[converter] Support SymInt repeats in aten.repeat lowering#15
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gokulkrishna98 merged 4 commits intoJun 16, 2026
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…amic shapes Six related fixes that surface together when exporting models whose FX graphs combine SymInt-derived shape arithmetic with mixed source element types and ranks: 1. _aten_to_core_resolver / replace_binary_ops _op_map: register a bare 'pow' entry alongside the variant-suffixed ones. Some torch.export rewrites leave ``aten.pow`` as the OpOverloadPacket target with no overload suffix; without this entry the converter raises ``Unsupported ATen op: pow``. 2. Same registries for bare 'round': torch.export can leave ``aten.round`` without a ``.default`` overload, mirroring the pow case. 3. upsample_build_output_shape_dynamic: ensure each (out_h, out_w) operand is rank-1 with int32 element type before the concat that builds the output shape — the dialect verifier rejects mixed-rank / mixed-element-type concat inputs. Hits when out_h/out_w are SymInts derived from ``round(SymFloat)`` arithmetic. 4. get_operand mixed-list path (SymInt + plain int): normalise each resolved Value to the same canonical rank-1 si32 form and emit plain-int constants with explicit ``dtype=np.int32`` so the dim-vector concat sees uniform operands. Hits ops like ``view``, ``expand``, ``reshape``, ``repeat`` whenever a dim list mixes SymInts with ints. 5. replace_cat: when one input has a dynamic non-concat axis and a sibling has a known static size for that axis, reshape the dynamic side to that static size before the concat. Localised shape inference using the fact that all non-concat dims must be equal — multiple distinct static sizes is left for the dialect verifier to reject. 6. replace_arange_start_step: unify start/end/step element types to the FX node's output dtype before ``coreai.range_``. Mirrors aten.arange's internal type promotion since coreai.range_'s verifier requires uniform element types. Adds a shared ``to_rank1_int32(v)`` helper in ``_utils.py`` so fixes 3 and 4 share one canonical normalization (rank-0 → rank-1, cast to signed int32 if needed); both call sites collapse to one line per operand. One regression test per non-trivial fix, each verified to FAIL without the fix and PASS with it (verified by reverting each fix individually): - TestRound: bare ``aten.round`` overload-packet target must lower. - TestUpsampleNearest2d / TestUpsampleBilinear2d::test_round_symfloat_size: ``round((num / aspect) ** 0.5) * 14`` output_size produces SymInts whose Value type doesn't match the int32 constants used elsewhere. Pre-fix: ``coreai.concat`` raises ``Operation creation failed``. - TestView::test_view_with_round_symfloat_dims: same trigger applied to a ``view([1, C, h, w])`` mixed list. Pre-fix: ``expected the same element type for all inputs to concat``. - TestCat::test_dynamic_vs_static_non_concat_axis: ``Dim.AUTO`` on one side + static sibling forces non-concat-axis promotion. - TestArange::test_symint_end_with_float_start_step: float ``arange`` with SymInt-derived end exercises the element-type unify.
…ill-dynamic siblings The previous replace_cat fix uses ``coreai.reshape(inp, new_shape)`` with a Python list to promote a dynamic non-concat axis to its known static size before the concat. The list form materializes the shape as an ``int32`` constant tensor, so every entry must be a real int — there is no slot for the dynamic-size sentinel. When a cat input has both a promotable axis (sibling has a known static size) and an axis that is dynamic on every input (no static sibling), post-promotion ``new_shape`` is a mix of concrete ints and the dynamic sentinel; passing it to the list-form reshape raises ``OverflowError: Python integer -9223372036854775808 out of bounds for int32``. Split ``replace_cat`` into two reshape paths: - All axes static post-promotion: keep the list-form reshape. - Some axes still dynamic post-promotion: build the shape vector at runtime — ``coreai.get_shape(inp)`` for the still-dynamic axes, ``coreai.constant`` slices for the promoted axes, concat along axis 0 to get a rank-1 ``int32`` Value, and pass that to Value-form ``coreai.ReshapeOp`` with a partially-static result type. Adds a numerical regression that exercises the mixed path: a 4-D cat where one input has a sibling-promotable axis and another axis that stays dynamic on every input. Verified to fail with the list-form-only fix and pass with this delta.
… dims The previous ``replace_repeat`` lowering called ``np.array(node.args[1], dtype=np.uint32)`` on the repeats list. Under dynamic shapes, ``tensor.repeat(...)`` can be called with at least one entry derived from a symbolic shape; that entry appears in the FX graph as a ``torch.fx.Node``, and ``np.array`` raises ``TypeError: int() argument must be a string ... not 'Node'`` trying to coerce it. Detect any ``fx.Node`` in the repeats list and take a dynamic path: build a rank-1 ``uint32`` dim vector at runtime by emitting per-axis ``coreai.constant`` chunks for plain ints and the resolved Value (cast to ``uint32``, lifted to rank-1 if scalar) for SymInts, concat along axis 0, and pass the resulting Value to ``coreai.tile`` (which accepts a runtime Value for its dims). The all-static fast path keeps the existing ``np.array(...)`` form. Adds an IR FileCheck test pinning the dynamic-tile shape and a numerical-output test. Both fail without the fix (the IR test reproduces the ``TypeError`` above).
gokulkrishna98
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Lewis300,
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cymbalrush and
jakesabathia2
Lewis300
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Jun 16, 2026
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