CANN: fix multi-thread set_tensor race conditions (llama/20151)

CANN: fix multi-thread set_tensor race conditions (llama/20151)

* CANN: fix multi-thread set_tensor race conditions

When ollama calls ggml_backend_tensor_set from multiple threads (each
writing a different chunk of the same tensor), the CANN backend had
three concurrency issues:

1. Quantized tensors (Q4_0/Q8_0) require a full-tensor format transform
   before uploading to device. Per-chunk transforms produced corrupt data.

2. ND-to-NZ weight conversion requires complete tensor data on device.
   Per-chunk conversion operated on incomplete data.

3. The global g_nz_workspaces array had unprotected concurrent access.

Fix by introducing a TensorSetTracker that accumulates write progress
per tensor. For quantized tensors, raw data is staged in a host buffer
and the transform + upload is deferred until all chunks arrive. For NZ
weights, chunks are uploaded directly but conversion is deferred. The
tracker and its staging buffer are released immediately after
post-processing completes.

Add per-device mutex to g_nz_workspaces to prevent data races.

* CANN: fix L2_NORM ignoring eps parameter

The L2_NORM implementation was not using the eps parameter from
op_params, causing incorrect results when eps is large (e.g. 10.0).
The CPU reference computes scale = 1/fmaxf(norm, eps), so add a
Clamp step to clamp the norm to at least eps before dividing.

* ggml/cann: compare op_params for POOL_2D in ACL graph cache matching

When ACL graph mode is enabled, the graph LRU cache checks whether a
cached graph matches the current computation graph. Previously,
GGML_OP_POOL_2D was not included in the op_params comparison, so two
POOL_2D nodes with different pooling parameters (kernel size, stride,
padding) but identical tensor shapes and addresses could incorrectly
reuse a cached graph, leading to wrong results or aclnn errors.

Add GGML_OP_POOL_2D to the list of ops that require op_params matching
in ggml_graph_node_properties::has_matching_properties().

* cann: fix ACL graph cache matching by adding tensor type and unconditional op_params comparison

The ACL graph LRU cache was incorrectly reusing cached graphs for
operations with different tensor types or op_params, causing test
failures for CPY (f16 vs bf16), POOL_2D, L2_NORM, NORM_MUL_ADD,
RMS_NORM_MUL_ADD, and ADD_RMS_NORM.

Changes:
- Add node_type and src_type[] fields to ggml_graph_node_properties
  so the cache can distinguish tensors with different types but
  identical ne/nb (e.g. f16 and bf16 both have 2-byte elements)
- Compare op_params unconditionally for all ops instead of only for
  SCALE/UNARY/GLU/ROPE/POOL_2D