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Merge pull request #29079 from varun-jaiswal17:feat/dnn-int8-optimization

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dnn int8 optimization #29079

all_layers.hpp 
- Add float_input flag to Conv2Int8Params and Conv2Int8Layer to let the first conv accept raw FP32 input and quantize internally.

graph_fusion_qdq.cpp : 
- Fuse DQ → Sigmoid → QL into SigmoidInt8, Similarly for MAxPool.
- Fuse the input QuantizeLinear node into the first Conv2Int8.

conv2_int8_layer.cpp
- Add quantizeInterleaveBlock()


conv2_int8_kernels.simd.hpp
- Add spatial tiling to both convInt8BlockVNNI and convInt8BlockDepthwise: splits output pixels into tiles so total task count is N × ngroups × Kblk × ntiles, fully utilizing all threads even when the channel count is small.

elementwise_layers.cpp
- Widen CV_Assert to accept CV_8U in addition to CV_8S.

eltwise2_int8_layer.cpp
- Add QLinearMul support: new Mul math path for both signed and unsigned int8.
- Add numpy-style broadcast support so QLinearMul / QLinearAdd with scalar

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
This commit is contained in:
Varun Jaiswal
2026-05-26 14:04:47 +05:30
committed by GitHub
parent be2c53c2b7
commit bae8cb1915
7 changed files with 1158 additions and 102 deletions
Download Patch File Download Diff File Expand all files Collapse all files
modules/dnn/include/opencv2/dnn/all_layers.hpp
+3
View File
@@ -395,6 +395,7 @@ CV__DNN_INLINE_NS_BEGIN
int output_zp = 0;
bool per_channel = true;
bool input_is_u8 = false;
bool float_input = false; // accept FP32 NCHW and quantize+interleave internally
// blobs[0] = quantized weights, blobs[1] = fused bias, blobs[2] = output multiplier
Mat weights, bias, outputMultiplier;
};
@@ -408,6 +409,7 @@ CV__DNN_INLINE_NS_BEGIN
int input_zp, output_zp;
float input_sc, output_sc;
bool per_channel;
bool float_input = false;
std::vector<int> strides, dilations, pads;
int ngroups;
@@ -1387,6 +1389,7 @@ CV__DNN_INLINE_NS_BEGIN
float output_sc = 1.f;
int output_zp = 0;
bool with_relu = false;
String operation = "add";
};
class CV_EXPORTS Eltwise2Int8Layer : public Layer
modules/dnn/src/graph_fusion_qdq.cpp
+166 -39
View File
@@ -174,14 +174,17 @@ struct ModelFusionQDQ
int8_inputs.push_back(dq->inputs[0]);
}
const int eltwise_out_type = !outputs.empty() ? netimpl->argData(outputs[0]).type : -1;
const bool eltwise_out_int8 = (eltwise_out_type == CV_8S || eltwise_out_type == CV_8U);
const int eltwise_out_zp_type = netimpl->argData(out_zp).type;
const bool eltwise_out_int8 = (eltwise_out_zp_type == CV_8S || eltwise_out_zp_type == CV_8U);
if (dq_ptrs.size() == add->inputs.size() && eltwise_out_int8) {
vector<float> in_scales(2);
vector<int> in_zps(2);
bool eltwise_in_int8 = true;
for (int k = 0; k < 2; k++) {
int inp_type = netimpl->argData(dq_ptrs[k]->inputs[0]).type;
const int inp_type0 = netimpl->argData(dq_ptrs[k]->inputs[0]).type;
const int zp_type = (dq_ptrs[k]->inputs.size() >= 3 && netimpl->isConstArg(dq_ptrs[k]->inputs[2]))
? netimpl->argData(dq_ptrs[k]->inputs[2]).type : -1;
const int inp_type = (inp_type0 >= 0) ? inp_type0 : zp_type;
eltwise_in_int8 = eltwise_in_int8 && (inp_type == CV_8S || inp_type == CV_8U);
in_scales[k] = netimpl->argTensor(dq_ptrs[k]->inputs[1]).at<float>(0);
const Mat& zp_m = netimpl->argTensor(dq_ptrs[k]->inputs[2]);
@@ -204,6 +207,8 @@ struct ModelFusionQDQ
ep.input_zeropoints = in_zps;
ep.output_sc = out_scale_val;
ep.output_zp = out_zp_val;
ep.operation = (add->op == NaryEltwiseLayer::OPERATION::PROD)
? "mul" : "add";
Ptr<Eltwise2Int8Layer> eltwiseInt8 = Eltwise2Int8Layer::create(ep);
eltwiseInt8->netimpl = netimpl;
fused_layer_idx = add_layer_idx;
@@ -290,6 +295,81 @@ struct ModelFusionQDQ
}
}
{
int sig_layer_idx = -1;
SigmoidLayer* sigmoid_layer = 0;
if (getQdqPatternContext<SigmoidLayer>(layer_ptr, ninputs, inputs, producer_of,
newprog, q_data_in, out_scale, out_zp,
sig_layer_idx, sigmoid_layer) &&
sigmoid_layer->inputs.size() == 1) {
Arg sig_in = sigmoid_layer->inputs[0];
int dq_idx = producer_of.at(sig_in.idx);
DequantizeLinearLayer* dq = getLayer<DequantizeLinearLayer>(newprog, dq_idx);
const int sig_out_zp_type = netimpl->argData(out_zp).type;
const bool sig_out_int8 = (sig_out_zp_type == CV_8S || sig_out_zp_type == CV_8U);
const int sig_in_zp_type = (dq && dq->inputs.size() >= 3 && netimpl->isConstArg(dq->inputs[2]))
? netimpl->argData(dq->inputs[2]).type : -1;
const bool sig_in_int8 = (sig_in_zp_type == CV_8S || sig_in_zp_type == CV_8U);
if (dq && dq->inputs.size() >= 3 &&
areDqArgsConst(dq) &&
sig_in_int8 && sig_out_int8 &&
usecounts.at(sig_in.idx) == 1) {
const float inp_sc = netimpl->argTensor(dq->inputs[1]).at<float>(0);
const Mat& sig_in_zp_m = netimpl->argTensor(dq->inputs[2]);
const int inp_zp = sig_in_zp_m.depth() == CV_8U
? (int)sig_in_zp_m.at<uint8_t>(0)
: (int)sig_in_zp_m.at<int8_t>(0);
const float out_sc = netimpl->argTensor(out_scale).at<float>(0);
const Mat& sig_out_zp_m = netimpl->argTensor(out_zp);
const int out_zp_i = sig_out_zp_m.depth() == CV_8U
? (int)sig_out_zp_m.at<uint8_t>(0)
: (int)sig_out_zp_m.at<int8_t>(0);
if (inp_sc > 0.f && out_sc > 0.f) {
const bool isU8 = (sig_in_zp_type == CV_8U);
Mat lookUpTable(1, 256, isU8 ? CV_8U : CV_8S);
if (isU8) {
uint8_t* table = lookUpTable.ptr<uint8_t>();
for (int t = 0; t < 256; t++) {
float x = inp_sc * (t - inp_zp);
float y = 1.f / (1.f + std::exp(-x));
int q = out_zp_i + cvRound(y / out_sc);
table[t] = saturate_cast<uint8_t>(q);
}
} else {
int8_t* table = lookUpTable.ptr<int8_t>();
for (int t = -128; t < 128; t++) {
float x = inp_sc * (t - inp_zp);
float y = 1.f / (1.f + std::exp(-x));
int q = out_zp_i + cvRound(y / out_sc);
table[t + 128] = saturate_cast<int8_t>(q);
}
}
ActivationInt8Params ap;
ap.name = sigmoid_layer->name;
ap.activationType = "SigmoidInt8";
ap.input_sc = inp_sc;
ap.input_zp = inp_zp;
ap.output_sc = out_sc;
ap.output_zp = out_zp_i;
ap.activationLUT = lookUpTable;
Ptr<ActivationLayerInt8> sigmoidInt8 = ActivationLayerInt8::create(ap);
sigmoidInt8->netimpl = netimpl;
fused_layer_idx = sig_layer_idx;
newprog[sig_layer_idx] = sigmoidInt8;
fused_inputs.assign(1, dq->inputs[0]);
removed_args.push_back(q_data_in);
removed_args.push_back(sig_in);
newprog[dq_idx] = Ptr<Layer>();
break;
}
}
}
}
QuantizeLinearLayer* ql_compound = dynamic_cast<QuantizeLinearLayer*>(layer_ptr);
if (ql_compound && ninputs == 3) {
Arg q_inp = inputs[0];
@@ -390,6 +470,8 @@ struct ModelFusionQDQ
ep2.output_sc = out_sc2;
ep2.output_zp = out_zp_val2;
ep2.with_relu = true;
ep2.operation = (add2->op == NaryEltwiseLayer::OPERATION::PROD)
? "mul" : "add";
Ptr<Eltwise2Int8Layer> eltInt8 = Eltwise2Int8Layer::create(ep2);
eltInt8->netimpl = netimpl;
@@ -490,10 +572,14 @@ struct ModelFusionQDQ
// SIMD iteration. When ngroups>1 and Kg<K0, multiple groups map
// to the same K0-wide output slot and clobber one another, so
// skip the fusion and fall back to the unfused float path.
// Exception: pure depthwise (ngroups==outCn, Cg==1) is routed to
// convInt8BlockDepthwise before the VNNI path and is correct.
// See https://github.com/opencv/opencv/issues/28798
const int outChannelsPerGroup = outCn / std::max(conv->ngroups, 1);
const int inChannelsPerGroup = (w_q.dims >= 2) ? w_q.size[1] : 1;
const bool isDepthwise = (conv->ngroups == outCn) && (inChannelsPerGroup == 1) && (outCn > 1);
const bool kernelSupportsGrouping =
conv->ngroups <= 1 || outChannelsPerGroup >= 8;
conv->ngroups <= 1 || outChannelsPerGroup >= 8 || isDepthwise;
if (all_wzp_zero && symmetric_pads && kernelSupportsGrouping) {
Mat bias = Mat::zeros(1, outCn, CV_32S);
@@ -531,8 +617,9 @@ struct ModelFusionQDQ
Mat bq = netimpl->argTensor(dq_b->inputs[0]);
if (bq.empty() || bq.total() != (size_t)outCn || bq.depth() != CV_32S)
biasOk = false;
else
else {
bias = bq.reshape(1, 1);
}
}
}
}
@@ -542,7 +629,7 @@ struct ModelFusionQDQ
const bool inputIsU8 =
(netimpl->argData(dq_x->inputs[0]).type == CV_8U) ||
(x_zp_m.depth() == CV_8U);
const int inp_zp_kernel = inputIsU8 ? (inp_zp - 128) : inp_zp;
const int inp_zp_kernel = inputIsU8 ? 0 : inp_zp;
Mat weights_2d = w_q.reshape(1, outCn);
Mat biasFused(1, outCn, CV_32S);
Mat outputMultiplier(1, outCn, CV_32F);
@@ -950,55 +1037,47 @@ struct ModelFusionQDQ
}
}
// Fuse DQ -> MaxPool -> QL into Pool2Int8 when scales/zps match.
{
int pool_layer_idx = -1;
PoolingLayer* pool = 0;
if (getQdqPatternContext<PoolingLayer>(layer_ptr, ninputs, inputs, producer_of,
MaxPoolLayer* maxpool = 0;
if (getQdqPatternContext<MaxPoolLayer>(layer_ptr, ninputs, inputs, producer_of,
newprog, q_data_in, out_scale, out_zp,
pool_layer_idx, pool) &&
pool->inputs.size() == 1) {
Arg pool_in = pool->inputs[0];
pool_layer_idx, maxpool) &&
maxpool->inputs.size() == 1) {
Arg pool_in = maxpool->inputs[0];
int dq_idx = producer_of.at(pool_in.idx);
DequantizeLinearLayer* dq = getLayer<DequantizeLinearLayer>(newprog, dq_idx);
float inp_sc = 0.f, out_sc = 0.f;
int inp_zp = 0, out_zp_i = 0;
const int pool_out_type = !outputs.empty() ? netimpl->argData(outputs[0]).type : -1;
const bool pool_out_int8 = (pool_out_type == CV_8S || pool_out_type == CV_8U);
const int pool_in_type = (dq && !dq->inputs.empty()) ? netimpl->argData(dq->inputs[0]).type : -1;
const int pool_out_zp_type = netimpl->argData(out_zp).type;
const bool pool_out_int8 = (pool_out_zp_type == CV_8S || pool_out_zp_type == CV_8U);
const int pool_in_type = (dq && dq->inputs.size() >= 3 && netimpl->isConstArg(dq->inputs[2]))
? netimpl->argData(dq->inputs[2]).type
: ((dq && !dq->inputs.empty()) ? netimpl->argData(dq->inputs[0]).type : -1);
const bool pool_in_int8 = (pool_in_type == CV_8S || pool_in_type == CV_8U);
if (dq && dq->inputs.size() >= 3 &&
areDqArgsConst(dq) &&
pool_in_int8 && pool_out_int8 &&
usecounts.at(pool_in.idx) == 1) {
inp_sc = netimpl->argTensor(dq->inputs[1]).at<float>(0);
float inp_sc = netimpl->argTensor(dq->inputs[1]).at<float>(0);
const Mat& pool_zp_m = netimpl->argTensor(dq->inputs[2]);
inp_zp = pool_zp_m.depth() == CV_8U
int inp_zp = pool_zp_m.depth() == CV_8U
? (int)pool_zp_m.at<uint8_t>(0)
: (int)pool_zp_m.at<int8_t>(0);
out_sc = netimpl->argTensor(out_scale).at<float>(0);
float out_sc = netimpl->argTensor(out_scale).at<float>(0);
const Mat& pool_out_zp_m = netimpl->argTensor(out_zp);
out_zp_i = pool_out_zp_m.depth() == CV_8U
int out_zp_i = pool_out_zp_m.depth() == CV_8U
? (int)pool_out_zp_m.at<uint8_t>(0)
: (int)pool_out_zp_m.at<int8_t>(0);
bool isGlobalAve = pool->globalPooling;
bool isMax = !isGlobalAve;
if ((isGlobalAve && inp_sc > 0.f && out_sc > 0.f) ||
(isMax && std::abs(inp_sc - out_sc) < 1e-6f && inp_zp == out_zp_i)) {
if (std::abs(inp_sc - out_sc) < 1e-6f && inp_zp == out_zp_i) {
Pool2Int8Params p;
p.name = pool->name;
p.kernel_shape.assign(pool->kernel_size.begin(), pool->kernel_size.end());
p.strides.assign(pool->strides.begin(), pool->strides.end());
if (!pool->pads_begin.empty()) {
p.pads.reserve(pool->pads_begin.size() + pool->pads_end.size());
for (size_t v : pool->pads_begin) p.pads.push_back((int)v);
for (size_t v : pool->pads_end) p.pads.push_back((int)v);
}
if (pool->padMode == "SAME")
p.auto_pad = AUTO_PAD_SAME_UPPER;
else if (pool->padMode == "VALID")
p.auto_pad = AUTO_PAD_VALID;
p.ceil_mode = pool->ceilMode;
p.is_global_pooling = isGlobalAve;
p.is_max_pool = isMax;
p.name = maxpool->name;
p.kernel_shape = maxpool->kernel_shape;
p.strides = maxpool->strides;
p.pads = maxpool->pads;
p.auto_pad = maxpool->auto_pad;
p.ceil_mode = maxpool->ceil_mode;
p.is_global_pooling = false;
p.is_max_pool = true;
p.input_sc = inp_sc;
p.input_zp = inp_zp;
p.output_sc = out_sc;
@@ -1015,6 +1094,7 @@ struct ModelFusionQDQ
}
}
}
}
// fuse DequantizeLinear -> DataShuffling/MaxPool -> QuantizeLinear
// when DQ and Q have same scale/zp, the op can work directly on int8 data.
@@ -1180,6 +1260,53 @@ struct ModelFusionQDQ
modified = true;
}
// Fuse input QuantizeLinear into the first Conv2Int8 (float_input=true).
for (size_t j = 0; j < newprog.size(); j++)
{
if (newprog[j].empty()) continue;
Conv2Int8Layer* conv = dynamic_cast<Conv2Int8Layer*>(newprog[j].get());
if (!conv || conv->inputs.empty() || conv->float_input)
continue;
Arg conv_in0 = conv->inputs[0];
size_t ql_idx = newprog.size();
for (size_t k = 0; k < j; k++) {
if (newprog[k].empty()) continue;
QuantizeLinearLayer* ql_cand = dynamic_cast<QuantizeLinearLayer*>(newprog[k].get());
if (!ql_cand || ql_cand->outputs.empty())
continue;
if (ql_cand->outputs[0].idx == conv_in0.idx) {
ql_idx = k;
break;
}
}
if (ql_idx >= newprog.size()) continue;
QuantizeLinearLayer* ql = dynamic_cast<QuantizeLinearLayer*>(newprog[ql_idx].get());
if (ql->inputs.size() < 2) continue;
const Arg& ql_data = ql->inputs[0];
if (netimpl->argKind(ql_data) != DNN_ARG_INPUT) continue;
int ql_out_uc = 0;
for (size_t k = 0; k < newprog.size(); k++) {
if (newprog[k].empty()) continue;
for (const Arg& a : newprog[k]->inputs)
if (a.idx == conv_in0.idx) ql_out_uc++;
}
if (ql_out_uc != 1) continue;
if (!netimpl->isConstArg(ql->inputs[1])) continue;
if (ql->inputs.size() >= 3 && !netimpl->isConstArg(ql->inputs[2])) continue;
if (ql->inputs.size() >= 3) {
int zp_type = netimpl->argData(ql->inputs[2]).type;
if (zp_type != CV_8S && zp_type != CV_8U) continue;
}
conv->float_input = true;
conv->inputs[0] = ql_data;
newprog[ql_idx] = Ptr<Layer>();
modified = true;
}
if (modified) {
std::vector<int> uc(nargs, 0);
for (const auto& layer : newprog) {
modules/dnn/src/int8layers/conv2_int8_kernels.simd.hpp
+679 -28
View File
@@ -6,6 +6,9 @@
#include "opencv2/core/hal/intrin.hpp"
#include "../layers/conv2_common.hpp"
#if CV_AVX2 && (defined(__x86_64__) || defined(_M_X64))
#include <immintrin.h>
#endif
#if !defined(CV_AVXVNNI_AVAILABLE)
#if (CV_TRY_AVX2 || CV_AVX2) && \
@@ -94,10 +97,16 @@ static void convInt8BlockVNNI(const void* inp_, const void* residual_,
size_t outtotal = outshape.total();
if ((Kg % K0) != 0) memset(out, 0, outtotal);
int total_blocks = N * ngroups * Kblk;
int nkb = N * ngroups * Kblk;
int nthreads = std::max(1, cv::getNumThreads());
int min_ntiles = std::max(1, (nthreads * 4 + nkb - 1) / nkb);
int TILE = std::max(SPAT_BLOCK_SIZE,
((planeblocks_ + min_ntiles - 1) / min_ntiles + SPAT_BLOCK_SIZE - 1) & ~(SPAT_BLOCK_SIZE - 1));
int ntiles = (planeblocks_ + TILE - 1) / TILE;
int total_tasks = nkb * ntiles;
const __m256i v_xor = inputIsU8 ? _mm256_setzero_si256() : _mm256_set1_epi8((char)0x80);
parallel_for_(Range(0, total_blocks), [&](const Range& range) {
parallel_for_(Range(0, total_tasks), [&](const Range& range) {
constexpr int C0 = 8, K0 = 8;
constexpr int C0shift = 3;
int D = D_, H = H_, W = W_;
@@ -115,9 +124,11 @@ static void convInt8BlockVNNI(const void* inp_, const void* residual_,
memset(zbuf, (uint8_t)inp_zp, C0);
for (int t = range.start; t < range.end; t++) {
int n = t / (ngroups * Kblk);
int rem = t - n * (ngroups * Kblk);
int g = rem / Kblk;
int tile = t % ntiles;
int kt = t / ntiles;
int n = kt / (ngroups * Kblk);
int rem = kt - n * (ngroups * Kblk);
int g = rem / Kblk;
int kblk = rem - g * Kblk;
int k_base = g * Kg + kblk * K0;
@@ -141,6 +152,12 @@ static void convInt8BlockVNNI(const void* inp_, const void* residual_,
alignas(32) float multbuf[K0];
memcpy(multbuf, multiplier + k_base, K0 * sizeof(float));
const int k0_off_dw = k_base & (K0-1);
if (cs.depthwise) {
biasbuf[k0_off_dw] = biasbuf[0];
multbuf[k0_off_dw] = multbuf[0];
}
int D_l = D, H_l = H, W_l = W;
int Di_l = Di, Hi_l = Hi, Wi_l = Wi;
int iplanesize_l = iplanesize;
@@ -155,11 +172,13 @@ static void convInt8BlockVNNI(const void* inp_, const void* residual_,
planeblocks_l = W_l;
}
int p = 0;
for (; p < planeblocks_l; p += SPAT_BLOCK_SIZE) {
if (p + SPAT_BLOCK_SIZE > planeblocks_l) {
if (p == 0) break;
p = planeblocks_l - SPAT_BLOCK_SIZE;
int p_start = tile * TILE;
int p_end = std::min(p_start + TILE, planeblocks_l);
int p = p_start;
for (; p < p_end; p += SPAT_BLOCK_SIZE) {
if (p + SPAT_BLOCK_SIZE > p_end) {
if (p == p_start) break;
p = p_end - SPAT_BLOCK_SIZE;
}
Vec3i pt[SPAT_BLOCK_SIZE];
@@ -275,8 +294,9 @@ static void convInt8BlockVNNI(const void* inp_, const void* residual_,
__m256 facc = _mm256_add_ps( \
_mm256_mul_ps(_mm256_cvtepi32_ps(s##j), vmult), vzp); \
if (resptr) { \
__m256i res32 = _mm256_cvtepi8_epi32( \
_mm_loadl_epi64((const __m128i*)(resptr + (p + (j)) * K0))); \
__m256i res32 = inputIsU8 \
? _mm256_cvtepu8_epi32(_mm_loadl_epi64((const __m128i*)(resptr + (p + (j)) * K0))) \
: _mm256_cvtepi8_epi32(_mm_loadl_epi64((const __m128i*)(resptr + (p + (j)) * K0))); \
facc = _mm256_add_ps(facc, \
_mm256_sub_ps(_mm256_cvtepi32_ps(res32), vzp)); \
} \
@@ -287,13 +307,24 @@ static void convInt8BlockVNNI(const void* inp_, const void* residual_,
__m128i packed8 = inputIsU8 ? _mm_packus_epi16(packed16, packed16) \
: _mm_packs_epi16(packed16, packed16); \
int8_t* optr = outptr + (p + (j)) * K0; \
if (activLUT) { \
alignas(16) int8_t tmp8[16]; \
_mm_store_si128((__m128i*)tmp8, packed8); \
for (int k = 0; k < K0; k++) \
optr[k] = (int8_t)activLUT[(int)tmp8[k] + 128]; \
alignas(16) int8_t tmp8[16]; \
_mm_store_si128((__m128i*)tmp8, packed8); \
if (!cs.depthwise) { \
if (activLUT) { \
for (int k = 0; k < K0; k++) { \
int lut_idx = inputIsU8 ? (int)(uint8_t)tmp8[k] : ((int)tmp8[k] + 128); \
optr[k] = (int8_t)activLUT[lut_idx]; \
} \
} else { \
_mm_storel_epi64((__m128i*)optr, packed8); \
} \
} else { \
_mm_storel_epi64((__m128i*)optr, packed8); \
if (activLUT) { \
int lut_idx = inputIsU8 ? (int)(uint8_t)tmp8[k0_off_dw] : ((int)tmp8[k0_off_dw] + 128); \
optr[k0_off_dw] = (int8_t)activLUT[lut_idx]; \
} else { \
optr[k0_off_dw] = tmp8[k0_off_dw]; \
} \
} \
}
CONV_VNNI_STORE(0); CONV_VNNI_STORE(1);
@@ -304,7 +335,7 @@ static void convInt8BlockVNNI(const void* inp_, const void* residual_,
}
// Scalar tail
for (; p < planeblocks_l; p++) {
for (; p < p_end; p++) {
alignas(32) int32_t acc[K0];
memcpy(acc, biasbuf, K0 * sizeof(int32_t));
@@ -326,15 +357,23 @@ static void convInt8BlockVNNI(const void* inp_, const void* residual_,
for (int c0 = 0; c0 < C0; c0++) {
int ival = inputIsU8 ? (int)(uint8_t)inptr[c0]
: (int)(uint8_t)((uint8_t)inptr[c0] ^ 0x80u);
const int8_t* wp = wptr + c0 * K0;
for (int k0 = 0; k0 < K0; k0++)
acc[k0] += ival * (int)wp[k0];
// VNNI-repacked weights: weight(c0,k0) = wptr[k0*4 + c0] (c0<4)
// else wptr[32 + k0*4 + (c0-4)]
if (c0 < 4) {
for (int k0 = 0; k0 < K0; k0++)
acc[k0] += ival * (int)wptr[k0 * 4 + c0];
} else {
for (int k0 = 0; k0 < K0; k0++)
acc[k0] += ival * (int)wptr[32 + k0 * 4 + (c0 - 4)];
}
}
}
}
int8_t* optr = outptr + p * K0;
const int8_t* rptr = resptr ? resptr + p * K0 : nullptr;
for (int k0 = 0; k0 < K0; k0++) {
int k0_lo = cs.depthwise ? k0_off_dw : 0;
int k0_hi = cs.depthwise ? k0_off_dw + 1 : K0;
for (int k0 = k0_lo; k0 < k0_hi; k0++) {
float val = (float)acc[k0] * multbuf[k0] + (float)out_zp;
if (rptr) val += (float)((int)rptr[k0] - out_zp);
int ival = cvRound(val);
@@ -352,6 +391,210 @@ static void convInt8BlockVNNI(const void* inp_, const void* residual_,
}
});
}
static void convInt8Block1x1(const void* inp_, const void* residual_,
void* out_, const ConvState& cs,
const void* weightsVNNI_,
const int* biasVNNI, const float* multiplier,
int inp_zp, int out_zp,
const int8_t* activLUT,
bool inputIsU8)
{
constexpr int C0 = 8, K0 = 8;
constexpr int SPAT_BLOCK_SIZE = 8;
const MatShape& inpshape = cs.inpshape;
const MatShape& outshape = cs.outshape;
int N = outshape[0];
int ndims = outshape.dims;
int K = outshape.channels();
int C = inpshape.channels();
int K1 = (K + K0 - 1) / K0;
int C1 = (C + C0 - 1) / C0;
int D_ = ndims >= 6 ? outshape[ndims-4] : 1;
int H_ = ndims >= 5 ? outshape[ndims-3] : 1;
int W_ = outshape[ndims-2];
int P = D_ * H_ * W_;
int Di_ = ndims >= 6 ? inpshape[ndims-4] : 1;
int Hi_ = ndims >= 5 ? inpshape[ndims-3] : 1;
int Wi_ = inpshape[ndims-2];
int Pi = Di_ * Hi_ * Wi_;
int ngroups = cs.ngroups;
int Kg = K / ngroups;
int Cg = C / ngroups;
int Kblk = cs.wshape[1];
int C1Max = cs.wshape[3];
const int8_t* inp = (const int8_t*)inp_;
const int8_t* residual = (const int8_t*)residual_;
int8_t* out = (int8_t*)out_;
const int8_t* wdata = (const int8_t*)weightsVNNI_;
int iplanesize = Pi * C0;
int planesize = P * K0;
if ((Kg % K0) != 0)
memset(out, 0, outshape.total());
constexpr int SB_TILE = 8;
int nthreads = std::max(1, cv::getNumThreads());
int nkb = N * ngroups * Kblk;
int min_ntiles = std::max(1, (nthreads * 4 + nkb - 1) / nkb);
int TILE = std::max(SB_TILE,
((P + min_ntiles - 1) / min_ntiles + SB_TILE - 1) & ~(SB_TILE - 1));
int ntiles = (P + TILE - 1) / TILE;
int total_tasks = nkb * ntiles;
const __m256i v_xor = inputIsU8 ? _mm256_setzero_si256() : _mm256_set1_epi8((char)0x80);
parallel_for_(Range(0, total_tasks), [&](const Range& range) {
constexpr int C0 = 8, K0 = 8;
constexpr int C0shift = 3;
for (int t = range.start; t < range.end; t++) {
int tile = t % ntiles;
int kt = t / ntiles;
int n = kt / (ngroups * Kblk);
int rem = kt - n * (ngroups * Kblk);
int g = rem / Kblk;
int kblk = rem - g * Kblk;
int k_base = g * Kg + kblk * K0;
if (k_base >= K) continue;
int c_start = g * Cg;
int c1_start = c_start >> C0shift;
int c00 = c_start & (C0 - 1);
int cblocks = (c00 + Cg + C0 - 1) >> C0shift;
const int8_t* inpbaseptr = inp + (size_t)(n * C1 + c1_start) * iplanesize;
const int8_t* wbaseptr = wdata + (size_t)(g * Kblk + kblk) * C1Max * C0 * K0;
int k1 = k_base >> C0shift;
int8_t* outptr = out + (size_t)(n * K1 + k1) * planesize;
const int8_t* resptr = residual ? residual + (size_t)(n * K1 + k1) * planesize : nullptr;
alignas(32) int32_t biasbuf[K0];
alignas(32) float multbuf[K0];
memcpy(biasbuf, biasVNNI + k_base, K0 * sizeof(int32_t));
memcpy(multbuf, multiplier + k_base, K0 * sizeof(float));
int p_start = tile * TILE;
int p_end = std::min(p_start + TILE, P);
int p = p_start;
for (; p + SPAT_BLOCK_SIZE <= p_end; p += SPAT_BLOCK_SIZE) {
__m256i vbias = _mm256_load_si256((const __m256i*)biasbuf);
__m256i s0 = vbias, s1 = vbias, s2 = vbias, s3 = vbias;
__m256i s4 = vbias, s5 = vbias, s6 = vbias, s7 = vbias;
const int8_t* inptr[SPAT_BLOCK_SIZE];
for (int j = 0; j < SPAT_BLOCK_SIZE; j++)
inptr[j] = inpbaseptr + (p + j) * C0;
const int8_t* wptr = wbaseptr;
for (int c1 = 0; c1 < cblocks; c1++, wptr += C0 * K0) {
__m256i wg0 = _mm256_load_si256((const __m256i*)(wptr));
__m256i wg1 = _mm256_load_si256((const __m256i*)(wptr + 32));
#define CONV_1x1_MAC(j) { \
__m256i x0 = _mm256_xor_si256(_mm256_set1_epi32(*(const int32_t*)(inptr[(j)] )), v_xor); \
__m256i x1 = _mm256_xor_si256(_mm256_set1_epi32(*(const int32_t*)(inptr[(j)] + 4)), v_xor); \
s##j = _mm256_dpbusd_epi32(s##j, x0, wg0); \
s##j = _mm256_dpbusd_epi32(s##j, x1, wg1); \
}
CONV_1x1_MAC(0); CONV_1x1_MAC(1);
CONV_1x1_MAC(2); CONV_1x1_MAC(3);
CONV_1x1_MAC(4); CONV_1x1_MAC(5);
CONV_1x1_MAC(6); CONV_1x1_MAC(7);
#undef CONV_1x1_MAC
for (int j = 0; j < SPAT_BLOCK_SIZE; j++)
inptr[j] += iplanesize;
}
__m256 vmult = _mm256_load_ps(multbuf);
__m256 vzp = _mm256_set1_ps((float)out_zp);
#define CONV_1x1_STORE(j) { \
__m256 facc = _mm256_add_ps( \
_mm256_mul_ps(_mm256_cvtepi32_ps(s##j), vmult), vzp); \
if (resptr) { \
__m256i res32 = inputIsU8 \
? _mm256_cvtepu8_epi32(_mm_loadl_epi64((const __m128i*)(resptr + (p + (j)) * K0))) \
: _mm256_cvtepi8_epi32(_mm_loadl_epi64((const __m128i*)(resptr + (p + (j)) * K0))); \
facc = _mm256_add_ps(facc, \
_mm256_sub_ps(_mm256_cvtepi32_ps(res32), vzp)); \
} \
__m256i ival = _mm256_cvtps_epi32(facc); \
__m128i lo = _mm256_castsi256_si128(ival); \
__m128i hi = _mm256_extracti128_si256(ival, 1); \
__m128i packed16 = _mm_packs_epi32(lo, hi); \
__m128i packed8 = inputIsU8 ? _mm_packus_epi16(packed16, packed16) \
: _mm_packs_epi16(packed16, packed16); \
int8_t* optr = outptr + (p + (j)) * K0; \
if (activLUT) { \
alignas(16) int8_t tmp8[16]; \
_mm_store_si128((__m128i*)tmp8, packed8); \
for (int k = 0; k < K0; k++) { \
int lut_idx = inputIsU8 ? (int)(uint8_t)tmp8[k] : ((int)tmp8[k] + 128); \
optr[k] = (int8_t)activLUT[lut_idx]; \
} \
} else { \
_mm_storel_epi64((__m128i*)optr, packed8); \
} \
}
CONV_1x1_STORE(0); CONV_1x1_STORE(1);
CONV_1x1_STORE(2); CONV_1x1_STORE(3);
CONV_1x1_STORE(4); CONV_1x1_STORE(5);
CONV_1x1_STORE(6); CONV_1x1_STORE(7);
#undef CONV_1x1_STORE
}
for (; p < p_end; p++) {
alignas(32) int32_t acc[K0];
memcpy(acc, biasbuf, K0 * sizeof(int32_t));
const int8_t* inptr_s = inpbaseptr + (size_t)p * C0;
const int8_t* wptr = wbaseptr;
for (int c1 = 0; c1 < cblocks; c1++, inptr_s += iplanesize, wptr += C0 * K0) {
for (int c0 = 0; c0 < C0; c0++) {
int iv = inputIsU8 ? (int)(uint8_t)inptr_s[c0]
: (int)(uint8_t)((uint8_t)inptr_s[c0] ^ 0x80u);
const int8_t* wp = wptr + c0 * K0;
for (int k0 = 0; k0 < K0; k0++)
acc[k0] += iv * (int)wp[k0];
}
}
int8_t* optr = outptr + (size_t)p * K0;
const int8_t* rptr = resptr ? resptr + (size_t)p * K0 : nullptr;
for (int k0 = 0; k0 < K0; k0++) {
float val = (float)acc[k0] * multbuf[k0] + (float)out_zp;
if (rptr) {
val += inputIsU8 ? (float)(int)(uint8_t)rptr[k0] - (float)out_zp
: (float)(int)rptr[k0] - (float)out_zp;
}
int iv = cvRound(val);
if (inputIsU8) {
iv = std::max(0, std::min(255, iv));
if (activLUT) iv = (int)(uint8_t)activLUT[iv];
((uint8_t*)optr)[k0] = (uint8_t)iv;
} else {
iv = std::max(-128, std::min(127, iv));
if (activLUT) iv = (int)activLUT[iv + 128];
optr[k0] = (int8_t)iv;
}
}
}
}
});
}
#ifdef __clang__
#pragma clang attribute pop
#else
@@ -861,6 +1104,395 @@ static void convInt8BlockNEON(const void* inp_, const void* residual_,
#undef CONV_NEON_STORE
#endif // CV_NEON && CV_NEON_DOT
// INT8 depthwise convolution kernel (ngroups==K==C, Kg==Cg==1).
static void convInt8BlockDepthwise(const void* inp_, const void* residual_,
void* out_, const ConvState& cs,
const void* weights_,
const int* bias, const float* multiplier,
int inp_zp, int out_zp,
const int8_t* activLUT,
bool inputIsU8)
{
constexpr int C0 = 8, K0 = 8;
constexpr int MAX_CONV_DIMS = ConvState::MAX_CONV_DIMS;
const MatShape& inpshape = cs.inpshape;
const MatShape& outshape = cs.outshape;
int N = outshape[0];
int ndims = outshape.dims;
int K = outshape.channels();
int C = inpshape.channels();
int K1 = (K + K0 - 1) / K0;
int C1 = (C + C0 - 1) / C0;
int D_ = ndims >= 6 ? outshape[ndims-4] : 1;
int H_ = ndims >= 5 ? outshape[ndims-3] : 1;
int W_ = outshape[ndims-2];
int planeblocks_ = D_ * H_ * W_;
int Di_ = ndims >= 6 ? inpshape[ndims-4] : 1;
int Hi_ = ndims >= 5 ? inpshape[ndims-3] : 1;
int Wi_ = inpshape[ndims-2];
int ngroups = cs.ngroups;
int ksize_ = cs.wshape[2];
int C1Max = cs.wshape[3];
int Sz = cs.strides[0], Sy = cs.strides[1], Sx = cs.strides[2];
int padZ = cs.pads[0], padY = cs.pads[1], padX = cs.pads[2];
int innerZ0 = cs.inner[0], innerZ1 = cs.inner[MAX_CONV_DIMS];
int innerY0 = cs.inner[1], innerY1 = cs.inner[MAX_CONV_DIMS+1];
int innerX0 = cs.inner[2], innerX1 = cs.inner[MAX_CONV_DIMS+2];
const int8_t* inp = (const int8_t*)inp_;
const int8_t* residual = (const int8_t*)residual_;
int8_t* out = (int8_t*)out_;
const int8_t* wdata = (const int8_t*)weights_;
const int* ofsZYX = cs.coordtab.data();
int iplanesize_ = Di_ * Hi_ * Wi_ * C0;
int planesize_ = planeblocks_ * K0;
// Spatial tiling: split planeblocks into ntiles tiles so the total task count
// is N*K1*ntiles, giving all threads work even when K is small (e.g. K=16 → K1=2).
constexpr int SB_TILE = 8;
int nthreads = std::max(1, cv::getNumThreads());
int TILE = std::max(SB_TILE,
((planeblocks_ + nthreads * 4 - 1) / (nthreads * 4) + SB_TILE - 1) & ~(SB_TILE - 1));
int ntiles = (planeblocks_ + TILE - 1) / TILE;
int total_tasks = N * K1 * ntiles;
parallel_for_(Range(0, total_tasks), [&](const Range& range) {
int H = H_, W = W_;
int Di = Di_, Hi = Hi_, Wi = Wi_;
int planeblocks = planeblocks_;
int iplanesize = iplanesize_;
int planesize = planesize_;
int ksize = ksize_;
for (int task = range.start; task < range.end; task++) {
int nk = task / ntiles;
int tile = task - nk * ntiles;
int n = nk / K1;
int k1 = nk - n * K1;
int g_start = k1 * K0;
int g_end = std::min(g_start + K0, ngroups);
int p_task_start = tile * TILE;
int p_task_end = std::min(p_task_start + TILE, planeblocks);
const int8_t* inpbaseptr = inp + (size_t)(n * C1 + k1) * iplanesize;
int8_t* outbase = out + (size_t)(n * K1 + k1) * planesize;
const int8_t* resbase = residual ? residual + (size_t)(n * K1 + k1) * planesize
: nullptr;
#if CV_AVX2 && (defined(__x86_64__) || defined(_M_X64))
if (ksize <= 128) {
int n_g = g_end - g_start;
constexpr int SB = 8;
// Pre-gather weights for this group block into wbuf[ksize * K0].
alignas(32) int8_t wbuf[128 * K0];
for (int i = 0; i < ksize; i++) {
for (int j = 0; j < n_g; j++) {
int g = g_start + j;
int c0 = g % C0;
wbuf[i * K0 + j] = wdata[(size_t)g * ksize * C1Max * C0 * K0
+ (size_t)i * C1Max * C0 * K0
+ (size_t)c0 * K0];
}
for (int j = n_g; j < K0; j++)
wbuf[i * K0 + j] = 0;
}
__m256i vbias = _mm256_loadu_si256((const __m256i*)(bias + g_start));
__m256 vmult = _mm256_loadu_ps(multiplier + g_start);
__m256 vzp = _mm256_set1_ps((float)out_zp);
__m128i v_xor = inputIsU8 ? _mm_setzero_si128()
: _mm_set1_epi8((char)0x80);
int p = p_task_start;
for (; p < p_task_end; p += SB) {
if (p + SB > p_task_end) {
if (p == p_task_start) break;
p = p_task_end - SB;
}
Vec3i pt[SB];
bool inner_pix[SB];
bool all_inner = true;
bool same_row = false;
if ((p % W) + SB <= W) {
same_row = true;
int zj = p / (H * W);
int yxj = p - zj * H * W;
int yj = yxj / W;
int xj0 = yxj - yj * W;
bool zy_inner = (zj >= innerZ0 && zj < innerZ1) &&
(yj >= innerY0 && yj < innerY1);
for (int j = 0; j < SB; j++) {
int xj = xj0 + j;
pt[j] = Vec3i(zj*Sz - padZ, yj*Sy - padY, xj*Sx - padX);
inner_pix[j] = zy_inner && (xj >= innerX0 && xj < innerX1);
all_inner &= inner_pix[j];
}
} else {
for (int j = 0; j < SB; j++) {
int pj = p + j;
int zj = pj / (H * W);
int yxj = pj - zj * H * W;
int yj = yxj / W;
int xj = yxj - yj * W;
pt[j] = Vec3i(zj*Sz - padZ, yj*Sy - padY, xj*Sx - padX);
inner_pix[j] = (zj >= innerZ0 && zj < innerZ1) &&
(yj >= innerY0 && yj < innerY1) &&
(xj >= innerX0 && xj < innerX1);
all_inner &= inner_pix[j];
}
}
__m256i s0 = vbias, s1 = vbias, s2 = vbias, s3 = vbias;
__m256i s4 = vbias, s5 = vbias, s6 = vbias, s7 = vbias;
if (all_inner && same_row) {
const int step = Sx * C0;
for (int i = 0; i < ksize; i++) {
__m128i w8 = _mm_loadl_epi64((const __m128i*)(wbuf + i * K0));
__m256i w32 = _mm256_cvtepi8_epi32(w8);
int zi_eff = pt[0][0] + ofsZYX[i*3];
int yi_eff = pt[0][1] + ofsZYX[i*3+1];
int xi_eff = pt[0][2] + ofsZYX[i*3+2];
const int8_t* ip = inpbaseptr +
(((zi_eff * Hi) + yi_eff) * Wi + xi_eff) * C0;
#define DW_HOT(j) { \
__m256i inp32 = _mm256_cvtepu8_epi32(_mm_xor_si128( \
_mm_loadl_epi64((const __m128i*)(ip+(j)*step)), v_xor)); \
s##j = _mm256_add_epi32(s##j, \
_mm256_mullo_epi32(inp32, w32)); \
}
DW_HOT(0); DW_HOT(1); DW_HOT(2); DW_HOT(3);
DW_HOT(4); DW_HOT(5); DW_HOT(6); DW_HOT(7);
#undef DW_HOT
}
} else if (all_inner) {
for (int i = 0; i < ksize; i++) {
__m128i w8 = _mm_loadl_epi64((const __m128i*)(wbuf + i * K0));
__m256i w32 = _mm256_cvtepi8_epi32(w8);
#define DW_INNER(j) { \
int zi = pt[j][0] + ofsZYX[i*3]; \
int yi = pt[j][1] + ofsZYX[i*3+1]; \
int xi = pt[j][2] + ofsZYX[i*3+2]; \
const int8_t* ip = inpbaseptr + \
(((zi * Hi) + yi) * Wi + xi) * C0; \
__m256i inp32 = _mm256_cvtepu8_epi32(_mm_xor_si128( \
_mm_loadl_epi64((const __m128i*)ip), v_xor)); \
s##j = _mm256_add_epi32(s##j, \
_mm256_mullo_epi32(inp32, w32)); \
}
DW_INNER(0); DW_INNER(1); DW_INNER(2); DW_INNER(3);
DW_INNER(4); DW_INNER(5); DW_INNER(6); DW_INNER(7);
#undef DW_INNER
}
} else {
for (int i = 0; i < ksize; i++) {
__m128i w8 = _mm_loadl_epi64((const __m128i*)(wbuf + i * K0));
__m256i w32 = _mm256_cvtepi8_epi32(w8);
#define DW_BORDER(j) { \
int zi = pt[j][0] + ofsZYX[i*3]; \
int yi = pt[j][1] + ofsZYX[i*3+1]; \
int xi = pt[j][2] + ofsZYX[i*3+2]; \
__m256i inp32; \
if (!((unsigned)zi >= (unsigned)Di || \
(unsigned)yi >= (unsigned)Hi || \
(unsigned)xi >= (unsigned)Wi)) { \
const int8_t* ip = inpbaseptr + \
(((zi * Hi) + yi) * Wi + xi) * C0; \
inp32 = _mm256_cvtepu8_epi32(_mm_xor_si128( \
_mm_loadl_epi64((const __m128i*)ip), v_xor)); \
} else { \
inp32 = inputIsU8 \
? _mm256_set1_epi32(inp_zp) \
: _mm256_set1_epi32((int)(uint8_t)((uint8_t)inp_zp ^ 0x80u)); \
} \
s##j = _mm256_add_epi32(s##j, _mm256_mullo_epi32(inp32, w32)); \
}
DW_BORDER(0); DW_BORDER(1); DW_BORDER(2); DW_BORDER(3);
DW_BORDER(4); DW_BORDER(5); DW_BORDER(6); DW_BORDER(7);
#undef DW_BORDER
}
}
#define DW_STORE(j) { \
__m256 facc = _mm256_add_ps( \
_mm256_mul_ps(_mm256_cvtepi32_ps(s##j), vmult), vzp); \
int8_t* optr = outbase + (p + (j)) * K0; \
const int8_t* rptr = resbase \
? resbase + (p + (j)) * K0 : nullptr; \
if (rptr) { \
__m256i r32 = _mm256_cvtepi8_epi32( \
_mm_loadl_epi64((const __m128i*)rptr)); \
facc = _mm256_add_ps(facc, _mm256_sub_ps( \
_mm256_cvtepi32_ps(r32), vzp)); \
} \
__m256i iv = _mm256_cvtps_epi32(facc); \
__m128i lo = _mm256_castsi256_si128(iv); \
__m128i hi = _mm256_extracti128_si256(iv, 1); \
__m128i p16 = _mm_packs_epi32(lo, hi); \
__m128i p8 = inputIsU8 ? _mm_packus_epi16(p16, p16) \
: _mm_packs_epi16(p16, p16); \
if (activLUT) { \
alignas(16) int8_t tmp8[16]; \
_mm_store_si128((__m128i*)tmp8, p8); \
if (inputIsU8) { \
for (int jj = 0; jj < n_g; jj++) \
((uint8_t*)optr)[jj] = \
(uint8_t)activLUT[(int)(uint8_t)tmp8[jj]]; \
} else { \
for (int jj = 0; jj < n_g; jj++) \
optr[jj] = (int8_t)activLUT[(int)tmp8[jj]+128]; \
} \
} else if (n_g == K0) { \
_mm_storel_epi64((__m128i*)optr, p8); \
} else { \
alignas(16) int8_t tmp8[16]; \
_mm_store_si128((__m128i*)tmp8, p8); \
memcpy(optr, tmp8, n_g); \
} \
}
DW_STORE(0); DW_STORE(1); DW_STORE(2); DW_STORE(3);
DW_STORE(4); DW_STORE(5); DW_STORE(6); DW_STORE(7);
#undef DW_STORE
}
for (; p < p_task_end; p++) {
int zj = p / (H * W);
int yxj = p - zj * H * W;
int yj = yxj / W;
int xj = yxj - yj * W;
int zi_base = zj * Sz - padZ, yi_base = yj * Sy - padY,
xi_base = xj * Sx - padX;
__m256i acc = vbias;
for (int i = 0; i < ksize; i++) {
int zi = zi_base + ofsZYX[i*3], yi = yi_base + ofsZYX[i*3+1],
xi = xi_base + ofsZYX[i*3+2];
if ((unsigned)zi>=(unsigned)Di || (unsigned)yi>=(unsigned)Hi ||
(unsigned)xi>=(unsigned)Wi) continue;
const int8_t* ip = inpbaseptr + (((zi*Hi)+yi)*Wi+xi)*C0;
__m256i inp32 = _mm256_cvtepu8_epi32(
_mm_xor_si128(_mm_loadl_epi64((const __m128i*)ip), v_xor));
__m256i w32 = _mm256_cvtepi8_epi32(
_mm_loadl_epi64((const __m128i*)(wbuf + i*K0)));
acc = _mm256_add_epi32(acc, _mm256_mullo_epi32(inp32, w32));
}
__m256 facc = _mm256_add_ps(
_mm256_mul_ps(_mm256_cvtepi32_ps(acc), vmult), vzp);
int8_t* optr = outbase + p * K0;
const int8_t* rptr = resbase ? resbase + p * K0 : nullptr;
if (rptr) {
facc = _mm256_add_ps(facc, _mm256_sub_ps(_mm256_cvtepi32_ps(
_mm256_cvtepi8_epi32(_mm_loadl_epi64((const __m128i*)rptr))), vzp));
}
__m256i iv = _mm256_cvtps_epi32(facc);
__m128i p16 = _mm_packs_epi32(_mm256_castsi256_si128(iv),
_mm256_extracti128_si256(iv, 1));
__m128i p8 = inputIsU8 ? _mm_packus_epi16(p16, p16)
: _mm_packs_epi16(p16, p16);
if (activLUT) {
alignas(16) int8_t tmp8[16];
_mm_store_si128((__m128i*)tmp8, p8);
if (inputIsU8)
for (int jj=0; jj<n_g; jj++)
((uint8_t*)optr)[jj]=(uint8_t)activLUT[(int)(uint8_t)tmp8[jj]];
else
for (int jj=0; jj<n_g; jj++)
optr[jj]=(int8_t)activLUT[(int)tmp8[jj]+128];
} else if (n_g == K0) {
_mm_storel_epi64((__m128i*)optr, p8);
} else {
alignas(16) int8_t tmp8[16];
_mm_store_si128((__m128i*)tmp8, p8);
memcpy(optr, tmp8, n_g);
}
}
continue;
}
#endif // CV_AVX2
for (int p = p_task_start; p < p_task_end; p++) {
int zj = p / (H * W);
int yxj = p - zj * H * W;
int yj = yxj / W;
int xj = yxj - yj * W;
bool is_inner = (zj >= innerZ0 && zj < innerZ1) &&
(yj >= innerY0 && yj < innerY1) &&
(xj >= innerX0 && xj < innerX1);
int zi_base = zj * Sz - padZ;
int yi_base = yj * Sy - padY;
int xi_base = xj * Sx - padX;
int8_t* optr = outbase + p * K0;
const int8_t* rptr = resbase ? resbase + p * K0 : nullptr;
for (int g = g_start; g < g_end; g++) {
int j = g - g_start;
// For Kg=1 depthwise: kin=0, k0=0, c0=g%C0
int c0 = g % C0;
const int8_t* wbase_g = wdata + (size_t)g * ksize * C1Max * C0 * K0;
int32_t acc = bias[g];
if (is_inner) {
for (int i = 0; i < ksize; i++) {
int zi = zi_base + ofsZYX[i * 3];
int yi = yi_base + ofsZYX[i * 3 + 1];
int xi = xi_base + ofsZYX[i * 3 + 2];
const int8_t* ip = inpbaseptr + (((zi * Hi) + yi) * Wi + xi) * C0;
int ival = inputIsU8 ? (int)(uint8_t)ip[c0]
: (int)(uint8_t)((uint8_t)ip[c0] ^ 0x80u);
acc += ival * (int)wbase_g[(size_t)i * C1Max * C0 * K0 + c0 * K0];
}
} else {
for (int i = 0; i < ksize; i++) {
int zi = zi_base + ofsZYX[i * 3];
int yi = yi_base + ofsZYX[i * 3 + 1];
int xi = xi_base + ofsZYX[i * 3 + 2];
int ival;
if ((unsigned)zi >= (unsigned)Di ||
(unsigned)yi >= (unsigned)Hi ||
(unsigned)xi >= (unsigned)Wi) {
ival = inputIsU8 ? inp_zp
: (int)(uint8_t)((uint8_t)inp_zp ^ 0x80u);
} else {
const int8_t* ip = inpbaseptr + (((zi * Hi) + yi) * Wi + xi) * C0;
ival = inputIsU8 ? (int)(uint8_t)ip[c0]
: (int)(uint8_t)((uint8_t)ip[c0] ^ 0x80u);
}
acc += ival * (int)wbase_g[(size_t)i * C1Max * C0 * K0 + c0 * K0];
}
}
float val = (float)acc * multiplier[g] + (float)out_zp;
if (rptr) val += (float)((int)rptr[j] - out_zp);
int ival = cvRound(val);
if (inputIsU8) {
ival = std::max(0, std::min(255, ival));
if (activLUT) ival = (int)(uint8_t)activLUT[ival];
((uint8_t*)optr)[j] = (uint8_t)ival;
} else {
ival = std::max(-128, std::min(127, ival));
if (activLUT) ival = (int)activLUT[ival + 128];
optr[j] = (int8_t)ival;
}
}
}
}
});
}
void convInt8Block(const void* inp_, const void* residual_,
void* out_, const ConvState& cs,
const void* weights_,
@@ -871,8 +1503,21 @@ void convInt8Block(const void* inp_, const void* residual_,
const int8_t* activLUT,
bool inputIsU8)
{
// Dispatch to dedicated depthwise kernel before the general VNNI path.
if (cs.depthwise) {
const int* dw_bias = biasVNNI_ ? biasVNNI_ : bias;
convInt8BlockDepthwise(inp_, residual_, out_, cs, weights_,
dw_bias, multiplier, inp_zp, out_zp, activLUT, inputIsU8);
return;
}
#if CV_AVXVNNI_AVAILABLE
if (cv::checkHardwareSupport(CV_CPU_AVX_VNNI) && weightsVNNI_ && biasVNNI_) {
if (cs.wshape[2] == 1 &&
cs.strides[0] == 1 && cs.strides[1] == 1 && cs.strides[2] == 1) {
convInt8Block1x1(inp_, residual_, out_, cs, weightsVNNI_,
biasVNNI_, multiplier, inp_zp, out_zp, activLUT, inputIsU8);
return;
}
convInt8BlockVNNI(inp_, residual_, out_, cs, weightsVNNI_,
biasVNNI_, multiplier, inp_zp, out_zp, activLUT, inputIsU8);
return;
@@ -999,10 +1644,14 @@ void convInt8Block(const void* inp_, const void* residual_,
}
alignas(32) int32_t biasbuf[K0];
memcpy(biasbuf, bias + k_base, K0 * sizeof(int32_t));
alignas(32) float multbuf[K0];
memcpy(multbuf, multiplier + k_base, K0 * sizeof(float));
{
int k_valid = std::min(K0, K - k_base);
memcpy(biasbuf, bias + k_base, k_valid * sizeof(int32_t));
memset(biasbuf + k_valid, 0, (K0 - k_valid) * sizeof(int32_t));
memcpy(multbuf, multiplier + k_base, k_valid * sizeof(float));
memset(multbuf + k_valid, 0, (K0 - k_valid) * sizeof(float));
}
#if CV_AVX2
int p = 0;
@@ -1143,8 +1792,10 @@ void convInt8Block(const void* inp_, const void* residual_,
if (activLUT) {
alignas(16) int8_t tmp8[16];
_mm_store_si128((__m128i*)tmp8, packed8);
for (int k = 0; k < K0; k++)
optr[k] = (int8_t)activLUT[(int)tmp8[k] + 128];
for (int k = 0; k < K0; k++) {
int lut_idx = inputIsU8 ? (int)(uint8_t)tmp8[k] : ((int)tmp8[k] + 128);
optr[k] = (int8_t)activLUT[lut_idx];
}
} else {
_mm_storel_epi64((__m128i*)optr, packed8);
}
modules/dnn/src/int8layers/conv2_int8_layer.cpp
+100 -11
View File
@@ -172,8 +172,10 @@ class Conv2Int8LayerImpl CV_FINAL : public Conv2Int8Layer
public:
Mat weights; // repacked int8 weights in block format
Mat weightsVNNI; // VNNI-transposed weights (pre-computed)
Mat biasInt32; // int32 fused bias
Mat biasVNNI; // int32 VNNI-adjusted bias (pre-computed)
Mat biasInt32;
Mat biasVNNI;
Mat biasU8;
Mat biasBaselineU8;
Mat outMultiplier; // float32 per-channel output multiplier
MatShape wshape0; // original weight shape (K x Cg x kH x kW)
MatShape prevInpshape;
@@ -228,6 +230,7 @@ public:
output_zp = p.output_zp;
per_channel = p.per_channel;
inputIsU8 = p.input_is_u8;
float_input = p.float_input;
addResidual = false;
if (!p.weights.empty()) {
@@ -243,8 +246,11 @@ public:
std::vector<MatType>& outputs,
std::vector<MatType>& internals) const CV_OVERRIDE
{
// Input can be CV_8SC1 or CV_8UC1; output matches input type
int outtype = !inputs.empty() ? inputs[0] : CV_8SC1;
int outtype;
if (float_input)
outtype = inputIsU8 ? CV_8UC1 : CV_8SC1;
else
outtype = !inputs.empty() ? inputs[0] : CV_8SC1;
outputs.assign(requiredOutputs, outtype);
internals.clear();
}
@@ -258,7 +264,14 @@ public:
CV_Assert(ninputs >= 1);
std::vector<int> emptyKernelShape;
outshapes.assign(1, convInferShape(inpshapes[0], wshape0, emptyKernelShape,
MatShape inpshape = inpshapes[0];
if (float_input && inpshape.layout != DATA_LAYOUT_BLOCK) {
if (inpshape.layout == DATA_LAYOUT_UNKNOWN)
inpshape.layout = DATA_LAYOUT_NCHW;
int C0 = getNetImpl(this)->defaultC0;
inpshape = inpshape.toLayout(DATA_LAYOUT_BLOCK, C0);
}
outshapes.assign(1, convInferShape(inpshape, wshape0, emptyKernelShape,
ngroups, strides, dilations,
pads, auto_pad, ceil_mode));
tempshapes.clear();
@@ -273,7 +286,7 @@ public:
size_t ninputs = actualInputs.size();
CV_Assert(ninputs >= 1u && requiredOutputs == 1u);
desiredInputs = actualInputs;
desiredInputs[0] = DATA_LAYOUT_BLOCK;
desiredInputs[0] = float_input ? DATA_LAYOUT_NCHW : DATA_LAYOUT_BLOCK;
for (size_t i = 1; i < ninputs; i++)
desiredInputs[i] = DATA_LAYOUT_UNKNOWN;
outputs.assign(requiredOutputs, DATA_LAYOUT_BLOCK);
@@ -301,13 +314,58 @@ public:
Ptr<ActivationLayerInt8> activ_int8 = layer.dynamicCast<ActivationLayerInt8>();
if (!activ_int8.empty()) {
activ = activ_int8;
if (!activ_int8->blobs.empty())
activ_int8->blobs[0].convertTo(activationLUT, CV_8S);
if (!activ_int8->activationLUT.empty())
activationLUT = activ_int8->activationLUT;
else if (!activ_int8->blobs.empty())
activ_int8->blobs[0].copyTo(activationLUT);
return true;
}
return false;
}
void quantizeInterleaveBlock(const Mat& fp32_nchw, Mat& int8_block, int C0) const
{
MatShape nchw = fp32_nchw.shape();
if (nchw.layout == DATA_LAYOUT_UNKNOWN)
nchw.layout = DATA_LAYOUT_NCHW;
CV_Assert(nchw.dims >= 2);
int N = nchw[0], C = nchw[1];
int planesize = 1;
for (int d = 2; d < nchw.dims; d++) planesize *= nchw[d];
int C1 = (C + C0 - 1) / C0;
MatShape blkshape = nchw.toLayout(DATA_LAYOUT_BLOCK, C0);
int8_block.fit(blkshape, inputIsU8 ? CV_8UC1 : CV_8SC1);
float inv_sc = input_sc > 0.f ? 1.f / input_sc : 0.f;
int zp = input_zp;
const bool is_u8 = inputIsU8;
const float* src = fp32_nchw.ptr<float>();
uint8_t* dst = int8_block.ptr<uint8_t>();
parallel_for_(Range(0, N * C1), [&](const Range& range) {
for (int nc = range.start; nc < range.end; nc++) {
int n = nc / C1, c1 = nc % C1;
int c_base = c1 * C0;
int nzc = std::min(C0, C - c_base);
const float* src_block = src + (n * C + c_base) * planesize;
uint8_t* dst_block = dst + (size_t)(n * C1 + c1) * planesize * C0;
for (int i = 0; i < planesize; i++) {
for (int c = 0; c < nzc; c++) {
float x = src_block[c * planesize + i];
int q = cvRound(x * inv_sc) + zp;
if (is_u8)
dst_block[i * C0 + c] = saturate_cast<uchar>(q);
else
dst_block[i * C0 + c] = (uint8_t)(int8_t)saturate_cast<schar>(q);
}
for (int c = nzc; c < C0; c++)
dst_block[i * C0 + c] = 0;
}
}
});
}
void forward(InputArrayOfArrays input_arrs,
OutputArrayOfArrays output_arrs,
OutputArrayOfArrays) CV_OVERRIDE
@@ -315,7 +373,16 @@ public:
int ninputs = (int)input_arrs.total();
CV_Assert(ninputs >= 1);
const Mat& inp = input_arrs.getMat(0);
Mat inp_storage;
Mat inp_ref = input_arrs.getMat(0);
if (float_input) {
CV_Assert(inp_ref.type() == CV_32F);
int C0 = getNetImpl(this)->defaultC0;
quantizeInterleaveBlock(inp_ref, inp_storage, C0);
}
const Mat& inp = float_input ? inp_storage : inp_ref;
MatShape inpshape = inp.shape();
CV_Assert(inpshape.layout == DATA_LAYOUT_BLOCK);
CV_Assert(inp.type() == CV_8SC1 || inp.type() == CV_8UC1);
@@ -332,6 +399,20 @@ public:
int Kg = K / ngroups;
repackWeightsForVNNI(weights, ngroups, Kg, Cg,
biasInt32, weightsVNNI, biasVNNI);
if (inputIsU8) {
biasU8.create({K}, CV_32SC1);
biasBaselineU8.create({K}, CV_32SC1);
const int32_t* bI = biasInt32.ptr<int32_t>();
const int32_t* bV = biasVNNI.ptr<int32_t>();
int32_t* bU = biasU8.ptr<int32_t>();
int32_t* bB = biasBaselineU8.ptr<int32_t>();
for (int k = 0; k < K; k++) {
int32_t wsum = (bI[k] - bV[k]) / 128;
bU[k] = bI[k] - input_zp * wsum;
bB[k] = bI[k] + (128 - input_zp) * wsum;
}
}
}
Mat residual;
@@ -376,11 +457,19 @@ public:
const int8_t* lutptr = !activationLUT.empty() ? activationLUT.ptr<int8_t>() : nullptr;
const int* effectiveBiasVNNI = inputIsU8
? (biasU8.empty() ? nullptr : biasU8.ptr<int>())
: (biasVNNI.empty() ? nullptr : biasVNNI.ptr<int>());
const int* baselineBias = (inputIsU8 && !biasBaselineU8.empty())
? biasBaselineU8.ptr<int>()
: biasInt32.ptr<int>();
convInt8Block(inp.data, resptr, out.data, cs,
weights.data,
weightsVNNI.empty() ? nullptr : weightsVNNI.data,
biasInt32.ptr<int>(),
biasVNNI.empty() ? nullptr : biasVNNI.ptr<int>(),
baselineBias,
effectiveBiasVNNI,
outMultiplier.ptr<float>(),
input_zp, output_zp,
lutptr, inputIsU8);
modules/dnn/src/int8layers/elementwise_layers.cpp
+16 -2
View File
@@ -307,9 +307,23 @@ public:
const int nstripes = getNumThreads();
Mat &dst = outputs[i];
CV_Assert(src.size == dst.size && src.type() == dst.type() &&
src.isContinuous() && dst.isContinuous() && src.type() == CV_8S);
src.isContinuous() && dst.isContinuous() &&
(src.type() == CV_8S || src.type() == CV_8U));
Activation::run(src, activationLUT, dst, nstripes);
if (src.type() == CV_8S) {
Activation::run(src, activationLUT, dst, nstripes);
} else {
const uint8_t* table = activationLUT.ptr<uint8_t>();
const uint8_t* srcptr = src.ptr<uint8_t>();
uint8_t* dstptr = dst.ptr<uint8_t>();
int total = (int)src.total();
parallel_for_(Range(0, nstripes), [&](const Range& r) {
int start = (int)((size_t)r.start * total / nstripes);
int end = (int)((size_t)r.end * total / nstripes);
for (int j = start; j < end; j++)
dstptr[j] = table[srcptr[j]];
}, nstripes);
}
}
else
{
modules/dnn/src/int8layers/eltwise2_int8_layer.cpp
+181 -19
View File
@@ -18,11 +18,48 @@
namespace cv {
namespace dnn {
static Mat broadcastExpand(const Mat& src, const MatShape& dstShape)
{
MatShape srcShape = src.shape();
const int ndst = (int)dstShape.size();
while ((int)srcShape.size() < ndst)
srcShape.insert(srcShape.begin(), 1);
if (srcShape == dstShape)
return src;
MatShape outShape = dstShape;
Mat out(outShape, src.type());
const int total = (int)out.total();
std::vector<int> dstStrides(ndst);
{ int s = 1; for (int d = ndst-1; d >= 0; d--) { dstStrides[d] = s; s *= dstShape[d]; } }
std::vector<int> srcStrides(ndst, 0);
{ int s = 1; for (int d = ndst-1; d >= 0; d--) {
srcStrides[d] = (srcShape[d] > 1) ? s : 0; s *= srcShape[d]; } }
const uint8_t* sp = src.ptr<uint8_t>();
uint8_t* dp = out.ptr<uint8_t>();
for (int i = 0; i < total; i++) {
int rem = i, srcIdx = 0;
for (int d = 0; d < ndst; d++) {
int coord = rem / dstStrides[d]; rem %= dstStrides[d];
srcIdx += coord * srcStrides[d];
}
dp[i] = sp[srcIdx];
}
return out;
}
class Eltwise2Int8LayerImpl CV_FINAL : public Eltwise2Int8Layer
{
public:
enum class Op { ADD, MUL };
Op op;
std::vector<float> coeffs;
float offset;
float mulCoeff;
bool withRelu;
Mat activationLUT;
@@ -36,6 +73,9 @@ public:
output_sc = params.get<float>("scales", 1.0f);
withRelu = params.get<bool>("with_relu", false);
std::string opStr = params.get<String>("operation", "add");
op = (opStr == "mul" || opStr == "prod") ? Op::MUL : Op::ADD;
if (params.has("input_scales"))
{
DictValue sc = params.get("input_scales");
@@ -55,6 +95,7 @@ public:
}
offset = 0.f;
mulCoeff = 0.f;
}
Eltwise2Int8LayerImpl(const Eltwise2Int8Params& p)
@@ -66,7 +107,9 @@ public:
output_sc = p.output_sc;
output_zp = p.output_zp;
withRelu = p.with_relu;
op = (p.operation == "mul" || p.operation == "prod") ? Op::MUL : Op::ADD;
offset = 0.f;
mulCoeff = 0.f;
}
void ensureCoeffs()
@@ -79,11 +122,19 @@ public:
CV_Assert(output_sc > 0.f);
coeffs.resize(scales.size());
offset = (float)output_zp;
for (size_t i = 0; i < scales.size(); i++)
{
coeffs[i] = scales[i] / output_sc;
offset -= coeffs[i] * zeropoints[i];
if (op == Op::MUL) {
CV_Assert(scales.size() == 2);
mulCoeff = (scales[0] * scales[1]) / output_sc;
coeffs[0] = (float)zeropoints[0];
coeffs[1] = (float)zeropoints[1];
offset = (float)output_zp;
} else {
offset = (float)output_zp;
for (size_t i = 0; i < scales.size(); i++)
{
coeffs[i] = scales[i] / output_sc;
offset -= coeffs[i] * zeropoints[i];
}
}
}
@@ -93,7 +144,24 @@ public:
std::vector<MatShape>& internals) const CV_OVERRIDE
{
CV_Assert(inputs.size() >= 2);
outputs.assign(1, inputs[0]);
int ndims = 0;
DataLayout outLayout = DATA_LAYOUT_UNKNOWN;
int outC = 0;
for (const auto& s : inputs) {
ndims = std::max(ndims, (int)s.size());
if (s.layout != DATA_LAYOUT_UNKNOWN && outLayout == DATA_LAYOUT_UNKNOWN)
outLayout = s.layout;
if (s.C > outC) outC = s.C;
}
MatShape outShape(ndims, 1);
for (const auto& s : inputs) {
int off = ndims - (int)s.size();
for (int d = 0; d < (int)s.size(); d++)
outShape[d + off] = std::max(outShape[d + off], s[d]);
}
outShape.layout = outLayout;
outShape.C = outC;
outputs.assign(1, outShape);
internals.clear();
return true;
}
@@ -145,8 +213,39 @@ public:
int ninputs = (int)input_arrs.total();
CV_Assert(ninputs >= 2 && coeffs.size() == (size_t)ninputs);
const Mat& inp0 = input_arrs.getMat(0);
std::vector<Mat> inputs(ninputs);
DataLayout outLayout = DATA_LAYOUT_UNKNOWN;
int outC = 0;
{
int ndims = 0;
for (int k = 0; k < ninputs; k++) {
inputs[k] = input_arrs.getMat(k);
const MatShape& sh = inputs[k].shape();
ndims = std::max(ndims, (int)sh.size());
if (sh.layout != DATA_LAYOUT_UNKNOWN && outLayout == DATA_LAYOUT_UNKNOWN)
outLayout = sh.layout;
if (sh.C > outC) outC = sh.C;
}
MatShape broadShape(ndims, 1);
for (int k = 0; k < ninputs; k++) {
const MatShape& sh = inputs[k].shape();
int off = ndims - (int)sh.size();
for (int d = 0; d < (int)sh.size(); d++)
broadShape[d + off] = std::max(broadShape[d + off], sh[d]);
}
broadShape.layout = outLayout;
broadShape.C = outC;
for (int k = 0; k < ninputs; k++)
if (inputs[k].shape() != broadShape)
inputs[k] = broadcastExpand(inputs[k], broadShape);
}
const Mat& inp0 = inputs[0];
MatShape outshape = inp0.shape();
if (outshape.layout == DATA_LAYOUT_UNKNOWN && outLayout != DATA_LAYOUT_UNKNOWN) {
outshape.layout = outLayout;
outshape.C = outC;
}
int outtype = inp0.type();
CV_Assert(outtype == CV_8SC1 || outtype == CV_8UC1);
const bool isU8 = (outtype == CV_8UC1);
@@ -175,6 +274,11 @@ public:
const float c0 = cptr[0];
const float c1 = cptr[1];
const bool isMul = (op == Op::MUL);
const float mc = mulCoeff;
const float zp0f = isMul ? (float)zeropoints[0] : 0.f;
const float zp1f = isMul ? (float)zeropoints[1] : 0.f;
const float ozpf = (float)output_zp;
const int out_min = isU8 ? (withRelu ? output_zp : 0) : (withRelu ? output_zp : -128);
const int out_max = isU8 ? 255 : 127;
@@ -184,7 +288,7 @@ public:
if (isU8) {
std::vector<const uint8_t*> inptrs(ninputs);
for (int k = 0; k < ninputs; k++)
inptrs[k] = input_arrs.getMat(k).ptr<uint8_t>();
inptrs[k] = inputs[k].ptr<uint8_t>();
const uint8_t* p0 = inptrs[0];
const uint8_t* p1 = inptrs[1];
uint8_t* outptr = out.ptr<uint8_t>();
@@ -192,7 +296,32 @@ public:
parallel_for_(Range(0, (int)total_elems), [&](const Range& r) {
int i = r.start;
#if CV_AVX2
if (!lutptr && ninputs == 2) {
if (isMul && !lutptr && ninputs == 2) {
__m256 vmc = _mm256_set1_ps(mc);
__m256 vzp0 = _mm256_set1_ps(zp0f);
__m256 vzp1 = _mm256_set1_ps(zp1f);
__m256 vozp = _mm256_set1_ps(ozpf);
__m256i vmin = _mm256_set1_epi32(out_min);
__m256i vmax = _mm256_set1_epi32(out_max);
for (; i <= r.end - 8; i += 8) {
__m128i a8 = _mm_loadl_epi64((const __m128i*)(p0 + i));
__m128i b8 = _mm_loadl_epi64((const __m128i*)(p1 + i));
__m256i a32 = _mm256_cvtepu8_epi32(a8);
__m256i b32 = _mm256_cvtepu8_epi32(b8);
__m256 af = _mm256_sub_ps(_mm256_cvtepi32_ps(a32), vzp0);
__m256 bf = _mm256_sub_ps(_mm256_cvtepi32_ps(b32), vzp1);
__m256 val = _mm256_fmadd_ps(_mm256_mul_ps(af, bf), vmc, vozp);
__m256i ival = _mm256_cvtps_epi32(val);
ival = _mm256_max_epi32(ival, vmin);
ival = _mm256_min_epi32(ival, vmax);
__m128i lo = _mm256_castsi256_si128(ival);
__m128i hi = _mm256_extracti128_si256(ival, 1);
__m128i packed16 = _mm_packs_epi32(lo, hi);
__m128i packed8 = _mm_packus_epi16(packed16, packed16);
_mm_storel_epi64((__m128i*)(outptr + i), packed8);
}
} else
if (!isMul && !lutptr && ninputs == 2) {
__m256 vc0 = _mm256_set1_ps(c0);
__m256 vc1 = _mm256_set1_ps(c1);
__m256 voff = _mm256_set1_ps(off);
@@ -274,10 +403,14 @@ public:
#endif
for (; i < r.end; i++)
{
float val = c0 * (float)p0[i] + c1 * (float)p1[i] + off;
for (int k = 2; k < ninputs; k++)
val += cptr[k] * (float)inptrs[k][i];
float val;
if (isMul) {
val = mc * ((float)p0[i] - zp0f) * ((float)p1[i] - zp1f) + ozpf;
} else {
val = c0 * (float)p0[i] + c1 * (float)p1[i] + off;
for (int k = 2; k < ninputs; k++)
val += cptr[k] * (float)inptrs[k][i];
}
int ival = cvRound(val);
ival = std::max(out_min, std::min(out_max, ival));
@@ -291,7 +424,7 @@ public:
} else {
std::vector<const int8_t*> inptrs(ninputs);
for (int k = 0; k < ninputs; k++)
inptrs[k] = input_arrs.getMat(k).ptr<int8_t>();
inptrs[k] = inputs[k].ptr<int8_t>();
const int8_t* p0 = inptrs[0];
const int8_t* p1 = inptrs[1];
int8_t* outptr = out.ptr<int8_t>();
@@ -299,7 +432,32 @@ public:
parallel_for_(Range(0, (int)total_elems), [&](const Range& r) {
int i = r.start;
#if CV_AVX2
if (!lutptr && ninputs == 2) {
if (isMul && !lutptr && ninputs == 2) {
__m256 vmc = _mm256_set1_ps(mc);
__m256 vzp0 = _mm256_set1_ps(zp0f);
__m256 vzp1 = _mm256_set1_ps(zp1f);
__m256 vozp = _mm256_set1_ps(ozpf);
__m256i vmin = _mm256_set1_epi32(out_min);
__m256i vmax = _mm256_set1_epi32(out_max);
for (; i <= r.end - 8; i += 8) {
__m128i a8 = _mm_loadl_epi64((const __m128i*)(p0 + i));
__m128i b8 = _mm_loadl_epi64((const __m128i*)(p1 + i));
__m256i a32 = _mm256_cvtepi8_epi32(a8);
__m256i b32 = _mm256_cvtepi8_epi32(b8);
__m256 af = _mm256_sub_ps(_mm256_cvtepi32_ps(a32), vzp0);
__m256 bf = _mm256_sub_ps(_mm256_cvtepi32_ps(b32), vzp1);
__m256 val = _mm256_fmadd_ps(_mm256_mul_ps(af, bf), vmc, vozp);
__m256i ival = _mm256_cvtps_epi32(val);
ival = _mm256_max_epi32(ival, vmin);
ival = _mm256_min_epi32(ival, vmax);
__m128i lo = _mm256_castsi256_si128(ival);
__m128i hi = _mm256_extracti128_si256(ival, 1);
__m128i packed16 = _mm_packs_epi32(lo, hi);
__m128i packed8 = _mm_packs_epi16(packed16, packed16);
_mm_storel_epi64((__m128i*)(outptr + i), packed8);
}
} else
if (!isMul && !lutptr && ninputs == 2) {
__m256 vc0 = _mm256_set1_ps(c0);
__m256 vc1 = _mm256_set1_ps(c1);
__m256 voff = _mm256_set1_ps(off);
@@ -381,10 +539,14 @@ public:
#endif
for (; i < r.end; i++)
{
float val = c0 * (float)p0[i] + c1 * (float)p1[i] + off;
for (int k = 2; k < ninputs; k++)
val += cptr[k] * (float)inptrs[k][i];
float val;
if (isMul) {
val = mc * ((float)p0[i] - zp0f) * ((float)p1[i] - zp1f) + ozpf;
} else {
val = c0 * (float)p0[i] + c1 * (float)p1[i] + off;
for (int k = 2; k < ninputs; k++)
val += cptr[k] * (float)inptrs[k][i];
}
int ival = cvRound(val);
ival = std::max(out_min, std::min(out_max, ival));
modules/dnn/test/test_model.cpp
+13 -3
View File
@@ -5,6 +5,8 @@
#include "test_precomp.hpp"
#include <opencv2/dnn/shape_utils.hpp>
#include "npy_blob.hpp"
#include <map>
#include <set>
namespace opencv_test { namespace {
template<typename TString>
@@ -817,12 +819,20 @@ TEST_P(Reproducibility_ResNet50_QDQ_ONNX, Accuracy)
topK(out, res, K);
ASSERT_EQ(int(res.size()), K);
// Top 4 class's score must be within eps of its reference value.
std::vector<std::pair<int, float> > ref = {{285, 10.44}, {287, 10.13}, {283, 8.89}, {278, 8.43}};
const float eps = 0.5f;
for (int i = 0; i < (int)ref.size(); i++) {
EXPECT_EQ(ref[i].first, res[i].first);
EXPECT_NEAR(ref[i].second, res[i].second, eps);
std::map<int, float> res_map;
for (int i = 0; i < (int)res.size(); i++)
res_map[res[i].first] = res[i].second;
for (const auto& r : ref) {
auto it = res_map.find(r.first);
EXPECT_NE(it, res_map.end()) << "Expected class " << r.first << " not found in top-4";
if (it != res_map.end()) {
EXPECT_NEAR(r.second, it->second, eps) << "Score mismatch for class " << r.first;
}
}
}
INSTANTIATE_TEST_CASE_P(/**/, Reproducibility_ResNet50_QDQ_ONNX,