Adding all project files

venv/Lib/site-packages/torch/distributed/optim/functional_adamw.py

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+# mypy: allow-untyped-defs
+from typing import Optional
+
+import torch
+import torch.optim._functional as F
+from torch import Tensor
+from torch.distributed.optim._deprecation_warning import (
+    _scripted_functional_optimizer_deprecation_warning,
+)
+
+
+__all__: list[str] = []
+
+
+# Define a TorchScript compatible Functional AdamW Optimizer
+# where we use these optimizer in a functional way.
+# Instead of using the `param.grad` when updating parameters,
+# we explicitly allow the distributed optimizer pass gradients to
+# the `step` function. In this way, we could separate the gradients
+# and parameters and allow multithreaded trainer to update the
+# parameters without data traces on accumulating to the same .grad.
+# NOTE: This should be only used by distributed optimizer internals
+# and not meant to expose to the user.
+@torch.jit.script
+class _FunctionalAdamW:
+    def __init__(
+        self,
+        params: list[Tensor],
+        lr: float = 1e-3,
+        betas: tuple[float, float] = (0.9, 0.999),
+        eps: float = 1e-8,
+        weight_decay: float = 1e-2,
+        amsgrad: bool = False,
+        maximize: bool = False,
+        foreach: bool = False,
+        fused: bool = False,
+        _allow_empty_param_list: bool = False,
+    ):
+        _scripted_functional_optimizer_deprecation_warning(stacklevel=2)
+        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.defaults = {
+            "lr": lr,
+            "eps": eps,
+            "beta1": betas[0],
+            "beta2": betas[1],
+            "weight_decay": weight_decay,
+        }
+        self.amsgrad = amsgrad
+        self.maximize = maximize
+        self.foreach = foreach
+        self.fused = fused
+        self.state = torch.jit.annotate(dict[torch.Tensor, dict[str, torch.Tensor]], {})
+
+        if len(params) == 0 and not _allow_empty_param_list:
+            raise ValueError("optimizer got an empty parameter list")
+
+        # NOTE: we only have one param_group and don't allow user to add additional
+        # param group as it's not a common use case.
+        self.param_group = {"params": params}
+
+    def step_param(self, param: Tensor, grad: Optional[Tensor]):
+        params_with_grad = []
+        grads = []
+        exp_avgs = []
+        exp_avg_sqs = []
+        max_exp_avg_sqs = []
+        state_steps: list[Tensor] = []
+        has_complex = torch.is_complex(param)
+        if grad is not None:
+            params_with_grad.append(param)
+            grads.append(grad)
+        # Lazy state initialization
+        if param not in self.state:
+            self.state[param] = {}
+            state = self.state[param]
+            state["step"] = torch.tensor(0.0)
+            # Exponential moving average of gradient values
+            state["exp_avg"] = torch.zeros_like(
+                param, memory_format=torch.preserve_format
+            )
+            # Exponential moving average of squared gradient values
+            state["exp_avg_sq"] = torch.zeros_like(
+                param, memory_format=torch.preserve_format
+            )
+            if self.amsgrad:
+                # Maintains max of all exp. moving avg. of sq. grad. values
+                state["max_exp_avg_sq"] = torch.zeros_like(
+                    param, memory_format=torch.preserve_format
+                )
+
+        state = self.state[param]
+
+        exp_avgs.append(state["exp_avg"])
+        exp_avg_sqs.append(state["exp_avg_sq"])
+
+        if self.amsgrad:
+            max_exp_avg_sqs.append(state["max_exp_avg_sq"])
+
+        state_steps.append(state["step"])
+        with torch.no_grad():
+            F.adamw(
+                params_with_grad,
+                grads,
+                exp_avgs,
+                exp_avg_sqs,
+                max_exp_avg_sqs,
+                state_steps,
+                amsgrad=self.amsgrad,
+                maximize=self.maximize,
+                beta1=self.defaults["beta1"],
+                beta2=self.defaults["beta2"],
+                lr=self.defaults["lr"],
+                weight_decay=self.defaults["weight_decay"],
+                eps=self.defaults["eps"],
+                foreach=self.foreach,
+                fused=self.fused,
+                grad_scale=None,
+                found_inf=None,
+                has_complex=has_complex,
+            )
+
+    def step(self, gradients: list[Optional[Tensor]]):
+        params = self.param_group["params"]
+        params_with_grad = []
+        grads = []
+        exp_avgs = []
+        exp_avg_sqs = []
+        max_exp_avg_sqs = []
+        state_steps: list[Tensor] = []
+
+        if len(params) != len(gradients):
+            raise ValueError(
+                "the gradients passed in does not equal to the size of the parameters!"
+                + f"Params length: {len(params)}. "
+                + f"Gradients length: {len(gradients)}"
+            )
+
+        has_complex = False
+        for param, gradient in zip(self.param_group["params"], gradients):
+            if gradient is not None:
+                has_complex |= torch.is_complex(param)
+                params_with_grad.append(param)
+                grads.append(gradient)
+                # Lazy state initialization
+                if param not in self.state:
+                    self.state[param] = {}
+                    state = self.state[param]
+                    state["step"] = torch.tensor(0.0)
+                    # Exponential moving average of gradient values
+                    state["exp_avg"] = torch.zeros_like(
+                        param, memory_format=torch.preserve_format
+                    )
+                    # Exponential moving average of squared gradient values
+                    state["exp_avg_sq"] = torch.zeros_like(
+                        param, memory_format=torch.preserve_format
+                    )
+                    if self.amsgrad:
+                        # Maintains max of all exp. moving avg. of sq. grad. values
+                        state["max_exp_avg_sq"] = torch.zeros_like(
+                            param, memory_format=torch.preserve_format
+                        )
+
+                state = self.state[param]
+
+                exp_avgs.append(state["exp_avg"])
+                exp_avg_sqs.append(state["exp_avg_sq"])
+
+                if self.amsgrad:
+                    max_exp_avg_sqs.append(state["max_exp_avg_sq"])
+
+                state_steps.append(state["step"])
+
+        with torch.no_grad():
+            F.adamw(
+                params_with_grad,
+                grads,
+                exp_avgs,
+                exp_avg_sqs,
+                max_exp_avg_sqs,
+                state_steps,
+                amsgrad=self.amsgrad,
+                maximize=self.maximize,
+                beta1=self.defaults["beta1"],
+                beta2=self.defaults["beta2"],
+                lr=self.defaults["lr"],
+                weight_decay=self.defaults["weight_decay"],
+                eps=self.defaults["eps"],
+                foreach=self.foreach,
+                fused=self.fused,
+                grad_scale=None,
+                found_inf=None,
+                has_complex=has_complex,
+            )