# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
import pytest
import torch

from megatron.core.tensor_parallel.layers import linear_with_frozen_weight
from megatron.core.tensor_parallel.mappings import gather_from_tensor_model_parallel_region
from tests.unit_tests.test_utilities import Utils


@pytest.mark.parametrize("tensor_parallel,allreduce_dgrad", [(1, False), (8, True)])
def test_LinearWithFrozenWeight(tensor_parallel, allreduce_dgrad):
    Utils.initialize_model_parallel(tensor_parallel, 1)

    size_per_partition = int(8 / tensor_parallel)

    # Input is an 8x8 identity matrix.
    input_data = torch.eye(8).cuda()
    input_data.requires_grad = True

    # Weight is an 8x8 matrix of all ones. If tensor parallelism > 1, the weight is partitioned evenly across GPUs.
    weight = torch.ones((size_per_partition, 8)).cuda()

    # Bias is a vector of length 8 of all zeros. If tensor parallelism > 1, the bias is partitioned evenly across GPUs
    bias = torch.zeros((size_per_partition)).cuda()

    gradient_accumulation_fusion = False
    sequence_parallel = False
    grad_output_buffer = None
    wgrad_deferral_limit = None

    output_parallel = linear_with_frozen_weight(
        input_data,
        weight,
        bias,
        gradient_accumulation_fusion,
        allreduce_dgrad,
        sequence_parallel,
        grad_output_buffer,
        wgrad_deferral_limit,
    )
    output = gather_from_tensor_model_parallel_region(
        output_parallel
    )  # no-op if tensor_parallel == 1.
    output.sum().backward()

    expected_output = torch.ones(8).cuda()
    expected_grad = 8 * torch.ones(8).cuda()

    assert torch.allclose(output, expected_output)
    assert torch.allclose(input_data.grad, expected_grad)

    Utils.destroy_model_parallel()
