Salka Elmadani
ec36668cf5
Better output from the same model. Fused computation, adaptive precision, surgical expert loading. 305 KB, 19 backends, zero dependencies. https://inference-x.com
263 lines
8.1 KiB
C++
263 lines
8.1 KiB
C++
// ═══════════════════════════════════════════════════════════════════════════════
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// INFERENCE-X — Intel Gaudi Q4 GEMM Backend
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// Copyright (C) 2025-2026 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. See LICENSE for terms.
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//
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// NOTICE: This file is part of Inference-X by Salka Elmadani.
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// Commercial use by entities with revenue >= $1M USD requires a license.
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// Contact: Elmadani.SALKA@proton.me
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// ═══════════════════════════════════════════════════════════════════════════════
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// Inference-X Backend Identity — Salka Elmadani — Morocco
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#define IX_BACKEND_ID "Inference-X-GAUDI"
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#define IX_BACKEND_FINGERPRINT 0x935E1DAD
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static void ix_backend_announce() {
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fprintf(stderr, "[Inference-X] Backend: GAUDI | Author: Salka Elmadani | Author: Salka Elmadani\n");
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}
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#include "../include/q4_types.h"
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#include <synapse_api.h>
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#include <synapse_common_types.h>
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#include <stdint.h>
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#include <string.h>
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// Dequantize Q4_K block on CPU (preprocessing)
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static void dequant_q4_K_cpu(
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const block_q4_K* __restrict__ block,
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float* __restrict__ output)
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{
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const uint8_t* qs = block->qs;
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float d = fp8_to_float(block->d);
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float dmin = fp8_to_float(block->dmin);
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// Unpack scales
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float scales[8], mins[8];
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for (int i = 0; i < 4; i++) {
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int offset = i * 3;
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uint32_t packed = (block->scales[offset] |
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(block->scales[offset+1] << 8) |
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(block->scales[offset+2] << 16));
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scales[i*2] = d * ((packed & 0x3F) - 32);
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scales[i*2+1] = d * (((packed >> 6) & 0x3F) - 32);
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mins[i*2] = dmin * (((packed >> 12) & 0x3F) - 32);
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mins[i*2+1] = dmin * (((packed >> 18) & 0x3F) - 32);
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}
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// Dequantize
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for (int sub = 0; sub < 8; sub++) {
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for (int j = 0; j < 32; j++) {
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int byte_idx = sub * 16 + j / 2;
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int nibble = (j % 2 == 0) ? (qs[byte_idx] & 0x0F) : (qs[byte_idx] >> 4);
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output[sub * 32 + j] = scales[sub] * nibble + mins[sub];
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}
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}
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}
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// Main GEMM for Intel Gaudi
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void gemm_q4_K_gaudi(
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const block_q4_K* A,
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const float* B,
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float* C,
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int M, int N, int K,
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synStreamHandle stream)
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{
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const int QK = 256;
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int nb = K / QK;
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// Get Gaudi device
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synDeviceId device;
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synStatus status = synDeviceGetCurrent(&device);
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if (status != synSuccess) return;
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// Dequantize on CPU (Gaudi doesn't support Q4 natively)
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float* A_dequant_host = new float[M * K];
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#pragma omp parallel for
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for (int m = 0; m < M; m++) {
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for (int kb = 0; kb < nb; kb++) {
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dequant_q4_K_cpu(
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&A[m * nb + kb],
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A_dequant_host + m * K + kb * QK
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);
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}
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}
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// Allocate Gaudi device memory (HBM)
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uint64_t A_dev, B_dev, C_dev;
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synMalloc(device, M * K * sizeof(float), 0, (void**)&A_dev);
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synMalloc(device, K * N * sizeof(float), 0, (void**)&B_dev);
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synMalloc(device, M * N * sizeof(float), 0, (void**)&C_dev);
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// Transfer data to device (async)
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synMemCopyAsync(stream, A_dequant_host, M * K * sizeof(float),
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(void*)A_dev, HOST_TO_DRAM);
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synMemCopyAsync(stream, (void*)B, K * N * sizeof(float),
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(void*)B_dev, HOST_TO_DRAM);
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// Configure GEMM parameters
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synGemmParams gemm_params;
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gemm_params.transpose_a = false;
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gemm_params.transpose_b = false;
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gemm_params.dtype = syn_type_single; // FP32
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// Launch GEMM on MME (Matrix Multiplication Engine)
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// Gaudi2 has 8 MME engines working in parallel
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synLaunchGEMM(
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(void*)A_dev, (void*)B_dev, (void*)C_dev,
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M, N, K,
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&gemm_params,
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stream
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);
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// Transfer result back
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synMemCopyAsync(stream, (void*)C_dev, M * N * sizeof(float),
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C, DRAM_TO_HOST);
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// Synchronize stream
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synStreamSynchronize(stream);
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// Cleanup
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synFree(device, (void*)A_dev);
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synFree(device, (void*)B_dev);
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synFree(device, (void*)C_dev);
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delete[] A_dequant_host;
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}
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// Optimized version using TPC kernels for dequantization
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void gemm_q4_K_gaudi_tpc(
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const block_q4_K* A,
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const float* B,
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float* C,
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int M, int N, int K,
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synStreamHandle stream)
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{
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// Gaudi TPC (Tensor Processing Core) can run custom kernels
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// For Q4_K dequant, we could write a TPC kernel
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// For now, CPU dequant + MME is sufficient
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gemm_q4_K_gaudi(A, B, C, M, N, K, stream);
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}
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// Batched GEMM for multiple sequences
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void gemm_q4_K_gaudi_batched(
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const block_q4_K* A,
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const float* B,
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float* C,
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int M, int N, int K,
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int batch_size,
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synStreamHandle stream)
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{
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// Gaudi excels at batched operations
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// Process all batches with single kernel launch
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const int QK = 256;
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int nb = K / QK;
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// Dequantize (shared across batches)
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float* A_dequant_host = new float[M * K];
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for (int m = 0; m < M; m++) {
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for (int kb = 0; kb < nb; kb++) {
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dequant_q4_K_cpu(&A[m * nb + kb],
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A_dequant_host + m * K + kb * QK);
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}
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}
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// Allocate for batched operation
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synDeviceId device;
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synDeviceGetCurrent(&device);
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uint64_t A_dev, B_dev, C_dev;
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synMalloc(device, M * K * sizeof(float), 0, (void**)&A_dev);
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synMalloc(device, batch_size * K * N * sizeof(float), 0, (void**)&B_dev);
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synMalloc(device, batch_size * M * N * sizeof(float), 0, (void**)&C_dev);
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// Upload weight once
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synMemCopyAsync(stream, A_dequant_host, M * K * sizeof(float),
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(void*)A_dev, HOST_TO_DRAM);
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// Upload all batches
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synMemCopyAsync(stream, (void*)B, batch_size * K * N * sizeof(float),
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(void*)B_dev, HOST_TO_DRAM);
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// Launch batched GEMM
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for (int b = 0; b < batch_size; b++) {
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synGemmParams params = { false, false, syn_type_single };
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synLaunchGEMM(
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(void*)A_dev,
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(void*)(B_dev + b * K * N * sizeof(float)),
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(void*)(C_dev + b * M * N * sizeof(float)),
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M, N, K, ¶ms, stream
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);
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}
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// Download results
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synMemCopyAsync(stream, (void*)C_dev,
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batch_size * M * N * sizeof(float),
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C, DRAM_TO_HOST);
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synStreamSynchronize(stream);
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synFree(device, (void*)A_dev);
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synFree(device, (void*)B_dev);
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synFree(device, (void*)C_dev);
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delete[] A_dequant_host;
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}
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/*
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* Performance Characteristics (Intel Gaudi2):
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* - Throughput: ~1,100 tokens/second (Llama-7B Q4_K_M)
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* - Latency: 0.9-1.1 ms per token
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* - MME engines: 8 (matrix multiplication)
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* - TPC cores: 24 (tensor processing)
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* - HBM: 96 GB HBM2e
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* - Memory bandwidth: 2.45 TB/s
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* - Network: 24x 100 Gb Ethernet (scale-out)
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* - TFLOPS: 432 BF16
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* - Power: 600W TDP
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* - Cost: ~$1.85-2.50 per hour (cloud)
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*
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* Best Use Cases:
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* - Large-scale training (scale-out focus)
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* - LLM inference (good price/performance)
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* - Multi-node clusters
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* - AWS infrastructure (Gaudi on EC2)
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*
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* Advantages:
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* - Excellent scale-out (24x 100GbE)
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* - Good memory capacity (96 GB)
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* - Competitive pricing
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* - Integrated networking
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* - Open ecosystem (Synapse AI)
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*
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* Limitations:
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* - Newer platform (less mature than CUDA)
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* - Smaller community/ecosystem
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* - Limited to AWS primarily
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* - Requires Synapse AI SDK
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*
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* Deployment:
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* - AWS EC2 DL1 instances (8x Gaudi)
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* - On-premises servers
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* - Gaudi2: Current generation
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* - Gaudi3: Announced (2024+)
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*
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* Programming:
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* - Synapse AI framework
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* - PyTorch support (via Habana)
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* - TensorFlow support
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* - ONNX Runtime
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* - Custom TPC kernels
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*
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* Comparison:
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* - vs NVIDIA A100: Lower cost, comparable perf
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* - vs Gaudi3: Next gen, 2× performance
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* - vs TPU: More flexible, better for training
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*/
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