import torch
import triton
import pytest


@pytest.mark.parametrize("MODE, TRANS_A, TRANS_B, BLOCK",
    [
    (mode, at, bt, block) for mode in ['sdd', 'dsd', 'dds']\
                          for at   in [False, True]\
                          for bt   in [False, True]\
                          for block in [16, 32, 64]
    ]
                         )
def test_matmul(MODE, TRANS_A, TRANS_B, BLOCK, DTYPE=torch.float16, Z=3, H=2, M=128, N=256, K=384):
    # set seed
    torch.random.manual_seed(0)
    # create inputs
    a = torch.randn((Z, H, K, M) if TRANS_A else (Z, H, M, K), dtype=DTYPE, device='cuda')
    b = torch.randn((Z, H, N, K) if TRANS_B else (Z, H, K, N), dtype=DTYPE, device='cuda')
    shape = {'sdd': (M, N), 'dsd': (a.shape[2], a.shape[3]), 'dds': (b.shape[2], b.shape[3])}[MODE]
    layout = torch.randint(2, (H, shape[0] // BLOCK, shape[1] // BLOCK))
    # triton result
    op = triton.ops.blocksparse.matmul(layout, BLOCK, MODE, trans_a=TRANS_A, trans_b=TRANS_B)
    ra = triton.testing.sparsify_tensor(a, layout, BLOCK) if MODE == 'dsd' else a
    rb = triton.testing.sparsify_tensor(b, layout, BLOCK) if MODE == 'dds' else b
    rc = op(ra, rb)
    # torch result
    ta = triton.testing.mask_tensor(a, layout, BLOCK) if MODE == 'dsd' else a
    tb = triton.testing.mask_tensor(b, layout, BLOCK) if MODE == 'dds' else b
    ta = ta.transpose(2, 3) if TRANS_A else ta
    tb = tb.transpose(2, 3) if TRANS_B else tb
    tc = torch.matmul(ta, tb)
    tc = triton.testing.mask_tensor(tc, layout, BLOCK) if MODE == 'sdd' else tc
    tc = triton.testing.sparsify_tensor(tc, layout, BLOCK) if MODE == 'sdd' else tc
    # compare
    assert triton.testing.allclose(rc, tc)

@pytest.mark.parametrize("BLOCK, WIDTH",
                         [
    (block, width) for block in [32]\
                   for width in [256, 576, 1024, 1792]
                         ]
                         )
def test_softmax(BLOCK, WIDTH, DTYPE=torch.float16):
    # set seed
    torch.random.manual_seed(0)
    Z, H, M, N = 2, 4, WIDTH, WIDTH
    scale = 0.4
    # create inputs
    layout = torch.randint(2, (H, M // BLOCK, N // BLOCK))
    x = torch.randn((Z, H, M, N), dtype=DTYPE, requires_grad=True, device='cuda')
    at_mask = torch.randint(low=0, high=2, size=(N, N), \
                            dtype=torch.bool, requires_grad=False, device='cuda')
    kp_mask = torch.randint(low=0, high=2, size=(Z, N), \
                            dtype=DTYPE, requires_grad=False, device='cuda')
    kp_mask[kp_mask == 1.] = float('-inf')
    # triton result
    op = triton.ops.blocksparse.softmax(layout, BLOCK)
    tx = triton.testing.sparsify_tensor(x, layout, BLOCK)
    ty = op(tx,
            scale=scale,
            key_padding_mask=kp_mask,
            key_padding_mask_mode='add',
            attn_mask=at_mask.to(DTYPE),
            attn_mask_mode='mul')
    # torch result
    rx = triton.testing.mask_tensor(x, layout, BLOCK, value=float('-inf'))
    if at_mask is not None:
        # broadcast at_mask to the same shape as rx
        M = at_mask[None, None, :, :] + torch.zeros_like(rx)
        rx[M == 0] = float('-inf')
    if kp_mask is not None:
        rx += kp_mask[:, None, None, :]
    ry = torch.softmax(rx * scale, -1)
    ry = torch.softmax(rx * scale, -1)
    ry = triton.testing.sparsify_tensor(ry, layout, BLOCK)
    # compare
    assert triton.testing.allclose(ry, ty)