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
//! Immutable shared vector storage, independent of search execution.
use crate::*;
impl Corpus {
/// Build shared, immutable storage from FP32 rows, normalizing before FP16 conversion.
/// Dimensions must be 1..=16384; rows must be finite and nonzero.
pub fn build<I, R>(device: &Device, dimensions: usize, rows: I) -> Result<Self>
where
I: IntoIterator<Item = R>,
I::IntoIter: ExactSizeIterator,
R: AsRef<[f32]>,
{
Self::build_encoded(device, dimensions, rows, MAX_ELEMENTS, |r, d, p, b| {
encode(r.as_ref(), d, p, b)
})
}
/// Build shared storage from little-endian FP16 rows, preserving their values.
/// Logical rows must be finite and nonzero. Dimension padding and inverse norms are added.
pub fn build_fp16<I, R>(device: &Device, dimensions: usize, rows: I) -> Result<Self>
where
I: IntoIterator<Item = R>,
I::IntoIter: ExactSizeIterator,
R: AsRef<[u8]>,
{
Self::build_encoded(device, dimensions, rows, MAX_ELEMENTS, |r, d, p, b| {
encode_fp16(r.as_ref(), d, p, b)
})
}
pub(crate) fn build_encoded<I, R>(
device: &Device,
dimensions: usize,
rows: I,
element_limit: usize,
mut encode_row: impl FnMut(&R, usize, usize, &mut Vec<u8>) -> Result<f32>,
) -> Result<Self>
where
I: IntoIterator<Item = R>,
I::IntoIter: ExactSizeIterator,
{
if device.target().as_str() != "gfx1151" {
return Err(invalid("hrxdb currently targets gfx1151"));
}
let mut rows = rows.into_iter();
let count = rows.len();
let (padded, _) = layout(dimensions, count)?;
let mut stream = device.stream()?;
let chunk_rows = (CHUNK_BYTES / (padded * 2)).max(1).min(count.max(1));
let mut chunk = Vec::with_capacity(chunk_rows * padded * 2);
let mut norm_chunk = Vec::with_capacity(chunk_rows * 4);
let mut shards = Vec::new();
let per_shard = shard_rows(padded, element_limit);
for start in (0..count).step_by(per_shard) {
let shard_count = (count - start).min(per_shard);
let capacity = shard_capacity(shard_count);
let data = stream.allocate(capacity * padded * 2)?;
let norms = stream.allocate(capacity * 4)?;
if capacity > shard_count {
// Initialize only the slack; these are not indexed zero rows.
// Contents are deliberately not part of the public contract.
// The ingestion synchronization below also completes the fills.
stream.fill(
data.try_slice(
shard_count * padded * 2,
(capacity - shard_count) * padded * 2,
)?,
0,
)?;
stream.fill(
norms.try_slice(shard_count * 4, (capacity - shard_count) * 4)?,
0,
)?;
}
for local in (0..shard_count).step_by(chunk_rows) {
chunk.clear();
norm_chunk.clear();
for _ in local..(local + chunk_rows).min(shard_count) {
let row = rows
.next()
.ok_or_else(|| invalid("row iterator returned fewer rows than declared"))?;
let inverse = encode_row(&row, dimensions, padded, &mut chunk)?;
norm_chunk.extend_from_slice(&inverse.to_le_bytes());
}
stream.upload(data.try_slice(local * padded * 2, chunk.len())?, &chunk)?;
stream.upload(norms.try_slice(local * 4, norm_chunk.len())?, &norm_chunk)?;
// Bound HRX's owned staging as well as the conversion buffers.
stream.synchronize()?;
}
shards.push(Shard {
data,
norms,
start,
count: shard_count,
capacity,
});
}
if rows.next().is_some() {
return Err(invalid("row iterator returned more rows than declared"));
}
Ok(Self {
workspace_budget: None,
inner: std::sync::Arc::new(CorpusStorage {
device: device.clone(),
device_id: stream.device_id(),
shards,
dimensions,
padded,
count,
gather: Default::default(),
}),
})
}
}
/// One owned resident FP16 allocation to adopt without copying its vectors.
/// Rows use the corpus's padded stride; capacity must be a multiple of 256.
/// Logical rows must be finite, nonzero, and have zero dimension padding.
pub struct ResidentShard {
/// Owned GPU allocation, including readable row slack.
pub vectors: Buffer,
/// Logical row count (slack is excluded).
pub rows: usize,
}
impl Corpus {
/// Adopt FP16 allocations on the producer's ordered stream, validating on
/// the GPU and computing inverse norms with FP32 accumulation.
///
/// FP32 norm reduction can round differently from host ingestion.
/// No vector data is transferred to the host or copied to another allocation.
/// The only readback is a four-byte validation result. This call completes
/// producer work and validation before publishing the immutable corpus.
/// Wait on a producer event first if its writes used another stream.
///
/// Buffers use a row stride of `ceil(dimensions/128)*128` FP16 elements.
/// Capacity must cover the logical rows, be divisible by 256, and fit the
/// 2^32-element per-shard limit. Shard order defines insertion IDs. Provided
/// allocations are consumed even on validation failure; no unchecked path
/// accepts inconsistent norms or invalid rows.
pub fn from_device(
device: &Device,
stream: &mut Stream,
dimensions: usize,
resident: Vec<ResidentShard>,
) -> Result<Self> {
let (padded, _) = layout(dimensions, 0)?;
if device.target().as_str() != "gfx1151" {
return Err(invalid("hrxdb currently targets gfx1151"));
}
let check = device.stream()?;
if check.device_id() != stream.device_id() {
return Err(invalid("producer stream belongs to another device"));
}
let mut count = 0usize;
for shard in &resident {
let row_bytes = padded * 2;
let capacity = shard.vectors.bytes() / row_bytes;
if shard.rows == 0
|| shard.rows > capacity
|| !shard.vectors.bytes().is_multiple_of(row_bytes)
|| !capacity.is_multiple_of(SHARD_ROW_ALIGNMENT)
|| capacity > MAX_ELEMENTS / padded
{
return Err(invalid(
"resident shard has invalid row count, stride, capacity, or element extent",
));
}
count = count
.checked_add(shard.rows)
.filter(|n| *n <= MAX_ROWS)
.ok_or_else(|| invalid("resident corpus exceeds 2^30 rows"))?;
}
let compiler = hrx::loom::Compiler::for_stream(None, stream)?;
let status = stream.allocate_zeroed(4)?;
let mut shards = Vec::with_capacity(resident.len());
let mut start = 0;
for shard in resident {
let capacity = shard.vectors.bytes() / (padded * 2);
let norms = stream.allocate_zeroed(capacity * 4)?;
let mut spec = kernels::named_spec("import_corpus");
for (key, value) in [
("import.rows", shard.rows),
("import.dimensions", dimensions),
("import.padded", padded),
] {
spec.set_config(key, value.to_string());
}
let (kernel, _) = compile(
&compiler,
stream,
include_str!("../kernels/import_corpus.loom"),
spec,
)?;
// SAFETY: validated extents cover every logical padded row. The
// kernel only reads vectors and writes owned norms/status; invalid
// values are counted on device, never used for addressing.
unsafe {
stream.dispatch(
&kernel,
[shard.rows as u32, 1, 1],
[128, 1, 1],
&Constants::new(),
&[shard.vectors.binding(), norms.binding(), status.binding()],
)?;
}
shards.push(Shard {
data: shard.vectors,
norms,
start,
count: shard.rows,
capacity,
});
start += shard.rows;
}
let mut errors = [0; 4];
stream.read_blocking(status.binding(), &mut errors)?;
if u32::from_le_bytes(errors) != 0 {
return Err(invalid(
"resident corpus contains nonfinite, zero-norm, or nonzero-padding rows",
));
}
Ok(Self {
workspace_budget: None,
inner: std::sync::Arc::new(CorpusStorage {
device: device.clone(),
device_id: stream.device_id(),
shards,
dimensions,
padded,
count,
gather: Default::default(),
}),
})
}
}