perf: move decode split partials to InferenceWorkspace

- Replace per-.cu-file static cached tensors with workspace-managed pre-allocated buffers

- InferenceWorkspace now owns decode_o_part / decode_ml_part (mirrors FlashInfer's workspace pattern)

- KVCache carries the buffers through the backend -> C++ kernel chain

- C++ kernels accept optional pre-allocated buffers; fallback to alloc_split_partials for backward compat

- Pre-allocates once at Executor init, zero allocation in the decode hot loop

- Prerequisite for CUDA-graph capture (all kernel addresses are stable)

astrai/inference/core/executor.py

@@ -78,9 +78,14 @@ class Executor:
         # (input_ids, decode mask, KV bind metadata).  Eagerly sized at init
         # so the workspace is CUDA-graph-capture friendly — no allocation
         # during capture.
+        config = model.config
+        max_q_heads = config.num_attention_heads
+        head_dim = config.hidden_size // config.num_attention_heads
         self._workspace = InferenceWorkspace(
             max_batch_size=kv_cache.max_batch_size,
             max_seq_len=kv_cache.max_seq_len,
+            max_q_heads=max_q_heads,
+            head_dim=head_dim,
             device=self.device,
             dtype=self.dtype,
         )