import cudnn
import pytest
import torch
import itertools
from looseversion import LooseVersion

import torch.nn as nn

from test_utils import torch_fork_set_rng


class RMSNorm(torch.nn.Module):
    """Root Mean Square Layer Normalization.

    Derived from https://github.com/bzhangGo/rmsnorm/blob/master/rmsnorm_torch.py. BSD 3-Clause License:
    https://github.com/bzhangGo/rmsnorm/blob/master/LICENSE.
    """

    def __init__(self, dim: int = -1, eps: float = 1e-5) -> None:
        super().__init__()
        self.eps = eps
        self.dim = dim

    def forward(
        self, x: torch.Tensor, weight: torch.Tensor, bias: torch.Tensor = None
    ) -> torch.Tensor:
        # NOTE: the original RMSNorm paper implementation is not equivalent
        norm_x = torch.mean(x * x, dim=self.dim, keepdim=True)
        inv_var = torch.rsqrt(norm_x + self.eps)
        x_normed = x * inv_var
        x_scaled = weight * x_normed
        if bias is not None:
            x_scaled += bias
        return x_scaled, inv_var


embedding_dim_options = [768, 1024, 1280, 1600]
input_type_options = [torch.float16, torch.bfloat16]
bias_options = [True, False]

all_options = [
    elem
    for elem in itertools.product(
        *[embedding_dim_options, input_type_options, bias_options]
    )
]


@pytest.fixture(params=all_options)
def param_extract(request):
    return request.param


@pytest.mark.skipif(
    LooseVersion(cudnn.backend_version_string()) < "8.9.6",
    reason="RmsNorm not supported below cudnn 8.9.6",
)
@torch_fork_set_rng(seed=0)
def test_rmsnorm(param_extract, cudnn_handle):

    embedding_dim, input_type, has_bias = param_extract

    batch_size, seq_size = 16, 128
    N, C, H, W = batch_size * seq_size, embedding_dim, 1, 1

    epsilon_value = 1e-3

    x_gpu = (
        2 * torch.randn(N, C, H, W, requires_grad=True, device="cuda", dtype=input_type)
        - 1.25
    )
    scale_gpu = (
        3 * torch.randn(1, C, H, W, requires_grad=True, device="cuda", dtype=input_type)
        - 2.75
    )
    bias_gpu = torch.randn(
        1, C, H, W, requires_grad=True, device="cuda", dtype=input_type
    )
    epsilon_cpu = torch.full(
        (1, 1, 1, 1),
        epsilon_value,
        requires_grad=False,
        device="cpu",
        dtype=torch.float32,
    )

    print("Running reference")

    model = RMSNorm(eps=epsilon_value, dim=(1, 2, 3)).float()
    Y_expected, inv_var_expected = model(
        x_gpu, scale_gpu, bias_gpu if has_bias else None
    )

    print("Building cudnn graph")

    stream = torch.cuda.current_stream().cuda_stream
    cudnn.set_stream(handle=cudnn_handle, stream=stream)

    graph = cudnn.pygraph(
        intermediate_data_type=cudnn.data_type.FLOAT,
        compute_data_type=cudnn.data_type.FLOAT,
        handle=cudnn_handle,
    )

    X = graph.tensor_like(x_gpu.detach())
    scale = graph.tensor_like(scale_gpu.detach())
    bias = graph.tensor_like(bias_gpu.detach()) if has_bias else None
    epsilon = graph.tensor_like(epsilon_cpu)

    Y, inv_var = graph.rmsnorm(
        name="RMS",
        norm_forward_phase=cudnn.norm_forward_phase.TRAINING,
        input=X,
        scale=scale,
        bias=bias,
        epsilon=epsilon,
    )

    Y.set_output(True).set_data_type(x_gpu.dtype)
    inv_var.set_output(True).set_data_type(inv_var_expected.dtype)

    graph.validate()
    graph.build_operation_graph()
    graph.create_execution_plans([cudnn.heur_mode.A, cudnn.heur_mode.FALLBACK])
    graph.check_support()
    graph.build_plans()

    Y_actual = torch.empty_like(x_gpu)
    inv_var_actual = torch.empty_like(inv_var_expected)

    workspace = torch.empty(
        graph.get_workspace_size(), device="cuda", dtype=torch.uint8
    )
    print("Executing cudnn graph")

    graph.execute(
        {
            X: x_gpu.detach(),
            scale: scale_gpu.detach(),
            bias: bias_gpu.detach(),
            epsilon: epsilon_cpu,
            Y: Y_actual,
            inv_var: inv_var_actual,
        },
        workspace,
        handle=cudnn_handle,
    )

    torch.cuda.synchronize()
    torch.testing.assert_close(Y_expected, Y_actual, atol=0.03125, rtol=0.03125)
    torch.testing.assert_close(inv_var_expected, inv_var_actual, atol=0.005, rtol=0.005)

    target = torch.randn_like(Y_expected)
    criterion = nn.MSELoss()
    loss = criterion(Y_expected, target)

    Y_expected.retain_grad()
    x_gpu.retain_grad()
    scale_gpu.retain_grad()
    bias_gpu.retain_grad()

    loss.backward()

    stream = torch.cuda.current_stream().cuda_stream
    cudnn.set_stream(handle=cudnn_handle, stream=stream)

    bwd_graph = cudnn.pygraph(
        intermediate_data_type=cudnn.data_type.FLOAT,
        compute_data_type=cudnn.data_type.FLOAT,
        handle=cudnn_handle,
    )

    DY = bwd_graph.tensor_like(Y_expected.grad)
    X_bwd = bwd_graph.tensor_like(x_gpu.detach())
    scale_bwd = bwd_graph.tensor_like(scale_gpu.detach())
    inv_var_bwd = bwd_graph.tensor_like(inv_var_actual)

    DX, Dscale, Dbias = bwd_graph.rmsnorm_backward(
        name="DRMS",
        grad=DY,
        input=X_bwd,
        scale=scale_bwd,
        inv_variance=inv_var_bwd,
        has_dbias=has_bias,
    )

    DX.set_output(True).set_data_type(x_gpu.dtype)
    Dscale.set_output(True).set_data_type(x_gpu.dtype)
    if has_bias:
        Dbias.set_output(True).set_data_type(x_gpu.dtype)
    else:
        assert Dbias is None

    bwd_graph.validate()
    bwd_graph.build_operation_graph()
    bwd_graph.create_execution_plans([cudnn.heur_mode.A, cudnn.heur_mode.FALLBACK])
    bwd_graph.check_support()
    bwd_graph.build_plans()

    DX_actual = torch.empty_like(x_gpu)
    DScale_actual = torch.empty_like(scale_gpu)
    Dbias_actual = torch.empty_like(bias_gpu)

    workspace = torch.empty(
        bwd_graph.get_workspace_size(), device="cuda", dtype=torch.uint8
    )
    print("Executing cudnn bwd_graph")

    bwd_graph.execute(
        {
            X_bwd: x_gpu.detach(),
            scale_bwd: scale_gpu.detach(),
            DY: Y_expected.grad,
            inv_var_bwd: inv_var_actual,
            DX: DX_actual,
            Dscale: DScale_actual,
            Dbias: Dbias_actual,
        },
        workspace,
        handle=cudnn_handle,
    )

    torch.cuda.synchronize()
    print("Comparing with reference")
    torch.testing.assert_close(x_gpu.grad, DX_actual, atol=2e-4, rtol=2e-4)
    torch.testing.assert_close(scale_gpu.grad, DScale_actual, atol=5e-4, rtol=5e-4)
    if has_bias:
        torch.testing.assert_close(bias_gpu.grad, Dbias_actual, atol=5e-4, rtol=5e-4)
    print("Success!!")
