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Merge pull request #7603 from kuke/simplify_erase

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Enhance GPU kernel of sequence erase op
add_depthwiseConv_op_gpu
Yibing Liu 7 years ago committed by GitHub
parent 41b8388492 8809d43ad7
commit a1c281f07e
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GPG Key ID: 4AEE18F83AFDEB23
3 changed files with 66 additions and 48 deletions
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3
paddle/operators/sequence_erase_op.cc
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@ -86,4 +86,5 @@ REGISTER_OP_WITHOUT_GRADIENT(sequence_erase, ops::SequenceEraseOp,
ops::SequenceEraseOpMaker);
REGISTER_OP_CPU_KERNEL(
sequence_erase,
ops::SequenceEraseKernel<paddle::platform::CPUDeviceContext, int32_t>);
ops::SequenceEraseKernel<paddle::platform::CPUDeviceContext, int32_t>,
ops::SequenceEraseKernel<paddle::platform::CPUDeviceContext, int64_t>);

79
paddle/operators/sequence_erase_op.cu
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@ -23,27 +23,22 @@ using platform::PADDLE_CUDA_NUM_THREADS;
using LoDTensor = framework::LoDTensor;
template <typename T>
__global__ void LabelErasedIdx(const T* in_dat, const int in_len,
const T* tokens, const int tokens_len,
int* num_erased) {
__global__ void LabelErasedIdx(const T* in_dat, const int64_t in_len,
const int* tokens, const size_t tokens_len,
size_t* num_erased) {
int index = blockIdx.x * blockDim.x + threadIdx.x;
if (index < in_len) {
int erased = 0;
for (int i = 0; i < tokens_len; ++i) {
for (size_t i = 0; i < tokens_len; ++i) {
if (in_dat[index] == tokens[i]) {
erased = 1;
num_erased[index + 1] = 1;
break;
}
}
num_erased[index + 1] = erased;
if (index == 0) {
num_erased[0] = 0;
}
}
}
template <typename T>
__global__ void GetOutLod(const T* num_erased, const int* in_lod,
const int lod_len, int* out_lod0) {
__global__ void GetOutLod(const size_t* num_erased, const size_t* in_lod,
const size_t lod_len, size_t* out_lod0) {
int index = blockIdx.x * blockDim.x + threadIdx.x;
if (index < lod_len) {
out_lod0[index] = in_lod[index] - num_erased[in_lod[index]];
@ -51,11 +46,11 @@ __global__ void GetOutLod(const T* num_erased, const int* in_lod,
}
template <typename T>
__global__ void SetOutput(const T* in_dat, const int in_len,
const int* num_erased, T* out_dat) {
__global__ void SetOutput(const T* in_dat, const int64_t in_len,
const size_t* num_erased, T* out_dat) {
int index = blockIdx.x * blockDim.x + threadIdx.x;
if (index < in_len) {
if (in_dat[index] != in_dat[index + 1]) {
if (num_erased[index] == num_erased[index + 1]) {
out_dat[index - num_erased[index]] = in_dat[index];
}
}
@ -72,53 +67,44 @@ class SequenceEraseOpCUDAKernel : public framework::OpKernel<T> {
PADDLE_ENFORCE_EQ(lod.size(), 1UL, "Only support one level sequence now.");
PADDLE_ENFORCE_EQ(lod[0].back(), (size_t)in->numel(),
"The actual size mismatches with the LoD information.");
auto tokens = ctx.Attr<std::vector<T>>("tokens");
auto tokens_len = tokens.size();
auto tokens = ctx.Attr<std::vector<int>>("tokens");
auto in_len = in->numel();
auto in_dat = in->data<T>();
auto lod0 = lod[0];
thrust::host_vector<T> host_tokens(tokens_len);
for (size_t i = 0; i < tokens.size(); ++i) {
host_tokens[i] = tokens[i];
}
thrust::device_vector<T> dev_tokens = host_tokens;
thrust::device_vector<int> num_erased(in_len + 1);
T* dev_tokens_ptr = thrust::raw_pointer_cast(dev_tokens.data());
int* num_erased_ptr = thrust::raw_pointer_cast(num_erased.data());
// Copy tokens to GPU
thrust::device_vector<int> dev_tokens(tokens.begin(), tokens.end());
int* dev_tokens_ptr = thrust::raw_pointer_cast(dev_tokens.data());
// Count number of elements to be erased
thrust::device_vector<size_t> num_erased(in_len + 1, 0);
size_t* num_erased_ptr = thrust::raw_pointer_cast(num_erased.data());
auto stream = ctx.cuda_device_context().stream();
LabelErasedIdx<<<(in_len - 1) / PADDLE_CUDA_NUM_THREADS + 1,
PADDLE_CUDA_NUM_THREADS, 0, stream>>>(
in_dat, in_len, dev_tokens_ptr, tokens_len, num_erased_ptr);
in_dat, in_len, dev_tokens_ptr, tokens.size(), num_erased_ptr);
thrust::inclusive_scan(num_erased.begin() + 1, num_erased.end(),
num_erased.begin() + 1);
// Calc LoD
// Copy LoD to GPU
auto lod0 = lod[0];
auto lod_len = lod0.size();
thrust::host_vector<int> host_lod(lod_len);
for (size_t i = 0; i < lod_len; ++i) {
host_lod[i] = lod0[i];
}
thrust::device_vector<int> dev_in_lod = host_lod;
thrust::device_vector<int> dev_out_lod(lod_len);
int* dev_in_lod_ptr = thrust::raw_pointer_cast(dev_in_lod.data());
int* dev_out_lod_ptr = thrust::raw_pointer_cast(dev_out_lod.data());
thrust::device_vector<size_t> dev_in_lod = lod0;
size_t* dev_in_lod_ptr = thrust::raw_pointer_cast(dev_in_lod.data());
// Calc output LoD
thrust::device_vector<size_t> dev_out_lod(lod_len);
size_t* dev_out_lod_ptr = thrust::raw_pointer_cast(dev_out_lod.data());
GetOutLod<<<(lod_len - 1) / PADDLE_CUDA_NUM_THREADS + 1,
PADDLE_CUDA_NUM_THREADS, 0, stream>>>(
num_erased_ptr, dev_in_lod_ptr, lod_len, dev_out_lod_ptr);
thrust::host_vector<int> host_out_lod = dev_out_lod;
std::vector<int> out_lod0(lod_len, 0);
for (size_t i = 0; i < lod_len; i++) {
out_lod0[i] = host_out_lod[i];
}
// Set LoD for output
thrust::host_vector<size_t> out_lod0 = dev_out_lod;
framework::LoD out_lod;
out_lod.push_back(out_lod0);
out->set_lod(out_lod);
// Set output
out->Resize({out_lod0.back(), 1});
out->Resize({static_cast<int64_t>(out_lod0.back()), 1});
auto out_dat = out->mutable_data<T>(ctx.GetPlace());
SetOutput<<<(in_len - 1) / PADDLE_CUDA_NUM_THREADS + 1,
PADDLE_CUDA_NUM_THREADS, 0, stream>>>(in_dat, in_len,
@ -130,4 +116,5 @@ class SequenceEraseOpCUDAKernel : public framework::OpKernel<T> {
} // namespace paddle
REGISTER_OP_CUDA_KERNEL(sequence_erase,
paddle::operators::SequenceEraseOpCUDAKernel<int32_t>);
paddle::operators::SequenceEraseOpCUDAKernel<int32_t>,
paddle::operators::SequenceEraseOpCUDAKernel<int64_t>);

32
python/paddle/v2/fluid/tests/test_sequence_erase_op.py
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@ -29,7 +29,7 @@ def sequence_erase(in_seq, lod0, tokens):
return np.array(out_seq).astype("int32"), new_lod0
class TestSequenceEraseOp(OpTest):
class TestSequenceEraseOpInt32(OpTest):
def setUp(self):
self.op_type = "sequence_erase"
in_seq = np.random.randint(0, 10, (30, 1)).astype("int32")
@ -44,5 +44,35 @@ class TestSequenceEraseOp(OpTest):
self.check_output()
class TestSequenceEraseOpInt64(OpTest):
def setUp(self):
self.op_type = "sequence_erase"
in_seq = np.random.randint(0, 10, (30, 1)).astype("int64")
lod = [[0, 9, 13, 24, 30]]
tokens = [2, 3, 5]
out_seq, new_lod0 = sequence_erase(in_seq, lod[0], tokens)
self.attrs = {'tokens': tokens}
self.inputs = {'X': (in_seq, lod)}
self.outputs = {'Out': (out_seq, [new_lod0])}
def test_check_output(self):
self.check_output()
class TestSequenceEraseOpEmpty(OpTest):
def setUp(self):
self.op_type = "sequence_erase"
in_seq = np.random.randint(0, 10, (30, 1)).astype("int32")
lod = [[0, 9, 13, 24, 30]]
tokens = []
out_seq, new_lod0 = sequence_erase(in_seq, lod[0], tokens)
self.attrs = {'tokens': tokens}
self.inputs = {'X': (in_seq, lod)}
self.outputs = {'Out': (out_seq, [new_lod0])}
def test_check_output(self):
self.check_output()
if __name__ == '__main__':
unittest.main()

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