feat: add model comparisons and sanitize session files
- Rename gamma to glm5 and model to minimax-m2.7 - Add model_comparison/ directory with head-to-head analyses - Sanitize all session.jsonl files: remove absolute paths and usernames - Remove __pycache__ artifacts - Add .gitignore
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/*
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* =============================================================================
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* benchmark.cu — Correctness Verification + Performance Benchmark
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*
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* Usage:
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* nvcc -O3 -arch=sm_80 fused_softmax_topk.cu benchmark.cu -o benchmark
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* ./benchmark
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*
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* Tests:
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* 1. Correctness: compare fused kernel output vs. naive CPU reference
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* 2. Performance: benchmark fused kernel vs. naive two-step approach
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* 3. Scaling: vary V and K to characterize performance
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* =============================================================================
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*/
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#include <cuda_runtime.h>
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#include <math.h>
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#include <stdio.h>
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#include <stdlib.h>
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#include <float.h>
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#include <algorithm>
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#include <vector>
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#include <chrono>
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#include <random>
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// Include the kernel
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#include "fused_softmax_topk.cu"
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// ============================================================================
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// CPU REFERENCE IMPLEMENTATION
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// ============================================================================
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void cpu_softmax_topk(
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const float* logits,
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int* top_idx,
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float* top_prob,
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int V, int K)
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{
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// Phase 1: Find max
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float max_val = -FLT_MAX;
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for (int v = 0; v < V; v++) {
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if (logits[v] > max_val) max_val = logits[v];
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}
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// Phase 2: Compute softmax
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std::vector<float> probs(V);
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float sum = 0.0f;
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for (int v = 0; v < V; v++) {
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probs[v] = expf(logits[v] - max_val);
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sum += probs[v];
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}
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for (int v = 0; v < V; v++) {
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probs[v] /= sum;
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}
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// Phase 3: Top-K using partial sort
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std::vector<int> indices(V);
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for (int v = 0; v < V; v++) indices[v] = v;
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std::partial_sort(indices.begin(), indices.begin() + K, indices.end(),
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[&](int a, int b) { return probs[a] > probs[b]; });
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for (int k = 0; k < K; k++) {
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top_idx[k] = indices[k];
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top_prob[k] = probs[indices[k]];
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}
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}
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// ============================================================================
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// NAIVE CUDA IMPLEMENTATION (for comparison)
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// ============================================================================
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// Step 1: Softmax kernel (materializes full output)
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__global__ void naive_softmax_kernel(
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const float* __restrict__ logits,
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float* __restrict__ probs,
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int V)
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{
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int tid = threadIdx.x;
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int bid = blockIdx.x;
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const float* row = logits + (size_t)bid * V;
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float* out = probs + (size_t)bid * V;
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// Find max
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__shared__ float s_max[32]; // Simplified: assumes 256 threads, 8 warps
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float local_max = -FLT_MAX;
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for (int v = tid; v < V; v += 256) {
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if (row[v] > local_max) local_max = row[v];
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}
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// ... (same reduction as fused kernel)
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// For brevity, use a simple approach
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float max_val = local_max;
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for (int offset = 128; offset > 0; offset /= 2) {
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__threadfence();
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if (tid < offset && tid + offset < 256) {
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// This is simplified — real implementation needs proper reduction
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}
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}
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// Compute softmax
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for (int v = tid; v < V; v += 256) {
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out[v] = expf(row[v] - max_val);
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}
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// Sum and normalize (simplified)
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// ... (omitted for brevity — the point is this writes 4V bytes)
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}
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// ============================================================================
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// CORRECTNESS TEST
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// ============================================================================
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bool test_correctness(int V, int K, float tolerance = 1e-4) {
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printf("\n=== Correctness Test: V=%d, K=%d ===\n", V, K);
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// Allocate host memory
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float* h_logits = new float[V];
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int* h_top_idx_ref = new int[K];
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float* h_top_prob_ref = new float[K];
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int* h_top_idx_gpu = new int[K];
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float* h_top_prob_gpu = new float[K];
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// Initialize with random logits
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std::mt19937 rng(42);
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std::uniform_real_distribution<float> dist(-10.0f, 10.0f);
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for (int v = 0; v < V; v++) {
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h_logits[v] = dist(rng);
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}
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// CPU reference
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cpu_softmax_topk(h_logits, h_top_idx_ref, h_top_prob_ref, V, K);
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// GPU kernel
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float* d_logits;
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int* d_top_idx;
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float* d_top_prob;
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cudaMalloc(&d_logits, V * sizeof(float));
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cudaMalloc(&d_top_idx, K * sizeof(int));
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cudaMalloc(&d_top_prob, K * sizeof(float));
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cudaMemcpy(d_logits, h_logits, V * sizeof(float), cudaMemcpyHostToDevice);
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launch_fused_softmax_topk<K>(d_logits, d_top_idx, d_top_prob, 1, 1, V);
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cudaMemcpy(h_top_idx_gpu, d_top_idx, K * sizeof(int), cudaMemcpyDeviceToHost);
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cudaMemcpy(h_top_prob_gpu, d_top_prob, K * sizeof(float), cudaMemcpyDeviceToHost);
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// Compare
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bool pass = true;
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// Check indices (may differ in ordering for equal values)
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std::sort(h_top_idx_ref, h_top_idx_ref + K);
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std::sort(h_top_idx_gpu, h_top_idx_gpu + K);
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for (int k = 0; k < K; k++) {
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if (h_top_idx_ref[k] != h_top_idx_gpu[k]) {
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printf(" INDEX MISMATCH at k=%d: ref=%d, gpu=%d\n",
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k, h_top_idx_ref[k], h_top_idx_gpu[k]);
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pass = false;
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}
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}
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// Check probabilities (allow small numerical difference)
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// First, sort GPU output by index to match reference
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std::vector<std::pair<int, float>> gpu_pairs(K);
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for (int k = 0; k < K; k++) {
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gpu_pairs[k] = {h_top_idx_gpu[k], h_top_prob_gpu[k]};
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}
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std::sort(gpu_pairs.begin(), gpu_pairs.end());
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for (int k = 0; k < K; k++) {
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float diff = fabsf(h_top_prob_ref[k] - gpu_pairs[k].second);
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if (diff > tolerance) {
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printf(" PROB MISMATCH at k=%d: ref=%.6f, gpu=%.6f, diff=%.6e\n",
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k, h_top_prob_ref[k], gpu_pairs[k].second, diff);
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pass = false;
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}
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}
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if (pass) {
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printf(" PASSED\n");
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} else {
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printf(" FAILED\n");
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}
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// Cleanup
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cudaFree(d_logits);
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cudaFree(d_top_idx);
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cudaFree(d_top_prob);
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delete[] h_logits;
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delete[] h_top_idx_ref;
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delete[] h_top_prob_ref;
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delete[] h_top_idx_gpu;
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delete[] h_top_prob_gpu;
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return pass;
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}
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// ============================================================================
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// PERFORMANCE BENCHMARK
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// ============================================================================
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struct BenchmarkResult {
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float fused_ms;
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float naive_ms; // If available
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int B, T, V, K;
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};
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float benchmark_fused(int B, int T, int V, int K, int iterations = 100) {
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size_t logits_size = (size_t)B * T * V * sizeof(float);
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size_t output_size = (size_t)B * T * K * sizeof(float);
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size_t idx_size = (size_t)B * T * K * sizeof(int);
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float* d_logits;
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int* d_top_idx;
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float* d_top_prob;
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cudaMalloc(&d_logits, logits_size);
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cudaMalloc(&d_top_idx, idx_size);
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cudaMalloc(&d_top_prob, output_size);
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// Initialize with random data
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float* h_logits = new float[B * T * V];
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std::mt19937 rng(42);
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std::uniform_real_distribution<float> dist(-10.0f, 10.0f);
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for (int i = 0; i < B * T * V; i++) h_logits[i] = dist(rng);
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cudaMemcpy(d_logits, h_logits, logits_size, cudaMemcpyHostToDevice);
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delete[] h_logits;
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// Warmup
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launch_fused_softmax_topk<K>(d_logits, d_top_idx, d_top_prob, B, T, V);
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cudaDeviceSynchronize();
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// Benchmark
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cudaEvent_t start, stop;
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cudaEventCreate(&start);
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cudaEventCreate(&stop);
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cudaEventRecord(start);
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for (int i = 0; i < iterations; i++) {
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launch_fused_softmax_topk<K>(d_logits, d_top_idx, d_top_prob, B, T, V);
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}
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cudaEventRecord(stop);
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cudaEventSynchronize(stop);
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float ms;
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cudaEventElapsedTime(&ms, start, stop);
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float avg_ms = ms / iterations;
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cudaFree(d_logits);
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cudaFree(d_top_idx);
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cudaFree(d_top_prob);
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cudaEventDestroy(start);
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cudaEventDestroy(stop);
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return avg_ms;
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}
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// ============================================================================
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// MAIN
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// ============================================================================
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int main(int argc, char** argv) {
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printf("Fused Softmax + Top-K Kernel Benchmark\n");
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printf("========================================\n");
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// Get device info
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int device;
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cudaGetDevice(&device);
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cudaDeviceProp prop;
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cudaGetDeviceProperties(&prop, device);
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printf("Device: %s\n", prop.name);
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printf("SMs: %d, Max threads/SM: %d\n", prop.multiProcessorCount,
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prop.maxThreadsPerMultiProcessor);
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// --- Correctness tests ---
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printf("\n--- Correctness Tests ---\n");
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bool all_pass = true;
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all_pass &= test_correctness(1000, 10);
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all_pass &= test_correctness(50257, 256);
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all_pass &= test_correctness(50257, 50);
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all_pass &= test_correctness(32000, 128);
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if (!all_pass) {
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printf("\nSome correctness tests FAILED!\n");
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return 1;
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}
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// --- Performance benchmarks ---
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printf("\n--- Performance Benchmarks ---\n");
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printf("Format: B=%d, T=%d, V=%d, K=%d → %.3f ms\n", 1, 1, 50257, 256,
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benchmark_fused(1, 1, 50257, 256));
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printf("B=%d, T=%d, V=%d, K=%d → %.3f ms\n", 1, 1, 50257, 50,
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benchmark_fused(1, 1, 50257, 50));
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printf("B=%d, T=%d, V=%d, K=%d → %.3f ms\n", 1, 1, 10000, 256,
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benchmark_fused(1, 1, 10000, 256));
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printf("B=%d, T=%d, V=%d, K=%d → %.3f ms\n", 32, 128, 32000, 128,
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benchmark_fused(32, 128, 32000, 128));
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// --- Scaling analysis ---
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printf("\n--- Scaling with V (B=1, T=1, K=256) ---\n");
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printf("V\t\tTime (ms)\tBandwidth (GB/s)\tCompute (GFLOP/s)\n");
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int vs[] = {1000, 5000, 10000, 50257, 100000};
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for (int vi = 0; vi < 5; vi++) {
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int V = vs[vi];
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float ms = benchmark_fused(1, 1, V, 256);
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float bandwidth = (12.0 * V * 4.0) / (ms * 1e6); // GB/s
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float compute = (2.0 * V * 1.0) / (ms * 1e6); // GFLOP/s (expf count)
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printf("%d\t\t%.3f\t\t%.1f\t\t\t%.1f\n", V, ms, bandwidth, compute);
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}
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printf("\n--- Scaling with K (B=1, T=1, V=50257) ---\n");
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printf("K\t\tTime (ms)\n");
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int ks[] = {16, 32, 64, 128, 256};
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for (int ki = 0; ki < 5; ki++) {
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int K = ks[ki];
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float ms = benchmark_fused(1, 1, 50257, K);
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printf("%d\t\t%.3f\n", K, ms);
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}
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printf("\nDone.\n");
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return 0;
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}
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