fix unit tests

exo/inference/mlx/sharded_inference_engine.py

@@ -77,15 +77,15 @@ class MLXDynamicShardInferenceEngine(InferenceEngine):
     await self.ensure_shard(shard)
     await asyncio.get_running_loop().run_in_executor(self.executor, self.model.load_weights, path)
     
-  async def infer_tensor(self, request_id: str, shard: Shard, input_data: np.ndarray, inference_state: dict = {}) -> tuple[np.ndarray, Optional[dict]]:
+  async def infer_tensor(self, request_id: str, shard: Shard, input_data: np.ndarray, inference_state: Optional[dict] = None) -> tuple[np.ndarray, Optional[dict]]:
     await self.ensure_shard(shard)
     loop = asyncio.get_running_loop()
     state = await self.poll_state(request_id) if self.model.model_type != 'StableDiffusionPipeline' else {}
     x = mx.array(input_data)
     if self.model.model_type != 'StableDiffusionPipeline':
-      output_data = await loop.run_in_executor(self.executor, lambda: self.model(x, **state, **inference_state))
+      output_data = await loop.run_in_executor(self.executor, lambda: self.model(x, **state, **(inference_state or {})))
     else:
-      output_data, inference_state = await loop.run_in_executor(self.executor, lambda: self.model(x, **state, **inference_state))
+      output_data, inference_state = await loop.run_in_executor(self.executor, lambda: self.model(x, **state, **(inference_state or {})))
     output_data = np.array(output_data)
     return output_data, inference_state
 

exo/inference/test_inference_engine.py

@@ -14,7 +14,7 @@ async def test_inference_engine(inference_engine_1: InferenceEngine, inference_e
   resp_full, _ = await inference_engine_1.infer_prompt("A", shard=Shard(model_id=model_id, start_layer=0, end_layer=n_layers - 1, n_layers=n_layers), prompt=prompt)
   token_full = await inference_engine_1.sample(resp_full)
   token_full = token_full.reshape(1, -1)
-  next_resp_full = await inference_engine_1.infer_tensor(
+  next_resp_full, _ = await inference_engine_1.infer_tensor(
     "A",
     shard=Shard(model_id=model_id, start_layer=0, end_layer=n_layers - 1, n_layers=n_layers),
     input_data=token_full,

test/test_tokenizers.py

@@ -24,7 +24,7 @@ def test_tokenizer(name, tokenizer, verbose=False):
     strip_tokens = lambda s: s.lstrip(tokenizer.decode([tokenizer.bos_token_id])).rstrip(tokenizer.decode([tokenizer.eos_token_id]))
     assert text == strip_tokens(decoded) == strip_tokens(reconstructed)
 
-ignore = ["TriAiExperiments/SFR-Iterative-DPO-LLaMA-3-70B-R", "mlx-community/DeepSeek-Coder-V2-Lite-Instruct-4bit-mlx", "mlx-community/DeepSeek-V2.5-MLX-AQ4_1_64", "llava-hf/llava-1.5-7b-hf", "mlx-community/Qwen*", "dummy", "mlx-community/Meta-Llama-3.1-405B-Instruct-8bit", "mlx-community/Phi-3.5-mini-instruct-4bit", "mlx-community/phi-4-4bit"]
+ignore = ["TriAiExperiments/SFR-Iterative-DPO-LLaMA-3-70B-R", "mlx-community/DeepSeek-Coder-V2-Lite-Instruct-4bit-mlx", "mlx-community/DeepSeek-V2.5-MLX-AQ4_1_64", "llava-hf/llava-1.5-7b-hf", "mlx-community/Qwen*", "dummy", "mlx-community/Meta-Llama-3.1-405B-Instruct-8bit", "mlx-community/Phi-3.5-mini-instruct-4bit", "mlx-community/phi-4-4bit", "stabilityai/stable-diffusion-2-1-base"]
 ignore_pattern = re.compile(r"^(" + "|".join(model.replace("*", ".*") for model in ignore) + r")")
 models = []
 for model_id in model_cards: