Update

[ghstack-poisoned]

CONTRIBUTING.md

@@ -18,7 +18,7 @@ aspects of contributing to PyTorch.
   - [Python Unit Testing](#python-unit-testing)
   - [Better local unit tests with `pytest`](#better-local-unit-tests-with-pytest)
   - [Local linting](#local-linting)
-    - [Running `mypy`](#running-mypy)
+    - [Running `pyrefly`](#running-pyrefly)
   - [C++ Unit Testing](#c-unit-testing)
   - [Run Specific CI Jobs](#run-specific-ci-jobs)
 - [Merging your Change](#merging-your-change)
@@ -281,7 +281,7 @@ dependencies as well as the nightly binaries into the repo directory.
 **Prerequisites**:
 The following packages should be installed with `pip`:
 - `expecttest` and `hypothesis` - required to run tests
-- `mypy` - recommended for linting
+- `pyrefly` - recommended for type checking. [Pyrefly](https://pyrefly.org/)
 - `pytest` - recommended to run tests more selectively
 Running
 ```
@@ -350,15 +350,32 @@ make lint
 
 Learn more about the linter on the [lintrunner wiki page](https://github.com/pytorch/pytorch/wiki/lintrunner)
 
-#### Running `mypy`
+#### Running `pyrefly`
 
-`mypy` is an optional static type checker for Python. We have multiple `mypy`
-configs for the PyTorch codebase that are automatically validated against whenever the linter is run.
+[Pyrefly](https://pyrefly.org/) is a high-performance static type checker for Python. It provides fast type checking along with IDE features like autocomplete and instant error feedback.
+
+PyTorch uses Pyrefly for type checking across the codebase. The configuration is managed in `pyrefly.toml` at the root of the repository.
+
+**Getting Started with Pyrefly:**
+
+To run type checking on the PyTorch codebase:
+```bash
+pyrefly check
+```
+
+For more detailed error information with summaries:
+```bash
+pyrefly check --summarize-errors
+```
+
+**Learn More:**
+- [Pyrefly Configuration](https://pyrefly.org/en/docs/configuration/) - Detailed configuration options
+- [Pyrefly IDE Features](https://pyrefly.org/en/docs/IDE-features/) - Set up Pyrefly in your editor for real-time type checking
+- [Python Typing Tutorial](https://pyrefly.org/en/docs/typing-for-python-developers/) - Learn about Python type annotations
 
 See [Guide for adding type annotations to
 PyTorch](https://github.com/pytorch/pytorch/wiki/Guide-for-adding-type-annotations-to-PyTorch)
-for more information on how to set up `mypy` and tackle type annotation
-tasks.
+for PyTorch-specific guidance on how to set up `pyrefly` and tackle type annotation tasks in this codebase.
 
 ### C++ Unit Testing
 

aten/src/ATen/native/cpu/Reduce.h

@@ -247,8 +247,8 @@ void binary_kernel_reduce(TensorIteratorBase& iter, ops_t ops, init_t init) {
   });
 }
 
-template <typename func_t, typename vec_func_t>
-void binary_kernel_reduce_vec(TensorIteratorBase& iter, func_t op, vec_func_t vop, double ident = 0) {
+template <typename func_t, typename vec_func_t, typename ident_t = double>
+void binary_kernel_reduce_vec(TensorIteratorBase& iter, func_t op, vec_func_t vop, ident_t ident = static_cast<ident_t>(0)) {
   using traits = binary_function_traits<func_t>;
   static_assert(
     all_same<

aten/src/ATen/native/cpu/ReduceOpsKernel.cpp

@@ -339,33 +339,13 @@ void or_kernel_impl(TensorIterator& iter) {
   }
 }
 
-template<typename scalar_t>
-struct MinValuesOps: public at::native::MinOps<scalar_t> {
-  using arg_t = typename MinOps<scalar_t>::arg_t;
-  static scalar_t project(arg_t arg) {
-    return arg.first;
-  }
-};
-
 void min_values_kernel_impl(TensorIterator& iter) {
-  // This case is special because of Vectorized<int64_t> does not
-  // handle upper_bound<int64_t>().
-  // See: https://github.com/pytorch/pytorch/issues/43254
-  if (iter.dtype() == kLong || iter.dtype() == kUInt64) {
-    AT_DISPATCH_V2(iter.dtype(), "min_values_cpu", AT_WRAP([&iter] {
-      binary_kernel_reduce(
-        iter,
-        MinValuesOps<scalar_t>{},
-        std::pair<scalar_t, int64_t>(upper_bound<scalar_t>(), -1));
-    }), kLong, kUInt64);
-    return;
-  }
   AT_DISPATCH_V2(iter.dtype(), "min_values_cpu", AT_WRAP([&iter] {
     binary_kernel_reduce_vec(
       iter,
       [](scalar_t a, scalar_t b) -> scalar_t { return min_impl(a, b); },
       [](Vectorized<scalar_t> a, Vectorized<scalar_t> b) { return minimum(a, b); },
-      static_cast<double>(upper_bound<scalar_t>()));
+      upper_bound<scalar_t>());
   }), AT_EXPAND(AT_ALL_TYPES), AT_EXPAND(AT_BAREBONES_UNSIGNED_TYPES), kBFloat16, kHalf, kBool);
 }
 

test/cpp_extensions/libtorch_agnostic_extension/libtorch_agnostic/csrc/kernel.cpp

@@ -47,20 +47,10 @@ Tensor sgd_out_of_place(
   STD_TORCH_CHECK(param.get_device() == -1, "CPU device index = -1");
   STD_TORCH_CHECK(param.get_device_index() == -1, "CPU device index = -1");
 
-  int64_t *param_sizes;
-  int64_t *param_strides;
-  aoti_torch_get_sizes(param.get(), &param_sizes);
-  aoti_torch_get_strides(param.get(), &param_strides);
+  // testing Tensor strides + stride
+  STD_TORCH_CHECK(param.strides()[0] == param.stride(0));
 
-  int32_t param_dtype;
-  aoti_torch_get_dtype(param.get(), &param_dtype);
-
-  int32_t param_device_type;
-  aoti_torch_get_device_type(param.get(), &param_device_type);
-
-  AtenTensorHandle out_ath;
-  aoti_torch_empty_strided(param.dim(), param_sizes, param_strides, param_dtype, param_device_type, param.get_device(), &out_ath);
-  auto out = Tensor(out_ath);
+  auto out = new_empty(param, param.sizes());
 
   sgd_math(
     reinterpret_cast<float*>(param.data_ptr()),
@@ -344,6 +334,8 @@ Tensor my_new_empty_dtype_variant(Tensor t) {
   // Still using a std::vector below even though people can just pass in an
   // initializer list (which will be implicitly converted to an HeaderOnlyArrayRef)
   // directly.
+  // This is to test that passing in a std::vector works for BC. (It gets
+  // implicitly converted to HeaderOnlyArrayRef too!)
   std::vector<int64_t> sizes = {2, 5};
   auto dtype = std::make_optional(torch::headeronly::ScalarType::BFloat16);
   return new_empty(t, sizes, dtype);

test/dynamo/test_graph_deduplication.py

@@ -8,21 +8,11 @@ from torch._dynamo.graph_deduplication import apply_graph_deduplication
 from torch._dynamo.graph_utils import _detect_cycles
 from torch._dynamo.output_graph import FakeRootModule
 from torch._dynamo.test_case import TestCase
-from torch._dynamo.testing import (
-    AotEagerAndRecordGraphs,
-    extract_graph_and_tracker,
-    normalize_gm,
-)
+from torch._dynamo.testing import extract_graph, extract_graph_and_tracker, normalize_gm
 from torch.compiler import allow_in_graph
 from torch.utils._ordered_set import OrderedSet
 
 
-def extract_graph(fn, *args, **kwargs):
-    backend = AotEagerAndRecordGraphs()
-    result = torch.compile(backend=backend)(fn)(*args, **kwargs)
-    return result, backend.graphs, backend.fw_graphs
-
-
 def graph_str(gm):
     return normalize_gm(gm.print_readable(print_output=False))
 
@@ -40,7 +30,7 @@ class GraphDededuplicationTests(TestCase):
         super().tearDown()
 
     def run_and_return_graphs(self, fn, *args, **kwargs):
-        return extract_graph(fn, *args, **kwargs)
+        return extract_graph(fn, *args, **kwargs)[0:3]
 
     def run_and_get_simple_graph(self):
         def fn(x, y):

test/dynamo/test_install_free_tensors.py

@@ -1,7 +1,7 @@
 # Owner(s): ["module: dynamo"]
 import unittest
-from collections.abc import Sequence
-from typing import Any, Callable, Union
+from collections.abc import Callable, Sequence
+from typing import Any, Union
 
 import torch
 import torch._dynamo

test/dynamo/test_python_autograd.py

@@ -1,5 +1,5 @@
 # Owner(s): ["module: dynamo"]
-from typing import Callable, NamedTuple, Optional
+from typing import NamedTuple, Optional, TYPE_CHECKING
 
 import torch
 import torch._dynamo
@@ -7,6 +7,10 @@ from torch._dynamo.test_case import run_tests, TestCase
 from torch._dynamo.testing import CompileCounter, same
 
 
+if TYPE_CHECKING:
+    from collections.abc import Callable
+
+
 """
 This is an example of a pure-python version of autograd implemented by
 @zdevito.  It represents a rather challenging test case for TorchDynamo

test/dynamo/test_streams.py

@@ -1,11 +1,17 @@
 # Owner(s): ["module: dynamo"]
 import functools
+import re
 import unittest
 import weakref
 
 import torch
 import torch._dynamo.test_case
 import torch._dynamo.testing
+from torch._dynamo.graph_bytecode_inputs import (
+    reset_user_object_tracking,
+    store_user_object_weakrefs,
+)
+from torch._dynamo.testing import extract_graph, remove_trailing_space
 from torch.testing._internal.common_cuda import TEST_MULTIGPU
 from torch.testing._internal.common_utils import requires_cuda
 
@@ -15,6 +21,14 @@ requires_multigpu = functools.partial(
 )
 
 
+def remove_file_comment(gm_str: str) -> str:
+    return remove_trailing_space(re.sub(r"File.*\n", "\n", gm_str))
+
+
+def print_graph(graph: torch.fx.GraphModule) -> str:
+    return remove_file_comment(graph.print_readable())
+
+
 class TestStreams(torch._dynamo.test_case.TestCase):
     @classmethod
     def setUpClass(cls):
@@ -36,9 +50,7 @@ class TestStreams(torch._dynamo.test_case.TestCase):
 
     @requires_cuda
     def test_stream_enter_exit(self):
-        def fn(x, y):
-            s2 = torch.Stream()
-            s1 = torch.Stream()
+        def fn(x, y, s1, s2):
             with s1:
                 z1 = torch.add(x, y)
             with s2:
@@ -47,13 +59,36 @@ class TestStreams(torch._dynamo.test_case.TestCase):
 
             return y
 
-        inp = (torch.ones(2, 2) + 1, torch.ones(2, 2))
+        inp = (torch.ones(2, 2) + 1, torch.ones(2, 2), torch.Stream(), torch.Stream())
         expected = fn(*inp)
-        fn_opt = torch.compile(fn, fullgraph=True)
-        actual = fn_opt(*inp)
+        (
+            actual,
+            _,
+            fw_graphs,
+            _,
+        ) = extract_graph(fn, *inp)
+        self.assertEqual(len(fw_graphs), 1)
         self.assertEqual(expected, actual)
+        self.assertExpectedInline(
+            print_graph(fw_graphs[0]),
+            """\
+class <lambda>(torch.nn.Module):
+    def forward(self, arg0_1: "f32[2, 2]", arg1_1: "f32[2, 2]"):
+        # Annotation: {'stream': None}
+        add: "f32[2, 2]" = torch.ops.aten.add.Tensor(arg0_1, arg1_1)
+
+        # Annotation: {'stream': None}
+        add_1: "f32[2, 2]" = torch.ops.aten.add.Tensor(arg0_1, arg1_1);  arg0_1 = arg1_1 = None
+
+        # Annotation: {'stream': None}
+        add_2: "f32[2, 2]" = torch.ops.aten.add.Tensor(add_1, 2);  add_1 = None
+        add_3: "f32[2, 2]" = torch.ops.aten.add.Tensor(add_2, add);  add_2 = add = None
+        return (add_3,)
+""",
+        )
 
     @requires_cuda
+    @unittest.skip("Needs graph break support with annotation context")
     def test_stream_context_graph_break(self):
         def fn(x, y):
             s2 = torch.Stream()
@@ -70,9 +105,16 @@ class TestStreams(torch._dynamo.test_case.TestCase):
 
         inp = (torch.ones(2, 2) + 1, torch.ones(2, 2))
         expected = fn(*inp)
-        fn_opt = torch.compile(fn)
-        actual = fn_opt(*inp)
+        (
+            actual,
+            _,
+            fw_graphs,
+            _,
+        ) = extract_graph(fn, *inp)
         self.assertEqual(expected, actual)
+        self.assertEqual(len(fw_graphs), 2)
+        self.assertExpectedInline(print_graph(fw_graphs[0]), """""")
+        self.assertExpectedInline(print_graph(fw_graphs[1]), """""")
 
     @requires_cuda
     def test_stream_input(self):
@@ -155,35 +197,310 @@ class TestStreams(torch._dynamo.test_case.TestCase):
         self.assertEqual(s_act, s_exp)
 
     def test_nested_stream_enter_exit(self):
-        pass
+        def fn(x, y, s0, s1, s2):
+            with s1:
+                with s2:
+                    z1 = torch.add(x, y)
+            with s0:
+                z0 = torch.add(x, y)
+                with s2:
+                    y = 2 + z1
 
+            return z0, y
+
+        inp = (
+            torch.ones(2, 2) + 1,
+            torch.ones(2, 2),
+            torch.Stream(),
+            torch.Stream(),
+            torch.Stream(),
+        )
+        expected = fn(*inp)
+        (
+            actual,
+            _,
+            fw_graphs,
+            _,
+        ) = extract_graph(fn, *inp)
+        self.assertEqual(len(fw_graphs), 1)
+        self.assertEqual(expected, actual)
+        self.assertExpectedInline(
+            print_graph(fw_graphs[0]),
+            """\
+class <lambda>(torch.nn.Module):
+    def forward(self, arg0_1: "f32[2, 2]", arg1_1: "f32[2, 2]"):
+        # Annotation: {'stream': None}
+        add: "f32[2, 2]" = torch.ops.aten.add.Tensor(arg0_1, arg1_1)
+
+        # Annotation: {'stream': None}
+        add_1: "f32[2, 2]" = torch.ops.aten.add.Tensor(arg0_1, arg1_1);  arg0_1 = arg1_1 = None
+
+        # Annotation: {'stream': None}
+        add_2: "f32[2, 2]" = torch.ops.aten.add.Tensor(add, 2);  add = None
+        return (add_1, add_2)
+""",
+        )
+
+    @unittest.skip("Needs graph break support with annotation context")
     def test_stream_enter_exit_graph_break(self):
         pass
 
+    @unittest.skip("Needs graph break support with annotation context")
     def test_nested_stream_enter_exit_graph_break(self):
         pass
 
     def test_local_stream_enter_exit(self):
-        pass
+        def fn(x, y):
+            s2 = torch.Stream()
+            s1 = torch.Stream()
+            with s1:
+                z1 = torch.add(x, y)
+            with s2:
+                z = torch.add(x, y)
+                y = z + 2 + z1
+
+            return y
+
+        inp = (torch.ones(2, 2) + 1, torch.ones(2, 2))
+        expected = fn(*inp)
+        (
+            actual,
+            _,
+            fw_graphs,
+            _,
+        ) = extract_graph(fn, *inp)
+        self.assertEqual(len(fw_graphs), 1)
+        self.assertEqual(expected, actual)
+        self.assertExpectedInline(
+            print_graph(fw_graphs[0]),
+            """\
+class <lambda>(torch.nn.Module):
+    def forward(self, arg0_1: "f32[2, 2]", arg1_1: "f32[2, 2]"):
+        # Annotation: {'stream': 1}
+        add: "f32[2, 2]" = torch.ops.aten.add.Tensor(arg0_1, arg1_1)
+
+        # Annotation: {'stream': 0}
+        add_1: "f32[2, 2]" = torch.ops.aten.add.Tensor(arg0_1, arg1_1);  arg0_1 = arg1_1 = None
+
+        # Annotation: {'stream': 0}
+        add_2: "f32[2, 2]" = torch.ops.aten.add.Tensor(add_1, 2);  add_1 = None
+        add_3: "f32[2, 2]" = torch.ops.aten.add.Tensor(add_2, add);  add_2 = add = None
+        return (add_3,)
+""",
+        )
 
     def test_local_stream_nested_enter_exit(self):
-        pass
+        def fn(x, y):
+            s2 = torch.Stream()
+            s1 = torch.Stream()
+            s0 = torch.Stream()
+            with s1:
+                with s2:
+                    z1 = torch.add(x, y)
+            with s0:
+                z0 = torch.add(x, y)
+                with s2:
+                    y = 2 + z1
+
+            return z0, y
+
+        inp = (torch.ones(2, 2) + 1, torch.ones(2, 2))
+        expected = fn(*inp)
+        (
+            actual,
+            _,
+            fw_graphs,
+            _,
+        ) = extract_graph(fn, *inp)
+        self.assertEqual(len(fw_graphs), 1)
+        self.assertEqual(expected, actual)
+        self.assertExpectedInline(
+            print_graph(fw_graphs[0]),
+            """\
+class <lambda>(torch.nn.Module):
+    def forward(self, arg0_1: "f32[2, 2]", arg1_1: "f32[2, 2]"):
+        # Annotation: {'stream': 0}
+        add: "f32[2, 2]" = torch.ops.aten.add.Tensor(arg0_1, arg1_1)
+
+        # Annotation: {'stream': 2}
+        add_1: "f32[2, 2]" = torch.ops.aten.add.Tensor(arg0_1, arg1_1);  arg0_1 = arg1_1 = None
+
+        # Annotation: {'stream': 0}
+        add_2: "f32[2, 2]" = torch.ops.aten.add.Tensor(add, 2);  add = None
+        return (add_1, add_2)
+""",
+        )
 
     def test_stream_with_mutation(self):
-        pass
+        def fn(x, y):
+            s2 = torch.Stream()
+            s1 = torch.Stream()
+            s0 = torch.Stream()
+            with s1:
+                with s2:
+                    x.add_(y)
+            with s0:
+                z1 = torch.add(y, y)
+                z0 = torch.add(z1, y)
+                with s2:
+                    y = 2 + z1
+
+            return z0, y
+
+        inp = (torch.ones(2, 2) + 1, torch.ones(2, 2))
+        expected = fn(*inp)
+        (
+            actual,
+            _,
+            fw_graphs,
+            _,
+        ) = extract_graph(fn, *inp)
+        self.assertEqual(len(fw_graphs), 1)
+        self.assertEqual(expected, actual)
+        self.assertExpectedInline(
+            print_graph(fw_graphs[0]),
+            """\
+class <lambda>(torch.nn.Module):
+    def forward(self, arg0_1: "f32[2, 2]", arg1_1: "f32[2, 2]"):
+        # Annotation: {'stream': 0}
+        add: "f32[2, 2]" = torch.ops.aten.add.Tensor(arg0_1, arg1_1)
+
+        # Annotation: {'stream': 2}
+        add_1: "f32[2, 2]" = torch.ops.aten.add.Tensor(arg1_1, arg1_1)
+
+        # Annotation: {'stream': 2}
+        add_2: "f32[2, 2]" = torch.ops.aten.add.Tensor(add_1, arg1_1);  arg1_1 = None
+
+        # Annotation: {'stream': 0}
+        add_3: "f32[2, 2]" = torch.ops.aten.add.Tensor(add_1, 2);  add_1 = None
+
+        #
+        copy_: "f32[2, 2]" = torch.ops.aten.copy_.default(arg0_1, add);  arg0_1 = add = copy_ = None
+        return (add_2, add_3)
+""",
+        )
+
+    def test_stream_backward(self) -> None:
+        def fn(x, y):
+            s2 = torch.Stream()
+            s0 = torch.Stream()
+            with s0:
+                y0 = 2 * x + y
+            with s2:
+                z = 2 * x + y
+
+            return y0, z
+
+        inp = (
+            torch.ones(2, 2, requires_grad=True) + 1,
+            torch.ones(2, 2, requires_grad=True),
+        )
+        expected = fn(*inp)
+        (
+            actual,
+            _,
+            fw_graphs,
+            bw_graphs,
+        ) = extract_graph(fn, *inp)
+        self.assertEqual(len(fw_graphs), 1)
+        self.assertEqual(expected, actual)
+        self.assertExpectedInline(
+            print_graph(fw_graphs[0]),
+            """\
+class GraphModule(torch.nn.Module):
+    def forward(self, primals_1: "f32[2, 2]", primals_2: "f32[2, 2]"):
+        # Annotation: {'stream': 1}
+        mul: "f32[2, 2]" = torch.ops.aten.mul.Tensor(primals_1, 2);  primals_1 = None
+        add: "f32[2, 2]" = torch.ops.aten.add.Tensor(mul, primals_2)
+
+        # Annotation: {'stream': 0}
+        add_1: "f32[2, 2]" = torch.ops.aten.add.Tensor(mul, primals_2);  mul = primals_2 = None
+        return (add, add_1)
+""",
+        )
+
+        actual[1].sum().backward()
+        self.assertExpectedInline(
+            print_graph(bw_graphs[0]),
+            """\
+class GraphModule(torch.nn.Module):
+    def forward(self, tangents_1: "f32[2, 2]", tangents_2: "f32[2, 2]"):
+        # Annotation: {'stream': 0}
+        mul_2: "f32[2, 2]" = torch.ops.aten.mul.Tensor(tangents_2, 2)
+
+        #
+        add_2: "f32[2, 2]" = torch.ops.aten.add.Tensor(tangents_2, tangents_1);  tangents_2 = None
+
+        # Annotation: {'stream': 1}
+        mul_3: "f32[2, 2]" = torch.ops.aten.mul.Tensor(tangents_1, 2);  tangents_1 = None
+
+        #
+        add_3: "f32[2, 2]" = torch.ops.aten.add.Tensor(mul_2, mul_3);  mul_2 = mul_3 = None
+        return (add_3, add_2)
+""",
+        )
 
     @requires_cuda
-    def test_run_opcheck(self):
+    def test_run_opcheck_fork_join(self):
         from torch._dynamo.variables.streams import fork_stream, join_stream
         from torch.library import opcheck
 
-        sample_inputs = [
-            (0, torch.device("cuda:0"), 1, torch.device("cuda:1")),
-            (2, torch.device("cuda:2"), 3, torch.device("cuda:1")),
-        ]
-        for args in sample_inputs:
-            opcheck(fork_stream, args)
-            opcheck(join_stream, args)
+        original_stream = torch.accelerator.current_stream()
+        try:
+            s0 = torch.Stream()
+            s1 = torch.Stream()
+            store_user_object_weakrefs(s0, s1)
+
+            sample_inputs = [
+                (0, 1),
+                (1, 0),
+            ]
+            for args in sample_inputs:
+                opcheck(fork_stream, args)
+                opcheck(join_stream, args)
+        finally:
+            torch.accelerator.set_stream(original_stream)
+            reset_user_object_tracking()
+
+    @requires_cuda
+    def test_run_opcheck_wait_record(self):
+        from torch._dynamo.variables.streams import record_event, wait_event
+        from torch.library import opcheck
+
+        original_stream = torch.accelerator.current_stream()
+        try:
+            s0 = torch.Stream()
+            s1 = torch.Stream()
+            e0 = torch.Event()
+            e1 = torch.Event()
+            store_user_object_weakrefs(s0, s1, e0, e1)
+
+            sample_inputs = [
+                (2, 0),
+                (3, 1),
+            ]
+            for args in sample_inputs:
+                opcheck(wait_event, args)
+                opcheck(record_event, args)
+        finally:
+            torch.accelerator.set_stream(original_stream)
+            reset_user_object_tracking()
+
+    def test_is_marked_side_effectful(self):
+        self.assertIn(
+            torch.ops.streams.fork.default, torch.fx.node._side_effectful_functions
+        )
+        self.assertIn(
+            torch.ops.streams.join.default, torch.fx.node._side_effectful_functions
+        )
+        self.assertIn(
+            torch.ops.streams.wait_event.default,
+            torch.fx.node._side_effectful_functions,
+        )
+        self.assertIn(
+            torch.ops.streams.record_event.default,
+            torch.fx.node._side_effectful_functions,
+        )
 
 
 if __name__ == "__main__":

test/inductor/test_torchinductor.py

@@ -14424,20 +14424,6 @@ def forward(self, arg0_1: "Sym(s77)", arg1_1: "Sym(s27)", arg2_1: "Sym(s53)", ar
 
         self.common(fn, (torch.randn(6, 4, device=GPU_TYPE).t().contiguous().t(),))
 
-    @skip_if_halide
-    @requires_cuda_and_triton
-    def test_unbacked_float_item(self):
-        def fn(x, max_val):
-            return torch.clamp(x, 0, max_val.item())
-
-        self.common(
-            fn,
-            (
-                torch.randn(10, 20, 30, device=self.device),
-                torch.tensor(5.0, device=self.device),
-            ),
-        )
-
     # end of class CommonTemplate - add new tests here
 
 

test/typing/pass/arithmetic_ops.py

@@ -1,5 +1,5 @@
-from typing import Union
-from typing_extensions import assert_type, TypeAlias
+from typing import TypeAlias, Union
+from typing_extensions import assert_type
 
 from torch import randn, Tensor
 

torch/_C/_distributed_c10d.pyi

@@ -1,8 +1,9 @@
 # mypy: allow-untyped-defs
 # mypy: disable-error-code="type-arg"
+from collections.abc import Callable
 from datetime import timedelta
 from enum import Enum
-from typing import Any, Callable, Optional, overload, Union
+from typing import Any, Optional, overload, Union
 
 import torch
 from torch import Tensor

torch/_dynamo/testing.py

@@ -87,6 +87,12 @@ def extract_graph_and_tracker(fn, *args, **kwargs):  # type: ignore[no-untyped-d
     return gm.graph, region_tracker  # type: ignore[union-attr]
 
 
+def extract_graph(fn, *args, **kwargs):  # type: ignore[no-untyped-def]
+    backend = AotEagerAndRecordGraphs()
+    result = torch.compile(backend=backend)(fn)(*args, **kwargs)
+    return result, backend.graphs, backend.fw_graphs, backend.bw_graphs
+
+
 def collect_results(
     model: torch.nn.Module, prediction: Any, loss: Any, example_inputs: Any
 ) -> list[Any]:

torch/_dynamo/variables/ctx_manager.py

@@ -21,9 +21,9 @@ restoring state changes.
 import inspect
 import sys
 import warnings
-from collections.abc import Callable, Sequence
+from collections.abc import Callable, Sequence, Sized
 from contextlib import ExitStack
-from typing import Any, ContextManager, Optional, Sized, TYPE_CHECKING, Union
+from typing import Any, ContextManager, Optional, TYPE_CHECKING, Union
 
 import torch._C
 from torch._guards import Guard

torch/_dynamo/variables/streams.py

@@ -5,11 +5,12 @@ from typing import Any, Optional
 import torch
 from torch._dynamo.variables.dicts import ConstDictVariable
 from torch._dynamo.variables.lists import TupleVariable
-from torch.fx import Proxy
+from torch.fx import has_side_effect, Proxy
 
 from .. import graph_break_hints
 from ..bytecode_transformation import create_call_function
 from ..exc import TYPE_CHECKING, unimplemented_v2
+from ..graph_bytecode_inputs import get_external_object_by_index
 from .base import VariableTracker
 from .constant import ConstantVariable
 from .ctx_manager import FxTracebackAnnotateVariable
@@ -27,46 +28,93 @@ from torch._library.custom_ops import custom_op
 Tensor = torch.Tensor
 
 
+def _get_stream_by_index(index: int) -> torch.Stream:
+    stream = get_external_object_by_index(index)
+    assert isinstance(stream, torch.Stream), (
+        f"Fork/join stream expected a stream object at index {index}"
+    )
+    return stream
+
+
+def _get_event_by_index(index: int) -> torch.Event:
+    event = get_external_object_by_index(index)
+    assert isinstance(event, torch.Event), (
+        f"Record/wait event expected an event object at index {index}"
+    )
+    return event
+
+
 @custom_op("streams::fork", mutates_args=())
 def fork_stream(
-    from_index: int,
-    from_device: torch.device,
+    from_index: int,  # kept to make stream transitions clearer
     to_index: int,
-    to_device: torch.device,
 ) -> None:
-    pass
+    torch.accelerator.set_stream(_get_stream_by_index(to_index))
 
 
 @fork_stream.register_fake
 def _(
-    from_index: int,
-    from_device: torch.device,
+    from_index: int,  # kept to make stream transitions clearer
     to_index: int,
-    to_device: torch.device,
 ) -> None:
     pass
 
 
+has_side_effect(torch.ops.streams.fork.default)
+
+
 @custom_op("streams::join", mutates_args=())
-def join_stream(
-    from_index: int,
-    from_device: torch.device,
-    to_index: int,
-    to_device: torch.device,
-) -> None:
-    pass
+def join_stream(from_index: int, to_index: int) -> None:
+    torch.accelerator.set_stream(_get_stream_by_index(to_index))
 
 
 @join_stream.register_fake
 def _(
     from_index: int,
-    from_device: torch.device,
     to_index: int,
-    to_device: torch.device,
 ) -> None:
     pass
 
 
+has_side_effect(torch.ops.streams.join.default)
+
+
+@custom_op("streams::record_event", mutates_args=())
+def record_event(event_index: int, stream_index: int) -> None:
+    event = _get_event_by_index(event_index)
+    stream = _get_stream_by_index(stream_index)
+    stream.record_event(event)
+
+
+@record_event.register_fake
+def _(
+    event_index: int,
+    stream_index: int,
+) -> None:
+    pass
+
+
+has_side_effect(torch.ops.streams.record_event.default)
+
+
+@custom_op("streams::wait_event", mutates_args=())
+def wait_event(event_index: int, stream_index: int) -> None:
+    event = _get_event_by_index(event_index)
+    stream = _get_stream_by_index(stream_index)
+    stream.wait_event(event)
+
+
+@wait_event.register_fake
+def _(
+    event_index: int,
+    stream_index: int,
+) -> None:
+    pass
+
+
+has_side_effect(torch.ops.streams.wait_event.default)
+
+
 class SymbolicStreamState:
     """Track the currently entered stream if any"""
 

torch/_inductor/codecache.py

@@ -2970,12 +2970,6 @@ class CppPythonBindingsCodeCache(CppCodeCache):
                 throw std::runtime_error("expected int arg");
             return reinterpret_cast<uintptr_t>(result);
         }}
-        template <> inline float parse_arg<float>(PyObject* args, size_t n) {{
-            auto result = PyFloat_AsDouble(PyTuple_GET_ITEM(args, n));
-            if(unlikely(result == -1.0 && PyErr_Occurred()))
-                throw std::runtime_error("expected float arg");
-            return static_cast<float>(result);
-        }}
 
         {extra_parse_arg}
 

torch/_inductor/codegen/common.py

@@ -1732,15 +1732,9 @@ class KernelArgs:
             call_args.append(self.wrap_ptr_arg(outer, dtype))
             arg_types.append(f"{cpp_dtype}*")
         for outer, inner in self.sizevars.items():
-            if isinstance(outer, sympy.Symbol) and symbol_is_type(
-                outer, (SymT.UNBACKED_FLOAT)
-            ):
-                arg_defs.append(f"const float {inner}")
-                arg_types.append("const float")
-            else:
-                arg_defs.append(f"const {INDEX_TYPE} {inner}")
-                arg_types.append(f"const {INDEX_TYPE}")
+            arg_defs.append(f"const {INDEX_TYPE} {inner}")
             call_args.append(self.wrap_size_arg(outer))
+            arg_types.append(f"const {INDEX_TYPE}")
             if V.graph.wrapper_code:
                 V.graph.wrapper_code.ensure_size_computed(outer)
         assert not self.workspace_args, "Workspace not supported on CPU "
@@ -2359,7 +2353,6 @@ class Kernel(CodeGen, Generic[CSEVariableType]):
                     SymT.UNBACKED_INT,
                     SymT.SIZE,
                     SymT.PRECOMPUTED_SIZE,
-                    SymT.UNBACKED_FLOAT,
                 ),
             )
         }

torch/_inductor/codegen/pallas.py

@@ -2,7 +2,7 @@
 from __future__ import annotations
 
 import hashlib
-from typing import Any, Callable, Optional, Sequence, TYPE_CHECKING
+from typing import Any, Optional, TYPE_CHECKING
 
 import sympy  # noqa: TC002
 
@@ -17,6 +17,8 @@ from .simd import SIMDKernel, SIMDScheduling
 
 
 if TYPE_CHECKING:
+    from collections.abc import Callable, Sequence
+
     from ..ir import IRNode
     from ..scheduler import BaseSchedulerNode
 

torch/_inductor/codegen/triton_combo_kernel.py

@@ -627,7 +627,7 @@ class ComboKernel(Kernel):
         if heuristics == "foreach":
             heuristics_line = f"""
                 @triton_heuristics.foreach(
-                    num_warps={self.num_warps},
+                    filename=__file__,
                     triton_meta={triton_meta!r},
                     inductor_meta={inductor_meta!r},
                 )

torch/_inductor/codegen/triton_utils.py

@@ -4,7 +4,6 @@ from typing import Any, Optional
 import sympy
 
 import torch
-from torch.utils._sympy.symbol import symbol_is_type, SymT
 
 from .. import config
 from ..runtime.hints import AttrsDescriptorWrapper
@@ -72,10 +71,6 @@ def signature_of(arg: KernelArgType, *, size_dtype: Optional[str]) -> str:
             return "constexpr"
         elif isinstance(arg.expr, (float, sympy.Float)):
             return "fp32"
-        elif isinstance(arg.expr, sympy.Symbol) and symbol_is_type(
-            arg.expr, (SymT.UNBACKED_FLOAT)
-        ):
-            return "fp32"
         elif isinstance(arg.expr, bool):
             return "i1"
 

torch/_inductor/comm_analysis.py

@@ -360,7 +360,7 @@ def estimate_nccl_collective_runtime_from_fx_node(
     fx_node: torch.fx.Node,
     override_size: Optional[int] = None,
     # TODO(ivankobzarev): NCCL estimator sometimes fail unexpectedly, enable back after fix.
-    use_nccl_estimator: bool = False,
+    use_nccl_estimator: bool = True,
 ) -> float:
     """
     Returns estimated NCCL collective runtime in nanoseconds (ns).

torch/_inductor/runtime/caching/config.py

@@ -1,6 +1,6 @@
 import os
+from collections.abc import Callable
 from functools import cache, partial
-from typing import Callable
 
 import torch
 from torch._environment import is_fbcode

torch/_inductor/runtime/triton_heuristics.py

@@ -3586,13 +3586,24 @@ def user_autotune(
     )
 
 
-def foreach(triton_meta, num_warps, filename=None, inductor_meta=None):
+def foreach(triton_meta, filename=None, inductor_meta=None):
     """
     Compile a triton foreach kernel
     """
+    configs = []
+
+    # Naive autotuning path for num_warps
+    if not (
+        inductor_meta.get("max_autotune") or inductor_meta.get("max_autotune_pointwise")
+    ):
+        configs.append(triton.Config({}, num_stages=1, num_warps=8))
+    else:
+        for warps in [1, 2, 4, 8]:
+            configs.append(triton.Config({}, num_stages=1, num_warps=warps))
+
     return cached_autotune(
         None,
-        [triton.Config({}, num_stages=1, num_warps=num_warps)],
+        configs,
         triton_meta=triton_meta,
         inductor_meta=inductor_meta,
         heuristic_type=HeuristicType.TEMPLATE,

torch/autograd/profiler.py

@@ -52,26 +52,7 @@ __all__ = [
     "MemRecordsAcc",
 ]
 
-try:
-    # Available in Python >= 3.2
-    from contextlib import ContextDecorator as _ContextDecorator
-except ImportError:
-    import functools
-
-    class _ContextDecorator:  # type: ignore[no-redef]
-        def __enter__(self):
-            raise NotImplementedError
-
-        def __exit__(self, exc_type, exc_val, exc_tb):
-            raise NotImplementedError
-
-        def __call__(self, func):
-            @functools.wraps(func)
-            def wrapped(*args, **kwargs):
-                with self:
-                    return func(*args, **kwargs)
-
-            return wrapped
+from contextlib import ContextDecorator
 
 
 # global python state - whether profiler is currently enabled
@@ -744,8 +725,7 @@ class profile:
         return all_function_events
 
 
-# pyrefly: ignore [invalid-inheritance]
-class record_function(_ContextDecorator):
+class record_function(ContextDecorator):
     """Context manager/function decorator that adds a label to a code block/function when running autograd profiler.
     Label will only appear if CPU activity tracing is enabled.
 

torch/csrc/distributed/c10d/ProcessGroupNCCL.cpp

@@ -3593,6 +3593,7 @@ float ProcessGroupNCCL::endTimeEstimate() {
 #ifdef NCCL_SIM_INFO_INITIALIZER
   ncclSimInfo_t simInfo = NCCL_SIM_INFO_INITIALIZER;
   C10D_NCCL_CHECK(ncclGroupSimulateEnd(&simInfo), std::nullopt);
+  --ncclActiveGroupCounter_;
   return simInfo.estimatedTime;
 #else
   TORCH_CHECK(

torch/csrc/stable/tensor_struct.h

@@ -4,6 +4,7 @@
 #include <torch/headeronly/core/ScalarType.h>
 #include <torch/headeronly/macros/Macros.h>
 #include <torch/headeronly/util/Exception.h>
+#include <torch/headeronly/util/HeaderOnlyArrayRef.h>
 #include <torch/headeronly/util/shim_utils.h>
 #include <climits>
 #include <memory>
@@ -13,6 +14,7 @@
 HIDDEN_NAMESPACE_BEGIN(torch, stable)
 
 using accelerator::DeviceIndex;
+using torch::headeronly::IntHeaderOnlyArrayRef;
 using torch::headeronly::ScalarType;
 
 // The torch::stable::Tensor class is a highlevel C++ wrapper around
@@ -93,6 +95,32 @@ class Tensor {
     return numel;
   }
 
+  // note: this API is, for all intents and purposes, the same as the one in
+  // TensorBase.h: it returns a borrowed reference of the dimension sizes of
+  // a Tensor.
+  //
+  // The only difference is that it returns a header-only IntHeaderOnlyArrayRef,
+  // which has slightly less functionality than a regular IntArrayRef. See
+  // [HeaderOnlyArrayRef vs ArrayRef note] for more details.
+  IntHeaderOnlyArrayRef sizes() const {
+    int64_t* sizes;
+    TORCH_ERROR_CODE_CHECK(aoti_torch_get_sizes(ath_.get(), &sizes));
+    return IntHeaderOnlyArrayRef(sizes, dim());
+  }
+
+  // note: this API is, for all intents and purposes, the same as the one in
+  // TensorBase.h: it returns a borrowed reference of the strides of a
+  // Tensor.
+  //
+  // The only difference is that it returns a header-only IntHeaderOnlyArrayRef,
+  // which has slightly less functionality than a regular IntArrayRef. See
+  // [HeaderOnlyArrayRef vs ArrayRef note] for more details.
+  IntHeaderOnlyArrayRef strides() const {
+    int64_t* strides;
+    TORCH_ERROR_CODE_CHECK(aoti_torch_get_strides(ath_.get(), &strides));
+    return IntHeaderOnlyArrayRef(strides, dim());
+  }
+
   // note: this is a subset of the original TensorBase API. It takes no
   // arguments whereas the original API takes in a kwarg of memory format.
   // Here, we assume the default contiguous memory format.

torch/distributed/_local_tensor/_c10d.py

@@ -1,9 +1,8 @@
 import functools
 import math
 import operator
-from collections.abc import Sequence
+from collections.abc import Callable, Sequence
 from datetime import timedelta
-from typing import Callable
 
 import torch
 from torch._C import ScriptObject

torch/distributed/tensor/experimental/_attention.py

@@ -10,6 +10,7 @@ from ._context_parallel._attention import (
     _enable_context_parallel_dispatcher,
     _is_causal_behavior,
     _RotateMethod,
+    _templated_ring_attention,
     context_parallel,
     context_parallel_unshard,
     set_rotate_method,
@@ -22,6 +23,7 @@ from ._context_parallel._load_balancer import (
 )
 
 
+# TODO(fegin): add deprecation message once the final interfaces are concluded.
 __all__ = [
     "_CausalBehavior",
     "_context_parallel_shard",
@@ -31,6 +33,7 @@ __all__ = [
     "_enable_context_parallel_dispatcher",
     "_is_causal_behavior",
     "_RotateMethod",
+    "_templated_ring_attention",
     "context_parallel",
     "context_parallel_unshard",
     "set_rotate_method",