feat: update llama.cpp submodule and bindings for Qwen 3.5 support

Updates the llama.cpp submodule to da348c9df which includes support for
the Qwen 3.5 model architecture (hybrid SSM + attention).

Changes to Python bindings:

1. llama_cpp.py: Sync llama_context_params struct with upstream C API
   - flash_attn (bool) → flash_attn_type (enum llama_flash_attn_type)
   - Add samplers (void*) and n_samplers (size_t) fields
   - Add LLAMA_FLASH_ATTN_TYPE_* enum constants

2. llama.py: Update flash_attn parameter handling
   - Map flash_attn=True/False to flash_attn_type=1/0

3. _ctypes_extensions.py: Graceful handling of deprecated symbols
   - ctypes_function decorator returns stub instead of crashing
     when a symbol is not found in the shared library

Tested with Qwen3.5-0.8B-Q4_K_M.gguf on Apple Silicon (M1 Pro):
- Cold start: ~4s (vs ~40s with mlx-vlm)
- Inference: ~0.6s per chat completion
- Model loads and runs correctly on Metal GPU

Author: codavidgarcia

llama_cpp/_ctypes_extensions.py

@@ -110,11 +110,22 @@ def ctypes_function(
     ):
         def decorator(f: F) -> F:
             if enabled:
-                func = getattr(lib, name)
-                func.argtypes = argtypes
-                func.restype = restype
-                functools.wraps(f)(func)
-                return func
+                try:
+                    func = getattr(lib, name)
+                    func.argtypes = argtypes
+                    func.restype = restype
+                    functools.wraps(f)(func)
+                    return func
+                except AttributeError:
+                    # Symbol not found in shared library (deprecated/removed)
+                    @functools.wraps(f)
+                    def stub(*args: Any, **kwargs: Any) -> Any:
+                        raise NotImplementedError(
+                            f"Symbol '{name}' not found in shared library. The C API might "
+                            "have been removed or deprecated."
+                        )
+
+                    return stub  # type: ignore
             else:
                 return f
 

llama_cpp/llama.py

@@ -341,7 +341,11 @@ def __init__(
         self._logits_all = logits_all if draft_model is None else True
         self.context_params.embeddings = embedding  # TODO: Rename to embeddings
         self.context_params.offload_kqv = offload_kqv
-        self.context_params.flash_attn = flash_attn
+        self.context_params.flash_attn_type = (
+            llama_cpp.LLAMA_FLASH_ATTN_TYPE_ENABLED
+            if flash_attn
+            else llama_cpp.LLAMA_FLASH_ATTN_TYPE_DISABLED
+        )
 
         if op_offload is not None:
             self.context_params.op_offload = op_offload
@@ -2096,7 +2100,10 @@ def __getstate__(self):
             logits_all=self._logits_all,
             embedding=self.context_params.embeddings,
             offload_kqv=self.context_params.offload_kqv,
-            flash_attn=self.context_params.flash_attn,
+            flash_attn=(
+                self.context_params.flash_attn_type
+                == llama_cpp.LLAMA_FLASH_ATTN_TYPE_ENABLED
+            ),
             op_offload=self.context_params.op_offload,
             swa_full=self.context_params.swa_full,
             # Sampling Params

llama_cpp/llama_cpp.py

@@ -463,6 +463,16 @@
 LLAMA_ATTENTION_TYPE_NON_CAUSAL = 1
 
 
+# enum llama_flash_attn_type {
+#     LLAMA_FLASH_ATTN_TYPE_AUTO     = -1,
+#     LLAMA_FLASH_ATTN_TYPE_DISABLED = 0,
+#     LLAMA_FLASH_ATTN_TYPE_ENABLED  = 1,
+# };
+LLAMA_FLASH_ATTN_TYPE_AUTO = -1
+LLAMA_FLASH_ATTN_TYPE_DISABLED = 0
+LLAMA_FLASH_ATTN_TYPE_ENABLED = 1
+
+
 # enum llama_split_mode {
 #     LLAMA_SPLIT_MODE_NONE  = 0, // single GPU
 #     LLAMA_SPLIT_MODE_LAYER = 1, // split layers and KV across GPUs
@@ -761,6 +771,7 @@ class llama_model_params(ctypes.Structure):
 #     enum llama_rope_scaling_type rope_scaling_type; // RoPE scaling type, from `enum llama_rope_scaling_type`
 #     enum llama_pooling_type      pooling_type;      // whether to pool (sum) embedding results by sequence id
 #     enum llama_attention_type    attention_type;    // attention type to use for embeddings
+#     enum llama_flash_attn_type   flash_attn_type;   // when to enable Flash Attention
 
 #     // ref: https://github.com/ggml-org/llama.cpp/pull/2054
 #     float    rope_freq_base;   // RoPE base frequency, 0 = from model
@@ -770,7 +781,7 @@ class llama_model_params(ctypes.Structure):
 #     float    yarn_beta_fast;   // YaRN low correction dim
 #     float    yarn_beta_slow;   // YaRN high correction dim
 #     uint32_t yarn_orig_ctx;    // YaRN original context size
-#     float    defrag_thold;     // defragment the KV cache if holes/size > thold, <= 0 disabled (default)
+#     float    defrag_thold;     // [DEPRECATED] defragment the KV cache if holes/size > thold, <= 0 disabled (default)
 
 #     ggml_backend_sched_eval_callback cb_eval;
 #     void * cb_eval_user_data;
@@ -787,15 +798,14 @@ class llama_model_params(ctypes.Structure):
 #     // Keep the booleans together and at the end of the struct to avoid misalignment during copy-by-value.
 #     bool embeddings;  // if true, extract embeddings (together with logits)
 #     bool offload_kqv; // offload the KQV ops (including the KV cache) to GPU
-#     bool flash_attn;  // use flash attention [EXPERIMENTAL]
 #     bool no_perf;     // measure performance timings
 #     bool op_offload;  // offload host tensor operations to device
-#     bool swa_full;    // use full-size SWA cache (https://github.com/ggml-org/llama.cpp/pull/13194#issuecomment-2868343055)
-#                       // NOTE: setting to false when n_seq_max > 1 can cause bad performance in some cases
-#                       //       ref: https://github.com/ggml-org/llama.cpp/pull/13845#issuecomment-2924800573
+#     bool swa_full;    // use full-size SWA cache
 #     bool kv_unified;  // use a unified buffer across the input sequences when computing the attention
-#                       // try to disable when n_seq_max > 1 for improved performance when the sequences do not share a large prefix
-#                       // ref: https://github.com/ggml-org/llama.cpp/pull/14363
+
+#     // [EXPERIMENTAL]
+#     struct llama_sampler_seq_config * samplers;
+#     size_t                            n_samplers;
 # };
 class llama_context_params(ctypes.Structure):
     """Parameters for llama_context
@@ -810,6 +820,7 @@ class llama_context_params(ctypes.Structure):
         rope_scaling_type (int): RoPE scaling type, from `enum llama_rope_scaling_type`
         pooling_type (int): whether to pool (sum) embedding results by sequence id (ignored if no pooling layer)
         attention_type (int): attention type to use for embeddings
+        flash_attn_type (int): when to enable Flash Attention, from `enum llama_flash_attn_type`
         rope_freq_base (float): RoPE base frequency, 0 = from model
         rope_freq_scale (float): RoPE frequency scaling factor, 0 = from model
         yarn_ext_factor (float): YaRN extrapolation mix factor, negative = from model
@@ -826,11 +837,12 @@ class llama_context_params(ctypes.Structure):
         abort_callback_data (ctypes.ctypes.c_void_p): data for abort_callback
         embeddings (bool): if true, extract embeddings (together with logits)
         offload_kqv (bool): whether to offload the KQV ops (including the KV cache) to GPU
-        flash_attn (bool): whether to use flash attention
         no_perf (bool): whether to measure performance timings
         op_offload (bool): offload host tensor operations to device
         swa_full (bool): use full-size SWA cache
         kv_unified (bool): use a unified buffer across the input sequences when computing the attention
+        samplers (ctypes.c_void_p): backend sampler chain configuration [EXPERIMENTAL]
+        n_samplers (ctypes.c_size_t): number of backend sampler chains
     """
 
     if TYPE_CHECKING:
@@ -843,6 +855,7 @@ class llama_context_params(ctypes.Structure):
         rope_scaling_type: int
         pooling_type: int
         attention_type: int
+        flash_attn_type: int
         rope_freq_base: float
         rope_freq_scale: float
         yarn_ext_factor: float
@@ -859,11 +872,12 @@ class llama_context_params(ctypes.Structure):
         abort_callback_data: ctypes.c_void_p
         embeddings: bool
         offload_kqv: bool
-        flash_attn: bool
         no_perf: bool
         op_offload: bool
         swa_full: bool
         kv_unified: bool
+        samplers: ctypes.c_void_p
+        n_samplers: ctypes.c_size_t
 
     _fields_ = [
         ("n_ctx", ctypes.c_uint32),
@@ -875,6 +889,7 @@ class llama_context_params(ctypes.Structure):
         ("rope_scaling_type", ctypes.c_int),
         ("pooling_type", ctypes.c_int),
         ("attention_type", ctypes.c_int),
+        ("flash_attn_type", ctypes.c_int),
         ("rope_freq_base", ctypes.c_float),
         ("rope_freq_scale", ctypes.c_float),
         ("yarn_ext_factor", ctypes.c_float),
@@ -891,11 +906,12 @@ class llama_context_params(ctypes.Structure):
         ("abort_callback_data", ctypes.c_void_p),
         ("embeddings", ctypes.c_bool),
         ("offload_kqv", ctypes.c_bool),
-        ("flash_attn", ctypes.c_bool),
         ("no_perf", ctypes.c_bool),
         ("op_offload", ctypes.c_bool),
         ("swa_full", ctypes.c_bool),
         ("kv_unified", ctypes.c_bool),
+        ("samplers", ctypes.c_void_p),
+        ("n_samplers", ctypes.c_size_t),
     ]