import torch

try:
    import triton
    import triton.language as tl

    HAS_TRITON = True
except ImportError:
    HAS_TRITON = False
    print("please install triton from https://github.com/openai/triton")


if HAS_TRITON:
    """
    this kernel function is modified from
    https://github.com/ModelTC/lightllm/blob/main/lightllm/models/llama/triton_kernel/rmsnorm.py
    """

    @triton.jit
    def _rms_norm_fwd_fused(
        X,  # pointer to the input
        Y,  # pointer to the output
        W,  # pointer to the weights
        stride,  # how much to increase the pointer when moving by 1 row
        N,  # number of columns in X
        eps,  # epsilon to avoid division by zero
        BLOCK_SIZE: tl.constexpr,
    ):
        # Map the program id to the row of X and Y it should compute.
        row = tl.program_id(0)
        Y += row * stride
        X += row * stride
        # Compute variance
        _var = tl.zeros([BLOCK_SIZE], dtype=tl.float32)
        for off in range(0, N, BLOCK_SIZE):
            cols = off + tl.arange(0, BLOCK_SIZE)
            x = tl.load(X + cols, mask=cols < N, other=0.0).to(tl.float32)
            _var += x * x
        var = tl.sum(_var, axis=0) / N
        rstd = 1 / tl.sqrt(var + eps)
        # Normalize and apply linear transformation
        for off in range(0, N, BLOCK_SIZE):
            cols = off + tl.arange(0, BLOCK_SIZE)
            mask = cols < N
            w = tl.load(W + cols, mask=mask).to(tl.float32)
            x = tl.load(X + cols, mask=mask, other=0.0).to(tl.float32)
            x_hat = x * rstd
            y = x_hat * w
            # Write output
            tl.store(Y + cols, y.to(tl.float16), mask=mask)

    def rmsnorm_forward(x, weight, eps):
        # allocate output
        y = torch.empty_like(x)
        # reshape input data into 2D tensor
        x_arg = x.view(-1, x.shape[-1])
        M, N = x_arg.shape
        # Less than 64KB per feature: enqueue fused kernel
        MAX_FUSED_SIZE = 65536 // x.element_size()
        BLOCK_SIZE = min(MAX_FUSED_SIZE, triton.next_power_of_2(N))
        # print("BLOCK_SIZE:", BLOCK_SIZE)
        if N > BLOCK_SIZE:
            raise RuntimeError("This layer norm doesn't support feature dim >= 64KB.")
        # heuristics for number of warps
        num_warps = min(max(BLOCK_SIZE // 256, 1), 8)
        # print(BLOCK_SIZE, num_warps, "block_size, numwarps")
        BLOCK_SIZE = 128 * 2 * 2 * 2 * 2 * 2 * 2 * 2
        num_warps = 8
        # enqueue kernel
        _rms_norm_fwd_fused[(M,)](x_arg, y, weight, x_arg.stride(0), N, eps, BLOCK_SIZE=BLOCK_SIZE, num_warps=num_warps)
        return y