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Posted to commits@tvm.apache.org by GitBox <gi...@apache.org> on 2020/12/04 00:10:12 UTC
[GitHub] [tvm] mbrookhart commented on a change in pull request #7029: [Relay][Pass] Clean up DCE tests in preparation for refactoring.
mbrookhart commented on a change in pull request #7029:
URL: https://github.com/apache/tvm/pull/7029#discussion_r535738565
##########
File path: tests/python/relay/test_pass_dead_code_elimination.py
##########
@@ -25,59 +25,106 @@
import pytest
-class env:
- def __init__(self):
- self.shape = tvm.runtime.convert([1, 2, 3])
- self.tt = relay.TensorType(self.shape, "float32")
- self.int32 = relay.TensorType([], "int32")
- self.float32 = relay.TensorType([], "float32")
- self.one = relay.const(1.0)
- self.two = relay.const(2.0)
- self.three = relay.const(3.0)
- self.a = relay.Var("a", self.float32)
- self.b = relay.Var("b", self.float32)
- self.c = relay.Var("c", self.float32)
- self.d = relay.Var("d", self.float32)
- self.e = relay.Var("e", self.float32)
- self.x = relay.Var("x", self.int32)
- self.y = relay.Var("y", self.int32)
- self.z = relay.Var("z", self.int32)
-
-
-e = env()
-
-
-def run_opt_pass(expr, opt_pass):
- assert isinstance(opt_pass, tvm.transform.Pass)
- mod = tvm.IRModule.from_expr(expr)
- mod = opt_pass(mod)
- entry = mod["main"]
- return entry if isinstance(expr, relay.Function) else entry.body
-
-
-def test_let():
- orig = relay.Let(e.x, e.y, e.z)
- orig = run_opt_pass(orig, transform.DeadCodeElimination())
- assert tvm.ir.structural_equal(Function(free_vars(orig), orig), Function([e.z], e.z))
-
-
-def test_used_let():
- orig = relay.Let(e.c, e.one, e.c + e.c)
- orig = run_opt_pass(orig, transform.DeadCodeElimination())
- expected = relay.Let(e.c, e.one, e.c + e.c)
- assert tvm.ir.structural_equal(Function([], orig), Function([], expected))
+# class env:
+# def __init__(self):
+# self.shape = tvm.runtime.convert([1, 2, 3])
+# self.tt = relay.TensorType(self.shape, "float32")
+# self.int32 = relay.TensorType([], "int32")
+# self.float32 = relay.TensorType([], "float32")
+# self.one = relay.const(1.0)
+# self.two = relay.const(2.0)
+# self.three = relay.const(3.0)
+# self.a = relay.Var("a", self.float32)
+# self.b = relay.Var("b", self.float32)
+# self.c = relay.Var("c", self.float32)
+# self.d = relay.Var("d", self.float32)
+# self.e = relay.Var("e", self.float32)
+# self.x = relay.Var("x", self.int32)
+# self.y = relay.Var("y", self.int32)
+# self.z = relay.Var("z", self.int32)
+
+
+# e = env()
+
+
+# def run_opt_pass(expr, opt_pass):
+# assert isinstance(opt_pass, tvm.transform.Pass)
+# mod = tvm.IRModule.from_expr(expr)
+# mod = opt_pass(mod)
+# entry = mod["main"]
+# return entry if isinstance(expr, relay.Function) else entry.body
+
+
+def optimize_source(source, passes):
+ if not isinstance(passes, list):
+ passes = [passes]
+
+ optimize = tvm.transform.Sequential(passes)
+ module = tvm.parser.parse(source)
+ return optimize(module)
+
+
+def optimize_and_check(before_source, after_source, passes):
+ optimize_module = optimize_source(before_source, passes)
+ after_module = tvm.parser.parse(after_source)
+ print(optimize_module)
+ print(after_module)
Review comment:
Remove Prints
##########
File path: tests/python/relay/test_pass_dead_code_elimination.py
##########
@@ -25,59 +25,106 @@
import pytest
-class env:
- def __init__(self):
- self.shape = tvm.runtime.convert([1, 2, 3])
- self.tt = relay.TensorType(self.shape, "float32")
- self.int32 = relay.TensorType([], "int32")
- self.float32 = relay.TensorType([], "float32")
- self.one = relay.const(1.0)
- self.two = relay.const(2.0)
- self.three = relay.const(3.0)
- self.a = relay.Var("a", self.float32)
- self.b = relay.Var("b", self.float32)
- self.c = relay.Var("c", self.float32)
- self.d = relay.Var("d", self.float32)
- self.e = relay.Var("e", self.float32)
- self.x = relay.Var("x", self.int32)
- self.y = relay.Var("y", self.int32)
- self.z = relay.Var("z", self.int32)
-
-
-e = env()
-
-
-def run_opt_pass(expr, opt_pass):
- assert isinstance(opt_pass, tvm.transform.Pass)
- mod = tvm.IRModule.from_expr(expr)
- mod = opt_pass(mod)
- entry = mod["main"]
- return entry if isinstance(expr, relay.Function) else entry.body
-
-
-def test_let():
- orig = relay.Let(e.x, e.y, e.z)
- orig = run_opt_pass(orig, transform.DeadCodeElimination())
- assert tvm.ir.structural_equal(Function(free_vars(orig), orig), Function([e.z], e.z))
-
-
-def test_used_let():
- orig = relay.Let(e.c, e.one, e.c + e.c)
- orig = run_opt_pass(orig, transform.DeadCodeElimination())
- expected = relay.Let(e.c, e.one, e.c + e.c)
- assert tvm.ir.structural_equal(Function([], orig), Function([], expected))
+# class env:
+# def __init__(self):
+# self.shape = tvm.runtime.convert([1, 2, 3])
+# self.tt = relay.TensorType(self.shape, "float32")
+# self.int32 = relay.TensorType([], "int32")
+# self.float32 = relay.TensorType([], "float32")
+# self.one = relay.const(1.0)
+# self.two = relay.const(2.0)
+# self.three = relay.const(3.0)
+# self.a = relay.Var("a", self.float32)
+# self.b = relay.Var("b", self.float32)
+# self.c = relay.Var("c", self.float32)
+# self.d = relay.Var("d", self.float32)
+# self.e = relay.Var("e", self.float32)
+# self.x = relay.Var("x", self.int32)
+# self.y = relay.Var("y", self.int32)
+# self.z = relay.Var("z", self.int32)
+
+
+# e = env()
+
+
+# def run_opt_pass(expr, opt_pass):
+# assert isinstance(opt_pass, tvm.transform.Pass)
+# mod = tvm.IRModule.from_expr(expr)
+# mod = opt_pass(mod)
+# entry = mod["main"]
+# return entry if isinstance(expr, relay.Function) else entry.body
Review comment:
Remove Comments
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