Comprehensive Complex Numerics Support

.github/workflows/build-test-linux-x86_64.yml

@@ -107,7 +107,7 @@ jobs:
         set -euo pipefail
         pushd .
         cd tests/py/dynamo
-        python -m pytest -ra -n 8 --junitxml=${RUNNER_TEST_RESULTS_DIR}/l0_dynamo_converter_tests_results.xml  --dist=loadscope --maxfail=20 conversion/
+        python -m pytest -ra -n 8 --junitxml=${RUNNER_TEST_RESULTS_DIR}/l0_dynamo_converter_tests_results.xml  --maxfail=20 conversion/
         popd
 
   L0-dynamo-core-tests:
@@ -236,6 +236,7 @@ jobs:
         cd tests/py/dynamo
         python -m pytest -ra -n 8 --junitxml=${RUNNER_TEST_RESULTS_DIR}/l1_dynamo_core_tests_results.xml  runtime/test_001_*
         python -m pytest -ra -n 8 --junitxml=${RUNNER_TEST_RESULTS_DIR}/l1_dynamo_core_partitioning_tests_results.xml partitioning/test_001_*
+        python -m pytest -ra -n 8 --junitxml=${RUNNER_TEST_RESULTS_DIR}/l1_dynamo_hlo_tests_results.xml hlo/
 
         popd
 

.github/workflows/build-test-linux-x86_64_rtx.yml

@@ -107,7 +107,7 @@ jobs:
         set -euo pipefail
         pushd .
         cd tests/py/dynamo
-        python -m pytest -ra -n 8 --junitxml=${RUNNER_TEST_RESULTS_DIR}/l0_dynamo_converter_tests_results.xml  --dist=loadscope --maxfail=20 conversion/
+        python -m pytest -ra -n 8 --junitxml=${RUNNER_TEST_RESULTS_DIR}/l0_dynamo_converter_tests_results.xml  --maxfail=20 conversion/
         popd
 
   L0-dynamo-core-tests:
@@ -204,6 +204,7 @@ jobs:
         pushd .
         cd tests/py/dynamo
         python -m pytest -ra -n 8 --junitxml=${RUNNER_TEST_RESULTS_DIR}/l1_dynamo_core_tests_results.xml  runtime/test_001_*
+        python -m pytest -ra -n 8 --junitxml=${RUNNER_TEST_RESULTS_DIR}/l1_dynamo_hlo_tests_results.xml hlo/
         popd
 
   L1-dynamo-compile-tests:

.github/workflows/build-test-windows.yml

@@ -226,6 +226,7 @@ jobs:
         cd tests/py/dynamo
         ../../../packaging/vc_env_helper.bat python -m pytest -ra -n 8 --junitxml=${RUNNER_TEST_RESULTS_DIR}/l1_dynamo_core_tests_results.xml runtime/test_001_*
         ../../../packaging/vc_env_helper.bat python -m pytest -ra -n 8 --junitxml=${RUNNER_TEST_RESULTS_DIR}/l1_dynamo_core_partitioning_tests_results.xml partitioning/test_001_*
+        ../../../packaging/vc_env_helper.bat python -m pytest -ra -n 8 --junitxml=${RUNNER_TEST_RESULTS_DIR}/l1_dynamo_hlo_tests_results.xml hlo/
         popd
 
   L1-dynamo-compile-tests:

.github/workflows/build-test-windows_rtx.yml

@@ -200,6 +200,7 @@ jobs:
         pushd .
         cd tests/py/dynamo
         ../../../packaging/vc_env_helper.bat python -m pytest -ra -n 8 --junitxml=${RUNNER_TEST_RESULTS_DIR}/l1_dynamo_core_tests_results.xml runtime/test_001_*
+        ../../../packaging/vc_env_helper.bat python -m pytest -ra -n 8 --junitxml=${RUNNER_TEST_RESULTS_DIR}/l1_dynamo_hlo_tests_results.xml hlo/
         popd
 
   L1-dynamo-compile-tests:

docsrc/contributors/complex_number_support.rst

@@ -135,19 +135,244 @@ runtime modules handle the conversion:
 Key Implementation Invariants
 -------------------------------
 
-* **``originally_complex`` set** — the set of nodes that were complex-dtype
-  *before* any rewrites. After ``replace_input_node``, complex placeholders become
-  ``float32`` so ``is_complex_dtype()`` returns ``False``. The ``originally_complex``
-  set is used to decide which ``mul.Tensor`` nodes need the complex mul rewrite.
+* **``node.meta["is_complex_layout"]``** — every node that represents a complex
+  quantity (either originally complex-dtype, or a real ``(..., 2)`` tensor produced
+  by the rewriter) is annotated with ``node.meta["is_complex_layout"] = True``.
+  This annotation is set during the detection phase (before any rewrites begin) and
+  propagated by every rewrite handler as it emits new nodes.  It survives dtype
+  changes: after ``replace_input_node`` converts a ``placeholder`` from complex to
+  ``float32``, the dtype-based check ``is_complex_dtype()`` would return ``False``,
+  but the metadata flag remains.  ``_is_complex_layout_node(n)`` is simply
+  ``n.meta.get("is_complex_layout", False)`` — no shape heuristics or recursion.
 * **FakeTensorMode reuse** — ``propagate_metadata`` must use the ``FakeTensorMode``
   from existing placeholder fake tensors (not a fresh mode) to avoid mode-mismatch
   errors under ``torch.compile`` and to preserve SymInt for dynamic shapes.
 * **Dotted buffer names** — ``register_buffer`` rejects names containing ``.``.
   Nested submodule parameter names (e.g. ``layers.0.weight``) must have ``.``
   replaced with ``__`` before registration.
 
+The Decomposition System — How It Is Built
+-------------------------------------------
+
+The rewriter is split across two classes and wired together by a lightweight
+dispatch mechanism.  This section walks through each piece and explains the
+design decisions.
+
+ComplexOpDetector — Subgraph Discovery
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+``ComplexOpDetector`` walks the graph to find the set of nodes that participate
+in complex arithmetic.
+
+``node_include_in_subgraph``
+""""""""""""""""""""""""""""
+
+A node is included in a complex subgraph if:
+
+1. Its output dtype is ``complex64`` or ``complex128`` (``is_complex_dtype``), **or**
+2. Any of its inputs are complex (``has_complex_input``).
+
+The second condition is necessary to catch real-output ops — ``abs``, ``angle``,
+``real``, ``imag`` — whose inputs are complex.  These must be rewritten alongside
+the rest of the subgraph even though their outputs are real.
+
+``subgraph_from_anchor``
+""""""""""""""""""""""""
+
+For ``view_as_real``-bounded subgraphs, detection starts at a ``view_as_real``
+*anchor* node and performs a backward BFS:
+
+.. code-block:: text
+
+    view_as_real  ←  mul (complex)  ←  reshape  ←  placeholder (complex)
+         ↑ anchor         ↑ subgraph              ↑ subgraph     ↑ input
+
+At each step, if an upstream node satisfies ``node_include_in_subgraph`` it is
+added to the subgraph; otherwise it becomes an *input node* (the boundary).  The
+result is a ``ComplexSubGraphInfo`` containing anchor nodes, subgraph nodes, and
+input nodes.
+
+After collection the subgraph is **sorted in topological order** (by position in
+the graph's node list).  This is critical: without it a ``mul`` node could be
+processed before its ``sin`` or ``cos`` operands, causing the rewriter to see the
+original complex node instead of the already-rewritten real node.
+
+``find_complex_op_subgraphs`` and subgraph merging
+"""""""""""""""""""""""""""""""""""""""""""""""""""
+
+When a model has multiple ``view_as_real`` anchors that share upstream nodes
+(e.g. ``xq_out`` and ``xk_out`` in a RoPE layer both descend from the same
+``freqs_cis`` placeholder), their subgraphs would otherwise be detected
+separately.  ``find_complex_op_subgraphs`` merges overlapping subgraphs by
+set intersection so each node is rewritten exactly once.
+
+``find_all_complex_subgraphs`` — unbounded complex ops
+"""""""""""""""""""""""""""""""""""""""""""""""""""""""
+
+Some models produce a complex tensor as a graph *output* without passing it
+through ``view_as_real``.  ``find_all_complex_subgraphs`` is a forward scan that
+collects every ``call_function`` node with a complex output, regardless of
+anchoring.  The resulting subgraph is processed the same way as an
+anchor-bounded one.
+
+ComplexGraphRewriter — Dispatch-Based Rewriting
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+``ComplexGraphRewriter`` is decorated with ``@_register_unpackers``, which at
+class-definition time scans every method for the ``@_complex_unpacker(op, ...)``
+decorator and builds a ``cls._DISPATCH`` dictionary mapping aten ops to rewrite
+methods.
+
+.. code-block:: python
+
+    @_complex_unpacker(torch.ops.aten.mul.Tensor)
+    def _rewrite_mul(self, node: Node, b: SubgraphBuilder, ...):
+        ...
+
+The entry point ``rewrite_subgraph_nodes`` iterates over the (topologically
+ordered) subgraph nodes and for each node:
+
+1. Looks up ``node.target`` in ``_DISPATCH``.
+2. If found, calls the corresponding rewrite method.
+3. If not found but the op is in ``_ELEMENTWISE_SAFE``, skips it (the op applies
+   independently to every scalar, so the ``(..., 2)`` real layout is already
+   correct).
+4. Otherwise logs a warning and leaves the node unchanged.
+
+``_ELEMENTWISE_SAFE``
+"""""""""""""""""""""
+
+The ``_ELEMENTWISE_SAFE`` set contains ops that apply to every element of the
+tensor independently — ``add.Tensor``, ``sub.Tensor``, ``neg``, ``mul.Scalar``,
+``clone``, ``where``, etc.  On the ``(..., 2)`` real layout these are already
+correct: adding two complex tensors element-wise is the same as adding their
+real and imaginary parts independently.
+
+Notably **excluded** from this set:
+
+* ``permute.default`` — must append the trailing real/imag dim index.
+* ``add.Scalar`` / ``sub.Scalar`` — a scalar added to a complex number only
+  shifts the real part; on the ``(..., 2)`` layout both parts would be shifted.
+* ``reshape`` / ``view`` — shape arguments need updating for the extra ``2`` dim.
+
+Complex Multiply Decomposition
+"""""""""""""""""""""""""""""""
+
+The most important rewrite is ``mul.Tensor`` between two complex operands.
+The rewriter calls ``complex_mul_replacement``:
+
+.. code-block:: python
+
+    # inputs a, b have shape (..., 2) — last dim is [real, imag]
+    re_a = select(a, -1, 0);  im_a = select(a, -1, 1)
+    re_b = select(b, -1, 0);  im_b = select(b, -1, 1)
+    real_out = re_a * re_b - im_a * im_b   # ac - bd
+    imag_out = re_a * im_b + im_a * re_b   # ad + bc
+    result   = stack([real_out, imag_out], dim=-1)
+
+Each step is inserted via a ``SubgraphBuilder`` anchored at the ``mul`` node,
+so all six new nodes appear immediately after it in topological order.  The
+original ``mul`` node is then replaced and erased.
+
+See :ref:`subgraph_builder` for more on how ``SubgraphBuilder`` manages
+cursor-based insertion.
+
+The ``is_complex_layout`` Metadata Invariant
+"""""""""""""""""""""""""""""""""""""""""""""
+
+Input replacement (Stage 2) converts complex ``placeholder`` nodes to
+``float32``.  After that, ``is_complex_dtype(node)`` returns ``False`` for those
+nodes even though they logically represent complex quantities.
+
+To avoid missed rewrites, every node that represents a complex quantity is
+annotated with ``node.meta["is_complex_layout"] = True`` during the detection
+phase (lines in ``rewrite_subgraph_nodes`` before any rewrites begin).  The
+annotation is then propagated forward by every rewrite handler:
+
+* ``replace_input_node`` stamps it on the new placeholder and ``get_attr`` nodes.
+* ``_inline_cat_re_im`` stamps it on every ``[re_u, im_u]`` concatenation node,
+  covering all math handlers (``exp``, ``log``, ``sin``, ``mul``, etc.) at once.
+* Each shape-manipulation handler (``reshape``, ``permute``, ``unsqueeze``,
+  ``cat``, ``stack``, etc.) stamps it on its output node explicitly.
+
+``_is_complex_layout_node(n)`` is therefore a direct metadata lookup — no shape
+heuristics (``val.shape[-1] == 2``), no recursive ``_SHAPE_TRANSPARENT_OPS``
+propagation.  This also eliminates false-positives on real parameters that
+coincidentally have a trailing dimension of size 2.
+
+FakeTensorMode Reuse for Dynamic Shapes
+"""""""""""""""""""""""""""""""""""""""""
+
+When inserting a new ``placeholder`` for a complex input, the pass must populate
+``meta["val"]`` with a ``FakeTensor`` of the new real shape.  Using a fresh
+``FakeTensorMode()`` would create a *new* ``ShapeEnv``, which is incompatible
+with the one that ``torch.export`` used to encode dynamic shape constraints
+(SymInt ranges).
+
+The fix is to extract the ``FakeTensorMode`` from the *original* placeholder's
+``meta["val"].fake_mode`` and reuse it.  The new fake tensor is then constructed
+by appending a concrete ``2`` to the symbolic shape list:
+
+.. code-block:: python
+
+    orig_fake = input_node.meta["val"]
+    sym_shape = list(orig_fake.shape) + [2]
+    with orig_fake.fake_mode:
+        fake_tensor = torch.empty(sym_shape, dtype=new_dtype, device=device)
+
+This preserves all SymInt identity across the graph and keeps
+dynamic-shape exports working correctly.
+
+Entry Point: ``complex_graph_detection``
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+The public entry point called by the lowering pipeline is
+``complex_graph_detection(gm, settings)``.  It:
+
+1. Instantiates ``ComplexOpDetector`` and ``ComplexGraphRewriter``.
+2. Calls ``find_complex_op_subgraphs`` anchored on ``view_as_real`` to find
+   bounded complex subgraphs.
+3. Calls ``find_all_complex_subgraphs`` for any remaining complex nodes that
+   are not ``view_as_real``-bounded.
+4. For each subgraph:
+
+   a. Calls ``replace_input_node`` on every boundary input node (Stage 2).
+   b. Calls ``rewrite_subgraph_nodes`` on the ordered subgraph (Stage 3).
+   c. Calls ``clean_up_graph_after_modifications`` to remove dead nodes.
+
+5. Returns the modified ``GraphModule``.
+
+Adding New Op Rewrites
+^^^^^^^^^^^^^^^^^^^^^^^
+
+To teach the rewriter about a new complex op, add a method to
+``ComplexGraphRewriter`` tagged with ``@_complex_unpacker``:
+
+.. code-block:: python
+
+    @_complex_unpacker(torch.ops.aten.my_new_op.default)
+    def _rewrite_my_new_op(self, node: Node) -> bool:
+        inp = node.args[0]
+        with SubgraphBuilder(self.gm.graph, node) as b:
+            re = b(torch.ops.aten.select.int, inp, -1, 0)
+            im = b(torch.ops.aten.select.int, inp, -1, 1)
+            out = b(my_real_impl, re, im)
+            # If the output is still a complex-layout [..., 2] tensor, annotate it.
+            # (Not needed if using _inline_cat_re_im, which sets the flag automatically.)
+            out.meta["is_complex_layout"] = True
+            node.replace_all_uses_with(out)
+            self.gm.graph.erase_node(node)
+        return True
+
+``@_register_unpackers`` (applied to the class) picks up the new entry
+automatically at import time — no other registration is required.
+
+If the new op is elementwise-safe on the ``(..., 2)`` layout (i.e. it acts
+independently on every scalar), add it to ``_ELEMENTWISE_SAFE`` instead.
+
 Related
 -------
 
 * :ref:`lowering` — the complex rewrite is a lowering pass.
+* :ref:`subgraph_builder` — the ``SubgraphBuilder`` helper used in every rewrite method.
 * :ref:`lowering_passes_catalog` — pass ordering and management.

docsrc/tutorials/advanced_usage.rst

@@ -2,7 +2,7 @@ Advanced Usage
 ==============
 
 Step-by-step tutorials covering engine caching, quantization, custom kernels,
-dynamic shapes, weight streaming, debugging, and more.
+dynamic shapes, weight streaming, debugging, complex numerics, and more.
 
 .. toctree::
    :maxdepth: 2
@@ -14,5 +14,6 @@ dynamic shapes, weight streaming, debugging, and more.
    weight_refit/index
    runtime_opt/index
    deployment/index
+   complex_numerics/index
    Example: Distributed Inference <_rendered_examples/distributed_inference/index>
    ../indices/supported_ops

docsrc/tutorials/deployment/complex_tensors.rst → docsrc/tutorials/complex_numerics/complex_tensors.rst

@@ -11,6 +11,12 @@ compilation.
 This page explains what the rewriter does, which patterns are supported, and what
 limitations to be aware of when compiling models with complex inputs.
 
+.. seealso::
+
+   :doc:`../_rendered_examples/dynamo/torch_export_3d_rope` — a runnable
+   end-to-end example compiling a video-transformer 3D RoPE attention block
+   (CogVideoX / Wan / HunyuanVideo style) with dynamic T×H×W shapes.
+
 ----
 
 How the Rewriter Works

docsrc/tutorials/complex_numerics/index.rst

@@ -0,0 +1,10 @@
+Complex Numerics
+===================
+
+Compatiblity support for numerical datatypes like complex numerics which are not natively supported by TensorRT
+
+.. toctree::
+   :maxdepth: 1
+
+   complex_tensors
+   Example: 3D RoPE with Complex Numerics <../_rendered_examples/dynamo/torch_export_3d_rope>

docsrc/tutorials/deployment/index.rst

@@ -12,4 +12,3 @@ complex-valued model support.
    cross_compile_windows
    Example: Cross-runtime Compilation for Windows <../_rendered_examples/dynamo/cross_runtime_compilation_for_windows>
    distributed_inference
-   complex_tensors

docsrc/tutorials/extensibility/lowering/index.rst

@@ -8,3 +8,4 @@ rewrite ATen ops before TensorRT compilation.
    :maxdepth: 1
 
    writing_dynamo_aten_lowering_passes
+   subgraph_builder