OP(warpctc, add_position_encoding, scaled_dot_product_attention) error message enhancement (#24261)

* enhance add_position_encoding error message, test=develop

* enhance warpctc & scaled_dot_product_attention error message, test=develop

* modified error message and ctest of scaled_dot_product_attention, test=develop

paddle/fluid/operators/add_position_encoding_op.cc

@@ -23,11 +23,9 @@ class AddPositionEncodingOp : public framework::OperatorWithKernel {
   using framework::OperatorWithKernel::OperatorWithKernel;
 
   void InferShape(framework::InferShapeContext* ctx) const override {
-    PADDLE_ENFORCE(ctx->HasInput("X"),
-                   "X(Input) of add_position_encoding_op should not be null.");
-    PADDLE_ENFORCE(
-        ctx->HasOutput("Out"),
-        "Out(Output) of add_position_encoding_op should not be null.");
+    OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "AddPositionEncoding");
+    OP_INOUT_CHECK(ctx->HasOutput("Out"), "Output", "Out",
+                   "AddPositionEncoding");
 
     auto x_dims = ctx->GetInputDim("X");
     ctx->SetOutputDim("Out", x_dims);

paddle/fluid/operators/add_position_encoding_op.h

@@ -39,25 +39,40 @@ class AddPositionEncodingKernel : public framework::OpKernel<T> {
     int enc_size = 0;
 
     if (x_lod.empty()) {
-      PADDLE_ENFORCE(
-          x_dim.size() == 3UL,
-          "The input X of Add Position Encoding should be 3-D Tensor!");
+      PADDLE_ENFORCE_EQ(x_dim.size(), 3,
+                        platform::errors::InvalidArgument(
+                            "The input(X)'s dimension of AddPositionEncodingOp "
+                            "should be equal to "
+                            "3, but received %d. ",
+                            x_dim.size()));
       batch_size = x_dim[0];
       max_seq_len = x_dim[1];
       enc_size = x_dim[2];
     } else {
-      PADDLE_ENFORCE(
-          x_dim.size() == 2UL,
-          "The input X of Add Position Encoding should be 2-D LoDTensor!");
-      PADDLE_ENFORCE(
-          x_lod.size() == 1UL,
-          "The Add Position Encoding Op only supports lod_level == 1!");
+      PADDLE_ENFORCE_EQ(x_dim.size(), 2,
+                        platform::errors::InvalidArgument(
+                            "The input(X)'s dimension of AddPositionEncodingOp "
+                            "should be equal to "
+                            "2, but received %d. ",
+                            x_dim.size()));
+      PADDLE_ENFORCE_EQ(x_lod.size(), 1,
+                        platform::errors::InvalidArgument(
+                            "The input(X)'s lod level of AddPositionEncodingOp "
+                            "should be equal to "
+                            "1, but received %d. ",
+                            x_lod.size()));
+
       batch_size = x_lod[0].size() - 1;
       max_seq_len = -1;
       enc_size = x_dim[1];
     }
 
-    PADDLE_ENFORCE(enc_size % 2 == 0, "Only support even encode size!");
+    PADDLE_ENFORCE_EQ(enc_size % 2, 0,
+                      platform::errors::InvalidArgument(
+                          "The input(X)'s feature size of "
+                          "AddPositionEncodingOp only support even, "
+                          "but received an odd number: %d. ",
+                          enc_size));
 
     const int half_size = enc_size / 2;
     for (int i = 0; i < batch_size; ++i) {

paddle/fluid/operators/warpctc_op.cc

@@ -28,14 +28,11 @@ class WarpCTCOp : public framework::OperatorWithKernel {
   using framework::OperatorWithKernel::OperatorWithKernel;
 
   void InferShape(framework::InferShapeContext* ctx) const override {
-    PADDLE_ENFORCE(ctx->HasInput("Logits"),
-                   "Input(Logits) of WarpCTCOp should not be null.");
-    PADDLE_ENFORCE(ctx->HasInput("Label"),
-                   "Input(Label) of WarpCTCOp should not be null.");
-    PADDLE_ENFORCE(ctx->HasOutput("WarpCTCGrad"),
-                   "Output(WarpCTCGrad) of WarpCTCOp should not be null.");
-    PADDLE_ENFORCE(ctx->HasOutput("Loss"),
-                   "Output(Loss) of WarpCTCOp should not be null.");
+    OP_INOUT_CHECK(ctx->HasInput("Logits"), "Input", "Logits", "WarpCTC");
+    OP_INOUT_CHECK(ctx->HasInput("Label"), "Input", "Label", "WarpCTC");
+    OP_INOUT_CHECK(ctx->HasOutput("WarpCTCGrad"), "Output", "WarpCTCGrad",
+                   "WarpCTC");
+    OP_INOUT_CHECK(ctx->HasOutput("Loss"), "Output", "Loss", "WarpCTC");
 
     auto logits_dims = ctx->GetInputDim("Logits");
     int blank = ctx->Attrs().Get<int>("blank");
@@ -47,9 +44,18 @@ class WarpCTCOp : public framework::OperatorWithKernel {
       sequence_width =
           static_cast<int>(framework::product(logits_dims) / logits_dims[0]);
     }
-    PADDLE_ENFORCE((blank >= 0) && (blank < sequence_width),
-                   "The value of Attr(blank) should be in interval [0, %d).",
-                   sequence_width);
+
+    PADDLE_ENFORCE_GE(
+        blank, 0, platform::errors::InvalidArgument(
+                      "The value of Attr(blank) should be in interval [0, %d), "
+                      "but received %d",
+                      blank));
+    PADDLE_ENFORCE_LT(
+        blank, sequence_width,
+        platform::errors::InvalidArgument(
+            "The value of Attr(blank) should be in interval [0, %d), "
+            "but received %d",
+            blank));
 
     // TODO(liuyiqun): it is tricky to set the wrong dimension here.
     ctx->SetOutputDim("Loss", {-1, 1});
@@ -160,10 +166,10 @@ class WarpCTCGradOp : public framework::OperatorWithKernel {
   using framework::OperatorWithKernel::OperatorWithKernel;
 
   void InferShape(framework::InferShapeContext* ctx) const override {
-    PADDLE_ENFORCE(ctx->HasInput("WarpCTCGrad"),
-                   "Input(WarpCTCGrad) of WarpCTCGradOp should not be null.");
-    PADDLE_ENFORCE(ctx->HasOutput(framework::GradVarName("Logits")),
-                   "Output(Logits@GRAD) of WarpCTCGradOp should not be null.");
+    OP_INOUT_CHECK(ctx->HasInput("WarpCTCGrad"), "Input", "WarpCTCGrad",
+                   "WarpCTCGrad");
+    OP_INOUT_CHECK(ctx->HasOutput(framework::GradVarName("Logits")), "Output",
+                   "WarpCTCGrad", "WarpCTCGrad");
     ctx->SetOutputDim(framework::GradVarName("Logits"),
                       ctx->GetInputDim("Logits"));
     ctx->ShareLoD("Logits", /*->*/ framework::GradVarName("Logits"));

paddle/fluid/operators/warpctc_op.h

@@ -65,13 +65,18 @@ class WarpCTCFunctor {
     ctcStatus_t status = platform::dynload::get_workspace_size(
         cpu_label_lengths, cpu_input_lengths, static_cast<int>(sequence_width),
         static_cast<int>(num_sequences), options_, &workspace_bytes);
-    PADDLE_ENFORCE_EQ(CTC_STATUS_SUCCESS, status,
-                      "warp-ctc [version %d] Error in get_workspace_size: ",
-                      warpctc_version_,
-                      platform::dynload::ctcGetStatusString(status));
-    PADDLE_ENFORCE_GT(workspace_bytes, 0UL,
-                      "Bytes of workspace got by warp-ctc function, "
-                      "get_workspace_size(), should be larger than 0.");
+
+    PADDLE_ENFORCE_EQ(
+        CTC_STATUS_SUCCESS, status,
+        platform::errors::PreconditionNotMet(
+            "warp-ctc [version %d] Error in get_workspace_size: %s",
+            warpctc_version_, platform::dynload::ctcGetStatusString(status)));
+    PADDLE_ENFORCE_GT(
+        workspace_bytes, 0UL,
+        platform::errors::InvalidArgument(
+            "Bytes of workspace got by warp-ctc function, "
+            "get_workspace_size() should be larger than 0, but received %d",
+            workspace_bytes));
 
     auto& dev_ctx = ctx.template device_context<DeviceContext>();
     size_t workspace_elements = workspace_bytes / sizeof(float) + 1UL;
@@ -88,10 +93,12 @@ class WarpCTCFunctor {
         input, gradient, cpu_labels, cpu_label_lengths, cpu_input_lengths,
         static_cast<int>(sequence_width), static_cast<int>(num_sequences),
         cpu_loss, workspace_data, options_);
-    PADDLE_ENFORCE_EQ(CTC_STATUS_SUCCESS, status,
-                      "warp-ctc [version %d] Error in compute_ctc_loss: ",
-                      warpctc_version_,
-                      platform::dynload::ctcGetStatusString(status));
+
+    PADDLE_ENFORCE_EQ(
+        CTC_STATUS_SUCCESS, status,
+        platform::errors::PreconditionNotMet(
+            "warp-ctc [version %d] Error in get_workspace_size: %s",
+            warpctc_version_, platform::dynload::ctcGetStatusString(status)));
   }
 
  protected:
@@ -156,23 +163,40 @@ class WarpCTCKernel : public framework::OpKernel<T> {
                             labels_length_cpu.data<int64_t>()[i]);
       }
     } else {
+      PADDLE_ENFORCE_GT(logits->NumLevels(), 0UL,
+                        platform::errors::InvalidArgument(
+                            "Input(Logits) Tensor of WarpCTC "
+                            "does not contain LoD information."));
+      PADDLE_ENFORCE_GT(label->NumLevels(), 0UL,
+                        platform::errors::InvalidArgument(
+                            "Input(Label) Tensor of WarpCTC "
+                            "does not contain LoD information."));
+
       logits_lod = framework::ToAbsOffset(logits->lod())[0];
       auto logits_dims = logits->dims();
+
       PADDLE_ENFORCE_EQ(
           logits_dims[0], static_cast<int64_t>(logits_lod.back()),
-          "The first dimension of Input(Logits) should be equal to "
-          "the sum of all sequences' lengths.");
+          platform::errors::InvalidArgument(
+              "The first dimension of Input(Logits) should be equal to "
+              "the sum of all sequences' lengths = %d., but received %d. ",
+              static_cast<int64_t>(logits_lod.back()), logits_dims[0]));
 
       label_lod = framework::ToAbsOffset(label->lod())[0];
       auto label_dims = label->dims();
-      PADDLE_ENFORCE_EQ(
-          label_dims[0], label->numel(),
-          "The width of each timestep in Input(Label) should be 1.");
+      PADDLE_ENFORCE_EQ(label_dims[1], 1,
+                        platform::errors::InvalidArgument(
+                            "The last dimension of Input(Label) should be 1, "
+                            "but received %d",
+                            label_dims[1]));
 
       num_sequences = logits_lod.size() - 1;
-      PADDLE_ENFORCE_EQ(num_sequences, label_lod.size() - 1,
-                        "The number of sequences of Input(Logits) should be "
-                        "equal to that of Input(Label).");
+      PADDLE_ENFORCE_EQ(
+          num_sequences, label_lod.size() - 1,
+          platform::errors::InvalidArgument(
+              "The number of sequences of Input(Logits) should be "
+              "equal to that of Input(Label) = %d, but received %d",
+              label_lod.size() - 1, num_sequences));
 
       sequence_width = logits->numel() / logits_dims[0];
       max_sequence_length = math::MaximumSequenceLength(logits_lod);

python/paddle/fluid/layers/loss.py

@@ -616,8 +616,14 @@ def warpctc(input,
             print(output)
     """
     helper = LayerHelper('warpctc', **locals())
+    check_variable_and_dtype(input, 'input', ['float32'], "warpctc")
+    check_variable_and_dtype(label, 'label', ['int32'], "warpctc")
     this_inputs = {'Logits': [input], 'Label': [label]}
     if input_length is not None and label_length is not None:
+        check_variable_and_dtype(input_length, 'LogitsLength', ['int64'],
+                                 "warpctc")
+        check_variable_and_dtype(label_length, 'LabelLength', ['int64'],
+                                 "warpctc")
         this_inputs['LogitsLength'] = [input_length]
         this_inputs['LabelLength'] = [label_length]
 

python/paddle/fluid/layers/nn.py

@@ -12463,6 +12463,8 @@ def add_position_encoding(input, alpha, beta, name=None):
 
     """
     helper = LayerHelper('add_position_encoding', **locals())
+    check_variable_and_dtype(input, 'input', ['float32', 'float64'],
+                             "add_position_encoding")
     dtype = helper.input_dtype()
 
     out = helper.create_variable_for_type_inference(dtype=dtype)

python/paddle/fluid/nets.py

@@ -15,6 +15,7 @@
 from __future__ import print_function
 import six
 from . import layers
+from .data_feeder import check_variable_and_dtype, convert_dtype
 
 __all__ = [
     "simple_img_conv_pool",
@@ -410,9 +411,10 @@ def scaled_dot_product_attention(queries,
             Multi-Head Attention.
 
     Raises:
+        TypeError: The dtype of inputs keys, values and queries should be the same.
         ValueError: Inputs queries, keys and values should all be 3-D tensors.
         ValueError: The hidden size of queries and keys should be the same.
-        ValueError: The max sequence length in query batch and in key batch should be the same.
+        ValueError: The max sequence length in value batch and in key batch should be the same.
         ValueError: he hidden size of keys must be divisible by the number of attention heads.
         ValueError: he hidden size of values must be divisible by the number of attention heads.
 
@@ -427,17 +429,38 @@ def scaled_dot_product_attention(queries,
             contexts = fluid.nets.scaled_dot_product_attention(queries, keys, values)
             contexts.shape  # [3, 5, 10]
     """
+    check_variable_and_dtype(queries, 'queries', ['float32', 'float64'],
+                             "scaled_dot_product_attention")
+    check_variable_and_dtype(keys, 'keys', ['float32', 'float64'],
+                             "scaled_dot_product_attention")
+    check_variable_and_dtype(values, 'values', ['float32', 'float64'],
+                             "scaled_dot_product_attention")
+
+    if not (queries.dtype == keys.dtype == values.dtype):
+        raise TypeError(
+            "The dtype of keys, values and queries should be the same."
+            "But received queries.dtype = %s, "
+            " keys.dtype = %s, values.dtype) = %s." %
+            (convert_dtype(queries.dtype), convert_dtype(keys.dtype),
+             convert_dtype(values.dtype)))
+
     if not (len(queries.shape) == len(keys.shape) == len(values.shape) == 3):
         raise ValueError(
-            "Inputs queries, keys and values should all be 3-D tensors.")
+            "Inputs queries, keys and values should all be 3-D tensors."
+            "But received len(queries.shape) = %d, "
+            "len(keys.shape) = %d, len(values.shape) = %d." %
+            (len(queries.shape), len(keys.shape), len(values.shape)))
 
     if queries.shape[-1] != keys.shape[-1]:
         raise ValueError(
-            "The hidden size of queries and keys should be the same.")
+            "The hidden size of queries and keys should be the same."
+            "But received queries' hidden size = %d and keys' hidden size = %d."
+            % (queries.shape[-1], keys.shape[-1]))
     if keys.shape[-2] != values.shape[-2]:
         raise ValueError(
-            "The max sequence length in query batch and in key batch "
-            "should be the same.")
+            "The max sequence length in value batch and in key batch "
+            "should be the same. But received max sequence length in value batch "
+            "= %d, in key batch = %d." % (values.shape[-2], keys.shape[-2]))
     if keys.shape[-1] % num_heads != 0:
         raise ValueError("The hidden size of keys (%d) must be divisible "
                          "by the number of attention heads (%d)." %

python/paddle/fluid/tests/unittests/test_add_position_encoding_op.py

@@ -16,6 +16,8 @@ import numpy as np
 import math
 import paddle.fluid.core as core
 from op_test import OpTest
+import paddle.fluid as fluid
+from paddle.fluid import Program, program_guard
 
 
 class TestAddPositionEncodingTensorOp(OpTest):
@@ -130,5 +132,18 @@ class TestAddPositionEncodingLoDTensorOp(OpTest):
             start += max_length
 
 
+class TestAddPositionEncodingOpError(unittest.TestCase):
+    def test_errors(self):
+        with program_guard(Program(), Program()):
+            input_data = np.random.random((4, 16, 8)).astype("float32")
+
+            def test_Variable():
+                # the input type must be Variable
+                fluid.layers.add_position_encoding(
+                    input=input_data, alpha=1.0, beta=1.0)
+
+            self.assertRaises(TypeError, test_Variable)
+
+
 if __name__ == '__main__':
     unittest.main()

python/paddle/fluid/tests/unittests/test_scaled_dot_product_attention.py

+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# 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.
+
+from __future__ import print_function
+
+import unittest
+import numpy as np
+import paddle.fluid as fluid
+from paddle.fluid import Program, program_guard
+
+
+class TestScaledDotProductAttentionError(unittest.TestCase):
+    def test_errors(self):
+        with program_guard(Program(), Program()):
+            queries = fluid.data(
+                name="queries", shape=[3, 5, 9], dtype="float32")
+            keys = fluid.data(name="keys", shape=[3, 6, 9], dtype="float32")
+            values = fluid.data(
+                name="values", shape=[3, 6, 10], dtype="float32")
+
+            def test_queries_Variable():
+                queries_data = np.random.rand(3, 5, 9).astype("float32")
+                fluid.nets.scaled_dot_product_attention(queries_data, keys,
+                                                        values)
+
+            self.assertRaises(TypeError, test_queries_Variable)
+
+            def test_keys_Variable():
+                keys_data = np.random.rand(3, 6, 9).astype("float32")
+                fluid.nets.scaled_dot_product_attention(queries, keys_data,
+                                                        values)
+
+            self.assertRaises(TypeError, test_keys_Variable)
+
+            def test_values_Variable():
+                values_data = np.random.rand(3, 6, 10).astype("float32")
+                fluid.nets.scaled_dot_product_attention(queries, keys,
+                                                        values_data)
+
+            self.assertRaises(TypeError, test_values_Variable)
+
+            def test_diff_dtype():
+                keys_error = fluid.data(
+                    name="keys_error", shape=[3, 6, 9], dtype="float64")
+                values_error = fluid.data(
+                    name="values_error", shape=[3, 6, 10], dtype="float64")
+                fluid.nets.scaled_dot_product_attention(queries, keys_error,
+                                                        values_error)
+
+            self.assertRaises(TypeError, test_diff_dtype)
+
+            def test_diff_dim():
+                keys_error_dim = fluid.data(
+                    name="keys_error_dim", shape=[3, 6], dtype="float32")
+                values_error_dim = fluid.data(
+                    name="values_error_dim", shape=[3], dtype="float32")
+                fluid.nets.scaled_dot_product_attention(queries, keys_error_dim,
+                                                        values_error_dim)
+
+            self.assertRaises(ValueError, test_diff_dim)
+
+            def test_diff_hidden_size():
+                queries_error_hs = fluid.data(
+                    name="queries_error_hs", shape=[3, 5, 9], dtype="float32")
+                keys_error_hs = fluid.data(
+                    name="keys_error_hs", shape=[3, 6, 10], dtype="float32")
+                fluid.nets.scaled_dot_product_attention(queries_error_hs,
+                                                        keys_error_hs, values)
+
+            self.assertRaises(ValueError, test_diff_hidden_size)
+
+            def test_diff_max_len():
+                keys_error_len = fluid.data(
+                    name="keys_error_len", shape=[3, 7, 9], dtype="float32")
+                values_error_len = fluid.data(
+                    name="values_error_len", shape=[3, 6, 10], dtype="float32")
+                fluid.nets.scaled_dot_product_attention(queries, keys_error_len,
+                                                        values_error_len)
+
+            self.assertRaises(ValueError, test_diff_max_len)
+
+
+if __name__ == "__main__":
+    unittest.main()

python/paddle/fluid/tests/unittests/test_warpctc_op.py

@@ -19,6 +19,8 @@ import unittest
 import numpy as np
 from op_test import OpTest
 from test_softmax_op import stable_softmax
+import paddle.fluid as fluid
+from paddle.fluid import Program, program_guard
 
 CUDA_BLOCK_SIZE = 512
 
@@ -335,5 +337,57 @@ class TestWarpCTCOpWithPaddingCase1(TestWarpCTCOpWithPadding):
         self.norm_by_times = False
 
 
+class TestWarpCTCOpError(unittest.TestCase):
+    def test_errors(self):
+        with program_guard(Program(), Program()):
+            logits = fluid.data(
+                name='logits', shape=[5, 16, 6], dtype='float32')
+            logits_length = fluid.data(
+                name='logits_length', shape=[None], dtype='int64')
+            label = fluid.data(name='label', shape=[16, 3], dtype='int32')
+            label_length = fluid.data(
+                name='labels_length', shape=[None], dtype='int64')
+
+            def test_logits_Variable():
+                logits_data = np.random.rand(5, 16, 6).astype("float32")
+                fluid.layers.warpctc(
+                    input=logits_data,
+                    label=label,
+                    input_length=logits_length,
+                    label_length=label_length)
+
+            self.assertRaises(TypeError, test_logits_Variable)
+
+            def test_label_Variable():
+                label_data = np.random.randint(0, 5, [5, 1]).astype("int32")
+                fluid.layers.warpctc(
+                    input=logits,
+                    label=label_data,
+                    input_length=logits_length,
+                    label_length=label_length)
+
+            self.assertRaises(TypeError, test_label_Variable)
+
+            def test_logits_len_Variable():
+                logits_length_data = np.array([5] * 16).astype("int64")
+                fluid.layers.warpctc(
+                    input=logits,
+                    label=label,
+                    input_length=logits_length_data,
+                    label_length=label_length)
+
+            self.assertRaises(TypeError, test_logits_len_Variable)
+
+            def test_label_len_Variable():
+                label_length_data = np.array([3] * 16).astype("int64")
+                fluid.layers.warpctc(
+                    input=logits,
+                    label=label,
+                    input_length=logits_length,
+                    label_length=label_length_data)
+
+            self.assertRaises(TypeError, test_label_len_Variable)
+
+
 if __name__ == "__main__":
     unittest.main()