diff --git a/_modules/dicee/models/adopt.html b/_modules/dicee/models/adopt.html new file mode 100644 index 00000000..750b5392 --- /dev/null +++ b/_modules/dicee/models/adopt.html @@ -0,0 +1,668 @@ + + + + + + + + dicee.models.adopt — DICE Embeddings 0.1.3.2 documentation + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +

Source code for dicee.models.adopt

+# CD: copy pasted from https://raw.githubusercontent.com/iShohei220/adopt/refs/heads/main/adopt.py
+# mypy: allow-untyped-decorators
+# mypy: allow-untyped-defs
+from typing import cast, Callable, List, Optional, Tuple, Union
+
+import torch
+from torch import Tensor
+
+from torch.optim.optimizer import (
+    _capturable_doc, # noqa: F401
+    _default_to_fused_or_foreach,
+    _device_dtype_check_for_fused,
+    _differentiable_doc, # noqa: F401
+    _disable_dynamo_if_unsupported,
+    _foreach_doc, # noqa: F401
+    _fused_doc, # noqa: F401
+    _get_capturable_supported_devices,
+    _get_scalar_dtype,
+    _get_value, # noqa: F401
+    _maximize_doc, # noqa: F401
+    _stack_if_compiling, # noqa: F401
+    _use_grad_for_differentiable,
+    _view_as_real,
+    DeviceDict, # noqa: F401
+    Optimizer,
+    ParamsT,
+)
+
+
+__all__ = ["ADOPT", "adopt"]
+
+
+

+[docs]
+class ADOPT(Optimizer):
+    def __init__(
+        self,
+        params: ParamsT,
+        lr: Union[float, Tensor] = 1e-3,
+        betas: Tuple[float, float] = (0.9, 0.9999),
+        eps: float = 1e-6,
+        clip_lambda: Optional[Callable[[int], float]] = lambda step: step**0.25,
+        weight_decay: float = 0.0,
+        decouple: bool = False,
+        *,
+        foreach: Optional[bool] = None,
+        maximize: bool = False,
+        capturable: bool = False,
+        differentiable: bool = False,
+        fused: Optional[bool] = None,
+    ):
+        if isinstance(lr, Tensor):
+            if foreach and not capturable:
+                raise ValueError(
+                    "lr as a Tensor is not supported for capturable=False and foreach=True"
+                )
+            if lr.numel() != 1:
+                raise ValueError("Tensor lr must be 1-element")
+        if not 0.0 <= lr:
+            raise ValueError(f"Invalid learning rate: {lr}")
+        if not 0.0 <= eps:
+            raise ValueError(f"Invalid epsilon value: {eps}")
+        if not 0.0 <= betas[0] < 1.0:
+            raise ValueError(f"Invalid beta parameter at index 0: {betas[0]}")
+        if not 0.0 <= betas[1] < 1.0:
+            raise ValueError(f"Invalid beta parameter at index 1: {betas[1]}")
+        if not 0.0 <= weight_decay:
+            raise ValueError(f"Invalid weight_decay value: {weight_decay}")
+
+        self.clip_lambda = clip_lambda
+
+        defaults = dict(
+            lr=lr,
+            betas=betas,
+            eps=eps,
+            weight_decay=weight_decay,
+            decouple=decouple,
+            maximize=maximize,
+            foreach=foreach,
+            capturable=capturable,
+            differentiable=differentiable,
+            fused=fused,
+        )
+        super().__init__(params, defaults)
+
+        if fused:
+            # TODO: support fused
+            raise RuntimeError("`fused` is not currently supported")
+
+            if differentiable:
+                raise RuntimeError("`fused` does not support `differentiable`")
+            self._step_supports_amp_scaling = True
+            # TODO(crcrpar): [low prec params & their higher prec copy]
+            # Support AMP with FP16/BF16 model params which would need
+            # higher prec copy of params to do update math in higher prec to
+            # alleviate the loss of information.
+            if foreach:
+                raise RuntimeError("`fused` and `foreach` cannot be `True` together.")
+
+

+[docs]
+    def __setstate__(self, state):
+        super().__setstate__(state)
+        for group in self.param_groups:
+            group.setdefault("maximize", False)
+            group.setdefault("foreach", None)
+            group.setdefault("capturable", False)
+            group.setdefault("differentiable", False)
+            fused = group.setdefault("fused", None)
+            for p in group["params"]:
+                p_state = self.state.get(p, [])
+                if len(p_state) != 0 and not torch.is_tensor(p_state["step"]):
+                    step_val = float(p_state["step"])
+                    p_state["step"] = (
+                        torch.tensor(
+                            step_val,
+                            dtype=_get_scalar_dtype(is_fused=fused),
+                            device=p.device,
+                        )
+                        if group["capturable"] or group["fused"]
+                        else torch.tensor(step_val, dtype=_get_scalar_dtype())
+                    )

+
+
+    def _init_group(
+        self,
+        group,
+        params_with_grad,
+        grads,
+        exp_avgs,
+        exp_avg_sqs,
+        state_steps,
+    ):
+        has_complex = False
+        for p in group["params"]:
+            if p.grad is not None:
+                has_complex |= torch.is_complex(p)
+                params_with_grad.append(p)
+                if p.grad.is_sparse:
+                    raise RuntimeError(
+                        "ADOPT does not support sparse gradients"
+                    )
+                grads.append(p.grad)
+
+                state = self.state[p]
+                # Lazy state initialization
+                if len(state) == 0:
+                    if group["fused"]:
+                        _device_dtype_check_for_fused(p)
+                    # note(crcrpar): [special device hosting for step]
+                    # Deliberately host `step` on CPU if both capturable and fused are off.
+                    # This is because kernel launches are costly on CUDA and XLA.
+                    state["step"] = (
+                        torch.zeros(
+                            (),
+                            dtype=_get_scalar_dtype(is_fused=group["fused"]),
+                            device=p.device,
+                        )
+                        if group["capturable"] or group["fused"]
+                        else torch.tensor(0.0, dtype=_get_scalar_dtype())
+                    )
+                    # Exponential moving average of gradient values
+                    state["exp_avg"] = torch.zeros_like(
+                        p, memory_format=torch.preserve_format
+                    )
+                    # Exponential moving average of squared gradient values
+                    state["exp_avg_sq"] = torch.zeros_like(
+                        p, memory_format=torch.preserve_format
+                    )
+
+                exp_avgs.append(state["exp_avg"])
+                exp_avg_sqs.append(state["exp_avg_sq"])
+
+                if group["differentiable"] and state["step"].requires_grad:
+                    raise RuntimeError(
+                        "`requires_grad` is not supported for `step` in differentiable mode"
+                    )
+
+                # Foreach without capturable does not support a tensor lr
+                if (
+                    group["foreach"]
+                    and torch.is_tensor(group["lr"])
+                    and not group["capturable"]
+                ):
+                    raise RuntimeError(
+                        "lr as a Tensor is not supported for capturable=False and foreach=True"
+                    )
+
+                state_steps.append(state["step"])
+        return has_complex
+
+

+[docs]
+    @_use_grad_for_differentiable
+    def step(self, closure=None):
+        """Perform a single optimization step.
+
+        Args:
+            closure (Callable, optional): A closure that reevaluates the model
+                and returns the loss.
+        """
+        self._cuda_graph_capture_health_check()
+
+        loss = None
+        if closure is not None:
+            with torch.enable_grad():
+                loss = closure()
+
+        for group in self.param_groups:
+            params_with_grad: List[Tensor] = []
+            grads: List[Tensor] = []
+            exp_avgs: List[Tensor] = []
+            exp_avg_sqs: List[Tensor] = []
+            state_steps: List[Tensor] = []
+            beta1, beta2 = group["betas"]
+
+            has_complex = self._init_group(
+                group,
+                params_with_grad,
+                grads,
+                exp_avgs,
+                exp_avg_sqs,
+                state_steps,
+            )
+
+            adopt(
+                params_with_grad,
+                grads,
+                exp_avgs,
+                exp_avg_sqs,
+                state_steps,
+                has_complex=has_complex,
+                beta1=beta1,
+                beta2=beta2,
+                lr=group["lr"],
+                clip_lambda=self.clip_lambda,
+                weight_decay=group["weight_decay"],
+                decouple=group["decouple"],
+                eps=group["eps"],
+                maximize=group["maximize"],
+                foreach=group["foreach"],
+                capturable=group["capturable"],
+                differentiable=group["differentiable"],
+                fused=group["fused"],
+                grad_scale=getattr(self, "grad_scale", None),
+                found_inf=getattr(self, "found_inf", None),
+            )
+
+        return loss

+

+
+
+
+def _single_tensor_adopt(
+    params: List[Tensor],
+    grads: List[Tensor],
+    exp_avgs: List[Tensor],
+    exp_avg_sqs: List[Tensor],
+    state_steps: List[Tensor],
+    grad_scale: Optional[Tensor],
+    found_inf: Optional[Tensor],
+    *,
+    has_complex: bool,
+    beta1: float,
+    beta2: float,
+    lr: Union[float, Tensor],
+    clip_lambda: Optional[Callable[[int], float]],
+    weight_decay: float,
+    decouple: bool,
+    eps: float,
+    maximize: bool,
+    capturable: bool,
+    differentiable: bool,
+):
+    assert grad_scale is None and found_inf is None
+
+    if torch.jit.is_scripting():
+        # this assert is due to JIT being dumb and not realizing that the ops below
+        # have overloads to handle both float and Tensor lrs, so we just assert it's
+        # a float since most people using JIT are using floats
+        assert isinstance(lr, float)
+
+    for i, param in enumerate(params):
+        grad = grads[i] if not maximize else -grads[i]
+        exp_avg = exp_avgs[i]
+        exp_avg_sq = exp_avg_sqs[i]
+        step_t = state_steps[i]
+
+        # If compiling, the compiler will handle cudagraph checks, see note [torch.compile x capturable]
+        if not torch._utils.is_compiling() and capturable:
+            capturable_supported_devices = _get_capturable_supported_devices()
+            assert (
+                param.device.type == step_t.device.type
+                and param.device.type in capturable_supported_devices
+            ), f"If capturable=True, params and state_steps must be on supported devices: {capturable_supported_devices}."
+
+        step = step_t if capturable or differentiable else _get_value(step_t)
+
+        if weight_decay != 0 and not decouple:
+            grad = grad.add(param, alpha=weight_decay)
+
+        if torch.is_complex(param):
+            grad = torch.view_as_real(grad)
+            if exp_avg is not None:
+                exp_avg = torch.view_as_real(exp_avg)
+            if exp_avg_sq is not None:
+                exp_avg_sq = torch.view_as_real(exp_avg_sq)
+            param = torch.view_as_real(param)
+
+        if step == 0:
+            exp_avg_sq.addcmul_(grad, grad.conj())
+            # update step
+            step_t += 1
+            continue
+
+        if weight_decay != 0 and decouple:
+            param.add_(param, alpha=-lr*weight_decay)
+
+        denom = torch.clamp(exp_avg_sq.sqrt(), eps)
+        normed_grad = grad.div(denom)
+        if clip_lambda is not None:
+            clip = clip_lambda(step)
+            normed_grad.clamp_(-clip, clip)
+
+        exp_avg.lerp_(normed_grad, 1 - beta1)
+
+        param.add_(exp_avg, alpha=-lr)
+        exp_avg_sq.mul_(beta2).addcmul_(grad, grad.conj(), value=1 - beta2)
+
+        # update step
+        step_t += 1
+
+
+def _multi_tensor_adopt(
+    params: List[Tensor],
+    grads: List[Tensor],
+    exp_avgs: List[Tensor],
+    exp_avg_sqs: List[Tensor],
+    state_steps: List[Tensor],
+    grad_scale: Optional[Tensor],
+    found_inf: Optional[Tensor],
+    *,
+    has_complex: bool,
+    beta1: float,
+    beta2: float,
+    lr: Union[float, Tensor],
+    clip_lambda: Optional[Callable[[int], float]],
+    weight_decay: float,
+    decouple: bool,
+    eps: float,
+    maximize: bool,
+    capturable: bool,
+    differentiable: bool,
+):
+    if len(params) == 0:
+        return
+
+    if isinstance(lr, Tensor) and not capturable:
+        raise RuntimeError(
+            "lr as a Tensor is not supported for capturable=False and foreach=True"
+        )
+
+    # If compiling, the compiler will handle cudagraph checks, see note [torch.compile x capturable]
+    if not torch._utils.is_compiling() and capturable:
+        capturable_supported_devices = _get_capturable_supported_devices(
+            supports_xla=False
+        )
+        assert all(
+            p.device.type == step.device.type
+            and p.device.type in capturable_supported_devices
+            for p, step in zip(params, state_steps)
+        ), f"If capturable=True, params and state_steps must be on supported devices: {capturable_supported_devices}."
+
+    assert grad_scale is None and found_inf is None
+
+    assert not differentiable, "_foreach ops don't support autograd"
+
+    grouped_tensors = Optimizer._group_tensors_by_device_and_dtype(
+        [params, grads, exp_avgs, exp_avg_sqs, state_steps]  # type: ignore[list-item]
+    )
+    for (
+        device_params_,
+        device_grads_,
+        device_exp_avgs_,
+        device_exp_avg_sqs_,
+        device_state_steps_,
+    ), _ in grouped_tensors.values():
+        device_params = cast(List[Tensor], device_params_)
+        device_grads = cast(List[Tensor], device_grads_)
+        device_exp_avgs = cast(List[Tensor], device_exp_avgs_)
+        device_exp_avg_sqs = cast(List[Tensor], device_exp_avg_sqs_)
+        device_state_steps = cast(List[Tensor], device_state_steps_)
+
+        # Handle complex parameters
+        if has_complex:
+            _view_as_real(
+                device_params, device_grads, device_exp_avgs, device_exp_avg_sqs
+            )
+
+        if maximize:
+            device_grads = torch._foreach_neg(device_grads)  # type: ignore[assignment]
+
+        if weight_decay != 0 and not decouple:
+            # Re-use the intermediate memory (device_grads) already allocated for maximize
+            if maximize:
+                torch._foreach_add_(device_grads, device_params, alpha=weight_decay)
+            else:
+                device_grads = torch._foreach_add(  # type: ignore[assignment]
+                    device_grads, device_params, alpha=weight_decay
+                )
+
+        if device_state_steps[0] == 0:
+            torch._foreach_addcmul_(device_exp_avg_sqs, device_grads, device_grads)
+
+            # Update steps
+            # If steps are on CPU, foreach will fall back to the slow path, which is a for-loop calling t.add(1) over
+            # and over. 1 will then be wrapped into a Tensor over and over again, which is slower than if we just
+            # wrapped it once now. The alpha is required to assure we go to the right overload.
+            if not torch._utils.is_compiling() and device_state_steps[0].is_cpu:
+                torch._foreach_add_(
+                    device_state_steps, torch.tensor(1.0, device="cpu"), alpha=1.0
+                )
+            else:
+                torch._foreach_add_(device_state_steps, 1)
+
+            continue
+
+        if weight_decay != 0 and decouple:
+            torch._foreach_add_(device_params, device_params, alpha=-lr*weight_decay)
+
+        exp_avg_sq_sqrt = torch._foreach_sqrt(device_exp_avg_sqs)
+        torch._foreach_maximum_(exp_avg_sq_sqrt, eps)
+
+        normed_grad = torch._foreach_div(device_grads, exp_avg_sq_sqrt)
+        if clip_lambda is not None:
+            clip = clip_lambda(device_state_steps[0])
+            torch._foreach_maximum_(normed_grad, -clip)
+            torch._foreach_minimum_(normed_grad, clip)
+
+        torch._foreach_lerp_(device_exp_avgs, normed_grad, 1 - beta1)
+
+        torch._foreach_add_(device_params, device_exp_avgs, alpha=-lr)
+        torch._foreach_mul_(device_exp_avg_sqs, beta2)
+        torch._foreach_addcmul_(
+            device_exp_avg_sqs, device_grads, device_grads, value=1 - beta2
+        )
+
+        # Update steps
+        # If steps are on CPU, foreach will fall back to the slow path, which is a for-loop calling t.add(1) over
+        # and over. 1 will then be wrapped into a Tensor over and over again, which is slower than if we just
+        # wrapped it once now. The alpha is required to assure we go to the right overload.
+        if not torch._utils.is_compiling() and device_state_steps[0].is_cpu:
+            torch._foreach_add_(
+                device_state_steps, torch.tensor(1.0, device="cpu"), alpha=1.0
+            )
+        else:
+            torch._foreach_add_(device_state_steps, 1)
+
+
+

+[docs]
+@_disable_dynamo_if_unsupported(single_tensor_fn=_single_tensor_adopt)
+def adopt(
+    params: List[Tensor],
+    grads: List[Tensor],
+    exp_avgs: List[Tensor],
+    exp_avg_sqs: List[Tensor],
+    state_steps: List[Tensor],
+    # kwonly args with defaults are not supported by functions compiled with torchscript issue #70627
+    # setting this as kwarg for now as functional API is compiled by torch/distributed/optim
+    foreach: Optional[bool] = None,
+    capturable: bool = False,
+    differentiable: bool = False,
+    fused: Optional[bool] = None,
+    grad_scale: Optional[Tensor] = None,
+    found_inf: Optional[Tensor] = None,
+    has_complex: bool = False,
+    *,
+    beta1: float,
+    beta2: float,
+    lr: Union[float, Tensor],
+    clip_lambda: Optional[Callable[[int], float]],
+    weight_decay: float,
+    decouple: bool,
+    eps: float,
+    maximize: bool,
+):
+    r"""Functional API that performs ADOPT algorithm computation.
+
+    """
+    # Respect when the user inputs False/True for foreach or fused. We only want to change
+    # the default when neither have been user-specified. Note that we default to foreach
+    # and pass False to use_fused. This is not a mistake--we want to give the fused impl
+    # bake-in time before making it the default, even if it is typically faster.
+    if fused is None and foreach is None:
+        _, foreach = _default_to_fused_or_foreach(
+            params, differentiable, use_fused=False
+        )
+        # Do not flip on foreach for the unsupported case where lr is a Tensor and capturable=False.
+        if foreach and isinstance(lr, Tensor) and not capturable:
+            foreach = False
+    if fused is None:
+        fused = False
+    if foreach is None:
+        foreach = False
+
+    # this check is slow during compilation, so we skip it
+    # if it's strictly needed we can add this check back in dynamo
+    if not torch._utils.is_compiling() and not all(
+        isinstance(t, torch.Tensor) for t in state_steps
+    ):
+        raise RuntimeError(
+            "API has changed, `state_steps` argument must contain a list of singleton tensors"
+        )
+
+    if foreach and torch.jit.is_scripting():
+        raise RuntimeError("torch.jit.script not supported with foreach optimizers")
+    if fused and torch.jit.is_scripting():
+        raise RuntimeError("torch.jit.script not supported with fused optimizers")
+
+    if fused and not torch.jit.is_scripting():
+        func = _fused_adopt # noqa: F821
+    elif foreach and not torch.jit.is_scripting():
+        func = _multi_tensor_adopt
+    else:
+        func = _single_tensor_adopt
+
+    func(
+        params,
+        grads,
+        exp_avgs,
+        exp_avg_sqs,
+        state_steps,
+        has_complex=has_complex,
+        beta1=beta1,
+        beta2=beta2,
+        lr=lr,
+        clip_lambda=clip_lambda,
+        weight_decay=weight_decay,
+        decouple=decouple,
+        eps=eps,
+        maximize=maximize,
+        capturable=capturable,
+        differentiable=differentiable,
+        grad_scale=grad_scale,
+        found_inf=found_inf,
+    )

+
+
+ +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/dicee/models/base_model.html b/_modules/dicee/models/base_model.html index 163da5ee..3d39cd84 100644 --- a/_modules/dicee/models/base_model.html +++ b/_modules/dicee/models/base_model.html @@ -94,6 +94,7 @@

Source code for dicee.models.base_model

 import torch
 from torch import nn
 from torch.nn import functional as F
+from .adopt import ADOPT
 
 

 [docs]
@@ -212,9 +213,12 @@ 
Source code for dicee.models.base_model
             self.selected_optimizer = torch.optim.SGD(params=parameters, lr=self.learning_rate,
                                                       momentum=0, dampening=0, weight_decay=self.weight_decay,
                                                       nesterov=False)
+
         elif self.optimizer_name == 'Adam':
             self.selected_optimizer = torch.optim.Adam(parameters, lr=self.learning_rate,
                                                        weight_decay=self.weight_decay)
+        elif self.optimizer_name == 'Adopt':
+            self.selected_optimizer = ADOPT(parameters, lr=self.learning_rate)
         elif self.optimizer_name == 'AdamW':
             self.selected_optimizer = torch.optim.AdamW(parameters, lr=self.learning_rate,
                                                        weight_decay=self.weight_decay)
@@ -230,7 +234,7 @@ 
Source code for dicee.models.base_model
                                                        lr=self.learning_rate, lambd=0.0001, alpha=0.75,
                                                        weight_decay=self.weight_decay)
         else:
-            raise KeyError()
+            raise KeyError(f"{self.optimizer_name} is not found!")
         print(self.selected_optimizer)
         return self.selected_optimizer

 

@@ -425,10 +429,8 @@ 
Source code for dicee.models.base_model
             self.normalizer_class = IdentityClass
         else:
             raise NotImplementedError()
-        if self.args.get("optim") in ['AdamW', 'Adam', 'SGD']:
-            self.optimizer_name = self.args['optim']
-        else:
-            self.optimizer_name = 'Adam'
+
+        self.optimizer_name = self.args['optim']
 
         if self.args.get("init_param") is None:
             self.param_init = IdentityClass
diff --git a/_modules/dicee/models/ensemble.html b/_modules/dicee/models/ensemble.html
new file mode 100644
index 00000000..6a77376f
--- /dev/null
+++ b/_modules/dicee/models/ensemble.html
@@ -0,0 +1,264 @@
+
+
+
+
+
+  
+  
+  dicee.models.ensemble — DICE Embeddings 0.1.3.2 documentation
+      
+      
+      
+      
+      
+      
+
+  
+    
+      
+      
+      
+      
+      
+    
+    
+     
+
+
+ 
+  

+    
+
+    

+
+      

+        

+          

+  
+  

+

+          

+           

+             
+  
Source code for dicee.models.ensemble
+import torch
+import copy
+
+import torch._dynamo
+
+torch._dynamo.config.suppress_errors = True
+
+
+

+[docs]
+class EnsembleKGE:
+    def __init__(self, seed_model):
+        self.models = []
+        self.optimizers = []
+        self.loss_history = []
+        for i in range(torch.cuda.device_count()):
+            i_model=copy.deepcopy(seed_model)
+            i_model.to(torch.device(f"cuda:{i}"))
+            # TODO: Why we cant send the compile model to cpu ?
+            # i_model = torch.compile(i_model)
+            self.optimizers.append(i_model.configure_optimizers())
+            self.models.append(i_model)
+        # Maybe use the original model's name ?
+        self.name="TP_"+self.models[0].name
+        self.train_mode=True
+
+

+[docs]
+    def named_children(self):
+        return self.models[0].named_children()

+
+    @property
+    def example_input_array(self):
+        return self.models[0].example_input_array
+    @property
+    def _trainer(self):
+        return self.models[0]._trainer
+
+

+[docs]
+    def parameters(self):
+        return [ x  for i in self.models for x in i.parameters()]

+
+        # return self.models[0].parameters()
+

+[docs]
+    def modules(self):
+        return [x for i in self.models for x in i.modules()]

+
+
+

+[docs]
+    def __iter__(self):
+        return (i for i in self.models)

+
+
+

+[docs]
+    def __len__(self):
+        return len(self.models)

+
+
+

+[docs]
+    def eval(self):
+        for model in self.models:
+            model.eval()
+        self.train_mode=False

+
+

+[docs]
+    def to(self,device):
+        for i in range(len(self.models)):
+            if device == "cpu":
+                self.models[i].cpu()
+            else:
+                raise NotImplementedError

+
+
+
+

+[docs]
+    def mem_of_model(self):
+        mem_of_ensemble={'EstimatedSizeMB': 0, 'NumParam': 0}
+        for i in self.models:
+            for k,v in i.mem_of_model().items():
+                mem_of_ensemble[k] += v
+        return mem_of_ensemble

+
+

+[docs]
+    def __call__(self,x_batch):
+
+        if self.train_mode is False:
+            yhat=0
+            for gpu_id, model in enumerate(self.models):
+                yhat += model(x_batch)
+            return yhat / len(self.models)
+        else:
+            for opt in self.optimizers:
+                opt.zero_grad()
+            yhat=None
+            for gpu_id, model in enumerate(self.models):
+                # Move batch into the GPU where the i.th model resides
+                if isinstance(x_batch, tuple):
+                    x_batch=(x_batch[0].to(f"cuda:{gpu_id}"),x_batch[1].to(f"cuda:{gpu_id}"))
+                else:
+                    x_batch=x_batch.to(f"cuda:{gpu_id}")
+                if yhat is None:
+                    yhat=model(x_batch)
+                else:
+                    yhat+=model(x_batch).to("cuda:0")
+            return yhat/len(self.models)

+
+    
+

+[docs]
+    def step(self):
+        for opt in self.optimizers:
+            opt.step()

+
+    
+    """
+    def __getattr__(self, name):
+        # Create a function that will call the same attribute/method on each model
+        def method(*args, **kwargs):
+            results = []
+            for model in self.models:
+                attr = getattr(model, name)
+                if callable(attr):
+                    # If it's a method, call it with provided arguments
+                    results.append(attr(*args, **kwargs))
+                else:
+                    # If it's an attribute, just get its value
+                    results.append(attr)
+            return results
+        return method
+    """
+

+[docs]
+    def __str__(self):
+        return f"EnsembleKGE of {len(self.models)} {self.models[0]}"

+

+
+

+
+           

+          

+          
+        

+      

+    

+  

+   
+
+
+
\ No newline at end of file
diff --git a/_modules/dicee/scripts/run.html b/_modules/dicee/scripts/run.html
index 89800321..d9165617 100644
--- a/_modules/dicee/scripts/run.html
+++ b/_modules/dicee/scripts/run.html
@@ -134,9 +134,9 @@ 
Source code for dicee.scripts.run
                         help="Available knowledge graph embedding models. "
                              "To use other knowledge graph embedding models available in python, e.g.,"
                              "**Pykeen_BoxE** and add this into choices")
-    parser.add_argument('--optim', type=str, default='Adam',
+    parser.add_argument('--optim', type=str, default='Adopt',
                         help='An optimizer',
-                        choices=['Adam', 'AdamW', 'SGD',"NAdam", "Adagrad", "ASGD"])
+                        choices=['Adam', 'AdamW', 'SGD',"NAdam", "Adagrad", "ASGD", "Adopt"])
     parser.add_argument('--embedding_dim', type=int, default=32,
                         help='Number of dimensions for an embedding vector. ')
     parser.add_argument("--num_epochs", type=int, default=10, help='Number of epochs for training. ')
@@ -147,8 +147,8 @@ 
Source code for dicee.scripts.run
                         default={},
                         help='{"PPE":{ "last_percent_to_consider": 10}}'
                              '"Perturb": {"level": "out", "ratio": 0.2, "method": "RN", "scaler": 0.3}')
-    parser.add_argument("--trainer", type=str, default='MP',
-                        choices=['torchCPUTrainer', 'PL', 'torchDDP', "MP"],
+    parser.add_argument("--trainer", type=str, default='TP',
+                        choices=['torchCPUTrainer', 'PL', 'torchDDP', "TP"],
                         help='PL (pytorch lightning trainer), torchDDP (custom ddp), torchCPUTrainer (custom cpu only), MP (Model Paralelisim)')
     parser.add_argument('--scoring_technique', default="KvsSample",
                         help="Training technique for knowledge graph embedding model",
diff --git a/_modules/dicee/static_funcs.html b/_modules/dicee/static_funcs.html
index 705af759..a38c7ef1 100644
--- a/_modules/dicee/static_funcs.html
+++ b/_modules/dicee/static_funcs.html
@@ -109,6 +109,7 @@ 
Source code for dicee.static_funcs
 import polars as pl
 import requests
 import csv
+from .models.ensemble import EnsembleKGE
 
 

 [docs]
@@ -415,6 +416,10 @@ 
Source code for dicee.static_funcs
             print(e)
             print(model.name)
             print('Could not save the model correctly')
+    elif isinstance(model, EnsembleKGE):
+        for i, partial_model in enumerate(model):
+            new_path=path.replace("model.pt",f"model_partial_{i}.pt")
+            torch.save(partial_model.state_dict(), new_path)
     else:
         torch.save(model.model.state_dict(), path)

 
@@ -437,13 +442,7 @@ 
Source code for dicee.static_funcs
     assert full_storage_path is not None
     assert isinstance(model_name, str)
     assert len(model_name) > 1
-
-    # (1) Save pytorch model in trained_model .
-    if hasattr(trained_model,"is_ensemble"):
-        for i,kge in enumerate(trained_model):
-            torch.save(kge.state_dict(), full_storage_path + f'/{model_name}_{i}.pt')
-    else:
-        save_checkpoint_model(model=trained_model, path=full_storage_path + f'/{model_name}.pt')
+    save_checkpoint_model(model=trained_model, path=full_storage_path + f'/{model_name}.pt')
 
     if save_embeddings_as_csv:
         entity_emb, relation_ebm = trained_model.get_embeddings()
diff --git a/_modules/dicee/static_funcs_training.html b/_modules/dicee/static_funcs_training.html
index bb045541..76907c94 100644
--- a/_modules/dicee/static_funcs_training.html
+++ b/_modules/dicee/static_funcs_training.html
@@ -138,13 +138,13 @@ 
Source code for dicee.static_funcs_training
                          torch.tensor(r).repeat(num_entities, ),
                          all_entities), dim=1)
 
-        predictions_tails = model.forward_triples(x)
+        predictions_tails = model(x)
         x = torch.stack((all_entities,
                          torch.tensor(r).repeat(num_entities, ),
                          torch.tensor(t).repeat(num_entities)
                          ), dim=1)
 
-        predictions_heads = model.forward_triples(x)
+        predictions_heads = model(x)
         del x
 
         # 3. Computed filtered ranks for missing tail entities.
diff --git a/_modules/dicee/static_preprocess_funcs.html b/_modules/dicee/static_preprocess_funcs.html
index c0dbae34..592d5e76 100644
--- a/_modules/dicee/static_preprocess_funcs.html
+++ b/_modules/dicee/static_preprocess_funcs.html
@@ -156,7 +156,7 @@ 
Source code for dicee.static_preprocess_funcs
sanity_checking_with_arguments(args)
     if args.model == 'Shallom':
         args.scoring_technique = 'KvsAll'
-    # TODO: we need need to define as "NONE ?
+
     if args.normalization == 'None':
         args.normalization = None
     assert args.normalization in [None, 'LayerNorm', 'BatchNorm1d']
diff --git a/_modules/dicee/trainer/dice_trainer.html b/_modules/dicee/trainer/dice_trainer.html
index 226742e5..08cd4be9 100644
--- a/_modules/dicee/trainer/dice_trainer.html
+++ b/_modules/dicee/trainer/dice_trainer.html
@@ -97,7 +97,8 @@ 
Source code for dicee.trainer.dice_trainer
 from dicee.dataset_classes import construct_dataset
 from .torch_trainer import TorchTrainer
 from .torch_trainer_ddp import TorchDDPTrainer
-from .model_parallelism import MP
+from .model_parallelism import TensorParallel
+from ..models.ensemble import EnsembleKGE
 from ..static_funcs import timeit
 import os
 import torch
@@ -107,74 +108,6 @@ 
Source code for dicee.trainer.dice_trainer
 from ..knowledge_graph import KG
 import numpy as np
 
-
-

-[docs]
-class EnsembleKGE:
-    """
-
-    """
-    def __init__(self, model):
-        self.models = []
-        self.optimizers=[]
-
-        for i in range(torch.cuda.device_count()):
-            i_model=copy.deepcopy(model)
-            i_model.to(torch.device(f"cuda:{i}"))
-            i_model = torch.compile(i_model)
-            self.optimizers.append(i_model.configure_optimizers())
-            self.models.append(i_model)
-
-

-[docs]
-    def __iter__(self):
-        return (i for i in self.models)

-
-
-

-[docs]
-    def __len__(self):
-        return len(self.models)

-
-
-

-[docs]
-    def __call__(self, *args, **kwargs):
-        # Forward
-        results = None
-        for model in self.models:
-            if results is None:
-                results=model(*args, **kwargs)
-            else:
-                results += model(*args, **kwargs)
-        return results/len(self.models)

-
-
-

-[docs]
-    def __getattr__(self, name):
-        # Create a function that will call the same attribute/method on each model
-        def method(*args, **kwargs):
-            results = []
-            for model in self.models:
-                attr = getattr(model, name)
-                if callable(attr):
-                    # If it's a method, call it with provided arguments
-                    results.append(attr(*args, **kwargs))
-                else:
-                    # If it's an attribute, just get its value
-                    results.append(attr)
-            return results
-        return method

-
-
-

-[docs]
-    def __str__(self):
-        return f"EnsembleKGE of {len(self.models)} {self.models[0]}"

-

-
-
 

 [docs]
 def load_term_mapping(file_path=str):
@@ -184,13 +117,13 @@ 
Source code for dicee.trainer.dice_trainer
 
 

 [docs]
-def initialize_trainer(args, callbacks)->TorchTrainer | MP | TorchDDPTrainer | pl.Trainer:
+def initialize_trainer(args, callbacks)->TorchTrainer | TensorParallel | TorchDDPTrainer | pl.Trainer:
     if args.trainer == 'torchCPUTrainer':
         print('Initializing TorchTrainer CPU Trainer...', end='\t')
         trainer = TorchTrainer(args, callbacks=callbacks)
-    elif args.trainer == 'MP':
-        print('Initializing MPTrainer...', end='\t')
-        trainer= MP(args, callbacks=callbacks)
+    elif args.trainer == 'TP':
+        print('Initializing TensorParallel...', end='\t')
+        trainer= TensorParallel(args, callbacks=callbacks)
     elif args.trainer == 'torchDDP':
         assert torch.cuda.is_available()
         print('Initializing TorchDDPTrainer GPU', end='\t')
@@ -351,7 +284,7 @@ 
Source code for dicee.trainer.dice_trainer
 

 [docs]
     @timeit
-    def initialize_trainer(self, callbacks: List) -> pl.Trainer | MP | TorchTrainer | TorchDDPTrainer:
+    def initialize_trainer(self, callbacks: List) -> pl.Trainer | TensorParallel | TorchTrainer | TorchDDPTrainer:
         """ Initialize Trainer from input arguments """
         return initialize_trainer(self.args, callbacks)

 
@@ -452,16 +385,19 @@ 
Source code for dicee.trainer.dice_trainer
         assert isinstance(knowledge_graph, np.memmap) or isinstance(knowledge_graph, KG), \
             f"knowledge_graph must be an instance of KG or np.memmap. Currently {type(knowledge_graph)}"
         if self.args.num_folds_for_cv == 0:
-            self.trainer: Union[MP, TorchTrainer, TorchDDPTrainer, pl.Trainer]
+            self.trainer: Union[TensorParallel, TorchTrainer, TorchDDPTrainer, pl.Trainer]
             self.trainer = self.initialize_trainer(callbacks=get_callbacks(self.args))
-
             model, form_of_labelling = self.initialize_or_load_model()
             self.trainer.evaluator = self.evaluator
             self.trainer.dataset = knowledge_graph
             self.trainer.form_of_labelling = form_of_labelling
-            if isinstance(self.trainer, MP):
-                model=EnsembleKGE(model)
-            self.trainer.fit(model, train_dataloaders=self.init_dataloader(self.init_dataset()))
+            # TODO: Later, maybe we should write a callback to save the models in disk
+
+            if isinstance(self.trainer, TensorParallel):
+                model = self.trainer.fit(model, train_dataloaders=self.init_dataloader(self.init_dataset()))
+                assert isinstance(model,EnsembleKGE)
+            else:
+                self.trainer.fit(model, train_dataloaders=self.init_dataloader(self.init_dataset()))
 
 
             return model, form_of_labelling
diff --git a/_modules/dicee/trainer/model_parallelism.html b/_modules/dicee/trainer/model_parallelism.html
index 4afffb35..f55b3749 100644
--- a/_modules/dicee/trainer/model_parallelism.html
+++ b/_modules/dicee/trainer/model_parallelism.html
@@ -91,78 +91,256 @@ 
Source code for dicee.trainer.model_parallelism
<
 import torch
 from ..abstracts import AbstractTrainer
 from ..static_funcs_training import make_iterable_verbose
+from ..models.ensemble import EnsembleKGE
+from typing import Tuple
 
-

-[docs]
-class MP(AbstractTrainer):
+

+[docs]
+def extract_input_outputs(z: list, device=None):
+    # pin arrays x,y, which allows us to move them to GPU asynchronously (non_blocking=True)
+    if len(z) == 2:
+        x_batch, y_batch = z
+        # pin arrays x,y, which allows us to move them to GPU asynchronously (non_blocking=True)
+        if device:
+            x_batch, y_batch = x_batch.to(device, non_blocking=True), y_batch.pin_memory().to(device,
+                                                                                              non_blocking=True)
+        return x_batch, y_batch
+    elif len(z) == 3:
+        x_batch, y_idx_batch, y_batch, = z
+        if device:
+            x_batch, y_batch, y_idx_batch = x_batch.pin_memory().to(device,
+                                                                    non_blocking=True), y_batch.pin_memory().to(
+                device, non_blocking=True), y_idx_batch.pin_memory().to(device, non_blocking=True)
+        return (x_batch, y_idx_batch), y_batch
+    else:
+        raise ValueError('Unexpected batch shape..')

+
+
+

+[docs]
+def find_good_batch_size(train_loader,ensemble_model,max_available_gpu_memory:float=0.05):
+    # () Initial batch size
+    batch_size=train_loader.batch_size
+    print("Automatic batch size finding")
+    for n in range(200):
+        # () Initialize a dataloader with a current batch_size
+        train_dataloaders = torch.utils.data.DataLoader(train_loader.dataset,
+                                                            batch_size=batch_size,
+                                                            shuffle=True,
+                                                            sampler=None,
+                                                            batch_sampler=None,
+                                                            num_workers=0,
+                                                            collate_fn=train_loader.dataset.collate_fn,
+                                                            pin_memory=False, drop_last=False,
+                                                            timeout=0,
+                                                            worker_init_fn=None,
+                                                            persistent_workers=False)
+        loss=None
+        for i, z in enumerate(train_dataloaders):
+            loss = forward_backward_update_loss(z,ensemble_model)
+            break
+        global_free_memory, total_memory = torch.cuda.mem_get_info()
+        available_gpu_memory = global_free_memory / total_memory
+        print(f"Random Batch Loss: {loss}\tFree/Total GPU Memory: {available_gpu_memory}\tBatch Size:{batch_size}")
+        # () Stepping criterion
+        if available_gpu_memory > max_available_gpu_memory and batch_size < len(train_loader.dataset) :
+            # Increment the current batch size
+            batch_size+=batch_size
+        else:
+            if batch_size >= len(train_loader.dataset):
+                print("Batch size equals to the training dataset size")
+            else:
+                print(f"Max GPU memory used\tFree/Total GPU Memory:{available_gpu_memory}")
+
+            return batch_size
 
+    raise RuntimeError("The computation should be here!")

+
+
+

+[docs]
+def forward_backward_update_loss(z:Tuple, ensemble_model):
+    # () Get the i-th batch of data points.
+    x_batch, y_batch = extract_input_outputs(z)
+    # () Move the batch of labels into the master GPU : GPU-0
+    y_batch = y_batch.to("cuda:0")
+    # () Forward Pass on the batch. Yhat located on the master GPU.
+    yhat = ensemble_model(x_batch)
+    # () Compute the loss
+    loss = torch.nn.functional.binary_cross_entropy_with_logits(yhat, y_batch)
+    # () Compute the gradient of the loss w.r.t. parameters.
+    loss.backward()
+    # () Parameter update.
+    ensemble_model.step()
+    # () Report the batch and epoch losses.
+    batch_loss = loss.item()
+    # () Accumulate batch loss
+    return batch_loss

+
+
+

+[docs]
+class TensorParallel(AbstractTrainer):
     def __init__(self, args, callbacks):
         super().__init__(args, callbacks)
-
-

-[docs]
+        self.models=[]
+

+[docs]
     def get_ensemble(self):
         return self.models

 
-

-[docs]
+
+

+[docs]
     def fit(self, *args, **kwargs):
         """ Train model        """
         assert len(args) == 1
-        models, = args
-        for i in models:
-            self.on_fit_start(self, i)
-
+        seed_model, = args
+        # () Init. ensemble model.
+        ensemble_model = EnsembleKGE(seed_model)
+        # () Run on_fit_start callbacks.
+        self.on_fit_start(self, ensemble_model)
+        # () Sanity checking
+        assert torch.cuda.device_count()== len(ensemble_model)
+        # ()
+        train_dataloader = kwargs['train_dataloaders']
+        # ()
+        train_dataloader = torch.utils.data.DataLoader(train_dataloader.dataset,
+                                                        batch_size=find_good_batch_size(train_dataloader, ensemble_model),
+                                                        shuffle=True,
+                                                        sampler=None,
+                                                        batch_sampler=None,
+                                                        num_workers=self.attributes.num_core,
+                                                        collate_fn=train_dataloader.dataset.collate_fn,
+                                                        pin_memory=False,
+                                                        drop_last=False,
+                                                        timeout=0,
+                                                        worker_init_fn=None,
+                                                        persistent_workers=False)
+        num_of_batches = len(train_dataloader)
+        # () Start training.
         for epoch in (tqdm_bar := make_iterable_verbose(range(self.attributes.num_epochs),
                                                         verbose=True, position=0, leave=True)):
             epoch_loss = 0
-            num_of_batches = len(kwargs['train_dataloaders'])
-            for i, z in enumerate(kwargs['train_dataloaders']):
-                source, targets = self.extract_input_outputs(z)
-                yhat = 0
-                # Perform forward for each model
-                for kge_model in models:
-                    source = tuple(_.to(kge_model.device) for _ in source) if isinstance(source, tuple) else source.to(kge_model.device)
-                    yhat+= kge_model(source).to("cpu")
-                # Normalize
-                yhat /=len(models)
-                loss = torch.nn.functional.binary_cross_entropy_with_logits(yhat, targets)
-
-                loss.backward()
-                for opt in models.optimizers:
-                    opt.step()
-                    opt.zero_grad(set_to_none=True)
-                batch_loss = loss.item()
+            # () Iterate over batches.
+            for i, z in enumerate(train_dataloader):
+                batch_loss = forward_backward_update_loss(z,ensemble_model)
                 epoch_loss += batch_loss
+
                 if hasattr(tqdm_bar, 'set_description_str'):
                     tqdm_bar.set_description_str(f"Epoch:{epoch + 1}")
                     if i > 0:
-                        tqdm_bar.set_postfix_str(f"batch={i} | {num_of_batches}, loss_step={batch_loss:.5f}, loss_epoch={epoch_loss / i:.5f}")
+                        tqdm_bar.set_postfix_str(
+                            f"batch={i} | {num_of_batches}, loss_step={batch_loss:.5f}, loss_epoch={epoch_loss / i:.5f}")
                     else:
                         tqdm_bar.set_postfix_str(f"loss_step={batch_loss:.5f}, loss_epoch={batch_loss:.5f}")
+            ensemble_model.loss_history.append(epoch_loss)
 
-        for kge_model in models:
-            self.on_fit_end(self, kge_model)

+        self.on_fit_end(self, ensemble_model)
+        # TODO: Later, maybe we should write a callback to save the models in disk
+        return ensemble_model

 
+    """
+    
+    def batchwisefit(self, *args, **kwargs):
+        assert len(args) == 1
+        model, = args
+        # (1) Run the fit the start callback.
+        self.on_fit_start(self, model)
+        # (2) Setup DDP.
+        optimizer = model.configure_optimizers()
+        num_gpus = torch.cuda.device_count()
+        for epoch in (tqdm_bar := make_iterable_verbose(range(self.attributes.num_epochs),
+                                                        verbose=True, position=0, leave=True)):
+            epoch_loss = 0
+            num_of_batches = len(kwargs['train_dataloaders'])
+            for i, (x_batch, y_batch) in enumerate(kwargs['train_dataloaders']):
+                # Define a large batch into small batches
+                x_splits = torch.chunk(x_batch, num_gpus)
+                y_splits = torch.chunk(y_batch, num_gpus)
 
+                # Forward pass. We need to paralelize it
+                gpu_losses = []
+                for gpu_id, (x_split, y_split) in enumerate(zip(x_splits, y_splits)):
+                    y_split = y_split.to(f"cuda:{gpu_id}")
+                    h_emb, r_emb, t_emb = model.get_triple_representation(x_split)
+                    h_emb, r_emb, t_emb = h_emb.pin_memory().to(f"cuda:{gpu_id}",
+                                                                non_blocking=True), r_emb.pin_memory().to(f"cuda:{gpu_id}", non_blocking=True), t_emb.pin_memory().to(f"cuda:{gpu_id}", non_blocking=True)
+                    yhat = model.score(h_emb, r_emb, t_emb)
+                    gpu_losses.append(torch.nn.functional.binary_cross_entropy_with_logits(yhat, y_split).to("cuda:0"))
 
-

-[docs]
-    def extract_input_outputs(self, z: list):
-        # pin arrays x,y, which allows us to move them to GPU asynchronously (non_blocking=True)
-        if len(z) == 2:
-            x_batch, y_batch = z
-            # pin arrays x,y, which allows us to move them to GPU asynchronously (non_blocking=True)
-            # x_batch, y_batch = x_batch.pin_memory().to(self.local_rank, non_blocking=True), y_batch.pin_memory().to(self.local_rank, non_blocking=True)
-            return x_batch, y_batch
-        elif len(z) == 3:
-            x_batch, y_idx_batch, y_batch, = z
-            # x_batch, y_batch,y_idx_batch = x_batch.pin_memory().to(self.local_rank, non_blocking=True), y_batch.pin_memory().to(self.local_rank, non_blocking=True),y_idx_batch.pin_memory().to(self.local_rank, non_blocking=True)
-            return (x_batch, y_idx_batch), y_batch
-        else:
-            raise ValueError('Unexpected batch shape..')

-

+                loss = sum(gpu_losses) / len(gpu_losses)
+
+                loss.backward()
+                batch_loss = loss.item()
+                optimizer.step()
+                optimizer.zero_grad(set_to_none=True)
+
+                epoch_loss += batch_loss
+
+                if hasattr(tqdm_bar, 'set_description_str'):
+                    tqdm_bar.set_description_str(f"Epoch:{epoch + 1}")
+                    if i > 0:
+                        tqdm_bar.set_postfix_str(
+                            f"batch={i} | {num_of_batches}, loss_step={batch_loss:.5f}, loss_epoch={epoch_loss / i:.5f}")
+                    else:
+                        tqdm_bar.set_postfix_str(f"loss_step={batch_loss:.5f}, loss_epoch={batch_loss:.5f}")
 
+    def torch_buggy_fit(self, *args, **kwargs):
+        assert len(args) == 1
+        model, = args
+        # () Run the fit the start callback.
+        self.on_fit_start(self, model)
+        # () Init Process Group with NCCL.
+        torch.distributed.init_process_group(backend="nccl")
+        # () Get Rank and World Size.
+        rank = dist.get_rank()
+        world_size = dist.get_world_size()
+        # () Reinitialize Rank based on manuel seed rank.
+        torch.manual_seed(rank)
+        model.param_init(model.entity_embeddings.weight.data) 
+        model.param_init(model.relation_embeddings.weight.data)
+        # () .
+        device = torch.device(f'cuda:{rank}')
+        model.to(device)
+        # () .
+        optimizer = model.configure_optimizers()
+        # () .
+        for epoch in (tqdm_bar := make_iterable_verbose(range(self.attributes.num_epochs),
+                                                        verbose=True, position=0, leave=True)):
+            epoch_loss = 0
+            num_of_batches = len(kwargs['train_dataloaders'])
+            # () .
+            for i, z in enumerate(kwargs['train_dataloaders']):
+                optimizer.zero_grad()
+                # () Get batch and move it on GPUs .
+                inputs,targets = extract_input_outputs(z,device)
+                # () Predict .
+                yhats = model(inputs)   
+                # () TODO: Pytorch Bug https://github.com/pytorch/pytorch/issues/58005 .
+                dist.all_reduce(yhats,op=dist.ReduceOp.SUM)
+                # () Compute loss .
+                loss = torch.nn.functional.binary_cross_entropy_with_logits(yhats, targets)
+                # () Backward .
+                loss.backward()
+                # () .
+                batch_loss = loss.item()
+                # () .
+                optimizer.step()
+                # () .
+                epoch_loss +=batch_loss
+                # () .
+                if rank==0 and hasattr(tqdm_bar, 'set_description_str'):
+                    tqdm_bar.set_description_str(f"Epoch:{epoch + 1}")
+                    if i > 0:
+                        tqdm_bar.set_postfix_str(f"batch={i} | {num_of_batches}, loss_step={batch_loss:.5f}, loss_epoch={epoch_loss / i:.5f}")
+                    else:
+                        tqdm_bar.set_postfix_str(f"loss_step={batch_loss:.5f}, loss_epoch={batch_loss:.5f}")
+        # () .
+        torch.distributed.destroy_process_group()
+        # () .
+        self.on_fit_end(self, model)
+    """

 
 

 
diff --git a/_modules/dicee/trainer/torch_trainer.html b/_modules/dicee/trainer/torch_trainer.html
index 19739a60..570c278a 100644
--- a/_modules/dicee/trainer/torch_trainer.html
+++ b/_modules/dicee/trainer/torch_trainer.html
@@ -96,176 +96,6 @@ 
Source code for dicee.trainer.torch_trainer
 import psutil
 from tqdm import tqdm
 
-

-[docs]
-class xMP(AbstractTrainer):
-    def __init__(self, args, callbacks):
-        super().__init__(args, callbacks)
-        self.loss_function = None
-        self.optimizer = None
-        self.model = None
-        self.train_dataloaders = None
-        self.training_step = None
-        torch.manual_seed(self.attributes.random_seed)
-        torch.cuda.manual_seed_all(self.attributes.random_seed)
-        assert torch.cuda.is_available(), "CUDA not available"
-        self.available_gpus = torch.cuda.device_count()
-        self.process = psutil.Process(os.getpid())
-
-    def _run_batch(self, i: int, x_batch, y_batch) -> float:
-        """
-            Forward anc Backward according to a mini-batch
-
-            Arguments
-           ----------
-           i : index of a batch
-           x_batch: torch.Tensor on selected device
-           y_batch: torch.Tensor on selected device
-           Returns
-           -------
-           batch loss (float)
-       """
-        if self.attributes.gradient_accumulation_steps > 1:
-            # (1) Update parameters every gradient_accumulation_steps mini-batch.
-            if i % self.attributes.gradient_accumulation_steps == 0:
-                self.optimizer.zero_grad(set_to_none=True)
-        else:
-            # (2) Do not accumulate gradient, zero the gradients per batch.
-            self.optimizer.zero_grad(set_to_none=True)
-        # (3) Loss Forward and Backward w.r.t the batch.
-        return self.forward_backward_update(x_batch, y_batch)
-
-

-[docs]
-    def fit(self, *args, train_dataloaders, **kwargs) -> None:
-        """
-            Training starts
-
-            Arguments
-           ----------
-           args:tuple
-           (BASEKGE,)
-           kwargs:Tuple
-               empty dictionary
-           Returns
-           -------
-           batch loss (float)
-       """
-        assert len(args) == 1
-        model, = args
-
-        import torch
-        import copy
-
-        self.models=[]
-        self.optimizers=[]
-        for i in range(0, self.available_gpus):
-            i_model=copy.deepcopy(model)
-            self.optimizers.append(model.configure_optimizers())
-            device = torch.device(f"cuda:{i}")
-            self.models.append(i_model.to(device))
-
-
-        del device
-        del i_model
-        # Create a copy of models
-        self.model = model
-        self.model.to(self.device)
-        self.train_dataloaders = train_dataloaders
-        self.loss_function = model.loss_function
-        # self.optimizer = self.model.configure_optimizers()
-        # self.training_step = self.model.training_step
-        # (1) Start running callbacks
-        # self.on_fit_start(self, self.model)
-
-        print(f'NumOfDataPoints:{len(self.train_dataloaders.dataset)} '
-              f'| NumOfEpochs:{self.attributes.max_epochs} '
-              f'| LearningRate:{self.model.learning_rate} '
-              f'| BatchSize:{self.train_dataloaders.batch_size} '
-              f'| EpochBatchsize:{len(train_dataloaders)}')
-
-        for epoch in (tqdm_bar := tqdm(range(self.attributes.max_epochs))):
-            epoch_loss = 0
-            i = 0
-            construct_mini_batch_time = None
-            batch: list
-            for i, batch in enumerate(self.train_dataloaders):
-                # (1) Extract Input and Outputs and set them on the dice
-                x_batch, y_batch = self.extract_input_outputs_set_device(batch)
-                start_time = time.time()
-                if construct_mini_batch_time:
-                    construct_mini_batch_time = start_time - construct_mini_batch_time
-                # (2) Forward-Backward-Update.
-                batch_loss = self._run_batch(i, x_batch, y_batch)
-                epoch_loss += batch_loss
-                tqdm_bar.set_description_str(f"Epoch:{epoch + 1}")
-                if i > 0:
-                    tqdm_bar.set_postfix_str(f"loss_step={batch_loss:.5f}, loss_epoch={epoch_loss / i:.5f}")
-                else:
-                    tqdm_bar.set_postfix_str(f"loss_step={batch_loss:.5f}, loss_epoch={batch_loss:.5f}")
-            avg_epoch_loss = epoch_loss / len(self.train_dataloaders)
-            self.model.loss_history.append(avg_epoch_loss)
-            self.on_train_epoch_end(self, self.model)
-        self.on_fit_end(self, self.model)

-
-
-

-[docs]
-    def forward_backward_update(self, x_batch: torch.Tensor, y_batch: torch.Tensor) -> torch.Tensor:
-        """
-            Compute forward, loss, backward, and parameter update
-
-            Arguments
-           ----------
-           x_batch:(torch.Tensor) mini-batch inputs
-           y_batch:(torch.Tensor) mini-batch outputs
-
-           Returns
-           -------
-           batch loss (float)
-       """
-        batch_loss = self.training_step(batch=(x_batch, y_batch))
-        batch_loss.backward()
-        self.optimizer.step()
-        return batch_loss.item()

-
-
-

-[docs]
-    def extract_input_outputs_set_device(self, batch: list) -> Tuple:
-        """
-            Construct inputs and outputs from a batch of inputs with outputs From a batch of inputs and put
-
-            Arguments
-           ----------
-           batch: (list) mini-batch inputs on CPU
-
-           Returns
-           -------
-           (tuple) mini-batch on select device
-       """
-        if len(batch) == 2:
-            x_batch, y_batch = batch
-
-            if isinstance(x_batch, tuple):
-                # Triple and Byte
-                return x_batch, y_batch
-            else:
-                # (1) NegSample: x is a triple, y is a float
-                x_batch, y_batch = batch
-                return x_batch.to(self.device), y_batch.to(self.device)
-        elif len(batch) == 3:
-            x_batch, y_idx_batch, y_batch, = batch
-            x_batch, y_idx_batch, y_batch = x_batch.to(self.device), y_idx_batch.to(self.device), y_batch.to(
-                self.device)
-            return (x_batch, y_idx_batch), y_batch
-        else:
-            print(len(batch))
-            print("Unexpected batch shape..")
-            raise RuntimeError

-

-
-
 

 [docs]
 class TorchTrainer(AbstractTrainer):
diff --git a/_modules/index.html b/_modules/index.html
index 1aff070c..35da74a7 100644
--- a/_modules/index.html
+++ b/_modules/index.html
@@ -97,10 +97,12 @@ 
All modules for which code is available

 
  • dicee.executer
    • 
 
  • dicee.knowledge_graph
    • 
 
  • dicee.knowledge_graph_embeddings
    • 
+
  • dicee.models.adopt
    • 
 
  • dicee.models.base_model
    • 
 
  • dicee.models.clifford
    • 
 
  • dicee.models.complex
    • 
 
  • dicee.models.dualE
    • 
+
  • dicee.models.ensemble
    • 
 
  • dicee.models.function_space
    • 
 
  • dicee.models.octonion
    • 
 
  • dicee.models.pykeen_models
    • 
diff --git a/_sources/autoapi/dicee/index.rst.txt b/_sources/autoapi/dicee/index.rst.txt
index 10d1f0bd..bf31fa70 100644
--- a/_sources/autoapi/dicee/index.rst.txt
+++ b/_sources/autoapi/dicee/index.rst.txt
@@ -66,6 +66,7 @@ Classes
    dicee.PykeenKGE
    dicee.BytE
    dicee.BaseKGE
+   dicee.EnsembleKGE
    dicee.DICE_Trainer
    dicee.KGE
    dicee.Execute
@@ -1988,6 +1989,67 @@ Package Contents
    .. py:method:: get_embeddings() -> Tuple[numpy.ndarray, numpy.ndarray]
 
 
+.. py:class:: EnsembleKGE(seed_model)
+
+   .. py:attribute:: models
+      :value: []
+
+
+
+   .. py:attribute:: optimizers
+      :value: []
+
+
+
+   .. py:attribute:: loss_history
+      :value: []
+
+
+
+   .. py:attribute:: name
+
+
+   .. py:attribute:: train_mode
+      :value: True
+
+
+
+   .. py:method:: named_children()
+
+
+   .. py:property:: example_input_array
+
+
+   .. py:method:: parameters()
+
+
+   .. py:method:: modules()
+
+
+   .. py:method:: __iter__()
+
+
+   .. py:method:: __len__()
+
+
+   .. py:method:: eval()
+
+
+   .. py:method:: to(device)
+
+
+   .. py:method:: mem_of_model()
+
+
+   .. py:method:: __call__(x_batch)
+
+
+   .. py:method:: step()
+
+
+   .. py:method:: __str__()
+
+
 .. py:function:: create_recipriocal_triples(x)
 
    Add inverse triples into dask dataframe
@@ -2183,7 +2245,7 @@ Package Contents
 
 
 
-   .. py:method:: initialize_trainer(callbacks: List) -> lightning.Trainer | dicee.trainer.model_parallelism.MP | dicee.trainer.torch_trainer.TorchTrainer | dicee.trainer.torch_trainer_ddp.TorchDDPTrainer
+   .. py:method:: initialize_trainer(callbacks: List) -> lightning.Trainer | dicee.trainer.model_parallelism.TensorParallel | dicee.trainer.torch_trainer.TorchTrainer | dicee.trainer.torch_trainer_ddp.TorchDDPTrainer
 
       Initialize Trainer from input arguments
 
diff --git a/_sources/autoapi/dicee/models/adopt/index.rst.txt b/_sources/autoapi/dicee/models/adopt/index.rst.txt
new file mode 100644
index 00000000..c791cc54
--- /dev/null
+++ b/_sources/autoapi/dicee/models/adopt/index.rst.txt
@@ -0,0 +1,67 @@
+dicee.models.adopt
+==================
+
+.. py:module:: dicee.models.adopt
+
+
+Classes
+-------
+
+.. autoapisummary::
+
+   dicee.models.adopt.ADOPT
+
+
+Functions
+---------
+
+.. autoapisummary::
+
+   dicee.models.adopt.adopt
+
+
+Module Contents
+---------------
+
+.. py:class:: ADOPT(params: torch.optim.optimizer.ParamsT, lr: Union[float, torch.Tensor] = 0.001, betas: Tuple[float, float] = (0.9, 0.9999), eps: float = 1e-06, clip_lambda: Optional[Callable[[int], float]] = lambda step: step**0.25, weight_decay: float = 0.0, decouple: bool = False, *, foreach: Optional[bool] = None, maximize: bool = False, capturable: bool = False, differentiable: bool = False, fused: Optional[bool] = None)
+
+   Bases: :py:obj:`torch.optim.optimizer.Optimizer`
+
+
+   Base class for all optimizers.
+
+   .. warning::
+       Parameters need to be specified as collections that have a deterministic
+       ordering that is consistent between runs. Examples of objects that don't
+       satisfy those properties are sets and iterators over values of dictionaries.
+
+   :param params: an iterable of :class:`torch.Tensor` s or
+                  :class:`dict` s. Specifies what Tensors should be optimized.
+   :type params: iterable
+   :param defaults: (dict): a dict containing default values of optimization
+                    options (used when a parameter group doesn't specify them).
+
+
+   .. py:attribute:: clip_lambda
+
+
+   .. py:method:: __setstate__(state)
+
+
+   .. py:method:: step(closure=None)
+
+      Perform a single optimization step.
+
+      :param closure: A closure that reevaluates the model
+                      and returns the loss.
+      :type closure: Callable, optional
+
+
+
+.. py:function:: adopt(params: List[torch.Tensor], grads: List[torch.Tensor], exp_avgs: List[torch.Tensor], exp_avg_sqs: List[torch.Tensor], state_steps: List[torch.Tensor], foreach: Optional[bool] = None, capturable: bool = False, differentiable: bool = False, fused: Optional[bool] = None, grad_scale: Optional[torch.Tensor] = None, found_inf: Optional[torch.Tensor] = None, has_complex: bool = False, *, beta1: float, beta2: float, lr: Union[float, torch.Tensor], clip_lambda: Optional[Callable[[int], float]], weight_decay: float, decouple: bool, eps: float, maximize: bool)
+
+   Functional API that performs ADOPT algorithm computation.
+
+
+
+
diff --git a/_sources/autoapi/dicee/models/ensemble/index.rst.txt b/_sources/autoapi/dicee/models/ensemble/index.rst.txt
new file mode 100644
index 00000000..7eb8b9b6
--- /dev/null
+++ b/_sources/autoapi/dicee/models/ensemble/index.rst.txt
@@ -0,0 +1,78 @@
+dicee.models.ensemble
+=====================
+
+.. py:module:: dicee.models.ensemble
+
+
+Classes
+-------
+
+.. autoapisummary::
+
+   dicee.models.ensemble.EnsembleKGE
+
+
+Module Contents
+---------------
+
+.. py:class:: EnsembleKGE(seed_model)
+
+   .. py:attribute:: models
+      :value: []
+
+
+
+   .. py:attribute:: optimizers
+      :value: []
+
+
+
+   .. py:attribute:: loss_history
+      :value: []
+
+
+
+   .. py:attribute:: name
+
+
+   .. py:attribute:: train_mode
+      :value: True
+
+
+
+   .. py:method:: named_children()
+
+
+   .. py:property:: example_input_array
+
+
+   .. py:method:: parameters()
+
+
+   .. py:method:: modules()
+
+
+   .. py:method:: __iter__()
+
+
+   .. py:method:: __len__()
+
+
+   .. py:method:: eval()
+
+
+   .. py:method:: to(device)
+
+
+   .. py:method:: mem_of_model()
+
+
+   .. py:method:: __call__(x_batch)
+
+
+   .. py:method:: step()
+
+
+   .. py:method:: __str__()
+
+
diff --git a/_sources/autoapi/dicee/models/index.rst.txt b/_sources/autoapi/dicee/models/index.rst.txt
index b405b773..9fe9a09c 100644
--- a/_sources/autoapi/dicee/models/index.rst.txt
+++ b/_sources/autoapi/dicee/models/index.rst.txt
@@ -10,10 +10,12 @@ Submodules
 .. toctree::
    :maxdepth: 1
 
+   /autoapi/dicee/models/adopt/index
    /autoapi/dicee/models/base_model/index
    /autoapi/dicee/models/clifford/index
    /autoapi/dicee/models/complex/index
    /autoapi/dicee/models/dualE/index
+   /autoapi/dicee/models/ensemble/index
    /autoapi/dicee/models/function_space/index
    /autoapi/dicee/models/octonion/index
    /autoapi/dicee/models/pykeen_models/index
@@ -28,6 +30,7 @@ Classes
 
 .. autoapisummary::
 
+   dicee.models.ADOPT
    dicee.models.BaseKGELightning
    dicee.models.BaseKGE
    dicee.models.IdentityClass
@@ -78,6 +81,41 @@ Functions
 Package Contents
 ----------------
 
+.. py:class:: ADOPT(params: torch.optim.optimizer.ParamsT, lr: Union[float, torch.Tensor] = 0.001, betas: Tuple[float, float] = (0.9, 0.9999), eps: float = 1e-06, clip_lambda: Optional[Callable[[int], float]] = lambda step: step**0.25, weight_decay: float = 0.0, decouple: bool = False, *, foreach: Optional[bool] = None, maximize: bool = False, capturable: bool = False, differentiable: bool = False, fused: Optional[bool] = None)
+
+   Bases: :py:obj:`torch.optim.optimizer.Optimizer`
+
+
+   Base class for all optimizers.
+
+   .. warning::
+       Parameters need to be specified as collections that have a deterministic
+       ordering that is consistent between runs. Examples of objects that don't
+       satisfy those properties are sets and iterators over values of dictionaries.
+
+   :param params: an iterable of :class:`torch.Tensor` s or
+                  :class:`dict` s. Specifies what Tensors should be optimized.
+   :type params: iterable
+   :param defaults: (dict): a dict containing default values of optimization
+                    options (used when a parameter group doesn't specify them).
+
+
+   .. py:attribute:: clip_lambda
+
+
+   .. py:method:: __setstate__(state)
+
+
+   .. py:method:: step(closure=None)
+
+      Perform a single optimization step.
+
+      :param closure: A closure that reevaluates the model
+                      and returns the loss.
+      :type closure: Callable, optional
+
+
+
 .. py:class:: BaseKGELightning(*args, **kwargs)
 
    Bases: :py:obj:`lightning.LightningModule`
diff --git a/_sources/autoapi/dicee/trainer/dice_trainer/index.rst.txt b/_sources/autoapi/dicee/trainer/dice_trainer/index.rst.txt
index 1ea68ec1..0082ca01 100644
--- a/_sources/autoapi/dicee/trainer/dice_trainer/index.rst.txt
+++ b/_sources/autoapi/dicee/trainer/dice_trainer/index.rst.txt
@@ -9,7 +9,6 @@ Classes
 
 .. autoapisummary::
 
-   dicee.trainer.dice_trainer.EnsembleKGE
    dicee.trainer.dice_trainer.DICE_Trainer
 
 
@@ -26,39 +25,9 @@ Functions
 Module Contents
 ---------------
 
-.. py:class:: EnsembleKGE(model)
-
-   
-
-
-   .. py:attribute:: models
-      :value: []
-
-
-
-   .. py:attribute:: optimizers
-      :value: []
-
-
-
-   .. py:method:: __iter__()
-
-
-   .. py:method:: __len__()
-
-
-   .. py:method:: __call__(*args, **kwargs)
-
-
-   .. py:method:: __getattr__(name)
-
-
-   .. py:method:: __str__()
-
-
 .. py:function:: load_term_mapping(file_path=str)
 
-.. py:function:: initialize_trainer(args, callbacks) -> dicee.trainer.torch_trainer.TorchTrainer | dicee.trainer.model_parallelism.MP | dicee.trainer.torch_trainer_ddp.TorchDDPTrainer | lightning.Trainer
+.. py:function:: initialize_trainer(args, callbacks) -> dicee.trainer.torch_trainer.TorchTrainer | dicee.trainer.model_parallelism.TensorParallel | dicee.trainer.torch_trainer_ddp.TorchDDPTrainer | lightning.Trainer
 
 .. py:function:: get_callbacks(args)
 
@@ -125,7 +94,7 @@ Module Contents
 
 
 
-   .. py:method:: initialize_trainer(callbacks: List) -> lightning.Trainer | dicee.trainer.model_parallelism.MP | dicee.trainer.torch_trainer.TorchTrainer | dicee.trainer.torch_trainer_ddp.TorchDDPTrainer
+   .. py:method:: initialize_trainer(callbacks: List) -> lightning.Trainer | dicee.trainer.model_parallelism.TensorParallel | dicee.trainer.torch_trainer.TorchTrainer | dicee.trainer.torch_trainer_ddp.TorchDDPTrainer
 
       Initialize Trainer from input arguments
 
diff --git a/_sources/autoapi/dicee/trainer/index.rst.txt b/_sources/autoapi/dicee/trainer/index.rst.txt
index 712f3832..b7361576 100644
--- a/_sources/autoapi/dicee/trainer/index.rst.txt
+++ b/_sources/autoapi/dicee/trainer/index.rst.txt
@@ -90,7 +90,7 @@ Package Contents
 
 
 
-   .. py:method:: initialize_trainer(callbacks: List) -> lightning.Trainer | dicee.trainer.model_parallelism.MP | dicee.trainer.torch_trainer.TorchTrainer | dicee.trainer.torch_trainer_ddp.TorchDDPTrainer
+   .. py:method:: initialize_trainer(callbacks: List) -> lightning.Trainer | dicee.trainer.model_parallelism.TensorParallel | dicee.trainer.torch_trainer.TorchTrainer | dicee.trainer.torch_trainer_ddp.TorchDDPTrainer
 
       Initialize Trainer from input arguments
 
diff --git a/_sources/autoapi/dicee/trainer/model_parallelism/index.rst.txt b/_sources/autoapi/dicee/trainer/model_parallelism/index.rst.txt
index 3ba28a7e..dbad67c3 100644
--- a/_sources/autoapi/dicee/trainer/model_parallelism/index.rst.txt
+++ b/_sources/autoapi/dicee/trainer/model_parallelism/index.rst.txt
@@ -9,13 +9,29 @@ Classes
 
 .. autoapisummary::
 
-   dicee.trainer.model_parallelism.MP
+   dicee.trainer.model_parallelism.TensorParallel
+
+
+Functions
+---------
+
+.. autoapisummary::
+
+   dicee.trainer.model_parallelism.extract_input_outputs
+   dicee.trainer.model_parallelism.find_good_batch_size
+   dicee.trainer.model_parallelism.forward_backward_update_loss
 
 
 Module Contents
 ---------------
 
-.. py:class:: MP(args, callbacks)
+.. py:function:: extract_input_outputs(z: list, device=None)
+
+.. py:function:: find_good_batch_size(train_loader, ensemble_model, max_available_gpu_memory: float = 0.05)
+
+.. py:function:: forward_backward_update_loss(z: Tuple, ensemble_model)
+
+.. py:class:: TensorParallel(args, callbacks)
 
    Bases: :py:obj:`dicee.abstracts.AbstractTrainer`
 
@@ -32,6 +48,11 @@ Module Contents
            ?
 
 
+   .. py:attribute:: models
+      :value: []
+
+
+
    .. py:method:: get_ensemble()
 
 
@@ -41,6 +62,3 @@ Module Contents
 
 
 
-   .. py:method:: extract_input_outputs(z: list)
-
-
diff --git a/_sources/autoapi/dicee/trainer/torch_trainer/index.rst.txt b/_sources/autoapi/dicee/trainer/torch_trainer/index.rst.txt
index 6340f1ae..7cf1e3f3 100644
--- a/_sources/autoapi/dicee/trainer/torch_trainer/index.rst.txt
+++ b/_sources/autoapi/dicee/trainer/torch_trainer/index.rst.txt
@@ -9,100 +9,12 @@ Classes
 
 .. autoapisummary::
 
-   dicee.trainer.torch_trainer.xMP
    dicee.trainer.torch_trainer.TorchTrainer
 
 
 Module Contents
 ---------------
 
-.. py:class:: xMP(args, callbacks)
-
-   Bases: :py:obj:`dicee.abstracts.AbstractTrainer`
-
-
-   Abstract class for Trainer class for knowledge graph embedding models
-
-
-   Parameter
-   ---------
-   args : str
-       ?
-
-   callbacks: list
-           ?
-
-
-   .. py:attribute:: loss_function
-      :value: None
-
-
-
-   .. py:attribute:: optimizer
-      :value: None
-
-
-
-   .. py:attribute:: model
-      :value: None
-
-
-
-   .. py:attribute:: train_dataloaders
-      :value: None
-
-
-
-   .. py:attribute:: training_step
-      :value: None
-
-
-
-   .. py:attribute:: available_gpus
-
-
-   .. py:attribute:: process
-
-
-   .. py:method:: fit(*args, train_dataloaders, **kwargs) -> None
-
-       Training starts
-
-       Arguments
-      ----------
-      args:tuple
-      (BASEKGE,)
-      kwargs:Tuple
-          empty dictionary
-      :rtype: batch loss (float)
-
-
-
-   .. py:method:: forward_backward_update(x_batch: torch.Tensor, y_batch: torch.Tensor) -> torch.Tensor
-
-       Compute forward, loss, backward, and parameter update
-
-       Arguments
-      ----------
-      x_batch:(torch.Tensor) mini-batch inputs
-      y_batch:(torch.Tensor) mini-batch outputs
-
-      :rtype: batch loss (float)
-
-
-
-   .. py:method:: extract_input_outputs_set_device(batch: list) -> Tuple
-
-       Construct inputs and outputs from a batch of inputs with outputs From a batch of inputs and put
-
-       Arguments
-      ----------
-      batch: (list) mini-batch inputs on CPU
-
-      :rtype: (tuple) mini-batch on select device
-
-
-
 .. py:class:: TorchTrainer(args, callbacks)
 
    Bases: :py:obj:`dicee.abstracts.AbstractTrainer`
diff --git a/autoapi/dicee/index.html b/autoapi/dicee/index.html
index 7775ecb8..2b0c3efd 100644
--- a/autoapi/dicee/index.html
+++ b/autoapi/dicee/index.html
@@ -384,6 +384,26 @@
 
  • BaseKGE.get_embeddings()
    • 
 
 
+
  • EnsembleKGE
+
    • 
 
  • create_recipriocal_triples()
    • 
 
  • get_er_vocab()
    • 
 
  • get_re_vocab()
    • 
@@ -777,49 +797,52 @@ 
    Classes
 
    BaseKGE
    
 
    Base class for all neural network modules.
    
 
-
    DICE_Trainer
    
+
    EnsembleKGE
    
+
    
+
+
    DICE_Trainer
    
 
    DICE_Trainer implement
    
 
-
    KGE
    
+
    KGE
    
 
    Knowledge Graph Embedding Class for interactive usage of pre-trained models
    
 
-
    Execute
    
+
    Execute
    
 
    A class for Training, Retraining and Evaluation a model.
    
 
-
    BPE_NegativeSamplingDataset
    
+
    BPE_NegativeSamplingDataset
    
 
    An abstract class representing a Dataset.
    
 
-
    MultiLabelDataset
    
+
    MultiLabelDataset
    
 
    An abstract class representing a Dataset.
    
 
-
    MultiClassClassificationDataset
    
+
    MultiClassClassificationDataset
    
 
    Dataset for the 1vsALL training strategy
    
 
-
    OnevsAllDataset
    
+
    OnevsAllDataset
    
 
    Dataset for the 1vsALL training strategy
    
 
-
    KvsAll
    
+
    KvsAll
    
 
    Creates a dataset for KvsAll training by inheriting from torch.utils.data.Dataset.
    
 
-
    AllvsAll
    
+
    AllvsAll
    
 
    Creates a dataset for AllvsAll training by inheriting from torch.utils.data.Dataset.
    
 
-
    OnevsSample
    
+
    OnevsSample
    
 
    A custom PyTorch Dataset class for knowledge graph embeddings, which includes
    
 
-
    KvsSampleDataset
    
+
    KvsSampleDataset
    
 
    KvsSample a Dataset:
    
 
-
    NegSampleDataset
    
+
    NegSampleDataset
    
 
    An abstract class representing a Dataset.
    
 
-
    TriplePredictionDataset
    
+
    TriplePredictionDataset
    
 
    Triple Dataset
    
 
-
    CVDataModule
    
+
    CVDataModule
    
 
    Create a Dataset for cross validation
    
 
-
    QueryGenerator
    
+
    QueryGenerator
    
 
    
 
 
@@ -3153,6 +3176,96 @@ 
    Output
 
 
 
+
    
+
    
+class dicee.EnsembleKGE(seed_model)[source]
    
+
    
+
    
+models = []
    
+
    
+
+
    
+
    
+optimizers = []
    
+
    
+
+
    
+
    
+loss_history = []
    
+
    
+
+
    
+
    
+name
    
+
    
+
+
    
+
    
+train_mode = True
    
+
    
+
+
    
+
    
+named_children()[source]
    
+
    
+
+
    
+
    
+property example_input_array
    
+
    
+
+
    
+
    
+parameters()[source]
    
+
    
+
+
    
+
    
+modules()[source]
    
+
    
+
+
    
+
    
+__iter__()[source]
    
+
    
+
+
    
+
    
+__len__()[source]
    
+
    
+
+
    
+
    
+eval()[source]
    
+
    
+
+
    
+
    
+to(device)[source]
    
+
    
+
+
    
+
    
+mem_of_model()[source]
    
+
    
+
+
    
+
    
+__call__(x_batch)[source]
    
+
    
+
+
    
+
    
+step()[source]
    
+
    
+
+
    
+
    
+__str__()[source]
    
+
    
+
+
    
+
 
    
 
    
 dicee.create_recipriocal_triples(x)[source]
    
@@ -3449,7 +3562,7 @@ 
    Parameter
    
 
    
-initialize_trainer(callbacks: List) → lightning.Trainer | dicee.trainer.model_parallelism.MP | dicee.trainer.torch_trainer.TorchTrainer | dicee.trainer.torch_trainer_ddp.TorchDDPTrainer[source]
    
+initialize_trainer(callbacks: List) → lightning.Trainer | dicee.trainer.model_parallelism.TensorParallel | dicee.trainer.torch_trainer.TorchTrainer | dicee.trainer.torch_trainer_ddp.TorchDDPTrainer[source]
 
    Initialize Trainer from input arguments
    
 
    
 
diff --git a/autoapi/dicee/models/adopt/index.html b/autoapi/dicee/models/adopt/index.html
new file mode 100644
index 00000000..4d40b83d
--- /dev/null
+++ b/autoapi/dicee/models/adopt/index.html
@@ -0,0 +1,259 @@
+
+
+
+
+
+  
+
+  
+  dicee.models.adopt — DICE Embeddings 0.1.3.2 documentation
+      
+      
+      
+      
+      
+      
+
+  
+    
+      
+      
+      
+      
+      
+    
+    
+    
+    
+     
+
+
+ 
+  
    
+    
+
+    
    
+
+      
    
+        
    
+          
    
+  
+  
    
+
    
+          
    
+           
    
+             
+  
    
+
    dicee.models.adopt
    
+
    
+
    Classes
    
+
+
+
+
+
+
+
    ADOPT
    Base class for all optimizers.
    
+
    
+
    
+
    Functions
    
+
+
+
+
+
+
+
    adopt(params, grads, exp_avgs, exp_avg_sqs, state_steps)
    Functional API that performs ADOPT algorithm computation.
    
+
    
+
    
+
    Module Contents
    
+
    
+
    
+class dicee.models.adopt.ADOPT(params: torch.optim.optimizer.ParamsT, lr: float | torch.Tensor = 0.001, betas: Tuple[float, float] = (0.9, 0.9999), eps: float = 1e-06, clip_lambda: Callable[[int], float] | None = lambda step: ..., weight_decay: float = 0.0, decouple: bool = False, *, foreach: bool | None = None, maximize: bool = False, capturable: bool = False, differentiable: bool = False, fused: bool | None = None)[source]
    
+
    Bases: torch.optim.optimizer.Optimizer
    
+
    Base class for all optimizers.
    
+
    
+
    Warning
    
+
    Parameters need to be specified as collections that have a deterministic
+ordering that is consistent between runs. Examples of objects that don’t
+satisfy those properties are sets and iterators over values of dictionaries.
    
+
    
+
    
+
    Parameters:
    
+
      
+
    • params (iterable) – an iterable of torch.Tensor s or
+dict s. Specifies what Tensors should be optimized.
      • 
+
    • defaults – (dict): a dict containing default values of optimization
+options (used when a parameter group doesn’t specify them).
      • 
+
    
+
    
+
    
+
    
+
    
+clip_lambda
    
+
    
+
+
    
+
    
+__setstate__(state)[source]
    
+
    
+
+
    
+
    
+step(closure=None)[source]
    
+
    Perform a single optimization step.
    
+
    
+
    Parameters:
    
+
    closure (Callable, optional) – A closure that reevaluates the model
+and returns the loss.
    
+
    
+
    
+
    
+
+
    
+
+
    
+
    
+dicee.models.adopt.adopt(params: List[torch.Tensor], grads: List[torch.Tensor], exp_avgs: List[torch.Tensor], exp_avg_sqs: List[torch.Tensor], state_steps: List[torch.Tensor], foreach: bool | None = None, capturable: bool = False, differentiable: bool = False, fused: bool | None = None, grad_scale: torch.Tensor | None = None, found_inf: torch.Tensor | None = None, has_complex: bool = False, *, beta1: float, beta2: float, lr: float | torch.Tensor, clip_lambda: Callable[[int], float] | None, weight_decay: float, decouple: bool, eps: float, maximize: bool)[source]
    
+
    Functional API that performs ADOPT algorithm computation.
    
+
    
+
+
    
+
    
+
+
+           
    
+          
    
+          
+        
    
+      
    
+    
    
+  
    
+   
+
+
+
\ No newline at end of file
diff --git a/autoapi/dicee/models/base_model/index.html b/autoapi/dicee/models/base_model/index.html
index 92080bdf..57437b9d 100644
--- a/autoapi/dicee/models/base_model/index.html
+++ b/autoapi/dicee/models/base_model/index.html
@@ -25,7 +25,7 @@
     
     
     
-     
+     
 
 
  
@@ -78,6 +78,7 @@
 
  • dicee.knowledge_graph_embeddings
    • 
 
  • dicee.models
    • 
 
diff --git a/autoapi/dicee/models/clifford/index.html b/autoapi/dicee/models/clifford/index.html
index 2ffbdb96..b46cf28c 100644
--- a/autoapi/dicee/models/clifford/index.html
+++ b/autoapi/dicee/models/clifford/index.html
@@ -79,6 +79,7 @@
 
  • dicee.knowledge_graph_embeddings
    • 
 
  • dicee.models
    • 
           
    
 
diff --git a/autoapi/dicee/models/index.html b/autoapi/dicee/models/index.html
index d583a295..6b05ff31 100644
--- a/autoapi/dicee/models/index.html
+++ b/autoapi/dicee/models/index.html
@@ -25,7 +25,7 @@
     
     
     
-    
+    
      
 
 
@@ -79,10 +79,12 @@
 
  • dicee.knowledge_graph_embeddings
    • 
 
  • dicee.models
    • 
+      
  • find_good_batch_size() (in module dicee.trainer.model_parallelism)
+
    • 
       
  • find_missing_triples() (dicee.KGE method)
 
       
    • 
-      
  • fit() (dicee.trainer.model_parallelism.MP method)
+      
  • fit() (dicee.trainer.model_parallelism.TensorParallel method)
 
       
    • 
       
  • forward_backward_update() (dicee.trainer.torch_trainer.TorchTrainer method)
-
-      
    • 
+      
  • forward_backward_update_loss() (in module dicee.trainer.model_parallelism)
+
    • 
       
  • forward_byte_pair_encoded_k_vs_all() (dicee.BaseKGE method)
 
       
    • 
-      
  • get_ensemble() (dicee.trainer.model_parallelism.MP method)
+      
  • get_ensemble() (dicee.trainer.model_parallelism.TensorParallel method)
 
    • 
       
  • get_entity_embeddings() (dicee.abstracts.BaseInteractiveKGE method)
 
    • 
@@ -2823,11 +2871,7 @@ 
    L
    
       
  • loss_func (dicee.trainer.torch_trainer_ddp.NodeTrainer attribute)
 
    • 
       
  • loss_function (dicee.trainer.torch_trainer.TorchTrainer attribute)
-
-      
    • 
       
  • loss_function() (dicee.BytE method)
 
       
    • 
       
  • model_name (dicee.analyse_experiments.Experiment attribute)
 
    • 
-      
  • models (dicee.trainer.dice_trainer.EnsembleKGE attribute)
+      
  • models (dicee.EnsembleKGE attribute)
+
+      
    • 
       
  • 
     module
 
@@ -3009,6 +3065,8 @@ 
    M
    
         
  • dicee.knowledge_graph_embeddings
 
    • 
         
  • dicee.models
+
    • 
+        
  • dicee.models.adopt
 
    • 
         
  • dicee.models.base_model
 
    • 
@@ -3017,6 +3075,8 @@ 
    M
    
         
  • dicee.models.complex
 
    • 
         
  • dicee.models.dualE
+
    • 
+        
  • dicee.models.ensemble
 
    • 
         
  • dicee.models.function_space
 
    • 
@@ -3073,8 +3133,12 @@ 
    M
    
       
   
   
+  
+  
-  
-      
  • optimizers (dicee.trainer.dice_trainer.EnsembleKGE attribute)
+      
  • optimizers (dicee.EnsembleKGE attribute)
+
+      
    • 
       
  • ordered_bpe_entities (dicee.BPE_NegativeSamplingDataset attribute)
 
       
      
@@ -3728,7 +3804,13 @@ 
      P
      
 
       
    • 
       
  • parameters() (dicee.abstracts.BaseInteractiveKGE method)
+
+      
    • 
       
  • path (dicee.abstracts.AbstractPPECallback attribute)
 
       
    • 
-      
  • pq (dicee.analyse_experiments.Experiment attribute)
-
    • 
   
   
-  
+  
+      
  • step() (dicee.EnsembleKGE method)
+
+      
    • 
       
  • storage_path (dicee.config.Namespace attribute)
@@ -4531,6 +4619,8 @@ 
    T
    
         
  • (dicee.knowledge_graph_embeddings.KGE method)
 
    • 
       
+      
  • TensorParallel (class in dicee.trainer.model_parallelism)
+
    • 
       
  • test_dataloader() (dicee.models.base_model.BaseKGELightning method)
 
       
    • 
-      
  • to() (dicee.KGE method)
+      
  • to() (dicee.EnsembleKGE method)
 
       
    • 
       
  • to_df() (dicee.analyse_experiments.Experiment method)
@@ -4636,21 +4730,17 @@ 
    T
    
 
    • 
       
       
  • train_dataloaders (dicee.trainer.torch_trainer.TorchTrainer attribute)
-
-      
    • 
       
  • train_dataset_loader (dicee.trainer.torch_trainer_ddp.NodeTrainer attribute)
 
    • 
       
  • train_h1 (dicee.analyse_experiments.Experiment attribute)
 
    • 
       
  • train_h10 (dicee.analyse_experiments.Experiment attribute)
-
    • 
-      
  • train_h3 (dicee.analyse_experiments.Experiment attribute)
 
    • 
   
   
-  
 
 
 
diff --git a/objects.inv b/objects.inv
index b355c5c8..d7d186d9 100644
Binary files a/objects.inv and b/objects.inv differ
diff --git a/py-modindex.html b/py-modindex.html
index 4325cdcb..a5fbcc37 100644
--- a/py-modindex.html
+++ b/py-modindex.html
@@ -166,6 +166,11 @@ 
    Python Module Index
    
           
        dicee.models
        
+     
+       
+          
+       dicee.models.adopt
+       
      
        
           
@@ -186,6 +191,11 @@ 
    Python Module Index
    
           
        dicee.models.dualE
        
+     
+       
+          
+       dicee.models.ensemble
+       
      
        
           
diff --git a/searchindex.js b/searchindex.js
index 301b92c1..ad6dfab2 100644
--- a/searchindex.js
+++ b/searchindex.js
@@ -1 +1 @@
-Search.setIndex({"alltitles": {"10FOLD CV results on California housing dataset (regression).": [[1, "fold-cv-results-on-california-housing-dataset-regression"]], "10FOLD CV results on TODO (binary classification).": [[1, "fold-cv-results-on-todo-binary-classification"]], "1vsAll Training Technique": [[46, null]], "Answering Complex Queries": [[49, "answering-complex-queries"]], "Attributes": [[11, "attributes"], [36, "attributes"], [39, "attributes"]], "Available KGE Models": [[50, "available-kge-models"]], "Benchmarking": [[1, "benchmarking"]], "Classes": [[3, "classes"], [4, "classes"], [5, "classes"], [6, "classes"], [7, "classes"], [9, "classes"], [10, "classes"], [11, "classes"], [12, "classes"], [13, "classes"], [14, "classes"], [15, "classes"], [16, "classes"], [17, "classes"], [18, "classes"], [19, "classes"], [20, "classes"], [21, "classes"], [22, "classes"], [23, "classes"], [25, "classes"], [26, "classes"], [27, "classes"], [28, "classes"], [29, "classes"], [30, "classes"], [36, "classes"], [40, "classes"], [41, "classes"], [42, "classes"], [43, "classes"], [44, "classes"]], "Contents:": [[45, null]], "Coverage Report": [[49, "coverage-report"]], "Creating an Embedding Vector Database": [[49, "creating-an-embedding-vector-database"]], "Dicee Manual": [[49, null]], "Docker": [[49, "docker"]], "Download Knowledge Graphs": [[49, "download-knowledge-graphs"]], "Download files": [[0, "download-files"]], "Downloading Pretrained Models": [[49, "downloading-pretrained-models"]], "Downloading and Preprocessing DBpedia 2022-03 for Knowledge Graph Embeddings": [[0, null]], "Example Usage:": [[31, "example-usage"]], "Examples": [[50, "examples"]], "Examples for Sharded Training": [[50, "examples-for-sharded-training"]], "Filtering Triples": [[0, "filtering-triples"]], "Functions": [[4, "functions"], [5, "functions"], [7, "functions"], [8, "functions"], [11, "functions"], [19, "functions"], [20, "functions"], [22, "functions"], [24, "functions"], [31, 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false]], "dicee.trainer.model_parallelism": [[42, "module-dicee.trainer.model_parallelism", false]], "dicee.trainer.torch_trainer": [[43, "module-dicee.trainer.torch_trainer", false]], "dicee.trainer.torch_trainer_ddp": [[44, "module-dicee.trainer.torch_trainer_ddp", false]], "discrete_points (dicee.models.fmult2 attribute)": [[19, "dicee.models.FMult2.discrete_points", false]], "discrete_points (dicee.models.function_space.fmult2 attribute)": [[18, "dicee.models.function_space.FMult2.discrete_points", false]], "dist_func (dicee.models.pyke attribute)": [[19, "dicee.models.Pyke.dist_func", false]], "dist_func (dicee.models.real.pyke attribute)": [[23, "dicee.models.real.Pyke.dist_func", false]], "dist_func (dicee.pyke attribute)": [[11, "dicee.Pyke.dist_func", false]], "distmult (class in dicee)": [[11, "dicee.DistMult", false]], "distmult (class in dicee.models)": [[19, "dicee.models.DistMult", false]], "distmult (class in dicee.models.real)": [[23, "dicee.models.real.DistMult", false]], "download_file() (in module dicee)": [[11, "dicee.download_file", false]], "download_file() (in module dicee.static_funcs)": [[37, "dicee.static_funcs.download_file", false]], "download_files_from_url() (in module dicee)": [[11, "dicee.download_files_from_url", false]], "download_files_from_url() (in module dicee.static_funcs)": [[37, "dicee.static_funcs.download_files_from_url", false]], "download_pretrained_model() (in module dicee)": [[11, "dicee.download_pretrained_model", false]], "download_pretrained_model() (in module dicee.static_funcs)": [[37, "dicee.static_funcs.download_pretrained_model", false]], "dropout (dicee.models.transformers.causalselfattention attribute)": [[25, "dicee.models.transformers.CausalSelfAttention.dropout", false]], "dropout (dicee.models.transformers.gptconfig attribute)": [[25, "dicee.models.transformers.GPTConfig.dropout", false]], "dropout (dicee.models.transformers.mlp attribute)": [[25, "dicee.models.transformers.MLP.dropout", false]], "duale (class in dicee)": [[11, "dicee.DualE", false]], "duale (class in dicee.models)": [[19, "dicee.models.DualE", false]], "duale (class in dicee.models.duale)": [[17, "dicee.models.dualE.DualE", false]], "dummy_eval() (dicee.evaluator.evaluator method)": [[9, "dicee.evaluator.Evaluator.dummy_eval", false]], "dummy_id (dicee.knowledge_graph.kg attribute)": [[12, "dicee.knowledge_graph.KG.dummy_id", false]], "during_training (dicee.evaluator.evaluator attribute)": [[9, "dicee.evaluator.Evaluator.during_training", false]], "ee_vocab (dicee.evaluator.evaluator attribute)": [[9, "dicee.evaluator.Evaluator.ee_vocab", false]], "efficient_zero_grad() (in module dicee.static_funcs_training)": [[38, "dicee.static_funcs_training.efficient_zero_grad", false]], "embedding_dim (dicee.analyse_experiments.experiment attribute)": [[4, "dicee.analyse_experiments.Experiment.embedding_dim", false]], "embedding_dim (dicee.basekge attribute)": [[11, "dicee.BaseKGE.embedding_dim", false]], "embedding_dim 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"conv2d (dicee.models.complex.conex attribute)": [[17, "dicee.models.complex.ConEx.conv2d", false]], "conv2d (dicee.models.conex attribute)": [[21, "dicee.models.ConEx.conv2d", false]], "conv2d (dicee.models.convo attribute)": [[21, "dicee.models.ConvO.conv2d", false]], "conv2d (dicee.models.convq attribute)": [[21, "dicee.models.ConvQ.conv2d", false]], "conv2d (dicee.models.octonion.aconvo attribute)": [[22, "dicee.models.octonion.AConvO.conv2d", false]], "conv2d (dicee.models.octonion.convo attribute)": [[22, "dicee.models.octonion.ConvO.conv2d", false]], "conv2d (dicee.models.quaternion.aconvq attribute)": [[24, "dicee.models.quaternion.AConvQ.conv2d", false]], "conv2d (dicee.models.quaternion.convq attribute)": [[24, "dicee.models.quaternion.ConvQ.conv2d", false]], "convo (class in dicee)": [[11, "dicee.ConvO", false]], "convo (class in dicee.models)": [[21, "dicee.models.ConvO", false]], "convo (class in dicee.models.octonion)": [[22, "dicee.models.octonion.ConvO", false]], "convq 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"dicee.read_preprocess_save_load_kg.preprocess": [[30, "module-dicee.read_preprocess_save_load_kg.preprocess", false]], "dicee.read_preprocess_save_load_kg.read_from_disk": [[31, "module-dicee.read_preprocess_save_load_kg.read_from_disk", false]], "dicee.read_preprocess_save_load_kg.save_load_disk": [[32, "module-dicee.read_preprocess_save_load_kg.save_load_disk", false]], "dicee.read_preprocess_save_load_kg.util": [[33, "module-dicee.read_preprocess_save_load_kg.util", false]], "dicee.sanity_checkers": [[34, "module-dicee.sanity_checkers", false]], "dicee.scripts": [[35, "module-dicee.scripts", false]], "dicee.scripts.index": [[36, "module-dicee.scripts.index", false]], "dicee.scripts.run": [[37, "module-dicee.scripts.run", false]], "dicee.scripts.serve": [[38, "module-dicee.scripts.serve", false]], "dicee.static_funcs": [[39, "module-dicee.static_funcs", false]], "dicee.static_funcs_training": [[40, "module-dicee.static_funcs_training", false]], "dicee.static_preprocess_funcs": [[41, "module-dicee.static_preprocess_funcs", false]], "dicee.trainer": [[43, "module-dicee.trainer", false]], "dicee.trainer.dice_trainer": [[42, "module-dicee.trainer.dice_trainer", false]], "dicee.trainer.model_parallelism": [[44, "module-dicee.trainer.model_parallelism", false]], "dicee.trainer.torch_trainer": [[45, "module-dicee.trainer.torch_trainer", false]], "dicee.trainer.torch_trainer_ddp": [[46, "module-dicee.trainer.torch_trainer_ddp", false]], "discrete_points (dicee.models.fmult2 attribute)": [[21, "dicee.models.FMult2.discrete_points", false]], "discrete_points (dicee.models.function_space.fmult2 attribute)": [[20, "dicee.models.function_space.FMult2.discrete_points", false]], "dist_func (dicee.models.pyke attribute)": [[21, "dicee.models.Pyke.dist_func", false]], "dist_func (dicee.models.real.pyke attribute)": [[25, "dicee.models.real.Pyke.dist_func", false]], "dist_func (dicee.pyke attribute)": [[11, "dicee.Pyke.dist_func", false]], "distmult (class in dicee)": [[11, 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"ensemblekge (class in dicee)": [[11, "dicee.EnsembleKGE", false]], "ensemblekge (class in dicee.models.ensemble)": [[19, "dicee.models.ensemble.EnsembleKGE", false]], "ent2id (dicee.query_generator.querygenerator attribute)": [[28, "dicee.query_generator.QueryGenerator.ent2id", false]], "ent2id (dicee.querygenerator attribute)": [[11, "dicee.QueryGenerator.ent2id", false]], "ent_in (dicee.query_generator.querygenerator attribute)": [[28, "dicee.query_generator.QueryGenerator.ent_in", false]], "ent_in (dicee.querygenerator attribute)": [[11, "dicee.QueryGenerator.ent_in", false]], "ent_out (dicee.query_generator.querygenerator attribute)": [[28, "dicee.query_generator.QueryGenerator.ent_out", false]], "ent_out (dicee.querygenerator attribute)": [[11, "dicee.QueryGenerator.ent_out", false]], "entities_str (dicee.knowledge_graph.kg property)": [[12, "dicee.knowledge_graph.KG.entities_str", false]], "entity_embeddings (dicee.aconvq attribute)": [[11, "dicee.AConvQ.entity_embeddings", false]], "entity_embeddings (dicee.convq attribute)": [[11, "dicee.ConvQ.entity_embeddings", false]], "entity_embeddings (dicee.decal attribute)": [[11, "dicee.DeCaL.entity_embeddings", false]], "entity_embeddings (dicee.duale attribute)": [[11, "dicee.DualE.entity_embeddings", false]], "entity_embeddings (dicee.lfmult attribute)": [[11, "dicee.LFMult.entity_embeddings", false]], "entity_embeddings (dicee.models.aconvq attribute)": [[21, "dicee.models.AConvQ.entity_embeddings", false]], "entity_embeddings (dicee.models.clifford.decal attribute)": [[16, "dicee.models.clifford.DeCaL.entity_embeddings", false]], "entity_embeddings (dicee.models.convq attribute)": [[21, "dicee.models.ConvQ.entity_embeddings", false]], "entity_embeddings (dicee.models.decal attribute)": [[21, "dicee.models.DeCaL.entity_embeddings", false]], "entity_embeddings (dicee.models.duale attribute)": [[21, "dicee.models.DualE.entity_embeddings", false]], "entity_embeddings (dicee.models.duale.duale attribute)": 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