shrink_group implementation to expose ncclCommShrink API (#164518)

Closes #164529

To expose the new [ncclCommShrink](https://docs.nvidia.com/deeplearning/nccl/user-guide/docs/api/comms.html#ncclcommshrink) API to PyTorch.

This is useful when you need to exclude certain GPUs or nodes from a collective operation, for example in fault tolerance scenarios or when dynamically adjusting resource utilization.

For more info:  [Shrinking a communicator](https://docs.nvidia.com/deeplearning/nccl/user-guide/docs/usage/communicators.html#shrinking-a-communicator)

Pull Request resolved: https://github.com/pytorch/pytorch/pull/164518
Approved by: https://github.com/kwen2501

docs/source/distributed.md

@@ -394,6 +394,10 @@ an opaque group handle that can be given as a `group` argument to all collective
 .. autofunction:: new_group
 ```
 
+```{eval-rst}
+.. autofunction:: torch.distributed.distributed_c10d.shrink_group
+```
+
 ```{eval-rst}
 .. autofunction:: get_group_rank
 ```

test/distributed/logging_utils.py

@@ -0,0 +1,43 @@
+import logging
+import time
+
+
+_start_time = time.time()
+_logger = logging.getLogger(__name__)
+
+
+def _ts():
+    return time.time() - _start_time
+
+
+def configure(level=logging.INFO, force=False):
+    try:
+        logging.basicConfig(
+            level=level,
+            format="%(asctime)s %(name)s %(levelname)s: %(message)s",
+            force=force,
+        )
+    except TypeError:
+        logging.basicConfig(
+            level=level, format="%(asctime)s %(name)s %(levelname)s: %(message)s"
+        )
+
+
+def log_test_info(rank, message):
+    _logger.info("[%7.3fs][Rank %s] %s", _ts(), rank, message)
+
+
+def log_test_success(rank, message):
+    _logger.info("[%7.3fs][Rank %s] ✅ %s", _ts(), rank, message)
+
+
+def log_test_validation(rank, message):
+    _logger.info("[%7.3fs][Rank %s] ✓ %s", _ts(), rank, message)
+
+
+def log_test_warning(rank, message):
+    _logger.warning("[%7.3fs][Rank %s] ⚠️ %s", _ts(), rank, message)
+
+
+def log_test_error(rank, message):
+    _logger.error("[%7.3fs][Rank %s] ✗ %s", _ts(), rank, message)

test/distributed/test_c10d_nccl.py

@@ -2,6 +2,7 @@
 
 import copy
 import json
+import logging
 import os
 import pickle
 import random
@@ -21,6 +22,7 @@ from unittest import mock, SkipTest
 import torch
 import torch.distributed as c10d
 import torch.distributed._functional_collectives as _functional_collectives
+from torch.distributed.distributed_c10d import SHRINK_ABORT as NCCL_SHRINK_ABORT
 
 
 if not c10d.is_available() or not c10d.is_nccl_available():
@@ -47,12 +49,15 @@ from torch._C._distributed_c10d import ErrorType, OpType, WorkResult
 from torch.nn.parallel import DistributedDataParallel
 from torch.testing._internal.common_cuda import _get_torch_rocm_version, TEST_MULTIGPU
 from torch.testing._internal.common_distributed import (
+    get_required_world_size,
     get_timeout,
     init_multigpu_helper,
     MultiProcessTestCase,
     requires_multicast_support,
     requires_nccl,
+    requires_nccl_shrink,
     requires_nccl_version,
+    requires_world_size,
     skip_if_lt_x_gpu,
     skip_if_rocm_multiprocess,
     sm_is_or_higher_than,
@@ -87,6 +92,17 @@ BFLOAT16_AVAILABLE = torch.cuda.is_available() and (
     torch.version.cuda is not None or torch.version.hip is not None
 )
 
+from logging_utils import (
+    configure as _log_configure,
+    log_test_info,
+    log_test_success,
+    log_test_validation,
+    log_test_warning,
+)
+
+
+_log_configure(level=logging.INFO, force=True)
+
 
 class RendezvousEnvTest(TestCase):
     @retry_on_connect_failures
@@ -317,7 +333,7 @@ class ProcessGroupNCCLGroupTest(MultiProcessTestCase):
 
     @property
     def world_size(self):
-        return 2
+        return get_required_world_size(self, 2)
 
     @property
     def rank_to_GPU(self):
@@ -1255,6 +1271,628 @@ class ProcessGroupNCCLGroupTest(MultiProcessTestCase):
         pg_2 = c10d.new_group([0, 1])
         self.assertEqual(pg_2.group_desc, "undefined")
 
+    @requires_nccl_shrink()
+    @requires_world_size(2)
+    def test_shrink_group_basic(self):
+        """Test basic shrink_group functionality."""
+        self._perform_shrink_test([1], "Basic shrink test")
+
+    @requires_nccl_shrink()
+    @requires_world_size(2)
+    def test_shrink_group_validation(self):
+        """Test input validation in shrink_group."""
+        device, pg = self._setup_shrink_test("validation")
+
+        def _test_invalid_input(ranks, description, expected_exception):
+            """Helper to test invalid inputs."""
+            try:
+                c10d.shrink_group(ranks)
+                self.fail(f"Expected {expected_exception.__name__} for {description}")
+            except expected_exception:
+                log_test_validation(self.rank, f"✓ {description}")
+            except Exception:
+                if expected_exception is Exception:  # Accept any exception
+                    log_test_validation(self.rank, f"✓ {description}")
+                else:
+                    raise
+
+        # Test cases
+        _test_invalid_input([], "Empty exclusion list", ValueError)
+        if self.world_size > 1:
+            _test_invalid_input([0, 0, 1], "Duplicate ranks", Exception)
+        _test_invalid_input([self.world_size + 1], "Out of bounds rank", Exception)
+
+        log_test_success(self.rank, "All validation tests passed")
+        dist.destroy_process_group()
+
+    @requires_nccl_shrink()
+    @requires_world_size(2)
+    def test_shrink_group_backend_properties(self):
+        """Test that backend properties are preserved after shrinking."""
+
+        test_name = "Backend Properties Test"
+        ranks_to_exclude = [0]
+
+        # Reuse _setup_shrink_test for complete setup (device, environment, and process group)
+        device, pg = self._setup_shrink_test("backend_properties")
+
+        # Follow _perform_shrink_test pattern from here
+        log_test_info(self.rank, f"{test_name} (world_size={self.world_size})")
+
+        is_excluded = self.rank in ranks_to_exclude
+        log_test_info(
+            self.rank,
+            f"Excluding ranks: {ranks_to_exclude}, am_excluded: {is_excluded}",
+        )
+
+        # Store original backend property values (not references) before shrinking
+        original_timeout = None
+        original_high_priority = None
+        if not is_excluded:
+            original_backend = pg._get_backend(device)
+            original_timeout = original_backend.options._timeout
+            original_high_priority = original_backend.options.is_high_priority_stream
+            log_test_info(
+                self.rank,
+                f"Storing original backend properties: timeout={original_timeout}, high_priority={original_high_priority}",
+            )
+
+        if is_excluded:
+            log_test_info(
+                self.rank,
+                f"Excluded rank {self.rank} - setup complete, skipping shrink operation",
+            )
+            dist.destroy_process_group()  # hang without it
+            return
+
+        # Only non-excluded ranks proceed with shrink (same as _perform_shrink_test)
+        log_test_info(self.rank, "Non-excluded rank calling shrink_group")
+        shrunk_pg = c10d.shrink_group(ranks_to_exclude)
+
+        # Reuse _validate_shrunk_group helper (same as _perform_shrink_test)
+        expected_size = self.world_size - len(ranks_to_exclude)
+        _ = self._validate_shrunk_group(shrunk_pg, expected_size, test_name)
+
+        # Add custom backend properties validation
+        new_backend = shrunk_pg._get_backend(device)
+        log_test_info(self.rank, "Validating backend properties are preserved")
+
+        new_timeout = new_backend.options._timeout
+        new_high_priority = new_backend.options.is_high_priority_stream
+
+        log_test_info(
+            self.rank,
+            f"Timeout comparison - original: {original_timeout}, new: {new_timeout}",
+        )
+        self.assertEqual(
+            original_timeout, new_timeout, f"{test_name}: timeout not preserved"
+        )
+
+        log_test_info(
+            self.rank,
+            f"High priority stream comparison - original: {original_high_priority}, new: {new_high_priority}",
+        )
+        self.assertEqual(
+            original_high_priority,
+            new_high_priority,
+            f"{test_name}: high_priority_stream not preserved",
+        )
+
+        log_test_validation(
+            self.rank, f"{test_name}: Backend properties preserved successfully"
+        )
+        log_test_success(
+            self.rank, f"{test_name} successful (shrink + backend validation)"
+        )
+
+        # Cleanup (same as _perform_shrink_test)
+        dist.destroy_process_group()
+
+    @requires_nccl_shrink()
+    @requires_world_size(2)
+    def test_shrink_group_multiple_comms(self):
+        """Test shrink_group with multiple communicators and subgroup invalidation."""
+
+        device, pg = self._setup_shrink_test("multiple_comms")
+
+        # Create subgroup [0, 1] and test shrinking it
+        subgroup = c10d.new_group([0, 1])
+        if self.rank <= 1:
+            # Shrink subgroup: exclude rank 1
+            if self.rank == 0:  # Only rank 0 remains
+                shrunk_subgroup = c10d.shrink_group([1], group=subgroup)
+                self.assertEqual(shrunk_subgroup.size(), 1)
+                # Test communication on shrunk subgroup
+                tensor = torch.full((1,), self.rank).cuda(device)
+                c10d.all_reduce(tensor, group=shrunk_subgroup)
+                self.assertEqual(tensor.item(), 0)  # Only rank 0
+                log_test_success(self.rank, "Subgroup shrinking successful")
+
+        dist.barrier()  # Sync before default group test
+
+        # Shrink default group: exclude last rank
+        ranks_to_exclude = [self.world_size - 1]
+        if self.rank not in ranks_to_exclude:
+            shrunk_default = c10d.shrink_group(ranks_to_exclude)
+            expected_size = self.world_size - 1
+            self.assertEqual(shrunk_default.size(), expected_size)
+
+            # Test collective on shrunk default group
+            tensor = torch.full((1,), self.rank).cuda(device)
+            c10d.all_reduce(tensor, group=shrunk_default)
+            expected_sum = sum(
+                range(self.world_size - 1)
+            )  # 0 + 1 + ... + (world_size-2)
+            self.assertEqual(tensor.item(), expected_sum)
+            log_test_success(self.rank, "Default group shrinking successful")
+
+            # Note: After shrinking default group, the old subgroup is invalid
+            # due to global rank reassignment
+
+        dist.destroy_process_group()
+
+    def _test_shrink_group_with_flag(self, shrink_flag, flag_name, rank_to_exclude):
+        """Helper method to test shrink_group with a specific flag."""
+        if self.world_size < 2:
+            log_test_info(self.rank, f"Skipping (needs ≥2 GPUs, got {self.world_size})")
+            return
+        ranks_to_exclude = [rank_to_exclude]
+        log_test_info(self.rank, f"Using {flag_name} flag (value: {shrink_flag})")
+        if flag_name == "NCCL_SHRINK_ABORT":
+            log_test_info(
+                self.rank,
+                "ABORT flag will terminate ongoing operations before shrinking",
+            )
+
+        self._perform_shrink_test(
+            ranks_to_exclude, f"{flag_name} flag test", shrink_flags=shrink_flag
+        )
+
+    @requires_nccl_shrink()
+    @requires_world_size(2)
+    def test_shrink_group_flags(self):
+        """Test shrink_group with different shrink flags."""
+        # Test ABORT flags
+        log_test_info(self.rank, "Testing NCCL_SHRINK_ABORT flag")
+        self._test_shrink_group_with_flag(NCCL_SHRINK_ABORT, "NCCL_SHRINK_ABORT", 1)
+
+    @requires_nccl_shrink()
+    @requires_world_size(2)
+    def test_shrink_group_nccl_config(self):
+        """Verify that passing NCCL config via pg_options influences the shrunk group's backend options."""
+        device, pg = self._setup_shrink_test("config")
+        if self.rank == self.world_size - 1:
+            # excluded rank should not call shrink_group
+            dist.destroy_process_group()
+            return
+
+        # Prepare pg_options with NCCL config overrides
+        # Capture parent's current backend options to ensure we can prove override vs inherit
+        parent_backend = pg._get_backend(torch.device("cuda"))
+        parent_hp = parent_backend.options.is_high_priority_stream
+        parent_blocking = parent_backend.options.config.blocking
+
+        # Choose overrides that differ from the parent (flip where possible)
+        override_hp = not parent_hp
+        if parent_blocking in (0, 1):
+            override_blocking = 1 - parent_blocking
+        else:
+            # If undefined or unexpected, set to 1 which is a concrete value
+            override_blocking = 1
+
+        opts = c10d.ProcessGroupNCCL.Options()
+        opts.is_high_priority_stream = override_hp
+        opts.config.blocking = override_blocking
+
+        shrunk_pg = c10d.shrink_group([self.world_size - 1], pg_options=opts)
+
+        # Validate backend options propagated
+        backend = shrunk_pg._get_backend(torch.device("cuda"))
+        # is_high_priority_stream should exactly match our override and differ from parent
+        self.assertEqual(backend.options.is_high_priority_stream, override_hp)
+        self.assertNotEqual(backend.options.is_high_priority_stream, parent_hp)
+        # config is a struct; check representative field and difference from parent when meaningful
+        self.assertEqual(backend.options.config.blocking, override_blocking)
+        if parent_blocking in (0, 1):
+            self.assertNotEqual(backend.options.config.blocking, parent_blocking)
+
+        dist.destroy_process_group()
+
+    @requires_nccl_shrink()
+    @requires_world_size(2)
+    def test_shrink_group_performance(self):
+        """Test shrink_group performance and regression detection."""
+        import time
+
+        ranks_to_exclude = self._get_default_ranks_to_exclude()
+        is_excluded = self.rank in ranks_to_exclude
+
+        if not ranks_to_exclude:
+            log_test_info(self.rank, "Skipping performance test (world_size=1)")
+            return
+
+        log_test_info(self.rank, f"Performance test with {self.world_size} processes")
+        device, pg = self._setup_shrink_test("performance")
+
+        if not is_excluded:
+            log_test_info(self.rank, "Measuring shrink_group performance")
+            start_time = time.time()
+            shrunk_pg = c10d.shrink_group(ranks_to_exclude)
+            end_time = time.time()
+
+            elapsed_time = end_time - start_time
+            log_test_info(self.rank, f"shrink_group: {elapsed_time:.3f}s")
+
+            # Regression check: should complete within reasonable time
+            self.assertLess(
+                elapsed_time,
+                30.0,
+                f"shrink_group took {elapsed_time:.3f}s, possible regression",
+            )
+
+            # Test collective performance
+            expected_size = self.world_size - len(ranks_to_exclude)
+            self._validate_shrunk_group(shrunk_pg, expected_size, "performance")
+
+            collective_start = time.time()
+            _ = self._test_collective_on_shrunk_group(
+                shrunk_pg, device, ranks_to_exclude, "performance"
+            )
+            collective_time = time.time() - collective_start
+
+            log_test_info(self.rank, f"all_reduce: {collective_time:.3f}s")
+            log_test_success(self.rank, "Performance test passed")
+        else:
+            log_test_info(self.rank, "Excluded rank - waiting")
+
+        dist.destroy_process_group()
+
+    @requires_nccl_shrink()
+    @requires_world_size(4)
+    def test_shrink_group_multiple_exclusions(self):
+        """Test shrink_group with multiple ranks excluded at once."""
+        # Scale exclusions with world size
+        ranks_to_exclude = list(range(2, self.world_size, 2))  # Every other rank from 2
+
+        self._perform_shrink_test(ranks_to_exclude, "Multiple exclusions test")
+
+    @requires_nccl_shrink()
+    @requires_world_size(3)
+    def test_shrink_group_multiple_iterations(self):
+        """Test multiple shrink operations in sequence."""
+        log_test_info(
+            self.rank,
+            f"Starting test_shrink_group_multiple_iterations with world_size={self.world_size}",
+        )
+
+        store = c10d.FileStore(self.file_name, self.world_size)
+        device = torch.device(f"cuda:{self.rank}")
+        _ = self._create_process_group_nccl(store, self.opts(), device_id=device)
+
+        # Track current effective world size throughout shrinking operations
+        current_world_size = self.world_size
+        log_test_info(self.rank, f"Initial world_size: {current_world_size}")
+
+        # First shrinking: exclude the last rank(s)
+        first_exclusion = [self.world_size - 1]
+        if self.world_size >= 6:
+            first_exclusion.append(
+                self.world_size - 2
+            )  # Exclude last two ranks for larger sizes
+
+        log_test_info(self.rank, f"First shrinking: excluding ranks {first_exclusion}")
+
+        if self.rank not in first_exclusion:
+            # Only non-excluded ranks should call shrink_group
+            first_pg = c10d.shrink_group(first_exclusion)
+            self.assertIsNotNone(first_pg)
+            # IMPORTANT: Update world size after first shrinking
+            current_world_size = first_pg.size()
+            expected_first_size = self.world_size - len(first_exclusion)
+            log_test_info(
+                self.rank,
+                f"After first shrinking: world_size {self.world_size} -> {current_world_size}",
+            )
+            self.assertEqual(first_pg.size(), expected_first_size)
+
+            # Second shrinking: exclude another rank from the remaining group
+            # Choose a rank that's in the middle range
+            if current_world_size >= 3:
+                second_exclusion = [
+                    current_world_size - 1
+                ]  # Exclude the new "last" rank
+                log_test_info(
+                    self.rank,
+                    f"Second shrinking from group of size {current_world_size}: excluding ranks {second_exclusion}",
+                )
+
+                if self.rank not in second_exclusion:
+                    # Only non-excluded ranks should call shrink_group for second iteration
+                    second_pg = c10d.shrink_group(second_exclusion, group=first_pg)
+                    self.assertIsNotNone(second_pg)
+                    # IMPORTANT: Update world size after second shrinking
+                    final_world_size = second_pg.size()
+                    expected_final_size = current_world_size - len(second_exclusion)
+                    log_test_info(
+                        self.rank,
+                        f"After second shrinking: world_size {current_world_size} -> {final_world_size}",
+                    )
+                    self.assertEqual(second_pg.size(), expected_final_size)
+
+                    # Test collective on final group
+                    tensor = torch.full((1,), self.rank).cuda(device)
+                    log_test_info(
+                        self.rank,
+                        f"Performing all_reduce on final group (size {final_world_size}) with tensor: {tensor.item()}",
+                    )
+                    c10d.all_reduce(tensor, group=second_pg)
+                    log_test_info(
+                        self.rank,
+                        f"Final all_reduce completed, result: {tensor.item()}",
+                    )
+
+                    # Calculate expected sum of remaining ranks
+                    all_excluded = set(first_exclusion + second_exclusion)
+                    remaining_ranks = [
+                        r for r in range(self.world_size) if r not in all_excluded
+                    ]
+                    expected_sum = sum(remaining_ranks)
+                    log_test_info(
+                        self.rank,
+                        f"Remaining ranks: {remaining_ranks}, expected sum: {expected_sum}, actual: {tensor.item()}",
+                    )
+                    self.assertEqual(tensor.item(), expected_sum)
+                    log_test_info(self.rank, "Final verification passed")
+                else:
+                    log_test_info(
+                        self.rank,
+                        "This rank excluded in second shrinking, not calling shrink_group",
+                    )
+            else:
+                log_test_info(
+                    self.rank, "Skipping second shrinking (remaining group too small)"
+                )
+        else:
+            log_test_info(
+                self.rank,
+                "This rank excluded in first shrinking, not calling shrink_group",
+            )
+
+        log_test_info(self.rank, "Destroying process group")
+        dist.destroy_process_group()
+        log_test_info(self.rank, "test_shrink_group_multiple_iterations completed")
+
+    # Helper methods for optimized shrink group tests
+    def _setup_shrink_test(self, test_suffix, world_size=None, warmup=True):
+        """Common setup for shrink group tests."""
+        os.environ["TORCH_NCCL_USE_COMM_NONBLOCKING"] = "1"
+        world_size = world_size or self.world_size
+        store = c10d.FileStore(self.file_name + f"_{test_suffix}", world_size)
+        device = torch.device(f"cuda:{self.rank}")
+        c10d.init_process_group(
+            "nccl",
+            world_size=world_size,
+            rank=self.rank,
+            store=store,
+            pg_options=self.opts(),
+            device_id=device,
+        )
+        pg = c10d.distributed_c10d._get_default_group()
+
+        if warmup:
+            c10d.all_reduce(torch.ones(1).cuda(device), group=pg)
+
+        return device, pg
+
+    def _validate_shrunk_group(self, shrunk_pg, expected_size, test_name=""):
+        """Validate properties of a shrunk process group."""
+        self.assertIsNotNone(shrunk_pg, f"{test_name}: shrunk_pg should not be None")
+        actual_size = shrunk_pg.size()
+        self.assertEqual(
+            actual_size, expected_size, f"{test_name}: group size mismatch"
+        )
+
+        new_rank = shrunk_pg.rank()
+        self.assertTrue(
+            0 <= new_rank < expected_size, f"{test_name}: invalid new rank {new_rank}"
+        )
+
+        log_test_info(
+            self.rank,
+            f"{test_name}: world_size {self.world_size} -> {actual_size}, rank {self.rank} -> {new_rank}",
+        )
+        return new_rank
+
+    def _test_collective_on_shrunk_group(
+        self, shrunk_pg, device, ranks_to_exclude, test_name=""
+    ):
+        """Test collective communication on shrunk group and verify correctness."""
+        test_tensor = torch.full((1,), self.rank, device=device, dtype=torch.float32)
+        c10d.all_reduce(test_tensor, group=shrunk_pg)
+
+        result = test_tensor.item()
+        expected_sum = sum(
+            r for r in range(self.world_size) if r not in ranks_to_exclude
+        )
+
+        self.assertEqual(
+            result, expected_sum, f"{test_name}: collective result mismatch"
+        )
+        log_test_info(
+            self.rank, f"{test_name}: collective passed ({result} == {expected_sum})"
+        )
+        return result
+
+    def _perform_shrink_test(
+        self, ranks_to_exclude, test_name, shrink_flags=0, with_collective=True
+    ):
+        """Complete shrink test flow: setup, shrink, validate, test collective, cleanup.
+
+        Consistent API: All ranks perform setup to initialize distributed environment.
+        ONLY non-excluded ranks call shrink_group() for both default and non-default groups.
+        Excluded ranks perform setup, then exit without calling shrink_group() or waiting.
+        """
+        log_test_info(self.rank, f"{test_name} (world_size={self.world_size})")
+
+        is_excluded = self.rank in ranks_to_exclude
+        log_test_info(
+            self.rank,
+            f"Excluding ranks: {ranks_to_exclude}, am_excluded: {is_excluded}",
+        )
+
+        # All ranks (including excluded ones) perform setup to initialize distributed environment
+        device, pg = self._setup_shrink_test(test_name.lower().replace(" ", "_"))
+        is_default_group = pg == c10d.distributed_c10d._get_default_group()
+
+        if is_excluded:
+            log_test_info(
+                self.rank,
+                f"Excluded rank {self.rank} - setup complete, skipping shrink operation",
+            )
+            if shrink_flags & NCCL_SHRINK_ABORT:
+                log_test_info(self.rank, f"Using abort for excluded rank {self.rank}")
+                pg._get_backend(torch.device(device)).abort()
+                log_test_info(
+                    self.rank, f"cleanup resources for excluded rank {self.rank}"
+                )
+                dist.destroy_process_group()
+                log_test_info(self.rank, f"Excluded rank {self.rank} - exit")
+            else:
+                log_test_info(
+                    self.rank, f"Using regular destroy for excluded rank {self.rank}"
+                )
+                dist.destroy_process_group()
+            return None
+
+        # Only non-excluded ranks proceed with shrink
+        log_test_info(
+            self.rank,
+            f"Non-excluded rank calling shrink_group (default_group={is_default_group})",
+        )
+        shrunk_pg = c10d.shrink_group(ranks_to_exclude, shrink_flags=shrink_flags)
+        log_test_info(
+            self.rank,
+            f"Non-excluded rank calling shrink_group (default_group={is_default_group}) done",
+        )
+
+        # Non-excluded ranks: validate and test the new group
+        expected_size = self.world_size - len(ranks_to_exclude)
+        _ = self._validate_shrunk_group(shrunk_pg, expected_size, test_name)
+
+        if with_collective:
+            _ = self._test_collective_on_shrunk_group(
+                shrunk_pg, device, ranks_to_exclude, test_name
+            )
+            log_test_success(self.rank, f"{test_name} successful (shrink + collective)")
+        else:
+            log_test_success(self.rank, f"{test_name} successful (shrink only)")
+
+        dist.destroy_process_group()
+        return shrunk_pg
+
+    def _get_default_ranks_to_exclude(self):
+        """Get default ranks to exclude based on world size."""
+        if self.world_size <= 1:
+            return []
+        return [self.world_size - 1]  # Exclude last rank by default
+
+    @requires_nccl_shrink()
+    @requires_world_size(3)
+    def test_shrink_group_vs_abort_reinit_performance(self):
+        """Compare performance of shrink_group vs traditional abort+reinit (simplified for reliability)."""
+        log_test_info(self.rank, "=== TEST 1: abort+reinit ===")
+
+        device, pg1 = self._setup_shrink_test("_perf_reinit")
+        torch.cuda.synchronize(device)
+
+        # Test 1: Traditional abort + reinit
+        start_time = time.perf_counter()
+        dist.destroy_process_group()
+
+        device, new_pg = self._setup_shrink_test("perf_shrink_test1")
+        reinit_time = time.perf_counter() - start_time
+
+        # Test collective with original rank values for fair comparison (non-blocking mode)
+        test_tensor = torch.full((1,), self.rank, device=device, dtype=torch.float32)
+        work = c10d.all_reduce(test_tensor, group=new_pg, async_op=True)
+        work.wait()
+
+        torch.cuda.synchronize(device)
+
+        # Verify correctness
+        expected_sum = sum(r for r in range(self.world_size))
+        self.assertEqual(test_tensor.item(), expected_sum, "Reinit collective failed")
+
+        log_test_info(self.rank, f"abort+reinit: {reinit_time:.4f}s")
+        dist.destroy_process_group(new_pg)
+
+        # Test 2: shrink_group with NCCL_SHRINK_ABORT
+        log_test_info(self.rank, "=== TEST 2: shrink_group ===")
+
+        ranks_to_exclude = [self.world_size - 1]
+        is_excluded = self.rank in ranks_to_exclude
+        log_test_info(
+            self.rank,
+            f"Excluding ranks: {ranks_to_exclude}, am_excluded: {is_excluded}",
+        )
+
+        device, pg1 = self._setup_shrink_test("perf_shrink_test2")  # Unique suffix
+
+        shrink_time = 0
+        if not is_excluded:
+            torch.cuda.synchronize(device)  # Ensure accurate timing
+            start_time = time.perf_counter()
+            shrunk_pg = c10d.shrink_group(
+                ranks_to_exclude, shrink_flags=NCCL_SHRINK_ABORT
+            )
+            c10d.all_reduce(torch.ones(1).cuda(device), group=shrunk_pg)
+            shrink_time = time.perf_counter() - start_time
+
+            # Test collective communication on shrunk group (non-blocking mode)
+            test_tensor = torch.full(
+                (1,), self.rank, device=device, dtype=torch.float32
+            )
+            work = c10d.all_reduce(test_tensor, group=shrunk_pg, async_op=True)
+            work.wait()
+
+            # Verify correctness
+            expected_sum = sum(
+                r for r in range(self.world_size) if r not in ranks_to_exclude
+            )
+            self.assertEqual(
+                test_tensor.item(),
+                expected_sum,
+                "shrink_test: collective result mismatch",
+            )
+
+            torch.cuda.synchronize(device)  # Ensure operations complete
+            log_test_info(self.rank, f"shrink_group: {shrink_time:.4f}s")
+            dist.destroy_process_group()
+        else:
+            log_test_info(self.rank, "Excluded from shrink test - exiting immediately")
+            dist.destroy_process_group()
+            return
+
+        # Performance analysis (only for participating ranks)
+        if shrink_time > 0 and reinit_time > 0:
+            speedup = reinit_time / shrink_time
+            time_saved = reinit_time - shrink_time
+
+            log_test_info(self.rank, "=== PERFORMANCE RESULTS ===")
+            log_test_info(self.rank, f"shrink_group:  {shrink_time:.4f}s")
+            log_test_info(self.rank, f"abort+reinit:  {reinit_time:.4f}s")
+            log_test_info(self.rank, f"time_saved:    {time_saved:+.4f}s")
+            log_test_info(self.rank, f"speedup:       {speedup:.2f}x")
+
+            if speedup > 1.1:
+                log_test_success(self.rank, "shrink_group significantly faster")
+            elif speedup > 0.9:
+                log_test_info(self.rank, "≈ comparable performance")
+            else:
+                log_test_warning(self.rank, "abort+reinit faster")
+
+        log_test_info(self.rank, "Performance test completed")
+
     @requires_nccl()
     @skip_but_pass_in_sandcastle_if(not TEST_MULTIGPU, "NCCL test requires 2+ GPUs")
     def test_deterministic_mode_no_break(self):

torch/csrc/distributed/c10d/Backend.hpp

@@ -79,6 +79,23 @@ class TORCH_API Backend : public torch::CustomClassHolder {
     return false;
   }
 
+  virtual bool supportsShrinking() const {
+    return false;
+  }
+
+  // Shrink the backend by excluding specified ranks. Backends that support
+  // communicator shrinking should override this and return a new backend
+  // instance representing the shrunken group. Backends may use opts_override
+  // to supply backend-specific options for the new group.
+  virtual c10::intrusive_ptr<Backend> shrink(
+      const std::vector<int64_t>& /*ranks_to_exclude*/,
+      int /*shrink_flags*/ = 0,
+      const c10::intrusive_ptr<Options>& /*opts_override*/ = nullptr) {
+    TORCH_CHECK(
+        false,
+        c10::str("Backend ", getBackendName(), " does not support shrink"));
+  }
+
   virtual void setTimeout(std::chrono::milliseconds timeout) {
     TORCH_CHECK(
         false,

torch/csrc/distributed/c10d/NCCLUtils.cpp

@@ -259,6 +259,65 @@ std::shared_ptr<NCCLComm> NCCLComm::split(
 }
 #endif
 
+#ifdef NCCL_HAS_COMM_SHRINK
+std::shared_ptr<NCCLComm> NCCLComm::shrink(
+    NCCLComm* source,
+    std::vector<int>& ranks_to_exclude,
+    ncclConfig_t* config,
+    int shrinkFlags) {
+  // Preconditions are validated in ProcessGroupNCCL::shrink
+
+  LOG(INFO) << "Rank " << source->rank_ << ": shrinking comm " << source->repr()
+            << " excluding " << ranks_to_exclude.size() << " ranks";
+
+  at::cuda::OptionalCUDAGuard gpuGuard(source->deviceIndex_);
+  auto comm = std::make_shared<NCCLComm>();
+
+  // This call will block until the source communicator is initialized
+  auto sourceComm = source->getNcclComm();
+
+  C10D_NCCL_CHECK_NONBLOCKING(
+      ncclCommShrink(
+          sourceComm,
+          ranks_to_exclude.data(),
+          ranks_to_exclude.size(),
+          reinterpret_cast<ncclComm_t*>(&(comm->ncclComm_)),
+          config,
+          shrinkFlags),
+      source->getNcclCommFailureReason());
+
+  // Wait for the child communicator to be ready
+  source->waitReady(true);
+  comm->initialized_ = true;
+
+  // NCCL automatically assigns rank during shrink - query it efficiently
+  int assigned_rank;
+  try {
+    C10D_NCCL_CHECK(
+        ncclCommUserRank(comm->ncclComm_, &assigned_rank), std::nullopt);
+    comm->rank_ = assigned_rank;
+  } catch (const std::exception& e) {
+    // Fallback: if ncclCommUserRank fails, we can't determine the rank
+    LOG(ERROR) << "Failed to query NCCL-assigned rank: " << e.what();
+    throw;
+  }
+
+  // Child comm should be on the same device as parent comm
+  comm->deviceIndex_ = source->deviceIndex_;
+  if (config != nullptr) {
+    comm->nonBlocking_ = config->blocking == 0;
+  } else {
+    // Inherit parent behavior if no config provided
+    comm->nonBlocking_ = source->nonBlocking_;
+  }
+
+  LOG(INFO) << "Rank " << source->rank_ << ": created shrunken comm "
+            << comm->repr() << " with NCCL-assigned rank " << assigned_rank;
+
+  return comm;
+}
+#endif
+
 void NCCLComm::finalize() {
   LockType lock(mutex_);
   if (aborted_) {

torch/csrc/distributed/c10d/NCCLUtils.hpp

@@ -90,6 +90,10 @@ static_assert(
 #define NCCL_HAS_NVLS_CTAS
 #endif
 
+#if NCCL_VERSION_CODE >= NCCL_VERSION(2, 27, 0)
+#define NCCL_HAS_COMM_SHRINK
+#endif
+
 // Macro to throw on a non-successful NCCL return value.
 #define C10D_NCCL_CHECK(cmd, failureReason)                                   \
   do {                                                                        \
@@ -294,6 +298,14 @@ class NCCLComm {
       ncclConfig_t& config);
 #endif // NCCL_HAS_COMM_SPLIT
 
+#ifdef NCCL_HAS_COMM_SHRINK
+  static std::shared_ptr<NCCLComm> shrink(
+      NCCLComm* source,
+      std::vector<int>& ranks_to_exclude,
+      ncclConfig_t* config,
+      int shrinkFlags = 0);
+#endif // NCCL_HAS_COMM_SHRINK
+
 #if (defined(IS_NCCLX) || defined(USE_ROCM)) && defined(NCCL_COMM_DUMP)
   std::unordered_map<std::string, std::string> ncclCommDump();
 #endif

torch/csrc/distributed/c10d/ProcessGroupNCCL.cpp

@@ -165,7 +165,7 @@ ncclRedOpRAII getNcclReduceOp(
 }
 
 // Get a key string from device
-inline std::string getKeyFromDevice(at::Device& device) {
+inline std::string getKeyFromDevice(const at::Device& device) {
   return std::to_string(device.index());
 }
 
@@ -5838,6 +5838,139 @@ at::Tensor ProcessGroupNCCL::allocateTensor(
   return tensor;
 }
 
+#ifdef NCCL_HAS_COMM_SHRINK
+c10::intrusive_ptr<Backend> ProcessGroupNCCL::shrink(
+    const std::vector<int64_t>& ranks_to_exclude,
+    int shrink_flags,
+    const c10::intrusive_ptr<Backend::Options>& opts_override) {
+  // Runtime version check with better error message
+  auto runtime_version = torch::cuda::nccl::version();
+  TORCH_CHECK(
+      runtime_version >= NCCL_VERSION(2, 27, 0),
+      "ProcessGroupNCCL::shrink requires NCCL version 2.27.0 or later. "
+      "Found version: ",
+      runtime_version);
+
+  // Early validation with detailed error messages
+  TORCH_CHECK_VALUE(
+      !ranks_to_exclude.empty(), "ranks_to_exclude cannot be empty");
+  TORCH_CHECK_VALUE(
+      static_cast<int>(ranks_to_exclude.size()) < size_,
+      "Cannot exclude all ranks (",
+      ranks_to_exclude.size(),
+      " >= ",
+      size_,
+      ")");
+
+  // Validate ranks and convert to int efficiently
+  std::vector<int> int_ranks_to_exclude;
+  int_ranks_to_exclude.reserve(ranks_to_exclude.size());
+  for (int64_t rank : ranks_to_exclude) {
+    TORCH_CHECK_VALUE(
+        rank >= 0 && rank < size_,
+        "Invalid rank ",
+        rank,
+        " for group size ",
+        size_);
+    int_ranks_to_exclude.push_back(static_cast<int>(rank));
+  }
+
+  // Get primary communicator with better error context
+  auto primary_device_index = guessDeviceId();
+  auto primary_device = at::Device(at::kCUDA, primary_device_index);
+  const auto primary_key = getKeyFromDevice(primary_device);
+
+  std::shared_ptr<NCCLComm> primary_comm = getNCCLComm(primary_key);
+  TORCH_CHECK(
+      primary_comm,
+      "Primary NCCL communicator for device ",
+      primary_device,
+      " (key: ",
+      primary_key,
+      ") is not initialized");
+
+  // Cache device index before shrink operation
+  at::DeviceIndex parent_device_index = primary_comm->getDeviceIndex();
+
+  ncclConfig_t* config = nullptr;
+  // Default to inheriting from parent options
+  bool high_priority_stream = options_->is_high_priority_stream;
+  if (opts_override) {
+    auto nccl_opts =
+        c10::static_intrusive_pointer_cast<ProcessGroupNCCL::Options>(
+            opts_override);
+    config = &nccl_opts->config;
+    // If user provided override options, honor is_high_priority_stream as well
+    high_priority_stream = nccl_opts->is_high_priority_stream;
+  }
+
+  std::shared_ptr<NCCLComm> shrunk_comm = NCCLComm::shrink(
+      primary_comm.get(),
+      int_ranks_to_exclude,
+      (config != nullptr ? config : &options_->config),
+      shrink_flags);
+
+  // Calculate new size and get NCCL-assigned rank
+  int new_size = size_ - static_cast<int>(ranks_to_exclude.size());
+  int new_rank = shrunk_comm->rank_;
+
+  // Create new ProcessGroupNCCL with optimized options cloning
+  auto new_store = store_->clone();
+  auto new_opts = ProcessGroupNCCL::Options::create(high_priority_stream);
+  new_opts->timeout = options_->timeout;
+  if (config != nullptr) {
+    new_opts->config = *config;
+  } else {
+    new_opts->config = options_->config;
+  }
+
+  auto new_pg = c10::make_intrusive<ProcessGroupNCCL>(
+      new_store, new_rank, new_size, new_opts);
+
+  // Set up the new process group with optimized device setup
+  new_pg->initializeDeviceStateForComm(
+      at::Device(at::kCUDA, parent_device_index), shrunk_comm);
+
+  return c10::static_intrusive_pointer_cast<Backend>(new_pg);
+}
+
+#else // !NCCL_HAS_COMM_SHRINK
+// Backend interface override: raise consistent error when shrink is
+// unsupported.
+c10::intrusive_ptr<Backend> ProcessGroupNCCL::shrink(
+    const std::vector<int64_t>& /*ranks_to_exclude*/,
+    int /*shrink_flags*/,
+    const c10::intrusive_ptr<Backend::Options>& /*opts_override*/) {
+  TORCH_CHECK(
+      false,
+      "ProcessGroupNCCL::shrink requires NCCL version 2.27.0 or later, "
+      "but PyTorch was built with an older version or without NCCL shrink support.");
+}
+
+#endif // NCCL_HAS_COMM_SHRINK
+
+void ProcessGroupNCCL::initializeDeviceStateForComm(
+    const at::Device& device,
+    std::shared_ptr<NCCLComm> comm) {
+  const auto key = getKeyFromDevice(device);
+  std::unique_lock<std::mutex> lock(mutex_);
+  at::cuda::OptionalCUDAGuard gpuGuard(device);
+
+  bool force_high = getCvarBool(TORCH_NCCL_HIGH_PRIORITY, false);
+  auto stream = at::cuda::getStreamFromPool(
+      options_->is_high_priority_stream || force_high);
+
+  devNCCLCommMap_[key] = comm;
+  ncclStreams_.emplace(key, stream);
+  ncclEvents_.emplace(key, at::cuda::CUDAEvent(cudaEventDisableTiming));
+  usedDeviceIdxs_.insert(device.index());
+
+  if (shouldAllCommunicatorsRegisterAllTensors()) {
+    std::lock_guard<std::mutex> map_lock(ncclCommMemPoolMapMutex);
+    ncclCommMemPoolMap.emplace(std::move(comm), MemPoolSet{});
+  }
+}
+
 } // namespace c10d
 
 #endif // USE_C10D_NCCL

torch/csrc/distributed/c10d/ProcessGroupNCCL.hpp

@@ -997,6 +997,21 @@ class TORCH_API ProcessGroupNCCL : public Backend {
 
   ErrorType getError() override;
 
+  bool supportsShrinking() const override {
+#ifdef NCCL_HAS_COMM_SHRINK
+    return true;
+#else
+    return false;
+#endif
+  }
+
+  // Backend-style shrink override that returns a Backend instance.
+  c10::intrusive_ptr<Backend> shrink(
+      const std::vector<int64_t>& ranks_to_exclude,
+      int shrink_flags = 0,
+      const c10::intrusive_ptr<Backend::Options>& opts_override =
+          nullptr) override;
+
   std::shared_ptr<c10::Allocator> getMemAllocator() override;
 
   // Allocate tensor from communication-optimized memory pool
@@ -1065,6 +1080,12 @@ class TORCH_API ProcessGroupNCCL : public Backend {
       int p2pRank = 0,
       bool isSendRecvSelf = false);
 
+  // Initialize device-specific state (comm, stream, event, bookkeeping) for a
+  // given communicator on this process group instance.
+  void initializeDeviceStateForComm(
+      const at::Device& device,
+      std::shared_ptr<NCCLComm> comm);
+
   // Wrapper method which can be overridden for tests.
   virtual std::exception_ptr checkForNCCLErrors(
       std::shared_ptr<NCCLComm>& ncclComm);

torch/csrc/distributed/c10d/init.cpp

@@ -2730,12 +2730,23 @@ Arguments:
               "supports_time_estimate",
               &::c10d::Backend::supportsTimeEstimation,
               "(test whether the backend supports collective time estimation)")
+          .def_property_readonly(
+              "supports_shrinking",
+              &::c10d::Backend::supportsShrinking,
+              "(test whether the backend supports communicator shrinking)")
           .def(
               "set_timeout",
               &::c10d::Backend::setTimeout,
               py::arg("timeout"),
               py::call_guard<py::gil_scoped_release>(),
               R"(Sets the default timeout for all future operations.)")
+          .def(
+              "shrink",
+              &::c10d::Backend::shrink,
+              py::arg("ranks_to_exclude"),
+              py::arg("shrink_flags") = 0,
+              py::arg("opts_override") = nullptr,
+              py::call_guard<py::gil_scoped_release>())
           .def(
               "broadcast",
               &::c10d::Backend::broadcast,

torch/distributed/distributed_c10d.py

@@ -130,6 +130,7 @@ __all__ = [
     "reduce_scatter_tensor",
     "get_node_local_rank",
     "split_group",
+    "shrink_group",
 ]
 
 _MPI_AVAILABLE = True
@@ -5713,3 +5714,517 @@ def _get_process_group_name(pg: ProcessGroup) -> str:
 
 def _get_process_group_store(pg: ProcessGroup) -> Store:
     return _world.pg_map[pg][1]
+
+
+# Shrink flags for process group backends
+SHRINK_DEFAULT = 0x00
+SHRINK_ABORT = 0x01
+
+
+@_time_logger
+def shrink_group(
+    ranks_to_exclude: list[int],
+    group: Optional[ProcessGroup] = None,
+    shrink_flags: int = SHRINK_DEFAULT,
+    pg_options: Optional[Any] = None,
+) -> ProcessGroup:
+    """
+    Shrinks a process group by excluding specified ranks.
+
+    Creates and returns a new, smaller process group comprising only the ranks
+    from the original group that were not in the ``ranks_to_exclude`` list.
+
+    Args:
+        ranks_to_exclude (List[int]): A list of ranks from the original
+            ``group`` to exclude from the new group.
+        group (ProcessGroup, optional): The process group to shrink. If ``None``,
+            the default process group is used. Defaults to ``None``.
+        shrink_flags (int, optional): Flags to control the shrinking behavior.
+            Can be ``SHRINK_DEFAULT`` (default) or ``SHRINK_ABORT``.
+            ``SHRINK_ABORT`` will attempt to terminate ongoing operations
+            in the parent communicator before shrinking.
+            Defaults to ``SHRINK_DEFAULT``.
+        pg_options (ProcessGroupOptions, optional): Backend-specific options to apply
+            to the shrunken process group. If provided, the backend will use
+            these options when creating the new group. If omitted, the new group
+            inherits defaults from the parent.
+
+    Returns:
+        ProcessGroup: a new group comprised of the remaining ranks. If the
+        default group was shrunk, the returned group becomes the new default group.
+
+    Raises:
+        TypeError: if the group’s backend does not support shrinking.
+        ValueError: if ``ranks_to_exclude`` is invalid (empty, out of bounds,
+        duplicates, or excludes all ranks).
+        RuntimeError: if an excluded rank calls this function or the backend
+        fails the operation.
+
+    Notes:
+        - Only non-excluded ranks should call this function; excluded ranks
+          must not participate in the shrink operation.
+        - Shrinking the default group destroys all other process groups since
+          rank reassignment makes them inconsistent.
+    """
+    # Step 1: Validate input parameters with comprehensive error checking
+    _validate_shrink_inputs(ranks_to_exclude, shrink_flags)
+
+    # Step 2: Get target group and essential properties
+    target_group_info = _prepare_shrink_target_group(group)
+
+    # Step 3: Validate backend requirements and availability
+    backend_impl = _validate_shrink_backend_requirements(target_group_info)
+
+    # Step 4: Validate ranks against group and check for duplicates
+    excluded_ranks_set = _validate_and_process_excluded_ranks(
+        ranks_to_exclude, target_group_info
+    )
+
+    # Step 5: Execute the actual shrink operation (backend-specific)
+    new_backend = backend_impl.shrink(
+        sorted(excluded_ranks_set),
+        shrink_flags,
+        pg_options if pg_options is not None else None,
+    )
+
+    # Step 6: Handle cleanup and creation of new process group
+    target_group_info["pg_options_override"] = pg_options
+    return _finalize_shrunk_group(target_group_info, excluded_ranks_set, new_backend)
+
+
+def _validate_shrink_inputs(ranks_to_exclude: list[int], shrink_flags: int) -> None:
+    """Validate input parameters for shrink_group."""
+    if not isinstance(ranks_to_exclude, list):
+        raise TypeError(
+            f"ranks_to_exclude must be a list, but got {type(ranks_to_exclude).__name__}. "
+            f"Example: [1, 3, 5] to exclude ranks 1, 3, and 5."
+        )
+
+    if not ranks_to_exclude:
+        raise ValueError(
+            "ranks_to_exclude cannot be empty. To shrink a group, you must specify at least "
+            "one rank to exclude. Example: [failed_rank_id]"
+        )
+
+    # Validate shrink_flags with clear explanation of valid values
+    valid_flags = [SHRINK_DEFAULT, SHRINK_ABORT]
+    if not isinstance(shrink_flags, int) or shrink_flags not in valid_flags:
+        raise ValueError(
+            f"Invalid shrink_flags value: {shrink_flags}. Must be one of: "
+            f"SHRINK_DEFAULT ({SHRINK_DEFAULT}) or SHRINK_ABORT ({SHRINK_ABORT}). "
+            f"Use SHRINK_ABORT to abort ongoing operations before shrinking."
+        )
+
+
+def _prepare_shrink_target_group(group: Optional[ProcessGroup]) -> dict:
+    """Prepare and validate the target group for shrinking."""
+    target_pg = group if group is not None else _get_default_group()
+
+    # Cache frequently accessed properties to avoid repeated calls
+    group_size = int(target_pg.size())
+    group_info = {
+        "process_group": target_pg,
+        "is_default_group": (target_pg == _get_default_group()),
+        "group_size": group_size,
+        "current_rank": target_pg.rank(),
+        "group_name": _get_process_group_name(target_pg),
+    }
+
+    # Validate that we have a valid process group
+    if group_size <= 1:
+        raise ValueError(
+            f"Cannot shrink a process group with size {group_size}. "
+            f"Group must have at least 2 ranks to support shrinking."
+        )
+
+    return group_info
+
+
+def _validate_shrink_backend_requirements(group_info: dict) -> Any:
+    """Return the backend implementation for the target group or raise if unsupported."""
+    target_pg = group_info["process_group"]
+    group_name = group_info["group_name"]
+
+    # Get the group's backend directly via ProcessGroup API. Prefer a bound device if present,
+    # otherwise try CUDA then fall back to CPU.
+    try:
+        preferred_device = getattr(target_pg, "bound_device_id", None)
+        if preferred_device is not None:
+            backend_impl = target_pg._get_backend(preferred_device)
+        else:
+            # Try CUDA first if available, else CPU
+            try:
+                backend_impl = target_pg._get_backend(torch.device("cuda"))
+            except Exception:
+                backend_impl = target_pg._get_backend(torch.device("cpu"))
+    except RuntimeError as e:
+        raise RuntimeError(
+            f"Cannot access device backend for process group '{group_name}'. "
+            f"Ensure the process group was initialized with a compatible device backend and devices are available."
+        ) from e
+
+    try:
+        supports = bool(backend_impl.supports_shrinking)
+    except Exception:
+        supports = False
+    if not supports:
+        raise TypeError(
+            f"Process group backend for '{group_name}' does not support shrinking operations."
+        )
+
+    return backend_impl
+
+
+def _validate_and_process_excluded_ranks(
+    ranks_to_exclude: list[int], group_info: dict
+) -> set:
+    """Validate excluded ranks and convert to set for efficient operations."""
+    group_size = group_info["group_size"]
+    current_rank = group_info["current_rank"]
+
+    # Use set for O(1) duplicate detection and membership testing
+    excluded_ranks_set = set()
+
+    # Validate each rank with detailed error messages
+    for i, rank in enumerate(ranks_to_exclude):
+        if not isinstance(rank, int):
+            raise TypeError(
+                f"All elements in ranks_to_exclude must be integers. "
+                f"Element at index {i} is {type(rank).__name__}: {rank}"
+            )
+
+        if not (0 <= rank < group_size):
+            raise ValueError(
+                f"Rank {rank} at index {i} is out of bounds for group size {group_size}. "
+                f"Valid ranks are in range [0, {group_size - 1}]."
+            )
+
+        if rank in excluded_ranks_set:
+            raise ValueError(
+                f"Duplicate rank {rank} found in ranks_to_exclude at index {i}. "
+                f"Each rank can only be excluded once."
+            )
+
+        excluded_ranks_set.add(rank)
+
+    # Ensure we don't exclude all ranks
+    if len(excluded_ranks_set) >= group_size:
+        raise ValueError(
+            f"Cannot exclude all {group_size} ranks from process group. "
+            f"At least one rank must remain. Excluding {len(excluded_ranks_set)} ranks."
+        )
+
+    # Critical check: current rank should not be in excluded list
+    if current_rank in excluded_ranks_set:
+        raise RuntimeError(
+            f"Current rank {current_rank} is in the exclusion list and should not call shrink_group(). "
+            f"Only non-excluded ranks should participate in the shrinking operation. "
+            f"Excluded ranks should terminate their processes instead."
+        )
+
+    return excluded_ranks_set
+
+
+def _finalize_shrunk_group(
+    group_info: dict, excluded_ranks_set: set, new_backend
+) -> ProcessGroup:
+    """Clean up old group and create new shrunk process group."""
+    target_pg = group_info["process_group"]
+    is_default_group = group_info["is_default_group"]
+
+    # Handle default group dependencies - destroy other groups first
+    if is_default_group:
+        _destroy_all_other_groups(exclude_group=target_pg)
+
+    # Gather original group metadata before cleanup
+    original_group_metadata = _extract_group_metadata(target_pg)
+
+    # Calculate remaining ranks efficiently
+    original_ranks = get_process_group_ranks(target_pg)
+    remaining_ranks = [
+        rank for rank in original_ranks if rank not in excluded_ranks_set
+    ]
+
+    # Clean up the original group
+    _cleanup_original_group(target_pg, is_default_group)
+
+    # Create and configure the new process group
+    new_pg = _create_shrunk_process_group(
+        new_backend, remaining_ranks, original_group_metadata, is_default_group
+    )
+
+    # Register the new group in global state
+    if is_default_group:
+        _update_default_pg(new_pg)
+
+    # Update global state with new group information
+    rank_mapping = {
+        global_rank: group_rank
+        for group_rank, global_rank in enumerate(remaining_ranks)
+    }
+    _update_process_group_global_state(
+        pg=new_pg,
+        backend_name=original_group_metadata["backend_name"],
+        store=original_group_metadata["store"],
+        group_name=original_group_metadata["new_group_name"],
+        backend_config=original_group_metadata["backend_config"],
+        rank_mapping=rank_mapping,
+    )
+
+    return new_pg
+
+
+def _extract_group_metadata(target_pg: ProcessGroup) -> dict:
+    """Extract metadata from the original group before cleanup."""
+    original_backend_name, original_store = _world.pg_map[target_pg]
+    original_backend_config = _world.pg_backend_config.get(target_pg, "")
+    original_group_name = _get_process_group_name(target_pg)
+
+    # Extract device binding information before cleanup to avoid accessing destroyed group
+    bound_device_id = None
+    if hasattr(target_pg, "bound_device_id"):
+        bound_device_id = target_pg.bound_device_id
+
+    # Generate new group name for the shrunk group; hash for uniqueness across backends
+    remaining_ranks = list(get_process_group_ranks(target_pg))
+    new_group_name = _process_group_name(remaining_ranks, use_hashed_name=True)
+
+    return {
+        "backend_name": original_backend_name,
+        "store": original_store,
+        "backend_config": original_backend_config,
+        "original_group_name": original_group_name,
+        "new_group_name": new_group_name,
+        "bound_device_id": bound_device_id,  # Safe to access after cleanup
+    }
+
+
+def _cleanup_original_group(target_pg: ProcessGroup, is_default_group: bool) -> None:
+    """Clean up the original process group safely."""
+    try:
+        destroy_process_group(target_pg)
+    except Exception as e:
+        group_type = "default" if is_default_group else "non-default"
+        logger.warning("Failed to destroy %s group during shrinking: %s", group_type, e)
+
+    # Ensure global state cleanup even if destroy_process_group fails
+    _cleanup_process_group_global_state(target_pg)
+
+
+def _create_shrunk_process_group(
+    new_backend, remaining_ranks: list[int], metadata: dict, is_default_group: bool
+) -> ProcessGroup:
+    """Create and configure the new shrunk process group."""
+    # Create new group properties
+    new_group_rank = new_backend.rank()
+    new_group_size = new_backend.size()
+    group_name = metadata["new_group_name"]
+
+    # Generate descriptive group description
+    if is_default_group:
+        group_desc = "default:shrunken"
+    else:
+        group_desc = f"{metadata['original_group_name']}:shrunk"
+
+    # Create process group with new communicator (clone the parent store like split does)
+    prefix_store = PrefixStore(f"{group_name}/", metadata["store"].clone())
+    new_pg = ProcessGroup(prefix_store, new_group_rank, new_group_size)
+
+    # Configure backend using the device type of the new backend's bound device if available,
+    # otherwise derive from the original group's bound device or fall back to CPU.
+    backend_device = metadata.get("bound_device_id")
+    if backend_device is None:
+        # Default to CPU if no bound device is present
+        backend_device = torch.device("cpu")
+
+    # Choose backend enum based on device type
+    if backend_device.type == "cuda":
+        backend_type = ProcessGroup.BackendType.NCCL
+    else:
+        backend_type = ProcessGroup.BackendType.GLOO
+
+    new_pg._register_backend(backend_device, backend_type, new_backend)
+    new_pg._set_default_backend(backend_type)
+
+    # Inherit device binding from original group if it was bound
+    bound_device_id = metadata.get("bound_device_id")
+    if bound_device_id is not None:
+        new_pg.bound_device_id = bound_device_id
+
+    # Set group metadata
+    new_pg._set_group_name(group_name)
+    new_pg._set_group_desc(group_desc)
+
+    # Persist backend configuration overrides (if provided via shrink_group)
+    backend_config_override = metadata.get("backend_config")
+    if backend_config_override is not None:
+        # Store for introspection/debugging and potential backend hooks
+        _world.pg_backend_config[new_pg] = backend_config_override
+
+    return new_pg
+
+
+def _destroy_all_other_groups(exclude_group: Optional[ProcessGroup] = None) -> None:
+    """
+    Destroy all process groups except the excluded group and clean up all global state.
+
+    This is necessary when shrinking the default group because global ranks
+    are reassigned by NCCL, making all existing process groups inconsistent.
+
+    Note: Uses abort for non-collective cleanup since excluded ranks may not
+    participate in collective operations. Backend cleanup is handled independently per group.
+
+    Args:
+        exclude_group (ProcessGroup, optional): Process group to exclude from destruction.
+            If None, destroys all process groups.
+    """
+    # Get list of groups to destroy (avoid modifying dict while iterating)
+    groups_to_destroy = []
+    for pg in list(_world.pg_group_ranks.keys()):
+        if exclude_group is not None and pg == exclude_group:
+            continue
+        groups_to_destroy.append(pg)
+
+    # Warn user about automatic destruction
+    if groups_to_destroy:
+        group_names = [_get_process_group_name(pg) for pg in groups_to_destroy]
+        logger.warning(
+            "Shrinking default group will destroy %d other process groups: %s. "
+            "This is necessary because shrinking the default group reassigns global ranks, "
+            "making existing groups inconsistent.",
+            len(groups_to_destroy),
+            ", ".join(group_names),
+        )
+
+    # Destroy each group and clean up global state
+    for pg in groups_to_destroy:
+        try:
+            # First call abort_process_group which handles the C++ cleanup non-collectively
+            _abort_process_group(pg)
+        except Exception as e:
+            # Log but don't fail - some groups might already be destroyed
+            logger.warning(
+                "Failed to abort process group %s: %s",
+                _get_process_group_name(pg),
+                e,
+            )
+
+        # Ensure all global state is cleaned up even if _abort_process_group fails
+        # or doesn't clean up everything
+        _cleanup_process_group_global_state(pg)
+
+
+def _cleanup_process_group_global_state(pg: ProcessGroup) -> None:
+    """
+    Clean up all global state associated with a process group.
+
+    This function ensures complete cleanup of process group state from all
+    global dictionaries and registries, even if destroy_process_group fails
+    or doesn't clean up everything. This is critical when destroying multiple
+    groups to prevent inconsistent state.
+
+    The cleanup removes the process group from:
+    - _world.pg_map (backend and store mapping)
+    - _world.pg_names (group name mapping)
+    - _world.pg_group_ranks (rank mappings)
+    - _world.pg_backend_config (backend configuration)
+    - _world.tags_to_pg and _world.pg_to_tag (tag mappings)
+    - _world.pg_coalesce_state (coalescing state)
+    - C++ internal registries via _unregister_process_group
+
+    Args:
+        pg (ProcessGroup): The process group to clean up.
+    """
+    try:
+        # Clean up main process group mappings
+        _world.pg_map.pop(pg, None)
+        _world.pg_group_ranks.pop(pg, None)
+        _world.pg_backend_config.pop(pg, None)
+
+        # Clean up process group name mapping
+        group_name = _world.pg_names.pop(pg, None)
+
+        # Clean up tag mappings
+        pg_tag = _world.pg_to_tag.pop(pg, None)
+        if pg_tag is not None and pg_tag in _world.tags_to_pg:
+            try:
+                _world.tags_to_pg[pg_tag].remove(pg)
+                # Remove the tag entry if list is empty
+                if not _world.tags_to_pg[pg_tag]:
+                    _world.tags_to_pg.pop(pg_tag, None)
+            except (ValueError, KeyError):
+                # Process group was already removed from the list
+                pass
+
+        # Clean up any registered process group names using C++ unregister function
+        if group_name is not None:
+            try:
+                _unregister_process_group(group_name)
+            except Exception:
+                # Process group name might not be registered or already unregistered
+                pass
+
+        # Clean up coalesce state if present
+        _world.pg_coalesce_state.pop(pg, None)
+
+    except Exception as e:
+        # Log cleanup failures but don't propagate - we want to continue with other cleanups
+        logger.warning("Failed to fully clean up global state for process group: %s", e)
+
+
+def _update_process_group_global_state(
+    pg: ProcessGroup,
+    backend_name: str,
+    store: Store,
+    group_name: str,
+    backend_config: str,
+    rank_mapping: Optional[dict[int, int]] = None,
+    pg_tag: Optional[str] = None,
+    user_tag: Optional[str] = None,
+) -> None:
+    """
+    Update all global state dictionaries for a process group.
+
+    This helper function consolidates the common pattern of updating multiple
+    global state dictionaries when creating or modifying process groups.
+
+    Args:
+        pg (ProcessGroup): The process group to update state for.
+        backend_name (str): Backend name for pg_map.
+        store (Store): Store instance for pg_map.
+        group_name (str): Group name for pg_names and registration.
+        backend_config (str): Backend configuration string.
+        rank_mapping (Dict[int, int], optional): Global rank to group rank mapping.
+            If None, skips updating pg_group_ranks.
+        pg_tag (str, optional): Process group tag. If None, defaults to f"ptd:{group_name}".
+        user_tag (str, optional): User-provided tag for special tag handling.
+            If provided, creates "user:{user_tag}" tag and also adds to default "".
+    """
+    # Update main process group mappings
+    _world.pg_map[pg] = (backend_name, store)
+    _world.pg_names[pg] = group_name
+    _world.pg_backend_config[pg] = backend_config
+
+    # Register the process group name
+    _register_process_group(group_name, pg)
+
+    # Update rank mapping if provided
+    if rank_mapping is not None:
+        _world.pg_group_ranks[pg] = rank_mapping
+
+    # Handle tag management
+    if pg_tag is None:
+        pg_tag = f"ptd:{group_name}"
+
+    if user_tag is not None:
+        # Special handling for user-provided tags
+        # Add to default "" tag first
+        _world.tags_to_pg.setdefault("", []).append(pg)
+        # Then create user-specific tag
+        user_pg_tag = f"user:{user_tag}"
+        _world.tags_to_pg.setdefault(user_pg_tag, []).append(pg)
+        _world.pg_to_tag[pg] = user_pg_tag
+    else:
+        # Standard process group tag
+        _world.tags_to_pg.setdefault(pg_tag, []).append(pg)
+        _world.pg_to_tag[pg] = pg_tag

torch/testing/_internal/common_distributed.py

@@ -238,6 +238,47 @@ def skip_if_lt_x_gpu(x):
     return decorator
 
 
+def requires_world_size(n: int):
+    """
+    Decorator to request a specific world size for a test. The test harness can
+    read this attribute to set the number of ranks to spawn. If there are fewer
+    than `n` CUDA devices available, the test should be skipped by the harness.
+
+    Usage:
+        @require_world_size(3)
+        def test_something(self):
+            ...
+    """
+
+    def decorator(func):
+        func._required_world_size = n
+        available = torch.cuda.device_count()
+        return unittest.skipUnless(
+            available >= n, f"requires {n} GPUs, found {available}"
+        )(func)
+
+    return decorator
+
+
+def get_required_world_size(obj: Any, default: int) -> int:
+    """
+    Returns the requested world size for the currently running unittest method on `obj`
+    if annotated via `@require_world_size(n)`, else returns `default`.
+    """
+    try:
+        # Try MultiProcessTestCase helper first, then unittest fallback
+        test_name = (
+            obj._current_test_name()  # type: ignore[attr-defined]
+            if hasattr(obj, "_current_test_name") and callable(obj._current_test_name)
+            else obj._testMethodName
+        )
+        fn = getattr(obj, test_name)
+        value = fn._required_world_size
+        return int(value)
+    except Exception:
+        return default
+
+
 # This decorator helps avoiding initializing cuda while testing other backends
 def nccl_skip_if_lt_x_gpu(backend, x):
     def decorator(func):
@@ -367,6 +408,13 @@ def requires_nccl_version(version, msg):
         )
 
 
+def requires_nccl_shrink():
+    """
+    Require NCCL shrink support (NCCL available and version >= 2.27).
+    """
+    return requires_nccl_version((2, 27), "Need NCCL 2.27+ for shrink_group")
+
+
 def requires_nccl():
     return skip_but_pass_in_sandcastle_if(
         not c10d.is_nccl_available(),