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https://github.com/pytorch/pytorch.git
synced 2025-10-20 21:14:14 +08:00
WIP Support python slicing with data depedennt inptu tensors maybe
ghstack-source-id: 4abcd9a5a4de65fa0d205c0b101998f48f6d9655 Pull Request resolved: https://github.com/pytorch/pytorch/pull/165074
This commit is contained in:
Laith Sakka
@ -528,30 +528,6 @@ Attempted to call function marked as skipped
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f(x)
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self.assertEqual(len(ws), 2)
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def test_slice_with_tensor(self):
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def fn(x, y):
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return x[:y]
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self.assertExpectedInlineMunged(
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Unsupported,
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lambda: torch.compile(fn, backend="eager", fullgraph=True)(
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torch.randn(10),
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torch.tensor([3]),
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),
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"""\
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Dynamic slicing with Tensor arguments
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Explanation: Creating slices with Tensor arguments is not supported. e.g. `l[:x]`, where `x` is a 1-element tensor.
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Hint: It may be possible to write Dynamo tracing rules for this code. Please report an issue to PyTorch if you encounter this graph break often and it is causing performance issues.
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Developer debug context: SliceVariable start: ConstantVariable(NoneType: None), stop: LazyVariableTracker(realized: TensorVariable()), step: ConstantVariable(NoneType: None)
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For more details about this graph break, please visit: https://meta-pytorch.github.io/compile-graph-break-site/gb/gb0038.html
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from user code:
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File "test_error_messages.py", line N, in fn
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return x[:y]""",
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)
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def test_observed_exception(self):
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def fn():
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raise RuntimeError("test")
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@ -3799,6 +3799,120 @@ def forward(self, arg0_1: "i64[2][1]cpu", arg1_1: "Sym(u2)", arg2_1: "Sym(u3)",
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def test_unbacked_slice_with_step_cpp_wrapper(self):
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self.test_unbacked_slice_with_step()
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@fresh_cache()
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@torch._dynamo.config.patch("capture_scalar_outputs", True)
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def test_slice_with_tensor_indices(self):
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# Test slicing with tensor start/stop/step on RHS (reading)
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# Test 1: Basic slice with tensor start and stop
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def f1(x, start_t, stop_t):
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return x[start_t:stop_t]
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x = torch.randn(20)
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start_t = torch.tensor(5)
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stop_t = torch.tensor(15)
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fn1 = torch.compile(f1, fullgraph=True, backend="inductor")
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self.assertTrue(torch.allclose(fn1(x, start_t, stop_t), f1(x, start_t, stop_t)))
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# Test 2: Slice with tensor step
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def f2(x, start_t, stop_t, step_t):
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return x[start_t:stop_t:step_t]
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step_t = torch.tensor(2)
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fn2 = torch.compile(f2, fullgraph=True, backend="inductor")
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self.assertTrue(
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torch.allclose(
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fn2(x, start_t, stop_t, step_t), f2(x, start_t, stop_t, step_t)
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)
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)
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# Test 3: Slice with only tensor start
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def f3(x, start_t):
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return x[start_t:]
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fn3 = torch.compile(f3, fullgraph=True, backend="inductor")
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self.assertTrue(torch.allclose(fn3(x, start_t), f3(x, start_t)))
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# Test 4: Slice with only tensor stop
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def f4(x, stop_t):
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return x[:stop_t]
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fn4 = torch.compile(f4, fullgraph=True, backend="inductor")
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self.assertTrue(torch.allclose(fn4(x, stop_t), f4(x, stop_t)))
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# Test 5: Negative indices with tensors
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def f5(x, start_t):
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return x[start_t:-1]
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start_t_neg = torch.tensor(-10)
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fn5 = torch.compile(f5, fullgraph=True, backend="inductor")
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self.assertTrue(torch.allclose(fn5(x, start_t_neg), f5(x, start_t_neg)))
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# Test 6: Multidimensional slice with tensor indices
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def f6(x, start_t, stop_t):
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return x[:, start_t:stop_t]
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x_2d = torch.randn(10, 20)
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fn6 = torch.compile(f6, fullgraph=True, backend="inductor")
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self.assertTrue(
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torch.allclose(fn6(x_2d, start_t, stop_t), f6(x_2d, start_t, stop_t))
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)
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@fresh_cache()
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@torch._dynamo.config.patch("capture_scalar_outputs", True)
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@torch._inductor.config.patch("cpp_wrapper", True)
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def test_slice_with_tensor_indices_cpp_wrapper(self):
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self.test_slice_with_tensor_indices()
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@fresh_cache()
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@torch._dynamo.config.patch("capture_scalar_outputs", True)
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def test_select_with_tensor_index(self):
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# Test direct tensor indexing (select) without calling .item()
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# # Test 1: Simple 0-d tensor as index
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# def f1(x, idx_tensor):
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# return x[idx_tensor]
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# x = torch.randn(10)
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idx_tensor = torch.tensor(5)
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# fn1 = torch.compile(f1, fullgraph=True, backend="inductor")
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# self.assertTrue(torch.allclose(fn1(x, idx_tensor), f1(x, idx_tensor)))
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# # Test 2: Negative tensor index
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# def f2(x, idx_tensor):
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# return x[idx_tensor]
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# idx_tensor_neg = torch.tensor(-2)
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# fn2 = torch.compile(f2, fullgraph=True, backend="inductor")
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# self.assertTrue(torch.allclose(fn2(x, idx_tensor_neg), f2(x, idx_tensor_neg)))
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# Test 3: Multidimensional select with tensor index
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def f3(x, idx_tensor):
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return x[:, idx_tensor]
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x_2d = torch.randn(5, 10)
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fn3 = torch.compile(f3, fullgraph=True, backend="inductor")
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self.assertTrue(torch.allclose(fn3(x_2d, idx_tensor), f3(x_2d, idx_tensor)))
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# # Hit inductor error
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# u7 = u6 + 1 * (u5 + 12 if u5 < 0 else u5)
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# NameError: name 'u6' is not defined
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# # Test 4: Multiple tensor indices
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# def f4(x, idx1, idx2):
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# return x[idx1, idx2]
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# x_2d = torch.randn(8, 12)
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# idx1 = torch.tensor(3)
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# idx2 = torch.tensor(7)
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# fn4 = torch.compile(f4, fullgraph=True, backend="inductor")
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# self.assertTrue(torch.allclose(fn4(x_2d, idx1, idx2), f4(x_2d, idx1, idx2)))
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@fresh_cache()
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@torch._dynamo.config.patch("capture_scalar_outputs", True)
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@torch._inductor.config.patch("cpp_wrapper", True)
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def test_select_with_tensor_index_cpp_wrapper(self):
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self.test_select_with_tensor_index()
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@fresh_cache()
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@torch._dynamo.config.patch("capture_scalar_outputs", True)
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def test_tensor_split(self):
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@ -3138,7 +3138,7 @@ class InstructionTranslatorBase(
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def BUILD_SLICE(self, inst: Instruction) -> None:
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items = self.popn(inst.argval)
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self.push(SliceVariable(items))
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self.push(SliceVariable(items, tx=self))
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def BUILD_LIST(self, inst: Instruction) -> None:
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items = self.popn(inst.argval)
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@ -1795,7 +1795,7 @@ class VariableBuilder:
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]
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self.install_guards(GuardBuilder.TYPE_MATCH)
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if isinstance(value, slice):
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return SliceVariable(items, source=self.source)
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return SliceVariable(items, self.tx, source=self.source)
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else:
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return RangeVariable(items, source=self.source)
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@ -1235,6 +1235,53 @@ class BuiltinVariable(VariableTracker):
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# scenario is to de-sugar eagerly.
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fn, args = IN_PLACE_DESUGARING_MAP[fn], [args[0], args[1]]
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# Convert size-1 TensorVariable indices to SymIntVariable by calling .item()
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# This decomposes tensor[t] to u=t.item(); tensor[u] at the dynamo level
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# Only convert if the tensor doesn't contain unbacked symints (data-dependent values)
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if fn is operator.getitem and len(args) == 2:
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from torch.fx.experimental.symbolic_shapes import has_free_symbols
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def should_convert_to_item(tensor_idx):
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"""Check if we should convert size-1 tensor to scalar."""
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if not isinstance(tensor_idx, variables.TensorVariable):
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return False
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# Only convert if size-1 or 0-d
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if tensor_idx._size is None or not all(
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s == 1 for s in tensor_idx._size
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):
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return False
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# Don't convert if it has unbacked symints (data-dependent)
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example_value = tensor_idx.proxy.node.meta.get("example_value")
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return example_value is None or not has_free_symbols(example_value)
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index_arg = args[1]
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if isinstance(
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index_arg, variables.TensorVariable
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) and should_convert_to_item(index_arg):
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args = list(args)
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args[1] = index_arg.call_method(tx, "item", [], {})
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args = tuple(args)
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elif isinstance(
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index_arg, (variables.TupleVariable, variables.ListVariable)
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):
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# Multi-dimensional indexing: tensor[:, idx] or tensor[idx1, idx2]
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new_items = []
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changed = False
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for item in index_arg.items:
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if should_convert_to_item(item):
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new_items.append(item.call_method(tx, "item", [], {}))
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changed = True
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else:
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new_items.append(item)
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if changed:
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args = list(args)
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args[1] = (
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variables.TupleVariable(new_items)
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if isinstance(index_arg, variables.TupleVariable)
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else variables.ListVariable(new_items)
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)
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args = tuple(args)
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if fn is operator.getitem and isinstance(args[1], SymNodeVariable):
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# Standard indexing will force specialization due to
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# __index__. Rewrite as a regular torch op which will
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@ -1247,6 +1294,83 @@ class BuiltinVariable(VariableTracker):
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args[1],
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],
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)
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elif fn is operator.getitem and isinstance(
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args[1], variables.TupleVariable
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):
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# Handle tuple with SymNodeVariables: x[symint1, symint2] or x[:, symint]
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# Decompose into sequential operations, tracking dimension changes
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index_items = args[1].items
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if any(isinstance(item, SymNodeVariable) for item in index_items):
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result = args[0]
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dims_removed = 0 # Track how many dimensions have been removed
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for original_dim, item in enumerate(index_items):
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# Current dimension in the result tensor
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current_dim = original_dim - dims_removed
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if isinstance(item, SymNodeVariable):
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# Apply torch.select at current_dim (removes this dimension)
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result = variables.TorchInGraphFunctionVariable(
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torch.select
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).call_function(
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tx,
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[
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result,
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variables.ConstantVariable.create(current_dim),
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item,
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],
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{},
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)
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dims_removed += 1
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elif isinstance(item, variables.SliceVariable):
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# Slicing keeps the dimension
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result = variables.BuiltinVariable(
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operator.getitem
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).call_function(tx, [result, item], {})
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else:
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# Regular scalar index (also removes dimension)
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result = variables.BuiltinVariable(
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operator.getitem
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).call_function(tx, [result, item], {})
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dims_removed += 1
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return result
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elif fn is operator.getitem and isinstance(
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args[1], (variables.TupleVariable, variables.ListVariable)
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):
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# Check if we have SymNodeVariable inside tuple: tensor[:, symnode]
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# Rewrite as torch.select to avoid DDE
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index_items = args[1].items
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symnode_indices = [
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i
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for i, item in enumerate(index_items)
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if isinstance(item, SymNodeVariable)
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]
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if len(symnode_indices) == 1:
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# Single SymNode in tuple - rewrite as torch.select
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symnode_idx = symnode_indices[0]
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symnode = index_items[symnode_idx]
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# Check that all other indices are slices or ellipsis
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non_symnode_indices = [
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i for i in range(len(index_items)) if i != symnode_idx
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]
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if all(
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isinstance(
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index_items[i],
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(variables.SliceVariable, variables.ConstantVariable),
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)
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for i in non_symnode_indices
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):
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fn, args = (
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torch.select,
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[
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args[0],
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variables.ConstantVariable.create(symnode_idx),
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symnode,
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],
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)
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# Interaction between ndarray and tensors:
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# We prefer the tensor op whenever there are tensors involved
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@ -1745,7 +1869,7 @@ class BuiltinVariable(VariableTracker):
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)
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def call_slice(self, tx: "InstructionTranslator", *args):
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return variables.SliceVariable(args)
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return variables.SliceVariable(args, tx)
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def _dyn_proxy(self, tx: "InstructionTranslator", *args, **kwargs):
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from .builder import wrap_fx_proxy
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@ -1386,7 +1386,7 @@ class NamedTupleVariable(TupleVariable):
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class SliceVariable(VariableTracker):
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def __init__(self, items, **kwargs) -> None:
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def __init__(self, items, tx=None, **kwargs) -> None:
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items_to_map = items
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start, stop, step = [variables.ConstantVariable.create(None)] * 3
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@ -1399,18 +1399,27 @@ class SliceVariable(VariableTracker):
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else:
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raise AssertionError
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if isinstance(start, variables.TensorVariable) or isinstance(
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stop, variables.TensorVariable
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):
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unimplemented_v2(
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gb_type="Dynamic slicing with Tensor arguments",
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context=f"SliceVariable start: {start}, stop: {stop}, step: {step}",
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explanation="Creating slices with Tensor arguments is not supported. "
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"e.g. `l[:x]`, where `x` is a 1-element tensor.",
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hints=[
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*graph_break_hints.SUPPORTABLE,
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],
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# Convert TensorVariable to SymIntVariable by calling .item()
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# This decomposes a[:t] to u=t.item(); a[:u] at the dynamo level
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if isinstance(start, variables.TensorVariable):
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assert tx is not None, (
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"tx is required when slice indices are TensorVariables"
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)
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assert start.size is None or all(s == 1 for s in start.size)
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start = start.call_method(tx, "item", [], {})
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if isinstance(stop, variables.TensorVariable):
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assert tx is not None, (
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"tx is required when slice indices are TensorVariables"
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)
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assert stop.size is None or all(s == 1 for s in stop.size)
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stop = stop.call_method(tx, "item", [], {})
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if isinstance(step, variables.TensorVariable):
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assert tx is not None, (
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"tx is required when slice indices are TensorVariables"
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)
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assert step.size is None or all(s == 1 for s in step.size)
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step = step.call_method(tx, "item", [], {})
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self.items = (start, stop, step)
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super().__init__(**kwargs)
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