delete test as instructed

[ghstack-poisoned]

test/inductor/test_torchinductor.py

@@ -8423,6 +8423,22 @@ def forward(self, arg0_1: "Sym(s77)", arg1_1: "Sym(s27)", arg2_1: "Sym(s53)", ar
             self.assertEqual(fn(x[0:]), x[16:][:16])
             self.assertEqual(fn(x[128:]), x[128 + 16 :][:16])
 
+    def test_index_float_zero(self):
+        def fn(arg0, arg1, arg2):
+            t1 = torch.tanh(arg0)
+            t2 = t1.clone()
+            t2.fill_(arg1.item())
+            t3 = torch.clamp(t2, 0, arg2.size(0) - 1).to(torch.long)
+            return torch.nn.functional.embedding(t3, arg2)
+
+        arg0 = torch.randint(0, 1000, [47], dtype=torch.int64, device=self.device)
+        arg1 = torch.randint(0, 1000, [], dtype=torch.int64, device=self.device)
+        arg2 = torch.rand([256, 88], dtype=torch.float16, device=self.device)
+
+        cfn = torch.compile(fullgraph=True, dynamic=True)(fn)
+
+        self.assertEqual(fn(arg0, arg1, arg2), cfn(arg0, arg1, arg2))
+
     # from GPT2ForSequenceClassification
     @skip_if_gpu_halide
     def test_index_tensor(self):

test/test_torchfuzz_repros.py

@@ -13,6 +13,7 @@ import pytest
 
 import torch
 from torch.testing._internal.common_utils import run_tests, TestCase
+from torch.testing._internal.inductor_utils import HAS_CUDA_AND_TRITON
 
 
 class TestFuzzerCompileIssues(TestCase):
@@ -220,67 +221,6 @@ class TestFuzzerCompileIssues(TestCase):
         out_compiled.sum().backward()
         print("Compile Success! ✅")
 
-    @pytest.mark.xfail(reason="Issue #164086")
-    def test_fuzzer_issue_164086(self):
-        torch.manual_seed(0)
-
-        def foo(arg0, arg1, arg2, arg3, arg4, arg5):
-            t0 = arg0  # size=(42, 56), stride=(42, 1), dtype=int64, device=cuda
-            t1 = torch.tanh(
-                t0
-            )  # size=(42, 56), stride=(42, 1), dtype=int64, device=cuda
-            t2 = t1.clone()
-            t2.zero_()  # size=(42, 56), stride=(42, 1), dtype=int64, device=cuda
-            t3 = (
-                arg1  # size=(50000, 128), stride=(50000, 1), dtype=float16, device=cuda
-            )
-            t4 = arg2  # size=(46, 128), stride=(46, 1), dtype=float16, device=cuda
-            t5 = torch.nn.functional.linear(
-                t3, t4
-            )  # size=(50000, 46), stride=(50000, 1), dtype=float16, device=cuda
-            t6 = arg3  # size=(50000, 4, 46), stride=(184, 46, 1), dtype=float16, device=cuda
-            t7 = t6.max(
-                dim=1
-            ).values  # size=(50000, 46), stride=(50000, 1), dtype=float16, device=cuda
-            t8 = arg4  # size=(25786, 46), stride=(46, 1), dtype=float16, device=cuda
-            t9 = arg5  # size=(24214, 46), stride=(46, 1), dtype=float16, device=cuda
-            t10 = torch.cat(
-                [t8, t9], dim=0
-            )  # size=(50000, 46), stride=(50000, 1), dtype=float16, device=cuda
-            t11 = torch.pow(
-                torch.pow(torch.pow(torch.pow(t5, t7), t10), t5), t7
-            )  # size=(50000, 46), stride=(50000, 1), dtype=float16, device=cuda
-            t12 = torch.nn.functional.embedding(
-                torch.clamp(t2, 0, t11.size(0) - 1).to(torch.long), t11
-            )  # size=(42, 56, 46), stride=(2576, 46, 1), dtype=float16, device=cuda
-            output = t12
-            return output
-
-        arg0 = torch.randint(0, 1000, [42, 56], dtype=torch.int64, device="cuda")
-        arg1 = torch.rand(
-            [50000, 128], dtype=torch.float16, device="cuda", requires_grad=True
-        )
-        arg2 = torch.rand(
-            [46, 128], dtype=torch.float16, device="cuda", requires_grad=True
-        )
-        arg3 = torch.rand(
-            [50000, 4, 46], dtype=torch.float16, device="cuda", requires_grad=True
-        )
-        arg4 = torch.rand(
-            [25786, 46], dtype=torch.float16, device="cuda", requires_grad=True
-        )
-        arg5 = torch.rand(
-            [24214, 46], dtype=torch.float16, device="cuda", requires_grad=True
-        )
-
-        out_eager = foo(arg0, arg1, arg2, arg3, arg4, arg5)
-        out_eager.sum().backward()
-        print("Eager Success! ✅")
-        compiled_foo = torch.compile(foo, fullgraph=True, dynamic=True)
-        out_compiled = compiled_foo(arg0, arg1, arg2, arg3, arg4, arg5)
-        out_compiled.sum().backward()
-        print("Compile Success! ✅")
-
     @pytest.mark.xfail(reason="Issue #163877")
     def test_fuzzer_issue_163877(self):
         torch.manual_seed(0)

torch/_inductor/codegen/mps.py

@@ -141,6 +141,15 @@ class MetalExprPrinter(ExprPrinter_):
         x = self.doprint(expr.args[0])
         return f"static_cast<float>({x})"
 
+    def _print_Float(self, expr: sympy.Expr) -> str:
+        if expr.is_integer:
+            # sympy considers 0.0 to be integer, but Metal doesn't.
+            # this workaround prints the float as an integer
+            # xref: https://github.com/sympy/sympy/issues/26620
+            return str(int(expr))
+        else:
+            return str(expr)
+
     def _print_FloorToInt(self, expr: sympy.Expr) -> str:
         assert len(expr.args) == 1
         x = self.doprint(expr.args[0])

torch/_inductor/codegen/triton.py

@@ -736,7 +736,12 @@ class TritonPrinter(PythonPrinter):
         )
 
     def _print_Float(self, expr: sympy.Expr) -> str:
-        if config.is_fbcode() and torch.version.hip:
+        if expr.is_integer:
+            # sympy considers 0.0 to be integer, but triton doesn't.
+            # this workaround prints the float as an integer
+            # xref: https://github.com/sympy/sympy/issues/26620
+            ret = str(int(expr))
+        elif config.is_fbcode() and torch.version.hip:
             ret = f"{expr}"
         else:
             ret = f"tl.full([], {expr}, tl.float64)"