Adding all project files

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

@@ -0,0 +1,107 @@
+# 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 Rprop 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 _FunctionalRprop:
+    def __init__(
+        self,
+        params: list[Tensor],
+        lr: float = 1e-2,
+        etas: tuple[float, float] = (0.5, 1.2),
+        step_sizes: tuple[float, float] = (1e-6, 50),
+        foreach: bool = False,
+        maximize: bool = False,
+        _allow_empty_param_list: bool = False,
+    ):
+        _scripted_functional_optimizer_deprecation_warning(stacklevel=2)
+        self.defaults = {
+            "lr": lr,
+        }
+        self.etas = etas
+        self.step_sizes = step_sizes
+        self.foreach = foreach
+        self.maximize = maximize
+
+        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}
+
+        self.state = torch.jit.annotate(dict[torch.Tensor, dict[str, torch.Tensor]], {})
+
+    def step(self, gradients: list[Optional[Tensor]]):
+        params = self.param_group["params"]
+        params_with_grad = []
+        grads = []
+        prevs = []
+        step_sizes = []
+        state_steps = []
+        lr = self.defaults["lr"]
+        etaminus, etaplus = self.etas
+        step_size_min, step_size_max = self.step_sizes
+
+        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(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)
+                    state["prev"] = torch.zeros_like(
+                        param, memory_format=torch.preserve_format
+                    )
+                    state["step_size"] = torch.full_like(gradient, lr)
+
+                state = self.state[param]
+                prevs.append(state["prev"])
+                step_sizes.append(state["step_size"])
+                state_steps.append(state["step"])
+
+        with torch.no_grad():
+            F.rprop(
+                params_with_grad,
+                grads,
+                prevs,
+                step_sizes,
+                state_steps,
+                step_size_min=step_size_min,
+                step_size_max=step_size_max,
+                etaminus=etaminus,
+                etaplus=etaplus,
+                foreach=self.foreach,
+                maximize=self.maximize,
+                has_complex=has_complex,
+            )