-
Notifications
You must be signed in to change notification settings - Fork 44
Expand file tree
/
Copy pathtest_mixed.cpp
More file actions
301 lines (248 loc) · 10.4 KB
/
Copy pathtest_mixed.cpp
File metadata and controls
301 lines (248 loc) · 10.4 KB
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
#include <gtest/gtest.h>
extern "C" {
#include "turboquant/turboquant.h"
void tq_mixed_4b8_quantize_ref(const float* src, void* dst, int n);
void tq_mixed_4b8_dequantize_ref(const void* src, float* dst, int n);
void tq_mixed_4b8_attention_ref(const float* query, const void* kv,
float* scores, int seq_len, int head_dim);
void tq_uniform_4b_quantize_ref(const float* src, void* dst, int n);
void tq_uniform_4b_dequantize_ref(const void* src, float* dst, int n);
}
#include <cmath>
#include <vector>
#include <algorithm>
#include <numeric>
/* ============================================================
* Block size verification
* ============================================================ */
TEST(Mixed4B8, BlockSize) {
/* 2(scale) + 2(zero) + 4(outlier_idx) + 8(outlier_vals) + 64(qs) = 80 */
EXPECT_EQ(sizeof(block_tq_mixed_4b8), 80u);
}
/* ============================================================
* Basic roundtrip — smooth data (no strong outliers)
* ============================================================ */
TEST(Mixed4B8, RoundtripBasic) {
std::vector<float> input(TQ_BK);
for (int i = 0; i < TQ_BK; i++) input[i] = sinf(i * 0.1f);
block_tq_mixed_4b8 block;
tq_mixed_4b8_quantize_ref(input.data(), &block, TQ_BK);
std::vector<float> output(TQ_BK);
tq_mixed_4b8_dequantize_ref(&block, output.data(), TQ_BK);
double mse = 0;
for (int i = 0; i < TQ_BK; i++) {
double d = input[i] - output[i];
mse += d * d;
}
mse /= TQ_BK;
EXPECT_LT(mse, 0.01); /* comparable or better than uniform_4b */
}
/* ============================================================
* Outlier detection: top-4 channels by |value| are found
* ============================================================ */
TEST(Mixed4B8, OutlierDetection) {
std::vector<float> input(TQ_BK, 0.0f);
/* Plant 4 known outliers at specific positions */
input[10] = 50.0f;
input[33] = -40.0f;
input[77] = 30.0f;
input[120] = -20.0f;
/* Fill rest with small values */
for (int i = 0; i < TQ_BK; i++) {
if (i != 10 && i != 33 && i != 77 && i != 120)
input[i] = sinf(i * 0.05f) * 0.5f;
}
block_tq_mixed_4b8 block;
tq_mixed_4b8_quantize_ref(input.data(), &block, TQ_BK);
/* Verify the 4 outlier indices are exactly {10, 33, 77, 120} (any order) */
std::vector<int> detected(TQ_MIXED_OUTLIERS);
for (int o = 0; o < TQ_MIXED_OUTLIERS; o++) detected[o] = block.outlier_idx[o];
std::sort(detected.begin(), detected.end());
EXPECT_EQ(detected[0], 10);
EXPECT_EQ(detected[1], 33);
EXPECT_EQ(detected[2], 77);
EXPECT_EQ(detected[3], 120);
}
/* ============================================================
* Quality: MSE is LOWER than uniform_4b on outlier-heavy data
* ============================================================ */
TEST(Mixed4B8, BetterThanUniformOnOutlierData) {
/* Create data that mimics real KV cache: mostly small, a few large */
std::vector<float> input(TQ_BK);
for (int i = 0; i < TQ_BK; i++) input[i] = sinf(i * 0.1f) * 0.5f;
/* Inject outliers */
input[5] = 25.0f;
input[42] = -30.0f;
input[99] = 20.0f;
input[110] = -15.0f;
/* Quantize with mixed_4b8 */
block_tq_mixed_4b8 mixed_block;
tq_mixed_4b8_quantize_ref(input.data(), &mixed_block, TQ_BK);
std::vector<float> mixed_out(TQ_BK);
tq_mixed_4b8_dequantize_ref(&mixed_block, mixed_out.data(), TQ_BK);
double mixed_mse = 0;
for (int i = 0; i < TQ_BK; i++) {
double d = input[i] - mixed_out[i];
mixed_mse += d * d;
}
mixed_mse /= TQ_BK;
/* Quantize with uniform_4b */
block_tq_uniform_4b uni_block;
tq_uniform_4b_quantize_ref(input.data(), &uni_block, TQ_BK);
std::vector<float> uni_out(TQ_BK);
tq_uniform_4b_dequantize_ref(&uni_block, uni_out.data(), TQ_BK);
double uni_mse = 0;
for (int i = 0; i < TQ_BK; i++) {
double d = input[i] - uni_out[i];
uni_mse += d * d;
}
uni_mse /= TQ_BK;
/* Mixed should be significantly better */
EXPECT_LT(mixed_mse, uni_mse * 0.5)
<< "mixed_mse=" << mixed_mse << " uni_mse=" << uni_mse;
}
/* ============================================================
* Quality: Attention cosine similarity is HIGHER than uniform_4b
* ============================================================ */
TEST(Mixed4B8, AttentionCosineBetterThanUniform) {
const int head_dim = TQ_BK;
/* Query vector */
std::vector<float> query(head_dim);
for (int i = 0; i < head_dim; i++) query[i] = cosf(i * 0.05f);
/* Key vector with outliers */
std::vector<float> key(head_dim);
for (int i = 0; i < head_dim; i++) key[i] = sinf(i * 0.1f) * 0.5f;
key[3] = 20.0f;
key[50] = -18.0f;
key[88] = 15.0f;
key[127] = -12.0f;
/* FP32 reference dot product */
double fp32_dot = 0;
for (int d = 0; d < head_dim; d++) fp32_dot += query[d] * key[d];
/* Mixed_4b8 dot product */
block_tq_mixed_4b8 mixed_block;
tq_mixed_4b8_quantize_ref(key.data(), &mixed_block, head_dim);
std::vector<float> mixed_deq(head_dim);
tq_mixed_4b8_dequantize_ref(&mixed_block, mixed_deq.data(), head_dim);
double mixed_dot = 0;
for (int d = 0; d < head_dim; d++) mixed_dot += query[d] * mixed_deq[d];
/* Uniform_4b dot product */
block_tq_uniform_4b uni_block;
tq_uniform_4b_quantize_ref(key.data(), &uni_block, head_dim);
std::vector<float> uni_deq(head_dim);
tq_uniform_4b_dequantize_ref(&uni_block, uni_deq.data(), head_dim);
double uni_dot = 0;
for (int d = 0; d < head_dim; d++) uni_dot += query[d] * uni_deq[d];
/* Mixed should be closer to fp32 than uniform */
double mixed_err = fabs(mixed_dot - fp32_dot);
double uni_err = fabs(uni_dot - fp32_dot);
EXPECT_LT(mixed_err, uni_err)
<< "mixed_err=" << mixed_err << " uni_err=" << uni_err;
}
/* ============================================================
* Attention function consistency: scores match dequantize+dot
* ============================================================ */
TEST(Mixed4B8, AttentionConsistency) {
const int head_dim = TQ_BK;
const int seq_len = 4;
std::vector<float> query(head_dim);
for (int i = 0; i < head_dim; i++) query[i] = cosf(i * 0.05f);
std::vector<block_tq_mixed_4b8> blocks(seq_len);
for (int s = 0; s < seq_len; s++) {
std::vector<float> key(head_dim);
for (int d = 0; d < head_dim; d++)
key[d] = sinf(s * 1.0f + d * 0.1f) + ((d == s * 30) ? 10.0f : 0.0f);
tq_mixed_4b8_quantize_ref(key.data(), &blocks[s], head_dim);
}
/* Compute via attention function */
std::vector<float> scores(seq_len);
tq_mixed_4b8_attention_ref(query.data(), blocks.data(), scores.data(),
seq_len, head_dim);
/* Compare with manual dequantize + dot */
for (int s = 0; s < seq_len; s++) {
std::vector<float> deq(head_dim);
tq_mixed_4b8_dequantize_ref(&blocks[s], deq.data(), head_dim);
float fp32_dot = 0;
for (int d = 0; d < head_dim; d++) fp32_dot += query[d] * deq[d];
EXPECT_NEAR(scores[s], fp32_dot, 1e-4f);
}
}
/* ============================================================
* Constant input: all same value
* ============================================================ */
TEST(Mixed4B8, ConstantInput) {
std::vector<float> input(TQ_BK, 3.14f);
block_tq_mixed_4b8 block;
tq_mixed_4b8_quantize_ref(input.data(), &block, TQ_BK);
std::vector<float> output(TQ_BK);
tq_mixed_4b8_dequantize_ref(&block, output.data(), TQ_BK);
for (int i = 0; i < TQ_BK; i++) {
EXPECT_NEAR(output[i], 3.14f, 0.1f);
}
}
/* ============================================================
* Traits table registration
* ============================================================ */
TEST(Mixed4B8, TraitsRegistered) {
EXPECT_STREQ(TQ_TRAITS[TQ_TYPE_MIXED_4B8].name, "mixed_4b8");
EXPECT_EQ(TQ_TRAITS[TQ_TYPE_MIXED_4B8].block_size, (size_t)TQ_BK);
EXPECT_EQ(TQ_TRAITS[TQ_TYPE_MIXED_4B8].type_size, sizeof(block_tq_mixed_4b8));
EXPECT_GT(TQ_TRAITS[TQ_TYPE_MIXED_4B8].bpe, 0.0f);
EXPECT_NE(TQ_TRAITS[TQ_TYPE_MIXED_4B8].quantize, nullptr);
EXPECT_NE(TQ_TRAITS[TQ_TYPE_MIXED_4B8].dequantize, nullptr);
EXPECT_NE(TQ_TRAITS[TQ_TYPE_MIXED_4B8].attention, nullptr);
}
/* ============================================================
* Type lookup by name
* ============================================================ */
TEST(Mixed4B8, TypeFromName) {
EXPECT_EQ(tq_type_from_name("mixed_4b8"), TQ_TYPE_MIXED_4B8);
}
/* ============================================================
* Format spec
* ============================================================ */
TEST(Mixed4B8, FormatSpec) {
tq_format_spec_t spec = tq_get_format_spec(TQ_TYPE_MIXED_4B8);
EXPECT_EQ(spec.algorithm, TQ_ALG_MIXED);
EXPECT_EQ(spec.key_bits, 4);
EXPECT_EQ(spec.outlier_count, TQ_MIXED_OUTLIERS);
EXPECT_EQ(spec.block_size, TQ_BK);
}
/* ============================================================
* Extreme outlier data: verify 10x+ MSE improvement
* ============================================================ */
TEST(Mixed4B8, ExtremeOutliers) {
std::vector<float> input(TQ_BK);
/* Most values in [-1, 1], four extreme outliers */
for (int i = 0; i < TQ_BK; i++) input[i] = sinf(i * 0.1f);
input[0] = 100.0f;
input[64] = -100.0f;
input[32] = 80.0f;
input[96] = -80.0f;
/* Mixed */
block_tq_mixed_4b8 mixed_block;
tq_mixed_4b8_quantize_ref(input.data(), &mixed_block, TQ_BK);
std::vector<float> mixed_out(TQ_BK);
tq_mixed_4b8_dequantize_ref(&mixed_block, mixed_out.data(), TQ_BK);
double mixed_mse = 0;
for (int i = 0; i < TQ_BK; i++) {
double d = input[i] - mixed_out[i];
mixed_mse += d * d;
}
mixed_mse /= TQ_BK;
/* Uniform */
block_tq_uniform_4b uni_block;
tq_uniform_4b_quantize_ref(input.data(), &uni_block, TQ_BK);
std::vector<float> uni_out(TQ_BK);
tq_uniform_4b_dequantize_ref(&uni_block, uni_out.data(), TQ_BK);
double uni_mse = 0;
for (int i = 0; i < TQ_BK; i++) {
double d = input[i] - uni_out[i];
uni_mse += d * d;
}
uni_mse /= TQ_BK;
/* With extreme outliers, mixed should be massively better */
EXPECT_LT(mixed_mse, uni_mse * 0.1)
<< "mixed_mse=" << mixed_mse << " uni_mse=" << uni_mse
<< " (expected 10x+ improvement)";
}