Merge pull request #14190 from jacquesqiao/dist-table-support-multi-table

Dist table support multi table

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

@@ -411,12 +411,12 @@ class TestDistLookupTableBase(TranspilerTest):
         self.emb_size = 64
         self.lookup_table_name = 'shared_w'
 
-        def emb_pool(ids):
+        def emb_pool(ids, table_name, is_distributed):
             emb = fluid.layers.embedding(
                 input=ids,
                 size=[self.table_size, self.emb_size],
                 dtype='float32',
-                param_attr=self.lookup_table_name,  # share parameter
+                param_attr=table_name,
                 is_sparse=is_sparse,
                 is_distributed=is_distributed)
             pool = fluid.layers.sequence_pool(input=emb, pool_type='average')
@@ -426,9 +426,13 @@ class TestDistLookupTableBase(TranspilerTest):
             name='title_ids', shape=[1], dtype='int64', lod_level=1)
         brand_ids = fluid.layers.data(
             name='brand_ids', shape=[1], dtype='int64', lod_level=1)
-        title_emb = emb_pool(title_ids)
-        brand_emb = emb_pool(brand_ids)
-        fc0 = fluid.layers.concat(input=[title_emb, brand_emb], axis=1)
+        profile_ids = fluid.layers.data(
+            name='brand_ids', shape=[1], dtype='int64', lod_level=1)
+        title_emb = emb_pool(title_ids, self.lookup_table_name, is_distributed)
+        brand_emb = emb_pool(brand_ids, self.lookup_table_name, is_distributed)
+        profile_emb = emb_pool(profile_ids, "profile_emb", False)
+        fc0 = fluid.layers.concat(
+            input=[title_emb, brand_emb, profile_emb], axis=1)
         predict = fluid.layers.fc(input=fc0,
                                   size=2,
                                   act=None,
@@ -449,7 +453,7 @@ class TestLocalLookupTable(TestDistLookupTableBase):
     def transpiler_test_impl(self):
         pserver1, startup1 = self.get_pserver(self.pserver1_ep)
 
-        self.assertEqual(len(pserver1.blocks), 3)
+        self.assertEqual(len(pserver1.blocks), 4)
         # 0 listen_and_serv
         # 1 optimize for fc_w or fc_b adam
         self.assertEqual([op.type for op in pserver1.blocks[1].ops],
@@ -459,16 +463,23 @@ class TestLocalLookupTable(TestDistLookupTableBase):
         self.assertEqual([op.type for op in pserver1.blocks[2].ops],
                          ["sum", "scale", "adam", "scale", "scale"])
 
+        # 3 optimize for table 2 adam
+        # NOTE: if param is not selected rows, the grad will scaled to grad / trainer_num
+        self.assertEqual([op.type for op in pserver1.blocks[3].ops],
+                         ["sum", "scale", "adam", "scale", "scale"])
+
         trainer, _ = self.get_trainer()
         self.assertEqual(len(trainer.blocks), 1)
         ops = [
             'lookup_table', 'sequence_pool', 'lookup_table', 'sequence_pool',
-            'concat', 'mul', 'elementwise_add', 'cross_entropy', 'mean',
-            'fill_constant', 'mean_grad', 'cross_entropy_grad',
-            'elementwise_add_grad', 'send', 'mul_grad', 'send', 'concat_grad',
-            'sequence_pool_grad', 'lookup_table_grad', 'sequence_pool_grad',
-            'lookup_table_grad', 'sum', 'split_selected_rows', 'send',
-            'send_barrier', 'recv', 'recv', 'recv', 'fetch_barrier', 'concat'
+            'lookup_table', 'sequence_pool', 'concat', 'mul', 'elementwise_add',
+            'cross_entropy', 'mean', 'fill_constant', 'mean_grad',
+            'cross_entropy_grad', 'elementwise_add_grad', 'send', 'mul_grad',
+            'send', 'concat_grad', 'sequence_pool_grad', 'lookup_table_grad',
+            'split_selected_rows', 'send', 'sequence_pool_grad',
+            'lookup_table_grad', 'sequence_pool_grad', 'lookup_table_grad',
+            'sum', 'split_selected_rows', 'send', 'send_barrier', 'recv',
+            'recv', 'recv', 'recv', 'fetch_barrier', 'concat', 'concat'
         ]
         self.assertEqual([op.type for op in trainer.blocks[0].ops], ops)
 
@@ -480,39 +491,45 @@ class TestDistLookupTable(TestDistLookupTableBase):
     def transpiler_test_impl(self):
         pserver1, startup1 = self.get_pserver(self.pserver1_ep)
 
-        self.assertEqual(len(pserver1.blocks), 5)
+        self.assertEqual(len(pserver1.blocks), 6)
         # 0 listen_and_serv
         # 1 optimize for fc_w or fc_b adam
         self.assertEqual([op.type for op in pserver1.blocks[1].ops],
                          ["sum", "scale", "adam", "scale", "scale"])
-        # 2 optimize for table sgd
+        # 4 prefetch -> lookup_sparse_table for data0
         self.assertEqual([op.type for op in pserver1.blocks[2].ops],
+                         ["sum", "scale", "adam", "scale", "scale"])
+        # 2 optimize for table sgd
+        self.assertEqual([op.type for op in pserver1.blocks[3].ops],
                          ["sum", "sgd"])
         # 3 prefetch -> lookup_sparse_table for data0
-        self.assertEqual([op.type for op in pserver1.blocks[3].ops],
+        self.assertEqual([op.type for op in pserver1.blocks[4].ops],
                          ["lookup_sparse_table"])
-        # 4 save table
-        self.assertEqual([op.type for op in pserver1.blocks[4].ops], ["save"])
+        # 5 save table
+        self.assertEqual([op.type for op in pserver1.blocks[5].ops], ["save"])
 
         trainer, trainer_startup = self.get_trainer()
         self.assertEqual(len(trainer.blocks), 1)
         ops = [
             'split_ids', 'prefetch', 'merge_ids', 'sequence_pool',
-            'sequence_pool', 'concat', 'mul', 'elementwise_add',
-            'cross_entropy', 'mean', 'fill_constant', 'mean_grad',
-            'cross_entropy_grad', 'elementwise_add_grad', 'send', 'mul_grad',
-            'send', 'concat_grad', 'sequence_pool_grad', 'lookup_table_grad',
-            'sequence_pool_grad', 'lookup_table_grad', 'sum', 'split_ids',
-            'send', 'send_barrier', 'recv', 'recv', 'fetch_barrier'
+            'sequence_pool', 'lookup_table', 'sequence_pool', 'concat', 'mul',
+            'elementwise_add', 'cross_entropy', 'mean', 'fill_constant',
+            'mean_grad', 'cross_entropy_grad', 'elementwise_add_grad', 'send',
+            'mul_grad', 'send', 'concat_grad', 'sequence_pool_grad',
+            'lookup_table_grad', 'split_selected_rows', 'send',
+            'sequence_pool_grad', 'lookup_table_grad', 'sequence_pool_grad',
+            'lookup_table_grad', 'sum', 'split_ids', 'send', 'send_barrier',
+            'recv', 'recv', 'recv', 'fetch_barrier', 'concat'
         ]
         self.assertEqual([op.type for op in trainer.blocks[0].ops], ops)
-
         startup_ops = [
             'fill_constant', 'fill_constant', 'fill_constant', 'fill_constant',
             'fill_constant', 'fill_constant', 'fill_constant', 'fill_constant',
             'fill_constant', 'fill_constant', 'fill_constant', 'fill_constant',
-            'fill_constant', 'fill_constant', 'uniform_random', 'recv', 'recv',
-            'fetch_barrier', 'fake_init'
+            'fill_constant', 'fill_constant', 'fill_constant', 'fill_constant',
+            'fill_constant', 'fill_constant', 'uniform_random',
+            'uniform_random', 'recv', 'recv', 'recv', 'fetch_barrier', 'concat',
+            'fake_init'
         ]
         self.assertEqual([op.type for op in trainer_startup.blocks[0].ops],
                          startup_ops)
@@ -526,7 +543,7 @@ class TestAsyncLocalLookupTable(TestDistLookupTableBase):
         config = fluid.DistributeTranspilerConfig()
         pserver1, startup1 = self.get_pserver(self.pserver1_ep, config, False)
 
-        self.assertEqual(len(pserver1.blocks), 3)
+        self.assertEqual(len(pserver1.blocks), 4)
         # 0 listen_and_serv
         # 1 optimize for fc_w or fc_b adam
         self.assertEqual([op.type for op in pserver1.blocks[1].ops],
@@ -535,17 +552,23 @@ class TestAsyncLocalLookupTable(TestDistLookupTableBase):
         # NOTE: if param is not selected rows, the grad will scaled to grad / trainer_num
         self.assertEqual([op.type for op in pserver1.blocks[2].ops],
                          ["adam", "scale", "scale"])
+        # 3 optimize for table adam
+        # NOTE: if param is not selected rows, the grad will scaled to grad / trainer_num
+        self.assertEqual([op.type for op in pserver1.blocks[3].ops],
+                         ["adam", "scale", "scale"])
 
         trainer, _ = self.get_trainer(config)
         self.assertEqual(len(trainer.blocks), 1)
         ops = [
             'lookup_table', 'sequence_pool', 'lookup_table', 'sequence_pool',
-            'concat', 'mul', 'elementwise_add', 'cross_entropy', 'mean',
-            'fill_constant', 'mean_grad', 'cross_entropy_grad',
-            'elementwise_add_grad', 'send', 'mul_grad', 'send', 'concat_grad',
-            'sequence_pool_grad', 'lookup_table_grad', 'sequence_pool_grad',
-            'lookup_table_grad', 'sum', 'split_selected_rows', 'send', 'recv',
-            'recv', 'recv', 'concat'
+            'lookup_table', 'sequence_pool', 'concat', 'mul', 'elementwise_add',
+            'cross_entropy', 'mean', 'fill_constant', 'mean_grad',
+            'cross_entropy_grad', 'elementwise_add_grad', 'send', 'mul_grad',
+            'send', 'concat_grad', 'sequence_pool_grad', 'lookup_table_grad',
+            'split_selected_rows', 'send', 'sequence_pool_grad',
+            'lookup_table_grad', 'sequence_pool_grad', 'lookup_table_grad',
+            'sum', 'split_selected_rows', 'send', 'recv', 'recv', 'recv',
+            'recv', 'concat', 'concat'
         ]
         self.assertEqual([op.type for op in trainer.blocks[0].ops], ops)
 
@@ -559,29 +582,34 @@ class TestAsyncDistLookupTable(TestDistLookupTableBase):
 
         pserver1, startup1 = self.get_pserver(self.pserver1_ep, config, False)
 
-        self.assertEqual(len(pserver1.blocks), 5)
+        self.assertEqual(len(pserver1.blocks), 6)
         # 0 listen_and_serv
         # 1 optimize for fc_w or fc_b adam
         self.assertEqual([op.type for op in pserver1.blocks[1].ops],
                          ["adam", "scale", "scale"])
-        # 2 optimize for table sgd
-        self.assertEqual([op.type for op in pserver1.blocks[2].ops], ["sgd"])
-        # 3 prefetch -> lookup_sparse_table for data0
-        self.assertEqual([op.type for op in pserver1.blocks[3].ops],
+        # 2 optimize for table adam
+        self.assertEqual([op.type for op in pserver1.blocks[2].ops],
+                         ["adam", "scale", "scale"])
+        # 3 optimize for table sgd
+        self.assertEqual([op.type for op in pserver1.blocks[3].ops], ["sgd"])
+        # 4 prefetch -> lookup_sparse_table for data0
+        self.assertEqual([op.type for op in pserver1.blocks[4].ops],
                          ["lookup_sparse_table"])
-        # 4 save table
-        self.assertEqual([op.type for op in pserver1.blocks[4].ops], ["save"])
+        # 5 save table
+        self.assertEqual([op.type for op in pserver1.blocks[5].ops], ["save"])
 
         trainer, _ = self.get_trainer(config)
         self.assertEqual(len(trainer.blocks), 1)
         ops = [
             'split_ids', 'prefetch', 'merge_ids', 'sequence_pool',
-            'sequence_pool', 'concat', 'mul', 'elementwise_add',
-            'cross_entropy', 'mean', 'fill_constant', 'mean_grad',
-            'cross_entropy_grad', 'elementwise_add_grad', 'send', 'mul_grad',
-            'send', 'concat_grad', 'sequence_pool_grad', 'lookup_table_grad',
-            'sequence_pool_grad', 'lookup_table_grad', 'sum', 'split_ids',
-            'send', 'recv', 'recv'
+            'sequence_pool', 'lookup_table', 'sequence_pool', 'concat', 'mul',
+            'elementwise_add', 'cross_entropy', 'mean', 'fill_constant',
+            'mean_grad', 'cross_entropy_grad', 'elementwise_add_grad', 'send',
+            'mul_grad', 'send', 'concat_grad', 'sequence_pool_grad',
+            'lookup_table_grad', 'split_selected_rows', 'send',
+            'sequence_pool_grad', 'lookup_table_grad', 'sequence_pool_grad',
+            'lookup_table_grad', 'sum', 'split_ids', 'send', 'recv', 'recv',
+            'recv', 'concat'
         ]
         self.assertEqual([op.type for op in trainer.blocks[0].ops], ops)
 

python/paddle/fluid/transpiler/distribute_transpiler.py

@@ -1065,7 +1065,12 @@ to transpile() call.")
             continue_search_lookup_table_op = False
             all_ops = program.global_block().ops
             for op in all_ops:
-                if op.type == LOOKUP_TABLE_TYPE:
+                if op.type == LOOKUP_TABLE_TYPE and self.table_name == op.input(
+                        "W")[0]:
+                    if not op.attr('is_distributed'):
+                        raise RuntimeError(
+                            "lookup_table_op that lookup an distributed embedding table"
+                            "should set is_distributed to true")
                     continue_search_lookup_table_op = True
 
                     lookup_table_op_index = lookup_table_op_index if lookup_table_op_index != -1 else list(