File size: 7,451 Bytes
57c2394 | 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 | #include "cpu_pool.h"
#include "cpu_threads.h"
#include "packed_matmul_cpu.h"
#include "../torch-ext/torch_binding.h"
#include <torch/headeronly/core/DeviceType.h>
#include <torch/headeronly/core/ScalarType.h>
#include <torch/headeronly/macros/Macros.h>
#include <algorithm>
#include <cstdlib>
#include <cstring>
#include <utility>
#include <vector>
namespace {
orbitquant::cpu::ScalarKind scalar_kind(OrbitQuantTensor const &tensor) {
using torch::headeronly::ScalarType;
switch (tensor.scalar_type()) {
case ScalarType::Float:
return orbitquant::cpu::ScalarKind::Float32;
case ScalarType::Half:
return orbitquant::cpu::ScalarKind::Float16;
case ScalarType::BFloat16:
return orbitquant::cpu::ScalarKind::BFloat16;
default:
STD_TORCH_CHECK(
false,
"CPU packed matmul supports float32, float16, and bfloat16 inputs");
}
return orbitquant::cpu::ScalarKind::Float32;
}
void parallel_packed_matmul(
orbitquant::cpu::PackedMatmulArgs const &args,
orbitquant::cpu::PackedMatmulRangeFn function) {
const std::int64_t arithmetic =
args.rows * args.out_features * args.in_features;
const int max_threads = orbitquant::cpu::requested_threads();
const int threads = arithmetic < 1'000'000
? 1
: std::max<int>(
1,
std::min<std::int64_t>(max_threads, args.out_features / 16));
if (threads == 1) {
function(args, 0, args.out_features);
return;
}
std::vector<std::pair<std::int64_t, std::int64_t>> ranges;
ranges.reserve(threads);
const std::int64_t columns_per_thread =
(args.out_features + threads - 1) / threads;
for (int thread = 0; thread < threads; ++thread) {
const std::int64_t start = thread * columns_per_thread;
const std::int64_t end = std::min(
args.out_features,
start + columns_per_thread);
if (start >= end) {
break;
}
ranges.emplace_back(start, end);
}
orbitquant::cpu::run_ranges(
ranges,
[&args, function](std::int64_t start, std::int64_t end) {
function(args, start, end);
});
}
orbitquant::cpu::PackedMatmulRangeFn select_packed_matmul() {
const char *requested = std::getenv("ORBITQUANT_CPU_ISA");
if (requested == nullptr || std::strcmp(requested, "auto") == 0) {
if (orbitquant::cpu::packed_matmul_x86_avx512_available()) {
return orbitquant::cpu::packed_matmul_x86_avx512_range;
}
if (orbitquant::cpu::packed_matmul_x86_avx2_available()) {
return orbitquant::cpu::packed_matmul_x86_avx2_range;
}
if (orbitquant::cpu::packed_matmul_neon_available()) {
return orbitquant::cpu::packed_matmul_neon_range;
}
return orbitquant::cpu::packed_matmul_scalar_range;
}
if (std::strcmp(requested, "scalar") == 0) {
return orbitquant::cpu::packed_matmul_scalar_range;
}
if (std::strcmp(requested, "avx2") == 0) {
STD_TORCH_CHECK(
orbitquant::cpu::packed_matmul_x86_avx2_available(),
"ORBITQUANT_CPU_ISA=avx2 requested AVX2/FMA/F16C on an unsupported CPU");
return orbitquant::cpu::packed_matmul_x86_avx2_range;
}
if (std::strcmp(requested, "avx512") == 0) {
STD_TORCH_CHECK(
orbitquant::cpu::packed_matmul_x86_avx512_available(),
"ORBITQUANT_CPU_ISA=avx512 requested AVX-512F/DQ/BW/VL on an unsupported CPU");
return orbitquant::cpu::packed_matmul_x86_avx512_range;
}
if (std::strcmp(requested, "neon") == 0) {
STD_TORCH_CHECK(
orbitquant::cpu::packed_matmul_neon_available(),
"ORBITQUANT_CPU_ISA=neon requested NEON on an unsupported CPU");
return orbitquant::cpu::packed_matmul_neon_range;
}
STD_TORCH_CHECK(
false,
"ORBITQUANT_CPU_ISA must be auto, scalar, avx2, avx512, or neon");
return orbitquant::cpu::packed_matmul_scalar_range;
}
} // namespace
void matmul_packed_weight(
OrbitQuantTensor &out,
OrbitQuantTensor const &x,
OrbitQuantTensor const &packed_weight_indices,
OrbitQuantTensor const &row_norms,
OrbitQuantTensor const ¢roids,
OrbitQuantTensor const &bias,
bool has_bias,
int64_t bits,
int64_t out_features,
int64_t in_features,
int64_t block_m,
int64_t block_n,
int64_t block_k) {
using torch::headeronly::DeviceType;
using torch::headeronly::ScalarType;
STD_TORCH_CHECK(x.device().type() == DeviceType::CPU, "x must be a CPU tensor");
STD_TORCH_CHECK(out.device().type() == DeviceType::CPU, "out must be a CPU tensor");
STD_TORCH_CHECK(
packed_weight_indices.device().type() == DeviceType::CPU,
"packed weights must be CPU tensors");
STD_TORCH_CHECK(
row_norms.device().type() == DeviceType::CPU,
"row norms must be CPU tensors");
STD_TORCH_CHECK(
centroids.device().type() == DeviceType::CPU,
"centroids must be CPU tensors");
STD_TORCH_CHECK(x.is_contiguous(), "x must be contiguous");
STD_TORCH_CHECK(out.is_contiguous(), "out must be contiguous");
STD_TORCH_CHECK(
packed_weight_indices.is_contiguous(), "packed weights must be contiguous");
STD_TORCH_CHECK(row_norms.is_contiguous(), "row norms must be contiguous");
STD_TORCH_CHECK(centroids.is_contiguous(), "centroids must be contiguous");
STD_TORCH_CHECK(
packed_weight_indices.scalar_type() == ScalarType::Byte,
"packed weights must be uint8");
STD_TORCH_CHECK(
row_norms.scalar_type() == ScalarType::Float,
"row_norms must be float32");
STD_TORCH_CHECK(
centroids.scalar_type() == ScalarType::Float,
"centroids must be float32");
STD_TORCH_CHECK(out.scalar_type() == x.scalar_type(), "out dtype must match x dtype");
STD_TORCH_CHECK(x.dim() == 2, "x must be rank 2");
STD_TORCH_CHECK(out.dim() == 2, "out must be rank 2");
STD_TORCH_CHECK(bits > 0 && bits <= 8, "bits must be in [1, 8]");
STD_TORCH_CHECK(block_m > 0 && block_n > 0 && block_k > 0, "tile sizes must be positive");
STD_TORCH_CHECK(x.size(1) == in_features, "x has an unexpected input dimension");
STD_TORCH_CHECK(out.size(0) == x.size(0), "out has an unexpected row count");
STD_TORCH_CHECK(out.size(1) == out_features, "out has an unexpected output dimension");
STD_TORCH_CHECK(row_norms.numel() == out_features, "row_norms must match out_features");
STD_TORCH_CHECK(centroids.numel() >= (1LL << bits), "centroids are too short");
const int64_t packed_bytes = (out_features * in_features * bits + 7) / 8;
STD_TORCH_CHECK(
packed_weight_indices.numel() >= packed_bytes,
"packed weights are too short");
if (has_bias) {
STD_TORCH_CHECK(
bias.device().type() == DeviceType::CPU,
"bias must be a CPU tensor");
STD_TORCH_CHECK(bias.is_contiguous(), "bias must be contiguous");
STD_TORCH_CHECK(
bias.scalar_type() == ScalarType::Float,
"bias must be float32");
STD_TORCH_CHECK(bias.numel() == out_features, "bias must match out_features");
}
if (x.numel() == 0 || out_features == 0) {
return;
}
orbitquant::cpu::PackedMatmulArgs args{
out.mutable_data_ptr(),
x.const_data_ptr(),
packed_weight_indices.const_data_ptr<std::uint8_t>(),
row_norms.const_data_ptr<float>(),
centroids.const_data_ptr<float>(),
has_bias ? bias.const_data_ptr<float>() : nullptr,
has_bias,
scalar_kind(x),
x.size(0),
out_features,
in_features,
bits,
};
parallel_packed_matmul(args, select_packed_matmul());
}
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