perf: finalize fp8 scale rings inside quantize kernels

- last-block epilogue (threadfence + counter elect) folds amax into hist[idx], reduces the window and publishes the next scale on device — zero extra launches per linear layer
- _ScaleRing packs [hist | scale | counter] into one CUDA buffer; the eager hist-write / max / scale-copy chain and update() are gone
- split FP8QuantizeParams out of FP8Params so each operator owns its fields; linear_forward/backward_fp8 take optional ring arguments
- e2e 12L/dim1024/B4xT512 (fused AdamW): fp8 137.8ms/step vs bf16 210.3ms, 1.53x; fwd 1.82x, bwd 1.50x

astrai/extension/fp8.py

@@ -105,26 +105,32 @@ class DynamicScaling(FP8Recipe):
 
 
 class _ScaleRing:
-    """One operand's delayed-scaling state: amax history ring + derived scale.
+    """One operand's delayed-scaling state, packed for in-kernel finalization.
 
-    The ring captures its recipe at construction; ``update`` records a fresh
+    ``state`` is a single float32 CUDA buffer ``[hist[n] | scale | counter]``
-    amax and refreshes the scale for the *next* step (delayed one step).
+    (``hist`` / ``scale`` are views). The quantize kernel's last-finishing
+    block records the freshly measured amax into ``hist[idx]``, reduces the
+    window and publishes the next step's scale entirely on device — the
+    Python-side hist-write / max / scale-write chain is gone. The counter
+    slot stays int32-zero (float bits) between launches. ``idx`` advances
+    host-side each step; ``margin`` is fixed by the recipe.
     """
 
-    __slots__ = ("recipe", "hist", "idx", "scale", "initialized")
+    __slots__ = ("recipe", "state", "hist", "scale", "idx", "initialized")
 
     def __init__(self, device: torch.device, recipe: FP8Recipe):
         self.recipe = recipe
         n = recipe.history_len
-        self.hist = torch.ones(n, device=device, dtype=torch.float32)
+        # [hist | scale | counter]; the counter slot must start at int 0.
+        self.state = torch.zeros(n + 2, device=device, dtype=torch.float32)
+        self.hist = self.state[:n]
+        self.scale = self.state[n : n + 1]
         self.idx = 0
-        self.scale = torch.ones(1, device=device, dtype=torch.float32)
         self.initialized = False
 
-    def update(self, amax: torch.Tensor, fmt: str) -> None:
+    def advance(self) -> None:
-        self.hist[self.idx] = amax.reshape(())
+        """Rotate to the next history slot after an in-kernel finalize."""
         self.idx = (self.idx + 1) % self.hist.numel()
-        self.scale.copy_(self.recipe.scale_from_history(self.hist, fmt))
 
     def seed(self, t: torch.Tensor, fmt: str) -> None:
         amax = t.abs().amax().to(torch.float32).clamp_min(1e-12)
@@ -235,8 +241,9 @@ def fp8_linear_forward(x: torch.Tensor, w: torch.Tensor, bias=None):
     """Scaled fp8 linear forward (called from the aten::linear impl).
 
     Pure FP8 path for both recipes: quantize x/w with the active scales, run
-    the pre-quantized GEMM, and feed the freshly measured amax back into the
+    the pre-quantized GEMM. With delayed scaling the rings finalize inside
-    delayed-scaling ring (dynamic scaling measures the current amax itself).
+    the quantize kernels (amax folded into the window, next step's scale
+    published on device); dynamic scaling measures the current amax itself.
     """
     if bias is None:
         bias = torch.empty(0, device=x.device, dtype=x.dtype)
@@ -246,17 +253,34 @@ def fp8_linear_forward(x: torch.Tensor, w: torch.Tensor, bias=None):
         meta = None
         sx = _dynamic_scale(x.reshape(-1, w.size(1)), state.recipe, fmt)
         sw = _dynamic_scale(w, state.recipe, fmt)
+        out, amax_x, amax_w = linear_forward_fp8(x, w, bias, sx, sw, fmt)
     else:
         meta = state.get_weight_meta(w)
         if not meta.w.initialized:
             meta.w.seed(w, fmt)
         if not meta.x.initialized:
             meta.x.seed(x, fmt)
-        sx, sw = meta.x.scale, meta.w.scale
+        # In-kernel ring finalization: the kernels write hist[idx] and the
-    out, amax_x, amax_w = linear_forward_fp8(x, w, bias, sx, sw, fmt)
+        # next scale; idx rotates host-side (the device counter self-rearms).
-    if meta is not None:
+        w_is_fp8 = w.dtype != torch.bfloat16
-        meta.x.update(amax_x, fmt)
+        out, amax_x, amax_w = linear_forward_fp8(
-        meta.w.update(amax_w, fmt)
+            x,
+            w,
+            bias,
+            meta.x.scale,
+            meta.w.scale,
+            fmt,
+            None,
+            meta.x.state,
+            meta.x.idx,
+            state.recipe.margin,
+            None if w_is_fp8 else meta.w.state,
+            meta.w.idx,
+            state.recipe.margin,
+        )
+        meta.x.advance()
+        if not w_is_fp8:
+            meta.w.advance()
     return out
 
 
@@ -290,18 +314,29 @@ class _LinearFp8(torch.autograd.Function):
             sg = _dynamic_scale(g, ctx.recipe, fmt)
             sw = _dynamic_scale(w, ctx.recipe, fmt)
             sx = _dynamic_scale(x, ctx.recipe, fmt)
+            grad_x, grad_w, grad_b, amax_g = linear_backward_fp8(
+                g, x, w, list(ctx.needs_input_grad), sg, sw, sx, fmt
+            )
         else:
             meta = ctx.meta
             if not meta.g.initialized:
                 meta.g.seed(g, fmt)
-            sg, sw, sx = meta.g.scale, meta.w.scale, meta.x.scale
+            # The g quantize kernel finalizes the gradient's ring in-kernel.
-        masks = list(ctx.needs_input_grad)
             grad_x, grad_w, grad_b, amax_g = linear_backward_fp8(
-            g, x, w, masks, sg, sw, sx, fmt
+                g,
+                x,
+                w,
+                list(ctx.needs_input_grad),
+                meta.g.scale,
+                meta.w.scale,
+                meta.x.scale,
+                fmt,
+                meta.g.state,
+                meta.g.idx,
+                ctx.recipe.margin,
             )
-        if not ctx.is_dynamic:
+            meta.g.advance()
-            ctx.meta.g.update(amax_g, fmt)
+        return grad_x, grad_w, grad_b if ctx.needs_input_grad[2] else None
-        return grad_x, grad_w, grad_b if masks[2] else None
 
 
 # ---------------------------------------------------------------------------

astrai/extension/ops/fp8.py

@@ -138,7 +138,21 @@ def mm_fp8(
     return fp8_gemm(a, b, sa, sb, int(out_dtype == "e4m3"), out_scale)
 
 
-def linear_forward_fp8(x, w, bias, sx, sw, fmt: str = "e4m3", bias_scale=None):
+def linear_forward_fp8(
+    x,
+    w,
+    bias,
+    sx,
+    sw,
+    fmt: str = "e4m3",
+    bias_scale=None,
+    x_ring=None,
+    x_ring_idx: int = 0,
+    x_ring_margin: int = 0,
+    w_ring=None,
+    w_ring_idx: int = 0,
+    w_ring_margin: int = 0,
+):
     """Pure FP8 linear forward: quantize x/w to ``fmt``, pre-quantized GEMM.
 
     Returns ``(out, amax_x, amax_w)``. ``bias`` may be ``None``. For static
@@ -146,6 +160,10 @@ def linear_forward_fp8(x, w, bias, sx, sw, fmt: str = "e4m3", bias_scale=None):
     (produced by :func:`quantize_bf16` with their scales as ``sw`` /
     ``bias_scale``); a pre-quantized ``bias`` requires ``bias_scale``, and
     its ``amax_w`` comes back 0. The bias is fused into the GEMM epilogue.
+    ``x_ring`` / ``w_ring`` (delayed scaling) are ``[hist | scale | counter]``
+    float32 buffers the quantize kernels finalize in-kernel: the measured
+    amax lands in ``hist[idx]`` and the next step's scale is published on
+    device, replacing the eager hist/max/scale update chain.
     """
     fmt8 = _fmt_dtype(fmt)
     if x.dtype != torch.bfloat16 or w.dtype not in (torch.bfloat16, fmt8):
@@ -155,16 +173,42 @@ def linear_forward_fp8(x, w, bias, sx, sw, fmt: str = "e4m3", bias_scale=None):
     if bias is None:
         bias = torch.empty(0, device=x.device, dtype=x.dtype)
     return get_module("fp8_ops").linear_forward_fp8(
-        x, w, bias, sx, sw, _fmt_int(fmt), bias_scale
+        x,
+        w,
+        bias,
+        sx,
+        sw,
+        _fmt_int(fmt),
+        bias_scale,
+        x_ring,
+        x_ring_idx,
+        x_ring_margin,
+        w_ring,
+        w_ring_idx,
+        w_ring_margin,
     )
 
 
-def linear_backward_fp8(g, x, w, masks, sg, sw, sx, fmt: str = "e5m2"):
+def linear_backward_fp8(
+    g,
+    x,
+    w,
+    masks,
+    sg,
+    sw,
+    sx,
+    fmt: str = "e5m2",
+    g_ring=None,
+    g_ring_idx: int = 0,
+    g_ring_margin: int = 0,
+):
     """FP8 linear backward; returns ``(grad_input, grad_weight, grad_bias, amax_g)``.
 
     The gradient (and the transposed w/x operands) are quantized to ``fmt``
     (default E5M2 — larger dynamic range for gradients) and the two GEMMs run
     as FP8 tensor-core products sharing a single gradient quantization.
+    ``g_ring`` (delayed scaling) is a ``[hist | scale | counter]`` buffer the
+    g quantize kernel finalizes in-kernel (see :func:`linear_forward_fp8`).
     """
     if not (
         g.dtype == torch.bfloat16
@@ -175,5 +219,15 @@ def linear_backward_fp8(g, x, w, masks, sg, sw, sx, fmt: str = "e5m2"):
             f"fp8 backward requires bf16 inputs, got {g.dtype}/{x.dtype}/{w.dtype}"
         )
     return get_module("fp8_ops").linear_backward_fp8(
-        g, x, w, list(masks), sg, sw, sx, _fmt_int(fmt)
+        g,
+        x,
+        w,
+        list(masks),
+        sg,
+        sw,
+        sx,
+        _fmt_int(fmt),
+        g_ring,
+        g_ring_idx,
+        g_ring_margin,
     )

csrc/kernels/fp8/common.h

@@ -63,16 +63,47 @@ struct Fp8GemmTraits {
     static constexpr float kFp8Max = kIsE5M2 ? 57344.0f : 448.0f;
 };
 
+// Quantize-kernel parameter POD: BF16 -> FP8 with fused amax and optional
+// delayed-scaling ring finalization. Separate from FP8Params so each
+// operator owns exactly the fields it touches (the GEMM never reads amax /
+// ring state). Same NSDMI rationale: amax / ring_state gate optional paths
+// via null checks. Still an aggregate, still trivially copyable.
+struct FP8QuantizeParams {
+    // BF16 input and FP8 output buffers; scale_a is the quantization step
+    // (device scalar). amax_a (may be null) is zero-initialized by the
+    // binding and receives the raw-domain absolute maximum.
+    const void* __restrict__ a_ptr = nullptr;
+    void* __restrict__ out_ptr = nullptr;
+    const float* __restrict__ scale_a = nullptr;
+    float* __restrict__ amax_a = nullptr;
+
+    // Optional delayed-scaling ring finalization. ring_state packs
+    // [hist[ring_len] | scale | counter] with ring_len = numel - 2. When
+    // non-null and amax_a is set, the last-finishing block records the
+    // measured amax into hist[ring_idx], reduces the window and publishes
+    // the next step's scale (max(hist) / fp8_max / 2^ring_margin) — the
+    // fused replacement for the eager hist-write / max / scale-write chain,
+    // at zero extra launches. The counter slot is a persistent zero-armed
+    // int32 (float bits) electing the last block each launch.
+    float* ring_state = nullptr;
+    int ring_len = 0;
+    int ring_idx = 0;
+    int ring_margin = 0;
+
+    // Element count (only the elementwise quantize kernel uses it).
+    int total = 0;
+};
+
 // Unified GEMM parameter POD, mirroring AttentionParams: one struct flows
-// through quantize / fused / pre-quantized kernels. Each kernel touches only
+// through the pre-quantized GEMM kernels. Each kernel touches only the
-// the fields it needs; buffers are raw pointers packed by the torch binding.
+// fields it needs; buffers are raw pointers packed by the torch binding.
 // Pointer members default to null (same NSDMI rationale as AttentionParams:
-// bias / amax / out_scale gate optional paths via null checks, so a partially
+// bias / out_scale gate optional paths via null checks, so a partially
 // packed struct must never hold garbage non-null pointers). Still an
 // aggregate, still trivially copyable.
 struct FP8Params {
-    // Inputs: a/b are BF16 for the fused (quantize-in-GEMM) path, FP8 for
+    // Inputs: a/b are FP8 for the pre-quantized path. Scales are
-    // the pre-quantized path. Scales are quantization steps (device scalars).
+    // quantization steps (device scalars).
     const void* __restrict__ a_ptr = nullptr;
     const void* __restrict__ b_ptr = nullptr;
     const void* __restrict__ bias = nullptr;
@@ -84,23 +115,16 @@ struct FP8Params {
     void* __restrict__ out_ptr = nullptr;
     const float* __restrict__ out_scale = nullptr;
 
-    // Fused forward extras: bias (may be null) and amax slots (may be null).
+    // Shapes. `int` covers every realistic LLM shape; the kernels promote
-    float* __restrict__ amax_a = nullptr;
+    // to int64 for all pointer arithmetic.
-    float* __restrict__ amax_b = nullptr;
-
-    // Shapes. total is only used by the elementwise quantize kernel. `int`
-    // covers every realistic LLM shape; the kernels promote to int64 for all
-    // pointer arithmetic.
     int m, n, k;
 
-    // Physical leading dimensions (column count, i.e. row stride) of A and B.
+    // Physical leading dimensions (column count, i.e. row stride) of A and
-    // For a non-transposed operand the stride equals the contract dim; for a
+    // B. For a non-transposed operand the stride equals the contract dim;
-    // transposed operand it is the operand's own column count. The binding
+    // for a transposed operand it is the operand's own column count. The
-    // packs these so the kernel reads both buffers either naturally or
+    // binding packs these so the kernel reads both buffers either naturally
-    // transposed depending on the LayoutA/LayoutB tags (see gemm.cuh).
+    // or transposed depending on the LayoutA/LayoutB tags (see gemm.cuh).
     int a_ld, b_ld;
-
-    int total;
 };
 
 }  // namespace fp8

csrc/kernels/fp8/gemm.cuh

@@ -70,7 +70,7 @@ __device__ __forceinline__ unsigned quantize2(unsigned pair, float inv,
 }
 
 template <FP8Format Fmt>
-__global__ void fp8_quantize_kernel(FP8Params p) {
+__global__ void fp8_quantize_kernel(FP8QuantizeParams p) {
     const float inv = 1.0f / *p.scale_a;
     const auto* x = reinterpret_cast<const __nv_bfloat16*>(p.a_ptr);
     void* x8 = p.out_ptr;
@@ -122,6 +122,51 @@ __global__ void fp8_quantize_kernel(FP8Params p) {
             atomic_max_float(amax, v);
         }
     }
+    if (p.ring_state && amax) {
+        // Delayed-scaling ring finalization as a last-block epilogue (the
+        // CUDA threadFenceReduction pattern): the fence + counter elect the
+        // final block once every block's atomic_max above is visible; warp 0
+        // folds the fresh amax into the window, reduces it and publishes the
+        // next step's scale, then re-arms the counter for the next launch.
+        // __fdiv_rn / ldexpf keep the scale bit-identical to the eager
+        // (peak / fp8_max) / 2^margin fp32 chain despite --use_fast_math.
+        __threadfence();
+        __shared__ bool ring_last;
+        if (threadIdx.x == 0)
+            ring_last = atomicAdd(reinterpret_cast<int*>(p.ring_state +
+                                                        p.ring_len + 1),
+                                  1) == gridDim.x - 1;
+        __syncthreads();
+        if (ring_last && threadIdx.x < 32) {
+            float* hist = p.ring_state;
+            const int lane = threadIdx.x;
+            float v = 0.0f;
+            if (lane < p.ring_len) v = hist[lane];
+            if (lane == p.ring_idx) {
+                v = *amax;  // the global amax is final now
+                hist[lane] = v;
+            }
+            // Windows longer than one warp (atypical) fold the tail.
+            for (int i = lane + 32; i < p.ring_len; i += 32) {
+                float h = hist[i];
+                if (i == p.ring_idx) {
+                    h = *amax;
+                    hist[i] = h;
+                }
+                v = fmaxf(v, h);
+            }
+            const float peak = warp_reduce_max(v);
+            if (lane == 0) {
+                constexpr float kFmtMax =
+                    Fmt == FP8Format::E5M2 ? 57344.0f : 448.0f;
+                p.ring_state[p.ring_len] = fmaxf(
+                    ldexpf(__fdiv_rn(peak, kFmtMax), -p.ring_margin), 1e-12f);
+                __threadfence();
+                // Re-arm the counter (0.0f bits == int32 0).
+                p.ring_state[p.ring_len + 1] = 0.0f;
+            }
+        }
+    }
 }
 
 // Swizzled address inside a flat [rows * K] staging tile: the 16-byte chunk
@@ -539,7 +584,7 @@ __global__ void
 // ---------------------------------------------------------------------------
 
 template <FP8Format Fmt>
-void launch_fp8_quantize(const FP8Params& p, cudaStream_t stream) {
+void launch_fp8_quantize(const FP8QuantizeParams& p, cudaStream_t stream) {
     constexpr int kThreads = 256;
     // One block per 256 vectors (8 elements each); at least one block so the
     // scalar tail of a tiny / misaligned tensor is still covered.

csrc/kernels/fp8/ops.cu

@@ -71,30 +71,34 @@ void pack_gemm_params(FP8Params& p, const void* a, const void* b, void* out,
     p.out_scale = out_scale ? out_scale->data_ptr<float>() : nullptr;
     p.bias = bias;
     p.bias_scale = bias_scale ? bias_scale->data_ptr<float>() : nullptr;
-    p.amax_a = nullptr;
-    p.amax_b = nullptr;
     p.m = static_cast<int>(m);
     p.n = static_cast<int>(n);
     p.k = static_cast<int>(k);
     p.a_ld = static_cast<int>(a_ld);
     p.b_ld = static_cast<int>(b_ld);
-    p.total = 0;
 }
 
-void pack_quantize_params(FP8Params& p, const void* x, void* x8,
+// Pack the quantize params, optionally wiring the delayed-scaling ring.
+// ring (may be null) packs [hist[len] | scale | counter]; len/margin come
+// from the active recipe and idx is the caller's slot for this step.
+void pack_quantize_params(FP8QuantizeParams& p, const void* x, void* x8,
                           const torch::Tensor& scale, torch::Tensor* amax,
-                          int64_t total) {
+                          const torch::Tensor* ring, int64_t ring_idx,
+                          int64_t ring_margin, int64_t total) {
     p.a_ptr = x;
-    p.b_ptr = nullptr;
     p.out_ptr = x8;
     p.scale_a = scale.data_ptr<float>();
-    p.scale_b = nullptr;
-    p.out_scale = nullptr;
-    p.bias = nullptr;
     p.amax_a = amax ? amax->data_ptr<float>() : nullptr;
-    p.amax_b = nullptr;
+    if (ring && ring->defined()) {
-    p.m = p.n = p.k = 0;
+        TORCH_CHECK(ring->is_cuda() && ring->scalar_type() == torch::kFloat32 &&
-    p.a_ld = p.b_ld = 0;
+                        ring->numel() >= 3 && ring->is_contiguous(),
+                    "ring must be a contiguous CUDA float32 tensor packing "
+                    "[hist | scale | counter]");
+        p.ring_state = ring->data_ptr<float>();
+        p.ring_len = static_cast<int>(ring->numel() - 2);
+        p.ring_idx = static_cast<int>(ring_idx);
+        p.ring_margin = static_cast<int>(ring_margin);
+    }
     p.total = static_cast<int>(total);
 }
 
@@ -154,9 +158,9 @@ std::tuple<torch::Tensor, torch::Tensor> quantize_bf16(torch::Tensor x,
         x_c, x_c.options().dtype(fmt ? torch::kFloat8_e5m2
                                       : torch::kFloat8_e4m3fn));
     auto amax = torch::zeros({1}, x_c.options().dtype(torch::kFloat32));
-    FP8Params p;
+    FP8QuantizeParams p;
     pack_quantize_params(p, x_c.data_ptr(), x8.data_ptr(), scale, &amax,
-                         x_c.numel());
+                         nullptr, 0, 0, x_c.numel());
     if (fmt) {
         launch_fp8_quantize<FP8Format::E5M2>(p, stream.stream());
     } else {
@@ -228,14 +232,19 @@ torch::Tensor mm_fp8(torch::Tensor a, torch::Tensor b, torch::Tensor sa,
 
 std::tuple<torch::Tensor, torch::Tensor, torch::Tensor> linear_forward_fp8(
     torch::Tensor x, torch::Tensor w, torch::Tensor bias, torch::Tensor sx,
-    torch::Tensor sw, int64_t fmt,
+    torch::Tensor sw, int64_t fmt, c10::optional<torch::Tensor> bias_scale,
-    c10::optional<torch::Tensor> bias_scale) {
+    c10::optional<torch::Tensor> x_ring, int64_t x_ring_idx,
+    int64_t x_ring_margin, c10::optional<torch::Tensor> w_ring,
+    int64_t w_ring_idx, int64_t w_ring_margin) {
     // Pure FP8 forward: quantize x/w (fmt: 0 = E4M3, 1 = E5M2), then the
     // pre-quantized GEMM; the dequantized BF16 output gets the bias added.
     // amax_x / amax_w come from the quantize kernels (zero-initialized here;
     // a pre-quantized w reports amax_w = 0 — nothing to feed a delayed ring).
     // w may itself be pre-quantized fp8 storage matching fmt (static
     // inference weights): the weight quantize is skipped, amax_w stays 0.
+    // When x_ring / w_ring are given (delayed scaling), the quantize kernels
+    // finalize them in-kernel: the returned amax is already folded into the
+    // ring window and the next step's scale is published on device.
     TORCH_CHECK(x.is_cuda() && w.is_cuda(), "CUDA tensors required");
     const auto f8opt = fmt ? torch::kFloat8_e5m2 : torch::kFloat8_e4m3fn;
     const bool w_prequant = w.scalar_type() == f8opt;
@@ -272,9 +281,12 @@ std::tuple<torch::Tensor, torch::Tensor, torch::Tensor> linear_forward_fp8(
     auto out = torch::empty({m, n}, x_c.options());
 
     auto quantize = [&](const torch::Tensor& src, torch::Tensor& dst,
-                        const torch::Tensor& scale, torch::Tensor* amax) {
+                        const torch::Tensor& scale, torch::Tensor* amax,
-        FP8Params qp;
+                        const c10::optional<torch::Tensor>& ring,
+                        int64_t ring_idx, int64_t ring_margin) {
+        FP8QuantizeParams qp;
         pack_quantize_params(qp, src.data_ptr(), dst.data_ptr(), scale, amax,
+                             ring ? &*ring : nullptr, ring_idx, ring_margin,
                              src.numel());
         if (fmt) {
             launch_fp8_quantize<FP8Format::E5M2>(qp, stream.stream());
@@ -282,13 +294,14 @@ std::tuple<torch::Tensor, torch::Tensor, torch::Tensor> linear_forward_fp8(
             launch_fp8_quantize<FP8Format::E4M3>(qp, stream.stream());
         }
     };
-    quantize(x_c, x8, sx, &amax_x);
+    quantize(x_c, x8, sx, &amax_x, x_ring, x_ring_idx, x_ring_margin);
     // Static inference weights arrive pre-quantized (w8 storage + its scale);
     // only freshly-loaded bf16 weights quantize here.
     torch::Tensor w8 = w_prequant
                            ? w_c
                            : torch::empty({n, k}, x_c.options().dtype(f8opt));
-    if (!w_prequant) quantize(w_c, w8, sw, &amax_w);
+    if (!w_prequant)
+        quantize(w_c, w8, sw, &amax_w, w_ring, w_ring_idx, w_ring_margin);
 
     FP8Params p;
     // Forward is the NT layout: A = x8 [M,K] (a_ld = k), B = w8 [N,K]
@@ -315,10 +328,16 @@ std::tuple<torch::Tensor, torch::Tensor, torch::Tensor> linear_forward_fp8(
 std::tuple<torch::Tensor, torch::Tensor, torch::Tensor, torch::Tensor>
 linear_backward_fp8(torch::Tensor g, torch::Tensor x, torch::Tensor w,
                     std::vector<int64_t> masks, torch::Tensor sg,
-                    torch::Tensor sw, torch::Tensor sx, int64_t fmt) {
+                    torch::Tensor sw, torch::Tensor sx, int64_t fmt,
+                    c10::optional<torch::Tensor> g_ring, int64_t g_ring_idx,
+                    int64_t g_ring_margin) {
     // Pre-quantized FP8 backward: grad is quantized once (E4M3 or E5M2 per
     // `fmt`), then dX / dW run as FP8 tensor-core GEMMs sharing g8.
-    // Returns (grad_input, grad_weight, grad_bias, amax_g).
+    // Returns (grad_input, grad_weight, grad_bias, amax_g). With g_ring
+    // (delayed scaling), the g quantize kernel finalizes the ring in-kernel
+    // (amax folded into the window, next step's scale published on device);
+    // the w/x quantizes for dX / dW never touch rings — each operand's ring
+    // is finalized exactly once per step (by the forward or this kernel).
     TORCH_CHECK(g.is_cuda() && x.is_cuda() && w.is_cuda(), "CUDA tensors required");
     TORCH_CHECK(g.scalar_type() == torch::kBFloat16 &&
                     x.scalar_type() == torch::kBFloat16 &&
@@ -346,9 +365,12 @@ linear_backward_fp8(torch::Tensor g, torch::Tensor x, torch::Tensor w,
                      : g.options().dtype(torch::kFloat8_e4m3fn);
 
     auto quantize = [&](const torch::Tensor& src, torch::Tensor& dst,
-                        const torch::Tensor& scale, torch::Tensor* amax) {
+                        const torch::Tensor& scale, torch::Tensor* amax,
-        FP8Params qp;
+                        const c10::optional<torch::Tensor>& ring,
+                        int64_t ring_idx, int64_t ring_margin) {
+        FP8QuantizeParams qp;
         pack_quantize_params(qp, src.data_ptr(), dst.data_ptr(), scale, amax,
+                             ring ? &*ring : nullptr, ring_idx, ring_margin,
                              src.numel());
         if (fmt) {
             launch_fp8_quantize<FP8Format::E5M2>(qp, stream.stream());
@@ -375,13 +397,13 @@ linear_backward_fp8(torch::Tensor g, torch::Tensor x, torch::Tensor w,
     torch::Tensor g8;
     if (masks[0] || masks[1]) {
         g8 = torch::empty({m, n}, f8opt);
-        quantize(g_c, g8, sg, &amax_g);
+        quantize(g_c, g8, sg, &amax_g, g_ring, g_ring_idx, g_ring_margin);
     }
     // dX = g @ w: A = g8 [M,N] (contract over N), B = w8 [N,K] read transposed
     // (b[p*b_ld + n] = w[p,n]); out = [M,K], a_ld = N, b_ld = K, contract = N.
     if (masks[0]) {
         auto w8 = torch::empty({n, k}, f8opt);
-        quantize(w_c, w8, sw, nullptr);
+        quantize(w_c, w8, sw, nullptr, c10::nullopt, 0, 0);
         auto grad_input_2d = grad_input.reshape({m, k});
         FP8Params gp;
         pack_gemm_params(gp, g8.data_ptr(), w8.data_ptr(),
@@ -394,7 +416,7 @@ linear_backward_fp8(torch::Tensor g, torch::Tensor x, torch::Tensor w,
     // b_ld = K, contract = M.
     if (masks[1]) {
         auto x8 = torch::empty({m, k}, f8opt);
-        quantize(x_c, x8, sx, nullptr);
+        quantize(x_c, x8, sx, nullptr, c10::nullopt, 0, 0);
         FP8Params gp;
         pack_gemm_params(gp, g8.data_ptr(), x8.data_ptr(),
                          grad_weight.data_ptr(), sg, sx, nullptr, nullptr,
@@ -423,12 +445,21 @@ PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
     m.def("linear_forward_fp8", &linear_forward_fp8, py::arg("x"),
           py::arg("w"), py::arg("bias"), py::arg("sx"), py::arg("sw"),
           py::arg("fmt") = 0, py::arg("bias_scale") = py::none(),
+          py::arg("x_ring") = py::none(), py::arg("x_ring_idx") = 0,
+          py::arg("x_ring_margin") = 0, py::arg("w_ring") = py::none(),
+          py::arg("w_ring_idx") = 0, py::arg("w_ring_margin") = 0,
           "Pure FP8 linear forward: quantize x/w, pre-quantized GEMM with the "
           "bias fused into the epilogue; w and bias may be pre-quantized fp8 "
           "matching fmt (static inference path; fp8 bias requires bias_scale);"
-          " returns (out, amax_x, amax_w)");
+          " x_ring/w_ring optionally finalize a delayed-scaling ring "
+          "([hist | scale | counter] float32 buffer) in-kernel; returns "
+          "(out, amax_x, amax_w)");
     m.def("linear_backward_fp8", &linear_backward_fp8, py::arg("g"),
           py::arg("x"), py::arg("w"), py::arg("masks"), py::arg("sg"),
           py::arg("sw"), py::arg("sx"), py::arg("fmt"),
-          "FP8 linear backward; returns (grad_input, grad_weight, grad_bias, amax_g)");
+          py::arg("g_ring") = py::none(), py::arg("g_ring_idx") = 0,
+          py::arg("g_ring_margin") = 0,
+          "FP8 linear backward; g_ring optionally finalizes the gradient's "
+          "delayed-scaling ring in-kernel; returns (grad_input, grad_weight, "
+          "grad_bias, amax_g)");
 }

tests/extension/test_fp8_mma.py

@@ -86,6 +86,51 @@ def test_quantize_bf16_e5m2_format():
     torch.testing.assert_close(amax, x.abs().amax().float().reshape(1))
 
 
+@skip_no_fp8
+def test_quantize_ring_in_kernel_finalize():
+    """The quantize kernel finalizes the delayed-scaling ring in-kernel: the
+    measured amax lands in hist[idx], the window reduces to the next step's
+    scale on device, and the counter re-arms for the next launch."""
+    from astrai.extension.fp8 import _ScaleRing
+
+    torch.manual_seed(21)
+    dev = torch.device("cuda")
+    ring = _ScaleRing(dev, DelayedScaling(history_len=4, margin=0))
+    x0 = torch.randn(256, 256, device=dev, dtype=torch.bfloat16)
+    w = torch.randn(256, 256, device=dev, dtype=torch.bfloat16)
+    sw = torch.tensor([1.0], device=dev)
+    ring.seed(x0, "e4m3")
+    hist0 = ring.hist.clone()
+
+    # Step over three fresh tensors: each launch folds its amax into
+    # hist[idx] and publishes max(hist)/448 as the next scale.
+    idx = 0
+    for _ in range(3):
+        x = torch.randn(256, 256, device=dev, dtype=torch.bfloat16) * (2.0 + 4.0 * _)
+        _ = linear_forward_fp8(
+            x,
+            w,
+            None,
+            ring.scale,
+            sw,
+            "e4m3",
+            None,
+            ring.state,
+            idx,
+            0,
+        )
+        torch.cuda.synchronize()
+        expected_hist = hist0.clone()
+        expected_hist[idx] = x.abs().amax().float()
+        torch.testing.assert_close(ring.hist, expected_hist)
+        expected_scale = (expected_hist.max() / 448.0).reshape(1)
+        torch.testing.assert_close(ring.scale, expected_scale, rtol=1e-6, atol=1e-12)
+        # counter re-armed to int32 zero
+        assert ring.state[-1].view(torch.int32).item() == 0
+        hist0 = expected_hist.clone()
+        idx = (idx + 1) % 4
+
+
 @skip_no_fp8
 def test_fp8_linear_forward_and_backward():
     torch.manual_seed(7)
@@ -196,9 +241,9 @@ def test_fp8_linear_backward_outside_autocast():
     calls = {"bwd": 0}
     orig = f8mod.linear_backward_fp8
 
-    def spy(g, xx, ww, masks, sg, sw, sx, fmt="e5m2"):
+    def spy(*args, **kwargs):
         calls["bwd"] += 1
-        return orig(g, xx, ww, masks, sg, sw, sx, fmt)
+        return orig(*args, **kwargs)
 
     f8mod.linear_backward_fp8 = spy
     try:
@@ -331,14 +376,20 @@ def test_fp8_autocast_context():
 
 
 def test_fp8_tensor_meta_delayed_update():
-    """Meta seeds from data and refreshes the scale from the amax ring."""
+    """Meta seeds from data; hist/scale are packed views of one state buffer."""
     meta = FP8TensorMeta(torch.device("cpu"), DelayedScaling(history_len=4, margin=0))
     w = torch.randn(8, 8)
     meta.w.seed(w, "e4m3")
     assert meta.w.initialized
     torch.testing.assert_close(meta.w.scale, (w.abs().amax() / 448.0).reshape(1))
-    meta.w.update(torch.tensor([4.0]), "e4m3")
+    # [hist | scale | counter] packing: views alias the single state buffer.
-    torch.testing.assert_close(meta.w.scale, torch.tensor(4.0 / 448.0).reshape(1))
+    assert meta.w.state.numel() == 4 + 2
+    assert meta.w.hist.data_ptr() == meta.w.state.data_ptr()
+    assert meta.w.scale.data_ptr() == meta.w.state[4:].data_ptr()
+    # counter slot stays int32-zero (float bits) between launches
+    assert meta.w.state[-1].view(torch.int32).item() == 0
+    meta.w.advance()
+    assert meta.w.idx == 1
 
 
 def test_quantize_bf16_cpu_fallback():