vllm.model_executor.layers.fla.ops.layernorm_guard ¶

def layer_norm_fwd(
    x: torch.Tensor,
    weight: torch.Tensor,
    bias: torch.Tensor,
    eps: float,
    z: torch.Tensor = None,
    out: torch.Tensor = None,
    group_size: int = None,
    norm_before_gate: bool = True,
    is_rms_norm: bool = False,
):
    M, N = x.shape
    if group_size is None:
        group_size = N
    assert N % group_size == 0
    ngroups = N // group_size
    assert x.stride(-1) == 1
    if z is not None:
        assert z.stride(-1) == 1
        assert z.shape == (M, N)
    assert weight.shape == (N, )
    assert weight.stride(-1) == 1
    if bias is not None:
        assert bias.stride(-1) == 1
        assert bias.shape == (N, )
    # allocate output
    if out is not None:
        assert out.shape == x.shape
    else:
        out = torch.empty_like(x)
    assert out.stride(-1) == 1
    mean = torch.empty((ngroups * M, ), dtype=torch.float32,
                       device=x.device) if not is_rms_norm else None
    rstd = torch.empty((ngroups * M, ), dtype=torch.float32, device=x.device)
    # Less than 64KB per feature: enqueue fused kernel
    MAX_FUSED_SIZE = 65536 // x.element_size()
    BLOCK_N = min(MAX_FUSED_SIZE, triton.next_power_of_2(group_size))
    if group_size > BLOCK_N:
        raise RuntimeError(
            "This layer norm doesn't support feature dim >= 64KB.")
    # heuristics for number of warps
    num_warps = min(max(BLOCK_N // 256, 1), 8)
    grid = (M, ngroups)
    layer_norm_fwd_kernel[grid](x,
                                out,
                                weight,
                                bias,
                                z,
                                mean,
                                rstd,
                                x.stride(0),
                                out.stride(0),
                                z.stride(0) if z is not None else 0,
                                M,
                                group_size,
                                eps,
                                BLOCK_N=BLOCK_N,
                                NORM_BEFORE_GATE=norm_before_gate,
                                IS_RMS_NORM=is_rms_norm,
                                num_warps=num_warps)
    return out, mean, rstd

@triton.heuristics({
    "HAS_BIAS": lambda args: args["B"] is not None,
    "HAS_Z": lambda args: args["Z"] is not None,
})
@triton.jit
def layer_norm_fwd_kernel(
    X,  # pointer to the input
    Y,  # pointer to the output
    W,  # pointer to the weights
    B,  # pointer to the biases
    Z,  # pointer to the other branch
    Mean,  # pointer to the mean
    Rstd,  # pointer to the 1/std
    stride_x_row,  # how much to increase the pointer when moving by 1 row
    stride_y_row,
    stride_z_row,
    M,  # number of rows in X
    N,  # number of columns in X
    eps,  # epsilon to avoid division by zero
    BLOCK_N: tl.constexpr,
    HAS_BIAS: tl.constexpr,
    HAS_Z: tl.constexpr,
    NORM_BEFORE_GATE: tl.constexpr,
    IS_RMS_NORM: tl.constexpr,
):
    # Map the program id to the row of X and Y it should compute.
    row = tl.program_id(0)
    group = tl.program_id(1)
    X += row * stride_x_row + group * N
    Y += row * stride_y_row + group * N
    if HAS_Z:
        Z += row * stride_z_row + group * N
    if not IS_RMS_NORM:
        Mean += group * M
    Rstd += group * M
    W += group * N
    if HAS_BIAS:
        B += group * N
    # Compute mean and variance
    cols = tl.arange(0, BLOCK_N)
    x = tl.load(X + cols, mask=cols < N, other=0.).to(tl.float32)
    if HAS_Z and not NORM_BEFORE_GATE:
        z = tl.load(Z + cols, mask=cols < N).to(tl.float32)
        x *= z * tl.sigmoid(z)
    if not IS_RMS_NORM:
        mean = tl.sum(x, axis=0) / N
        tl.store(Mean + row, mean)
        xbar = tl.where(cols < N, x - mean, 0.)
        var = tl.sum(xbar * xbar, axis=0) / N
    else:
        xbar = tl.where(cols < N, x, 0.)
        var = tl.sum(xbar * xbar, axis=0) / N
    rstd = 1 / tl.sqrt(var + eps)
    tl.store(Rstd + row, rstd)
    # Normalize and apply linear transformation
    mask = cols < N
    w = tl.load(W + cols, mask=mask).to(tl.float32)
    if HAS_BIAS:
        b = tl.load(B + cols, mask=mask).to(tl.float32)
    x_hat = (x - mean) * rstd if not IS_RMS_NORM else x * rstd
    y = x_hat * w + b if HAS_BIAS else x_hat * w
    if HAS_Z and NORM_BEFORE_GATE:
        z = tl.load(Z + cols, mask=mask).to(tl.float32)
        y *= z * tl.sigmoid(z)
    # Write output
    tl.store(Y + cols, y, mask=mask)

def layernorm_fn(x,
                 weight,
                 bias,
                 z=None,
                 eps=1e-6,
                 group_size=None,
                 norm_before_gate=True,
                 is_rms_norm=False):
    return LayerNormFn.apply(x, weight, bias, z, eps, group_size,
                             norm_before_gate, is_rms_norm)

def rms_norm_ref(x,
                 weight,
                 bias,
                 z=None,
                 eps=1e-6,
                 group_size=None,
                 norm_before_gate=True,
                 upcast=True):
    dtype = x.dtype
    weight = weight.float()
    bias = bias.float() if bias is not None else None
    if upcast:
        x = x.float()
        z = z.float() if z is not None else z
    if z is not None and not norm_before_gate:
        x = x * F.silu(z)
    if group_size is None:
        rstd = 1 / torch.sqrt((x.square()).mean(dim=-1, keepdim=True) + eps)
        out = (x * rstd * weight) + bias if bias is not None else (x * rstd *
                                                                   weight)
    else:
        x_group = rearrange(x, "... (g d) -> ... g d", d=group_size)
        rstd = 1 / torch.sqrt((x_group.square()).mean(dim=-1, keepdim=True) +
                              eps)
        out = rearrange(x_group * rstd, "... g d -> ... (g d)") * weight
        if bias is not None:
            out = out + bias
    if z is not None and norm_before_gate:
        out *= F.silu(z)
    return out.to(dtype)

def rmsnorm_fn(x,
               weight,
               bias,
               z=None,
               eps=1e-6,
               group_size=None,
               norm_before_gate=True):
    return LayerNormFn.apply(x, weight, bias, z, eps, group_size,
                             norm_before_gate, True)