vllm.model_executor.layers.quantization.utils.int8_utils

logger module-attribute

logger = getLogger(__name__)

_per_token_group_quant_int8

_per_token_group_quant_int8(
    y_ptr,
    y_q_ptr,
    y_s_ptr,
    y_stride,
    N,
    eps,
    int8_min,
    int8_max,
    BLOCK: constexpr,
)

A Triton-accelerated function to perform per-token-group quantization on a tensor.

This function converts the tensor values into int8 values.

Source code in vllm/model_executor/layers/quantization/utils/int8_utils.py

@triton.jit
def _per_token_group_quant_int8(
    # Pointers to inputs and output
    y_ptr,
    y_q_ptr,
    y_s_ptr,
    # Stride of input
    y_stride,
    # Columns of input
    N,
    # Avoid to divide zero
    eps,
    # Information for int8
    int8_min,
    int8_max,
    # 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 int8 values.
    """
    # Map the program id to the row of X and Y it should compute.
    g_id = tl.program_id(0)
    y_ptr += g_id * y_stride
    y_q_ptr += g_id * y_stride
    y_s_ptr += g_id

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

    y = tl.load(y_ptr + cols, mask=mask, other=0.0).to(tl.float32)
    # Quant
    _absmax = tl.maximum(tl.max(tl.abs(y)), eps)
    y_s = _absmax / int8_max
    y_q = tl.clamp(y / y_s, int8_min, int8_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)

_per_token_quant_int8

_per_token_quant_int8(
    x_ptr,
    xq_ptr,
    scale_ptr,
    stride_x,
    stride_xq,
    N,
    BLOCK: constexpr,
)

Source code in vllm/model_executor/layers/quantization/utils/int8_utils.py

@triton.jit
def _per_token_quant_int8(
    x_ptr,
    xq_ptr,
    scale_ptr,
    stride_x,
    stride_xq,
    N,
    BLOCK: tl.constexpr,
):
    # Adapted from https://github.com/InternLM/lmdeploy/blob/086481ed84b59bee3b8e4274e5fc69620040c048/lmdeploy/pytorch/kernels/cuda/w8a8_triton_kernels.py#L282
    row_id = tl.program_id(0)

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

    x = tl.load(x_ptr + row_id * stride_x + cols, mask=mask,
                other=0.0).to(tl.float32)
    absmax = tl.maximum(tl.max(tl.abs(x)), 1e-10)
    scale_x = absmax / 127
    x_q = x * (127 / absmax)
    x_q = round_int8(x_q)

    tl.store(xq_ptr + row_id * stride_xq + cols, x_q, mask=mask)
    tl.store(scale_ptr + row_id, scale_x)

_w8a8_block_int8_matmul

_w8a8_block_int8_matmul(
    A,
    B,
    C,
    As,
    Bs,
    M,
    N,
    K,
    group_n,
    group_k,
    stride_am,
    stride_ak,
    stride_bk,
    stride_bn,
    stride_cm,
    stride_cn,
    stride_As_m,
    stride_As_k,
    stride_Bs_k,
    stride_Bs_n,
    BLOCK_SIZE_M: constexpr,
    BLOCK_SIZE_N: constexpr,
    BLOCK_SIZE_K: constexpr,
    GROUP_SIZE_M: 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.

Source code in vllm/model_executor/layers/quantization/utils/int8_utils.py

@triton.jit
def _w8a8_block_int8_matmul(
    # 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).to(tl.float32) * 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)

apply_w8a8_block_int8_linear

apply_w8a8_block_int8_linear(
    input: Tensor,
    weight: Tensor,
    block_size: list[int],
    weight_scale: Tensor,
    input_scale: Optional[Tensor] = None,
    bias: Optional[Tensor] = None,
) -> Tensor

Source code in vllm/model_executor/layers/quantization/utils/int8_utils.py

def apply_w8a8_block_int8_linear(
    input: torch.Tensor,
    weight: torch.Tensor,
    block_size: list[int],
    weight_scale: torch.Tensor,
    input_scale: Optional[torch.Tensor] = None,
    bias: Optional[torch.Tensor] = None,
) -> torch.Tensor:
    assert input_scale is None
    # View input as 2D matrix for fp8 methods
    input_2d = input.view(-1, input.shape[-1])
    output_shape = [*input.shape[:-1], weight.shape[0]]

    q_input, x_scale = per_token_group_quant_int8(input_2d, block_size[1])
    output = w8a8_block_int8_matmul(q_input,
                                    weight,
                                    x_scale,
                                    weight_scale,
                                    block_size,
                                    output_dtype=input.dtype)

    if bias is not None:
        output = output + bias
    return output.to(dtype=input.dtype).view(*output_shape)

block_dequant

block_dequant(
    x_q_block: Tensor, x_s: Tensor, block_size: list[int]
) -> Tensor

This function conducts block-wise dequantization. The inputs are block-wise quantization tensor x_q_block, block-wise quantization scale and the block size. The outputs are dequantized tensor.

Source code in vllm/model_executor/layers/quantization/utils/int8_utils.py

def block_dequant(
    x_q_block: torch.Tensor,
    x_s: torch.Tensor,
    block_size: list[int],
) -> torch.Tensor:
    """This function conducts block-wise dequantization.
    The inputs are block-wise quantization tensor `x_q_block`,
    block-wise quantization scale and the block size.
    The outputs are dequantized tensor.
    """
    block_n, block_k = block_size[0], block_size[1]
    n, k = x_q_block.shape
    n_tiles = (n + block_n - 1) // block_n
    k_tiles = (k + block_k - 1) // block_k
    assert n_tiles == x_s.shape[0]
    assert k_tiles == x_s.shape[1]

    x_dq_block = x_q_block.to(torch.float32)

    for i in range(k_tiles):
        for j in range(n_tiles):
            x_dq_block[
                j * block_n:min((j + 1) * block_n, n),
                i * block_k:min((i + 1) * block_k, k),
            ] *= x_s[j][i]

    return x_dq_block

get_w8a8_block_int8_configs cached

get_w8a8_block_int8_configs(
    N: int, K: int, block_n: int, block_k: int
) -> Optional[dict[int, Any]]

Return optimized configurations for the w8a8 block fp8 kernel.

The return value will be a dictionary that maps an irregular grid of batch sizes to configurations of the w8a8 block fp8 kernel. To evaluate the kernel on a given batch size bs, the closest batch size in the grid should be picked and the associated configuration chosen to invoke the kernel.

Source code in vllm/model_executor/layers/quantization/utils/int8_utils.py

@functools.lru_cache
def get_w8a8_block_int8_configs(N: int, K: int, block_n: int,
                                block_k: int) -> Optional[dict[int, Any]]:
    """
    Return optimized configurations for the w8a8 block fp8 kernel.

    The return value will be a dictionary that maps an irregular grid of
    batch sizes to configurations of the w8a8 block fp8 kernel. To evaluate the
    kernel on a given batch size bs, the closest batch size in the grid should
    be picked and the associated configuration chosen to invoke the kernel.
    """

    # First look up if an optimized configuration is available in the configs
    # directory
    device_name = current_platform.get_device_name().replace(" ", "_")
    json_file_name = f"N={N},K={K},device_name={device_name},dtype=int8_w8a8,block_shape=[{block_n}, {block_k}].json"  # noqa: E501

    config_file_path = os.path.join(
        os.path.dirname(os.path.realpath(__file__)), "configs", json_file_name)
    if os.path.exists(config_file_path):
        with open(config_file_path) as f:
            logger.info(
                "Using configuration from %s for W8A8 Block INT8 kernel.",
                config_file_path,
            )
            # If a configuration has been found, return it
            return {int(key): val for key, val in json.load(f).items()}

    # If no optimized configuration is available, we will use the default
    # configuration
    logger.warning(
        ("Using default W8A8 Block INT8 kernel config. Performance might "
         "be sub-optimal! Config file not found at %s"),
        config_file_path,
    )
    return None

input_to_int8

input_to_int8(
    x: Tensor, dtype: dtype = int8
) -> tuple[Tensor, Tensor]

This function quantizes input values to int8 values with tensor-wise quantization.

Source code in vllm/model_executor/layers/quantization/utils/int8_utils.py

def input_to_int8(
        x: torch.Tensor,
        dtype: torch.dtype = torch.int8) -> tuple[torch.Tensor, torch.Tensor]:
    """This function quantizes input values to int8 values with
    tensor-wise quantization."""
    iinfo = torch.iinfo(dtype)
    min_val, max_val = x.aminmax()
    amax = torch.maximum(min_val.abs(), max_val.abs()).clamp(min=1e-12)
    int8_min, int8_max = iinfo.min, iinfo.max
    scale = int8_max / amax
    x_scl_sat = (x * scale).clamp(min=int8_min, max=int8_max)
    return x_scl_sat.to(dtype).contiguous(), scale.float().reciprocal()

per_token_group_quant_int8

per_token_group_quant_int8(
    x: Tensor,
    group_size: int,
    eps: float = 1e-10,
    dtype: dtype = int8,
) -> tuple[Tensor, Tensor]

Function to perform per-token-group quantization on an input tensor x.

It converts the tensor values into signed int8 values and returns the quantized tensor along with the scaling factor used for quantization.

Parameters:

Name	Type	Description	Default
x	Tensor	The input tensor with ndim >= 2.	required
group_size	int	The group size used for quantization.	required
eps	float	The minimum to avoid dividing zero.	1e-10
dtype	dtype	The dype of output tensor. Note that only torch.int8 is supported for now.	int8

Returns:

Type	Description
tuple[Tensor, Tensor]	tuple[torch.Tensor, torch.Tensor]: The quantized tensor and the scaling factor for quantization.

Source code in vllm/model_executor/layers/quantization/utils/int8_utils.py

def per_token_group_quant_int8(
    x: torch.Tensor,
    group_size: int,
    eps: float = 1e-10,
    dtype: torch.dtype = torch.int8,
) -> tuple[torch.Tensor, torch.Tensor]:
    """Function to perform per-token-group quantization on an input tensor `x`.

    It converts the tensor values into signed int8 values and returns the
    quantized tensor along with the scaling factor used for quantization.

    Args:
        x: The input tensor with ndim >= 2.
        group_size: The group size used for quantization.
        eps: The minimum to avoid dividing zero.
        dtype: The dype of output tensor. Note that only `torch.int8`
            is supported for now.

    Returns:
        tuple[torch.Tensor, torch.Tensor]: The quantized tensor and the
            scaling factor for quantization.
    """
    assert (x.shape[-1] % group_size == 0
            ), "the last dimension of `x` cannot be divisible by `group_size`"
    assert x.is_contiguous(), "`x` is not contiguous"

    iinfo = torch.iinfo(dtype)
    int8_max = iinfo.max
    int8_min = iinfo.min

    x_q = torch.empty_like(x, device=x.device, dtype=dtype)
    M = x.numel() // group_size
    N = group_size
    x_s = torch.empty(
        x.shape[:-1] + (x.shape[-1] // group_size, ),
        device=x.device,
        dtype=torch.float32,
    )

    BLOCK = triton.next_power_of_2(N)
    # heuristics for number of warps
    num_warps = min(max(BLOCK // 256, 1), 8)
    num_stages = 1
    _per_token_group_quant_int8[(M, )](
        x,
        x_q,
        x_s,
        group_size,
        N,
        eps,
        int8_min=int8_min,
        int8_max=int8_max,
        BLOCK=BLOCK,
        num_warps=num_warps,
        num_stages=num_stages,
    )

    return x_q, x_s

per_token_quant_int8

per_token_quant_int8(x)

Source code in vllm/model_executor/layers/quantization/utils/int8_utils.py

def per_token_quant_int8(x):
    M = x.numel() // x.shape[-1]
    N = x.shape[-1]
    x_q = torch.empty_like(x, device=x.device, dtype=torch.int8)
    scales = torch.empty(x.shape[:-1] + (1, ),
                         device=x.device,
                         dtype=torch.float32)
    BLOCK = triton.next_power_of_2(N)
    # heuristics for number of warps
    num_warps = min(max(BLOCK // 256, 1), 8)

    assert x.is_contiguous()
    _per_token_quant_int8[(M, )](
        x,
        x_q,
        scales,
        stride_x=x.stride(-2),
        stride_xq=x_q.stride(-2),
        N=N,
        BLOCK=BLOCK,
        num_warps=num_warps,
        num_stages=1,
    )

    return x_q, scales

round_f32

round_f32(arg0, _builder=None)

Source code in vllm/model_executor/layers/quantization/utils/int8_utils.py

@core.extern
def round_f32(arg0, _builder=None):
    return core.extern_elementwise("",
                                   "", [arg0], {
                                       (core.dtype("fp32"), ):
                                       ("llvm.round", core.dtype("fp32")),
                                       (core.dtype("fp64"), ):
                                       ("llvm.round", core.dtype("fp64")),
                                   },
                                   is_pure=True,
                                   _builder=_builder)

round_int8

round_int8(x)

Source code in vllm/model_executor/layers/quantization/utils/int8_utils.py

@triton.jit
def round_int8(x):
    return tl.extra.cuda.libdevice.round(x).to(tl.int8)

w8a8_block_int8_matmul

w8a8_block_int8_matmul(
    A: Tensor,
    B: Tensor,
    As: Tensor,
    Bs: Tensor,
    block_size: list[int],
    output_dtype: dtype = float16,
) -> Tensor

This function performs matrix multiplication with block-wise quantization.

It takes two input tensors A and B with scales As and Bs. The output is returned in the specified output_dtype.

Parameters:

Name	Type	Description	Default
A	Tensor	The input tensor, e.g., activation.	required
B	Tensor	The input tensor, e.g., weight.	required
As	Tensor	The per-token-group quantization scale for A.	required
Bs	Tensor	The per-block quantization scale for B.	required
block_size	list[int]	The block size for per-block quantization. It should be 2-dim, e.g., [128, 128].	required
output_dytpe		The dtype of the returned tensor.	required

Returns:

Type	Description
Tensor	torch.Tensor: The result of matmul.

Source code in vllm/model_executor/layers/quantization/utils/int8_utils.py

def w8a8_block_int8_matmul(
    A: torch.Tensor,
    B: torch.Tensor,
    As: torch.Tensor,
    Bs: torch.Tensor,
    block_size: list[int],
    output_dtype: torch.dtype = torch.float16,
) -> torch.Tensor:
    """This function performs matrix multiplication with block-wise
    quantization.

    It takes two input tensors `A` and `B` with scales `As` and `Bs`.
    The output is returned in the specified `output_dtype`.

    Args:
        A: The input tensor, e.g., activation.
        B: The input tensor, e.g., weight.
        As: The per-token-group quantization scale for `A`.
        Bs: The per-block quantization scale for `B`.
        block_size: The block size for per-block quantization. It should be
            2-dim, e.g., [128, 128].
        output_dytpe: The dtype of the returned tensor.

    Returns:
        torch.Tensor: The result of matmul.
    """
    assert len(block_size) == 2
    block_n, block_k = block_size[0], block_size[1]

    assert A.shape[-1] == B.shape[-1]
    assert A.shape[:-1] == As.shape[:-1] and A.is_contiguous()
    assert triton.cdiv(A.shape[-1], block_k) == As.shape[-1]
    M = A.numel() // A.shape[-1]

    assert B.ndim == 2 and B.is_contiguous() and Bs.ndim == 2
    N, K = B.shape
    assert triton.cdiv(N, block_n) == Bs.shape[0]
    assert triton.cdiv(K, block_k) == Bs.shape[1]

    C_shape = A.shape[:-1] + (N, )
    C = A.new_empty(C_shape, dtype=output_dtype)

    configs = get_w8a8_block_int8_configs(N, K, block_size[0], block_size[1])
    if configs:
        # If an optimal configuration map has been found, look up the
        # optimal config
        config = configs[min(configs.keys(), key=lambda x: abs(x - M))]
    else:
        # Default config
        # Block-wise quant: BLOCK_SIZE_K must be divisible by block_size[1]
        config = {
            "BLOCK_SIZE_M": 64,
            "BLOCK_SIZE_N": block_size[0],
            "BLOCK_SIZE_K": block_size[1],
            "GROUP_SIZE_M": 32,
            "num_warps": 4,
            "num_stages": 3,
        }

    def grid(META):
        return (triton.cdiv(M, META["BLOCK_SIZE_M"]) *
                triton.cdiv(N, META["BLOCK_SIZE_N"]), )

    _w8a8_block_int8_matmul[grid](
        A,
        B,
        C,
        As,
        Bs,
        M,
        N,
        K,
        block_n,
        block_k,
        A.stride(-2),
        A.stride(-1),
        B.stride(1),
        B.stride(0),
        C.stride(-2),
        C.stride(-1),
        As.stride(-2),
        As.stride(-1),
        Bs.stride(1),
        Bs.stride(0),
        **config,
    )

    return C