Automated submodule update: tensorpipe

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 `pyrefly`](#running-pyrefly)
+    - [Running `mypy`](#running-mypy)
   - [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
-- `pyrefly` - recommended for type checking. [Pyrefly](https://pyrefly.org/)
+- `mypy` - recommended for linting
 - `pytest` - recommended to run tests more selectively
 Running
 ```
@@ -350,32 +350,15 @@ make lint
 
 Learn more about the linter on the [lintrunner wiki page](https://github.com/pytorch/pytorch/wiki/lintrunner)
 
-#### Running `pyrefly`
+#### Running `mypy`
 
-[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
+`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.
 
 See [Guide for adding type annotations to
 PyTorch](https://github.com/pytorch/pytorch/wiki/Guide-for-adding-type-annotations-to-PyTorch)
-for PyTorch-specific guidance on how to set up `pyrefly` and tackle type annotation tasks in this codebase.
+for more information on how to set up `mypy` and tackle type annotation
+tasks.
 
 ### C++ Unit Testing
 

aten/src/ATen/core/CachingHostAllocator.h

@@ -226,8 +226,8 @@ template <
     typename B = HostBlock<S>>
 struct CachingHostAllocatorImpl {
   virtual ~CachingHostAllocatorImpl() {
-    if (active_) {
-      active_ = false;
+    active_ = false;
+    if (pinned_use_background_threads()) {
       getBackgroundThreadPool()->waitWorkComplete();
     }
   }
@@ -260,7 +260,6 @@ struct CachingHostAllocatorImpl {
     if (pinned_use_background_threads()) {
       // Launch the background thread and process events in a loop.
       static bool background_thread_flag [[maybe_unused]] = [this] {
-        active_ = true;
         getBackgroundThreadPool()->run([&]() {
           while (active_) {
             process_events();
@@ -684,9 +683,9 @@ struct CachingHostAllocatorImpl {
   alignas(hardware_destructive_interference_size) std::mutex events_mutex_;
   std::deque<std::pair<E, B*>> events_; // event queue paired with block
 
-  // Indicates whether the event-processing thread pool is active.
+  // Indicates whether the object is active.
   // Set to false in the destructor to signal background threads to stop.
-  std::atomic<bool> active_{false};
+  std::atomic<bool> active_{true};
 protected:
   alignas(hardware_destructive_interference_size) HostStatsStaged stats_;
 };

aten/src/ATen/cuda/cub.h

@@ -24,13 +24,7 @@ namespace detail {
 // radix_sort_pairs doesn't interact with value_t other than to copy
 // the data, so we can save template instantiations by reinterpreting
 // it as an opaque type.
-// We use native integer types for 1/2/4/8-byte values to reduce
-// register usage in CUDA kernels. For sizes > 8 fall back to char array.
 template <int N> struct alignas(N) OpaqueType { char data[N]; };
-template <> struct alignas(1) OpaqueType<1> { uint8_t data; };
-template <> struct alignas(2) OpaqueType<2> { uint16_t data; };
-template <> struct alignas(4) OpaqueType<4> { uint32_t data; };
-template <> struct alignas(8) OpaqueType<8> { uint64_t data; };
 
 template<typename key_t, int value_size>
 void radix_sort_pairs_impl(

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, 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)) {
+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) {
   using traits = binary_function_traits<func_t>;
   static_assert(
     all_same<

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

@@ -339,13 +339,33 @@ 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); },
-      upper_bound<scalar_t>());
+      static_cast<double>(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

@@ -47,10 +47,20 @@ 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");
 
-  // testing Tensor strides + stride
-  STD_TORCH_CHECK(param.strides()[0] == param.stride(0));
+  int64_t *param_sizes;
+  int64_t *param_strides;
+  aoti_torch_get_sizes(param.get(), &param_sizes);
+  aoti_torch_get_strides(param.get(), &param_strides);
 
-  auto out = new_empty(param, param.sizes());
+  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);
 
   sgd_math(
     reinterpret_cast<float*>(param.data_ptr()),
@@ -334,8 +344,6 @@ 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/distributed/tensor/debug/test_debug_mode.py

@@ -5,16 +5,8 @@ import contextlib
 import torch
 import torch.distributed as dist
 from torch._subclasses.fake_tensor import FakeTensorMode
-from torch.distributed.tensor import (
-    DeviceMesh,
-    distribute_tensor,
-    DTensor,
-    Partial,
-    Replicate,
-    Shard,
-)
+from torch.distributed.tensor import DeviceMesh, DTensor, Partial, Replicate, Shard
 from torch.distributed.tensor._dtensor_spec import ShardOrderEntry
-from torch.fx.experimental.proxy_tensor import make_fx
 from torch.testing._internal.common_utils import (
     instantiate_parametrized_tests,
     parametrize,
@@ -434,31 +426,6 @@ class TestDTensorDebugMode(TestCase):
         ][-1]
         self.assertTrue("self.l2(self.l1(x))" in sum_op.fwd_stack_trace)
 
-    def test_pretty_print_dtensor_make_fx(self):
-        mesh = DeviceMesh(self.device_type, list(range(self.world_size)))
-
-        A = torch.randn(8, 32)
-        B = torch.randn(32, 32)
-        dA = distribute_tensor(A, mesh, [Shard(0)]).requires_grad_()
-        dB = distribute_tensor(B, mesh, [Replicate()]).requires_grad_()
-
-        def f(dA, dB):
-            dy = dA @ dB
-            loss = dy.sum()
-            loss.backward()
-            return dA.grad, dB.grad
-
-        # We actually need the tracing_mode='fake' here, or to trace under a FakeTensorMode.
-        # make_fx has some logic to ensure we don't accidentally stash real tensors in the graph
-        # so we won't stash our DTensors properly if they don't hold Fake inner tensors
-        gm = make_fx(f, tracing_mode="fake")(dA, dB)
-        # DCE isn't necessary here, there were just a lot of dead detach() nodes that spammed the graph
-        gm.graph.eliminate_dead_code()
-        gm.recompile()
-        # Colored is nice for actual viewing, not using in this test though
-        gm_str = gm.print_readable(colored=False, print_output=False)
-        self.assertTrue('"DTensor(f32[8, 32], S(0))" = torch.ops.aten.mm' in gm_str)
-
 
 instantiate_parametrized_tests(TestDTensorDebugMode)
 

test/distributed/test_c10d_nccl.py

@@ -5789,229 +5789,6 @@ class NCCLTraceTest(NCCLTraceTestBase):
         else:
             self.assertTrue("duration_ms" not in t["entries"][0])
 
-    @requires_nccl()
-    @skip_but_pass_in_sandcastle_if(not TEST_MULTIGPU, "NCCL test requires 2+ GPUs")
-    @parametrize("timing_enabled", [True, False])
-    def test_fr_record_reset_circular_buffer_full(self, timing_enabled):
-        """
-        Test that when the circular buffer in entries_ is full and we call reset,
-        then fill the buffer with new entries, dump_entries returns only the new
-        entries and not the old ones.
-        """
-        if self.rank == self.MAIN_PROCESS_RANK:
-            return
-
-        # Override buffer size to 10 for faster testing
-        os.environ["TORCH_NCCL_TRACE_BUFFER_SIZE"] = "10"
-
-        pg = self._create_process_group_nccl()
-        if timing_enabled:
-            pg._enable_collectives_timing()
-        device = self.local_device
-        self.set_thread_name("fr_test_thread")
-        a = torch.full((3, 4), float(self.rank), device=device)
-
-        # Fill the buffer completely with 10 entries
-        for _ in range(10):
-            f = pg.allreduce(a)
-        f.wait()
-        torch.cuda.synchronize(device=device)
-        time.sleep(1)
-
-        # Verify buffer is full with 10 entries
-        t = pickle.loads(torch._C._distributed_c10d._dump_nccl_trace())
-        self.assertEqual(len(t["entries"]), 10)
-
-        # Now reset the flight recorder
-        torch._C._distributed_c10d._reset_fr_recording_nccl()
-
-        # Add new entries after reset - fill the buffer completely again
-        for _ in range(10):
-            f = pg.allreduce(a)
-        f.wait()
-        torch.cuda.synchronize(device=device)
-        time.sleep(1)
-
-        # Verify we get exactly 10 new entries, not 20
-        t = pickle.loads(torch._C._distributed_c10d._dump_nccl_trace())
-        self.assertEqual(len(t["entries"]), 10)
-
-        # Verify all entries have the expected properties (from after reset)
-        # After reset, record IDs should start from 0 again
-        for i, entry in enumerate(t["entries"]):
-            self.assertIn("profiling_name", entry)
-            self.assertEqual(entry["profiling_name"], "nccl:all_reduce")
-            self.assertIn("record_id", entry)
-            # Record IDs should be sequential starting from 0 after reset
-            self.assertEqual(entry["record_id"], i)
-
-        dist.destroy_process_group()
-
-    @requires_nccl()
-    @skip_but_pass_in_sandcastle_if(not TEST_MULTIGPU, "NCCL test requires 2+ GPUs")
-    @parametrize("timing_enabled", [True, False])
-    def test_fr_record_reset_partial_overwrite(self, timing_enabled):
-        """
-        Test that when the circular buffer is full, we reset, and then add fewer
-        entries than the buffer size, we only get the new entries.
-        This tests that old entries at the end of the circular buffer are properly
-        filtered out based on reset_epoch.
-        """
-        if self.rank == self.MAIN_PROCESS_RANK:
-            return
-
-        # Override buffer size to 10 for faster testing
-        os.environ["TORCH_NCCL_TRACE_BUFFER_SIZE"] = "10"
-
-        pg = self._create_process_group_nccl()
-        if timing_enabled:
-            pg._enable_collectives_timing()
-        device = self.local_device
-        self.set_thread_name("fr_test_thread")
-        a = torch.full((3, 4), float(self.rank), device=device)
-
-        # Fill the buffer completely
-        for _ in range(10):
-            f = pg.allreduce(a)
-        f.wait()
-        torch.cuda.synchronize(device=device)
-        time.sleep(1)
-
-        # Reset the flight recorder
-        torch._C._distributed_c10d._reset_fr_recording_nccl()
-
-        # Add only 3 new entries (much less than buffer size)
-        for _ in range(3):
-            f = pg.allreduce(a)
-        f.wait()
-        torch.cuda.synchronize(device=device)
-        time.sleep(1)
-
-        # Verify we only get the 3 new entries, not 10
-        t = pickle.loads(torch._C._distributed_c10d._dump_nccl_trace())
-        self.assertEqual(len(t["entries"]), 3)
-
-        # Verify record IDs start from 0 after reset
-        for i, entry in enumerate(t["entries"]):
-            self.assertIn("record_id", entry)
-            self.assertEqual(entry["record_id"], i)
-
-        dist.destroy_process_group()
-
-    @requires_nccl()
-    @skip_but_pass_in_sandcastle_if(not TEST_MULTIGPU, "NCCL test requires 2+ GPUs")
-    @parametrize("timing_enabled", [True, False])
-    def test_fr_record_reset_wraparound(self, timing_enabled):
-        """
-        Test that when we reset in the middle of the circular buffer and then
-        wrap around, dump_entries correctly returns only entries from the current
-        epoch in the correct order.
-        """
-        if self.rank == self.MAIN_PROCESS_RANK:
-            return
-
-        # Override buffer size to 10 for faster testing
-        os.environ["TORCH_NCCL_TRACE_BUFFER_SIZE"] = "10"
-
-        pg = self._create_process_group_nccl()
-        if timing_enabled:
-            pg._enable_collectives_timing()
-        device = self.local_device
-        self.set_thread_name("fr_test_thread")
-        a = torch.full((3, 4), float(self.rank), device=device)
-
-        # Fill half the buffer
-        for _ in range(5):
-            f = pg.allreduce(a)
-        f.wait()
-        torch.cuda.synchronize(device=device)
-        time.sleep(1)
-
-        # Reset at this point (reset happens at index 5)
-        torch._C._distributed_c10d._reset_fr_recording_nccl()
-
-        # Now add 8 entries, which will wrap around
-        # (5->9 fills rest of buffer, then 0->2 wraps around)
-        for _ in range(8):
-            f = pg.allreduce(a)
-        f.wait()
-        torch.cuda.synchronize(device=device)
-        time.sleep(1)
-
-        # Should get exactly 8 entries, properly ordered
-        t = pickle.loads(torch._C._distributed_c10d._dump_nccl_trace())
-        self.assertEqual(len(t["entries"]), 8)
-
-        # Entries should be in chronological order
-        # The dump_entries() method returns entries from next_ to end, then 0 to next_
-        # After filtering old entries, we should have 8 entries in order
-        # Verify record IDs start from 0 after reset (id_ is reset in reset_all())
-        for i, entry in enumerate(t["entries"]):
-            self.assertIn("profiling_name", entry)
-            self.assertIn("record_id", entry)
-            self.assertEqual(entry["record_id"], i)
-
-        dist.destroy_process_group()
-
-    @requires_nccl()
-    @skip_but_pass_in_sandcastle_if(not TEST_MULTIGPU, "NCCL test requires 2+ GPUs")
-    @parametrize("timing_enabled", [True, False])
-    def test_fr_record_multiple_resets(self, timing_enabled):
-        """
-        Test multiple consecutive resets to ensure each reset properly increments
-        the epoch and filters out entries from previous epochs.
-        """
-        if self.rank == self.MAIN_PROCESS_RANK:
-            return
-
-        # Override buffer size to 10 for faster testing
-        os.environ["TORCH_NCCL_TRACE_BUFFER_SIZE"] = "10"
-
-        pg = self._create_process_group_nccl()
-        if timing_enabled:
-            pg._enable_collectives_timing()
-        device = self.local_device
-        self.set_thread_name("fr_test_thread")
-        a = torch.full((3, 4), float(self.rank), device=device)
-
-        # First batch: 2 entries
-        for _ in range(2):
-            f = pg.allreduce(a)
-        f.wait()
-        torch.cuda.synchronize(device=device)
-        time.sleep(1)
-
-        # First reset
-        torch._C._distributed_c10d._reset_fr_recording_nccl()
-
-        # Second batch: 3 entries
-        for _ in range(3):
-            f = pg.allreduce(a)
-        f.wait()
-        torch.cuda.synchronize(device=device)
-        time.sleep(1)
-
-        # Second reset
-        torch._C._distributed_c10d._reset_fr_recording_nccl()
-
-        # Third batch: 4 entries
-        for _ in range(4):
-            f = pg.allreduce(a)
-        f.wait()
-        torch.cuda.synchronize(device=device)
-        time.sleep(1)
-
-        # Should only see the last 4 entries
-        t = pickle.loads(torch._C._distributed_c10d._dump_nccl_trace())
-        self.assertEqual(len(t["entries"]), 4)
-
-        # Verify record IDs start from 0 after the last reset
-        for i, entry in enumerate(t["entries"]):
-            self.assertIn("record_id", entry)
-            self.assertEqual(entry["record_id"], i)
-
-        dist.destroy_process_group()
-
 
 def check_if_test_is_skipped(fn):
     def wrapper(self, *args, **kwargs):

test/dynamo/test_graph_deduplication.py

@@ -8,11 +8,21 @@ 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 extract_graph, extract_graph_and_tracker, normalize_gm
+from torch._dynamo.testing import (
+    AotEagerAndRecordGraphs,
+    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))
 
@@ -30,7 +40,7 @@ class GraphDededuplicationTests(TestCase):
         super().tearDown()
 
     def run_and_return_graphs(self, fn, *args, **kwargs):
-        return extract_graph(fn, *args, **kwargs)[0:3]
+        return extract_graph(fn, *args, **kwargs)
 
     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 Callable, Sequence
-from typing import Any, Union
+from collections.abc import Sequence
+from typing import Any, Callable, Union
 
 import torch
 import torch._dynamo

test/dynamo/test_python_autograd.py

@@ -1,5 +1,5 @@
 # Owner(s): ["module: dynamo"]
-from typing import NamedTuple, Optional, TYPE_CHECKING
+from typing import Callable, NamedTuple, Optional
 
 import torch
 import torch._dynamo
@@ -7,10 +7,6 @@ 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

@@ -1,13 +1,11 @@
 # 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.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
 
@@ -17,14 +15,6 @@ 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):
@@ -46,7 +36,9 @@ class TestStreams(torch._dynamo.test_case.TestCase):
 
     @requires_cuda
     def test_stream_enter_exit(self):
-        def fn(x, y, s1, s2):
+        def fn(x, y):
+            s2 = torch.Stream()
+            s1 = torch.Stream()
             with s1:
                 z1 = torch.add(x, y)
             with s2:
@@ -55,36 +47,13 @@ class TestStreams(torch._dynamo.test_case.TestCase):
 
             return y
 
-        inp = (torch.ones(2, 2) + 1, torch.ones(2, 2), torch.Stream(), torch.Stream())
+        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)
+        fn_opt = torch.compile(fn, fullgraph=True)
+        actual = fn_opt(*inp)
         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()
@@ -101,16 +70,9 @@ class <lambda>(torch.nn.Module):
 
         inp = (torch.ones(2, 2) + 1, torch.ones(2, 2))
         expected = fn(*inp)
-        (
-            actual,
-            _,
-            fw_graphs,
-            _,
-        ) = extract_graph(fn, *inp)
+        fn_opt = torch.compile(fn)
+        actual = fn_opt(*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):
@@ -193,248 +155,22 @@ class <lambda>(torch.nn.Module):
         self.assertEqual(s_act, s_exp)
 
     def test_nested_stream_enter_exit(self):
-        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
+        pass
 
-            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):
-        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,)
-""",
-        )
+        pass
 
     def test_local_stream_nested_enter_exit(self):
-        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)
-""",
-        )
+        pass
 
     def test_stream_with_mutation(self):
-        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)
-""",
-        )
+        pass
 
     @requires_cuda
     def test_run_opcheck(self):

test/inductor/test_torchinductor.py

@@ -14424,6 +14424,20 @@ 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/test_scaled_matmul_cuda.py

@@ -1864,8 +1864,6 @@ class TestFP8Matmul(TestCase):
     ], name_fn=lambda mkn: f"{mkn[0]}_{mkn[1]}_{mkn[2]}")
     @parametrize("recipe", ["mxfp8", "mxfp4", "nvfp4"])
     def test_blockwise_mxfp8_nvfp4_mxfp4_numerics(self, test_case_name, fast_accum, mkn, recipe) -> None:
-        if torch.version.hip and recipe == "nvfp4":
-            raise unittest.SkipTest("nvfp4 not supported on ROCm, skipping")
         if (recipe == "nvfp4" or recipe == "mxfp4") and fast_accum:
             raise unittest.SkipTest("fast_accum not supported in nvfp4/mxfp4 cublas gemm, skipping")
 

test/test_transformers.py

@@ -1914,7 +1914,6 @@ class TestSDPAFailureModes(NNTestCase):
                     q, k, v, None, 0.0, is_causal=True))
 
     @onlyCUDA
-    @unittest.skipIf(not PLATFORM_SUPPORTS_MEM_EFF_ATTENTION, "Does not support Efficient Attention")
     def test_mem_eff_attention_fail_with_batch_size_geq_65536(self):
         batch_size = 2**16
         query = torch.rand([batch_size, 2, 2, 8], device='cuda', dtype=torch.float16, requires_grad=True)
@@ -1936,7 +1935,6 @@ class TestSDPAFailureModes(NNTestCase):
         self.assertEqual(value.grad, v_cpu.grad, atol=2e-3, rtol=1e-4)
 
     @onlyCUDA
-    @unittest.skipIf(not PLATFORM_SUPPORTS_MEM_EFF_ATTENTION, "Does not support Efficient Attention")
     def test_mem_eff_attention_fail_with_batch_size_geq_65536_error(self):
         query = torch.rand([2**16, 2, 2, 8], device='cuda', dtype=torch.float16)
         key = torch.rand([2**16, 2, 2, 8], device='cuda', dtype=torch.float16)
@@ -1950,7 +1948,6 @@ class TestSDPAFailureModes(NNTestCase):
 
     @largeTensorTest("15GB", "cuda")
     @onlyCUDA
-    @unittest.skipIf(not PLATFORM_SUPPORTS_MEM_EFF_ATTENTION, "Does not support Efficient Attention")
     def test_mem_eff_attention_large_seq_len_uniform_attention(self):
         device = torch.device("cuda")
         dtype = torch.bfloat16

test/typing/pass/arithmetic_ops.py

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

torch/_C/_distributed_c10d.pyi

@@ -1,9 +1,8 @@
 # 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, Optional, overload, Union
+from typing import Any, Callable, Optional, overload, Union
 
 import torch
 from torch import Tensor

torch/_dynamo/symbolic_convert.py

@@ -3320,7 +3320,7 @@ class InstructionTranslatorBase(
         obj = self.stack[-inst.arg]
         assert isinstance(obj, SetVariable)
         assert obj.is_mutable()
-        obj.call_method(self, "add", [v], {})  # type: ignore[arg-type]
+        obj.call_method(self, "add", [v], {})
 
     def SET_UPDATE(self, inst: Instruction) -> None:
         v = self.pop()
@@ -3329,7 +3329,7 @@ class InstructionTranslatorBase(
         obj = self.stack[-inst.arg]
         assert isinstance(obj, SetVariable)
         assert obj.is_mutable()
-        obj.call_method(self, "update", [v], {})  # type: ignore[arg-type]
+        obj.call_method(self, "update", [v], {})
 
     def LIST_APPEND(self, inst: Instruction) -> None:
         v = self.pop()
@@ -3637,7 +3637,7 @@ class InstructionTranslatorBase(
         obj = self.stack[-inst.arg].realize()
         assert isinstance(obj, ConstDictVariable)
         assert obj.is_mutable()
-        obj.call_method(self, "update", [v], {})  # type: ignore[arg-type]
+        obj.call_method(self, "update", [v], {})
 
     DICT_UPDATE = DICT_MERGE
 

torch/_dynamo/testing.py

@@ -87,12 +87,6 @@ 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, Sized
+from collections.abc import Callable, Sequence
 from contextlib import ExitStack
-from typing import Any, ContextManager, Optional, TYPE_CHECKING, Union
+from typing import Any, ContextManager, Optional, Sized, TYPE_CHECKING, Union
 
 import torch._C
 from torch._guards import Guard

torch/_dynamo/variables/dicts.py

@@ -1,3 +1,5 @@
+# mypy: ignore-errors
+
 """
 Dictionary-related variable tracking classes for PyTorch Dynamo.
 
@@ -24,7 +26,7 @@ import inspect
 import operator
 import types
 from collections.abc import Hashable as py_Hashable
-from typing import Any, Optional, TYPE_CHECKING, Union
+from typing import Optional, TYPE_CHECKING
 
 from torch._subclasses.fake_tensor import is_fake
 
@@ -57,13 +59,11 @@ if TYPE_CHECKING:
 # - (perhaps) Define how it is compared in _HashableTracker._eq_impl
 
 
-def was_instancecheck_override(obj: Any) -> bool:
+def was_instancecheck_override(obj):
     return type(obj).__dict__.get("__instancecheck__", False)
 
 
-def raise_unhashable(
-    arg: VariableTracker, tx: Optional["InstructionTranslator"] = None
-) -> None:
+def raise_unhashable(arg, tx=None):
     if tx is None:
         from torch._dynamo.symbolic_convert import InstructionTranslator
 
@@ -75,7 +75,7 @@ def raise_unhashable(
     )
 
 
-def is_hashable(x: VariableTracker) -> bool:
+def is_hashable(x):
     # NB - performing isinstance check on a LazVT realizes the VT, accidentally
     # inserting the guard. To avoid this, lazyVT `is_hashable` methods looks at
     # the underlying value without realizing the VT. Consider updating the
@@ -143,7 +143,7 @@ class ConstDictVariable(VariableTracker):
         Note that it's also fine to put VTs into dictionaries and sets, but doing so does not take into account aliasing
         """
 
-        def __init__(self, vt: VariableTracker) -> None:
+        def __init__(self, vt) -> None:
             # We specialize SymNodes
             vt = specialize_symnode(vt)
             # TODO Temporarily remove to figure out what keys are we breaking on
@@ -153,7 +153,7 @@ class ConstDictVariable(VariableTracker):
             self.vt = vt
 
         @property
-        def underlying_value(self) -> Any:
+        def underlying_value(self):
             if (
                 isinstance(self.vt, variables.LazyVariableTracker)
                 and not self.vt.is_realized()
@@ -178,8 +178,7 @@ class ConstDictVariable(VariableTracker):
             elif isinstance(self.vt, variables.FrozenDataClassVariable):
                 Hashable = ConstDictVariable._HashableTracker
                 fields_values = {
-                    k: Hashable(v).underlying_value
-                    for k, v in self.vt.fields.items()  # type: ignore[attr-defined]
+                    k: Hashable(v).underlying_value for k, v in self.vt.fields.items()
                 }
                 return variables.FrozenDataClassVariable.HashWrapper(
                     self.vt.python_type(), fields_values
@@ -188,16 +187,16 @@ class ConstDictVariable(VariableTracker):
                 # The re module in Python 3.13+ has a dictionary (_cache2) with
                 # an object as key (`class _ZeroSentinel(int): ...`):
                 # python test/dynamo/test_unittest.py CPythonTestLongMessage.test_baseAssertEqual
-                return self.vt.value  # type: ignore[attr-defined,union-attr]
+                return self.vt.value
             else:
                 x = self.vt.as_python_constant()
             return x
 
-        def __hash__(self) -> int:
+        def __hash__(self):
             return hash(self.underlying_value)
 
         @staticmethod
-        def _eq_impl(a: Any, b: Any) -> bool:
+        def _eq_impl(a, b):
             # TODO: Put this in utils and share it between variables/builtin.py and here
             type_a, type_b = type(a), type(b)
             if not (issubclass(type_a, type_b) or issubclass(type_b, type_a)):
@@ -213,7 +212,7 @@ class ConstDictVariable(VariableTracker):
             else:
                 return a == b
 
-        def __eq__(self, other: object) -> bool:
+        def __eq__(self, other: "ConstDictVariable._HashableTracker") -> bool:
             Hashable = ConstDictVariable._HashableTracker
             assert isinstance(other, Hashable) or ConstantVariable.is_literal(other), (
                 type(other)
@@ -227,8 +226,8 @@ class ConstDictVariable(VariableTracker):
     def __init__(
         self,
         items: dict[VariableTracker, VariableTracker],
-        user_cls: type = dict,
-        **kwargs: Any,
+        user_cls=dict,
+        **kwargs,
     ) -> None:
         # .clone() pass these arguments in kwargs but they're recreated a few
         # lines below
@@ -248,22 +247,18 @@ class ConstDictVariable(VariableTracker):
             for x, v in items.items()
         )
 
-        def make_hashable(
-            key: Union[VariableTracker, "ConstDictVariable._HashableTracker"],
-        ) -> "ConstDictVariable._HashableTracker":
+        def make_hashable(key):
             return key if isinstance(key, Hashable) else Hashable(key)
 
         dict_cls = self._get_dict_cls_from_user_cls(user_cls)
         self.items = dict_cls({make_hashable(x): v for x, v in items.items()})
         # need to reconstruct everything if the dictionary is an intermediate value
         # or if a pop/delitem was executed
-        self.should_reconstruct_all = (
-            not is_from_local_source(self.source) if self.source else True
-        )
+        self.should_reconstruct_all = not is_from_local_source(self.source)
         self.original_items = items.copy()
         self.user_cls = user_cls
 
-    def _get_dict_cls_from_user_cls(self, user_cls: type) -> type:
+    def _get_dict_cls_from_user_cls(self, user_cls):
         accepted_dict_types = (dict, collections.OrderedDict, collections.defaultdict)
 
         # avoid executing user code if user_cls is a dict subclass
@@ -282,10 +277,10 @@ class ConstDictVariable(VariableTracker):
             dict_cls = dict
         return dict_cls
 
-    def as_proxy(self) -> dict[Any, Any]:
+    def as_proxy(self):
         return {k.vt.as_proxy(): v.as_proxy() for k, v in self.items.items()}
 
-    def debug_repr(self) -> str:
+    def debug_repr(self):
         return (
             "{"
             + ", ".join(
@@ -294,20 +289,20 @@ class ConstDictVariable(VariableTracker):
             + "}"
         )
 
-    def as_python_constant(self) -> dict[Any, Any]:
+    def as_python_constant(self):
         return {
             k.vt.as_python_constant(): v.as_python_constant()
             for k, v in self.items.items()
         }
 
-    def keys_as_python_constant(self) -> dict[Any, VariableTracker]:
+    def keys_as_python_constant(self):
         self.install_dict_keys_match_guard()
         return {k.vt.as_python_constant(): v for k, v in self.items.items()}
 
-    def python_type(self) -> type:
+    def python_type(self):
         return self.user_cls
 
-    def __contains__(self, vt: VariableTracker) -> bool:
+    def __contains__(self, vt) -> bool:
         assert isinstance(vt, VariableTracker)
         Hashable = ConstDictVariable._HashableTracker
         return (
@@ -327,15 +322,13 @@ class ConstDictVariable(VariableTracker):
             for key, value in self.items.items()
         )
 
-    def is_new_item(
-        self, value: Optional[VariableTracker], other: VariableTracker
-    ) -> bool:
+    def is_new_item(self, value, other):
         # compare the id of the realized values if both values are not lazy VTs
         if value and value.is_realized() and other.is_realized():
             return id(value.realize()) != id(other.realize())
         return id(value) != id(other)
 
-    def reconstruct_kvs_into_new_dict(self, codegen: "PyCodegen") -> None:
+    def reconstruct_kvs_into_new_dict(self, codegen):
         # Build a dictionary that contains the keys and values.
         num_args = 0
         for key, value in self.items.items():
@@ -347,7 +340,7 @@ class ConstDictVariable(VariableTracker):
                 num_args += 1
         codegen.append_output(create_instruction("BUILD_MAP", arg=num_args))
 
-    def reconstruct(self, codegen: "PyCodegen") -> None:
+    def reconstruct(self, codegen: "PyCodegen"):
         if self.user_cls is collections.OrderedDict:
             # emit `OrderedDict(constructed_dict)`
             codegen.add_push_null(
@@ -365,21 +358,19 @@ class ConstDictVariable(VariableTracker):
 
     def getitem_const_raise_exception_if_absent(
         self, tx: "InstructionTranslator", arg: VariableTracker
-    ) -> VariableTracker:
+    ):
         key = ConstDictVariable._HashableTracker(arg)
         if key not in self.items:
             raise_observed_exception(KeyError, tx)
         return self.items[key]
 
-    def getitem_const(
-        self, tx: "InstructionTranslator", arg: VariableTracker
-    ) -> VariableTracker:
+    def getitem_const(self, tx: "InstructionTranslator", arg: VariableTracker):
         key = ConstDictVariable._HashableTracker(arg)
         if key not in self.items:
-            msg = f"Dictionary key {arg.value} not found during tracing"  # type: ignore[attr-defined]
+            msg = f"Dictionary key {arg.value} not found during tracing"
             unimplemented_v2(
                 gb_type="key not found in dict",
-                context=f"Key {arg.value}",  # type: ignore[attr-defined]
+                context=f"Key {arg.value}",
                 explanation=msg,
                 hints=[
                     "Check if the key exists in the dictionary before accessing it.",
@@ -388,13 +379,13 @@ class ConstDictVariable(VariableTracker):
             )
         return self.items[key]
 
-    def maybe_getitem_const(self, arg: VariableTracker) -> Optional[VariableTracker]:
+    def maybe_getitem_const(self, arg: VariableTracker):
         key = ConstDictVariable._HashableTracker(arg)
         if key not in self.items:
             return None
         return self.items[key]
 
-    def realize_key_vt(self, arg: VariableTracker) -> None:
+    def realize_key_vt(self, arg: VariableTracker):
         # Realize the LazyVT on a particular index
         assert arg in self
         key = ConstDictVariable._HashableTracker(arg)
@@ -403,13 +394,11 @@ class ConstDictVariable(VariableTracker):
         if isinstance(original_key_vt, variables.LazyVariableTracker):
             original_key_vt.realize()
 
-    def install_dict_keys_match_guard(self) -> None:
+    def install_dict_keys_match_guard(self):
         if self.source:
             install_guard(self.make_guard(GuardBuilder.DICT_KEYS_MATCH))
 
-    def install_dict_contains_guard(
-        self, tx: "InstructionTranslator", args: list[VariableTracker]
-    ) -> None:
+    def install_dict_contains_guard(self, tx, args):
         # Key guarding - These are the cases to consider
         # 1) The dict has been mutated. In this case, we would have already
         # inserted a DICT_KEYS_MATCH guard, so we can skip.
@@ -450,11 +439,11 @@ class ConstDictVariable(VariableTracker):
 
     def call_method(
         self,
-        tx: "InstructionTranslator",
-        name: str,
-        args: list[VariableTracker],
-        kwargs: dict[str, VariableTracker],
-    ) -> VariableTracker:
+        tx,
+        name,
+        args: "list[VariableTracker]",
+        kwargs: "dict[str, VariableTracker]",
+    ) -> "VariableTracker":
         # NB - Both key and value are LazyVariableTrackers in the beginning. So,
         # we have to insert guards when a dict method is accessed. For this to
         # be simple, we are conservative and overguard. We skip guard only for
@@ -473,7 +462,7 @@ class ConstDictVariable(VariableTracker):
                 tx, *args, **kwargs
             )
             tx.output.side_effects.mutation(self)
-            self.items.update(temp_dict_vt.items)  # type: ignore[attr-defined]
+            self.items.update(temp_dict_vt.items)
             return ConstantVariable.create(None)
         elif name == "__getitem__":
             # Key guarding - Nothing to do. LazyVT for value will take care.
@@ -537,7 +526,7 @@ class ConstDictVariable(VariableTracker):
             return ConstantVariable.create(len(self.items))
         elif name == "__setitem__" and self.is_mutable():
             if not arg_hashable:
-                raise_unhashable(args[0], tx)
+                raise_unhashable(args[0])
 
             self.install_dict_keys_match_guard()
             if kwargs or len(args) != 2:
@@ -561,7 +550,7 @@ class ConstDictVariable(VariableTracker):
                 raise_args_mismatch(tx, name, "1 or 2 args", f"{len(args)} args")
 
             if not arg_hashable:
-                raise_unhashable(args[0], tx)
+                raise_unhashable(args[0])
 
             if args[0] not in self:
                 self.install_dict_contains_guard(tx, args)
@@ -576,7 +565,7 @@ class ConstDictVariable(VariableTracker):
                 raise_args_mismatch(tx, name, "1 or 2 args", f"{len(args)} args")
 
             if not arg_hashable:
-                raise_unhashable(args[0], tx)
+                raise_unhashable(args[0])
 
             if args[0] not in self:
                 # missing item, return the default value. Install no DICT_CONTAINS guard.
@@ -610,7 +599,7 @@ class ConstDictVariable(VariableTracker):
                     last = v.value
                 else:
                     raise_args_mismatch(tx, name)
-                k, v = self.items.popitem(last=last)  # type: ignore[possibly-undefined]
+                k, v = self.items.popitem(last=last)
             else:
                 k, v = self.items.popitem()
 
@@ -643,17 +632,17 @@ class ConstDictVariable(VariableTracker):
                         # NB - Guard on all the keys of the other dict to ensure
                         # correctness.
                         args[0].install_dict_keys_match_guard()
-                        dict_vt: ConstDictVariable = args[0]
+                        dict_vt = args[0]
                     else:
-                        dict_vt = BuiltinVariable.call_custom_dict(tx, dict, args[0])  # type: ignore[assignment]
-                    self.items.update(dict_vt.items)  # type: ignore[attr-defined]
+                        dict_vt = BuiltinVariable.call_custom_dict(tx, dict, args[0])
+                    self.items.update(dict_vt.items)
                 if has_kwargs:
                     # Handle kwargs
-                    kwargs_hashable = {
+                    kwargs = {
                         Hashable(ConstantVariable.create(k)): v
                         for k, v in kwargs.items()
                     }
-                    self.items.update(kwargs_hashable)
+                    self.items.update(kwargs)
                 return ConstantVariable.create(None)
             else:
                 return super().call_method(tx, name, args, kwargs)
@@ -667,7 +656,7 @@ class ConstDictVariable(VariableTracker):
                 )
 
             if not arg_hashable:
-                raise_unhashable(args[0], tx)
+                raise_unhashable(args[0])
 
             self.install_dict_contains_guard(tx, args)
             contains = args[0] in self
@@ -682,7 +671,7 @@ class ConstDictVariable(VariableTracker):
                 )
 
             if not arg_hashable:
-                raise_unhashable(args[0], tx)
+                raise_unhashable(args[0])
 
             self.install_dict_keys_match_guard()
             if kwargs or len(args) > 2:
@@ -718,7 +707,7 @@ class ConstDictVariable(VariableTracker):
                 and "last" in kwargs
                 and isinstance(kwargs["last"], ConstantVariable)
             ):
-                last = kwargs.get("last").value  # type: ignore[union-attr]
+                last = kwargs.get("last").value
 
             key = Hashable(args[0])
             self.items.move_to_end(key, last=last)
@@ -734,7 +723,7 @@ class ConstDictVariable(VariableTracker):
             )
         elif name == "__ne__":
             return ConstantVariable.create(
-                not self.call_method(tx, "__eq__", args, kwargs).value  # type: ignore[attr-defined]
+                not self.call_method(tx, "__eq__", args, kwargs).value
             )
         elif name == "__or__":
             if len(args) != 1:
@@ -761,14 +750,14 @@ class ConstDictVariable(VariableTracker):
             if not istype(
                 other, (ConstDictVariable, variables.UserDefinedDictVariable)
             ):
-                err_msg = (
+                msg = (
                     f"unsupported operand type(s) for |: '{self.python_type().__name__}'"
                     f"and '{other.python_type().__name__}'"
                 )
-                raise_observed_exception(TypeError, tx, args=[err_msg])
+                raise_observed_exception(TypeError, tx, args=[msg])
 
             # OrderedDict overloads __ror__
-            ts = {self.user_cls, other.user_cls}  # type: ignore[attr-defined]
+            ts = {self.user_cls, other.user_cls}
             user_cls = (
                 collections.OrderedDict
                 if any(issubclass(t, collections.OrderedDict) for t in ts)
@@ -785,8 +774,8 @@ class ConstDictVariable(VariableTracker):
 
             # NB - Guard on all the keys of the other dict to ensure
             # correctness.
-            args[0].install_dict_keys_match_guard()  # type: ignore[attr-defined]
-            new_dict_vt.items.update(args[0].items)  # type: ignore[attr-defined]
+            args[0].install_dict_keys_match_guard()
+            new_dict_vt.items.update(args[0].items)
             return new_dict_vt
         elif name == "__ior__":
             self.call_method(tx, "update", args, kwargs)
@@ -800,13 +789,11 @@ class ConstDictVariable(VariableTracker):
         else:
             return super().call_method(tx, name, args, kwargs)
 
-    def unpack_var_sequence(self, tx: "InstructionTranslator") -> list[VariableTracker]:
+    def unpack_var_sequence(self, tx):
         self.install_dict_keys_match_guard()
         return [x.vt for x in self.items.keys()]
 
-    def call_obj_hasattr(
-        self, tx: "InstructionTranslator", name: str
-    ) -> VariableTracker:
+    def call_obj_hasattr(self, tx, name):
         # dict not allow setting arbitrary attributes.  OrderedDict and
         # defaultdict allow arbitrary setattr, but not deletion of default attrs
         if any(
@@ -829,25 +816,25 @@ class ConstDictVariable(VariableTracker):
             ],
         )
 
-    def clone(self, **kwargs: Any) -> VariableTracker:
+    def clone(self, **kwargs):
         self.install_dict_keys_match_guard()
         return super().clone(**kwargs)
 
 
 class MappingProxyVariable(VariableTracker):
     # proxies to the original dict_vt
-    def __init__(self, dv_dict: ConstDictVariable, **kwargs: Any) -> None:
+    def __init__(self, dv_dict: ConstDictVariable, **kwargs) -> None:
         super().__init__(**kwargs)
         assert isinstance(dv_dict, ConstDictVariable)
         self.dv_dict = dv_dict
 
-    def python_type(self) -> type:
+    def python_type(self):
         return types.MappingProxyType
 
-    def unpack_var_sequence(self, tx: "InstructionTranslator") -> list[VariableTracker]:
+    def unpack_var_sequence(self, tx):
         return self.dv_dict.unpack_var_sequence(tx)
 
-    def reconstruct(self, codegen: "PyCodegen") -> None:
+    def reconstruct(self, codegen: "PyCodegen"):
         # load types.MappingProxyType
         if self.source:
             msg = (
@@ -876,11 +863,11 @@ class MappingProxyVariable(VariableTracker):
 
     def call_method(
         self,
-        tx: "InstructionTranslator",
-        name: str,
-        args: list[VariableTracker],
-        kwargs: dict[str, VariableTracker],
-    ) -> VariableTracker:
+        tx,
+        name,
+        args: list["VariableTracker"],
+        kwargs: dict[str, "VariableTracker"],
+    ) -> "VariableTracker":
         if self.source and tx.output.side_effects.has_existing_dict_mutation():
             msg = (
                 "A dict has been modified while we have an existing mappingproxy object. "
@@ -905,7 +892,7 @@ class MappingProxyVariable(VariableTracker):
 
     def call_obj_hasattr(
         self, tx: "InstructionTranslator", name: str
-    ) -> VariableTracker:
+    ) -> "VariableTracker":
         if self.python_type() is types.MappingProxyType:
             return ConstantVariable.create(name in types.MappingProxyType.__dict__)
         return super().call_obj_hasattr(tx, name)
@@ -913,44 +900,35 @@ class MappingProxyVariable(VariableTracker):
 
 class NNModuleHooksDictVariable(ConstDictVariable):
     # Special class to avoid adding any guards on the nn module hook ids.
-    def install_dict_keys_match_guard(self) -> None:
+    def install_dict_keys_match_guard(self):
         pass
 
-    def install_dict_contains_guard(
-        self, tx: "InstructionTranslator", args: list[VariableTracker]
-    ) -> None:
+    def install_dict_contains_guard(self, tx, args):
         pass
 
 
 class DefaultDictVariable(ConstDictVariable):
-    def __init__(
-        self,
-        items: dict[VariableTracker, VariableTracker],
-        user_cls: type,
-        default_factory: Optional[VariableTracker] = None,
-        **kwargs: Any,
-    ) -> None:
+    def __init__(self, items, user_cls, default_factory=None, **kwargs) -> None:
         super().__init__(items, user_cls, **kwargs)
         assert user_cls is collections.defaultdict
         if default_factory is None:
             default_factory = ConstantVariable.create(None)
         self.default_factory = default_factory
 
-    def is_python_constant(self) -> bool:
+    def is_python_constant(self):
         # Return false for unsupported defaults. This ensures that a bad handler
         # path is not taken in BuiltinVariable for getitem.
         if self.default_factory not in [list, tuple, dict] and not self.items:
             return False
         return super().is_python_constant()
 
-    def debug_repr(self) -> str:
-        assert self.default_factory is not None
+    def debug_repr(self):
         return (
             f"defaultdict({self.default_factory.debug_repr()}, {super().debug_repr()})"
         )
 
     @staticmethod
-    def is_supported_arg(arg: VariableTracker) -> bool:
+    def is_supported_arg(arg):
         if isinstance(arg, variables.BuiltinVariable):
             return arg.fn in (list, tuple, dict, set)
         else:
@@ -964,11 +942,11 @@ class DefaultDictVariable(ConstDictVariable):
 
     def call_method(
         self,
-        tx: "InstructionTranslator",
-        name: str,
-        args: list[VariableTracker],
-        kwargs: dict[str, VariableTracker],
-    ) -> VariableTracker:
+        tx,
+        name,
+        args: "list[VariableTracker]",
+        kwargs: "dict[str, VariableTracker]",
+    ) -> "VariableTracker":
         if name == "__getitem__":
             if len(args) != 1:
                 raise_args_mismatch(tx, name, "1 args", f"{len(args)} args")
@@ -984,13 +962,13 @@ class DefaultDictVariable(ConstDictVariable):
                 else:
                     default_var = self.default_factory.call_function(tx, [], {})
                     super().call_method(
-                        tx, "__setitem__", [args[0], default_var], kwargs
+                        tx, "__setitem__", (args[0], default_var), kwargs
                     )
                     return default_var
         else:
             return super().call_method(tx, name, args, kwargs)
 
-    def reconstruct(self, codegen: "PyCodegen") -> None:
+    def reconstruct(self, codegen):
         # emit `defaultdict(default_factory, new_dict)`
         codegen.add_push_null(
             lambda: codegen.extend_output(
@@ -1016,48 +994,40 @@ class SetVariable(ConstDictVariable):
     def __init__(
         self,
         items: list[VariableTracker],
-        **kwargs: Any,
+        **kwargs,
     ) -> None:
-        # pyrefly: ignore[bad-assignment]
         items = dict.fromkeys(items, SetVariable._default_value())
-        # pyrefly: ignore[bad-argument-type]
         super().__init__(items, **kwargs)
 
-    def debug_repr(self) -> str:
+    def debug_repr(self):
         if not self.items:
             return "set()"
         else:
             return "{" + ",".join(k.vt.debug_repr() for k in self.items.keys()) + "}"
 
     @property
-    def set_items(self) -> set["ConstDictVariable._HashableTracker"]:
+    def set_items(self):
         return set(self.items.keys())
 
     @staticmethod
-    def _default_value() -> VariableTracker:
+    def _default_value():
         # Variable to fill in he keys of the dictionary
         return ConstantVariable.create(None)
 
-    def as_proxy(self) -> Any:
+    def as_proxy(self):
         return {k.vt.as_proxy() for k in self.set_items}
 
-    def python_type(self) -> type:
+    def python_type(self):
         return set
 
-    def as_python_constant(self) -> Any:
+    def as_python_constant(self):
         return {k.vt.as_python_constant() for k in self.set_items}
 
-    def reconstruct(self, codegen: "PyCodegen") -> None:
+    def reconstruct(self, codegen: "PyCodegen"):
         codegen.foreach([x.vt for x in self.set_items])
         codegen.append_output(create_instruction("BUILD_SET", arg=len(self.set_items)))
 
-    def _fast_set_method(
-        self,
-        tx: "InstructionTranslator",
-        fn: Any,
-        args: list[VariableTracker],
-        kwargs: dict[str, VariableTracker],
-    ) -> VariableTracker:
+    def _fast_set_method(self, tx, fn, args, kwargs):
         try:
             res = fn(
                 *[x.as_python_constant() for x in [self, *args]],
@@ -1067,16 +1037,15 @@ class SetVariable(ConstDictVariable):
             raise_observed_exception(
                 type(exc), tx, args=list(map(ConstantVariable.create, exc.args))
             )
-        # pyrefly: ignore[unbound-name]
         return VariableTracker.build(tx, res)
 
     def call_method(
         self,
-        tx: "InstructionTranslator",
-        name: str,
+        tx,
+        name,
         args: list[VariableTracker],
         kwargs: dict[str, VariableTracker],
-    ) -> VariableTracker:
+    ) -> "VariableTracker":
         # We forward the calls to the dictionary model
         from ..utils import check_constant_args
 
@@ -1096,10 +1065,10 @@ class SetVariable(ConstDictVariable):
             return self._fast_set_method(tx, getattr(py_type, name), args, kwargs)
 
         if name == "__init__":
-            temp_set_vt = variables.BuiltinVariable(set).call_set(tx, *args, **kwargs)
+            temp_set_vt = variables.BuiltinVariable(set).call_set(tx, *args, *kwargs)
             tx.output.side_effects.mutation(self)
             self.items.clear()
-            self.items.update(temp_set_vt.items)  # type: ignore[attr-defined]
+            self.items.update(temp_set_vt.items)
             return ConstantVariable.create(None)
         elif name == "add":
             if kwargs or len(args) != 1:
@@ -1110,7 +1079,7 @@ class SetVariable(ConstDictVariable):
                     f"{len(args)} args and {len(kwargs)} kwargs",
                 )
             name = "__setitem__"
-            args = [args[0], SetVariable._default_value()]
+            args = (args[0], SetVariable._default_value())
         elif name == "pop":
             if kwargs or args:
                 raise_args_mismatch(
@@ -1121,14 +1090,12 @@ class SetVariable(ConstDictVariable):
                 )
             # Choose an item at random and pop it via the Dict.pop method
             try:
-                result: VariableTracker = self.set_items.pop().vt  # type: ignore[assignment]
+                result = self.set_items.pop().vt
             except KeyError as e:
                 raise_observed_exception(
                     KeyError, tx, args=list(map(ConstantVariable.create, e.args))
                 )
-            # pyrefly: ignore[unbound-name]
-            super().call_method(tx, name, [result], kwargs)
-            # pyrefly: ignore[unbound-name]
+            super().call_method(tx, name, (result,), kwargs)
             return result
         elif name == "isdisjoint":
             if kwargs or len(args) != 1:
@@ -1250,7 +1217,6 @@ class SetVariable(ConstDictVariable):
                     f"unsupported operand type(s) for {name}: '{self.python_type_name()}' and '{args[0].python_type_name()}'"
                 )
                 raise_observed_exception(TypeError, tx, args=[msg])
-            assert m is not None
             return self.call_method(tx, m, args, kwargs)
         elif name in ("__iand__", "__ior__", "__ixor__", "__isub__"):
             if not isinstance(args[0], (SetVariable, variables.UserDefinedSetVariable)):
@@ -1264,34 +1230,29 @@ class SetVariable(ConstDictVariable):
                 "__ixor__": "symmetric_difference_update",
                 "__isub__": "difference_update",
             }.get(name)
-            assert m is not None
             self.call_method(tx, m, args, kwargs)
             return self
         elif name == "__eq__":
             if not isinstance(args[0], (SetVariable, variables.UserDefinedSetVariable)):
                 return ConstantVariable.create(False)
             r = self.call_method(tx, "symmetric_difference", args, kwargs)
-            return ConstantVariable.create(len(r.set_items) == 0)  # type: ignore[attr-defined]
+            return ConstantVariable.create(len(r.set_items) == 0)
         elif name in cmp_name_to_op_mapping:
             if not isinstance(args[0], (SetVariable, variables.UserDefinedSetVariable)):
                 return ConstantVariable.create(NotImplemented)
             return ConstantVariable.create(
-                cmp_name_to_op_mapping[name](self.set_items, args[0].set_items)  # type: ignore[attr-defined]
+                cmp_name_to_op_mapping[name](self.set_items, args[0].set_items)
             )
         return super().call_method(tx, name, args, kwargs)
 
-    def getitem_const(
-        self, tx: "InstructionTranslator", arg: VariableTracker
-    ) -> VariableTracker:
+    def getitem_const(self, tx: "InstructionTranslator", arg: VariableTracker):
         raise RuntimeError("Illegal to getitem on a set")
 
-    def install_dict_keys_match_guard(self) -> None:
+    def install_dict_keys_match_guard(self):
         # Already EQUALS_MATCH guarded
         pass
 
-    def install_dict_contains_guard(
-        self, tx: "InstructionTranslator", args: list[VariableTracker]
-    ) -> None:
+    def install_dict_contains_guard(self, tx, args):
         super().install_dict_contains_guard(tx, args)
 
 
@@ -1299,27 +1260,27 @@ class FrozensetVariable(SetVariable):
     def __init__(
         self,
         items: list[VariableTracker],
-        **kwargs: Any,
+        **kwargs,
     ) -> None:
         super().__init__(items, **kwargs)
 
-    def debug_repr(self) -> str:
+    def debug_repr(self):
         if not self.items:
             return "frozenset()"
         else:
             return "{" + ",".join(k.vt.debug_repr() for k in self.items.keys()) + "}"
 
     @property
-    def set_items(self) -> set["ConstDictVariable._HashableTracker"]:
+    def set_items(self):
         return self.items.keys()
 
-    def python_type(self) -> type:
+    def python_type(self):
         return frozenset
 
-    def as_python_constant(self) -> Any:
+    def as_python_constant(self):
         return frozenset({k.vt.as_python_constant() for k in self.set_items})
 
-    def reconstruct(self, codegen: "PyCodegen") -> None:
+    def reconstruct(self, codegen: "PyCodegen"):
         codegen.foreach([x.vt for x in self.set_items])
         codegen.add_push_null(
             lambda: codegen.extend_output(
@@ -1332,11 +1293,11 @@ class FrozensetVariable(SetVariable):
 
     def call_method(
         self,
-        tx: "InstructionTranslator",
-        name: str,
+        tx,
+        name,
         args: list[VariableTracker],
         kwargs: dict[str, VariableTracker],
-    ) -> VariableTracker:
+    ) -> "VariableTracker":
         if name in ["add", "pop", "update", "remove", "discard", "clear"]:
             raise RuntimeError(f"Illegal call_method {name} on a frozenset")
         elif name == "__init__":
@@ -1355,7 +1316,7 @@ class FrozensetVariable(SetVariable):
             "symmetric_difference",
         ):
             r = super().call_method(tx, name, args, kwargs)
-            return FrozensetVariable(r.items)  # type: ignore[attr-defined]
+            return FrozensetVariable(r.items)
         return super().call_method(tx, name, args, kwargs)
 
 
@@ -1363,11 +1324,11 @@ class DictKeySetVariable(SetVariable):
     def __init__(
         self,
         items: list[VariableTracker],
-        **kwargs: Any,
+        **kwargs,
     ) -> None:
         super().__init__(items, **kwargs)
 
-    def debug_repr(self) -> str:
+    def debug_repr(self):
         if not self.items:
             return "dict_keys([])"
         else:
@@ -1377,35 +1338,33 @@ class DictKeySetVariable(SetVariable):
                 + "])"
             )
 
-    def install_dict_keys_match_guard(self) -> None:
+    def install_dict_keys_match_guard(self):
         # Already EQUALS_MATCH guarded
         pass
 
-    def install_dict_contains_guard(
-        self, tx: "InstructionTranslator", args: list[VariableTracker]
-    ) -> None:
+    def install_dict_contains_guard(self, tx, args):
         # Already EQUALS_MATCH guarded
         pass
 
     @property
-    def set_items(self) -> Any:
+    def set_items(self):
         return self.items
 
-    def python_type(self) -> type:
+    def python_type(self):
         return dict_keys
 
-    def as_python_constant(self) -> Any:
+    def as_python_constant(self):
         return dict.fromkeys(
             {k.vt.as_python_constant() for k in self.set_items}, None
         ).keys()
 
     def call_method(
         self,
-        tx: "InstructionTranslator",
-        name: str,
+        tx,
+        name,
         args: list[VariableTracker],
         kwargs: dict[str, VariableTracker],
-    ) -> VariableTracker:
+    ) -> "VariableTracker":
         if name in ["add", "pop", "update", "remove", "discard", "clear"]:
             raise RuntimeError(f"Illegal call_method {name} on a dict_keys")
         return super().call_method(tx, name, args, kwargs)
@@ -1420,47 +1379,42 @@ class DictViewVariable(VariableTracker):
 
     kv: Optional[str] = None
 
-    def __init__(self, dv_dict: ConstDictVariable, **kwargs: Any) -> None:
+    def __init__(self, dv_dict: ConstDictVariable, **kwargs) -> None:
         super().__init__(**kwargs)
         assert self.kv in ("keys", "values", "items")
         assert isinstance(dv_dict, ConstDictVariable)
         self.dv_dict = dv_dict
 
     @property
-    def view_items(self) -> Any:
-        assert self.kv is not None
+    def view_items(self):
         return getattr(self.dv_dict.items, self.kv)()
 
     @property
-    def view_items_vt(self) -> list[VariableTracker]:
+    def view_items_vt(self):
         # Returns an iterable of the unpacked items
         # Implement in the subclasses
         raise NotImplementedError
 
-    def unpack_var_sequence(self, tx: "InstructionTranslator") -> list[VariableTracker]:
+    def unpack_var_sequence(self, tx):
         return self.view_items_vt
 
-    def reconstruct(self, codegen: "PyCodegen") -> None:
-        assert self.kv is not None
+    def reconstruct(self, codegen: "PyCodegen"):
         codegen(self.dv_dict)
         codegen.load_method(self.kv)
         codegen.call_method(0)
 
-    def call_obj_hasattr(
-        self, tx: "InstructionTranslator", name: str
-    ) -> VariableTracker:
-        assert self.kv is not None
+    def call_obj_hasattr(self, tx, name):
         if name in self.python_type().__dict__:
             return ConstantVariable.create(True)
         return ConstantVariable.create(False)
 
     def call_method(
         self,
-        tx: "InstructionTranslator",
-        name: str,
-        args: list[VariableTracker],
-        kwargs: dict[str, VariableTracker],
-    ) -> VariableTracker:
+        tx,
+        name,
+        args: list["VariableTracker"],
+        kwargs: dict[str, "VariableTracker"],
+    ) -> "VariableTracker":
         if name == "__len__":
             return self.dv_dict.call_method(tx, name, args, kwargs)
         elif name == "__iter__":
@@ -1474,24 +1428,24 @@ class DictKeysVariable(DictViewVariable):
     kv = "keys"
 
     @property
-    def set_items(self) -> set[VariableTracker]:
+    def set_items(self):
         return set(self.view_items)
 
     @property
-    def view_items_vt(self) -> list[VariableTracker]:
+    def view_items_vt(self):
         # Returns an iterable of the unpacked items
         return [x.vt for x in self.view_items]
 
-    def python_type(self) -> type:
+    def python_type(self):
         return dict_keys
 
     def call_method(
         self,
-        tx: "InstructionTranslator",
-        name: str,
-        args: list[VariableTracker],
-        kwargs: dict[str, VariableTracker],
-    ) -> VariableTracker:
+        tx,
+        name,
+        args: list["VariableTracker"],
+        kwargs: dict[str, "VariableTracker"],
+    ) -> "VariableTracker":
         if name == "__contains__":
             return self.dv_dict.call_method(tx, name, args, kwargs)
         elif name in (
@@ -1506,13 +1460,13 @@ class DictKeysVariable(DictViewVariable):
         ):
             # These methods always returns a set
             m = getattr(self.set_items, name)
-            r = m(args[0].set_items)  # type: ignore[attr-defined]
+            r = m(args[0].set_items)
             return SetVariable(r)
         if name in cmp_name_to_op_mapping:
             if not isinstance(args[0], (SetVariable, DictKeysVariable)):
                 return ConstantVariable.create(NotImplemented)
             return ConstantVariable.create(
-                cmp_name_to_op_mapping[name](self.set_items, args[0].set_items)  # type: ignore[attr-defined]
+                cmp_name_to_op_mapping[name](self.set_items, args[0].set_items)
             )
         return super().call_method(tx, name, args, kwargs)
 
@@ -1522,10 +1476,10 @@ class DictValuesVariable(DictViewVariable):
     kv = "values"
 
     @property
-    def view_items_vt(self) -> list[VariableTracker]:
+    def view_items_vt(self):
         return list(self.view_items)
 
-    def python_type(self) -> type:
+    def python_type(self):
         return dict_values
 
 
@@ -1533,20 +1487,14 @@ class DictItemsVariable(DictViewVariable):
     kv = "items"
 
     @property
-    def view_items_vt(self) -> list[VariableTracker]:
+    def view_items_vt(self):
         # Returns an iterable of the unpacked items
         return [variables.TupleVariable([k.vt, v]) for k, v in self.view_items]
 
-    def python_type(self) -> type:
+    def python_type(self):
         return dict_items
 
-    def call_method(
-        self,
-        tx: "InstructionTranslator",
-        name: str,
-        args: list[VariableTracker],
-        kwargs: dict[str, VariableTracker],
-    ) -> VariableTracker:
+    def call_method(self, tx, name, args, kwargs):
         # TODO(guilhermeleobas): This should actually check if args[0]
         # implements the mapping protocol.
         if name == "__eq__":

torch/_inductor/codecache.py

@@ -2970,6 +2970,12 @@ 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

@@ -1732,9 +1732,15 @@ class KernelArgs:
             call_args.append(self.wrap_ptr_arg(outer, dtype))
             arg_types.append(f"{cpp_dtype}*")
         for outer, inner in self.sizevars.items():
-            arg_defs.append(f"const {INDEX_TYPE} {inner}")
+            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}")
             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 "
@@ -2353,6 +2359,7 @@ 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, Optional, TYPE_CHECKING
+from typing import Any, Callable, Optional, Sequence, TYPE_CHECKING
 
 import sympy  # noqa: TC002
 
@@ -17,8 +17,6 @@ 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(
-                    filename=__file__,
+                    num_warps={self.num_warps},
                     triton_meta={triton_meta!r},
                     inductor_meta={inductor_meta!r},
                 )

torch/_inductor/codegen/triton_utils.py

@@ -4,6 +4,7 @@ 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
@@ -71,6 +72,10 @@ 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 = True,
+    use_nccl_estimator: bool = False,
 ) -> 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

@@ -3586,24 +3586,13 @@ def user_autotune(
     )
 
 
-def foreach(triton_meta, filename=None, inductor_meta=None):
+def foreach(triton_meta, num_warps, 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,
-        configs,
+        [triton.Config({}, num_stages=1, num_warps=num_warps)],
         triton_meta=triton_meta,
         inductor_meta=inductor_meta,
         heuristic_type=HeuristicType.TEMPLATE,

torch/autograd/profiler.py

@@ -52,7 +52,26 @@ __all__ = [
     "MemRecordsAcc",
 ]
 
-from contextlib import ContextDecorator
+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
 
 
 # global python state - whether profiler is currently enabled
@@ -725,7 +744,8 @@ class profile:
         return all_function_events
 
 
-class record_function(ContextDecorator):
+# pyrefly: ignore [invalid-inheritance]
+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/FlightRecorder.hpp

@@ -108,14 +108,12 @@ struct FlightRecorder {
     capture_cpp_stack_ = getCvarBool(
         {"TORCH_FR_CPP_STACK", "TORCH_NCCL_TRACE_CPP_STACK"}, false);
     enabled_ = max_entries_ > 0;
-    reset_epoch_start_idx_[0] = 0;
   }
   struct Entry {
     size_t id_; // incremented id in the trace buffer
                 // used to figure out where in the circular entries
                 // buffer this entry will be located to
                 // update state information
-    size_t reset_epoch_; // epoch when this entry was created
     size_t pg_id_;
     std::tuple<std::string, std::string> pg_name_; // <group_name, group_desc>
 
@@ -185,34 +183,11 @@ struct FlightRecorder {
   size_t max_entries_ = 0;
   size_t next_ = 0;
   size_t id_ = 0;
-  size_t reset_epoch_ = 0;
-  std::unordered_map<size_t, size_t>
-      reset_epoch_start_idx_; // maps reset_epoch to the idx where it starts
   std::map<size_t, std::shared_ptr<ProcessGroupStatus>> all_pg_status_;
   std::map<std::tuple<std::string, std::string>, std::vector<uint64_t>>
       pg_name_to_ranks_;
   std::string comm_lib_version_;
 
-  struct TraceIdentifier {
-    std::optional<size_t> id;
-    std::optional<size_t> reset_epoch;
-  };
-
-  TraceIdentifier recordWithResetEnabled(
-      size_t pg_id,
-      const std::tuple<std::string, std::string>& pg_name,
-      size_t collective_seq_id,
-      size_t p2p_seq_id,
-      size_t op_id,
-      std::string profiling_name,
-      const std::vector<at::Tensor>& inputs,
-      const std::vector<at::Tensor>& outputs,
-      EventType* start,
-      EventType* end,
-      std::chrono::milliseconds timeout_ms,
-      std::shared_ptr<ProcessGroupStatus> pg_status,
-      bool isP2P);
-
   std::optional<size_t> record(
       size_t pg_id,
       const std::tuple<std::string, std::string>& pg_name,
@@ -238,16 +213,8 @@ struct FlightRecorder {
 
   std::vector<Entry> dump_entries();
 
-  // Returns the index in entries_ for the given id and reset_epoch.
-  // Caller must hold mutex_lock before calling this method.
-  size_t getIdxFromId(size_t id, size_t reset_epoch) const;
-
-  // Returns the entry with the given id and reset_epoch, if it exists.
-  // Otherwise, returns std::nullopt.
-  TORCH_API std::optional<Entry> getEntry(
-      std::optional<size_t> id,
-      std::optional<size_t> reset_epoch);
-
+  // Returns the entry with the given id, if it exists. Otherwise, returns
+  // std::nullopt.
   TORCH_API std::optional<Entry> getEntry(std::optional<size_t> id);
 
   /*
@@ -260,11 +227,6 @@ struct FlightRecorder {
   never hang. (timing must also be enabled for compute_duration - see
   TORCH_NCCL_ENABLE_TIMING).
   */
-  TORCH_API void retire_id(
-      std::optional<size_t> id,
-      std::optional<size_t> reset_epoch,
-      bool compute_duration = true);
-
   TORCH_API void retire_id(
       std::optional<size_t> id,
       bool compute_duration = true);

torch/csrc/distributed/c10d/FlightRecorderDetail.hpp

@@ -53,41 +53,8 @@ std::optional<size_t> FlightRecorder<EventType>::record(
     std::chrono::milliseconds timeout_ms,
     std::shared_ptr<ProcessGroupStatus> pg_status,
     bool isP2P) {
-  auto result = recordWithResetEnabled(
-      pg_id,
-      pg_name,
-      collective_seq_id,
-      p2p_seq_id,
-      op_id,
-      std::move(profiling_name),
-      inputs,
-      outputs,
-      start,
-      end,
-      timeout_ms,
-      std::move(pg_status),
-      isP2P);
-  return result.id;
-}
-
-template <typename EventType>
-typename FlightRecorder<EventType>::TraceIdentifier FlightRecorder<EventType>::
-    recordWithResetEnabled(
-        size_t pg_id,
-        const std::tuple<std::string, std::string>& pg_name,
-        size_t collective_seq_id,
-        size_t p2p_seq_id,
-        size_t op_id,
-        std::string profiling_name,
-        const std::vector<at::Tensor>& inputs,
-        const std::vector<at::Tensor>& outputs,
-        EventType* start,
-        EventType* end,
-        std::chrono::milliseconds timeout_ms,
-        std::shared_ptr<ProcessGroupStatus> pg_status,
-        bool isP2P) {
   if (!enabled_) {
-    return TraceIdentifier{std::nullopt, std::nullopt};
+    return std::nullopt;
   }
   if (all_pg_status_.find(pg_id) == all_pg_status_.end()) {
     // Current pg_status is not in FR.
@@ -97,13 +64,8 @@ typename FlightRecorder<EventType>::TraceIdentifier FlightRecorder<EventType>::
       torch::CapturedTraceback::gather(true, true, capture_cpp_stack_);
   std::lock_guard<std::mutex> guard(mutex_);
 
-  TORCH_CHECK(
-      reset_epoch_start_idx_.find(reset_epoch_) !=
-      reset_epoch_start_idx_.end());
-
   auto te = Entry{
       id_,
-      reset_epoch_,
       pg_id,
       pg_name,
       collective_seq_id,
@@ -142,20 +104,15 @@ typename FlightRecorder<EventType>::TraceIdentifier FlightRecorder<EventType>::
     te.sizes_.insert(te.sizes_.end(), sizes.begin(), sizes.end());
   }
 
-  const auto next = next_++;
-
   if (entries_.size() < max_entries_) {
     entries_.emplace_back(std::move(te));
   } else {
-    entries_[next] = std::move(te);
+    entries_[next_++] = std::move(te);
+    if (next_ == max_entries_) {
+      next_ = 0;
+    }
   }
-
-  if (next_ == max_entries_) {
-    next_ = 0;
-  }
-
-  const auto id = id_++;
-  return TraceIdentifier{id, reset_epoch_};
+  return id_++;
 }
 
 template <typename EventType>
@@ -206,20 +163,15 @@ std::vector<typename FlightRecorder<EventType>::Entry> FlightRecorder<
   std::vector<Entry> result;
   {
     std::lock_guard<std::mutex> guard(mutex_);
-    // Filter entries during insertion - only keep entries from current epoch
-    auto filter = [this](const Entry& e) {
-      return e.reset_epoch_ == reset_epoch_;
-    };
-    std::copy_if(
+    result.reserve(entries_.size());
+    result.insert(
+        result.end(),
         entries_.begin() + static_cast<std::ptrdiff_t>(next_),
-        entries_.end(),
-        std::back_inserter(result),
-        filter);
-    std::copy_if(
+        entries_.end());
+    result.insert(
+        result.end(),
         entries_.begin(),
-        entries_.begin() + static_cast<std::ptrdiff_t>(next_),
-        std::back_inserter(result),
-        filter);
+        entries_.begin() + static_cast<std::ptrdiff_t>(next_));
   }
   // query any remaining events
   for (auto& r : result) {
@@ -230,47 +182,28 @@ std::vector<typename FlightRecorder<EventType>::Entry> FlightRecorder<
 }
 
 template <typename EventType>
-// Returns the index in entries_ for the given id and reset_epoch.
-// Caller must hold mutex_lock before calling this method.
-size_t FlightRecorder<EventType>::getIdxFromId(size_t id, size_t reset_epoch)
-    const {
-  // Look up the starting idx for the given reset epoch
-  auto it = reset_epoch_start_idx_.find(reset_epoch);
-  TORCH_CHECK(it != reset_epoch_start_idx_.end());
-  // Calculate idx based on where the epoch started
-  return (it->second + id) % max_entries_;
-}
-
-template <typename EventType>
-// Returns the entry with the given id and reset_epoch, if it exists. Otherwise,
-// returns std::nullopt.
+// Returns the entry with the given id, if it exists. Otherwise, returns
+// std::nullopt.
 std::optional<typename FlightRecorder<EventType>::Entry> FlightRecorder<
-    EventType>::
-    getEntry(std::optional<size_t> id, std::optional<size_t> reset_epoch) {
-  if (!enabled_ || !id || !reset_epoch) {
+    EventType>::getEntry(std::optional<size_t> id) {
+  if (!enabled_ || !id) {
     return std::nullopt;
   }
 
   std::unique_lock<std::mutex> guard(mutex_);
-  Entry entry = entries_.at(getIdxFromId(*id, *reset_epoch));
-  if (entry.id_ == *id && entry.reset_epoch_ == *reset_epoch) {
+  Entry entry = entries_.at(*id % max_entries_);
+  if (entry.id_ == *id) {
     return entry;
+  } else {
+    return std::nullopt;
   }
-  return std::nullopt;
-}
-
-template <typename EventType>
-std::optional<typename FlightRecorder<EventType>::Entry> FlightRecorder<
-    EventType>::getEntry(std::optional<size_t> id) {
-  return getEntry(id, 0);
 }
 
 template <typename EventType>
 void FlightRecorder<EventType>::retire_id(
     std::optional<size_t> id,
-    std::optional<size_t> reset_epoch,
     bool compute_duration) {
-  if (!enabled_ || !id || !reset_epoch) {
+  if (!enabled_ || !id) {
     return;
   }
 
@@ -281,8 +214,8 @@ void FlightRecorder<EventType>::retire_id(
 
   std::unique_lock<std::mutex> guard(mutex_);
 
-  Entry* entry = &entries_.at(getIdxFromId(*id, *reset_epoch));
-  if (entry->id_ == *id && entry->reset_epoch_ == *reset_epoch) {
+  Entry* entry = &entries_.at(*id % max_entries_);
+  if (entry->id_ == *id) {
     update_state(*entry);
 
     if (compute_duration) {
@@ -304,8 +237,8 @@ void FlightRecorder<EventType>::retire_id(
     guard.lock();
 
     // Refresh the entry pointer, see if the entry has been overwritten
-    entry = &entries_.at(getIdxFromId(*id, *reset_epoch));
-    if (!(entry->id_ == *id && entry->reset_epoch_ == *reset_epoch)) {
+    entry = &entries_.at(*id % max_entries_);
+    if (entry->id_ != *id) {
       LOG(INFO) << "retire_id abandoned for id " << *id
                 << ", event was overwritten while waiting to compute duration.";
       return;
@@ -316,23 +249,12 @@ void FlightRecorder<EventType>::retire_id(
   }
 }
 
-template <typename EventType>
-void FlightRecorder<EventType>::retire_id(
-    std::optional<size_t> id,
-    bool compute_duration) {
-  retire_id(id, 0, compute_duration);
-}
-
 template <typename EventType>
 void FlightRecorder<EventType>::reset_all() {
   std::lock_guard<std::mutex> guard(mutex_);
-  if (!entries_.empty()) {
-    // Soft delete: increment epoch to mark all existing entries as old
-    // Store where the new epoch starts in the circular buffer
-    reset_epoch_++;
-    reset_epoch_start_idx_[reset_epoch_] = next_;
-    id_ = 0;
-  }
+  next_ = 0;
+  id_ = 0;
+  entries_.clear();
 }
 
 template <typename EventType>

torch/csrc/distributed/c10d/ProcessGroupGloo.cpp

@@ -708,8 +708,7 @@ void ProcessGroupGloo::runLoop(int workerIndex) {
     // TODO: We need to have numel of tensors for gloo as well.
     pgStatus_->lastCompletedNumelIn = 0;
     pgStatus_->lastCompletedNumelOut = 0;
-    FlightRecorder<c10::Event>::get()->retire_id(
-        work->trace_id_, work->trace_reset_epoch_, false);
+    FlightRecorder<c10::Event>::get()->retire_id(work->trace_id_, false);
     lock.lock();
     workInProgress_[workerIndex].reset();
   }
@@ -781,7 +780,7 @@ void ProcessGroupGloo::enqueue(c10::intrusive_ptr<AsyncWork> work) {
   pgStatus_->lastEnqueuedNumelOut = 0;
   // using c10d::FlightRecorder;
   // TODO: We need to have a way to use c10::Event inside gloo as well.
-  auto traceId = FlightRecorder<c10::Event>::get()->recordWithResetEnabled(
+  work->trace_id_ = FlightRecorder<c10::Event>::get()->record(
       local_id_,
       std::make_tuple(pg_uid_, pg_desc_),
       collectiveCounter_,
@@ -796,8 +795,6 @@ void ProcessGroupGloo::enqueue(c10::intrusive_ptr<AsyncWork> work) {
       work->getTimeout(),
       pgStatus_,
       false);
-  work->trace_id_ = traceId.id;
-  work->trace_reset_epoch_ = traceId.reset_epoch;
   workQueue_.push_back(std::move(work));
   lock.unlock();
 

torch/csrc/distributed/c10d/ProcessGroupGloo.hpp

@@ -99,7 +99,6 @@ class TORCH_API ProcessGroupGloo : public Backend {
     // unique id used to tell the trace buffer that this
     // work has completed
     std::optional<uint64_t> trace_id_;
-    std::optional<uint64_t> trace_reset_epoch_;
     std::shared_ptr<gloo::Context> context_;
     const std::chrono::milliseconds timeout_;
 

torch/csrc/distributed/c10d/ProcessGroupNCCL.cpp

@@ -575,7 +575,6 @@ ProcessGroupNCCL::WorkNCCL::WorkNCCL(const WorkNCCL& w)
       futureWorkResult_(w.futureWorkResult_),
       timingEnabled_(w.timingEnabled_),
       trace_id_(w.trace_id_),
-      trace_reset_epoch_(w.trace_reset_epoch_),
       distDebugLevel_(w.distDebugLevel_) {
   exception_ = w.exception_;
 }
@@ -705,9 +704,9 @@ bool ProcessGroupNCCL::WorkNCCL::checkTimeout(
 // Print the traceback of the collective at call time
 std::string ProcessGroupNCCL::WorkNCCL::getTraceback() const {
   // First step we get the corresponding record entry from FR, based on work's
-  // trace_id_ and trace_reset_epoch_
+  // trace_id_
   std::optional<FlightRecorderCUDA::Entry> entry =
-      FlightRecorderCUDA::get()->getEntry(trace_id_, trace_reset_epoch_);
+      FlightRecorderCUDA::get()->getEntry(trace_id_);
   if (entry.has_value()) {
     auto entryVal = entry.value();
     // Get stack trace from FR entry, in string format
@@ -2395,8 +2394,7 @@ void ProcessGroupNCCL::Watchdog::runLoop() {
         pg_->pgStatus_->lastCompletedWorkName = opTypeToString(work.opType_);
         pg_->pgStatus_->lastCompletedNumelIn = work.numelIn_;
         pg_->pgStatus_->lastCompletedNumelOut = work.numelOut_;
-        FlightRecorderCUDA::get()->retire_id(
-            work.trace_id_, work.trace_reset_epoch_, true);
+        FlightRecorderCUDA::get()->retire_id(work.trace_id_, true);
         if (pg_->onCompletionHook_) {
           // Move Work object to completedWorkList_ to be consumed by the hook
           // thread
@@ -3362,7 +3360,7 @@ c10::intrusive_ptr<ProcessGroupNCCL::WorkNCCL> ProcessGroupNCCL::initWork(
     //   these objects to the Work because it has implications for keeping those
     //   tensors alive longer and adds overhead when copying Work objects
     //   between threads
-    auto traceId = FlightRecorderCUDA::get()->recordWithResetEnabled(
+    r->trace_id_ = FlightRecorderCUDA::get()->record(
         local_id_,
         std::make_tuple(pg_uid_, pg_desc_),
         seqCollective_,
@@ -3376,8 +3374,6 @@ c10::intrusive_ptr<ProcessGroupNCCL::WorkNCCL> ProcessGroupNCCL::initWork(
         options_->timeout,
         pgStatus_,
         isP2P);
-    r->trace_id_ = traceId.id;
-    r->trace_reset_epoch_ = traceId.reset_epoch;
   }
   return r;
 }
@@ -3597,7 +3593,6 @@ 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(
@@ -3681,7 +3676,7 @@ c10::intrusive_ptr<Work> ProcessGroupNCCL::collective(
     // later in endCoalescing we record a 'coalesced' Work which has
     // timing/state updates via watchdog thread, but lacks op metadata such as
     // input/output sizes and profilingTitle per-op in the group.
-    FlightRecorderCUDA::get()->recordWithResetEnabled(
+    FlightRecorderCUDA::get()->record(
         local_id_,
         std::make_tuple(pg_uid_, pg_desc_),
         seqCollective_,
@@ -4173,7 +4168,7 @@ c10::intrusive_ptr<Work> ProcessGroupNCCL::pointToPoint(
     // TODO(whc) because we don't pass output {tensor} to initWork, we tell
     // initWork to not record, and then we manually call record passing all the
     // information it wants.
-    auto traceId = FlightRecorderCUDA::get()->recordWithResetEnabled(
+    work->trace_id_ = FlightRecorderCUDA::get()->record(
         local_id_,
         std::make_tuple(pg_uid_, pg_desc_),
         seqCollective_,
@@ -4187,8 +4182,6 @@ c10::intrusive_ptr<Work> ProcessGroupNCCL::pointToPoint(
         options_->timeout,
         pgStatus_,
         /*isP2P=*/true);
-    work->trace_id_ = traceId.id;
-    work->trace_reset_epoch_ = traceId.reset_epoch;
   }
 
   // Only check for NaN for send ops, for recv ops `tensor` can be a random

torch/csrc/distributed/c10d/ProcessGroupNCCL.hpp

@@ -505,7 +505,6 @@ class TORCH_API ProcessGroupNCCL : public Backend {
     // unique id used to tell the trace buffer that this
     // work has completed
     std::optional<uint64_t> trace_id_;
-    std::optional<uint64_t> trace_reset_epoch_;
     DebugLevel distDebugLevel_;
     friend class ProcessGroupNCCL;
   };

torch/csrc/stable/tensor_struct.h

@@ -4,7 +4,6 @@
 #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>
@@ -14,7 +13,6 @@
 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
@@ -95,32 +93,6 @@ 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

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

torch/distributed/tensor/experimental/_attention.py

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

torch/fx/graph.py

@@ -647,15 +647,6 @@ class CodeGen:
 
             if verbose:
                 # override annotation with more detailed information
-                try:
-                    from torch.distributed.tensor._api import DTensor, DTensorSpec
-
-                    dtensorspec_format_shard_order_str = (
-                        DTensorSpec.format_shard_order_str
-                    )
-                except ModuleNotFoundError:
-                    DTensor = None  # type: ignore[assignment,misc]
-                    dtensorspec_format_shard_order_str = None
                 from torch.fx.experimental.proxy_tensor import py_sym_types
                 from torch.fx.passes.shape_prop import TensorMetadata
 
@@ -686,16 +677,6 @@ class CodeGen:
                     core = _tensor_annotation(meta_val)
                     if is_plain:
                         maybe_type_annotation = f': "{core}"'
-                    elif type(meta_val) is DTensor:
-                        assert dtensorspec_format_shard_order_str is not None
-                        dtensor_meta = dtensorspec_format_shard_order_str(
-                            meta_val._spec.placements,  # type: ignore[attr-defined]
-                            meta_val._spec.shard_order,  # type: ignore[attr-defined]
-                        )
-                        cls = meta_val.__class__.__name__
-                        maybe_type_annotation = (
-                            f': "{cls}({core}, {dim_green(dtensor_meta)})"'
-                        )
                     else:
                         cls = meta_val.__class__.__name__
                         maybe_type_annotation = f': "{cls}({core})"'