pytorch lightning Fail

[niitsuma]

Following code stop with

AttributeError: 'builtin_function_or_method' object has no attribute 'code'. Did you mean: 'call'?

(ns pytorchlightning.core
 (:gen-class)

 (:require 
 [libpython-clj2.python :as py
 :refer
 [ py. py.- 
 as-jvm
 set-attr!
 get-item
 ->py-tuple
 ->py-list
 ]]
 
 [libpython-clj2.require :refer [require-python]]
 ))

;;(py/initialize!)

(require-python
 '[builtins :as python]
 '[torch]
 '[torch.nn :as nn :refer [Linear]]
 '[torch.nn.functional :refer [mse_loss]]
 '[torch.utils.data :refer [DataLoader Dataset]]
 '[torch.optim]
 '[pytorch_lightning]
 )


(defonce model (atom nil))

(def LitModel
 (py/create-class
 "LitModel" [pytorch_lightning/LightningModule]
 {"__init__"
 (py/make-tuple-instance-fn
 (fn [self]
 (py. pytorch_lightning/LightningModule __init__ self)
 (py/set-attr! self "layer" (Linear 1 1))
 nil))
 
 "forward"
 (py/make-tuple-instance-fn
 (fn [self x] (py. self layer x))
 :arg-converter as-jvm
 :method-name "forward"
 )
 
 "training_step"
 (py/make-tuple-instance-fn
 (fn [self batch batch_idx]
 (mse_loss (py/get-item batch 1) (py. self forward (py/get-item batch 0)))
 ))
 "validation_step"
 (py/make-tuple-instance-fn
 (fn [self batch batch_idx]
 (mse_loss (py/get-item batch 1) (py. self forward (py/get-item batch 0)))
 ))
 "test_step"
 (py/make-tuple-instance-fn
 (fn [self batch batch_idx]
 (mse_loss (py/get-item batch 1) (py. self forward (py/get-item batch 0)))
 ))
 
 "configure_optimizers"
 (py/make-tuple-instance-fn
 (fn [self]
 (torch.optim/SGD
 (py. self parameters)
 :lr 0.02)
 ))
 }))

(def SimpleDataset
 (py/create-class
 "SimpleDataset" [Dataset]
 {"__init__"
 (py/make-tuple-instance-fn
 (fn [self data] (py/set-attr! self "data" data) nil))
 "__len__"
 (py/make-tuple-instance-fn 
 (fn [self] (python/len (py.- self data))))
 "__getitem__"
 (py/make-tuple-instance-fn
 (fn [self idx]
 (py/->py-tuple
 [
 (torch/tensor [(py/get-item (py/get-item (py.- self data) idx) 0)] :dtype torch/float32)
 (torch/tensor [(py/get-item (py/get-item (py.- self data) idx) 1)] :dtype torch/float32)
 ]
 )
 ))
 }))


(def SimpleDataModule
 (py/create-class
 "SimpleDataModule" [pytorch_lightning/LightningDataModule]
 {"__init__"
 (py/make-tuple-instance-fn
 (fn [self data] (py/set-attr! self "data" data) nil))
 
 "train_dataloader"
 (py/make-tuple-instance-fn
 (fn [self]
 (DataLoader (SimpleDataset (py.- self data )) :batch_size 2 :shuffle false)))
 "val_dataloader"
 (py/make-tuple-instance-fn
 (fn [self]
 (DataLoader (SimpleDataset (py.- self data )) :batch_size 2 :shuffle false)))
 "test_dataloader"
 (py/make-tuple-instance-fn
 (fn [self]
 (DataLoader (SimpleDataset (py.- self data )) :batch_size 2 :shuffle false)))
 }))




(defn -main [& args]

 (reset! model (LitModel))

 (def data [[1.0 3.0] [2.0 5.0] [3.0 7.0] [4.0 9.0] [5.0 11.0]] )
 (def data_pylist (py/->py-list data))

 ;; (println (py/get-item (py/get-item data_pylist 0) 0))

 (def train_dataset (SimpleDataset data_pylist))
 (def train_loader (DataLoader train_dataset :batch_size 2 :shuffle false))

 ;; ;;for debug
 ;; (def train_pylist (python/list train_loader))
 ;; (def train_pylist_0 (py/get-item train_pylist 0))
 ;; (println (py. @model training_step train_pylist_0 0) );;;OK. maybe this part work

 
 (def trainer (pytorch_lightning/Trainer :max_epochs 10 ))

 (def datamodu (SimpleDataModule data_pylist))
 
 (py. trainer fit @model train_loader)
 ;;(py. trainer fit @model datamodu) ;;also fail

 )

[cnuernber]

It appears to me that this happens when you pass in a function that doesn't have its source code thus the autodiff system can't autodiff. Keep in mind that functions defined via clojure are translated to python as opaque C function pointers so they won't be autodifferentiable.

[tani]

@cnuernber Thank you for the awesome project!
__code__ is used in the following parts, and it is likely only to check whether instance_method has been overridden as intended. Now, I would be happy if there were a way to give a fake __code__ value to a function represented by a C pointer—something like (set-attr! func "__code__" ""). Do you have any good ideas?

https://github.com/Lightning-AI/utilities/blob/b2359648d26af67d5f97fd8e00c47103c976a485/src/lightning_utilities/core/overrides.py#L9-L34

https://github.com/Lightning-AI/torchmetrics/blob/c57197e7e68e226aefb28f4496a751555074c352/src/torchmetrics/utilities/checks.py#L740-L762

Execution error at libpython-clj2.python.ffi/check-error-throw (ffi.clj:717).
Traceback (most recent call last):
 File "/Users/tani/Documents/libpython-clj/.venv/lib/python3.13/site-packages/lightning/pytorch/trainer/trainer.py", line 539, in fit
 call._call_and_handle_interrupt(
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^
 self, self._fit_impl, model, train_dataloaders, val_dataloaders, datamodule, ckpt_path
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 )
 ^
 File "/Users/tani/Documents/libpython-clj/.venv/lib/python3.13/site-packages/lightning/pytorch/trainer/call.py", line 47, in _call_and_handle_interrupt
 return trainer_fn(*args, **kwargs)
 File "/Users/tani/Documents/libpython-clj/.venv/lib/python3.13/site-packages/lightning/pytorch/trainer/trainer.py", line 575, in _fit_impl
 self._run(model, ckpt_path=ckpt_path)
 ~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 File "/Users/tani/Documents/libpython-clj/.venv/lib/python3.13/site-packages/lightning/pytorch/trainer/trainer.py", line 932, in _run
 _verify_loop_configurations(self)
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^
 File "/Users/tani/Documents/libpython-clj/.venv/lib/python3.13/site-packages/lightning/pytorch/trainer/configuration_validator.py", line 36, in _verify_loop_configurations
 __verify_train_val_loop_configuration(trainer, model)
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^
 File "/Users/tani/Documents/libpython-clj/.venv/lib/python3.13/site-packages/lightning/pytorch/trainer/configuration_validator.py", line 51, in __verify_train_val_loop_configuration
 has_training_step = is_overridden("training_step", model)
 File "/Users/tani/Documents/libpython-clj/.venv/lib/python3.13/site-packages/lightning/pytorch/utilities/model_helpers.py", line 46, in is_overridden
 return _is_overridden(method_name, instance, parent)
 File "/Users/tani/Documents/libpython-clj/.venv/lib/python3.13/site-packages/lightning_utilities/core/overrides.py", line 34, in is_overridden
 return instance_attr.__code__ != parent_attr.__code__
 ^^^^^^^^^^^^^^^^^^^^^^
AttributeError: 'builtin_function_or_method' object has no attribute '__code__'. Did you mean: '__call__'?

(ns sample.core
 (:require
 [libpython-clj2.require :refer [require-python]]
 [libpython-clj2.python :as py :refer [py. py.- py.. py* py**]]
 ))

(py/initialize! :python-executable "/Users/tani/Documents/libpython-clj/.venv/bin/python")

(require-python
 'os
 '[torch.nn :refer [Linear ReLU Sequential]]
 '[torch.nn.functional :refer [mse_loss]]
 '[torch.optim :as o :refer [Adam]]
 '[torch.utils.data :refer [DataLoader]]
 '[lightning :refer [LightningModule Trainer]]
 '[torchvision.datasets :refer [MNIST]]
 '[torchvision.transforms :refer [ToTensor]])

(def encoder (Sequential (Linear (* 28 28) 64) (ReLU) (Linear 64 3)))
(def decoder (Sequential (Linear 64 3) (ReLU) (Linear (* 28 28) 64)))
(def LitModel
 (py/create-class
 "LitModel" [LightningModule]
 {"__init__"
 (py/make-tuple-instance-fn
 (fn [self encoder decoder]
 (py. LightningModule __init__ self)
 (py/set-attr! self "encoder" encoder)
 (py/set-attr! self "decoder" decoder)
 nil))
 "training_step"
 (py/make-tuple-instance-fn
 (fn [self batch batch_idx]
 (let [head (py/get-item batch 0)
 x (py. head view (py. head size 0) -1)
 z (py. self encoder x)
 xhat (py. self decoder z)
 loss (mse_loss xhat x)]
 loss)))
 "configure_optimizers"
 (py/make-tuple-instance-fn
 (fn [self]
 (py** Adam (py. self parameters) {:lr 0.0001})))}))

(def model (LitModel encoder decoder))
(def dataset (MNIST (os/getcwd) :download true :transform (ToTensor)))
(def train_loader (DataLoader dataset))
(def trainer (Trainer :limit_train_batches 2 :max_epochs 1 :logger []))
(py. trainer fit (py/as-python model) train_loader)

[amano-kenji]

I asked grok chatbot about this. It told me to create a subclass of torch.autograd.Function with libpython-clj as below.

import torch
from torch.autograd import Function
from ctypes import cdll

# Load the C library
lib = cdll.LoadLibrary("./mylib.so")
lib.double_value.argtypes = [ctypes.c_float]
lib.double_value.restype = ctypes.c_float

# Custom autograd function
class DoubleFunction(Function):
 @staticmethod
 def forward(ctx, input):
 # Extract scalar value from tensor, call C function
 scalar_input = input.item()
 result = lib.double_value(scalar_input)
 # Return as a tensor
 output = torch.tensor([result], dtype=input.dtype, device=input.device)
 # Save input for backward pass if needed
 ctx.save_for_backward(input)
 return output

 @staticmethod
 def backward(ctx, grad_output):
 # Retrieve saved input if needed (here, not strictly necessary)
 input, = ctx.saved_tensors
 # Gradient of doubling is 2, multiplied by incoming gradient
 grad_input = grad_output * 2
 return grad_input

# Python wrapper for convenience
def double_tensor(x):
 return DoubleFunction.apply(x)

Another way is to replace pytorch lightning with pytorch.