fix(hip): share MatMul and Gemm shapes with dynamic batch validation

[fhanuman]

Summary

Unify MatMul, Gemm, and MatMulNBits result-shape construction across ONNX conversion, HIP reification, and static verification.

• derive MatMul batch/M/N dimensions and transpose-aware Gemm extents from shared semantic helpers;
• support independent whole-matrix batch broadcasting for A and B;
• pass operand batch counts and strides through the final hipBLASLt runtime ABI;
• reject dynamically concealed partial-batch layouts through the recoverable runtime error path;
• keep converter, dialect, lowering, real/mock runtime, and ABI changes atomic.

Compiled model artifacts using the previous MatMul wrapper ABI must be invalidated.

Stack

This is PR 4 of 12 and depends on PR 3:

1. Constant carrier/externalization
2. Shape-rule foundation
3. Reshape provenance
4. MatMul/Gemm ← this PR
5. Broadcast/Reduction
6. Loop carriers
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 refactoring and stack reconstruction. The complete compiler/runtime ABI slice was checked with the authoritative local build and focused MatMul/Gemm tests.

Made with Cursor

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L2 Accuracy Results (EP vs CPU)

Model	Combined L2	Total Elems	Skipped NaN/Inf
conv_test_hybrid	4.8668E-07	64	0
GroupQueryAttention_seq256	25.2366	2621440	0
MatMulNBits_o_seq128	259.906	368640	0
QMoE_seq128	34.957	368640	0

Threshold: 0.01 | Run: 3890 - Commit: 9d7fdec

[github-actions]

MorphiZen EP Performance Results

Model	QPS	Session (s)	1st Infer (ms)	CPU%	Mem (MB)
full_model_seq128	7.52	6.37	358	3	1243
GroupQueryAttention_seq128	4385.73	1.77629	12	6	312
matmul_down_seq128	530.51	2.53	72	3	354

EPContext Export Performance

Model	QPS	Session (s)	1st Infer (ms)	CPU%	Mem (MB)
full_model_seq128	7.54	45.92	359	3	15590

EPContext Import Performance

Model	QPS	Session (s)	1st Infer (ms)	CPU%	Mem (MB)
full_model_seq128	7.54	10.05	357	3	15758

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	157.7	79.6	1.33	13.53
Llama-3.1-8B-awq-g128-int4-asym-fp16-onnx-dml	1	5	128	128	256.9	41.8	1.22	6.43

OGA Wheel Smoke (Python benchmark_e2e.py)

Model	TTFT (ms)	TPS
Llama-3.1-8B-awq-g128-int4-asym-fp16-onnx-dml	191	41.2

Run: 3890 - Commit: 9d7fdec