Add unit testing for gemv and fix the gradien check for bais.

paddle/framework/lod_tensor_test.cu

@@ -36,8 +36,8 @@ TEST(LoDTensor, LoDInGPU) {
   lod_tensor.mutable_data<float>(place);
 
   lod_tensor.set_lod(src_lod);
-  CHECK_EQ(lod_tensor.lod_element(0, 2).first, 4UL);
-  CHECK_EQ(lod_tensor.lod_element(0, 4).first, 8UL);
+  EXPECT_EQ(lod_tensor.lod_element(0, 2).first, 4UL);
+  EXPECT_EQ(lod_tensor.lod_element(0, 4).first, 8UL);
 
   auto lod = lod_tensor.lod();
 
@@ -45,6 +45,6 @@ TEST(LoDTensor, LoDInGPU) {
   cudaDeviceSynchronize();
 
   for (size_t i = 0; i < src_lod[0].size(); ++i) {
-    CHECK_EQ(lod[0].data()[i], src_lod[0].data()[i] * 2);
+    EXPECT_EQ(lod[0].data()[i], src_lod[0].data()[i] * 2);
   }
-}
\ No newline at end of file
+}

paddle/operators/lstm_op.h

@@ -162,9 +162,9 @@ class LSTMGradKernel : public framework::OpKernel<T> {
     auto* bias_g = ctx.Output<Tensor>(framework::GradVarName("Bias"));
 
     auto& device_ctx = ctx.device_context();
+    math::SetConstant<Place, T> zero;
     if (weight_g) {
       weight_g->mutable_data<T>(ctx.GetPlace());
-      math::SetConstant<Place, T> zero;
       zero(device_ctx, weight_g, static_cast<T>(0.0));
     }
 
@@ -188,6 +188,7 @@ 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()));
+      zero(device_ctx, bias_g, static_cast<T>(0.0));
       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;
@@ -219,6 +220,8 @@ class LSTMGradKernel : public framework::OpKernel<T> {
     batch_cell_g.mutable_data<T>(out_dims, ctx.GetPlace());
     batch_cell_g.set_lod(batch_gate->lod());
     to_batch(device_ctx, *cell_g, batch_cell_g, false);
+    // TODO(qingqing) support the case output cell has gradient.
+    zero(device_ctx, &batch_cell_g, static_cast<T>(0.0));
 
     LoDTensor batch_gate_g;
     batch_gate_g.mutable_data<T>(batch_gate->dims(), ctx.GetPlace());
@@ -304,7 +307,7 @@ class LSTMGradKernel : public framework::OpKernel<T> {
       int n = static_cast<int>(batch_gate_g.dims()[1]);
 
       Tensor ones;
-      ones.mutable_data<T>({1, m}, ctx.GetPlace());
+      ones.mutable_data<T>({m}, ctx.GetPlace());
       math::SetConstant<Place, T> set;
       set(device_ctx, &ones, static_cast<T>(1.0));
 

paddle/operators/math/math_function_test.cc

@@ -89,3 +89,53 @@ TEST(math_function, zero) {
   EXPECT_EQ(t[2], 1);
   EXPECT_EQ(t[3], 1);
 }
+
+template <typename T>
+void GemvTest(int m, int n, bool trans) {
+  paddle::framework::Tensor mat_a;
+  paddle::framework::Tensor vec_b;
+  paddle::framework::Tensor vec_c;
+  auto* cpu_place = new paddle::platform::CPUPlace();
+  int b_num = trans ? m : n;
+  int c_num = trans ? n : m;
+
+  T* data_a = mat_a.mutable_data<T>({m, n}, *cpu_place);
+  T* data_b = vec_b.mutable_data<T>({b_num}, *cpu_place);
+  T* data_c = vec_c.mutable_data<T>({c_num}, *cpu_place);
+  for (int i = 0; i < mat_a.numel(); ++i) {
+    data_a[i] = static_cast<T>(i);
+  }
+  for (int i = 0; i < vec_b.numel(); ++i) {
+    data_b[i] = static_cast<T>(i);
+  }
+
+  paddle::platform::CPUDeviceContext context(*cpu_place);
+  paddle::operators::math::gemv<paddle::platform::CPUPlace, T>(
+      context, trans, static_cast<int>(m), static_cast<int>(n), 1., data_a,
+      data_b, 0., data_c);
+
+  if (!trans) {
+    for (int i = 0; i < m; ++i) {
+      T sum = 0.0;
+      for (int j = 0; j < n; ++j) {
+        sum += data_a[i * n + j] * data_b[j];
+      }
+      ASSERT_FLOAT_EQ(data_c[i], sum);
+    }
+  } else {
+    for (int i = 0; i < n; ++i) {
+      T sum = 0.0;
+      for (int j = 0; j < m; ++j) {
+        sum += data_a[j * n + i] * data_b[j];
+      }
+      ASSERT_FLOAT_EQ(data_c[i], sum);
+    }
+  }
+}
+
+TEST(math_function, gemv) {
+  GemvTest<float>(3, 13, false);
+  GemvTest<double>(4, 5, false);
+  GemvTest<float>(12, 7, true);
+  GemvTest<double>(7, 9, true);
+}

paddle/operators/math/math_function_test.cu

@@ -177,3 +177,65 @@ TEST(math_function, gemm_trans_cublas) {
   EXPECT_EQ(input3_ptr[7], 99);
   delete gpu_place;
 }
+
+template <typename T>
+void GemvTest(int m, int n, bool trans) {
+  paddle::framework::Tensor mat_a;
+  paddle::framework::Tensor vec_b;
+  paddle::framework::Tensor vec_c;
+  auto* cpu_place = new paddle::platform::CPUPlace();
+
+  T* data_a = mat_a.mutable_data<T>({m, n}, *cpu_place);
+  T* data_b = vec_b.mutable_data<T>({trans ? m : n}, *cpu_place);
+  T* data_c = vec_c.mutable_data<T>({trans ? n : m}, *cpu_place);
+
+  auto* gpu_place = new paddle::platform::GPUPlace(0);
+  paddle::framework::Tensor g_mat_a;
+  paddle::framework::Tensor g_vec_b;
+  paddle::framework::Tensor g_vec_c;
+  T* g_data_a = g_mat_a.mutable_data<T>(mat_a.dims(), *gpu_place);
+  T* g_data_b = g_vec_b.mutable_data<T>(vec_b.dims(), *gpu_place);
+  T* g_data_c = g_vec_c.mutable_data<T>(vec_c.dims(), *gpu_place);
+
+  for (int i = 0; i < mat_a.numel(); ++i) {
+    data_a[i] = static_cast<T>(i);
+  }
+  for (int i = 0; i < vec_b.numel(); ++i) {
+    data_b[i] = static_cast<T>(i);
+  }
+
+  paddle::platform::CUDADeviceContext context(*gpu_place);
+  g_mat_a.CopyFrom(mat_a, *gpu_place, context);
+  g_vec_b.CopyFrom(vec_b, *gpu_place, context);
+
+  paddle::operators::math::gemv<paddle::platform::GPUPlace, T>(
+      context, trans, static_cast<int>(m), static_cast<int>(n), 1., g_data_a,
+      g_data_b, 0., g_data_c);
+
+  vec_c.CopyFrom(g_vec_c, paddle::platform::CPUPlace(), context);
+
+  if (!trans) {
+    for (int i = 0; i < m; ++i) {
+      T sum = 0.0;
+      for (int j = 0; j < n; ++j) {
+        sum += data_a[i * n + j] * data_b[j];
+      }
+      ASSERT_FLOAT_EQ(data_c[i], sum);
+    }
+  } else {
+    for (int i = 0; i < n; ++i) {
+      T sum = 0.0;
+      for (int j = 0; j < m; ++j) {
+        sum += data_a[j * n + i] * data_b[j];
+      }
+      ASSERT_FLOAT_EQ(data_c[i], sum);
+    }
+  }
+}
+
+TEST(math_function, gemv) {
+  GemvTest<float>(3, 13, false);
+  GemvTest<double>(3, 13, false);
+  GemvTest<float>(3, 13, true);
+  GemvTest<double>(3, 13, true);
+}

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

@@ -114,26 +114,20 @@ def lstm(
 
 
 class TestLstmOp(OpTest):
-    def set_data(self):
-        # self.lod = [[0, 2, 6, 9]]
-        # self.D = 64
-        # self.sort_idx = [2, 6, 0, 3, 7, 1, 4, 8, 5]
-
-        self.lod = [[0, 1]]
-        self.D = 4
-        self.sort_idx = [0]
-
-        # self.act_gate = 'identity'
-        # self.act_cell = 'identity'
-        # self.act_cand = 'identity'
+    def set_argument(self):
+        self.lod = [[0, 2, 6, 9]]
+        self.D = 16
+        self.sort_idx = [2, 6, 0, 3, 7, 1, 4, 8, 5]
+
         self.act_gate = 'sigmoid'
         self.act_cell = 'tanh'
         self.act_cand = 'tanh'
 
+        self.has_initial_state = True
         self.is_reverse = False
 
     def setUp(self):
-        self.set_data()
+        self.set_argument()
         self.op_type = 'lstm'
 
         T = self.lod[0][-1]
@@ -155,17 +149,14 @@ class TestLstmOp(OpTest):
         for i, j in enumerate(self.sort_idx):
             g_sort[i, :] = g[j, :]
 
-        self.inputs = {
-            'Input': (x, self.lod),
-            'H0': h0,
-            'C0': c0,
-            'Weight': w,
-            'Bias': b
-        }
+        self.inputs = {'Input': (x, self.lod), 'Weight': w, 'Bias': b}
+        self.inputs['H0'] = h0
+        self.inputs['C0'] = c0
+
         self.outputs = {
             'Hidden': (h, self.lod),
             'Cell': (c, self.lod),
-            #'BatchGate': g_sort,
+            'BatchGate': g_sort,
         }
         self.attrs = {
             'usePeepholes': True,
@@ -175,26 +166,43 @@ class TestLstmOp(OpTest):
             'candidateActivation': self.act_cand
         }
 
-    def not_test_check_output(self):
+    def test_check_output(self):
         self.check_output()
 
+    #TODO(qingqing) add more unit testing case
     def test_check_grad(self):
+        # TODO(qingqing) remove folowing two lines after the check_grad is refined.
         self.outputs['BatchGate'] = None
         self.outputs['BatchCellPreAct'] = None
-        self.check_grad(['Input', 'Weight'], ['Hidden', 'Cell'])
-        #['Input', 'Weight', 'Bias'], ['Hidden', 'Cell'])
-
-    #class TestLstmOpRerverse(TestLstmOp):
-    #    def set_data(self):
-    #        self.lod = [[0, 2, 6, 9]]
-    #        self.D = 64
-    #        self.sort_idx = [2, 6, 0, 3, 7, 1, 4, 8, 5]
-    #
-    #        self.act_gate = 'sigmoid'
-    #        self.act_cell = 'tanh'
-    #        self.act_cand = 'tanh'
-    #
-    #        self.is_reverse = True
+        self.check_grad(['Input', 'Weight', 'Bias'], ['Hidden'])
+
+
+class TestLstmOpHasNoInitial(TestLstmOp):
+    def set_argument(self):
+        self.lod = [[0, 2, 6, 9]]
+        self.D = 64
+        self.sort_idx = [2, 6, 0, 3, 7, 1, 4, 8, 5]
+
+        self.act_gate = 'sigmoid'
+        self.act_cell = 'tanh'
+        self.act_cand = 'tanh'
+
+        self.has_initial_state = False
+        self.is_reverse = True
+
+
+class TestLstmOpRerverse(TestLstmOp):
+    def set_argument(self):
+        self.lod = [[0, 2, 6, 9]]
+        self.D = 64
+        self.sort_idx = [2, 6, 0, 3, 7, 1, 4, 8, 5]
+
+        self.act_gate = 'sigmoid'
+        self.act_cell = 'tanh'
+        self.act_cand = 'tanh'
+
+        self.has_initial_state = True
+        self.is_reverse = True
 
 
 if __name__ == '__main__':