include DTensor metadata when pretty-printing fx.Graphs #166750
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/166750
Note: Links to docs will display an error until the docs builds have been completed. ✅ You can merge normally! (2 Unrelated Failures)As of commit bffb8b7 with merge base 5c63946 ( FLAKY - The following jobs failed but were likely due to flakiness present on trunk:
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bdhirsh
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release notes: distributed (dtensor)
ezyang
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Oct 31, 2025
Skylion007
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Nov 1, 2025
Example below. You need to trace your function with DTensor inputs in order for the graph proxies to run on DTensor (and not the inner local tensor). You also need to run with `tracing_mode="fake"`, or with your own `FakeTensorMode`, to see the nice DTensor printing. If this doesn't feel very ergonomic then maybe we can find some better UX for printing a graph with DTensor in it: <img width="1446" height="582" alt="image" src="https://github.com/user-attachments/assets/99ea5ce6-1008-4ba5-b58e-542cd34a340b" /> ``` import torch from torch.testing._internal.distributed.fake_pg import FakeStore from torch.distributed.tensor import distribute_tensor, Shard, Replicate from torch.utils._debug_mode import DebugMode from torch.fx.experimental.proxy_tensor import make_fx from torch.utils._python_dispatch import TorchDispatchMode from torch.utils import _pytree as pytree world_size = 8 device_type = "cpu" fake_store = FakeStore() torch.distributed.init_process_group("fake", store=fake_store, rank=0, world_size=world_size) device_mesh = torch.distributed.init_device_mesh(device_type, (world_size,)) dim = 128 A = torch.randn(8, dim) B = torch.randn(dim, dim) dA = distribute_tensor(A, device_mesh, [Shard(0)]).requires_grad_() dB = distribute_tensor(B, device_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() gm.print_readable(colored=True) ``` cc ezyang EikanWang jgong5 wenzhe-nrv [ghstack-poisoned]
pytorch-bot
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bdhirsh
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Nov 3, 2025
| 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) |
|
@pytorchbot merge |
Merge startedYour change will be merged once all checks pass (ETA 0-4 Hours). Learn more about merging in the wiki. Questions? Feedback? Please reach out to the PyTorch DevX Team |
Merge failedReason: 1 jobs have failed, first few of them are: trunk / macos-py3-arm64 / test (default, 1, 3, macos-m1-stable) Details for Dev Infra teamRaised by workflow job |
Example below. You need to trace your function with DTensor inputs in order for the graph proxies to run on DTensor (and not the inner local tensor). You also need to run with `tracing_mode="fake"`, or with your own `FakeTensorMode`, to see the nice DTensor printing. If this doesn't feel very ergonomic then maybe we can find some better UX for printing a graph with DTensor in it: <img width="1446" height="582" alt="image" src="https://github.com/user-attachments/assets/99ea5ce6-1008-4ba5-b58e-542cd34a340b" /> ``` import torch from torch.testing._internal.distributed.fake_pg import FakeStore from torch.distributed.tensor import distribute_tensor, Shard, Replicate from torch.utils._debug_mode import DebugMode from torch.fx.experimental.proxy_tensor import make_fx from torch.utils._python_dispatch import TorchDispatchMode from torch.utils import _pytree as pytree world_size = 8 device_type = "cpu" fake_store = FakeStore() torch.distributed.init_process_group("fake", store=fake_store, rank=0, world_size=world_size) device_mesh = torch.distributed.init_device_mesh(device_type, (world_size,)) dim = 128 A = torch.randn(8, dim) B = torch.randn(dim, dim) dA = distribute_tensor(A, device_mesh, [Shard(0)]).requires_grad_() dB = distribute_tensor(B, device_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() gm.print_readable(colored=True) ``` cc H-Huang awgu wanchaol fegin fduwjj wz337 wconstab d4l3k pragupta msaroufim dcci ezyang EikanWang jgong5 wenzhe-nrv [ghstack-poisoned]
Example below. You need to trace your function with DTensor inputs in order for the graph proxies to run on DTensor (and not the inner local tensor). You also need to run with `tracing_mode="fake"`, or with your own `FakeTensorMode`, to see the nice DTensor printing. If this doesn't feel very ergonomic then maybe we can find some better UX for printing a graph with DTensor in it: <img width="1446" height="582" alt="image" src="https://github.com/user-attachments/assets/99ea5ce6-1008-4ba5-b58e-542cd34a340b" /> ``` import torch from torch.testing._internal.distributed.fake_pg import FakeStore from torch.distributed.tensor import distribute_tensor, Shard, Replicate from torch.utils._debug_mode import DebugMode from torch.fx.experimental.proxy_tensor import make_fx from torch.utils._python_dispatch import TorchDispatchMode from torch.utils import _pytree as pytree world_size = 8 device_type = "cpu" fake_store = FakeStore() torch.distributed.init_process_group("fake", store=fake_store, rank=0, world_size=world_size) device_mesh = torch.distributed.init_device_mesh(device_type, (world_size,)) dim = 128 A = torch.randn(8, dim) B = torch.randn(dim, dim) dA = distribute_tensor(A, device_mesh, [Shard(0)]).requires_grad_() dB = distribute_tensor(B, device_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() gm.print_readable(colored=True) ``` cc H-Huang awgu wanchaol fegin fduwjj wz337 wconstab d4l3k pragupta msaroufim dcci ezyang EikanWang jgong5 wenzhe-nrv [ghstack-poisoned]
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@pytorchbot merge |
Merge startedYour change will be merged once all checks pass (ETA 0-4 Hours). Learn more about merging in the wiki. Questions? Feedback? Please reach out to the PyTorch DevX Team |
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Example below. You need to trace your function with DTensor inputs in order for the graph proxies to run on DTensor (and not the inner local tensor). You also need to run with
tracing_mode="fake", or with your ownFakeTensorMode, to see the nice DTensor printing. If this doesn't feel very ergonomic then maybe we can find some better UX for printing a graph with DTensor in it:Stack from ghstack (oldest at bottom):
cc @H-Huang @awgu @wanchaol @fegin @fduwjj @wz337 @wconstab @d4l3k @pragupta @msaroufim @dcci @ezyang @EikanWang @jgong5 @wenzhe-nrv