# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import contextlib
import os
from collections import namedtuple
from collections.abc import Callable
from typing import Any

import torch

import vllm.envs as envs
from vllm.logger import init_logger
from vllm.triton_utils import tl, triton

logger = init_logger(__name__)


def _matmul_launch_metadata(
    grid: Callable[..., Any], kernel: Any, args: dict[str, Any]
) -> dict[str, Any]:
    ret = {}
    m, n, k = args["M"], args["N"], args["K"]
    ret["name"] = f"{kernel.name} [M={m}, N={n}, K={k}]"
    if "tiles_per_update" in args:
        ret["name"] = (
            f"{kernel.name} [M={m}, N={n}, K={k}, "
            f"tiles_per_update={args['tiles_per_update']:02}]"
        )
    if "c_ptr" in args:
        bytes_per_elem = args["c_ptr"].element_size()
    else:
        bytes_per_elem = 1 if args["FP8_OUTPUT"] else 2
    ret[f"flops{bytes_per_elem * 8}"] = 2.0 * m * n * k
    ret["bytes"] = bytes_per_elem * (m * k + n * k + m * n)
    return ret


@triton.jit
def _compute_pid(tile_id, num_pid_in_group, num_pid_m, GROUP_SIZE_M, NUM_SMS):
    group_id = tile_id // num_pid_in_group
    first_pid_m = group_id * GROUP_SIZE_M
    group_size_m = min(num_pid_m - first_pid_m, GROUP_SIZE_M)
    pid_m = first_pid_m + (tile_id % group_size_m)
    pid_n = (tile_id % num_pid_in_group) // group_size_m
    return pid_m, pid_n


@triton.jit(launch_metadata=_matmul_launch_metadata)
def matmul_kernel_persistent(
    a_ptr,
    b_ptr,
    c_ptr,  #
    bias_ptr,
    M,
    N,
    K,  #
    stride_am,
    stride_ak,
    stride_bk,
    stride_bn,
    stride_cm,
    stride_cn,
    BLOCK_SIZE_M: tl.constexpr,  #
    BLOCK_SIZE_N: tl.constexpr,  #
    BLOCK_SIZE_K: tl.constexpr,  #
    GROUP_SIZE_M: tl.constexpr,  #
    NUM_SMS: tl.constexpr,  #
    A_LARGE: tl.constexpr,
    B_LARGE: tl.constexpr,
    C_LARGE: tl.constexpr,
    HAS_BIAS: tl.constexpr,
):
    start_pid = tl.program_id(axis=0)
    num_pid_m = tl.cdiv(M, BLOCK_SIZE_M)
    num_pid_n = tl.cdiv(N, BLOCK_SIZE_N)
    k_tiles = tl.cdiv(K, BLOCK_SIZE_K)
    num_tiles = num_pid_m * num_pid_n

    tile_id_c = start_pid - NUM_SMS

    offs_k_for_mask = tl.arange(0, BLOCK_SIZE_K)
    num_pid_in_group = GROUP_SIZE_M * num_pid_n

    for tile_id in tl.range(start_pid, num_tiles, NUM_SMS, flatten=True):
        pid_m, pid_n = _compute_pid(
            tile_id, num_pid_in_group, num_pid_m, GROUP_SIZE_M, NUM_SMS
        )
        start_m = pid_m * BLOCK_SIZE_M
        start_n = pid_n * BLOCK_SIZE_N
        offs_am = start_m + tl.arange(0, BLOCK_SIZE_M)
        offs_bn = start_n + tl.arange(0, BLOCK_SIZE_N)
        if A_LARGE:
            offs_am = offs_am.to(tl.int64)
        if B_LARGE:
            offs_bn = offs_bn.to(tl.int64)
        offs_am = tl.where(offs_am < M, offs_am, 0)
        offs_bn = tl.where(offs_bn < N, offs_bn, 0)
        offs_am = tl.max_contiguous(tl.multiple_of(offs_am, BLOCK_SIZE_M), BLOCK_SIZE_M)
        offs_bn = tl.max_contiguous(tl.multiple_of(offs_bn, BLOCK_SIZE_N), BLOCK_SIZE_N)

        accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)
        for ki in range(k_tiles):
            if A_LARGE or B_LARGE:
                offs_k = ki * BLOCK_SIZE_K + tl.arange(0, BLOCK_SIZE_K).to(tl.int64)
            else:
                offs_k = ki * BLOCK_SIZE_K + tl.arange(0, BLOCK_SIZE_K)
            a_ptrs = a_ptr + (
                offs_am[:, None] * stride_am + offs_k[None, :] * stride_ak
            )
            b_ptrs = b_ptr + (
                offs_k[:, None] * stride_bk + offs_bn[None, :] * stride_bn
            )

            a = tl.load(
                a_ptrs, mask=offs_k_for_mask[None, :] < K - ki * BLOCK_SIZE_K, other=0.0
            )
            b = tl.load(
                b_ptrs, mask=offs_k_for_mask[:, None] < K - ki * BLOCK_SIZE_K, other=0.0
            )
            accumulator = tl.dot(a, b, accumulator)

        tile_id_c += NUM_SMS
        pid_m, pid_n = _compute_pid(
            tile_id_c, num_pid_in_group, num_pid_m, GROUP_SIZE_M, NUM_SMS
        )
        offs_cm = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
        offs_cn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
        if C_LARGE:
            offs_cm = offs_cm.to(tl.int64)
            offs_cn = offs_cn.to(tl.int64)
        c_ptrs = c_ptr + stride_cm * offs_cm[:, None] + stride_cn * offs_cn[None, :]
        c_mask = (offs_cm[:, None] < M) & (offs_cn[None, :] < N)
        if HAS_BIAS:
            bias_ptrs = bias_ptr + offs_cn
            bias = tl.load(bias_ptrs, mask=offs_cn < N, other=0.0).to(tl.float32)
            accumulator += bias
        if c_ptr.dtype.element_ty == tl.float8e4nv:
            c = accumulator.to(tl.float8e4nv)
        else:
            c = accumulator.to(tl.float16)
        tl.store(c_ptrs, c, mask=c_mask)


def matmul_persistent(
    a: torch.Tensor, b: torch.Tensor, bias: torch.Tensor | None = None
):
    # Check constraints.
    assert a.shape[1] == b.shape[0], "Incompatible dimensions"
    assert a.dtype == b.dtype, "Incompatible dtypes"
    assert bias is None or bias.dim() == 1, (
        "Currently assuming bias is 1D, let Horace know if you run into this"
    )
    NUM_SMS = torch.cuda.get_device_properties("cuda").multi_processor_count
    M, K = a.shape
    K, N = b.shape
    dtype = a.dtype
    # Allocates output.
    c = torch.empty((M, N), device=a.device, dtype=dtype)

    # 1D launch kernel where each block gets its own program.
    def grid(META):
        return (
            min(
                NUM_SMS,
                triton.cdiv(M, META["BLOCK_SIZE_M"])
                * triton.cdiv(N, META["BLOCK_SIZE_N"]),
            ),
        )

    configs = {
        torch.bfloat16: {
            "BLOCK_SIZE_M": 128,
            "BLOCK_SIZE_N": 128,
            "BLOCK_SIZE_K": 64,
            "GROUP_SIZE_M": 8,
            "num_stages": 3,
            "num_warps": 8,
        },
        torch.float16: {
            "BLOCK_SIZE_M": 128,
            "BLOCK_SIZE_N": 256,
            "BLOCK_SIZE_K": 64,
            "GROUP_SIZE_M": 8,
            "num_stages": 3,
            "num_warps": 8,
        },
        torch.float32: {
            "BLOCK_SIZE_M": 128,
            "BLOCK_SIZE_N": 128,
            "BLOCK_SIZE_K": 32,
            "GROUP_SIZE_M": 8,
            "num_stages": 3,
            "num_warps": 8,
        },
    }
    # print(a.device, b.device, c.device)
    matmul_kernel_persistent[grid](
        a,
        b,
        c,  #
        bias,
        M,
        N,
        K,  #
        a.stride(0),
        a.stride(1),  #
        b.stride(0),
        b.stride(1),  #
        c.stride(0),
        c.stride(1),  #
        NUM_SMS=NUM_SMS,  #
        A_LARGE=a.numel() > 2**31,
        B_LARGE=b.numel() > 2**31,
        C_LARGE=c.numel() > 2**31,
        HAS_BIAS=bias is not None,
        **configs[dtype],
    )
    return c


@triton.jit
def _log_softmax_kernel(
    input_ptr,
    output_ptr,
    input_row_stride,
    output_row_stride,
    n_cols,
    BLOCK_SIZE: tl.constexpr,
):
    """
    Compute log_softmax along the last dimension of a 2D tensor.
    Each block handles one row of the input tensor.
    """
    # Get the row index for this block
    row_idx = tl.program_id(0).to(tl.int64)

    # Compute base pointers for input and output rows
    row_start_ptr = input_ptr + row_idx * input_row_stride
    output_row_start_ptr = output_ptr + row_idx * output_row_stride

    # Step 1: Find maximum value in the row for numerical stability
    max_val = -float("inf")
    for col_offset in range(0, n_cols, BLOCK_SIZE):
        col_idx = col_offset + tl.arange(0, BLOCK_SIZE)
        mask = col_idx < n_cols

        # Load values
        vals = tl.load(row_start_ptr + col_idx, mask=mask, other=-float("inf"))

        # Update maximum
        max_val = tl.max(tl.maximum(vals, max_val))

    # Step 2: Compute sum of exp(x - max_val)
    sum_exp = 0.0
    for col_offset in range(0, n_cols, BLOCK_SIZE):
        col_idx = col_offset + tl.arange(0, BLOCK_SIZE)
        mask = col_idx < n_cols

        # Load values
        vals = tl.load(row_start_ptr + col_idx, mask=mask, other=0.0)

        # Compute exp(x - max_val) and accumulate
        exp_vals = tl.exp(vals - max_val)
        sum_exp += tl.sum(tl.where(mask, exp_vals, 0.0))

    # Compute log(sum_exp)
    log_sum_exp = tl.log(sum_exp)

    # Step 3: Compute final log_softmax values: x - max_val - log_sum_exp
    for col_offset in range(0, n_cols, BLOCK_SIZE):
        col_idx = col_offset + tl.arange(0, BLOCK_SIZE)
        mask = col_idx < n_cols

        # Load values
        vals = tl.load(row_start_ptr + col_idx, mask=mask)

        # Compute log_softmax
        output = vals - max_val - log_sum_exp

        # Store results
        tl.store(output_row_start_ptr + col_idx, output, mask=mask)


def log_softmax(input: torch.Tensor, dim: int = -1) -> torch.Tensor:
    """
    Compute log_softmax using Triton kernel.

    Args:
        input: Input tensor
        dim: Dimension along which to compute log_softmax
             (only -1 or last dim supported)
    >> Stashed changes
    Returns:
        Tensor with log_softmax applied along the specified dimension
    """
    if dim != -1 and dim != input.ndim - 1:
        raise ValueError(
            "This implementation only supports log_softmax along the last dimension"
        )

    # Flatten all dimensions except the last one
    original_shape = input.shape
    input_2d = input.reshape(-1, input.shape[-1])
    input_2d = input_2d.contiguous()

    n_rows, n_cols = input_2d.shape

    # Allocate output tensor
    output = torch.empty_like(input_2d)

    # Choose block size based on the number of columns
    BLOCK_SIZE = 1024

    # Launch kernel with one block per row
    grid = (n_rows,)
    _log_softmax_kernel[grid](
        input_2d,
        output,
        input_2d.stride(0),
        output.stride(0),
        n_cols,
        BLOCK_SIZE=BLOCK_SIZE,
    )
    # Reshape output back to original shape
    return output.reshape(original_shape)


@triton.jit
def mean_kernel(
    input_ptr,
    output_ptr,
    input_stride0,
    input_stride1,
    input_stride2,
    output_stride0,
    output_stride1,
    M,  # size before reduction dim
    N,  # size of reduction dim
    K,  # size after reduction dim
    BLOCK_SIZE: tl.constexpr,
):
    """
    Kernel for computing mean along a single dimension.
    Input is viewed as (M, N, K) where N is the dimension being reduced.
    """
    # Program ID gives us which output element we're computing
    pid = tl.program_id(0)

    # Compute output indices
    m_idx = pid // K
    k_idx = pid % K

    # Bounds check
    if m_idx >= M or k_idx >= K:
        return

    # Accumulate sum across reduction dimension
    acc = 0.0
    for n_start in range(0, N, BLOCK_SIZE):
        n_offsets = n_start + tl.arange(0, BLOCK_SIZE)
        mask = n_offsets < N

        # Calculate input indices
        input_idx = (
            m_idx * input_stride0 + n_offsets * input_stride1 + k_idx * input_stride2
        )

        # Load and accumulate
        vals = tl.load(input_ptr + input_idx, mask=mask, other=0.0)
        acc += tl.sum(vals)

    # Compute mean and store
    mean_val = acc / N
    output_idx = m_idx * output_stride0 + k_idx * output_stride1
    tl.store(output_ptr + output_idx, mean_val)


def mean_dim(
    input: torch.Tensor,
    dim: int,
    keepdim: bool = False,
    dtype: torch.dtype | None = None,
) -> torch.Tensor:
    """
    Triton implementation of torch.mean with single dimension reduction.

    Args:
        input: Input tensor
        dim: Single dimension along which to compute mean
        keepdim: Whether to keep the reduced dimension
        dtype: Output dtype. If None, uses input dtype
               (or float32 for integer inputs)

    Returns:
        Tensor with mean values along specified dimension
    """
    # Validate inputs
    assert -input.ndim <= dim < input.ndim, (
        f"Invalid dimension {dim} for tensor with {input.ndim} dimensions"
    )

    # Handle negative dim
    if dim < 0:
        dim = dim + input.ndim

    # Handle dtype
    if dtype is None:
        if input.dtype in [torch.int8, torch.int16, torch.int32, torch.int64]:
            dtype = torch.float32
        else:
            dtype = input.dtype

    # Convert input to appropriate dtype if needed
    if input.dtype != dtype:
        input = input.to(dtype)

    # Get input shape and strides
    shape = list(input.shape)

    # Calculate dimensions for kernel
    M = 1
    for i in range(dim):
        M *= shape[i]

    N = shape[dim]

    K = 1
    for i in range(dim + 1, len(shape)):
        K *= shape[i]

    # Reshape input to 3D view (M, N, K)
    input_3d = input.reshape(M, N, K)

    # Create output shape
    if keepdim:
        output_shape = shape.copy()
        output_shape[dim] = 1
    else:
        output_shape = shape[:dim] + shape[dim + 1 :]

    # Create output tensor
    output = torch.empty(output_shape, dtype=dtype, device=input.device)

    # Reshape output for kernel
    output_2d = output.reshape(M, 1, K).squeeze(1) if keepdim else output.reshape(M, K)

    # Launch kernel
    grid = (M * K,)
    BLOCK_SIZE = 1024

    mean_kernel[grid](
        input_3d,
        output_2d,
        input_3d.stride(0),
        input_3d.stride(1),
        input_3d.stride(2),
        output_2d.stride(0),
        output_2d.stride(1) if output_2d.ndim > 1 else 0,
        M,
        N,
        K,
        BLOCK_SIZE,
    )

    return output


def mm_batch_invariant(a, b):
    return matmul_persistent(a, b)


def matmul_batch_invariant(a, b, *, out=None):
    # torch.matmul can handle various dimensions
    # For 2D x 2D, it's the same as mm
    if a.ndim == 2 and b.ndim == 2:
        result = matmul_persistent(a, b)
        if out is not None:
            out.copy_(result)
            return out
        return result
    elif a.ndim == 3 and b.ndim == 3:
        # Handle batched case like bmm
        return bmm_batch_invariant(a, b, out=out)
    else:
        raise ValueError(
            f"matmul_batch_invariant currently only supports 2D x 2D and 3D x 3D, "
            f"got shapes {a.shape} and {b.shape}"
        )


def bmm_batch_invariant(a, b, *, out=None):
    # Batched matrix multiply: (B, M, K) x (B, K, N) -> (B, M, N)
    # Process each batch separately with our persistent kernel
    if a.ndim == 3 and b.ndim == 3:
        results = []
        for i in range(a.shape[0]):
            results.append(matmul_persistent(a[i], b[i]))
        result = torch.stack(results, dim=0)

        if out is not None:
            out.copy_(result)
            return out
        return result
    else:
        raise ValueError(
            f"bmm_batch_invariant expects 3D tensors, "
            f"got shapes {a.shape} and {b.shape}"
        )


def addmm_batch_invariant(bias, a, b):
    return matmul_persistent(a, b, bias=bias)


def _log_softmax_batch_invariant(input, dim, _half_to_float):
    assert not _half_to_float, "not implemented"
    return log_softmax(input, dim=dim)


def softmax_batch_invariant(input, dim, dtype=None):
    # Compute softmax in a deterministic way
    # First subtract max for numerical stability (standard practice)
    input_max = torch.amax(input, dim=dim, keepdim=True)
    input = input - input_max
    exp_x = torch.exp(input)
    sum_exp_x = torch.sum(exp_x, dim=dim, keepdim=True)
    return exp_x / sum_exp_x


def mean_batch_invariant(input, dim, keepdim=False, dtype: torch.dtype | None = None):
    assert dtype is None or dtype == torch.float32, f"unsupported dtype: {dtype}"

    result = input.to(torch.float32)

    if len(dim) == 0:
        dim = [i for i in range(len(input.shape))]

    # Sort dimensions to reduce from largest to smallest to handle shifting dims
    # during iterative reduction.
    sorted_dims = sorted([d % input.ndim for d in dim], reverse=True)

    # Iteratively apply a deterministic mean.
    for d in sorted_dims:
        result = mean_dim(result, dim=d, keepdim=True)

    if not keepdim:
        # Squeeze the reduced dimensions.
        for d in sorted_dims:
            result = result.squeeze(d)

    return result


@triton.jit
def _rms_norm_kernel(
    input_ptr,
    weight_ptr,
    output_ptr,
    input_row_stride,
    output_row_stride,
    n_cols,
    eps,
    BLOCK_SIZE: tl.constexpr,
):
    """
    Compute RMS normalization along the last dimension of a 2D tensor.
    RMS Norm: y = x / sqrt(mean(x^2) + eps) * weight
    Each block handles one row of the input tensor.
    """
    row_idx = tl.program_id(0).to(tl.int64)
    row_start_ptr = input_ptr + row_idx * input_row_stride
    output_row_start_ptr = output_ptr + row_idx * output_row_stride

    # Step 1: Compute sum of squares in float32 to avoid overflow
    sum_sq = tl.zeros([1], dtype=tl.float32)
    for col_offset in range(0, n_cols, BLOCK_SIZE):
        col_idx = col_offset + tl.arange(0, BLOCK_SIZE)
        mask = col_idx < n_cols

        vals = tl.load(row_start_ptr + col_idx, mask=mask, other=0.0)
        # Convert to float32 for accumulation to prevent overflow
        vals_f32 = vals.to(tl.float32)
        sq_vals = vals_f32 * vals_f32
        sum_sq += tl.sum(tl.where(mask, sq_vals, 0.0))

    # Step 2: Compute RMS (root mean square) in float32
    mean_sq = sum_sq / n_cols
    rms = tl.sqrt(mean_sq + eps)
    inv_rms = 1.0 / rms

    # Step 3: Normalize and apply weight
    for col_offset in range(0, n_cols, BLOCK_SIZE):
        col_idx = col_offset + tl.arange(0, BLOCK_SIZE)
        mask = col_idx < n_cols
        vals = tl.load(row_start_ptr + col_idx, mask=mask, other=0.0)
        weight = tl.load(weight_ptr + col_idx, mask=mask, other=1.0)
        # Compute in float32 then convert back to input dtype
        vals_f32 = vals.to(tl.float32)
        weight_f32 = weight.to(tl.float32)
        output_f32 = vals_f32 * inv_rms * weight_f32
        output = output_f32.to(vals.dtype)
        tl.store(output_row_start_ptr + col_idx, output, mask=mask)


def rms_norm(
    input: torch.Tensor, weight: torch.Tensor, eps: float = 1e-6
) -> torch.Tensor:
    """
    Compute RMS normalization using Triton kernel.

    RMS Norm normalizes the input by the root mean square and scales by weight:
    output = input / sqrt(mean(input^2) + eps) * weight

    Args:
        input: Input tensor of shape (..., hidden_size)
        weight: Weight tensor of shape (hidden_size,)
        eps: Small constant for numerical stability

    Returns:
        Tensor with RMS normalization applied along the last dimension
    """
    assert weight.dim() == 1, "Weight must be 1-dimensional"
    assert input.shape[-1] == weight.shape[0], (
        f"Input last dimension ({input.shape[-1]}) must match "
        f"weight dimension ({weight.shape[0]})"
    )

    # Flatten all dimensions except the last one
    original_shape = input.shape
    input_2d = input.reshape(-1, input.shape[-1])
    input_2d = input_2d.contiguous()
    weight = weight.contiguous()

    n_rows, n_cols = input_2d.shape

    output = torch.empty_like(input_2d)
    BLOCK_SIZE = 1024
    grid = (n_rows,)
    _rms_norm_kernel[grid](
        input_2d,
        weight,
        output,
        input_2d.stride(0),
        output.stride(0),
        n_cols,
        eps,
        BLOCK_SIZE=BLOCK_SIZE,
    )
    return output.reshape(original_shape)


def rms_norm_batch_invariant(
    input: torch.Tensor, weight: torch.Tensor, eps: float = 1e-6
) -> torch.Tensor:
    """
    Batch-invariant wrapper for RMS normalization.

    This function provides a deterministic, batch-invariant implementation
    of RMS normalization for use with the batch_invariant mode.

    Args:
        input: Input tensor of shape (..., hidden_size)
        weight: Weight tensor of shape (hidden_size,)
        eps: Small constant for numerical stability

    Returns:
        RMS normalized tensor
    """
    return rms_norm(input, weight, eps=eps)


def linear_batch_invariant(input, weight, bias=None):
    output = mm_batch_invariant(input, weight.t())
    if bias is not None:
        output = output + bias
    return output


_batch_invariant_MODE = False
_batch_invariant_LIB = None
_original_torch_bmm = None


def is_batch_invariant_mode_enabled():
    return _batch_invariant_MODE


def enable_batch_invariant_mode():
    global _batch_invariant_MODE, _batch_invariant_LIB, _original_torch_bmm
    if _batch_invariant_MODE:
        return

    _batch_invariant_MODE = True
    _batch_invariant_LIB = torch.library.Library("aten", "IMPL")
    _batch_invariant_LIB.impl("aten::mm", mm_batch_invariant, "CUDA")
    _batch_invariant_LIB.impl("aten::addmm", addmm_batch_invariant, "CUDA")
    _batch_invariant_LIB.impl("aten::matmul", matmul_batch_invariant, "CUDA")
    _batch_invariant_LIB.impl("aten::bmm", bmm_batch_invariant, "CUDA")
    _batch_invariant_LIB.impl("aten::linear", linear_batch_invariant, "CUDA")
    _batch_invariant_LIB.impl(
        "aten::_log_softmax", _log_softmax_batch_invariant, "CUDA"
    )
    _batch_invariant_LIB.impl("aten::softmax", softmax_batch_invariant, "CUDA")
    _batch_invariant_LIB.impl("aten::_softmax", softmax_batch_invariant, "CUDA")
    _batch_invariant_LIB.impl("aten::mean.dim", mean_batch_invariant, "CUDA")

    # Also monkeypatch torch.bmm directly as a fallback
    _original_torch_bmm = torch.bmm
    torch.bmm = bmm_batch_invariant


def disable_batch_invariant_mode():
    global _batch_invariant_MODE, _batch_invariant_LIB, _original_torch_bmm
    if _batch_invariant_LIB is not None:
        _batch_invariant_LIB._destroy()
    if _original_torch_bmm is not None:
        torch.bmm = _original_torch_bmm
        _original_torch_bmm = None
    _batch_invariant_MODE = False
    _batch_invariant_LIB = None


@contextlib.contextmanager
def set_batch_invariant_mode(enabled: bool = True):
    global _batch_invariant_MODE, _batch_invariant_LIB
    old_data = (_batch_invariant_MODE, _batch_invariant_LIB)
    if enabled:
        enable_batch_invariant_mode()
    else:
        disable_batch_invariant_mode()
    yield
    if _batch_invariant_LIB is not None:
        _batch_invariant_LIB._destroy()
    _batch_invariant_MODE, _batch_invariant_LIB = old_data


AttentionBlockSize = namedtuple("AttentionBlockSize", ["block_m", "block_n"])


def get_batch_invariant_attention_block_size() -> AttentionBlockSize:
    return AttentionBlockSize(block_m=16, block_n=16)


def vllm_is_batch_invariant():
    env_key = "VLLM_BATCH_INVARIANT"
    is_overridden = False
    val = os.getenv(env_key, "0")
    try:
        is_overridden = int(val) != 0
    except ValueError:
        is_overridden = False
    return is_overridden


def override_envs_for_invariance():
    curr_attn_backend = envs.VLLM_ATTENTION_BACKEND
    supported_backends = [
        "FLASH_ATTN",  # best supported backend
        "FLEX_ATTENTION",
        "FLASHINFER",
        "FLASH_ATTN_MLA",
        "TRITON_MLA",
        # Not yet supported MLA backends
        # "FLASHMLA",
        # "FLASHINFER_MLA",
    ]
    if curr_attn_backend not in supported_backends:
        warning = (
            "Forcibly updating attention backend to"
            f" {supported_backends[0]} for batch_invariant. "
            f" Supported backends: {supported_backends}."
        )
        logger.warning_once(warning)
        os.environ["VLLM_ATTENTION_BACKEND"] = supported_backends[0]
    if os.environ["VLLM_ATTENTION_BACKEND"] != supported_backends[0]:
        warning = (
            "You are using a decode-invariant form of batch invariance. "
            "This will not be invariant between prefill and decode."
        )
        logger.warning_once(warning)
    os.environ["VLLM_ALLREDUCE_USE_SYMM_MEM"] = "0"

    os.environ["CUBLAS_WORKSPACE_CONFIG"] = ":4096:8"

    # NCCL determinism settings
    os.environ["NCCL_LAUNCH_MODE"] = "GROUP"
    os.environ["NCCL_COLLNET_ENABLE"] = "0"
    os.environ["NCCL_NVLS_ENABLE"] = "0"
    os.environ["NCCL_P2P_NET_DISABLE"] = "1"
    os.environ["NCCL_MIN_NCHANNELS"] = "1"
    os.environ["NCCL_MAX_NCHANNELS"] = "1"
    os.environ["NCCL_PROTO"] = "Simple"
    os.environ["NCCL_ALGO"] = "allreduce:tree"
    os.environ["NCCL_NTHREADS"] = "1"
    os.environ["NCCL_SOCKET_NTHREADS"] = "1"


def init_batch_invariance():
    # this will hit all the csrc overrides as well
    if vllm_is_batch_invariant():
        override_envs_for_invariance()
        enable_batch_invariant_mode()

        # Disable TF32 for batch invariance - it causes non-deterministic rounding
        torch.backends.cuda.matmul.allow_tf32 = False
        torch.backends.cudnn.allow_tf32 = False