Replacing assertions (quic#98)

* Replacing assertions Signed-off-by: quic-meet <[email protected]> * Changes Signed-off-by: Meet Doshi <[email protected]> * misisng device ids param Signed-off-by: Meet Doshi <[email protected]> * changes Signed-off-by: Meet Doshi <[email protected]> * changes Signed-off-by: Meet Doshi <[email protected]> --------- Signed-off-by: quic-meet <[email protected]> Signed-off-by: Meet Doshi <[email protected]> Co-authored-by: Meet Doshi <[email protected]> Co-authored-by: Amit Raj <[email protected]>
quic-amitraj · Oct 23, 2024 · b8cb759 · b8cb759
1 parent c433c11
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Showing 8 changed files with 43 additions and 33 deletions.
diff --git a/QEfficient/base/common.py b/QEfficient/base/common.py
@@ -46,9 +46,10 @@ def get_hf_model_type(hf_model_path: str) -> QEFF_MODEL_TYPE:
     """
     Loads model config file and returns the type of the model (i.e. LLMs, SD, quantized etc.) as supported by the library.
     """
-    assert os.path.isdir(
-        hf_model_path
-    ), "Pleae pass local dir path where the model is downloaded; use `QEfficient.utils.login_and_download_hf_lm` for downloading hf model"
+    if not os.path.isdir(hf_model_path):
+        raise FileNotFoundError(
+            "Please pass local dir path where the model is downloaded; use `QEfficient.utils.login_and_download_hf_lm` for downloading hf model"
+        )
     config, kwargs = AutoConfig.from_pretrained(
         hf_model_path,
         return_unused_kwargs=True,
@@ -84,9 +85,8 @@ def from_pretrained(cls, pretrained_model_name_or_path: str, *args, **kwargs) ->
             pretrained_model_name_or_path = login_and_download_hf_lm(pretrained_model_name_or_path, *args, **kwargs)
         model_type = get_hf_model_type(hf_model_path=pretrained_model_name_or_path)
         qeff_auto_model_class = MODEL_TYPE_TO_QEFF_AUTO_MODEL_MAP[model_type]
-        assert issubclass(
-            qeff_auto_model_class, QEFFBaseModel
-        ), f"Expected class that inherits {QEFFBaseModel}, got {type(qeff_auto_model_class)}"
+        if not issubclass(qeff_auto_model_class, QEFFBaseModel):
+            raise Exception(f"Expected class that inherits {QEFFBaseModel}, got {type(qeff_auto_model_class)}")
 
         return qeff_auto_model_class.from_pretrained(
             pretrained_model_name_or_path=pretrained_model_name_or_path, **kwargs

diff --git a/QEfficient/compile/compile_helper.py b/QEfficient/compile/compile_helper.py
@@ -59,10 +59,10 @@ def compile_kv_model_on_cloud_ai_100(
     if os.path.isdir(aic_binary_dir):
         shutil.rmtree(aic_binary_dir)
 
-    assert os.path.isfile(
-        specializations_json
-    ), f"Please use 'QEfficient.compile', as {specializations_json} file was not found"
-    assert os.path.isfile(custom_io_path), f"{custom_io_path} file was not found!"
+    if not os.path.isfile(specializations_json):
+        raise FileNotFoundError(f"Please use 'QEfficient.compile', as {specializations_json} file was not found")
+    if not os.path.isfile(custom_io_path):
+        raise FileNotFoundError(f"{custom_io_path} file was not found!")
     command = [
         "/opt/qti-aic/exec/qaic-exec",
         f"-m={onnx_path}",

diff --git a/QEfficient/exporter/export_hf_to_cloud_ai_100.py b/QEfficient/exporter/export_hf_to_cloud_ai_100.py
@@ -177,7 +177,8 @@ def convert_to_cloud_kvstyle(
         logger.warning(f"Overriding {onnx_dir_path}")
         shutil.rmtree(onnx_dir_path)
 
-    assert qeff_model.is_transformed, f"please pass the {qeff_model.__class__.__name__} after transform API"
+    if not qeff_model.is_transformed:
+        raise Exception(f"please pass the {qeff_model.__class__.__name__} after transform API")
 
     # Decide path for saving exported ONNX files.
     model_name = export_kvstyle_transformed_model_to_onnx(
@@ -220,8 +221,10 @@ def export_kvstyle_transformed_model_to_onnx(
     output_names = list(pt_outputs.keys())
 
     # Raise error if expected outputs are not present
-    assert "logits" in output_names, "logits not found in output"
-    assert "past_key_values" in output_names, "past_key_values not found in output"
+    if "logits" not in output_names:
+        raise KeyError("logits not found in output")
+    if "past_key_values" not in output_names:
+        raise KeyError("past_key_values not found in output")
 
     # Build inputs for next iteration from outputs
     # Build inputs for decode

diff --git a/QEfficient/generation/cloud_infer.py b/QEfficient/generation/cloud_infer.py
@@ -66,14 +66,15 @@ def __init__(
             self.context = qaicrt.Context()
             self.queue = qaicrt.Queue(self.context, 0)  # Async API
         if enable_debug_logs:
-            assert (
-                self.context.setLogLevel(qaicrt.QLogLevel.QL_DEBUG) == qaicrt.QStatus.QS_SUCCESS
-            ), "Failed to setLogLevel"
+            if self.context.setLogLevel(qaicrt.QLogLevel.QL_DEBUG) != qaicrt.QStatus.QS_SUCCESS:
+                raise RuntimeError("Failed to setLogLevel")
+
         qpc = qaicrt.Qpc(qpc_path)
         # Load IO Descriptor
         iodesc = aicapi.IoDesc()
         status, iodesc_data = qpc.getIoDescriptor()
-        assert status == qaicrt.QStatus.QS_SUCCESS, "Failed to getIoDescriptor"
+        if status != qaicrt.QStatus.QS_SUCCESS:
+            raise RuntimeError("Failed to getIoDescriptor")
         iodesc.ParseFromString(bytes(iodesc_data))
         self.allowed_shapes = [
             [(aic_to_np_dtype_mapping[x.type].itemsize, list(x.dims)) for x in allowed_shape.shapes]
@@ -87,7 +88,8 @@ def __init__(
         if device_ids and len(device_ids) > 1:
             prog_properties.devMapping = ":".join(map(str, device_ids))
         self.program = qaicrt.Program(self.context, None, qpc, prog_properties)
-        assert self.program.load() == qaicrt.QStatus.QS_SUCCESS, "Failed to load program"
+        if self.program.load() != qaicrt.QStatus.QS_SUCCESS:
+            raise RuntimeError("Failed to load program")
         if activate:
             self.activate()
         # Create input qbuffers and buf_dims
@@ -157,11 +159,13 @@ def run(self, inputs: Dict[str, np.ndarray]) -> Dict[str, np.ndarray]:
         """
         # Set inputs
         self.set_buffers(inputs)
-        assert self.execObj.setData(self.qbuffers, self.buf_dims) == qaicrt.QStatus.QS_SUCCESS, "Failed to setData"
+        if self.execObj.setData(self.qbuffers, self.buf_dims) != qaicrt.QStatus.QS_SUCCESS:
+            raise MemoryError("Failed to setData")
         # # Run with sync API
         # if self.execObj.run(self.qbuffers) != qaicrt.QStatus.QS_SUCCESS:
         # Run with async API
-        assert self.queue.enqueue(self.execObj) == qaicrt.QStatus.QS_SUCCESS, "Failed to enqueue"
+        if self.queue.enqueue(self.execObj) != qaicrt.QStatus.QS_SUCCESS:
+            raise MemoryError("Failed to enqueue")
         if self.execObj.waitForCompletion() != qaicrt.QStatus.QS_SUCCESS:
             error_message = "Failed to run"
             # Print additional error messages for unmatched dimension error
@@ -187,7 +191,8 @@ def run(self, inputs: Dict[str, np.ndarray]) -> Dict[str, np.ndarray]:
             raise ValueError(error_message)
         # Get output buffers
         status, output_qbuffers = self.execObj.getData()
-        assert status == qaicrt.QStatus.QS_SUCCESS, "Failed to getData"
+        if status != qaicrt.QStatus.QS_SUCCESS:
+            raise MemoryError("Failed to getData")
         # Build output
         outputs = {}
         for output_name in self.output_names:

diff --git a/QEfficient/generation/text_generation_inference.py b/QEfficient/generation/text_generation_inference.py
@@ -157,9 +157,8 @@ def get_compilation_dims(qpc_path: str) -> Tuple[int, int]:
 
 
 def get_input_prompts(prompt: str, prompts_txt_file_path: str) -> List[str]:
-    assert (
-        prompt is not None or prompts_txt_file_path is not None
-    ), "Please pass at least one argument either using --prompt or --prompts_txt_file_path"
+    if prompt is None and prompts_txt_file_path is None:
+        raise ValueError("Please pass at least one argument either using --prompt or --prompts_txt_file_path")
     if prompts_txt_file_path is not None:
         if prompt is not None:
             logger.warning("Found inputs passed using txt file as well as CLI, taking inputs from given txt file")
@@ -444,7 +443,8 @@ def _fetch_generation_len(self, generation_len, max_gen_len):
                 "Passed generation_len is greater than allowed length. "
                 "Make sure this model supports sliding window, such as Mistral"
             )
-        assert generation_len > 0, "generation length should be greater than zero"
+        if generation_len <= 0:
+            raise ValueError("generation length should be greater than zero")
         return generation_len
 
     def prepare_decode_inputs(self):

diff --git a/QEfficient/transformers/models/modeling_auto.py b/QEfficient/transformers/models/modeling_auto.py
@@ -211,7 +211,6 @@ def export(self) -> str:
         Returns:
             :str: Path of the generated ``ONNX`` graph.
         """
-        assert self.is_transformed, "Please first run transform on the QEFFAutoModelForCausalLM object"
         # Export
         _, onnx_model_path = QEfficient.export(
             model_name=self.model_card_name,
@@ -366,11 +365,13 @@ def generate(self, prompts: List[str], device_id: List[int] = None, runtime: str
         ``optional`` Args:
             :runtime (str, optional): Only ``AI_100`` runtime is supported as of now; ``ONNXRT`` and ``PyTorch`` coming soon. Defaults to "AI_100".
         """
-        assert Runtime(runtime) == Runtime.AI_100, "Only AI_100 runtime is supported right now via generate API"
+        if Runtime(runtime) != Runtime.AI_100:
+            raise ValueError("Only AI_100 runtime is supported right now via generate API")
         self.run_cloud_ai_100(prompts=prompts, device_id=device_id, **kwargs)
 
     def run_cloud_ai_100(self, prompts: List[str], device_id: List[int] = None, **kwargs):
-        assert isinstance(self.qpc_path, str), "Please run compile API first!"
+        if not isinstance(self.qpc_path, str):
+            raise TypeError("Please run compile API first!")
         generation_len = kwargs.pop("generation_len", None)
         return QEfficient.cloud_ai_100_exec_kv(
             self.tokenizer,

diff --git a/QEfficient/transformers/transform.py b/QEfficient/transformers/transform.py
@@ -75,9 +75,8 @@ def transform_lm(model: nn.Module) -> nn.Module:
 
     # Check with new params hash
     later_params_hash = get_params_hash(model)
-    assert (
-        prior_params_hash == later_params_hash
-    ), "Weights were changed in the transform process, please report an issue"
+    if prior_params_hash != later_params_hash:
+        raise RuntimeError("Weights were changed in the transform process, please report an issue")
 
     # Replace the Dyanmic cache utils update api
     transformers.cache_utils.DynamicCache.update = QEffDynamicCache.update
@@ -94,7 +93,8 @@ def transform(model: QEFFBaseModel, form_factor="cloud"):
     model (torch.nn.Module): object of any instance of class that is child of `QEFFBaseAutoModelFactory`
     form_factor (str): form factor configuration for optimizing the model, available options=["cloud", "edge"].
     """
-    assert form_factor == "cloud", "Only form_factor='cloud' is supported as of now!"
+    if form_factor != "cloud":
+        raise ValueError("Only form_factor='cloud' is supported as of now!")
     # FIXME: move this to class and use model.transform()
     if AUTO_MODEL_MAP_TO_MODEL_TYPE_MAP.get(model.__class__, None) == QEFF_MODEL_TYPE.CAUSALLM:
         transform_lm(model.model)  # type: ignore

diff --git a/QEfficient/utils/_utils.py b/QEfficient/utils/_utils.py
@@ -224,7 +224,8 @@ def padding_check_and_fix(tokenizer: Union[PreTrainedTokenizer, PreTrainedTokeni
         tokenizer.padding_side = "right"
 
     if tokenizer.pad_token_id is None:
-        assert tokenizer.eos_token_id is not None, "Found tokenizer.eos_token_id to be None, expected int"
+        if not isinstance(tokenizer.eos_token_id, int):
+            raise TypeError("Found tokenizer.eos_token_id to be None, expected int")
         # If Pad token is out of range of vocab size
         if tokenizer.eos_token_id < tokenizer.vocab_size:
             tokenizer.pad_token_id = tokenizer.eos_token_id