459 lines
21 KiB
C++
459 lines
21 KiB
C++
// ═══════════════════════════════════════════════════════════════════════════════
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// INFERENCE-X — Hardware Kernel Dispatch (Central Routing)
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// Copyright (C) 2024-2026 Salka Elmadani. All rights reserved.
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// Licensed under the Business Source License 1.1 (BSL-1.1)
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// See LICENSE file for full terms.
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//
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// INTELLECTUAL PROPERTY PROTECTION:
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// - INPI eSoleau deposit: 7phf-Ueye-2nWr-Vsgu (16/02/2026)
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// - GitHub: git.inference-x.com/salka/inference-x
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// - Author: Salka Elmadani | Morocco | Morocco
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//
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// MANUFACTURER NOTICE: Any manufacturer, company, or entity that
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// incorporates, embeds, distributes, or commercially uses Inference-X
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// or any derivative work without explicit written authorization from
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// the copyright holder is in violation of BSL-1.1 and applicable
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// intellectual property laws. This includes but is not limited to:
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// hardware vendors, cloud providers, SaaS platforms, and OEMs.
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//
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// Contact: Elmadani.SALKA@proton.me for licensing.
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// ═══════════════════════════════════════════════════════════════════════════════
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#pragma once
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// Inference-X Provenance — this engine was created by Salka Elmadani
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// Unauthorized commercial use (revenue >= $1M) requires licensing
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__attribute__((unused)) static const char* ix_provenance() { return "Inference-X | Salka Elmadani | BSL-1.1 | 935"; }
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#include "backends.h" // ix::Platform, ix::HWProfile, ix::detect_hardware()
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#include "gemm.h" // ix::gemm::matmul (proven v6 — ran Kimi K2.5 1T)
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#include "expert_mmap.h" // ix::ExpertMmapManager
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// ═══════════════════════════════════════════════════════════════════════════════
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// BACKEND DECLARATIONS (conditional)
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//
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// Each backend is a .c/.cpp file under backends/q4_kernels/<platform>/
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// It compiles ONLY when the Makefile detects its SDK (sets IX_USE_*).
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// Without the SDK → the #ifdef is dead, zero code emitted, zero link error.
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//
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// Contract: every backend implements Q4_K GEMM as
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// void gemm_q4_K_<platform>(A, B, C, M, N, K [, stream])
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// This dispatch calls them with N=1 (GEMV: out[M] = W[M×K] × x[K]).
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// ═══════════════════════════════════════════════════════════════════════════════
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// ── Clean-signature backends (no stream) ─────────────────────────────────────
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#ifdef IX_USE_CPU_AVX512
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extern "C" void gemm_q4_K_fp32_cpu(
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const block_q4_K* __restrict__ A, const float* __restrict__ B,
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float* __restrict__ C, int M, int N, int K);
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#endif
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#ifdef IX_USE_HEXAGON
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extern "C" void gemm_q4_K_hexagon(
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const block_q4_K* A, const float* B, float* C, int M, int N, int K);
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#endif
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#ifdef IX_USE_CEREBRAS
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extern "C" void gemm_q4_K_wse(
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const block_q4_K* A, const float* B, float* C, int M, int N, int K);
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#endif
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#ifdef IX_USE_SAMBANOVA
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extern "C" void gemm_q4_K_sambanova(
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const block_q4_K* A, const float* B, float* C, int M, int N, int K);
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#endif
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#ifdef IX_USE_GRAPHCORE
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extern "C" void gemm_q4_K_ipu(
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const block_q4_K* A, const float* B, float* C, int M, int N, int K);
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#endif
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#ifdef IX_USE_FPGA_XILINX
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extern "C" void gemm_q4_K_xilinx(
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const block_q4_K* A, const float* B, float* C, int M, int N, int K);
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#endif
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// ── Stream-based backends (need runtime context) ─────────────────────────────
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#ifdef IX_USE_GROQ
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#include <groq/groq_runtime.h>
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extern "C" void gemm_q4_K_groq(
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const void* A, const void* B, void* C, int M, int N, int K,
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groq_stream_t stream);
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#endif
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#ifdef IX_USE_GAUDI
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#include <synapse_api.h>
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extern "C" void gemm_q4_K_gaudi(
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const block_q4_K* A, const float* B, float* C, int M, int N, int K,
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synStreamHandle stream);
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#endif
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#ifdef IX_USE_INFERENTIA
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extern "C" void gemm_q4_K_aws_inferentia(
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const void* A, const void* B, void* C, int M, int N, int K,
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void* stream);
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#endif
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#ifdef IX_USE_MAIA
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#include <maia_runtime.h>
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extern "C" void gemm_q4_K_maia(
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const block_q4_K* A, const float* B, float* C, int M, int N, int K,
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maia_stream_t stream);
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#endif
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// ── Snapdragon: hybrid NEON+Hexagon DSP path ────────────────────────────────
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#ifdef IX_USE_SNAPDRAGON
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extern "C" void gemm_q4_K_hexagon_fused(
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const block_q4_K* A, const float* B, float* C, int M, int N, int K);
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#endif
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namespace ix {
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// ─── Backend enum ────────────────────────────────────────────────────────────
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enum class KernelBackend {
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GENERIC, // v6 gemm.h — proven on Kimi K2.5 1T (226GB, 17GB RAM)
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CPU_AVX512, // backends/q4_kernels/cpu
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ARM_NEON, // backends.h NEON intrinsics
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HEXAGON_HVX, // backends/q4_kernels/hexagon
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SNAPDRAGON_HYBRID, // backends/q4_kernels/snapdragon
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CEREBRAS_WSE, // backends/q4_kernels/cerebras
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TPU_XLA, // backends/q4_kernels/tpu (Python — needs bridge)
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GAUDI_HABANA, // backends/q4_kernels/gaudi
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INFERENTIA_AWS, // backends/q4_kernels/inferentia
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FPGA_XILINX, // backends/q4_kernels/fpga_xilinx
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GRAPHCORE_IPU, // backends/q4_kernels/graphcore
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SAMBANOVA_RDU, // backends/q4_kernels/sambanova
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MAIA_AZURE, // backends/q4_kernels/maia
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GROQ_LPU, // backends/q4_kernels/groq
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};
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// ═══════════════════════════════════════════════════════════════════════════════
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// KERNEL DISPATCH — Singleton
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//
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// init() once at startup.
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// After that, every matmul() call auto-routes to the optimal kernel.
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// If the selected backend SDK wasn't compiled in, falls through to generic.
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// No crash, no undefined symbol, no runtime check. Compiler eliminates it.
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// ═══════════════════════════════════════════════════════════════════════════════
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class KernelDispatch {
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public:
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static KernelDispatch& instance() {
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static KernelDispatch kd;
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return kd;
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}
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// ─── STARTUP ─────────────────────────────────────────────────────────
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void init() {
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hw_ = detect_hardware();
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select_backend();
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init_streams();
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print_hw_report(hw_);
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printf("[IX-DISPATCH] Kernel backend: %s\n", backend_name());
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fflush(stdout);
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initialized_ = true;
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}
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// Init ExpertMmap for MoE weight streaming
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void init_expert_mmap(int n_layers) {
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emm_.init(n_layers);
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use_expert_mmap_ = true;
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printf("[IX-DISPATCH] ExpertMmap enabled: %d layers\n", n_layers);
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fflush(stdout);
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}
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// Register expert tensors for a layer (call during model load)
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void register_experts(int layer,
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void* gate_data, size_t gate_expert_bytes, int n_experts,
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void* up_data, size_t up_expert_bytes,
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void* down_data, size_t down_expert_bytes) {
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if (!use_expert_mmap_) return;
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emm_.register_layer(layer,
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gate_data, gate_expert_bytes, n_experts,
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up_data, up_expert_bytes,
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down_data, down_expert_bytes);
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}
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// ═════════════════════════════════════════════════════════════════════
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// GEMM DISPATCH — the central weld
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//
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// Contract: out[M] = W[M×K quantized] × x[K]
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//
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// Specialized backends handle Q4_K only (the bottleneck format for
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// large MoE models). All other dtypes go through the proven v6 path
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// which already handles Q4_K, Q6_K, Q8_0, IQ2_XXS, IQ4_XS, F16.
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//
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// If a backend's SDK wasn't compiled → its #ifdef is dead →
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// the case exists in the enum but the code inside is empty →
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// falls through to default → generic. Zero penalty.
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// ═════════════════════════════════════════════════════════════════════
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inline void matmul(float* out, const void* W, dtype type,
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const float* x, int M, int K) {
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// Only Q4_K has specialized backends. Everything else → proven v6.
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if (type != dtype::Q4_K) {
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gemm::matmul(out, W, type, x, M, K);
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return;
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}
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const auto* A __attribute__((unused)) = static_cast<const block_q4_K*>(W);
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switch (backend_) {
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// ── CPU: AVX-512 fused dequant+GEMM in zmm registers ────────
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#ifdef IX_USE_CPU_AVX512
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case KernelBackend::CPU_AVX512:
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gemm_q4_K_fp32_cpu(A, x, out, M, 1, K);
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return;
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#endif
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// ── ARM NEON: vectorized in backends.h ───────────────────────
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// Uses v6 gemm.h with NEON intrinsics already compiled in.
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// No separate backend file needed — it's in the generic path.
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case KernelBackend::ARM_NEON:
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break; // → generic (which IS NEON-optimized when compiled on ARM)
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// ── Qualcomm Hexagon: HVX vector DSP ─────────────────────────
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#ifdef IX_USE_HEXAGON
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case KernelBackend::HEXAGON_HVX:
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gemm_q4_K_hexagon(A, x, out, M, 1, K);
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return;
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#endif
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// ── Snapdragon SoC: hybrid NEON + Hexagon DSP ────────────────
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#ifdef IX_USE_SNAPDRAGON
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case KernelBackend::SNAPDRAGON_HYBRID:
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gemm_q4_K_hexagon_fused(A, x, out, M, 1, K);
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return;
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#endif
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// ── Cerebras WSE: 850K cores, weight-stationary dataflow ─────
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#ifdef IX_USE_CEREBRAS
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case KernelBackend::CEREBRAS_WSE:
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gemm_q4_K_wse(A, x, out, M, 1, K);
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return;
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#endif
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// ── Google TPU: XLA backend (Python) ─────────────────────────
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// TPU backend is q4_gemm_tpu.py (134 lines).
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// Requires pybind11 or subprocess bridge to wire in.
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// Falls through to generic until bridge is integrated.
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// This is the ONE backend that needs external glue.
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case KernelBackend::TPU_XLA:
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break; // → generic (TODO: pybind11 bridge)
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// ── Intel Gaudi: Habana TPC kernels ──────────────────────────
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#ifdef IX_USE_GAUDI
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case KernelBackend::GAUDI_HABANA:
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gemm_q4_K_gaudi(A, x, out, M, 1, K, gaudi_stream_);
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return;
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#endif
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// ── AWS Inferentia: NeuronCore pipeline ──────────────────────
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#ifdef IX_USE_INFERENTIA
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case KernelBackend::INFERENTIA_AWS:
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gemm_q4_K_aws_inferentia(A, x, out, M, 1, K, inferentia_stream_);
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return;
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#endif
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// ── Xilinx FPGA: Vitis HLS dataflow ─────────────────────────
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#ifdef IX_USE_FPGA_XILINX
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case KernelBackend::FPGA_XILINX:
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gemm_q4_K_xilinx(A, x, out, M, 1, K);
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return;
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#endif
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// ── Graphcore IPU: BSP tile compute ──────────────────────────
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#ifdef IX_USE_GRAPHCORE
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case KernelBackend::GRAPHCORE_IPU:
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gemm_q4_K_ipu(A, x, out, M, 1, K);
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return;
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#endif
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// ── SambaNova RDU: reconfigurable dataflow ───────────────────
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#ifdef IX_USE_SAMBANOVA
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case KernelBackend::SAMBANOVA_RDU:
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gemm_q4_K_sambanova(A, x, out, M, 1, K);
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return;
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#endif
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// ── Microsoft Maia: Azure custom ASIC ────────────────────────
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#ifdef IX_USE_MAIA
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case KernelBackend::MAIA_AZURE:
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gemm_q4_K_maia(A, x, out, M, 1, K, maia_stream_);
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return;
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#endif
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// ── Groq LPU: deterministic SRAM compute ────────────────────
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#ifdef IX_USE_GROQ
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case KernelBackend::GROQ_LPU:
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gemm_q4_K_groq(A, x, out, M, 1, K, groq_stream_);
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return;
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#endif
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default:
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break;
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}
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// ── Fallthrough: proven v6 generic path ──────────────────────
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// This ran Kimi K2.5 (1T params, 384 experts, 226GB) on 17GB RAM.
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// It works. Everything above is optimization.
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gemm::matmul(out, W, type, x, M, K);
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}
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// ─── MoE EXPERT PREFETCH ─────────────────────────────────────────────
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void prefetch_experts(int layer, const int* expert_ids, int n_active) {
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if (!use_expert_mmap_) return;
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emm_.prefetch_active(layer, expert_ids, n_active);
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}
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void evict_layer(int layer) {
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if (!use_expert_mmap_) return;
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emm_.evict_layer(layer);
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}
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void print_stats() {
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if (use_expert_mmap_) emm_.print_stats();
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}
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// ─── ACCESSORS ───────────────────────────────────────────────────────
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const HWProfile& hardware() const { return hw_; }
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KernelBackend backend_type() const { return backend_; }
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bool initialized() const { return initialized_; }
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const char* backend_name() const {
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switch (backend_) {
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case KernelBackend::GENERIC: return "GENERIC (v6 proven)";
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case KernelBackend::CPU_AVX512: return "CPU_AVX512";
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case KernelBackend::ARM_NEON: return "ARM_NEON";
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case KernelBackend::HEXAGON_HVX: return "HEXAGON_HVX";
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case KernelBackend::SNAPDRAGON_HYBRID: return "SNAPDRAGON_HYBRID";
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case KernelBackend::CEREBRAS_WSE: return "CEREBRAS_WSE";
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case KernelBackend::TPU_XLA: return "TPU_XLA (Python bridge)";
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case KernelBackend::GAUDI_HABANA: return "GAUDI_HABANA";
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case KernelBackend::INFERENTIA_AWS: return "INFERENTIA_AWS";
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case KernelBackend::FPGA_XILINX: return "FPGA_XILINX";
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case KernelBackend::GRAPHCORE_IPU: return "GRAPHCORE_IPU";
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case KernelBackend::SAMBANOVA_RDU: return "SAMBANOVA_RDU";
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case KernelBackend::MAIA_AZURE: return "MAIA_AZURE";
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case KernelBackend::GROQ_LPU: return "GROQ_LPU";
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default: return "UNKNOWN";
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}
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}
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private:
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KernelDispatch() = default;
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// ─── BACKEND SELECTION ───────────────────────────────────────────────
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// Maps detected Platform → optimal KernelBackend.
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// detect_hardware() in backends.h already resolved the Platform,
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// including IX_USE_* overrides for accelerators.
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// ─────────────────────────────────────────────────────────────────────
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void select_backend() {
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switch (hw_.platform) {
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// ── x86 ──────────────────────────────────────────────────
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case Platform::X86_AVX512:
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#ifdef IX_USE_CPU_AVX512
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backend_ = KernelBackend::CPU_AVX512; break;
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#else
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backend_ = KernelBackend::GENERIC; break; // AVX512 detected but backend not compiled
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#endif
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case Platform::X86_AVX2:
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case Platform::X86_SSE42:
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case Platform::X86_GENERIC:
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backend_ = KernelBackend::GENERIC; break;
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// ── ARM ──────────────────────────────────────────────────
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case Platform::ARM64_NEON:
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case Platform::ARM64_SVE:
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case Platform::ARM32_NEON:
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case Platform::APPLE_SILICON:
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backend_ = KernelBackend::ARM_NEON; break;
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// ── Mobile SoC ───────────────────────────────────────────
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case Platform::SNAPDRAGON:
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backend_ = KernelBackend::SNAPDRAGON_HYBRID; break;
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case Platform::MEDIATEK:
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case Platform::EXYNOS:
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backend_ = KernelBackend::ARM_NEON; break;
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// ── Cloud accelerators ───────────────────────────────────
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case Platform::TPU:
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backend_ = KernelBackend::TPU_XLA; break;
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case Platform::GAUDI:
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backend_ = KernelBackend::GAUDI_HABANA; break;
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case Platform::INFERENTIA:
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backend_ = KernelBackend::INFERENTIA_AWS; break;
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case Platform::CEREBRAS:
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backend_ = KernelBackend::CEREBRAS_WSE; break;
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case Platform::GROQ:
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backend_ = KernelBackend::GROQ_LPU; break;
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case Platform::GRAPHCORE:
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backend_ = KernelBackend::GRAPHCORE_IPU; break;
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case Platform::SAMBANOVA:
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backend_ = KernelBackend::SAMBANOVA_RDU; break;
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case Platform::MAIA:
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backend_ = KernelBackend::MAIA_AZURE; break;
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case Platform::FPGA_XILINX:
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backend_ = KernelBackend::FPGA_XILINX; break;
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case Platform::HEXAGON:
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backend_ = KernelBackend::HEXAGON_HVX; break;
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// ── Edge/Embedded → scalar generic ───────────────────────
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case Platform::RISCV:
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case Platform::XTENSA:
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case Platform::CORTEX_M:
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backend_ = KernelBackend::GENERIC; break;
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default:
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backend_ = KernelBackend::GENERIC; break;
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}
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}
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// ─── STREAM INIT ─────────────────────────────────────────────────────
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// Backends that need a stream/context get it here. Called once.
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// Without the SDK → empty function. Compiler eliminates it.
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// ─────────────────────────────────────────────────────────────────────
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void init_streams() {
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#ifdef IX_USE_GROQ
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if (backend_ == KernelBackend::GROQ_LPU)
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groq_create_stream(&groq_stream_);
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#endif
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#ifdef IX_USE_GAUDI
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if (backend_ == KernelBackend::GAUDI_HABANA)
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synStreamCreate(&gaudi_stream_, 0);
|
||
#endif
|
||
#ifdef IX_USE_MAIA
|
||
if (backend_ == KernelBackend::MAIA_AZURE)
|
||
maia_create_stream(&maia_stream_);
|
||
#endif
|
||
// Inferentia uses default stream (nullptr). No init needed.
|
||
}
|
||
|
||
// ─── STATE ───────────────────────────────────────────────────────────
|
||
HWProfile hw_{};
|
||
KernelBackend backend_ = KernelBackend::GENERIC;
|
||
ExpertMmapManager emm_;
|
||
bool use_expert_mmap_ = false;
|
||
bool initialized_ = false;
|
||
|
||
// Stream handles — only exist when SDK compiled in
|
||
#ifdef IX_USE_GROQ
|
||
groq_stream_t groq_stream_{};
|
||
#endif
|
||
#ifdef IX_USE_GAUDI
|
||
synStreamHandle gaudi_stream_{};
|
||
#endif
|
||
#ifdef IX_USE_INFERENTIA
|
||
void* inferentia_stream_ = nullptr;
|
||
#endif
|
||
#ifdef IX_USE_MAIA
|
||
maia_stream_t maia_stream_{};
|
||
#endif
|
||
};
|
||
|
||
} // namespace ix
|