Dist op compatible (#37994)

* dist matmul op compatible

* dist op unittest

* modify dist matmul

* modify dist reshape

* modify dist reshape

* add a space

* add a space

* delete dist matmul op

* modify reshape

* add dist op unittest

* modify dist op unittest

python/paddle/distributed/auto_parallel/operators/common.py

@@ -57,6 +57,9 @@ class DistributedOperatorImpl:
         return self.is_input_compatible(dist_op) and \
             self.is_output_compatible(dist_op)
 
+    def is_auto_compatible(self, dist_op):
+        raise NotImplementedError("Please Implement this method in Subclass.")
+
     def update_dims_mapping(self, dist_op):
         raise NotImplementedError("Please Implement this method in Subclass.")
 

python/paddle/distributed/auto_parallel/operators/dist_embedding.py

@@ -80,6 +80,32 @@ class DistributedEmbeddingImpl(DistributedOperatorImpl):
                 return False
         return True
 
+    def is_auto_compatible(self, dist_op):
+        op_desc = dist_op.serial_op.desc
+        op_dist_attr = dist_op.dist_attr
+        ids_name = op_desc.input('Ids')[0]
+        w_name = op_desc.input('W')[0]
+        out_name = op_desc.output('Out')[0]
+        out_dims_mapping = op_dist_attr.get_output_dims_mapping(out_name)
+        ids_dims_mapping = op_dist_attr.get_input_dims_mapping(ids_name)
+        w_dims_mapping = op_dist_attr.get_input_dims_mapping(w_name)
+        if is_dim_replicate(w_dims_mapping[-2]) or is_dim_shard(w_dims_mapping[
+                -1]):
+            return False
+        # Other dimensions must be replicate except the batch dimension
+        for mapping in ids_dims_mapping[1:]:
+            if is_dim_shard(mapping):
+                return False
+        for mapping in out_dims_mapping[1:]:
+            if is_dim_shard(mapping):
+                return False
+        if w_dims_mapping[-1] != out_dims_mapping[-1]:
+            return False
+        if ids_dims_mapping != out_dims_mapping[:len(ids_dims_mapping)]:
+            return False
+
+        return True
+
     def update_dims_mapping(self, dist_op):
         changed = False
         op_desc = dist_op.serial_op.desc

python/paddle/distributed/auto_parallel/operators/dist_reshape.py

@@ -74,6 +74,36 @@ class DistributedReshapeImpl0(DistributedOperatorImpl):
 
         return True
 
+    def is_auto_compatible(self, dist_op):
+        op_desc = dist_op.serial_op.desc
+        op_dist_attr = dist_op.dist_attr
+        x_name = op_desc.input('X')[0]
+        out_name = op_desc.output('Out')[0]
+        x_shape_name = op_desc.output('XShape')[0]
+        x_shape_dims_mapping = op_dist_attr.get_output_dims_mapping(
+            x_shape_name)
+        x_dims_mapping = op_dist_attr.get_input_dims_mapping(x_name)
+        out_dims_mapping = op_dist_attr.get_output_dims_mapping(out_name)
+        if len(x_dims_mapping) != len(out_dims_mapping) - 1:
+            return False
+
+        if is_dim_shard(out_dims_mapping[-1]):
+            return False
+
+        for idx, item in enumerate(out_dims_mapping[:-2]):
+            if x_dims_mapping[idx] != item:
+                return False
+        if out_dims_mapping[-2] != x_dims_mapping[-1]:
+            return False
+
+        if x_shape_dims_mapping[0] != -1:
+            return False
+
+        if x_shape_dims_mapping[1:] != x_dims_mapping[:]:
+            return False
+
+        return True
+
     def update_dims_mapping(self, dist_op):
         changed = False
         op_desc = dist_op.serial_op.desc
@@ -201,6 +231,43 @@ class DistributedReshapeImpl1(DistributedOperatorImpl):
 
         return True
 
+    def is_auto_compatible(self, dist_op):
+        op_desc = dist_op.serial_op.desc
+        op_dist_attr = dist_op.dist_attr
+        x_name = op_desc.input('X')[0]
+        out_name = op_desc.output('Out')[0]
+        x_shape_name = op_desc.output('XShape')[0]
+        x_dims_mapping = op_dist_attr.get_input_dims_mapping(x_name)
+        out_dims_mapping = op_dist_attr.get_output_dims_mapping(out_name)
+        x_shape_dims_mapping = op_dist_attr.get_output_dims_mapping(
+            x_shape_name)
+
+        if len(x_dims_mapping) == len(out_dims_mapping) + 2:
+            if out_dims_mapping[0] != x_dims_mapping[0]:
+                return False
+            if x_dims_mapping[-1] != -1 or x_dims_mapping[-2] != -1:
+                return False
+        elif len(x_dims_mapping) != len(out_dims_mapping) + 1:
+            return False
+
+        if is_dim_shard(x_dims_mapping[-1]):
+            return False
+
+        for idx, item in enumerate(x_dims_mapping[:-2]):
+            if out_dims_mapping[idx] != item:
+                return False
+
+        if x_dims_mapping[-2] != out_dims_mapping[-1]:
+            return False
+
+        if x_shape_dims_mapping[0] != -1:
+            return False
+
+        if x_shape_dims_mapping[1:] != x_dims_mapping[:]:
+            return False
+
+        return True
+
     def update_dims_mapping(self, dist_op):
         changed = False
         op_desc = dist_op.serial_op.desc

python/paddle/distributed/auto_parallel/operators/dist_softmax.py

@@ -71,6 +71,25 @@ class DistributedSoftmaxImpl(DistributedOperatorImpl):
 
         return True
 
+    def is_auto_compatible(self, dist_op):
+        op_desc = dist_op.serial_op.desc
+        op_dist_attr = dist_op.dist_attr
+        x_name = op_desc.input('X')[0]
+        axis = op_desc.attr('axis')
+        out_name = op_desc.output('Out')[0]
+        x_dims_mapping = op_dist_attr.get_input_dims_mapping(x_name)
+        out_dims_mapping = op_dist_attr.get_output_dims_mapping(out_name)
+        if axis != -1 and axis != len(x_dims_mapping) - 1:
+            return False
+
+        if is_dim_shard(x_dims_mapping[axis]):
+            return False
+
+        if x_dims_mapping != out_dims_mapping:
+            return False
+
+        return True
+
     def update_dims_mapping(self, dist_op):
         changed = False
         op_desc = dist_op.serial_op.desc

python/paddle/distributed/auto_parallel/operators/dist_transpose.py

@@ -47,6 +47,35 @@ class DistributedTranspose2Impl(DistributedOperatorImpl):
     def is_output_compatible(self, dist_op):
         return True
 
+    def is_auto_compatible(self, dist_op):
+        op_desc = dist_op.serial_op.desc
+        op_dist_attr = dist_op.dist_attr
+        perm = op_desc.attr('axis')
+        x_name = op_desc.input('X')[0]
+        out_name = op_desc.output('Out')[0]
+        x_shape_name = op_desc.output('XShape')[0]
+        x_shape_dims_mapping = op_dist_attr.get_output_dims_mapping(
+            x_shape_name)
+        x_dims_mapping = op_dist_attr.get_input_dims_mapping(x_name)
+        out_dims_mapping = op_dist_attr.get_output_dims_mapping(out_name)
+        new_dims_mapping = [-1 for i in range(len(x_dims_mapping))]
+        for i in range(len(x_dims_mapping)):
+            new_dims_mapping[i] = x_dims_mapping[perm[i]]
+
+        if len(x_dims_mapping) != len(out_dims_mapping):
+            return False
+
+        if new_dims_mapping != out_dims_mapping:
+            return False
+
+        if x_shape_dims_mapping[0] != -1:
+            return False
+
+        if x_shape_dims_mapping[1:] != x_dims_mapping[:]:
+            return False
+
+        return True
+
     def update_dims_mapping(self, dist_op):
         changed = False
         op_desc = dist_op.serial_op.desc

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

+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from __future__ import print_function
+import unittest
+import copy
+
+import numpy as np
+
+import paddle
+import paddle.nn as nn
+import paddle.static as static
+import paddle.nn.functional as F
+import paddle.utils as utils
+import paddle.fluid.core as core
+from paddle.fluid import layers
+from paddle.distributed.auto_parallel.operators.common import DistributedOperatorImplContainer
+from paddle.distributed.auto_parallel.operators.common import DistributedOperatorImpl
+from paddle.distributed.auto_parallel.operators.common import get_distributed_operator_impl_container
+from paddle.distributed.auto_parallel.dist_context import DistributedContext, DistributedOperatorContext
+from paddle.distributed.auto_parallel.dist_attribute import OperatorDistributedAttribute, TensorDistributedAttribute
+from paddle.distributed.auto_parallel.dist_op import DistributedOperator
+paddle.enable_static()
+device = "gpu" if core.is_compiled_with_cuda() else "cpu"
+
+
+class MLPLayer(nn.Layer):
+    def __init__(self,
+                 hidden_size=1024,
+                 intermediate_size=4 * 1024,
+                 initializer_range=0.02):
+        super(MLPLayer, self).__init__()
+        d_model = hidden_size
+        dim_feedforward = intermediate_size
+        weight_attr = paddle.ParamAttr(initializer=nn.initializer.Normal(
+            mean=0.0, std=initializer_range))
+        bias_attr = None
+
+        self.linear0 = nn.Linear(
+            d_model, dim_feedforward, weight_attr, bias_attr=bias_attr)
+        self.linear1 = nn.Linear(
+            dim_feedforward, d_model, weight_attr, bias_attr=bias_attr)
+        self.norm = nn.LayerNorm(d_model, epsilon=1e-5)
+
+    def forward(self, input):
+        out = self.norm(input)
+        out = self.linear0(out)
+        out = F.gelu(out, approximate=True)
+        out = self.linear1(out)
+
+        return out
+
+
+def mlp_forward(train_program, start_program):
+    with static.program_guard(train_program,
+                              start_program), utils.unique_name.guard():
+        batch_size = 4
+        hidden_size = 1024
+        sqrt_hidden_size = 32
+        double_hidden_size = 64
+
+        input = static.data(name="input", shape=[8, 8, 16], dtype='int32')
+        input = paddle.reshape(input, [hidden_size])
+        input = paddle.reshape(input, [sqrt_hidden_size, sqrt_hidden_size])
+        embedding = paddle.nn.Embedding(2, batch_size, sparse=True)
+        input = embedding(input)
+        input = paddle.reshape(input, [hidden_size, batch_size])
+        input = paddle.transpose(input, perm=[1, 0])
+        matmulinput = static.data(
+            name="matmulinput",
+            shape=[hidden_size, hidden_size],
+            dtype='float32')
+        input = layers.matmul(x=input, y=matmulinput)
+        label = static.data(
+            name="label", shape=[batch_size, 1], dtype='float32')
+        mlp = MLPLayer(
+            hidden_size=hidden_size,
+            intermediate_size=4 * hidden_size,
+            initializer_range=0.02)
+
+        predict = mlp(input)
+        error_cost = paddle.nn.functional.square_error_cost(predict, label)
+        loss = paddle.mean(error_cost)
+        m = paddle.nn.Softmax()
+        loss = m(loss)
+    return loss, train_program, start_program
+
+
+class Testcompatible(unittest.TestCase):
+    def test_raise_compatible(self):
+        valid_op_dist_attr_list = []
+        program = paddle.static.Program()
+        startup_program = paddle.static.Program()
+        loss, program, start_program = mlp_forward(program, startup_program)
+        ops = program.global_block().ops
+        for idx, op in enumerate(ops):
+            if op.type == 'transpose2':
+                op_dist_attr = OperatorDistributedAttribute()
+                dist_op = DistributedOperator(op, op_dist_attr)
+                impls = DistributedOperatorImpl()
+                try:
+                    impls.is_auto_compatible(dist_op)
+                except NotImplementedError:
+                    e = False
+                self.assertTrue(e == False)
+
+    def test_reshape_remove_compatible(self):
+        valid_op_dist_attr_list = []
+        program = paddle.static.Program()
+        startup_program = paddle.static.Program()
+        loss, program, start_program = mlp_forward(program, startup_program)
+        ops = program.global_block().ops
+        for idx, op in enumerate(ops):
+            if op.type == 'reshape2':
+                dist_op_impl_container = get_distributed_operator_impl_container(
+                    op.type)
+                impls = dist_op_impl_container.get_impls()
+                op_dist_attr = OperatorDistributedAttribute()
+                op_dist_attr.set_input_dims_mapping(op.input_arg_names[0],
+                                                    [-1, -1, -1])
+                op_dist_attr.set_output_dims_mapping(op.output_arg_names[0],
+                                                     [-1, -1])
+                op_dist_attr.set_output_dims_mapping(op.output_arg_names[1],
+                                                     [-1, -1, -1, -1])
+                self.assertTrue(impls[1].is_auto_compatible(
+                    DistributedOperator(op, op_dist_attr)))
+                op_dist_attr.set_output_dims_mapping(op.output_arg_names[1],
+                                                     [-1, -1, -1, 1])
+                self.assertFalse(impls[1].is_auto_compatible(
+                    DistributedOperator(op, op_dist_attr)))
+                op_dist_attr.set_output_dims_mapping(op.output_arg_names[1],
+                                                     [0, -1, -1, 1])
+                self.assertFalse(impls[1].is_auto_compatible(
+                    DistributedOperator(op, op_dist_attr)))
+                op_dist_attr.set_output_dims_mapping(op.output_arg_names[1],
+                                                     [-1, 1, -1, -1])
+                self.assertFalse(impls[1].is_auto_compatible(
+                    DistributedOperator(op, op_dist_attr)))
+                op_dist_attr.set_output_dims_mapping(op.output_arg_names[1],
+                                                     [-1, -1, 1, -1])
+                self.assertFalse(impls[1].is_auto_compatible(
+                    DistributedOperator(op, op_dist_attr)))
+
+                op_dist_attr.set_input_dims_mapping(op.input_arg_names[0],
+                                                    [1, -1, -1])
+                self.assertFalse(impls[1].is_auto_compatible(
+                    DistributedOperator(op, op_dist_attr)))
+                op_dist_attr.set_input_dims_mapping(op.input_arg_names[0],
+                                                    [0, -1, -1])
+                self.assertFalse(impls[1].is_auto_compatible(
+                    DistributedOperator(op, op_dist_attr)))
+                op_dist_attr.set_output_dims_mapping(op.output_arg_names[1],
+                                                     [0, -1, -1, -1])
+                self.assertFalse(impls[1].is_auto_compatible(
+                    DistributedOperator(op, op_dist_attr)))
+
+                op_dist_attr.set_input_dims_mapping(op.input_arg_names[0],
+                                                    [-1, 0, -1])
+                op_dist_attr.set_output_dims_mapping(op.output_arg_names[0],
+                                                     [-1, -1])
+
+                self.assertFalse(impls[1].is_auto_compatible(
+                    DistributedOperator(op, op_dist_attr)))
+
+    def test_reshape_remove_two_compatible(self):
+        valid_op_dist_attr_list = []
+        program = paddle.static.Program()
+        startup_program = paddle.static.Program()
+        loss, program, start_program = mlp_forward(program, startup_program)
+        ops = program.global_block().ops
+        for idx, op in enumerate(ops):
+            if op.type == 'reshape2':
+                dist_op_impl_container = get_distributed_operator_impl_container(
+                    op.type)
+                impls = dist_op_impl_container.get_impls()
+                op_dist_attr = OperatorDistributedAttribute()
+                op_dist_attr.set_input_dims_mapping(op.input_arg_names[0],
+                                                    [-1, -1, -1])
+                op_dist_attr.set_output_dims_mapping(op.output_arg_names[0],
+                                                     [-1])
+                op_dist_attr.set_output_dims_mapping(op.output_arg_names[1],
+                                                     [-1, -1, -1, -1])
+                dist_op = DistributedOperator(op, op_dist_attr)
+                self.assertTrue(impls[1].is_auto_compatible(
+                    DistributedOperator(op, op_dist_attr)))
+                op_dist_attr.set_input_dims_mapping(op.input_arg_names[0],
+                                                    [-1, 1, 0])
+                self.assertFalse(impls[1].is_auto_compatible(
+                    DistributedOperator(op, op_dist_attr)))
+                op_dist_attr.set_input_dims_mapping(op.input_arg_names[0],
+                                                    [0, 1, 1])
+                self.assertFalse(impls[1].is_auto_compatible(
+                    DistributedOperator(op, op_dist_attr)))
+
+                op_dist_attr.set_output_dims_mapping(op.output_arg_names[1],
+                                                     [1, -1, -1, -1])
+                self.assertFalse(impls[1].is_auto_compatible(
+                    DistributedOperator(op, op_dist_attr)))
+                op_dist_attr.set_input_dims_mapping(op.input_arg_names[0],
+                                                    [-1, 1, 1])
+                self.assertFalse(impls[1].is_auto_compatible(
+                    DistributedOperator(op, op_dist_attr)))
+
+                op_dist_attr.set_output_dims_mapping(op.output_arg_names[1],
+                                                     [-1, -1, -1, 1])
+                self.assertFalse(impls[1].is_auto_compatible(
+                    DistributedOperator(op, op_dist_attr)))
+
+                op_dist_attr.set_output_dims_mapping(op.output_arg_names[1],
+                                                     [-1, 1, -1, -1])
+                self.assertFalse(impls[1].is_auto_compatible(
+                    DistributedOperator(op, op_dist_attr)))
+                op_dist_attr.set_output_dims_mapping(op.output_arg_names[1],
+                                                     [-1, -1, 1, -1])
+                self.assertFalse(impls[1].is_auto_compatible(
+                    DistributedOperator(op, op_dist_attr)))
+
+                op_dist_attr.set_input_dims_mapping(op.input_arg_names[0],
+                                                    [1, -1, -1])
+                self.assertFalse(impls[1].is_auto_compatible(
+                    DistributedOperator(op, op_dist_attr)))
+
+    def test_reshape_add_compatible(self):
+        valid_op_dist_attr_list = []
+        program = paddle.static.Program()
+        startup_program = paddle.static.Program()
+        loss, program, start_program = mlp_forward(program, startup_program)
+        ops = program.global_block().ops
+        for idx, op in enumerate(ops):
+            if op.type == 'reshape2':
+                dist_op_impl_container = get_distributed_operator_impl_container(
+                    op.type)
+                impls = dist_op_impl_container.get_impls()
+                op_dist_attr = OperatorDistributedAttribute()
+                op_dist_attr.set_input_dims_mapping(op.input_arg_names[0], [-1])
+                op_dist_attr.set_output_dims_mapping(op.output_arg_names[0],
+                                                     [-1, -1])
+                op_dist_attr.set_output_dims_mapping(op.output_arg_names[1],
+                                                     [-1, -1])
+                op_dist_attr.set_output_dims_mapping(op.output_arg_names[1],
+                                                     [-1, -1])
+                self.assertTrue(impls[0].is_auto_compatible(
+                    DistributedOperator(op, op_dist_attr)))
+                op_dist_attr.set_output_dims_mapping(op.output_arg_names[1],
+                                                     [-1, 0])
+                self.assertFalse(impls[0].is_auto_compatible(
+                    DistributedOperator(op, op_dist_attr)))
+
+                op_dist_attr.set_input_dims_mapping(op.input_arg_names[0], [-1])
+                op_dist_attr.set_output_dims_mapping(op.output_arg_names[0],
+                                                     [0, -1])
+
+                op_dist_attr.set_output_dims_mapping(op.output_arg_names[1],
+                                                     [-1])
+                self.assertFalse(impls[0].is_auto_compatible(
+                    DistributedOperator(op, op_dist_attr)))
+
+                op_dist_attr.set_output_dims_mapping(op.output_arg_names[1],
+                                                     [-1, 1])
+                self.assertFalse(impls[0].is_auto_compatible(
+                    DistributedOperator(op, op_dist_attr)))
+
+                op_dist_attr.set_output_dims_mapping(op.output_arg_names[1],
+                                                     [1, -1])
+                self.assertFalse(impls[0].is_auto_compatible(
+                    DistributedOperator(op, op_dist_attr)))
+                op_dist_attr.set_output_dims_mapping(op.output_arg_names[1],
+                                                     [1, 1])
+                self.assertFalse(impls[0].is_auto_compatible(
+                    DistributedOperator(op, op_dist_attr)))
+                op_dist_attr.set_output_dims_mapping(op.output_arg_names[0],
+                                                     [-1, -1, 1])
+                self.assertFalse(impls[0].is_auto_compatible(
+                    DistributedOperator(op, op_dist_attr)))
+                op_dist_attr.set_input_dims_mapping(op.input_arg_names[0], [-1])
+                op_dist_attr.set_output_dims_mapping(op.output_arg_names[0],
+                                                     [0, -1])
+                self.assertFalse(impls[0].is_auto_compatible(
+                    DistributedOperator(op, op_dist_attr)))
+
+    def test_transpose_compatible(self):
+        valid_op_dist_attr_list = []
+        program = paddle.static.Program()
+        startup_program = paddle.static.Program()
+        loss, program, start_program = mlp_forward(program, startup_program)
+        ops = program.global_block().ops
+        for idx, op in enumerate(ops):
+            if op.type == 'transpose2':
+                dist_op_impl_container = get_distributed_operator_impl_container(
+                    op.type)
+                impls = dist_op_impl_container.get_impls()
+                op_dist_attr = OperatorDistributedAttribute()
+                op_dist_attr.set_input_dims_mapping(op.input_arg_names[0],
+                                                    [-1, -1])
+                op_dist_attr.set_output_dims_mapping(op.output_arg_names[0],
+                                                     [-1, -1])
+                op_dist_attr.set_output_dims_mapping(op.output_arg_names[1],
+                                                     [-1, -1, -1])
+                dist_op = DistributedOperator(op, op_dist_attr)
+                self.assertTrue(impls[0].is_auto_compatible(dist_op))
+
+                op_dist_attr.set_output_dims_mapping(op.output_arg_names[1],
+                                                     [-1, 0, 0])
+                dist_op = DistributedOperator(op, op_dist_attr)
+                self.assertFalse(impls[0].is_auto_compatible(dist_op))
+
+                op_dist_attr.set_output_dims_mapping(op.output_arg_names[1],
+                                                     [0, 0, 0])
+                dist_op = DistributedOperator(op, op_dist_attr)
+                self.assertFalse(impls[0].is_auto_compatible(dist_op))
+
+                op_dist_attr.set_input_dims_mapping(op.input_arg_names[0],
+                                                    [1, -1])
+                dist_op = DistributedOperator(op, op_dist_attr)
+                self.assertFalse(impls[0].is_auto_compatible(dist_op))
+                op_dist_attr.set_output_dims_mapping(op.output_arg_names[1],
+                                                     [-1, 0, 0])
+                dist_op = DistributedOperator(op, op_dist_attr)
+                self.assertFalse(impls[0].is_auto_compatible(dist_op))
+
+                op_dist_attr.set_output_dims_mapping(op.output_arg_names[1],
+                                                     [0, -1, -1])
+                self.assertFalse(impls[0].is_auto_compatible(dist_op))
+
+                op_dist_attr.set_input_dims_mapping(op.input_arg_names[0],
+                                                    [-1, -1])
+
+                op_dist_attr.set_output_dims_mapping(op.output_arg_names[1],
+                                                     [0, 1, 1])
+                self.assertFalse(impls[0].is_auto_compatible(dist_op))
+
+    def test_softmax_compatible(self):
+        valid_op_dist_attr_list = []
+        program = paddle.static.Program()
+        startup_program = paddle.static.Program()
+        loss, program, start_program = mlp_forward(program, startup_program)
+        ops = program.global_block().ops
+        for idx, op in enumerate(ops):
+            if op.type == 'softmax':
+                dist_op_impl_container = get_distributed_operator_impl_container(
+                    op.type)
+                impls = dist_op_impl_container.get_impls()
+                op_dist_attr = OperatorDistributedAttribute()
+                op_dist_attr.set_input_dims_mapping(op.input_arg_names[0],
+                                                    [-1, -1])
+                op_dist_attr.set_output_dims_mapping(op.output_arg_names[0],
+                                                     [-1, -1])
+                dist_op = DistributedOperator(op, op_dist_attr)
+                self.assertTrue(impls[0].is_auto_compatible(dist_op))
+                op_dist_attr.set_output_dims_mapping(op.output_arg_names[0],
+                                                     [1])
+                dist_op = DistributedOperator(op, op_dist_attr)
+                self.assertFalse(impls[0].is_auto_compatible(dist_op))
+
+                op_dist_attr.set_input_dims_mapping(op.input_arg_names[0],
+                                                    [-1, 1])
+                dist_op = DistributedOperator(op, op_dist_attr)
+                self.assertFalse(impls[0].is_auto_compatible(dist_op))
+                op.all_attrs()['axis'] = 2
+                self.assertFalse(impls[0].is_auto_compatible(dist_op))
+
+    def test_embedding_compatible(self):
+        valid_op_dist_attr_list = []
+        program = paddle.static.Program()
+        startup_program = paddle.static.Program()
+        loss, program, start_program = mlp_forward(program, startup_program)
+        ops = program.global_block().ops
+        for idx, op in enumerate(ops):
+            if op.type == 'c_embedding' or op.type == 'lookup_table_v2':
+                dist_op_impl_container = get_distributed_operator_impl_container(
+                    op.type)
+                impls = dist_op_impl_container.get_impls()
+                op_dist_attr = OperatorDistributedAttribute()
+                op_dist_attr.set_input_dims_mapping(op.input_arg_names[0],
+                                                    [-1, -1])
+                op_dist_attr.set_input_dims_mapping(op.input_arg_names[1],
+                                                    [1, -1])
+                op_dist_attr.set_output_dims_mapping(op.output_arg_names[0],
+                                                     [-1, -1, -1])
+                dist_op = DistributedOperator(op, op_dist_attr)
+                self.assertTrue(impls[0].is_auto_compatible(dist_op))
+                op_dist_attr.set_output_dims_mapping(op.output_arg_names[0],
+                                                     [-1, 0, 0])
+                dist_op = DistributedOperator(op, op_dist_attr)
+                self.assertFalse(impls[0].is_auto_compatible(dist_op))
+                op_dist_attr.set_input_dims_mapping(op.input_arg_names[0],
+                                                    [-1, 1])
+                dist_op = DistributedOperator(op, op_dist_attr)
+                self.assertFalse(impls[0].is_auto_compatible(dist_op))
+                op_dist_attr.set_input_dims_mapping(op.input_arg_names[1],
+                                                    [-1, 1])
+                dist_op = DistributedOperator(op, op_dist_attr)
+
+                op_dist_attr.set_input_dims_mapping(op.input_arg_names[1],
+                                                    [1, 1])
+                op_dist_attr.set_output_dims_mapping(op.output_arg_names[0],
+                                                     [-1, -1, -1])
+                dist_op = DistributedOperator(op, op_dist_attr)
+                self.assertFalse(impls[0].is_auto_compatible(dist_op))
+
+                self.assertFalse(impls[0].is_auto_compatible(dist_op))
+                op_dist_attr.set_input_dims_mapping(op.input_arg_names[0],
+                                                    [-1, 1])
+                dist_op = DistributedOperator(op, op_dist_attr)
+                self.assertFalse(impls[0].is_auto_compatible(dist_op))
+                op_dist_attr.set_input_dims_mapping(op.input_arg_names[1],
+                                                    [1, 1])
+                op_dist_attr.set_output_dims_mapping(op.output_arg_names[0],
+                                                     [-1, -1, -1])
+                dist_op = DistributedOperator(op, op_dist_attr)
+                self.assertFalse(impls[0].is_auto_compatible(dist_op))
+                op_dist_attr.set_input_dims_mapping(op.input_arg_names[0],
+                                                    [-1, -1])
+                op_dist_attr.set_output_dims_mapping(op.output_arg_names[0],
+                                                     [1, 1, -1])
+                dist_op = DistributedOperator(op, op_dist_attr)
+                self.assertFalse(impls[0].is_auto_compatible(dist_op))
+
+
+if __name__ == "__main__":
+    unittest.main()