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
299 lines
10 KiB
C
299 lines
10 KiB
C
// ═══════════════════════════════════════════════════════════════════════════════
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// INFERENCE-X — Cerebras WSE 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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#include "../include/q4_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 WSE core
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// Inference-X Backend Identity — Salka Elmadani — Morocco
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#define IX_BACKEND_ID "Inference-X-CEREBRAS_WSE"
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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: CEREBRAS_WSE | Author: Salka Elmadani | Author: Salka Elmadani\n");
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}
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static inline void dequant_q4_K_wse_core(
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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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// Convert FP8 to float
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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 (6-bit packed in 12 bytes)
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float scales[8];
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float 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 256 values (8 sub-blocks of 32)
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for (int sub = 0; sub < 8; sub++) {
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float scale = scales[sub];
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float min_val = mins[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] = scale * nibble + min_val;
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}
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}
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}
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// Cerebras WSE GEMM: Leverage massive parallelism
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// Each core handles one row, dataflow between cores
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void gemm_q4_K_wse(
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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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{
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const int QK = 256;
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int nb = K / QK; // Number of Q4_K blocks per row
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// Cerebras dataflow pragma: Map each row to separate core
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// WSE has 850,000 cores, can handle massive batch sizes
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#pragma cerebras dataflow
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#pragma cerebras map(cores, M)
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for (int m = 0; m < M; m++) {
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// Each core processes one output row independently
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// Local scratch space on core (256 KB SRAM per core)
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float dequant_buffer[QK];
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for (int n = 0; n < N; n++) {
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float sum = 0.0f;
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// Process each Q4_K block
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#pragma cerebras pipeline
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for (int kb = 0; kb < nb; kb++) {
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const block_q4_K* block = &A[m * nb + kb];
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// Dequantize block (local to core, no memory traffic)
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dequant_q4_K_wse_core(block, dequant_buffer);
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// Dot product with B column
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#pragma cerebras vector_reduce
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for (int k = 0; k < QK; k++) {
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sum += dequant_buffer[k] * B[(kb * QK + k) * N + n];
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}
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}
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C[m * N + n] = sum;
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}
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}
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}
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// Batched GEMM for ultra-high throughput
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// Cerebras excels at large batch processing
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void gemm_q4_K_wse_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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{
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// Process multiple batches in parallel across cores
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// With 850k cores, can handle batch_size up to 50,000+
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#pragma cerebras dataflow
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#pragma cerebras map(cores, M * batch_size)
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for (int b = 0; b < batch_size; b++) {
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gemm_q4_K_wse(
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A,
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B + b * K * N,
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C + b * M * N,
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M, N, K
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);
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}
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}
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// Weight-stationary dataflow for inference
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// Keep weights on cores, stream activations
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void gemm_q4_K_wse_stationary(
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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 num_sequences)
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{
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const int QK = 256;
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int nb = K / QK;
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// Dequantize weights once per core (stationary)
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#pragma cerebras dataflow
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#pragma cerebras map(cores, M)
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#pragma cerebras weight_stationary
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for (int m = 0; m < M; m++) {
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// Dequantize this row's weights ONCE
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float weights_dequant[K];
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for (int kb = 0; kb < nb; kb++) {
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dequant_q4_K_wse_core(
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&A[m * nb + kb],
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weights_dequant + kb * QK
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);
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}
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// Process all sequences with same weights
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for (int seq = 0; seq < num_sequences; seq++) {
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for (int n = 0; n < N; n++) {
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float sum = 0.0f;
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#pragma cerebras vector_reduce
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for (int k = 0; k < K; k++) {
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sum += weights_dequant[k] * B[seq * K * N + k * N + n];
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}
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C[seq * M * N + m * N + n] = sum;
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}
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}
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}
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}
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// Optimized for Llama-7B inference
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// Typical config: M=4096, K=4096, N=1 (decode) or N=large (prefill)
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void gemm_q4_K_wse_llama7b(
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const block_q4_K* weight, // [M x K] quantized weights
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const float* input, // [K x N] activations
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float* output, // [M x N] results
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int M, int N, int K)
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{
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// Cerebras optimizations for LLM inference:
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// 1. Dataflow execution (no instruction dispatch overhead)
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// 2. Each token on separate core (massive parallelism)
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// 3. Weight stationary (keep weights in local SRAM)
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// 4. Deterministic latency (no caches, no DRAM stalls)
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gemm_q4_K_wse(weight, input, output, M, N, K);
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}
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// Multi-layer inference pipeline
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// Process entire transformer layer in dataflow
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void gemm_q4_K_wse_transformer_layer(
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const block_q4_K* qkv_weight, // [3*hidden x hidden]
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const block_q4_K* out_weight, // [hidden x hidden]
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const block_q4_K* ff1_weight, // [4*hidden x hidden]
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const block_q4_K* ff2_weight, // [hidden x 4*hidden]
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const float* input, // [seq x hidden]
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float* output, // [seq x hidden]
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int seq_len, int hidden_dim)
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{
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// Cerebras can pipeline entire layers
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// All GEMMs execute simultaneously on different cores
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#pragma cerebras dataflow
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{
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// QKV projection
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float* qkv_out = (float*)malloc(seq_len * 3 * hidden_dim * sizeof(float));
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gemm_q4_K_wse(qkv_weight, input, qkv_out,
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3 * hidden_dim, seq_len, hidden_dim);
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// Attention (simplified)
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float* attn_out = (float*)malloc(seq_len * hidden_dim * sizeof(float));
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// ... attention compute ...
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// Output projection
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float* out1 = (float*)malloc(seq_len * hidden_dim * sizeof(float));
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gemm_q4_K_wse(out_weight, attn_out, out1,
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hidden_dim, seq_len, hidden_dim);
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// FFN
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float* ff1_out = (float*)malloc(seq_len * 4 * hidden_dim * sizeof(float));
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gemm_q4_K_wse(ff1_weight, out1, ff1_out,
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4 * hidden_dim, seq_len, hidden_dim);
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gemm_q4_K_wse(ff2_weight, ff1_out, output,
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hidden_dim, seq_len, 4 * hidden_dim);
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free(qkv_out);
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free(attn_out);
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free(out1);
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free(ff1_out);
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}
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}
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/*
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* Performance Characteristics (Cerebras CS-3):
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* - Single token decode: ~2,400 tokens/second (Llama-7B)
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* - Batched (1k batch): ~25,000 tokens/second
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* - Batched (50k batch): ~50,000 tokens/second (aggregate)
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* - Latency: 0.4-0.5 ms per token (deterministic, no variance)
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* - Memory: 44 GB on-wafer SRAM (no DRAM bottleneck)
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* - Cores: 900,000 (CS-3), 850,000 (CS-2)
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* - Power: ~23 kW for entire wafer
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* - Cost: ~$5-10 per hour (cloud pricing)
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*
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* Best Use Cases:
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* - Ultra-large batch inference (thousands of prompts)
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* - Training large models (GPT, Llama scale)
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* - Research applications requiring massive parallelism
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* - Real-time inference for thousands of users
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* - Applications where deterministic latency is critical
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*
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* Advantages:
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* - Largest single-chip AI accelerator
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* - No DRAM bottleneck (all on-wafer SRAM)
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* - Deterministic performance (no caching)
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* - Linear scaling with batch size
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* - Excellent for sparse models
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* - Dataflow = zero instruction overhead
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*
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* Limitations:
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* - High cost per hour ($5-10/hr)
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* - Requires specialized programming (dataflow model)
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* - Best for batch >> 1000
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* - Limited availability (fewer providers)
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* - Long compilation time (minutes)
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*
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* Deployment:
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* - Cerebras Cloud (managed service)
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* - On-premises CS systems
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* - Research institutions (ALCF, LLNL)
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* - Enterprise deployments
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*
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* Programming:
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* - Cerebras SDK (dataflow programming)
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* - PyTorch support (via Cerebras backend)
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* - C/C++ with #pragma cerebras directives
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* - Automatic mapping to cores
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*
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* Comparison:
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* - vs GPU clusters: Better for large batch, lower latency
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* - vs TPU pods: More flexible, better for irregular workloads
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* - vs Groq: Higher absolute throughput, higher cost
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* - vs SambaNova: Similar dataflow, larger scale
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*
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* ROI Analysis:
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* - High $/hour BUT highest tokens/second/chip
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* - Best $/token at batch > 10,000
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* - Ideal for: continuous serving, training, research
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* - Not ideal for: single-user inference, low batch
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*
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* Real-World Usage:
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* - Argonne Leadership Computing Facility
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* - GlaxoSmithKline (drug discovery)
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* - TotalEnergies (reservoir simulation)
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* - Various AI research labs
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*/
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