update ut

Author: sneaxiy

python/paddle/distributed/fleet/meta_optimizers/raw_program_optimizer.py

@@ -165,7 +165,7 @@ def _insert_allreduce_ops_for_gm(self, gm_block):
         block = self.main_program.global_block()
 
         first_optimize_op_idx = None
-        for i, op in reversed(enumerate(list(gm_block.ops))):
+        for i, op in reversed(list(enumerate(gm_block.ops))):
             if is_backward_op(op) and first_optimize_op_idx is None:
                 first_optimize_op_idx = i + 1
                 break

python/paddle/fluid/tests/unittests/CMakeLists.txt

@@ -778,7 +778,7 @@ endif()
 if (WITH_DISTRIBUTE AND NOT APPLE)
     if(WITH_GPU OR WITH_ROCM)
         set_tests_properties(test_c_comm_init_op PROPERTIES TIMEOUT 120)
-        set_tests_properties(test_dist_mnist_gradient_merge PROPERTIES TIMEOUT 120)
+        set_tests_properties(test_dist_mnist_gradient_merge PROPERTIES TIMEOUT 160)
     endif()
 endif()
 

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

@@ -12,6 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import os
 import paddle
 import paddle.nn as nn
 import paddle.fluid as fluid
@@ -44,9 +45,10 @@ def get_model(self, batch_size=2, single_device=False):
         strategy.build_strategy = build_strategy
 
         strategy.gradient_merge = True
+        avg = os.environ['enable_gm_avg'] == "True"
         strategy.gradient_merge_configs = {
             "k_steps": 2,
-            "avg": False,
+            "avg": avg,
         }
         strategy.without_graph_optimization = True
 
@@ -65,9 +67,25 @@ def get_model(self, batch_size=2, single_device=False):
                 optimizer,
                 k_steps=strategy.gradient_merge_configs["k_steps"],
                 avg=strategy.gradient_merge_configs["avg"])
+            world_size = 1
         else:
             optimizer = fleet.distributed_optimizer(optimizer)
+            world_size = fleet.world_size()
         optimizer.minimize(cost)
+        if world_size > 1:
+            assert paddle.static.default_main_program().num_blocks == 2
+            gm_block = paddle.static.default_main_program().block(1)
+            start_allreduce_idx = None
+            for i, op in enumerate(gm_block.ops):
+                if op.type == "c_allreduce_sum":
+                    start_allreduce_idx = i
+                    break
+            # the magic number 1 below means skip the c_sync_calc_stream op
+            if avg:
+                assert start_allreduce_idx > 1
+            else:
+                assert start_allreduce_idx == 1
+
         train_reader = paddle.batch(
             paddle.dataset.mnist.test(), batch_size=batch_size)
         test_reader = paddle.batch(

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

@@ -52,21 +52,35 @@ def test_dist_train(self):
                 log_name=flag_name + "_no_fuse")
 
 
-class TestDistMnistGradMergeRawOptimizer(TestDistBase):
+class TestDistMnistGradMergeRawOptimizerBase(TestDistBase):
     def _setup_config(self):
         self._use_reader_alloc = False
         self._nccl2_mode = True
         self._use_fleet_api = True
         self._use_fleet_api_20 = True
 
+    def enable_avg(self):
+        return False
+
     def test_dist_train(self):
         if fluid.core.is_compiled_with_cuda():
+            avg = str(self.enable_avg())
+            log_name = flag_name + "_raw_optimizer_gm_avg_" + avg
             self.check_with_place(
                 "dist_mnist_gradient_merge_raw_optimizer.py",
                 delta=1e-5,
                 check_error_log=True,
-                log_name=flag_name + "_raw_optimizer",
-                need_envs={'FLAGS_apply_pass_to_program': '1'})
+                log_name=log_name,
+                need_envs={
+                    'FLAGS_apply_pass_to_program': '1',
+                    'enable_gm_avg': avg,
+                })
+
+
+class TestDistMnistGradMergeRawOptimizerAvg(
+        TestDistMnistGradMergeRawOptimizerBase):
+    def enable_avg(self):
+        return True
 
 
 if __name__ == "__main__":