from megatron.core.tensor_parallel import mappings
from tests.test_utilities import Utils
import torch

def test_CopyToModelParallelRegion():
    Utils.initialize_model_parallel(4,2)
    input_data = torch.ones((1)).cuda()*Utils.rank
    output_data = mappings._CopyToModelParallelRegion.backward(None, input_data)
    result = torch.ones(1).cuda()
    result = result * 22 if Utils.rank >= 4 else result * 6
    assert(torch.equal(output_data, result))
    assert(torch.equal(input_data, mappings.copy_to_tensor_model_parallel_region(input_data)))
    assert(torch.equal(input_data, mappings._CopyToModelParallelRegion.symbolic(None, input_data)))
    Utils.destroy_model_parallel()

def test_ReduceFromModelParallelRegion():
    Utils.initialize_model_parallel(4,2)
    input_data = torch.ones((1)).cuda()*Utils.rank
    output_data = mappings._ReduceFromModelParallelRegion.symbolic(None, input_data)
    result = torch.ones(1).cuda()
    result = result * 22 if Utils.rank >= 4 else result * 6
    assert(torch.equal(output_data, result))
    input_data = torch.ones((1)).cuda()*Utils.rank
    assert(torch.equal(mappings.reduce_from_tensor_model_parallel_region(input_data), result))
    assert(torch.equal(input_data, mappings._ReduceFromModelParallelRegion.backward(None, input_data)))
    Utils.destroy_model_parallel()

def test_ScatterToModelParallelRegion():
    Utils.initialize_model_parallel(4,2)
    input_data = torch.rand((8,4)).cuda()
    output_data = mappings.scatter_to_tensor_model_parallel_region(input_data)
    req_dim = int(Utils.rank%(Utils.world_size/2))
    assert(torch.equal(output_data, input_data[:,req_dim].reshape((8,1))))
    output_data = mappings._ScatterToModelParallelRegion.symbolic(None, input_data)
    assert(torch.equal(output_data, input_data[:, req_dim].reshape((8,1))))

    input_data = torch.ones(8).cuda() * Utils.rank
    actual_output_data = mappings._ScatterToModelParallelRegion.backward(None, input_data)
    expected_output = torch.cat((
        torch.ones(8)*0,
        torch.ones(8)*1,
        torch.ones(8)*2,
        torch.ones(8)*3)).cuda()
    if (Utils.rank >= 4):
        expected_output = expected_output + 4
    assert(torch.equal(actual_output_data, expected_output))
    Utils.destroy_model_parallel()

def test_GatherFromModelParallelRegion():
    Utils.initialize_model_parallel(4,2)
    input_data = torch.rand((8,4)).cuda()
    req_dim = int(Utils.rank%(Utils.world_size/2))
    output_data = mappings._GatherFromModelParallelRegion.backward(None, input_data)
    assert(torch.equal(output_data, input_data[:, req_dim].reshape((8,1))))
    input_data = torch.ones(8).cuda() * Utils.rank
    actual_output_data = mappings.gather_from_tensor_model_parallel_region(input_data)
    expected_output = torch.cat((
        torch.ones(8)*0,
        torch.ones(8)*1,
        torch.ones(8)*2,
        torch.ones(8)*3)).cuda()
    if (Utils.rank >= 4):
        expected_output = expected_output + 4
    assert(torch.equal(actual_output_data, expected_output))
    assert(torch.equal(mappings._GatherFromModelParallelRegion.symbolic(None, input_data), expected_output))
    Utils.destroy_model_parallel()
 
def test_ScatterToSequenceParallelRegion():
    Utils.initialize_model_parallel(4,2)
    input_data = torch.rand((8,4)).cuda()
    req_dim = int(Utils.rank%(Utils.world_size/2))*2
    output_data = mappings._ScatterToSequenceParallelRegion.symbolic(None, input_data)
    assert(torch.equal(output_data, input_data[req_dim:req_dim+2, :]))
    output_data = mappings.scatter_to_sequence_parallel_region(input_data)
    assert(torch.equal(output_data, input_data[req_dim:req_dim+2, :]))
    input_data = torch.ones(4).cuda() * Utils.rank
    output_data = mappings._ScatterToModelParallelRegion.backward(None, input_data)
    expected_output = torch.concat((
        torch.ones(4)*0,
        torch.ones(4)*1,
        torch.ones(4)*2,
        torch.ones(4)*3)).cuda()
    if (Utils.rank >= 4):
        expected_output = expected_output + 4
    assert(torch.equal(output_data, expected_output))
    Utils.destroy_model_parallel()

def test_GatherFromSequenceParallelRegion():
    Utils.initialize_model_parallel(4,2)
    input_data = torch.ones(4).cuda() * Utils.rank
    output_data = mappings.gather_from_sequence_parallel_region(input_data)
    expected_output = torch.concat((
        torch.ones(4)*0,
        torch.ones(4)*1,
        torch.ones(4)*2,
        torch.ones(4)*3)).cuda()
    if (Utils.rank >= 4):
        expected_output = expected_output + 4
    assert(torch.equal(output_data, expected_output))
    assert(torch.equal(mappings._GatherFromSequenceParallelRegion.symbolic(None, input_data), expected_output))
    input_data = torch.vstack((
        torch.ones(4)*0,
        torch.ones(4)*1,
        torch.ones(4)*2,
        torch.ones(4)*3)).cuda()
    class Ctx:
        tensor_parallel_output_grad = True
    output_data = mappings._GatherFromSequenceParallelRegion.backward(Ctx(), input_data)
    expected_output = torch.ones((1,4)).cuda() * 4 * int(Utils.rank % 4)
    assert(torch.equal(output_data[0], expected_output))
    Utils.destroy_model_parallel()

def test_ReduceScatterToSequenceParallelRegion():
    Utils.initialize_model_parallel(4,2)
    input_data = torch.vstack((
        torch.ones(4)*0,
        torch.ones(4)*1,
        torch.ones(4)*2,
        torch.ones(4)*3)).cuda()
    output_data = mappings.reduce_scatter_to_sequence_parallel_region(input_data)
    expected_output = torch.ones(4).cuda() * 4 * int(Utils.rank % 4)
    assert(torch.equal(output_data[0], expected_output))
    assert(torch.equal(mappings._ReduceScatterToSequenceParallelRegion.symbolic(None, input_data) , expected_output.reshape((1,4))))
    input_data = torch.ones(4).cuda() * Utils.rank
    output_data = mappings._ReduceScatterToSequenceParallelRegion.backward(None,input_data)
    expected_output = torch.concat((
        torch.ones(4)*0,
        torch.ones(4)*1,
        torch.ones(4)*2,
        torch.ones(4)*3)).cuda()
    if (Utils.rank >= 4):
        expected_output = expected_output + 4
    assert(torch.equal(output_data, expected_output))
    Utils.destroy_model_parallel()