From dc9d82253e3dfb5f23b902a3add53b057e5d47ed Mon Sep 17 00:00:00 2001
From: Haifeng Jin <5476582+haifeng-jin@users.noreply.github.com>
Date: Wed, 19 Jul 2023 11:25:46 -0700
Subject: [PATCH] add adagrad for torch (#548)

Co-authored-by: Haifeng Jin <haifeng-jin@users.noreply.github.com>
---
 .../backend/torch/optimizers/torch_adagrad.py | 37 +++++++++++++++++++
 .../torch/optimizers/torch_optimizer.py       |  2 +
 keras_core/optimizers/adagrad_test.py         |  9 +++--
 3 files changed, 44 insertions(+), 4 deletions(-)
 create mode 100644 keras_core/backend/torch/optimizers/torch_adagrad.py

diff --git a/keras_core/backend/torch/optimizers/torch_adagrad.py b/keras_core/backend/torch/optimizers/torch_adagrad.py
new file mode 100644
index 0000000000..117a93bc4c
--- /dev/null
+++ b/keras_core/backend/torch/optimizers/torch_adagrad.py
@@ -0,0 +1,37 @@
+import torch
+
+from keras_core import ops
+from keras_core import optimizers
+from keras_core.backend.torch.optimizers import torch_parallel_optimizer
+
+
+class Adagrad(
+    torch_parallel_optimizer.TorchParallelOptimizer, optimizers.Adagrad
+):
+    def _parallel_update_step(
+        self,
+        grads,
+        variables,
+        learning_rate,
+    ):
+        keras_variables = variables
+        variables = [v.value for v in variables]
+
+        dtype = variables[0].dtype
+        lr = ops.cast(learning_rate, dtype)
+
+        accumulators = [
+            self._accumulators[self._get_variable_index(variable)].value
+            for variable in keras_variables
+        ]
+        torch._foreach_add_(accumulators, torch._foreach_mul(grads, grads))
+        torch._foreach_add_(
+            variables,
+            torch._foreach_div(
+                torch._foreach_mul(grads, lr),
+                torch._foreach_sqrt(
+                    torch._foreach_add(accumulators, self.epsilon)
+                ),
+            ),
+            alpha=-1,
+        )
diff --git a/keras_core/backend/torch/optimizers/torch_optimizer.py b/keras_core/backend/torch/optimizers/torch_optimizer.py
index ca83cd84bd..57cb44ccb0 100644
--- a/keras_core/backend/torch/optimizers/torch_optimizer.py
+++ b/keras_core/backend/torch/optimizers/torch_optimizer.py
@@ -8,6 +8,7 @@ class TorchOptimizer(BaseOptimizer):
     def __new__(cls, *args, **kwargs):
         # Import locally to avoid circular imports.
         from keras_core.backend.torch.optimizers import torch_adadelta
+        from keras_core.backend.torch.optimizers import torch_adagrad
         from keras_core.backend.torch.optimizers import torch_adam
         from keras_core.backend.torch.optimizers import torch_adamw
         from keras_core.backend.torch.optimizers import torch_rmsprop
@@ -15,6 +16,7 @@ def __new__(cls, *args, **kwargs):
 
         OPTIMIZERS = {
             optimizers.Adadelta: torch_adadelta.Adadelta,
+            optimizers.Adagrad: torch_adagrad.Adagrad,
             optimizers.Adam: torch_adam.Adam,
             optimizers.AdamW: torch_adamw.AdamW,
             optimizers.RMSprop: torch_rmsprop.RMSprop,
diff --git a/keras_core/optimizers/adagrad_test.py b/keras_core/optimizers/adagrad_test.py
index 3112f345b3..a531090294 100644
--- a/keras_core/optimizers/adagrad_test.py
+++ b/keras_core/optimizers/adagrad_test.py
@@ -4,6 +4,7 @@
 import numpy as np
 
 from keras_core import backend
+from keras_core import ops
 from keras_core import testing
 from keras_core.optimizers.adagrad import Adagrad
 
@@ -19,7 +20,7 @@ def test_config(self):
 
     def test_single_step(self):
         optimizer = Adagrad(learning_rate=0.5)
-        grads = np.array([1.0, 6.0, 7.0, 2.0])
+        grads = ops.array([1.0, 6.0, 7.0, 2.0])
         vars = backend.Variable([1.0, 2.0, 3.0, 4.0])
         optimizer.apply_gradients(zip([grads], [vars]))
         self.assertAllClose(
@@ -28,7 +29,7 @@ def test_single_step(self):
 
     def test_weight_decay(self):
         grads, var1, var2, var3 = (
-            np.zeros(()),
+            ops.zeros(()),
             backend.Variable(2.0),
             backend.Variable(2.0, name="exclude"),
             backend.Variable(2.0),
@@ -54,8 +55,8 @@ def test_correctness_with_golden(self):
         )
 
         x = backend.Variable(np.ones([10]))
-        grads = np.arange(0.1, 1.1, 0.1)
-        first_grads = np.full((10,), 0.01)
+        grads = ops.arange(0.1, 1.1, 0.1)
+        first_grads = ops.full((10,), 0.01)
 
         # fmt: off
         golden = np.array(