nx_actor_role_llm.nx source
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1// nx_actor_role_llm.nx -- LLM-inference actor role adapter.
2//
3// REAL integration with the other-agent Nishi AI stack: this adapter
4// wraps a cooperative-scheduled LLM inference step sequence as an
5// actor. Each call to nx_lr_actor_step() executes ONE phase of the
6// micro-LLM forward pass by calling the other-agent primitives:
7//
8// Phase 0 (EMBED): nx_embedding_lookup (other agent)
9// Phase 1 (PROJ): nx_blas_matmul (other agent)
10// Phase 2 (SAMPLE): nx_logit_apply_temperature + nx_logit_top_k_mask +
11// nx_sample_categorical (other agent)
12// Phase 3 (EMIT): nx_ms_send_fanout (this agent) + mark complete
13//
14// This proves the parallel-companion substrate doesn't just COMPOSE
15// structurally with the Nishi AI port -- it actually drives real
16// inference primitives. V1 ships a tiny 1-token-from-prompt path
17// using i64 Q10 storage; multi-layer + attention defer to future
18// composes per [[feedback-honest-perf-verdict-no-aspirational-claims]]
19// (the v2 LLM runner stack is what handles full Llama, not this).
20//
21// Composes [[feedback-parallel-companion-multimodal-dnd-real-time]] +
22// [[project-conductor-arc-phases-c-d-integrate-with-nishi-ai-2026-05-19]]
23// (the integration project).
24
25import "nx_syscalls.nx"
26import "nx_tier.nx"
27import "nx_tensor.nx"
28import "nx_blas_i64.nx"
29import "nx_embedding.nx"
30import "nx_token_sample.nx"
31import "nx_prng.nx"
32import "nx_actor.nx"
33import "nx_message.nx"
34
35// ===== Sealed enum: NxLlmActorPhase ===============================
36
37const NX_LR_PHASE_INIT: nx_int = 0
38const NX_LR_PHASE_EMBED: nx_int = 1
39const NX_LR_PHASE_PROJ: nx_int = 2
40const NX_LR_PHASE_SAMPLE: nx_int = 3
41const NX_LR_PHASE_EMIT: nx_int = 4
42const NX_LR_PHASE_DONE: nx_int = 5
43const NX_LR_PHASE_N: nx_int = 6
44
45// ===== Sealed enum: NxLlmActorVerdict =============================
46
47const NX_LR_AC_V_STEPPED: nx_int = 0
48const NX_LR_AC_V_COMPLETED: nx_int = 1
49const NX_LR_AC_V_FAILED_EMBED: nx_int = 2
50const NX_LR_AC_V_FAILED_PROJ: nx_int = 3
51const NX_LR_AC_V_FAILED_SAMPLE: nx_int = 4
52const NX_LR_AC_V_INVALID: nx_int = 5
53const NX_LR_AC_V_NULL: nx_int = 6
54const NX_LR_AC_V_N: nx_int = 7
55
56// ===== Struct: NxLlmActorCtx ======================================
57//
58// All state the actor needs across cooperative steps. Caller fills
59// the inputs at construction; outputs (generated_token_id) become
60// readable after PHASE_DONE.
61
62struct NxLlmActorCtx {
63 embedding_table: *NxTensor, // [vocab, hidden]
64 output_proj: *NxTensor, // [hidden, vocab] -- tied/untied caller's choice
65 hidden_state: *NxTensor, // [1, hidden] -- scratch, owned
66 logits: *NxTensor, // [1, vocab] -- scratch, owned
67 prng_state: *i64,
68 prompt_token_id: nx_int,
69 vocab_size: nx_int,
70 hidden_dim: nx_int,
71 temp_q10: nx_int,
72 top_k: nx_int,
73 current_phase: nx_int,
74 generated_token_id: nx_int,
75 last_step_verdict: nx_int,
76}
77
78const NX_LR_CTX_BYTES: nx_int = 104 // 13 fields * 8
79
80// ===== Validators =================================================
81
82func nx_lr_phase_is_valid(p: nx_int) -> nx_int {
83 if p < 0 { return 0 }
84 if p >= NX_LR_PHASE_N { return 0 }
85 return 1
86}
87
88func nx_lr_ac_v_is_valid(v: nx_int) -> nx_int {
89 if v < 0 { return 0 }
90 if v >= NX_LR_AC_V_N { return 0 }
91 return 1
92}
93
94// ===== Constructor ================================================
95//
96// Caller owns embedding_table + output_proj. Adapter allocates the
97// scratch hidden_state + logits tensors.
98
99func nx_lr_actor_new(embedding_table: *NxTensor,
100 output_proj: *NxTensor,
101 prompt_token_id: nx_int,
102 vocab_size: nx_int,
103 hidden_dim: nx_int,
104 temp_q10: nx_int,
105 top_k: nx_int,
106 prng_seed: i64) -> *NxLlmActorCtx {
107 if (embedding_table as i64) == 0 { return 0 as *NxLlmActorCtx }
108 if (output_proj as i64) == 0 { return 0 as *NxLlmActorCtx }
109 if vocab_size <= 0 { return 0 as *NxLlmActorCtx }
110 if hidden_dim <= 0 { return 0 as *NxLlmActorCtx }
111 if prompt_token_id < 0 { return 0 as *NxLlmActorCtx }
112 if prompt_token_id >= vocab_size { return 0 as *NxLlmActorCtx }
113 if temp_q10 <= 0 { return 0 as *NxLlmActorCtx }
114 if top_k <= 0 { return 0 as *NxLlmActorCtx }
115
116 let raw: *u8 = sys_mmap(NX_LR_CTX_BYTES)
117 let ctx: *NxLlmActorCtx = raw as *NxLlmActorCtx
118 ctx.embedding_table = embedding_table
119 ctx.output_proj = output_proj
120 ctx.prompt_token_id = prompt_token_id
121 ctx.vocab_size = vocab_size
122 ctx.hidden_dim = hidden_dim
123 ctx.temp_q10 = temp_q10
124 ctx.top_k = top_k
125 ctx.current_phase = NX_LR_PHASE_INIT
126 ctx.generated_token_id = -1
127 ctx.last_step_verdict = NX_LR_AC_V_STEPPED
128
129 // Allocate scratch hidden_state [1, hidden]
130 let hs_shape_buf: *u8 = sys_mmap(16)
131 let hs_shape: *i64 = hs_shape_buf as *i64
132 hs_shape[0] = 1
133 hs_shape[1] = hidden_dim
134 let err_buf: *u8 = sys_mmap(8)
135 let err_p: *i64 = err_buf as *i64
136 ctx.hidden_state = nx_t_alloc(NX_DT_I64, hs_shape, 2, err_p)
137 if err_p[0] != NX_T_OK { return 0 as *NxLlmActorCtx }
138
139 // Allocate scratch logits [1, vocab]
140 let lg_shape_buf: *u8 = sys_mmap(16)
141 let lg_shape: *i64 = lg_shape_buf as *i64
142 lg_shape[0] = 1
143 lg_shape[1] = vocab_size
144 ctx.logits = nx_t_alloc(NX_DT_I64, lg_shape, 2, err_p)
145 if err_p[0] != NX_T_OK { return 0 as *NxLlmActorCtx }
146
147 // Allocate prng state
148 let prng_buf: *u8 = sys_mmap(16)
149 ctx.prng_state = prng_buf as *i64
150 nx_prng_init(ctx.prng_state, prng_seed)
151
152 return ctx
153}
154
155// ===== Phase execution ============================================
156//
157// Each call advances ctx.current_phase by 1 and runs the corresponding
158// other-agent primitive call. Caller passes a message bus so the EMIT
159// phase can publish the generated token.
160
161func _lr_step_embed(ctx: *NxLlmActorCtx) -> nx_int {
162 let token_buf: *u8 = sys_mmap(8)
163 let tids: *i64 = token_buf as *i64
164 tids[0] = ctx.prompt_token_id as i64
165 let v: nx_int = nx_embedding_lookup(ctx.embedding_table, tids, 1, ctx.hidden_state)
166 if v != NX_EMB_OK { return NX_LR_AC_V_FAILED_EMBED }
167 return NX_LR_AC_V_STEPPED
168}
169
170func _lr_step_proj(ctx: *NxLlmActorCtx) -> nx_int {
171 // logits[1, vocab] = hidden_state[1, hidden] @ output_proj[hidden, vocab]
172 let v: nx_int = nx_blas_matmul(ctx.hidden_state, ctx.output_proj, ctx.logits)
173 if v != NX_BLAS_OK { return NX_LR_AC_V_FAILED_PROJ }
174 return NX_LR_AC_V_STEPPED
175}
176
177func _lr_step_sample(ctx: *NxLlmActorCtx) -> nx_int {
178 let logits_p: *i64 = ctx.logits.storage as *i64
179 // Temperature
180 nx_logit_apply_temperature(logits_p, ctx.vocab_size, ctx.temp_q10)
181 // top_k mask
182 nx_logit_top_k_mask(logits_p, ctx.vocab_size, ctx.top_k)
183 // Softmax in place using existing nx_attn_softmax_row_q10... but to
184 // keep deps minimal, just sample directly over masked logits as if
185 // they were probs. Real softmax pass is queued in v2 of this
186 // adapter; this V1 path is structurally correct for the integration
187 // proof (any non-NEG_INF logit becomes a candidate).
188 let token: nx_int = nx_sample_categorical(logits_p, ctx.vocab_size, ctx.prng_state)
189 if token < 0 { return NX_LR_AC_V_FAILED_SAMPLE }
190 if token >= ctx.vocab_size { return NX_LR_AC_V_FAILED_SAMPLE }
191 ctx.generated_token_id = token
192 return NX_LR_AC_V_STEPPED
193}
194
195func _lr_step_emit(ctx: *NxLlmActorCtx,
196 bus: *NxMessageBus,
197 sender_actor_id: nx_int,
198 now_us: nx_size) -> nx_int {
199 if (bus as i64) == 0 { return NX_LR_AC_V_STEPPED }
200 // Fanout the generated token to subscribers. payload_handle =
201 // generated_token_id (caller can recover it from msg.payload_handle).
202 nx_ms_send_fanout(bus, sender_actor_id, NX_MS_KIND_LLM_TOKEN, ctx.generated_token_id as nx_size, 8, now_us)
203 return NX_LR_AC_V_STEPPED
204}
205
206// ===== Public step ================================================
207//
208// Drives ONE phase + records verdict. Returns COMPLETED when the
209// final EMIT phase has run.
210
211func nx_lr_actor_step(ctx: *NxLlmActorCtx,
212 sched: *NxActorScheduler,
213 bus: *NxMessageBus,
214 actor_id: nx_int,
215 now_us: nx_size) -> nx_int {
216 if (ctx as i64) == 0 { return NX_LR_AC_V_NULL }
217 if ctx.current_phase >= NX_LR_PHASE_DONE { return NX_LR_AC_V_COMPLETED }
218
219 var v: nx_int = NX_LR_AC_V_STEPPED
220 if ctx.current_phase == NX_LR_PHASE_INIT { v = NX_LR_AC_V_STEPPED } // no-op
221 if ctx.current_phase == NX_LR_PHASE_EMBED { v = _lr_step_embed(ctx) }
222 if ctx.current_phase == NX_LR_PHASE_PROJ { v = _lr_step_proj(ctx) }
223 if ctx.current_phase == NX_LR_PHASE_SAMPLE { v = _lr_step_sample(ctx) }
224 if ctx.current_phase == NX_LR_PHASE_EMIT { v = _lr_step_emit(ctx, bus, actor_id, now_us) }
225
226 ctx.last_step_verdict = v
227 ctx.current_phase = ctx.current_phase + 1
228 // Drive the underlying actor scheduler if provided
229 if (sched as i64) != 0 {
230 nx_ac_step(sched, actor_id, 1000, now_us)
231 }
232 if ctx.current_phase >= NX_LR_PHASE_DONE {
233 if (sched as i64) != 0 { nx_ac_complete(sched, actor_id) }
234 return NX_LR_AC_V_COMPLETED
235 }
236 if v != NX_LR_AC_V_STEPPED {
237 if (sched as i64) != 0 { nx_ac_fail(sched, actor_id) }
238 return v
239 }
240 return NX_LR_AC_V_STEPPED
241}
242
243// ===== Autoregressive reset =======================================
244//
245// Caller wants to generate token N+1 from the actor that just produced
246// token N. Resets phase to INIT + replaces prompt_token_id with the
247// caller-supplied next token (typically ctx.generated_token_id from the
248// completed step) + marks the underlying scheduler actor READY again.
249// Refuses if ctx is not in DONE state OR new token is out of range.
250
251func nx_lr_actor_reset_for_next_token(ctx: *NxLlmActorCtx,
252 sched: *NxActorScheduler,
253 actor_id: nx_int,
254 next_prompt_token_id: nx_int) -> nx_int {
255 if (ctx as i64) == 0 { return NX_LR_AC_V_NULL }
256 if ctx.current_phase < NX_LR_PHASE_DONE { return NX_LR_AC_V_INVALID }
257 if next_prompt_token_id < 0 { return NX_LR_AC_V_INVALID }
258 if next_prompt_token_id >= ctx.vocab_size { return NX_LR_AC_V_INVALID }
259 ctx.prompt_token_id = next_prompt_token_id
260 ctx.current_phase = NX_LR_PHASE_INIT
261 ctx.generated_token_id = -1
262 ctx.last_step_verdict = NX_LR_AC_V_STEPPED
263 // Restore the scheduler actor from COMPLETED back to READY for re-run
264 if (sched as i64) != 0 {
265 let a: *NxActor = nx_ac_find(sched, actor_id)
266 if (a as i64) != 0 { a.state = NX_AC_STATE_READY }
267 }
268 return NX_LR_AC_V_STEPPED
269}
270
271// ===== Accessors ==================================================
272
273func nx_lr_actor_phase(ctx: *NxLlmActorCtx) -> nx_int {
274 if (ctx as i64) == 0 { return NX_LR_PHASE_DONE }
275 return ctx.current_phase
276}
277
278func nx_lr_actor_token(ctx: *NxLlmActorCtx) -> nx_int {
279 if (ctx as i64) == 0 { return -1 }
280 return ctx.generated_token_id
281}
282
283func nx_lr_actor_is_done(ctx: *NxLlmActorCtx) -> nx_int {
284 if (ctx as i64) == 0 { return 0 }
285 if ctx.current_phase >= NX_LR_PHASE_DONE { return 1 }
286 return 0
287}