ViperEkura
7da1439c9e
- linear_forward_fp8 accepts pre-quantized w8 (matching fmt) and skips the weight quantize; amax_w returns 0 on that path since no bf16 values are seen - bias is now fused into the GEMM epilogue for both dtypes, replacing the separate torch-level add (one elementwise kernel per linear removed) - FP8Params.bias becomes void* with a new bias_scale slot: null scale = raw bf16 bias, non-null = fp8 storage dequantized in the epilogue after the operand scaling and before any output quantization - ops/fp8.py relaxes the w dtype check to bf16-or-fp8 and passes bias_scale through - regression test covers w8/b8, w8/bf16-bias and the amax_w = 0 contract vs an explicit quantization reference
108 lines
4.3 KiB
C++
108 lines
4.3 KiB
C++
#pragma once
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#include <cuda_bf16.h>
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#include <cuda_fp8.h>
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#include <cuda_runtime.h>
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#include <cstdint>
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// Pure POD/traits header — no .cuh/CUDA-kernel includes; raw __nv_* type
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// spellings only.
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namespace astrai {
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namespace fp8 {
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// Compile-time FP8 format: E4M3 (forward / high precision, max 448) or
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// E5M2 (gradient / large dynamic range, max 57344).
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enum class FP8Format : int {
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E4M3 = 0,
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E5M2 = 1,
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};
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// Operand memory layouts as types (CUTLASS-style tags). The tag names the
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// storage order of the raw buffer relative to the operand's canonical GEMM
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// matrix — A is [M][K], B is [K][N]:
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// A RowMajor = [M][K] storage (K-contiguous rows; the default)
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// A ColMajor = [K][M] storage (M-contiguous; A^T)
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// B RowMajor = [K][N] storage (N-contiguous; the plain a @ b operand)
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// B ColMajor = [N][K] storage (K-contiguous; the nn.Linear weight layout)
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// Empty tags: selection happens by type at compile time (see load_operand_tile).
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struct RowMajor {};
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struct ColMajor {};
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// Transpose of a layout tag: the same buffer with the rows and contract dims
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// swapped. B's tag is relative to the canonical [K][N] GEMM matrix, so the
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// stage-load (which views any operand as [rows][contract]) sees the transposed
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// tag — this trait makes that inversion explicit.
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template <typename Layout>
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struct transpose_layout;
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template <>
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struct transpose_layout<RowMajor> {
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using type = ColMajor;
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};
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template <>
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struct transpose_layout<ColMajor> {
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using type = RowMajor;
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};
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template <typename Layout>
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using transpose_layout_t = typename transpose_layout<Layout>::type;
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// Compile-time tile configuration, mirroring KernelTraits<HEAD_DIM, BC,
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// WARPS, STAGES> in the attention kernels. `Fmt` selects the FP8 conversion
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// and the MMA PTX mnemonic; the remaining parameters shape the CTA tile and
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// the cp.async pipeline depth.
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template <FP8Format Fmt, int BlockM, int BlockN, int K, int Stages>
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struct Fp8GemmTraits {
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static constexpr FP8Format kFormat = Fmt;
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static constexpr int kBlockM = BlockM;
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static constexpr int kBlockN = BlockN;
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static constexpr int kK = K;
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static constexpr int kStages = Stages;
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static constexpr bool kIsE5M2 = (Fmt == FP8Format::E5M2);
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static constexpr __nv_fp8_interpretation_t kNvFormat =
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kIsE5M2 ? __NV_E5M2 : __NV_E4M3;
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static constexpr float kFp8Max = kIsE5M2 ? 57344.0f : 448.0f;
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};
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// Unified GEMM parameter POD, mirroring AttentionParams: one struct flows
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// through quantize / fused / pre-quantized kernels. Each kernel touches only
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// the fields it needs; buffers are raw pointers packed by the torch binding.
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// Pointer members default to null (same NSDMI rationale as AttentionParams:
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// bias / amax / out_scale gate optional paths via null checks, so a partially
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// packed struct must never hold garbage non-null pointers). Still an
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// aggregate, still trivially copyable.
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struct FP8Params {
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// Inputs: a/b are BF16 for the fused (quantize-in-GEMM) path, FP8 for
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// the pre-quantized path. Scales are quantization steps (device scalars).
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const void* __restrict__ a_ptr = nullptr;
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const void* __restrict__ b_ptr = nullptr;
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const void* __restrict__ bias = nullptr;
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const float* __restrict__ scale_a = nullptr;
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const float* __restrict__ scale_b = nullptr;
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const float* __restrict__ bias_scale = nullptr;
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// Output: BF16 or FP8 (E4M3). out_scale is the output quantization step
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// (FP8 output only).
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void* __restrict__ out_ptr = nullptr;
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const float* __restrict__ out_scale = nullptr;
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// Fused forward extras: bias (may be null) and amax slots (may be null).
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float* __restrict__ amax_a = nullptr;
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float* __restrict__ amax_b = nullptr;
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// Shapes. total is only used by the elementwise quantize kernel. `int`
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// covers every realistic LLM shape; the kernels promote to int64 for all
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// pointer arithmetic.
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int m, n, k;
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// Physical leading dimensions (column count, i.e. row stride) of A and B.
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// For a non-transposed operand the stride equals the contract dim; for a
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// transposed operand it is the operand's own column count. The binding
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// packs these so the kernel reads both buffers either naturally or
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// transposed depending on the LayoutA/LayoutB tags (see gemm.cuh).
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int a_ld, b_ld;
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int total;
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};
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} // namespace fp8
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} // namespace astrai
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