dist matmul op compatible (#37949)

* dist matmul op compatible

* modify common dist op

* modify common

* add a space

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

@@ -296,6 +296,83 @@ class DistributedMatmulImpl0(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
+        x_name = op_desc.input('X')[0]
+        y_name = op_desc.input('Y')[0]
+        out_name = op_desc.output('Out')[0]
+        out_dims_mapping = op_dist_attr.get_output_dims_mapping(out_name)
+        x_dims_mapping = op_dist_attr.get_input_dims_mapping(x_name)
+        y_dims_mapping = op_dist_attr.get_input_dims_mapping(y_name)
+
+        assert len(x_dims_mapping) >= len(
+            y_dims_mapping), "now just support x dims > y dims"
+        if len(x_dims_mapping) == len(y_dims_mapping) and len(
+                x_dims_mapping) == 4:
+            if x_dims_mapping[:2] != y_dims_mapping[:2]:
+                return False
+            if x_dims_mapping[:2] != out_dims_mapping[:2]:
+                return False
+            x_dims_mapping = x_dims_mapping[-2:]
+            y_dims_mapping = y_dims_mapping[-2:]
+            out_dims_mapping = out_dims_mapping[-2:]
+        elif len(x_dims_mapping) != len(y_dims_mapping) and len(
+                x_dims_mapping) == 3:
+            if x_dims_mapping[0] != out_dims_mapping[0]:
+                return False
+            x_dims_mapping = x_dims_mapping[-2:]
+            y_dims_mapping = y_dims_mapping[-2:]
+            out_dims_mapping = out_dims_mapping[-2:]
+
+        if is_dim_replicate(out_dims_mapping[-1]):
+            return False
+
+        for mapping in out_dims_mapping[1:-1]:
+            if is_dim_shard(mapping):
+                return False
+
+        input_dims_mapping = []
+        ordered_input_shard_dims_mapping = []
+
+        for dim in (x_dims_mapping + y_dims_mapping):
+            input_dims_mapping.append(dim)
+
+        for item in input_dims_mapping:
+            if item not in ordered_input_shard_dims_mapping and item != -1:
+                ordered_input_shard_dims_mapping.append(item)
+
+        for mapping in out_dims_mapping:
+            if mapping not in input_dims_mapping:
+                return False
+
+        if is_dim_shard(x_dims_mapping[0]):
+            order_index = 0
+            for idx, item in enumerate(out_dims_mapping):
+                if item != -1:
+                    if item != ordered_input_shard_dims_mapping[order_index]:
+                        return False
+                    else:
+                        order_index += 1
+            if order_index != len(ordered_input_shard_dims_mapping):
+                return False
+
+        if is_dim_shard(x_dims_mapping[-1]):
+            return False
+        if is_dim_shard(y_dims_mapping[0]) or is_dim_replicate(y_dims_mapping[
+                1]):
+            return False
+        for mapping in x_dims_mapping[1:-1]:
+            if is_dim_shard(mapping):
+                return False
+
+        if is_dim_shard(x_dims_mapping[0]):
+            for mapping in y_dims_mapping[1:]:
+                if is_dim_shard(mapping) and mapping == x_dims_mapping[0]:
+                    return False
+
+        return True
+
     def update_dims_mapping(self, dist_op):
         changed = False
         dim_changed = _update_dims_mapping_for_matmul(dist_op)
@@ -510,6 +587,95 @@ class DistributedMatmulImpl1(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
+        x_name = op_desc.input('X')[0]
+        y_name = op_desc.input('Y')[0]
+        x_dims_mapping = op_dist_attr.get_input_dims_mapping(x_name)
+        y_dims_mapping = op_dist_attr.get_input_dims_mapping(y_name)
+
+        if op_desc.attr('transpose_X') or op_desc.attr('transpose_Y'):
+            return False
+        out_name = op_desc.output('Out')[0]
+        out_dims_mapping = op_dist_attr.get_output_dims_mapping(out_name)
+        # for gpt2, x dims > y dims, this is a temporary solution
+        assert len(x_dims_mapping) >= len(
+            y_dims_mapping), "now just support x dims > y dims"
+        if len(x_dims_mapping) == len(y_dims_mapping) and len(
+                x_dims_mapping) == 4:
+            if x_dims_mapping[:2] != y_dims_mapping[:2]:
+                return False
+            if x_dims_mapping[:2] != out_dims_mapping[:2]:
+                return False
+            x_dims_mapping = x_dims_mapping[-2:]
+            y_dims_mapping = y_dims_mapping[-2:]
+            out_dims_mapping = out_dims_mapping[-2:]
+        elif len(x_dims_mapping) != len(y_dims_mapping) and len(
+                x_dims_mapping) == 3:
+            if x_dims_mapping[0] != out_dims_mapping[0]:
+                return False
+            x_dims_mapping = x_dims_mapping[-2:]
+            y_dims_mapping = y_dims_mapping[-2:]
+            out_dims_mapping = out_dims_mapping[-2:]
+
+        if is_dim_shard(out_dims_mapping[-1]):
+            return False
+        # Other dimensions must be replicate except the batch dimension
+        for mapping in out_dims_mapping[1:-1]:
+            if is_dim_shard(mapping):
+                return False
+
+        if is_dim_replicate(x_dims_mapping[-1]):
+            return False
+
+        if is_dim_replicate(y_dims_mapping[-2]) or is_dim_shard(y_dims_mapping[
+                -1]):
+            return False
+
+        # Other dimensions must be replicate except the batch dimension
+        for mapping in x_dims_mapping[1:-1]:
+            if is_dim_shard(mapping):
+                return False
+
+        x_shard_dim_count = 0
+        x_shard_dims = []
+        y_shard_dim_count = 0
+        y_shard_dims = []
+        for dim in x_dims_mapping:
+            if is_dim_shard(dim):
+                x_shard_dim_count += 1
+                x_shard_dims.append(dim)
+
+        for dim in y_dims_mapping:
+            if is_dim_shard(dim):
+                y_shard_dim_count += 1
+                y_shard_dims.append(dim)
+
+        if not x_shard_dims and not y_shard_dims:
+            return False
+
+        if x_shard_dims[-1] != y_shard_dims[0]:
+            return False
+
+        if x_shard_dim_count == y_shard_dim_count:
+            for dim in out_dims_mapping:
+                if is_dim_shard(dim):
+                    return False
+            if x_shard_dims != y_shard_dims:
+                return False
+        else:
+            if x_shard_dim_count < y_shard_dim_count:
+                return False
+            output_shard_dims = []
+            for dim in out_dims_mapping:
+                if is_dim_shard(dim):
+                    output_shard_dims.append(dim)
+            if not output_shard_dims or output_shard_dims[0] != x_shard_dims[0]:
+                return False
+
+        return True
+
     def update_dims_mapping(self, dist_op):
         changed = False
         dim_changed = _update_dims_mapping_for_matmul(dist_op)
@@ -710,6 +876,59 @@ class DistributedMatmulImpl2(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]
+        y_name = op_desc.input('Y')[0]
+        out_name = op_desc.output('Out')[0]
+        out_dims_mapping = op_dist_attr.get_output_dims_mapping(out_name)
+        x_dims_mapping = op_dist_attr.get_input_dims_mapping(x_name)
+        y_dims_mapping = op_dist_attr.get_input_dims_mapping(y_name)
+        assert len(x_dims_mapping) >= len(
+            y_dims_mapping
+        ), "now just support x dims > y dims,but x:{0} and y:{1}".format(
+            x_dims_mapping, y_dims_mapping)
+        if len(x_dims_mapping) == len(y_dims_mapping) and len(
+                x_dims_mapping) == 4:
+            if x_dims_mapping[:2] != y_dims_mapping[:2]:
+                return False
+            if x_dims_mapping[:2] != out_dims_mapping[:2]:
+                return False
+            x_dims_mapping = x_dims_mapping[-2:]
+            y_dims_mapping = y_dims_mapping[-2:]
+            out_dims_mapping = out_dims_mapping[-2:]
+        elif len(x_dims_mapping) != len(y_dims_mapping) and len(
+                x_dims_mapping) == 3:
+            if x_dims_mapping[0] != out_dims_mapping[0]:
+                return False
+            x_dims_mapping = x_dims_mapping[-2:]
+            y_dims_mapping = y_dims_mapping[-2:]
+            out_dims_mapping = out_dims_mapping[-2:]
+
+        if is_dim_shard(out_dims_mapping[-1]):
+            return False
+
+        if is_valid_list_index(out_dims_mapping,
+                               -2) and is_dim_shard(out_dims_mapping[-2]):
+            return False
+
+        if is_dim_shard(x_dims_mapping[-1]):
+            return False
+
+        if is_valid_list_index(x_dims_mapping,
+                               -2) and is_dim_shard(x_dims_mapping[-2]):
+            return False
+
+        if is_dim_shard(y_dims_mapping[-1]):
+            return False
+
+        if is_valid_list_index(y_dims_mapping,
+                               -2) and is_dim_shard(y_dims_mapping[-2]):
+            return False
+
+        return True
+
     def update_dims_mapping(self, dist_op):
         changed = False
         dim_changed = _update_dims_mapping_for_matmul(dist_op)
@@ -777,6 +996,86 @@ class DistributedMatmulV2Impl0(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
+        x_name = op_desc.input('X')[0]
+        y_name = op_desc.input('Y')[0]
+        out_name = op_desc.output('Out')[0]
+        out_dims_mapping = op_dist_attr.get_output_dims_mapping(out_name)
+        x_dims_mapping = op_dist_attr.get_input_dims_mapping(x_name)
+        y_dims_mapping = op_dist_attr.get_input_dims_mapping(y_name)
+
+        if op_desc.attr('trans_x') or op_desc.attr('trans_y'):
+            return False
+        assert len(x_dims_mapping) >= len(
+            y_dims_mapping), "now just support x dims > y dims"
+        if len(x_dims_mapping) == len(y_dims_mapping) and len(
+                x_dims_mapping) == 4:
+            if x_dims_mapping[:2] != y_dims_mapping[:2]:
+                return False
+            if x_dims_mapping[:2] != out_dims_mapping[:2]:
+                return False
+            x_dims_mapping = x_dims_mapping[-2:]
+            y_dims_mapping = y_dims_mapping[-2:]
+            out_dims_mapping = out_dims_mapping[-2:]
+        elif len(x_dims_mapping) != len(y_dims_mapping) and len(
+                x_dims_mapping) == 3:
+            if x_dims_mapping[0] != out_dims_mapping[0]:
+                return False
+            x_dims_mapping = x_dims_mapping[-2:]
+            y_dims_mapping = y_dims_mapping[-2:]
+            out_dims_mapping = out_dims_mapping[-2:]
+
+        if is_dim_replicate(out_dims_mapping[-1]):
+            return False
+
+        for mapping in out_dims_mapping[1:-1]:
+            if is_dim_shard(mapping):
+                return False
+        input_dims_mapping = []
+        ordered_input_shard_dims_mapping = []
+
+        for dim in (x_dims_mapping + y_dims_mapping):
+            input_dims_mapping.append(dim)
+
+        for item in input_dims_mapping:
+            if item not in ordered_input_shard_dims_mapping and item != -1:
+                ordered_input_shard_dims_mapping.append(item)
+
+        for mapping in out_dims_mapping:
+            if mapping not in input_dims_mapping:
+                return False
+
+        if is_dim_shard(x_dims_mapping[0]):
+            order_index = 0
+            for idx, item in enumerate(out_dims_mapping):
+                if item != -1:
+                    if item != ordered_input_shard_dims_mapping[order_index]:
+                        return False
+                    else:
+                        order_index += 1
+            if order_index != len(ordered_input_shard_dims_mapping):
+                return False
+
+        if is_dim_shard(x_dims_mapping[-1]):
+            return False
+
+        if is_dim_shard(y_dims_mapping[0]) or is_dim_replicate(y_dims_mapping[
+                1]):
+            return False
+
+        for mapping in x_dims_mapping[1:-1]:
+            if is_dim_shard(mapping):
+                return False
+
+        if is_dim_shard(x_dims_mapping[0]):
+            for mapping in y_dims_mapping[1:]:
+                if is_dim_shard(mapping) and mapping == x_dims_mapping[0]:
+                    return False
+
+        return True
+
     def update_dims_mapping(self, dist_op):
         changed = False
         dim_changed = _update_dims_mapping_for_matmul(dist_op)
@@ -985,6 +1284,94 @@ class DistributedMatmulV2Impl1(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
+        x_name = op_desc.input('X')[0]
+        y_name = op_desc.input('Y')[0]
+        x_dims_mapping = op_dist_attr.get_input_dims_mapping(x_name)
+        y_dims_mapping = op_dist_attr.get_input_dims_mapping(y_name)
+        if op_desc.attr('trans_x') or op_desc.attr('trans_y'):
+            return False
+        out_name = op_desc.output('Out')[0]
+        out_dims_mapping = op_dist_attr.get_output_dims_mapping(out_name)
+        assert len(x_dims_mapping) >= len(
+            y_dims_mapping), "now just support x dims > y dims"
+        if len(x_dims_mapping) == len(y_dims_mapping) and len(
+                x_dims_mapping) == 4:
+            if x_dims_mapping[:2] != y_dims_mapping[:2]:
+                return False
+            if x_dims_mapping[:2] != out_dims_mapping[:2]:
+                return False
+            x_dims_mapping = x_dims_mapping[-2:]
+            y_dims_mapping = y_dims_mapping[-2:]
+            out_dims_mapping = out_dims_mapping[-2:]
+
+        elif len(x_dims_mapping) != len(y_dims_mapping) and len(
+                x_dims_mapping) == 3:
+            if x_dims_mapping[0] != out_dims_mapping[0]:
+                return False
+            x_dims_mapping = x_dims_mapping[-2:]
+            y_dims_mapping = y_dims_mapping[-2:]
+            out_dims_mapping = out_dims_mapping[-2:]
+
+        if is_dim_shard(out_dims_mapping[-1]):
+            return False
+
+        # Other dimensions must be replicate except the batch dimension
+        for mapping in out_dims_mapping[1:-1]:
+            if is_dim_shard(mapping):
+                return False
+
+        if is_dim_replicate(x_dims_mapping[-1]):
+            return False
+
+        if is_dim_replicate(y_dims_mapping[-2]) or is_dim_shard(y_dims_mapping[
+                -1]):
+            return False
+
+        # Other dimensions must be replicate except the batch dimension
+        for mapping in x_dims_mapping[1:-1]:
+            if is_dim_shard(mapping):
+                return False
+
+        x_shard_dim_count = 0
+        x_shard_dims = []
+        y_shard_dim_count = 0
+        y_shard_dims = []
+        for dim in x_dims_mapping:
+            if is_dim_shard(dim):
+                x_shard_dim_count += 1
+                x_shard_dims.append(dim)
+
+        for dim in y_dims_mapping:
+            if is_dim_shard(dim):
+                y_shard_dim_count += 1
+                y_shard_dims.append(dim)
+
+        if not x_shard_dims and not y_shard_dims:
+            return False
+
+        if x_shard_dims[-1] != y_shard_dims[0]:
+            return False
+
+        if x_shard_dim_count == y_shard_dim_count:
+            for dim in out_dims_mapping:
+                if is_dim_shard(dim):
+                    return False
+            if x_shard_dims != y_shard_dims:
+                return False
+        else:
+            if x_shard_dim_count < y_shard_dim_count:
+                return False
+            output_shard_dims = []
+            for dim in out_dims_mapping:
+                if is_dim_shard(dim):
+                    output_shard_dims.append(dim)
+            if not output_shard_dims or output_shard_dims[0] != x_shard_dims[0]:
+                return False
+        return True
+
     def update_dims_mapping(self, dist_op):
         changed = False
         dim_changed = _update_dims_mapping_for_matmul(dist_op)
@@ -1183,6 +1570,61 @@ class DistributedMatmulV2Impl2(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]
+        y_name = op_desc.input('Y')[0]
+        out_name = op_desc.output('Out')[0]
+        out_dims_mapping = op_dist_attr.get_output_dims_mapping(out_name)
+        x_dims_mapping = op_dist_attr.get_input_dims_mapping(x_name)
+        y_dims_mapping = op_dist_attr.get_input_dims_mapping(y_name)
+        assert len(x_dims_mapping) >= len(
+            y_dims_mapping
+        ), "now just support x dims > y dims,but x:{0} and y:{1}".format(
+            x_dims_mapping, y_dims_mapping)
+
+        if len(x_dims_mapping) == len(y_dims_mapping) and len(
+                x_dims_mapping) == 4:
+            if x_dims_mapping[:2] != y_dims_mapping[:2]:
+                return False
+            if x_dims_mapping[:2] != out_dims_mapping[:2]:
+                return False
+            x_dims_mapping = x_dims_mapping[-2:]
+            y_dims_mapping = y_dims_mapping[-2:]
+            out_dims_mapping = out_dims_mapping[-2:]
+
+        elif len(x_dims_mapping) != len(y_dims_mapping) and len(
+                x_dims_mapping) == 3:
+            if x_dims_mapping[0] != out_dims_mapping[0]:
+                return False
+            x_dims_mapping = x_dims_mapping[-2:]
+            y_dims_mapping = y_dims_mapping[-2:]
+            out_dims_mapping = out_dims_mapping[-2:]
+
+        if is_dim_shard(out_dims_mapping[-1]):
+            return False
+
+        if is_valid_list_index(out_dims_mapping,
+                               -2) and is_dim_shard(out_dims_mapping[-2]):
+            return False
+
+        if is_dim_shard(x_dims_mapping[-1]):
+            return False
+
+        if is_valid_list_index(x_dims_mapping,
+                               -2) and is_dim_shard(x_dims_mapping[-2]):
+            return False
+
+        if is_dim_shard(y_dims_mapping[-1]):
+            return False
+
+        if is_valid_list_index(y_dims_mapping,
+                               -2) and is_dim_shard(y_dims_mapping[-2]):
+            return False
+
+        return True
+
     def update_dims_mapping(self, dist_op):
         changed = False
         dim_changed = _update_dims_mapping_for_matmul(dist_op)

python/paddle/fluid/tests/unittests/test_auto_search_dist_matmul_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_matmulv2_matmul_2_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)
+
+        with static.program_guard(program,
+                                  start_program), utils.unique_name.guard():
+            matmulx3 = static.data(
+                name="matmulx3", shape=[6, 2, 6], dtype='float32')
+            matmuly3 = static.data(
+                name="matmuly3", shape=[6, 6], dtype='float32')
+            output1 = paddle.matmul(x=matmulx3, y=matmuly3)
+            output_1 = layers.matmul(x=matmulx3, y=matmuly3)
+            matmulx4 = static.data(
+                name="matmulx4", shape=[6, 6, 2, 6], dtype='float32')
+            matmuly4 = static.data(
+                name="matmuly4", shape=[6, 6, 6, 6], dtype='float32')
+            output2 = paddle.matmul(x=matmulx4, y=matmuly4)
+            output_2 = layers.matmul(x=matmulx4, y=matmuly4)
+        ops = program.global_block().ops
+        vars = program.global_block().vars
+        for idx, op in enumerate(ops):
+            if op.type == 'matmul_v2' or op.type == 'matmul':
+                dist_op_impl_container = get_distributed_operator_impl_container(
+                    op.type)
+                impls = dist_op_impl_container.get_impls()
+                op_dist_attr = OperatorDistributedAttribute()
+                X = op.input_arg_names[0]
+                Y = op.input_arg_names[1]
+                out = op.output_arg_names[0]
+                if len(vars[X].shape) == 2 and len(vars[Y].shape) == 2:
+                    op_dist_attr.set_input_dims_mapping(X, [-1, -1])
+                    op_dist_attr.set_input_dims_mapping(Y, [-1, -1])
+                    op_dist_attr.set_output_dims_mapping(out, [-1, -1])
+                    self.assertTrue(impls[2].is_auto_compatible(
+                        DistributedOperator(op, op_dist_attr)))
+                    op_dist_attr.set_input_dims_mapping(X, [1, -1])
+                    self.assertFalse(impls[2].is_auto_compatible(
+                        DistributedOperator(op, op_dist_attr)))
+                    op_dist_attr.set_input_dims_mapping(X, [-1, 1])
+                    self.assertFalse(impls[2].is_auto_compatible(
+                        DistributedOperator(op, op_dist_attr)))
+                    op_dist_attr.set_input_dims_mapping(Y, [1, -1])
+                    self.assertFalse(impls[2].is_auto_compatible(
+                        DistributedOperator(op, op_dist_attr)))
+                    op_dist_attr.set_input_dims_mapping(Y, [-1, 1])
+                    self.assertFalse(impls[2].is_auto_compatible(
+                        DistributedOperator(op, op_dist_attr)))
+                    op_dist_attr.set_output_dims_mapping(out, [-1, 1])
+                    self.assertFalse(impls[2].is_auto_compatible(
+                        DistributedOperator(op, op_dist_attr)))
+                    op_dist_attr.set_output_dims_mapping(out, [1, -1])
+                    self.assertFalse(impls[2].is_auto_compatible(
+                        DistributedOperator(op, op_dist_attr)))
+                if len(vars[X].shape) == 3 and len(vars[Y].shape) == 2:
+                    op_dist_attr.set_input_dims_mapping(X, [-1, -1, -1])
+                    op_dist_attr.set_input_dims_mapping(Y, [-1, -1])
+                    op_dist_attr.set_output_dims_mapping(out, [-1, -1, -1])
+                    self.assertTrue(impls[2].is_auto_compatible(
+                        DistributedOperator(op, op_dist_attr)))
+                    op_dist_attr.set_output_dims_mapping(out, [1, -1, -1])
+                    op_dist_attr.set_input_dims_mapping(X, [-1, -1, 1])
+                    self.assertFalse(impls[2].is_auto_compatible(
+                        DistributedOperator(op, op_dist_attr)))
+                    op_dist_attr.set_input_dims_mapping(Y, [1, -1])
+                    self.assertFalse(impls[2].is_auto_compatible(
+                        DistributedOperator(op, op_dist_attr)))
+                    self.assertFalse(impls[2].is_auto_compatible(
+                        DistributedOperator(op, op_dist_attr)))
+                    op_dist_attr.set_output_dims_mapping(out, [-1, 1, -1])
+                    self.assertFalse(impls[2].is_auto_compatible(
+                        DistributedOperator(op, op_dist_attr)))
+                if len(vars[X].shape) == 4 and len(vars[Y].shape) == 4:
+                    op_dist_attr.set_input_dims_mapping(X, [-1, -1, -1, -1])
+                    op_dist_attr.set_input_dims_mapping(Y, [-1, -1, -1, -1])
+                    op_dist_attr.set_output_dims_mapping(out, [-1, -1, -1, -1])
+                    self.assertTrue(impls[2].is_auto_compatible(
+                        DistributedOperator(op, op_dist_attr)))
+                    op_dist_attr.set_input_dims_mapping(Y, [0, -1, -1, -1])
+                    self.assertFalse(impls[2].is_auto_compatible(
+                        DistributedOperator(op, op_dist_attr)))
+                    op_dist_attr.set_output_dims_mapping(out, [0, -1, -1, -1])
+                    self.assertFalse(impls[2].is_auto_compatible(
+                        DistributedOperator(op, op_dist_attr)))
+                    op_dist_attr.set_input_dims_mapping(Y, [-1, -1, 0, -1])
+                    self.assertFalse(impls[2].is_auto_compatible(
+                        DistributedOperator(op, op_dist_attr)))
+                    op_dist_attr.set_output_dims_mapping(out, [-1, -1, 0, -1])
+                    self.assertFalse(impls[2].is_auto_compatible(
+                        DistributedOperator(op, op_dist_attr)))
+                    op_dist_attr.set_input_dims_mapping(Y, [-1, -1, -1, 1])
+                    self.assertFalse(impls[2].is_auto_compatible(
+                        DistributedOperator(op, op_dist_attr)))
+                    op_dist_attr.set_output_dims_mapping(out, [-1, -1, 0, -1])
+                    self.assertFalse(impls[2].is_auto_compatible(
+                        DistributedOperator(op, op_dist_attr)))
+
+    def test_matmulv2_matmul_1_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)
+        with static.program_guard(program,
+                                  start_program), utils.unique_name.guard():
+            matmulx3 = static.data(
+                name="matmulx3", shape=[6, 2, 6], dtype='float32')
+            matmuly3 = static.data(
+                name="matmuly3", shape=[6, 6], dtype='float32')
+            output1 = paddle.matmul(x=matmulx3, y=matmuly3)
+            output_1 = layers.matmul(x=matmulx3, y=matmuly3)
+            matmulx4 = static.data(
+                name="matmulx4", shape=[6, 6, 6, 6], dtype='float32')
+            matmuly4 = static.data(
+                name="matmuly4", shape=[6, 6, 6, 6], dtype='float32')
+            output2 = paddle.matmul(x=matmulx4, y=matmuly4)
+            output_2 = layers.matmul(x=matmulx4, y=matmuly4)
+        ops = program.global_block().ops
+        vars = program.global_block().vars
+        for idx, op in enumerate(ops):
+            if op.type == 'matmul_v2' or op.type == 'matmul':
+                dist_op_impl_container = get_distributed_operator_impl_container(
+                    op.type)
+                impls = dist_op_impl_container.get_impls()
+                op_dist_attr = OperatorDistributedAttribute()
+                X = op.input_arg_names[0]
+                Y = op.input_arg_names[1]
+                out = op.output_arg_names[0]
+                if len(vars[X].shape) == 2 and len(vars[Y].shape) == 2:
+                    op_dist_attr.set_input_dims_mapping(X, [-1, 1])
+                    op_dist_attr.set_input_dims_mapping(Y, [1, -1])
+                    op_dist_attr.set_output_dims_mapping(out, [-1, -1])
+                    dist_op = DistributedOperator(op, op_dist_attr)
+                    op_dist_attr.set_output_dims_mapping(out, [1, -1])
+                    self.assertFalse(impls[1].is_auto_compatible(
+                        DistributedOperator(op, op_dist_attr)))
+                    op_dist_attr.set_input_dims_mapping(X, [-1, -1])
+                    self.assertFalse(impls[1].is_auto_compatible(
+                        DistributedOperator(op, op_dist_attr)))
+                    op_dist_attr.set_input_dims_mapping(Y, [-1, -1])
+                    self.assertFalse(impls[1].is_auto_compatible(
+                        DistributedOperator(op, op_dist_attr)))
+                if len(vars[X].shape) == 3 and len(vars[Y].shape) == 2:
+                    op_dist_attr.set_input_dims_mapping(X, [-1, -1, 1])
+                    op_dist_attr.set_input_dims_mapping(Y, [1, -1])
+                    op_dist_attr.set_output_dims_mapping(out, [-1, -1, -1])
+                    self.assertTrue(impls[1].is_auto_compatible(
+                        DistributedOperator(op, op_dist_attr)))
+                    op_dist_attr.set_output_dims_mapping(out, [1, -1, 1])
+                    self.assertFalse(impls[1].is_auto_compatible(
+                        DistributedOperator(op, op_dist_attr)))
+                    op_dist_attr.set_input_dims_mapping(out, [-1, -1, -1])
+                    self.assertFalse(impls[1].is_auto_compatible(
+                        DistributedOperator(op, op_dist_attr)))
+                    op_dist_attr.set_output_dims_mapping(out, [-1, 0, -1])
+                    self.assertFalse(impls[1].is_auto_compatible(
+                        DistributedOperator(op, op_dist_attr)))
+                    op_dist_attr.set_input_dims_mapping(X, [-1, -1, -1])
+                    self.assertFalse(impls[1].is_auto_compatible(
+                        DistributedOperator(op, op_dist_attr)))
+                if len(vars[X].shape) == 4 and len(vars[Y].shape) == 4:
+                    op_dist_attr.set_input_dims_mapping(X, [-1, -1, -1, 1])
+                    op_dist_attr.set_input_dims_mapping(Y, [-1, -1, 1, -1])
+                    op_dist_attr.set_output_dims_mapping(out, [-1, -1, -1, -1])
+                    self.assertTrue(impls[1].is_auto_compatible(
+                        DistributedOperator(op, op_dist_attr)))
+                    op_dist_attr.set_input_dims_mapping(Y, [0, -1, -1, -1])
+                    self.assertFalse(impls[1].is_auto_compatible(
+                        DistributedOperator(op, op_dist_attr)))
+                    op_dist_attr.set_output_dims_mapping(out, [0, -1, -1, -1])
+                    self.assertFalse(impls[1].is_auto_compatible(
+                        DistributedOperator(op, op_dist_attr)))
+                    op_dist_attr.set_input_dims_mapping(Y, [-1, -1, 0, -1])
+                    self.assertFalse(impls[1].is_auto_compatible(
+                        DistributedOperator(op, op_dist_attr)))
+                    op_dist_attr.set_output_dims_mapping(out, [-1, -1, 0, -1])
+                    self.assertFalse(impls[1].is_auto_compatible(
+                        DistributedOperator(op, op_dist_attr)))
+                    op_dist_attr.set_input_dims_mapping(Y, [-1, -1, -1, 1])
+                    self.assertFalse(impls[1].is_auto_compatible(
+                        DistributedOperator(op, op_dist_attr)))
+                    op_dist_attr.set_output_dims_mapping(out, [-1, -1, 0, -1])
+                    self.assertFalse(impls[1].is_auto_compatible(
+                        DistributedOperator(op, op_dist_attr)))
+
+    def test_matmulv2_matmul_0_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)
+        with static.program_guard(program,
+                                  start_program), utils.unique_name.guard():
+            matmulx3 = static.data(
+                name="matmulx3", shape=[6, 2, 6], dtype='float32')
+            matmuly3 = static.data(
+                name="matmuly3", shape=[6, 6], dtype='float32')
+            output1 = paddle.matmul(x=matmulx3, y=matmuly3)
+            output_1 = layers.matmul(x=matmulx3, y=matmuly3)
+            matmulx4 = static.data(
+                name="matmulx4", shape=[6, 6, 2, 6], dtype='float32')
+            matmuly4 = static.data(
+                name="matmuly4", shape=[6, 6, 6, 6], dtype='float32')
+            output2 = paddle.matmul(x=matmulx4, y=matmuly4)
+            output_2 = layers.matmul(x=matmulx4, y=matmuly4)
+        ops = program.global_block().ops
+        vars = program.global_block().vars
+        for idx, op in enumerate(ops):
+            if op.type == 'matmul_v2' or op.type == 'matmul':
+                dist_op_impl_container = get_distributed_operator_impl_container(
+                    op.type)
+                impls = dist_op_impl_container.get_impls()
+                op_dist_attr = OperatorDistributedAttribute()
+                X = op.input_arg_names[0]
+                Y = op.input_arg_names[1]
+                out = op.output_arg_names[0]
+                if len(vars[X].shape) == 2 and len(vars[Y].shape) == 2:
+                    op_dist_attr.set_input_dims_mapping(X, [-1, -1])
+                    op_dist_attr.set_input_dims_mapping(Y, [-1, 1])
+                    op_dist_attr.set_output_dims_mapping(out, [-1, 1])
+                    self.assertTrue(impls[0].is_auto_compatible(
+                        DistributedOperator(op, op_dist_attr)))
+                    op_dist_attr.set_input_dims_mapping(X, [-1, 1])
+                    self.assertFalse(impls[0].is_auto_compatible(
+                        DistributedOperator(op, op_dist_attr)))
+                    op_dist_attr.set_input_dims_mapping(Y, [1, 1])
+                    self.assertFalse(impls[0].is_auto_compatible(
+                        DistributedOperator(op, op_dist_attr)))
+                    op_dist_attr.set_output_dims_mapping(out, [0, 0])
+                    self.assertFalse(impls[0].is_auto_compatible(
+                        DistributedOperator(op, op_dist_attr)))
+                    op_dist_attr.set_input_dims_mapping(X, [0, -1])
+                    op_dist_attr.set_output_dims_mapping(out, [1, 1])
+                    self.assertFalse(impls[0].is_auto_compatible(
+                        DistributedOperator(op, op_dist_attr)))
+                    op_dist_attr.set_input_dims_mapping(Y, [1, -1])
+                    self.assertFalse(impls[0].is_auto_compatible(
+                        DistributedOperator(op, op_dist_attr)))
+                if len(vars[X].shape) == 3 and len(vars[Y].shape) == 2:
+                    op_dist_attr.set_input_dims_mapping(X, [-1, -1, -1])
+                    op_dist_attr.set_input_dims_mapping(Y, [-1, 1])
+                    op_dist_attr.set_output_dims_mapping(out, [-1, -1, 1])
+                    self.assertTrue(impls[0].is_auto_compatible(
+                        DistributedOperator(op, op_dist_attr)))
+                    op_dist_attr.set_input_dims_mapping(X, [-1, 0, -1])
+                    self.assertFalse(impls[0].is_auto_compatible(
+                        DistributedOperator(op, op_dist_attr)))
+                    op_dist_attr.set_input_dims_mapping(X, [-1, 1, -1])
+                    self.assertFalse(impls[0].is_auto_compatible(
+                        DistributedOperator(op, op_dist_attr)))
+                    op_dist_attr.set_input_dims_mapping(Y, [-1, -1])
+                    self.assertFalse(impls[0].is_auto_compatible(
+                        DistributedOperator(op, op_dist_attr)))
+                    op_dist_attr.set_output_dims_mapping(out, [1, -1, 1])
+                    self.assertFalse(impls[0].is_auto_compatible(
+                        DistributedOperator(op, op_dist_attr)))
+                    op_dist_attr.set_output_dims_mapping(out, [-1, -1, -1])
+                    self.assertFalse(impls[0].is_auto_compatible(
+                        DistributedOperator(op, op_dist_attr)))
+                    op_dist_attr.set_output_dims_mapping(out, [-1, 1, -1])
+                    self.assertFalse(impls[0].is_auto_compatible(
+                        DistributedOperator(op, op_dist_attr)))
+                if len(vars[X].shape) == 4 and len(vars[Y].shape) == 4:
+                    op_dist_attr.set_input_dims_mapping(X, [-1, -1, -1, -1])
+                    op_dist_attr.set_input_dims_mapping(Y, [-1, -1, -1, 1])
+                    op_dist_attr.set_output_dims_mapping(out, [-1, -1, -1, 1])
+                    self.assertTrue(impls[0].is_auto_compatible(
+                        DistributedOperator(op, op_dist_attr)))
+                    op_dist_attr.set_output_dims_mapping(out, [0, -1, -1, 1])
+                    self.assertFalse(impls[0].is_auto_compatible(
+                        DistributedOperator(op, op_dist_attr)))
+                    op_dist_attr.set_input_dims_mapping(X, [-1, 1, 1, -1])
+                    self.assertFalse(impls[0].is_auto_compatible(
+                        DistributedOperator(op, op_dist_attr)))
+                    op_dist_attr.set_input_dims_mapping(X, [-1, 1, -1, -1])
+                    self.assertFalse(impls[0].is_auto_compatible(
+                        DistributedOperator(op, op_dist_attr)))
+                    op_dist_attr.set_input_dims_mapping(X, [-1, -1, 1, -1])
+                    self.assertFalse(impls[0].is_auto_compatible(
+                        DistributedOperator(op, op_dist_attr)))
+                    op_dist_attr.set_input_dims_mapping(Y, [0, -1, -1, 1])
+                    self.assertFalse(impls[0].is_auto_compatible(
+                        DistributedOperator(op, op_dist_attr)))
+                    op_dist_attr.set_output_dims_mapping(out, [-1, 1, 1, 1])
+                    self.assertFalse(impls[0].is_auto_compatible(
+                        DistributedOperator(op, op_dist_attr)))
+                    op_dist_attr.set_input_dims_mapping(Y, [-1, -1, -1, -1])
+                    self.assertFalse(impls[0].is_auto_compatible(
+                        DistributedOperator(op, op_dist_attr)))
+                    op_dist_attr.set_output_dims_mapping(out, [-1, -1, 1, -1])
+                    self.assertFalse(impls[0].is_auto_compatible(
+                        DistributedOperator(op, op_dist_attr)))
+                    op_dist_attr.set_input_dims_mapping(Y, [-1, -1, 1, -1])
+                    self.assertFalse(impls[0].is_auto_compatible(
+                        DistributedOperator(op, op_dist_attr)))
+
+
+if __name__ == "__main__":
+    unittest.main()