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fully_connected_op_prune.h
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fully_connected_op_prune.h
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/**
* Copyright (c) 2016-present, Facebook, Inc.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#ifndef CAFFE2_OPERATORS_FULLY_CONNECTED_OP_PRUNE_H_
#define CAFFE2_OPERATORS_FULLY_CONNECTED_OP_PRUNE_H_
#include "caffe2/core/context.h"
#include "caffe2/core/operator.h"
#include "caffe2/utils/math.h"
namespace caffe2 {
namespace {
template<int N>
using Shape = std::array<int, N>;
template<int N>
const std::vector<int64_t>& shape(Shape<N> vs) {
static thread_local std::vector<int64_t> cache;
cache.resize(vs.size());
for (auto i = 0; i < vs.size(); ++i) {
cache[i] = vs[i];
}
return cache;
}
inline const std::vector<int64_t>& shape(int i) {
return shape<1>(Shape<1>({i}));
}
inline const std::vector<int64_t>& shape(int i, int j) {
return shape<2>(Shape<2>({i, j}));
}
template <typename T, class Context>
void MaskMatrix(const T* mask, T* mat,
int M, int N);
template <typename T, class Context>
void MaskMatrix_Inc(T* mask_seq, T* mat,
int M, int N, int seq_len, T target);
template <typename T, class Context>
void AggrDW(T* ag_dw, const T* dw, int N, int K, Context* context);
template <typename T>
int MatrixCompare_LT(const T* mat, float thres,
T* mask_seq, int M, int N);
// TODO(wyiming): write an incremental Mask
// Incremental Mask: only give the new mask positions;
// Assuming that weights masked will not be mask again;
// The incremental mask can also be used to update mask matrix;
// But this will include template for bool and float;
template <>
void MaskMatrix<float, CPUContext>(
const float* mask, float* mat, int M, int N) {
int offset = 0;
for (int i = 0; i < M; ++i) {
for (int j = 0; j < N; ++j) {
mat[offset] = mask[offset]? mat[offset] : 0;
offset++;
}
}
}
template <>
void MaskMatrix_Inc<float, CPUContext>(
float* mask_seq,
float* mat,
int /*M*/,
int /*N*/,
int seq_len,
float target) {
for (int i = 0; i < seq_len; ++i) {
// assume that the mask_seq is smaller than size
// Although it seems that random access gets bad performance,
// we make sure that seq is in order;
mat[static_cast<int>(mask_seq[i])] = target;
}
}
template <>
void AggrDW<float, CPUContext>(
float* ag_dw, const float* dw,
int N, int K, CPUContext* context) {
math::Add<float, CPUContext>(N*K, dw, ag_dw, ag_dw, context);
}
template <>
int MatrixCompare_LT<float>(
const float* mat, float thres,
float* mask_seq, int M, int N) {
int seq_len = 0;
int offset = 0;
for (int i = 0 ; i < M; ++i) {
for (int j = 0; j < N; ++j) {
if (mat[offset] != 0 &&
(mat[offset] < thres && mat[offset] > -thres)) {
mask_seq[seq_len++] = static_cast<float>(offset);
}
offset++;
}
}
return seq_len;
}
}
// This is Caffe's InnerProductOp, with a name that fits its purpose better.
template <typename T, class Context, class Engine=DefaultEngine>
class FullyConnectedOpPrune final : public Operator<Context> {
public:
USE_OPERATOR_CONTEXT_FUNCTIONS;
FullyConnectedOpPrune(const OperatorDef& operator_def, Workspace* ws)
: Operator<Context>(operator_def, ws) {}
~FullyConnectedOpPrune() {}
bool RunOnDevice() override {
const auto& X = Input(0);
const auto& W = Input(1);
const auto& Mask = Input(2);
const auto& b = Input(3);
CAFFE_ENFORCE_GE(X.dim(), 1);
CAFFE_ENFORCE_GE(W.dim(), 2);
if (X.dim() > 2 || W.dim() > 2) {
VLOG(1) << "Using legacy support for arbitrary input and weight "
"dimensions.";
}
CAFFE_ENFORCE_EQ(b.dim(), 1);
// batch size
int M = X.dim() > 1 ? X.dim32(0) : 1;
// Feature dimension
int K = X.numel() / M;
// number of outputs.
int N = W.dim32(0);
CAFFE_ENFORCE_EQ(K, W.numel() / W.dim32(0));
CAFFE_ENFORCE_EQ(N, b.dim32(0));
std::vector<int64_t> dims;
if (X.dim() > 1) {
dims = {M, N};
} else {
dims = {N};
}
auto* Y = Output(0, dims, at::dtype<T>());
// W * x
math::Gemm<T, Context, Engine>(
CblasNoTrans, CblasTrans, M, N, K, 1, X.template data<T>(),
W.template data<T>(), 0, Y->template mutable_data<T>(),
&context_);
// Add bias term
if (bias_multiplier_.numel() != M) {
// If the helper bias multiplier is not M,
// reshape and fill it with one.
bias_multiplier_.Resize(M);
math::Set<T, Context>(
M, static_cast<T>(1),
bias_multiplier_.template mutable_data<T>(),
&context_);
}
math::Gemm<T, Context, Engine>(
CblasNoTrans, CblasNoTrans, M, N, 1, 1,
bias_multiplier_.template data<T>(), b.template data<T>(), 1,
Y->template mutable_data<T>(), &context_);
if (OutputSize() == 2){
auto* Comp_rate = Output(1, vector<int64_t>(), at::dtype<T>());
T* comp_data = Comp_rate->template mutable_data<T>();
math::Sum<T, Context>(
Mask.numel(), Mask.template data<T>(), comp_data, &context_);
math::Scale<float, T, Context>(
1,
static_cast<T>(1.) / Mask.numel(),
comp_data,
comp_data,
&context_);
}
return true;
}
protected:
Tensor bias_multiplier_{Context::GetDeviceType()};
};
template <typename T, class Context, class Engine=DefaultEngine>
class FullyConnectedPruneGradientOp : public Operator<Context> {
public:
int iter_offset;
public:
USE_OPERATOR_CONTEXT_FUNCTIONS;
FullyConnectedPruneGradientOp
(const OperatorDef& operator_def, Workspace* ws)
: Operator<Context>(operator_def, ws) { iter_offset = 0; }
~FullyConnectedPruneGradientOp() {}
bool RunOnDevice() override {
const auto& X = Input(0);
//const auto& W = Input(1);
auto* W_ptr = Output(2);
auto& W = *W_ptr;
//const auto& Mask = Input(2);
auto* Mask_ptr = Output(3);
auto& Mask = *Mask_ptr;
const auto& dY = Input(3);
//const auto& Ag_dW = Input(4);
auto* Ag_dW_ptr = Output(4);
auto& Ag_dW = *Ag_dW_ptr;
// it is also the Input(5)
// how about get threshold
auto& thres = Input(6);
//TODO(wyiming): check comp_lb is a float
auto& comp_lb = Input(7);
DCHECK_GE(X.dim(), 1);
DCHECK_GE(W.dim(), 2);
DCHECK_LE(dY.dim(), 2);
// batch size
int M = X.dim() > 1 ? X.dim32(0) : 1;
// Feature dimension
int K = X.numel() / M;
// number of outputs.
int N = W.dim32(0);
// TODO(wyiming): add this window_size to workspace?
int window_size = 100;
// TODO(wyiming): this threshold should be
// based on distribution of the layer weight
float thr = 0.01;
DCHECK_EQ(Mask.dim32(0), W.dim32(0));
DCHECK_EQ(Mask.dim32(1), W.dim32(1));
DCHECK_EQ(Ag_dW.dim32(0), W.dim32(0));
DCHECK_EQ(Ag_dW.dim32(1), W.dim32(1));
DCHECK_EQ(K, W.numel() / W.dim32(0));
if (dY.dim() > 1) {
DCHECK_EQ(M, dY.dim32(0));
DCHECK_EQ(N, dY.dim32(1));
} else {
DCHECK_EQ(X.dim(), 1);
DCHECK_EQ(N, dY.numel());
}
auto* dW = Output(0, W.sizes(), at::dtype<T>());
auto* db = Output(1, {N}, at::dtype<T>());
// Compute dW
math::Gemm<T, Context, Engine>(
CblasTrans, CblasNoTrans, N, K, M, 1,
dY.template data<T>(), X.template data<T>(),
0, dW->template mutable_data<T>(),
&context_);
comp_r_buf_.Resize(vector<int64_t>());
T* comp_data = comp_r_buf_.template mutable_data<T>();
math::Sum<T, Context>(
Mask.numel(), Mask.template data<T>(), comp_data, &context_);
math::Scale<float, T, Context>(
1,
static_cast<T>(1.) / Mask.numel(),
comp_data,
comp_data,
&context_);
// update W size window
// Notice here we need to maintain state in OP.
// This is new in Caffe2.
// And this is something we might need to discuss in the future.
// at most mask half of the matrix at time
// 1. mask dw with previous mask
MaskMatrix<T, Context>(Mask.template mutable_data<T>(),
dW->template mutable_data<T>(), N, K);
if(*comp_data > *(comp_lb.template data<T>())){
iter_offset++;
if (iter_offset % window_size == 0) {
// TODO(wyiming):do the prune here;
sum_buffer_.ResizeLike(W);
math::Add<T, Context>(
W.numel(),
W.template mutable_data<T>(),
Ag_dW.template mutable_data<T>(),
sum_buffer_.template mutable_data<T>(),
&context_);
auto* mask_seq_auto = Output(5, W.sizes(), at::dtype<T>());
T* mask_seq = mask_seq_auto->template mutable_data<T>();
math::Set<T, Context>(N*K, static_cast<T>(0),
mask_seq_auto->template mutable_data<T>(), &context_);
// 2. find dw below thres but not eq 0
int seq_len = MatrixCompare_LT<T>(
Ag_dW_ptr->template mutable_data<T>(),
*thres.template data<T>(), mask_seq, N, K);
// 3. use the mask_seq to update W and dw
MaskMatrix_Inc<T, Context>(mask_seq,
dW->template mutable_data<T>(),
N, K, seq_len, 0);
MaskMatrix_Inc<T, Context>(mask_seq,
W.template mutable_data<T>(),
N, K, seq_len, 0);
MaskMatrix_Inc<T, Context>(mask_seq,
Mask.template mutable_data<T>(),
N, K, seq_len, 0);
math::Set<T, Context>(N*K, static_cast<T>(0),
Ag_dW.template mutable_data<T>(),
&context_);
} else {
// add dW to Aggregate dW.
AggrDW<T, Context>(
Ag_dW.template mutable_data<T>(),
dW->template mutable_data<T>(),
N, K, &context_);
}
}
if (bias_multiplier_.numel() != M) {
// If the helper bias multiplier is not M,
// reshape and fill it with one.
bias_multiplier_.Resize(M);
math::Set<T, Context>(
M, static_cast<T>(1),
bias_multiplier_.template mutable_data<T>(),
&context_);
}
// Compute dB
math::Gemv<T, Context>(
CblasTrans, M, N, 1, dY.template data<T>(),
bias_multiplier_.template data<T>(), 0,
db->template mutable_data<T>(),
&context_);
// Compute dX if necessary.
if (OutputSize() == 7) {
auto* dX = Output(6, X.sizes(), at::dtype<T>());
math::Gemm<T, Context, Engine>(
CblasNoTrans, CblasNoTrans, M, K, N, 1,
dY.template data<T>(), W.template data<T>(),
0, dX->template mutable_data<T>(),
&context_);
}
return true;
}
protected:
Tensor bias_multiplier_{Context::GetDeviceType()};
Tensor sum_buffer_{Context::GetDeviceType()};
Tensor comp_r_buf_{Context::GetDeviceType()};
};
} // namespace caffe2
#endif // CAFFE2_OPERATORS_FULLY_CONNECTED_OP_H_