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未验证 提交 eaa3fd45 编写于 作者: S sneaxiy 提交者： GitHub
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add more int type support for softmax_with_cross_entropy (#39409)

上级 8d87b3bc
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Showing with 307 addition and 154 deletion
paddle/fluid/operators/math/cross_entropy.cc
浏览文件 @ eaa3fd45
......@@ -30,59 +30,90 @@ template <typename T, int MajorType = Eigen::RowMajor,
using EigenMatrix = framework::EigenMatrix<T, MajorType, IndexType>;
template <typename T>
class CrossEntropyFunctor<platform::CPUDeviceContext, T> {
public:
void operator()(const platform::CPUDeviceContext& ctx, framework::Tensor* out,
struct HardLabelCrossEntropyCPUFunctorImpl {
HardLabelCrossEntropyCPUFunctorImpl(framework::Tensor* out,
const framework::Tensor* prob,
const framework::Tensor* labels, const bool softLabel,
const int ignore_index, const int axis_dim) {
const int batch_size = prob->dims()[0];
const int num_classes = prob->dims()[1];
const int num_remain = num_classes / axis_dim;
const framework::Tensor* labels,
const int ignore_index,
const int axis_dim)
: out_(out),
prob_(prob),
labels_(labels),
ignore_index_(ignore_index),
axis_dim_(axis_dim) {}
Eigen::DSizes<int, 3> batch_axis_remain(batch_size, axis_dim, num_remain);
template <typename U>
void apply() const {
const int batch_size = prob_->dims()[0];
const int num_classes = prob_->dims()[1];
const int num_remain = num_classes / axis_dim_;
if (softLabel) {
auto in = EigenMatrix<T>::From(*prob);
auto lbl = EigenMatrix<T>::From(*labels);
auto loss = EigenMatrix<T>::From(*out);
const T* prob_data = prob_->template data<T>();
T* loss_data = out_->template data<T>();
loss.device(*ctx.eigen_device()) =
-((lbl * in.log().unaryExpr(math::TolerableValue<T>()))
.reshape(batch_axis_remain)
.sum(Eigen::DSizes<int, 1>(1)));
} else {
const T* prob_data = prob->data<T>();
T* loss_data = out->data<T>();
const int64_t* label_data = labels->data<int64_t>();
const auto* label_data = labels_->template data<U>();
for (int i = 0; i < batch_size; ++i) {
for (int j = 0; j < num_remain; j++) {
int lbl = label_data[i * num_remain + j];
if (lbl != ignore_index) {
int lbl = static_cast<int>(label_data[i * num_remain + j]);
if (lbl != ignore_index_) {
PADDLE_ENFORCE_GE(lbl, 0,
platform::errors::OutOfRange(
"label value should >= 0 when label "
"value(%f) not equal to ignore_index(%f)",
lbl, ignore_index));
lbl, ignore_index_));
PADDLE_ENFORCE_LT(
lbl, axis_dim,
lbl, axis_dim_,
platform::errors::OutOfRange(
"label value should less than the shape of axis dimension "
"when label value(%f) not equal to ignore_index(%f), But "
"received label value as %ld and shape of axis dimension "
"is %d",
lbl, ignore_index, lbl, axis_dim));
lbl, ignore_index_, lbl, axis_dim_));
}
int index = i * num_classes + lbl * num_remain + j;
int loss_idx = i * num_remain + j;
loss_data[loss_idx] =
lbl == ignore_index
lbl == ignore_index_
? 0
: -math::TolerableValue<T>()(std::log(prob_data[index]));
}
}
}
private:
framework::Tensor* out_;
const framework::Tensor* prob_;
const framework::Tensor* labels_;
const int ignore_index_;
const int axis_dim_;
};
template <typename T>
class CrossEntropyFunctor<platform::CPUDeviceContext, T> {
public:
void operator()(const platform::CPUDeviceContext& ctx, framework::Tensor* out,
const framework::Tensor* prob,
const framework::Tensor* labels, const bool softLabel,
const int ignore_index, const int axis_dim) {
if (softLabel) {
const int batch_size = prob->dims()[0];
const int num_classes = prob->dims()[1];
const int num_remain = num_classes / axis_dim;
Eigen::DSizes<int, 3> batch_axis_remain(batch_size, axis_dim, num_remain);
auto in = EigenMatrix<T>::From(*prob);
auto lbl = EigenMatrix<T>::From(*labels);
auto loss = EigenMatrix<T>::From(*out);
loss.device(*ctx.eigen_device()) =
-((lbl * in.log().unaryExpr(math::TolerableValue<T>()))
.reshape(batch_axis_remain)
.sum(Eigen::DSizes<int, 1>(1)));
} else {
HardLabelCrossEntropyCPUFunctorImpl<T> functor_impl(
out, prob, labels, ignore_index, axis_dim);
framework::VisitIntDataType(labels->type(), functor_impl);
}
}
};
......
paddle/fluid/operators/math/cross_entropy.cu
浏览文件 @ eaa3fd45
......@@ -21,18 +21,19 @@ namespace paddle {
namespace operators {
namespace math {
template <typename T>
__global__ void CrossEntropyKernel(T* Y, const T* X, const int64_t* label,
template <typename T, typename LabelT>
__global__ void CrossEntropyKernel(T* Y, const T* X, const LabelT* label,
const int N, const int D,
const int ignore_index) {
CUDA_KERNEL_LOOP(i, N) {
PADDLE_ENFORCE(label[i] >= 0 && label[i] < D || label[i] == ignore_index,
auto lbl = static_cast<int64_t>(label[i]);
PADDLE_ENFORCE(lbl >= 0 && lbl < D || lbl == ignore_index,
"The value of label[%d] expected >= 0 and < %ld, or == %ld, "
"but got %ld. Please check input value.",
i, D, ignore_index, label[i]);
Y[i] = ignore_index == label[i]
i, D, ignore_index, lbl);
Y[i] = ignore_index == lbl
? static_cast<T>(0)
: -math::TolerableValue<T>()(real_log(X[i * D + label[i]]));
: -math::TolerableValue<T>()(real_log(X[i * D + lbl]));
}
}
......@@ -54,6 +55,43 @@ __global__ void SoftCrossEntropyKernel(T* Y, const T* X, const T* label,
}
}
template <typename T>
struct HardLabelCrossEntropyCUDAFunctorImpl {
public:
HardLabelCrossEntropyCUDAFunctorImpl(T* loss_data, const T* prob_data,
const void* label_data,
const int batch_size,
const int class_num,
const int ignore_index,
const int block_size, gpuStream_t stream)
: loss_data_(loss_data),
prob_data_(prob_data),
label_data_(label_data),
batch_size_(batch_size),
class_num_(class_num),
ignore_index_(ignore_index),
block_size_(block_size),
stream_(stream) {}
template <typename U>
void apply() const {
int grid_size = (batch_size_ + block_size_ - 1) / block_size_;
CrossEntropyKernel<T, U><<<grid_size, block_size_, 0, stream_>>>(
loss_data_, prob_data_, static_cast<const U*>(label_data_), batch_size_,
class_num_, ignore_index_);
}
private:
T* loss_data_;
const T* prob_data_;
const void* label_data_;
const int batch_size_;
const int class_num_;
const int ignore_index_;
const int block_size_;
gpuStream_t stream_;
};
template <typename T>
class CrossEntropyFunctor<platform::CUDADeviceContext, T> {
public:
......@@ -81,12 +119,10 @@ class CrossEntropyFunctor<platform::CUDADeviceContext, T> {
SoftCrossEntropyKernel<T><<<batch_size, block, 0, ctx.stream()>>>(
loss_data, prob_data, label_data, class_num);
} else {
const int64_t* label_data = labels->data<int64_t>();
int block = kMaxBlockDim;
int grid = (batch_size + block - 1) / block;
CrossEntropyKernel<T><<<grid, block, 0, ctx.stream()>>>(
loss_data, prob_data, label_data, batch_size, class_num,
ignore_index);
HardLabelCrossEntropyCUDAFunctorImpl<T> functor(
loss_data, prob_data, labels->data(), batch_size, class_num,
ignore_index, kMaxBlockDim, ctx.stream());
framework::VisitDataType(labels->type(), functor);
}
}
};
......
paddle/fluid/operators/softmax_with_cross_entropy_op.cu
浏览文件 @ eaa3fd45
......@@ -59,9 +59,9 @@ enum class SoftmaxMode { kSoftmax, kLogSoftmax, kCrossEntropy };
/*
Hard label cross entropy.
*/
template <typename T, bool IgnoreIndex>
template <typename T, typename LabelT, bool IgnoreIndex>
__global__ void CrossEntropyHardLabel(T* loss, const T* softmax,
const int64_t* labels, const int n,
const LabelT* labels, const int n,
const int dim, const int d,
const int ignore_idx) {
int64_t ids = blockIdx.x * blockDim.x + threadIdx.x;
......@@ -70,13 +70,14 @@ __global__ void CrossEntropyHardLabel(T* loss, const T* softmax,
// thread ids compute loss[ids] using softmax[idx]
if (ids < n * d) {
if (labels[ids] < 0) { // label is negative
auto lbl = static_cast<int64_t>(labels[ids]);
if (lbl < 0) { // label is negative
loss[ids] = static_cast<T>(0.0);
} else { // label is positive of zero
int64_t idx = idx_n * dim * d + labels[ids] * d + idx_d;
int64_t idx = idx_n * dim * d + lbl * d + idx_d;
if (IgnoreIndex == true) {
// IgnoreIndex is true
if (labels[ids] == ignore_idx) {
if (lbl == ignore_idx) {
loss[ids] = static_cast<T>(0.0);
} else {
loss[ids] = -Log(softmax[idx]);
......@@ -94,9 +95,9 @@ __global__ void CrossEntropyHardLabel(T* loss, const T* softmax,
Input: log softmax
Output: loss and exp(input)
*/
template <typename T, bool IgnoreIndex>
template <typename T, typename LabelT, bool IgnoreIndex>
__global__ void CrossEntropyExpHardLabel(T* loss, T* softmax,
const int64_t* labels, const int n,
const LabelT* labels, const int n,
const int dim, const int d,
const int ignore_idx) {
int64_t idx = blockIdx.x * blockDim.x + threadIdx.x;
......@@ -106,10 +107,11 @@ __global__ void CrossEntropyExpHardLabel(T* loss, T* softmax,
int64_t ids = idx_n * d + idx_d;
if (idx < n * dim * d) {
auto lbl = static_cast<int64_t>(labels[ids]);
if (IgnoreIndex == true) {
// IgnoreIndex is true
if (idx_dim == labels[ids]) {
if (labels[ids] == ignore_idx) {
if (idx_dim == lbl) {
if (lbl == ignore_idx) {
loss[ids] = static_cast<T>(0.0);
} else {
loss[ids] = -softmax[idx];
......@@ -117,8 +119,8 @@ __global__ void CrossEntropyExpHardLabel(T* loss, T* softmax,
}
} else {
// IgnoreIndex is false
if (labels[ids] >= 0 && labels[ids] < dim) {
if (labels[ids] == idx_dim) {
if (lbl >= 0 && lbl < dim) {
if (lbl == idx_dim) {
loss[ids] = -softmax[idx];
}
} else {
......@@ -151,10 +153,10 @@ __global__ void CrossEntropyExpHardLabel(T* loss, T* softmax,
For reduction max (sum), firstly compute max (sum) to one warp, then use
shuffle api to compute max (sum) in one warp.
*/
template <typename T, typename VecT, typename AccT, int Log2Elements,
SoftmaxMode mode, bool IgnoreIndex>
template <typename T, typename LabelT, typename VecT, typename AccT,
int Log2Elements, SoftmaxMode mode, bool IgnoreIndex>
__global__ void WarpSoftmaxForward(T* loss, T* softmax, const T* src,
const int64_t* label, const int batch_size,
const LabelT* label, const int batch_size,
const int stride, const int element_count,
const int ignore_index) {
constexpr int kDimCeil = 1 << Log2Elements;
......@@ -299,10 +301,11 @@ __global__ void WarpSoftmaxForward(T* loss, T* softmax, const T* src,
softmax[(first_batch + i) * stride + idx] = std::exp(logsoftmax);
// label
int loss_idx = (threadIdx.x + it * kWarpSize) * kVSize;
auto lbl = static_cast<int64_t>(label[first_batch + i]);
if (IgnoreIndex == true) {
// IgnoreIndex is true
if (label[first_batch + i] == loss_idx) {
if (label[first_batch + i] != ignore_index) {
if (lbl == loss_idx) {
if (lbl != ignore_index) {
loss[first_batch + i] = -logsoftmax;
} else {
loss[first_batch + i] = static_cast<T>(0.0);
......@@ -310,9 +313,8 @@ __global__ void WarpSoftmaxForward(T* loss, T* softmax, const T* src,
}
} else {
// IgnoreIndex is false
if (label[first_batch + i] >= 0 &&
label[first_batch + i] < element_count) {
if (label[first_batch + i] == loss_idx) {
if (lbl >= 0 && lbl < element_count) {
if (lbl == loss_idx) {
loss[first_batch + i] = -logsoftmax;
}
} else {
......@@ -342,17 +344,16 @@ __global__ void WarpSoftmaxForward(T* loss, T* softmax, const T* src,
tmpptr[s] = std::exp(logsoftmax);
// label
int loss_idx = (threadIdx.x + it * kWarpSize) * kVSize + s;
auto lbl = static_cast<int64_t>(label[first_batch + i]);
if (IgnoreIndex == true) {
// IgnoreIndex is true
if (label[first_batch + i] == loss_idx &&
label[first_batch + i] != ignore_index) {
if (lbl == loss_idx && lbl != ignore_index) {
loss[first_batch + i] = -logsoftmax;
}
} else {
// IgnoreIndex is false
if (label[first_batch + i] >= 0 &&
label[first_batch + i] < element_count) {
if (label[first_batch + i] == loss_idx) {
if (lbl >= 0 && lbl < element_count) {
if (lbl == loss_idx) {
loss[first_batch + i] = -logsoftmax;
}
} else {
......@@ -373,9 +374,9 @@ __global__ void WarpSoftmaxForward(T* loss, T* softmax, const T* src,
}
}
#define SOFTMAX_WARP_FORWARD_CASE(Log2Elements, VecT, AccT) \
#define SOFTMAX_WARP_FORWARD_CASE(Log2Elements, LabelT, VecT, AccT) \
case Log2Elements: \
WarpSoftmaxForward<T, VecT, AccT, Log2Elements, mode, \
WarpSoftmaxForward<T, LabelT, VecT, AccT, Log2Elements, mode, \
IgnoreIndex><<<blocks, threads, 0, stream>>>( \
loss, softmax, src, label, batch_size, stride, element_count, \
ignore_index); \
......@@ -384,9 +385,9 @@ __global__ void WarpSoftmaxForward(T* loss, T* softmax, const T* src,
/*
Wrapper of softmax with cross entropy forward hard label.
*/
template <typename T, SoftmaxMode mode, bool IgnoreIndex>
template <typename T, typename LabelT, SoftmaxMode mode, bool IgnoreIndex>
void SwitchWarpSoftmaxForward(T* loss, T* softmax, const T* src,
const int64_t* label, const int batch_size,
const LabelT* label, const int batch_size,
const int stride, const int element_count,
const int ignore_index, gpuStream_t stream) {
using AccT = typename details::MPTypeTrait<T>::Type;
......@@ -403,16 +404,16 @@ void SwitchWarpSoftmaxForward(T* loss, T* softmax, const T* src,
dim3 threads(kWarpSize, warps_per_block, 1);
switch (log2_elements) {
SOFTMAX_WARP_FORWARD_CASE(0, T, AccT);
SOFTMAX_WARP_FORWARD_CASE(1, T, AccT);
SOFTMAX_WARP_FORWARD_CASE(2, T, AccT);
SOFTMAX_WARP_FORWARD_CASE(3, T, AccT);
SOFTMAX_WARP_FORWARD_CASE(4, T, AccT);
SOFTMAX_WARP_FORWARD_CASE(5, T, AccT);
SOFTMAX_WARP_FORWARD_CASE(6, T, AccT);
SOFTMAX_WARP_FORWARD_CASE(7, T, AccT);
SOFTMAX_WARP_FORWARD_CASE(8, T, AccT);
SOFTMAX_WARP_FORWARD_CASE(9, T, AccT);
SOFTMAX_WARP_FORWARD_CASE(0, LabelT, T, AccT);
SOFTMAX_WARP_FORWARD_CASE(1, LabelT, T, AccT);
SOFTMAX_WARP_FORWARD_CASE(2, LabelT, T, AccT);
SOFTMAX_WARP_FORWARD_CASE(3, LabelT, T, AccT);
SOFTMAX_WARP_FORWARD_CASE(4, LabelT, T, AccT);
SOFTMAX_WARP_FORWARD_CASE(5, LabelT, T, AccT);
SOFTMAX_WARP_FORWARD_CASE(6, LabelT, T, AccT);
SOFTMAX_WARP_FORWARD_CASE(7, LabelT, T, AccT);
SOFTMAX_WARP_FORWARD_CASE(8, LabelT, T, AccT);
SOFTMAX_WARP_FORWARD_CASE(9, LabelT, T, AccT);
default:
break;
}
......@@ -423,16 +424,16 @@ void SwitchWarpSoftmaxForward(T* loss, T* softmax, const T* src,
- SwitchWarpSoftmaxForward for small size
- cudnn function for large size
*/
template <typename T, bool IgnoreIndex>
template <typename T, typename LabelT, bool IgnoreIndex>
static void SoftmaxWithCrossEntropyHardLabel(
const platform::CUDADeviceContext& ctx, int rank, int axis,
const T* logits_data, const int64_t* labels_data, T* loss_data,
const T* logits_data, const LabelT* labels_data, T* loss_data,
T* softmax_data, int N, int dim, int D, const int ignore_index) {
auto stream = ctx.stream();
constexpr int max_dim = 320;
if (D == 1 && dim <= max_dim) { // small size
const SoftmaxMode mode = SoftmaxMode::kCrossEntropy;
SwitchWarpSoftmaxForward<T, mode, IgnoreIndex>(
SwitchWarpSoftmaxForward<T, LabelT, mode, IgnoreIndex>(
loss_data, softmax_data, logits_data, labels_data, N, dim, dim,
ignore_index, stream);
} else {
......@@ -465,7 +466,8 @@ static void SoftmaxWithCrossEntropyHardLabel(
int threads = 128;
int blocks = (N * dim * D + threads - 1) / threads;
// compute cross entropy, input is log softmax
CrossEntropyExpHardLabel<T, IgnoreIndex><<<blocks, threads, 0, stream>>>(
CrossEntropyExpHardLabel<T, LabelT,
IgnoreIndex><<<blocks, threads, 0, stream>>>(
loss_data, softmax_data, labels_data, N, dim, D, ignore_index);
}
}
......@@ -473,9 +475,9 @@ static void SoftmaxWithCrossEntropyHardLabel(
/*
Wrapper of softmax with cross entropy grad hard label.
*/
template <typename T>
template <typename T, typename LabelT>
__global__ void SoftmaxWithCrossEntropyGradHardLabel(
T* logits_grad, const T* loss_grad, const int64_t* labels, const int64_t n,
T* logits_grad, const T* loss_grad, const LabelT* labels, const int64_t n,
const int64_t dim, const int64_t d, const int ignore_index) {
int64_t idx = blockIdx.x * blockDim.x + threadIdx.x;
int64_t idx_n = idx / (d * dim);
......@@ -484,9 +486,10 @@ __global__ void SoftmaxWithCrossEntropyGradHardLabel(
int64_t ids = idx_n * d + idx_d;
if (idx < n * dim * d) {
if (labels[ids] == ignore_index) {
auto lbl = static_cast<int64_t>(labels[ids]);
if (lbl == ignore_index) {
logits_grad[idx] = static_cast<T>(0.0);
} else if (labels[ids] == idx_dim) {
} else if (lbl == idx_dim) {
logits_grad[idx] =
(logits_grad[idx] - static_cast<T>(1.0)) * loss_grad[ids];
} else {
......@@ -887,16 +890,16 @@ __global__ void SoftLabelCrossEntropyGradientKernel(T* logit_grad,
}
}
template <typename T>
template <typename T, typename LabelT>
__global__ void HardLabelCrossEntropyGradientKernel(T* logit_grad,
const int64_t* labels,
const LabelT* labels,
const int n, const int d,
const int remain,
const int ignore_index) {
CUDA_KERNEL_LOOP(index, n * remain) {
int idx_n = index / remain;
int idx_remain = index % remain;
int tmp = labels[index];
int tmp = static_cast<int>(labels[index]);
int idx = idx_n * d + tmp * remain + idx_remain;
if (ignore_index != tmp) {
logit_grad[idx] = -static_cast<T>(1.) / logit_grad[idx];
......@@ -904,18 +907,19 @@ __global__ void HardLabelCrossEntropyGradientKernel(T* logit_grad,
}
}
template <typename T>
template <typename T, typename LabelT>
__global__ void ScaleCrossEntropyGradient(T* logit_grad, const T* loss_grad,
const int num, const int d,
const int remain,
const int64_t* labels,
const LabelT* labels,
const int ignore_index) {
CUDA_KERNEL_LOOP(index, num) {
int idx_n = index / d;
int idx_remain = index % remain;
int idx_lbl = idx_n * remain + idx_remain;
int k = (index % d) / remain;
if (labels[idx_lbl] == ignore_index || labels[idx_lbl] != k) {
auto lbl = static_cast<int64_t>(labels[idx_lbl]);
if (lbl == ignore_index || lbl != k) {
logit_grad[index] = static_cast<T>(0.);
} else {
logit_grad[index] *= loss_grad[idx_lbl];
......@@ -927,6 +931,12 @@ template <typename T>
class SoftmaxWithCrossEntropyCUDAKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
RunSoftmaxWithCrossEntropyFunctor<T>(context, *this);
}
template <typename LabelT>
static void Apply(const framework::ExecutionContext& context,
const framework::Tensor& labels, const bool soft_label) {
PADDLE_ENFORCE_EQ(
platform::is_gpu_place(context.GetPlace()), true,
platform::errors::Unavailable("softmax_with_cross_entropy operator's "
......@@ -936,7 +946,6 @@ class SoftmaxWithCrossEntropyCUDAKernel : public framework::OpKernel<T> {
// do not with softmax op, and input is softmax
if (!use_softmax) {
const Tensor* softmax = context.Input<Tensor>("Logits");
const Tensor* labels = context.Input<Tensor>("Label");
Tensor* softmax_out = context.Output<Tensor>("Softmax");
Tensor* loss = context.Output<Tensor>("Loss");
......@@ -947,8 +956,9 @@ class SoftmaxWithCrossEntropyCUDAKernel : public framework::OpKernel<T> {
const int n = SizeToAxis(axis, softmax->dims());
const int d = SizeFromAxis(axis, softmax->dims());
auto* softmax_out_data = softmax_out->mutable_data<T>(context.GetPlace());
auto* loss_data = loss->mutable_data<T>(context.GetPlace());
auto* softmax_out_data =
softmax_out->template mutable_data<T>(context.GetPlace());
auto* loss_data = loss->template mutable_data<T>(context.GetPlace());
math::SetConstant<platform::CUDADeviceContext, T> set_constant;
set_constant(context.cuda_device_context(), loss, static_cast<T>(0));
......@@ -958,12 +968,11 @@ class SoftmaxWithCrossEntropyCUDAKernel : public framework::OpKernel<T> {
return;
}
auto soft_label = context.Attr<bool>("soft_label");
auto ignore_index = context.Attr<int>("ignore_index");
Tensor softmax_2d, labels_2d, loss_2d, softmax_out_2d;
softmax_2d.ShareDataWith(*softmax).Resize({n, d});
labels_2d.ShareDataWith(*labels).Resize({n, labels->numel() / n});
labels_2d.ShareDataWith(labels).Resize({n, labels.numel() / n});
loss_2d.ShareDataWith(*loss).Resize({n, 1});
softmax_out_2d.ShareDataWith(*softmax_out).Resize({n, d});
......@@ -977,8 +986,8 @@ class SoftmaxWithCrossEntropyCUDAKernel : public framework::OpKernel<T> {
// if axis is not the last, we need a new impliment
if (soft_label) {
auto* logits_data = softmax->data<T>();
auto* labels_data = labels->data<T>();
auto* logits_data = softmax->template data<T>();
auto* labels_data = labels.template data<T>();
const int kDimLog2 = static_cast<int>(Log2Ceil(axis_dim));
const int kDimCeil = 1 << kDimLog2;
......@@ -996,17 +1005,17 @@ class SoftmaxWithCrossEntropyCUDAKernel : public framework::OpKernel<T> {
loss_data, NULL, logits_data, labels_data, n, axis_dim,
d / axis_dim, kDimLog2);
} else { // HardLabel
auto* logits_data = softmax->data<T>();
auto* labels_data = labels->data<int64_t>();
auto* logits_data = softmax->template data<T>();
auto* labels_data = labels.template data<LabelT>();
int threads = 128;
int blocks = (n * d / axis_dim + threads - 1) / threads;
if (ignore_index >= 0 && ignore_index < axis_dim) {
CrossEntropyHardLabel<T, true><<<
CrossEntropyHardLabel<T, LabelT, true><<<
blocks, threads, 0, context.cuda_device_context().stream()>>>(
loss_data, logits_data, labels_data, n, axis_dim, d / axis_dim,
ignore_index);
} else {
CrossEntropyHardLabel<T, false><<<
CrossEntropyHardLabel<T, LabelT, false><<<
blocks, threads, 0, context.cuda_device_context().stream()>>>(
loss_data, logits_data, labels_data, n, axis_dim, d / axis_dim,
ignore_index);
......@@ -1022,7 +1031,6 @@ class SoftmaxWithCrossEntropyCUDAKernel : public framework::OpKernel<T> {
}
const Tensor* logits = context.Input<Tensor>("Logits");
const Tensor* labels = context.Input<Tensor>("Label");
Tensor* softmax = context.Output<Tensor>("Softmax");
Tensor* loss = context.Output<Tensor>("Loss");
......@@ -1033,8 +1041,8 @@ class SoftmaxWithCrossEntropyCUDAKernel : public framework::OpKernel<T> {
const int64_t n = SizeToAxis(axis, logits->dims());
const int64_t d = SizeFromAxis(axis, logits->dims());
auto* softmax_data = softmax->mutable_data<T>(context.GetPlace());
auto* loss_data = loss->mutable_data<T>(context.GetPlace());
auto* softmax_data = softmax->template mutable_data<T>(context.GetPlace());
auto* loss_data = loss->template mutable_data<T>(context.GetPlace());
if (axis_dim == 1) {
math::SetConstant<platform::CUDADeviceContext, T> set_constant;
......@@ -1043,12 +1051,11 @@ class SoftmaxWithCrossEntropyCUDAKernel : public framework::OpKernel<T> {
return;
}
auto soft_label = context.Attr<bool>("soft_label");
auto ignore_index = context.Attr<int>("ignore_index");
if (soft_label) {
auto* logits_data = logits->data<T>();
auto* labels_data = labels->data<T>();
auto* logits_data = logits->template data<T>();
auto* labels_data = labels.template data<T>();
SoftmaxWithCrossEntropySoftLabel<T>(
context.cuda_device_context(), rank, axis, logits_data, labels_data,
softmax_data, loss_data, n, axis_dim, d / axis_dim);
......@@ -1058,7 +1065,7 @@ class SoftmaxWithCrossEntropyCUDAKernel : public framework::OpKernel<T> {
Tensor logits_2d, softmax_2d, labels_2d, loss_2d;
logits_2d.ShareDataWith(*logits).Resize({n, d});
softmax_2d.ShareDataWith(*softmax).Resize({n, d});
labels_2d.ShareDataWith(*labels).Resize({n, labels->numel() / n});
labels_2d.ShareDataWith(labels).Resize({n, labels.numel() / n});
loss_2d.ShareDataWith(*loss).Resize({n, 1});
math::SoftmaxCUDNNFunctor<T>()(context.cuda_device_context(),
&logits_2d, &softmax_2d);
......@@ -1066,15 +1073,15 @@ class SoftmaxWithCrossEntropyCUDAKernel : public framework::OpKernel<T> {
context.cuda_device_context(), &loss_2d, &softmax_2d, &labels_2d,
false, ignore_index, axis_dim);
} else {
auto* logits_data = logits->data<T>();
auto* labels_data = labels->data<int64_t>();
auto* logits_data = logits->template data<T>();
auto* labels_data = labels.template data<LabelT>();
if (ignore_index >= 0 && ignore_index < axis_dim) {
SoftmaxWithCrossEntropyHardLabel<T, true>(
SoftmaxWithCrossEntropyHardLabel<T, LabelT, true>(
context.cuda_device_context(), rank, axis, logits_data,
labels_data, loss_data, softmax_data, n, axis_dim, d / axis_dim,
ignore_index);
} else {
SoftmaxWithCrossEntropyHardLabel<T, false>(
SoftmaxWithCrossEntropyHardLabel<T, LabelT, false>(
context.cuda_device_context(), rank, axis, logits_data,
labels_data, loss_data, softmax_data, n, axis_dim, d / axis_dim,
ignore_index);
......@@ -1088,13 +1095,19 @@ template <typename T>
class SoftmaxWithCrossEntropyGradCUDAKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
RunSoftmaxWithCrossEntropyFunctor<T>(context, *this);
}
template <typename LabelT>
static void Apply(const framework::ExecutionContext& context,
const framework::Tensor& labels, const bool soft_label) {
PADDLE_ENFORCE_EQ(
platform::is_gpu_place(context.GetPlace()), true,
platform::errors::Unavailable("softmax_with_cross_entropy operator's "
"CUDA kernel only runs on GPU device."));
const Tensor* labels = context.Input<Tensor>("Label");
const T* loss_grad_data =
context.Input<Tensor>(framework::GradVarName("Loss"))->data<T>();
context.Input<Tensor>(framework::GradVarName("Loss"))
->template data<T>();
Tensor* logit_grad =
context.Output<Tensor>(framework::GradVarName("Logits"));
const Tensor* softmax = context.Input<Tensor>("Softmax");
......@@ -1102,7 +1115,7 @@ class SoftmaxWithCrossEntropyGradCUDAKernel : public framework::OpKernel<T> {
framework::TensorCopy(*softmax, context.GetPlace(),
context.device_context(), logit_grad);
}
T* logit_grad_data = logit_grad->data<T>();
T* logit_grad_data = logit_grad->template data<T>();
const int rank = logit_grad->dims().size();
const int axis = CanonicalAxis(context.Attr<int>("axis"), rank);
......@@ -1123,21 +1136,22 @@ class SoftmaxWithCrossEntropyGradCUDAKernel : public framework::OpKernel<T> {
// do not with softmax op, and input is softmax
if (!use_softmax) {
if (context.Attr<bool>("soft_label")) {
if (soft_label) {
int grid = (n * d + block - 1) / block;
const T* label_data = labels->data<T>();
const T* label_data = labels.template data<T>();
SoftLabelCrossEntropyGradientKernel<T><<<grid, block, 0, stream>>>(
logit_grad_data, loss_grad_data, label_data, n, d, remain);
} else {
Tensor logits_grad_2d;
logits_grad_2d.ShareDataWith(*logit_grad).Resize({n, d});
int grid = (n * remain + block - 1) / block;
const int64_t* label_data = labels->data<int64_t>();
HardLabelCrossEntropyGradientKernel<T><<<grid, block, 0, stream>>>(
const auto* label_data = labels.template data<LabelT>();
HardLabelCrossEntropyGradientKernel<T,
LabelT><<<grid, block, 0, stream>>>(
logit_grad_data, label_data, n, d, remain, ignore_index);
int num = n * d;
grid = (num + block - 1) / block;
ScaleCrossEntropyGradient<T><<<grid, block, 0, stream>>>(
ScaleCrossEntropyGradient<T, LabelT><<<grid, block, 0, stream>>>(
logit_grad_data, loss_grad_data, num, d, remain, label_data,
ignore_index);
}
......@@ -1147,13 +1161,13 @@ class SoftmaxWithCrossEntropyGradCUDAKernel : public framework::OpKernel<T> {
// with softmax, continue
if (context.Attr<bool>("soft_label")) {
if (soft_label) {
int64_t grid = (n * d + block - 1) / block;
const T* label_data = labels->data<T>();
const T* label_data = labels.template data<T>();
SoftCrossEntropyGradientKernel<T><<<grid, block, 0, stream>>>(
logit_grad_data, loss_grad_data, label_data, n, d, remain);
} else {
const int64_t* label_data = labels->data<int64_t>();
const auto* label_data = labels.template data<LabelT>();
int grid = (n * d + block - 1) / block;
SoftmaxWithCrossEntropyGradHardLabel<T><<<grid, block, 0, stream>>>(
logit_grad_data, loss_grad_data, label_data, n, d / remain, remain,
......
paddle/fluid/operators/softmax_with_cross_entropy_op.h
浏览文件 @ eaa3fd45
......@@ -24,6 +24,48 @@ namespace operators {
using Tensor = framework::Tensor;
template <typename T, typename Visitor>
struct SoftmaxWithCrossEntropyFunctor {
public:
SoftmaxWithCrossEntropyFunctor(const framework::ExecutionContext& context,
const framework::Tensor& labels,
const bool soft_label, const Visitor& visitor)
: context_(context),
labels_(labels),
soft_label_(soft_label),
visitor_(visitor) {}
template <typename U>
void apply() const {
visitor_.template Apply<U>(context_, labels_, soft_label_);
}
private:
const framework::ExecutionContext& context_;
const framework::Tensor& labels_;
const bool soft_label_;
const Visitor& visitor_;
};
template <typename T, typename Visitor>
static void RunSoftmaxWithCrossEntropyFunctor(
const framework::ExecutionContext& context, const Visitor& visitor) {
const auto* labels = context.Input<framework::Tensor>("Label");
const bool soft_label = context.Attr<bool>("soft_label");
SoftmaxWithCrossEntropyFunctor<T, Visitor> functor(context, *labels,
soft_label, visitor);
auto dtype = labels->type();
if (soft_label) {
PADDLE_ENFORCE_EQ(
dtype, framework::DataTypeTrait<T>::DataType(),
platform::errors::InvalidArgument("The Input(Label) should be with the "
"same data type as Input(Logits)."));
functor.template apply<T>();
} else {
framework::VisitIntDataType(dtype, functor);
}
}
template <typename T>
class SoftmaxWithCrossEntropyKernel : public framework::OpKernel<T> {
public:
......@@ -32,14 +74,14 @@ class SoftmaxWithCrossEntropyKernel : public framework::OpKernel<T> {
platform::is_cpu_place(context.GetPlace()), true,
platform::errors::Unimplemented("This kernel only runs on CPU."));
const bool use_softmax = context.Attr<bool>("use_softmax");
const Tensor* labels = context.Input<Tensor>("Label");
const bool soft_label = context.Attr<bool>("soft_label");
// do not with softmax op, and input is softmax
if (!use_softmax) {
const Tensor* softmax = context.Input<Tensor>("Logits");
const Tensor* labels = context.Input<Tensor>("Label");
Tensor* softmax_out = context.Output<Tensor>("Softmax");
Tensor* loss = context.Output<Tensor>("Loss");
const bool soft_label = context.Attr<bool>("soft_label");
const int rank = softmax->dims().size();
const int axis = CanonicalAxis(context.Attr<int>("axis"), rank);
int axis_dim = softmax->dims()[axis];
......@@ -86,10 +128,8 @@ class SoftmaxWithCrossEntropyKernel : public framework::OpKernel<T> {
}
const Tensor* logits = context.Input<Tensor>("Logits");
const Tensor* labels = context.Input<Tensor>("Label");
Tensor* softmax = context.Output<Tensor>("Softmax");
Tensor* loss = context.Output<Tensor>("Loss");
const bool soft_label = context.Attr<bool>("soft_label");
const int rank = logits->dims().size();
const int axis = CanonicalAxis(context.Attr<int>("axis"), rank);
......@@ -132,9 +172,14 @@ template <typename T>
class SoftmaxWithCrossEntropyGradKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
RunSoftmaxWithCrossEntropyFunctor<T>(context, *this);
}
template <typename LabelT>
static void Apply(const framework::ExecutionContext& context,
const framework::Tensor& labels, const bool soft_label) {
const Tensor* out_grad =
context.Input<Tensor>(framework::GradVarName("Loss"));
const Tensor* labels = context.Input<Tensor>("Label");
Tensor* logit_grad =
context.Output<Tensor>(framework::GradVarName("Logits"));
const Tensor* softmax = context.Input<Tensor>("Softmax");
......@@ -143,7 +188,6 @@ class SoftmaxWithCrossEntropyGradKernel : public framework::OpKernel<T> {
framework::TensorCopy(*softmax, context.GetPlace(),
context.device_context(), logit_grad);
}
const bool soft_label = context.Attr<bool>("soft_label");
auto ignore_index = context.Attr<int>("ignore_index");
const int rank = logit_grad->dims().size();
......@@ -166,7 +210,7 @@ class SoftmaxWithCrossEntropyGradKernel : public framework::OpKernel<T> {
const int d = SizeFromAxis(axis, logit_grad->dims());
Tensor logit_grad_2d, labels_2d, out_grad_2d;
logit_grad_2d.ShareDataWith(*logit_grad).Resize({n, d});
labels_2d.ShareDataWith(*labels).Resize({n, labels->numel() / n});
labels_2d.ShareDataWith(labels).Resize({n, labels.numel() / n});
out_grad_2d.ShareDataWith(*out_grad).Resize({n, d / axis_dim});
auto out_grad_mat = framework::EigenMatrix<T>::From(out_grad_2d);
auto logit_grad_mat = framework::EigenMatrix<T>::From(logit_grad_2d);
......@@ -183,23 +227,24 @@ class SoftmaxWithCrossEntropyGradKernel : public framework::OpKernel<T> {
logit_grad_mat;
} else {
// use_softmax step2
const int64_t* label_data = labels->data<int64_t>();
T* logit_grad_data = logit_grad->data<T>();
const T* out_grad_data = out_grad->data<T>();
const auto* label_data = labels.template data<LabelT>();
T* logit_grad_data = logit_grad->template data<T>();
const T* out_grad_data = out_grad->template data<T>();
const int remain = d / axis_dim;
for (int i = 0; i < n; ++i) { // for each sample_1_dim
for (int j = 0; j < remain; j++) { // for each sample_other_dims
int idx = i * remain + j; // this sample's label_idx. for 1d case,
// remain=1 and j=0, so, idx = i
if (label_data[idx] == ignore_index) {
auto lbl = static_cast<int64_t>(label_data[idx]);
if (lbl == ignore_index) {
for (int k = 0; k < axis_dim; ++k) { // for each class id's label
logit_grad_data[i * d + k * remain + j] = 0;
}
} else {
// only for this sample's label_idx, the label is 1, others is 0,
// so, only compute this label_idx's class
logit_grad_data[i * d + label_data[idx] * remain + j] =
(-1 / logit_grad_data[i * d + label_data[idx] * remain + j]) *
logit_grad_data[i * d + lbl * remain + j] =
(-1 / logit_grad_data[i * d + lbl * remain + j]) *
out_grad_data[idx];
for (int k = 0; k < axis_dim; ++k) { // for each class id's label
if (k !=
......@@ -233,15 +278,16 @@ class SoftmaxWithCrossEntropyGradKernel : public framework::OpKernel<T> {
logit_grad_mat * // element_wise multiply
out_grad_mat.broadcast(Eigen::DSizes<int, 2>(1, axis_dim));
const int64_t* label_data = labels->data<int64_t>();
T* logit_grad_data = logit_grad->data<T>();
const T* out_grad_data = out_grad->data<T>();
const auto* label_data = labels.template data<LabelT>();
T* logit_grad_data = logit_grad->template data<T>();
const T* out_grad_data = out_grad->template data<T>();
const int remain = d / axis_dim;
for (int i = 0; i < n; ++i) { // for each sample_1_dim
for (int j = 0; j < remain; j++) { // for each sample_other_dims
int idx = i * remain + j; // this sample's label_idx. for 1d case,
// remain=1 and j=0, so, idx = i
if (label_data[idx] == ignore_index) {
auto lbl = static_cast<int64_t>(label_data[idx]);
if (lbl == ignore_index) {
for (int k = 0; k < axis_dim; ++k) { // for each class id's label
logit_grad_data[i * d + k * remain + j] = 0;
}
......@@ -258,8 +304,7 @@ class SoftmaxWithCrossEntropyGradKernel : public framework::OpKernel<T> {
// out_grad_data[idx]
// means: dy/dp * dy= ( p - y ) * dy
logit_grad_data[i * d + label_data[idx] * remain + j] -=
out_grad_data[idx];
logit_grad_data[i * d + lbl * remain + j] -= out_grad_data[idx];
}
}
}
......
python/paddle/fluid/tests/unittests/test_softmax_with_cross_entropy_op.py
浏览文件 @ eaa3fd45
......@@ -16,6 +16,7 @@ from __future__ import print_function
import unittest
import numpy as np
import paddle
import paddle.fluid.core as core
from op_test import OpTest
......@@ -58,6 +59,9 @@ class TestSoftmaxWithCrossEntropyOp(OpTest):
self.shape = [41, 37]
self.use_softmax = True
def hard_label_dtype(self):
return "int64"
def setUp(self):
self.initParams()
......@@ -72,7 +76,8 @@ class TestSoftmaxWithCrossEntropyOp(OpTest):
else:
axis_dim = self.shape[self.axis]
self.shape[self.axis] = 1
labels = np.random.randint(0, axis_dim, self.shape, dtype="int64")
labels = np.random.randint(
0, axis_dim, self.shape, dtype=self.hard_label_dtype())
loss = cross_entropy(softmax, labels, self.soft_label, self.axis,
self.ignore_index)
......@@ -107,6 +112,26 @@ class TestSoftmaxWithCrossEntropyOp(OpTest):
self.check_grad(["Logits"], "Loss", numeric_grad_delta=0.001)
class TestSoftmaxWithCrossEntropyOpInt32(TestSoftmaxWithCrossEntropyOp):
def hard_label_dtype(self):
return "int32"
class TestSoftmaxWithCrossEntropyOpInt16(TestSoftmaxWithCrossEntropyOp):
def hard_label_dtype(self):
return "int16"
class TestSoftmaxWithCrossEntropyOpInt8(TestSoftmaxWithCrossEntropyOp):
def hard_label_dtype(self):
return "int8"
class TestSoftmaxWithCrossEntropyOpUInt8(TestSoftmaxWithCrossEntropyOp):
def hard_label_dtype(self):
return "uint8"
class TestSoftmaxWithCrossEntropyOp_NotWithSoftmax_SoftLabel_1D(
TestSoftmaxWithCrossEntropyOp):
def initParams(self):
......@@ -711,4 +736,5 @@ class TestSoftmaxWithCrossEntropyOpBoundary1(TestSoftmaxWithCrossEntropyOp):
if __name__ == "__main__":
paddle.enable_static()
unittest.main()
python/paddle/nn/functional/loss.py
浏览文件 @ eaa3fd45
......@@ -1783,7 +1783,8 @@ def cross_entropy(input,
fluid.data_feeder.check_variable_and_dtype(
input, 'input', ['float32', 'float64'], 'softmax_cross_entropy')
fluid.data_feeder.check_variable_and_dtype(
label, 'label', ['int32', 'int64', 'float32', 'float64'],
label, 'label',
['uint8', 'int8', 'int16', 'int32', 'int64', 'float32', 'float64'],
'softmax_cross_entropy')
attrs = {
'soft_label': soft_label,
......
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