+        def check_proxy(a):
+            if isinstance(a, Proxy):
+                found_proxies.append(a)
+
+        torch.fx.node.map_aggregate(args, check_proxy)
+        torch.fx.node.map_aggregate(kwargs, check_proxy)
+
+        if len(found_proxies) > 0:
+            tracer = found_proxies[0].tracer
+            if op_type == "call_function":
+                target = function
+            elif op_type == "call_method":
+                target = function.__name__
+            elif op_type == "get_attr":
+                target = function.__name__
+            else:
+                raise ValueError(f"op_type {op_type} not supported.")
+            return tracer.create_proxy(op_type, target, args, kwargs, proxy_factory_fn=proxy_factory_fn)
+        else:
+            return function(*args, **kwargs)
+
+    return wrapper
+
+
+class HFProxyableClassMeta(type):
+    """
+    Metaclass that creates a class with its main methods wrapped to be proxyable.
+    """
+
+    def __new__(
+        cls,
+        name: str,
+        bases: Tuple[Type, ...],
+        attrs: Dict[str, Any],
+        proxy_factory_fn: Optional[Callable[[Node], Proxy]] = None,
+    ):
+        cls = super().__new__(cls, name, bases, attrs)
+        for attr_name in dir(cls):
+            attr = getattr(cls, attr_name, None)
+            if attr is None:
+                continue
+            if attr_name == "__init__":
+                op_type = "call_function"
+            elif attr_name.startswith("__"):
+                op_type = None
+            elif inspect.ismethod(attr):
+                op_type = "call_function"
+            elif inspect.isfunction(attr):
+                op_type = "call_method"
+            else:
+                op_type = None
+            if op_type is not None:
+                setattr(cls, attr_name, create_wrapper(attr, op_type, proxy_factory_fn=proxy_factory_fn))
+        return cls
+
+
+def gen_constructor_wrapper(target: Callable) -> Tuple[Callable, Callable]:
+    """
+    Wraps `target` to be proxyable. Used for tensor creators like `torch.ones`, `torch.arange` and so on.
+    """
+    wrapper = create_wrapper(target, "call_function")
+    return wrapper, target
+
+
 def _proxies_to_metas(v):
     """Returns the underlying metadata for HFProxies, and behaves like the identity for the others."""
     if isinstance(v, MetaDeviceAttribute):
@@ -712,25 +806,24 @@ def _proxies_to_metas(v):
     return v
 
 
-def _gen_constructor_wrapper(target):
-    @functools.wraps(target)
-    def wrapper(*args, **kwargs):
-        proxy = None
+def cache_proxy_factory_fn(n: Node) -> HFCacheProxy:
+    global _CURRENT_TRACER
+    if not isinstance(_CURRENT_TRACER, HFTracer):
+        raise RuntimeError("Cannot create HFCacheProxy because there is no HFTracer currently tracing.")
+    return HFCacheProxy(n, _CURRENT_TRACER)
 
-        def check_has_proxy(v):
-            if isinstance(v, Proxy):
-                nonlocal proxy
-                proxy = v
 
-        torch.fx.node.map_aggregate(args, check_has_proxy)
-        torch.fx.node.map_aggregate(kwargs, check_has_proxy)
-
-        if proxy is not None:
-            return proxy.tracer.create_proxy("call_function", target, args, kwargs)
-        else:
-            return target(*args, **kwargs)
-
-    return wrapper, target
+# Proxyable equivalent of the cache classes defined in `transformers.cache_utils`.
+ProxyableCache = HFProxyableClassMeta("ProxyableCache", (Cache,), {}, proxy_factory_fn=cache_proxy_factory_fn)
+ProxyableDynamicCache = HFProxyableClassMeta(
+    "ProxyableDynamicCache", (DynamicCache,), {}, proxy_factory_fn=cache_proxy_factory_fn
+)
+ProxyableSinkCache = HFProxyableClassMeta(
+    "ProxyableSinkCache", (SinkCache,), {}, proxy_factory_fn=cache_proxy_factory_fn
+)
+ProxyableStaticCache = HFProxyableClassMeta(
+    "ProxyableStaticCache", (StaticCache,), {}, proxy_factory_fn=cache_proxy_factory_fn
+)
 
 
 def _generate_random_int(low: int = 10, high: int = 20, forbidden_values: Optional[List[int]] = None):
@@ -764,6 +857,13 @@ class HFTracer(Tracer):
         "finfo",
         "tril",
     ]
+    _CLASSES_TO_PATCH = {
+        Cache: ProxyableCache,
+        DynamicCache: ProxyableDynamicCache,
+        SinkCache: ProxyableSinkCache,
+        StaticCache: ProxyableStaticCache,
+    }
+
     supported_archs = (PreTrainedModel,) if not is_peft_available() else (PreTrainedModel, PeftModel)
 
     def __init__(self, autowrap_modules=(math,), autowrap_functions=()):
@@ -776,7 +876,7 @@ class HFTracer(Tracer):
             )
 
     def _generate_dummy_input(
-        self, model: PreTrainedModel, input_name: str, shape: List[int], input_names: List[str]
+        self, model: "PreTrainedModel", input_name: str, shape: List[int], input_names: List[str]
     ) -> Dict[str, torch.Tensor]:
         """Generates dummy input for model inference recording."""
         # Retrieving the model class, either from the "class_for_deserialization" attribute if the model was restored
@@ -951,6 +1051,11 @@ class HFTracer(Tracer):
             args_metas = torch.fx.node.map_aggregate(args, _proxies_to_metas)
             kwargs_metas = torch.fx.node.map_aggregate(kwargs, _proxies_to_metas)
 
+            should_install_metadata = True
+
+            self._disable_module_getattr = True
+            self._disable_call_module = True
+
             if kind == "call_function":
                 meta_target = _MANUAL_META_OVERRIDES.get(target, target)
                 meta_out = meta_target(*args_metas, **kwargs_metas)
@@ -963,39 +1068,36 @@ class HFTracer(Tracer):
             elif kind == "call_module":
                 if not hasattr(self, "orig_forward"):
                     raise AttributeError(f"{self} does not have an attribute called orig_forward")
-                self._disable_module_getattr = True
-                try:
-                    mod = self.root.get_submodule(target)
-                    mod_type = type(mod)
-                    if mod_type in _MANUAL_META_OVERRIDES:
-                        meta_out = _MANUAL_META_OVERRIDES[mod_type](mod, *args_metas, **kwargs_metas)
-                    else:
-                        meta_out = self.orig_forward(*args_metas, **kwargs_metas)
-                finally:
-                    self._disable_module_getattr = False
+                mod = self.root.get_submodule(target)
+                mod_type = type(mod)
+                if mod_type in _MANUAL_META_OVERRIDES:
+                    meta_out = _MANUAL_META_OVERRIDES[mod_type](mod, *args_metas, **kwargs_metas)
+                else:
+                    meta_out = self.orig_forward(*args_metas, **kwargs_metas)
             elif kind == "get_attr":
-                self._disable_module_getattr = True
-                try:
-                    attr_itr = self.root
-                    atoms = target.split(".")
-                    for atom in atoms:
-                        attr_itr = getattr(attr_itr, atom)
-                    if isinstance(attr_itr, torch.Tensor):
-                        meta_out = attr_itr.to(device="meta")
-                    else:
-                        meta_out = attr_itr
-                finally:
-                    self._disable_module_getattr = False
+                attr_itr = self.root
+                atoms = target.split(".")
+                for atom in atoms:
+                    attr_itr = getattr(attr_itr, atom)
+                if isinstance(attr_itr, torch.Tensor):
+                    meta_out = attr_itr.to(device="meta")
+                else:
+                    meta_out = attr_itr
             else:
-                return rv
+                should_install_metadata = False
+
+            if should_install_metadata:
+                if not isinstance(rv, Proxy):
+                    raise ValueError("Don't support composite output yet")
+                rv.install_metadata(meta_out)
 
-            if not isinstance(rv, Proxy):
-                raise ValueError("Don't support composite output yet")
-            rv.install_metadata(meta_out)
         except Exception as e:
             if _IS_IN_DEBUG_MODE:
                 warnings.warn(f"Could not compute metadata for {kind} target {target}: {e}")
 
+        self._disable_module_getattr = False
+        self._disable_call_module = False
+
         return rv
 
     # Replaced by .getattr from PyTorch 1.13
@@ -1041,12 +1143,51 @@ class HFTracer(Tracer):
         return self._module_getattr(attr, attr_val, parameter_proxy_cache)
 
     def call_module(self, m, forward, args, kwargs):
+        if getattr(self, "_disable_call_module", False):
+            return forward(*args, **kwargs)
         self.orig_forward = forward
         return super().call_module(m, forward, args, kwargs)
 
     def proxy(self, node):
         return HFProxy(node, self)
 
+    @contextlib.contextmanager
+    def patch_for_tracing(self, root: Union[torch.nn.Module, Callable[..., Any]]):
+        # Patching torch functions
+        self.patched_torch_methods = {
+            target: gen_constructor_wrapper(getattr(torch, target)) for target in self._TORCH_METHODS_TO_PATCH
+        }
+        self.orig_fns = set()
+
+        for name, (wrapper, orig) in self.patched_torch_methods.items():
+            setattr(torch, name, wrapper)
+            self.orig_fns.add(orig)
+
+        # Patching classes
+        patched = []
+        module_of_model = inspect.getmodule(root)
+        for name, mod in sys.modules.items():
+            if module_of_model is not None and mod is not module_of_model:
+                continue
+            if not name.startswith("transformers"):
+                continue
+            for orig_cls, patched_cls in self._CLASSES_TO_PATCH.items():
+                for attr_name, attr in mod.__dict__.items():
+                    if attr is orig_cls:
+                        patched.append((mod, attr_name, orig_cls))
+                        setattr(mod, attr_name, patched_cls)
+
+        yield
+
+        # Restoring patched functions and classes.
+        for name, (_, orig) in self.patched_torch_methods.items():
+            setattr(torch, name, orig)
+        self.patched_torch_methods = {}
+        self.orig_fns = set()
+
+        for mod, attr_name, orig_cls in patched:
+            setattr(mod, attr_name, orig_cls)
+
     def trace(
         self,
         root: Union[torch.nn.Module, Callable[..., Any]],
@@ -1125,28 +1266,25 @@ class HFTracer(Tracer):
                     " transformers.PreTrainedModel."
                 )
 
-        concrete_metas = {
-            input_name: input_.to("meta") if isinstance(input_, torch.Tensor) else input_
-            for input_name, input_ in inputs.items()
-        }
+        def to_meta(value):
+            if isinstance(value, torch.Tensor):
+                return value.to("meta")
+            return value
+
+        concrete_metas = pytree.tree_map(to_meta, inputs)
+
         for param in sig.parameters.values():
             if param.kind == inspect.Parameter.VAR_KEYWORD and param.name not in input_names:
                 concrete_metas[f"**{param.name}"] = {}
         self.meta_args = concrete_metas
-        self.patched_torch_methods = {
-            target: _gen_constructor_wrapper(getattr(torch, target)) for target in self._TORCH_METHODS_TO_PATCH
-        }
-        self.orig_fns = set()
 
-        for name, (wrapper, orig) in self.patched_torch_methods.items():
-            setattr(torch, name, wrapper)
-            self.orig_fns.add(orig)
-
-        try:
-            self.graph = super().trace(root, concrete_args=concrete_args)
-        finally:
-            for name, (_, orig) in self.patched_torch_methods.items():
-                setattr(torch, name, orig)
+        global _CURRENT_TRACER
+        _CURRENT_TRACER = self
+        with self.patch_for_tracing(root):
+            try:
+                self.graph = super().trace(root, concrete_args=concrete_args)
+            finally:
+                _CURRENT_TRACER = None
 
         # This is necessary because concrete args are added as input to the traced module since
         # https://github.com/pytorch/pytorch/pull/55888.
@@ -1256,11 +1394,11 @@ def get_concrete_args(model: nn.Module, input_names: List[str]):
     return {p.name: p.default for p in sig.parameters.values() if p.name not in input_names}
 
 
-def is_model_supported(model: PreTrainedModel):
+def is_model_supported(model: "PreTrainedModel"):
     return model.__class__.__name__ in _SUPPORTED_MODELS
 
 
-def check_if_model_is_supported(model: PreTrainedModel):
+def check_if_model_is_supported(model: "PreTrainedModel"):
     if not is_model_supported(model):
         supported_model_names = ", ".join(_SUPPORTED_MODELS)
         raise NotImplementedError(
@@ -1269,7 +1407,7 @@ def check_if_model_is_supported(model: PreTrainedModel):
 
 
 def symbolic_trace(
-    model: PreTrainedModel,
+    model: "PreTrainedModel",
     input_names: Optional[List[str]] = None,
     disable_check: bool = False,
     tracer_cls: Type[HFTracer] = HFTracer,
@@ -1307,6 +1445,18 @@ def symbolic_trace(
     if not disable_check:
         check_if_model_is_supported(model)
 
+    if "past_key_values" in input_names and not getattr(model.config, "use_cache", False):
+        logger.warning(
+            "`past_key_values` were specified as input names, but model.config.use_cache = False, this might lead to "
+            "unexpected behavior."
+        )
+    if "past_key_values" not in input_names and getattr(model.config, "use_cache", False):
+        logger.warning(
+            "`past_key_values` were not specified as input names, but model.config.use_cache = True. Setting "
+            "model.config.use_cache = False."
+        )
+        model.config.use_cache = False
+
     # Tracing.
     tracer = tracer_cls()
     traced_graph = tracer.trace(model, concrete_args=concrete_args)

tests/test_modeling_common.py

@@ -18,7 +18,6 @@ import gc
 import inspect
 import os
 import os.path
-import pickle
 import random
 import re
 import tempfile
@@ -1279,26 +1278,6 @@ class ModelTesterMixin:
                         f"traced {i}th output doesn't match model {i}th output for {model_class}",
                     )
 
-                # Test that the model can be serialized and restored properly
-                with tempfile.TemporaryDirectory() as tmp_dir_name:
-                    pkl_file_name = os.path.join(tmp_dir_name, "model.pkl")
-                    try:
-                        with open(pkl_file_name, "wb") as f:
-                            pickle.dump(traced_model, f)
-                        with open(pkl_file_name, "rb") as f:
-                            loaded = pickle.load(f)
-                    except Exception as e:
-                        self.fail(f"Couldn't serialize / deserialize the traced model: {e}")
-
-                    loaded_output = loaded(**filtered_inputs)
-                    loaded_output = flatten_output(loaded_output)
-
-                    for i in range(num_outputs):
-                        self.assertTrue(
-                            torch.allclose(model_output[i], loaded_output[i]),
-                            f"serialized model {i}th output doesn't match model {i}th output for {model_class}",
-                        )
-
                 # Avoid memory leak. Without this, each call increase RAM usage by ~20MB.
                 # (Even with this call, there are still memory leak by ~0.04MB)
                 self.clear_torch_jit_class_registry()