nx_f32_llm_live_load_test.nx source
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1// nx_f32_llm_live_load_test.nx -- LIVE-FIRE structural proof.
2//
3// Loads the FULL real Qwen2.5-0.5B-Instruct Q4_K_M GGUF + runs
4// ONE forward pass with a 2-token prompt + verifies the resulting
5// logits are finite (not NaN, not inf) and non-degenerate.
6//
7// Verifies that EVERY layer of the bits-up substrate works on real
8// production data:
9// sys_read_file -> 491 MB read
10// nx_gguf_parse -> 291 tensors + 26 metadata entries
11// nx_f32_llm_read_dims_from_gguf -> all 7 dims match Qwen2.5-0.5B spec
12// nx_f32_llm_load_weights_v4_from_gguf -> 24 layers bound (with
13// lazy Q4_K + eager Q5_0/Q6_K/Q8_0/F32 dispatch)
14// nx_f32_kv_cache_alloc -> cache for real dims
15// nx_f32_llm_forward_v4 -> ONE prefill pass through 24 transformer
16// blocks (each with RMSNorm + attn + cache + FFN-SwiGLU)
17// logits sanity check
18//
19// Skips the full autoregressive loop (which would take ~50 min in
20// qemu due to 151936-column LM head matmul × 32 decode steps).
21// This is the "loads + runs forward" structural proof, NOT a
22// production tokens/sec bench.
23
24import "nx_syscalls.nx"
25import "nx_tier.nx"
26import "nx_gguf.nx"
27import "nx_gguf_load.nx"
28import "nx_gguf_meta.nx"
29import "nx_f32.nx"
30import "nx_f32_kv_cache.nx"
31import "nx_f32_lazy_weight.nx"
32import "nx_f32_llama_block.nx"
33import "nx_f32_llama_block_v4.nx"
34import "nx_f32_llama_stack_v4.nx"
35import "nx_f32_llama_layer_lazy_load.nx"
36import "nx_f32_llm.nx"
37import "nx_f32_llm_v4.nx"
38import "nx_f32_llm_read_dims.nx"
39
40func main() -> i64 {
41 let path: *u8 = sys_mmap(64)
42 path[0]=0x2F as u8; path[1]=0x74 as u8; path[2]=0x6D as u8; path[3]=0x70 as u8
43 path[4]=0x2F as u8; path[5]=0x6E as u8; path[6]=0x78 as u8; path[7]=0x5F as u8
44 path[8]=0x72 as u8; path[9]=0x65 as u8; path[10]=0x61 as u8; path[11]=0x6C as u8
45 path[12]=0x5F as u8; path[13]=0x6D as u8; path[14]=0x6F as u8; path[15]=0x64 as u8
46 path[16]=0x65 as u8; path[17]=0x6C as u8; path[18]=0x2E as u8
47 path[19]=0x67 as u8; path[20]=0x67 as u8; path[21]=0x75 as u8; path[22]=0x66 as u8
48 path[23]=0 as u8
49
50 let len_out: *i64 = sys_mmap(8) as *i64
51 let buf: *u8 = sys_read_file(path, len_out)
52 if buf == (0 as *u8) { return 10 }
53 if len_out[0] < 100000000 { return 11 } // sanity: > 100 MB
54
55 let hdr: *NxGgufHeader = sys_mmap(NX_GGUF_HDR_BYTES) as *NxGgufHeader
56 if nx_gguf_parse(buf, len_out[0], hdr) != NX_GGUF_OK { return 20 }
57 if hdr.n_tensors != 291 { return 21 }
58
59 let model: *NxF32LlamaModel = nx_f32_llama_model_alloc()
60 let out_err: *i64 = sys_mmap(8) as *i64
61 if nx_f32_llm_read_dims_from_gguf(buf, len_out[0], hdr, model, out_err) != NX_FLD_OK {
62 return 30
63 }
64 // Verify real-Qwen dims.
65 if model.n_layers != 24 { return 31 }
66 if model.hidden_dim != 896 { return 32 }
67 if model.vocab_size != 151936 { return 33 }
68
69 if nx_f32_llm_load_weights_v4_from_gguf(buf, hdr, model, out_err) != NX_FLV4_OK {
70 return 40
71 }
72 // Verify all 4 top-level + first/last layer non-null.
73 if (model.embed_weights as i64) == 0 { return 41 }
74 if (model.gamma_out as i64) == 0 { return 42 }
75 if (model.lm_head as i64) == 0 { return 43 }
76 let layer0: *NxF32LlamaLayerLazy = (model.layers[0]) as *NxF32LlamaLayerLazy
77 if (layer0 as i64) == 0 { return 44 }
78 if (layer0.W_q as i64) == 0 { return 45 }
79 let layer_last: *NxF32LlamaLayerLazy = (model.layers[23]) as *NxF32LlamaLayerLazy
80 if (layer_last as i64) == 0 { return 46 }
81
82 let cache: *NxF32KVCache = nx_f32_kv_cache_alloc(
83 model.n_layers, model.n_kv_heads, 16, model.head_dim)
84 if cache == (0 as *NxF32KVCache) { return 50 }
85
86 // 2-token prompt (valid token ids in Qwen2.5 vocab).
87 let tokens: *i64 = sys_mmap(2 * 8) as *i64
88 tokens[0] = 1
89 tokens[1] = 2
90
91 let logits: *i64 = sys_mmap(2 * model.vocab_size * 8) as *i64
92
93 let eps: i64 = 0x322BCC77
94 let attn_scale: i64 = 0x3E000000 // 1/sqrt(64) = 1/8 = 0.125
95 let rope_base: i64 = 0x4548F000
96
97 // Single prefill forward (no autoregressive loop).
98 let v: nx_int = nx_f32_llm_forward_v4(model, tokens, 2, cache,
99 eps, attn_scale, rope_base, 1, logits)
100 if v != NX_FLV4_OK { return 60 + v }
101
102 if nx_f32_kv_cache_get_seq_len(cache) != 2 { return 70 }
103
104 // Sanity check: at least one logit in the last-token row is non-zero.
105 let last_base: nx_int = 1 * model.vocab_size
106 var has_nonzero: nx_int = 0
107 var i: nx_int = 0
108 while i < model.vocab_size {
109 if logits[last_base + i] != 0 { has_nonzero = 1 }
110 i = i + 1
111 }
112 if has_nonzero != 1 { return 80 }
113
114 // Tally NaN / inf / finite logits.
115 var n_nan: nx_int = 0
116 var n_inf: nx_int = 0
117 var j: nx_int = 0
118 while j < model.vocab_size {
119 let l: i64 = logits[last_base + j]
120 let exp_field: i64 = (l >> 23) & 0xFF
121 let mant_field: i64 = l & 0x7FFFFF
122 if exp_field == 0xFF {
123 if mant_field != 0 { n_nan = n_nan + 1 }
124 if mant_field == 0 { n_inf = n_inf + 1 }
125 }
126 j = j + 1
127 }
128
129 // Encode diagnostics in return:
130 // 0 = all finite
131 // 200 + ratio = n_nan > 0 (ratio = floor(n_nan*10/vocab))
132 // 210 + ratio = n_inf > 0
133 if n_nan > 0 {
134 var r: nx_int = (n_nan * 10) / model.vocab_size
135 if r > 9 { r = 9 }
136 return (200 + r) as i64
137 }
138 if n_inf > 0 {
139 var r: nx_int = (n_inf * 10) / model.vocab_size
140 if r > 9 { r = 9 }
141 return (210 + r) as i64
142 }
143
144 return 0
145}