fix: rename device_id → device_ids for API consistency

QEfficient/cloud/execute.py

@@ -87,7 +87,7 @@ def main(
     cloud_ai_100_exec_kv(
         tokenizer=tokenizer,
         qpc_path=qpc_path,
-        device_id=device_group,
+        device_ids=device_group,
         prompt=prompt,
         prompts_txt_file_path=prompts_txt_file_path,
         generation_len=generation_len,

QEfficient/cloud/infer.py

@@ -315,7 +315,7 @@ def main(
         _ = qeff_model.generate(
             tokenizer,
             prompts=prompt,
-            device_id=device_group,
+            device_ids=device_group,
             prompts_txt_file_path=prompts_txt_file_path,
             generation_len=generation_len,
             iteration=iteration,

QEfficient/generation/text_generation_inference.py

@@ -127,7 +127,7 @@ def latency_stats_bertstyle(
     qpc_path: str,
     seq_len: int,
     prompt: str,
-    device_id: Optional[List[int]] = None,
+    device_ids: Optional[List[int]] = None,
 ):
     """
     Function to execute Bertstyle ONNX model on Cloud AI 100.
@@ -137,9 +137,9 @@ def latency_stats_bertstyle(
         :qpc_path (str): Path to save generated binary file after compilation.
         :seq_len (int): Sequence length.
         :prompt (str): Sample prompt for the model text generation.
-        :device_id (List[int]): Device Ids to be used for compilation. If devices > 1, it enables multiple card setup.
+        :device_ids (List[int]): Device Ids to be used for compilation. If devices > 1, it enables multiple card setup.
     """
-    session = QAICInferenceSession(qpc_path, device_id)
+    session = QAICInferenceSession(qpc_path, device_ids)
     tokenizer = transformers.AutoTokenizer.from_pretrained(model_name, padding_side="left")
     padding_check_and_fix(tokenizer)  # Check and fix tokenizer viability
     inputs = tokenizer(prompt, return_tensors="np", max_length=seq_len, padding="max_length")
@@ -319,7 +319,7 @@ def cloud_ai_100_exec_kv(
     qpc_path: str,
     prompt: Optional[str] = None,
     prompts_txt_file_path: Optional[str] = None,
-    device_id: Optional[List[int]] = None,
+    device_ids: Optional[List[int]] = None,
     generation_len: Optional[int] = None,
     comp_ctx_lengths_prefill: Optional[List[int]] = None,
     comp_ctx_lengths_decode: Optional[List[int]] = None,
@@ -348,7 +348,7 @@ def cloud_ai_100_exec_kv(
         :prompt (str): Sample prompt for the model text generation. ``Defaults to None``.
         :prompts_txt_file_path (str): Path of the prompt text file. ``Defaults to None``.
         :generation_len (int): Maximum context length for the model during compilation. ``Defaults to None``.
-        :device_id (List[int]): Device IDs to be used for execution. If ``len(device_id) > 1``, it enables multiple card setup. If ``None``, auto-device-picker will be used. ``Defaults to None``.
+        :device_ids (List[int]): Device IDs to be used for execution. If ``len(device_ids) > 1``, it enables multiple card setup. If ``None``, auto-device-picker will be used. ``Defaults to None``.
         :enable_debug_logs (bool): If True, it enables debugging logs. ``Defaults to False``.
         :stream (bool): If True, enable streamer, which returns tokens one by one as the model generates them. ``Defaults to True``.
         :Write_io_dir (str): Path to write the input and output files. ``Defaults to None``.
@@ -377,7 +377,7 @@ def cloud_ai_100_exec_kv(
         base_path, onnx_model_path = QEfficient.export(model_name="gpt2")
         qpc_path = QEfficient.compile(onnx_path=onnx_model_path, qpc_path=os.path.join(base_path, "qpc"), num_cores=14, device_group=[0])
         tokenizer = transformers.AutoTokenizer.from_pretrained("gpt2")
-        exec_info = QEfficient.cloud_ai_100_exec_kv(tokenizer=tokenizer, qpc_path=qpc_path, prompt="Hi there!!", device_id=[0])
+        exec_info = QEfficient.cloud_ai_100_exec_kv(tokenizer=tokenizer, qpc_path=qpc_path, prompt="Hi there!!", device_ids=[0])
 
     """
     batch_size, ctx_len, full_batch_size = get_compilation_dims(qpc_path)
@@ -390,7 +390,7 @@ def cloud_ai_100_exec_kv(
     generate_text = TextGeneration(
         tokenizer=tokenizer,
         qpc_path=qpc_path,
-        device_id=device_id,
+        device_ids=device_ids,
         ctx_len=ctx_len,
         comp_ctx_lengths_prefill=comp_ctx_lengths_prefill,
         comp_ctx_lengths_decode=comp_ctx_lengths_decode,
@@ -443,7 +443,7 @@ def __init__(
         ctx_len: Optional[int] = None,
         comp_ctx_lengths_prefill: Optional[List[int]] = None,
         comp_ctx_lengths_decode: Optional[List[int]] = None,
-        device_id: Optional[List[int]] = None,
+        device_ids: Optional[List[int]] = None,
         enable_debug_logs: bool = False,
         write_io_dir: Optional[str] = None,
         is_tlm: Optional[int] = None,
@@ -465,7 +465,7 @@ def __init__(
 
         # Load QPC
         self._session = QAICInferenceSession(
-            qpc_path, device_id, activate=activate, enable_debug_logs=enable_debug_logs
+            qpc_path, device_ids, activate=activate, enable_debug_logs=enable_debug_logs
         )
 
         # Validate sampler inputs for On-Device Sampling
@@ -1083,7 +1083,7 @@ def __init__(
         ctx_len: Optional[int] = None,
         comp_ctx_lengths_prefill: Optional[List[int]] = None,
         comp_ctx_lengths_decode: Optional[List[int]] = None,
-        device_id: Optional[List[int]] = None,
+        device_ids: Optional[List[int]] = None,
         enable_debug_logs: bool = False,
         write_io_dir: Optional[str] = None,
         is_tlm: bool = False,
@@ -1099,7 +1099,7 @@ def __init__(
             ctx_len=ctx_len,
             comp_ctx_lengths_prefill=comp_ctx_lengths_prefill,
             comp_ctx_lengths_decode=comp_ctx_lengths_decode,
-            device_id=device_id,
+            device_ids=device_ids,
             enable_debug_logs=enable_debug_logs,
             write_io_dir=write_io_dir,
             is_tlm=is_tlm,

QEfficient/generation/vlm_generation.py

@@ -54,7 +54,7 @@ class VisionLanguageGeneration(QEffTextGenerationBase):
         ...     processor=processor,
         ...     lang_qpc_path="path/to/lang.qpc",
         ...     vision_qpc_path="path/to/vision.qpc",
-        ...     device_id=[0]
+        ...     device_ids=[0]
         ... )
         >>> result = vlm.generate(
         ...     images=["image1.jpg"],
@@ -68,7 +68,7 @@ class VisionLanguageGeneration(QEffTextGenerationBase):
         ...     processor=processor,
         ...     lang_qpc_path="path/to/lang.qpc",
         ...     vision_qpc_path="path/to/vision.qpc",
-        ...     device_id=[0],
+        ...     device_ids=[0],
         ...     full_batch_size=8,  # Enable continuous batching
         ...     include_sampler=True,  # Enable on-device sampling
         ...     sampling_params=sampling_config
@@ -82,7 +82,7 @@ def __init__(
         processor: AutoImageProcessor,
         lang_qpc_path: str,
         vision_qpc_path: str,
-        device_id: Optional[List[int]] = None,
+        device_ids: Optional[List[int]] = None,
         ctx_len: Optional[int] = None,
         comp_ctx_lengths_prefill: Optional[List[int]] = None,
         comp_ctx_lengths_decode: Optional[List[int]] = None,
@@ -106,7 +106,7 @@ def __init__(
             processor: Image processor
             lang_qpc_path: Path to language model QPC
             vision_qpc_path: Path to vision encoder QPC
-            device_id: Device IDs for execution (default: [0])
+            device_ids: Device IDs for execution (default: [0])
             ctx_len: Context length
             enable_debug_logs: Enable debug logging
             write_io_dir: Directory for I/O file writing
@@ -134,7 +134,7 @@ def __init__(
             ctx_len=ctx_len,
             comp_ctx_lengths_prefill=comp_ctx_lengths_prefill,
             comp_ctx_lengths_decode=comp_ctx_lengths_decode,
-            device_id=device_id,
+            device_ids=device_ids,
             enable_debug_logs=enable_debug_logs,
             write_io_dir=write_io_dir,
             is_tlm=is_tlm,
@@ -157,7 +157,7 @@ def __init__(
         self.image_height = image_height
         self.image_width = image_width
         self._vision_qpc_path = vision_qpc_path
-        self.device_id = device_id  # Store device_id for vision components
+        self.device_ids = device_ids  # Store device_ids for vision components
         self.enable_debug_logs = enable_debug_logs  # Store for vision components
         self._vision_outputs_cache = LRUCache(max_size=100)  # LRU cache for vision outputs
         self._vision_cache = {}  # Cache for vision outputs across batches
@@ -177,7 +177,7 @@ def _init_vision_components(self):
         """Initialize vision-specific components"""
         # Vision session (separate from base class language session)
         self._vision_session = QAICInferenceSession(
-            self._vision_qpc_path, self.device_id, activate=False, enable_debug_logs=self.enable_debug_logs
+            self._vision_qpc_path, self.device_ids, activate=False, enable_debug_logs=self.enable_debug_logs
         )
 
         # Vision handler with language session coordination
@@ -801,7 +801,7 @@ def generate_stream_tokens(
             tokenizer=self.tokenizer,
             qpc_path=self._qpc_path,
             ctx_len=self._ctx_len,
-            device_id=self.device_id,
+            device_ids=self.device_ids,
             enable_debug_logs=self.enable_debug_logs,
             is_tlm=self.is_tlm,
             include_sampler=self.include_sampler,

QEfficient/peft/lora/auto.py

@@ -395,7 +395,7 @@ def generate(
         tokenizer: Union[PreTrainedTokenizerFast, PreTrainedTokenizer],
         prompts: List[str],
         prompt_to_adapter_mapping: List[str] = None,
-        device_id: Optional[List[int]] = None,
+        device_ids: Optional[List[int]] = None,
         runtime: Optional[str] = "AI_100",
         **kwargs,
     ):
@@ -410,7 +410,7 @@ def generate(
             tokenizer (PreTrainedTokenizerFast or PreTrainedTokenizer): Tokenizer used for inference.
             prompts (List[str]): List of prompts to generate outputs for.
             prompt_to_adapter_mapping (List[str]): List of adapter names to use for each prompt. Use "base" for the base model (no adapter).
-            device_id (List[int], optional): Device IDs to use for execution. If `None`, auto-device-picker is used.
+            device_ids (List[int], optional): Device IDs to use for execution. If `None`, auto-device-picker is used.
             runtime (str, optional): Runtime to use. Only "AI_100" is currently supported. Default is "AI_100".
             **kwargs: Additional generation parameters.
 
@@ -440,7 +440,7 @@ def generate(
             tokenizer,
             self.qpc_path,
             prompt=prompts,
-            device_id=device_id,
+            device_ids=device_ids,
             generation_len=generation_len,
             prompt_to_lora_id_mapping=[
                 self.active_adapter_to_id[name] if name != "base" else 0 for name in prompt_to_adapter_mapping

QEfficient/transformers/models/modeling_auto.py

@@ -1717,7 +1717,7 @@ def generate(
                 vision_qpc_path=self.vision_model.qpc_path,
                 tokenizer=tokenizer,
                 processor=processor,
-                device_id=device_ids,  # if device_ids is not None else [0],
+                device_ids=device_ids,  # if device_ids is not None else [0],
                 ctx_len=ctx_len_comp,
                 full_batch_size=fbs,
                 comp_ctx_lengths_prefill=self.comp_ctx_lengths_prefill,
@@ -3674,7 +3674,7 @@ def generate(
         self,
         tokenizer: Union[PreTrainedTokenizerFast, PreTrainedTokenizer],
         prompts: List[str],
-        device_id: List[int] = None,
+        device_ids: List[int] = None,
         runtime_ai100: bool = True,
         **kwargs,
     ):
@@ -3690,7 +3690,7 @@ def generate(
             Tokenizer for the model.
         prompts : list of str
             List of prompts to generate output for.
-        device_id : list of int, optional
+        device_ids : list of int, optional
             Device IDs for running the QPC. Defaults to `[0]` if not specified.
         runtime_ai100 : bool, optional
             Whether to use AI 100 runtime. Default is True.
@@ -3724,7 +3724,7 @@ def generate(
                 prompt=prompts,
                 comp_ctx_lengths_prefill=self.comp_ctx_lengths_prefill,
                 comp_ctx_lengths_decode=self.comp_ctx_lengths_decode,
-                device_id=device_id,
+                device_ids=device_ids,
                 generation_len=generation_len,
                 automation=kwargs.pop("automation", False),
                 iteration=kwargs.pop("iteration", 1),
@@ -4363,7 +4363,7 @@ def generate(
             :inputs (Union[torch.Tensor, np.ndarray]): inputs to run the execution.
             :processor (AutoProcessor): The Processor to use for encoding the waveform.
         ``optional`` Args:
-            :device_id (List[int]): Ids of devices for running the qpc pass as [0] in case of normal model / [0, 1, 2, 3] in case of tensor slicing model
+            :device_ids (List[int]): Ids of devices for running the qpc pass as [0] in case of normal model / [0, 1, 2, 3] in case of tensor slicing model
             :runtime_ai100 (bool, optional): ``AI_100`` and ``PyTorch`` runtime is supported as of now. Defaults to ``True`` for ``AI_100`` runtime.
         Returns:
             :dict: Output from the ``AI_100`` or ``PyTorch`` runtime.

QEfficient/utils/run_utils.py

@@ -234,7 +234,7 @@ def run_kv_model_on_cloud_ai_100(self, qpc_path, device_group=None):
         execinfo = TextGeneration(
             tokenizer=self.input_handler.tokenizer,
             qpc_path=qpc_path,
-            device_id=device_group,
+            device_ids=device_group,
             ctx_len=self.input_handler.ctx_len,
             full_batch_size=self.input_handler.full_batch_size,
         ).generate(prompt=self.input_handler.prompt, generation_len=self.gen_len, stream=False)

examples/peft/multi_adapter.py

@@ -74,7 +74,7 @@
 qeff_model.generate(
     tokenizer=load_hf_tokenizer(pretrained_model_name_or_path=base_model_name),
     prompts=prompts,
-    device_id=device_group,
+    device_ids=device_group,
     prompt_to_adapter_mapping=[
         "gsm8k",
         "tldr_content_gen",

examples/performance/cpp_execution/text_inference_cpp.py

@@ -131,7 +131,7 @@ def main(
         qpc_path=qpc_dir_path,
         prompt_len=prompt_len,
         prompt=prompt,
-        device_id=device_group,
+        device_ids=device_group,
         prompts_txt_file_path=prompts_txt_file_path,
         generation_len=generation_len,
         full_batch_size=full_batch_size,
@@ -144,7 +144,7 @@ def cloud_ai_100_exec_kv_cpp(
     prompt_len: int,
     prompt: Optional[List[str]] = None,
     prompts_txt_file_path: Optional[str] = None,
-    device_id: Optional[List[int]] = None,
+    device_ids: Optional[List[int]] = None,
     generation_len: Optional[int] = None,
     enable_debug_logs: bool = False,
     stream: bool = True,
@@ -156,7 +156,7 @@ def cloud_ai_100_exec_kv_cpp(
 
     # ********* CPP Calling ********
     InferenceSetIOBuffer.generatePrompt(
-        tokenizer, qpc_path, prompt_len, ctx_len, batch_size, prompt, generation_len, device_id
+        tokenizer, qpc_path, prompt_len, ctx_len, batch_size, prompt, generation_len, device_ids
     )
 
 

examples/performance/on_device_sampling.py

@@ -101,7 +101,7 @@ def main(args, **kwargs):
         tokenizer=load_hf_tokenizer(pretrained_model_name_or_path=args.model_name),
         prompts=args.prompt,
         prompts_txt_file_path=args.prompts_txt_file_path,
-        device_id=args.device_group,
+        device_ids=args.device_group,
         generation_len=args.generation_len,
         include_sampler=include_sampler,
         return_pdfs=return_pdfs,

examples/text_generation/basic_inference.py

@@ -47,7 +47,7 @@ def main():
     exec_info = model.generate(
         tokenizer=tokenizer,
         prompts=[args.prompt],
-        device_id=args.device_group,
+        device_ids=args.device_group,
         generation_len=args.generation_len,
     )
 

examples/text_generation/continuous_batching.py

@@ -56,7 +56,7 @@ def main():
     exec_info = model.generate(
         tokenizer=tokenizer,
         prompts=prompt_list,
-        device_id=args.device_group,
+        device_ids=args.device_group,
         generation_len=args.generation_len,
     )
 

examples/text_generation/moe_inference.py

@@ -54,7 +54,7 @@ def main():
     exec_info = model.generate(
         tokenizer=tokenizer,
         prompts=[args.prompt],
-        device_id=args.device_group,
+        device_ids=args.device_group,
         generation_len=args.generation_len,
     )