[Edge][CPU] W4A8 via vLLM's own AMX kernel, a per-device weight path, and stop recompiling on every start

Four changes, each measured on a Xeon 8480C with Spark-X2.5-1.7B at 24
threads. Numbers are best-of-interleaved-passes; this host is shared and a
single sequential grid swung the llama.cpp control 30%, so anything under
~8% is not treated as a result.

**vLLM's CPU backend does have a 4-bit kernel.** An earlier note in this
tree said it did not, on the strength of `woq_int4_linear`,
`int4_scaled_mm_with_quant` and `da8w4_linear` all being absent. Those are
simply not the names it uses: `CPUWNA16LinearKernel` dispatches GPTQ/AWQ
checkpoints to `int4_scaled_mm_cpu`, an AMX W4A8 GEMM, whenever activations
are bf16 and the checkpoint carries no act-order. Four-bit weights times
eight-bit activations is the arithmetic llama.cpp's Q4_K_M already does.
`gptq_cpu_export` builds checkpoints for it; the kernel pins the zero point
at 8, so the grid must be symmetric, and a per-group scale search recovers
most of what that costs.

**A per-device weight path** (`edge/weight_path.py`). The fast kernel on each
device needs a different, mutually exclusive weight layout, so the choice has
to come from a probe rather than a flag: AMX x86 -> W4A8/GPTQ; x86 without
AMX, or priority="fidelity" -> W4A16/tinygemm (bf16 activations, the more
faithful of the two); ARM -> W4A16 but flagged unverified, because PyTorch's
4-bit CPU GEMM dispatches a different kernel there than the AVX-512 one
measured here; phone -> runs_in_vllm=False, the deliverable is an exported
graph with calibrated w8a16.

**The CPU compile cache never loaded.** `_try_load_aot_compiled_fn` finishes
with `compiled_fn.finalize_loading(cfg)`, which the CUDA wrapper object has
and the inductor CPU backend's plain function does not. The AttributeError
was swallowed as a cache miss, so every engine start recompiled the model:
warm start 49.8 s against 14.9 s with the missing no-op attached. Self-
disabling if upstream gives the CPU path a real one.

**The head gate stops being a GEMM.** A 2048x8 projection cannot amortize a
thread fan-out: dispatched as `aten::mm` it costs 1.155 ms of a decode step,
nearly all barrier. As an eager reduction behind an opaque op it costs 0.444
ms. It has to be opaque -- written inline the reduction is traced, and
inductor's loop made the whole step 1.8x slower (78.8 -> 44.5 tok/s).

Also included and *off by default*: `cpu_ir_ops` registers the fused
`_C::rms_norm` / `_C::fused_add_rms_norm` kernels that vLLM's CPU build ships
but gates on `GPGPU_DEVICE`. In isolation they are 3.6x and 4.5x faster than
the native path; end to end they are slightly slower (72.7 vs 74.0 tok/s),
because inductor was already fusing the native norm into its neighbours and
an opaque call breaks that fusion. Kept with the measurement recorded so the
next person does not repeat it. `gelu_and_mul` is deliberately not registered:
its fused CPU kernel is 2.7x slower at Spark's GEGLU shape.

Decode, W4A16 g64 -> W4A8 g128 with the gate fix: 69.1 -> 84.5 tok/s (+22%),
against llama.cpp Q4_K_M's 90.0; prefill 3347 against 1121 (3.0x). The two
4-bit paths are not equally faithful: against each build's own bf16 over 12
greedy prompts, Q4_K_M reproduces 4/12, W4A16 2/12 and W4A8 1/12.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>

tests/model_executor/models/spark2_5/test_spark2_5.py

@@ -147,6 +147,49 @@ def test_headwise_gate_scales_each_head_independently():
     )
 
 
+@pytest.mark.core_model
+@pytest.mark.cpu
+@pytest.mark.parametrize("tokens", [1, 2, 3, 16])
+def test_head_gate_branches_compute_the_same_projection(tokens):
+    """The gate op picks a reduction at one or two tokens and a GEMM above.
+
+    Both arms have to be the same linear map, or a sequence would be gated
+    one way while it is prefilled and another way while it decodes.
+    """
+    from vllm_omni.model_executor.models.spark2_5.spark2_5 import _head_gate_score
+
+    torch.manual_seed(0)
+    g = torch.randn(8, 2048, dtype=torch.bfloat16)
+    x = torch.randn(tokens, 2048, dtype=torch.bfloat16)
+
+    got = _head_gate_score(x, g)
+    expected = torch.nn.functional.linear(x.float(), g.float())
+    assert got.shape == (tokens, 8)
+    assert got.dtype == torch.float32
+    # The reduction accumulates in fp32 and the GEMM in bf16, so the GEMM arm
+    # is the looser of the two against an fp32 reference.
+    torch.testing.assert_close(got, expected, rtol=2e-2, atol=2e-2)
+
+
+@pytest.mark.core_model
+@pytest.mark.cpu
+def test_head_gate_op_is_registered_and_traceable():
+    """It must stay opaque. Written inline the reduction gets traced, and
+    inductor's generated loop made the whole decode step 1.8x slower --
+    78.8 tok/s to 44.5 -- than leaving the matmul alone."""
+    from vllm_omni.model_executor.models.spark2_5 import spark2_5  # noqa: F401
+
+    g = torch.randn(8, 64, dtype=torch.bfloat16)
+    x = torch.randn(1, 64, dtype=torch.bfloat16)
+    out = torch.ops.vllm.spark_head_gate(x, g)
+    assert out.shape == (1, 8)
+
+    compiled = torch.compile(
+        lambda a, b: torch.ops.vllm.spark_head_gate(a, b), dynamic=True
+    )
+    torch.testing.assert_close(compiled(x, g), out)
+
+
 @pytest.mark.core_model
 @pytest.mark.cpu
 def test_registered_in_the_omni_registry():