Merge branch 'master' into ansor-gpu-tutorial

Author: merrymercy

python/tvm/exec/rpc_server.py

@@ -69,7 +69,7 @@ def init_utvm(args):
     args : argparse.Namespace
         parsed args from command-line invocation
     """
-    from tvm import micro
+    from tvm import micro  # pylint: disable=import-outside-toplevel
 
     if args.utvm_dev_config and args.utvm_dev_id:
         raise RuntimeError("only one of --utvm-dev-config and --utvm-dev-id allowed")

python/tvm/hybrid/_ffi_api.py

@@ -17,5 +17,4 @@
 """FFI APIs for tvm.hybrid"""
 import tvm._ffi
 
-
-tvm._ffi._init_api("tir.hybrid", __name__)
+tvm._ffi._init_api("hybrid", __name__)

python/tvm/hybrid/intrin.py

@@ -23,114 +23,146 @@
 from .registry import register_intrin
 
 
-@register_intrin
+@register_intrin()
 def bool(imm):
-    return tvm.tir.const(imm.value, "bool")
+    return tvm.tir.const(imm, "bool")
 
 
-@register_intrin
+@register_intrin()
 def int8(imm):
-    return tvm.tir.const(imm.value, "int8")
+    return tvm.tir.const(imm, "int8")
 
 
-@register_intrin
+@register_intrin()
 def int16(imm):
-    return tvm.tir.const(imm.value, "int16")
+    return tvm.tir.const(imm, "int16")
 
 
-@register_intrin
+@register_intrin()
 def int32(imm):
-    return tvm.tir.const(imm.value, "int32")
+    return tvm.tir.const(imm, "int32")
 
 
-@register_intrin
+@register_intrin()
 def int64(imm):
-    return tvm.tir.const(imm.value, "int64")
+    return tvm.tir.const(imm, "int64")
 
 
-@register_intrin
+@register_intrin()
 def uint8(imm):
-    return tvm.tir.const(imm.value, "uint8")
+    return tvm.tir.const(imm, "uint8")
 
 
-@register_intrin
+@register_intrin()
 def uint16(imm):
-    return tvm.tir.const(imm.value, "uint16")
+    return tvm.tir.const(imm, "uint16")
 
 
-@register_intrin
+@register_intrin()
 def uint32(imm):
-    return tvm.tir.const(imm.value, "uint32")
+    return tvm.tir.const(imm, "uint32")
 
 
-@register_intrin
+@register_intrin()
 def uint64(imm):
-    return tvm.tir.const(imm.value, "uint64")
+    return tvm.tir.const(imm, "uint64")
 
 
-@register_intrin
+@register_intrin()
 def float8(imm):
-    return tvm.tir.const(imm.value, "float8")
+    return tvm.tir.const(imm, "float8")
 
 
-@register_intrin
+@register_intrin()
 def float16(imm):
-    return tvm.tir.const(imm.value, "float16")
+    return tvm.tir.const(imm, "float16")
 
 
-@register_intrin
+@register_intrin()
 def float32(imm):
-    return tvm.tir.const(imm.value, "float32")
+    return tvm.tir.const(imm, "float32")
 
 
-@register_intrin
+@register_intrin()
 def float64(imm):
-    return tvm.tir.const(imm.value, "float64")
+    return tvm.tir.const(imm, "float64")
 
 
-@register_intrin
+@register_intrin()
 def floordiv(x, y):
     return tvm.tir.floordiv(x, y)
 
 
-@register_intrin
+@register_intrin()
 def floormod(x, y):
     return tvm.tir.floormod(x, y)
 
 
-@register_intrin
+@register_intrin()
 def load(dtype, var, index, predicate=True):
     return tvm.tir.Load(dtype, var, index, predicate)
 
 
-@register_intrin
-def cast(dtype, value):
+@register_intrin()
+def cast(value, dtype):
     return tvm.tir.Cast(dtype, value)
 
 
-@register_intrin
+@register_intrin()
 def ramp(base, stride, lanes):
-    lanes = lanes.value if not isinstance(lanes, int) else lanes
     return tvm.tir.Ramp(base, stride, lanes)
 
 
-@register_intrin
+@register_intrin()
 def broadcast(value, lanes):
-    lanes = lanes.value if not isinstance(lanes, int) else lanes
     return tvm.tir.Broadcast(value, lanes)
 
 
-@register_intrin
+@register_intrin()
 def evaluate(value):
     return tvm.tir.Evaluate(value)
 
 
-@register_intrin
+@register_intrin()
 def store(var, index, value, predicate=True):
     return tvm.tir.Store(var, value, index, predicate)
 
 
-@register_intrin
+@register_intrin()
 def iter_var(var, dom, iter_type, thread_tag):
     iter_type = getattr(tvm.tir.IterVar, iter_type)
     return tvm.tir.IterVar(dom, var, iter_type, thread_tag)
+
+
+@register_intrin()
+def max(a, b):  # pylint: disable=redefined-builtin
+    return tvm.tir.Max(a, b)
+
+
+def get_axis(begin, end, iter_type):
+    ana = tvm.arith.Analyzer()
+    extent = ana.simplify(end - begin)
+    block_var_dom = tvm.ir.Range.from_min_extent(begin, extent)
+
+    iter_type_dict = {"data_par": 0, "reduce": 2, "scan": 3, "opaque": 4}
+    return tvm.tir.IterVar(block_var_dom, "bv", iter_type_dict[iter_type])
+
+
+@register_intrin()
+def range(begin, end):
+    return get_axis(begin, end, "data_par")
+
+
+@register_intrin()
+def reduce_axis(begin, end):
+    return get_axis(begin, end, "reduce")
+
+
+@register_intrin()
+def scan_axis(begin, end):
+    return get_axis(begin, end, "scan")
+
+
+@register_intrin()
+def opaque_axis(begin, end):
+    return get_axis(begin, end, "opaque")