initial

optimum/exporters/onnx/base.py

@@ -177,6 +177,7 @@ class OnnxConfig(ExportConfig, ABC):
         "text2text-generation": OrderedDict({"logits": {0: "batch_size", 1: "decoder_sequence_length"}}),
         "text-classification": OrderedDict({"logits": {0: "batch_size"}}),
         "text-generation": OrderedDict({"logits": {0: "batch_size", 1: "sequence_length"}}),
+        "time-series-forecasting": OrderedDict({"prediction_outputs": {0: "batch_size"}}),
         "token-classification": OrderedDict({"logits": {0: "batch_size", 1: "sequence_length"}}),
         "visual-question-answering": OrderedDict({"logits": {0: "batch_size", 1: "sequence_length"}}),
         "zero-shot-image-classification": OrderedDict(

optimum/exporters/onnx/model_configs.py

@@ -59,6 +59,7 @@
     NormalizedTextAndVisionConfig,
     NormalizedTextConfig,
     NormalizedTextConfigWithGQA,
+    NormalizedTimeSeriesForecastingConfig,
     NormalizedVisionConfig,
     check_if_diffusers_greater,
     check_if_transformers_greater,
@@ -2499,3 +2500,46 @@ class EncoderDecoderOnnxConfig(EncoderDecoderBaseOnnxConfig):
     NORMALIZED_CONFIG_CLASS = NormalizedEncoderDecoderConfig
 
     DEFAULT_ONNX_OPSET = 14  # uses SDPA in Transformers, hence opset>=14.
+
+
+
+
+class TimesFMDummyInputGenerator(DummyInputGenerator):
+    SUPPORTED_INPUT_NAMES = ("inputs",)
+
+    def __init__(
+        self,
+        task: str,
+        normalized_config: NormalizedConfig,
+        batch_size: int = DEFAULT_DUMMY_SHAPES["batch_size"],
+        **kwargs,
+    ):
+        self.task = task
+        self.normalized_config = normalized_config
+
+        self.batch_size = batch_size
+        self.context_len = normalized_config.context_len
+
+    def generate(self, input_name: str, framework: str = "pt", int_dtype: str = "int64", float_dtype: str = "fp32"):
+        return self.random_float_tensor(
+            shape=[self.batch_size, self.context_len],
+            min_value=-1,
+            max_value=1,
+            framework=framework,
+            dtype=float_dtype,
+        )
+
+
+class TimesFMOnnxConfig(OnnxConfig):
+    NORMALIZED_CONFIG_CLASS = NormalizedTimeSeriesForecastingConfig
+    MIN_TRANSFORMERS_VERSION = version.parse("4.47.0")
+    DUMMY_INPUT_GENERATOR_CLASSES = (TimesFMDummyInputGenerator,)
+
+
+    @property
+    def inputs(self) -> Dict[str, Dict[int, str]]:
+        return {"inputs": {0: "batch_size", 1: "sequence_length"}}
+
+    @property
+    def outputs(self) -> Dict[str, Dict[int, str]]:
+        return super().outputs

optimum/exporters/tasks.py

@@ -320,6 +320,7 @@ class TasksManager:
         ("pt", "visual-bert", "question-answering"): ("transformers", "VisualBertForQuestionAnswering"),
         # VisionEncoderDecoderModel is not registered in AutoModelForDocumentQuestionAnswering
         ("pt", "vision-encoder-decoder", "document-question-answering"): ("transformers", "VisionEncoderDecoderModel"),
+        ("pt", "timesfm", "time-series-forecasting"): ("transformers", "TimesFMModelForPrediction"),
     }
 
     _ENCODER_DECODER_TASKS = (
@@ -939,6 +940,10 @@ class TasksManager:
             "text-classification",
             onnx="Qwen2OnnxConfig",
         ),
+        "timesfm": supported_tasks_mapping(
+            "time-series-forecasting",
+            onnx="TimesFMOnnxConfig",
+        ),
         "llama": supported_tasks_mapping(
             "feature-extraction",
             "feature-extraction-with-past",

optimum/utils/__init__.py

@@ -91,5 +91,6 @@
     NormalizedTextAndVisionConfig,
     NormalizedTextConfig,
     NormalizedTextConfigWithGQA,
+    NormalizedTimeSeriesForecastingConfig,
     NormalizedVisionConfig,
 )

optimum/utils/normalized_config.py

@@ -96,6 +96,10 @@ class NormalizedSeq2SeqConfig(NormalizedTextConfig):
     DECODER_NUM_ATTENTION_HEADS = NormalizedTextConfig.NUM_ATTENTION_HEADS
 
 
+class NormalizedTimeSeriesForecastingConfig(NormalizedConfig):
+    CONTEXT_LEN = "context_len"
+
+
 class NormalizedVisionConfig(NormalizedConfig):
     IMAGE_SIZE = "image_size"
     NUM_CHANNELS = "num_channels"