# Copyright (c) 2024, Tri Dao.
# Implement dropout + residual + layer_norm / rms_norm.

# Based on the Triton LayerNorm tutorial: https://triton-lang.org/main/getting-started/tutorials/05-layer-norm.html
# For the backward pass, we keep weight_grad and bias_grad in registers and accumulate.
# This is faster for dimensions up to 8k, but after that it's much slower due to register spilling.
# The models we train have hidden dim up to 8k anyway (e.g. Llama 70B), so this is fine.

import math

import torch
import torch.nn.functional as F

import triton
import triton.language as tl


from typing import Callable
from flash_attn.ops.rms_norm import RMSNorm 

def custom_amp_decorator(dec: Callable, cuda_amp_deprecated: bool):
    def decorator(*args, **kwargs):
        if cuda_amp_deprecated:
            kwargs["device_type"] = "cuda"
        return dec(*args, **kwargs)
    return decorator


if hasattr(torch.amp, "custom_fwd"): # type: ignore[attr-defined]
    deprecated = True
    from torch.amp import custom_fwd, custom_bwd # type: ignore[attr-defined]
else:
    deprecated = False
    from torch.cuda.amp import custom_fwd, custom_bwd

custom_fwd = custom_amp_decorator(custom_fwd, deprecated)
custom_bwd = custom_amp_decorator(custom_bwd, deprecated)


def triton_autotune_configs():
    # Return configs with a valid warp count for the current device
    configs=[]
    # Maximum threads per block is architecture-dependent in theory, but in reality all are 1024
    max_threads_per_block=1024
    # Default to warp size 32 if not defined by device
    warp_size=getattr(torch.cuda.get_device_properties(torch.cuda.current_device()), "warp_size", 32)
    # Autotune for warp counts which are powers of 2 and do not exceed thread per block limit
    warp_count=1
    while warp_count*warp_size <= max_threads_per_block:
        configs.append(triton.Config({}, num_warps=warp_count))
        warp_count*=2
    return configs

def layer_norm_ref(
    x,
    weight,
    bias,
    residual=None,
    x1=None,
    weight1=None,
    bias1=None,
    eps=1e-6,
    dropout_p=0.0,
    rowscale=None,
    prenorm=False,
    zero_centered_weight=False,
    dropout_mask=None,
    dropout_mask1=None,
    upcast=False,
):
    dtype = x.dtype
    if upcast:
        x = x.float()
        weight = weight.float()
        bias = bias.float() if bias is not None else None
        residual = residual.float() if residual is not None else residual
        x1 = x1.float() if x1 is not None else None
        weight1 = weight1.float() if weight1 is not None else None
        bias1 = bias1.float() if bias1 is not None else None
    if zero_centered_weight:
        weight = weight + 1.0
        if weight1 is not None:
            weight1 = weight1 + 1.0
    if x1 is not None:
        assert rowscale is None, "rowscale is not supported with parallel LayerNorm"
    if rowscale is not None:
        x = x * rowscale[..., None]
    if dropout_p > 0.0:
        if dropout_mask is not None:
            x = x.masked_fill(~dropout_mask, 0.0) / (1.0 - dropout_p)
        else:
            x = F.dropout(x, p=dropout_p)
        if x1 is not None:
            if dropout_mask1 is not None:
                x1 = x1.masked_fill(~dropout_mask1, 0.0) / (1.0 - dropout_p)
            else:
                x1 = F.dropout(x1, p=dropout_p)
    if x1 is not None:
        x = x + x1
    if residual is not None:
        x = (x + residual).to(x.dtype)
    out = F.layer_norm(x.to(weight.dtype), x.shape[-1:], weight=weight, bias=bias, eps=eps).to(
        dtype
    )
    if weight1 is None:
        return out if not prenorm else (out, x)
    else:
        out1 = F.layer_norm(
            x.to(weight1.dtype), x.shape[-1:], weight=weight1, bias=bias1, eps=eps
        ).to(dtype)
        return (out, out1) if not prenorm else (out, out1, x)


def rms_norm_ref(
    x,
    weight,
    bias,
    residual=None,
    x1=None,
    weight1=None,
    bias1=None,
    eps=1e-6,
    dropout_p=0.0,
    rowscale=None,
    prenorm=False,
    zero_centered_weight=False,
    dropout_mask=None,
    dropout_mask1=None,
    upcast=False,
):
    dtype = x.dtype
    if upcast:
        x = x.float()
        weight = weight.float()
        bias = bias.float() if bias is not None else None
        residual = residual.float() if residual is not None else residual
        x1 = x1.float() if x1 is not None else None
        weight1 = weight1.float() if weight1 is not None else None
        bias1 = bias1.float() if bias1 is not None else None
    if zero_centered_weight:
        weight = weight + 1.0
        if weight1 is not None:
            weight1 = weight1 + 1.0
    if x1 is not None:
        assert rowscale is None, "rowscale is not supported with parallel LayerNorm"
    if rowscale is not None:
        x = x * rowscale[..., None]
    if dropout_p > 0.0:
        if dropout_mask is not None:
            x = x.masked_fill(~dropout_mask, 0.0) / (1.0 - dropout_p)
        else:
            x = F.dropout(x, p=dropout_p)
        if x1 is not None:
            if dropout_mask1 is not None:
                x1 = x1.masked_fill(~dropout_mask1, 0.0) / (1.0 - dropout_p)
            else:
                x1 = F.dropout(x1, p=dropout_p)
    if x1 is not None:
        x = x + x1
    if residual is not None:
        x = (x + residual).to(x.dtype)
    rstd = 1 / torch.sqrt((x.square()).mean(dim=-1, keepdim=True) + eps)
    out = ((x * rstd * weight) + bias if bias is not None else (x * rstd * weight)).to(dtype)
    if weight1 is None:
        return out if not prenorm else (out, x)
    else:
        out1 = ((x * rstd * weight1) + bias1 if bias1 is not None else (x * rstd * weight1)).to(
            dtype
        )
        return (out, out1) if not prenorm else (out, out1, x)


@triton.autotune(
    configs=triton_autotune_configs(),
    key=["N", "HAS_RESIDUAL", "STORE_RESIDUAL_OUT", "IS_RMS_NORM", "HAS_BIAS"],
)
# @triton.heuristics({"HAS_BIAS": lambda args: args["B"] is not None})
# @triton.heuristics({"HAS_RESIDUAL": lambda args: args["RESIDUAL"] is not None})
@triton.heuristics({"HAS_X1": lambda args: args["X1"] is not None})
@triton.heuristics({"HAS_W1": lambda args: args["W1"] is not None})
@triton.heuristics({"HAS_B1": lambda args: args["B1"] is not None})
@triton.jit
def _layer_norm_fwd_1pass_kernel(
    X,  # pointer to the input
    Y,  # pointer to the output
    W,  # pointer to the weights
    B,  # pointer to the biases
    RESIDUAL,  # pointer to the residual
    X1,
    W1,
    B1,
    Y1,
    RESIDUAL_OUT,  # pointer to the residual
    ROWSCALE,
    SEEDS,  # Dropout seeds for each row
    DROPOUT_MASK,
    Mean,  # pointer to the mean
    Rstd,  # pointer to the 1/std
    stride_x_row,  # how much to increase the pointer when moving by 1 row
    stride_y_row,
    stride_res_row,
    stride_res_out_row,
    stride_x1_row,
    stride_y1_row,
    M,  # number of rows in X
    N,  # number of columns in X
    eps,  # epsilon to avoid division by zero
    dropout_p,  # Dropout probability
    zero_centered_weight,  # If true, add 1.0 to the weight
    IS_RMS_NORM: tl.constexpr,
    BLOCK_N: tl.constexpr,
    HAS_RESIDUAL: tl.constexpr,
    STORE_RESIDUAL_OUT: tl.constexpr,
    HAS_BIAS: tl.constexpr,
    HAS_DROPOUT: tl.constexpr,
    STORE_DROPOUT_MASK: tl.constexpr,
    HAS_ROWSCALE: tl.constexpr,
    HAS_X1: tl.constexpr,
    HAS_W1: tl.constexpr,
    HAS_B1: tl.constexpr,
):
    # Map the program id to the row of X and Y it should compute.
    row = tl.program_id(0)
    X += row * stride_x_row
    Y += row * stride_y_row
    if HAS_RESIDUAL:
        RESIDUAL += row * stride_res_row
    if STORE_RESIDUAL_OUT:
        RESIDUAL_OUT += row * stride_res_out_row
    if HAS_X1:
        X1 += row * stride_x1_row
    if HAS_W1:
        Y1 += row * stride_y1_row
    # Compute mean and variance
    cols = tl.arange(0, BLOCK_N)
    x = tl.load(X + cols, mask=cols < N, other=0.0).to(tl.float32)
    if HAS_ROWSCALE:
        rowscale = tl.load(ROWSCALE + row).to(tl.float32)
        x *= rowscale
    if HAS_DROPOUT:
        # Compute dropout mask
        # 7 rounds is good enough, and reduces register pressure
        keep_mask = tl.rand(tl.load(SEEDS + row).to(tl.uint32), cols, n_rounds=7) > dropout_p
        x = tl.where(keep_mask, x / (1.0 - dropout_p), 0.0)
        if STORE_DROPOUT_MASK:
            tl.store(DROPOUT_MASK + row * N + cols, keep_mask, mask=cols < N)
    if HAS_X1:
        x1 = tl.load(X1 + cols, mask=cols < N, other=0.0).to(tl.float32)
        if HAS_ROWSCALE:
            rowscale = tl.load(ROWSCALE + M + row).to(tl.float32)
            x1 *= rowscale
        if HAS_DROPOUT:
            # Compute dropout mask
            # 7 rounds is good enough, and reduces register pressure
            keep_mask = (
                tl.rand(tl.load(SEEDS + M + row).to(tl.uint32), cols, n_rounds=7) > dropout_p
            )
            x1 = tl.where(keep_mask, x1 / (1.0 - dropout_p), 0.0)
            if STORE_DROPOUT_MASK:
                tl.store(DROPOUT_MASK + (M + row) * N + cols, keep_mask, mask=cols < N)
        x += x1
    if HAS_RESIDUAL:
        residual = tl.load(RESIDUAL + cols, mask=cols < N, other=0.0).to(tl.float32)
        x += residual
    if STORE_RESIDUAL_OUT:
        tl.store(RESIDUAL_OUT + cols, x, mask=cols < N)
    if not IS_RMS_NORM:
        mean = tl.sum(x, axis=0) / N
        tl.store(Mean + row, mean)
        xbar = tl.where(cols < N, x - mean, 0.0)
        var = tl.sum(xbar * xbar, axis=0) / N
    else:
        xbar = tl.where(cols < N, x, 0.0)
        var = tl.sum(xbar * xbar, axis=0) / N
    rstd = 1 / tl.sqrt(var + eps)
    tl.store(Rstd + row, rstd)
    # Normalize and apply linear transformation
    mask = cols < N
    w = tl.load(W + cols, mask=mask).to(tl.float32)
    if zero_centered_weight:
        w += 1.0
    if HAS_BIAS:
        b = tl.load(B + cols, mask=mask).to(tl.float32)
    x_hat = (x - mean) * rstd if not IS_RMS_NORM else x * rstd
    y = x_hat * w + b if HAS_BIAS else x_hat * w
    # Write output
    tl.store(Y + cols, y, mask=mask)
    if HAS_W1:
        w1 = tl.load(W1 + cols, mask=mask).to(tl.float32)
        if zero_centered_weight:
            w1 += 1.0
        if HAS_B1:
            b1 = tl.load(B1 + cols, mask=mask).to(tl.float32)
        y1 = x_hat * w1 + b1 if HAS_B1 else x_hat * w1
        tl.store(Y1 + cols, y1, mask=mask)


def _layer_norm_fwd(
    x,
    weight,
    bias,
    eps,
    residual=None,
    x1=None,
    weight1=None,
    bias1=None,
    dropout_p=0.0,
    rowscale=None,
    out_dtype=None,
    residual_dtype=None,
    zero_centered_weight=False,
    is_rms_norm=False,
    return_dropout_mask=False,
    out=None,
    residual_out=None
):
    if residual is not None:
        residual_dtype = residual.dtype
    M, N = x.shape
    assert x.stride(-1) == 1
    if residual is not None:
        assert residual.stride(-1) == 1
        assert residual.shape == (M, N)
    assert weight.shape == (N,)
    assert weight.stride(-1) == 1
    if bias is not None:
        assert bias.stride(-1) == 1
        assert bias.shape == (N,)
    if x1 is not None:
        assert x1.shape == x.shape
        assert rowscale is None
        assert x1.stride(-1) == 1
    if weight1 is not None:
        assert weight1.shape == (N,)
        assert weight1.stride(-1) == 1
    if bias1 is not None:
        assert bias1.shape == (N,)
        assert bias1.stride(-1) == 1
    if rowscale is not None:
        assert rowscale.is_contiguous()
        assert rowscale.shape == (M,)
    # allocate output
    if out is None:
        out = torch.empty_like(x, dtype=x.dtype if out_dtype is None else out_dtype)
    else:
        assert out.shape == x.shape
    assert out.stride(-1) == 1
    if weight1 is not None:
        y1 = torch.empty_like(out)
        assert y1.stride(-1) == 1
    else:
        y1 = None
    if (
        residual is not None
        or (residual_dtype is not None and residual_dtype != x.dtype)
        or dropout_p > 0.0
        or rowscale is not None
        or x1 is not None
    ):
        if residual_out is None:
            residual_out = torch.empty(
                M, N, device=x.device, dtype=residual_dtype if residual_dtype is not None else x.dtype
            )
        else:
            assert residual_out.shape == x.shape
        assert residual_out.stride(-1) == 1
    else:
        residual_out = None
    mean = torch.empty((M,), dtype=torch.float32, device=x.device) if not is_rms_norm else None
    rstd = torch.empty((M,), dtype=torch.float32, device=x.device)
    if dropout_p > 0.0:
        seeds = torch.randint(
            2**32, (M if x1 is None else 2 * M,), device=x.device, dtype=torch.int64
        )
    else:
        seeds = None
    if return_dropout_mask and dropout_p > 0.0:
        dropout_mask = torch.empty(M if x1 is None else 2 * M, N, device=x.device, dtype=torch.bool)
    else:
        dropout_mask = None
    # Less than 64KB per feature: enqueue fused kernel
    MAX_FUSED_SIZE = 65536 // x.element_size()
    BLOCK_N = min(MAX_FUSED_SIZE, triton.next_power_of_2(N))
    if N > BLOCK_N:
        raise RuntimeError("This layer norm doesn't support feature dim >= 64KB.")
    with torch.cuda.device(x.device.index):
        _layer_norm_fwd_1pass_kernel[(M,)](
            x,
            out,
            weight,
            bias,
            residual,
            x1,
            weight1,
            bias1,
            y1,
            residual_out,
            rowscale,
            seeds,
            dropout_mask,
            mean,
            rstd,
            x.stride(0),
            out.stride(0),
            residual.stride(0) if residual is not None else 0,
            residual_out.stride(0) if residual_out is not None else 0,
            x1.stride(0) if x1 is not None else 0,
            y1.stride(0) if y1 is not None else 0,
            M,
            N,
            eps,
            dropout_p,
            zero_centered_weight,
            is_rms_norm,
            BLOCK_N,
            residual is not None,
            residual_out is not None,
            bias is not None,
            dropout_p > 0.0,
            dropout_mask is not None,
            rowscale is not None,
        )
    # residual_out is None if residual is None and residual_dtype == input_dtype and dropout_p == 0.0
    if dropout_mask is not None and x1 is not None:
        dropout_mask, dropout_mask1 = dropout_mask.tensor_split(2, dim=0)
    else:
        dropout_mask1 = None
    return (
        out,
        y1,
        mean,
        rstd,
        residual_out if residual_out is not None else x,
        seeds,
        dropout_mask,
        dropout_mask1,
    )


@triton.autotune(
    configs=triton_autotune_configs(),
    key=["N", "HAS_DRESIDUAL", "STORE_DRESIDUAL", "IS_RMS_NORM", "HAS_BIAS", "HAS_DROPOUT"],
)
# @triton.heuristics({"HAS_BIAS": lambda args: args["B"] is not None})
# @triton.heuristics({"HAS_DRESIDUAL": lambda args: args["DRESIDUAL"] is not None})
# @triton.heuristics({"STORE_DRESIDUAL": lambda args: args["DRESIDUAL_IN"] is not None})
@triton.heuristics({"HAS_ROWSCALE": lambda args: args["ROWSCALE"] is not None})
@triton.heuristics({"HAS_DY1": lambda args: args["DY1"] is not None})
@triton.heuristics({"HAS_DX1": lambda args: args["DX1"] is not None})
@triton.heuristics({"HAS_B1": lambda args: args["DB1"] is not None})
@triton.heuristics({"RECOMPUTE_OUTPUT": lambda args: args["Y"] is not None})
@triton.jit
def _layer_norm_bwd_kernel(
    X,  # pointer to the input
    W,  # pointer to the weights
    B,  # pointer to the biases
    Y,  # pointer to the output to be recomputed
    DY,  # pointer to the output gradient
    DX,  # pointer to the input gradient
    DW,  # pointer to the partial sum of weights gradient
    DB,  # pointer to the partial sum of biases gradient
    DRESIDUAL,
    W1,
    DY1,
    DX1,
    DW1,
    DB1,
    DRESIDUAL_IN,
    ROWSCALE,
    SEEDS,
    Mean,  # pointer to the mean
    Rstd,  # pointer to the 1/std
    stride_x_row,  # how much to increase the pointer when moving by 1 row
    stride_y_row,
    stride_dy_row,
    stride_dx_row,
    stride_dres_row,
    stride_dy1_row,
    stride_dx1_row,
    stride_dres_in_row,
    M,  # number of rows in X
    N,  # number of columns in X
    eps,  # epsilon to avoid division by zero
    dropout_p,
    zero_centered_weight,
    rows_per_program,
    IS_RMS_NORM: tl.constexpr,
    BLOCK_N: tl.constexpr,
    HAS_DRESIDUAL: tl.constexpr,
    STORE_DRESIDUAL: tl.constexpr,
    HAS_BIAS: tl.constexpr,
    HAS_DROPOUT: tl.constexpr,
    HAS_ROWSCALE: tl.constexpr,
    HAS_DY1: tl.constexpr,
    HAS_DX1: tl.constexpr,
    HAS_B1: tl.constexpr,
    RECOMPUTE_OUTPUT: tl.constexpr,
):
    # Map the program id to the elements of X, DX, and DY it should compute.
    row_block_id = tl.program_id(0)
    row_start = row_block_id * rows_per_program
    # Do not early exit if row_start >= M, because we need to write DW and DB
    cols = tl.arange(0, BLOCK_N)
    mask = cols < N
    X += row_start * stride_x_row
    if HAS_DRESIDUAL:
        DRESIDUAL += row_start * stride_dres_row
    if STORE_DRESIDUAL:
        DRESIDUAL_IN += row_start * stride_dres_in_row
    DY += row_start * stride_dy_row
    DX += row_start * stride_dx_row
    if HAS_DY1:
        DY1 += row_start * stride_dy1_row
    if HAS_DX1:
        DX1 += row_start * stride_dx1_row
    if RECOMPUTE_OUTPUT:
        Y += row_start * stride_y_row
    w = tl.load(W + cols, mask=mask).to(tl.float32)
    if zero_centered_weight:
        w += 1.0
    if RECOMPUTE_OUTPUT and HAS_BIAS:
        b = tl.load(B + cols, mask=mask, other=0.0).to(tl.float32)
    if HAS_DY1:
        w1 = tl.load(W1 + cols, mask=mask).to(tl.float32)
        if zero_centered_weight:
            w1 += 1.0
    dw = tl.zeros((BLOCK_N,), dtype=tl.float32)
    if HAS_BIAS:
        db = tl.zeros((BLOCK_N,), dtype=tl.float32)
    if HAS_DY1:
        dw1 = tl.zeros((BLOCK_N,), dtype=tl.float32)
        if HAS_B1:
            db1 = tl.zeros((BLOCK_N,), dtype=tl.float32)
    row_end = min((row_block_id + 1) * rows_per_program, M)
    for row in range(row_start, row_end):
        # Load data to SRAM
        x = tl.load(X + cols, mask=mask, other=0).to(tl.float32)
        dy = tl.load(DY + cols, mask=mask, other=0).to(tl.float32)
        if HAS_DY1:
            dy1 = tl.load(DY1 + cols, mask=mask, other=0).to(tl.float32)
        if not IS_RMS_NORM:
            mean = tl.load(Mean + row)
        rstd = tl.load(Rstd + row)
        # Compute dx
        xhat = (x - mean) * rstd if not IS_RMS_NORM else x * rstd
        xhat = tl.where(mask, xhat, 0.0)
        if RECOMPUTE_OUTPUT:
            y = xhat * w + b if HAS_BIAS else xhat * w
            tl.store(Y + cols, y, mask=mask)
        wdy = w * dy
        dw += dy * xhat
        if HAS_BIAS:
            db += dy
        if HAS_DY1:
            wdy += w1 * dy1
            dw1 += dy1 * xhat
            if HAS_B1:
                db1 += dy1
        if not IS_RMS_NORM:
            c1 = tl.sum(xhat * wdy, axis=0) / N
            c2 = tl.sum(wdy, axis=0) / N
            dx = (wdy - (xhat * c1 + c2)) * rstd
        else:
            c1 = tl.sum(xhat * wdy, axis=0) / N
            dx = (wdy - xhat * c1) * rstd
        if HAS_DRESIDUAL:
            dres = tl.load(DRESIDUAL + cols, mask=mask, other=0).to(tl.float32)
            dx += dres
        # Write dx
        if STORE_DRESIDUAL:
            tl.store(DRESIDUAL_IN + cols, dx, mask=mask)
        if HAS_DX1:
            if HAS_DROPOUT:
                keep_mask = (
                    tl.rand(tl.load(SEEDS + M + row).to(tl.uint32), cols, n_rounds=7) > dropout_p
                )
                dx1 = tl.where(keep_mask, dx / (1.0 - dropout_p), 0.0)
            else:
                dx1 = dx
            tl.store(DX1 + cols, dx1, mask=mask)
        if HAS_DROPOUT:
            keep_mask = tl.rand(tl.load(SEEDS + row).to(tl.uint32), cols, n_rounds=7) > dropout_p
            dx = tl.where(keep_mask, dx / (1.0 - dropout_p), 0.0)
        if HAS_ROWSCALE:
            rowscale = tl.load(ROWSCALE + row).to(tl.float32)
            dx *= rowscale
        tl.store(DX + cols, dx, mask=mask)

        X += stride_x_row
        if HAS_DRESIDUAL:
            DRESIDUAL += stride_dres_row
        if STORE_DRESIDUAL:
            DRESIDUAL_IN += stride_dres_in_row
        if RECOMPUTE_OUTPUT:
            Y += stride_y_row
        DY += stride_dy_row
        DX += stride_dx_row
        if HAS_DY1:
            DY1 += stride_dy1_row
        if HAS_DX1:
            DX1 += stride_dx1_row
    tl.store(DW + row_block_id * N + cols, dw, mask=mask)
    if HAS_BIAS:
        tl.store(DB + row_block_id * N + cols, db, mask=mask)
    if HAS_DY1:
        tl.store(DW1 + row_block_id * N + cols, dw1, mask=mask)
        if HAS_B1:
            tl.store(DB1 + row_block_id * N + cols, db1, mask=mask)


def _layer_norm_bwd(
    dy,
    x,
    weight,
    bias,
    eps,
    mean,
    rstd,
    dresidual=None,
    dy1=None,
    weight1=None,
    bias1=None,
    seeds=None,
    dropout_p=0.0,
    rowscale=None,
    has_residual=False,
    has_x1=False,
    zero_centered_weight=False,
    is_rms_norm=False,
    x_dtype=None,
    recompute_output=False,
):
    M, N = x.shape
    assert x.stride(-1) == 1
    assert dy.stride(-1) == 1
    assert dy.shape == (M, N)
    if dresidual is not None:
        assert dresidual.stride(-1) == 1
        assert dresidual.shape == (M, N)
    assert weight.shape == (N,)
    assert weight.stride(-1) == 1
    if bias is not None:
        assert bias.stride(-1) == 1
        assert bias.shape == (N,)
    if dy1 is not None:
        assert weight1 is not None
        assert dy1.shape == dy.shape
        assert dy1.stride(-1) == 1
    if weight1 is not None:
        assert weight1.shape == (N,)
        assert weight1.stride(-1) == 1
    if bias1 is not None:
        assert bias1.shape == (N,)
        assert bias1.stride(-1) == 1
    if seeds is not None:
        assert seeds.is_contiguous()
        assert seeds.shape == (M if not has_x1 else M * 2,)
    if rowscale is not None:
        assert rowscale.is_contiguous()
        assert rowscale.shape == (M,)
    # allocate output
    dx = (
        torch.empty_like(x)
        if x_dtype is None
        else torch.empty(M, N, dtype=x_dtype, device=x.device)
    )
    dresidual_in = (
        torch.empty_like(x)
        if has_residual
        and (dx.dtype != x.dtype or dropout_p > 0.0 or rowscale is not None or has_x1)
        else None
    )
    dx1 = torch.empty_like(dx) if (has_x1 and dropout_p > 0.0) else None
    y = torch.empty(M, N, dtype=dy.dtype, device=dy.device) if recompute_output else None
    if recompute_output:
        assert weight1 is None, "recompute_output is not supported with parallel LayerNorm"

    # Less than 64KB per feature: enqueue fused kernel
    MAX_FUSED_SIZE = 65536 // x.element_size()
    BLOCK_N = min(MAX_FUSED_SIZE, triton.next_power_of_2(N))
    if N > BLOCK_N:
        raise RuntimeError("This layer norm doesn't support feature dim >= 64KB.")
    # Increasing the multiple (e.g. 8) will allow more thread blocks to be launched and hide the
    # latency of the gmem reads/writes, but will increase the time of summing up dw / db.
    sm_count = torch.cuda.get_device_properties(x.device).multi_processor_count * 8
    _dw = torch.empty((sm_count, N), dtype=torch.float32, device=weight.device)
    _db = (
        torch.empty((sm_count, N), dtype=torch.float32, device=bias.device)
        if bias is not None
        else None
    )
    _dw1 = torch.empty_like(_dw) if weight1 is not None else None
    _db1 = torch.empty_like(_db) if bias1 is not None else None
    rows_per_program = math.ceil(M / sm_count)
    grid = (sm_count,)
    with torch.cuda.device(x.device.index):
        _layer_norm_bwd_kernel[grid](
            x,
            weight,
            bias,
            y,
            dy,
            dx,
            _dw,
            _db,
            dresidual,
            weight1,
            dy1,
            dx1,
            _dw1,
            _db1,
            dresidual_in,
            rowscale,
            seeds,
            mean,
            rstd,
            x.stride(0),
            0 if not recompute_output else y.stride(0),
            dy.stride(0),
            dx.stride(0),
            dresidual.stride(0) if dresidual is not None else 0,
            dy1.stride(0) if dy1 is not None else 0,
            dx1.stride(0) if dx1 is not None else 0,
            dresidual_in.stride(0) if dresidual_in is not None else 0,
            M,
            N,
            eps,
            dropout_p,
            zero_centered_weight,
            rows_per_program,
            is_rms_norm,
            BLOCK_N,
            dresidual is not None,
            dresidual_in is not None,
            bias is not None,
            dropout_p > 0.0,
        )
    dw = _dw.sum(0).to(weight.dtype)
    db = _db.sum(0).to(bias.dtype) if bias is not None else None
    dw1 = _dw1.sum(0).to(weight1.dtype) if weight1 is not None else None
    db1 = _db1.sum(0).to(bias1.dtype) if bias1 is not None else None
    # Don't need to compute dresidual_in separately in this case
    if has_residual and dx.dtype == x.dtype and dropout_p == 0.0 and rowscale is None:
        dresidual_in = dx
    if has_x1 and dropout_p == 0.0:
        dx1 = dx
    return (
        (dx, dw, db, dresidual_in, dx1, dw1, db1)
        if not recompute_output
        else (dx, dw, db, dresidual_in, dx1, dw1, db1, y)
    )


class LayerNormFn(torch.autograd.Function):
    @staticmethod
    def forward(
        ctx,
        x,
        weight,
        bias,
        residual=None,
        x1=None,
        weight1=None,
        bias1=None,
        eps=1e-6,
        dropout_p=0.0,
        rowscale=None,
        prenorm=False,
        residual_in_fp32=False,
        zero_centered_weight=False,
        is_rms_norm=False,
        return_dropout_mask=False,
        out=None,
        residual_out=None
    ):
        x_shape_og = x.shape
        # Check for zero sequence length
        if x.numel() == 0:
            ctx.zero_seq_length = True
            # Only save minimal required tensors for backward
            # ctx.save_for_backward(weight, bias, weight1, bias1)
            ctx.x_shape_og = x_shape_og
            ctx.weight_shape = weight.shape
            ctx.weight_dtype = weight.dtype
            ctx.weight_device = weight.device

            ctx.has_bias = bias is not None
            ctx.bias_shape = bias.shape if bias is not None else None
            ctx.bias_dtype = bias.dtype if bias is not None else None
            ctx.bias_device = bias.device if bias is not None else None

            ctx.has_weight1 = weight1 is not None
            ctx.weight1_shape = weight1.shape if weight1 is not None else None
            ctx.weight1_dtype = weight1.dtype if weight1 is not None else None
            ctx.weight1_device = weight1.device if weight1 is not None else None

            ctx.has_bias1 = bias1 is not None
            ctx.bias1_shape = bias1.shape if bias1 is not None else None
            ctx.bias1_dtype = bias1.dtype if bias1 is not None else None
            ctx.bias1_device = bias1.device if bias1 is not None else None

            ctx.has_residual = residual is not None
            ctx.has_x1 = x1 is not None
            ctx.dropout_p = dropout_p

            # Handle output tensors with correct dtype
            y = x  # Preserve input tensor properties
            y1 = torch.empty_like(x) if x1 is not None else None
            
            # Only create residual_out if prenorm is True
            residual_out = torch.empty(x.shape, 
                                    dtype=torch.float32 if residual_in_fp32 else x.dtype,
                                    device=x.device) if prenorm else None
            
            # Handle dropout masks
            dropout_mask = None
            dropout_mask1 = None
            if return_dropout_mask:
                dropout_mask = torch.empty_like(x, dtype=torch.uint8)
                if x1 is not None:
                    dropout_mask1 = torch.empty_like(x, dtype=torch.uint8)

            # Return based on configuration
            if not return_dropout_mask:
                if weight1 is None:
                    return y if not prenorm else (y, residual_out)
                else:
                    return (y, y1) if not prenorm else (y, y1, residual_out)
            else:
                if weight1 is None:
                    return ((y, dropout_mask, dropout_mask1) if not prenorm 
                        else (y, residual_out, dropout_mask, dropout_mask1))
                else:
                    return ((y, y1, dropout_mask, dropout_mask1) if not prenorm 
                        else (y, y1, residual_out, dropout_mask, dropout_mask1))

        ctx.zero_seq_length = False  
        # reshape input data into 2D tensor
        x = x.reshape(-1, x.shape[-1])
        if x.stride(-1) != 1:
            x = x.contiguous()
        if residual is not None:
            assert residual.shape == x_shape_og
            residual = residual.reshape(-1, residual.shape[-1])
            if residual.stride(-1) != 1:
                residual = residual.contiguous()
        if x1 is not None:
            assert x1.shape == x_shape_og
            assert rowscale is None, "rowscale is not supported with parallel LayerNorm"
            x1 = x1.reshape(-1, x1.shape[-1])
            if x1.stride(-1) != 1:
                x1 = x1.contiguous()
        weight = weight.contiguous()
        if bias is not None:
            bias = bias.contiguous()
        if weight1 is not None:
            weight1 = weight1.contiguous()
        if bias1 is not None:
            bias1 = bias1.contiguous()
        if rowscale is not None:
            rowscale = rowscale.reshape(-1).contiguous()
        residual_dtype = (
            residual.dtype
            if residual is not None
            else (torch.float32 if residual_in_fp32 else None)
        )
        if out is not None:
            out = out.reshape(-1, out.shape[-1])
        if residual_out is not None:
            residual_out = residual_out.reshape(-1, residual_out.shape[-1])
        y, y1, mean, rstd, residual_out, seeds, dropout_mask, dropout_mask1 = _layer_norm_fwd(
            x,
            weight,
            bias,
            eps,
            residual,
            x1,
            weight1,
            bias1,
            dropout_p=dropout_p,
            rowscale=rowscale,
            residual_dtype=residual_dtype,
            zero_centered_weight=zero_centered_weight,
            is_rms_norm=is_rms_norm,
            return_dropout_mask=return_dropout_mask,
            out=out,
            residual_out=residual_out
        )
        ctx.save_for_backward(
            residual_out, weight, bias, weight1, bias1, rowscale, seeds, mean, rstd
        )
        ctx.x_shape_og = x_shape_og
        ctx.eps = eps
        ctx.dropout_p = dropout_p
        ctx.is_rms_norm = is_rms_norm
        ctx.has_residual = residual is not None
        ctx.has_x1 = x1 is not None
        ctx.prenorm = prenorm
        ctx.x_dtype = x.dtype
        ctx.zero_centered_weight = zero_centered_weight
        y = y.reshape(x_shape_og)
        y1 = y1.reshape(x_shape_og) if y1 is not None else None
        residual_out = residual_out.reshape(x_shape_og) if residual_out is not None else None
        dropout_mask = dropout_mask.reshape(x_shape_og) if dropout_mask is not None else None
        dropout_mask1 = dropout_mask1.reshape(x_shape_og) if dropout_mask1 is not None else None
        if not return_dropout_mask:
            if weight1 is None:
                return y if not prenorm else (y, residual_out)
            else:
                return (y, y1) if not prenorm else (y, y1, residual_out)
        else:
            if weight1 is None:
                return (
                    (y, dropout_mask, dropout_mask1)
                    if not prenorm
                    else (y, residual_out, dropout_mask, dropout_mask1)
                )
            else:
                return (
                    (y, y1, dropout_mask, dropout_mask1)
                    if not prenorm
                    else (y, y1, residual_out, dropout_mask, dropout_mask1)
                )

    @staticmethod
    def backward(ctx, dy, *args):
        if ctx.zero_seq_length:
            return (
                torch.zeros(ctx.x_shape_og, dtype=dy.dtype, device=dy.device),
                torch.zeros(ctx.weight_shape, dtype=ctx.weight_dtype, device=ctx.weight_device),
                torch.zeros(ctx.bias_shape, dtype=ctx.bias_dtype, device=ctx.bias_device) if ctx.has_bias else None,
                torch.zeros(ctx.x_shape_og, dtype=dy.dtype, device=dy.device) if ctx.has_residual else None,
                torch.zeros(ctx.x_shape_og, dtype=dy.dtype, device=dy.device) if ctx.has_x1 and ctx.dropout_p > 0.0 else None,
                torch.zeros(ctx.weight1_shape, dtype=ctx.weight1_dtype, device=ctx.weight1_device) if ctx.has_weight1 else None,
                torch.zeros(ctx.bias1_shape, dtype=ctx.bias1_dtype, device=ctx.bias1_device) if ctx.has_bias1 else None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
            )
        
        x, weight, bias, weight1, bias1, rowscale, seeds, mean, rstd = ctx.saved_tensors
        dy = dy.reshape(-1, dy.shape[-1])
        if dy.stride(-1) != 1:
            dy = dy.contiguous()
        assert dy.shape == x.shape
        if weight1 is not None:
            dy1, args = args[0], args[1:]
            dy1 = dy1.reshape(-1, dy1.shape[-1])
            if dy1.stride(-1) != 1:
                dy1 = dy1.contiguous()
            assert dy1.shape == x.shape
        else:
            dy1 = None
        if ctx.prenorm:
            dresidual = args[0]
            dresidual = dresidual.reshape(-1, dresidual.shape[-1])
            if dresidual.stride(-1) != 1:
                dresidual = dresidual.contiguous()
            assert dresidual.shape == x.shape
        else:
            dresidual = None
        
        dx, dw, db, dresidual_in, dx1, dw1, db1 = _layer_norm_bwd(
            dy,
            x,
            weight,
            bias,
            ctx.eps,
            mean,
            rstd,
            dresidual,
            dy1,
            weight1,
            bias1,
            seeds,
            ctx.dropout_p,
            rowscale,
            ctx.has_residual,
            ctx.has_x1,
            ctx.zero_centered_weight,
            ctx.is_rms_norm,
            x_dtype=ctx.x_dtype,
        )
        return (
            dx.reshape(ctx.x_shape_og),
            dw,
            db,
            dresidual_in.reshape(ctx.x_shape_og) if ctx.has_residual else None,
            dx1.reshape(ctx.x_shape_og) if dx1 is not None else None,
            dw1,
            db1,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
        )


def layer_norm_fn(
    x,
    weight,
    bias,
    residual=None,
    x1=None,
    weight1=None,
    bias1=None,
    eps=1e-6,
    dropout_p=0.0,
    rowscale=None,
    prenorm=False,
    residual_in_fp32=False,
    zero_centered_weight=False,
    is_rms_norm=False,
    return_dropout_mask=False,
    out=None,
    residual_out=None
):
    return LayerNormFn.apply(
        x,
        weight,
        bias,
        residual,
        x1,
        weight1,
        bias1,
        eps,
        dropout_p,
        rowscale,
        prenorm,
        residual_in_fp32,
        zero_centered_weight,
        is_rms_norm,
        return_dropout_mask,
        out,
        residual_out
    )


def rms_norm_fn(
    x,
    weight,
    bias,
    residual=None,
    x1=None,
    weight1=None,
    bias1=None,
    eps=1e-6,
    dropout_p=0.0,
    rowscale=None,
    prenorm=False,
    residual_in_fp32=False,
    zero_centered_weight=False,
    return_dropout_mask=False,
    out=None,
    residual_out=None
):
    return LayerNormFn.apply(
        x,
        weight,
        bias,
        residual,
        x1,
        weight1,
        bias1,
        eps,
        dropout_p,
        rowscale,
        prenorm,
        residual_in_fp32,
        zero_centered_weight,
        True,
        return_dropout_mask,
        out,
        residual_out
    )

'''
class RMSNorm(torch.nn.Module):

    def __init__(self, hidden_size, eps=1e-5, dropout_p=0.0, zero_centered_weight=False,
                 device=None, dtype=None):
        factory_kwargs = {"device": device, "dtype": dtype}
        super().__init__()
        self.eps = eps
        if dropout_p > 0.0:
            self.drop = torch.nn.Dropout(dropout_p)
        else:
            self.drop = None
        self.zero_centered_weight = zero_centered_weight
        self.weight = torch.nn.Parameter(torch.empty(hidden_size, **factory_kwargs))
        self.register_parameter("bias", None)
        self.reset_parameters()

    def reset_parameters(self):
        if not self.zero_centered_weight:
            torch.nn.init.ones_(self.weight)
        else:
            torch.nn.init.zeros_(self.weight)

    def forward(self, x, residual=None, prenorm=False, residual_in_fp32=False):
        return rms_norm_fn(
            x,
            self.weight,
            self.bias,
            residual=residual,
            eps=self.eps,
            dropout_p=self.drop.p if self.drop is not None and self.training else 0.0,
            prenorm=prenorm,
            residual_in_fp32=residual_in_fp32,
            zero_centered_weight=self.zero_centered_weight,
        )
'''

class LayerNormLinearFn(torch.autograd.Function):
    @staticmethod
    @custom_fwd
    def forward(
        ctx,
        x,
        norm_weight,
        norm_bias,
        linear_weight,
        linear_bias,
        residual=None,
        eps=1e-6,
        prenorm=False,
        residual_in_fp32=False,
        is_rms_norm=False,
    ):
        x_shape_og = x.shape
        # reshape input data into 2D tensor
        x = x.reshape(-1, x.shape[-1])
        if x.stride(-1) != 1:
            x = x.contiguous()
        if residual is not None:
            assert residual.shape == x_shape_og
            residual = residual.reshape(-1, residual.shape[-1])
            if residual.stride(-1) != 1:
                residual = residual.contiguous()
        norm_weight = norm_weight.contiguous()
        if norm_bias is not None:
            norm_bias = norm_bias.contiguous()
        residual_dtype = (
            residual.dtype
            if residual is not None
            else (torch.float32 if residual_in_fp32 else None)
        )
        y, _, mean, rstd, residual_out, *rest = _layer_norm_fwd(
            x,
            norm_weight,
            norm_bias,
            eps,
            residual,
            out_dtype=None if not torch.is_autocast_enabled() else torch.get_autocast_dtype("cuda"),
            residual_dtype=residual_dtype,
            is_rms_norm=is_rms_norm,
        )
        y = y.reshape(x_shape_og)
        dtype = torch.get_autocast_dtype("cuda") if torch.is_autocast_enabled() else y.dtype
        linear_weight = linear_weight.to(dtype)
        linear_bias = linear_bias.to(dtype) if linear_bias is not None else None
        out = F.linear(y.to(linear_weight.dtype), linear_weight, linear_bias)
        # We don't store y, will be recomputed in the backward pass to save memory
        ctx.save_for_backward(residual_out, norm_weight, norm_bias, linear_weight, mean, rstd)
        ctx.x_shape_og = x_shape_og
        ctx.eps = eps
        ctx.is_rms_norm = is_rms_norm
        ctx.has_residual = residual is not None
        ctx.prenorm = prenorm
        ctx.x_dtype = x.dtype
        ctx.linear_bias_is_none = linear_bias is None
        return out if not prenorm else (out, residual_out.reshape(x_shape_og))

    @staticmethod
    @custom_bwd
    def backward(ctx, dout, *args):
        x, norm_weight, norm_bias, linear_weight, mean, rstd = ctx.saved_tensors
        dout = dout.reshape(-1, dout.shape[-1])
        dy = F.linear(dout, linear_weight.t())
        dlinear_bias = None if ctx.linear_bias_is_none else dout.sum(0)
        if dy.stride(-1) != 1:
            dy = dy.contiguous()
        assert dy.shape == x.shape
        if ctx.prenorm:
            dresidual = args[0]
            dresidual = dresidual.reshape(-1, dresidual.shape[-1])
            if dresidual.stride(-1) != 1:
                dresidual = dresidual.contiguous()
            assert dresidual.shape == x.shape
        else:
            dresidual = None
        dx, dnorm_weight, dnorm_bias, dresidual_in, _, _, _, y = _layer_norm_bwd(
            dy,
            x,
            norm_weight,
            norm_bias,
            ctx.eps,
            mean,
            rstd,
            dresidual=dresidual,
            has_residual=ctx.has_residual,
            is_rms_norm=ctx.is_rms_norm,
            x_dtype=ctx.x_dtype,
            recompute_output=True,
        )
        dlinear_weight = torch.einsum("bo,bi->oi", dout, y)
        return (
            dx.reshape(ctx.x_shape_og),
            dnorm_weight,
            dnorm_bias,
            dlinear_weight,
            dlinear_bias,
            dresidual_in.reshape(ctx.x_shape_og) if ctx.has_residual else None,
            None,
            None,
            None,
            None,
        )


def layer_norm_linear_fn(
    x,
    norm_weight,
    norm_bias,
    linear_weight,
    linear_bias,
    residual=None,
    eps=1e-6,
    prenorm=False,
    residual_in_fp32=False,
    is_rms_norm=False,
):
    return LayerNormLinearFn.apply(
        x,
        norm_weight,
        norm_bias,
        linear_weight,
        linear_bias,
        residual,
        eps,
        prenorm,
        residual_in_fp32,
        is_rms_norm,
    )
