Merge pull request #16945 from xuezhong/cherry-pick_fix_infershape_bug

phlrain · web-flow · commit e06e8fd6d096 · 2019-04-18T14:09:07.000+08:00
merge pull request 16907 cherry-pick
diff --git a/paddle/fluid/operators/linear_chain_crf_op.cc b/paddle/fluid/operators/linear_chain_crf_op.cc
@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include "paddle/fluid/operators/linear_chain_crf_op.h"
+
 #include <memory>
 
 namespace paddle {
@@ -152,21 +153,28 @@ class LinearChainCRFOp : public framework::OperatorWithKernel {
     auto transition_dims = ctx->GetInputDim("Transition");
     PADDLE_ENFORCE_EQ(transition_dims.size(), 2,
                       "The Input(Transition) should be a 2-D tensor.");
-    PADDLE_ENFORCE_EQ(
-        transition_dims[0] - 2, transition_dims[1],
-        "An invalid dimension for the Input(Transition), which should "
-        "be a 2-D tensor with shape [(D + 2) x D].");
-    PADDLE_ENFORCE_EQ(
-        emission_dims[1], transition_dims[1],
+    bool check = true;
+    if ((!ctx->IsRuntime()) &&
+        (transition_dims[0] <= 0 || transition_dims[1] <= 0)) {
+      check = false;
+    }
+    if (check) {
+      PADDLE_ENFORCE_EQ(
+          transition_dims[0] - 2, transition_dims[1],
+          "An invalid dimension for the Input(Transition), which should "
+          "be a 2-D tensor with shape [(D + 2) x D].");
+    }
+    PADDLE_INFERSHAPE_ENFORCE_EQ(
+        ctx, emission_dims[1], transition_dims[1],
         "The 2nd dimension of the Input(Emission) and the Input(Transition) "
         "should be equal to the tag number.");
 
     auto label_dims = ctx->GetInputDim("Label");
     PADDLE_ENFORCE(label_dims.size() == 2UL && label_dims[1] == 1UL,
                    "The Input(Label) should be a 2-D tensor with the 2nd "
                    "dimensions fixed to 1.");
-    PADDLE_ENFORCE_EQ(
-        emission_dims[0], label_dims[0],
+    PADDLE_INFERSHAPE_ENFORCE_EQ(
+        ctx, emission_dims[0], label_dims[0],
         "The height of Input(Emission) and the height of Input(Label) "
         "should be the same.");
 
@@ -211,21 +219,28 @@ class LinearChainCRFGradOp : public framework::OperatorWithKernel {
     auto transition_exps_dims = ctx->GetInputDim("TransitionExps");
     PADDLE_ENFORCE_EQ(transition_exps_dims.size(), 2,
                       "The Input(TransitionExps) should be a 2-D tensor.");
-    PADDLE_ENFORCE_EQ(
-        transition_exps_dims[0] - 2, transition_exps_dims[1],
-        "An invalid dimension for the Input(TransitionExps), which should "
-        "be a 2-D tensor with shape [(D + 2) x D].");
-    PADDLE_ENFORCE_EQ(
-        emission_exps_dims[1], transition_exps_dims[1],
+    bool check = true;
+    if ((!ctx->IsRuntime()) &&
+        (transition_exps_dims[0] <= 0 || transition_exps_dims[1] <= 0)) {
+      check = false;
+    }
+    if (check) {
+      PADDLE_ENFORCE_EQ(
+          transition_exps_dims[0] - 2, transition_exps_dims[1],
+          "An invalid dimension for the Input(TransitionExps), which should "
+          "be a 2-D tensor with shape [(D + 2) x D].");
+    }
+    PADDLE_INFERSHAPE_ENFORCE_EQ(
+        ctx, emission_exps_dims[1], transition_exps_dims[1],
         "The 2nd dimension of the Input(EmissionExps) and the "
         "Input(TransitionExps) should be equal to the tag number.");
 
     auto label_dims = ctx->GetInputDim("Label");
     PADDLE_ENFORCE(label_dims.size() == 2UL && label_dims[1] == 1UL,
                    "The Input(Label) should be a 2-D tensor with the 2nd "
                    "dimensions fixed to 1.");
-    PADDLE_ENFORCE_EQ(
-        emission_exps_dims[0], label_dims[0],
+    PADDLE_INFERSHAPE_ENFORCE_EQ(
+        ctx, emission_exps_dims[0], label_dims[0],
         "The height of Input(EmissionExps) and the height of Input(Label) "
         "should be the same.");
 
diff --git a/paddle/fluid/operators/metrics/accuracy_op.cc b/paddle/fluid/operators/metrics/accuracy_op.cc
@@ -41,10 +41,11 @@ class AccuracyOp : public framework::OperatorWithKernel {
     // it's the output of topk.
 
     PADDLE_ENFORCE_EQ(label_dim.size(), 2, "label's rank must be 2.");
-    PADDLE_ENFORCE_EQ(label_dim[1], 1, "label's second dimension must be 1");
-    PADDLE_ENFORCE_EQ(inference_dim[0], label_dim[0],
-                      "the inference tensor's num_rows must be"
-                      " the same as label.");
+    PADDLE_INFERSHAPE_ENFORCE_EQ(ctx, label_dim[1], 1,
+                                 "label's second dimension must be 1");
+    PADDLE_INFERSHAPE_ENFORCE_EQ(ctx, inference_dim[0], label_dim[0],
+                                 "the inference tensor's num_rows must be"
+                                 " the same as label.");
 
     ctx->SetOutputDim("Accuracy", {1});
     ctx->SetOutputDim("Correct", {1});
diff --git a/paddle/fluid/operators/metrics/auc_op.cc b/paddle/fluid/operators/metrics/auc_op.cc
@@ -28,12 +28,13 @@ class AucOp : public framework::OperatorWithKernel {
     PADDLE_ENFORCE(ctx->HasInput("Label"),
                    "Input of Label should not be null.");
     auto predict_width = ctx->GetInputDim("Predict")[1];
-    PADDLE_ENFORCE_EQ(predict_width, 2, "Only support binary classification");
+    PADDLE_INFERSHAPE_ENFORCE_EQ(ctx, predict_width, 2,
+                                 "Only support binary classification");
     auto predict_height = ctx->GetInputDim("Predict")[0];
     auto label_height = ctx->GetInputDim("Label")[0];
 
-    PADDLE_ENFORCE_EQ(predict_height, label_height,
-                      "Out and Label should have same height.");
+    PADDLE_INFERSHAPE_ENFORCE_EQ(ctx, predict_height, label_height,
+                                 "Out and Label should have same height.");
 
     int num_pred_buckets = ctx->Attrs().Get<int>("num_thresholds") + 1;
     int slide_steps = ctx->Attrs().Get<int>("slide_steps");
diff --git a/paddle/fluid/operators/sample_logits_op.cc b/paddle/fluid/operators/sample_logits_op.cc
@@ -11,7 +11,6 @@ distributed under the License is distributed on an "AS IS" BASIS,
 WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 See the License for the specific language governing permissions and
 limitations under the License. */
-
 #include "paddle/fluid/operators/sample_logits_op.h"
 #include <memory>
 #include "paddle/fluid/operators/math/sample_prob.h"
@@ -141,7 +140,10 @@ class SampleLogitsOp : public framework::OperatorWithKernel {
                       "The labels should be a 2-D tensor.");
 
     const int num_samples = ctx->Attrs().Get<int>("num_samples");
-    const int num_sampled_classes = labels_dims[1] + num_samples;
+    int num_sampled_classes = labels_dims[1] + num_samples;
+    if ((!ctx->IsRuntime()) && labels_dims[1] <= 0) {
+      num_sampled_classes = -1;
+    }
     ctx->SetOutputDim("Samples", {logits_dims[0], num_sampled_classes});
     ctx->SetOutputDim("Probabilities", {logits_dims[0], num_sampled_classes});
     ctx->SetOutputDim("SampledLogits", {logits_dims[0], num_sampled_classes});
diff --git a/paddle/fluid/operators/smooth_l1_loss_op.cc b/paddle/fluid/operators/smooth_l1_loss_op.cc
@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include "paddle/fluid/operators/smooth_l1_loss_op.h"
+#include <memory>
 
 namespace paddle {
 namespace operators {
@@ -27,15 +28,39 @@ class SmoothL1LossOp : public framework::OperatorWithKernel {
 
     auto x_dims = ctx->GetInputDim("X");
     auto y_dims = ctx->GetInputDim("Y");
-    PADDLE_ENFORCE_EQ(x_dims, y_dims);
+    bool check = true;
+    if ((!ctx->IsRuntime()) &&
+        (framework::product(x_dims) <= 0 || framework::product(y_dims) <= 0)) {
+      check = false;
+    }
+    if (check) {
+      PADDLE_ENFORCE_EQ(x_dims, y_dims);
+    }
     PADDLE_ENFORCE_GE(x_dims.size(), 2,
                       "The tensor rank of Input(X) should not be less than 2.");
     if (ctx->HasInput("InsideWeight")) {
       PADDLE_ENFORCE(ctx->HasInput("OutsideWeight"),
                      "If weights are provided, must specify both "
                      "inside and outside weights.");
-      PADDLE_ENFORCE_EQ(ctx->GetInputDim("InsideWeight"), x_dims);
-      PADDLE_ENFORCE_EQ(ctx->GetInputDim("OutsideWeight"), x_dims);
+      auto dims = ctx->GetInputDim("InsideWeight");
+      bool check = true;
+      if ((!ctx->IsRuntime()) &&
+          (framework::product(dims) <= 0 || framework::product(x_dims) <= 0)) {
+        check = false;
+      }
+      if (check) {
+        PADDLE_ENFORCE_EQ(dims, x_dims);
+      }
+
+      dims = ctx->GetInputDim("OutsideWeight");
+      check = true;
+      if ((!ctx->IsRuntime()) &&
+          (framework::product(dims) <= 0 || framework::product(x_dims) <= 0)) {
+        check = false;
+      }
+      if (check) {
+        PADDLE_ENFORCE_EQ(dims, x_dims);
+      }
     }
 
     ctx->SetOutputDim("Diff", x_dims);
@@ -110,11 +135,11 @@ class SmoothL1LossGradOp : public framework::OperatorWithKernel {
 
     PADDLE_ENFORCE_GE(out_dims.size(), 2,
                       "The tensor rank of Input(Out@Grad) should be 2.");
-    PADDLE_ENFORCE_EQ(out_dims[0], in_dims[0],
-                      "The 1st dimension of Input(Out@Grad) must be "
-                      "same as input.");
-    PADDLE_ENFORCE_EQ(out_dims[1], 1,
-                      "The 2nd dimension of Input(Out@Grad) must be 1.");
+    PADDLE_INFERSHAPE_ENFORCE_EQ(ctx, out_dims[0], in_dims[0],
+                                 "The 1st dimension of Input(Out@Grad) must be "
+                                 "same as input.");
+    PADDLE_INFERSHAPE_ENFORCE_EQ(
+        ctx, out_dims[1], 1, "The 2nd dimension of Input(Out@Grad) must be 1.");
 
     auto x_grad_name = framework::GradVarName("X");
     auto y_grad_name = framework::GradVarName("Y");
diff --git a/paddle/fluid/operators/squared_l2_distance_op.cc b/paddle/fluid/operators/squared_l2_distance_op.cc
@@ -41,13 +41,26 @@ class SquaredL2DistanceOp : public framework::OperatorWithKernel {
 
     int rank = framework::arity(x_dims);
     PADDLE_ENFORCE_GE(rank, 2, "Tensor rank should be at least equal to 2.");
-    PADDLE_ENFORCE_EQ(product(x_dims) / x_dims[0], product(y_dims) / y_dims[0],
-                      "Product of dimensions expcet the first dimension of "
-                      "input and target must be equal.");
-    PADDLE_ENFORCE(y_dims[0] == 1 || y_dims[0] == x_dims[0],
-                   "First dimension of target must be equal to input "
-                   "or to 1.");
-
+    bool check = true;
+    if ((!ctx->IsRuntime()) &&
+        (framework::product(x_dims) <= 0 || framework::product(y_dims) <= 0)) {
+      check = false;
+    }
+    if (check) {
+      PADDLE_ENFORCE_EQ(product(x_dims) / x_dims[0],
+                        product(y_dims) / y_dims[0],
+                        "Product of dimensions expcet the first dimension of "
+                        "input and target must be equal.");
+    }
+    check = true;
+    if ((!ctx->IsRuntime()) && (y_dims[0] <= 0 || x_dims[0] <= 0)) {
+      check = false;
+    }
+    if (check) {
+      PADDLE_ENFORCE(y_dims[0] == 1 || y_dims[0] == x_dims[0],
+                     "First dimension of target must be equal to input "
+                     "or to 1.");
+    }
     ctx->SetOutputDim("sub_result", {x_dims[0], product(x_dims) / x_dims[0]});
     ctx->SetOutputDim("Out", {x_dims[0], 1});
     ctx->ShareLoD("X", /*->*/ "Out");
@@ -91,12 +104,12 @@ class SquaredL2DistanceGradOp : public framework::OperatorWithKernel {
     auto out_dims = ctx->GetInputDim(framework::GradVarName("Out"));
     auto x_dims = ctx->GetInputDim("X");
     auto y_dims = ctx->GetInputDim("Y");
-    PADDLE_ENFORCE_EQ(out_dims[0], x_dims[0],
-                      "First dimension of output gradient and "
-                      "input value must be equal.");
-    PADDLE_ENFORCE_EQ(out_dims[1], 1,
-                      "Second dimension of output gradient "
-                      "must be 1.");
+    PADDLE_INFERSHAPE_ENFORCE_EQ(ctx, out_dims[0], x_dims[0],
+                                 "First dimension of output gradient and "
+                                 "input value must be equal.");
+    PADDLE_INFERSHAPE_ENFORCE_EQ(ctx, out_dims[1], 1,
+                                 "Second dimension of output gradient "
+                                 "must be 1.");
     auto x_grad_name = framework::GradVarName("X");
     auto y_grad_name = framework::GradVarName("Y");
     if (ctx->HasOutput(x_grad_name)) ctx->SetOutputDim(x_grad_name, x_dims);
diff --git a/paddle/fluid/platform/enforce.h b/paddle/fluid/platform/enforce.h
@@ -356,5 +356,46 @@ using CommonType2 = typename std::add_lvalue_reference<
 #define PADDLE_ENFORCE_LE(__VAL0, __VAL1, ...) \
   __PADDLE_BINARY_COMPARE(__VAL0, __VAL1, <=, >, __VA_ARGS__)
 
+#define __PADDLE_INFERSHAPE_BINARY_COMPARE(__CTX, __VAL1, __VAL2, __CMP, \
+                                           __INV_CMP, ...)               \
+  do {                                                                   \
+    auto __val1 = (__VAL1);                                              \
+    auto __val2 = (__VAL2);                                              \
+    if (!__CTX->IsRuntime()) {                                           \
+      if (__val1 == -1 || __val2 == -1) {                                \
+        break;                                                           \
+      }                                                                  \
+    }                                                                    \
+    using __TYPE1__ = decltype(__val1);                                  \
+    using __TYPE2__ = decltype(__val2);                                  \
+    using __COMMON_TYPE1__ =                                             \
+        ::paddle::platform::details::CommonType1<__TYPE1__, __TYPE2__>;  \
+    using __COMMON_TYPE2__ =                                             \
+        ::paddle::platform::details::CommonType2<__TYPE1__, __TYPE2__>;  \
+    bool __is_not_error = (static_cast<__COMMON_TYPE1__>(__val1))__CMP(  \
+        static_cast<__COMMON_TYPE2__>(__val2));                          \
+    if (UNLIKELY(!__is_not_error)) {                                     \
+      PADDLE_THROW("Enforce failed. Expected %s " #__CMP                 \
+                   " %s, but received %s:%s " #__INV_CMP " %s:%s.\n%s",  \
+                   #__VAL1, #__VAL2, #__VAL1,                            \
+                   ::paddle::string::to_string(__val1), #__VAL2,         \
+                   ::paddle::string::to_string(__val2),                  \
+                   ::paddle::string::Sprintf(__VA_ARGS__));              \
+    }                                                                    \
+  } while (0)
+
+#define PADDLE_INFERSHAPE_ENFORCE_EQ(__CTX, __VAL0, __VAL1, ...) \
+  __PADDLE_INFERSHAPE_BINARY_COMPARE(__CTX, __VAL0, __VAL1, ==, !=, __VA_ARGS__)
+#define PADDLE_INFERSHAPE_ENFORCE_NE(__CTX, __VAL0, __VAL1, ...) \
+  __PADDLE_INFERSHAPE_BINARY_COMPARE(__CTX, __VAL0, __VAL1, !=, ==, __VA_ARGS__)
+#define PADDLE_INFERSHAPE_ENFORCE_GT(__CTX, __VAL0, __VAL1, ...) \
+  __PADDLE_INFERSHAPE_BINARY_COMPARE(__CTX, __VAL0, __VAL1, >, <=, __VA_ARGS__)
+#define PADDLE_INFERSHAPE_ENFORCE_GE(__CTX, __VAL0, __VAL1, ...) \
+  __PADDLE_INFERSHAPE_BINARY_COMPARE(__CTX, __VAL0, __VAL1, >=, <, __VA_ARGS__)
+#define PADDLE_INFERSHAPE_ENFORCE_LT(__CTX, __VAL0, __VAL1, ...) \
+  __PADDLE_INFERSHAPE_BINARY_COMPARE(__CTX, __VAL0, __VAL1, <, >=, __VA_ARGS__)
+#define PADDLE_INFERSHAPE_ENFORCE_LE(__CTX, __VAL0, __VAL1, ...) \
+  __PADDLE_INFERSHAPE_BINARY_COMPARE(__CTX, __VAL0, __VAL1, <=, >, __VA_ARGS__)
+
 }  // namespace platform
 }  // namespace paddle
