# Copyright 2022 Huawei Technologies Co., Ltd
#
# 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.

import numpy as np
import mindspore as ms
import mindspore.nn as nn
from mindspore import Tensor, context, jit
from mindspore import dataset as ds
from mindspore.ops import operations as P
from mindspore.common.jit_config import JitConfig


context.set_context(mode=ms.GRAPH_MODE)
context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", dataset_strategy="full_batch")


def setup_function():
    context.set_auto_parallel_context(dataset_strategy="full_batch")


class Attention(nn.Cell):
    def __init__(self):
        super(Attention, self).__init__()
        self.fc_a = nn.Dense(128, 768, activation='relu')
        self.fc_b = nn.Dense(128, 768, activation='relu')
        self.fc_c = nn.Dense(128, 768, activation='relu')
        self.fc_a.matmul.shard(((1, 1), (8, 1)))
        self.fc_b.matmul.shard(((1, 1), (8, 1)))
        self.fc_c.matmul.shard(((1, 1), (8, 1)))

    def construct(self, x):
        q = self.fc_a(x)
        k = self.fc_b(x)
        v = self.fc_c(x)
        return q, k, v

attention = Attention()
relu = nn.ReLU()


@jit
def dense_func(x, label):
    q, k, v = attention(x)
    k = P.Transpose()(k, (1, 0)) # (728, 32)
    c = relu(P.MatMul()(q, k)) # (32, 32)
    s = relu(P.MatMul()(c, v)) # (32, 768)
    s = s - label
    return P.ReduceMean()(s * s)

optimizer_adam = nn.Adam(attention.trainable_params(), learning_rate=0.001)
attention.set_train()
attention.update_parameters_name("attn")
optimizer_adam.update_parameters_name("opt")
grad_dens_func = ms.ops.value_and_grad(dense_func, None, optimizer_adam.parameters)


@jit
def train_step(input_, label_):
    loss, grad = grad_dens_func(input_, label_)
    optimizer_adam(grad)
    return loss


def test_sink():
    """
    Feature: Function mode in auto parallel
    Description: sink mode
    Expectation: compile ok
    """
    context.reset_auto_parallel_context()
    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel",
                                      dataset_strategy="data_parallel", device_num=8)
    data = {"input": np.ones([16, 32, 128]).astype(np.float32), "label": np.zeros([16, 32, 768]).astype(np.float32)}
    dataset = ds.NumpySlicesDataset(data=data)
    jitconfig = JitConfig(jit_level="O1", exc_mode='auto')
    sink_process = ms.train.data_sink(dense_func, dataset, sink_size=4, jit_config=jitconfig)
    _ = sink_process()


def METHOD_NAME():
    """
    Feature: Function mode in auto parallel
    Description: no sink mode
    Expectation: compile ok
    """
    context.reset_auto_parallel_context()
    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", dataset_strategy="full_batch", device_num=8)
    _ = dense_func(Tensor(np.ones([32, 128]).astype(np.float32)), Tensor(np.zeros([32, 768]).astype(np.float32)))


def test_sink_with_grad():
    """
    Feature: Function mode in auto parallel
    Description: sink mode with grad
    Expectation: compile ok
    """
    context.reset_auto_parallel_context()
    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel",
                                      dataset_strategy="data_parallel", device_num=8)
    data = {"input": np.ones([16, 32, 128]).astype(np.float32), "label": np.zeros([16, 32, 768]).astype(np.float32)}
    dataset = ds.NumpySlicesDataset(data=data)
    jitconfig = JitConfig(jit_level="O1", exc_mode='no_sink')
    sink_process = ms.train.data_sink(train_step, dataset, sink_size=4, jit_config=jitconfig)
    _ = sink_process()


def test_no_sink_with_grad():
    """
    Feature: Function mode in auto parallel
    Description: no sink mode with grad
    Expectation: compile ok
    """
    context.reset_auto_parallel_context()
    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", dataset_strategy="full_batch", device_num=8)
    _ = train_step(Tensor(np.ones([32, 128]).astype(np.float32)), Tensor(np.zeros([32, 768]).astype(np.float32)))