[CODEGEN] fix vector conversion for opencl (#783)

* support more argument type in depthwise_conv2d

* mark all pointer as 'restrict' & fix vector conversion for opencl

src/codegen/codegen_c.cc

@@ -38,14 +38,17 @@ void CodeGenC::AddFunction(LoweredFunc f) {
     if (i != 0) stream << ", ";
     if (v.type().is_handle()) {
       auto it = alloc_storage_scope_.find(v.get());
-      if (it != alloc_storage_scope_.end()) {
+      if (it != alloc_storage_scope_.end())
         PrintStorageScope(it->second, stream);
       stream << ' ';
-      }
-    }
+
       if (handle_data_type_.count(v.get())) {
         PrintType(handle_data_type_.at(v.get()), stream);
+      } else {
+        stream << "void";
+      }
       stream << "*";
+
       if (f->is_restricted && restrict_keyword_.length() != 0) {
         stream << ' ' << restrict_keyword_;
       }
@@ -402,12 +405,9 @@ inline void PrintBinaryIntrinsitc(const Call* op,
   }
 }
 void CodeGenC::VisitExpr_(const Cast *op, std::ostream& os) {  // NOLINT(*)
-  os << "(";
-  this->PrintType(op->type, os);
-  os << ")";
-  os << '(';
-  this->PrintExpr(op->value, os);
-  os << ')';
+  std::stringstream value;
+  this->PrintExpr(op->value, value);
+  os << CastFromTo(value.str(), op->value.type(), op->type);
 }
 void CodeGenC::VisitExpr_(const Variable *op, std::ostream& os) {  // NOLINT(*)
   os << GetVarID(op);

src/codegen/codegen_c.h

@@ -142,6 +142,8 @@ class CodeGenC :
   // print store of single element.
   virtual void PrintVecElemStore(
       const std::string& vec, Type t, int i, const std::string& value);
+  // Get a cast type from to
+  virtual std::string CastFromTo(std::string value, Type from, Type target);
 
  protected:
   // Print reference to struct location
@@ -150,8 +152,6 @@ class CodeGenC :
   // print reference to a buffer as type t in index.
   std::string GetBufferRef(
       Type t, const Variable* buffer, Expr index);
-  // Get a cast type from to
-  std::string CastFromTo(std::string value, Type from, Type target);
   /*!
    * \brief If buffer is allocated as type t.
    * \param buf_var The buffer variable.

src/codegen/codegen_opencl.cc

@@ -175,6 +175,22 @@ void CodeGenOpenCL::PrintStorageScope(
   }
 }
 
+std::string CodeGenOpenCL::CastFromTo(std::string value, Type from, Type target) {
+  if (from == target) return value;
+  std::ostringstream os;
+  if (target.lanes() == 1) {
+    os << "((";
+    this->PrintType(target, os);
+    os << ")" << value << ")";
+  } else {  // convert vector type
+    os << "(";
+    os << "convert_";
+    this->PrintType(target, os);
+    os << "(" << value << "))";
+  }
+  return os.str();
+}
+
 void CodeGenOpenCL::VisitExpr_(const Broadcast* op, std::ostream& os) {   // NOLINT(*)
   std::string v = PrintExpr(op->value);
   os << "((";

src/codegen/codegen_opencl.h

@@ -34,6 +34,8 @@ class CodeGenOpenCL final : public CodeGenC {
   // the address of load/store
   void PrintVecAddr(const Variable* buffer, Type t,
                     Expr base, std::ostream& os);  // NOLINT(*)
+  std::string CastFromTo(std::string value, Type from, Type target); // NOLINT(*)
+
   // overload visitor
   void VisitExpr_(const Broadcast* op, std::ostream& os) final; // NOLINT(*)
 

src/pass/split_host_device.cc

@@ -191,9 +191,6 @@ class HostDeviceSplitter : public IRMutator {
         auto it = handle_data_type_.find(v.get());
         if (it != handle_data_type_.end()) {
           n->handle_data_type.Set(v, it->second);
-        } else {
-          // int32 as a placeholder
-          n->handle_data_type.Set(v, make_const(UInt(32), 0));
         }
       }
     }

topi/python/topi/nn/depthwise_conv2d.py

@@ -31,8 +31,13 @@ def depthwise_conv2d_nchw(Input, Filter, stride, padding, out_dtype='float32'):
     Output : tvm.Tensor
         4-D with shape [batch, out_channel, out_height, out_width]
     """
+    out_dtype = Input.dtype
+
     batch, in_channel, in_height, in_width = Input.shape
     filter_channel, channel_multiplier, filter_height, filter_width = Filter.shape
+    if isinstance(stride, int):
+        stride_h = stride_w = stride
+    else:
         stride_h, stride_w = stride
 
     pad_top, pad_left, pad_down, pad_right = get_pad_tuple(
@@ -82,6 +87,9 @@ def depthwise_conv2d_nhwc(Input, Filter, stride, padding):
     """
     batch, in_height, in_width, in_channel = Input.shape
     filter_height, filter_width, filter_channel, channel_multiplier = Filter.shape
+    if isinstance(stride, int):
+        stride_h = stride_w = stride
+    else:
         stride_h, stride_w = stride
 
     pad_top, pad_left, pad_down, pad_right = get_pad_tuple(
@@ -131,6 +139,9 @@ def depthwise_conv2d_backward_input_nhwc(Filter, Out_grad, oshape, ishape, strid
     batch, in_h, in_w, in_c = ishape
     _, out_h, out_w, out_c = oshape
     filter_h, filter_w, _, channel_multiplier = Filter.shape
+    if isinstance(stride, int):
+        stride_h = stride_w = stride
+    else:
         stride_h, stride_w = stride
 
     dilated_out_grad = dilate(Out_grad, [1, stride_h, stride_w, 1], name='dilated_out_grad')
@@ -186,6 +197,9 @@ def depthwise_conv2d_backward_weight_nhwc(Input, Out_grad, oshape, fshape, strid
     batch, out_h, out_w, out_c = oshape
     filter_h, filter_w, _, channel_multiplier = fshape
     in_c = Input.shape[3].value
+    if isinstance(stride, int):
+        stride_h = stride_w = stride
+    else:
         stride_h, stride_w = stride
 
     pad_top, pad_left, pad_bottom, pad_right = get_pad_tuple(padding, (filter_h, filter_w))

topi/python/topi/testing/depthwise_conv2d_python.py

@@ -27,7 +27,11 @@ def depthwise_conv2d_python_nchw(input_np, filter_np, stride, padding):
     """
     batch, in_channel, in_height, in_width = input_np.shape
     _, channel_multiplier, filter_height, filter_width = filter_np.shape
+    if isinstance(stride, int):
+        stride_h = stride_w = stride
+    else:
         stride_h, stride_w = stride
+
     # calculate output shape
     if padding == 'VALID':
         out_channel = in_channel * channel_multiplier
@@ -84,7 +88,11 @@ def depthwise_conv2d_python_nhwc(input_np, filter_np, stride, padding):
     """
     batch, in_height, in_width, in_channel = input_np.shape
     filter_height, filter_width, _, channel_multiplier = filter_np.shape
+    if isinstance(stride, int):
+        stride_h = stride_w = stride
+    else:
         stride_h, stride_w = stride
+
     # calculate output shape
     if padding == 'VALID':
         out_channel = in_channel * channel_multiplier

topi/tests/python/test_topi_depthwise_conv2d.py

@@ -7,18 +7,17 @@ from tvm.contrib.pickle_memoize import memoize
 from topi.cuda.depthwise_conv2d import schedule_depthwise_conv2d_nhwc
 
 
-def depthwise_conv2d_with_workload_nchw(batch, in_channel, in_height, channel_multiplier, filter_height, stride_h, padding):
+def depthwise_conv2d_with_workload_nchw(batch, in_channel, in_height, channel_multiplier, filter_height, stride, padding):
     in_width = in_height
     filter_channel = in_channel
     filter_width = filter_height
-    stride_w = stride_h
     # placeholder
     Input = tvm.placeholder((batch, in_channel, in_height, in_width), name='Input')
     Filter = tvm.placeholder((filter_channel, channel_multiplier, filter_height, filter_width), name='Filter')
     Scale = tvm.placeholder((in_channel * channel_multiplier,), name='Scale')
     Shift = tvm.placeholder((in_channel * channel_multiplier,), name='Shift')
     # declare
-    DepthwiseConv2d = topi.nn.depthwise_conv2d_nchw(Input, Filter, stride=[stride_h, stride_w], padding=padding)
+    DepthwiseConv2d = topi.nn.depthwise_conv2d_nchw(Input, Filter, stride=stride, padding=padding)
     ScaleShift = topi.nn.scale_shift_nchw(DepthwiseConv2d, Scale, Shift)
     Relu = topi.nn.relu(ScaleShift)
 
@@ -56,7 +55,7 @@ def depthwise_conv2d_with_workload_nchw(batch, in_channel, in_height, channel_mu
             shift_np = np.random.uniform(size=shift_shape).astype(dtype)
             # correctness with scipy
             depthwise_conv2d_scipy = topi.testing.depthwise_conv2d_python_nchw(
-                input_np, filter_np, stride=[stride_h, stride_w], padding=padding)
+                input_np, filter_np, stride=stride, padding=padding)
             scale_shift_scipy = np.zeros(shape=scale_shift_shape)
             for c in range(in_channel * channel_multiplier):
                 scale_shift_scipy[:,c,:,:] = depthwise_conv2d_scipy[:,c,:,:] * scale_np[c] + shift_np[c]