Enhance unit testing.

1. user can disable peephole connections.
2. not calculate some gradients.

paddle/operators/lstm_op.cc

@@ -164,16 +164,19 @@ class LSTMOpMaker : public framework::OpProtoAndCheckerMaker {
         "(string, default: sigmoid)"
         "The activation for input gate, forget gate and output "
         "gate, `sigmoid` by default.")
-        .SetDefault("sigmoid");
+        .SetDefault("sigmoid")
+        .InEnum({"sigmoid", "tanh", "relu", "identity"});
     AddAttr<std::string>("cell_activation",
                          "(string, default: tanh)"
                          "The activation for cell output, `tanh` by defalut.")
-        .SetDefault("tanh");
+        .SetDefault("tanh")
+        .InEnum({"sigmoid", "tanh", "relu", "identity"});
     AddAttr<std::string>("candidate_activation",
                          "(string, default: tanh)"
                          "The activation for candidate hidden state, "
                          "`tanh` by default.")
-        .SetDefault("tanh");
+        .SetDefault("tanh")
+        .InEnum({"sigmoid", "tanh", "relu", "identity"});
     AddComment(R"DOC(
 Long-Short Term Memory (LSTM) Operator.
 

paddle/operators/lstm_op.h

@@ -69,7 +69,7 @@ class LSTMKernel : public framework::OpKernel<T> {
     }
 
     math::LstmMetaValue<T> lstm_value;
-    if (bias) {
+    if (bias && ctx.Attr<bool>("use_peepholes")) {
       T* bias_data = const_cast<T*>(bias->data<T>());
       // the code style in LstmMetaValue will be updated later.
 
@@ -85,6 +85,7 @@ class LSTMKernel : public framework::OpKernel<T> {
     Tensor ordered_c0;
     if (cell_t0) {
       math::CopyMatrixRowsFunctor<Place, T> row_shuffle;
+      ordered_c0.mutable_data<T>(cell_t0->dims(), ctx.GetPlace());
       const size_t* order = batch_gate->lod()[2].data();
       row_shuffle(device_ctx, *cell_t0, order, ordered_c0, true);
       lstm_value.prevStateValue = ordered_c0.data<T>();
@@ -124,6 +125,7 @@ class LSTMKernel : public framework::OpKernel<T> {
       } else if (hidden_t0) {
         math::CopyMatrixRowsFunctor<Place, T> row_shuffle;
         Tensor ordered_h0;
+        ordered_h0.mutable_data<T>(hidden_t0->dims(), ctx.GetPlace());
         const size_t* order = batch_gate->lod()[2].data();
         row_shuffle(device_ctx, *hidden_t0, order, ordered_h0, true);
         math::matmul<Place, T>(device_ctx, ordered_h0, false, *weight, false,
@@ -199,7 +201,7 @@ class LSTMGradKernel : public framework::OpKernel<T> {
     PADDLE_ENFORCE_EQ(frame_size, out_dims[1]);
 
     math::LstmMetaValue<T> lstm_value;
-    if (bias) {
+    if (bias && ctx.Attr<bool>("use_peepholes")) {
       T* bias_data = const_cast<T*>(bias->data<T>());
       lstm_value.checkIg = bias_data + 4 * frame_size;
       lstm_value.checkFg = lstm_value.checkIg + frame_size;
@@ -211,9 +213,13 @@ class LSTMGradKernel : public framework::OpKernel<T> {
     }
 
     math::LstmMetaGrad<T> lstm_grad;
+
     if (bias && bias_g) {
-      T* bias_g_data = const_cast<T*>(bias_g->mutable_data<T>(ctx.GetPlace()));
+      bias_g->mutable_data<T>(ctx.GetPlace());
       zero(device_ctx, bias_g, static_cast<T>(0.0));
+    }
+    if (bias && bias_g && ctx.Attr<bool>("use_peepholes")) {
+      T* bias_g_data = bias_g->data<T>();
       lstm_grad.checkIgGrad = bias_g_data + 4 * frame_size;
       lstm_grad.checkFgGrad = lstm_grad.checkIgGrad + frame_size;
       lstm_grad.checkOgGrad = lstm_grad.checkFgGrad + frame_size;

paddle/operators/math/detail/lstm_cpu_kernel.h

@@ -52,9 +52,9 @@ void naive_lstm_forward_one_sequence(Op op, LstmMetaValue<T> value,
     rValueIg = valueIg[i];
     rValueFg = valueFg[i];
     rValueOg = valueOg[i];
-    rCheckI = value.checkIg[i];
-    rCheckF = value.checkFg[i];
-    rCheckO = value.checkOg[i];
+    rCheckI = value.checkIg ? value.checkIg[i] : 0;
+    rCheckF = value.checkFg ? value.checkFg[i] : 0;
+    rCheckO = value.checkOg ? value.checkOg[i] : 0;
 
     if (value.prevStateValue) {
       rPrevState = value.prevStateValue[i];
@@ -114,9 +114,9 @@ void naive_lstm_backward_one_sequence(Op op, LstmMetaValue<T> value,
     rValueIg = valueIg[i];
     rValueFg = valueFg[i];
     rValueOg = valueOg[i];
-    rCheckI = value.checkIg[i];
-    rCheckF = value.checkFg[i];
-    rCheckO = value.checkOg[i];
+    rCheckI = value.checkIg ? value.checkIg[i] : 0;
+    rCheckF = value.checkFg ? value.checkFg[i] : 0;
+    rCheckO = value.checkOg ? value.checkOg[i] : 0;
     rState = value.stateValue[i];
     rStateAtv = value.stateActiveValue[i];
     rOutputGrad = grad.outputGrad[i];
@@ -155,9 +155,9 @@ void avx_lstm_forward_one_sequence(Op op, LstmMetaValue<T> value, int frameSize,
   __m256 rValueIg;
   __m256 rValueFg;
   __m256 rValueOg;
-  __m256 rCheckI;
-  __m256 rCheckF;
-  __m256 rCheckO;
+  __m256 rCheckI = _mm256_set1_ps(0.0f);
+  __m256 rCheckF = _mm256_set1_ps(0.0f);
+  __m256 rCheckO = _mm256_set1_ps(0.0f);
   __m256 rState;
   __m256 rPrevState = _mm256_set1_ps(0.0f);
   __m256 rStateAtv;
@@ -173,9 +173,11 @@ void avx_lstm_forward_one_sequence(Op op, LstmMetaValue<T> value, int frameSize,
     rValueIg = valueIg[i];
     rValueFg = valueFg[i];
     rValueOg = valueOg[i];
+    if (value.checkIg) {
       rCheckI = ((__m256 *)value.checkIg)[i];
       rCheckF = ((__m256 *)value.checkFg)[i];
       rCheckO = ((__m256 *)value.checkOg)[i];
+    }
 
     if (value.prevStateValue) {
       rPrevState = ((__m256 *)value.prevStateValue)[i];
@@ -216,9 +218,9 @@ void avx_lstm_backward_one_sequence(Op op, LstmMetaValue<T> value,
   __m256 rState;
   __m256 rStateAtv;
   __m256 rOutputGrad;
-  __m256 rCheckI;
-  __m256 rCheckF;
-  __m256 rCheckO;
+  __m256 rCheckI = _mm256_set1_ps(0.0f);
+  __m256 rCheckF = _mm256_set1_ps(0.0f);
+  __m256 rCheckO = _mm256_set1_ps(0.0f);
   __m256 rCheckIGrad;
   __m256 rCheckFGrad;
   __m256 rCheckOGrad;
@@ -237,9 +239,11 @@ void avx_lstm_backward_one_sequence(Op op, LstmMetaValue<T> value,
     rValueIg = valueIg[i];
     rValueFg = valueFg[i];
     rValueOg = valueOg[i];
+    if (value.checkIg) {
       rCheckI = ((__m256 *)value.checkIg)[i];
       rCheckF = ((__m256 *)value.checkFg)[i];
       rCheckO = ((__m256 *)value.checkOg)[i];
+    }
     rState = ((__m256 *)value.stateValue)[i];
     rStateAtv = ((__m256 *)value.stateActiveValue)[i];
     rOutputGrad = ((__m256 *)grad.outputGrad)[i];

paddle/operators/math/detail/lstm_gpu_kernel.h

@@ -55,9 +55,10 @@ __global__ void KeLstmForward(Op op, LstmMetaValue<T> value, int frameSize,
   T rValueIg;
   T rValueFg;
   T rValueOg;
-  T rCheckI = value.checkIg[frameIdx];
-  T rCheckF = value.checkFg[frameIdx];
-  T rCheckO = value.checkOg[frameIdx];
+
+  T rCheckI = value.checkIg ? value.checkIg[frameIdx] : 0;
+  T rCheckF = value.checkFg ? value.checkFg[frameIdx] : 0;
+  T rCheckO = value.checkOg ? value.checkOg[frameIdx] : 0;
 
   rValueIn = value.gateValue[frameIdx];
   rValueIg = value.gateValue[frameIdx + frameSize];
@@ -121,9 +122,10 @@ __global__ void KeLstmBackward(Op op, LstmMetaValue<T> value,
   T rStateGrad;
   T rStateAtv;
   T rOutputGrad;
-  T rCheckI = value.checkIg[frameIdx];
-  T rCheckF = value.checkFg[frameIdx];
-  T rCheckO = value.checkOg[frameIdx];
+  T rCheckI = value.checkIg ? value.checkIg[frameIdx] : 0;
+  T rCheckF = value.checkFg ? value.checkFg[frameIdx] : 0;
+  T rCheckO = value.checkOg ? value.checkOg[frameIdx] : 0;
+
   T rCheckIGrad;
   T rCheckFGrad;
   T rCheckOGrad;

paddle/operators/math/sequence2batch.h

@@ -31,7 +31,7 @@ class CopyMatrixRowsFunctor {
   // The indexed rows are based on the input index.
   void operator()(const platform::DeviceContext& context,
                   const framework::Tensor& src, const size_t* index,
-                  framework::Tensor* dst, bool is_src_index);
+                  framework::Tensor& dst, bool is_src_index);
 };
 
 template <typename Place, typename T>
@@ -57,7 +57,7 @@ class LoDTensor2BatchFunctor {
                   bool is_reverse = false) const {
     if (!is_cal_batch_lod) {
       auto lods = batch.lod();
-      PADDLE_ENFORCE_LE(lods.size(), 2UL);
+      PADDLE_ENFORCE_GT(lods.size(), 2UL);
       PADDLE_ENFORCE_EQ(lods[1].size(),
                         static_cast<size_t>(lod_tensor.dims()[0]));
       CopyMatrixRowsFunctor<Place, T> to_batch;
@@ -68,8 +68,6 @@ class LoDTensor2BatchFunctor {
     auto lods = lod_tensor.lod();
     auto lod = lods[0];
     PADDLE_ENFORCE_EQ(lods.size(), 1UL, "Only support one level sequence now.");
-    PADDLE_ENFORCE_EQ(lod_tensor.dims()[0],
-                      static_cast<int64_t>(lod.size() - 1));
 
     std::vector<SeqInfo> seq_info;
     for (size_t seq_id = 0; seq_id < lod.size() - 1; ++seq_id) {
@@ -112,7 +110,7 @@ class LoDTensor2BatchFunctor {
     int num_batch = seq_info[0].length;
     batch_lods[0].resize(static_cast<size_t>(num_batch + 1));
     // batch_lods[1] is the raw index in the input LoDTensor
-    batch_lods[1].resize(static_cast<size_t>(seq_info.size()));
+    batch_lods[1].resize(static_cast<size_t>(lod_tensor.dims()[0]));
     // batch_lods[2] is the sort order for the input LoDTensor.
     batch_lods[2].resize(seq_info.size());
 
@@ -152,8 +150,7 @@ class Batch2LoDTensorFunctor {
                   const framework::LoDTensor& batch,
                   framework::LoDTensor& lod_tensor) const {
     auto in_lod = batch.lod();
-    PADDLE_ENFORCE_LT(in_lod.size(), 2UL,
-                      "The LoD size of input `batch` should be 2.");
+    PADDLE_ENFORCE_GT(in_lod.size(), 2UL);
     PADDLE_ENFORCE_EQ(in_lod[1].size(),
                       static_cast<size_t>(lod_tensor.dims()[0]));
     CopyMatrixRowsFunctor<Place, T> to_seq;

python/paddle/v2/framework/tests/test_lstm_op.py

@@ -117,9 +117,9 @@ class TestLstmOp(OpTest):
         self.act_cell = 'tanh'
         self.act_cand = 'tanh'
 
-        self.has_initial_state = True
-        self.has_bias = True
+        self.has_initial_state = False
         self.is_reverse = False
+        self.use_peepholes = True
 
     def setUp(self):
         self.set_argument()
@@ -129,20 +129,26 @@ class TestLstmOp(OpTest):
         N = len(self.lod[0]) - 1
 
         x = np.random.normal(size=(T, 4 * self.D)).astype('float64')
+        if self.has_initial_state:
+            h0 = np.random.normal(size=(N, self.D)).astype('float64')
+            c0 = np.random.normal(size=(N, self.D)).astype('float64')
+        else:
             h0 = np.zeros((N, self.D)).astype('float64')
             c0 = np.zeros((N, self.D)).astype('float64')
         w = np.random.normal(size=(self.D, 4 * self.D)).astype('float64')
+        if self.use_peepholes:
             b = np.random.normal(size=(1, 7 * self.D)).astype('float64')
+        else:
+            b = np.random.normal(size=(1, 4 * self.D)).astype('float64')
 
-        w_b = b[:, 0:4 * self.D] if self.has_bias else None
-        w_c = b[:, 4 * self.D:] if self.has_bias else None
+        w_b = b[:, 0:4 * self.D]
+        w_c = b[:, 4 * self.D:] if self.use_peepholes else None
         h, c = lstm(x, self.lod, h0, c0, w, w_b, w_c, self.is_reverse,
                     ACTVATION[self.act_gate], ACTVATION[self.act_cell],
                     ACTVATION[self.act_cand])
 
         self.inputs = {'Input': (x, self.lod), 'Weight': w}
 
-        if self.has_bias:
         self.inputs['Bias'] = b
 
         if self.has_initial_state:
@@ -154,18 +160,17 @@ class TestLstmOp(OpTest):
             'Cell': (c, self.lod),
         }
         self.attrs = {
-            'use_peepholes': True,
+            'use_peepholes': self.use_peepholes,
             'is_reverse': self.is_reverse,
             'gate_activation': self.act_gate,
             'cell_activation': self.act_cell,
             'candidate_activation': self.act_cand
         }
 
-    def not_test_check_output(self):
+    def test_check_output(self):
         self.check_output(atol=1e-8)
 
-    #TODO(qingqing) add more unit testing case
-    def not_test_check_grad(self):
+    def test_check_grad(self):
         # TODO(qingqing) remove folowing lines after the check_grad is refined.
         N = len(self.lod[0]) - 1
         self.outputs['BatchGate'] = np.zeros((N, 4 * self.D)).astype('float64')
@@ -174,8 +179,38 @@ class TestLstmOp(OpTest):
         self.check_grad(
             ['Input', 'Weight', 'Bias'], ['Hidden'], max_relative_error=5e-4)
 
+    def test_check_grad_ingore_bias(self):
+        N = len(self.lod[0]) - 1
+        self.outputs['BatchGate'] = np.zeros((N, 4 * self.D)).astype('float64')
+        self.outputs['BatchCellPreAct'] = np.zeros(
+            (N, self.D)).astype('float64')
+        self.check_grad(
+            ['Input', 'Weight'], ['Hidden'],
+            max_relative_error=5e-4,
+            no_grad_set=set('Bias'))
+
+    def test_check_grad_ingore_weight(self):
+        N = len(self.lod[0]) - 1
+        self.outputs['BatchGate'] = np.zeros((N, 4 * self.D)).astype('float64')
+        self.outputs['BatchCellPreAct'] = np.zeros(
+            (N, self.D)).astype('float64')
+        self.check_grad(
+            ['Input', 'Bias'], ['Hidden'],
+            max_relative_error=5e-4,
+            no_grad_set=set('Weight'))
+
+    def test_check_grad_ingore_input(self):
+        N = len(self.lod[0]) - 1
+        self.outputs['BatchGate'] = np.zeros((N, 4 * self.D)).astype('float64')
+        self.outputs['BatchCellPreAct'] = np.zeros(
+            (N, self.D)).astype('float64')
+        self.check_grad(
+            ['Weight', 'Bias'], ['Hidden'],
+            max_relative_error=5e-4,
+            no_grad_set=set('Input'))
+
 
-class TestLstmOpHasNoInitial(TestLstmOp):
+class TestLstmOpHasInitial(TestLstmOp):
     def set_argument(self):
         self.lod = [[0, 2, 5, 7]]
         self.D = 16
@@ -184,12 +219,52 @@ class TestLstmOpHasNoInitial(TestLstmOp):
         self.act_cell = 'tanh'
         self.act_cand = 'tanh'
 
-        self.has_initial_state = False
+        self.has_initial_state = True
         self.is_reverse = True
-        self.has_bias = True
+        self.use_peepholes = True
 
+    def test_check_grad(self):
+        # TODO(qingqing) remove folowing lines after the check_grad is refined.
+        N = len(self.lod[0]) - 1
+        self.outputs['BatchGate'] = np.zeros((N, 4 * self.D)).astype('float64')
+        self.outputs['BatchCellPreAct'] = np.zeros(
+            (N, self.D)).astype('float64')
+        self.check_grad(
+            ['Input', 'Weight', 'Bias', 'H0', 'C0'], ['Hidden'],
+            max_relative_error=5e-4)
 
-class TestLstmOpHasNoBias(TestLstmOp):
+    # In order to speed up, skip following testing
+    def test_check_grad_ingore_bias(self):
+        return
+
+    def test_check_grad_ingore_weight(self):
+        return
+
+    def test_check_grad_ingore_input(self):
+        return
+
+    def test_check_grad_ingore_h0(self):
+        N = len(self.lod[0]) - 1
+        self.outputs['BatchGate'] = np.zeros((N, 4 * self.D)).astype('float64')
+        self.outputs['BatchCellPreAct'] = np.zeros(
+            (N, self.D)).astype('float64')
+        self.check_grad(
+            ['Input', 'Weight', 'Bias', 'C0'], ['Hidden'],
+            max_relative_error=5e-4,
+            no_grad_set=set('H0'))
+
+    def test_check_grad_ingore_c0(self):
+        N = len(self.lod[0]) - 1
+        self.outputs['BatchGate'] = np.zeros((N, 4 * self.D)).astype('float64')
+        self.outputs['BatchCellPreAct'] = np.zeros(
+            (N, self.D)).astype('float64')
+        self.check_grad(
+            ['Input', 'Weight', 'Bias', 'H0'], ['Hidden'],
+            max_relative_error=5e-4,
+            no_grad_set=set('C0'))
+
+
+class TestLstmOpRerverse(TestLstmOp):
     def set_argument(self):
         self.lod = [[0, 2, 5, 7]]
         self.D = 16
@@ -198,15 +273,22 @@ class TestLstmOpHasNoBias(TestLstmOp):
         self.act_cell = 'tanh'
         self.act_cand = 'tanh'
 
-        self.has_initial_state = True
-        self.is_reverse = False
-        self.has_bias = False
+        self.has_initial_state = False
+        self.is_reverse = True
+        self.use_peepholes = True
 
-    def test_check_output(self):
-        self.check_output(atol=1e-8)
+    # In order to speed up, skip following testing
+    def test_check_grad_ingore_bias(self):
+        return
 
+    def test_check_grad_ingore_weight(self):
+        return
 
-class TestLstmOpRerverse(TestLstmOp):
+    def test_check_grad_ingore_input(self):
+        return
+
+
+class TestLstmOpNotUsePeepholes(TestLstmOp):
     def set_argument(self):
         self.lod = [[0, 2, 5, 7]]
         self.D = 16
@@ -215,9 +297,19 @@ class TestLstmOpRerverse(TestLstmOp):
         self.act_cell = 'tanh'
         self.act_cand = 'tanh'
 
-        self.has_initial_state = True
+        self.has_initial_state = False
         self.is_reverse = True
-        self.has_bias = True
+        self.use_peepholes = False
+
+    # In order to speed up, skip following testing
+    def test_check_grad_ingore_bias(self):
+        return
+
+    def test_check_grad_ingore_weight(self):
+        return
+
+    def test_check_grad_ingore_input(self):
+        return
 
 
 if __name__ == '__main__':

python/paddle/v2/optimizer.py

@@ -102,7 +102,7 @@ class Momentum(Optimizer):
 
     ..  math::
 
-        v_{t} &= k * v_{t-1} - \\gamma_t / (g_{t} + \\lambda w_{t-1}) \\\\
+        v_{t} &= k * v_{t-1} - \\gamma_t (g_{t} + \\lambda w_{t-1}) \\\\
         w_{t} &= w_{t-1} + v_{t} \\\\
 
     where, :math:`k` is momentum, :math:`\\lambda` is decay rate,