rrm workspace: TRM/HRM/SRM code, Maze dataset, dynamical-analysis pipelineHEADmain

Curated export for clone-and-run Maze training (2x A6000) + diagnostics.
trm/hrm pretrain.py carry trajectory-augmentation code (backward-compatible).
Heavy artifacts (checkpoints/wandb/npz) gitignored; see PROVENANCE.md.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

1 files changed, 40 insertions, 0 deletions

diff --git a/trm/models/ema.py b/trm/models/ema.py
new file mode 100644
index 0000000..2e52933
--- /dev/null
+++ b/trm/models/ema.py
@@ -0,0 +1,40 @@
+import copy
+import torch.nn as nn
+
+class EMAHelper(object):
+    def __init__(self, mu=0.999):
+        self.mu = mu
+        self.shadow = {}
+
+    def register(self, module):
+        if isinstance(module, nn.DataParallel):
+            module = module.module
+        for name, param in module.named_parameters():
+            if param.requires_grad:
+                self.shadow[name] = param.data.clone()
+
+    def update(self, module):
+        if isinstance(module, nn.DataParallel):
+            module = module.module
+        for name, param in module.named_parameters():
+            if param.requires_grad:
+                self.shadow[name].data = (1. - self.mu) * param.data + self.mu * self.shadow[name].data
+
+    def ema(self, module):
+        if isinstance(module, nn.DataParallel):
+            module = module.module
+        for name, param in module.named_parameters():
+            if param.requires_grad:
+                param.data.copy_(self.shadow[name].data)
+
+    def ema_copy(self, module):
+        module_copy = copy.deepcopy(module)
+        self.ema(module_copy)
+        return module_copy
+
+    def state_dict(self):
+        return self.shadow
+
+    def load_state_dict(self, state_dict):
+        self.shadow = state_dict
+