merge lazy mode

paddle/fluid/API.spec

@@ -367,7 +367,7 @@ paddle.fluid.optimizer.MomentumOptimizer.__init__ ArgSpec(args=['self', 'learnin
 paddle.fluid.optimizer.MomentumOptimizer.minimize ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None))
 paddle.fluid.optimizer.AdagradOptimizer.__init__ ArgSpec(args=['self', 'learning_rate', 'epsilon', 'regularization', 'name'], varargs=None, keywords=None, defaults=(1e-06, None, None))
 paddle.fluid.optimizer.AdagradOptimizer.minimize ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None))
-paddle.fluid.optimizer.AdamOptimizer.__init__ ArgSpec(args=['self', 'learning_rate', 'beta1', 'beta2', 'epsilon', 'regularization', 'name'], varargs=None, keywords=None, defaults=(0.001, 0.9, 0.999, 1e-08, None, None))
+paddle.fluid.optimizer.AdamOptimizer.__init__ ArgSpec(args=['self', 'learning_rate', 'beta1', 'beta2', 'epsilon', 'regularization', 'name', 'lazy_mode'], varargs=None, keywords=None, defaults=(0.001, 0.9, 0.999, 1e-08, None, None, False))
 paddle.fluid.optimizer.AdamOptimizer.minimize ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None))
 paddle.fluid.optimizer.AdamaxOptimizer.__init__ ArgSpec(args=['self', 'learning_rate', 'beta1', 'beta2', 'epsilon', 'regularization', 'name'], varargs=None, keywords=None, defaults=(0.001, 0.9, 0.999, 1e-08, None, None))
 paddle.fluid.optimizer.AdamaxOptimizer.minimize ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None))

paddle/fluid/operators/optimizers/adam_op.cc

@@ -109,6 +109,11 @@ class AdamOpMaker : public framework::OpProtoAndCheckerMaker {
                    "(float, default 1.0e-8) "
                    "Constant for numerical stability")
         .SetDefault(1.0e-8f);
+    AddAttr<bool>(
+        "lazy_mode",
+        "(bool, default false) "
+        "only update the parameter that has gradient in sparse update")
+        .SetDefault(false);
 
     AddComment(R"DOC(
 Adam Optimizer.

paddle/fluid/operators/optimizers/adam_op.h

@@ -178,12 +178,13 @@ struct SparseAdamFunctor {
   const int64_t* rows_;
   int64_t row_numel_;
   int64_t row_count_;
+  bool lazy_mode_;
 
   SparseAdamFunctor(T beta1, T beta2, T epsilon, const T* beta1_pow,
                     const T* beta2_pow, const T* mom1, T* mom1_out,
                     const T* mom2, T* mom2_out, const T* lr, const T* grad,
                     const T* param, T* param_out, const int64_t* rows,
-                    int64_t row_numel, int64_t row_count)
+                    int64_t row_numel, int64_t row_count, bool lazy_mode)
       : beta1_(beta1),
         beta2_(beta2),
         epsilon_(epsilon),
@@ -199,13 +200,10 @@ struct SparseAdamFunctor {
         param_out_(param_out),
         rows_(rows),
         row_numel_(row_numel),
-        row_count_(row_count) {}
-
-  inline HOSTDEVICE void operator()(size_t i) const {
-    auto row_idx =
-        math::BinarySearch<int64_t>(rows_, row_count_, i / row_numel_);
-    T g = row_idx >= 0 ? grad_[row_idx * row_numel_ + i % row_numel_] : 0;
+        row_count_(row_count),
+        lazy_mode_(lazy_mode) {}
 
+  inline HOSTDEVICE void adam_update(size_t i, T g) const {
     // The following code is the same as dense
     T mom1 = moment1_[i];
     T mom2 = moment2_[i];
@@ -226,6 +224,17 @@ struct SparseAdamFunctor {
     moment2_out_[i] = mom2;
     param_out_[i] = p;
   }
+
+  inline HOSTDEVICE void operator()(size_t i) const {
+    auto row_idx =
+        math::BinarySearch<int64_t>(rows_, row_count_, i / row_numel_);
+    if (lazy_mode_ && row_idx < 0) {
+      return;
+    } else {
+      T g = row_idx >= 0 ? grad_[row_idx * row_numel_ + i % row_numel_] : 0;
+      adam_update(i, g);
+    }
+  }
 };
 
 template <typename DeviceContext, typename T>
@@ -241,6 +250,7 @@ class AdamOpKernel : public framework::OpKernel<T> {
     using paddle::framework::LoDTensor;
     using paddle::operators::detail::Ref;
 
+    bool lazy_mode = ctx.Attr<bool>("lazy_mode");
     T beta1 = static_cast<T>(ctx.Attr<float>("beta1"));
     T beta2 = static_cast<T>(ctx.Attr<float>("beta2"));
     T epsilon = static_cast<T>(ctx.Attr<float>("epsilon"));
@@ -352,17 +362,27 @@ class AdamOpKernel : public framework::OpKernel<T> {
           mom2_out.template mutable_data<T>(ctx.GetPlace()),
           lr.template data<T>(), grad_data, param.template data<T>(),
           param_out.template mutable_data<T>(ctx.GetPlace()), rows, row_numel,
-          grad_merge.rows().size());
-      int inner_op_parallelism = FLAGS_inner_op_parallelism;
-      if (inner_op_parallelism > 1 &&
-          FLAGS_min_param_size_to_use_multithread > 0 &&
-          param.numel() > FLAGS_min_param_size_to_use_multithread) {
+          grad_merge.rows().size(), lazy_mode);
+      VLOG(3) << "lazy_mode :" << lazy_mode;
+      if (lazy_mode && platform::is_cpu_place(ctx.GetPlace())) {
+        size_t row_count = grad_merge.rows().size();
+        std::vector<int64_t> cpu_rows(grad_merge.rows());
+        for (size_t row_index = 0; row_index < row_count; ++row_index) {
+          for (size_t offset = 0; offset < row_numel; ++offset) {
+            size_t i = cpu_rows[row_index] * row_numel + offset;
+            functor.adam_update(i, grad_data[row_index * row_numel + offset]);
+          }
+        }
+      } else if (FLAGS_inner_op_parallelism > 1 &&
+                 FLAGS_min_param_size_to_use_multithread > 0 &&
+                 param.numel() > FLAGS_min_param_size_to_use_multithread) {
         VLOG(3) << "use multi thread, inner_op_parallelism="
-                << inner_op_parallelism << " min_param_size_to_use_multithread="
+                << FLAGS_inner_op_parallelism
+                << " min_param_size_to_use_multithread="
                 << FLAGS_min_param_size_to_use_multithread;
         std::vector<std::future<void>> fs;
-        int64_t block_size = param.numel() / inner_op_parallelism;
-        for (int i = 0; i < inner_op_parallelism; ++i) {
+        int64_t block_size = param.numel() / FLAGS_inner_op_parallelism;
+        for (int i = 0; i < FLAGS_inner_op_parallelism; ++i) {
           int64_t start = i * block_size;
           int64_t end = (i + 1) * block_size;
           if (end > param.numel()) {

python/paddle/fluid/optimizer.py

@@ -641,9 +641,14 @@ class AdamOptimizer(Optimizer):
         beta1 (float): The exponential decay rate for the 1st moment estimates.
         beta2 (float): The exponential decay rate for the 2nd moment estimates.
         epsilon (float): a small float value for numerical stability.
-        regularization: A Regularizer, such as
-                        fluid.regularizer.L2DecayRegularizer.
+        regularization: A Regularizer, such as fluid.regularizer.L2DecayRegularizer.
         name: A optional name prefix.
+        lazy_mode(bool: false): The official Adam algorithm has two moving-average accumulators
+        the accumulators are updated at every step. Every element of the two moving-average is updated
+        in both dense mode and sparse mode. If the size of parameter is very large, then the update
+        may be very slow. The lazy mode only update the element that has gradient is the current
+        mini-batch, so it will be much more faster. But this mode has different semantics with the
+        original Adam algorithm and may lead to different result.
 
     Examples:
         .. code-block:: python
@@ -663,7 +668,8 @@ class AdamOptimizer(Optimizer):
                  beta2=0.999,
                  epsilon=1e-8,
                  regularization=None,
-                 name=None):
+                 name=None,
+                 lazy_mode=False):
         assert learning_rate is not None
         assert beta1 is not None
         assert beta2 is not None
@@ -676,6 +682,7 @@ class AdamOptimizer(Optimizer):
         self._beta1 = beta1
         self._beta2 = beta2
         self._epsilon = epsilon
+        self._lazy_mode = lazy_mode
 
     def _create_accumulators(self, block, parameters):
         assert isinstance(block, framework.Block)
@@ -729,7 +736,8 @@ class AdamOptimizer(Optimizer):
             attrs={
                 "beta1": self._beta1,
                 "beta2": self._beta2,
-                "epsilon": self._epsilon
+                "epsilon": self._epsilon,
+                "lazy_mode": self._lazy_mode
             })
 
         return adam_op

python/paddle/fluid/tests/unittests/test_adam_op.py

@@ -194,7 +194,8 @@ def adam_step(inputs, attributes):
     return param_out, moment1_out, moment2_out
 
 
-def adam_step_sparse(inputs, attributes, height, rows, row_numel, np_grad):
+def adam_step_sparse(inputs, attributes, height, rows, row_numel, np_grad,
+                     lazy_mode):
     '''
     Simulate one step of the adam optimizer
     :param inputs: dict of inputs
@@ -218,19 +219,30 @@ def adam_step_sparse(inputs, attributes, height, rows, row_numel, np_grad):
     moment2_out = np.zeros(shape=[height, row_numel])
     param_out = np.zeros(shape=[height, row_numel])
 
-    for idx, row_id in enumerate(rows):
+    def update_row(row_id, update_value):
         moment1_out[row_id] = beta1 * moment1[row_id] + (1 - beta1
-                                                         ) * np_grad[idx]
+                                                         ) * update_value
         moment2_out[row_id] = beta2 * moment2[row_id] + (
-            1 - beta2) * np.square(np_grad[idx])
+            1 - beta2) * np.square(update_value)
         lr_t = lr * np.sqrt(1 - beta2_pow) / (1 - beta1_pow)
         param_out[row_id] = param[row_id] - lr_t * (moment1_out[row_id] / (
             np.sqrt(moment2_out[row_id]) + epsilon))
+
+    if lazy_mode:
+        for idx, row_id in enumerate(rows):
+            update_row(row_id, np_grad[idx])
+    else:
+        for row_id in range(param_out.shape[0]):
+            update_value = np.zeros(np_grad[0].shape).astype("float32")
+            if row_id in rows:
+                update_value = np_grad[rows.index(row_id)]
+            update_row(row_id, update_value)
+
     return param_out, moment1_out, moment2_out
 
 
 class TestSparseAdamOp(unittest.TestCase):
-    def setup(self, scope, place):
+    def setup(self, scope, place, lazy_mode):
         beta1 = 0.78
         beta2 = 0.836
         epsilon = 1e-4
@@ -248,6 +260,7 @@ class TestSparseAdamOp(unittest.TestCase):
             'Beta2Pow': np.array([beta2**10]).astype("float32"),
             "LearningRate": np.full((1), 2.0).astype("float32")
         }
+        self.init_output = np.full((height, row_numel), 0.0).astype("float32")
         self.attrs = {'epsilon': epsilon, 'beta1': beta1, 'beta2': beta2}
 
         grad_selected_rows = scope.var('Grad').get_selected_rows()
@@ -262,19 +275,21 @@ class TestSparseAdamOp(unittest.TestCase):
 
         self.sparse_inputs = ["Grad"]
 
-        param_out, mom1, mom2 = adam_step_sparse(
-            self.dense_inputs, self.attrs, height, rows, row_numel, np_array)
+        param_out, mom1, mom2 = adam_step_sparse(self.dense_inputs, self.attrs,
+                                                 height, rows, row_numel,
+                                                 np_array, lazy_mode)
         self.outputs = {
             "ParamOut": param_out,
             "Moment1Out": mom1,
             "Moment2Out": mom2
         }
 
-    def check_with_place(self, place):
+    def check_with_place(self, place, lazy_mode):
         scope = core.Scope()
-        self.setup(scope, place)
+        self.setup(scope, place, lazy_mode)
 
         op_args = dict()
+        op_args['lazy_mode'] = lazy_mode
         for key, np_array in self.dense_inputs.items():
             var = scope.var(key).get_tensor()
             var.set(np_array, place)
@@ -283,7 +298,7 @@ class TestSparseAdamOp(unittest.TestCase):
             op_args[s] = s
         for s in self.outputs:
             var = scope.var(s).get_tensor()
-            var.set(self.outputs[s], place)
+            var.set(self.init_output, place)
             op_args[s] = s
         for k in self.attrs:
             op_args[k] = self.attrs[k]
@@ -297,20 +312,17 @@ class TestSparseAdamOp(unittest.TestCase):
             actual = np.array(out_var)
             actual = actual.reshape([actual.size])
             np_array = np_array.reshape([np_array.size])
-            for idx, row_id in enumerate(self.rows):
-                j = 0
-                while j < self.row_numel:
-                    pos = row_id * self.row_numel + j
-                    self.assertLess((actual[pos] - np_array[pos]) / actual[pos],
-                                    0.00001)
-                    j += 1
-
-    def test_sparse_sgd(self):
+
+            for i in range(np_array.size):
+                self.assertLess((actual[i] - np_array[i]), 0.00001)
+
+    def test_sparse_adam(self):
         places = [core.CPUPlace()]
         if core.is_compiled_with_cuda():
             places.append(core.CUDAPlace(0))
         for place in places:
-            self.check_with_place(place)
+            for lazy_mode in (True, False):
+                self.check_with_place(place, lazy_mode)
 
 
 if __name__ == "__main__":