fix(dynamo): keep the complex decomposition inside the TRT op set

py/torch_tensorrt/dynamo/lowering/passes/complex_decomposition_adapter.py

@@ -8,9 +8,13 @@
   1. capture the complex I/O signature before rewriting (drives the boundary
      adapters in ``_compiler._insert_complex_io_adapters``);
   2. call upstream to expand every complex op into real ops on separate re/im;
-  3. normalize the SoA seams (``aten.complex`` / ``aten.real`` / ``aten.imag``)
-     into the interleaved ``[..., 2]`` layout the rest of the TRT flow expects,
-     so the engine only ever sees real tensors (Option A in the RFC).
+  3. put back what the retrace undid: upstream returns a module rebuilt from
+     scratch, so the decomposition and constant-folding stages that ran earlier
+     find their work partly reverted;
+  4. normalize the seams (``aten.view_as_real`` / ``aten.complex`` /
+     ``aten.real`` / ``aten.imag``) into the interleaved ``[..., 2]`` layout the
+     rest of the TRT flow expects, so the engine only ever sees real tensors
+     (Option A in the RFC).
 
 Gated by ``settings.use_complex_decomposition``; falls back to the legacy pass on
 older torch (the upstream API only exists in torch>=2.14.0.dev), or if
@@ -26,6 +30,10 @@
 from torch.fx import GraphModule
 from torch_tensorrt._features import has_complex_decomposition
 from torch_tensorrt.dynamo._settings import CompilationSettings
+from torch_tensorrt.dynamo.lowering.passes.constant_folding import constant_fold
+from torch_tensorrt.dynamo.lowering.passes.mark_constant_fold_exclusions import (
+    mark_constant_fold_exclusions,
+)
 from torch_tensorrt.dynamo.lowering.passes.pass_utils import (
     clean_up_graph_after_modifications,
 )
@@ -91,7 +99,9 @@ def complex_decomposition_adapter(
         # call either fully succeeds or raises; it never partially mutates
         # anything, since it builds a new GraphModule rather than editing
         # the existing one in place).
-        decomposed_gm = decompose_complex_in_graph(gm, flat_args)
+        decomposed_gm = decompose_complex_in_graph(
+            gm, flat_args, decompositions=_trt_decomposition_table(settings)
+        )
     except Exception as e:
         # decompose_complex_in_graph is upstream, experimental PyTorch code
         # (torch._functorch._aot_autograd.complex_decomposition) with
@@ -107,11 +117,18 @@ def complex_decomposition_adapter(
         return complex_graph_detection(gm, settings)
     gm = decomposed_gm
 
-    # (3) Normalize SoA seams into the interleaved [..., 2] layout used by TRT.
+    # (3) Fold constants again. The retrace builds the module from scratch, so
+    #     a constant that the constant-folding stage had already reduced to one
+    #     frozen tensor comes back as a live chain of ops reading it, and the
+    #     converters for those ops expect a TRT tensor rather than a weight.
+    gm = mark_constant_fold_exclusions(gm, settings)
+    gm = constant_fold(gm, settings)
+
+    # (4) Normalize the seams into the interleaved [..., 2] layout used by TRT.
     gm = _normalize_complex_boundary_for_trt(gm)
     gm = clean_up_graph_after_modifications(gm)
 
-    # (4) Re-attach the captured I/O signature onto the RETURNED module so
+    # (5) Re-attach the captured I/O signature onto the RETURNED module so
     #     _insert_complex_io_adapters can restore the complex boundary.
     gm.meta["complex_output_indices"] = complex_output_indices
     gm.meta["complex_input_names"] = complex_input_names
@@ -123,6 +140,29 @@ def complex_decomposition_adapter(
     return gm
 
 
+def _trt_decomposition_table(
+    settings: CompilationSettings,
+) -> dict[Any, Any]:
+    """The op set the rest of the TRT flow expects to see.
+
+    ``decompose_complex_in_graph`` re-traces through ``make_fx``, so the module
+    it returns is built from whatever ``ComplexTensor`` dispatched to, not from
+    the ops that survived the decomposition run at export time. Without a table
+    that retrace re-introduces ops the flow has already decided to expand
+    (``aten.stack``) or to drop (``aten.alias``), long after the stage that
+    would have handled them, and the partitioner then cuts the graph around
+    them. Handing the retrace the same table keeps the two in step.
+    """
+    from torch_tensorrt.dynamo.lowering import get_decompositions
+
+    return get_decompositions(
+        settings.enable_experimental_decompositions,
+        settings.decompose_attention,
+        settings.use_distributed_mode_trace,
+        use_fp32_acc=settings.use_fp32_acc,
+    )
+
+
 def _graph_has_complex(gm: GraphModule) -> bool:
     from torch_tensorrt.dynamo.utils import COMPLEX_DTYPES
 
@@ -174,20 +214,30 @@ def _fake_flat_args(gm: GraphModule) -> list[torch.Tensor]:
 
 
 def _normalize_complex_boundary_for_trt(gm: GraphModule) -> GraphModule:
-    """Fold upstream's SoA seams into the interleaved ``[..., 2]`` real layout.
+    """Fold upstream's seams into the interleaved ``[..., 2]`` real layout.
 
-    Upstream leaves the graph in terms of separate re/im joined by
-    ``aten.complex(re, im)`` and unpacked by ``aten.real`` / ``aten.imag``.  TRT
-    has no converter for any of those.  We:
+    Every op below reinterprets a complex value as a pair of real halves, or
+    the other way round, and TRT has no converter for any of them.  Leaving one
+    in the graph does not just fail an op check: the partitioner cuts the graph
+    around it and the model comes back split into several engines with PyTorch
+    blocks in between.  We rewrite:
 
+      * ``view_as_real(z)``  -> the interleaved ``[..., 2]`` tensor it would
+        have returned, for each producer ``z`` can have (see Pass A)
       * ``real(z)`` / ``imag(z)`` where ``z = complex(re, im)``  -> re / im
         (cancel the pack/unpack round-trip)
-      * remaining ``aten.complex(re, im)``  -> ``stack([re, im], -1)`` tagged as
+      * remaining ``aten.complex(re, im)``  -> ``cat([re, im], -1)`` tagged as
         complex-layout, so the [..., 2] tensor flows to the boundary adapters.
 
-    Confirmed against real decompose_complex_in_graph output: the boundary op
-    set is aten.complex/real/imag as assumed above, unpacked via real/imag
-    (not view_as_real).
+    Which of these appear depends on where the complex region sits.  A graph
+    whose own inputs or outputs are complex meets upstream at ``aten.complex``,
+    because that is what ``ComplexTensor`` packs with on the way out.  A graph
+    that is real on both ends but complex in the middle, as a rotary embedding
+    is, meets it at ``view_as_real`` instead: the entry ``view_as_complex`` is
+    left alone (its argument is a plain real tensor, so ``ComplexTensor`` never
+    sees it), and ``ComplexTensor`` splits its halves with ``torch.real`` /
+    ``torch.imag``, which are composite ops that expand to ``view_as_real``
+    plus ``select`` before any subclass can intercept them.
     """
     g = gm.graph
     aten = torch.ops.aten
@@ -210,7 +260,43 @@ def _propagate_meta(node: torch.fx.Node) -> None:
         with fake_mode:
             node.meta["val"] = node.target(*arg_vals)
 
-    # Pass A: cancel real(complex(re,im)) / imag(complex(re,im)) round-trips.
+    def _pack_interleaved(re: Any, im: Any) -> torch.fx.Node:
+        """Build the ``[..., 2]`` tensor holding ``re`` and ``im`` side by side.
+
+        Callers set the insertion point.  ``cat`` of two unsqueezed halves
+        rather than ``stack`` because the stage that expands ``stack`` has
+        already run by the time this pass does.
+        """
+        re_u = g.call_function(aten.unsqueeze.default, (re, -1))
+        _propagate_meta(re_u)
+        im_u = g.call_function(aten.unsqueeze.default, (im, -1))
+        _propagate_meta(im_u)
+        packed = g.call_function(aten.cat.default, ([re_u, im_u], -1))
+        _propagate_meta(packed)
+        return packed
+
+    # Pass A: rewrite view_as_real over each producer it can have.
+    for node in list(g.nodes):
+        if node.op != "call_function" or node.target != aten.view_as_real.default:
+            continue
+        src = node.args[0]
+        if not isinstance(src, torch.fx.Node):
+            continue
+        if src.target == aten.view_as_complex.default:
+            # view_as_complex consumes a trailing dimension of 2 and
+            # view_as_real puts it back holding the same values, so the pair is
+            # an identity on the real tensor that went in.  Dropping it leaves
+            # the halves to be picked out of that tensor directly.
+            replacement = src.args[0]
+        elif src.target == aten.complex.default:
+            with g.inserting_before(node):
+                replacement = _pack_interleaved(src.args[0], src.args[1])
+        else:
+            continue
+        node.replace_all_uses_with(replacement)
+        g.erase_node(node)
+
+    # Pass B: cancel real(complex(re,im)) / imag(complex(re,im)) round-trips.
     for node in list(g.nodes):
         if node.op != "call_function" or node.target not in (
             aten.real.default,
@@ -233,18 +319,12 @@ def _propagate_meta(node: torch.fx.Node) -> None:
             node.replace_all_uses_with(src.args[idx])
             g.erase_node(node)
 
-    # Pass B: turn surviving aten.complex(re, im) into stack([re, im], -1).
+    # Pass C: turn surviving aten.complex(re, im) into the interleaved layout.
     for node in list(g.nodes):
         if node.op != "call_function" or node.target != aten.complex.default:
             continue
-        re, im = node.args[0], node.args[1]
         with g.inserting_before(node):
-            re_u = g.call_function(aten.unsqueeze.default, (re, -1))
-            _propagate_meta(re_u)
-            im_u = g.call_function(aten.unsqueeze.default, (im, -1))
-            _propagate_meta(im_u)
-            packed = g.call_function(aten.cat.default, ([re_u, im_u], -1))
-            _propagate_meta(packed)
+            packed = _pack_interleaved(node.args[0], node.args[1])
         packed.meta["is_complex_layout"] = True
         node.replace_all_uses_with(packed)
         g.erase_node(node)

tests/py/dynamo/conversion/test_cumsum_aten.py

@@ -33,9 +33,7 @@ def forward(self, x):
                 )
             return
 
-        self.run_test(
-            Cumsum(), inputs, immutable_weights=False, use_dynamo_tracer=True
-        )
+        self.run_test(Cumsum(), inputs, immutable_weights=False, use_dynamo_tracer=True)
 
     @parameterized.expand(
         [
@@ -108,10 +106,7 @@ def forward(self, x):
             == opt_shape[positive_dim]
             == max_shape[positive_dim]
         )
-        if (
-            has_static_trip_count
-            and not is_tensorrt_rtx_version_supported("1.7")
-        ):
+        if has_static_trip_count and not is_tensorrt_rtx_version_supported("1.7"):
             with self.assertRaises(UnsupportedOperatorException):
                 self.run_test_with_dynamic_shape(
                     Cumsum(),

tests/py/dynamo/lowering/test_complex_decomposition_adapter.py

@@ -3,9 +3,10 @@
 These tests exercise the TRT-specific glue around PyTorch's upstream complex
 decomposition -- they do NOT require a GPU or a TRT build:
 
-  * _normalize_complex_boundary_for_trt: folds the aten.complex / real / imag
-    seams that decompose_complex_in_graph leaves behind into the interleaved
-    [..., 2] real layout the rest of the TRT flow expects.
+  * _normalize_complex_boundary_for_trt: folds the aten.view_as_real /
+    aten.complex / real / imag seams that decompose_complex_in_graph leaves
+    behind into the interleaved [..., 2] real layout the rest of the TRT flow
+    expects.
   * the torch-version feature gate: when the upstream API is unavailable the
     adapter must fall back to the legacy complex_graph_detection pass.
 """
@@ -81,6 +82,118 @@ def forward(self, re, im):
     assert torch.equal(out[..., 1], im)
 
 
+def test_normalize_folds_view_as_real_of_view_as_complex():
+    """view_as_real(view_as_complex(x)) -> x, and both nodes are erased."""
+
+    class M(torch.nn.Module):
+        def forward(self, x):
+            z = torch.ops.aten.view_as_complex.default(x)
+            return torch.ops.aten.view_as_real.default(z)
+
+    from torch_tensorrt.dynamo.lowering.passes.pass_utils import (
+        clean_up_graph_after_modifications,
+    )
+
+    x = torch.randn(3, 2)
+    gm = torch.fx.symbolic_trace(M())
+
+    gm = cda._normalize_complex_boundary_for_trt(gm)
+    # The view_as_complex node is left without users, and the adapter runs this
+    # right after the pass to collect nodes in exactly that state.
+    gm = clean_up_graph_after_modifications(gm)
+    targets = _targets(gm)
+
+    assert aten.view_as_real.default not in targets
+    assert aten.view_as_complex.default not in targets
+    assert torch.equal(gm(x), x)
+
+
+def test_normalize_folds_view_as_real_of_complex():
+    """view_as_real(complex(re,im)) -> the interleaved [..., 2] layout.
+
+    Without this, Pass C would rewrite the complex() node into that layout and
+    leave a view_as_real reading it, which is no longer a complex tensor.
+    """
+
+    class M(torch.nn.Module):
+        def forward(self, re, im):
+            z = torch.ops.aten.complex.default(re, im)
+            return torch.ops.aten.view_as_real.default(z)
+
+    re = torch.randn(3)
+    im = torch.randn(3)
+    gm = torch.fx.symbolic_trace(M())
+
+    gm = cda._normalize_complex_boundary_for_trt(gm)
+    targets = _targets(gm)
+
+    assert aten.view_as_real.default not in targets
+    assert aten.complex.default not in targets
+
+    out = gm(re, im)
+    assert out.shape == (3, 2)
+    assert torch.equal(out[..., 0], re)
+    assert torch.equal(out[..., 1], im)
+
+
+# ---------------------------------------------------------------------------
+# whole pass over a graph that is real at both ends and complex in the middle
+# ---------------------------------------------------------------------------
+
+
+@pytest.mark.skipif(
+    not cda.has_complex_decomposition(),
+    reason="decompose_complex_in_graph requires torch>=2.14.dev",
+)
+def test_interior_complex_region_leaves_nothing_trt_cannot_convert():
+    """A rotary-embedding shaped graph must come out with real ops only.
+
+    The complex region here sits in the middle: the graph takes a real tensor
+    and returns one, so upstream meets it at view_as_real rather than at
+    aten.complex, and its retrace also brings back ops the decomposition and
+    constant-folding stages had already dealt with. Any of those left behind
+    is not just an unconverted op: the partitioner cuts the graph around it and
+    the model comes back as several engines with PyTorch blocks in between.
+    """
+    from torch_tensorrt.dynamo._settings import CompilationSettings
+    from torch_tensorrt.dynamo.lowering import get_decompositions
+
+    class M(torch.nn.Module):
+        def __init__(self):
+            super().__init__()
+            self.register_buffer(
+                "freqs",
+                torch.complex(torch.randn(1, 2, 1, 1), torch.randn(1, 2, 1, 1)),
+            )
+
+        def forward(self, x):
+            z = torch.view_as_complex(x.reshape(1, 2, 1, 1, 2))
+            return torch.view_as_real(z * self.freqs)
+
+    model = M().eval()
+    x = torch.randn(1, 2, 1, 2)
+    expected = model(x)
+
+    gm = (
+        torch.export.export(model, (x,))
+        .run_decompositions(get_decompositions())
+        .module()
+    )
+    out = cda.complex_decomposition_adapter(gm, CompilationSettings())
+
+    targets = set(_targets(out))
+    for target in (
+        aten.view_as_complex.default,
+        aten.view_as_real.default,
+        aten.complex.default,
+        aten.alias.default,
+        aten.stack.default,
+    ):
+        assert target not in targets, f"{target} survived the pass"
+
+    torch.testing.assert_close(out(x), expected)
+
+
 # ---------------------------------------------------------------------------
 # feature gate / fallback
 # ---------------------------------------------------------------------------
@@ -172,7 +285,11 @@ def fake_decompose(g, flat_args, *a, **k):
     )
     monkeypatch.setattr(cda, "has_complex_decomposition", lambda: True)
 
-    out = cda.complex_decomposition_adapter(gm, settings=object())
+    # Real settings, not a stub: the adapter reads the decomposition options off
+    # them to build the table it hands to the retrace.
+    from torch_tensorrt.dynamo._settings import CompilationSettings
+
+    out = cda.complex_decomposition_adapter(gm, settings=CompilationSettings())
 
     # Functional: we did NOT get the original module back...
     assert out is not gm