nx_llm_run.nx source
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1// nx_llm_run.nx -- end-to-end LLM inference scaffold.
2//
3// L5 integration brick. Closes the SUBSTRATE COMPLETENESS PROOF:
4// every primitive shipped this session (BPE + embedding + RMSNorm +
5// attention + RoPE + SiLU + sampler + ...) composes into a SINGLE
6// callable that maps text -> next-token-text. Per the bits-up
7// cardinal: no new math, no new kernels -- pure composition.
8//
9// ===== End-to-end pipeline =======================================
10//
11// text input
12// -> nx_bpe_encode (token_ids)
13// -> nx_embedding_lookup or _q8 (input vectors)
14// -> for each layer in 0..n_layers:
15// nx_transformer_block_forward
16// -> final RMSNorm (or LayerNorm) on output
17// -> output projection (matmul to vocab_size)
18// -> nx_attn_softmax_row_q10 (treat logits as 1-row tensor)
19// -> nx_logit_apply_temperature
20// -> nx_logit_top_k_mask
21// -> nx_sample_categorical (next token)
22// -> nx_bpe_decode (token -> text bytes)
23//
24// ===== Per the bits-up cardinal ===================================
25//
26// This file ONLY composes existing primitives. No new structs, no
27// new sealed enums, no new math. Imports surface every layer:
28//
29// nx_tensor (NxTensor L1 container)
30// nx_image (only via downstream consumers; not direct)
31// nx_bpe (text <-> tokens)
32// nx_embedding (token -> vector)
33// nx_model_spec (config envelope)
34// nx_transformer_block (per-layer compute)
35// nx_rmsnorm / nx_layernorm (final norm)
36// nx_attention (softmax + scale)
37// nx_token_sample (logits -> token)
38// nx_loop (bounded loops)
39//
40// ===== v1 scope ===================================================
41//
42// Synthetic-data smoke. We construct a tiny model spec + zeroed
43// weights + a 4-token "test" prompt and walk it through the pipeline
44// to prove every API composes. The output token is meaningless
45// (zero weights -> uniform logits) but the SHAPES and VERDICTS
46// must all pass.
47//
48// Real GGUF-backed run (load Llama-7B weights, generate a real
49// completion) waits on:
50// * Mass weight loader (nx_gguf already shipped) wired into the
51// transformer-block weight bundle struct.
52// * Host-side memory budget (the substrate must alloc ~6 GB for
53// q4_K-quant Llama-7B weights).
54// * Real BPE vocab file loaded into nx_intern.
55//
56// Those are the next workstream. THIS file is the API-composition
57// proof.
58//
59// genealogy_id: gpt2_inference_loop_radford_2019 +
60// llama_cpp_main_loop_gerganov_2023 +
61// pytorch_generate_pattern
62// lineage_id: substrate_llm_run_v1_scaffold
63
64// nx_safety_envelope:
65// intended_use: AUTO_APPLIED -- primitive-specific tuning queued
66// sil_target: SIL1
67// evidence: [bulk_applied_2026-05-16, see-file-comment-for-detail]
68// verdict: NOT_YET_EVALUATED
69
70import "nx_syscalls.nx"
71import "nx_tier.nx"
72import "nx_loop.nx"
73import "nx_tensor.nx"
74import "nx_bpe.nx"
75import "nx_embedding.nx"
76import "nx_model_spec.nx"
77import "nx_rmsnorm.nx"
78import "nx_attention.nx"
79import "nx_token_sample.nx"
80import "nx_prng.nx"
81
82// ===== Sealed-enum: LlmRunVerdict =================================
83
84const NX_LLM_OK: nx_int = 0
85const NX_LLM_ERR_BAD_SPEC: nx_int = 1
86const NX_LLM_ERR_BAD_VOCAB: nx_int = 2
87const NX_LLM_ERR_OOM: nx_int = 3
88const NX_LLM_ERR_INTERNAL: nx_int = 4
89const NX_LLM_N_VERDICTS: nx_int = 5
90
91func nx_llm_verdict_is_valid(v: nx_int) -> nx_int {
92 if v < 0 { return 0 }
93 if v >= NX_LLM_N_VERDICTS { return 0 }
94 return 1
95}
96
97// ===== Minimal end-to-end token-generation function ===============
98//
99// Synthetic: caller provides a model spec, an embedding table (Q10
100// NxTensor [vocab_size, hidden_dim]), a BPE vocab, an input prompt,
101// a temperature, top_k, and a PRNG state. We tokenize, embed,
102// "pass through" the synthetic forward pass (matmul against a
103// zeroed final-projection matrix in v1 -- replaced with real
104// weights in v2), and sample the next token.
105//
106// Returns: the next token ID (>= 0) or a sealed error verdict
107// (negative).
108//
109// v1 NOTE: the actual transformer-block forward is OMITTED here
110// because it requires a full weights bundle. We embed the prompt,
111// then run final-softmax + sample. This proves the BPE + embedding
112// + softmax + sampler surfaces compose; full per-layer compute is
113// added when the weight loader lands.
114
115func nx_llm_generate_one(spec: *NxModelSpec, embed: *NxTensor,
116 bpe: *NxBpeVocab,
117 prompt_text: *u8, prompt_len: nx_int,
118 temperature_q10: nx_int, top_k: nx_int,
119 prng_state: *i64) -> nx_int {
120 let v_spec: nx_int = nx_model_spec_validate(spec)
121 if v_spec != NX_MS_OK { return 0 - NX_LLM_ERR_BAD_SPEC }
122
123 // Step 1: tokenize prompt.
124 let token_buf: *i64 = sys_mmap((prompt_len + 16) * 8) as *i64
125 let n_tokens: nx_int = nx_bpe_encode(bpe, prompt_text, prompt_len, token_buf)
126 if n_tokens <= 0 { return 0 - NX_LLM_ERR_BAD_VOCAB }
127 if n_tokens > spec.max_seq_len { return 0 - NX_LLM_ERR_BAD_SPEC }
128
129 // Step 2: embed. Output is [n_tokens, hidden_dim] Q10.
130 let in_sh: *nx_int = sys_mmap(2 * 8) as *nx_int
131 in_sh[0] = n_tokens; in_sh[1] = spec.hidden_dim
132 let err: *nx_int = sys_mmap(8) as *nx_int
133 err[0] = 0
134 let embedded: *NxTensor = nx_t_alloc(NX_DT_I64, in_sh, 2, err)
135 if err[0] != 0 { return 0 - NX_LLM_ERR_OOM }
136
137 let v_emb: nx_int = nx_embedding_lookup(embed, token_buf, n_tokens, embedded)
138 if v_emb != NX_EMB_OK { return 0 - NX_LLM_ERR_INTERNAL }
139
140 // Step 3: (omitted in v1) for each layer:
141 // nx_transformer_block_forward(embedded, positions, weights[i], ...)
142 //
143 // Real call requires a populated NxTransformerBlockWeights bundle
144 // per layer. The bundle struct is defined; the WEIGHTS need to
145 // come from nx_gguf parse + dequantize. That wiring is the next
146 // workstream. For v1 we PASSTHROUGH embedded -> hidden.
147
148 // Step 4: take the last-token hidden vector (the prediction
149 // position) as our pre-output state.
150 let hidden_last: *i64 = sys_mmap(spec.hidden_dim * 8) as *i64
151 let pe: *i64 = embedded.storage as *i64
152 let base: nx_int = (n_tokens - 1) * spec.hidden_dim
153 var c: nx_int = 0
154 var c_iter: nx_int = 0
155 var c_verdict: nx_int = NX_LOOP_RUNNING
156 let C_BUDGET: nx_int = spec.hidden_dim
157 while c_verdict == NX_LOOP_RUNNING && c_iter < C_BUDGET {
158 hidden_last[c] = pe[base + c]
159 c = c + 1
160 c_iter = c_iter + 1
161 }
162
163 // Step 5: output projection. Caller supplies the output weight
164 // matrix downstream. For v1: we project to "logits" by
165 // SIMULATING a uniform projection -- copy hidden_last to a
166 // vocab-sized buffer (padded with zeros). Real path: matmul.
167 let logits: *i64 = sys_mmap(spec.vocab_size * 8) as *i64
168 var lz: nx_int = 0
169 while lz < spec.vocab_size { logits[lz] = 0; lz = lz + 1 }
170 // Copy the (smaller of hidden_dim, vocab_size) values for sanity.
171 var lc: nx_int = 0
172 var copy_n: nx_int = spec.hidden_dim
173 if copy_n > spec.vocab_size { copy_n = spec.vocab_size }
174 while lc < copy_n {
175 logits[lc] = hidden_last[lc]
176 lc = lc + 1
177 }
178
179 // Step 6: temperature + top-K masking.
180 nx_logit_apply_temperature(logits, spec.vocab_size, temperature_q10)
181 nx_logit_top_k_mask(logits, spec.vocab_size, top_k)
182
183 // Step 7: softmax over logits. We treat logits as a [1, vocab_size]
184 // tensor for nx_attn_softmax_row_q10's API.
185 let lo_sh: *nx_int = sys_mmap(2 * 8) as *nx_int
186 lo_sh[0] = 1; lo_sh[1] = spec.vocab_size
187 let lo_t: *NxTensor = nx_t_alloc(NX_DT_I64, lo_sh, 2, err)
188 if err[0] != 0 { return 0 - NX_LLM_ERR_OOM }
189 let lo_p: *i64 = lo_t.storage as *i64
190 var k: nx_int = 0
191 while k < spec.vocab_size { lo_p[k] = logits[k]; k = k + 1 }
192 nx_attn_softmax_row_q10(lo_t)
193
194 // Step 8: sample a token.
195 let next_token: nx_int = nx_sample_categorical(lo_p, spec.vocab_size, prng_state)
196 return next_token
197}
198
199// ===== Self-test ==================================================
200//
201// Builds a tiny model (vocab=16, hidden=8, ffn=16, 2 layers, max_seq=8)
202// + a 4-token BPE vocab + a prompt + calls nx_llm_generate_one.
203// We don't verify the output token's identity (zero weights -> any
204// token is "valid"); we verify the function COMPLETES with a non-
205// negative return (= valid token ID).
206
207func main() -> i64 {
208 // Tiny model spec.
209 let spec: *NxModelSpec = nx_model_spec_new()
210 spec.n_layers = 2
211 spec.hidden_dim = 8
212 spec.n_heads = 2
213 spec.head_dim = 4
214 spec.n_kv_heads = 2
215 spec.ffn_dim = 16
216 spec.vocab_size = 16
217 spec.max_seq_len = 8
218
219 // Validate.
220 if nx_model_spec_validate(spec) != NX_MS_OK { return 5 }
221
222 // Embedding table: [vocab=16, hidden=8] Q10.
223 let sh: *nx_int = sys_mmap(2 * 8) as *nx_int
224 sh[0] = spec.vocab_size; sh[1] = spec.hidden_dim
225 let err: *nx_int = sys_mmap(8) as *nx_int
226 err[0] = 0
227 let embed: *NxTensor = nx_t_alloc(NX_DT_I64, sh, 2, err)
228 if err[0] != 0 { return 6 }
229 // Fill with token_id * 100 + dim_index in Q10.
230 let pe: *i64 = embed.storage as *i64
231 var i: nx_int = 0
232 while i < spec.vocab_size {
233 var j: nx_int = 0
234 while j < spec.hidden_dim {
235 pe[i * spec.hidden_dim + j] = (i * 100 + j) * 1024 / 100
236 j = j + 1
237 }
238 i = i + 1
239 }
240
241 // Tiny BPE vocab: 4 single-char tokens 'a'/'b'/'c'/'d'.
242 let bpe: *NxBpeVocab = nx_bpe_vocab_new(256, 16, 8)
243 let a: *u8 = sys_mmap(1); a[0] = 0x61
244 let b: *u8 = sys_mmap(1); b[0] = 0x62
245 let c2: *u8 = sys_mmap(1); c2[0] = 0x63
246 let d: *u8 = sys_mmap(1); d[0] = 0x64
247 nx_bpe_add_token(bpe, a, 1)
248 nx_bpe_add_token(bpe, b, 1)
249 nx_bpe_add_token(bpe, c2, 1)
250 nx_bpe_add_token(bpe, d, 1)
251
252 // Prompt "abcd".
253 let prompt: *u8 = sys_mmap(4)
254 prompt[0]=0x61; prompt[1]=0x62; prompt[2]=0x63; prompt[3]=0x64
255
256 // PRNG.
257 let prng: *i64 = sys_mmap(8) as *i64
258 nx_prng_init(prng, 0xcafebabe)
259
260 // Run.
261 let next_token: nx_int = nx_llm_generate_one(
262 spec, embed, bpe, prompt, 4,
263 1024, // temperature = 1.0 in Q10
264 4, // top_k
265 prng)
266 if next_token < 0 { return 10 - next_token }
267 if next_token >= spec.vocab_size { return 20 }
268
269 // --- Verdict gate ---
270 var vi: nx_int = 0
271 while vi < NX_LLM_N_VERDICTS {
272 if nx_llm_verdict_is_valid(vi) != 1 { return 30 + vi }
273 vi = vi + 1
274 }
275
276 return 0
277}