[Refactor] Introduce target generic dispatch system

docs/api/python/index.rst

@@ -8,6 +8,7 @@ Python API
    intrin
    tensor
    schedule
+   target
    build
    module
    ndarray

docs/api/python/target.rst

@@ -0,0 +1,13 @@
+tvm.target
+----------
+.. automodule:: tvm.target
+
+.. autofunction:: tvm.target.generic_func
+
+.. autoclass:: tvm.target.Target
+    :members:
+
+.. autofunction:: tvm.target.cuda
+.. autofunction:: tvm.target.rocm
+.. autofunction:: tvm.target.rasp
+.. autofunction:: tvm.target.create

docs/api/python/topi.rst

@@ -37,13 +37,11 @@ Index
 
 .. autosummary::
 
-   topi.cuda.schedule_conv2d_nchw
-   topi.cuda.schedule_conv2d_hwcn
-   topi.cuda.schedule_depthwise_conv2d_nchw
-   topi.cuda.schedule_depthwise_conv2d_nhwc
-   topi.cuda.schedule_reduce
-   topi.cuda.schedule_broadcast
-   topi.cuda.schedule_injective
+   topi.generic.schedule_conv2d_nchw
+   topi.generic.schedule_depthwise_conv2d_nchw
+   topi.generic.schedule_reduce
+   topi.generic.schedule_broadcast
+   topi.generic.schedule_injective
 
 topi
 ~~~~
@@ -75,14 +73,12 @@ topi.nn
 .. autofunction:: topi.nn.depthwise_conv2d_nhwc
 
 
-topi.cuda
-~~~~~~~~~
-.. automodule:: topi.cuda
+topi.generic
+~~~~~~~~~~~~
+.. automodule:: topi.generic
 
-.. autofunction:: topi.cuda.schedule_conv2d_nchw
-.. autofunction:: topi.cuda.schedule_conv2d_hwcn
-.. autofunction:: topi.cuda.schedule_depthwise_conv2d_nchw
-.. autofunction:: topi.cuda.schedule_depthwise_conv2d_nhwc
-.. autofunction:: topi.cuda.schedule_reduce
-.. autofunction:: topi.cuda.schedule_broadcast
-.. autofunction:: topi.cuda.schedule_injective
+.. autofunction:: topi.generic.schedule_conv2d_nchw
+.. autofunction:: topi.generic.schedule_depthwise_conv2d_nchw
+.. autofunction:: topi.generic.schedule_reduce
+.. autofunction:: topi.generic.schedule_broadcast
+.. autofunction:: topi.generic.schedule_injective

python/tvm/_ffi/ndarray.py

@@ -56,11 +56,7 @@ def context(dev_type, dev_id=0):
       assert tvm.context("cuda", 0) == tvm.gpu(0)
     """
     if isinstance(dev_type, string_types):
-        if dev_type not in TVMContext.STR2MASK:
-            if dev_type.find("nvptx") != -1:
-                dev_type = "cuda"
-            if dev_type.find("rocm") != -1:
-                dev_type = "rocm"
+        dev_type = dev_type.split()[0]
         if dev_type not in TVMContext.STR2MASK:
             raise ValueError("Unknown device type %s" % dev_type)
         dev_type = TVMContext.STR2MASK[dev_type]

python/tvm/_ffi/runtime_ctypes.py

@@ -100,9 +100,12 @@ class TVMContext(ctypes.Structure):
         12: 'ext_dev',
     }
     STR2MASK = {
+        'llvm': 1,
+        'stackvm': 1,
         'cpu': 1,
         'gpu': 2,
         'cuda': 2,
+        'nvptx': 2,
         'cl': 4,
         'opencl': 4,
         'metal': 8,

python/tvm/build_module.py

@@ -15,6 +15,7 @@
 from . import module
 from . import codegen
 from . import ndarray
+from . import target as _target
 
 class BuildConfig(object):
     """Configuration scope to set a build config option.
@@ -238,7 +239,7 @@ def lower(sch,
 
 def build(sch,
           args=None,
-          target="llvm",
+          target=None,
           target_host=None,
           name="default_function",
           binds=None):
@@ -252,36 +253,10 @@ def build(sch,
     args : list of Buffer or Tensor or Var, optional
         The argument lists to the function.
 
-    target : str, optional
+    target : str or :any:`tvm.target.Target`, optional
         The target and option of the compilation.
-        When the target is llvm, you can set options like:
 
-          - **-mtriple=<target triple>** or **-target**
-
-            Specify the target triple, which is useful for cross
-            compilation.
-
-          - **-mcpu=<cpuname>**
-
-            Specify a specific chip in the current architecture to
-            generate code for. By default this is infered from the
-            target triple and autodetected to the current architecture.
-
-          - **-mattr=a1,+a2,-a3,...**
-
-            Override or control specific attributes of the target,
-            such as whether SIMD operations are enabled or not. The
-            default set of attributes is set by the current CPU.
-
-          - **-system-lib**
-
-            Build TVM system library module. System lib is a global module that contains
-            self registered functions in program startup. User can get the module using
-            :any:`tvm.module.system_lib`.
-            It is useful in environments where dynamic loading api like dlopen is banned.
-            The system lib will be available as long as the result code is linked by the program.
-
-    target_host : str, optional
+    target_host : str or :any:`tvm.target.Target` optional
         Host compilation target, if target is device.
         When TVM compiles device specific program such as CUDA,
         we also need host(CPU) side code to interact with the driver
@@ -301,6 +276,10 @@ def build(sch,
     -------
     f : Function, or pair of functions
        The result function.
+
+    Note
+    ----
+    See the note on :any:`tvm.target` on target string format.
     """
     if isinstance(sch, schedule.Schedule):
         if args is None:
@@ -325,14 +304,17 @@ def build(sch,
         if x.name in fname_set:
             raise ValueError("Duplicate function name %s" % x.name)
 
+    target = _target.current_target() if target is None else target
+    target = _target.create(target) if target else _target.create("llvm")
+
     fhost = []
     fdevice = []
     for func in flist:
         if func.func_type == container.LoweredFunc.MixedFunc:
             if BuildConfig.current.detect_global_barrier:
                 func = ir_pass.ThreadSync(func, "global")
             func = ir_pass.ThreadSync(func, "shared")
-            warp_size = 32 if target == "cuda" else 1
+            warp_size = target.thread_warp_size
             func = ir_pass.LowerThreadAllreduce(func, warp_size)
             fsplits = [s for s in ir_pass.SplitHostDevice(func)]
             fhost.append(fsplits[0])
@@ -345,29 +327,28 @@ def build(sch,
         else:
             raise ValueError("unknown function type %d" % func.func_type)
 
-    if not target.startswith("llvm") and target not in ("stackvm", "ext_dev") and not fdevice:
+    if "gpu" in target.keys and not fdevice:
         warnings.warn(
             "Specified target %s, but cannot find device code, did you do bind?" % target)
 
-    device = "cpu" if target.startswith("llvm") or target == "stackvm" else target
-    device_type = ndarray.context(device, 0).device_type
+    device_type = ndarray.context(target.target_name, 0).device_type
     fhost = [ir_pass.BindDeviceType(x, device_type) for x in fhost]
     fhost = [ir_pass.LowerTVMBuiltin(x) for x in fhost]
 
     if not target_host:
-        if device == "cpu":
+        if device_type == ndarray.cpu(0).device_type:
             target_host = target
             assert not fdevice
         else:
             target_host = "llvm" if module.enabled("llvm") else "stackvm"
-
+    target_host = _target.create(target_host)
     target_device = target
-    fdevice = [ir_pass.LowerIntrin(x, target_device) for x in fdevice]
-    fhost = [ir_pass.LowerIntrin(x, target_host) for x in fhost]
+    fdevice = [ir_pass.LowerIntrin(x, target_device.target_name) for x in fdevice]
+    fhost = [ir_pass.LowerIntrin(x, target_host.target_name) for x in fhost]
     fhost = [ir_pass.CombineContextCall(x) for x in fhost]
-    mhost = codegen.build_module(fhost, target_host)
+    mhost = codegen.build_module(fhost, str(target_host))
 
     if fdevice:
-        mdev = codegen.build_module(fdevice, target_device)
+        mdev = codegen.build_module(fdevice, str(target_device))
         mhost.import_module(mdev)
     return mhost