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https://github.com/leejet/stable-diffusion.cpp.git
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refactor: register CPU INT8 GEMM and ConvRot extensions
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@@ -1,6 +1,7 @@
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#include "core/ggml_extensions.h"
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#ifndef SD_USE_UPSTREAM_GGML
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#include "core/int8_ops.h"
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#include "ggml-ext.h"
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#include <cmath>
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@@ -186,6 +187,7 @@ void sd_ggml_register_extensions(ggml_backend_t backend) {
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#endif
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return;
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}
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sd_ggml_register_cpu_i8(device);
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ggml_ext_kernel_desc desc = {};
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desc.abi_version = GGML_EXT_ABI_VERSION;
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desc.name = "sd.rope_apply.cpu.v1";
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@@ -0,0 +1,373 @@
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#include "core/int8_ops.h"
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#ifndef SD_USE_UPSTREAM_GGML
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#include <algorithm>
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#include <cmath>
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#include <cstring>
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#include <limits>
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#include <utility>
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struct I8Attributes {
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int64_t shape[3];
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int32_t group_size;
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int32_t flags;
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};
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enum I8Flags { I8_PACKED = 1,
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I8_INPUT_SCALE = 2,
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I8_WEIGHT_SCALE = 4,
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I8_BIAS = 8 };
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static bool valid_group(int group) {
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return group == 1 || group == 4 || group == 16 || group == 64 || group == 256;
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}
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static constexpr int64_t i8_size_limit = int64_t(std::min(uint64_t(INT64_MAX), uint64_t(SIZE_MAX)));
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static bool packed_height(int64_t k, int64_t rows, int64_t* height) {
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if (k <= 0 || rows <= 0 || rows > i8_size_limit / 4)
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return false;
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const int64_t padded = (rows + 3) / 4 * 4;
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const int64_t scales = (rows * 4 - 1) / k + 1;
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if (padded > i8_size_limit / k || scales > i8_size_limit / k - padded)
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return false;
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*height = padded + scales;
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return true;
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}
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static bool scale_matches(const ggml_tensor* scale, int64_t count) {
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return !scale || (scale->type == GGML_TYPE_F32 && ggml_is_contiguous(scale) && ggml_nelements(scale) == count);
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}
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static bool i8_shape(const SDI8Operation& op, const int64_t* shape) {
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const auto* w = op.weight;
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const auto* x = op.input;
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if (!w || !x || w->type != GGML_TYPE_I8 || w->ne[2] != 1 || w->ne[3] != 1 ||
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!ggml_is_contiguous(w) || !ggml_is_contiguous(x) || x->ne[0] != w->ne[0] ||
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(op.group_size != 0 && (!valid_group(op.group_size) || w->ne[0] % op.group_size)))
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return false;
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int64_t rows = 1;
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for (int i = 0; i < 3; ++i) {
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if (shape[i] <= 0 || shape[i] > (INT64_MAX - 3) / rows)
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return false;
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rows *= shape[i];
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}
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const int64_t k = w->ne[0], n = w->ne[1];
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int64_t height;
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if (n <= 0 || n > (i8_size_limit - 4) / 4 || k <= 0 || k > i8_size_limit - 4 - n * 4 ||
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rows > (i8_size_limit - 3) / (k + 4 + n * 4) || !packed_height(k, rows, &height))
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return false;
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if (op.packed) {
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if (x->type != GGML_TYPE_I8 || !valid_group(op.group_size) || op.input_scale ||
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x->ne[1] != height || x->ne[2] != 1 || x->ne[3] != 1)
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return false;
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} else {
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if (std::memcmp(shape, x->ne + 1, 3 * sizeof(int64_t)))
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return false;
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if (x->type == GGML_TYPE_I8) {
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if (!op.input_scale || op.group_size != 0)
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return false;
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} else if (x->type != GGML_TYPE_F32 || op.input_scale)
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return false;
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}
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return scale_matches(op.input_scale, rows) && scale_matches(op.weight_scale, w->ne[1]) && scale_matches(op.bias, w->ne[1]);
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}
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static bool i8_infer(ggml_tensor* const* inputs, int count, const void* attributes, ggml_ext_tensor_desc* output) {
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I8Attributes attr;
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std::memcpy(&attr, attributes, sizeof(attr));
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if (count < 2 || (attr.flags & ~15))
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return false;
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SDI8Operation op = {inputs[0], inputs[1], nullptr, nullptr, nullptr, attr.group_size, (attr.flags & I8_PACKED) != 0};
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int index = 2;
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for (auto entry : {std::make_pair(I8_INPUT_SCALE, &op.input_scale), std::make_pair(I8_WEIGHT_SCALE, &op.weight_scale), std::make_pair(I8_BIAS, &op.bias)}) {
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if (attr.flags & entry.first) {
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if (index >= count)
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return false;
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*entry.second = inputs[index++];
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}
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}
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if (index != count || !i8_shape(op, attr.shape))
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return false;
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output->type = GGML_TYPE_F32;
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output->ne[0] = op.weight->ne[1];
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std::memcpy(output->ne + 1, attr.shape, sizeof(attr.shape));
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return true;
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}
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static bool convrot_infer(ggml_tensor* const* inputs, int count, const void* attributes, ggml_ext_tensor_desc* output) {
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int32_t group;
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std::memcpy(&group, attributes, sizeof(group));
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if (count != 1 || inputs[0]->type != GGML_TYPE_F32 || !ggml_is_contiguous(inputs[0]) || !valid_group(group) || inputs[0]->ne[0] <= 0 || inputs[0]->ne[0] % group)
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return false;
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const int64_t k = inputs[0]->ne[0], rows = ggml_nrows(inputs[0]);
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int64_t height;
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if (!packed_height(k, rows, &height))
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return false;
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output->type = GGML_TYPE_I8;
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output->ne[0] = k;
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output->ne[1] = height;
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output->ne[2] = output->ne[3] = 1;
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return true;
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}
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ggml_ext_op_t sd_ggml_i8_op() {
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static const auto op = [] { ggml_ext_op_desc d = {GGML_EXT_ABI_VERSION, "sd.linear_i8.v1", sizeof(I8Attributes), i8_infer}; auto result = ggml_ext_register_op(&d); GGML_ASSERT(result); return result; }();
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return op;
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}
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ggml_ext_op_t sd_ggml_convrot_op() {
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static const auto op = [] { ggml_ext_op_desc d = {GGML_EXT_ABI_VERSION, "sd.quantize_i8_convrot.v1", sizeof(int32_t), convrot_infer}; auto result = ggml_ext_register_op(&d); GGML_ASSERT(result); return result; }();
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return op;
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}
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bool sd_ggml_i8_describe(const ggml_tensor* node, SDI8Operation* op) {
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if (!node || node->type != GGML_TYPE_F32 || !ggml_is_contiguous(node))
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return false;
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*op = {node->src[0], node->src[1], nullptr, nullptr, nullptr, 0, false};
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if (node->op == GGML_OP_MUL_MAT) {
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op->weight_scale = node->src[2];
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op->bias = node->src[3];
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op->group_size = node->op_params[2];
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op->packed = node->op_params[4] > 0;
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if (op->packed)
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op->group_size = node->op_params[4];
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} else if (ggml_ext_op_get(node) == sd_ggml_i8_op()) {
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I8Attributes attr;
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std::memcpy(&attr, ggml_ext_op_attributes(node), sizeof(attr));
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if (attr.flags & ~15 || std::memcmp(attr.shape, node->ne + 1, sizeof(attr.shape)))
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return false;
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op->group_size = attr.group_size;
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op->packed = (attr.flags & I8_PACKED) != 0;
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int index = 2;
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if (attr.flags & I8_INPUT_SCALE)
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op->input_scale = node->src[index++];
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if (attr.flags & I8_WEIGHT_SCALE)
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op->weight_scale = node->src[index++];
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if (attr.flags & I8_BIAS)
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op->bias = node->src[index++];
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if (((attr.flags & I8_INPUT_SCALE) && !op->input_scale) || ((attr.flags & I8_WEIGHT_SCALE) && !op->weight_scale) || ((attr.flags & I8_BIAS) && !op->bias))
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return false;
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} else
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return false;
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return i8_shape(*op, node->ne + 1) && node->ne[0] == op->weight->ne[1];
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}
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bool sd_ggml_convrot_describe(const ggml_tensor* node, int* group) {
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if (!node || !node->src[0] || !ggml_is_contiguous(node))
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return false;
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if (node->op == GGML_OP_QUANTIZE_I8_CONVROT)
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*group = node->op_params[0];
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else if (ggml_ext_op_get(node) == sd_ggml_convrot_op())
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std::memcpy(group, ggml_ext_op_attributes(node), sizeof(int32_t));
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else
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return false;
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ggml_ext_tensor_desc output;
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return convrot_infer(node->src, 1, group, &output) && output.type == node->type && !std::memcmp(output.ne, node->ne, sizeof(output.ne));
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}
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static ggml_tensor* i8_create(ggml_context* ctx, ggml_tensor* w, ggml_tensor* x, ggml_tensor* xs, ggml_tensor* ws, ggml_tensor* bias, int group, const ggml_tensor* logical) {
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I8Attributes attr = {{logical->ne[1], logical->ne[2], logical->ne[3]}, group, logical != x ? I8_PACKED : 0};
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ggml_tensor* inputs[5] = {w, x};
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int count = 2;
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if (xs) {
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attr.flags |= I8_INPUT_SCALE;
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inputs[count++] = xs;
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}
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if (ws) {
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attr.flags |= I8_WEIGHT_SCALE;
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inputs[count++] = ws;
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}
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if (bias) {
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attr.flags |= I8_BIAS;
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inputs[count++] = bias;
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}
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return ggml_ext_op_create(ctx, sd_ggml_i8_op(), inputs, count, &attr);
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}
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ggml_tensor* sd_ggml_mul_mat_i8(ggml_context* ctx, ggml_tensor* w, ggml_tensor* x, ggml_tensor* xs, ggml_tensor* ws, ggml_tensor* bias) {
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if (!w || !x || !xs || x->type != GGML_TYPE_I8)
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return nullptr;
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return i8_create(ctx, w, x, xs, ws, bias, 0, x);
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}
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ggml_tensor* sd_ggml_linear_i8(ggml_context* ctx, ggml_tensor* w, ggml_tensor* x, ggml_tensor* ws, ggml_tensor* bias, int group) {
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if (!w || !x)
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return nullptr;
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const auto* logical = x;
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if (x->type == GGML_TYPE_I8) {
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int packed_group;
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if (!sd_ggml_convrot_describe(x, &packed_group) || packed_group != group)
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return nullptr;
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logical = x->src[0];
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}
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return i8_create(ctx, w, x, nullptr, ws, bias, group, logical);
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}
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ggml_tensor* sd_ggml_quantize_i8_convrot(ggml_context* ctx, ggml_tensor* x, int group) {
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int32_t attr = group;
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return ggml_ext_op_create(ctx, sd_ggml_convrot_op(), &x, 1, &attr);
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}
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static void transform(float* values, int group) {
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const float scale = 1.f / std::sqrt(float(group));
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for (int i = 0; i < group; ++i)
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values[i] *= scale;
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for (int stride = 1; stride < group; stride *= 4)
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for (int base = 0; base < group; base += 4 * stride)
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for (int j = 0; j < stride; ++j) {
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const int i = base + j;
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const float a = values[i], b = values[i + stride], c = values[i + 2 * stride], d = values[i + 3 * stride];
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values[i] = a + b + c - d;
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values[i + stride] = a + b - c + d;
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values[i + 2 * stride] = a - b + c + d;
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values[i + 3 * stride] = -a + b + c + d;
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}
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}
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struct I8Execution {
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SDI8Operation op;
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int64_t rows;
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int8_t* quant;
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float* scales;
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float* output;
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};
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static void quantize(ggml_tensor*, int ith, int nth, void* data) {
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auto& e = *static_cast<I8Execution*>(data);
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const int64_t k = e.op.input->ne[0];
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const int group = e.op.group_size;
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const auto* input = static_cast<const float*>(e.op.input->data);
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for (int64_t row = ith; row < e.rows; row += nth) {
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const float* src = input + row * k;
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int8_t* dst = e.quant + row * k;
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float amax = 0.f, values[256];
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auto visit = [&](auto fn) { if (!group) { for (int64_t i = 0; i < k; ++i) fn(i, src[i]); }
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else for (int64_t base = 0; base < k; base += group) { std::memcpy(values, src + base, group * sizeof(float)); transform(values, group); for (int j = 0; j < group; ++j) fn(base + j, values[j]); } };
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visit([&](int64_t, float value) { amax = std::max(amax, std::fabs(value)); });
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const float scale = amax / 127.f, inverse = scale == 0.f ? 0.f : 1.f / scale;
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e.scales[row] = scale;
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if (scale == 0.f)
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std::memset(dst, 0, k);
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else
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visit([&](int64_t i, float value) { dst[i] = static_cast<int8_t>(std::max(-127L, std::min(127L, std::lrintf(value * inverse)))); });
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}
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}
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static void multiply(ggml_tensor*, int ith, int nth, void* data) {
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auto& e = *static_cast<I8Execution*>(data);
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const auto& op = e.op;
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const int64_t k = op.weight->ne[0], n = op.weight->ne[1];
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const auto* w = static_cast<const int8_t*>(op.weight->data);
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const auto* ws = op.weight_scale ? static_cast<const float*>(op.weight_scale->data) : nullptr;
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const auto* bias = op.bias ? static_cast<const float*>(op.bias->data) : nullptr;
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for (int64_t index = ith; index < e.rows * n; index += nth) {
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const int64_t row = index / n, column = index % n;
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uint32_t sum = 0;
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for (int64_t i = 0; i < k; ++i)
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sum += uint32_t(int32_t(w[column * k + i]) * int32_t(e.quant[row * k + i]));
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int32_t signed_sum;
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std::memcpy(&signed_sum, &sum, sizeof(sum));
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float value = float(signed_sum) * e.scales[row];
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if (ws)
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value *= ws[column];
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if (bias)
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value += bias[column];
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e.output[index] = value;
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}
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}
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static bool overlaps(const ggml_tensor* dst, const ggml_tensor* src) {
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if (!src)
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return false;
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const auto a = reinterpret_cast<uintptr_t>(dst->data), b = reinterpret_cast<uintptr_t>(src->data);
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return a <= b ? b - a < ggml_nbytes(dst) : a - b < ggml_nbytes(src);
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}
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static size_t quant_workspace(const ggml_tensor* node, const SDI8Operation& op) {
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return op.input->type == GGML_TYPE_F32 ? (size_t(ggml_nelements(op.input)) + 3) / 4 * 4 + size_t(ggml_nrows(node)) * 4 : 0;
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}
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static bool i8_supports(ggml_backend_dev_t, const ggml_tensor* node, void*) {
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SDI8Operation op;
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return sd_ggml_i8_describe(node, &op);
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}
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static bool convrot_supports(ggml_backend_dev_t, const ggml_tensor* node, void*) {
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int group;
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return sd_ggml_convrot_describe(node, &group);
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}
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static ggml_status i8_prepare(ggml_backend_t, ggml_tensor* const* nodes, int, void**, size_t* bytes, void*) {
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SDI8Operation op;
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if (!sd_ggml_i8_describe(nodes[0], &op))
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return GGML_STATUS_FAILED;
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*bytes = quant_workspace(nodes[0], op) + ggml_nbytes(nodes[0]);
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return GGML_STATUS_SUCCESS;
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}
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static ggml_status convrot_prepare(ggml_backend_t, ggml_tensor* const* nodes, int, void**, size_t* bytes, void*) {
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*bytes = ggml_nbytes(nodes[0]);
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return GGML_STATUS_SUCCESS;
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}
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static void destroy(void*, void*) {}
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static ggml_ext_cpu_parallel_fn parallel(ggml_backend_t backend) {
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return reinterpret_cast<ggml_ext_cpu_parallel_fn>(ggml_backend_reg_get_proc_address(ggml_backend_dev_backend_reg(ggml_backend_get_device(backend)), "ggml_ext_cpu_parallel_v1"));
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}
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static ggml_status i8_execute(const ggml_ext_execution_context* context, ggml_tensor* const* nodes, int, void*, void*) {
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auto* dst = nodes[0];
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I8Execution e = {};
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if (!sd_ggml_i8_describe(dst, &e.op))
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return GGML_STATUS_FAILED;
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e.rows = ggml_nrows(dst);
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auto run = parallel(context->backend);
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if (!run)
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return GGML_STATUS_FAILED;
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char* scratch = static_cast<char*>(ggml_backend_buffer_get_base(context->workspace));
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const size_t quant_size = quant_workspace(dst, e.op);
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if (e.op.input->type == GGML_TYPE_F32) {
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e.quant = reinterpret_cast<int8_t*>(scratch);
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e.scales = reinterpret_cast<float*>(scratch + (size_t(ggml_nelements(e.op.input)) + 3) / 4 * 4);
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auto status = run(context->backend, dst, quantize, &e);
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if (status != GGML_STATUS_SUCCESS)
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return status;
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} else {
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e.quant = static_cast<int8_t*>(e.op.input->data);
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e.scales = e.op.packed ? reinterpret_cast<float*>(e.quant + e.op.input->ne[0] * ((e.rows + 3) / 4 * 4)) : static_cast<float*>(e.op.input_scale->data);
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}
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bool alias = false;
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for (auto* input : dst->src)
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alias |= overlaps(dst, input);
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e.output = alias ? reinterpret_cast<float*>(scratch + quant_size) : static_cast<float*>(dst->data);
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auto status = run(context->backend, dst, multiply, &e);
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if (status == GGML_STATUS_SUCCESS && alias)
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std::memcpy(dst->data, e.output, ggml_nbytes(dst));
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return status;
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}
|
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static ggml_status convrot_execute(const ggml_ext_execution_context* context, ggml_tensor* const* nodes, int, void*, void*) {
|
||||
auto* dst = nodes[0];
|
||||
I8Execution e = {};
|
||||
if (!sd_ggml_convrot_describe(dst, &e.op.group_size))
|
||||
return GGML_STATUS_FAILED;
|
||||
auto run = parallel(context->backend);
|
||||
if (!run)
|
||||
return GGML_STATUS_FAILED;
|
||||
e.op.input = dst->src[0];
|
||||
e.rows = ggml_nrows(e.op.input);
|
||||
bool alias = overlaps(dst, e.op.input);
|
||||
e.quant = static_cast<int8_t*>(alias ? ggml_backend_buffer_get_base(context->workspace) : dst->data);
|
||||
const int64_t k = dst->ne[0], padded = (e.rows + 3) / 4 * 4;
|
||||
e.scales = reinterpret_cast<float*>(e.quant + k * padded);
|
||||
auto status = run(context->backend, dst, quantize, &e);
|
||||
if (status == GGML_STATUS_SUCCESS) {
|
||||
std::memset(e.quant + k * e.rows, 0, k * (padded - e.rows));
|
||||
if (alias)
|
||||
std::memcpy(dst->data, e.quant, ggml_nbytes(dst));
|
||||
}
|
||||
return status;
|
||||
}
|
||||
void sd_ggml_register_cpu_i8(ggml_backend_dev_t device) {
|
||||
ggml_ext_kernel_desc d = {};
|
||||
d.abi_version = GGML_EXT_ABI_VERSION;
|
||||
d.priority = 100;
|
||||
d.destroy = destroy;
|
||||
d.name = "sd.linear_i8.cpu.v1";
|
||||
d.op = GGML_OP_CUSTOM;
|
||||
d.custom_op = sd_ggml_i8_op();
|
||||
d.supports = i8_supports;
|
||||
d.prepare = i8_prepare;
|
||||
d.execute = i8_execute;
|
||||
GGML_ASSERT(ggml_ext_register_kernel(device, &d));
|
||||
d.name = "sd.linear_i8.cpu.override.v1";
|
||||
d.op = GGML_OP_MUL_MAT;
|
||||
d.custom_op = nullptr;
|
||||
GGML_ASSERT(ggml_ext_register_kernel(device, &d));
|
||||
d.name = "sd.quantize_i8_convrot.cpu.v1";
|
||||
d.op = GGML_OP_CUSTOM;
|
||||
d.custom_op = sd_ggml_convrot_op();
|
||||
d.supports = convrot_supports;
|
||||
d.prepare = convrot_prepare;
|
||||
d.execute = convrot_execute;
|
||||
GGML_ASSERT(ggml_ext_register_kernel(device, &d));
|
||||
d.name = "sd.quantize_i8_convrot.cpu.override.v1";
|
||||
d.op = GGML_OP_QUANTIZE_I8_CONVROT;
|
||||
d.custom_op = nullptr;
|
||||
GGML_ASSERT(ggml_ext_register_kernel(device, &d));
|
||||
}
|
||||
#endif
|
||||
@@ -0,0 +1,28 @@
|
||||
#ifndef __SD_CORE_INT8_OPS_H__
|
||||
#define __SD_CORE_INT8_OPS_H__
|
||||
|
||||
#include "ggml-backend.h"
|
||||
|
||||
#ifndef SD_USE_UPSTREAM_GGML
|
||||
#include "ggml-ext.h"
|
||||
struct SDI8Operation {
|
||||
const ggml_tensor* weight;
|
||||
const ggml_tensor* input;
|
||||
const ggml_tensor* input_scale;
|
||||
const ggml_tensor* weight_scale;
|
||||
const ggml_tensor* bias;
|
||||
int group_size;
|
||||
bool packed;
|
||||
};
|
||||
|
||||
ggml_ext_op_t sd_ggml_i8_op();
|
||||
ggml_ext_op_t sd_ggml_convrot_op();
|
||||
bool sd_ggml_i8_describe(const ggml_tensor* node, SDI8Operation* operation);
|
||||
bool sd_ggml_convrot_describe(const ggml_tensor* node, int* group_size);
|
||||
ggml_tensor* sd_ggml_mul_mat_i8(ggml_context* ctx, ggml_tensor* weight, ggml_tensor* input, ggml_tensor* input_scale, ggml_tensor* weight_scale = nullptr, ggml_tensor* bias = nullptr);
|
||||
ggml_tensor* sd_ggml_linear_i8(ggml_context* ctx, ggml_tensor* weight, ggml_tensor* input, ggml_tensor* weight_scale, ggml_tensor* bias, int group_size);
|
||||
ggml_tensor* sd_ggml_quantize_i8_convrot(ggml_context* ctx, ggml_tensor* input, int group_size);
|
||||
void sd_ggml_register_cpu_i8(ggml_backend_dev_t device);
|
||||
#endif
|
||||
|
||||
#endif
|
||||
Reference in new issue
Block a user