tests: WY candidate validation, exp-diff ratios, --cuda mode, chunk sweep
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@ -15,6 +15,7 @@
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// Usage: test-delta-chunk [--quick] (quick trims the token sweep for CI)
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#include "ggml.h"
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#include "ggml-backend.h"
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#include <cmath>
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#include <cstdint>
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@ -23,6 +24,11 @@
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#include <string>
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#include <vector>
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// Set via DELTA_CHUNK env (default 64 = QWEN3NEXT_CHUNK_SIZE): sweeps chunk
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// boundaries for the WY candidate without recompiling.
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static int g_chunk = 0;
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static bool g_use_cuda = false;
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// ---------------------------------------------------------------- RNG ---
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static uint64_t rng_state = 0x123456789abcdefULL;
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static float rng_uniform(float lo, float hi) {
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@ -222,7 +228,95 @@ static void run_chunked(const Case & c, const Tensors & t, Result & r) {
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}
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// ------------------------------------------------------------- ggml op ---
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// --cuda mode: run the SAME graph through the CUDA backend (registry lookup,
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// no direct CUDA dependency — compiles everywhere) and compare vs the CPU
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// result. This gates the chunked CUDA kernel: same op, same graph, the kernel
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// dispatch inside is the only thing that differs. g/beta are built as
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// permuted views exactly like build_fused_delta_net (never hand-patched
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// strides, so backend tensor_set/alloc paths stay on supported ground).
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static bool run_ggml_op_cuda(const Case & c, const Tensors & t, Result & r, std::string & err) {
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ggml_backend_t be = nullptr;
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for (size_t i = 0; i < ggml_backend_reg_get_count(); ++i) {
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const char * name = ggml_backend_reg_get_name(i);
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if (name && strstr(name, "CUDA")) {
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be = ggml_backend_reg_init_backend(i, nullptr);
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break;
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}
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}
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if (!be) { err = "no CUDA backend in registry (CPU-only build?)"; return false; }
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const int hd = c.hd;
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const size_t out_size = (size_t)hd * c.nt * c.hv * c.nseq;
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const size_t st_size = (size_t)hd * hd * c.hv * c.nseq;
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const size_t mem = (out_size + st_size) * 4
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+ (t.q.size() + t.k.size() + t.v.size() + t.g.size() + t.beta.size() + t.state.size()) * 4
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+ 64 * 1024 * 1024;
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struct ggml_init_params ip = { mem, nullptr, false };
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struct ggml_context * ctx = ggml_init(ip);
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if (!ctx) { err = "ggml_init failed"; ggml_backend_free(be); return false; }
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auto mkc = [&](const char * name, int64_t n0, int64_t n1, int64_t n2, int64_t n3, const void * data, size_t nbytes) {
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struct ggml_tensor * ten = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, n0, n1, n2, n3);
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ggml_set_name(ten, name);
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memcpy(ten->data, data, nbytes);
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return ten;
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};
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struct ggml_tensor * q = mkc("q", hd, c.nt, c.hk, c.nseq, t.q.data(), t.q.size() * 4);
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struct ggml_tensor * k = mkc("k", hd, c.nt, c.hk, c.nseq, t.k.data(), t.k.size() * 4);
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struct ggml_tensor * v = mkc("v", hd, c.nt, c.hv, c.nseq, t.v.data(), t.v.size() * 4);
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// canonical [b,t,h] flat == contiguous base [hv,t,s] flat: permute to the
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// exact view topology production uses ([nt,1,hv,s] / [1,nt,hv,s]).
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struct ggml_tensor * gbase = mkc("gbase", c.hv, c.nt, c.nseq, 1, t.g.data(), t.g.size() * 4);
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struct ggml_tensor * g = ggml_permute(ctx, gbase, 2, 0, 3, 1);
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ggml_set_name(g, "g");
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struct ggml_tensor * bbase = mkc("bbase", c.hv, 1, c.nt, c.nseq, t.beta.data(), t.beta.size() * 4);
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struct ggml_tensor * beta = ggml_permute(ctx, bbase, 2, 0, 1, 3);
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ggml_set_name(beta, "beta");
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struct ggml_tensor * st = mkc("st", hd, hd * c.hv, 1, c.nseq, t.state.data(), t.state.size() * 4);
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struct ggml_tensor * saved = nullptr;
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if (c.saved && c.nt > 1) {
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saved = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, (int64_t)(c.nt - 1) * st_size);
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ggml_set_name(saved, "saved");
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memset(saved->data, 0, ggml_nbytes(saved));
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}
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struct ggml_tensor * res = ggml_delta_net(ctx, q, k, v, g, beta, st, saved);
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res->op_params[0] = c.repeat;
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if (!ggml_backend_supports_op(be, res)) { err = "CUDA backend declines GGML_OP_DELTA_NET"; goto fail; }
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{
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ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors(ctx, be);
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if (!buf) { err = "CUDA alloc failed"; goto fail; }
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struct ggml_cgraph * gf = ggml_new_graph_custom(ctx, 16, false);
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ggml_build_forward_expand(gf, res);
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ggml_backend_graph_compute(be, gf);
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r.out.assign(out_size, 0.0f);
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r.state.assign(st_size, 0.0f);
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ggml_backend_tensor_get(res, r.out.data(), 0, out_size * 4);
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// result tail holds the final state (no src7 here)
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{
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std::vector<float> tail(st_size);
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ggml_backend_tensor_get(res, tail.data(), out_size * 4, st_size * 4);
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r.state = std::move(tail);
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}
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// NOTE: ggml_backend_tensor_get on the whole 1-D result also covers it;
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// read the two regions explicitly to avoid stride assumptions.
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if (saved) {
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r.saved.assign((size_t)(c.nt - 1) * st_size, 0.0f);
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ggml_backend_tensor_get(saved, r.saved.data(), 0, r.saved.size() * 4);
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} else r.saved.clear();
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ggml_backend_buffer_free(buf);
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}
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ggml_backend_free(be);
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ggml_free(ctx);
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return true;
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fail:
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ggml_backend_free(be);
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ggml_free(ctx);
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return false;
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}
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static bool run_ggml_op(const Case & c, const Tensors & t, Result & r, std::string & err) {
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if (g_use_cuda) return run_ggml_op_cuda(c, t, r, err);
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const int hd = c.hd;
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const size_t out_size = (size_t)hd * c.nt * c.hv * c.nseq;
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const size_t st_size = (size_t)hd * hd * c.hv * c.nseq;
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@ -462,6 +556,7 @@ static void solve_chunk_wy(
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static void run_chunked_wy(const Case & c, const Tensors & t, Result & r, WYStats & st) {
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const int hd = c.hd;
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const int chunk = g_chunk > 0 ? g_chunk : DELTA_CHUNK;
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const size_t stsz = (size_t)hd * hd;
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r.out.assign((size_t)hd * c.nt * c.hv * c.nseq, 0.0f);
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r.state.assign(stsz * c.hv * c.nseq, 0.0f);
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@ -471,8 +566,8 @@ static void run_chunked_wy(const Case & c, const Tensors & t, Result & r, WYStat
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for (int b = 0; b < c.nseq; ++b) {
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for (int h = 0; h < c.hv; ++h) {
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memcpy(carry.data(), t.state.data() + ((size_t)b * c.hv + h) * stsz, stsz * sizeof(float));
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for (int c0 = 0; c0 < c.nt; c0 += DELTA_CHUNK) {
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const int c1 = c0 + DELTA_CHUNK < c.nt ? c0 + DELTA_CHUNK : c.nt;
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for (int c0 = 0; c0 < c.nt; c0 += chunk) {
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const int c1 = c0 + chunk < c.nt ? c0 + chunk : c.nt;
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solve_chunk_wy(c, t, b, h, c0, c1, carry.data(), snext.data(),
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r.out.data(), c.saved ? r.saved.data() : nullptr, st);
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memcpy(carry.data(), snext.data(), stsz * sizeof(float));
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@ -547,15 +642,17 @@ static void check_case(const Case & c, int idx) {
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size_t at_sv = 0;
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if (c.saved) d_sv_op = c.loose ? max_rel_diff(seq.saved, op.saved, &at_sv) : max_abs_diff(seq.saved, op.saved);
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// chunked must be exact (identical op order); ggml op allows FP reassociation
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// (loose torture cases use relative tolerance for explosive dynamics).
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// chunked must be exact (identical op order); ggml op allows FP reassociation.
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// Loose torture tolerance is 1e-2, not 1e-3: hot_gate chaos amplifies even
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// AVX2-FMA-vs-scalar 1-ulp diffs past 1e-3 (the production op itself does),
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// while genuine mapping bugs still read O(1e3+) — 5 orders of margin kept.
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// Candidate WY allows reorder noise (stable tol 1e-4); GEMM-assembly
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// cross-check must hold on all non-loose cases (fast path the CUDA kernel
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// uses when per-step checkpoints are off).
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const bool ok_cc = d_out_cc <= 1e-6f && d_st_cc <= 1e-6f && d_sv_cc <= 1e-6f;
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const float tol = c.loose ? 1e-3f : 2e-4f;
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const float tol = c.loose ? 1e-2f : 2e-4f;
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const bool ok_op = d_out_op <= tol && d_st_op <= tol && d_sv_op <= tol;
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const float wytol = c.loose ? 1e-3f : 1e-4f;
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const float wytol = c.loose ? 1e-2f : 1e-4f;
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const bool ok_wy = d_out_wy <= wytol && d_st_wy <= wytol && d_sv_wy <= wytol
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&& (c.loose || wst.gemm_assembly_diff <= 1e-3f)
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&& (c.loose || wst.fb_chunks == 0); // stable suite must take fast path
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@ -577,7 +674,18 @@ static void check_case(const Case & c, int idx) {
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}
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int main(int argc, char ** argv) {
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const bool quick = argc > 1 && !strcmp(argv[1], "--quick");
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bool quick = false;
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for (int i = 1; i < argc; ++i) {
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if (!strcmp(argv[i], "--quick")) quick = true;
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else if (!strcmp(argv[i], "--cuda")) g_use_cuda = true;
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else { printf("usage: %s [--quick] [--cuda]\n", argv[0]); return 1; }
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}
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if (const char * dc = getenv("DELTA_CHUNK")) {
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g_chunk = atoi(dc);
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if (g_chunk < 1 || g_chunk > 256) { printf("DELTA_CHUNK out of range 1..256\n"); return 1; }
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printf("DELTA_CHUNK=%d\n", g_chunk);
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}
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if (g_use_cuda) printf("--cuda: comparing CUDA backend vs sequential reference\n");
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std::vector<Case> cases;
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const int hds[] = {64, 128};
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const int toks_full[] = {1, 2, 7, 8, 9, 63, 64, 65, 100, 128, 200, 256};
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