From fdaf0772c4ac2ee2e766ddfa804cf49c65f0904d Mon Sep 17 00:00:00 2001
From: Kexin Zhao <zhaokexin01@baidu.com>
Date: Fri, 20 Oct 2017 00:28:00 -0700
Subject: [PATCH] add adagrad optimizer python implementation

---
 python/paddle/v2/framework/optimizer.py       | 59 ++++++++++++++++++-
 .../v2/framework/tests/test_optimizer.py      | 41 +++++++++++++
 2 files changed, 99 insertions(+), 1 deletion(-)

diff --git a/python/paddle/v2/framework/optimizer.py b/python/paddle/v2/framework/optimizer.py
index f992a42c40..51d435668c 100644
--- a/python/paddle/v2/framework/optimizer.py
+++ b/python/paddle/v2/framework/optimizer.py
@@ -1,7 +1,7 @@
 import paddle.v2.framework.framework as framework
 from collections import defaultdict
 
-__all__ = ['SGDOptimizer', 'MomentumOptimizer']
+__all__ = ['SGDOptimizer', 'MomentumOptimizer', 'AdagradOptimizer']
 
 
 class Optimizer(object):
@@ -272,3 +272,60 @@ class MomentumOptimizer(Optimizer):
             attrs={"mu": self._momentum})
 
         return momentum_op
+
+
+class AdagradOptimizer(Optimizer):
+    """Simple Adagrad optimizer with moment state
+    """
+    _moment_acc_str = "moment"
+
+    def __init__(self, learning_rate, epsilon):
+        assert learning_rate is not None
+        assert epsilon is not None
+        super(AdagradOptimizer, self).__init__()
+        self.type = "adagrad"
+        self._learning_rate = learning_rate
+        self._epsilon = epsilon
+
+    def _initialize_tensors(self, block):
+        assert isinstance(block, framework.Block)
+        lr_shape = [1]
+        # create a variable for learning_rate
+        self._lr = block.create_var(
+            dtype="float32", shape=lr_shape, lod_level=0)
+
+        # create an op to init the learning_rate
+        # FIXME: Fix when Initialization design has been implemented
+        # https://github.com/PaddlePaddle/Paddle/pull/4852
+        block.append_op(
+            type="fill_constant",
+            outputs={"Out": self._lr},
+            attrs={"shape": lr_shape,
+                   "value": self._learning_rate})
+
+    def _create_accumulators(self, block, parameters):
+        assert isinstance(block, framework.Block)
+
+        for p in parameters:
+            self._add_accumulator(block, self._moment_acc_str, p, 'float32')
+
+    def _append_optimize_op(self, block, param_and_grad):
+        assert isinstance(block, framework.Block)
+
+        moment_acc = self._get_accumulator(self._moment_acc_str,
+                                           param_and_grad[0])
+
+        # create the adagrad optimizer op
+        adagrad_op = block.append_op(
+            type=self.type,
+            inputs={
+                "Param": param_and_grad[0],
+                "Grad": param_and_grad[1],
+                "Moment": moment_acc,
+                "LearningRate": self._lr
+            },
+            outputs={"ParamOut": param_and_grad[0],
+                     "MomentOut": moment_acc},
+            attrs={"epsilon": self._epsilon})
+
+        return adagrad_op
diff --git a/python/paddle/v2/framework/tests/test_optimizer.py b/python/paddle/v2/framework/tests/test_optimizer.py
index e6a142ac36..3d1715bf62 100644
--- a/python/paddle/v2/framework/tests/test_optimizer.py
+++ b/python/paddle/v2/framework/tests/test_optimizer.py
@@ -69,5 +69,46 @@ class TestMomentumOptimizer(unittest.TestCase):
         self.assertTrue(mul_x.name in velocity_acc)
 
 
+class TestAdagradOptimizer(unittest.TestCase):
+    class MockAdagrad(optimizer.AdagradOptimizer):
+        def get_accumulators(self):
+            return self._accumulators
+
+        def get_moment_str(self):
+            return self._moment_acc_str
+
+    def test_adagrad_optimizer(self):
+        program = framework.Program()
+        block = program.global_block()
+        mul_x = block.create_parameter(
+            dtype="float32", shape=[5, 10], lod_level=0, name="mul.x")
+        mul_y = block.create_var(
+            dtype="float32", shape=[10, 8], lod_level=0, name="mul.y")
+        mul_out = block.create_var(
+            dtype="float32", shape=[5, 8], lod_level=0, name="mul.out")
+        block.append_op(
+            type="mul",
+            inputs={"X": mul_x,
+                    "Y": mul_y},
+            outputs={"Out": mul_out},
+            attrs={"x_num_col_dims": 1})
+        adagrad_optimizer = self.MockAdagrad(learning_rate=0.01, epsilon=1.0e-6)
+        params_grads = adagrad_optimizer.create_backward_pass(mul_out)
+        self.assertEqual(len(params_grads), 1)
+        self.assertEqual(len(adagrad_optimizer.get_accumulators()), 0)
+        opts = adagrad_optimizer.create_optimization_pass(params_grads, mul_out)
+        self.assertEqual(len(opts), 1)
+        adagrad_op = opts[0]
+        self.assertEqual(adagrad_op.type, "adagrad")
+
+        # check accumulators
+        accumulators = adagrad_optimizer.get_accumulators()
+        self.assertEqual(len(accumulators), 1)
+        self.assertTrue(adagrad_optimizer.get_moment_str() in accumulators)
+        moment_acc = accumulators[adagrad_optimizer.get_moment_str()]
+        self.assertEqual(len(moment_acc), 1)
+        self.assertTrue(mul_x.name in moment_acc)
+
+
 if __name__ == '__main__':
     unittest.main()
-- 
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