Added multi-vector embeddings

1 files changed, 56 insertions, 4 deletions

diff --git a/training/util.py b/training/util.py
index d0f7fcd..43a55e1 100644
--- a/training/util.py
+++ b/training/util.py
@@ -1,5 +1,6 @@
 from pathlib import Path
 import json
+from typing import Iterable
 
 import torch
 from PIL import Image
@@ -39,8 +40,6 @@ class CheckpointerBase:
         self,
         datamodule,
         output_dir: Path,
-        placeholder_token,
-        placeholder_token_id,
         sample_image_size,
         sample_batches,
         sample_batch_size,
@@ -48,8 +47,6 @@ class CheckpointerBase:
     ):
         self.datamodule = datamodule
         self.output_dir = output_dir
-        self.placeholder_token = placeholder_token
-        self.placeholder_token_id = placeholder_token_id
         self.sample_image_size = sample_image_size
         self.seed = seed or torch.random.seed()
         self.sample_batches = sample_batches
@@ -117,3 +114,58 @@ class CheckpointerBase:
             del image_grid
 
         del generator
+
+
+class EMAModel:
+    """
+    Exponential Moving Average of models weights
+    """
+
+    def __init__(self, parameters: Iterable[torch.nn.Parameter], decay=0.9999):
+        parameters = list(parameters)
+        self.shadow_params = [p.clone().detach() for p in parameters]
+
+        self.decay = decay
+        self.optimization_step = 0
+
+    @torch.no_grad()
+    def step(self, parameters):
+        parameters = list(parameters)
+
+        self.optimization_step += 1
+
+        # Compute the decay factor for the exponential moving average.
+        value = (1 + self.optimization_step) / (10 + self.optimization_step)
+        one_minus_decay = 1 - min(self.decay, value)
+
+        for s_param, param in zip(self.shadow_params, parameters):
+            if param.requires_grad:
+                s_param.sub_(one_minus_decay * (s_param - param))
+            else:
+                s_param.copy_(param)
+
+        torch.cuda.empty_cache()
+
+    def copy_to(self, parameters: Iterable[torch.nn.Parameter]) -> None:
+        """
+        Copy current averaged parameters into given collection of parameters.
+        Args:
+            parameters: Iterable of `torch.nn.Parameter`; the parameters to be
+                updated with the stored moving averages. If `None`, the
+                parameters with which this `ExponentialMovingAverage` was
+                initialized will be used.
+        """
+        parameters = list(parameters)
+        for s_param, param in zip(self.shadow_params, parameters):
+            param.data.copy_(s_param.data)
+
+    def to(self, device=None, dtype=None) -> None:
+        r"""Move internal buffers of the ExponentialMovingAverage to `device`.
+        Args:
+            device: like `device` argument to `torch.Tensor.to`
+        """
+        # .to() on the tensors handles None correctly
+        self.shadow_params = [
+            p.to(device=device, dtype=dtype) if p.is_floating_point() else p.to(device=device)
+            for p in self.shadow_params
+        ]