add support to max_len is None

paddle/fluid/API.spec

@@ -162,7 +162,7 @@ paddle.fluid.layers.crop ArgSpec(args=['x', 'shape', 'offsets', 'name'], varargs
 paddle.fluid.layers.rank_loss ArgSpec(args=['label', 'left', 'right', 'name'], varargs=None, keywords=None, defaults=(None,))
 paddle.fluid.layers.prelu ArgSpec(args=['x', 'mode', 'param_attr', 'name'], varargs=None, keywords=None, defaults=(None, None))
 paddle.fluid.layers.flatten ArgSpec(args=['x', 'axis', 'name'], varargs=None, keywords=None, defaults=(1, None))
-paddle.fluid.layers.sequence_mask ArgSpec(args=['x', 'max_len', 'mask_dtype'], varargs=None, keywords=None, defaults=('int64',))
+paddle.fluid.layers.sequence_mask ArgSpec(args=['x', 'maxlen', 'dtype', 'name'], varargs=None, keywords=None, defaults=(None, 'int64', None))
 paddle.fluid.layers.data ArgSpec(args=['name', 'shape', 'append_batch_size', 'dtype', 'lod_level', 'type', 'stop_gradient'], varargs=None, keywords=None, defaults=(True, 'float32', 0, VarType.LOD_TENSOR, True))
 paddle.fluid.layers.open_recordio_file ArgSpec(args=['filename', 'shapes', 'lod_levels', 'dtypes', 'pass_num', 'for_parallel'], varargs=None, keywords=None, defaults=(1, True))
 paddle.fluid.layers.open_files ArgSpec(args=['filenames', 'shapes', 'lod_levels', 'dtypes', 'thread_num', 'buffer_size', 'pass_num', 'is_test'], varargs=None, keywords=None, defaults=(None, None, 1, None))

paddle/fluid/operators/sequence_mask_op.h

@@ -14,6 +14,14 @@
 
 #pragma once
 
+#ifdef __NVCC__
+#include <thrust/device_ptr.h>
+#include <thrust/functional.h>
+#include <thrust/reduce.h>
+#else
+#include <algorithm>
+#endif
+
 #include "paddle/fluid/framework/op_registry.h"
 #include "paddle/fluid/platform/for_range.h"
 
@@ -26,50 +34,60 @@ class SequenceMaskOp : public framework::OperatorWithKernel {
 
   void InferShape(framework::InferShapeContext *ctx) const override {
     PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) must exist");
-    auto max_len = ctx->Attrs().Get<int>("max_len");
-    PADDLE_ENFORCE_GT(max_len, 1, "Attr(max_len) must be larger than 1");
     PADDLE_ENFORCE(ctx->HasOutput("Y"), "Output(Y) must exist");
-    auto dim = framework::vectorize2int(ctx->GetInputDim("X"));
-    dim.push_back(max_len);
-    ctx->SetOutputDim("Y", framework::make_ddim(dim));
+
+    auto maxlen = ctx->Attrs().Get<int>("maxlen");
+    if (maxlen > 0) {  // We can only infershape when maxlen > 0
+      auto dim = framework::vectorize2int(ctx->GetInputDim("X"));
+      dim.push_back(maxlen);
+      ctx->SetOutputDim("Y", framework::make_ddim(dim));
+    }
   }
 };
 
 class SequenceMaskOpMaker : public framework::OpProtoAndCheckerMaker {
  public:
   void Make() override {
-    AddInput("X", "The input of sequence_mask op.");
+    AddInput("X", "The input tensor of sequence_mask op.");
     AddOutput("Y", "The output mask of sequence_mask op.");
-    AddAttr<int>("max_len", "The maximum length of the sequence.")
-        .GreaterThan(1);
+    AddAttr<int>("maxlen",
+                 "The maximum length of the sequence. If maxlen < 0, maxlen "
+                 "= max(Input(X)).")
+        .SetDefault(-1)
+        .AddCustomChecker([](int &v) {
+          PADDLE_ENFORCE(v < 0 || v >= 1,
+                         "Attr(maxlen) must be less than 0 or larger than 1");
+        });
     AddAttr<int>("out_dtype", "Output data type");
     AddComment(R"DOC(
 SequenceMask Operator
 
-This operator outputs a Mask according to Input(X) and Attr(max_len).
+This operator outputs a Mask according to Input(X) and Attr(maxlen).
 Supposing Input(X) is a Tensor with shape [d_1, d_2, ..., d_n], the
-Output(Y) is a mask with shape [d_1, d_2, ..., d_n, max_len], where:
+Output(Y) is a mask with shape [d_1, d_2, ..., d_n, maxlen], where:
 
 Y(i_1, i_2, ..., i_n, j) = (j < X(i_1, i_2, ..., i_n)) 
+
+If maxlen < 0, maxlen = max(X)
     )DOC");
   }
 };
 
 template <typename Tx, typename Ty>
 struct SequenceMaskForRangeFunctor {
-  HOSTDEVICE SequenceMaskForRangeFunctor(const Tx *x, Ty *y, int max_len)
-      : x_(x), y_(y), max_len_(max_len) {}
+  HOSTDEVICE SequenceMaskForRangeFunctor(const Tx *x, Ty *y, int maxlen)
+      : x_(x), y_(y), maxlen_(maxlen) {}
 
   HOSTDEVICE void operator()(int y_idx) const {
-    int x_idx = y_idx / max_len_;
-    int j = y_idx % max_len_;
+    int x_idx = y_idx / maxlen_;
+    int j = y_idx % maxlen_;
     y_[y_idx] = static_cast<Ty>(j < x_[x_idx] ? 1 : 0);
   }
 
  private:
   const Tx *x_;
   Ty *y_;
-  int max_len_;
+  int maxlen_;
 };
 
 template <typename DeviceContext, typename Tx>
@@ -77,14 +95,14 @@ struct SequenceMaskFunctor {
   using Tensor = framework::LoDTensor;
 
   SequenceMaskFunctor(const DeviceContext &ctx, const Tx *x, Tensor *y,
-                      int limits, int max_len)
-      : ctx_(ctx), x_(x), y_(y), limits_(limits), max_len_(max_len) {}
+                      int limits, int maxlen)
+      : ctx_(ctx), x_(x), y_(y), limits_(limits), maxlen_(maxlen) {}
 
   template <typename Ty>
   void operator()() const {
     auto *y_data = y_->mutable_data<Ty>(ctx_.GetPlace());
     platform::ForRange<DeviceContext> for_range(ctx_, limits_);
-    for_range(SequenceMaskForRangeFunctor<Tx, Ty>(x_, y_data, max_len_));
+    for_range(SequenceMaskForRangeFunctor<Tx, Ty>(x_, y_data, maxlen_));
   }
 
  private:
@@ -92,7 +110,7 @@ struct SequenceMaskFunctor {
   const Tx *x_;
   Tensor *y_;
   int limits_;
-  int max_len_;
+  int maxlen_;
 };
 
 template <typename DeviceContext, typename Tx>
@@ -103,13 +121,32 @@ class SequenceMaskKernel : public framework::OpKernel<Tx> {
   void Compute(const framework::ExecutionContext &ctx) const override {
     auto *x = ctx.Input<Tensor>("X");
     auto *y = ctx.Output<Tensor>("Y");
-    auto max_len = ctx.Attr<int>("max_len");
+    auto maxlen = ctx.Attr<int>("maxlen");
+
+    auto *x_data = x->data<Tx>();
+    auto x_numel = x->numel();
+    if (maxlen < 0) {
+#ifdef __NVCC__
+      VLOG(10)
+          << "SequenceMaskOp on GPU may be slow when maxlen is not provided.";
+      maxlen = static_cast<int>(
+          thrust::reduce(thrust::device_pointer_cast(x_data),
+                         thrust::device_pointer_cast(x_data) + x_numel,
+                         static_cast<Tx>(0), thrust::maximum<Tx>()));
+#else
+      maxlen = static_cast<int>(*std::max_element(x_data, x_data + x_numel));
+#endif
+      auto y_dim = framework::vectorize2int(x->dims());
+      y_dim.push_back(maxlen);
+      y->Resize(framework::make_ddim(y_dim));
+    }
+
     auto out_dtype = static_cast<framework::proto::VarType::Type>(
         ctx.Attr<int>("out_dtype"));
     auto &dev_ctx = ctx.template device_context<DeviceContext>();
-    framework::VisitDataType(out_dtype, SequenceMaskFunctor<DeviceContext, Tx>(
-                                            dev_ctx, x->data<Tx>(), y,
-                                            x->numel() * max_len, max_len));
+    framework::VisitDataType(out_dtype,
+                             SequenceMaskFunctor<DeviceContext, Tx>(
+                                 dev_ctx, x_data, y, x_numel * maxlen, maxlen));
   }
 };
 

python/paddle/fluid/layers/nn.py

@@ -5525,13 +5525,46 @@ def flatten(x, axis=1, name=None):
     return out
 
 
-def sequence_mask(x, max_len, mask_dtype='int64'):
+def sequence_mask(x, maxlen=None, dtype='int64', name=None):
+    """
+    **SequenceMask Layer**
+
+    This layer outputs a mask according to the input :code:`x` and
+    :code:`maxlen` with data type of :code:`dtype`.
+
+    Supposing :code:`x` is a Tensor with shape [d_1, d_2, ..., d_n], the
+    :code:`y` is a mask with shape [d_1, d_2, ..., d_n, maxlen], where:
+    
+    .. math::
+     
+        y(i_1, i_2,..., i_n, j) = (j < x(i_1, i_2,..., i_n))
+
+    Args:
+        x (Variable): Input tensor of sequence_mask layer, 
+                      whose elements are integers less than :code:`maxlen`.
+        maxlen (int|None): Maximum length of the sequence. If :code:`maxlen`
+                           is None, it would be replace with :math:`max(x)`.
+        dtype (np.dtype|core.VarDesc.VarType|str): Data type of the output.
+        name (str|None): A name for this layer(optional). If set None, the 
+                         layer will be named automatically.  
+    
+    Returns:
+        Variable: The output sequence mask.
+    
+    """
+
     helper = LayerHelper('sequence_mask', **locals())
-    y = helper.create_tmp_variable(dtype=mask_dtype)
+    if name is None:
+        out = helper.create_tmp_variable(dtype=dtype)
+    else:
+        out = helper.create_tmp_variable(dtype=dtype, name=name)
+
     helper.append_op(
         type='sequence_mask',
         inputs={'X': [x]},
-        outputs={'Y': y},
-        attrs={'max_len': max_len,
-               'out_dtype': y.dtype})
-    return y
+        outputs={'Y': out},
+        attrs={
+            'max_len': maxlen if maxlen is not None else -1,
+            'out_dtype': out.dtype
+        })
+    return out

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

@@ -13,7 +13,9 @@
 # limitations under the License.
 
 from op_test import OpTest
+import paddle.fluid as fluid
 from paddle.fluid.framework import convert_np_dtype_to_dtype_
+import paddle.fluid.core as core
 import numpy as np
 import copy
 import unittest
@@ -22,7 +24,7 @@ import unittest
 class SequenceMaskTestBase(OpTest):
     def initDefaultParameters(self):
         self.op_type = 'sequence_mask'
-        self.max_len = 10
+        self.maxlen = 10
         self.mask_dtype = 'int64'
         self.x = [[0, 3, 4], [5, 7, 9]]
 
@@ -38,15 +40,16 @@ class SequenceMaskTestBase(OpTest):
         self.inputs = {'X': self.x}
         self.outputs = {'Y': self.calc_ground_truth_mask()}
         self.attrs = {
-            'max_len': self.max_len,
+            'maxlen': self.maxlen,
             'out_dtype': convert_np_dtype_to_dtype_(self.mask_dtype)
         }
 
     def calc_ground_truth_mask(self):
-        shape = self.x.shape + (self.max_len, )
+        maxlen = np.max(self.x) if self.maxlen < 0 else self.maxlen
+        shape = self.x.shape + (maxlen, )
         index_broadcast = np.broadcast_to(
             np.reshape(
-                range(self.max_len), newshape=[1] * self.x.ndim + [-1]),
+                range(maxlen), newshape=[1] * self.x.ndim + [-1]),
             shape=shape)
         x_broadcast = np.broadcast_to(
             np.reshape(
@@ -82,5 +85,10 @@ class SequenceMaskTest5(SequenceMaskTestBase):
         self.mask_dtype = 'float64'
 
 
+class SequenceMaskTest6(SequenceMaskTestBase):
+    def initParameters(self):
+        self.maxlen = -1
+
+
 if __name__ == '__main__':
     unittest.main()