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llama.cpp / ggml /src /ggml-openvino /ggml-quants.cpp
dlxj
todo: 基于 CUDA 13.0 编译
2517be1
#include "ggml-quants.h"
#include "ggml-common.h"
#include "ggml-impl.h"
#include "ggml.h"
#include <algorithm>
#include <cassert>
#include <cmath>
#include <cstddef>
#include <cstdint>
#include <limits>
#include <memory>
#include <openvino/core/except.hpp>
#include <openvino/core/node.hpp>
#include <openvino/core/node_output.hpp>
#include <openvino/core/parallel.hpp>
#include <openvino/core/shape.hpp>
#include <openvino/core/type/element_type.hpp>
#include <openvino/core/type/element_type_traits.hpp>
#include <openvino/core/type/float16.hpp>
#include <openvino/op/add.hpp>
#include <openvino/op/constant.hpp>
#include <openvino/op/convert.hpp>
#include <openvino/op/multiply.hpp>
#include <openvino/op/reshape.hpp>
#include <openvino/op/subtract.hpp>
#include <openvino/op/util/attr_types.hpp>
#include <openvino/runtime/tensor.hpp>
#include <string>
#include <vector>
void unpack_32_4(const uint8_t * data, uint8_t * dst) {
std::fill_n(dst, 16, 0);
for (int j = 0; j < 16; ++j) {
uint8_t x = (data[j] & 0x0F);
uint8_t y = (data[j] >> 4);
if (j % 2 != 0) {
x <<= 4;
y <<= 4;
}
dst[j / 2] |= x;
dst[8 + j / 2] |= y; // Last 16 weights are in the higher bits
}
}
// Extracts (weight, scales, zp) from Q4_0 tensors.
// Data layout is: |16 bit scale|32 x 4bit weights|.
void extract_q4_0_data(const ggml_tensor * tensor,
ov::Tensor & weights_arr,
ov::Tensor & scales_arr,
ov::Tensor & zp_arr) {
const uint64_t bytes_per_block = 18; // 2 bytes scale, 32x0.5 byte weights
auto * data = static_cast<uint8_t *>(tensor->data);
auto * weights = static_cast<uint8_t *>(weights_arr.data());
auto * scales = scales_arr.data<ov::element_type_traits<ov::element::f16>::value_type>();
auto * zp = static_cast<uint8_t *>(zp_arr.data());
bool is_scalar_zp = (zp_arr.get_size() == 1); // Symmetric quantization
// For Q4_0, zero point is always 8
if (is_scalar_zp) {
zp[0] = 8 | (8 << 4); // Pack two 4-bit values
}
ov::parallel_for(scales_arr.get_size(), [&](size_t i) {
scales[i] = ov::float16::from_bits(*((uint16_t *) (data + i * bytes_per_block)));
// For asymmetric quantization, compute per-block zero points
if (!is_scalar_zp) {
// Pack two 4-bit zero points per byte
if (i % 2 == 0) {
zp[i / 2] = 8; // Lower nibble
} else {
zp[i / 2] |= (8 << 4); // Upper nibble
}
}
unpack_32_4(data + i * bytes_per_block + 2, weights + i * 16);
});
}
// Extracts (weight, scales, zp) from Q4_1 tensors.
// Data layout is: |16 bit scale|16 bit min|32 x 4bit weights|.
void extract_q4_1_data(const ggml_tensor * tensor,
ov::Tensor & weights_arr,
ov::Tensor & scales_arr,
ov::Tensor & zp_arr,
bool use_bias) {
const uint64_t bytes_per_block = 20; // 2 bytes scale, 2 bytes min, 32x0.5 byte weights
auto * data = static_cast<uint8_t *>(tensor->data);
auto * weights = static_cast<uint8_t *>(weights_arr.data());
auto * scales = scales_arr.data<ov::element_type_traits<ov::element::f16>::value_type>();
if (use_bias) {
// Store bias (min) directly as f16 instead of computing u4 zero points
auto * bias = zp_arr.data<ov::element_type_traits<ov::element::f16>::value_type>();
ov::parallel_for(scales_arr.get_size(), [&](size_t i) {
float scale = static_cast<float>(ov::float16::from_bits(*((uint16_t *) (data + i * bytes_per_block))));
float min = static_cast<float>(ov::float16::from_bits(*((uint16_t *) (data + i * bytes_per_block + 2))));
scales[i] = ov::float16(scale);
bias[i] = ov::float16(min); // bias = min, dequant: w*s + bias
unpack_32_4(data + i * bytes_per_block + 4, weights + i * 16);
});
} else {
auto * zp = static_cast<uint8_t *>(zp_arr.data());
ov::parallel_for(scales_arr.get_size(), [&](size_t i) {
float scale = static_cast<float>(ov::float16::from_bits(*((uint16_t *) (data + i * bytes_per_block))));
float min = static_cast<float>(ov::float16::from_bits(*((uint16_t *) (data + i * bytes_per_block + 2))));
scales[i] = ov::float16(scale);
// zp = -min / scale (bias = min, so zp = -bias/scale)
uint8_t zp_val = (scale != 0.0f) ? (uint8_t) std::round(-min / scale) : 0;
// Pack two 4-bit zero points per byte
if (i % 2 == 0) {
zp[i / 2] = zp_val & 0x0F; // Lower nibble
} else {
zp[i / 2] |= (zp_val << 4); // Upper nibble
}
unpack_32_4(data + i * bytes_per_block + 4, weights + i * 16);
});
}
}
// Extracts (weight, scales, zp) from Q8_0 tensors.
// Data layout is: |16 bit scale|32 x 8bit weights|.
void extract_q8_0_data(const ggml_tensor * tensor,
ov::Tensor & weights_arr,
ov::Tensor & scales_arr,
ov::Tensor & zp_arr) {
const uint64_t weights_per_block = 32;
const uint64_t bytes_per_block = 34; // 2 bytes scale, 32x1 byte weights
auto * data = static_cast<uint8_t *>(tensor->data);
auto * weights = static_cast<uint8_t *>(weights_arr.data());
auto * scales = scales_arr.data<ov::element_type_traits<ov::element::f16>::value_type>();
auto * zp = static_cast<uint8_t *>(zp_arr.data());
bool is_scalar_zp = (zp_arr.get_size() == 1); // Symmetric quantization
// For Q8_0, zero point is always 128
if (is_scalar_zp) {
zp[0] = 128;
}
ov::parallel_for(scales_arr.get_size(), [&](size_t i) {
uint8_t * block_data = data + i * bytes_per_block;
scales[i] = ov::float16::from_bits(*(uint16_t *) block_data);
// For asymmetric quantization, store per-block zero points
if (!is_scalar_zp) {
zp[i] = 128;
}
for (size_t j = 0; j < weights_per_block; ++j) {
uint8_t x = block_data[j + 2]; // j+2 to skip the scale bytes.
// Original data is in int8_t, so we add a bias of -128 and invert the first bit.
x ^= 1 << 7;
weights[i * weights_per_block + j] = x;
}
});
}
void unpack_256_4(const uint8_t * data, uint8_t * dst) {
// Initialize the output array with zeros
std::fill_n(dst, 128, 0);
for (size_t i = 0; i < 4; ++i) {
for (int j = 0; j < 32; ++j) {
uint8_t x = (data[i * 32 + j] & 0x0F);
uint8_t y = (data[i * 32 + j] >> 4);
if (j % 2 != 0) {
x <<= 4;
y <<= 4;
}
dst[i * 32 + j / 2] |= x;
dst[i * 32 + 16 + j / 2] |= y; // Last 16 weights are in the higher bits
}
}
}
void extract_q4_k_data(const ggml_tensor * tensor,
ov::Tensor & weights_arr,
ov::Tensor & scales_arr,
ov::Tensor & zp_arr,
bool use_bias) {
const uint64_t bytes_per_block = 2 + 2 + 12 + 128;
const uint64_t n_super_block = tensor->nb[3] / bytes_per_block;
auto * data = static_cast<uint8_t *>(tensor->data);
auto * weights = static_cast<uint8_t *>(weights_arr.data());
auto * scales = scales_arr.data<ov::element_type_traits<ov::element::f16>::value_type>();
// For bias path, zp_arr holds f16 bias values; for zp path, it holds packed u4 zero points
auto * zp_u4 = use_bias ? nullptr : static_cast<uint8_t *>(zp_arr.data());
auto * bias_f16 = use_bias ? zp_arr.data<ov::element_type_traits<ov::element::f16>::value_type>() : nullptr;
ov::parallel_for(n_super_block, [&](size_t i) {
uint8_t * block_data = data + i * bytes_per_block;
// Extract scale factors and offsets
float scale_scales = static_cast<float>(ov::float16::from_bits(*((uint16_t *) block_data)));
float scale_mins = static_cast<float>(ov::float16::from_bits(*((uint16_t *) block_data + 1)));
// Extract qs1 and qs2
uint8_t * qs1 = block_data + 4;
// Calculate scales
float scale_vals[8];
scale_vals[0] = scale_scales * static_cast<float>((*(qs1) & 0b111111));
scale_vals[1] = scale_scales * static_cast<float>((*(qs1 + 1) & 0b111111));
scale_vals[2] = scale_scales * static_cast<float>((*(qs1 + 2) & 0b111111));
scale_vals[3] = scale_scales * static_cast<float>((*(qs1 + 3) & 0b111111));
scale_vals[4] = scale_scales * static_cast<float>((*(qs1 + 8) & 0b00001111) | ((*(qs1) >> 6) << 4));
scale_vals[5] = scale_scales * static_cast<float>((*(qs1 + 9) & 0b00001111) | ((*(qs1 + 1) >> 6) << 4));
scale_vals[6] = scale_scales * static_cast<float>((*(qs1 + 10) & 0b00001111) | ((*(qs1 + 2) >> 6) << 4));
scale_vals[7] = scale_scales * static_cast<float>((*(qs1 + 11) & 0b00001111) | ((*(qs1 + 3) >> 6) << 4));
// Calculate min values (bias = -min)
float min_vals[8];
min_vals[0] = scale_mins * static_cast<float>((*(qs1 + 4) & 0b111111));
min_vals[1] = scale_mins * static_cast<float>((*(qs1 + 5) & 0b111111));
min_vals[2] = scale_mins * static_cast<float>((*(qs1 + 6) & 0b111111));
min_vals[3] = scale_mins * static_cast<float>((*(qs1 + 7) & 0b111111));
min_vals[4] = scale_mins * static_cast<float>((*(qs1 + 8) >> 4) | ((*(qs1 + 4) >> 6) << 4));
min_vals[5] = scale_mins * static_cast<float>((*(qs1 + 9) >> 4) | ((*(qs1 + 5) >> 6) << 4));
min_vals[6] = scale_mins * static_cast<float>((*(qs1 + 10) >> 4) | ((*(qs1 + 6) >> 6) << 4));
min_vals[7] = scale_mins * static_cast<float>((*(qs1 + 11) >> 4) | ((*(qs1 + 7) >> 6) << 4));
// Store scales and compute zero points or bias
for (int j = 0; j < 8; j++) {
scales[i * 8 + j] = ov::float16(scale_vals[j]);
if (use_bias) {
// Store bias = -min directly as f16, dequant: w*s + bias
bias_f16[i * 8 + j] = ov::float16(-min_vals[j]);
} else {
// zp = min / scale (since bias = -min and zp = -bias/scale)
uint8_t zp_val = (scale_vals[j] != 0.0f) ? (uint8_t) std::round(min_vals[j] / scale_vals[j]) : 0;
// Pack two 4-bit zero points per byte
size_t idx = i * 8 + j;
if (idx % 2 == 0) {
zp_u4[idx / 2] = zp_val & 0x0F;
} else {
zp_u4[idx / 2] |= (zp_val << 4);
}
}
}
unpack_256_4(block_data + 16, weights + i * 128);
});
}
void extract_q6_k_data(const ggml_tensor * tensor,
ov::Tensor & weights_arr,
ov::Tensor & scales_arr,
ov::Tensor & zp_arr) {
const uint64_t bytes_per_block = 128 + 64 + 16 + 2;
const uint64_t n_super_block = tensor->nb[3] / bytes_per_block;
auto * data = static_cast<uint8_t *>(tensor->data);
auto * weights = static_cast<uint8_t *>(weights_arr.data());
auto * scales = scales_arr.data<ov::element_type_traits<ov::element::f16>::value_type>();
auto * zp = static_cast<uint8_t *>(zp_arr.data());
bool is_scalar_zp = (zp_arr.get_size() == 1); // Symmetric quantization
// For Q6_K, zero point is always 32
if (is_scalar_zp) {
zp[0] = 32;
}
ov::parallel_for(n_super_block, [&](size_t i) {
uint8_t * block_data = data + i * bytes_per_block;
float scale_factor =
static_cast<float>(ov::float16::from_bits(*((uint16_t *) block_data + 104))); // (128+64+16)/2
for (size_t j = 0; j < 16; j++) {
scales[j + i * 16] =
ov::float16(scale_factor * static_cast<float>(*((int8_t *) (block_data + 128 + 64 + j))));
// For asymmetric quantization, store per-block zero points
if (!is_scalar_zp) {
zp[j + i * 16] = 32;
}
}
uint8_t * ql = block_data;
uint8_t * qh = block_data + 128;
for (int64_t j = 0; j < 32; ++j) {
weights[i * 256 + j] = (ql[j] & 0xF) | (((qh[j] >> 0) & 3) << 4);
weights[i * 256 + j + 32] = (ql[32 + j] & 0xF) | (((qh[j] >> 2) & 3) << 4);
weights[i * 256 + j + 64] = (ql[j] >> 4) | (((qh[j] >> 4) & 3) << 4);
weights[i * 256 + j + 96] = (ql[32 + j] >> 4) | (((qh[j] >> 6) & 3) << 4);
weights[i * 256 + j + 128] = (ql[64 + j] & 0xF) | (((qh[32 + j] >> 0) & 3) << 4);
weights[i * 256 + j + 160] = (ql[96 + j] & 0xF) | (((qh[32 + j] >> 2) & 3) << 4);
weights[i * 256 + j + 192] = (ql[64 + j] >> 4) | (((qh[32 + j] >> 4) & 3) << 4);
weights[i * 256 + j + 224] = (ql[96 + j] >> 4) | (((qh[32 + j] >> 6) & 3) << 4);
}
});
}
static inline void get_scale_min_k4(int j, const uint8_t * q, uint8_t * d, uint8_t * m) {
if (j < 4) {
*d = q[j] & 63;
*m = q[j + 4] & 63;
} else {
*d = (q[j + 4] & 0xF) | ((q[j - 4] >> 6) << 4);
*m = (q[j + 4] >> 4) | ((q[j - 0] >> 6) << 4);
}
}
void extract_q5_k_data(const ggml_tensor * tensor,
ov::Tensor & weights_arr,
ov::Tensor & scales_arr,
ov::Tensor & zp_arr,
bool use_bias) {
const uint64_t bytes_per_block = 4 + 12 + 32 + 128;
const uint64_t n_super_block = tensor->nb[3] / bytes_per_block;
auto * data = static_cast<uint8_t *>(tensor->data);
auto * weights = static_cast<uint8_t *>(weights_arr.data());
auto * scales = scales_arr.data<ov::element_type_traits<ov::element::f16>::value_type>();
// For bias path, zp_arr holds f16 bias values; for zp path, it holds u8 zero points
auto * zp_u8 = use_bias ? nullptr : static_cast<uint8_t *>(zp_arr.data());
auto * bias_f16 = use_bias ? zp_arr.data<ov::element_type_traits<ov::element::f16>::value_type>() : nullptr;
ov::parallel_for(n_super_block, [&](size_t i) {
uint8_t * block_data = data + i * bytes_per_block;
const float d = static_cast<float>(ov::float16::from_bits(*((uint16_t *) block_data)));
const float min_factor = static_cast<float>(ov::float16::from_bits(*((uint16_t *) block_data + 1)));
const uint8_t * scales_data = block_data + 4; // 12 bytes of scales
const uint8_t * qh = block_data + 4 + 12; // 32 bytes of high bits
const uint8_t * ql = block_data + 4 + 12 + 32; // 128 bytes of low bits
int is = 0;
uint8_t u1 = 1;
uint8_t u2 = 2;
// Process 2 blocks in one iteration
for (int j = 0; j < 256; j += 64) { // 256 = QK_K, so 4 iterations of 64
uint8_t sc;
uint8_t m;
// Get scale and min for first 32 elements
get_scale_min_k4(is + 0, scales_data, &sc, &m);
const float d1 = d * sc;
const float m1 = min_factor * m;
// Get scale and min for second 32 elements
get_scale_min_k4(is + 1, scales_data, &sc, &m);
const float d2 = d * sc;
const float m2 = min_factor * m;
scales[i * 8 + is] = ov::float16(d1);
scales[i * 8 + is + 1] = ov::float16(d2);
if (use_bias) {
// Store bias = -min directly as f16, dequant: w*s + bias
bias_f16[i * 8 + is] = ov::float16(-m1);
bias_f16[i * 8 + is + 1] = ov::float16(-m2);
} else {
// zp = min / scale (since bias = -min and zp = -bias/scale)
zp_u8[i * 8 + is] = (d1 != 0.0f) ? (uint8_t) std::round(m1 / d1) : 0;
zp_u8[i * 8 + is + 1] = (d2 != 0.0f) ? (uint8_t) std::round(m2 / d2) : 0;
}
// Extract weights for first 32 elements (matching deq formula exactly)
for (int l = 0; l < 32; ++l) {
weights[i * 256 + j + l] = (ql[l] & 0xF) + ((qh[l] & u1) ? 16 : 0);
}
// Extract weights for second 32 elements
for (int l = 0; l < 32; ++l) {
weights[i * 256 + j + l + 32] = (ql[l] >> 4) + ((qh[l] & u2) ? 16 : 0);
}
ql += 32;
is += 2;
u1 <<= 2;
u2 <<= 2;
}
});
}
// TODO Reorder for make_intX_weights
ov::Output<ov::Node> make_int8_weights(ov::Tensor & weight,
ov::Tensor & scales,
ov::Tensor & zp,
size_t group_size,
bool use_bias) {
ov::Shape orig_shape = weight.get_shape();
// Expand dimensions for scales and zp/bias
auto scale_shape = scales.get_shape();
auto zp_shape = zp.get_shape();
bool is_scalar_zp = zp_shape.empty(); // Symmetric quantization
ov::Shape packed_shape = {orig_shape[0], orig_shape[1] / group_size, group_size};
if (packed_shape[1] == 1) {
// Requantized channel-wise case
packed_shape.erase(packed_shape.begin() + 1);
} else {
scale_shape.push_back(1);
scales.set_shape(scale_shape);
// For symmetric quantization, zp remains scalar (don't resize)
if (!is_scalar_zp) {
zp_shape.push_back(1);
zp.set_shape(zp_shape);
}
}
// Create graph nodes
auto weights_node = std::make_shared<ov::op::v0::Constant>(ov::element::u8, packed_shape,
static_cast<uint8_t *>(weight.data()), nullptr);
weights_node->get_rt_info()["__gguf_tensor_holder"] = weight;
auto scales_f16 = std::make_shared<ov::op::v0::Constant>(scales);
auto weights_f16 = std::make_shared<ov::op::v0::Convert>(weights_node, ov::element::f16);
ov::Output<ov::Node> result;
if (use_bias && !is_scalar_zp) {
// Bias path: w * s + b (zp tensor holds f16 bias values)
auto bias_f16 = std::make_shared<ov::op::v0::Constant>(zp);
auto w_s = std::make_shared<ov::op::v1::Multiply>(weights_f16, scales_f16, ov::op::AutoBroadcastType::NUMPY);
result = std::make_shared<ov::op::v1::Add>(w_s, bias_f16, ov::op::AutoBroadcastType::NUMPY);
} else {
// Zero point path: (w - zp) * s
auto zero_point = std::make_shared<ov::op::v0::Constant>(zp);
float zp_value;
if (ov::op::util::get_single_value(zero_point, zp_value)) {
zero_point = ov::op::v0::Constant::create(zero_point->get_element_type(), {}, {zp_value});
}
auto zero_point_f16 = std::make_shared<ov::op::v0::Convert>(zero_point, ov::element::f16);
auto w_zp =
std::make_shared<ov::op::v1::Subtract>(weights_f16, zero_point_f16, ov::op::AutoBroadcastType::NUMPY);
result = std::make_shared<ov::op::v1::Multiply>(w_zp, scales_f16, ov::op::AutoBroadcastType::NUMPY);
}
if (packed_shape.size() != 2) {
// If not requantized channel-wise case, reshape back to original shape
auto final_shape =
std::make_shared<ov::op::v0::Constant>(ov::element::i64, ov::Shape{orig_shape.size()}, orig_shape);
result = std::make_shared<ov::op::v1::Reshape>(result, final_shape, false);
}
return std::make_shared<ov::op::v0::Convert>(result, ov::element::f32);
}
ov::Output<ov::Node> make_int4_weights(ov::Tensor & weight,
ov::Tensor & scales,
ov::Tensor & zp,
size_t group_size,
bool use_bias) {
ov::Shape orig_weight_shape = weight.get_shape();
// Expand dimensions for scales and zp/bias
ov::Shape scale_shape = scales.get_shape();
auto zp_shape = zp.get_shape();
bool is_scalar_zp = zp_shape.empty(); // Symmetric quantization
// Create INT4 weight tensor
ov::Shape packed_shape = {orig_weight_shape[0], orig_weight_shape[1] / group_size, group_size};
if (packed_shape[1] == 1) {
// Requantized channel-wise case
packed_shape.erase(packed_shape.begin() + 1);
} else {
scale_shape.push_back(1);
scales.set_shape(scale_shape);
// For symmetric quantization, zp remains scalar (don't resize)
if (!is_scalar_zp) {
zp_shape.push_back(1);
zp.set_shape(zp_shape);
}
}
auto weights_node = std::make_shared<ov::op::v0::Constant>(ov::element::u4, packed_shape,
static_cast<uint8_t *>(weight.data()), nullptr);
weights_node->get_rt_info()["__gguf_tensor_holder"] = weight;
auto weights_f16 = std::make_shared<ov::op::v0::Convert>(weights_node, ov::element::f16);
auto scales_f16 = std::make_shared<ov::op::v0::Constant>(scales);
ov::Output<ov::Node> result;
if (use_bias && !is_scalar_zp) {
// Bias path: w * s + b (zp tensor holds f16 bias values)
auto bias_f16 = std::make_shared<ov::op::v0::Constant>(zp);
auto w_s = std::make_shared<ov::op::v1::Multiply>(weights_f16, scales_f16, ov::op::AutoBroadcastType::NUMPY);
result = std::make_shared<ov::op::v1::Add>(w_s, bias_f16, ov::op::AutoBroadcastType::NUMPY);
} else {
// Zero point path: (w - zp) * s
auto zero_points_node = std::make_shared<ov::op::v0::Constant>(zp);
float zp_value;
if (ov::op::util::get_single_value(zero_points_node, zp_value)) {
zero_points_node = ov::op::v0::Constant::create(zero_points_node->get_element_type(), {}, {zp_value});
}
auto zero_points_f16 = std::make_shared<ov::op::v0::Convert>(zero_points_node, ov::element::f16);
auto w_zp =
std::make_shared<ov::op::v1::Subtract>(weights_f16, zero_points_f16, ov::op::AutoBroadcastType::NUMPY);
result = std::make_shared<ov::op::v1::Multiply>(w_zp, scales_f16, ov::op::AutoBroadcastType::NUMPY);
}
if (packed_shape.size() != 2) {
// If not requantized channel-wise case, reshape back to original shape
auto final_shape = std::make_shared<ov::op::v0::Constant>(ov::element::i64, ov::Shape{orig_weight_shape.size()},
orig_weight_shape);
result = std::make_shared<ov::op::v1::Reshape>(result, final_shape, false);
}
return std::make_shared<ov::op::v0::Convert>(result, ov::element::f32);
}
// Extract quantized weights from tensor and create weight subgraph
std::shared_ptr<ov::Node> extract_quantized_weights(const ggml_tensor * tensor,
const void * data,
ov::Tensor & weights,
ov::Tensor & scales,
ov::Tensor & zp,
bool use_bias) {
// Create a temporary tensor for extraction functions that read from tensor->data
ggml_tensor temp_tensor = *tensor;
temp_tensor.data = const_cast<void *>(data);
// Determine block size based on tensor type
int64_t weights_per_block;
bool is_u4;
switch (tensor->type) {
case GGML_TYPE_Q4_0:
case GGML_TYPE_Q4_1:
case GGML_TYPE_Q4_K:
is_u4 = true;
weights_per_block = 32;
break;
case GGML_TYPE_Q8_0:
case GGML_TYPE_Q5_K:
is_u4 = false;
weights_per_block = 32;
break;
case GGML_TYPE_Q6_K:
is_u4 = false;
weights_per_block = 16;
break;
default:
throw std::runtime_error("Unsupported quantized type for extraction: " +
std::string(ggml_type_name(tensor->type)));
}
// Extract quantized data
switch (tensor->type) {
case GGML_TYPE_Q4_0:
extract_q4_0_data(&temp_tensor, weights, scales, zp);
break;
case GGML_TYPE_Q4_1:
extract_q4_1_data(&temp_tensor, weights, scales, zp, use_bias);
break;
case GGML_TYPE_Q4_K:
extract_q4_k_data(&temp_tensor, weights, scales, zp, use_bias);
break;
case GGML_TYPE_Q8_0:
extract_q8_0_data(&temp_tensor, weights, scales, zp);
break;
case GGML_TYPE_Q6_K:
extract_q6_k_data(&temp_tensor, weights, scales, zp);
break;
case GGML_TYPE_Q5_K:
extract_q5_k_data(&temp_tensor, weights, scales, zp, use_bias);
break;
default:
throw std::runtime_error("Unsupported quantized type: " + std::string(ggml_type_name(tensor->type)));
}
// Create the OpenVINO weight subgraph
ov::Output<ov::Node> weight_node;
if (is_u4) {
weight_node = make_int4_weights(weights, scales, zp, weights_per_block, use_bias);
} else {
weight_node = make_int8_weights(weights, scales, zp, weights_per_block, use_bias);
}
auto result = weight_node.get_node_shared_ptr();
result->set_friendly_name(tensor->name);
return result;
}
// Requantize weights to target format, writing to provided buffers
std::shared_ptr<ov::Node> requantize_to_buffers(const ggml_tensor * tensor,
const void * data,
ExtraQuantType requant_type,
int64_t block_size,
ov::Tensor & weights,
ov::Tensor & scales,
ov::Tensor & zp) {
int64_t n_elements = ggml_nelements(tensor);
// First dequantize to F32
std::vector<float> weights_f32(n_elements);
ggml_get_type_traits(tensor->type)->to_float(data, weights_f32.data(), n_elements);
// Handle F16 case - just convert and create constant
if (requant_type == ExtraQuantType::F16) {
ggml_get_type_traits(GGML_TYPE_F16)->from_float_ref(weights_f32.data(), weights.data(), n_elements);
auto result = std::make_shared<ov::op::v0::Constant>(weights);
result->set_friendly_name(tensor->name);
return result;
}
// Requantize to target quantized format
bool is_u4 = (requant_type == ExtraQuantType::Q4_0_C || requant_type == ExtraQuantType::Q4_0_128);
if (is_u4) {
quantize_q4_0(weights_f32.data(), weights, scales, zp, n_elements, block_size);
} else if (requant_type == ExtraQuantType::Q8_1_C) {
quantize_q8_1(weights_f32.data(), weights, scales, zp, n_elements, block_size);
} else {
quantize_q8_0(weights_f32.data(), weights, scales, zp, n_elements, block_size);
}
// Create the OpenVINO weight subgraph
ov::Output<ov::Node> weight_node;
if (is_u4) {
weight_node = make_int4_weights(weights, scales, zp, block_size);
} else {
weight_node = make_int8_weights(weights, scales, zp, block_size);
}
auto result = weight_node.get_node_shared_ptr();
result->set_friendly_name(tensor->name);
return result;
}
OvWeight process_weight_tensor(const ggml_tensor * tensor, const void * data, void * output_base_ptr, bool use_bias) {
GGML_ASSERT(tensor != nullptr);
GGML_ASSERT(data != nullptr);
OvWeight result;
// Get 2D shape for weights [rows, cols]
ov::Shape node_shape = {static_cast<size_t>(tensor->ne[1]), static_cast<size_t>(tensor->ne[0])};
// Handle F16/F32/BF16 weights
if (tensor->type == GGML_TYPE_F32 || tensor->type == GGML_TYPE_F16 || tensor->type == GGML_TYPE_BF16) {
ov::element::Type element_type;
switch (tensor->type) {
case GGML_TYPE_F32:
element_type = ov::element::f32;
break;
case GGML_TYPE_F16:
element_type = ov::element::f16;
break;
case GGML_TYPE_BF16:
element_type = ov::element::bf16;
break;
default:
OPENVINO_THROW("Unexpected tensor type in F16/F32/BF16 path");
}
if (output_base_ptr && output_base_ptr != data) {
// Using external buffer - copy data and create shared-memory constant
size_t tensor_bytes = ggml_nbytes(tensor);
memcpy(output_base_ptr, data, tensor_bytes);
result.weights = ov::Tensor(element_type, node_shape, output_base_ptr);
} else {
result.weights = ov::Tensor(element_type, node_shape, data);
}
result.weight_node = std::make_shared<ov::op::v0::Constant>(result.weights);
return result;
}
// Handle quantized weights
if (!ggml_is_quantized(tensor->type)) {
OPENVINO_THROW("Unsupported weight tensor type: ", ggml_type_name(tensor->type));
}
result.layout = ggml_openvino_get_extracted_layout(tensor, use_bias);
const auto & layout = result.layout;
if (layout.total_size == 0) {
OPENVINO_THROW("Unsupported quantized type: ", ggml_type_name(tensor->type));
}
if (use_bias) {
OPENVINO_ASSERT(!layout.is_requant,
"use_bias is only used for test-backend-ops, which should not have requantization");
// bias node will be created on the fly and not use backend buffer
output_base_ptr = nullptr;
}
// F16 requant path - no separate scales/zp needed in result
if (layout.is_requant && layout.requant_type.has_value() && layout.requant_type.value() == ExtraQuantType::F16) {
if (output_base_ptr) {
result.weights = ov::Tensor(ov::element::f16, node_shape,
static_cast<uint8_t *>(output_base_ptr) + layout.weights_offset);
} else {
result.weights = ov::Tensor(ov::element::f16, node_shape);
}
ov::Tensor dummy_scales, dummy_zp; // Not used for F16
result.weight_node =
requantize_to_buffers(tensor, data, ExtraQuantType::F16, 0, result.weights, dummy_scales, dummy_zp);
return result;
}
// Quantized path (normal extraction or quantized requant)
// Create weight/scale/zp tensors - shared between both paths
ov::element::Type weight_type = layout.is_u4 ? ov::element::u4 : ov::element::u8;
ov::Shape scale_shape = {node_shape[0], node_shape[1] / layout.weights_per_block};
ov::Shape zp_shape = layout.is_symmetric ? ov::Shape{} : scale_shape;
if (output_base_ptr) {
uint8_t * buf_base = static_cast<uint8_t *>(output_base_ptr);
result.weights = ov::Tensor(weight_type, node_shape, buf_base + layout.weights_offset);
result.scales = ov::Tensor(ov::element::f16, scale_shape, buf_base + layout.scales_offset);
result.zp = ov::Tensor(weight_type, zp_shape, buf_base + layout.zp_offset);
} else {
result.weights = ov::Tensor(weight_type, node_shape);
result.scales = ov::Tensor(ov::element::f16, scale_shape);
if (use_bias && !layout.is_symmetric) {
// bias only has effect for asymmetric quant
result.zp = ov::Tensor(ov::element::f16, zp_shape);
} else {
result.zp = ov::Tensor(weight_type, zp_shape);
}
}
if (layout.is_requant && layout.requant_type.has_value()) {
result.weight_node = requantize_to_buffers(tensor, data, layout.requant_type.value(), layout.weights_per_block,
result.weights, result.scales, result.zp);
} else {
result.weight_node =
extract_quantized_weights(tensor, data, result.weights, result.scales, result.zp, use_bias);
}
return result;
}
void quantize_q4_0(const float * x,
ov::Tensor & weights_arr,
ov::Tensor & scales_arr,
ov::Tensor & zp_arr,
int64_t k,
int64_t qk) {
assert(k % qk == 0);
const int nb = k / qk;
auto * weights = static_cast<uint8_t *>(weights_arr.data());
auto * scales = scales_arr.data<ov::element_type_traits<ov::element::f16>::value_type>();
auto * zp = static_cast<uint8_t *>(zp_arr.data());
bool is_scalar_zp = (zp_arr.get_size() == 1); // Symmetric quantization
// For Q4_0, zero point is always 8
if (is_scalar_zp) {
zp[0] = 8 | (8 << 4); // Pack two 4-bit values
}
for (int i = 0; i < nb; i++) {
float amax = 0.0f; // absolute max
float max = 0.0f;
for (int j = 0; j < qk; j++) {
const float v = x[i * qk + j];
if (amax < fabsf(v)) {
amax = fabsf(v);
max = v;
}
}
const float d = max / -8;
if (d == 0) {
scales[i] = ov::float16(1.0f);
// zp is already set to 8 for symmetric, or set per-block for asymmetric
if (!is_scalar_zp) {
if (i % 2 == 0) {
zp[i / 2] = 8;
} else {
zp[i / 2] |= (8 << 4);
}
}
memset(weights + i * qk / 2, 8 | (8 << 4), qk / 2);
continue;
}
const float id = 1.0f / d;
scales[i] = ov::float16(d);
// For asymmetric quantization, store per-block zero points
if (!is_scalar_zp) {
if (i % 2 == 0) {
zp[i / 2] = 8;
} else {
zp[i / 2] |= (8 << 4);
}
}
for (int j = 0; j < qk / 2; ++j) {
const float x0 = x[i * qk + 2 * j] * id;
const float x1 = x[i * qk + 2 * j + 1] * id;
const uint8_t xi0 = MIN(15, (int8_t) (x0 + 8.5f));
const uint8_t xi1 = MIN(15, (int8_t) (x1 + 8.5f));
weights[i * qk / 2 + j] = xi0 | (xi1 << 4);
}
}
}
void quantize_q8_0(const float * x,
ov::Tensor & weights_arr,
ov::Tensor & scales_arr,
ov::Tensor & zp_arr,
int64_t k,
int64_t qk) {
assert(k % qk == 0);
const int nb = k / qk;
auto * weights = static_cast<uint8_t *>(weights_arr.data());
auto * scales = scales_arr.data<ov::element_type_traits<ov::element::f16>::value_type>();
auto * zp = static_cast<uint8_t *>(zp_arr.data());
bool is_scalar_zp = (zp_arr.get_size() == 1); // Symmetric quantization
// For Q8_0, zero point is always 128
if (is_scalar_zp) {
zp[0] = 128;
}
for (int i = 0; i < nb; i++) {
float amax = 0.0f; // absolute max
for (int j = 0; j < qk; j++) {
const float v = x[i * qk + j];
if (amax < fabsf(v)) {
amax = fabsf(v);
}
}
const float d = amax / 127.0f;
const float id = d ? 1.0f / d : 0.0f;
scales[i] = ov::float16(d);
// For asymmetric quantization, store per-block zero points
if (!is_scalar_zp) {
zp[i] = 128;
}
for (int j = 0; j < qk; ++j) {
const float x0 = x[i * qk + j] * id;
const int8_t xi0 = roundf(x0);
weights[i * qk + j] = (uint8_t) (xi0 + 128);
}
}
}
void quantize_q8_1(const float * x,
ov::Tensor & weights_arr,
ov::Tensor & scales_arr,
ov::Tensor & zp_arr,
int64_t k,
int64_t qk) {
assert(k % qk == 0);
const int nb = k / qk;
auto * weights = static_cast<uint8_t *>(weights_arr.data());
auto * scales = scales_arr.data<ov::element_type_traits<ov::element::f16>::value_type>();
auto * zp = static_cast<uint8_t *>(zp_arr.data());
for (int i = 0; i < nb; i++) {
float min = std::numeric_limits<float>::max();
float max = std::numeric_limits<float>::lowest();
for (int j = 0; j < qk; j++) {
const float v = x[i * qk + j];
if (v < min) {
min = v;
}
if (v > max) {
max = v;
}
}
const float d = (max - min) / ((1 << 8) - 1);
const float id = d ? 1.0f / d : 0.0f;
scales[i] = ov::float16(d);
// zp = -min / scale (Q8_1 is asymmetric)
zp[i] = (d != 0.0f) ? (uint8_t) std::round(-min / d) : 0;
for (int j = 0; j < qk; ++j) {
const float x0 = (x[i * qk + j] - min) * id;
const uint8_t xi0 = roundf(x0);
weights[i * qk + j] = xi0;
}
}
}