nix log /nix/store/5khy3vr59v193zla77izslymvn1w2l3c-python3.10-torchinfo-1.64.drv

5khy3vr59v193zla77izslymvn1w2l3c-python3.10-torchinfo-1.64.drv.log

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running bdist_wheel
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copying torchinfo/layer_info.py -> build/lib/torchinfo
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copying torchinfo/torchinfo.py -> build/lib/torchinfo
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copying torchinfo/py.typed -> build/lib/torchinfo
/nix/store/8zx4h7r5mn1b913hgi8rwjfynwg1wgdi-python3.10-setuptools-67.4.0/lib/python3.10/site-packages/setuptools/command/install.py:34: SetuptoolsDeprecationWarning: setup.py install is deprecated. Use build and pip and other standards-based tools.
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installing to build/bdist.linux-x86_64/wheel
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creating build/bdist.linux-x86_64/wheel/torchinfo
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adding 'torchinfo/__init__.py'
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Processing ./torchinfo-1.6.4-py3-none-any.whl
Installing collected packages: torchinfo
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============================= test session starts ==============================
platform linux -- Python 3.10.10, pytest-7.2.1, pluggy-1.0.0
rootdir: /build/source
collecting ... 
collected 64 items / 2 deselected / 62 selected                                

tests/exceptions_test.py ...F.                                           [  8%]
tests/gpu_test.py ss                                                     [ 11%]
tests/half_precision_test.py sss                                         [ 16%]
tests/torchinfo_test.py ............................................     [ 87%]
tests/torchinfo_xl_test.py .....s..                                      [100%]

=================================== FAILURES ===================================
________________________ test_input_size_half_precision ________________________

model = Linear(in_features=2, out_features=5, bias=True)
x = [tensor([[0.7124, 0.1774],
        [0.6299, 0.7700],
        [0.0745, 0.4951],
        [0.0251, 0.7275],
        [0.86... [0.0835, 0.0176],
        [0.0797, 0.2688],
        [0.7529, 0.1649],
        [0.9116, 0.8252]], dtype=torch.float16)]
batch_dim = None, cache_forward_pass = False, device = 'cpu'
mode = <Mode.EVAL: 'eval'>, kwargs = {}, model_name = 'Linear'
all_layers = [Linear: 0], summary_list = [Linear: 0]
hooks = {140728432927184: (<torch.utils.hooks.RemovableHandle object at 0x7ffde44134f0>, <torch.utils.hooks.RemovableHandle object at 0x7ffde4411240>)}
named_module = ('Linear', Linear(in_features=2, out_features=5, bias=True))
saved_model_mode = True

    def forward_pass(
        model: nn.Module,
        x: CORRECTED_INPUT_DATA_TYPE,
        batch_dim: int | None,
        cache_forward_pass: bool,
        device: torch.device | str,
        mode: Mode,
        **kwargs: Any,
    ) -> list[LayerInfo]:
        """Perform a forward pass on the model using forward hooks."""
        global _cached_forward_pass  # pylint: disable=global-variable-not-assigned
        model_name = model.__class__.__name__
        if cache_forward_pass and model_name in _cached_forward_pass:
            return _cached_forward_pass[model_name]
    
        all_layers: list[LayerInfo] = []
        summary_list: list[LayerInfo] = []
        hooks: dict[int, tuple[RemovableHandle, RemovableHandle]] | None = (
            None if x is None else {}
        )
        named_module = (model_name, model)
        apply_hooks(named_module, model, batch_dim, summary_list, hooks, all_layers)
    
        if x is None:
            if not summary_list or summary_list[0].var_name != model_name:
                summary_list.insert(0, LayerInfo("", model, 0))
            set_depth_index(summary_list)
            return summary_list
    
        kwargs = set_device(kwargs, device)
        saved_model_mode = model.training
        try:
            if mode == Mode.TRAIN:
                model.train()
            elif mode == Mode.EVAL:
                model.eval()
            else:
                raise RuntimeError(
                    f"Specified model mode ({list(Mode)}) not recognized: {mode}"
                )
    
            with torch.no_grad():  # type: ignore[no-untyped-call]
                if isinstance(x, (list, tuple)):
>                   _ = model.to(device)(*x, **kwargs)

torchinfo/torchinfo.py:294: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Linear(in_features=2, out_features=5, bias=True)
args = (tensor([[0.7124, 0.1774],
        [0.6299, 0.7700],
        [0.0745, 0.4951],
        [0.0251, 0.7275],
        [0.86...[0.0835, 0.0176],
        [0.0797, 0.2688],
        [0.7529, 0.1649],
        [0.9116, 0.8252]], dtype=torch.float16),)
kwargs = {}
forward_call = <bound method Linear.forward of Linear(in_features=2, out_features=5, bias=True)>
full_backward_hooks = [], non_full_backward_hooks = [], backward_pre_hooks = []
hook_id = 24, hook = <function apply_hooks.<locals>.pre_hook at 0x7ffde45e67a0>
result = None, bw_hook = None

    def _call_impl(self, *args, **kwargs):
        forward_call = (self._slow_forward if torch._C._get_tracing_state() else self.forward)
        # If we don't have any hooks, we want to skip the rest of the logic in
        # this function, and just call forward.
        if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks
                or _global_backward_pre_hooks or _global_backward_hooks
                or _global_forward_hooks or _global_forward_pre_hooks):
            return forward_call(*args, **kwargs)
        # Do not call functions when jit is used
        full_backward_hooks, non_full_backward_hooks = [], []
        backward_pre_hooks = []
        if self._backward_pre_hooks or _global_backward_pre_hooks:
            backward_pre_hooks = self._get_backward_pre_hooks()
    
        if self._backward_hooks or _global_backward_hooks:
            full_backward_hooks, non_full_backward_hooks = self._get_backward_hooks()
    
        if _global_forward_pre_hooks or self._forward_pre_hooks:
            for hook_id, hook in (
                *_global_forward_pre_hooks.items(),
                *self._forward_pre_hooks.items(),
            ):
                if hook_id in self._forward_pre_hooks_with_kwargs:
                    result = hook(self, args, kwargs)  # type: ignore[misc]
                    if result is not None:
                        if isinstance(result, tuple) and len(result) == 2:
                            args, kwargs = result
                        else:
                            raise RuntimeError(
                                "forward pre-hook must return None or a tuple "
                                f"of (new_args, new_kwargs), but got {result}."
                            )
                else:
                    result = hook(self, args)
                    if result is not None:
                        if not isinstance(result, tuple):
                            result = (result,)
                        args = result
    
        bw_hook = None
        if full_backward_hooks or backward_pre_hooks:
            bw_hook = hooks.BackwardHook(self, full_backward_hooks, backward_pre_hooks)
            args = bw_hook.setup_input_hook(args)
    
>       result = forward_call(*args, **kwargs)

/nix/store/x2s2mb5i6skm7galc1y72w1q2329mjqr-python3.10-torch-2.0.0/lib/python3.10/site-packages/torch/nn/modules/module.py:1538: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Linear(in_features=2, out_features=5, bias=True)
input = tensor([[0.7124, 0.1774],
        [0.6299, 0.7700],
        [0.0745, 0.4951],
        [0.0251, 0.7275],
        [0.866...  [0.0835, 0.0176],
        [0.0797, 0.2688],
        [0.7529, 0.1649],
        [0.9116, 0.8252]], dtype=torch.float16)

    def forward(self, input: Tensor) -> Tensor:
>       return F.linear(input, self.weight, self.bias)
E       RuntimeError: "addmm_impl_cpu_" not implemented for 'Half'

/nix/store/x2s2mb5i6skm7galc1y72w1q2329mjqr-python3.10-torch-2.0.0/lib/python3.10/site-packages/torch/nn/modules/linear.py:114: RuntimeError

The above exception was the direct cause of the following exception:

    def test_input_size_half_precision() -> None:
        test = torch.nn.Linear(2, 5).half()
        with pytest.warns(
            UserWarning,
            match=(
                "Half precision is not supported with input_size parameter, and "
                "may output incorrect results. Try passing input_data directly."
            ),
        ):
>           summary(test, dtypes=[torch.float16], input_size=(10, 2), device="cpu")

tests/exceptions_test.py:59: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
torchinfo/torchinfo.py:215: in summary
    summary_list = forward_pass(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

model = Linear(in_features=2, out_features=5, bias=True)
x = [tensor([[0.7124, 0.1774],
        [0.6299, 0.7700],
        [0.0745, 0.4951],
        [0.0251, 0.7275],
        [0.86... [0.0835, 0.0176],
        [0.0797, 0.2688],
        [0.7529, 0.1649],
        [0.9116, 0.8252]], dtype=torch.float16)]
batch_dim = None, cache_forward_pass = False, device = 'cpu'
mode = <Mode.EVAL: 'eval'>, kwargs = {}, model_name = 'Linear'
all_layers = [Linear: 0], summary_list = [Linear: 0]
hooks = {140728432927184: (<torch.utils.hooks.RemovableHandle object at 0x7ffde44134f0>, <torch.utils.hooks.RemovableHandle object at 0x7ffde4411240>)}
named_module = ('Linear', Linear(in_features=2, out_features=5, bias=True))
saved_model_mode = True

    def forward_pass(
        model: nn.Module,
        x: CORRECTED_INPUT_DATA_TYPE,
        batch_dim: int | None,
        cache_forward_pass: bool,
        device: torch.device | str,
        mode: Mode,
        **kwargs: Any,
    ) -> list[LayerInfo]:
        """Perform a forward pass on the model using forward hooks."""
        global _cached_forward_pass  # pylint: disable=global-variable-not-assigned
        model_name = model.__class__.__name__
        if cache_forward_pass and model_name in _cached_forward_pass:
            return _cached_forward_pass[model_name]
    
        all_layers: list[LayerInfo] = []
        summary_list: list[LayerInfo] = []
        hooks: dict[int, tuple[RemovableHandle, RemovableHandle]] | None = (
            None if x is None else {}
        )
        named_module = (model_name, model)
        apply_hooks(named_module, model, batch_dim, summary_list, hooks, all_layers)
    
        if x is None:
            if not summary_list or summary_list[0].var_name != model_name:
                summary_list.insert(0, LayerInfo("", model, 0))
            set_depth_index(summary_list)
            return summary_list
    
        kwargs = set_device(kwargs, device)
        saved_model_mode = model.training
        try:
            if mode == Mode.TRAIN:
                model.train()
            elif mode == Mode.EVAL:
                model.eval()
            else:
                raise RuntimeError(
                    f"Specified model mode ({list(Mode)}) not recognized: {mode}"
                )
    
            with torch.no_grad():  # type: ignore[no-untyped-call]
                if isinstance(x, (list, tuple)):
                    _ = model.to(device)(*x, **kwargs)
                elif isinstance(x, dict):
                    _ = model.to(device)(**x, **kwargs)
                else:
                    # Should not reach this point, since process_input_data ensures
                    # x is either a list, tuple, or dict
                    raise ValueError("Unknown input type")
        except Exception as e:
            executed_layers = [layer for layer in summary_list if layer.executed]
>           raise RuntimeError(
                "Failed to run torchinfo. See above stack traces for more details. "
                f"Executed layers up to: {executed_layers}"
            ) from e
E           RuntimeError: Failed to run torchinfo. See above stack traces for more details. Executed layers up to: []

torchinfo/torchinfo.py:303: RuntimeError
=============================== warnings summary ===============================
tests/exceptions_test.py: 1 warning
tests/torchinfo_test.py: 39 warnings
tests/torchinfo_xl_test.py: 7 warnings
  /build/source/torchinfo/torchinfo.py:455: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
    action_fn=lambda data: sys.getsizeof(data.storage()),

tests/exceptions_test.py: 1 warning
tests/torchinfo_test.py: 39 warnings
tests/torchinfo_xl_test.py: 7 warnings
  /nix/store/x2s2mb5i6skm7galc1y72w1q2329mjqr-python3.10-torch-2.0.0/lib/python3.10/site-packages/torch/storage.py:665: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
    return super().__sizeof__() + self.nbytes()

-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
=========================== short test summary info ============================
FAILED tests/exceptions_test.py::test_input_size_half_precision - RuntimeError: Failed to run torchinfo. See above stack traces for more deta...
= 1 failed, 55 passed, 6 skipped, 2 deselected, 94 warnings in 138.34s (0:02:18) =
/nix/store/pw17yc3mwmsci4jygwalj8ppg0drz31v-stdenv-linux/setup: line 1593: pop_var_context: head of shell_variables not a function context