realizar 0.8.6

Pure Rust ML inference engine built from scratch - model serving for GGUF and safetensors
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419

//! T-COV-95 Synthetic Falsification: convert/mod.rs via Pygmy GGUF models
//!
//! Tests GGUF to APR conversion using GGUFBuilder synthetic models.
//! A 1KB GGUF file exercises the same conversion logic as a 100GB one.

use crate::gguf::test_factory::{
    build_minimal_llama_gguf, build_minimal_phi2_gguf, create_f32_embedding_data,
    create_f32_norm_weights, create_q4_k_data, create_q6_k_data, create_q8_0_data, GGUFBuilder,
};

use super::GgufToAprConverter;
use crate::apr_transformer::AprTransformer;
use crate::gguf::{GGUFModel, GGUFTransformer};

// ============================================================================
// GgufToAprConverter::convert with Pygmy Models
// ============================================================================

#[test]
fn test_convert_llama_pygmy() {
    let gguf_data = build_minimal_llama_gguf(32, 64, 128, 4, 4);
    let result = GgufToAprConverter::convert(&gguf_data);
    assert!(result.is_ok(), "Conversion failed: {:?}", result.err());

    let apr = result.unwrap();
    assert_eq!(apr.config.architecture, "llama");
    assert_eq!(apr.config.hidden_dim, 64);
    assert_eq!(apr.config.num_layers, 1);
    assert_eq!(apr.config.num_heads, 4);
}

#[test]
fn test_convert_phi2_pygmy() {
    let gguf_data = build_minimal_phi2_gguf(32, 64, 128, 4);
    let result = GgufToAprConverter::convert(&gguf_data);
    assert!(result.is_ok(), "Conversion failed: {:?}", result.err());

    let apr = result.unwrap();
    assert_eq!(apr.config.architecture, "phi2");
    assert_eq!(apr.config.hidden_dim, 64);
}

#[test]
fn test_convert_empty_vocab() {
    // Minimal GGUF with vocab_size=1
    let embed_data = create_f32_embedding_data(1, 32);
    let norm_data = create_f32_norm_weights(32);
    let q_data = create_q4_k_data(32 * 32);

    let gguf_data = GGUFBuilder::new()
        .architecture("llama")
        .hidden_dim("llama", 32)
        .num_layers("llama", 1)
        .num_heads("llama", 2)
        .num_kv_heads("llama", 2)
        .context_length("llama", 64)
        .rope_freq_base("llama", 10000.0)
        .rms_epsilon("llama", 1e-5)
        .ffn_hidden_dim("llama", 64)
        .add_f32_tensor("token_embd.weight", &[1, 32], &embed_data)
        .add_f32_tensor("blk.0.attn_norm.weight", &[32], &norm_data)
        .add_q4_k_tensor("blk.0.attn_q.weight", &[32, 32], &q_data)
        .add_q4_k_tensor("blk.0.attn_k.weight", &[32, 32], &q_data)
        .add_q4_k_tensor("blk.0.attn_v.weight", &[32, 32], &q_data)
        .add_q4_k_tensor("blk.0.attn_output.weight", &[32, 32], &q_data)
        .add_f32_tensor("blk.0.ffn_norm.weight", &[32], &norm_data)
        .add_q4_k_tensor("blk.0.ffn_up.weight", &[32, 64], &q_data)
        .add_q4_k_tensor("blk.0.ffn_down.weight", &[64, 32], &q_data)
        .add_q4_k_tensor("blk.0.ffn_gate.weight", &[32, 64], &q_data)
        .add_f32_tensor("output_norm.weight", &[32], &norm_data)
        .build();

    let result = GgufToAprConverter::convert(&gguf_data);
    assert!(result.is_ok(), "Conversion failed: {:?}", result.err());
}

#[test]
fn test_convert_truncated_data() {
    // Truncated GGUF data should fail
    let gguf_data = build_minimal_llama_gguf(32, 64, 128, 4, 4);
    let truncated = &gguf_data[..gguf_data.len() / 2];

    let result = GgufToAprConverter::convert(truncated);
    assert!(result.is_err());
}

#[test]
fn test_convert_invalid_magic() {
    // Invalid GGUF magic
    let result = GgufToAprConverter::convert(b"INVALID_MAGIC_12345678");
    assert!(result.is_err());
}

// ============================================================================
// GgufToAprConverter::from_gguf_transformer
// ============================================================================

#[test]
fn test_from_gguf_transformer_llama() {
    let gguf_data = build_minimal_llama_gguf(32, 64, 128, 4, 4);
    let gguf_model = GGUFModel::from_bytes(&gguf_data).unwrap();
    let gguf_transformer = GGUFTransformer::from_gguf(&gguf_model, &gguf_data).unwrap();

    let apr = GgufToAprConverter::from_gguf_transformer(&gguf_transformer);

    assert_eq!(
        apr.config.architecture,
        gguf_transformer.config.architecture
    );
    assert_eq!(apr.config.hidden_dim, gguf_transformer.config.hidden_dim);
    assert_eq!(apr.config.num_layers, gguf_transformer.config.num_layers);
    assert_eq!(apr.layers.len(), gguf_transformer.layers.len());
    assert_eq!(
        apr.token_embedding.len(),
        gguf_transformer.token_embedding.len()
    );
}

#[test]
fn test_from_gguf_transformer_phi2() {
    let gguf_data = build_minimal_phi2_gguf(32, 64, 128, 4);
    let gguf_model = GGUFModel::from_bytes(&gguf_data).unwrap();
    let gguf_transformer = GGUFTransformer::from_gguf(&gguf_model, &gguf_data).unwrap();

    let apr = GgufToAprConverter::from_gguf_transformer(&gguf_transformer);
    assert_eq!(apr.config.architecture, "phi2");
}

#[test]
fn test_from_gguf_transformer_preserves_weights() {
    let gguf_data = build_minimal_llama_gguf(32, 64, 128, 4, 4);
    let gguf_model = GGUFModel::from_bytes(&gguf_data).unwrap();
    let gguf_transformer = GGUFTransformer::from_gguf(&gguf_model, &gguf_data).unwrap();

    let apr = GgufToAprConverter::from_gguf_transformer(&gguf_transformer);

    // Check weight sizes match
    assert_eq!(
        apr.output_norm_weight.len(),
        gguf_transformer.output_norm_weight.len()
    );
    assert_eq!(
        apr.lm_head_weight.len(),
        gguf_transformer.lm_head_weight.len()
    );

    // Check first layer weights match
    if !apr.layers.is_empty() {
        assert_eq!(
            apr.layers[0].attn_norm_weight.len(),
            gguf_transformer.layers[0].attn_norm_weight.len()
        );
        assert_eq!(
            apr.layers[0].qkv_weight.len(),
            gguf_transformer.layers[0].qkv_weight.len()
        );
    }
}

// ============================================================================
// GgufToAprConverter::to_apr_bytes and round-trip
// ============================================================================

#[test]
fn test_to_apr_bytes_basic() {
    let gguf_data = build_minimal_llama_gguf(32, 64, 128, 4, 4);
    let apr = GgufToAprConverter::convert(&gguf_data).unwrap();

    let apr_bytes = GgufToAprConverter::to_apr_bytes(&apr).unwrap();

    // Check APR header magic
    assert!(apr_bytes.len() >= 64);
    assert_eq!(&apr_bytes[0..3], b"APR");
}

#[test]
fn test_to_apr_bytes_header_layout() {
    let gguf_data = build_minimal_llama_gguf(32, 64, 128, 4, 4);
    let apr = GgufToAprConverter::convert(&gguf_data).unwrap();

    let apr_bytes = GgufToAprConverter::to_apr_bytes(&apr).unwrap();

    // Parse header fields
    let version_major = apr_bytes[4];
    let version_minor = apr_bytes[5];
    let tensor_count =
        u32::from_le_bytes([apr_bytes[8], apr_bytes[9], apr_bytes[10], apr_bytes[11]]);

    assert_eq!(version_major, 2);
    assert_eq!(version_minor, 0);
    assert_eq!(tensor_count, 1); // Single "weights" tensor
}

#[test]
fn test_apr_round_trip() {
    // Convert GGUF to APR
    let gguf_data = build_minimal_llama_gguf(32, 64, 128, 4, 4);
    let original = GgufToAprConverter::convert(&gguf_data).unwrap();

    // Serialize to bytes
    let apr_bytes = GgufToAprConverter::to_apr_bytes(&original).unwrap();

    // Deserialize back
    let loaded = GgufToAprConverter::from_apr_bytes(&apr_bytes).unwrap();

    // Verify config matches
    assert_eq!(original.config.architecture, loaded.config.architecture);
    assert_eq!(original.config.hidden_dim, loaded.config.hidden_dim);
    assert_eq!(original.config.num_layers, loaded.config.num_layers);
    assert_eq!(original.config.num_heads, loaded.config.num_heads);
    assert_eq!(original.config.vocab_size, loaded.config.vocab_size);
}

#[test]
fn test_apr_round_trip_weights_preserved() {
    let gguf_data = build_minimal_llama_gguf(32, 64, 128, 4, 4);
    let original = GgufToAprConverter::convert(&gguf_data).unwrap();

    let apr_bytes = GgufToAprConverter::to_apr_bytes(&original).unwrap();
    let loaded = GgufToAprConverter::from_apr_bytes(&apr_bytes).unwrap();

    // Check embedding weights
    assert_eq!(original.token_embedding.len(), loaded.token_embedding.len());
    for (o, l) in original
        .token_embedding
        .iter()
        .zip(loaded.token_embedding.iter())
    {
        assert!((o - l).abs() < 1e-6, "Embedding mismatch: {} vs {}", o, l);
    }

    // Check layer weights
    assert_eq!(original.layers.len(), loaded.layers.len());
}

// ============================================================================
// GgufToAprConverter::from_apr_bytes error cases
// ============================================================================

#[test]
fn test_from_apr_bytes_too_small() {
    let result = GgufToAprConverter::from_apr_bytes(&[0; 32]);
    assert!(result.is_err());
}

#[test]
fn test_from_apr_bytes_invalid_magic() {
    let mut data = vec![0u8; 128];
    data[0..4].copy_from_slice(b"NOAP");
    let result = GgufToAprConverter::from_apr_bytes(&data);
    assert!(result.is_err());
}

#[test]
fn test_from_apr_bytes_truncated_metadata() {
    // Valid header but truncated metadata
    let mut data = vec![0u8; 128];
    data[0..4].copy_from_slice(b"APR\0");
    data[4] = 2; // version
    data[8..12].copy_from_slice(&1u32.to_le_bytes()); // tensor_count
    data[12..20].copy_from_slice(&64u64.to_le_bytes()); // metadata_offset
    data[20..24].copy_from_slice(&1000u32.to_le_bytes()); // metadata_size > file size

    let result = GgufToAprConverter::from_apr_bytes(&data);
    assert!(result.is_err());
}

// ============================================================================
// Mixed tensor type conversions
// ============================================================================

#[test]
fn test_convert_with_q4_k_tensors() {
    let vocab_size = 32;
    let hidden_dim = 64;
    let kv_dim = 64;
    let intermediate_dim = 128;

    let embed_data = create_f32_embedding_data(vocab_size, hidden_dim);
    let norm_data = create_f32_norm_weights(hidden_dim);
    let q_data = create_q4_k_data(hidden_dim * hidden_dim);
    let k_data = create_q4_k_data(hidden_dim * kv_dim);
    let v_data = create_q4_k_data(hidden_dim * kv_dim);
    let out_data = create_q4_k_data(hidden_dim * hidden_dim);
    let up_data = create_q4_k_data(hidden_dim * intermediate_dim);
    let down_data = create_q4_k_data(intermediate_dim * hidden_dim);
    let gate_data = create_q4_k_data(hidden_dim * intermediate_dim);

    let gguf_data = GGUFBuilder::new()
        .architecture("llama")
        .hidden_dim("llama", hidden_dim as u32)
        .num_layers("llama", 1)
        .num_heads("llama", 4)
        .num_kv_heads("llama", 4)
        .context_length("llama", 64)
        .rope_freq_base("llama", 10000.0)
        .rms_epsilon("llama", 1e-5)
        .ffn_hidden_dim("llama", intermediate_dim as u32)
        .add_f32_tensor(
            "token_embd.weight",
            &[vocab_size as u64, hidden_dim as u64],
            &embed_data,
        )
        .add_f32_tensor("blk.0.attn_norm.weight", &[hidden_dim as u64], &norm_data)
        .add_q4_k_tensor(
            "blk.0.attn_q.weight",
            &[hidden_dim as u64, hidden_dim as u64],
            &q_data,
        )
        .add_q4_k_tensor(
            "blk.0.attn_k.weight",
            &[hidden_dim as u64, kv_dim as u64],
            &k_data,
        )
        .add_q4_k_tensor(
            "blk.0.attn_v.weight",
            &[hidden_dim as u64, kv_dim as u64],
            &v_data,
        )
        .add_q4_k_tensor(
            "blk.0.attn_output.weight",
            &[hidden_dim as u64, hidden_dim as u64],
            &out_data,
        )
        .add_f32_tensor("blk.0.ffn_norm.weight", &[hidden_dim as u64], &norm_data)
        .add_q4_k_tensor(
            "blk.0.ffn_up.weight",
            &[hidden_dim as u64, intermediate_dim as u64],
            &up_data,
        )
        .add_q4_k_tensor(
            "blk.0.ffn_down.weight",
            &[intermediate_dim as u64, hidden_dim as u64],
            &down_data,
        )
        .add_q4_k_tensor(
            "blk.0.ffn_gate.weight",
            &[hidden_dim as u64, intermediate_dim as u64],
            &gate_data,
        )
        .add_f32_tensor("output_norm.weight", &[hidden_dim as u64], &norm_data)
        .build();

    let apr = GgufToAprConverter::convert(&gguf_data).unwrap();
    assert_eq!(apr.config.hidden_dim, hidden_dim);
    assert!(!apr.layers.is_empty());
}

#[test]
fn test_convert_with_q8_0_tensors() {
    let vocab_size = 32;
    let hidden_dim = 64;
    let intermediate_dim = 128;

    let embed_data = create_f32_embedding_data(vocab_size, hidden_dim);
    let norm_data = create_f32_norm_weights(hidden_dim);
    let q8_data = create_q8_0_data(hidden_dim * hidden_dim);
    let ffn_data = create_q8_0_data(hidden_dim * intermediate_dim);

    let gguf_data = GGUFBuilder::new()
        .architecture("llama")
        .hidden_dim("llama", hidden_dim as u32)
        .num_layers("llama", 1)
        .num_heads("llama", 4)
        .num_kv_heads("llama", 4)
        .context_length("llama", 64)
        .rope_freq_base("llama", 10000.0)
        .rms_epsilon("llama", 1e-5)
        .ffn_hidden_dim("llama", intermediate_dim as u32)
        .add_f32_tensor(
            "token_embd.weight",
            &[vocab_size as u64, hidden_dim as u64],
            &embed_data,
        )
        .add_f32_tensor("blk.0.attn_norm.weight", &[hidden_dim as u64], &norm_data)
        .add_q8_0_tensor(
            "blk.0.attn_q.weight",
            &[hidden_dim as u64, hidden_dim as u64],
            &q8_data,
        )
        .add_q8_0_tensor(
            "blk.0.attn_k.weight",
            &[hidden_dim as u64, hidden_dim as u64],
            &q8_data,
        )
        .add_q8_0_tensor(
            "blk.0.attn_v.weight",
            &[hidden_dim as u64, hidden_dim as u64],
            &q8_data,
        )
        .add_q8_0_tensor(
            "blk.0.attn_output.weight",
            &[hidden_dim as u64, hidden_dim as u64],
            &q8_data,
        )
        .add_f32_tensor("blk.0.ffn_norm.weight", &[hidden_dim as u64], &norm_data)
        .add_q8_0_tensor(
            "blk.0.ffn_up.weight",
            &[hidden_dim as u64, intermediate_dim as u64],
            &ffn_data,
        )
        .add_q8_0_tensor(
            "blk.0.ffn_down.weight",
            &[intermediate_dim as u64, hidden_dim as u64],
            &ffn_data,
        )
        .add_q8_0_tensor(
            "blk.0.ffn_gate.weight",
            &[hidden_dim as u64, intermediate_dim as u64],
            &ffn_data,
        )
        .add_f32_tensor("output_norm.weight", &[hidden_dim as u64], &norm_data)
        .build();

    let apr = GgufToAprConverter::convert(&gguf_data).unwrap();
    assert_eq!(apr.config.hidden_dim, hidden_dim);
}

include!("tests_convert_02.rs");