This repository has been archived by the owner on Feb 7, 2023. It is now read-only.
-
Notifications
You must be signed in to change notification settings - Fork 1.9k
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
Reviewed By: Yangqing Differential Revision: D5607549 fbshipit-source-id: dfdd7f78d4c64c1f71e11106c57f2c4007581e48
- Loading branch information
1 parent
be0fc14
commit 861d224
Showing
5 changed files
with
1,471 additions
and
0 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -0,0 +1,378 @@ | ||
#include "ulp.h" | ||
#include "caffe2/operators/conv_pool_op_base.h" | ||
#include "ulp_neon.h" | ||
|
||
namespace caffe2 { | ||
|
||
void uniformQuantize2b1b(const TensorCPU& X, | ||
const std::vector<std::unique_ptr<TensorCPU>>& XQ, | ||
float offset, | ||
float inter_center_distance) { | ||
CAFFE_ENFORCE_GT(X.ndim(), 1); | ||
const auto N = X.size_to_dim(X.ndim() - 1); | ||
auto C = X.size() / N; | ||
const auto QC = divRoundUp(C, 8); | ||
auto XQs = X.dims(); | ||
XQs[X.ndim() - 1] = QC; | ||
CAFFE_ENFORCE_EQ(XQ.size(), k2b1bXBits); | ||
for (auto i = 0; i < k2b1bXBits; ++i) { | ||
XQ[i]->Resize(XQs); | ||
} | ||
const float* Xdata = X.data<float>(); | ||
std::array<uint8_t*, k2b1bXBits> XQdata; | ||
for (auto i = 0; i < k2b1bXBits; ++i) { | ||
XQdata[i] = XQ[i]->mutable_data<uint8_t>(); | ||
} | ||
for (auto n = 0; n < N; ++n) { | ||
for (auto qc = 0; qc < QC; ++qc) { | ||
// compute the block in X. | ||
std::array<uint8_t, k2b1bXBits> p = {{0, 0}}; | ||
for (auto b = 0; b < 8; ++b) { | ||
const auto c = qc * 8 + b; | ||
if (c < C) { | ||
float v = Xdata[qc * 8 + b + C * n]; | ||
if (v < offset) { | ||
// zero'd already. | ||
} else if (v < offset + inter_center_distance) { | ||
p[0] |= 1 << b; | ||
} else if (v < offset + 2 * inter_center_distance) { | ||
p[1] |= 1 << b; | ||
} else { | ||
p[0] |= 1 << b; | ||
p[1] |= 1 << b; | ||
} | ||
} | ||
} | ||
for (auto i = 0; i < k2b1bXBits; ++i) { | ||
XQdata[i][qc + QC * n] = p[i]; | ||
} | ||
} | ||
} | ||
} | ||
|
||
void qconv(const ConvArgs& args, | ||
const TensorCPU& X, | ||
const TensorCPU& W, | ||
const TensorCPU* b, | ||
TensorCPU* Y) { | ||
const auto N = X.dim32(0); | ||
const auto IH = X.dim32(1); | ||
const auto IW = X.dim32(2); | ||
const auto KH = W.dim32(1); | ||
const auto KW = W.dim32(2); | ||
const auto KC = W.dim32(3); | ||
Y->Resize(X.dim32(0), | ||
(X.dim32(1) - KH + args.pad_t + args.pad_b) / args.stride_h + 1, | ||
(X.dim32(2) - KW + args.pad_l + args.pad_r) / args.stride_w + 1, | ||
W.dim32(0)); | ||
const auto OH = Y->dim32(1); | ||
const auto OW = Y->dim32(2); | ||
const auto OC = Y->dim32(3); | ||
|
||
CAFFE_ENFORCE_EQ(W.dim32(3), X.dim32(3)); | ||
|
||
const auto* Xdata = X.data<uint8_t>(); | ||
const auto* Wdata = W.data<uint8_t>(); | ||
auto* Ydata = Y->mutable_data<float>(); | ||
for (size_t n = 0; n < N; ++n) { | ||
for (size_t oh = 0; oh < OH; ++oh) { | ||
for (size_t ow = 0; ow < OW; ++ow) { | ||
for (size_t oc = 0; oc < OC; ++oc) { | ||
float acc = 0.0; | ||
for (size_t kh = 0; kh < KH; ++kh) { | ||
const int32_t ih = (int32_t)kh + (int32_t)args.stride_h * oh - (int32_t)args.pad_t; | ||
for (size_t kw = 0; kw < KW; ++kw) { | ||
const int32_t iw = (int32_t)kw + (int32_t)args.stride_w * ow - (int32_t)args.pad_l; | ||
for (size_t kc = 0; kc < KC; ++kc) { | ||
const uint8_t w = Wdata[kc + KC * kw + KC * KW * kh + KC * KW * KH * oc]; | ||
// Use unsigned integer math to avoid multiple comparisons (>= H, < 0). | ||
if ((size_t)ih >= (size_t)IH || (size_t)iw >= (size_t)IW) { | ||
acc += __builtin_popcount(0 ^ w); | ||
} else { | ||
const uint8_t x = | ||
Xdata[kc + KC * (size_t)iw + KC * IW * (size_t)ih + n * KC * IW * IH]; | ||
const uint8_t w = Wdata[kc + KC * kw + KC * KW * kh + KC * KW * KH * oc]; | ||
acc += __builtin_popcount(x ^ w); | ||
} | ||
} | ||
} | ||
} | ||
Ydata[oc + OC * ow + OC * OW * oh + n * OC * OW * OH] = | ||
KW * KH * KC * 8 - 2 * acc + (b ? b->data<float>()[oc] : 0.0); | ||
; | ||
} | ||
} | ||
} | ||
} | ||
} | ||
|
||
void qpad_zero(const ConvArgs& args, const TensorCPU& X, TensorCPU* Y) { | ||
CAFFE_ENFORCE_EQ(args.stride_h, 1); | ||
CAFFE_ENFORCE_EQ(args.stride_w, 1); | ||
const auto* Xdata = X.data<uint8_t>(); | ||
Y->Resize(X.dim32(0), | ||
X.dim32(1) + args.pad_t + args.pad_b, | ||
X.dim32(2) + args.pad_l + args.pad_r, | ||
X.dim32(3)); | ||
auto* Ydata = Y->mutable_data<uint8_t>(); | ||
::memset(Ydata, Y->nbytes(), 0); | ||
const auto C = Y->dim32(3); | ||
const auto XrowSize = X.dim32(3) * X.dim32(2); | ||
const auto YrowSize = Y->dim32(3) * Y->dim32(2); | ||
math::CopyMatrix<CPUContext>(1, | ||
X.dim32(1), | ||
XrowSize, | ||
Xdata, | ||
XrowSize, | ||
Ydata + C * args.pad_l + YrowSize * args.pad_t, | ||
YrowSize, | ||
nullptr); | ||
} | ||
|
||
void signQuantize(const TensorCPU& X, TensorCPU* XQ) { | ||
CAFFE_ENFORCE_GT(X.ndim(), 1); | ||
const auto N = X.size_to_dim(X.ndim() - 1); | ||
auto C = X.size() / N; | ||
const auto QC = divRoundUp(C, 8); | ||
auto XQs = X.dims(); | ||
XQs[X.ndim() - 1] = QC; | ||
XQ->Resize(XQs); | ||
const float* Xdata = X.data<float>(); | ||
uint8_t* XQdata = XQ->mutable_data<uint8_t>(); | ||
for (auto n = 0; n < N; ++n) { | ||
for (auto qc = 0; qc < QC; ++qc) { | ||
// compute the block in X. | ||
uint8_t p = 0; | ||
for (auto b = 0; b < 8; ++b) { | ||
const auto c = qc * 8 + b; | ||
if (c < C) { | ||
p |= (Xdata[c + C * n] > 0) << b; | ||
} | ||
} | ||
XQdata[qc + QC * n] = p; | ||
} | ||
} | ||
} | ||
|
||
void filterNormalization11(const TensorCPU& WQ, TensorCPU* WQN) { | ||
const auto F = WQ.dim32(0); | ||
// In our NEON kernel we read up to TileSize, so align allocation to TileSize elements. | ||
WQN->Resize(divRoundUp(F, kGEMMTileSize) * kGEMMTileSize); | ||
const auto WQs = WQ.size() / F; | ||
const auto WQbits = 8 * WQs; | ||
const auto* WQdata = WQ.data<uint8_t>(); | ||
auto* WQNdata = WQN->mutable_data<float>(); | ||
for (auto f = 0; f < F; ++f) { | ||
int32_t bitSum = 0; | ||
for (auto j = 0; j < WQs; ++j) { | ||
bitSum += __builtin_popcount(WQdata[f * WQs + j]); | ||
} | ||
DCHECK_LE(bitSum, WQbits); | ||
WQNdata[f] = 2 * bitSum - WQbits; | ||
} | ||
} | ||
|
||
void filterNormalizationL1(const TensorCPU& W, TensorCPU* WL1) { | ||
const auto F = W.dim32(0); | ||
WL1->Resize(F); | ||
const auto Ws = W.size() / F; | ||
const auto* Wdata = W.data<float>(); | ||
auto* WL1data = WL1->mutable_data<float>(); | ||
for (auto f = 0; f < F; ++f) { | ||
double l1sum = 0.0; | ||
for (auto j = 0; j < Ws; ++j) { | ||
l1sum += std::abs(Wdata[f * Ws + j]); | ||
} | ||
WL1data[f] = l1sum / Ws; | ||
} | ||
} | ||
|
||
void qim2col(const ConvArgs& args, const TensorCPU& XQ, const TensorCPU& WQ, TensorCPU* XQcol) { | ||
// TODO: pass pre-resized output? | ||
// TODO: handle strides? | ||
|
||
CAFFE_ENFORCE_EQ(XQ.dim32(3), WQ.dim32(3)); | ||
const size_t N = XQ.dim32(0); | ||
const size_t IH = XQ.dim32(1); | ||
const size_t IW = XQ.dim32(2); | ||
const size_t KH = WQ.dim32(1); | ||
const size_t KW = WQ.dim32(2); | ||
const size_t KC = WQ.dim32(3); | ||
|
||
XQcol->Resize(XQ.dim32(0), | ||
(XQ.dim32(1) - KH + args.pad_t + args.pad_b) / args.stride_h + 1, | ||
(XQ.dim32(2) - KW + args.pad_l + args.pad_r) / args.stride_w + 1, | ||
KH * KW * KC); | ||
|
||
if (args.pad_l == 0 && args.pad_r == 0 && args.pad_b == 0 && args.pad_t == 0 && | ||
args.stride_h == 1 && args.stride_w == 1 && KH == 1 && KW == 1) { | ||
CAFFE_ENFORCE_EQ(XQ.size(), XQcol->size()); | ||
XQcol->ShareExternalPointer(const_cast<uint8_t*>(XQ.data<uint8_t>()), XQ.size()); | ||
return; | ||
} | ||
const size_t OH = XQcol->dim32(1); | ||
const size_t OW = XQcol->dim32(2); | ||
|
||
const uint8_t* XQdata = XQ.data<uint8_t>(); | ||
uint8_t* XQcoldata = XQcol->mutable_data<uint8_t>(); | ||
for (size_t n = 0; n < N; ++n) { | ||
for (size_t oh = 0; oh < OH; ++oh) { | ||
int32_t h_pad = (int32_t)(args.stride_h * oh) - (int32_t)args.pad_t; | ||
for (size_t ow = 0; ow < OW; ++ow) { | ||
int32_t w_pad = (int32_t)(args.stride_w * ow) - (int32_t)args.pad_l; | ||
for (size_t kh = 0; kh < KH; ++kh) { | ||
int32_t ih = (int32_t)kh + h_pad; | ||
if ((size_t)ih < (size_t)IH && (size_t)w_pad < (size_t)IW && | ||
(size_t)((int32_t)w_pad + (int32_t)KW) < (size_t)IW) { | ||
// We can do a larger memcpy, of size KW * KC | ||
size_t off = kh * KW * KC + ow * KH * KW * KC + oh * KH * KW * KC * OW + | ||
n * KH * KW * KC * OW * OH; | ||
std::memcpy(&XQcoldata[off], | ||
&XQdata[((int32_t)w_pad) * KC + ih * IW * KC + n * IW * KC * IH], | ||
KW * KC); | ||
} else { | ||
for (size_t kw = 0; kw < KW; ++kw) { | ||
int32_t iw = (int32_t)kw + w_pad; | ||
// Use unsigned integer math to avoid multiple comparisons (>= H, < 0). | ||
size_t off = kw * KC + kh * KW * KC + ow * KH * KW * KC + oh * KH * KW * KC * OW + | ||
n * KH * KW * KC * OW * OH; | ||
if ((size_t)ih < (size_t)IH && (size_t)iw < (size_t)IW) { | ||
std::memcpy( | ||
&XQcoldata[off], &XQdata[iw * KC + ih * IW * KC + n * KC * IW * IH], KC); | ||
} else { | ||
// This should be simply padded with zero. | ||
std::memset(&XQcoldata[off], 0, KC); | ||
} | ||
} | ||
} | ||
} | ||
} | ||
} | ||
} | ||
} | ||
|
||
std::unique_ptr<QConvState> create2b1bConvState(Workspace* ws, | ||
const TensorCPU& W, | ||
const TensorCPU* b) { | ||
auto state = caffe2::make_unique<QConvState>(); | ||
state->XQs.resize(k2b1bXBits); | ||
state->YQs.resize(k2b1bXBits); | ||
for (auto i = 0; i < k2b1bXBits; ++i) { | ||
state->XQs[i] = caffe2::make_unique<TensorCPU>(); | ||
state->YQs[i] = caffe2::make_unique<TensorCPU>(); | ||
} | ||
state->WQ = caffe2::make_unique<TensorCPU>(); | ||
state->WQN = caffe2::make_unique<TensorCPU>(); | ||
state->WQL1Norm = caffe2::make_unique<TensorCPU>(); | ||
state->scratch = caffe2::make_unique<TensorCPU>(); | ||
state->scratchColBuffer = caffe2::make_unique<TensorCPU>(); | ||
|
||
signQuantize(W, state->WQ.get()); | ||
filterNormalization11(*(state->WQ), state->WQN.get()); | ||
filterNormalizationL1(W, state->WQL1Norm.get()); | ||
// TODO: incorporate center distance normalization. | ||
// Since inputs to convs are [0, 1, 2, 3], instead of [0, x, 2 * x, ...], | ||
// we can just uniformly rescale the outputs by x, i.e., | ||
// for (auto i = 0; i < r->WQL1Norm.size(); ++i) { | ||
// r->WQL1Norm.mutable_data<float>()[i] *= center_distance; | ||
// } | ||
state->parallelFor = [ws](size_t range, std::function<void(size_t)> f) { | ||
return ws->GetThreadPool()->run([&](int, size_t v) { f(v); }, range); | ||
}; | ||
if (b) { | ||
state->bias = caffe2::make_unique<TensorCPU>(*b); | ||
} | ||
return state; | ||
} | ||
|
||
void run2b1bConvGeneric(QConvState* state, const ConvArgs& args, const TensorCPU& X, TensorCPU* Y) { | ||
#ifdef __ARM_NEON__ | ||
if (run2b1bConvNeon(state, args, X, Y)) { | ||
return; | ||
} | ||
#endif | ||
uniformQuantize2b1b(X, state->XQs, 0.5, 1.0); | ||
for (auto i = 0; i < k2b1bXBits; ++i) { | ||
qconv(args, *(state->XQs[i]), *(state->WQ), nullptr, state->YQs[i].get()); | ||
} | ||
Y->ResizeLike(*(state->YQs[0])); | ||
const auto F = state->WQ->dim(0); | ||
const auto N = Y->size() / F; | ||
run2b1bUnification(state, | ||
N, | ||
F, | ||
state->WQN->data<float>(), | ||
state->YQs[0]->data<float>(), | ||
state->YQs[1]->data<float>(), | ||
F, | ||
Y->mutable_data<float>(), | ||
F, | ||
state->bias ? state->bias->data<float>() : nullptr); | ||
} | ||
|
||
void run2b1bUnification(QConvState* state, | ||
size_t N, | ||
size_t C, | ||
const float* WQNVdata, | ||
const float* YQs0Vdata, | ||
const float* YQs1Vdata, | ||
size_t YQstride, | ||
float* Ydata, | ||
size_t Ystride, | ||
const float* bias) { | ||
ConstEigenVectorArrayMap<float> WQNV(WQNVdata, C); | ||
|
||
for (size_t j = 0; j < N; ++j) { | ||
ConstEigenVectorArrayMap<float> YQs0V(YQs0Vdata + YQstride * j, C); | ||
ConstEigenVectorArrayMap<float> YQs1V(YQs1Vdata + YQstride * j, C); | ||
EigenVectorArrayMap<float> YNV(Ydata + Ystride * j, C); | ||
if (bias) { | ||
ConstEigenVectorArrayMap<float> BV(bias, C); | ||
YNV = (std::pow<float>(2, k2b1bXBits) - 1) / 2 * WQNV + std::pow<float>(2, -1) * YQs0V + | ||
std::pow<float>(2, 0) * YQs1V + BV; | ||
} else { | ||
YNV = (std::pow<float>(2, k2b1bXBits) - 1) / 2 * WQNV + std::pow<float>(2, -1) * YQs0V + | ||
std::pow<float>(2, 0) * YQs1V; | ||
} | ||
} | ||
} | ||
|
||
class QConvOp final : public ConvPoolOpBase<CPUContext> { | ||
public: | ||
QConvOp(const OperatorDef& operator_def, Workspace* ws) | ||
: ConvPoolOpBase<CPUContext>(operator_def, ws), ws_(ws) { | ||
OPERATOR_NEEDS_FEATURE(this->order_ == StorageOrder::NHWC, "QConvOp only supports NHWC order"); | ||
OPERATOR_NEEDS_FEATURE(this->dilation_h() == 1, ""); | ||
OPERATOR_NEEDS_FEATURE(this->dilation_w() == 1, ""); | ||
OPERATOR_NEEDS_FEATURE(this->group_ == 1, ""); | ||
} | ||
|
||
bool RunOnDeviceWithOrderNHWC() override { | ||
auto& X = Input(0); | ||
auto& filter = Input(1); | ||
const auto* bias = InputSize() == 3 ? &Input(2) : nullptr; | ||
auto* Y = Output(0); | ||
|
||
// TODO: Support multiple quantization methods instead of assuming 2b1b. | ||
if (!state_) { | ||
state_ = create2b1bConvState(ws_, filter, bias); | ||
} | ||
ConvArgs args; | ||
args.pad_l = this->pad_l(); | ||
args.pad_t = this->pad_t(); | ||
args.pad_b = this->pad_b(); | ||
args.pad_r = this->pad_r(); | ||
args.stride_h = this->stride_h(); | ||
args.stride_w = this->stride_w(); | ||
run2b1bConvGeneric(state_.get(), args, X, Y); | ||
return true; | ||
} | ||
|
||
private: | ||
std::unique_ptr<QConvState> state_; | ||
Workspace* ws_; | ||
}; | ||
|
||
REGISTER_CPU_OPERATOR(QConv, QConvOp); | ||
|
||
} // namespace caffe2 |
Oops, something went wrong.