fix(loop): support shape-changing loop carriers

[fhanuman]

Summary

Support shape-changing ranked Loop carriers without aliasing results to immutable initialization storage.

• return exact carrier descriptors through a status-aware loop body ABI;
• manage per-invocation double-buffered carrier storage with zero-trip aliasing and transactional publication;
• copy exact loop results to graph outputs and remove the sequence-length-specific accumulator workaround.

Stack

This is PR 6 of 12 and depends on PR 5:

1. Constant carrier/externalization
2. Shape-rule foundation
3. Reshape provenance
4. MatMul/Gemm
5. Broadcast/Reduction
6. Loop carriers ← this PR
7. Payload/Readback
8. Conv/Pool
9. Gather/Tensor
10. Attention/Normalization
11. Explicit contract enforcement
12. Refinement hardening

AI assistance

AI tools assisted with the loop ABI implementation and stack reconstruction. The changes were checked with the authoritative local build, focused compiler/runtime tests, and strict loop numerics.

Made with Cursor

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L2 Accuracy Results (EP vs CPU)

Model	Combined L2	Total Elems	Skipped NaN/Inf
conv_test_hybrid	EP inference failed	-	-
GroupQueryAttention_seq256	EP inference failed	-	-
MatMulNBits_o_seq128	EP inference failed	-	-
QMoE_seq128	EP inference failed	-	-

Threshold: 0.01 | Run: 3871 - Commit: 08c707b

[github-actions]

MorphiZen EP Performance Results

Model	QPS	Session (s)	1st Infer (ms)	CPU%	Mem (MB)
full_model_seq128	failed	-	-	-	-
GroupQueryAttention_seq128	failed	-	-	-	-
matmul_down_seq128	failed	-	-	-	-

EPContext Export Performance

Model	QPS	Session (s)	1st Infer (ms)	CPU%	Mem (MB)
full_model_seq128	failed	-	-	-	-

OGA Benchmark Results

Model	Warmup	Reps	Prompt Len	Gen Tokens	TTFT (ms)	TPS	Peak Mem (GB)	GPU Mem (GB)
gpt-oss-20b-webgpu-int4-rtn-block-32	1	5	128	128	-	-	-	0.00
Llama-3.1-8B-awq-g128-int4-asym-fp16-onnx-dml	1	5	128	128	-	-	-	0.00

OGA Wheel Smoke (Python benchmark_e2e.py)

Model	TTFT (ms)	TPS
Llama-3.1-8B-awq-g128-int4-asym-fp16-onnx-dml	failed	-

Run: 3871 - Commit: 08c707b