# 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 unittest.mock
from io import StringIO

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.tensor as tensor
from paddle.fluid import layers
from paddle.nn.layer.transformer import _convert_param_attr_to_list
import paddle.distributed.auto_parallel as auto
from paddle.distributed.auto_parallel.utils import check_distributed_attr_for_program
from paddle.distributed.auto_parallel.utils import print_program_with_distributed_attr
from paddle.distributed.auto_parallel.utils import append_distributed_attr_suffix
from paddle.distributed.auto_parallel.context import DistributedContext
from paddle.distributed.auto_parallel.context import set_default_distributed_context
paddle.enable_static()
_global_parallel_strategy = None
_global_process_mesh = None
_global_process_mesh2 = None
ROOT_MESH = auto.ProcessMesh([[0, 1, 2, 3], [4, 5, 6, 7]])


class MLPLayer(nn.Layer):
    def __init__(self,
                 hidden_size=1024,
                 intermediate_size=4 * 1024,
                 dropout_ratio=0.1,
                 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)
        self.dropout = nn.Dropout(dropout_ratio, mode="upscale_in_train")

    def forward(self, input):
        if _global_parallel_strategy == "mp":
            auto.shard_tensor(
                self.linear0.weight, _global_process_mesh, dim_mapping=[-1, 0])
            auto.shard_tensor(
                self.linear1.weight, _global_process_mesh, dim_mapping=[0, -1])
        elif _global_parallel_strategy == "dp_mp":
            auto.shard_tensor(
                self.linear0.weight, _global_process_mesh, dim_mapping=[-1, 1])
            auto.shard_tensor(
                self.linear1.weight, _global_process_mesh, dim_mapping=[1, -1])
        elif _global_parallel_strategy == "pp":
            auto.shard_tensor(
                self.linear0.weight, _global_process_mesh, dim_mapping=[-1, 1])
            auto.shard_tensor(
                self.linear1.weight, _global_process_mesh2,
                dim_mapping=[1, -1])

        out = self.norm(input)
        out = self.linear0(out)
        out = F.gelu(out, approximate=True)
        out = self.linear1(out)
        out = self.dropout(out)

        return out


def mlp_pretrain_forward(train_program, start_program):
    with static.program_guard(train_program,
                              start_program), utils.unique_name.guard():
        batch_size = 4
        hidden_size = 1024
        sequence_len = 512
        input = static.data(
            name="input",
            shape=[batch_size, sequence_len, hidden_size],
            dtype='float32')

        if _global_parallel_strategy == "dp":
            auto.shard_tensor(
                input, _global_process_mesh, dim_mapping=[0, -1, -1])
        elif _global_parallel_strategy == "dp_mp":
            auto.shard_tensor(
                input, _global_process_mesh, dim_mapping=[0, -1, -1])

        mlp = MLPLayer(
            hidden_size=hidden_size,
            intermediate_size=4 * hidden_size,
            dropout_ratio=0.1,
            initializer_range=0.02)
        out = mlp(input)
    return train_program, start_program


class TestMLPAutoCompletion(unittest.TestCase):
    def test_mlp_dp(self):
        global _global_parallel_strategy
        _global_parallel_strategy = "dp"
        global _global_process_mesh
        _global_process_mesh = auto.ProcessMesh(
            mesh=[0, 1, 2, 3], parent=ROOT_MESH)
        train_program = static.Program()
        start_program = static.Program()
        dist_context = DistributedContext()
        train_program, start_program = mlp_pretrain_forward(train_program,
                                                            start_program)
        complete_train_program = auto.complete_annotation(train_program,
                                                          dist_context)
        # print_program_with_distributed_attr(complete_train_program,
        #                                     dist_context)
        self.assertTrue(
            check_distributed_attr_for_program(complete_train_program,
                                               dist_context))

    def test_mlp_mp(self):
        global _global_parallel_strategy
        _global_parallel_strategy = "mp"
        global _global_process_mesh
        _global_process_mesh = auto.ProcessMesh(
            mesh=[0, 1, 2, 3], parent=ROOT_MESH)

        train_program = static.Program()
        start_program = static.Program()
        dist_context = DistributedContext()
        train_program, start_program = mlp_pretrain_forward(train_program,
                                                            start_program)
        complete_train_program = auto.complete_annotation(train_program,
                                                          dist_context)
        # print_program_with_distributed_attr(complete_train_program,
        #                                     dist_context)
        self.assertTrue(
            check_distributed_attr_for_program(complete_train_program,
                                               dist_context))

    def test_mlp_dp_mp(self):
        global _global_parallel_strategy
        _global_parallel_strategy = "dp_mp"
        global _global_process_mesh
        _global_process_mesh = auto.ProcessMesh(
            mesh=[[0, 1, 2, 3], [4, 5, 6, 7]], parent=ROOT_MESH)

        train_program = static.Program()
        start_program = static.Program()
        dist_context = DistributedContext()
        train_program, start_program = mlp_pretrain_forward(train_program,
                                                            start_program)
        complete_train_program = auto.complete_annotation(train_program,
                                                          dist_context)
        # print_program_with_distributed_attr(complete_train_program,
        #                                     dist_context)
        self.assertTrue(
            check_distributed_attr_for_program(complete_train_program,
                                               dist_context))

    def test_mlp_misc(self):
        # import pdb
        global _global_parallel_strategy
        _global_parallel_strategy = "pp"
        global _global_process_mesh
        _global_process_mesh = auto.ProcessMesh(
            mesh=[[0, 1], [2, 3]], parent=ROOT_MESH)
        global _global_process_mesh2
        _global_process_mesh2 = auto.ProcessMesh(
            mesh=[[4, 5], [6, 7]], parent=ROOT_MESH)

        train_program = static.Program()
        start_program = static.Program()
        dist_context = DistributedContext()
        train_program, start_program = mlp_pretrain_forward(train_program,
                                                            start_program)
        # pdb.set_trace()
        complete_train_program = auto.complete_annotation(train_program,
                                                          dist_context)
        # print_program_with_distributed_attr(complete_train_program,
        #                                     dist_context)
        dist_context.finalize_distributed_attr_for_program(
            complete_train_program)
        from paddle.distributed.auto_parallel.interface import _g_process_mesh_map
        for block in complete_train_program.blocks:
            for tensor in block.vars.values():
                desc = tensor.desc
                attr_name = append_distributed_attr_suffix("mesh_id")
                self.assertIsNotNone(desc.has_attr(attr_name))
                attr_name = append_distributed_attr_suffix("dim_mapping")
                self.assertIsNotNone(desc.has_attr(attr_name))
            for op in block.ops:
                desc = op.desc
                attr_name = append_distributed_attr_suffix("mesh_id")
                self.assertIsNotNone(desc.has_attr(attr_name))
                for tensor_name in desc.input_arg_names():
                    attr_name = append_distributed_attr_suffix("IN_" +
                                                               tensor_name)
                    self.assertIsNotNone(desc.has_attr(attr_name))
                for tensor_name in desc.output_arg_names():
                    attr_name = append_distributed_attr_suffix("OUT_" +
                                                               tensor_name)
                    self.assertIsNotNone(desc.has_attr(attr_name))
        set_default_distributed_context(dist_context)
        self.assertTrue("dist_attr" in str(complete_train_program))
        with unittest.mock.patch(
                "sys.stdout", new_callable=StringIO) as mock_stdout:
            print_program_with_distributed_attr(complete_train_program)
            self.assertIsNotNone(mock_stdout.getvalue())


class AttentionLayer(nn.Layer):
    def __init__(self,
                 hidden_size=1024,
                 sequence_len=512,
                 intermediate_size=4 * 1024,
                 num_heads=16,
                 dropout_ratio=0.1,
                 initializer_range=0.02):
        super(AttentionLayer, self).__init__()
        self.hidden_size = hidden_size
        self.sequence_len = sequence_len
        self.embed_dim = self.hidden_size
        self.kdim = self.embed_dim
        self.vdim = self.embed_dim
        self.num_heads = num_heads
        self.head_dim = self.embed_dim // self.num_heads
        assert self.head_dim * self.num_heads == self.embed_dim, \
            "embed_dim must be divisible by num_heads"
        self.dropout_ratio = dropout_ratio
        self.initializer_range = initializer_range
        self.training = True
        self.attn_mask = None
        weight_attr = paddle.ParamAttr(initializer=nn.initializer.Normal(
            mean=0.0, std=initializer_range))
        bias_attr = None

        self.q_proj = nn.Linear(
            self.embed_dim, self.embed_dim, weight_attr, bias_attr=bias_attr)
        self.k_proj = nn.Linear(
            self.kdim, self.embed_dim, weight_attr, bias_attr=bias_attr)
        self.v_proj = nn.Linear(
            self.vdim, self.embed_dim, weight_attr, bias_attr=bias_attr)
        self.out_proj = nn.Linear(
            self.embed_dim, self.embed_dim, weight_attr, bias_attr=bias_attr)

    def forward(self, input):
        if _global_parallel_strategy == "dp":
            auto.shard_tensor(
                input, _global_process_mesh, dim_mapping=[0, -1, -1])
        elif _global_parallel_strategy == "dp_mp":
            auto.shard_tensor(
                input, _global_process_mesh, dim_mapping=[0, -1, -1])

        q = self.q_proj(input)
        q = tensor.reshape(x=q, shape=[0, 0, self.num_heads, self.head_dim])
        q = tensor.transpose(x=q, perm=[0, 2, 1, 3])

        k = self.k_proj(input)
        v = self.v_proj(input)

        if _global_parallel_strategy == "mp":
            auto.shard_tensor(
                self.q_proj.weight, _global_process_mesh, dim_mapping=[-1, 0])
            auto.shard_tensor(
                self.k_proj.weight, _global_process_mesh, dim_mapping=[-1, 0])
            auto.shard_tensor(
                self.v_proj.weight, _global_process_mesh, dim_mapping=[-1, 0])
        elif _global_parallel_strategy == "dp_mp":
            auto.shard_tensor(
                self.q_proj.weight, _global_process_mesh, dim_mapping=[-1, 1])
            auto.shard_tensor(
                self.k_proj.weight, _global_process_mesh, dim_mapping=[-1, 1])
            auto.shard_tensor(
                self.v_proj.weight, _global_process_mesh, dim_mapping=[-1, 1])

        k = tensor.reshape(x=k, shape=[0, 0, self.num_heads, self.head_dim])
        k = tensor.transpose(x=k, perm=[0, 2, 1, 3])
        v = tensor.reshape(x=v, shape=[0, 0, self.num_heads, self.head_dim])
        v = tensor.transpose(x=v, perm=[0, 2, 1, 3])

        # scale dot product attention
        product = layers.matmul(
            x=q, y=k, transpose_y=True, alpha=self.head_dim**-0.5)

        if self.attn_mask is not None:
            product = product + self.attn_mask

        weights = F.softmax(product)

        if self.dropout_ratio:
            weights = F.dropout(
                weights,
                self.dropout_ratio,
                training=self.training,
                mode="upscale_in_train")

        out = tensor.matmul(weights, v)

        # combine heads
        out = tensor.transpose(out, perm=[0, 2, 1, 3])
        out = tensor.reshape(x=out, shape=[0, 0, out.shape[2] * out.shape[3]])

        # project to output
        out = self.out_proj(out)
        if _global_parallel_strategy == "mp":
            auto.shard_tensor(
                self.out_proj.weight, _global_process_mesh,
                dim_mapping=[0, -1])
        elif _global_parallel_strategy == "dp_mp":
            auto.shard_tensor(
                self.out_proj.weight, _global_process_mesh,
                dim_mapping=[1, -1])

        return out


def attn_pretrain_forward(train_program, start_program):
    with static.program_guard(train_program,
                              start_program), utils.unique_name.guard():
        batch_size = 4
        hidden_size = 1024
        sequence_len = 512
        input = static.data(
            name="query",
            shape=[batch_size, sequence_len, hidden_size],
            dtype='float32')
        attn = AttentionLayer(
            hidden_size=hidden_size,
            sequence_len=sequence_len,
            intermediate_size=4 * hidden_size,
            num_heads=16,
            dropout_ratio=0.1,
            initializer_range=0.02)
        out = attn(input)

    return train_program, start_program


class TestAttentionAutoCompletion(unittest.TestCase):
    def test_attn_dp(self):
        global _global_parallel_strategy
        _global_parallel_strategy = "dp"
        global _global_process_mesh
        _global_process_mesh = auto.ProcessMesh(
            mesh=[0, 1, 2, 3], parent=ROOT_MESH)
        train_program = static.Program()
        start_program = static.Program()
        dist_context = DistributedContext()
        train_program, start_program = attn_pretrain_forward(train_program,
                                                             start_program)
        complete_train_program = auto.complete_annotation(train_program,
                                                          dist_context)
        # print_program_with_distributed_attr(complete_train_program,
        #                                     dist_context)
        self.assertTrue(
            check_distributed_attr_for_program(complete_train_program,
                                               dist_context))

    def test_attn_mp(self):
        global _global_parallel_strategy
        _global_parallel_strategy = "mp"
        global _global_process_mesh
        _global_process_mesh = auto.ProcessMesh(
            mesh=[0, 1, 2, 3], parent=ROOT_MESH)

        train_program = static.Program()
        start_program = static.Program()
        dist_context = DistributedContext()
        train_program, start_program = attn_pretrain_forward(train_program,
                                                             start_program)
        complete_train_program = auto.complete_annotation(train_program,
                                                          dist_context)
        # print_program_with_distributed_attr(complete_train_program,
        #                                     dist_context)
        self.assertTrue(
            check_distributed_attr_for_program(complete_train_program,
                                               dist_context))

    def test_attn_dp_mp(self):
        global _global_parallel_strategy
        _global_parallel_strategy = "dp_mp"
        global _global_process_mesh
        _global_process_mesh = auto.ProcessMesh(
            mesh=[[0, 1, 2, 3], [4, 5, 6, 7]], parent=ROOT_MESH)

        train_program = static.Program()
        start_program = static.Program()
        dist_context = DistributedContext()
        train_program, start_program = attn_pretrain_forward(train_program,
                                                             start_program)
        complete_train_program = auto.complete_annotation(train_program,
                                                          dist_context)
        # print_program_with_distributed_attr(complete_train_program,
        #                                     dist_context)
        self.assertTrue(
            check_distributed_attr_for_program(complete_train_program,
                                               dist_context))


class DecoderLayer(nn.Layer):
    def __init__(self,
                 vocab_size=32768,
                 hidden_size=1024,
                 sequence_len=512,
                 max_position_embeddings=512,
                 intermediate_size=4 * 1024,
                 num_heads=16,
                 dropout_ratio=0.1,
                 initializer_range=0.02):
        super(DecoderLayer, self).__init__()
        self.vocab_size = vocab_size
        self.hidden_size = hidden_size
        self.max_position_embeddings = max_position_embeddings
        self.sequence_len = sequence_len
        self.embed_dim = self.hidden_size
        self.kdim = self.embed_dim
        self.vdim = self.embed_dim
        self.num_heads = num_heads
        self.dropout_ratio = dropout_ratio
        self.initializer_range = initializer_range
        self.training = True
        self.attn_mask = None

        self.head_dim = self.embed_dim // self.num_heads
        assert self.head_dim * self.num_heads == self.embed_dim, \
            "embed_dim must be divisible by num_heads"
        self.word_embeddings = nn.Embedding(
            self.vocab_size,
            self.hidden_size,
            weight_attr=paddle.ParamAttr(
                name="word_embeddings",
                initializer=nn.initializer.Normal(
                    mean=0.0, std=self.initializer_range)))
        self.position_embeddings = nn.Embedding(
            self.max_position_embeddings,
            self.hidden_size,
            weight_attr=paddle.ParamAttr(
                name="pos_embeddings",
                initializer=nn.initializer.Normal(
                    mean=0.0, std=self.initializer_range)))

        weight_attr = paddle.ParamAttr(initializer=nn.initializer.Normal(
            mean=0.0, std=self.initializer_range))
        bias_attr = None
        self.q_proj = nn.Linear(
            self.embed_dim, self.embed_dim, weight_attr, bias_attr=bias_attr)
        self.k_proj = nn.Linear(
            self.kdim, self.embed_dim, weight_attr, bias_attr=bias_attr)
        self.v_proj = nn.Linear(
            self.vdim, self.embed_dim, weight_attr, bias_attr=bias_attr)
        self.out_proj = nn.Linear(
            self.embed_dim, self.embed_dim, weight_attr, bias_attr=bias_attr)

        intermediate_size = 4 * self.hidden_size
        d_model = self.hidden_size
        dim_feedforward = intermediate_size
        weight_attr = paddle.ParamAttr(initializer=nn.initializer.Normal(
            mean=0.0, std=self.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.norm1 = nn.LayerNorm(d_model, epsilon=1e-5)
        self.norm2 = nn.LayerNorm(d_model, epsilon=1e-5)
        self.dropout1 = nn.Dropout(self.dropout_ratio)
        self.dropout2 = nn.Dropout(self.dropout_ratio, mode="upscale_in_train")
        self.dropout3 = nn.Dropout(self.dropout_ratio, mode="upscale_in_train")

    def forward(self, input_ids, position_ids):
        if _global_parallel_strategy == "dp":
            auto.shard_tensor(
                input_ids, _global_process_mesh, dim_mapping=[0, -1])
        elif _global_parallel_strategy == "dp_mp":
            auto.shard_tensor(
                input_ids, _global_process_mesh, dim_mapping=[0, -1])

        input_embeddings = self.word_embeddings(input_ids)
        position_embeddings = self.position_embeddings(position_ids)

        if _global_parallel_strategy == "mp":
            auto.shard_tensor(
                self.word_embeddings.weight,
                _global_process_mesh,
                dim_mapping=[0, -1])
        elif _global_parallel_strategy == "dp_mp":
            auto.shard_tensor(
                self.word_embeddings.weight,
                _global_process_mesh,
                dim_mapping=[1, -1])

        embeddings = input_embeddings + position_embeddings
        embeddings = self.dropout1(embeddings)

        # Pre-norm
        target = self.norm1(embeddings)

        # The following is the attention part
        q = self.q_proj(target)
        q = tensor.reshape(x=q, shape=[0, 0, self.num_heads, self.head_dim])
        q = tensor.transpose(x=q, perm=[0, 2, 1, 3])

        k = self.k_proj(target)
        v = self.v_proj(target)

        if _global_parallel_strategy == "mp":
            auto.shard_tensor(
                self.q_proj.weight, _global_process_mesh, dim_mapping=[-1, 0])
            auto.shard_tensor(
                self.k_proj.weight, _global_process_mesh, dim_mapping=[-1, 0])
            auto.shard_tensor(
                self.v_proj.weight, _global_process_mesh, dim_mapping=[-1, 0])
        elif _global_parallel_strategy == "dp_mp":
            auto.shard_tensor(
                self.q_proj.weight, _global_process_mesh, dim_mapping=[-1, 1])
            auto.shard_tensor(
                self.k_proj.weight, _global_process_mesh, dim_mapping=[-1, 1])
            auto.shard_tensor(
                self.v_proj.weight, _global_process_mesh, dim_mapping=[-1, 1])

        k = tensor.reshape(x=k, shape=[0, 0, self.num_heads, self.head_dim])
        k = tensor.transpose(x=k, perm=[0, 2, 1, 3])
        v = tensor.reshape(x=v, shape=[0, 0, self.num_heads, self.head_dim])
        v = tensor.transpose(x=v, perm=[0, 2, 1, 3])

        # scale dot product attention
        product = layers.matmul(
            x=q, y=k, transpose_y=True, alpha=self.head_dim**-0.5)

        if self.attn_mask is not None:
            product = product + self.attn_mask

        weights = F.softmax(product)

        if self.dropout_ratio:
            weights = F.dropout(
                weights,
                self.dropout_ratio,
                training=self.training,
                mode="upscale_in_train")

        out = tensor.matmul(weights, v)

        # combine heads
        out = tensor.transpose(out, perm=[0, 2, 1, 3])
        out = tensor.reshape(x=out, shape=[0, 0, out.shape[2] * out.shape[3]])

        # project to output
        out = self.out_proj(out)

        if _global_parallel_strategy == "mp":
            auto.shard_tensor(
                self.out_proj.weight, _global_process_mesh,
                dim_mapping=[0, -1])
        elif _global_parallel_strategy == "dp_mp":
            auto.shard_tensor(
                self.out_proj.weight, _global_process_mesh,
                dim_mapping=[1, -1])

        # Add residual
        residual = embeddings + self.dropout2(out)

        # Pre-norm
        out0 = self.norm2(residual)

        # The following is the MLP part
        out1 = self.linear0(out0)
        out2 = F.gelu(out1, approximate=True)
        out3 = self.linear1(out2)

        if _global_parallel_strategy == "mp":
            auto.shard_tensor(
                self.linear0.weight, _global_process_mesh, dim_mapping=[-1, 0])
            auto.shard_tensor(
                self.linear1.weight, _global_process_mesh, dim_mapping=[0, -1])
        elif _global_parallel_strategy == "dp_mp":
            auto.shard_tensor(
                self.linear0.weight, _global_process_mesh, dim_mapping=[-1, 1])
            auto.shard_tensor(
                self.linear1.weight, _global_process_mesh, dim_mapping=[1, -1])

        # Add residual
        final = residual + self.dropout3(out3)
        return final


def decoder_pretrain_forward(train_program, start_program):
    with static.program_guard(train_program,
                              start_program), utils.unique_name.guard():
        batch_size = 4
        hidden_size = 1024
        sequence_len = 512
        input_ids = static.data(
            name="input_ids", shape=[batch_size, sequence_len], dtype='int64')
        position_ids = static.data(
            name="position_ids",
            shape=[batch_size, sequence_len],
            dtype='int64')
        decoder = DecoderLayer(
            vocab_size=32768,
            hidden_size=hidden_size,
            sequence_len=sequence_len,
            max_position_embeddings=512,
            intermediate_size=4 * hidden_size,
            num_heads=16,
            dropout_ratio=0.1,
            initializer_range=0.02)
        out = decoder(input_ids, position_ids)

    return train_program, start_program


class TestDecoderLayerAutoCompletion(unittest.TestCase):
    def test_decoder_dp(self):
        global _global_parallel_strategy
        _global_parallel_strategy = "dp"
        global _global_process_mesh
        _global_process_mesh = auto.ProcessMesh(
            mesh=[0, 1, 2, 3], parent=ROOT_MESH)
        train_program = static.Program()
        start_program = static.Program()
        dist_context = DistributedContext()
        train_program, start_program = decoder_pretrain_forward(train_program,
                                                                start_program)
        complete_train_program = auto.complete_annotation(train_program,
                                                          dist_context)
        # print_program_with_distributed_attr(complete_train_program,
        #                                     dist_context)
        self.assertTrue(
            check_distributed_attr_for_program(complete_train_program,
                                               dist_context))

    def test_decoder_mp(self):
        global _global_parallel_strategy
        _global_parallel_strategy = "mp"
        global _global_process_mesh
        _global_process_mesh = auto.ProcessMesh(
            mesh=[0, 1, 2, 3], parent=ROOT_MESH)

        train_program = static.Program()
        start_program = static.Program()
        dist_context = DistributedContext()
        train_program, start_program = decoder_pretrain_forward(train_program,
                                                                start_program)
        complete_train_program = auto.complete_annotation(train_program,
                                                          dist_context)
        # print_program_with_distributed_attr(complete_train_program,
        #                                     dist_context)
        self.assertTrue(
            check_distributed_attr_for_program(complete_train_program,
                                               dist_context))

    def test_decoder_dp_mp(self):
        global _global_parallel_strategy
        _global_parallel_strategy = "dp_mp"
        global _global_process_mesh
        _global_process_mesh = auto.ProcessMesh(
            mesh=[[0, 1, 2, 3], [4, 5, 6, 7]], parent=ROOT_MESH)

        train_program = static.Program()
        start_program = static.Program()
        dist_context = DistributedContext()
        train_program, start_program = decoder_pretrain_forward(train_program,
                                                                start_program)
        complete_train_program = auto.complete_annotation(train_program,
                                                          dist_context)
        # print_program_with_distributed_attr(complete_train_program,
        #                                     dist_context)
        self.assertTrue(
            check_distributed_attr_for_program(complete_train_program,
                                               dist_context))


if __name__ == "__main__":
    unittest.main()
