[TVM] upgrade to generic schedule (apache#173)

nnvm/python/nnvm/compiler/build_module.py

@@ -161,7 +161,7 @@ def optimize(graph, shape, dtype="float32"):
     return graph
 
 
-def build(graph, target, shape, dtype="float32", params=None):
+def build(graph, target=None, shape=None, dtype="float32", params=None):
     """Build graph into runtime library.
 
     The build function will optimize the graph and do the compilation.
@@ -175,10 +175,10 @@ def build(graph, target, shape, dtype="float32", params=None):
     graph : Graph
         The graph to be used in lowering
 
-    target : str
+    target : str or :any:`tvm.target.Target`, optional
         The build target
 
-    shape : dict of str to tuple
+    shape : dict of str to tuple, optional
         The input shape to the graph
 
     dtype : str or dict of str to str
@@ -201,8 +201,12 @@ def build(graph, target, shape, dtype="float32", params=None):
         The updated parameters of graph if params is passed.
         This can be different from the params passed in.
     """
-    if not isinstance(target, str):
-        raise TypeError("require target to be str")
+    target = target if target else tvm.target.current_target()
+    if target is None:
+        raise ValueError("Target is not set in env or passed as argument.")
+    target = tvm.target.create(target)
+
+    shape = shape if shape else {}
     if not isinstance(shape, dict):
         raise TypeError("require shape to be dict")
     cfg = BuildConfig.current
@@ -223,13 +227,14 @@ def build(graph, target, shape, dtype="float32", params=None):
     # Operator Fusion and generatiom
     graph = graph_attr.set_shape_inputs(graph, shape)
     graph = graph_attr.set_dtype_inputs(graph, dtype)
-    graph._set_json_attr("target", target, "str")
+    graph._set_json_attr("target", str(target), "str")
     if cfg.pass_enabled("OpFusion"):
         graph._set_json_attr("opt_level", 1, "int")
     else:
         graph._set_json_attr("opt_level", 0, "int")
     graph = graph.apply("InferShape").apply("InferType")
-    graph = graph.apply("GraphFusePartition").apply("GraphFuseCompile")
+    with target:
+        graph = graph.apply("GraphFusePartition").apply("GraphFuseCompile")
     libmod = graph_attr._move_out_module(graph, "module")
     return graph, libmod, params
 

nnvm/python/nnvm/top/nn.py

@@ -50,10 +50,9 @@ def compute_softmax(attrs, inputs, _):
 @reg.register_schedule("softmax")
 def schedule_softmax(_, outs, target):
     """Schedule definition of softmax"""
-    if target == "cuda":
-        return topi.cuda.schedule_softmax(outs)
-    # naive schedule
-    return tvm.create_schedule([x.op for x in outs])
+    with tvm.target.create(target):
+        return topi.generic.schedule_softmax(outs)
+
 
 reg.register_pattern("softmax", OpPattern.OPAQUE)
 
@@ -68,10 +67,8 @@ def compute_log_softmax(attrs, inputs, _):
 @reg.register_schedule("log_softmax")
 def schedule_log_softmax(_, outs, target):
     """Schedule definition of softmax"""
-    if target == "cuda":
-        return topi.cuda.schedule_softmax(outs)
-    # naive schedule
-    return tvm.create_schedule([x.op for x in outs])
+    with tvm.target.create(target):
+        return topi.generic.schedule_softmax(outs)
 
 # Mark softmax as extern as we do not fuse it in call cases
 reg.register_pattern("log_softmax", OpPattern.OPAQUE)
@@ -87,10 +84,8 @@ def compute_dense(attrs, inputs, _):
 @reg.register_schedule("dense")
 def schedule_dense(_, outs, target):
     """Schedule definition of dense"""
-    if target == "cuda":
-        return topi.cuda.schedule_dense(outs)
-    # naive schedule
-    return tvm.create_schedule([x.op for x in outs])
+    with tvm.target.create(target):
+        return topi.generic.schedule_dense(outs)
 
 reg.register_pattern("dense", OpPattern.OUT_ELEMWISE_FUSABLE)
 
@@ -123,18 +118,10 @@ def compute_conv2d(attrs, inputs, _):
 def schedule_conv2d(attrs, outs, target):
     """Schedule definition of conv2d"""
     groups = attrs.get_int("groups")
-    if target == "cuda":
-        if groups == 1:
-            return topi.cuda.schedule_conv2d_nchw(outs)
-        return topi.cuda.schedule_depthwise_conv2d_nchw(outs)
-    # naive schedule
-
-    if tvm.target.current_target() == tvm.target.rasp():
+    with tvm.target.create(target):
         if groups == 1:
-            return topi.rasp.schedule_conv2d(outs)
-        return topi.rasp.schedule_depthwise_conv2d(outs)
-
-    return tvm.create_schedule([x.op for x in outs])
+            return topi.generic.schedule_conv2d_nchw(outs)
+        return topi.generic.schedule_depthwise_conv2d_nchw(outs)
 
 reg.register_pattern("conv2d", OpPattern.OUT_ELEMWISE_FUSABLE)
 
@@ -155,10 +142,8 @@ def compute_max_pool2d(attrs, inputs, _):
 @reg.register_schedule("max_pool2d")
 def schedule_max_pool2d(_, outs, target):
     """Schedule definition of max_pool2d"""
-    if target == "cuda":
-        return topi.cuda.schedule_pool(outs)
-    # naive schedule
-    return tvm.create_schedule([x.op for x in outs])
+    with tvm.target.create(target):
+        return topi.generic.schedule_pool(outs)
 
 reg.register_pattern("max_pool2d", OpPattern.OUT_ELEMWISE_FUSABLE)
 
@@ -179,10 +164,8 @@ def compute_avg_pool2d(attrs, inputs, _):
 @reg.register_schedule("avg_pool2d")
 def schedule_avg_pool2d(_, outs, target):
     """Schedule definition of avg_pool2d"""
-    if target == "cuda":
-        return topi.cuda.schedule_pool(outs)
-    # naive schedule
-    return tvm.create_schedule([x.op for x in outs])
+    with tvm.target.create(target):
+        return topi.generic.schedule_pool(outs)
 
 reg.register_pattern("avg_pool2d", OpPattern.OUT_ELEMWISE_FUSABLE)
 
@@ -198,10 +181,8 @@ def compute_global_max_pool2d(attrs, inputs, _):
 @reg.register_schedule("global_max_pool2d")
 def schedule_global_max_pool2d(_, outs, target):
     """Schedule definition of global_max_pool2d"""
-    if target == "cuda":
-        return topi.cuda.schedule_global_pool(outs)
-    # naive schedule
-    return tvm.create_schedule([x.op for x in outs])
+    with tvm.target.create(target):
+        return topi.generic.schedule_global_pool(outs)
 
 reg.register_pattern("global_max_pool2d", OpPattern.OUT_ELEMWISE_FUSABLE)
 
@@ -217,9 +198,7 @@ def compute_global_avg_pool2d(attrs, inputs, _):
 @reg.register_schedule("global_avg_pool2d")
 def schedule_global_avg_pool2d(_, outs, target):
     """Schedule definition of global_avg_pool2d"""
-    if target == "cuda":
-        return topi.cuda.schedule_global_pool(outs)
-    # naive schedule
-    return tvm.create_schedule([x.op for x in outs])
+    with tvm.target.create(target):
+        return topi.generic.schedule_global_pool(outs)
 
 reg.register_pattern("global_avg_pool2d", OpPattern.OUT_ELEMWISE_FUSABLE)

nnvm/python/nnvm/top/reduction.py

@@ -10,14 +10,9 @@
 
 def _schedule_reduce(_, outs, target):
     """Generic schedule for reduce"""
-    if target == "cuda":
-        return topi.cuda.schedule_reduce(outs)
-    assert target.startswith("llvm")
-    s = tvm.create_schedule([x.op for x in outs])
-    x = outs[0]
-    tvm.schedule.AutoInlineInjective(s)
-    s[x].fuse(s[x].op.axis)
-    return s
+    with tvm.target.create(target):
+        return topi.generic.schedule_reduce(outs)
+
 
 _fschedule_reduce = tvm.convert(_schedule_reduce)
 

nnvm/python/nnvm/top/tensor.py

@@ -10,15 +10,8 @@
 
 def _schedule_injective(_, outs, target):
     """Generic schedule for binary bcast"""
-    if target == "cuda":
-        return topi.cuda.schedule_injective(outs)
-    assert target.startswith("llvm")
-    s = tvm.create_schedule([x.op for x in outs])
-    x = outs[0]
-    tvm.schedule.AutoInlineInjective(s)
-    s[x].fuse(s[x].op.axis)
-    return s
-
+    with tvm.target.create(target):
+        return topi.generic.schedule_injective(outs)
 
 def _compute_binary_scalar(f):
     """auxiliary function"""
@@ -174,7 +167,7 @@ def _compute(attrs, x, _):
 
 # broadcast_to
 @reg.register_compute("broadcast_to")
-def compute_softmax(attrs, inputs, out_info):
+def compute_broadcast_to(attrs, inputs, out_info):
     """Compute definition of softmax"""
     return topi.broadcast_to(inputs[0], shape=out_info[0].shape)
 reg.register_pattern("broadcast_to", OpPattern.BROADCAST)