IREE gap: TensorEncoding not propagated through intermediate ops in TraceToGraphBuilder.addTrace (P2) #492

Context

Surfaced during the int8 NPU readiness audit on 2026-04-13. The `TensorEncoding` metadata plumbing landed in #471 (accessor helpers) and is used in `TraceToGraphBuilder.finalize()` to attach encodings to session-resolved weight nodes. The #478 StableHLO emitter reads that metadata and emits a module-level `skainet.tensor_encodings = {...}` attribute.

The gap: `TraceToGraphBuilder.addTrace` — the path that processes every traced op — builds its input and output `TensorSpec` objects from `trace.attributes["inputShapes"]` / `outputShapes` / `inputDTypes` / `outputDTypes` only. It does not consult the underlying tensor's `TensorData` subtype, and it does not propagate `TensorEncoding` from a producer's output spec to a consumer's input spec.

Consequence: a Q8_0 weight that flows through any intermediate op (`transpose(w)`, `reshape(w)`, `cast(w)`) reaches the downstream matmul's input spec without its `tensorEncoding` set. The StableHLO emitter's `skainet.tensor_encodings` dictionary correctly lists the original weight, but any consumer pass that wants to identify a matmul operand as quantized would have to walk the def-use chain backwards through the IR to find the original constant — which defeats the point of carrying the metadata in the first place.

Why this is P2 and not P0

• Not blocking NNAPI / runtime backends. A runtime NNAPI backend dispatches on `TensorData` subtype directly (`is Q8_0TensorData`), not on `TensorSpec.metadata`. The encoding channel is only load-bearing for the compile path (StableHLO → IREE).
• Not blocking the first IREE spike. The first validation question is "does IREE even accept a SKaiNET-emitted StableHLO module." Consuming the encoding metadata is downstream of that.
• A weight that's used directly by a matmul (without an intervening transpose/reshape) still gets its encoding attached correctly via `finalize()`. Many model traces take that shape.

Scope

1. In `TraceToGraphBuilder.addTrace`, after `buildInputSpecs` / `buildOutputSpecs` produce their initial `TensorSpec` instances, propagate `tensorEncoding` from the corresponding producer node's output spec for every input that has a known producer (`producersByTensorId[tRef.id]` is non-null). This is the "forward propagation" step that makes encoding flow through intermediate ops.
2. Decide a policy for the pass-through ops (`reshape`, `transpose`, `squeeze`, `unsqueeze`, `concat`, `slice`, `cast`): does the output of a transpose of a Q8_0 tensor inherit the Q8_0 encoding? Default: yes for type-preserving structural ops (transpose, reshape, squeeze, unsqueeze, concat, slice); no for `cast` since it changes the element type.
3. Unit test that builds a trace with a Q8_0 weight → transpose → matmul and asserts the matmul's input spec at the graph node level carries `TensorEncoding.Q8_0`.
4. `./gradlew :skainet-compile:skainet-compile-hlo:allTests` and `:skainet-compile:skainet-compile-dag:allTests` before pushing.

Out of scope

• Quantized-aware cast that preserves encoding across element-type transitions (handled by a separate `TensorEncoding.Dense(bytesPerElement=…)` adjustment; later).
• Emitting the quantized constant values in the StableHLO output (separate gap, separate issue).
• Changing the `TensorEncoding` sealed type.

Relationship to other IREE work

This is one of two gaps surfaced in the 2026-04-13 audit. The other is "quantized weight values not emitted as stablehlo.constant" which is tracked in its own issue and is the more load-bearing IREE gap. Both are tagged into the project at https://github.com/orgs/SKaiNET-developers/projects/1.