ops: transformer op fwd/bwd CUDA kernels + Tensor wrappers
add/mul/add_bias(+sum_rows)/rms_norm/silu/rope/softmax/cross_entropy, each with its analytic backward, in csrc/ops/nn.cu (inlined warp/block reductions). FFI declarations + nn.cu in build.rs (no_cuda gated). Tensor gains the matching thin wrappers; DType grows I32 for cross-entropy targets. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
This commit is contained in:
@@ -32,6 +32,7 @@ fn main() {
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.file("../../csrc/test/vecadd.cu")
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.file("../../csrc/ops/elementwise.cu")
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.file("../../csrc/ops/gemm.cu")
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.file("../../csrc/ops/nn.cu")
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.compile("xtrain_cuda_kernels");
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}
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@@ -63,6 +63,120 @@ unsafe extern "C" {
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);
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}
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// Transformer / autograd op kernels (csrc/ops/nn.cu). Forward + backward for the
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// ops the Phase T4 tape engine needs. All F32, row-major, contiguous.
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#[cfg(not(no_cuda))]
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unsafe extern "C" {
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// Elementwise: out = a + b ; out = a * b.
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pub fn launch_add_f32(a: *const f32, b: *const f32, out: *mut f32, n: i32, s: CudaStream);
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pub fn launch_mul_f32(a: *const f32, b: *const f32, out: *mut f32, n: i32, s: CudaStream);
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// Broadcast bias add: out[r,c] = x[r,c] + bias[c]. x:[rows,cols], bias:[cols].
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pub fn launch_add_bias_f32(
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x: *const f32,
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bias: *const f32,
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out: *mut f32,
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rows: i32,
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cols: i32,
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s: CudaStream,
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);
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// Column-sum (over rows): dbias[c] = sum_r dout[r,c]. Bias backward.
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pub fn launch_sum_rows_f32(
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dout: *const f32,
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dbias: *mut f32,
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rows: i32,
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cols: i32,
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s: CudaStream,
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);
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// RMSNorm forward: writes y[rows,cols] and inv_rms[rows] (cached for bwd).
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pub fn launch_rms_norm_f32(
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x: *const f32,
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gamma: *const f32,
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y: *mut f32,
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inv_rms: *mut f32,
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rows: i32,
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cols: i32,
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eps: f32,
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s: CudaStream,
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);
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pub fn launch_rms_norm_dx_f32(
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x: *const f32,
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gamma: *const f32,
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dy: *const f32,
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inv_rms: *const f32,
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dx: *mut f32,
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rows: i32,
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cols: i32,
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s: CudaStream,
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);
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pub fn launch_rms_norm_dgamma_f32(
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x: *const f32,
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dy: *const f32,
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inv_rms: *const f32,
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dgamma: *mut f32,
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rows: i32,
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cols: i32,
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s: CudaStream,
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);
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// SiLU: y = x*sigmoid(x); backward dx.
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pub fn launch_silu_f32(x: *const f32, y: *mut f32, n: i32, s: CudaStream);
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pub fn launch_silu_dx_f32(x: *const f32, dy: *const f32, dx: *mut f32, n: i32, s: CudaStream);
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// RoPE (rotate_half), x:[tokens,heads,head_dim], position = token index.
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pub fn launch_rope_f32(
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x: *const f32,
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y: *mut f32,
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tokens: i32,
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heads: i32,
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head_dim: i32,
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theta: f32,
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s: CudaStream,
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);
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pub fn launch_rope_dx_f32(
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dy: *const f32,
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dx: *mut f32,
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tokens: i32,
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heads: i32,
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head_dim: i32,
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theta: f32,
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s: CudaStream,
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);
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// Row-wise softmax + Jacobian backward.
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pub fn launch_softmax_f32(x: *const f32, y: *mut f32, rows: i32, cols: i32, s: CudaStream);
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pub fn launch_softmax_dx_f32(
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y: *const f32,
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dy: *const f32,
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dx: *mut f32,
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rows: i32,
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cols: i32,
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s: CudaStream,
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);
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// Cross-entropy: fwd writes probs[rows,cols] + per-row loss[rows];
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// bwd dx = scale*(probs - onehot).
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pub fn launch_cross_entropy_fwd_f32(
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x: *const f32,
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target: *const i32,
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probs: *mut f32,
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loss: *mut f32,
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rows: i32,
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cols: i32,
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s: CudaStream,
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);
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pub fn launch_cross_entropy_dx_f32(
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probs: *const f32,
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target: *const i32,
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dx: *mut f32,
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rows: i32,
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cols: i32,
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scale: f32,
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s: CudaStream,
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);
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}
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// cuBLAS — used ONLY as a correctness reference for the hand-written GEMM in
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// tests. Declared (and linked, see build.rs) only when CUDA is compiled in.
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#[cfg(not(no_cuda))]
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@@ -7,18 +7,22 @@
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#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash)]
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pub enum DType {
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F32,
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/// 32-bit signed integers. Used for cross-entropy targets (token ids).
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I32,
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}
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impl DType {
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pub fn size_bytes(self) -> usize {
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match self {
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DType::F32 => 4,
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DType::I32 => 4,
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}
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}
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pub fn name(self) -> &'static str {
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match self {
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DType::F32 => "f32",
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DType::I32 => "i32",
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}
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}
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}
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@@ -45,3 +49,13 @@ impl TensorDType for f32 {
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v as f32
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}
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}
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impl TensorDType for i32 {
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const DTYPE: DType = DType::I32;
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fn to_f64(self) -> f64 {
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self as f64
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}
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fn from_f64(v: f64) -> Self {
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v as i32
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}
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}
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@@ -247,6 +247,334 @@ impl Tensor {
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let db = a.transpose_2d().matmul(dc); // [K,M] @ [M,N] = [K,N]
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(da, db)
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}
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// --- Transformer / autograd op primitives (the T4 kernels) ---
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//
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// Each is a thin, contiguous-F32-on-GPU wrapper over a kernel in
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// csrc/ops/nn.cu. The autograd `Var` layer (xtrain-autodiff) builds nodes on
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// top of these; the analytic backwards are derived in docs/03-autograd-engine.md.
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/// Elementwise `out = self + other` (same shape).
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#[cfg(not(no_cuda))]
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pub fn add(&self, other: &Tensor) -> Self {
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self.check_binary(other, "add");
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let out = Tensor::zeros(&self.shape, DType::F32, self.device());
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unsafe {
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xtrain_cuda::ffi::launch_add_f32(
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self.data_ptr() as *const f32,
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other.data_ptr() as *const f32,
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out.data_ptr() as *mut f32,
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self.numel() as i32,
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std::ptr::null_mut(),
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);
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}
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xtrain_cuda::device::synchronize().expect("add sync failed");
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out
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}
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/// Elementwise `out = self * other` (same shape, Hadamard product).
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#[cfg(not(no_cuda))]
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pub fn mul(&self, other: &Tensor) -> Self {
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self.check_binary(other, "mul");
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let out = Tensor::zeros(&self.shape, DType::F32, self.device());
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unsafe {
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xtrain_cuda::ffi::launch_mul_f32(
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self.data_ptr() as *const f32,
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other.data_ptr() as *const f32,
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out.data_ptr() as *mut f32,
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self.numel() as i32,
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std::ptr::null_mut(),
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);
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}
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xtrain_cuda::device::synchronize().expect("mul sync failed");
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out
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}
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/// Broadcast bias add: `out[r,c] = self[r,c] + bias[c]`.
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/// `self`:[rows,cols], `bias`:[cols].
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#[cfg(not(no_cuda))]
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pub fn add_bias(&self, bias: &Tensor) -> Self {
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assert_eq!(self.ndim(), 2, "add_bias requires 2D input");
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assert_eq!(bias.ndim(), 1, "bias must be 1D");
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assert_eq!(self.shape[1], bias.shape[0], "bias len != cols");
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let (rows, cols) = (self.shape[0], self.shape[1]);
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let out = Tensor::zeros(&self.shape, DType::F32, self.device());
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unsafe {
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xtrain_cuda::ffi::launch_add_bias_f32(
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self.data_ptr() as *const f32,
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bias.data_ptr() as *const f32,
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out.data_ptr() as *mut f32,
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rows as i32,
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cols as i32,
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std::ptr::null_mut(),
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);
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}
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xtrain_cuda::device::synchronize().expect("add_bias sync failed");
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out
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}
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/// Column-sum over rows: `out[c] = sum_r self[r,c]`. This is the bias
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/// backward (sum the upstream grad over the broadcast dim). `self`:[rows,cols]
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/// → [cols].
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#[cfg(not(no_cuda))]
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pub fn sum_rows(&self) -> Self {
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assert_eq!(self.ndim(), 2, "sum_rows requires 2D input");
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let (rows, cols) = (self.shape[0], self.shape[1]);
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let out = Tensor::zeros(&[cols], DType::F32, self.device());
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unsafe {
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xtrain_cuda::ffi::launch_sum_rows_f32(
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self.data_ptr() as *const f32,
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out.data_ptr() as *mut f32,
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rows as i32,
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cols as i32,
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std::ptr::null_mut(),
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);
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}
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xtrain_cuda::device::synchronize().expect("sum_rows sync failed");
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out
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}
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/// RMSNorm forward: `y[r,c] = x[r,c] * inv_rms[r] * gamma[c]` with
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/// `inv_rms = rsqrt(mean(x²) + eps)`. `self`:[rows,cols], `gamma`:[cols].
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/// Returns `(y, inv_rms)`; `inv_rms`:[rows] is cached for backward.
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#[cfg(not(no_cuda))]
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pub fn rms_norm(&self, gamma: &Tensor, eps: f32) -> (Tensor, Tensor) {
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assert_eq!(self.ndim(), 2, "rms_norm requires 2D input");
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assert_eq!(gamma.ndim(), 1, "gamma must be 1D");
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assert_eq!(self.shape[1], gamma.shape[0], "gamma len != cols");
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let (rows, cols) = (self.shape[0], self.shape[1]);
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let y = Tensor::zeros(&self.shape, DType::F32, self.device());
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let inv_rms = Tensor::zeros(&[rows], DType::F32, self.device());
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unsafe {
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xtrain_cuda::ffi::launch_rms_norm_f32(
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self.data_ptr() as *const f32,
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gamma.data_ptr() as *const f32,
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y.data_ptr() as *mut f32,
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inv_rms.data_ptr() as *mut f32,
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rows as i32,
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cols as i32,
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eps,
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std::ptr::null_mut(),
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);
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}
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xtrain_cuda::device::synchronize().expect("rms_norm sync failed");
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(y, inv_rms)
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}
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/// RMSNorm backward. Inputs are the forward `x`, `gamma`, upstream `dy`, and
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/// the cached `inv_rms`. Returns `(dx, dgamma)`.
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#[cfg(not(no_cuda))]
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pub fn rms_norm_backward(
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x: &Tensor,
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gamma: &Tensor,
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dy: &Tensor,
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inv_rms: &Tensor,
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) -> (Tensor, Tensor) {
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let (rows, cols) = (x.shape[0], x.shape[1]);
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let dx = Tensor::zeros(&[rows, cols], DType::F32, x.device());
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let dgamma = Tensor::zeros(&[cols], DType::F32, x.device());
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unsafe {
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xtrain_cuda::ffi::launch_rms_norm_dx_f32(
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x.data_ptr() as *const f32,
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gamma.data_ptr() as *const f32,
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dy.data_ptr() as *const f32,
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inv_rms.data_ptr() as *const f32,
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dx.data_ptr() as *mut f32,
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rows as i32,
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cols as i32,
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std::ptr::null_mut(),
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);
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xtrain_cuda::ffi::launch_rms_norm_dgamma_f32(
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x.data_ptr() as *const f32,
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dy.data_ptr() as *const f32,
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inv_rms.data_ptr() as *const f32,
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dgamma.data_ptr() as *mut f32,
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rows as i32,
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cols as i32,
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std::ptr::null_mut(),
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||||
);
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||||
}
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xtrain_cuda::device::synchronize().expect("rms_norm_backward sync failed");
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(dx, dgamma)
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||||
}
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/// SiLU forward: `y = x * sigmoid(x)`, elementwise.
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#[cfg(not(no_cuda))]
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pub fn silu(&self) -> Self {
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assert_eq!(self.dtype, DType::F32, "silu only supports F32");
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let out = Tensor::zeros(&self.shape, DType::F32, self.device());
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unsafe {
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xtrain_cuda::ffi::launch_silu_f32(
|
||||
self.data_ptr() as *const f32,
|
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out.data_ptr() as *mut f32,
|
||||
self.numel() as i32,
|
||||
std::ptr::null_mut(),
|
||||
);
|
||||
}
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||||
xtrain_cuda::device::synchronize().expect("silu sync failed");
|
||||
out
|
||||
}
|
||||
|
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/// SiLU backward: `dx = dy * (sig + x*sig*(1-sig))`, `sig = sigmoid(x)`.
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/// Inputs are the forward `x` and upstream `dy`.
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#[cfg(not(no_cuda))]
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pub fn silu_backward(x: &Tensor, dy: &Tensor) -> Self {
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||||
let dx = Tensor::zeros(&x.shape, DType::F32, x.device());
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unsafe {
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xtrain_cuda::ffi::launch_silu_dx_f32(
|
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x.data_ptr() as *const f32,
|
||||
dy.data_ptr() as *const f32,
|
||||
dx.data_ptr() as *mut f32,
|
||||
x.numel() as i32,
|
||||
std::ptr::null_mut(),
|
||||
);
|
||||
}
|
||||
xtrain_cuda::device::synchronize().expect("silu_backward sync failed");
|
||||
dx
|
||||
}
|
||||
|
||||
/// RoPE forward (rotate_half). `self`:[tokens,heads,head_dim]; the position
|
||||
/// of each token is its row index. Returns the rotated tensor.
|
||||
#[cfg(not(no_cuda))]
|
||||
pub fn rope(&self, theta: f32) -> Self {
|
||||
assert_eq!(self.ndim(), 3, "rope requires [tokens,heads,head_dim]");
|
||||
let (tokens, heads, head_dim) = (self.shape[0], self.shape[1], self.shape[2]);
|
||||
assert_eq!(head_dim % 2, 0, "head_dim must be even");
|
||||
let out = Tensor::zeros(&self.shape, DType::F32, self.device());
|
||||
unsafe {
|
||||
xtrain_cuda::ffi::launch_rope_f32(
|
||||
self.data_ptr() as *const f32,
|
||||
out.data_ptr() as *mut f32,
|
||||
tokens as i32,
|
||||
heads as i32,
|
||||
head_dim as i32,
|
||||
theta,
|
||||
std::ptr::null_mut(),
|
||||
);
|
||||
}
|
||||
xtrain_cuda::device::synchronize().expect("rope sync failed");
|
||||
out
|
||||
}
|
||||
|
||||
/// RoPE backward: apply the inverse (transpose) rotation to `dy`. RoPE is an
|
||||
/// orthogonal map, so it needs no cached forward values, only `theta`.
|
||||
#[cfg(not(no_cuda))]
|
||||
pub fn rope_backward(dy: &Tensor, theta: f32) -> Self {
|
||||
let (tokens, heads, head_dim) = (dy.shape[0], dy.shape[1], dy.shape[2]);
|
||||
let dx = Tensor::zeros(&dy.shape, DType::F32, dy.device());
|
||||
unsafe {
|
||||
xtrain_cuda::ffi::launch_rope_dx_f32(
|
||||
dy.data_ptr() as *const f32,
|
||||
dx.data_ptr() as *mut f32,
|
||||
tokens as i32,
|
||||
heads as i32,
|
||||
head_dim as i32,
|
||||
theta,
|
||||
std::ptr::null_mut(),
|
||||
);
|
||||
}
|
||||
xtrain_cuda::device::synchronize().expect("rope_backward sync failed");
|
||||
dx
|
||||
}
|
||||
|
||||
/// Row-wise safe softmax over the last dim. `self`:[rows,cols].
|
||||
#[cfg(not(no_cuda))]
|
||||
pub fn softmax(&self) -> Self {
|
||||
assert_eq!(self.ndim(), 2, "softmax requires 2D input");
|
||||
let (rows, cols) = (self.shape[0], self.shape[1]);
|
||||
let out = Tensor::zeros(&self.shape, DType::F32, self.device());
|
||||
unsafe {
|
||||
xtrain_cuda::ffi::launch_softmax_f32(
|
||||
self.data_ptr() as *const f32,
|
||||
out.data_ptr() as *mut f32,
|
||||
rows as i32,
|
||||
cols as i32,
|
||||
std::ptr::null_mut(),
|
||||
);
|
||||
}
|
||||
xtrain_cuda::device::synchronize().expect("softmax sync failed");
|
||||
out
|
||||
}
|
||||
|
||||
/// Softmax backward (Jacobian): `dx[r,c] = y[r,c]*(dy[r,c] - sum_c'(dy*y))`.
|
||||
/// Inputs are the forward output `y` and upstream `dy`.
|
||||
#[cfg(not(no_cuda))]
|
||||
pub fn softmax_backward(y: &Tensor, dy: &Tensor) -> Self {
|
||||
let (rows, cols) = (y.shape[0], y.shape[1]);
|
||||
let dx = Tensor::zeros(&y.shape, DType::F32, y.device());
|
||||
unsafe {
|
||||
xtrain_cuda::ffi::launch_softmax_dx_f32(
|
||||
y.data_ptr() as *const f32,
|
||||
dy.data_ptr() as *const f32,
|
||||
dx.data_ptr() as *mut f32,
|
||||
rows as i32,
|
||||
cols as i32,
|
||||
std::ptr::null_mut(),
|
||||
);
|
||||
}
|
||||
xtrain_cuda::device::synchronize().expect("softmax_backward sync failed");
|
||||
dx
|
||||
}
|
||||
|
||||
/// Cross-entropy forward over logits `self`:[rows,cols] with one I32 target
|
||||
/// per row. Returns `(probs, loss)` where `probs`:[rows,cols] is the softmax
|
||||
/// (cached for backward) and `loss`:[rows] is the per-row negative log-likelihood.
|
||||
#[cfg(not(no_cuda))]
|
||||
pub fn cross_entropy(&self, target: &Tensor) -> (Tensor, Tensor) {
|
||||
assert_eq!(self.ndim(), 2, "cross_entropy requires 2D logits");
|
||||
assert_eq!(target.dtype, DType::I32, "target must be I32");
|
||||
assert_eq!(target.numel(), self.shape[0], "one target per row");
|
||||
let (rows, cols) = (self.shape[0], self.shape[1]);
|
||||
let probs = Tensor::zeros(&self.shape, DType::F32, self.device());
|
||||
let loss = Tensor::zeros(&[rows], DType::F32, self.device());
|
||||
unsafe {
|
||||
xtrain_cuda::ffi::launch_cross_entropy_fwd_f32(
|
||||
self.data_ptr() as *const f32,
|
||||
target.data_ptr() as *const i32,
|
||||
probs.data_ptr() as *mut f32,
|
||||
loss.data_ptr() as *mut f32,
|
||||
rows as i32,
|
||||
cols as i32,
|
||||
std::ptr::null_mut(),
|
||||
);
|
||||
}
|
||||
xtrain_cuda::device::synchronize().expect("cross_entropy sync failed");
|
||||
(probs, loss)
|
||||
}
|
||||
|
||||
/// Cross-entropy backward: `dx = scale * (probs - onehot(target))`. With
|
||||
/// `scale = upstream / rows`, this is the gradient of the mean per-row loss.
|
||||
#[cfg(not(no_cuda))]
|
||||
pub fn cross_entropy_backward(probs: &Tensor, target: &Tensor, scale: f32) -> Self {
|
||||
let (rows, cols) = (probs.shape[0], probs.shape[1]);
|
||||
let dx = Tensor::zeros(&probs.shape, DType::F32, probs.device());
|
||||
unsafe {
|
||||
xtrain_cuda::ffi::launch_cross_entropy_dx_f32(
|
||||
probs.data_ptr() as *const f32,
|
||||
target.data_ptr() as *const i32,
|
||||
dx.data_ptr() as *mut f32,
|
||||
rows as i32,
|
||||
cols as i32,
|
||||
scale,
|
||||
std::ptr::null_mut(),
|
||||
);
|
||||
}
|
||||
xtrain_cuda::device::synchronize().expect("cross_entropy_backward sync failed");
|
||||
dx
|
||||
}
|
||||
|
||||
// Shared validation for same-shape binary elementwise ops.
|
||||
#[cfg(not(no_cuda))]
|
||||
fn check_binary(&self, other: &Tensor, op: &str) {
|
||||
assert_eq!(self.dtype, DType::F32, "{op} only supports F32");
|
||||
assert_eq!(other.dtype, DType::F32, "{op} only supports F32");
|
||||
assert_eq!(self.shape(), other.shape(), "{op} shape mismatch");
|
||||
assert_eq!(self.device(), other.device(), "{op} device mismatch");
|
||||
assert!(
|
||||
self.is_contiguous() && other.is_contiguous(),
|
||||
"{op} requires contiguous tensors"
|
||||
);
|
||||
}
|
||||
}
|
||||
|
||||
impl std::fmt::Debug for Tensor {
|
||||
|
||||
358
csrc/ops/nn.cu
Normal file
358
csrc/ops/nn.cu
Normal file
@@ -0,0 +1,358 @@
|
||||
// Forward + backward CUDA kernels for the transformer ops the autograd engine
|
||||
// (Phase T4) needs: elementwise add/mul, broadcast bias add + its row-sum
|
||||
// backward, RMSNorm, SiLU, RoPE, row-wise softmax, and cross-entropy.
|
||||
//
|
||||
// All F32, row-major, contiguous. Forward kernels mirror xserv
|
||||
// (docs/04-transformer-kernels.md, docs/05-attention.md); the backward kernels
|
||||
// are new (xserv is inference-only). Reduction helpers are inlined here so this
|
||||
// file is self-contained (no shared header), matching the existing csrc/ layout.
|
||||
|
||||
#include <math.h>
|
||||
|
||||
extern "C" {
|
||||
|
||||
// --- Warp / block reductions (sum + max), block handles one row ---
|
||||
|
||||
__device__ __forceinline__ float warp_reduce_sum(float v) {
|
||||
#pragma unroll
|
||||
for (int off = 16; off > 0; off >>= 1)
|
||||
v += __shfl_down_sync(0xffffffff, v, off);
|
||||
return v;
|
||||
}
|
||||
|
||||
__device__ __forceinline__ float warp_reduce_max(float v) {
|
||||
#pragma unroll
|
||||
for (int off = 16; off > 0; off >>= 1)
|
||||
v = fmaxf(v, __shfl_down_sync(0xffffffff, v, off));
|
||||
return v;
|
||||
}
|
||||
|
||||
__device__ __forceinline__ float block_reduce_sum(float v) {
|
||||
__shared__ float shared[32];
|
||||
int lane = threadIdx.x & 31;
|
||||
int warp = threadIdx.x >> 5;
|
||||
int nwarps = (blockDim.x + 31) >> 5;
|
||||
v = warp_reduce_sum(v);
|
||||
if (lane == 0) shared[warp] = v;
|
||||
__syncthreads();
|
||||
v = (threadIdx.x < nwarps) ? shared[threadIdx.x] : 0.0f;
|
||||
if (warp == 0) v = warp_reduce_sum(v);
|
||||
// broadcast warp-0 lane-0 result to whole block
|
||||
__shared__ float bcast;
|
||||
if (threadIdx.x == 0) bcast = v;
|
||||
__syncthreads();
|
||||
return bcast;
|
||||
}
|
||||
|
||||
__device__ __forceinline__ float block_reduce_max(float v) {
|
||||
__shared__ float shared[32];
|
||||
int lane = threadIdx.x & 31;
|
||||
int warp = threadIdx.x >> 5;
|
||||
int nwarps = (blockDim.x + 31) >> 5;
|
||||
v = warp_reduce_max(v);
|
||||
if (lane == 0) shared[warp] = v;
|
||||
__syncthreads();
|
||||
v = (threadIdx.x < nwarps) ? shared[threadIdx.x] : -INFINITY;
|
||||
if (warp == 0) v = warp_reduce_max(v);
|
||||
__shared__ float bcast;
|
||||
if (threadIdx.x == 0) bcast = v;
|
||||
__syncthreads();
|
||||
return bcast;
|
||||
}
|
||||
|
||||
// =====================================================================
|
||||
// Elementwise add / mul (same-shape)
|
||||
// =====================================================================
|
||||
|
||||
__global__ void add_k(const float* a, const float* b, float* out, int n) {
|
||||
int i = blockIdx.x * blockDim.x + threadIdx.x;
|
||||
if (i < n) out[i] = a[i] + b[i];
|
||||
}
|
||||
void launch_add_f32(const float* a, const float* b, float* out, int n, void* s) {
|
||||
int blk = 256, grid = (n + blk - 1) / blk;
|
||||
add_k<<<grid, blk, 0, (cudaStream_t)s>>>(a, b, out, n);
|
||||
}
|
||||
|
||||
__global__ void mul_k(const float* a, const float* b, float* out, int n) {
|
||||
int i = blockIdx.x * blockDim.x + threadIdx.x;
|
||||
if (i < n) out[i] = a[i] * b[i];
|
||||
}
|
||||
void launch_mul_f32(const float* a, const float* b, float* out, int n, void* s) {
|
||||
int blk = 256, grid = (n + blk - 1) / blk;
|
||||
mul_k<<<grid, blk, 0, (cudaStream_t)s>>>(a, b, out, n);
|
||||
}
|
||||
|
||||
// =====================================================================
|
||||
// Broadcast bias add: out[r,c] = x[r,c] + bias[c] (x:[rows,cols])
|
||||
// Backward for bias is a column-sum (sum over rows): dbias[c] = sum_r dout[r,c].
|
||||
// =====================================================================
|
||||
|
||||
__global__ void add_bias_k(const float* x, const float* bias, float* out,
|
||||
int rows, int cols) {
|
||||
int i = blockIdx.x * blockDim.x + threadIdx.x;
|
||||
if (i < rows * cols) out[i] = x[i] + bias[i % cols];
|
||||
}
|
||||
void launch_add_bias_f32(const float* x, const float* bias, float* out,
|
||||
int rows, int cols, void* s) {
|
||||
int n = rows * cols, blk = 256, grid = (n + blk - 1) / blk;
|
||||
add_bias_k<<<grid, blk, 0, (cudaStream_t)s>>>(x, bias, out, rows, cols);
|
||||
}
|
||||
|
||||
// dbias[c] = sum_r dout[r,c]. One block per column, threads stride over rows.
|
||||
__global__ void sum_rows_k(const float* dout, float* dbias, int rows, int cols) {
|
||||
int col = blockIdx.x;
|
||||
float acc = 0.0f;
|
||||
for (int r = threadIdx.x; r < rows; r += blockDim.x)
|
||||
acc += dout[r * cols + col];
|
||||
acc = block_reduce_sum(acc);
|
||||
if (threadIdx.x == 0) dbias[col] = acc;
|
||||
}
|
||||
void launch_sum_rows_f32(const float* dout, float* dbias, int rows, int cols, void* s) {
|
||||
int blk = 256;
|
||||
sum_rows_k<<<cols, blk, 0, (cudaStream_t)s>>>(dout, dbias, rows, cols);
|
||||
}
|
||||
|
||||
// =====================================================================
|
||||
// RMSNorm: y[r,c] = x[r,c] * inv_rms[r] * gamma[c], inv_rms = rsqrt(mean(x²)+eps)
|
||||
// x:[rows,cols], gamma:[cols]. Forward also writes inv_rms[rows] for backward.
|
||||
// =====================================================================
|
||||
|
||||
__global__ void rms_norm_k(const float* x, const float* gamma, float* y,
|
||||
float* inv_rms, int rows, int cols, float eps) {
|
||||
int r = blockIdx.x;
|
||||
const float* xr = x + r * cols;
|
||||
float* yr = y + r * cols;
|
||||
float ss = 0.0f;
|
||||
for (int c = threadIdx.x; c < cols; c += blockDim.x) ss += xr[c] * xr[c];
|
||||
ss = block_reduce_sum(ss);
|
||||
float ir = rsqrtf(ss / cols + eps);
|
||||
if (threadIdx.x == 0) inv_rms[r] = ir;
|
||||
for (int c = threadIdx.x; c < cols; c += blockDim.x)
|
||||
yr[c] = xr[c] * ir * gamma[c];
|
||||
}
|
||||
void launch_rms_norm_f32(const float* x, const float* gamma, float* y,
|
||||
float* inv_rms, int rows, int cols, float eps, void* s) {
|
||||
int blk = cols < 1024 ? cols : 1024;
|
||||
if (blk < 32) blk = 32;
|
||||
rms_norm_k<<<rows, blk, 0, (cudaStream_t)s>>>(x, gamma, y, inv_rms, rows, cols, eps);
|
||||
}
|
||||
|
||||
// RMSNorm backward.
|
||||
// Let g[c] = dy[r,c]*gamma[c], ir = inv_rms[r], n = cols.
|
||||
// dx[r,c] = ir*g[c] - x[r,c]*ir³/n * sum_c(g[c]*x[r,c])
|
||||
// dgamma[c] = sum_r dy[r,c] * x[r,c] * ir (accumulated across rows)
|
||||
__global__ void rms_norm_dx_k(const float* x, const float* gamma, const float* dy,
|
||||
const float* inv_rms, float* dx, int rows, int cols) {
|
||||
int r = blockIdx.x;
|
||||
const float* xr = x + r * cols;
|
||||
const float* dyr = dy + r * cols;
|
||||
float* dxr = dx + r * cols;
|
||||
float ir = inv_rms[r];
|
||||
float dot = 0.0f; // sum_c g[c]*x[c]
|
||||
for (int c = threadIdx.x; c < cols; c += blockDim.x)
|
||||
dot += dyr[c] * gamma[c] * xr[c];
|
||||
dot = block_reduce_sum(dot);
|
||||
float coeff = ir * ir * ir / (float)cols * dot;
|
||||
for (int c = threadIdx.x; c < cols; c += blockDim.x)
|
||||
dxr[c] = ir * dyr[c] * gamma[c] - xr[c] * coeff;
|
||||
}
|
||||
void launch_rms_norm_dx_f32(const float* x, const float* gamma, const float* dy,
|
||||
const float* inv_rms, float* dx, int rows, int cols, void* s) {
|
||||
int blk = cols < 1024 ? cols : 1024;
|
||||
if (blk < 32) blk = 32;
|
||||
rms_norm_dx_k<<<rows, blk, 0, (cudaStream_t)s>>>(x, gamma, dy, inv_rms, dx, rows, cols);
|
||||
}
|
||||
|
||||
// dgamma[c] = sum_r dy[r,c] * x[r,c] * inv_rms[r]. One block per column.
|
||||
__global__ void rms_norm_dgamma_k(const float* x, const float* dy, const float* inv_rms,
|
||||
float* dgamma, int rows, int cols) {
|
||||
int col = blockIdx.x;
|
||||
float acc = 0.0f;
|
||||
for (int r = threadIdx.x; r < rows; r += blockDim.x)
|
||||
acc += dy[r * cols + col] * x[r * cols + col] * inv_rms[r];
|
||||
acc = block_reduce_sum(acc);
|
||||
if (threadIdx.x == 0) dgamma[col] = acc;
|
||||
}
|
||||
void launch_rms_norm_dgamma_f32(const float* x, const float* dy, const float* inv_rms,
|
||||
float* dgamma, int rows, int cols, void* s) {
|
||||
int blk = 256;
|
||||
rms_norm_dgamma_k<<<cols, blk, 0, (cudaStream_t)s>>>(x, dy, inv_rms, dgamma, rows, cols);
|
||||
}
|
||||
|
||||
// =====================================================================
|
||||
// SiLU: y = x * sigmoid(x). Backward: dx = dy * (sig + x*sig*(1-sig)).
|
||||
// =====================================================================
|
||||
|
||||
__global__ void silu_k(const float* x, float* y, int n) {
|
||||
int i = blockIdx.x * blockDim.x + threadIdx.x;
|
||||
if (i < n) { float xv = x[i]; y[i] = xv / (1.0f + expf(-xv)); }
|
||||
}
|
||||
void launch_silu_f32(const float* x, float* y, int n, void* s) {
|
||||
int blk = 256, grid = (n + blk - 1) / blk;
|
||||
silu_k<<<grid, blk, 0, (cudaStream_t)s>>>(x, y, n);
|
||||
}
|
||||
|
||||
__global__ void silu_dx_k(const float* x, const float* dy, float* dx, int n) {
|
||||
int i = blockIdx.x * blockDim.x + threadIdx.x;
|
||||
if (i < n) {
|
||||
float xv = x[i];
|
||||
float sig = 1.0f / (1.0f + expf(-xv));
|
||||
dx[i] = dy[i] * (sig + xv * sig * (1.0f - sig));
|
||||
}
|
||||
}
|
||||
void launch_silu_dx_f32(const float* x, const float* dy, float* dx, int n, void* s) {
|
||||
int blk = 256, grid = (n + blk - 1) / blk;
|
||||
silu_dx_k<<<grid, blk, 0, (cudaStream_t)s>>>(x, dy, dx, n);
|
||||
}
|
||||
|
||||
// =====================================================================
|
||||
// RoPE (rotate_half layout). x:[tokens, heads, head_dim]; position = token index.
|
||||
// y[i] = x[i]*cos - x[i+h]*sin
|
||||
// y[i+h] = x[i+h]*cos + x[i]*sin (i in [0,half), h=half_dim)
|
||||
// freq[i] = theta^(-2i/head_dim); angle = pos*freq[i].
|
||||
// Backward is the inverse (transpose) rotation: apply +angle's transpose ≡ -angle.
|
||||
// dx[i] = dy[i]*cos + dy[i+h]*sin
|
||||
// dx[i+h] = dy[i+h]*cos - dy[i]*sin
|
||||
// =====================================================================
|
||||
|
||||
__global__ void rope_k(const float* x, float* y, int heads, int head_dim, float theta) {
|
||||
int tok = blockIdx.x;
|
||||
int head = blockIdx.y;
|
||||
int half = head_dim / 2;
|
||||
int i = threadIdx.x;
|
||||
if (i >= half) return;
|
||||
float freq = powf(theta, -(float)(2 * i) / (float)head_dim);
|
||||
float angle = (float)tok * freq;
|
||||
float c = cosf(angle), sn = sinf(angle);
|
||||
int base = (tok * heads + head) * head_dim;
|
||||
float x0 = x[base + i], x1 = x[base + i + half];
|
||||
y[base + i] = x0 * c - x1 * sn;
|
||||
y[base + i + half] = x1 * c + x0 * sn;
|
||||
}
|
||||
void launch_rope_f32(const float* x, float* y, int tokens, int heads,
|
||||
int head_dim, float theta, void* s) {
|
||||
dim3 grid(tokens, heads);
|
||||
int blk = head_dim / 2;
|
||||
rope_k<<<grid, blk, 0, (cudaStream_t)s>>>(x, y, heads, head_dim, theta);
|
||||
}
|
||||
|
||||
__global__ void rope_dx_k(const float* dy, float* dx, int heads, int head_dim, float theta) {
|
||||
int tok = blockIdx.x;
|
||||
int head = blockIdx.y;
|
||||
int half = head_dim / 2;
|
||||
int i = threadIdx.x;
|
||||
if (i >= half) return;
|
||||
float freq = powf(theta, -(float)(2 * i) / (float)head_dim);
|
||||
float angle = (float)tok * freq;
|
||||
float c = cosf(angle), sn = sinf(angle);
|
||||
int base = (tok * heads + head) * head_dim;
|
||||
float d0 = dy[base + i], d1 = dy[base + i + half];
|
||||
dx[base + i] = d0 * c + d1 * sn;
|
||||
dx[base + i + half] = d1 * c - d0 * sn;
|
||||
}
|
||||
void launch_rope_dx_f32(const float* dy, float* dx, int tokens, int heads,
|
||||
int head_dim, float theta, void* s) {
|
||||
dim3 grid(tokens, heads);
|
||||
int blk = head_dim / 2;
|
||||
rope_dx_k<<<grid, blk, 0, (cudaStream_t)s>>>(dy, dx, heads, head_dim, theta);
|
||||
}
|
||||
|
||||
// =====================================================================
|
||||
// Row-wise safe softmax. x:[rows,cols] → y. Backward (Jacobian):
|
||||
// dx[r,c] = y[r,c] * (dy[r,c] - sum_c'(dy[r,c']*y[r,c']))
|
||||
// =====================================================================
|
||||
|
||||
__global__ void softmax_k(const float* x, float* y, int rows, int cols) {
|
||||
int r = blockIdx.x;
|
||||
const float* xr = x + r * cols;
|
||||
float* yr = y + r * cols;
|
||||
float m = -INFINITY;
|
||||
for (int c = threadIdx.x; c < cols; c += blockDim.x) m = fmaxf(m, xr[c]);
|
||||
m = block_reduce_max(m);
|
||||
float sum = 0.0f;
|
||||
for (int c = threadIdx.x; c < cols; c += blockDim.x) {
|
||||
float e = expf(xr[c] - m);
|
||||
yr[c] = e;
|
||||
sum += e;
|
||||
}
|
||||
sum = block_reduce_sum(sum);
|
||||
float inv = 1.0f / sum;
|
||||
for (int c = threadIdx.x; c < cols; c += blockDim.x) yr[c] *= inv;
|
||||
}
|
||||
void launch_softmax_f32(const float* x, float* y, int rows, int cols, void* s) {
|
||||
int blk = cols < 1024 ? cols : 1024;
|
||||
if (blk < 32) blk = 32;
|
||||
softmax_k<<<rows, blk, 0, (cudaStream_t)s>>>(x, y, rows, cols);
|
||||
}
|
||||
|
||||
__global__ void softmax_dx_k(const float* y, const float* dy, float* dx,
|
||||
int rows, int cols) {
|
||||
int r = blockIdx.x;
|
||||
const float* yr = y + r * cols;
|
||||
const float* dyr = dy + r * cols;
|
||||
float* dxr = dx + r * cols;
|
||||
float dot = 0.0f; // sum_c dy*y
|
||||
for (int c = threadIdx.x; c < cols; c += blockDim.x) dot += dyr[c] * yr[c];
|
||||
dot = block_reduce_sum(dot);
|
||||
for (int c = threadIdx.x; c < cols; c += blockDim.x)
|
||||
dxr[c] = yr[c] * (dyr[c] - dot);
|
||||
}
|
||||
void launch_softmax_dx_f32(const float* y, const float* dy, float* dx,
|
||||
int rows, int cols, void* s) {
|
||||
int blk = cols < 1024 ? cols : 1024;
|
||||
if (blk < 32) blk = 32;
|
||||
softmax_dx_k<<<rows, blk, 0, (cudaStream_t)s>>>(y, dy, dx, rows, cols);
|
||||
}
|
||||
|
||||
// =====================================================================
|
||||
// Cross-entropy over logits x:[rows,cols] with int target per row.
|
||||
// Forward writes per-row loss[r] = -log(softmax(x)[target]) and the softmax
|
||||
// probs[r,:] (cached for backward). Backward: dx[r,c] = (probs[r,c]-onehot)/rows
|
||||
// (mean reduction; the *rows scale folds the 1/rows of mean loss into dx).
|
||||
// =====================================================================
|
||||
|
||||
__global__ void cross_entropy_fwd_k(const float* x, const int* target,
|
||||
float* probs, float* loss, int rows, int cols) {
|
||||
int r = blockIdx.x;
|
||||
const float* xr = x + r * cols;
|
||||
float* pr = probs + r * cols;
|
||||
float m = -INFINITY;
|
||||
for (int c = threadIdx.x; c < cols; c += blockDim.x) m = fmaxf(m, xr[c]);
|
||||
m = block_reduce_max(m);
|
||||
float sum = 0.0f;
|
||||
for (int c = threadIdx.x; c < cols; c += blockDim.x) {
|
||||
float e = expf(xr[c] - m);
|
||||
pr[c] = e;
|
||||
sum += e;
|
||||
}
|
||||
sum = block_reduce_sum(sum);
|
||||
float inv = 1.0f / sum;
|
||||
for (int c = threadIdx.x; c < cols; c += blockDim.x) pr[c] *= inv;
|
||||
if (threadIdx.x == 0) {
|
||||
int t = target[r];
|
||||
loss[r] = -logf(pr[t]);
|
||||
}
|
||||
}
|
||||
void launch_cross_entropy_fwd_f32(const float* x, const int* target,
|
||||
float* probs, float* loss, int rows, int cols, void* s) {
|
||||
int blk = cols < 1024 ? cols : 1024;
|
||||
if (blk < 32) blk = 32;
|
||||
cross_entropy_fwd_k<<<rows, blk, 0, (cudaStream_t)s>>>(x, target, probs, loss, rows, cols);
|
||||
}
|
||||
|
||||
// dx[r,c] = scale * (probs[r,c] - [c==target]). scale = upstream/rows.
|
||||
__global__ void cross_entropy_dx_k(const float* probs, const int* target,
|
||||
float* dx, int rows, int cols, float scale) {
|
||||
int i = blockIdx.x * blockDim.x + threadIdx.x;
|
||||
if (i >= rows * cols) return;
|
||||
int r = i / cols, c = i % cols;
|
||||
float g = probs[i] - (c == target[r] ? 1.0f : 0.0f);
|
||||
dx[i] = g * scale;
|
||||
}
|
||||
void launch_cross_entropy_dx_f32(const float* probs, const int* target,
|
||||
float* dx, int rows, int cols, float scale, void* s) {
|
||||
int n = rows * cols, blk = 256, grid = (n + blk - 1) / blk;
|
||||
cross_entropy_dx_k<<<grid, blk, 0, (cudaStream_t)s>>>(probs, target, dx, rows, cols, scale);
|
||||
}
|
||||
|
||||
} // extern "C"
|
||||
Reference in New Issue
Block a user