aprender-serve 0.64.0

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


#[test]
fn test_block_weights_with_gate() {
    let hidden_dim = 32;
    let intermediate_dim = 64;

    let block = BlockWeights {
        attn_norm_weight: vec![1.0; hidden_dim],
        attn_norm_bias: vec![0.0; hidden_dim],
        qkv_weight: vec![0.01; hidden_dim * 96],
        qkv_bias: vec![0.0; 96],
        out_weight: vec![0.01; hidden_dim * hidden_dim],
        out_bias: vec![0.0; hidden_dim],
        ffn_norm_weight: vec![1.0; hidden_dim],
        ffn_norm_bias: vec![0.0; hidden_dim],
        ffn_fc1_weight: vec![0.01; hidden_dim * intermediate_dim],
        ffn_fc1_bias: vec![0.0; intermediate_dim],
        ffn_fc2_weight: vec![0.01; intermediate_dim * hidden_dim],
        ffn_fc2_bias: vec![0.0; hidden_dim],
        ffn_gate_weight: Some(vec![0.01; hidden_dim * intermediate_dim]),
        linear_attn: None,
    moe_experts: None,
    };

    assert!(block.ffn_gate_weight.is_some());
    assert_eq!(
        block.ffn_gate_weight.as_ref().expect("test value should be present").len(),
        hidden_dim * intermediate_dim
    );
}

// ============================================================================
// WeightType Debug/Clone Tests
// ============================================================================

#[test]
fn test_weight_type_debug() {
    let qkv = WeightType::Qkv;
    let output = WeightType::Output;
    let fc1 = WeightType::FfnFc1;
    let fc2 = WeightType::FfnFc2;
    let lm_head = WeightType::LmHead;

    // Test Debug trait
    let qkv_debug = format!("{:?}", qkv);
    assert!(qkv_debug.contains("Qkv"));

    let output_debug = format!("{:?}", output);
    assert!(output_debug.contains("Output"));

    let fc1_debug = format!("{:?}", fc1);
    assert!(fc1_debug.contains("FfnFc1"));

    let fc2_debug = format!("{:?}", fc2);
    assert!(fc2_debug.contains("FfnFc2"));

    let lm_head_debug = format!("{:?}", lm_head);
    assert!(lm_head_debug.contains("LmHead"));
}

#[test]
fn test_weight_type_clone() {
    let original = WeightType::Qkv;
    let cloned = original;

    // Clone should work (Copy trait)
    assert!(matches!(cloned, WeightType::Qkv));
}

// ============================================================================
// GpuModel layer_norm_static Tests (via forward operations)
// ============================================================================

#[test]
fn test_gpu_model_layer_norm_via_forward() {
    // layer_norm_static is tested indirectly via forward_block_idx
    let config = create_minimal_config();
    let mut model = GpuModel::new(config.clone()).expect("test value should be present");

    let mock = MockExecutor::new("layer_norm_test");
    model.with_test_executor(Box::new(mock));

    // This exercises layer_norm_static internally
    let input = vec![1.0f32; config.hidden_dim];
    let result = model.forward_block_idx(&input, 1, 0);

    assert!(result.is_ok());
}

// ============================================================================
// Edge Cases and Error Handling Tests
// ============================================================================

#[test]
fn test_gpu_model_forward_single_token_only() {
    let config = create_minimal_config();
    let mut model = GpuModel::new(config.clone()).expect("test value should be present");

    let mock = MockExecutor::new("single_token");
    model.with_test_executor(Box::new(mock));

    let token_ids = vec![5];
    let result = model.forward_gpu(&token_ids);

    assert!(result.is_ok());
    let logits = result.expect("test value should be present");
    assert_eq!(logits.len(), config.vocab_size);
}

#[test]
fn test_gpu_model_generate_max_tokens_zero() {
    let config = create_minimal_config();
    let mut model = GpuModel::new(config).expect("test value should be present");

    let mock = MockExecutor::new("zero_max_tokens");
    model.with_test_executor(Box::new(mock));

    let gen_config = GpuGenerateConfig::deterministic(0);
    let prompt = vec![1];
    let result = model.generate(&prompt, &gen_config);

    assert!(result.is_ok());
    // Should return at least the prompt
    let tokens = result.expect("test value should be present");
    assert!(!tokens.is_empty());
}

#[test]
fn test_gpu_model_forward_block_incremental_optimized() {
    let config = create_minimal_config();
    let mut model = GpuModel::new(config.clone()).expect("test value should be present");

    let mock = MockExecutor::new("block_incremental");
    model.with_test_executor(Box::new(mock));

    let mut kv_cache = StreamingKVCache::new(
        config.num_layers,
        64,
        config.num_kv_heads,
        config.head_dim(),
    );

    let input = vec![0.1f32; config.hidden_dim];
    let result = model.forward_block_incremental_optimized(&input, 0, &mut kv_cache);

    assert!(result.is_ok());
    let output = result.expect("test value should be present");
    assert_eq!(output.len(), config.hidden_dim);
}

// ============================================================================
// GpuModel GQA Attention Path Tests
// ============================================================================

#[test]
fn test_gpu_model_gqa_forward() {
    let config = create_gqa_config();
    let mut model = GpuModel::new(config.clone()).expect("test value should be present");

    let mock = MockExecutor::new("gqa_forward");
    model.with_test_executor(Box::new(mock));

    let token_ids = vec![1, 2];
    let result = model.forward_gpu(&token_ids);

    assert!(result.is_ok());
}

#[test]
fn test_gpu_model_gqa_incremental() {
    let config = create_gqa_config();
    let mut model = GpuModel::new(config.clone()).expect("test value should be present");

    let mock = MockExecutor::new("gqa_incremental");
    model.with_test_executor(Box::new(mock));

    let mut kv_cache = StreamingKVCache::new(
        config.num_layers,
        64,
        config.num_kv_heads,
        config.head_dim(),
    );

    let result = model.forward_gpu_incremental_optimized(1, &mut kv_cache);
    assert!(result.is_ok());
}

#[test]
fn test_gpu_model_gqa_generate() {
    let config = create_gqa_config();
    let mut model = GpuModel::new(config).expect("test value should be present");

    let mock = MockExecutor::new("gqa_generate");
    model.with_test_executor(Box::new(mock));

    let gen_config = GpuGenerateConfig::deterministic(2);
    let result = model.generate(&[1], &gen_config);

    assert!(result.is_ok());
}