mirror of https://github.com/maderix/ANE.git
722 lines
40 KiB
Mathematica
722 lines
40 KiB
Mathematica
// train_large.m — Train stories110M (12 layers, 768dim, 3072hidden) on ANE
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// Uses pretokenized TinyStories data with cross-entropy loss
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// 5 weight-bearing ANE kernels per layer × 12 layers = 60 per compile batch
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#include "stories_io.h"
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#include "stories_mil.h"
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#include "stories_cpu_ops.h"
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#define CKPT_PATH_DEFAULT "ane_stories110M_ckpt.bin"
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#define MODEL_PATH_DEFAULT "../../assets/models/stories110M.bin"
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#define DATA_PATH "tinystories_data00.bin"
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static const char *get_path(const char *env_var, const char *default_val) {
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const char *v = getenv(env_var);
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return (v && v[0]) ? v : default_val;
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}
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// ===== Weight loading from llama2.c format =====
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static bool load_pretrained(LayerWeights *lw, float *rms_final, float *embed, const char *path) {
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FILE *f = fopen(path, "rb");
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if (!f) { printf("Cannot open %s\n", path); return false; }
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Llama2Config cfg;
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// Validate config read — gatekeeper before any dimension-based logic (CRIT-03)
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if (fread(&cfg, sizeof(cfg), 1, f) != 1) {
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printf(" ERROR: Config read failed (truncated file?)\n");
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fclose(f); return false;
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}
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printf(" Model config: dim=%d hidden=%d layers=%d heads=%d vocab=%d seq=%d\n",
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cfg.dim, cfg.hidden_dim, cfg.n_layers, cfg.n_heads, abs(cfg.vocab_size), cfg.seq_len);
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if (cfg.dim != DIM || cfg.hidden_dim != HIDDEN || cfg.n_layers != NLAYERS) {
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printf(" ERROR: Config mismatch! Expected dim=%d hidden=%d layers=%d\n", DIM, HIDDEN, NLAYERS);
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fclose(f); return false;
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}
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int V = abs(cfg.vocab_size);
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bool shared = cfg.vocab_size > 0;
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// Read in llama2.c order: embed, rms_att[all], wq[all], wk[all], wv[all], wo[all],
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// rms_ffn[all], w1[all], w2[all], w3[all], rms_final, [wcls]
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fread(embed, 4, V * DIM, f);
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// rms_att weights for all layers (contiguous)
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for (int L = 0; L < NLAYERS; L++) fread(lw[L].rms_att, 4, DIM, f);
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// wq for all layers
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for (int L = 0; L < NLAYERS; L++) fread(lw[L].Wq, 4, WQ_SZ, f);
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// wk for all layers
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for (int L = 0; L < NLAYERS; L++) fread(lw[L].Wk, 4, WQ_SZ, f);
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// wv for all layers
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for (int L = 0; L < NLAYERS; L++) fread(lw[L].Wv, 4, WQ_SZ, f);
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// wo for all layers
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for (int L = 0; L < NLAYERS; L++) fread(lw[L].Wo, 4, WO_SZ, f);
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// rms_ffn weights for all layers
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for (int L = 0; L < NLAYERS; L++) fread(lw[L].rms_ffn, 4, DIM, f);
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// w1 for all layers
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for (int L = 0; L < NLAYERS; L++) fread(lw[L].W1, 4, W1_SZ, f);
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// w2 for all layers
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for (int L = 0; L < NLAYERS; L++) fread(lw[L].W2, 4, W2_SZ, f);
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// w3 for all layers
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for (int L = 0; L < NLAYERS; L++) fread(lw[L].W3, 4, W3_SZ, f);
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// rms_final
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fread(rms_final, 4, DIM, f);
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// wcls = embed if shared (we just use embed pointer)
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fclose(f);
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printf(" Loaded pretrained weights (%s)\n", shared ? "shared embed/cls" : "separate cls");
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return true;
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}
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// ===== Compile one layer's kernels =====
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static bool compile_layer_kernels(LayerKernels *lk, LayerWeights *w) {
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lk->fwdAttn = compile_kern_mil_w(gen_sdpa_fwd_taps(), (@{
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@"@model_path/weights/rms1.bin": @{@"offset":@0, @"data":build_blob(w->rms_att,1,DIM)},
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@"@model_path/weights/wq.bin": @{@"offset":@0, @"data":build_blob(w->Wq,DIM,DIM)},
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@"@model_path/weights/wk.bin": @{@"offset":@0, @"data":build_blob(w->Wk,DIM,DIM)},
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@"@model_path/weights/wv.bin": @{@"offset":@0, @"data":build_blob(w->Wv,DIM,DIM)},
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@"@model_path/weights/wo.bin": @{@"offset":@0, @"data":build_blob(w->Wo,DIM,DIM)},
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@"@model_path/weights/mask.bin": @{@"offset":@0, @"data":get_mask_blob()},
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}), DIM*SEQ*2, 6*DIM*SEQ*2);
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lk->fwdFFN = compile_kern_mil_w(gen_ffn_fwd_taps(), (@{
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@"@model_path/weights/rms2.bin": @{@"offset":@0, @"data":build_blob(w->rms_ffn,1,DIM)},
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@"@model_path/weights/w1.bin": @{@"offset":@0, @"data":build_blob(w->W1,HIDDEN,DIM)},
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@"@model_path/weights/w3.bin": @{@"offset":@0, @"data":build_blob(w->W3,HIDDEN,DIM)},
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@"@model_path/weights/w2.bin": @{@"offset":@0, @"data":build_blob(w->W2,DIM,HIDDEN)},
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}), DIM*SEQ*2, (2*DIM+3*HIDDEN)*SEQ*2);
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lk->ffnBwd = compile_kern_mil_w(gen_ffn_bwd(), (@{
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@"@model_path/weights/w2t.bin": @{@"offset":@0, @"data":build_blob_t(w->W2,DIM,HIDDEN)},
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@"@model_path/weights/w1t.bin": @{@"offset":@0, @"data":build_blob_t(w->W1,HIDDEN,DIM)},
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@"@model_path/weights/w3t.bin": @{@"offset":@0, @"data":build_blob_t(w->W3,HIDDEN,DIM)},
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}), (DIM+2*HIDDEN)*SEQ*2, (DIM+2*HIDDEN)*SEQ*2);
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lk->sdpaBwd1 = compile_kern_mil_w(gen_sdpa_bwd1(), (@{
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@"@model_path/weights/mask.bin": @{@"offset":@0, @"data":get_mask_blob()},
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@"@model_path/weights/wot.bin": @{@"offset":@0, @"data":build_blob_t(w->Wo,DIM,DIM)},
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}), 4*DIM*SEQ*2, (DIM+2*SCORE_CH)*SEQ*2);
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lk->qkvBwd = compile_kern_mil_w(gen_qkvb(), (@{
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@"@model_path/weights/wqt.bin": @{@"offset":@0, @"data":build_blob_t(w->Wq,DIM,DIM)},
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@"@model_path/weights/wkt.bin": @{@"offset":@0, @"data":build_blob_t(w->Wk,DIM,DIM)},
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@"@model_path/weights/wvt.bin": @{@"offset":@0, @"data":build_blob_t(w->Wv,DIM,DIM)},
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}), 3*DIM*SEQ*2, DIM*SEQ*2);
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return lk->fwdAttn && lk->fwdFFN && lk->ffnBwd && lk->sdpaBwd1 && lk->qkvBwd;
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}
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// Compile weight-free sdpaBwd2 (only needs once, no weights)
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static Kern *compile_sdpa_bwd2(void) {
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return compile_kern_mil_w(gen_sdpa_bwd2(), @{},
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(2*SCORE_CH+2*DIM)*SEQ*2, 2*DIM*SEQ*2);
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}
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static void free_layer_kernels(LayerKernels *lk) {
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free_kern(lk->fwdAttn); free_kern(lk->fwdFFN); free_kern(lk->ffnBwd);
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free_kern(lk->sdpaBwd1); free_kern(lk->qkvBwd);
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// sdpaBwd2 is shared, freed separately
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lk->fwdAttn = lk->fwdFFN = lk->ffnBwd = lk->sdpaBwd1 = lk->qkvBwd = NULL;
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}
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// ===== Checkpoint save/load =====
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static void save_checkpoint(const char *path, int step, int total_steps, float lr, float loss,
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double cc, double ct, double cw, int cs, int cb, int adam_t,
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LayerWeights *lw, LayerAdam *la, float *rms_final, AdamState *arms_final,
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float *embed, AdamState *aembed) {
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FILE *f = fopen(path, "wb");
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if (!f) { fprintf(stderr, "save_checkpoint: cannot open %s\n", path); return; } // CRIT-03
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CkptHdr h = {0};
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h.magic = 0x424C5A54; h.version = 2;
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h.step = step; h.total_steps = total_steps;
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h.n_layers = NLAYERS; h.vocab_size = VOCAB; h.dim = DIM;
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h.hidden_dim = HIDDEN; h.n_heads = HEADS; h.seq_len = SEQ;
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h.lr = lr; h.loss = loss;
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h.cum_compile = cc; h.cum_train = ct; h.cum_wall = cw;
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h.cum_steps = cs; h.cum_batches = cb; h.adam_t = adam_t;
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fwrite(&h, sizeof(h), 1, f);
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// Per-layer weights + adam
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for (int L = 0; L < NLAYERS; L++) {
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fwrite(lw[L].Wq,4,WQ_SZ,f); fwrite(lw[L].Wk,4,WQ_SZ,f);
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fwrite(lw[L].Wv,4,WQ_SZ,f); fwrite(lw[L].Wo,4,WO_SZ,f);
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fwrite(lw[L].W1,4,W1_SZ,f); fwrite(lw[L].W2,4,W2_SZ,f); fwrite(lw[L].W3,4,W3_SZ,f);
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fwrite(lw[L].rms_att,4,DIM,f); fwrite(lw[L].rms_ffn,4,DIM,f);
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// Adam state
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fwrite(la[L].Wq.m,4,WQ_SZ,f); fwrite(la[L].Wq.v,4,WQ_SZ,f);
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fwrite(la[L].Wk.m,4,WQ_SZ,f); fwrite(la[L].Wk.v,4,WQ_SZ,f);
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fwrite(la[L].Wv.m,4,WQ_SZ,f); fwrite(la[L].Wv.v,4,WQ_SZ,f);
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fwrite(la[L].Wo.m,4,WO_SZ,f); fwrite(la[L].Wo.v,4,WO_SZ,f);
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fwrite(la[L].W1.m,4,W1_SZ,f); fwrite(la[L].W1.v,4,W1_SZ,f);
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fwrite(la[L].W2.m,4,W2_SZ,f); fwrite(la[L].W2.v,4,W2_SZ,f);
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fwrite(la[L].W3.m,4,W3_SZ,f); fwrite(la[L].W3.v,4,W3_SZ,f);
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fwrite(la[L].rms_att.m,4,DIM,f); fwrite(la[L].rms_att.v,4,DIM,f);
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fwrite(la[L].rms_ffn.m,4,DIM,f); fwrite(la[L].rms_ffn.v,4,DIM,f);
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}
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fwrite(rms_final,4,DIM,f);
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fwrite(arms_final->m,4,DIM,f); fwrite(arms_final->v,4,DIM,f);
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fwrite(embed,4,VOCAB*DIM,f);
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fwrite(aembed->m,4,VOCAB*DIM,f); fwrite(aembed->v,4,VOCAB*DIM,f);
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fclose(f);
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}
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static bool load_checkpoint(const char *path, int *step, int *total_steps, float *lr, float *loss,
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double *cc, double *ct, double *cw, int *cs, int *cb, int *adam_t,
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LayerWeights *lw, LayerAdam *la, float *rms_final, AdamState *arms_final,
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float *embed, AdamState *aembed) {
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FILE *f = fopen(path, "rb");
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if (!f) return false;
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CkptHdr h;
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// Validate header read before magic-byte check (CRIT-03)
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if (fread(&h, sizeof(h), 1, f) != 1) {
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fprintf(stderr, "load_checkpoint: header read failed\n");
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fclose(f); return false;
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}
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if (h.magic != 0x424C5A54 || h.version != 2) { fclose(f); return false; }
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*step = h.step; *total_steps = h.total_steps; *lr = h.lr; *loss = h.loss;
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*cc = h.cum_compile; *ct = h.cum_train; *cw = h.cum_wall;
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*cs = h.cum_steps; *cb = h.cum_batches; *adam_t = h.adam_t;
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for (int L = 0; L < NLAYERS; L++) {
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fread(lw[L].Wq,4,WQ_SZ,f); fread(lw[L].Wk,4,WQ_SZ,f);
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fread(lw[L].Wv,4,WQ_SZ,f); fread(lw[L].Wo,4,WO_SZ,f);
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fread(lw[L].W1,4,W1_SZ,f); fread(lw[L].W2,4,W2_SZ,f); fread(lw[L].W3,4,W3_SZ,f);
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fread(lw[L].rms_att,4,DIM,f); fread(lw[L].rms_ffn,4,DIM,f);
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fread(la[L].Wq.m,4,WQ_SZ,f); fread(la[L].Wq.v,4,WQ_SZ,f);
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fread(la[L].Wk.m,4,WQ_SZ,f); fread(la[L].Wk.v,4,WQ_SZ,f);
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fread(la[L].Wv.m,4,WQ_SZ,f); fread(la[L].Wv.v,4,WQ_SZ,f);
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fread(la[L].Wo.m,4,WO_SZ,f); fread(la[L].Wo.v,4,WO_SZ,f);
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fread(la[L].W1.m,4,W1_SZ,f); fread(la[L].W1.v,4,W1_SZ,f);
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fread(la[L].W2.m,4,W2_SZ,f); fread(la[L].W2.v,4,W2_SZ,f);
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fread(la[L].W3.m,4,W3_SZ,f); fread(la[L].W3.v,4,W3_SZ,f);
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fread(la[L].rms_att.m,4,DIM,f); fread(la[L].rms_att.v,4,DIM,f);
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fread(la[L].rms_ffn.m,4,DIM,f); fread(la[L].rms_ffn.v,4,DIM,f);
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}
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fread(rms_final,4,DIM,f);
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fread(arms_final->m,4,DIM,f); fread(arms_final->v,4,DIM,f);
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fread(embed,4,VOCAB*DIM,f);
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fread(aembed->m,4,VOCAB*DIM,f); fread(aembed->v,4,VOCAB*DIM,f);
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fclose(f);
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return true;
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}
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// ===== Main =====
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int main(int argc, char *argv[]) {
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@autoreleasepool {
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setbuf(stdout, NULL);
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ane_init();
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init_accum_steps();
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mach_timebase_info(&g_tb);
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int total_steps = 10000;
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float lr = 3e-4f;
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float adam_b1=0.9f, adam_b2=0.999f, adam_eps=1e-8f;
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int adam_t = 0, start_step = 0;
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// Parse args (env vars set defaults, CLI flags override)
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const char *ckpt_path = get_path("ANE_CKPT_PATH", CKPT_PATH_DEFAULT);
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const char *model_path = get_path("ANE_MODEL_PATH", MODEL_PATH_DEFAULT);
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bool do_resume = false;
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int pos = 0;
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for (int i=1; i<argc; i++) {
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if (strcmp(argv[i], "--resume") == 0) do_resume = true;
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else if (strcmp(argv[i], "--steps") == 0 && i+1<argc) total_steps = atoi(argv[++i]);
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else if (strcmp(argv[i], "--lr") == 0 && i+1<argc) lr = atof(argv[++i]);
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else if (strcmp(argv[i], "--ckpt") == 0 && i+1<argc) ckpt_path = argv[++i];
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else if (strcmp(argv[i], "--model") == 0 && i+1<argc) model_path = argv[++i];
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else if (argv[i][0] != '-') {
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if (pos == 0) model_path = argv[i];
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else if (pos == 1) { /* seq - compile-time constant */ }
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else if (pos == 2) total_steps = atoi(argv[i]);
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else if (pos == 3) lr = atof(argv[i]);
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pos++;
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}
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}
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// Allocate per-layer state
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LayerWeights lw[NLAYERS];
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LayerAdam la[NLAYERS];
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LayerActs acts[NLAYERS];
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LayerGrads grads[NLAYERS];
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LayerKernels kern[NLAYERS];
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for (int L=0; L<NLAYERS; L++) {
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lw[L] = layer_weights_alloc();
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la[L] = layer_adam_alloc();
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acts[L] = layer_acts_alloc();
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grads[L] = layer_grads_alloc();
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memset(&kern[L], 0, sizeof(LayerKernels));
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}
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// Final RMSNorm + embedding + classifier
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float *rms_final = (float*)malloc(DIM*4);
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float *embed = (float*)malloc(VOCAB*DIM*4); // [VOCAB, DIM] row-major
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float *grms_final = (float*)calloc(DIM, 4);
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float *gembed = (float*)calloc(VOCAB*DIM, 4);
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AdamState arms_final = adam_alloc(DIM);
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AdamState aembed = adam_alloc((size_t)VOCAB*DIM);
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double cum_compile=0, cum_train=0, cum_wall=0;
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int cum_steps=0, cum_batches=0;
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float resume_loss = 0;
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bool resuming = false;
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if (do_resume) {
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resuming = load_checkpoint(ckpt_path, &start_step, &total_steps, &lr, &resume_loss,
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&cum_compile, &cum_train, &cum_wall, &cum_steps, &cum_batches, &adam_t,
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lw, la, rms_final, &arms_final, embed, &aembed);
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if (resuming) printf("[RESUMED step %d, loss=%.4f]\n", start_step, resume_loss);
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}
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if (!resuming) {
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printf("=== ANE Training: Stories110M (12 layers) ===\n");
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printf("dim=%d hidden=%d heads=%d seq=%d vocab=%d layers=%d\n", DIM, HIDDEN, HEADS, SEQ, VOCAB, NLAYERS);
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printf("model=%s data=%s ckpt=%s\n", model_path, DATA_PATH, ckpt_path);
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if (!load_pretrained(lw, rms_final, embed, model_path)) {
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printf("Pretrained load failed, using random init\n");
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srand48(42);
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float scale_d=1.0f/sqrtf(DIM), scale_h=1.0f/sqrtf(HIDDEN);
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for (int L=0; L<NLAYERS; L++) {
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for(size_t i=0;i<WQ_SZ;i++){lw[L].Wq[i]=scale_d*(2*drand48()-1);lw[L].Wk[i]=scale_d*(2*drand48()-1);}
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for(size_t i=0;i<WQ_SZ;i++){lw[L].Wv[i]=scale_d*(2*drand48()-1);lw[L].Wo[i]=scale_d*(2*drand48()-1);}
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for(size_t i=0;i<W1_SZ;i++) lw[L].W1[i]=scale_h*(2*drand48()-1);
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for(size_t i=0;i<W2_SZ;i++) lw[L].W2[i]=scale_d*(2*drand48()-1);
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for(size_t i=0;i<W3_SZ;i++) lw[L].W3[i]=scale_h*(2*drand48()-1);
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for(int i=0;i<DIM;i++){lw[L].rms_att[i]=1.0f; lw[L].rms_ffn[i]=1.0f;}
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}
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for(int i=0;i<DIM;i++) rms_final[i]=1.0f;
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float escale = 0.02f;
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for(size_t i=0;i<(size_t)VOCAB*DIM;i++) embed[i]=escale*(2*drand48()-1);
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}
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size_t tp = (size_t)NLAYERS*LAYER_PARAMS + DIM + (size_t)VOCAB*DIM;
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double xfmr_params = (double)NLAYERS*LAYER_PARAMS;
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double embed_params = (double)VOCAB*DIM;
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printf("Params: %.2fM (transformer %.2fM + embed %.2fM)\n", tp/1e6, xfmr_params/1e6, embed_params/1e6);
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printf("Kernels: %d (%d weight-bearing + %d static sdpaBwd2)\n",
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TOTAL_WEIGHT_KERNELS+NLAYERS, TOTAL_WEIGHT_KERNELS, NLAYERS);
|
||
printf("Accum %d steps per recompile | Adam LR=%.1e b1=%.1f b2=%.3f\n", ACCUM_STEPS, lr, adam_b1, adam_b2);
|
||
double fwd_f = NLAYERS*(4.0*2*DIM*DIM*SEQ + 2.0*2*DIM*HIDDEN*SEQ + 2.0*HIDDEN*DIM*SEQ);
|
||
double bwd_dx_f = fwd_f, bwd_dw_f = fwd_f;
|
||
double sdpa_f = NLAYERS*2.0*HEADS*5*SEQ*SEQ*HD;
|
||
double cls_f = 2.0*VOCAB*DIM*SEQ;
|
||
double total_f = fwd_f + bwd_dx_f + bwd_dw_f + sdpa_f + cls_f*3;
|
||
double ane_f = fwd_f + bwd_dx_f + sdpa_f;
|
||
printf("FLOPs/step: fwd=%.0fM bwd_dx=%.0fM bwd_dW=%.0fM sdpa_bwd=%.0fM total=%.0fM\n",
|
||
fwd_f/1e6, bwd_dx_f/1e6, bwd_dw_f/1e6, sdpa_f/1e6, total_f/1e6);
|
||
printf("ANE FLOPs/step: %.0fM (fwd+bwd_dx+sdpa_bwd) | CPU: dW+cls (cblas)\n\n", ane_f/1e6);
|
||
}
|
||
|
||
// mmap token data
|
||
int data_fd = open(DATA_PATH, O_RDONLY);
|
||
if (data_fd < 0) { printf("Cannot open %s\n", DATA_PATH); return 1; }
|
||
struct stat st; fstat(data_fd, &st);
|
||
size_t data_len = st.st_size;
|
||
uint16_t *token_data = (uint16_t*)mmap(NULL, data_len, PROT_READ, MAP_PRIVATE, data_fd, 0);
|
||
if (token_data == MAP_FAILED) { printf("mmap failed\n"); return 1; }
|
||
size_t n_tokens = data_len / 2;
|
||
if (n_tokens <= (size_t)(SEQ + 1)) {
|
||
printf("Token data too short: need at least %d tokens, got %zu\n", SEQ + 2, n_tokens);
|
||
munmap(token_data, data_len);
|
||
close(data_fd);
|
||
return 1;
|
||
}
|
||
printf("Token data: %zu tokens (%.1f MB)\n", n_tokens, data_len/1e6);
|
||
|
||
// Gradient buffers shared across layers (reused each step)
|
||
float *dy = (float*)malloc(SEQ*DIM*4); // gradient flowing backward
|
||
float *dffn = (float*)malloc(SEQ*DIM*4);
|
||
float *dh1 = (float*)malloc(SEQ*HIDDEN*4);
|
||
float *dh3 = (float*)malloc(SEQ*HIDDEN*4);
|
||
float *dx_ffn = (float*)malloc(SEQ*DIM*4);
|
||
float *dx2 = (float*)malloc(SEQ*DIM*4);
|
||
float *do_out_buf = (float*)malloc(SEQ*DIM*4);
|
||
float *dq = (float*)malloc(SEQ*DIM*4);
|
||
float *dk = (float*)malloc(SEQ*DIM*4);
|
||
float *dv = (float*)malloc(SEQ*DIM*4);
|
||
float *dx_attn = (float*)malloc(SEQ*DIM*4);
|
||
|
||
// x buffer for input to each layer (channel-first [DIM, SEQ])
|
||
float *x_cur = (float*)malloc(SEQ*DIM*4);
|
||
float *x_final = (float*)malloc(SEQ*DIM*4); // after final rmsnorm
|
||
float *logits = (float*)malloc(SEQ*VOCAB*4); // [VOCAB, SEQ] for cross-entropy
|
||
float *dlogits = (float*)malloc(SEQ*VOCAB*4);
|
||
|
||
// Compile static sdpaBwd2 kernels (no weights, one per layer)
|
||
Kern *sdpaBwd2[NLAYERS];
|
||
for (int L=0; L<NLAYERS; L++) {
|
||
sdpaBwd2[L] = compile_sdpa_bwd2();
|
||
if (!sdpaBwd2[L]) { printf("sdpaBwd2 compile failed\n"); return 1; }
|
||
}
|
||
|
||
dispatch_queue_t dw_q = dispatch_queue_create("dw_cblas", DISPATCH_QUEUE_SERIAL);
|
||
dispatch_group_t dw_grp = dispatch_group_create();
|
||
|
||
float last_loss = 999.0f;
|
||
double total_compile_ms=0, total_train_ms=0;
|
||
int total_steps_done=0, total_batches=0;
|
||
uint64_t t_wall_start = mach_absolute_time();
|
||
|
||
srand48(42 + start_step);
|
||
|
||
int step = start_step;
|
||
while (step < total_steps) {
|
||
// Check compile budget
|
||
if (g_compile_count + TOTAL_WEIGHT_KERNELS > MAX_COMPILES) {
|
||
for (int L=0; L<NLAYERS; L++) { free_layer_kernels(&kern[L]); free_kern(sdpaBwd2[L]); }
|
||
double wall = tb_ms(mach_absolute_time() - t_wall_start);
|
||
save_checkpoint(ckpt_path, step, total_steps, lr, last_loss,
|
||
total_compile_ms+cum_compile, total_train_ms+cum_train, wall+cum_wall,
|
||
total_steps_done+cum_steps, total_batches+cum_batches, adam_t,
|
||
lw, la, rms_final, &arms_final, embed, &aembed);
|
||
printf("[exec() restart step %d, %d compiles, loss=%.4f]\n", step, g_compile_count, last_loss);
|
||
fflush(stdout);
|
||
execl(argv[0], argv[0], "--resume", "--ckpt", ckpt_path, NULL);
|
||
perror("execl"); return 1;
|
||
}
|
||
|
||
// Compile all layers' weight-bearing kernels
|
||
uint64_t tc = mach_absolute_time();
|
||
for (int L=0; L<NLAYERS; L++) free_layer_kernels(&kern[L]);
|
||
|
||
bool compile_ok = true;
|
||
for (int L=0; L<NLAYERS; L++) {
|
||
printf(" Compiling layer %d/%d... (%d compiles)\r", L+1, NLAYERS, g_compile_count);
|
||
fflush(stdout);
|
||
if (!compile_layer_kernels(&kern[L], &lw[L])) {
|
||
printf("\nCompile failed at layer %d, restart\n", L);
|
||
compile_ok = false; break;
|
||
}
|
||
}
|
||
if (!compile_ok) { g_compile_count = MAX_COMPILES; continue; }
|
||
|
||
// Re-compile sdpaBwd2 if needed (after exec restart)
|
||
for (int L=0; L<NLAYERS; L++) {
|
||
if (!sdpaBwd2[L]) {
|
||
sdpaBwd2[L] = compile_sdpa_bwd2();
|
||
if (!sdpaBwd2[L]) { printf("sdpaBwd2 recompile failed\n"); return 1; }
|
||
}
|
||
}
|
||
|
||
double cms = tb_ms(mach_absolute_time() - tc);
|
||
total_compile_ms += cms;
|
||
printf(" Compiled %d kernels in %.0fms \n", TOTAL_WEIGHT_KERNELS, cms);
|
||
|
||
// Zero gradient accumulators
|
||
for (int L=0; L<NLAYERS; L++) layer_grads_zero(&grads[L]);
|
||
memset(grms_final, 0, DIM*4);
|
||
memset(gembed, 0, (size_t)VOCAB*DIM*4);
|
||
|
||
int steps_batch = 0;
|
||
uint64_t tt = mach_absolute_time();
|
||
double t_ane=0,t_io=0,t_elem=0,t_rms=0,t_cblas_wait=0,t_cls=0;
|
||
|
||
for (int a=0; a<ACCUM_STEPS && step<total_steps; a++, step++) {
|
||
uint64_t t0,t1;
|
||
// Sample random position in token data
|
||
size_t max_pos = n_tokens - SEQ - 1;
|
||
size_t pos = (size_t)(drand48() * max_pos);
|
||
uint16_t *input_tokens = token_data + pos;
|
||
uint16_t *target_tokens = token_data + pos + 1;
|
||
|
||
// Embedding lookup → x_cur [DIM, SEQ] channel-first
|
||
t0=mach_absolute_time();
|
||
embed_lookup(x_cur, embed, input_tokens, DIM, SEQ);
|
||
t1=mach_absolute_time(); t_elem+=tb_ms(t1-t0);
|
||
|
||
// ===== FORWARD (12 layers) =====
|
||
for (int L=0; L<NLAYERS; L++) {
|
||
LayerActs *ac = &acts[L];
|
||
|
||
// Save layer input for rmsnorm1 backward
|
||
memcpy(ac->layer_in, x_cur, SEQ*DIM*4);
|
||
// Attention forward: x_cur → o_out,Q,K,V,attn_out,xnorm
|
||
t0=mach_absolute_time();
|
||
dispatch_group_wait(dw_grp, DISPATCH_TIME_FOREVER);
|
||
t1=mach_absolute_time(); t_cblas_wait+=tb_ms(t1-t0); t0=t1;
|
||
io_write_fp16(kern[L].fwdAttn->ioIn, x_cur, DIM, SEQ);
|
||
t1=mach_absolute_time(); t_io+=tb_ms(t1-t0); t0=t1;
|
||
ane_eval(kern[L].fwdAttn);
|
||
t1=mach_absolute_time(); t_ane+=tb_ms(t1-t0); t0=t1;
|
||
io_read_fp16(kern[L].fwdAttn->ioOut, ac->o_out, 0, DIM, SEQ);
|
||
io_read_fp16(kern[L].fwdAttn->ioOut, ac->attn_out, 4*DIM, DIM, SEQ);
|
||
io_read_fp16(kern[L].fwdAttn->ioOut, ac->xnorm, 5*DIM, DIM, SEQ);
|
||
t1=mach_absolute_time(); t_io+=tb_ms(t1-t0); t0=t1;
|
||
|
||
vDSP_vadd(x_cur, 1, ac->o_out, 1, ac->x2, 1, (vDSP_Length)(SEQ*DIM));
|
||
t1=mach_absolute_time(); t_elem+=tb_ms(t1-t0); t0=t1;
|
||
|
||
// FFN forward
|
||
io_write_fp16(kern[L].fwdFFN->ioIn, ac->x2, DIM, SEQ);
|
||
t1=mach_absolute_time(); t_io+=tb_ms(t1-t0); t0=t1;
|
||
ane_eval(kern[L].fwdFFN);
|
||
t1=mach_absolute_time(); t_ane+=tb_ms(t1-t0); t0=t1;
|
||
io_read_fp16(kern[L].fwdFFN->ioOut, ac->ffn_out, 0, DIM, SEQ);
|
||
io_read_fp16(kern[L].fwdFFN->ioOut, ac->h1, DIM, HIDDEN, SEQ);
|
||
io_read_fp16(kern[L].fwdFFN->ioOut, ac->h3, DIM+HIDDEN, HIDDEN, SEQ);
|
||
io_read_fp16(kern[L].fwdFFN->ioOut, ac->silu_out, DIM+2*HIDDEN, HIDDEN, SEQ);
|
||
io_read_fp16(kern[L].fwdFFN->ioOut, ac->x2norm, DIM+3*HIDDEN, DIM, SEQ);
|
||
t1=mach_absolute_time(); t_io+=tb_ms(t1-t0); t0=t1;
|
||
|
||
vDSP_vadd(ac->x2, 1, ac->ffn_out, 1, x_cur, 1, (vDSP_Length)(SEQ*DIM));
|
||
t1=mach_absolute_time(); t_elem+=tb_ms(t1-t0);
|
||
}
|
||
|
||
// Final RMSNorm (CPU)
|
||
t0=mach_absolute_time();
|
||
rmsnorm(x_final, x_cur, rms_final, DIM, SEQ);
|
||
t1=mach_absolute_time(); t_rms+=tb_ms(t1-t0); t0=t1;
|
||
|
||
// Classifier: logits = embed^T @ x_final
|
||
cblas_sgemm(CblasRowMajor, CblasNoTrans, CblasNoTrans,
|
||
VOCAB, SEQ, DIM, 1.0f,
|
||
embed, DIM, x_final, SEQ, 0.0f, logits, SEQ);
|
||
t1=mach_absolute_time(); t_cls+=tb_ms(t1-t0); t0=t1;
|
||
|
||
// Cross-entropy loss
|
||
float loss = cross_entropy_loss(dlogits, logits, target_tokens, VOCAB, SEQ);
|
||
last_loss = loss;
|
||
t1=mach_absolute_time(); t_elem+=tb_ms(t1-t0); t0=t1;
|
||
|
||
// ===== BACKWARD =====
|
||
// dlogits already computed by cross_entropy_loss
|
||
|
||
// Classifier backward: dx_final = embed^T @ dlogits, dembed += dlogits @ x_final^T
|
||
// dx_final[DIM,SEQ] = embed^T[DIM,VOCAB] @ dlogits[VOCAB,SEQ]
|
||
cblas_sgemm(CblasRowMajor, CblasTrans, CblasNoTrans,
|
||
DIM, SEQ, VOCAB, 1.0f,
|
||
embed, DIM, dlogits, SEQ, 0.0f, dy, SEQ);
|
||
|
||
// dembed[VOCAB,DIM] += dlogits[VOCAB,SEQ] @ x_final^T[SEQ,DIM]
|
||
dispatch_group_async(dw_grp, dw_q, ^{
|
||
cblas_sgemm(CblasRowMajor, CblasNoTrans, CblasTrans,
|
||
VOCAB, DIM, SEQ, 1.0f,
|
||
dlogits, SEQ, x_final, SEQ, 1.0f, gembed, DIM);
|
||
});
|
||
|
||
// Final RMSNorm backward
|
||
float *dx_rms_final = (float*)calloc(SEQ*DIM, 4);
|
||
rmsnorm_bwd(dx_rms_final, grms_final, dy, x_cur, rms_final, DIM, SEQ);
|
||
memcpy(dy, dx_rms_final, SEQ*DIM*4);
|
||
free(dx_rms_final);
|
||
|
||
// ===== BACKWARD (12 layers, reverse) =====
|
||
for (int L=NLAYERS-1; L>=0; L--) {
|
||
LayerActs *ac = &acts[L];
|
||
LayerGrads *gr = &grads[L];
|
||
|
||
// dy is the gradient at the output of this layer
|
||
// dffn = dy (residual connection: d(x2 + ffn) = dy for both)
|
||
memcpy(dffn, dy, SEQ*DIM*4);
|
||
|
||
// FFN backward (ANE)
|
||
io_write_fp16_at(kern[L].ffnBwd->ioIn, 0, dffn, DIM, SEQ);
|
||
io_copy(kern[L].ffnBwd->ioIn, DIM, kern[L].fwdFFN->ioOut, DIM, 2*HIDDEN, SEQ);
|
||
ane_eval(kern[L].ffnBwd);
|
||
io_read_fp16(kern[L].ffnBwd->ioOut, dx_ffn, 0, DIM, SEQ);
|
||
io_read_fp16(kern[L].ffnBwd->ioOut, dh1, DIM, HIDDEN, SEQ);
|
||
io_read_fp16(kern[L].ffnBwd->ioOut, dh3, DIM+HIDDEN, HIDDEN, SEQ);
|
||
|
||
// dW FFN async
|
||
float *capt_dffn = (float*)malloc(SEQ*DIM*4); memcpy(capt_dffn, dffn, SEQ*DIM*4);
|
||
float *capt_silu = (float*)malloc(SEQ*HIDDEN*4); memcpy(capt_silu, ac->silu_out, SEQ*HIDDEN*4);
|
||
float *capt_dh1 = (float*)malloc(SEQ*HIDDEN*4); memcpy(capt_dh1, dh1, SEQ*HIDDEN*4);
|
||
float *capt_dh3 = (float*)malloc(SEQ*HIDDEN*4); memcpy(capt_dh3, dh3, SEQ*HIDDEN*4);
|
||
float *capt_x2n = (float*)malloc(SEQ*DIM*4); memcpy(capt_x2n, ac->x2norm, SEQ*DIM*4);
|
||
dispatch_group_async(dw_grp, dw_q, ^{
|
||
cblas_sgemm(CblasRowMajor, CblasNoTrans, CblasTrans, DIM, HIDDEN, SEQ,
|
||
1.0f, capt_dffn, SEQ, capt_silu, SEQ, 1.0f, gr->W2, HIDDEN);
|
||
cblas_sgemm(CblasRowMajor, CblasNoTrans, CblasTrans, HIDDEN, DIM, SEQ,
|
||
1.0f, capt_dh1, SEQ, capt_x2n, SEQ, 1.0f, gr->W1, DIM);
|
||
cblas_sgemm(CblasRowMajor, CblasNoTrans, CblasTrans, HIDDEN, DIM, SEQ,
|
||
1.0f, capt_dh3, SEQ, capt_x2n, SEQ, 1.0f, gr->W3, DIM);
|
||
free(capt_dffn); free(capt_silu); free(capt_dh1); free(capt_dh3); free(capt_x2n);
|
||
});
|
||
|
||
// RMSNorm2 backward
|
||
memset(dx2, 0, SEQ*DIM*4);
|
||
rmsnorm_bwd(dx2, gr->rms_ffn, dx_ffn, ac->x2, lw[L].rms_ffn, DIM, SEQ);
|
||
// Add residual: dx2 += dy (from skip connection)
|
||
for(int i=0;i<SEQ*DIM;i++) dx2[i] += dy[i];
|
||
|
||
// dWo async
|
||
memcpy(do_out_buf, dx2, SEQ*DIM*4);
|
||
float *capt_do = (float*)malloc(SEQ*DIM*4); memcpy(capt_do, do_out_buf, SEQ*DIM*4);
|
||
float *capt_attn = (float*)malloc(SEQ*DIM*4); memcpy(capt_attn, ac->attn_out, SEQ*DIM*4);
|
||
dispatch_group_async(dw_grp, dw_q, ^{
|
||
cblas_sgemm(CblasRowMajor, CblasNoTrans, CblasTrans, DIM, DIM, SEQ,
|
||
1.0f, capt_do, SEQ, capt_attn, SEQ, 1.0f, gr->Wo, DIM);
|
||
free(capt_do); free(capt_attn);
|
||
});
|
||
|
||
// SDPA backward (ANE)
|
||
io_copy(kern[L].sdpaBwd1->ioIn, 0, kern[L].fwdAttn->ioOut, DIM, 3*DIM, SEQ);
|
||
io_write_fp16_at(kern[L].sdpaBwd1->ioIn, 3*DIM, dx2, DIM, SEQ);
|
||
ane_eval(kern[L].sdpaBwd1);
|
||
io_copy(sdpaBwd2[L]->ioIn, 0, kern[L].sdpaBwd1->ioOut, DIM, 2*SCORE_CH, SEQ);
|
||
io_copy(sdpaBwd2[L]->ioIn, 2*SCORE_CH, kern[L].fwdAttn->ioOut, DIM, 2*DIM, SEQ);
|
||
ane_eval(sdpaBwd2[L]);
|
||
|
||
io_read_fp16(sdpaBwd2[L]->ioOut, dq, 0, DIM, SEQ);
|
||
io_read_fp16(sdpaBwd2[L]->ioOut, dk, DIM, DIM, SEQ);
|
||
io_read_fp16(kern[L].sdpaBwd1->ioOut, dv, 0, DIM, SEQ);
|
||
|
||
// dWq/dWk/dWv async
|
||
float *capt_dq = (float*)malloc(SEQ*DIM*4); memcpy(capt_dq, dq, SEQ*DIM*4);
|
||
float *capt_dk = (float*)malloc(SEQ*DIM*4); memcpy(capt_dk, dk, SEQ*DIM*4);
|
||
float *capt_dv = (float*)malloc(SEQ*DIM*4); memcpy(capt_dv, dv, SEQ*DIM*4);
|
||
float *capt_xn = (float*)malloc(SEQ*DIM*4); memcpy(capt_xn, ac->xnorm, SEQ*DIM*4);
|
||
dispatch_group_async(dw_grp, dw_q, ^{
|
||
cblas_sgemm(CblasRowMajor, CblasNoTrans, CblasTrans, DIM, DIM, SEQ,
|
||
1.0f, capt_dq, SEQ, capt_xn, SEQ, 1.0f, gr->Wq, DIM);
|
||
cblas_sgemm(CblasRowMajor, CblasNoTrans, CblasTrans, DIM, DIM, SEQ,
|
||
1.0f, capt_dk, SEQ, capt_xn, SEQ, 1.0f, gr->Wk, DIM);
|
||
cblas_sgemm(CblasRowMajor, CblasNoTrans, CblasTrans, DIM, DIM, SEQ,
|
||
1.0f, capt_dv, SEQ, capt_xn, SEQ, 1.0f, gr->Wv, DIM);
|
||
free(capt_dq); free(capt_dk); free(capt_dv); free(capt_xn);
|
||
});
|
||
|
||
// QKV backward (ANE)
|
||
io_copy(kern[L].qkvBwd->ioIn, 0, sdpaBwd2[L]->ioOut, 0, 2*DIM, SEQ);
|
||
io_copy(kern[L].qkvBwd->ioIn, 2*DIM, kern[L].sdpaBwd1->ioOut, 0, DIM, SEQ);
|
||
ane_eval(kern[L].qkvBwd);
|
||
io_read_fp16(kern[L].qkvBwd->ioOut, dx_attn, 0, DIM, SEQ);
|
||
|
||
// RMSNorm1 backward (using saved layer input)
|
||
float *dx_rms1 = (float*)calloc(SEQ*DIM, 4);
|
||
rmsnorm_bwd(dx_rms1, gr->rms_att, dx_attn, ac->layer_in, lw[L].rms_att, DIM, SEQ);
|
||
|
||
// dy for next layer (going backward) = dx_rms1 + dx2 residual
|
||
// Actually: layer output = layer_input + o_out, and x2 = layer_input + o_out
|
||
// So dx(layer_input) = dx_attn_rmsnorm + dx2 (residual from attn skip)
|
||
// Wait, dx2 already includes the attn skip residual gradient.
|
||
// dy = dx_rms1 (through rmsnorm1) is the gradient to the layer input
|
||
// But there's also the skip connection: layer_input → x2 directly
|
||
// So total gradient to layer_input = dx_rms1 + dx2_skip
|
||
// dx2 was computed as rmsnorm2_bwd + dy(ffn_skip), which already flows to x2
|
||
// x2 = layer_input + o_out, so d(layer_input) from x2 path = dx2
|
||
// And d(layer_input) from attn path through rmsnorm1 = dx_rms1
|
||
// Total: dy_prev = dx_rms1 (attn rmsnorm path)
|
||
// Wait no - dx2 = d(loss)/d(x2), not d(loss)/d(layer_input)
|
||
// d(layer_input) = d(loss)/d(x2) * d(x2)/d(layer_input) = dx2 (since x2 = input + o_out, d(x2)/d(input) = 1)
|
||
// Plus the path through rmsnorm1: dx_rms1
|
||
// Hmm but dx2 was already used as input to SDPA backward... let me reconsider.
|
||
//
|
||
// Actually the gradient flow is:
|
||
// dy → split to (dffn, dy_skip) [dy_skip = dy due to residual]
|
||
// dffn → ffnBwd → dx_ffn
|
||
// dx_ffn → rmsnorm2_bwd → dx_rms2
|
||
// dx2 = dx_rms2 + dy (skip connection from residual x2 → output)
|
||
// dx2 → sdpaBwd → dx_attn through Wo^T
|
||
// dx_attn → qkvBwd → dx_qkv
|
||
// dx_qkv → rmsnorm1_bwd → dx_rms1
|
||
// dy_prev_layer = dx_rms1 + dx2 (skip connection input → x2)
|
||
//
|
||
// So: dy for previous layer = dx_rms1 + dx2
|
||
for(int i=0;i<SEQ*DIM;i++) dy[i] = dx_rms1[i] + dx2[i];
|
||
free(dx_rms1);
|
||
}
|
||
|
||
// Embedding backward
|
||
dispatch_group_wait(dw_grp, DISPATCH_TIME_FOREVER);
|
||
embed_backward(gembed, dy, input_tokens, DIM, SEQ);
|
||
|
||
steps_batch++;
|
||
if (step % 10 == 0 || step == start_step)
|
||
printf("step %-4d loss=%.4f\n", step, loss);
|
||
|
||
// JSON telemetry to stderr
|
||
double step_ane = t_ane/steps_batch, step_io = t_io/steps_batch;
|
||
double step_cls = t_cls/steps_batch, step_elem = t_elem/steps_batch;
|
||
double step_rms = t_rms/steps_batch, step_cbw = t_cblas_wait/steps_batch;
|
||
fprintf(stderr, "{\"type\":\"step\",\"step\":%d,\"loss\":%.6f,"
|
||
"\"t_ane\":%.3f,\"t_io\":%.3f,\"t_cls\":%.3f,"
|
||
"\"t_elem\":%.3f,\"t_rms\":%.3f,\"t_cblas_wait\":%.3f,"
|
||
"\"compiles\":%d}\n",
|
||
step, loss, step_ane, step_io, step_cls, step_elem, step_rms, step_cbw, g_compile_count);
|
||
}
|
||
double tms = tb_ms(mach_absolute_time() - tt);
|
||
total_train_ms += tms;
|
||
total_steps_done += steps_batch;
|
||
total_batches++;
|
||
|
||
// Ensure all async dW finished
|
||
dispatch_group_wait(dw_grp, DISPATCH_TIME_FOREVER);
|
||
|
||
// Adam update (scale gradients by 1/steps_batch)
|
||
float gsc = 1.0f / steps_batch;
|
||
adam_t++;
|
||
for (int L=0; L<NLAYERS; L++) {
|
||
LayerGrads *g = &grads[L];
|
||
for(size_t i=0;i<WQ_SZ;i++){g->Wq[i]*=gsc;g->Wk[i]*=gsc;g->Wv[i]*=gsc;g->Wo[i]*=gsc;}
|
||
for(size_t i=0;i<W1_SZ;i++) g->W1[i]*=gsc;
|
||
for(size_t i=0;i<W2_SZ;i++) g->W2[i]*=gsc;
|
||
for(size_t i=0;i<W3_SZ;i++) g->W3[i]*=gsc;
|
||
for(int i=0;i<DIM;i++){g->rms_att[i]*=gsc; g->rms_ffn[i]*=gsc;}
|
||
|
||
adam_update(lw[L].Wq, g->Wq, &la[L].Wq, adam_t, lr, adam_b1, adam_b2, adam_eps);
|
||
adam_update(lw[L].Wk, g->Wk, &la[L].Wk, adam_t, lr, adam_b1, adam_b2, adam_eps);
|
||
adam_update(lw[L].Wv, g->Wv, &la[L].Wv, adam_t, lr, adam_b1, adam_b2, adam_eps);
|
||
adam_update(lw[L].Wo, g->Wo, &la[L].Wo, adam_t, lr, adam_b1, adam_b2, adam_eps);
|
||
adam_update(lw[L].W1, g->W1, &la[L].W1, adam_t, lr, adam_b1, adam_b2, adam_eps);
|
||
adam_update(lw[L].W2, g->W2, &la[L].W2, adam_t, lr, adam_b1, adam_b2, adam_eps);
|
||
adam_update(lw[L].W3, g->W3, &la[L].W3, adam_t, lr, adam_b1, adam_b2, adam_eps);
|
||
adam_update(lw[L].rms_att, g->rms_att, &la[L].rms_att, adam_t, lr, adam_b1, adam_b2, adam_eps);
|
||
adam_update(lw[L].rms_ffn, g->rms_ffn, &la[L].rms_ffn, adam_t, lr, adam_b1, adam_b2, adam_eps);
|
||
}
|
||
for(int i=0;i<DIM;i++) grms_final[i]*=gsc;
|
||
adam_update(rms_final, grms_final, &arms_final, adam_t, lr, adam_b1, adam_b2, adam_eps);
|
||
// Scale and update embed
|
||
for(size_t i=0;i<(size_t)VOCAB*DIM;i++) gembed[i]*=gsc;
|
||
adam_update(embed, gembed, &aembed, adam_t, lr, adam_b1, adam_b2, adam_eps);
|
||
|
||
printf(" [batch %d: compile=%.0fms train=%.1fms (%.1fms/step) compiles=%d]\n",
|
||
steps_batch, cms, tms, tms/steps_batch, g_compile_count);
|
||
printf(" ane=%.1f io=%.1f cls=%.1f elem=%.1f rms=%.1f cblas_wait=%.1f ms/step\n",
|
||
t_ane/steps_batch, t_io/steps_batch, t_cls/steps_batch, t_elem/steps_batch,
|
||
t_rms/steps_batch, t_cblas_wait/steps_batch);
|
||
|
||
// JSON batch telemetry to stderr
|
||
{
|
||
double bf = NLAYERS * (4.0*2*DIM*DIM*SEQ + 2.0*2*DIM*HIDDEN*SEQ + 2.0*HIDDEN*DIM*SEQ);
|
||
double bs = NLAYERS * 2.0*HEADS*5*SEQ*SEQ*HD;
|
||
double ane_f_batch = (bf*2 + bs) * steps_batch;
|
||
double ane_tflops = ane_f_batch / (tms * 1e9);
|
||
fprintf(stderr, "{\"type\":\"batch\",\"batch\":%d,\"compile_ms\":%.1f,"
|
||
"\"train_ms\":%.1f,\"ms_per_step\":%.1f}\n",
|
||
steps_batch, cms, tms, tms/steps_batch);
|
||
fprintf(stderr, "{\"type\":\"perf\",\"ane_tflops\":%.3f,\"ane_util_pct\":%.2f}\n",
|
||
ane_tflops, 100.0*ane_tflops/15.8);
|
||
}
|
||
}
|
||
|
||
// Efficiency report
|
||
double wall = tb_ms(mach_absolute_time() - t_wall_start);
|
||
total_compile_ms += cum_compile; total_train_ms += cum_train;
|
||
wall += cum_wall; total_steps_done += cum_steps; total_batches += cum_batches;
|
||
double fwd_flops = NLAYERS * (4.0*2*DIM*DIM*SEQ + 2.0*2*DIM*HIDDEN*SEQ + 2.0*HIDDEN*DIM*SEQ);
|
||
double sdpa_flops = NLAYERS * 2.0*HEADS*5*SEQ*SEQ*HD;
|
||
double cls_flops = 2.0*VOCAB*DIM*SEQ;
|
||
double total_flops = (fwd_flops*3 + sdpa_flops + cls_flops*3) * total_steps_done;
|
||
double ane_flops = (fwd_flops*2 + sdpa_flops) * total_steps_done;
|
||
printf("\n=== Efficiency Report ===\n");
|
||
printf("Total steps: %d\n", total_steps_done);
|
||
printf("Wall time: %.0f ms (%.1f s)\n", wall, wall/1000);
|
||
printf("Compile time: %.0f ms (%.1f%%)\n", total_compile_ms, 100*total_compile_ms/wall);
|
||
printf("Train time: %.0f ms (%.1f%%)\n", total_train_ms, 100*total_train_ms/wall);
|
||
printf("Avg train: %.1f ms/step\n", total_train_ms/total_steps_done);
|
||
printf("ANE TFLOPS: %.2f sustained\n", ane_flops / (total_train_ms * 1e9));
|
||
printf("Total TFLOPS: %.2f (ANE+CPU)\n", total_flops / (total_train_ms * 1e9));
|
||
printf("ANE utilization: %.1f%% of 15.8 TFLOPS\n", 100*ane_flops/(total_train_ms*1e9)/15.8);
|
||
|
||
// Cleanup
|
||
for (int L=0; L<NLAYERS; L++) {
|
||
free_layer_kernels(&kern[L]);
|
||
free_kern(sdpaBwd2[L]);
|
||
layer_weights_free(&lw[L]);
|
||
layer_adam_free(&la[L]);
|
||
layer_acts_free(&acts[L]);
|
||
layer_grads_free(&grads[L]);
|
||
}
|
||
munmap(token_data, data_len);
|
||
close(data_fd);
|
||
free(rms_final); free(embed); free(grms_final); free(gembed);
|
||
adam_free(&arms_final); adam_free(&aembed);
|
||
free(dy); free(dffn); free(dh1); free(dh3); free(dx_ffn); free(dx2);
|
||
free(do_out_buf); free(dq); free(dk); free(dv); free(dx_attn);
|
||
free(x_cur); free(x_final); free(logits); free(dlogits);
|
||
}
|
||
return 0;
|
||
}
|