Merge pull request #7603 from kuke/simplify_erase

Enhance GPU kernel of sequence erase op 

paddle/operators/sequence_erase_op.cc

@@ -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>);

paddle/operators/sequence_erase_op.cu

@@ -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>);

python/paddle/v2/fluid/tests/test_sequence_erase_op.py

@@ -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()