flash-attention/tests/ops/test_dropout_layer_norm.py

import math

import torch
import torch.nn.functional as F
import pytest

from einops import rearrange, repeat

from flash_attn.ops.layer_norm import DropoutAddLayerNorm, dropout_add_layer_norm
from flash_attn.ops.layer_norm import dropout_add_layer_norm_subset
from flash_attn.ops.rms_norm import DropoutAddRMSNorm, dropout_add_rms_norm
from flash_attn.ops.rms_norm import dropout_add_rms_norm_subset
from flash_attn.ops.layer_norm import dropout_add_layer_norm_parallel_residual
from flash_attn.ops.rms_norm import dropout_add_rms_norm_parallel_residual

try:
    from apex.normalization import FusedRMSNorm
    from apex.normalization.fused_layer_norm import fused_rms_norm_affine
except:
    FusedRMSNorm, fused_rms_norm_affine = None, None


is_sm8x = torch.cuda.get_device_capability('cuda')[0] >= 8

@pytest.mark.parametrize('is_rms_norm', [False, True])
@pytest.mark.parametrize('has_colscale', [True, False])
# @pytest.mark.parametrize('has_colscale', [False])
@pytest.mark.parametrize('has_rowscale', [True, False])
# @pytest.mark.parametrize('has_rowscale', [True])
@pytest.mark.parametrize('has_residual', [True, False])
# @pytest.mark.parametrize('has_residual', [False])
@pytest.mark.parametrize('dropout_p', [0.37, 0.0])
# @pytest.mark.parametrize('dropout_p', [0.0])
@pytest.mark.parametrize('weight_dtype', [torch.float32, torch.float16])
# @pytest.mark.parametrize('weight_dtype', [torch.float32])
@pytest.mark.parametrize('input_dtype,residual_dtype',
                         [(torch.float16, torch.float16), (torch.float16, torch.float32),
                          (torch.float32, torch.float32)]
                         + ([(torch.bfloat16, torch.bfloat16), (torch.bfloat16, torch.float32)] if is_sm8x else []))
# @pytest.mark.parametrize('input_dtype,residual_dtype', [(torch.float16, torch.float32)])
@pytest.mark.parametrize('hidden_size', [192, 256, 384, 768, 1024, 1280, 1536, 1600, 2048, 2560, 3000, 3072, 4096, 5120, 6144])
# @pytest.mark.parametrize('hidden_size', [256])
def test_dropout_layer_norm_training(hidden_size, input_dtype, residual_dtype, weight_dtype,
                                     dropout_p, has_residual, has_rowscale, has_colscale, is_rms_norm):
    if weight_dtype == torch.float16 and input_dtype == torch.bfloat16:
        pytest.skip()  # Not supported
    if is_rms_norm and FusedRMSNorm is None:
        pytest.skip()  # We need Apex's FusedRMSNorm to test
    layer_norm_cls = torch.nn.LayerNorm if not is_rms_norm else FusedRMSNorm
    our_layer_norm_cls = DropoutAddLayerNorm if not is_rms_norm else DropoutAddRMSNorm
    our_layer_norm_func = dropout_add_layer_norm if not is_rms_norm else dropout_add_rms_norm
    device = 'cuda'
    # rtol, atol = (1e-5, 1e-6) if input_dtype == torch.float32 else (1e-3, 1e-4)
    rtol, atol = (1e-3, 1e-4)
    # set seed
    torch.random.manual_seed(0)
    batch_size = 8
    seqlen = 512
    x0_pt = torch.randn(batch_size, seqlen, hidden_size, device=device, dtype=input_dtype,
                        requires_grad=True)
    x0 = x0_pt.detach().clone().requires_grad_()
    x0_ref = x0_pt.detach().clone().float().requires_grad_()
    if has_colscale:
        colscale = torch.randn(hidden_size, device=device, dtype=weight_dtype, requires_grad=True)
        colscale_pt = colscale.detach().clone().requires_grad_()
        colscale_ref = colscale.detach().clone().float().requires_grad_()
    else:
        colscale = None
    if has_residual:
        res_pt = torch.randn_like(x0, dtype=residual_dtype, requires_grad=True)
        res = res_pt.detach().clone().requires_grad_()
        res_ref = res_pt.detach().clone().float().requires_grad_()
    else:
        res = None
    if has_rowscale:
        rowscale = torch.empty(batch_size, seqlen, device=device, dtype=input_dtype)
        survival_rate = 0.87
        rowscale = rowscale.bernoulli_(survival_rate) / survival_rate
        x0_scaled_pt = x0_pt * rearrange(rowscale, '... -> ... 1')
        x0_scaled_ref = x0_ref * rearrange(rowscale, '... -> ... 1')
    else:
        rowscale = None
        x0_scaled_pt = x0_pt
        x0_scaled_ref = x0_ref
    if has_colscale:
        x0_scaled_pt = x0_scaled_pt * colscale_pt
        x0_scaled_ref = x0_scaled_ref * colscale_ref
    model_pt = layer_norm_cls(hidden_size).to(device=device, dtype=weight_dtype)
    torch.nn.init.normal_(model_pt.weight)
    if not is_rms_norm:
        torch.nn.init.normal_(model_pt.bias)
    model_ref = layer_norm_cls(hidden_size).to(device=device, dtype=torch.float32)
    model = our_layer_norm_cls(hidden_size, p=dropout_p, device=device, dtype=weight_dtype)
    with torch.no_grad():
        model.weight.copy_(model_pt.weight)
        model_ref.weight.copy_(model_pt.weight)
        if not is_rms_norm:
            model.bias.copy_(model_pt.bias)
            model_ref.bias.copy_(model_pt.bias)
    residual_in_fp32 = (not has_residual) and residual_dtype == torch.float32
    out, dmask = our_layer_norm_func(x0, res, model.weight, model.bias, model.p,
                                     model.epsilon, rowscale=rowscale, layerscale=colscale,
                                     residual_in_fp32=residual_in_fp32, return_dropout_mask=True)
    assert out.dtype == input_dtype
    print(f'Actual dropout fraction: {1 - dmask.float().mean().item()}')
    if has_residual:
        residual_pt = ((x0_scaled_pt.float() * dmask.float()) / (1 - dropout_p) + res_pt.float()).to(dtype=residual_dtype)
        residual_ref = (x0_scaled_ref * dmask.float()) / (1 - dropout_p) + res_ref
    else:
        residual_pt = ((x0_scaled_pt.float() * dmask.float()) / (1 - dropout_p)).to(dtype=residual_dtype)
        residual_ref = (x0_scaled_ref * dmask.float()) / (1 - dropout_p)
    out_pt = model_pt(residual_pt.to(dtype=weight_dtype)).to(dtype=input_dtype)
    out_ref = model_ref(residual_ref)
    assert (out - out_ref).abs().max() <= 4 * (out_pt - out_ref).abs().max() + 1e-4

    g = torch.randn_like(out) / batch_size
    out_pt.backward(g)
    out.backward(g)
    out_ref.backward(g)
    assert (x0.grad - x0_ref.grad).abs().max() <= 4 * (x0_pt.grad - x0_ref.grad).abs().max() + 1e-4
    if has_residual:
        assert (res.grad - res_ref.grad).abs().max() <= 4 * (res_pt.grad - res_ref.grad).abs().max() + 1e-4
    assert (model.weight.grad - model_ref.weight.grad).abs().max() <= 3 * (model_pt.weight.grad - model_ref.weight.grad).abs().max() + 3e-5
    if not is_rms_norm:
        assert (model.bias.grad - model_ref.bias.grad).abs().max() <= 2 * (model_pt.bias.grad - model_ref.bias.grad).abs().max() + 3e-5
    if has_colscale:
        assert (colscale.grad - colscale_ref.grad).abs().max() <= 2 * (colscale_pt.grad - colscale_ref.grad).abs().max() + 2e-4


@pytest.mark.parametrize('weight_dtype', [torch.float32, torch.float16])
@pytest.mark.parametrize('input_dtype,residual_dtype',
                         [(torch.float16, torch.float16), (torch.float16, torch.float32),
                          (torch.float32, torch.float32)]
                         + ([(torch.bfloat16, torch.bfloat16), (torch.bfloat16, torch.float32)] if is_sm8x else []))
@pytest.mark.parametrize('hidden_size', [768, 1024, 1280, 1536, 1600, 2048, 2560, 3072, 4096, 5120])
def test_dropout_layer_norm_eval(hidden_size, input_dtype, residual_dtype, weight_dtype):
    if weight_dtype == torch.float16 and input_dtype == torch.bfloat16:
        pytest.skip()  # Not supported
    device = 'cuda'
    # rtol, atol = (1e-5, 1e-6) if dtype == torch.float32 else (1e-3, 1e-4)
    rtol, atol = (1e-3, 1e-4)
    dropout_p = 0.37
    # set seed
    torch.random.manual_seed(0)
    batch_size = 32
    seqlen = 512
    x0_pt = torch.randn(batch_size, seqlen, hidden_size, device=device, dtype=input_dtype,
                        requires_grad=True)
    x0 = x0_pt.detach().clone().requires_grad_()
    x0_ref = x0_pt.detach().clone().float().requires_grad_()
    res_pt = torch.randn_like(x0, dtype=residual_dtype, requires_grad=True)
    res = res_pt.detach().clone().requires_grad_()
    res_ref = res_pt.detach().clone().float().requires_grad_()
    model_pt = torch.nn.LayerNorm(hidden_size, device=device, dtype=weight_dtype)
    torch.nn.init.normal_(model_pt.weight)
    torch.nn.init.normal_(model_pt.bias)
    model = DropoutAddLayerNorm(hidden_size, p=dropout_p, device=device, dtype=weight_dtype)
    model_ref = torch.nn.LayerNorm(hidden_size, device=device, dtype=torch.float32)
    with torch.no_grad():
        model.weight.copy_(model_pt.weight)
        model.bias.copy_(model_pt.bias)
        model_ref.weight.copy_(model_pt.weight)
        model_ref.bias.copy_(model_pt.bias)
    model_pt.eval()
    model.eval()
    model_ref.eval()
    out = model(x0, res)
    residual_pt = (x0_pt.float() + res_pt.float()).to(dtype=residual_dtype)
    residual_ref = x0_ref + res_ref
    out_pt = model_pt(residual_pt.to(dtype=weight_dtype)).to(input_dtype)
    out_ref = model_ref(residual_ref)
    assert (out - out_ref).abs().max() <= 4 * (out_pt - out_ref).abs().max() + 1e-4


@pytest.mark.parametrize('is_rms_norm', [False, True])
@pytest.mark.parametrize('has_colscale', [True, False])
@pytest.mark.parametrize('has_rowscale', [True, False])
@pytest.mark.parametrize('has_residual', [True, False])
@pytest.mark.parametrize('dropout_p', [0.37, 0.0])
@pytest.mark.parametrize('weight_dtype', [torch.float32, torch.float16])
@pytest.mark.parametrize('input_dtype,residual_dtype',
                         [(torch.float16, torch.float16), (torch.float16, torch.float32),
                          (torch.float32, torch.float32)]
                         + ([(torch.bfloat16, torch.bfloat16), (torch.bfloat16, torch.float32)] if is_sm8x else []))
# @pytest.mark.parametrize('has_colscale', [True])
# @pytest.mark.parametrize('has_rowscale', [False])
# @pytest.mark.parametrize('has_residual', [True])
# @pytest.mark.parametrize('dropout_p', [0.0])
# @pytest.mark.parametrize('weight_dtype', [torch.float32])
# @pytest.mark.parametrize('input_dtype,residual_dtype', [(torch.float32, torch.float32)])
@pytest.mark.parametrize('hidden_size', [192, 256, 384, 768, 1024, 1280, 1536, 1600, 2048, 2560, 3000, 3072, 4096, 5120, 6144])
# @pytest.mark.parametrize('hidden_size', [256])
def test_dropout_layer_norm_prenorm_training(hidden_size, input_dtype, residual_dtype, weight_dtype,
                                             dropout_p, has_residual, has_rowscale, has_colscale,
                                             is_rms_norm):
    if weight_dtype == torch.float16 and input_dtype == torch.bfloat16:
        pytest.skip()  # Not supported
    if is_rms_norm and FusedRMSNorm is None:
        pytest.skip()  # We need Apex's FusedRMSNorm to test
    layer_norm_cls = torch.nn.LayerNorm if not is_rms_norm else FusedRMSNorm
    our_layer_norm_cls = DropoutAddLayerNorm if not is_rms_norm else DropoutAddRMSNorm
    our_layer_norm_func = dropout_add_layer_norm if not is_rms_norm else dropout_add_rms_norm
    device = 'cuda'
    # rtol, atol = (1e-5, 1e-6) if input_dtype == torch.float32 else (1e-3, 1e-4)
    rtol, atol = (1e-3, 2e-4)
    # set seed
    torch.random.manual_seed(0)
    batch_size = 8
    seqlen = 512
    x0_pt = torch.randn(batch_size, seqlen, hidden_size, device=device, dtype=input_dtype,
                        requires_grad=True)
    x0 = x0_pt.detach().clone().requires_grad_()
    x0_ref = x0_pt.detach().clone().float().requires_grad_()
    if has_colscale:
        colscale = torch.randn(hidden_size, device=device, dtype=weight_dtype, requires_grad=True)
        colscale_pt = colscale.detach().clone().requires_grad_()
        colscale_ref = colscale.detach().clone().float().requires_grad_()
    else:
        colscale = None
    if has_residual:
        res_pt = torch.randn_like(x0, dtype=residual_dtype, requires_grad=True)
        res = res_pt.detach().clone().requires_grad_()
        res_ref = res_pt.detach().clone().float().requires_grad_()
    else:
        res = None
    if has_rowscale:
        rowscale = torch.empty(batch_size, seqlen, device=device, dtype=input_dtype)
        survival_rate = 0.87
        rowscale = rowscale.bernoulli_(survival_rate) / survival_rate
        x0_scaled_pt = x0_pt * rearrange(rowscale, '... -> ... 1')
        x0_scaled_ref = x0_ref * rearrange(rowscale, '... -> ... 1')
    else:
        rowscale = None
        x0_scaled_pt = x0_pt
        x0_scaled_ref = x0_ref
    if has_colscale:
        x0_scaled_pt = x0_scaled_pt * colscale_pt
        x0_scaled_ref = x0_scaled_ref * colscale_ref
    model_pt = layer_norm_cls(hidden_size).to(device=device, dtype=weight_dtype)
    torch.nn.init.normal_(model_pt.weight)
    if not is_rms_norm:
        torch.nn.init.normal_(model_pt.bias)
    model_ref = layer_norm_cls(hidden_size).to(device=device, dtype=torch.float32)
    model = our_layer_norm_cls(hidden_size, prenorm=True, p=dropout_p, device=device,
                               dtype=weight_dtype)
    with torch.no_grad():
        model.weight.copy_(model_pt.weight)
        model_ref.weight.copy_(model_pt.weight)
        if not is_rms_norm:
            model.bias.copy_(model_pt.bias)
            model_ref.bias.copy_(model_pt.bias)
    residual_in_fp32 = (not has_residual) and residual_dtype == torch.float32
    out, residual, dmask = our_layer_norm_func(x0, res, model.weight, model.bias, model.p,
                                               model.epsilon, rowscale=rowscale,
                                               layerscale=colscale, prenorm=True,
                                               residual_in_fp32=residual_in_fp32,
                                               return_dropout_mask=True)
    print(f'Actual dropout fraction: {1 - dmask.float().mean().item()}')
    if has_residual:
        residual_pt = ((x0_scaled_pt.float() * dmask.float()) / (1 - dropout_p) + res_pt.float()).to(dtype=residual_dtype)
        residual_ref = (x0_scaled_ref * dmask.float()) / (1 - dropout_p) + res_ref
    else:
        residual_pt = ((x0_scaled_pt.float() * dmask.float()) / (1 - dropout_p)).to(dtype=residual_dtype)
        residual_ref = (x0_scaled_ref * dmask.float()) / (1 - dropout_p)
    out_pt = model_pt(residual_pt.to(dtype=weight_dtype)).to(dtype=input_dtype)
    out_ref = model_ref(residual_ref)
    assert out.dtype == input_dtype
    assert residual.dtype == residual_dtype
    assert (out - out_ref).abs().max() <= 4 * (out_pt - out_ref).abs().max() + 1e-4
    assert (residual - residual_ref).abs().max() <= 4 * (residual_pt - residual_ref).abs().max() + 1e-4

    g = torch.randn_like(out) / batch_size
    (out_pt * F.sigmoid(residual_pt)).backward(g)
    (out * F.sigmoid(residual)).backward(g)
    (out_ref * F.sigmoid(residual_ref.to(dtype=residual_dtype))).backward(g)
    assert (x0.grad - x0_ref.grad).abs().max() <= 4 * (x0_pt.grad - x0_ref.grad).abs().max() + 1e-4
    if has_residual:
        assert (res.grad - res_ref.grad).abs().max() <= 4 * (res_pt.grad - res_ref.grad).abs().max() + 1e-4
    assert (model.weight.grad - model_ref.weight.grad).abs().max() <= 2 * (model_pt.weight.grad - model_ref.weight.grad).abs().max() + 2e-4
    if not is_rms_norm:
        assert (model.bias.grad - model_ref.bias.grad).abs().max() <= 2 * (model_pt.bias.grad - model_ref.bias.grad).abs().max() + 2e-4
    if has_colscale:
        assert (colscale.grad - colscale_ref.grad).abs().max() <= 2 * (colscale_pt.grad - colscale_ref.grad).abs().max() + 2e-4


@pytest.mark.parametrize('weight_dtype', [torch.float32, torch.float16])
@pytest.mark.parametrize('input_dtype,residual_dtype',
                         [(torch.float16, torch.float16), (torch.float16, torch.float32),
                          (torch.float32, torch.float32)]
                         + ([(torch.bfloat16, torch.bfloat16), (torch.bfloat16, torch.float32)] if is_sm8x else []))
@pytest.mark.parametrize('hidden_size', [768, 1024, 1280, 1536, 1600, 2048, 2560, 3072, 4096, 5120])
def test_dropout_layer_norm_prenorm_eval(hidden_size, input_dtype, residual_dtype, weight_dtype):
    if weight_dtype == torch.float16 and input_dtype == torch.bfloat16:
        pytest.skip()  # Not supported
    device = 'cuda'
    # rtol, atol = (1e-5, 1e-6) if dtype == torch.float32 else (1e-3, 1e-4)
    rtol, atol = (1e-3, 1e-4)
    dropout_p = 0.37
    # set seed
    torch.random.manual_seed(0)
    batch_size = 32
    seqlen = 512
    x0_pt = torch.randn(batch_size, seqlen, hidden_size, device=device, dtype=input_dtype,
                        requires_grad=True)
    x0 = x0_pt.detach().clone().requires_grad_()
    x0_ref = x0_pt.detach().clone().float().requires_grad_()
    res_pt = torch.randn_like(x0, dtype=residual_dtype, requires_grad=True)
    res = res_pt.detach().clone().requires_grad_()
    res_ref = res_pt.detach().clone().float().requires_grad_()
    model_pt = torch.nn.LayerNorm(hidden_size, device=device, dtype=weight_dtype)
    torch.nn.init.normal_(model_pt.weight)
    torch.nn.init.normal_(model_pt.bias)
    model = DropoutAddLayerNorm(hidden_size, prenorm=True, p=dropout_p, device=device,
                                dtype=weight_dtype)
    model_ref = torch.nn.LayerNorm(hidden_size, device=device, dtype=torch.float32)
    with torch.no_grad():
        model.weight.copy_(model_pt.weight)
        model.bias.copy_(model_pt.bias)
        model_ref.weight.copy_(model_pt.weight)
        model_ref.bias.copy_(model_pt.bias)
    model_pt.eval()
    model.eval()
    model_ref.eval()
    out, residual = model(x0, res)
    residual_pt = (x0_pt.float() + res_pt.float()).to(dtype=residual_dtype)
    residual_ref = x0_ref + res_ref
    out_pt = model_pt(residual_pt.to(dtype=weight_dtype)).to(input_dtype)
    out_ref = model_ref(residual_ref)
    assert (out - out_ref).abs().max() <= 4 * (out_pt - out_ref).abs().max() + 1e-4
    assert (residual - residual_ref).abs().max() <= 4 * (residual_pt - residual_ref).abs().max() + 1e-4


@pytest.mark.parametrize('has_colscale', [True, False])
@pytest.mark.parametrize('has_residual', [True, False])
@pytest.mark.parametrize('dropout_p', [0.37, 0.0])
@pytest.mark.parametrize('weight_dtype', [torch.float32, torch.float16])
@pytest.mark.parametrize('input_dtype,residual_dtype',
                         [(torch.float16, torch.float16), (torch.float16, torch.float32),
                          (torch.float32, torch.float32)]
                         + ([(torch.bfloat16, torch.bfloat16), (torch.bfloat16, torch.float32)] if is_sm8x else []))
# @pytest.mark.parametrize('has_colscale', [True])
# @pytest.mark.parametrize('has_residual', [True])
# @pytest.mark.parametrize('dropout_p', [0.0])
# @pytest.mark.parametrize('weight_dtype', [torch.float32])
# @pytest.mark.parametrize('input_dtype,residual_dtype', [(torch.float32, torch.float32)])
@pytest.mark.parametrize('hidden_size', [192, 256, 384, 768, 1024, 1280, 1536, 1600, 2048, 2560, 3000, 3072, 4096, 5120, 6144])
# @pytest.mark.parametrize('hidden_size', [256])
def test_dropout_layer_norm_subset_training(
        hidden_size, input_dtype, residual_dtype, weight_dtype, dropout_p,
        has_residual, has_colscale):
    if weight_dtype == torch.float16 and input_dtype == torch.bfloat16:
        pytest.skip()  # Not supported
    device = 'cuda'
    # rtol, atol = (1e-5, 1e-6) if input_dtype == torch.float32 else (1e-3, 1e-4)
    rtol, atol = (1e-3, 2e-4)
    # set seed
    torch.random.manual_seed(0)
    batch_size = 8
    seqlen = 512
    drop_path_rate = 0.4
    drop_path_scale = 1 / (1 - drop_path_rate)
    def generate_droppath_masks(batch_size, seqlen, drop_path_rate, device):
        # Do it on CPU so we can get the numrows (with .item()) without GPU-CPU sync
        mask_batch = torch.rand(batch_size) < 1 - drop_path_rate
        numrows = (mask_batch).sum().item() * seqlen
        mask_batch = mask_batch.to(device=device, non_blocking=True)
        mask_batch_seqlen = repeat(mask_batch, 'b -> (b s)', s=seqlen)
        subset = torch.cumsum(mask_batch_seqlen, dim=0,
                              dtype=torch.int32).masked_fill_(~mask_batch_seqlen, 0)
        return mask_batch, numrows, rearrange(subset, '(b s) -> b s', b=batch_size)

    x0_mask_batch, x0_numrows, x0_subset = generate_droppath_masks(batch_size, seqlen,
                                                                   drop_path_rate, device)
    out_mask_batch, out_numrows, out_subset = generate_droppath_masks(batch_size, seqlen,
                                                                      drop_path_rate, device)

    x0_pt = torch.randn(batch_size, seqlen, hidden_size, device=device, dtype=input_dtype,
                        requires_grad=True)
    x0 = x0_pt.detach().clone()[x0_mask_batch].requires_grad_()
    x0_ref = x0_pt.detach().clone().float().requires_grad_()
    if has_colscale:
        colscale = torch.randn(hidden_size, device=device, dtype=weight_dtype, requires_grad=True)
        colscale_pt = colscale.detach().clone().requires_grad_()
        colscale_ref = colscale.detach().clone().float().requires_grad_()
    else:
        colscale = None
    if has_residual:
        res_pt = torch.randn_like(x0_pt, dtype=residual_dtype, requires_grad=True)
        res = res_pt.detach().clone().requires_grad_()
        res_ref = res_pt.detach().clone().float().requires_grad_()
    else:
        res = None

    if has_colscale:
        x0_scaled_pt = x0_pt * colscale_pt
        x0_scaled_ref = x0_ref * colscale_ref
    else:
        x0_scaled_pt = x0_pt
        x0_scaled_ref = x0_ref

    model_pt = torch.nn.LayerNorm(hidden_size, device=device, dtype=weight_dtype)
    torch.nn.init.normal_(model_pt.weight)
    torch.nn.init.normal_(model_pt.bias)
    model_ref = torch.nn.LayerNorm(hidden_size, device=device, dtype=torch.float32)
    model = DropoutAddLayerNorm(hidden_size, prenorm=False, p=dropout_p, device=device,
                                dtype=weight_dtype)
    with torch.no_grad():
        model.weight.copy_(model_pt.weight)
        model.bias.copy_(model_pt.bias)
        model_ref.weight.copy_(model_pt.weight)
        model_ref.bias.copy_(model_pt.bias)

    residual_in_fp32 = (not has_residual) and residual_dtype == torch.float32
    out, dmask = dropout_add_layer_norm_subset(
        x0, res, model.weight, model.bias, model.p, model.epsilon, layerscale=colscale,
        x0_subset=x0_subset, out_subset=out_subset, rowscale_const=drop_path_scale,
        out_numrows = out_numrows, prenorm=False, residual_in_fp32=residual_in_fp32,
        return_dropout_mask=True)
    print(f'Actual dropout fraction: {1 - dmask.float().mean().item()}')

    x0_scaled_pt = x0_scaled_pt.masked_fill(
        repeat(~x0_mask_batch, 'b -> b s d', s=seqlen, d=hidden_size), 0
    ) * drop_path_scale
    x0_scaled_ref = x0_scaled_ref.masked_fill(
        repeat(~x0_mask_batch, 'b -> b s d', s=seqlen, d=hidden_size), 0
    ) * drop_path_scale
    dmask_expanded = torch.zeros_like(x0_pt, dtype=torch.uint8)
    dmask_expanded[x0_mask_batch] = dmask
    if has_residual:
        residual_pt = ((x0_scaled_pt.float() * dmask_expanded.float()) / (1 - dropout_p) + res_pt.float()).to(dtype=residual_dtype)
        residual_ref = (x0_scaled_ref * dmask_expanded.float()) / (1 - dropout_p) + res_ref
    else:
        residual_pt = ((x0_scaled_pt.float() * dmask_expanded.float()) / (1 - dropout_p)).to(dtype=residual_dtype)
        residual_ref = (x0_scaled_ref * dmask_expanded.float()) / (1 - dropout_p)
    out_pt = model_pt(residual_pt.to(dtype=weight_dtype)).to(dtype=input_dtype)[out_mask_batch]
    out_ref = model_ref(residual_ref)[out_mask_batch]
    assert out.dtype == input_dtype
    assert (out - out_ref).abs().max() <= 4 * (out_pt - out_ref).abs().max() + 1e-4

    g = torch.randn_like(out) / batch_size
    out_pt.backward(g)
    out.backward(g)
    out_ref.backward(g)
    assert (x0.grad - x0_ref.grad[x0_mask_batch]).abs().max() <= 4 * (x0_pt.grad - x0_ref.grad)[x0_mask_batch].abs().max() + 1e-4
    if has_residual:
        assert (res.grad - res_ref.grad).abs().max() <= 4 * (res_pt.grad - res_ref.grad).abs().max() + 1e-4
    assert (model.weight.grad - model_ref.weight.grad).abs().max() <= 2 * (model_pt.weight.grad - model_ref.weight.grad).abs().max() + 2e-4
    assert (model.bias.grad - model_ref.bias.grad).abs().max() <= 2 * (model_pt.bias.grad - model_ref.bias.grad).abs().max() + 2e-4
    if has_colscale:
        assert (colscale.grad - colscale_ref.grad).abs().max() <= 2 * (colscale_pt.grad - colscale_ref.grad).abs().max() + 2e-4


@pytest.mark.parametrize('has_colscale', [True, False])
@pytest.mark.parametrize('has_residual', [True, False])
@pytest.mark.parametrize('dropout_p', [0.37, 0.0])
@pytest.mark.parametrize('weight_dtype', [torch.float32, torch.float16])
@pytest.mark.parametrize('input_dtype,residual_dtype',
                         [(torch.float16, torch.float16), (torch.float16, torch.float32),
                          (torch.float32, torch.float32)]
                         + ([(torch.bfloat16, torch.bfloat16), (torch.bfloat16, torch.float32)] if is_sm8x else []))
# @pytest.mark.parametrize('has_colscale', [True])
# @pytest.mark.parametrize('has_residual', [True])
# @pytest.mark.parametrize('dropout_p', [0.0])
# @pytest.mark.parametrize('weight_dtype', [torch.float32])
# @pytest.mark.parametrize('input_dtype,residual_dtype', [(torch.float32, torch.float32)])
@pytest.mark.parametrize('hidden_size', [192, 256, 384, 768, 1024, 1280, 1536, 1600, 2048, 2560, 3000, 3072, 4096, 5120, 6144])
# @pytest.mark.parametrize('hidden_size', [256])
def test_dropout_layer_norm_subset_prenorm_training(
        hidden_size, input_dtype, residual_dtype, weight_dtype, dropout_p,
        has_residual, has_colscale):
    if weight_dtype == torch.float16 and input_dtype == torch.bfloat16:
        pytest.skip()  # Not supported
    device = 'cuda'
    # rtol, atol = (1e-5, 1e-6) if input_dtype == torch.float32 else (1e-3, 1e-4)
    rtol, atol = (1e-3, 2e-4)
    # set seed
    torch.random.manual_seed(0)
    batch_size = 8
    seqlen = 512
    drop_path_rate = 0.4
    drop_path_scale = 1 / (1 - drop_path_rate)
    def generate_droppath_masks(batch_size, seqlen, drop_path_rate, device):
        # Do it on CPU so we can get the numrows (with .item()) without GPU-CPU sync
        mask_batch = torch.rand(batch_size) < 1 - drop_path_rate
        numrows = (mask_batch).sum().item() * seqlen
        mask_batch = mask_batch.to(device=device, non_blocking=True)
        mask_batch_seqlen = repeat(mask_batch, 'b -> (b s)', s=seqlen)
        subset = torch.cumsum(mask_batch_seqlen, dim=0,
                              dtype=torch.int32).masked_fill_(~mask_batch_seqlen, 0)
        return mask_batch, numrows, rearrange(subset, '(b s) -> b s', b=batch_size)

    x0_mask_batch, x0_numrows, x0_subset = generate_droppath_masks(batch_size, seqlen,
                                                                   drop_path_rate, device)
    out_mask_batch, out_numrows, out_subset = generate_droppath_masks(batch_size, seqlen,
                                                                      drop_path_rate, device)

    x0_pt = torch.randn(batch_size, seqlen, hidden_size, device=device, dtype=input_dtype,
                        requires_grad=True)
    x0 = x0_pt.detach().clone()[x0_mask_batch].requires_grad_()
    x0_ref = x0_pt.detach().clone().float().requires_grad_()
    if has_colscale:
        colscale = torch.randn(hidden_size, device=device, dtype=weight_dtype, requires_grad=True)
        colscale_pt = colscale.detach().clone().requires_grad_()
        colscale_ref = colscale.detach().clone().float().requires_grad_()
    else:
        colscale = None
    if has_residual:
        res_pt = torch.randn_like(x0_pt, dtype=residual_dtype, requires_grad=True)
        res = res_pt.detach().clone().requires_grad_()
        res_ref = res_pt.detach().clone().float().requires_grad_()
    else:
        res = None

    if has_colscale:
        x0_scaled_pt = x0_pt * colscale_pt
        x0_scaled_ref = x0_ref * colscale_ref
    else:
        x0_scaled_pt = x0_pt
        x0_scaled_ref = x0_ref

    model_pt = torch.nn.LayerNorm(hidden_size, device=device, dtype=weight_dtype)
    torch.nn.init.normal_(model_pt.weight)
    torch.nn.init.normal_(model_pt.bias)
    model_ref = torch.nn.LayerNorm(hidden_size, device=device, dtype=torch.float32)
    model = DropoutAddLayerNorm(hidden_size, prenorm=True, p=dropout_p, device=device,
                                dtype=weight_dtype)
    with torch.no_grad():
        model.weight.copy_(model_pt.weight)
        model.bias.copy_(model_pt.bias)
        model_ref.weight.copy_(model_pt.weight)
        model_ref.bias.copy_(model_pt.bias)

    residual_in_fp32 = (not has_residual) and residual_dtype == torch.float32
    out, residual, dmask = dropout_add_layer_norm_subset(
        x0, res, model.weight, model.bias, model.p, model.epsilon, layerscale=colscale,
        x0_subset=x0_subset, out_subset=out_subset, rowscale_const=drop_path_scale,
        out_numrows = out_numrows, prenorm=True, residual_in_fp32=residual_in_fp32,
        return_dropout_mask=True)
    print(f'Actual dropout fraction: {1 - dmask.float().mean().item()}')

    x0_scaled_pt = x0_scaled_pt.masked_fill(
        repeat(~x0_mask_batch, 'b -> b s d', s=seqlen, d=hidden_size), 0
    ) * drop_path_scale
    x0_scaled_ref = x0_scaled_ref.masked_fill(
        repeat(~x0_mask_batch, 'b -> b s d', s=seqlen, d=hidden_size), 0
    ) * drop_path_scale
    dmask_expanded = torch.zeros_like(x0_pt, dtype=torch.uint8)
    dmask_expanded[x0_mask_batch] = dmask
    if has_residual:
        residual_pt = ((x0_scaled_pt.float() * dmask_expanded.float()) / (1 - dropout_p) + res_pt.float()).to(dtype=residual_dtype)
        residual_ref = (x0_scaled_ref * dmask_expanded.float()) / (1 - dropout_p) + res_ref
    else:
        residual_pt = ((x0_scaled_pt.float() * dmask_expanded.float()) / (1 - dropout_p)).to(dtype=residual_dtype)
        residual_ref = (x0_scaled_ref * dmask_expanded.float()) / (1 - dropout_p)
    out_pt = model_pt(residual_pt.to(dtype=weight_dtype)).to(dtype=input_dtype)[out_mask_batch]
    out_ref = model_ref(residual_ref)[out_mask_batch]
    assert out.dtype == input_dtype
    assert residual.dtype == residual_dtype
    assert (out - out_ref).abs().max() <= 4 * (out_pt - out_ref).abs().max() + 1e-4
    assert (residual - residual_ref).abs().max() <= 4 * (residual_pt - residual_ref).abs().max() + 1e-4

    g = torch.randn_like(out) / batch_size
    (out_pt * F.sigmoid(residual_pt[out_mask_batch]) + residual_pt.mean(0, keepdim=True)).backward(g)
    (out * F.sigmoid(residual[out_mask_batch]) + residual.mean(0, keepdim=True)).backward(g)
    (out_ref * F.sigmoid(residual_ref[out_mask_batch].to(dtype=residual_dtype)) + residual_ref.mean(0, keepdim=True)).backward(g)
    assert (x0.grad - x0_ref.grad[x0_mask_batch]).abs().max() <= 4 * (x0_pt.grad - x0_ref.grad)[x0_mask_batch].abs().max() + 1e-4
    if has_residual:
        assert (res.grad - res_ref.grad).abs().max() <= 4 * (res_pt.grad - res_ref.grad).abs().max() + 1e-4
    assert (model.weight.grad - model_ref.weight.grad).abs().max() <= 2 * (model_pt.weight.grad - model_ref.weight.grad).abs().max() + 2e-4
    assert (model.bias.grad - model_ref.bias.grad).abs().max() <= 2 * (model_pt.bias.grad - model_ref.bias.grad).abs().max() + 2e-4
    if has_colscale:
        assert (colscale.grad - colscale_ref.grad).abs().max() <= 2 * (colscale_pt.grad - colscale_ref.grad).abs().max() + 2e-4


@pytest.mark.parametrize('is_rms_norm', [False, True])
# @pytest.mark.parametrize('is_rms_norm', [False])
@pytest.mark.parametrize('tied_norm', [False, True])
# @pytest.mark.parametrize('tied_norm', [False])
@pytest.mark.parametrize('has_residual', [True, False])
# @pytest.mark.parametrize('has_residual', [False])
@pytest.mark.parametrize('has_x1', [True, False])
# @pytest.mark.parametrize('has_x1', [True])
@pytest.mark.parametrize('dropout_p', [0.37, 0.0])
# @pytest.mark.parametrize('dropout_p', [0.0])
@pytest.mark.parametrize('weight_dtype', [torch.float32, torch.float16])
# @pytest.mark.parametrize('weight_dtype', [torch.float16])
@pytest.mark.parametrize('input_dtype,residual_dtype',
                         [(torch.float16, torch.float16), (torch.float16, torch.float32),
                          (torch.float32, torch.float32)]
                         + ([(torch.bfloat16, torch.bfloat16), (torch.bfloat16, torch.float32)] if is_sm8x else []))
# @pytest.mark.parametrize('input_dtype,residual_dtype', [(torch.float16, torch.float32)])
@pytest.mark.parametrize('hidden_size', [192, 256, 384, 768, 1024, 1280, 1536, 1600, 2048, 2560, 3000, 3072, 4096, 5120, 6144])
# @pytest.mark.parametrize('hidden_size', [256])
def test_dropout_layer_norm_parallel_residual_training(
    hidden_size, input_dtype, residual_dtype, weight_dtype,
    dropout_p, has_x1, has_residual, tied_norm, is_rms_norm
):
    if weight_dtype == torch.float16 and input_dtype == torch.bfloat16:
        pytest.skip()  # Not supported
    if is_rms_norm and fused_rms_norm_affine is None:
        pytest.skip()  # We need Apex's FusedRMSNorm to test
    our_layer_norm_func = (dropout_add_layer_norm_parallel_residual if not is_rms_norm
                           else dropout_add_rms_norm_parallel_residual)
    device = 'cuda'
    # rtol, atol = (1e-5, 1e-6) if input_dtype == torch.float32 else (1e-3, 1e-4)
    rtol, atol = (1e-3, 1e-4)
    # set seed
    torch.random.manual_seed(0)
    batch_size = 8
    seqlen = 512
    x0_pt = torch.randn(batch_size, seqlen, hidden_size, device=device, dtype=input_dtype,
                        requires_grad=True)
    x0 = x0_pt.detach().clone().requires_grad_()
    x0_ref = x0_pt.detach().clone().float().requires_grad_()
    if has_x1:
        x1_pt = torch.randn(batch_size, seqlen, hidden_size, device=device, dtype=input_dtype,
                            requires_grad=True)
        x1 = x1_pt.detach().clone().requires_grad_()
        x1_ref = x1_pt.detach().clone().float().requires_grad_()
    else:
        x1 = None
    if has_residual:
        res_pt = torch.randn_like(x0, dtype=residual_dtype, requires_grad=True)
        res = res_pt.detach().clone().requires_grad_()
        res_ref = res_pt.detach().clone().float().requires_grad_()
    else:
        res = None
    weight0 = torch.randn(hidden_size, device=device, dtype=weight_dtype, requires_grad=True)
    bias0 = (torch.randn(hidden_size, device=device, dtype=weight_dtype, requires_grad=True)
             if not is_rms_norm else None)
    weight0_pt = weight0.detach().clone().requires_grad_()
    weight0_ref = weight0.detach().clone().float().requires_grad_()
    bias0_pt = bias0.detach().clone().requires_grad_() if bias0 is not None else None
    bias0_ref = bias0.detach().clone().float().requires_grad_() if bias0 is not None else None
    if not tied_norm:
        weight1 = torch.randn(hidden_size, device=device, dtype=weight_dtype, requires_grad=True)
        bias1 = (torch.randn(hidden_size, device=device, dtype=weight_dtype, requires_grad=True)
                 if not is_rms_norm else None)
        weight1_pt = weight1.detach().clone().requires_grad_()
        weight1_ref = weight1.detach().clone().float().requires_grad_()
        bias1_pt = bias1.detach().clone().requires_grad_() if bias1 is not None else None
        bias1_ref = bias1.detach().clone().float().requires_grad_() if bias1 is not None else None
    else:
        weight1, bias1 = None, None
    epsilon = 1e-5
    residual_in_fp32 = (not has_residual) and residual_dtype == torch.float32

    out0, out1, dmask0, dmask1 = our_layer_norm_func(
        x0, x1, res, weight0, bias0, weight1, bias1, dropout_p,
        epsilon, residual_in_fp32=residual_in_fp32, return_dropout_mask=True
    )
    assert out0.dtype == input_dtype
    if not tied_norm:
        assert out1.dtype == input_dtype
    print(f'Actual dropout fraction: {1 - dmask0.float().mean().item()}')
    if has_residual:
        if has_x1:
            residual_pt = ((x0_pt.float() * dmask0.float()) / (1 - dropout_p)
                          + (x1_pt.float() * dmask1.float()) / (1 - dropout_p)
                          + res_pt.float()).to(dtype=residual_dtype)
            residual_ref = ((x0_ref * dmask0.float()) / (1 - dropout_p)
                            + (x1_ref * dmask1.float()) / (1 - dropout_p)) + res_ref
        else:
            residual_pt = ((x0_pt.float() * dmask0.float()) / (1 - dropout_p)
                          + res_pt.float()).to(dtype=residual_dtype)
            residual_ref = (x0_ref * dmask0.float()) / (1 - dropout_p) + res_ref
    else:
        if has_x1:
            residual_pt = ((x0_pt.float() * dmask0.float()) / (1 - dropout_p)
                          + (x1_pt.float() * dmask1.float()) / (1 - dropout_p)).to(dtype=residual_dtype)
            residual_ref = ((x0_ref * dmask0.float()) / (1 - dropout_p)
                            + (x1_ref * dmask1.float()) / (1 - dropout_p))
        else:
            residual_pt = ((x0_pt.float() * dmask0.float()) / (1 - dropout_p)).to(dtype=residual_dtype)
            residual_ref = (x0_ref * dmask0.float()) / (1 - dropout_p)
    if not is_rms_norm:
        out0_pt = F.layer_norm(residual_pt.to(dtype=weight_dtype), (hidden_size,), weight0_pt, bias0_pt,
                               eps=epsilon).to(dtype=input_dtype)
        out0_ref = F.layer_norm(residual_ref, (hidden_size,), weight0_ref, bias0_ref, eps=epsilon)
        if not tied_norm:
            out1_pt = F.layer_norm(residual_pt.to(dtype=weight_dtype), (hidden_size,), weight1_pt,
                                   bias1_pt, eps=epsilon).to(dtype=input_dtype)
            out1_ref = F.layer_norm(residual_ref, (hidden_size,), weight1_ref, bias1_ref, eps=epsilon)
    else:
        out0_pt = fused_rms_norm_affine(residual_pt.to(dtype=weight_dtype), weight0_pt, (hidden_size,),
                                        eps=epsilon).to(dtype=input_dtype)
        out0_ref = fused_rms_norm_affine(residual_ref, weight0_ref, (hidden_size,), eps=epsilon)
        if not tied_norm:
            out1_pt = fused_rms_norm_affine(residual_pt.to(dtype=weight_dtype), weight1_pt,
                                            (hidden_size,), eps=epsilon).to(dtype=input_dtype)
            out1_ref = fused_rms_norm_affine(residual_ref, weight1_ref, (hidden_size,), eps=epsilon)

    assert (out0 - out0_ref).abs().max() <= 4 * (out0_pt - out0_ref).abs().max() + 1e-4
    if not tied_norm:
        assert (out1 - out1_ref).abs().max() <= 4 * (out1_pt - out1_ref).abs().max() + 1e-4

    g0 = torch.randn_like(out0) / batch_size
    if tied_norm:
        out0.backward(g0)
        out0_pt.backward(g0)
        out0_ref.backward(g0)
    else:
        g1 = torch.randn_like(out1) / batch_size
        (out0 * g0 + out1 * g1).sum().backward()
        (out0_pt * g0 + out1_pt * g1).sum().backward()
        (out0_ref * g0 + out1_ref * g1).sum().backward()
    assert (x0.grad - x0_ref.grad).abs().max() <= 4 * (x0_pt.grad - x0_ref.grad).abs().max() + 1e-4
    if has_x1:
        assert (x1.grad - x1_ref.grad).abs().max() <= 4 * (x1_pt.grad - x1_ref.grad).abs().max() + 1e-4
    if has_residual:
        assert (res.grad - res_ref.grad).abs().max() <= 4 * (res_pt.grad - res_ref.grad).abs().max() + 1e-4
    assert (weight0.grad - weight0_ref.grad).abs().max() <= 3 * (weight0_pt.grad - weight0_ref.grad).abs().max() + 3e-5
    if not is_rms_norm:
        assert (bias0.grad - bias0_ref.grad).abs().max() <= 2 * (bias0_pt.grad - bias0_ref.grad).abs().max() + 3e-5
    if not tied_norm:
        assert (weight1.grad - weight1_ref.grad).abs().max() <= 3 * (weight1_pt.grad - weight1_ref.grad).abs().max() + 3e-5
        if not is_rms_norm:
            assert (bias1.grad - bias1_ref.grad).abs().max() <= 2 * (bias1_pt.grad - bias1_ref.grad).abs().max() + 3e-5


@pytest.mark.parametrize('is_rms_norm', [False, True])
# @pytest.mark.parametrize('is_rms_norm', [False])
@pytest.mark.parametrize('tied_norm', [False, True])
# @pytest.mark.parametrize('tied_norm', [False])
@pytest.mark.parametrize('has_residual', [True, False])
# @pytest.mark.parametrize('has_residual', [False])
@pytest.mark.parametrize('has_x1', [True, False])
# @pytest.mark.parametrize('has_x1', [True])
@pytest.mark.parametrize('dropout_p', [0.37, 0.0])
# @pytest.mark.parametrize('dropout_p', [0.0])
@pytest.mark.parametrize('weight_dtype', [torch.float32, torch.float16])
# @pytest.mark.parametrize('weight_dtype', [torch.float16])
@pytest.mark.parametrize('input_dtype,residual_dtype',
                         [(torch.float16, torch.float16), (torch.float16, torch.float32),
                          (torch.float32, torch.float32)]
                         + ([(torch.bfloat16, torch.bfloat16), (torch.bfloat16, torch.float32)] if is_sm8x else []))
# @pytest.mark.parametrize('input_dtype,residual_dtype', [(torch.float16, torch.float32)])
@pytest.mark.parametrize('hidden_size', [192, 256, 384, 768, 1024, 1280, 1536, 1600, 2048, 2560, 3000, 3072, 4096, 5120, 6144])
# @pytest.mark.parametrize('hidden_size', [256])
def test_dropout_layer_norm_parallel_residual_prenorm_training(
    hidden_size, input_dtype, residual_dtype, weight_dtype,
    dropout_p, has_x1, has_residual, tied_norm, is_rms_norm
):
    if weight_dtype == torch.float16 and input_dtype == torch.bfloat16:
        pytest.skip()  # Not supported
    if is_rms_norm and fused_rms_norm_affine is None:
        pytest.skip()  # We need Apex's FusedRMSNorm to test
    our_layer_norm_func = (dropout_add_layer_norm_parallel_residual if not is_rms_norm
                           else dropout_add_rms_norm_parallel_residual)
    device = 'cuda'
    # rtol, atol = (1e-5, 1e-6) if input_dtype == torch.float32 else (1e-3, 1e-4)
    rtol, atol = (1e-3, 1e-4)
    # set seed
    torch.random.manual_seed(0)
    batch_size = 8
    seqlen = 512
    x0_pt = torch.randn(batch_size, seqlen, hidden_size, device=device, dtype=input_dtype,
                        requires_grad=True)
    x0 = x0_pt.detach().clone().requires_grad_()
    x0_ref = x0_pt.detach().clone().float().requires_grad_()
    if has_x1:
        x1_pt = torch.randn(batch_size, seqlen, hidden_size, device=device, dtype=input_dtype,
                            requires_grad=True)
        x1 = x1_pt.detach().clone().requires_grad_()
        x1_ref = x1_pt.detach().clone().float().requires_grad_()
    else:
        x1 = None
    if has_residual:
        res_pt = torch.randn_like(x0, dtype=residual_dtype, requires_grad=True)
        res = res_pt.detach().clone().requires_grad_()
        res_ref = res_pt.detach().clone().float().requires_grad_()
    else:
        res = None
    weight0 = torch.randn(hidden_size, device=device, dtype=weight_dtype, requires_grad=True)
    bias0 = (torch.randn(hidden_size, device=device, dtype=weight_dtype, requires_grad=True)
             if not is_rms_norm else None)
    weight0_pt = weight0.detach().clone().requires_grad_()
    weight0_ref = weight0.detach().clone().float().requires_grad_()
    bias0_pt = bias0.detach().clone().requires_grad_() if bias0 is not None else None
    bias0_ref = bias0.detach().clone().float().requires_grad_() if bias0 is not None else None
    if not tied_norm:
        weight1 = torch.randn(hidden_size, device=device, dtype=weight_dtype, requires_grad=True)
        bias1 = (torch.randn(hidden_size, device=device, dtype=weight_dtype, requires_grad=True)
                 if not is_rms_norm else None)
        weight1_pt = weight1.detach().clone().requires_grad_()
        weight1_ref = weight1.detach().clone().float().requires_grad_()
        bias1_pt = bias1.detach().clone().requires_grad_() if bias1 is not None else None
        bias1_ref = bias1.detach().clone().float().requires_grad_() if bias1 is not None else None
    else:
        weight1, bias1 = None, None
    epsilon = 1e-5
    residual_in_fp32 = (not has_residual) and residual_dtype == torch.float32

    out0, out1, residual, dmask0, dmask1 = our_layer_norm_func(
        x0, x1, res, weight0, bias0, weight1, bias1, dropout_p,
        epsilon, prenorm=True, residual_in_fp32=residual_in_fp32, return_dropout_mask=True
    )
    assert out0.dtype == input_dtype
    if not tied_norm:
        assert out1.dtype == input_dtype
    print(f'Actual dropout fraction: {1 - dmask0.float().mean().item()}')
    if has_residual:
        if has_x1:
            residual_pt = ((x0_pt.float() * dmask0.float()) / (1 - dropout_p)
                          + (x1_pt.float() * dmask1.float()) / (1 - dropout_p)
                          + res_pt.float()).to(dtype=residual_dtype)
            residual_ref = ((x0_ref * dmask0.float()) / (1 - dropout_p)
                            + (x1_ref * dmask1.float()) / (1 - dropout_p)) + res_ref
        else:
            residual_pt = ((x0_pt.float() * dmask0.float()) / (1 - dropout_p)
                          + res_pt.float()).to(dtype=residual_dtype)
            residual_ref = (x0_ref * dmask0.float()) / (1 - dropout_p) + res_ref
    else:
        if has_x1:
            residual_pt = ((x0_pt.float() * dmask0.float()) / (1 - dropout_p)
                          + (x1_pt.float() * dmask1.float()) / (1 - dropout_p)).to(dtype=residual_dtype)
            residual_ref = ((x0_ref * dmask0.float()) / (1 - dropout_p)
                            + (x1_ref * dmask1.float()) / (1 - dropout_p))
        else:
            residual_pt = ((x0_pt.float() * dmask0.float()) / (1 - dropout_p)).to(dtype=residual_dtype)
            residual_ref = (x0_ref * dmask0.float()) / (1 - dropout_p)
    if not is_rms_norm:
        out0_pt = F.layer_norm(residual_pt.to(dtype=weight_dtype), (hidden_size,), weight0_pt, bias0_pt,
                               eps=epsilon).to(dtype=input_dtype)
        out0_ref = F.layer_norm(residual_ref, (hidden_size,), weight0_ref, bias0_ref, eps=epsilon)
        if not tied_norm:
            out1_pt = F.layer_norm(residual_pt.to(dtype=weight_dtype), (hidden_size,), weight1_pt,
                                   bias1_pt, eps=epsilon).to(dtype=input_dtype)
            out1_ref = F.layer_norm(residual_ref, (hidden_size,), weight1_ref, bias1_ref, eps=epsilon)
    else:
        out0_pt = fused_rms_norm_affine(residual_pt.to(dtype=weight_dtype), weight0_pt, (hidden_size,),
                                        eps=epsilon).to(dtype=input_dtype)
        out0_ref = fused_rms_norm_affine(residual_ref, weight0_ref, (hidden_size,), eps=epsilon)
        if not tied_norm:
            out1_pt = fused_rms_norm_affine(residual_pt.to(dtype=weight_dtype), weight1_pt,
                                            (hidden_size,), eps=epsilon).to(dtype=input_dtype)
            out1_ref = fused_rms_norm_affine(residual_ref, weight1_ref, (hidden_size,), eps=epsilon)

    assert (out0 - out0_ref).abs().max() <= 4 * (out0_pt - out0_ref).abs().max() + 1e-4
    if not tied_norm:
        assert (out1 - out1_ref).abs().max() <= 4 * (out1_pt - out1_ref).abs().max() + 1e-4
    assert (residual - residual_ref).abs().max() <= 4 * (residual_pt - residual_ref).abs().max() + 1e-4

    g0 = torch.randn_like(out0) / batch_size
    if tied_norm:
        (out0 * F.sigmoid(residual)).backward(g0)
        (out0_pt * F.sigmoid(residual_pt)).backward(g0)
        (out0_ref * F.sigmoid(residual_ref)).backward(g0)
    else:
        g1 = torch.randn_like(out1) / batch_size
        (out0 * F.sigmoid(residual) * g0 + out1 * g1).sum().backward()
        (out0_pt * F.sigmoid(residual_pt) * g0 + out1_pt * g1).sum().backward()
        (out0_ref * F.sigmoid(residual_ref) * g0 + out1_ref * g1).sum().backward()
    assert (x0.grad - x0_ref.grad).abs().max() <= 4 * (x0_pt.grad - x0_ref.grad).abs().max() + 1e-4
    if has_x1:
        assert (x1.grad - x1_ref.grad).abs().max() <= 4 * (x1_pt.grad - x1_ref.grad).abs().max() + 1e-4
    if has_residual:
        assert (res.grad - res_ref.grad).abs().max() <= 4 * (res_pt.grad - res_ref.grad).abs().max() + 1e-4
    assert (weight0.grad - weight0_ref.grad).abs().max() <= 3 * (weight0_pt.grad - weight0_ref.grad).abs().max() + 3e-5
    if not is_rms_norm:
        assert (bias0.grad - bias0_ref.grad).abs().max() <= 2 * (bias0_pt.grad - bias0_ref.grad).abs().max() + 3e-5
    if not tied_norm:
        assert (weight1.grad - weight1_ref.grad).abs().max() <= 3 * (weight1_pt.grad - weight1_ref.grad).abs().max() + 3e-5
        if not is_rms_norm:
            assert (bias1.grad - bias1_ref.grad).abs().max() <= 2 * (bias1_pt.grad - bias1_ref.grad).abs().max() + 3e-5