@pytest.mark.push
    def test_torch_training():
        model = MatmulParam()
        shape = (1, 1024)
        inputs = torch.rand(shape)
        # Fake targets
        target = torch.zeros(shape)
    
        loss_fn = torch.nn.MSELoss()
        optimizer = torch.optim.SGD(model.parameters(), lr=0.1)
    
        tt_model = forge.compile(model, sample_inputs=[torch.rand(shape)], optimizer=optimizer)
    
        num_epochs = 20
    
        model.train()
        for epoch in range(num_epochs):
            golden = model(inputs)
            output = tt_model(inputs)
    
            output = [co.to("cpu") for co in output]
            assert compare_with_golden(golden=golden, calculated=output[0])
    
            optimizer.zero_grad()
    
            loss = loss_fn(output[0], target)
            loss.backward()
    
            golden_loss = loss_fn(golden, target)
            print(f"epoch: {epoch} loss: {loss}")
            print(f"epoch: {epoch} golden_loss: {golden_loss}")
            print(f"output.grad: {output[0].grad}")
    
            loss_grad = output[0].grad
            assert loss_grad is not None
            grad = tt_model.backward()
    
            # HACK to run the optimizer step
            # i'm not sure what's the right way to tie the torch optimizer to our params,
            # but this can be done automatically after backward() (hidden from user)
>           model.p.grad = grad[0]
E           TypeError: 'NoneType' object is not subscriptable

forge/test/mlir/test_training.py:66: TypeError

shape = (33, 27, 127)

    @pytest.mark.parametrize(
        "shape",
        [
            # input, hidden, output
            (784, 784, 10),
            (33, 27, 127),
            (128, 10, 20),
        ],
    )
    @pytest.mark.push
    def test_sgd(shape):
        torch.manual_seed(0)
        num_epochs = 10
        # Large learning rate to propagate possible errors faster
        learning_rate = 1
        batch_size = 10
    
        framework_model = MNISTLinear(input_size=shape[0], hidden_size=shape[1], output_size=shape[2], bias=False)
        golden_model = MNISTLinear(input_size=shape[0], hidden_size=shape[1], output_size=shape[2], bias=False)
    
        copy_params(framework_model, golden_model)
    
        tt_optimizer = forge.optimizers.SGD(learning_rate=learning_rate)
        golden_optimizer = torch.optim.SGD(golden_model.parameters(), lr=learning_rate)
    
        tt_model = forge.compile(
            framework_model, sample_inputs=[torch.randn(batch_size, shape[0])], optimizer=tt_optimizer, training=True
        )
    
        loss_fn = nn.MSELoss()
    
>       train_and_compare_optimizers(
            num_epochs=num_epochs,
            batch_size=batch_size,
            shape=shape,
            loss_fn=loss_fn,
            tt_model=tt_model,
            tt_optimizer=tt_optimizer,
            golden_model=golden_model,
            golden_optimizer=golden_optimizer,
        )

forge/test/mlir/test_optimizers.py:88: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
forge/test/mlir/test_optimizers.py:41: in train_and_compare_optimizers
    tt_optimizer.step()
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/optimizers.py:93: in step
    module.step()
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <forge.compiled_graph_state.CompiledModel object at 0x7f51e717de40>

    def step(self) -> None:
        assert self.fwd_compiled_graph_state.graph.training(), "Model not compiled for training."
        assert self.opt_compiled_graph_state is not None, "Optimizer graph should be present for training."
        assert self.bwd_compiled_graph_state is not None, "Backward graph should be present for training."
    
        inputs = [
            *self.gradient_outputs,
        ]
    
        logger.info(
            f"Running optimizer step on model {self.framework_module.get_name()} {self.opt_compiled_graph_state.graph.get_name()} on device..."
        )
    
>       self.runtime_model_state.run_program(ProgramType.Optimizer, inputs)
E       TypeError: run_program(): incompatible function arguments. The following argument types are supported:
E           1. (self: forge._C.runtime.ModelState, arg0: forge._C.runtime.ProgramType, arg1: List[forge._C.runtime.Tensor]) -> None
E       
E       Invoked with: <forge._C.runtime.ModelState object at 0x7f51e6dd70b0>, <ProgramType.Optimizer: 2>, [tensor([[-9.4843097031e-04,  3.4172273445e-05,  1.6523615341e-04,  ...,  4.5211389079e-04,  0.0000000000e+00, -2.1080239094e-05],
E               [ 3.8541443064e-04,  1.0857859161e-03, -3.3083641028e-05,  ...,  5.8670114959e-04,  0.0000000000e+00,  1.0534250177e-03],
E               [ 8.0792124209e-05, -6.7598145688e-04,  1.3021877385e-04,  ..., -3.1630668673e-04,  0.0000000000e+00, -8.0112746218e-04],
E               ...,
E               [ 1.6876119480e-04,  2.2889114916e-04, -2.4292142189e-04,  ..., -2.9746314976e-04,  0.0000000000e+00,  1.7288178788e-04],
E               [ 3.9924151497e-04, -4.2486837629e-05, -7.9519704741e-06,  ...,  5.9461832279e-05,  0.0000000000e+00, -3.7011798122e-04],
E               [-9.2521269107e-04, -5.4025981808e-04,  3.0508343480e-04,  ...,  6.9242290920e-04,  0.0000000000e+00, -4.7237664694e-04]]), tensor([[ 6.4303055406e-03,  6.9811330177e-03,  3.8233557716e-03,  9.9330907688e-04,  3.3100037836e-03,  5.9511169093e-04,  5.7607307099e-04, -7.7128119301e-05,  2.1128805820e-03,  5.3538451903e-03, -5.1653088303e-04, -8.8843902631e-05,  1.7779468326e-03,  2.6980042458e-04, -1.0302674491e-04,  5.3980229422e-03,
E                 4.2273843428e-04,  5.1447725855e-03,  1.4378665946e-03,  1.6906103119e-03,  3.3745747060e-03,  2.6021862868e-03,  2.2594677284e-03,  5.7131289504e-03,  2.1521663293e-03,  1.9366035704e-03,  5.7209935039e-03],
E               [ 6.2206282746e-04,  2.9119350947e-03,  4.9570556730e-03,  1.7205695622e-03,  2.6777780149e-03,  4.1070394218e-03,  9.2267506989e-04,  4.7140014358e-03,  1.0539437644e-03,  5.6521082297e-04,  6.3468917506e-04,  1.8682159716e-03,  2.3887879215e-03,  1.1673929403e-03,  0.0000000000e+00,  2.4207283277e-03,
E                 4.2909416370e-03,  2.8574203607e-03,  2.3792255670e-03,  4.8426981084e-04,  1.7831360456e-03,  8.3693105262e-04,  4.1714720428e-03,  3.4615453333e-03,  0.0000000000e+00,  1.3692971552e-03,  2.9608707409e-03],
E               [ 0.0000000000e+00,  0.0000000000e+00,  0.0000000000e+00,  3.4660808160e-04,  0.0000000000e+00,  1.3483430666e-04,  0.0000000000e+00,  6.6527783929e-05,  4.2637586012e-04,  0.0000000000e+00,  2.0297043375e-04,  1.8745809211e-04,  0.0000000000e+00,  0.0000000000e+00,  4.8298868933e-05,  0.0000000000e+00,
E                 2.5729348999e-04,  0.0000000000e+00,  0.0000000000e+00,  0.0000000000e+00,  0.0000000000e+00,  4.3533838470e-04,  0.0000000000e+00,  2.8892670525e-04,  0.0000000000e+00,  9.6132818726e-05,  1.2921080634e-04],
E               [ 3.0939036515e-03,  8.2832891494e-03,  1.2870236533e-03,  7.7504711226e-04,  1.0626323055e-03,  4.3296110816e-03,  1.2887135381e-04, -6.1300053494e-04,  5.8389460901e-04,  4.1752500692e-04,  2.3926366121e-03,  4.9534480786e-04, -3.5757632577e-04,  1.5251083532e-03,  0.0000000000e+00,  2.4306862615e-03,
E                 1.0658912361e-03,  1.5269919531e-03, -1.1623164755e-04,  2.3681162857e-03,  1.9047097303e-03,  1.5561352484e-03,  1.9056287128e-03,  1.7117161769e-03,  9.5889403019e-04,  2.8407264035e-03,  5.3852610290e-03],
E               [ 0.0000000000e+00,  0.0000000000e+00,  0.0000000000e+00,  0.0000000000e+00,  0.0000000000e+00,  0.0000000000e+00,  0.0000000000e+00,  0.0000000000e+00,  0.0000000000e+00,  0.0000000000e+00,  0.0000000000e+00,  0.0000000000e+00,  0.0000000000e+00,  0.0000000000e+00,  0.0000000000e+00,  0.0000000000e+00,
E                 0.0000000000e+00,  0.0000000000e+00,  0.0000000000e+00,  0.0000000000e+00,  0.0000000000e+00,  0.0000000000e+00,  0.0000000000e+00,  0.0000000000e+00,  0.0000000000e+00,  0.0000000000e+00,  0.0000000000e+00],
E               [-9.4243610511e-04,  8.5196481086e-04,  1.9158066716e-03,  7.1636983193e-04,  1.4600690920e-03,  3.0360266101e-03,  1.8454261590e-04,  4.1742931353e-04,  1.1317373719e-03, -2.6777635794e-03,  7.7434652485e-04,  9.6786773065e-04, -1.1782072252e-03,  1.4880883973e-03,  4.2541611037e-05,  5.6593323825e-04,
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E                 6.3124077860e-04, -1.7989965854e-03, -2.1661160281e-04, -3.1866499921e-04, -1.1967524188e-05,  3.3596418798e-03, -1.6879591858e-03,  1.5827936586e-03,  7.7789981151e-04, -1.9584260881e-03, -5.4573439993e-03],
E               [ 0.0000000000e+00,  4.9140371630e-06, -4.1346109356e-04,  3.0753196916e-04,  4.2908239993e-04,  2.1182966884e-04, -2.4843707797e-04, -1.7019909574e-03,  8.2122889580e-04, -5.4922467098e-04,  5.5391003843e-04, -5.3646836022e-05, -8.2888465840e-04,  0.0000000000e+00,  1.2345786672e-04,  2.1502359596e-04,
E                -7.3966843775e-04,  7.1642006515e-04, -6.2265014276e-04, -4.8854881243e-06,  3.0066809268e-04,  1.2316857465e-03, -1.3708855258e-03,  1.3640265679e-03,  0.0000000000e+00, -1.0552137974e-04,  3.5190783092e-04],
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E                 2.3666825145e-03,  0.0000000000e+00,  6.4850377385e-05,  0.0000000000e+00,  0.0000000000e+00,  7.8603159636e-05,  2.9340935871e-03, -6.5550586442e-04,  0.0000000000e+00,  1.7815716565e-03,  1.0001654737e-02],
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/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/compiled_graph_state.py:420: TypeError