import triton
import triton.language as tl

# fmt: off


MXFP_BLOCK_SIZE = tl.constexpr(32)


@triton.jit
def _get_max_quant_val(dtype: tl.constexpr):
    if dtype == tl.uint8:
        return 6.0
    elif dtype == tl.float8e5:
        return 57344.0
    elif dtype == tl.float8e4nv:
        return 448.0
    else:
        tl.static_assert(False, f"Invalid {dtype=}")

@triton.jit
def _compute_quant_and_scale(src_tensor, valid_src_mask, mx_tensor_dtype: tl.constexpr,
                             DEQUANT_SCALE_ROUNDING_MODE: tl.constexpr = 0):
    is_fp8: tl.constexpr = mx_tensor_dtype == tl.float8e4nv or mx_tensor_dtype == tl.float8e5
    BLOCK_SIZE_OUT_DIM: tl.constexpr = src_tensor.shape[0]
    BLOCK_SIZE_QUANT_DIM: tl.constexpr = src_tensor.shape[1]
    BLOCK_SIZE_QUANT_MX_SCALE: tl.constexpr = src_tensor.shape[1] // MXFP_BLOCK_SIZE

    # Explicit cast to fp32 since most ops are not supported on bfloat16. We avoid needless conversions to and from bf16
    f32_tensor = src_tensor.to(tl.float32)
    abs_tensor = tl.abs(f32_tensor)
    abs_tensor = tl.where(valid_src_mask, abs_tensor, -1.0)  # Don't consider padding tensors in scale computation
    abs_tensor = tl.reshape(abs_tensor, [BLOCK_SIZE_OUT_DIM, BLOCK_SIZE_QUANT_MX_SCALE, MXFP_BLOCK_SIZE])
    max_val = tl.max(abs_tensor, axis=2, keep_dims=True)
    dequant_scale = max_val / _get_max_quant_val(mx_tensor_dtype)
    if DEQUANT_SCALE_ROUNDING_MODE == 0:
        # DequantScaleRoundingMode.ROUND_UP
        # compute 2 ** ceil(log2(dequant_scale))
        # Adding 0x007FFFFF adds exponent by 1 unless mantissa is all zeros
        # A corner case: exponent is 0xFF that will overflow but that's already
        # NaN so assume we don't care.
        dequant_scale_exponent = (dequant_scale.to(tl.uint32, bitcast=True) + 0x007FFFFF) & 0x7F800000
    else:
        # DequantScaleRoundingMode.ROUND_DOWN
        # compute 2 ** floor(log2(dequant_scale))
        assert DEQUANT_SCALE_ROUNDING_MODE == 1
        dequant_scale_exponent = dequant_scale.to(tl.uint32, bitcast=True) & 0x7F800000
    dequant_scale_rounded = dequant_scale_exponent.to(tl.float32, bitcast=True)
    quant_scale = tl.where(dequant_scale_rounded == 0, 0, 1.0 / dequant_scale_rounded)

    f32_tensor = tl.reshape(f32_tensor, [BLOCK_SIZE_OUT_DIM, BLOCK_SIZE_QUANT_MX_SCALE, MXFP_BLOCK_SIZE])
    quant_tensor = f32_tensor * quant_scale

    # Reshape the tensors after scaling
    quant_tensor = quant_tensor.reshape([BLOCK_SIZE_OUT_DIM, BLOCK_SIZE_QUANT_DIM])
    # Set the invalid portions of the tensor to 0. This will ensure that any padding tensors are 0 in the mx format.
    quant_tensor = tl.where(valid_src_mask, quant_tensor, 0)
    dequant_scale_exponent = dequant_scale_exponent.reshape([BLOCK_SIZE_OUT_DIM, BLOCK_SIZE_QUANT_MX_SCALE])

    # First, we simply extract the exponent part of the scales and store the result
    dequant_scale_exponent = (dequant_scale_exponent >> 23).to(tl.uint8)
    # Now we must convert the tensors to the mx format.
    if is_fp8:
        out_tensor = quant_tensor.to(mx_tensor_dtype)
    else:
        quant_tensor = quant_tensor.to(tl.uint32, bitcast=True)
        signs = quant_tensor & 0x80000000
        exponents = (quant_tensor >> 23) & 0xFF
        mantissas = (quant_tensor & 0x7FFFFF)

        # 0.25 <= x < 0.75 maps to 0.5, a denormal number
        E8_BIAS = 127
        E2_BIAS = 1
        # Move implicit bit 1 at the beginning to mantissa for denormals
        adjusted_exponents = tl.core.sub(E8_BIAS, exponents + 1, sanitize_overflow=False)
        mantissas = tl.where(exponents < E8_BIAS, (0x400000 | (mantissas >> 1)) >> adjusted_exponents, mantissas)

        # For normal numbers, we change the bias from 127 to 1, and for subnormals, we keep exponent as 0.
        exponents = tl.maximum(exponents, E8_BIAS - E2_BIAS) - (E8_BIAS - E2_BIAS)

        # Combine sign, exponent, and mantissa, while saturating
        # rounding nearest with tie breaking up by adding +1 to one bit right of the LSB, then shift right
        e2m1_tmp = tl.minimum((((exponents << 2) | (mantissas >> 21)) + 1) >> 1, 0x7)
        e2m1_value = ((signs >> 28) | e2m1_tmp).to(tl.uint8)

        e2m1_value = tl.reshape(e2m1_value, [BLOCK_SIZE_OUT_DIM, BLOCK_SIZE_QUANT_DIM // 2, 2])
        evens, odds = tl.split(e2m1_value)
        out_tensor = evens | (odds << 4)

    return out_tensor, dequant_scale_exponent

@triton.jit
def _downcast_to_mxfp(mx_tensor_ptr, stride_mxt_outer, stride_mxt_quant: tl.constexpr,
                      mx_scale_ptr, stride_mx_scale_outer, stride_mx_scale_quant,
                      src_ptr, stride_src_outer, stride_src_quant,
                      outer_dim, quant_dim,
                      BLOCK_SIZE_OUT_DIM: tl.constexpr, BLOCK_SIZE_QUANT_DIM: tl.constexpr,
                      DEQUANT_SCALE_ROUNDING_MODE: tl.constexpr):

    tl.static_assert(stride_mxt_quant == 1, f"Output stride, {stride_mxt_quant=} must be 1.")
    tl.static_assert(BLOCK_SIZE_QUANT_DIM % MXFP_BLOCK_SIZE == 0, f"{BLOCK_SIZE_QUANT_DIM=} must be a multiple of 32")

    # uint8 signifies two fp4 e2m1 values packed into a single byte
    mx_tensor_dtype: tl.constexpr = mx_tensor_ptr.dtype.element_ty
    tl.static_assert(mx_tensor_dtype == tl.uint8 or (mx_tensor_dtype == tl.float8e4nv or mx_tensor_dtype == tl.float8e5),
                     f"Invalid {mx_tensor_dtype=}. Must be uint8 or float8.")

    src_dtype: tl.constexpr = src_ptr.dtype.element_ty
    tl.static_assert(mx_scale_ptr.dtype.element_ty == tl.uint8, f"{mx_scale_ptr.dtype.element_ty=} must be uint8")
    tl.static_assert((src_dtype == tl.bfloat16) or (src_dtype == tl.float16) or (src_dtype == tl.float32), f"{src_dtype=} must be bfloat16 or float16 or float32")
    is_fp4: tl.constexpr = mx_tensor_dtype == tl.uint8

    outer_block = tl.program_id(0).to(tl.int64)
    quant_block = tl.program_id(1).to(tl.int64)

    K_DIVISOR: tl.constexpr = 2 if is_fp4 else 1
    BLOCK_SIZE_QUANT_MX_SCALE: tl.constexpr = BLOCK_SIZE_QUANT_DIM // MXFP_BLOCK_SIZE
    BLOCK_SIZE_QUANT_MX_TENSOR: tl.constexpr = BLOCK_SIZE_QUANT_DIM // K_DIVISOR

    start_src_quant = quant_block * BLOCK_SIZE_QUANT_DIM
    start_mx_scale_quant = quant_block * BLOCK_SIZE_QUANT_MX_SCALE
    start_mx_quant = quant_block * BLOCK_SIZE_QUANT_MX_TENSOR
    start_out = outer_block * BLOCK_SIZE_OUT_DIM

    src_ptr += start_src_quant * stride_src_quant + start_out * stride_src_outer
    mx_scale_ptr += start_mx_scale_quant * stride_mx_scale_quant + start_out * stride_mx_scale_outer
    mx_tensor_ptr += start_mx_quant * stride_mxt_quant + start_out * stride_mxt_outer

    offs_src_quant = tl.arange(0, BLOCK_SIZE_QUANT_DIM)[None, :].to(tl.int64)
    offs_mxt_quant = tl.arange(0, BLOCK_SIZE_QUANT_MX_TENSOR)[None, :].to(tl.int64)
    offs_scale_quant = tl.arange(0, BLOCK_SIZE_QUANT_MX_SCALE)[None, :].to(tl.int64)
    offs_outer = tl.arange(0, BLOCK_SIZE_OUT_DIM)[:, None].to(tl.int64)

    mask_src_quant = start_src_quant + offs_src_quant < quant_dim
    mask_n = start_out + offs_outer < outer_dim
    full_mask_src = mask_src_quant & mask_n

    mask_mxt_quant = start_mx_quant + offs_mxt_quant < tl.cdiv(quant_dim, K_DIVISOR)
    full_mask_mxt = mask_mxt_quant & mask_n

    scale_mask_k = start_mx_scale_quant + offs_scale_quant < tl.cdiv(quant_dim, MXFP_BLOCK_SIZE)
    full_scale_mask = scale_mask_k & mask_n

    src_tensor_offsets = offs_src_quant * stride_src_quant + offs_outer * stride_src_outer
    mx_scale_offsets = offs_scale_quant * stride_mx_scale_quant + offs_outer * stride_mx_scale_outer
    mx_tensor_offsets = offs_mxt_quant * stride_mxt_quant + offs_outer * stride_mxt_outer
    src_tensor = tl.load(src_ptr + src_tensor_offsets, mask=full_mask_src)

    out_tensor, scale_tensor = _compute_quant_and_scale(src_tensor, full_mask_src, mx_tensor_dtype,
                                                        DEQUANT_SCALE_ROUNDING_MODE)

    tl.store(mx_scale_ptr + mx_scale_offsets, scale_tensor, mask=full_scale_mask)
    tl.store(mx_tensor_ptr + mx_tensor_offsets, out_tensor, mask=full_mask_mxt)


@triton.jit(repr=lambda _: "_dequantize_mxfp8")
def _quantize_mxfp8_fn(input, mask, pid=None):
    return _compute_quant_and_scale(input, mask, tl.float8e4nv)