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“5c18decf3c680f44121c77682f884be04791f30b”上不存在“develop/doc/design/refactor/parameter_server.html”
未验证 提交 33d1e565 编写于 作者: Y Yibing Liu 提交者： GitHub
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Enable seq_pool op to accept len 0 input (#17284) 

* Enable seq_pool op to accept len 0 input

test=develop

* Update sequence_pool's api

test=develop

* Add more unittest cases for seq_pool op

test=develop

* Remove legacy comments

test=develop

* Don't use template in op maker

test=develop
上级 90ebce9e
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Showing with 357 addition and 157 deletion
paddle/fluid/API.spec
浏览文件 @ 33d1e565
...@@ -87,7 +87,7 @@ paddle.fluid.layers.chunk_eval (ArgSpec(args=['input', 'label', 'chunk_scheme', ...@@ -87,7 +87,7 @@ paddle.fluid.layers.chunk_eval (ArgSpec(args=['input', 'label', 'chunk_scheme',
paddle.fluid.layers.sequence_conv (ArgSpec(args=['input', 'num_filters', 'filter_size', 'filter_stride', 'padding', 'bias_attr', 'param_attr', 'act', 'name'], varargs=None, keywords=None, defaults=(3, 1, None, None, None, None, None)), ('document', '3d8e8f3e0e1cf520156be37605e83ccd')) paddle.fluid.layers.sequence_conv (ArgSpec(args=['input', 'num_filters', 'filter_size', 'filter_stride', 'padding', 'bias_attr', 'param_attr', 'act', 'name'], varargs=None, keywords=None, defaults=(3, 1, None, None, None, None, None)), ('document', '3d8e8f3e0e1cf520156be37605e83ccd'))
paddle.fluid.layers.conv2d (ArgSpec(args=['input', 'num_filters', 'filter_size', 'stride', 'padding', 'dilation', 'groups', 'param_attr', 'bias_attr', 'use_cudnn', 'act', 'name'], varargs=None, keywords=None, defaults=(1, 0, 1, None, None, None, True, None, None)), ('document', '8ca6121acd6d23cd8806a93f493c2e17')) paddle.fluid.layers.conv2d (ArgSpec(args=['input', 'num_filters', 'filter_size', 'stride', 'padding', 'dilation', 'groups', 'param_attr', 'bias_attr', 'use_cudnn', 'act', 'name'], varargs=None, keywords=None, defaults=(1, 0, 1, None, None, None, True, None, None)), ('document', '8ca6121acd6d23cd8806a93f493c2e17'))
paddle.fluid.layers.conv3d (ArgSpec(args=['input', 'num_filters', 'filter_size', 'stride', 'padding', 'dilation', 'groups', 'param_attr', 'bias_attr', 'use_cudnn', 'act', 'name'], varargs=None, keywords=None, defaults=(1, 0, 1, None, None, None, True, None, None)), ('document', '37042620f9bd3a2da6e5d3138b2f724b')) paddle.fluid.layers.conv3d (ArgSpec(args=['input', 'num_filters', 'filter_size', 'stride', 'padding', 'dilation', 'groups', 'param_attr', 'bias_attr', 'use_cudnn', 'act', 'name'], varargs=None, keywords=None, defaults=(1, 0, 1, None, None, None, True, None, None)), ('document', '37042620f9bd3a2da6e5d3138b2f724b'))
paddle.fluid.layers.sequence_pool (ArgSpec(args=['input', 'pool_type', 'is_test'], varargs=None, keywords=None, defaults=(False,)), ('document', 'a194fb80614023f543df3949fbd0d0b8')) paddle.fluid.layers.sequence_pool (ArgSpec(args=['input', 'pool_type', 'is_test', 'pad_value'], varargs=None, keywords=None, defaults=(False, 0.0)), ('document', 'e90a93251c52dc4e6fb34fb3991b3f82'))
paddle.fluid.layers.sequence_softmax (ArgSpec(args=['input', 'use_cudnn', 'name'], varargs=None, keywords=None, defaults=(False, None)), ('document', '19ef6f9cdd27feac8a1ae060f19c10b4')) paddle.fluid.layers.sequence_softmax (ArgSpec(args=['input', 'use_cudnn', 'name'], varargs=None, keywords=None, defaults=(False, None)), ('document', '19ef6f9cdd27feac8a1ae060f19c10b4'))
paddle.fluid.layers.softmax (ArgSpec(args=['input', 'use_cudnn', 'name', 'axis'], varargs=None, keywords=None, defaults=(False, None, -1)), ('document', 'cee673c79e3ff4582656a24e04f841e5')) paddle.fluid.layers.softmax (ArgSpec(args=['input', 'use_cudnn', 'name', 'axis'], varargs=None, keywords=None, defaults=(False, None, -1)), ('document', 'cee673c79e3ff4582656a24e04f841e5'))
paddle.fluid.layers.pool2d (ArgSpec(args=['input', 'pool_size', 'pool_type', 'pool_stride', 'pool_padding', 'global_pooling', 'use_cudnn', 'ceil_mode', 'name', 'exclusive'], varargs=None, keywords=None, defaults=(-1, 'max', 1, 0, False, True, False, None, True)), ('document', 'bbd84e855e660cd1084bb71a2fd0cdaa')) paddle.fluid.layers.pool2d (ArgSpec(args=['input', 'pool_size', 'pool_type', 'pool_stride', 'pool_padding', 'global_pooling', 'use_cudnn', 'ceil_mode', 'name', 'exclusive'], varargs=None, keywords=None, defaults=(-1, 'max', 1, 0, False, True, False, None, True)), ('document', 'bbd84e855e660cd1084bb71a2fd0cdaa'))
......
paddle/fluid/operators/math/sequence_pooling.cc
浏览文件 @ 33d1e565
...@@ -36,8 +36,8 @@ template <typename T, bool is_test> ...@@ -36,8 +36,8 @@ template <typename T, bool is_test>
class MaxSeqPoolFunctor { class MaxSeqPoolFunctor {
public: public:
void operator()(const platform::CPUDeviceContext& context, void operator()(const platform::CPUDeviceContext& context,
const framework::LoDTensor& input, framework::Tensor* output, const framework::LoDTensor& input, T pad_value,
framework::Tensor* index) { framework::Tensor* output, framework::Tensor* index) {
auto in_dims = input.dims(); auto in_dims = input.dims();
auto out_dims = output->dims(); auto out_dims = output->dims();
auto idx_dims = index->dims(); auto idx_dims = index->dims();
...@@ -56,6 +56,13 @@ class MaxSeqPoolFunctor { ...@@ -56,6 +56,13 @@ class MaxSeqPoolFunctor {
int64_t num_seq = out_dims[0]; int64_t num_seq = out_dims[0];
int64_t dim = output->numel() / num_seq; int64_t dim = output->numel() / num_seq;
for (int64_t i = 0; i < num_seq; ++i) { for (int64_t i = 0; i < num_seq; ++i) {
if (starts[i] == starts[i + 1]) {
for (int64_t k = 0; k < dim; ++k) {
out_data[i * dim + k] = pad_value;
max_index[i * dim + k] = -1;
}
continue;
}
for (int64_t k = 0; k < dim; ++k) { for (int64_t k = 0; k < dim; ++k) {
out_data[i * dim + k] = in_data[starts[i] * dim + k]; out_data[i * dim + k] = in_data[starts[i] * dim + k];
max_index[i * dim + k] = starts[i]; max_index[i * dim + k] = starts[i];
...@@ -77,8 +84,8 @@ template <typename T> ...@@ -77,8 +84,8 @@ template <typename T>
class MaxSeqPoolFunctor<T, true> { class MaxSeqPoolFunctor<T, true> {
public: public:
void operator()(const platform::CPUDeviceContext& context, void operator()(const platform::CPUDeviceContext& context,
const framework::LoDTensor& input, framework::Tensor* output, const framework::LoDTensor& input, T pad_value,
framework::Tensor* index) { framework::Tensor* output, framework::Tensor* index) {
auto in_dims = input.dims(); auto in_dims = input.dims();
auto out_dims = output->dims(); auto out_dims = output->dims();
PADDLE_ENFORCE_GT(in_dims.size(), 1); PADDLE_ENFORCE_GT(in_dims.size(), 1);
...@@ -94,6 +101,12 @@ class MaxSeqPoolFunctor<T, true> { ...@@ -94,6 +101,12 @@ class MaxSeqPoolFunctor<T, true> {
int64_t num_seq = out_dims[0]; int64_t num_seq = out_dims[0];
int64_t dim = output->numel() / num_seq; int64_t dim = output->numel() / num_seq;
for (int64_t i = 0; i < num_seq; ++i) { for (int64_t i = 0; i < num_seq; ++i) {
if (starts[i] == starts[i + 1]) {
for (int64_t k = 0; k < dim; ++k) {
out_data[i * dim + k] = pad_value;
}
continue;
}
std::memcpy(&out_data[i * dim], &in_data[starts[i] * dim], std::memcpy(&out_data[i * dim], &in_data[starts[i] * dim],
dim * sizeof(T)); dim * sizeof(T));
for (size_t j = starts[i] + 1; j < starts[i + 1]; ++j) { for (size_t j = starts[i] + 1; j < starts[i + 1]; ++j) {
...@@ -134,6 +147,7 @@ class MaxSeqPoolGradFunctor { ...@@ -134,6 +147,7 @@ class MaxSeqPoolGradFunctor {
for (int64_t i = 0; i < num_seq; ++i) { for (int64_t i = 0; i < num_seq; ++i) {
for (int64_t j = 0; j < dim; ++j) { for (int64_t j = 0; j < dim; ++j) {
int step_id = max_index[i * dim + j]; int step_id = max_index[i * dim + j];
if (step_id == -1) continue;
ig_data[step_id * dim + j] = og_data[i * dim + j]; ig_data[step_id * dim + j] = og_data[i * dim + j];
} }
} }
...@@ -144,7 +158,7 @@ template <typename T> ...@@ -144,7 +158,7 @@ template <typename T>
class LastSeqPoolFunctor { class LastSeqPoolFunctor {
public: public:
void operator()(const platform::CPUDeviceContext& context, void operator()(const platform::CPUDeviceContext& context,
const framework::LoDTensor& input, const framework::LoDTensor& input, T pad_value,
framework::Tensor* output) { framework::Tensor* output) {
// Create pointers to input and output data // Create pointers to input and output data
auto* in_data = input.data<T>(); auto* in_data = input.data<T>();
...@@ -157,10 +171,16 @@ class LastSeqPoolFunctor { ...@@ -157,10 +171,16 @@ class LastSeqPoolFunctor {
for (int i = 0; i < seq_num; ++i) { for (int i = 0; i < seq_num; ++i) {
// Calculate the length of each sequence // Calculate the length of each sequence
int64_t seq_len = static_cast<int64_t>(lod[i + 1] - lod[i]); int64_t seq_len = static_cast<int64_t>(lod[i + 1] - lod[i]);
if (seq_len == 0) {
for (int j = 0; j < item_size; ++j) {
out_data[j] = pad_value;
}
} else {
// Point to the begin of next sequence // Point to the begin of next sequence
in_data += seq_len * item_size; in_data += seq_len * item_size;
// Copy the last item of sequence to output // Copy the last item of sequence to output
std::memcpy(out_data, (in_data - item_size), item_size * sizeof(T)); std::memcpy(out_data, (in_data - item_size), item_size * sizeof(T));
}
out_data += item_size; out_data += item_size;
} }
} }
...@@ -170,7 +190,7 @@ template <typename T> ...@@ -170,7 +190,7 @@ template <typename T>
class FirstSeqPoolFunctor { class FirstSeqPoolFunctor {
public: public:
void operator()(const platform::CPUDeviceContext& context, void operator()(const platform::CPUDeviceContext& context,
const framework::LoDTensor& input, const framework::LoDTensor& input, T pad_value,
framework::Tensor* output) { framework::Tensor* output) {
// Create pointers to input and output data // Create pointers to input and output data
auto* in_data = input.data<T>(); auto* in_data = input.data<T>();
...@@ -183,10 +203,16 @@ class FirstSeqPoolFunctor { ...@@ -183,10 +203,16 @@ class FirstSeqPoolFunctor {
for (int i = 0; i < seq_num; ++i) { for (int i = 0; i < seq_num; ++i) {
// Calculate the length of each sequence // Calculate the length of each sequence
int64_t seq_len = static_cast<int64_t>(lod[i + 1] - lod[i]); int64_t seq_len = static_cast<int64_t>(lod[i + 1] - lod[i]);
if (seq_len == 0) {
for (int j = 0; j < item_size; ++j) {
out_data[j] = pad_value;
}
} else {
// Copy the first item of sequence to output // Copy the first item of sequence to output
std::memcpy(out_data, in_data, item_size * sizeof(T)); std::memcpy(out_data, in_data, item_size * sizeof(T));
// Point to the next sequence // Point to the next sequence
in_data += seq_len * item_size; in_data += seq_len * item_size;
}
out_data += item_size; out_data += item_size;
} }
} }
...@@ -207,6 +233,7 @@ class SumSeqPoolGradFunctor { ...@@ -207,6 +233,7 @@ class SumSeqPoolGradFunctor {
auto blas = math::GetBlas<platform::CPUDeviceContext, T>(context); auto blas = math::GetBlas<platform::CPUDeviceContext, T>(context);
for (int i = 0; i < static_cast<int>(lod.size()) - 1; ++i) { for (int i = 0; i < static_cast<int>(lod.size()) - 1; ++i) {
int64_t h = static_cast<int64_t>(lod[i + 1] - lod[i]); int64_t h = static_cast<int64_t>(lod[i + 1] - lod[i]);
if (h == 0) continue;
int64_t in_offset = lod[i] * in_w; int64_t in_offset = lod[i] * in_w;
const T* out_pos = out_g_data + i * out_w; const T* out_pos = out_g_data + i * out_w;
T* in_pos = in_g_data + in_offset; T* in_pos = in_g_data + in_offset;
...@@ -222,27 +249,27 @@ class SequencePoolFunctor<platform::CPUDeviceContext, T> { ...@@ -222,27 +249,27 @@ class SequencePoolFunctor<platform::CPUDeviceContext, T> {
public: public:
/* max pool has index output */ /* max pool has index output */
void operator()(const platform::CPUDeviceContext& context, void operator()(const platform::CPUDeviceContext& context,
const std::string pooltype, const framework::LoDTensor& input, const std::string pooltype, T pad_value,
framework::Tensor* output, bool is_test, const framework::LoDTensor& input, framework::Tensor* output,
framework::Tensor* index = nullptr) { bool is_test, framework::Tensor* index = nullptr) {
if (pooltype == "MAX") { if (pooltype == "MAX") {
if (is_test) { if (is_test) {
math::MaxSeqPoolFunctor<T, true> max_pool; math::MaxSeqPoolFunctor<T, true> max_pool;
max_pool(context, input, output, index); max_pool(context, input, pad_value, output, index);
} else { } else {
math::MaxSeqPoolFunctor<T, false> max_pool; math::MaxSeqPoolFunctor<T, false> max_pool;
max_pool(context, input, output, index); max_pool(context, input, pad_value, output, index);
} }
return; return;
} }
if (pooltype == "LAST") { if (pooltype == "LAST") {
math::LastSeqPoolFunctor<T> last_pool; math::LastSeqPoolFunctor<T> last_pool;
last_pool(context, input, output); last_pool(context, input, pad_value, output);
return; return;
} }
if (pooltype == "FIRST") { if (pooltype == "FIRST") {
math::FirstSeqPoolFunctor<T> first_pool; math::FirstSeqPoolFunctor<T> first_pool;
first_pool(context, input, output); first_pool(context, input, pad_value, output);
return; return;
} }
...@@ -260,7 +287,13 @@ class SequencePoolFunctor<platform::CPUDeviceContext, T> { ...@@ -260,7 +287,13 @@ class SequencePoolFunctor<platform::CPUDeviceContext, T> {
.At(attr); .At(attr);
for (int i = 0; i < static_cast<int>(lod.size()) - 1; ++i) { for (int i = 0; i < static_cast<int>(lod.size()) - 1; ++i) {
attr.h = static_cast<int>(lod[i + 1] - lod[i]); attr.h = static_cast<int>(lod[i + 1] - lod[i]);
if (attr.h == 0) {
for (int j = 0; j < attr.w; ++j) {
dst[j] = pad_value;
}
} else {
seqpool(src, dst, &attr); seqpool(src, dst, &attr);
}
dst += attr.w; dst += attr.w;
src += attr.h * attr.w; src += attr.h * attr.w;
} }
...@@ -268,11 +301,17 @@ class SequencePoolFunctor<platform::CPUDeviceContext, T> { ...@@ -268,11 +301,17 @@ class SequencePoolFunctor<platform::CPUDeviceContext, T> {
} }
auto& place = *context.eigen_device(); auto& place = *context.eigen_device();
for (int i = 0; i < static_cast<int>(lod.size()) - 1; ++i) { for (int i = 0; i < static_cast<int>(lod.size()) - 1; ++i) {
Tensor out_t = output->Slice(i, i + 1);
int64_t w = input.numel() / input.dims()[0];
if (lod[i] == lod[i + 1]) {
for (int j = 0; j < w; ++j) {
out_t.data<T>()[j] = pad_value;
}
continue;
}
Tensor in_t = Tensor in_t =
input.Slice(static_cast<int>(lod[i]), static_cast<int>(lod[i + 1])); input.Slice(static_cast<int>(lod[i]), static_cast<int>(lod[i + 1]));
Tensor out_t = output->Slice(i, i + 1);
int64_t h = static_cast<int64_t>(lod[i + 1] - lod[i]); int64_t h = static_cast<int64_t>(lod[i + 1] - lod[i]);
int64_t w = input.numel() / input.dims()[0];
auto in_e = EigenMatrix<T>::From(in_t, framework::make_ddim({h, w})); auto in_e = EigenMatrix<T>::From(in_t, framework::make_ddim({h, w}));
auto out_e = EigenVector<T>::Flatten(out_t); auto out_e = EigenVector<T>::Flatten(out_t);
if (pooltype == "AVERAGE") { if (pooltype == "AVERAGE") {
...@@ -316,6 +355,7 @@ class SequencePoolGradFunctor<platform::CPUDeviceContext, T> { ...@@ -316,6 +355,7 @@ class SequencePoolGradFunctor<platform::CPUDeviceContext, T> {
auto lod = in_grad->lod()[0]; auto lod = in_grad->lod()[0];
auto& place = *context.eigen_device(); auto& place = *context.eigen_device();
for (int i = 0; i < static_cast<int>(lod.size()) - 1; ++i) { for (int i = 0; i < static_cast<int>(lod.size()) - 1; ++i) {
if (lod[i] == lod[i + 1]) continue;
auto in_g_t = in_grad->Slice(static_cast<int>(lod[i]), auto in_g_t = in_grad->Slice(static_cast<int>(lod[i]),
static_cast<int>(lod[i + 1])); static_cast<int>(lod[i + 1]));
auto out_g_t = out_grad.Slice(i, i + 1); auto out_g_t = out_grad.Slice(i, i + 1);
......
paddle/fluid/operators/math/sequence_pooling.cu
浏览文件 @ 33d1e565
...@@ -24,12 +24,16 @@ namespace math { ...@@ -24,12 +24,16 @@ namespace math {
template <typename T> template <typename T>
struct MaxPoolFunctor { struct MaxPoolFunctor {
HOSTDEVICE void operator()(const T* input, const size_t start, HOSTDEVICE void operator()(const T* input, const T pad_value,
const size_t end, const size_t item_dim, T* output, const size_t start, const size_t end,
int* index) { const size_t item_dim, T* output, int* index) {
for (int tid = threadIdx.x; tid < item_dim; tid += blockDim.x) { for (int tid = threadIdx.x; tid < item_dim; tid += blockDim.x) {
T max_val = static_cast<T>(-FLT_MAX); T max_val = static_cast<T>(-FLT_MAX);
int max_index = -1; int max_index = -1;
if (start == end) {
output[tid] = pad_value;
index[tid] = -1;
} else {
for (int i = start; i < end; ++i) { for (int i = start; i < end; ++i) {
if (max_val < input[item_dim * i + tid]) { if (max_val < input[item_dim * i + tid]) {
max_val = input[item_dim * i + tid]; max_val = input[item_dim * i + tid];
...@@ -40,14 +44,18 @@ struct MaxPoolFunctor { ...@@ -40,14 +44,18 @@ struct MaxPoolFunctor {
index[tid] = max_index; index[tid] = max_index;
} }
} }
}
}; };
template <typename T> template <typename T>
struct AvgPoolFunctor { struct AvgPoolFunctor {
HOSTDEVICE void operator()(const T* input, const size_t start, HOSTDEVICE void operator()(const T* input, const T pad_value,
const size_t end, const size_t item_dim, T* output, const size_t start, const size_t end,
int* index) { const size_t item_dim, T* output, int* index) {
for (int tid = threadIdx.x; tid < item_dim; tid += blockDim.x) { for (int tid = threadIdx.x; tid < item_dim; tid += blockDim.x) {
if (start == end) {
output[tid] = pad_value;
} else {
T val = static_cast<T>(0); T val = static_cast<T>(0);
for (int i = start; i < end; ++i) { for (int i = start; i < end; ++i) {
val += input[item_dim * i + tid]; val += input[item_dim * i + tid];
...@@ -56,14 +64,18 @@ struct AvgPoolFunctor { ...@@ -56,14 +64,18 @@ struct AvgPoolFunctor {
output[tid] = val / static_cast<T>(end - start); output[tid] = val / static_cast<T>(end - start);
} }
} }
}
}; };
template <typename T> template <typename T>
struct SumPoolFunctor { struct SumPoolFunctor {
HOSTDEVICE void operator()(const T* input, const size_t start, HOSTDEVICE void operator()(const T* input, const T pad_value,
const size_t end, const size_t item_dim, T* output, const size_t start, const size_t end,
int* index) { const size_t item_dim, T* output, int* index) {
for (int tid = threadIdx.x; tid < item_dim; tid += blockDim.x) { for (int tid = threadIdx.x; tid < item_dim; tid += blockDim.x) {
if (start == end) {
output[tid] = pad_value;
} else {
T val = static_cast<T>(0); T val = static_cast<T>(0);
for (int i = start; i < end; ++i) { for (int i = start; i < end; ++i) {
val += input[item_dim * i + tid]; val += input[item_dim * i + tid];
...@@ -71,14 +83,18 @@ struct SumPoolFunctor { ...@@ -71,14 +83,18 @@ struct SumPoolFunctor {
output[tid] = val; output[tid] = val;
} }
} }
}
}; };
template <typename T> template <typename T>
struct SqrtPoolFunctor { struct SqrtPoolFunctor {
HOSTDEVICE void operator()(const T* input, const size_t start, HOSTDEVICE void operator()(const T* input, const T pad_value,
const size_t end, const size_t item_dim, T* output, const size_t start, const size_t end,
int* index) { const size_t item_dim, T* output, int* index) {
for (int tid = threadIdx.x; tid < item_dim; tid += blockDim.x) { for (int tid = threadIdx.x; tid < item_dim; tid += blockDim.x) {
if (start == end) {
output[tid] = pad_value;
} else {
T val = static_cast<T>(0); T val = static_cast<T>(0);
for (int i = start; i < end; ++i) { for (int i = start; i < end; ++i) {
val += input[item_dim * i + tid]; val += input[item_dim * i + tid];
...@@ -87,33 +103,43 @@ struct SqrtPoolFunctor { ...@@ -87,33 +103,43 @@ struct SqrtPoolFunctor {
output[tid] = val / sqrt(end - start); output[tid] = val / sqrt(end - start);
} }
} }
}
}; };
template <typename T> template <typename T>
struct LastPoolFunctor { struct LastPoolFunctor {
HOSTDEVICE void operator()(const T* input, const size_t start, HOSTDEVICE void operator()(const T* input, const T pad_value,
const size_t end, const size_t item_dim, T* output, const size_t start, const size_t end,
int* index) { const size_t item_dim, T* output, int* index) {
for (int tid = threadIdx.x; tid < item_dim; tid += blockDim.x) { for (int tid = threadIdx.x; tid < item_dim; tid += blockDim.x) {
if (start == end) {
output[tid] = pad_value;
} else {
output[tid] = input[item_dim * (end - 1) + tid]; output[tid] = input[item_dim * (end - 1) + tid];
} }
} }
}
}; };
template <typename T> template <typename T>
struct FirstPoolFunctor { struct FirstPoolFunctor {
HOSTDEVICE void operator()(const T* input, const size_t start, HOSTDEVICE void operator()(const T* input, const T pad_value,
const size_t end, const size_t item_dim, T* output, const size_t start, const size_t end,
int* index) { const size_t item_dim, T* output, int* index) {
for (int tid = threadIdx.x; tid < item_dim; tid += blockDim.x) { for (int tid = threadIdx.x; tid < item_dim; tid += blockDim.x) {
if (start == end) {
output[tid] = pad_value;
} else {
output[tid] = input[item_dim * start + tid]; output[tid] = input[item_dim * start + tid];
} }
} }
}
}; };
template <typename T, typename Range_OP> template <typename T, typename Range_OP>
__global__ void sequence_pool_kernel(Range_OP op, const T* input, __global__ void sequence_pool_kernel(Range_OP op, const T* input,
const size_t* lod, const size_t lod_size, const T pad_value, const size_t* lod,
const size_t lod_size,
const size_t item_dim, T* output, const size_t item_dim, T* output,
int* index) { int* index) {
int bid = blockIdx.x; int bid = blockIdx.x;
...@@ -124,16 +150,17 @@ __global__ void sequence_pool_kernel(Range_OP op, const T* input, ...@@ -124,16 +150,17 @@ __global__ void sequence_pool_kernel(Range_OP op, const T* input,
if (index != nullptr) { if (index != nullptr) {
index_offset = &index[bid * item_dim]; index_offset = &index[bid * item_dim];
} }
op(input, start, end, item_dim, &output[bid * item_dim], index_offset); op(input, pad_value, start, end, item_dim, &output[bid * item_dim],
index_offset);
} }
template <typename T> template <typename T>
class SequencePoolFunctor<platform::CUDADeviceContext, T> { class SequencePoolFunctor<platform::CUDADeviceContext, T> {
public: public:
void operator()(const platform::CUDADeviceContext& context, void operator()(const platform::CUDADeviceContext& context,
const std::string pooltype, const framework::LoDTensor& input, const std::string pooltype, T pad_value,
framework::Tensor* output, bool is_test, const framework::LoDTensor& input, framework::Tensor* output,
framework::Tensor* index = nullptr) { bool is_test, framework::Tensor* index = nullptr) {
auto& lod = input.lod()[0]; auto& lod = input.lod()[0];
const size_t item_dim = output->numel() / output->dims()[0]; const size_t item_dim = output->numel() / output->dims()[0];
dim3 threads(1024, 1); dim3 threads(1024, 1);
...@@ -141,37 +168,37 @@ class SequencePoolFunctor<platform::CUDADeviceContext, T> { ...@@ -141,37 +168,37 @@ class SequencePoolFunctor<platform::CUDADeviceContext, T> {
if (pooltype == "MAX") { if (pooltype == "MAX") {
sequence_pool_kernel< sequence_pool_kernel<
T, MaxPoolFunctor<T>><<<grid, threads, 0, context.stream()>>>( T, MaxPoolFunctor<T>><<<grid, threads, 0, context.stream()>>>(
MaxPoolFunctor<T>(), input.data<T>(), MaxPoolFunctor<T>(), input.data<T>(), pad_value,
lod.CUDAData(context.GetPlace()), lod.size(), item_dim, lod.CUDAData(context.GetPlace()), lod.size(), item_dim,
output->mutable_data<T>(context.GetPlace()), index->data<int>()); output->mutable_data<T>(context.GetPlace()), index->data<int>());
} else if (pooltype == "AVERAGE") { } else if (pooltype == "AVERAGE") {
sequence_pool_kernel< sequence_pool_kernel<
T, AvgPoolFunctor<T>><<<grid, threads, 0, context.stream()>>>( T, AvgPoolFunctor<T>><<<grid, threads, 0, context.stream()>>>(
AvgPoolFunctor<T>(), input.data<T>(), AvgPoolFunctor<T>(), input.data<T>(), pad_value,
lod.CUDAData(context.GetPlace()), lod.size(), item_dim, lod.CUDAData(context.GetPlace()), lod.size(), item_dim,
output->mutable_data<T>(context.GetPlace()), nullptr); output->mutable_data<T>(context.GetPlace()), nullptr);
} else if (pooltype == "SUM") { } else if (pooltype == "SUM") {
sequence_pool_kernel< sequence_pool_kernel<
T, SumPoolFunctor<T>><<<grid, threads, 0, context.stream()>>>( T, SumPoolFunctor<T>><<<grid, threads, 0, context.stream()>>>(
SumPoolFunctor<T>(), input.data<T>(), SumPoolFunctor<T>(), input.data<T>(), pad_value,
lod.CUDAData(context.GetPlace()), lod.size(), item_dim, lod.CUDAData(context.GetPlace()), lod.size(), item_dim,
output->mutable_data<T>(context.GetPlace()), nullptr); output->mutable_data<T>(context.GetPlace()), nullptr);
} else if (pooltype == "SQRT") { } else if (pooltype == "SQRT") {
sequence_pool_kernel< sequence_pool_kernel<
T, SqrtPoolFunctor<T>><<<grid, threads, 0, context.stream()>>>( T, SqrtPoolFunctor<T>><<<grid, threads, 0, context.stream()>>>(
SqrtPoolFunctor<T>(), input.data<T>(), SqrtPoolFunctor<T>(), input.data<T>(), pad_value,
lod.CUDAData(context.GetPlace()), lod.size(), item_dim, lod.CUDAData(context.GetPlace()), lod.size(), item_dim,
output->mutable_data<T>(context.GetPlace()), nullptr); output->mutable_data<T>(context.GetPlace()), nullptr);
} else if (pooltype == "LAST") { } else if (pooltype == "LAST") {
sequence_pool_kernel< sequence_pool_kernel<
T, LastPoolFunctor<T>><<<grid, threads, 0, context.stream()>>>( T, LastPoolFunctor<T>><<<grid, threads, 0, context.stream()>>>(
LastPoolFunctor<T>(), input.data<T>(), LastPoolFunctor<T>(), input.data<T>(), pad_value,
lod.CUDAData(context.GetPlace()), lod.size(), item_dim, lod.CUDAData(context.GetPlace()), lod.size(), item_dim,
output->mutable_data<T>(context.GetPlace()), nullptr); output->mutable_data<T>(context.GetPlace()), nullptr);
} else if (pooltype == "FIRST") { } else if (pooltype == "FIRST") {
sequence_pool_kernel< sequence_pool_kernel<
T, FirstPoolFunctor<T>><<<grid, threads, 0, context.stream()>>>( T, FirstPoolFunctor<T>><<<grid, threads, 0, context.stream()>>>(
FirstPoolFunctor<T>(), input.data<T>(), FirstPoolFunctor<T>(), input.data<T>(), pad_value,
lod.CUDAData(context.GetPlace()), lod.size(), item_dim, lod.CUDAData(context.GetPlace()), lod.size(), item_dim,
output->mutable_data<T>(context.GetPlace()), nullptr); output->mutable_data<T>(context.GetPlace()), nullptr);
} else { } else {
......
paddle/fluid/operators/math/sequence_pooling.h
浏览文件 @ 33d1e565
...@@ -27,8 +27,9 @@ class SequencePoolFunctor { ...@@ -27,8 +27,9 @@ class SequencePoolFunctor {
public: public:
/* max pool has index output */ /* max pool has index output */
void operator()(const DeviceContext& context, const std::string pooltype, void operator()(const DeviceContext& context, const std::string pooltype,
const framework::LoDTensor& input, framework::Tensor* output, T pad_value, const framework::LoDTensor& input,
bool is_test = false, framework::Tensor* index = nullptr); framework::Tensor* output, bool is_test = false,
framework::Tensor* index = nullptr);
}; };
template <typename DeviceContext, typename T> template <typename DeviceContext, typename T>
......
paddle/fluid/operators/sequence_ops/sequence_pool_op.cc
浏览文件 @ 33d1e565
...@@ -57,6 +57,9 @@ class SequencePoolOpMaker : public framework::OpProtoAndCheckerMaker { ...@@ -57,6 +57,9 @@ class SequencePoolOpMaker : public framework::OpProtoAndCheckerMaker {
"(string, default 'AVERAGE') the pooling pooltype of SequencePoolOp.") "(string, default 'AVERAGE') the pooling pooltype of SequencePoolOp.")
.SetDefault("AVERAGE") .SetDefault("AVERAGE")
.InEnum({"AVERAGE", "SUM", "SQRT", "LAST", "FIRST", "MAX"}); .InEnum({"AVERAGE", "SUM", "SQRT", "LAST", "FIRST", "MAX"});
AddAttr<float>("pad_value",
"(float, default 0.0) The value to pad for empty sequence.")
.SetDefault(0.0);
AddComment(R"DOC( AddComment(R"DOC(
Sequence Pool Operator. Sequence Pool Operator.
...@@ -69,6 +72,8 @@ It supports six pooling types: ...@@ -69,6 +72,8 @@ It supports six pooling types:
5. FIRST: Out[i] = first instance in i-th sequence X[i] 5. FIRST: Out[i] = first instance in i-th sequence X[i]
6. MAX: $$Out[i] = max(X_i)$$ 6. MAX: $$Out[i] = max(X_i)$$
and for the empty sequence Out[i] = attr(pad_value).
The following example explains how this works: The following example explains how this works:
For a mini-batch of 3 variable-length sentences, For a mini-batch of 3 variable-length sentences,
containing 2, 3, and 2 time-steps: containing 2, 3, and 2 time-steps:
......
paddle/fluid/operators/sequence_ops/sequence_pool_op.h
浏览文件 @ 33d1e565
...@@ -32,6 +32,7 @@ class SequencePoolKernel : public framework::OpKernel<T> { ...@@ -32,6 +32,7 @@ class SequencePoolKernel : public framework::OpKernel<T> {
auto* in = context.Input<LoDTensor>("X"); auto* in = context.Input<LoDTensor>("X");
auto* out = context.Output<Tensor>("Out"); auto* out = context.Output<Tensor>("Out");
std::string pooltype = context.Attr<std::string>("pooltype"); std::string pooltype = context.Attr<std::string>("pooltype");
T pad_value = static_cast<T>(context.Attr<float>("pad_value"));
auto dims = in->dims(); auto dims = in->dims();
auto lod = in->lod(); auto lod = in->lod();
...@@ -58,8 +59,8 @@ class SequencePoolKernel : public framework::OpKernel<T> { ...@@ -58,8 +59,8 @@ class SequencePoolKernel : public framework::OpKernel<T> {
index->mutable_data<int>(context.GetPlace()); index->mutable_data<int>(context.GetPlace());
} }
math::SequencePoolFunctor<DeviceContext, T> pool; math::SequencePoolFunctor<DeviceContext, T> pool;
pool(context.template device_context<DeviceContext>(), pooltype, *in, out, pool(context.template device_context<DeviceContext>(), pooltype, pad_value,
is_test, index); *in, out, is_test, index);
} }
}; };
......
python/paddle/fluid/layers/nn.py
浏览文件 @ 33d1e565
...@@ -2346,7 +2346,7 @@ def conv3d(input, ...@@ -2346,7 +2346,7 @@ def conv3d(input,
return helper.append_activation(pre_act) return helper.append_activation(pre_act)
def sequence_pool(input, pool_type, is_test=False): def sequence_pool(input, pool_type, is_test=False, pad_value=0.0):
""" """
This function add the operator for sequence pooling. This function add the operator for sequence pooling.
It pools features of all time-steps of each instance, and is applied It pools features of all time-steps of each instance, and is applied
...@@ -2361,29 +2361,32 @@ def sequence_pool(input, pool_type, is_test=False): ...@@ -2361,29 +2361,32 @@ def sequence_pool(input, pool_type, is_test=False):
.. code-block:: text .. code-block:: text
x is a 1-level LoDTensor: x is a 1-level LoDTensor and **pad_value** = 0.0:
x.lod = [[2, 3, 2]] x.lod = [[2, 3, 2, 0]]
x.data = [1, 3, 2, 4, 6, 5, 1] x.data = [1, 3, 2, 4, 6, 5, 1]
x.dims = [7, 1] x.dims = [7, 1]
then output is a Tensor: then output is a Tensor:
out.dim = [3, 1] out.dim = [4, 1]
with condition len(x.lod[-1]) == out.dims[0] with condition len(x.lod[-1]) == out.dims[0]
for different pool_type: for different pool_type:
average: out.data = [2, 4, 3], where 2=(1+3)/2, 4=(2+4+6)/3, 3=(5+1)/2 average: out.data = [2, 4, 3, 0.0], where 2=(1+3)/2, 4=(2+4+6)/3, 3=(5+1)/2
sum : out.data = [4, 12, 6], where 4=1+3, 12=2+4+6, 6=5+1 sum : out.data = [4, 12, 6, 0.0], where 4=1+3, 12=2+4+6, 6=5+1
sqrt : out.data = [2.82, 6.93, 4.24], where 2.82=(1+3)/sqrt(2), sqrt : out.data = [2.82, 6.93, 4.24, 0.0], where 2.82=(1+3)/sqrt(2),
6.93=(2+4+6)/sqrt(3), 4.24=(5+1)/sqrt(2) 6.93=(2+4+6)/sqrt(3), 4.24=(5+1)/sqrt(2)
max : out.data = [3, 6, 5], where 3=max(1,3), 6=max(2,4,6), 5=max(5,1) max : out.data = [3, 6, 5, 0.0], where 3=max(1,3), 6=max(2,4,6), 5=max(5,1)
last : out.data = [3, 6, 1], where 3=last(1,3), 6=last(2,4,6), 1=last(5,1) last : out.data = [3, 6, 1, 0.0], where 3=last(1,3), 6=last(2,4,6), 1=last(5,1)
first : out.data = [1, 2, 5], where 1=first(1,3), 2=first(2,4,6), 5=first(5,1) first : out.data = [1, 2, 5, 0.0], where 1=first(1,3), 2=first(2,4,6), 5=first(5,1)
and all above 0.0 = **pad_value**.
Args: Args:
input(variable): The input variable which is a LoDTensor. input (variable): The input variable which is a LoDTensor.
pool_type (string): The pooling type of sequence_pool. pool_type (string): The pooling type of sequence_pool.
It supports average, sum, sqrt and max. It supports average, sum, sqrt and max.
is_test(bool, Default False): Used distinguish training from scoring mode. is_test (bool): Used to distinguish training from scoring mode. Default False.
pad_value (float): Used to pad the pooling result for empty input sequence.
Returns: Returns:
The sequence pooling variable which is a Tensor. The sequence pooling variable which is a Tensor.
...@@ -2392,6 +2395,8 @@ def sequence_pool(input, pool_type, is_test=False): ...@@ -2392,6 +2395,8 @@ def sequence_pool(input, pool_type, is_test=False):
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
x = fluid.layers.data(name='x', shape=[7, 1], x = fluid.layers.data(name='x', shape=[7, 1],
dtype='float32', lod_level=1) dtype='float32', lod_level=1)
avg_x = fluid.layers.sequence_pool(input=x, pool_type='average') avg_x = fluid.layers.sequence_pool(input=x, pool_type='average')
...@@ -2413,8 +2418,11 @@ def sequence_pool(input, pool_type, is_test=False): ...@@ -2413,8 +2418,11 @@ def sequence_pool(input, pool_type, is_test=False):
inputs={"X": input}, inputs={"X": input},
outputs={"Out": pool_out, outputs={"Out": pool_out,
"MaxIndex": max_index}, "MaxIndex": max_index},
attrs={"pooltype": pool_type.upper(), attrs={
"is_test": is_test}) "pooltype": pool_type.upper(),
"is_test": is_test,
"pad_value": pad_value
})
# when pool_type is max, variable max_index is initialized, # when pool_type is max, variable max_index is initialized,
# so we stop the gradient explicitly here # so we stop the gradient explicitly here
......
python/paddle/fluid/tests/unittests/test_seq_pool.py
浏览文件 @ 33d1e565
...@@ -20,31 +20,42 @@ from op_test import OpTest ...@@ -20,31 +20,42 @@ from op_test import OpTest
from test_reorder_lod_tensor import convert_to_offset from test_reorder_lod_tensor import convert_to_offset
def compute_seqpool_sum(x, offset, out): def compute_seqpool_sum(x, offset, out, pad_value=0.0):
for i in range(len(offset[0]) - 1): for i in range(len(offset[0]) - 1):
if offset[0][i] == offset[0][i + 1]:
out[i] = pad_value
else:
sub_x = x[offset[0][i]:offset[0][i + 1], :] sub_x = x[offset[0][i]:offset[0][i + 1], :]
out[i] = sub_x.sum(axis=0) out[i] = sub_x.sum(axis=0)
def compute_seqpool_avg(x, offset, out): def compute_seqpool_avg(x, offset, out, pad_value=0.0):
for i in range(len(offset[0]) - 1): for i in range(len(offset[0]) - 1):
if offset[0][i] == offset[0][i + 1]:
out[i] = pad_value
else:
sub_x = x[offset[0][i]:offset[0][i + 1], :] sub_x = x[offset[0][i]:offset[0][i + 1], :]
out[i] = sub_x.mean(axis=0) out[i] = sub_x.mean(axis=0)
def compute_seqpool_sqrt(x, offset, out): def compute_seqpool_sqrt(x, offset, out, pad_value=0.0):
for i in range(len(offset[0]) - 1): for i in range(len(offset[0]) - 1):
if offset[0][i] == offset[0][i + 1]:
out[i] = pad_value
else:
sub_x = x[offset[0][i]:offset[0][i + 1], :] sub_x = x[offset[0][i]:offset[0][i + 1], :]
seq_len = offset[0][i + 1] - offset[0][i] seq_len = offset[0][i + 1] - offset[0][i]
out[i] = sub_x.sum(axis=0) / np.sqrt(seq_len) out[i] = sub_x.sum(axis=0) / np.sqrt(seq_len)
class TestSeqAvgPool(OpTest): class TestSeqAvgPool(OpTest):
def set_lod(self):
return [[11]]
def set_data(self): def set_data(self):
self.op_type = 'sequence_pool' self.op_type = 'sequence_pool'
# one level, batch size is 4
x = np.random.uniform(0.1, 1, [11, 23]).astype('float32') x = np.random.uniform(0.1, 1, [11, 23]).astype('float32')
lod = [[11]] lod = self.set_lod()
self.inputs = {'X': (x, lod)} self.inputs = {'X': (x, lod)}
offset = convert_to_offset(lod) offset = convert_to_offset(lod)
out = np.zeros((len(lod[0]), 23)).astype('float32') out = np.zeros((len(lod[0]), 23)).astype('float32')
...@@ -52,8 +63,8 @@ class TestSeqAvgPool(OpTest): ...@@ -52,8 +63,8 @@ class TestSeqAvgPool(OpTest):
return x, offset, out return x, offset, out
def compute(self, x, offset, out): def compute(self, x, offset, out):
self.attrs = {'pooltype': "AVERAGE"} self.attrs = {"pad_value": 0.0, 'pooltype': "AVERAGE"}
compute_seqpool_avg(x, offset, out) compute_seqpool_avg(x, offset, out, self.attrs["pad_value"])
def setUp(self): def setUp(self):
x, offset, out = self.set_data() x, offset, out = self.set_data()
...@@ -69,95 +80,160 @@ class TestSeqAvgPool(OpTest): ...@@ -69,95 +80,160 @@ class TestSeqAvgPool(OpTest):
self.check_grad(["X"], "Out") self.check_grad(["X"], "Out")
class TestSeqAvgPoolLen0(TestSeqAvgPool):
def set_lod(self):
return [[0, 4, 0, 7, 0]]
class TestSeqSumPool(TestSeqAvgPool): class TestSeqSumPool(TestSeqAvgPool):
def compute(self, x, offset, out): def compute(self, x, offset, out):
self.attrs = {'pooltype': "SUM"} self.attrs = {"pad_value": 0.1, 'pooltype': "SUM"}
compute_seqpool_sum(x, offset, out) compute_seqpool_sum(x, offset, out, self.attrs["pad_value"])
class TestSeqSumPoolLen0(TestSeqSumPool):
def set_lod(self):
return [[0, 4, 0, 7, 0]]
class TestSeqMaxPool(TestSeqAvgPool): class TestSeqMaxPool(TestSeqAvgPool):
def set_lod(self):
return [[13]]
def set_data(self): def set_data(self):
self.op_type = 'sequence_pool' self.op_type = 'sequence_pool'
x = np.random.uniform(0.1, 1, [13, 23]).astype('float32') x = np.random.uniform(0.1, 1, [13, 23]).astype('float32')
lod = [[13]] lod = self.set_lod()
offset = convert_to_offset(lod) offset = convert_to_offset(lod)
for i in range(len(offset[0]) - 1): for i in range(len(offset[0]) - 1):
l = offset[0][i + 1] - offset[0][i] l = offset[0][i + 1] - offset[0][i]
if l > 0:
x[offset[0][i] + np.random.randint(l), :] += 2.0 x[offset[0][i] + np.random.randint(l), :] += 2.0
self.inputs = {'X': (x, lod)} self.inputs = {'X': (x, lod)}
out = np.zeros((1, 23)).astype('float32') out = np.zeros((len(lod[0]), 23)).astype('float32')
self.outputs = {'Out': out} self.outputs = {'Out': out}
return x, offset, out return x, offset, out
def compute(self, x, offset, out): def compute(self, x, offset, out):
self.attrs = {'pooltype': "MAX"} self.attrs = {"pad_value": 0.5, 'pooltype': "MAX"}
for i in range(len(offset[0]) - 1): for i in range(len(offset[0]) - 1):
if offset[0][i] == offset[0][i + 1]:
out[i] = self.attrs["pad_value"]
else:
sub_x = x[offset[0][i]:offset[0][i + 1], :] sub_x = x[offset[0][i]:offset[0][i + 1], :]
out[i] = np.amax(sub_x, axis=0) out[i] = np.amax(sub_x, axis=0)
class TestSeqMaxPoolLen0(TestSeqMaxPool):
def set_lod(self):
return [[0, 1, 1, 5, 6, 0]]
class TestSeqSqrtPool(TestSeqAvgPool): class TestSeqSqrtPool(TestSeqAvgPool):
def compute(self, x, offset, out): def compute(self, x, offset, out):
self.attrs = {'pooltype': "SQRT"} self.attrs = {"pad_value": 0.0, 'pooltype': "SQRT"}
compute_seqpool_sqrt(x, offset, out) compute_seqpool_sqrt(x, offset, out, self.attrs["pad_value"])
class TestSeqSqrtPoolLen0(TestSeqSqrtPool):
def set_lod(self):
return [[0, 7, 0, 2, 2, 0]]
class TestSeqLastPool(TestSeqAvgPool): class TestSeqLastPool(TestSeqAvgPool):
def compute(self, x, offset, out): def compute(self, x, offset, out):
self.attrs = {'pooltype': "LAST"} self.attrs = {"pad_value": 0.0, 'pooltype': "LAST"}
for i in range(len(offset[0]) - 1): for i in range(len(offset[0]) - 1):
if offset[0][i] == offset[0][i + 1]:
out[i] = self.attrs["pad_value"]
else:
sub_x = x[offset[0][i]:offset[0][i + 1], :] sub_x = x[offset[0][i]:offset[0][i + 1], :]
out[i] = sub_x[-1, :] out[i] = sub_x[-1, :]
class TestSeqLastPoolLen0(TestSeqLastPool):
def set_lod(self):
return [[0, 3, 4, 0, 4, 0]]
class TestSeqFirstPool(TestSeqAvgPool): class TestSeqFirstPool(TestSeqAvgPool):
def compute(self, x, offset, out): def compute(self, x, offset, out):
self.attrs = {'pooltype': "FIRST"} self.attrs = {"pad_value": 0.3, 'pooltype': "FIRST"}
for i in range(len(offset[0]) - 1): for i in range(len(offset[0]) - 1):
if offset[0][i] == offset[0][i + 1]:
out[i] = self.attrs["pad_value"]
else:
sub_x = x[offset[0][i]:offset[0][i + 1], :] sub_x = x[offset[0][i]:offset[0][i + 1], :]
out[i] = sub_x[0, :] out[i] = sub_x[0, :]
class TestSeqFirstPoolLen0(TestSeqFirstPool):
def set_lod(self):
return [[0, 2, 0, 3, 6, 0]]
class TestSeqAvgPool2D(TestSeqAvgPool): class TestSeqAvgPool2D(TestSeqAvgPool):
def set_lod(self):
return [[4, 1, 3, 5]]
def set_data(self): def set_data(self):
self.op_type = 'sequence_pool' self.op_type = 'sequence_pool'
# one level, batch size is 4
x = np.random.uniform(0.1, 1, [13, 3, 17]).astype('float32') x = np.random.uniform(0.1, 1, [13, 3, 17]).astype('float32')
lod = [[4, 1, 3, 5]] lod = self.set_lod()
self.inputs = {'X': (x, lod)} self.inputs = {'X': (x, lod)}
offset = convert_to_offset(lod) offset = convert_to_offset(lod)
out = np.zeros((4, 3, 17)).astype('float32') out = np.zeros((len(lod[0]), 3, 17)).astype('float32')
self.outputs = {'Out': out} self.outputs = {'Out': out}
return x, offset, out return x, offset, out
def compute(self, x, offset, out): def compute(self, x, offset, out):
self.attrs = {'pooltype': "AVERAGE"} self.attrs = {"pad_value": 0.0, 'pooltype': "AVERAGE"}
for i in range(len(offset[0]) - 1): for i in range(len(offset[0]) - 1):
if offset[0][i] == offset[0][i + 1]:
out[i] = self.attrs["pad_value"] * np.ones((3, 17))
else:
sub_x = np.reshape(x[offset[0][i]:offset[0][i + 1], :], sub_x = np.reshape(x[offset[0][i]:offset[0][i + 1], :],
(-1, 3 * 17)) (-1, 3 * 17))
out[i] = np.reshape(sub_x.mean(axis=0), (3, 17)) out[i] = np.reshape(sub_x.mean(axis=0), (3, 17))
class TestSeqAvgPool2DLen0(TestSeqAvgPool2D):
def set_lod(self):
return [[0, 5, 0, 8, 0]]
class TestSeqSumPool2D(TestSeqAvgPool2D): class TestSeqSumPool2D(TestSeqAvgPool2D):
def compute(self, x, offset, out): def compute(self, x, offset, out):
self.attrs = {'pooltype': "SUM"} self.attrs = {"pad_value": 0.2, 'pooltype': "SUM"}
for i in range(len(offset[0]) - 1): for i in range(len(offset[0]) - 1):
if offset[0][i] == offset[0][i + 1]:
out[i] = self.attrs["pad_value"] * np.ones((3, 17))
else:
sub_x = np.reshape(x[offset[0][i]:offset[0][i + 1], :], sub_x = np.reshape(x[offset[0][i]:offset[0][i + 1], :],
(-1, 3 * 17)) (-1, 3 * 17))
out[i] = np.reshape(sub_x.sum(axis=0), (3, 17)) out[i] = np.reshape(sub_x.sum(axis=0), (3, 17))
class TestSeqSumPool2DLen0(TestSeqSumPool2D):
def set_lod(self):
return [[0, 8, 0, 5, 0]]
class TestSeqSqrtPool2D(TestSeqAvgPool2D): class TestSeqSqrtPool2D(TestSeqAvgPool2D):
def compute(self, x, offset, out): def compute(self, x, offset, out):
self.attrs = {'pooltype': "SQRT"} self.attrs = {"pad_value": 0.0, 'pooltype': "SQRT"}
for i in range(len(offset[0]) - 1): for i in range(len(offset[0]) - 1):
if offset[0][i] == offset[0][i + 1]:
out[i] = self.attrs["pad_value"] * np.ones((3, 17))
else:
sub_x = np.reshape(x[offset[0][i]:offset[0][i + 1], :], sub_x = np.reshape(x[offset[0][i]:offset[0][i + 1], :],
(-1, 3 * 17)) (-1, 3 * 17))
seq_len = offset[0][i + 1] - offset[0][i] seq_len = offset[0][i + 1] - offset[0][i]
out[i] = np.reshape(sub_x.sum(axis=0) / np.sqrt(seq_len), (3, 17)) out[i] = np.reshape(
sub_x.sum(axis=0) / np.sqrt(seq_len), (3, 17))
def test_check_grad(self): def test_check_grad(self):
# Remove MaxIndex after check_grad is refined. # Remove MaxIndex after check_grad is refined.
...@@ -166,33 +242,54 @@ class TestSeqSqrtPool2D(TestSeqAvgPool2D): ...@@ -166,33 +242,54 @@ class TestSeqSqrtPool2D(TestSeqAvgPool2D):
self.check_grad(["X"], "Out", max_relative_error=0.06) self.check_grad(["X"], "Out", max_relative_error=0.06)
class TestSeqSqrtPool2DLen0(TestSeqSqrtPool2D):
def set_lod(self):
return [[0, 8, 0, 5, 0]]
class TestSeqMaxPool2D(TestSeqAvgPool2D): class TestSeqMaxPool2D(TestSeqAvgPool2D):
def set_lod(self):
return [[4, 1, 3, 5]]
def set_data(self): def set_data(self):
self.op_type = 'sequence_pool' self.op_type = 'sequence_pool'
x = np.random.uniform(0.1, 1, [13, 3, 11]).astype('float32') x = np.random.uniform(0.1, 1, [13, 3, 11]).astype('float32')
lod = [[4, 1, 3, 5]] self.lod = self.set_lod()
self.inputs = {'X': (x, lod)} self.inputs = {'X': (x, self.lod)}
offset = convert_to_offset(lod) offset = convert_to_offset(self.lod)
for i in range(len(offset[0]) - 1): for i in range(len(offset[0]) - 1):
l = offset[0][i + 1] - offset[0][i] l = offset[0][i + 1] - offset[0][i]
if l == 0:
continue
x[offset[0][i] + np.random.randint(l), :] += 1.0 x[offset[0][i] + np.random.randint(l), :] += 1.0
out = np.zeros((4, 3, 11)).astype('float32') out = np.zeros((len(self.lod[0]), 3, 11)).astype('float32')
self.outputs = {'Out': out} self.outputs = {'Out': out}
return x, offset, out return x, offset, out
def compute(self, x, offset, out): def compute(self, x, offset, out):
self.attrs = {'pooltype': "MAX"} self.attrs = {"pad_value": 0.0, 'pooltype': "MAX"}
for i in range(len(offset[0]) - 1): for i in range(len(offset[0]) - 1):
if offset[0][i] == offset[0][i + 1]:
out[i] = self.attrs["pad_value"] * np.ones((3, 11))
continue
sub_x = np.reshape(x[offset[0][i]:offset[0][i + 1], :], sub_x = np.reshape(x[offset[0][i]:offset[0][i + 1], :],
(-1, 3 * 11)) (-1, 3 * 11))
out[i] = np.reshape(np.amax(sub_x, axis=0), (3, 11)) out[i] = np.reshape(np.amax(sub_x, axis=0), (3, 11))
class TestSeqMaxPool2DLen0(TestSeqMaxPool2D):
def set_lod(self):
return [[0, 3, 0, 10, 0]]
class TestSeqMaxPool2DInference(TestSeqMaxPool2D): class TestSeqMaxPool2DInference(TestSeqMaxPool2D):
def compute(self, x, offset, out): def compute(self, x, offset, out):
self.attrs = {'pooltype': "MAX", 'is_test': True} self.attrs = {"pad_value": 1.0, 'pooltype': "MAX", 'is_test': True}
for i in range(len(offset[0]) - 1): for i in range(len(offset[0]) - 1):
if offset[0][i] == offset[0][i + 1]:
out[i] = self.attrs["pad_value"] * np.ones((3, 11))
else:
sub_x = np.reshape(x[offset[0][i]:offset[0][i + 1], :], sub_x = np.reshape(x[offset[0][i]:offset[0][i + 1], :],
(-1, 3 * 11)) (-1, 3 * 11))
out[i] = np.reshape(np.amax(sub_x, axis=0), (3, 11)) out[i] = np.reshape(np.amax(sub_x, axis=0), (3, 11))
...@@ -203,23 +300,44 @@ class TestSeqMaxPool2DInference(TestSeqMaxPool2D): ...@@ -203,23 +300,44 @@ class TestSeqMaxPool2DInference(TestSeqMaxPool2D):
return return
class TestSeqMaxPool2DInferenceLen0(TestSeqMaxPool2DInference):
def set_lod(self):
return [[0, 3, 0, 10, 0]]
class TestSeqLastPool2D(TestSeqAvgPool2D): class TestSeqLastPool2D(TestSeqAvgPool2D):
def compute(self, x, offset, out): def compute(self, x, offset, out):
self.attrs = {'pooltype': "LAST"} self.attrs = {"pad_value": 0.0, 'pooltype': "LAST"}
for i in range(len(offset[0]) - 1): for i in range(len(offset[0]) - 1):
if offset[0][i] == offset[0][i + 1]:
out[i] = self.attrs["pad_value"] * np.ones((3, 17))
else:
sub_x = np.reshape(x[offset[0][i]:offset[0][i + 1], :], sub_x = np.reshape(x[offset[0][i]:offset[0][i + 1], :],
(-1, 3 * 17)) (-1, 3 * 17))
out[i] = np.reshape(sub_x[-1, :], (3, 17)) out[i] = np.reshape(sub_x[-1, :], (3, 17))
class TestSeqLastPool2DLen0(TestSeqLastPool2D):
def set_lod(self):
return [[0, 3, 0, 1, 9, 0]]
class TestSeqFirstPool2D(TestSeqAvgPool2D): class TestSeqFirstPool2D(TestSeqAvgPool2D):
def compute(self, x, offset, out): def compute(self, x, offset, out):
self.attrs = {'pooltype': "FIRST"} self.attrs = {"pad_value": 0.0, 'pooltype': "FIRST"}
for i in range(len(offset[0]) - 1): for i in range(len(offset[0]) - 1):
if offset[0][i] == offset[0][i + 1]:
out[i] = self.attrs["pad_value"] * np.ones((3, 17))
else:
sub_x = np.reshape(x[offset[0][i]:offset[0][i + 1], :], sub_x = np.reshape(x[offset[0][i]:offset[0][i + 1], :],
(-1, 3 * 17)) (-1, 3 * 17))
out[i] = np.reshape(sub_x[0, :], (3, 17)) out[i] = np.reshape(sub_x[0, :], (3, 17))
class TestSeqFirstPool2DLen0(TestSeqFirstPool2D):
def set_lod(self):
return [[0, 3, 0, 3, 7, 0]]
if __name__ == '__main__': if __name__ == '__main__':
unittest.main() unittest.main()
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