[TOPI] CUDNN integration (#730)

* add target.libs to target str representation

* integrate cudnn into topi cuda

* append target.libs to target.options

python/tvm/target.py

@@ -88,17 +88,15 @@ class Target(object):
                  target_name,
                  options=None):
         self.target_name = target_name
-        self.options = []
+        self.options = _merge_opts([], options)
         self.device_name = ""
         self.libs = []
         # Parse device option
-        for item in _merge_opts([], options):
+        for item in self.options:
             if item.startswith("-libs="):
                 self.libs.append(item.split("=")[1])
-                continue
             elif item.startswith("-device="):
                 self.device_name = item.split("=")[1]
-            self.options.append(item)
         # Target query searchs device name first
         if self.device_name:
             self.keys = (self.device_name,)

src/codegen/llvm/llvm_common.cc

@@ -82,7 +82,7 @@ GetLLVMTargetMachine(const std::string& target_str,
       } else {
         LOG(FATAL) << "invalid -mfloat-abi option " << value;
       }
-    } else if (key == "-device") {
+    } else if (key == "-device" || key == "-libs") {
       // pass
     } else {
       LOG(FATAL) << "unknown option " << key;

tests/python/contrib/test_cudnn.py

@@ -41,6 +41,7 @@ def test_conv2d():
                              tensor_format=0,
                              algo=1)
     yshape = [x.value for x in Y.shape]
+    with tvm.target.create("cuda -libs=cudnn"):
         s = tvm.create_schedule(Y.op)
     
     def verify():

topi/python/topi/cuda/__init__.py

@@ -2,6 +2,7 @@
 """CUDA specific declaration and schedules."""
 from __future__ import absolute_import as _abs
 
+from .conv2d import conv2d_cuda
 from .conv2d_nchw import schedule_conv2d_nchw
 from .conv2d_hwcn import schedule_conv2d_hwcn
 from .depthwise_conv2d import schedule_depthwise_conv2d_nchw, schedule_depthwise_conv2d_nhwc

topi/python/topi/cuda/conv2d.py

+# pylint: disable=invalid-name, no-member, too-many-locals, too-many-statements, too-many-arguments, too-many-branches, line-too-long
+"""Compute definition for conv2d with cuda backend"""
+import tvm
+from tvm.contrib import cudnn
+import topi
+from ..nn.conv2d import conv2d
+
+@conv2d.register("cuda")
+def conv2d_cuda(data, kernel, stride, padding, layout='NCHW', out_dtype='float32'):
+    """Conv2D operator for cuda backend.
+
+    Parameters
+    ----------
+    input : tvm.Tensor
+        4-D with shape [batch, in_channel, in_height, in_width]
+
+    filter : tvm.Tensor
+        4-D with shape [num_filter, in_channel, filter_height, filter_width]
+
+    stride : int or a list/tuple of two ints
+        stride size, or [stride_height, stride_width]
+
+    padding : int or a list/tuple of two ints
+        padding size, or [pad_height, pad_width]
+
+    layout : str
+        layout of data
+
+    Returns
+    -------
+    output : tvm.Tensor
+        4-D with shape [batch, out_channel, out_height, out_width]
+    """
+    assert isinstance(stride, int) or len(stride) == 2
+    if isinstance(stride, int):
+        stride_h = stride_w = stride
+    else:
+        stride_h, stride_w = stride
+    if isinstance(padding, int):
+        pad_h = pad_w = padding
+    else:
+        pad_h, pad_w = padding
+    target = tvm.target.current_target()
+    if "cudnn" in target.libs:
+        assert layout != 'HWCN', "HWCN layout not supported with CUDNN."
+        tensor_format = 0 # CUDNN_TENSOR_NCHW
+        if layout == 'NHWC':
+            tensor_format = 1 # CUDNN_TENSOR_NHWC
+        return cudnn.conv2d_forward(data,
+                                    kernel,
+                                    stride_h,
+                                    stride_w,
+                                    pad_h,
+                                    pad_w,
+                                    1,  # dilation_h
+                                    1,  # dilation_w
+                                    conv_mode=1,
+                                    tensor_format=tensor_format,
+                                    algo=0)
+    elif layout == 'NCHW':
+        return topi.nn.conv2d_nchw(data, kernel, stride, padding, out_dtype)
+    elif layout == 'HWCN':
+        return topi.nn.conv2d_hwcn(data, kernel, stride, padding, out_dtype)
+    else:
+        raise ValueError("not support this layout {} yet".format(layout))

topi/python/topi/cuda/conv2d_nchw.py

 #pylint: disable=invalid-name, no-member, too-many-locals, too-many-statements, too-many-arguments, too-many-branches, line-too-long
 """Schedule for conv2d_nchw with auto fusion"""
 import tvm
+import topi
 from .. import util
 from .. import tag
 from .. import generic
@@ -516,6 +517,10 @@ def schedule_conv2d_nchw(outs):
     s: Schedule
         The computation schedule for conv2d_nchw.
     """
+    target = tvm.target.current_target()
+    if target.target_name == "cuda" and "cudnn" in target.libs:
+        return topi.generic.schedule_extern(outs)
+
     outs = [outs] if isinstance(outs, tvm.tensor.Tensor) else outs
     batch_size = util.get_const_int(outs[0].op.output(0).shape[0])
     if batch_size > 1:

topi/python/topi/rocm/conv2d.py

 # pylint: disable=invalid-name, no-member, too-many-locals, too-many-statements, too-many-arguments, too-many-branches, line-too-long
-"""Schedule for rocm conv2d_nchw with auto fusion"""
+"""Compute and schedule for rocm conv2d_nchw with auto fusion"""
 import tvm
 from tvm.contrib import miopen
 import topi