[OUTDATED!, Autograd] Cond Higher-Order Operation #126007
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/126007
Note: Links to docs will display an error until the docs builds have been completed. ❌ 40 New Failures, 5 Unrelated FailuresAs of commit b5a2b34 with merge base ee804d2 ( NEW FAILURES - The following jobs have failed:
FLAKY - The following jobs failed but were likely due to flakiness present on trunk:
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ydwu4
reviewed
May 14, 2024
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Overall looks good. Should probably fix existing test failures first and add more tests for the following cases:
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More scenerios for autograd beyond a simple function: e.g. 1. map + cond, 2. cond + nn modules (with parameters and buffers), closures, 3. nested cond autograd and others (can use your imagination to come up with more).
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Tracing the forward and backward graph with make_fx (with different tracing mode) for cases listed in a, also use expectedInline tests to make sure the produced forward and backward graphs are correct (we have examples for map).
| @@ -57,6 +57,8 @@ if(CMAKE_VERSION VERSION_GREATER_EQUAL 3.12.0) | |||
| endif() | |||
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| find_package(CUDAToolkit REQUIRED) | |||
| add_library(CUDA::nvToolsExt INTERFACE IMPORTED) | |||
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Why do we want to change this?
| from torch._higher_order_ops.map import ( # noqa: F401 | ||
| _stack_pytree, | ||
| _unstack_pytree, | ||
| map, | ||
| ) | ||
| from torch._higher_order_ops.cond import ( # noqa: F401 |
| result = cond(pred, true_fn, false_fn, [x]) | ||
| self.assertEqual(result, torch.cos(x)) | ||
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| grad_out = torch.ones_like(result) |
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can you also use "assertExpectedInline" to show the forward and backward graph? Similar as what we did for map.
| def test_cond_make_fx_preserve_stack_trace_for_nodes_in_subgraph(self): | ||
| def true_fn(x): | ||
| return x + x.cos() | ||
| # def test_cond_make_fx_preserve_stack_trace_for_nodes_in_subgraph(self): |
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Does this test starts to fail because of the change? Why is that?
| @@ -1912,7 +1912,8 @@ def fn(model: Callable): | |||
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| from torch import export as export | |||
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| from torch._higher_order_ops import cond | |||
| # from torch._higher_order_ops import cond | |||
| num_mapped_args = len(operands) | ||
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| unwrapped_mapped_operands = pytree.tree_map(_from_fun, operands) | ||
| # example_operands = _unstack_pytree(unwrapped_mapped_operands)[0] |
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yeah, I don't think we need _unstack_pytree because for cond, the operands are already flattened by dynamo (i.e. becomes a sequence of tensors) by the time we entering autograd key of cond.
| fw_true_graph = make_fx(true_fn)(*example_operands) | ||
| fw_false_graph = make_fx(false_fn)(*example_operands) | ||
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| def joint_f(fn, *joint_mapped_args): |
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Didn't look too carefully into the implementation of these. Can you add more tests to make check whether the true graph and false graph creates correct backward graph?
| @@ -256,6 +256,23 @@ def false_fn(x): | |||
| pred = torch.tensor(False, device="cuda") | |||
| result = cond(pred, true_fn, false_fn, [x]) | |||
| self.assertEqual(result, torch.cos(x)) | |||
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| def test_cond_autograd_simple(self): | |||
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Can we also add opinfo tests for cond here https://github.com/pytorch/pytorch/blob/main/torch/testing/_internal/hop_db.py? Following the map autograd tests.
bohnstingl
changed the title
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This PR is intended to provide the Autograd mechanism for the
condoperation.@ydwu4