Move TestIndexingSimplification to its own file (pytorch#97941)

test_torchinductor has gotten too big (almost 10k lines), this stack is trying to split it into smaller pieces.

Pull Request resolved: pytorch#97941
Approved by: https://github.com/ngimel

test/inductor/test_indexing.py

@@ -0,0 +1,208 @@
+# Owner(s): ["module: inductor"]
+import sympy
+
+from torch._inductor.codegen.cpp import cexpr
+from torch._inductor.codegen.triton import texpr
+from torch._inductor.codegen.wrapper import pexpr
+
+from torch._inductor.ir import ModularIndexing
+from torch._inductor.sizevars import SizeVarAllocator
+from torch.fx.experimental.symbolic_shapes import FloorDiv
+from torch.testing._internal.common_utils import TestCase as TorchTestCase
+
+
+class TestIndexingSimplification(TorchTestCase):
+    def test_indexing_simplification(self):
+        sizevars = SizeVarAllocator()
+        i0 = sympy.Symbol("i0", integer=True)
+        i1 = sympy.Symbol("i1", integer=True)
+        i2 = sympy.Symbol("i2", integer=True)
+        r3 = sympy.Symbol("r3", integer=True)
+
+        var_ranges = {i0: 3136, i1: 64, i2: 32, r3: 3}
+        expr = (
+            128 * i2
+            + ModularIndexing(i1, 1, 64)
+            + 64 * ModularIndexing(i1 + 64 * r3, 64, 2)
+        )
+        # check that `i1//64` is removed when i1 is always less than 64,
+        # and the next simplificaton doesn't happen
+        self.assertEqual(
+            sizevars.simplify_with_ranges(expr, var_ranges),
+            i1 + 128 * i2 + 64 * ModularIndexing(r3, 1, 2),
+        )
+        # all the modular indexing should be removed when the body cant be larger than the modulus
+        var_ranges[r3] = 2
+        self.assertEqual(
+            sizevars.simplify_with_ranges(expr, var_ranges), i1 + 128 * i2 + 64 * r3
+        )
+        # if there are negative terms in ModularIndexing base, we cannot replace it with FloorDiv
+        expr = ModularIndexing(i1 - 15, 1, 64)
+        self.assertEqual(
+            sizevars.simplify_with_ranges(expr, var_ranges),
+            ModularIndexing(i1 - 15, 1, 64),
+        )
+        # small terms should be kept if the rest is not guaranteed to be divisible
+        self.assertEqual(
+            sizevars.simplify_with_ranges(FloorDiv(r3 + i2 + i1, 32), var_ranges),
+            FloorDiv(r3 + i2 + i1, 32),
+        )
+
+        expr = ModularIndexing(2 * i2 + r3, 1, 64)
+        # modular indexing is removed if base is smaller than modulo
+        self.assertEqual(sizevars.simplify_with_ranges(expr, var_ranges), 2 * i2 + r3)
+
+        # check the same thing but with symbolic divisor
+        self.assertEqual(FloorDiv(r3 * i0, r3), i0)
+        self.assertEqual(ModularIndexing(r3 * i0, r3, 10), ModularIndexing(i0, 1, 10))
+
+        # (10*i) % 10 is always zero and should get optimized away
+        self.assertEqual(
+            ModularIndexing(i0 + i1 * 10, 1, 10), ModularIndexing(i0, 1, 10)
+        )
+
+        # ((20*i)//2) % 10 is always zero and should get optimized away
+        self.assertEqual(
+            ModularIndexing(i0 + i1 * 20, 2, 10), ModularIndexing(i0, 2, 10)
+        )
+
+        # the same things happens with symbolic divisor
+        self.assertEqual(
+            ModularIndexing(i0 + i1 * i2 * r3, i2, r3), ModularIndexing(i0, i2, r3)
+        )
+
+        # if there are negative terms, we cannot optimize away zero terms due to https://github.com/openai/triton/issues/619
+        self.assertEqual(
+            ModularIndexing(-i0 + i1 * 20, 2, 10), ModularIndexing(-i0 + i1 * 20, 2, 10)
+        )
+        self.assertEqual(
+            ModularIndexing(-15 + i1 * 20, 2, 10), ModularIndexing(-15 + i1 * 20, 2, 10)
+        )
+
+        # Constant fold from divisor into base
+        self.assertEqual(ModularIndexing(i0 * 4, 2, 10), ModularIndexing(i0 * 2, 1, 10))
+        self.assertEqual(FloorDiv(i0 * 4, 2), i0 * 2)
+
+        # Nested modular indexing is correctly simplified
+        var_ranges = {"i1": 13, "i2": 121}
+        expr = ModularIndexing(ModularIndexing(121 * i1 + i2, 1, 784), 1, 28)
+        self.assertEqual(sizevars.simplify_with_ranges(expr, var_ranges), expr)
+        expr = ModularIndexing(ModularIndexing(121 * i1 + i2, 1, 784) + 1, 1, 28)
+        self.assertEqual(sizevars.simplify_with_ranges(expr, var_ranges), expr)
+        var_ranges = {"i2": 784}
+        expr = ModularIndexing(ModularIndexing(i2, 1, 28), 7, 4)
+        expected = FloorDiv(ModularIndexing(i2, 1, 28), 7)
+        self.assertEqual(sizevars.simplify_with_ranges(expr, var_ranges), expected)
+        expr = ModularIndexing(ModularIndexing(i2, 1, 28) + 1, 7, 4)
+        self.assertEqual(sizevars.simplify_with_ranges(expr, var_ranges), expr)
+
+    def test_indexing_join(self):
+        sizevars = SizeVarAllocator()
+        i0 = sympy.Symbol("i0", integer=True)
+        i1 = sympy.Symbol("i1", integer=True)
+        i2 = sympy.Symbol("i2", integer=True)
+
+        # join two ModularIndexing calls into one larger one when possible
+        expr1 = ModularIndexing(i0, 1, 32) + 32 * ModularIndexing(i0, 32, 4)
+        self.assertEqual(
+            sizevars.simplify_with_ranges(expr1, {}), ModularIndexing(i0, 1, 128)
+        )
+
+        # it should also work with a scale
+        self.assertEqual(
+            sizevars.simplify_with_ranges(2 * expr1, {}),
+            2 * ModularIndexing(i0, 1, 128),
+        )
+
+        # it should work when divisor is not 1
+        expr2 = ModularIndexing(i0, 3, 32) + 32 * ModularIndexing(i0, 32 * 3, 4)
+        simplified = sizevars.simplify_with_ranges(expr2, {})
+        self.assertEqual(simplified, ModularIndexing(i0, 3, 128))
+        self.assertEqual(expr2.subs({i0: 39485}), simplified.subs({i0: 39485}))
+
+        # it should not happen in this case as the modulus is wrong
+        expr3 = ModularIndexing(i0, 1, 30) + 32 * ModularIndexing(i0, 32, 4)
+        self.assertEqual(sizevars.simplify_with_ranges(expr3, {}), expr3)
+
+        # check that it also works with a modulus>1
+        expr4 = ModularIndexing(i0, 10, i1) + i1 * ModularIndexing(i0, i1 * 10, i2)
+        res0 = expr4.subs({i0: 24056, i1: 13, i2: 19})
+        simplified = sizevars.simplify_with_ranges(expr4, {})
+        res1 = simplified.subs({i0: 24056, i1: 13, i2: 19})
+        self.assertEqual(res0, res1)
+        self.assertEqual(simplified, ModularIndexing(i0, 10, i1 * i2))
+
+        # and also works with an offset
+        self.assertEqual(
+            sizevars.simplify_with_ranges(expr4 + 10, {}),
+            ModularIndexing(i0, 10, i1 * i2) + 10,
+        )
+
+        # works for ModularIndexing + FloorDiv
+        expr5 = 197 * FloorDiv(i0, 197) + ModularIndexing(i0, 1, 197)
+        simplified = sizevars.simplify_with_ranges(expr5, {})
+        self.assertEqual(simplified, i0)
+        self.assertEqual(expr5.subs({i0: 39485}), simplified.subs({i0: 39485}))
+
+        # works with a scale
+        self.assertEqual(
+            sizevars.simplify_with_ranges(2 * expr5, {}),
+            2 * i0,
+        )
+
+        # divisor != 1
+        expr6 = 197 * FloorDiv(i0, 197 * 3) + ModularIndexing(i0, 3, 197)
+        simplified = sizevars.simplify_with_ranges(expr6, {})
+        self.assertEqual(simplified, FloorDiv(i0, 3))
+        self.assertEqual(expr6.subs({i0: 39485}), simplified.subs({i0: 39485}))
+
+
+class ExprPrinterTests(TorchTestCase):
+    def test_print_pow(self):
+        s1 = sympy.Symbol("foo", integer=True)
+        s2 = sympy.Symbol("bar", integer=True)
+        s3 = sympy.Symbol("baz", integer=True)
+
+        cases = (
+            # expr, result
+            # Test exprs.
+            (
+                s1 / (2 * s1 - 1) - 1 / (2 * s1 - 1),
+                lambda c: f"((-1)*({c}/((-1) + (2*foo)))) + (foo*({c}/((-1) + (2*foo))))",
+            ),
+            (s1 / (s2 - s3), lambda c: f"foo*({c}/(bar + ((-1)*baz)))"),
+            # Test Pow directly.
+            (
+                sympy.Pow(s1 + s2, 0),
+                lambda _: "1",
+            ),  # note: simplified before _print_Pow
+            (
+                sympy.Pow(s1 + s2, -3),
+                lambda c: f"{c}/((bar + foo)*(bar + foo)*(bar + foo))",
+            ),
+            (sympy.Pow(s1 + s2, 2), lambda _: "(bar + foo)*(bar + foo)"),
+        )
+
+        for expr, result in cases:
+            self.assertEqual(cexpr(expr), result(1.0))  # 1.0 for FP div
+            self.assertEqual(texpr(expr), result(1))
+            self.assertEqual(pexpr(expr), result(1))
+
+    def test_print_floor(self):
+        s1 = sympy.Symbol("s1", integer=False)
+        expr = sympy.floor(s1)
+        self.assertEqual(texpr(expr), "tl.math.floor(s1)")
+        self.assertEqual(pexpr(expr), "math.floor(s1)")
+
+    def test_print_ceil(self):
+        s1 = sympy.Symbol("s1", integer=False)
+        expr = sympy.ceiling(s1)
+        self.assertEqual(pexpr(expr), "math.ceil(s1)")
+
+
+if __name__ == "__main__":
+    from torch._dynamo.test_case import run_tests
+    from torch.testing._internal.inductor_utils import HAS_CPU, HAS_CUDA
+
+    if HAS_CPU or HAS_CUDA:
+        run_tests("sympy")