vllm.model_executor.layers.quantization.utils.fp8_utils ¶

def _fp8_gemm_nt_op(q_input: torch.Tensor, input_scale: torch.Tensor,
                    weight: torch.Tensor, weight_scale: torch.Tensor,
                    output: torch.Tensor, use_deep_gemm_e8m0: bool) -> None:
    fp8_gemm_nt((q_input, input_scale), (weight, weight_scale),
                output,
                is_deep_gemm_e8m0_used=use_deep_gemm_e8m0)

def _fp8_gemm_nt_op_fake(q_input: torch.Tensor, input_scale: torch.Tensor,
                         weight: torch.Tensor, weight_scale: torch.Tensor,
                         output: torch.Tensor,
                         use_deep_gemm_e8m0: bool) -> None:
    return None

def _maybe_pad_fp8_weight(weight: torch.Tensor) -> torch.Tensor:
    """Pad the weight tensor. This is an optimization on ROCm platform, which
    can benefit from tensors located far enough from one another in memory"""
    if (envs.VLLM_ROCM_FP8_PADDING and current_platform.is_rocm()
            and weight.stride(-1) == 1
            and (weight.stride(-2) * weight.element_size()) % 512 == 0):
        num_pad = 256 // weight.element_size()
        import torch.nn.functional as F
        weight = F.pad(weight, (0, num_pad), "constant", 0)[..., :-num_pad]
        torch.cuda.empty_cache()
    return weight

def _padded_cutlass(
    qx: torch.Tensor,
    weight: torch.Tensor,
    x_scale: torch.Tensor,
    weight_scale: torch.Tensor,
    block_size: list[int],
    output_dtype: torch.dtype,
) -> torch.Tensor:
    pad_multiple = 4
    dim = qx.shape[0]
    padded = dim if dim % pad_multiple == 0 else dim + pad_multiple - (
        dim % pad_multiple)

    padded_shape = [padded, *qx.shape[1:]]
    padded_qx = torch.zeros(padded_shape, device=qx.device, dtype=qx.dtype)
    padded_qx[0:qx.shape[0], ...].copy_(qx)

    padded_x_scale_shape = [*x_scale.shape[1:], padded]
    padded_x_scale = torch.ones(padded_x_scale_shape,
                                device=x_scale.device,
                                dtype=x_scale.dtype).permute(-1, -2)
    padded_x_scale[0:x_scale.shape[0], ...].copy_(x_scale)

    output = cutlass_scaled_mm(padded_qx, weight, padded_x_scale, weight_scale,
                               block_size, output_dtype, True)
    return output[0:qx.shape[0], ...]

def _padded_cutlass_fake(
    qx: torch.Tensor,
    weight: torch.Tensor,
    x_scale: torch.Tensor,
    weight_scale: torch.Tensor,
    block_size: list[int],
    output_dtype: torch.dtype,
) -> torch.Tensor:
    return torch.empty((qx.size(0), weight.size(0)),
                       dtype=output_dtype,
                       device=qx.device)

@triton.jit
def _per_token_group_quant_fp8(
    # Pointers to inputs and output
    y_ptr,
    y_q_ptr,
    y_s_ptr,
    group_size,
    # Num columns of y
    y_num_columns,
    y_row_stride,
    # Avoid to divide zero
    eps,
    # Information for float8
    fp8_min,
    fp8_max,
    use_ue8m0: tl.constexpr,
    # Meta-parameters
    BLOCK: tl.constexpr,
):
    """A Triton-accelerated function to perform per-token-group
    quantization on a tensor.
    This function converts the tensor values into float8 values.
    """
    groups_per_row = y_num_columns // group_size

    # Map the program id to the row of X and Y it should compute.
    g_id = tl.program_id(0)
    row = g_id // groups_per_row
    row_g_id = g_id % groups_per_row

    # Ensure offset calculations use int64 to prevent overflow
    y_ptr_offset = (row.to(tl.int64) * y_row_stride) + (row_g_id.to(tl.int64) *
                                                        group_size)
    y_ptr += y_ptr_offset

    y_q_ptr_offset = g_id.to(tl.int64) * group_size
    y_q_ptr += y_q_ptr_offset
    y_s_ptr += g_id

    cols = tl.arange(0, BLOCK)  # N <= BLOCK
    mask = cols < group_size

    y = tl.load(y_ptr + cols, mask=mask, other=0.0).to(tl.float32)
    # Quant
    _absmax = tl.maximum(tl.max(tl.abs(y)), eps)
    scale_raw = _absmax / fp8_max
    y_s = tl.math.exp2(tl.ceil(tl.log2(scale_raw))) if use_ue8m0 else scale_raw
    y_q = tl.clamp(y / y_s, fp8_min, fp8_max).to(y_q_ptr.dtype.element_ty)

    tl.store(y_q_ptr + cols, y_q, mask=mask)
    tl.store(y_s_ptr, y_s)

@triton.jit
def _per_token_group_quant_fp8_colmajor(
    # Pointers to inputs and output
    y_ptr,
    y_q_ptr,
    y_s_ptr,
    group_size,
    # Num columns of y
    y_num_columns,
    y_row_stride,
    # Stride from one column to the next of y_s
    y_s_col_stride,
    # Avoid to divide zero
    eps,
    # Information for float8
    fp8_min,
    fp8_max,
    use_ue8m0: tl.constexpr,
    # Meta-parameters
    BLOCK: tl.constexpr,
):
    """A Triton-accelerated function to perform per-token-group
    quantization on a tensor.
    This function converts the tensor values into float8 values.
    """
    groups_per_row = y_num_columns // group_size

    # Map the program id to the row of X and Y it should compute.
    g_id = tl.program_id(0)
    row = g_id // groups_per_row
    row_g_id = g_id % groups_per_row

    # Ensure offset calculations use int64 to prevent overflow
    y_ptr_offset = (row.to(tl.int64) * y_row_stride) + (row_g_id.to(tl.int64) *
                                                        group_size)
    y_ptr += y_ptr_offset

    y_q_ptr_offset = g_id.to(tl.int64) * group_size
    y_q_ptr += y_q_ptr_offset

    # Convert g_id the flattened block coordinate to 2D so we can index
    # into the output y_scales matrix
    blocks_per_row = y_num_columns // group_size
    scale_col = g_id % blocks_per_row
    scale_row = g_id // blocks_per_row
    # Ensure offset calculation uses int64 for y_s_ptr
    y_s_ptr_offset = (scale_col.to(tl.int64) * y_s_col_stride) + scale_row.to(
        tl.int64)
    y_s_ptr += y_s_ptr_offset

    cols = tl.arange(0, BLOCK)  # group_size <= BLOCK
    mask = cols < group_size

    y = tl.load(y_ptr + cols, mask=mask, other=0.0).to(tl.float32)
    # Quant
    _absmax = tl.maximum(tl.max(tl.abs(y)), eps)
    scale_raw = _absmax / fp8_max
    y_s = tl.math.exp2(tl.ceil(tl.log2(scale_raw))) if use_ue8m0 else scale_raw
    y_q = tl.clamp(y / y_s, fp8_min, fp8_max).to(y_q_ptr.dtype.element_ty)

    tl.store(y_q_ptr + cols, y_q, mask=mask)
    tl.store(y_s_ptr, y_s)

@triton.jit
def _w8a8_triton_block_scaled_mm(
    # Pointers to inputs and output
    A,
    B,
    C,
    As,
    Bs,
    # Shape for matmul
    M,
    N,
    K,
    # Block size for block-wise quantization
    group_n,
    group_k,
    # Stride for inputs and output
    stride_am,
    stride_ak,
    stride_bk,
    stride_bn,
    stride_cm,
    stride_cn,
    stride_As_m,
    stride_As_k,
    stride_Bs_k,
    stride_Bs_n,
    # Meta-parameters
    BLOCK_SIZE_M: tl.constexpr,
    BLOCK_SIZE_N: tl.constexpr,
    BLOCK_SIZE_K: tl.constexpr,
    GROUP_SIZE_M: tl.constexpr,
):
    """Triton-accelerated function used to perform linear operations (dot
    product) on input tensors `A` and `B` with block-wise quantization, and
    store the result in output tensor `C`.
    """

    pid = tl.program_id(axis=0)
    num_pid_m = tl.cdiv(M, BLOCK_SIZE_M)
    num_pid_n = tl.cdiv(N, BLOCK_SIZE_N)
    num_pid_in_group = GROUP_SIZE_M * num_pid_n
    group_id = pid // 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 + (pid % group_size_m)
    pid_n = (pid % num_pid_in_group) // group_size_m

    offs_am = (pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)) % M
    offs_bn = (pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)) % N
    offs_k = tl.arange(0, BLOCK_SIZE_K)
    a_ptrs = A + (offs_am[:, None] * stride_am + offs_k[None, :] * stride_ak)
    b_ptrs = B + (offs_k[:, None] * stride_bk + offs_bn[None, :] * stride_bn)

    As_ptrs = As + offs_am * stride_As_m
    offs_bsn = offs_bn // group_n
    Bs_ptrs = Bs + offs_bsn * stride_Bs_n

    accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)
    for k in range(0, tl.cdiv(K, BLOCK_SIZE_K)):
        a = tl.load(a_ptrs,
                    mask=offs_k[None, :] < K - k * BLOCK_SIZE_K,
                    other=0.0)
        b = tl.load(b_ptrs,
                    mask=offs_k[:, None] < K - k * BLOCK_SIZE_K,
                    other=0.0)

        k_start = k * BLOCK_SIZE_K
        offs_ks = k_start // group_k
        a_s = tl.load(As_ptrs + offs_ks * stride_As_k)
        b_s = tl.load(Bs_ptrs + offs_ks * stride_Bs_k)

        accumulator += tl.dot(a, b) * a_s[:, None] * b_s[None, :]
        a_ptrs += BLOCK_SIZE_K * stride_ak
        b_ptrs += BLOCK_SIZE_K * stride_bk

    if C.dtype.element_ty == tl.bfloat16:
        c = accumulator.to(tl.bfloat16)
    elif C.dtype.element_ty == tl.float16:
        c = accumulator.to(tl.float16)
    else:
        c = accumulator.to(tl.float32)

    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)
    c_ptrs = C + stride_cm * offs_cm[:, None] + stride_cn * offs_cn[None, :]
    c_mask = (offs_cm[:, None] < M) & (offs_cn[None, :] < N)
    tl.store(c_ptrs, c, mask=c_mask)

def _w8a8_triton_block_scaled_mm_fake(
    qx: torch.Tensor,
    weight: torch.Tensor,
    x_scale: torch.Tensor,
    weight_scale: torch.Tensor,
    block_size: list[int],
    output_dtype: torch.dtype,
) -> torch.Tensor:
    return torch.empty((qx.size(0), weight.size(0)),
                       dtype=output_dtype,
                       device=qx.device)

def _w8a8_triton_block_scaled_mm_func(
    qx: torch.Tensor,
    weight: torch.Tensor,
    x_scale: torch.Tensor,
    weight_scale: torch.Tensor,
    block_size: list[int],
    output_dtype: torch.dtype,
) -> torch.Tensor:
    return w8a8_triton_block_scaled_mm(qx, weight, x_scale, weight_scale,
                                       block_size, output_dtype)

def block_quant_to_tensor_quant(
    x_q_block: torch.Tensor,
    x_s: torch.Tensor,
) -> tuple[torch.Tensor, torch.Tensor]:
    """This function converts block-wise quantization to tensor-wise
    quantization. The inputs are block-wise quantization tensor `x_q_block`,
    block-wise quantization scale and the block size.
    The outputs are tensor-wise quantization tensor and tensor-wise
    quantization scale. Note only float8 is supported for now.
    """
    x_dq_block = group_broadcast(x_q_block, x_s)
    x_q_tensor, scale = input_to_float8(x_dq_block, dtype=x_q_block.dtype)
    return x_q_tensor, scale

def check_aiter_fp8_linear_support() -> bool:
    """AITER is only supported on ROCm and only for FP8_FNUZ
    and at the moment are MI300 series"""
    return (current_platform.is_rocm() and envs.VLLM_ROCM_USE_AITER
            and envs.VLLM_ROCM_USE_AITER_LINEAR
            and current_platform.is_fp8_fnuz())

def create_fp8_input_scale(
        output_partition_sizes: list[int],
        weight_loader: Optional[Callable]) -> torch.nn.Parameter:
    """Create input scale parameter for static activation quantization."""
    from vllm.model_executor.parameter import PerTensorScaleParameter

    scale = PerTensorScaleParameter(data=torch.empty(
        len(output_partition_sizes), dtype=torch.float32),
                                    weight_loader=weight_loader)
    scale[:] = torch.finfo(torch.float32).min
    return scale

def create_fp8_scale_parameter(
        parameter_type: torch.nn.Parameter, output_partition_sizes: list[int],
        input_size_per_partition: int, block_size: Optional[list[int]],
        weight_loader: Optional[Callable]) -> torch.nn.Parameter:
    """Create scale parameter based on quantization strategy."""
    if parameter_type == ChannelQuantScaleParameter:
        scale = parameter_type(data=torch.empty(
            (sum(output_partition_sizes), 1), dtype=torch.float32),
                               output_dim=0,
                               weight_loader=weight_loader)
    elif parameter_type == BlockQuantScaleParameter:
        assert block_size is not None
        block_n, block_k = block_size[0], block_size[1]
        output_size_per_partition = sum(output_partition_sizes)
        scale = parameter_type(
            data=torch.empty(
                (output_size_per_partition + block_n - 1) // block_n,
                (input_size_per_partition + block_k - 1) // block_k,
                dtype=torch.float32,
            ),
            input_dim=1,
            output_dim=0,
            weight_loader=weight_loader,
        )
    elif parameter_type == PerTensorScaleParameter:
        scale = parameter_type(data=torch.empty(len(output_partition_sizes),
                                                dtype=torch.float32),
                               weight_loader=weight_loader)
    else:
        raise ValueError(f"Unknown parameter type: {parameter_type}")

    scale[:] = torch.finfo(torch.float32).min
    return scale

def create_fp8_weight_parameter(
        output_size_per_partition: int, input_size_per_partition: int,
        weight_loader: Optional[Callable]) -> torch.nn.Parameter:
    """Create FP8 weight parameter."""
    from vllm.model_executor.parameter import ModelWeightParameter

    return ModelWeightParameter(data=torch.empty(output_size_per_partition,
                                                 input_size_per_partition,
                                                 dtype=torch.float8_e4m3fn),
                                input_dim=1,
                                output_dim=0,
                                weight_loader=weight_loader)