chore: import upstream snapshot with attribution

python/sglang/srt/layers/quantization/int8_kernel.py

@@ -0,0 +1,450 @@
+import functools
+import json
+import logging
+import os
+from typing import Any, Dict, List, Optional, Tuple
+
+import torch
+import triton
+import triton.language as tl
+from triton.language.extra import libdevice
+
+from sglang.srt.utils import get_device_name, is_cuda, is_hip
+
+_is_cuda = is_cuda()
+_is_hip = is_hip()
+if _is_cuda:
+    # Temporary
+    try:
+        from sgl_kernel import sgl_per_token_group_quant_8bit
+
+        enable_sgl_per_token_group_quant_8bit = True
+    except ImportError:
+        from sgl_kernel import sgl_per_token_group_quant_int8
+
+        enable_sgl_per_token_group_quant_8bit = False
+
+logger = logging.getLogger(__name__)
+
+
+@triton.jit
+def _per_token_quant_int8(
+    x_ptr,
+    xq_ptr,
+    scale_ptr,
+    x_sum_ptr,
+    stride_x,
+    stride_xq,
+    N,
+    CAL_SUM: tl.constexpr,
+    BLOCK: tl.constexpr,
+    IS_HIP: 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)
+    if IS_HIP:
+        # ROCm Triton dropped the CUDA `tl.extra.cuda.libdevice.*` shim
+        # (`__nv_roundf`); use the backend-agnostic libdevice instead.
+        x_q = libdevice.round(x_q).to(tl.int8)
+    else:
+        x_q = tl.extra.cuda.libdevice.round(x_q).to(tl.int8)
+    if CAL_SUM:
+        x_sum = tl.sum(x, axis=0)
+        tl.store(x_sum_ptr + row_id, x_sum.to(x_sum_ptr.dtype.element_ty))
+
+    tl.store(xq_ptr + row_id * stride_xq + cols, x_q, mask=mask)
+    tl.store(scale_ptr + row_id, scale_x.to(scale_ptr.dtype.element_ty))
+
+
+def per_token_quant_int8(x, scale_dtype=torch.float32, cal_sum=False):
+    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=scale_dtype)
+    if cal_sum:
+        x_sum = torch.empty(x.shape[:-1], device=x.device, dtype=x.dtype)
+    else:
+        x_sum = None
+    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,
+        x_sum,
+        stride_x=x.stride(-2),
+        stride_xq=x_q.stride(-2),
+        N=N,
+        CAL_SUM=cal_sum,
+        BLOCK=BLOCK,
+        IS_HIP=_is_hip,
+        num_warps=num_warps,
+        num_stages=1,
+    )
+    if cal_sum:
+        return x_q, scales, x_sum
+    else:
+        return x_q, scales
+
+
+@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)
+
+
+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
+
+
+def sglang_per_token_group_quant_int8(
+    x: torch.Tensor,
+    group_size: int,
+    eps: float = 1e-10,
+    dtype: torch.dtype = torch.int8,
+    enable_v2: Optional[bool] = None,
+):
+    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)
+    x_s = torch.empty(
+        x.shape[:-1] + (x.shape[-1] // group_size,),
+        device=x.device,
+        dtype=torch.float32,
+    )
+
+    # Temporary
+    if enable_sgl_per_token_group_quant_8bit:
+        sgl_per_token_group_quant_8bit(
+            x, x_q, x_s, group_size, eps, int8_min, int8_max, enable_v2=enable_v2
+        )
+    else:
+        assert not enable_v2
+        sgl_per_token_group_quant_int8(x, x_q, x_s, group_size, eps, int8_min, int8_max)
+
+    return x_q, x_s
+
+
+@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)
+
+
+@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 = 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"
+
+    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
+
+
+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