2025-02-10 nightly release (e8304f08fe)

aten/src/ATen/native/cuda/Math.cuh

@@ -758,11 +758,10 @@ const auto sinc_string = jiterator_stringify(
   T sinc(T a) {
     if (a == T(0)) {
       return T(1);
-    } else {
-      constexpr T pi = T(3.14159265358979323846L);
-      T product = pi * a;
-      return std::sin(product) / product;
     }
+    constexpr T pi = T(3.14159265358979323846L);
+    T product = pi * a;
+    return std::sin(product) / product;
   }
 ); // sinc_string
 

c10/metal/special_math.h

@@ -477,5 +477,31 @@ inline float2 sinc(float2 inp) {
   return float2(re, im) / a2;
 }
 
+template <typename T>
+inline T spherical_bessel_j0(T x) {
+  if (::metal::isinf(x))
+    return T(0.0);
+  T x2 = x * x;
+  T k1 = static_cast<T>(-1.0);
+  T k2 = static_cast<T>(1.0);
+
+  if (::metal::abs(x) < T(0.5)) {
+    return T(1.0) +
+        x2 *
+        (k1 / T(6.0) +
+         x2 *
+             (k2 / T(120.0) +
+              x2 *
+                  (k1 / T(5040.0) +
+                   x2 *
+                       (k2 / T(362880.0) +
+                        x2 *
+                            (k1 / T(39916800.0) +
+                             x2 * (k2 / T(6227020800.0)))))));
+  }
+
+  return ::metal::sin(x) / x;
+}
+
 } // namespace metal
 } // namespace c10

test/dynamo/test_repros.py

@@ -6510,6 +6510,27 @@ def forward(self, s0 : torch.SymInt, s1 : torch.SymInt, L_x_ : torch.Tensor):
             ).sum()
             self.assertEqual(actual, expected)
 
+    def test_incompatible_configs(self):
+        with torch._dynamo.config.patch(
+            suppress_errors=False, fail_on_recompile_limit_hit=False
+        ):
+            torch.compile(lambda: None)
+
+        with torch._dynamo.config.patch(
+            suppress_errors=True, fail_on_recompile_limit_hit=False
+        ):
+            torch.compile(lambda: None)
+
+        with torch._dynamo.config.patch(
+            suppress_errors=False, fail_on_recompile_limit_hit=True
+        ):
+            torch.compile(lambda: None)
+
+        with torch._dynamo.config.patch(
+            suppress_errors=True, fail_on_recompile_limit_hit=True
+        ), self.assertRaises(AssertionError):
+            torch.compile(lambda: None)
+
 
 class ReproTestsDevice(torch._dynamo.test_case.TestCase):
     def test_sub_alpha_scalar_repro(self, device):

test/inductor/test_flex_attention.py

@@ -2510,9 +2510,9 @@ def forward(self, arg0_1, arg1_1, arg2_1, arg3_1, arg4_1):
     @supported_platform
     def test_strided_backwards(self):
         shape = (1, 2, 4096, 64)
-        Q = torch.randn(shape, requires_grad=True, device="cuda", dtype=torch.bfloat16)
-        K = torch.randn(shape, requires_grad=True, device="cuda", dtype=torch.bfloat16)
-        V = torch.randn(shape, requires_grad=True, device="cuda", dtype=torch.bfloat16)
+        Q = torch.randn(shape, requires_grad=True, device="cuda")
+        K = torch.randn(shape, requires_grad=True, device="cuda")
+        V = torch.randn(shape, requires_grad=True, device="cuda")
         func = torch.compile(flex_attention, dynamic=True, fullgraph=True)
 
         K_sliced = K[:, :, :-128]

tools/build_with_debinfo.py

@@ -78,6 +78,9 @@ def create_build_plan() -> list[tuple[str, str]]:
         if line.startswith(": &&") and line.endswith("&& :"):
             line = line[4:-4]
         line = line.replace("-O2", "-g").replace("-O3", "-g")
+        # Build Metal shaders with debug infomation
+        if "xcrun metal " in line and "-frecord-sources" not in line:
+            line += " -frecord-sources -gline-tables-only"
         try:
             name = line.split("-o ", 1)[1].split(" ")[0]
             rc.append((name, line))

torch/_dynamo/config.py

@@ -52,6 +52,7 @@ skip_code_recursive_on_recompile_limit_hit = True
 # raise a hard error if cache limit is hit.  If you are on a model where you
 # know you've sized the cache correctly, this can help detect problems when
 # you regress guards/specialization.  This works best when recompile_limit = 1.
+# This flag is incompatible with: suppress_errors.
 # [@compile_ignored: runtime_behaviour]
 fail_on_recompile_limit_hit = False
 
@@ -164,6 +165,7 @@ traceable_tensor_subclasses: set[type[Any]] = set()
 # This is a good way to get your model to work one way or another, but you may
 # lose optimization opportunities this way.  Devs, if your benchmark model is failing
 # this way, you should figure out why instead of suppressing it.
+# This flag is incompatible with: fail_on_recompile_limit_hit.
 suppress_errors = bool(os.environ.get("TORCHDYNAMO_SUPPRESS_ERRORS", False))
 
 # Record and write an execution record of the current frame to a file

torch/_dynamo/eval_frame.py

@@ -833,6 +833,13 @@ def is_inductor_supported():
         return False
 
 
+def check_for_incompatible_configs():
+    # Some of the configs should be mutually exclusive
+    assert not (
+        config.suppress_errors and config.fail_on_recompile_limit_hit
+    ), "Dynamo configs suppress_error and fail_on_recompile_limit_hit can not both be active at the same time."
+
+
 def optimize(*args, **kwargs):
     def rebuild_ctx():
         ca_kwargs_override = config.compiled_autograd_kwargs_override
@@ -885,6 +892,7 @@ def _optimize(
             ...
     """
     check_if_dynamo_supported()
+    check_for_incompatible_configs()
     # Note: The hooks object could be global instead of passed around, *however* that would make
     # for a confusing API usage and plumbing story wherein we nest multiple .optimize calls.
     # There is some prior art around this, w/r/t nesting backend calls are enforced to be the same

torch/_torch_docs.py

@@ -11100,8 +11100,8 @@ are designed to work with this function. See the examples below.
 
 Args:
     {input}
-    indices (tensor): the indices into :attr:`input`. Must have long dtype.
-    dim (int, optional): dimension to select along.
+    indices (LongTensor): the indices into :attr:`input`. Must have long dtype.
+    dim (int, optional): dimension to select along. Default: 0
 
 Keyword args:
     {out}