feat(AINode): [Issue-17301] Import PatchTST-FM-R1 architecture and re…

iotdb-core/ainode/iotdb/ainode/core/model/model_info.py

@@ -158,4 +158,16 @@ def __repr__(self):
         },
         transformers_registered=True,
     ),
+    "patchtst_fm": ModelInfo(
+        model_id = "patchtst_fm",
+        category=ModelCategory.BUILTIN,
+        state=ModelStates.INACTIVE,
+        model_type="patchtst_fm",
+        pipeline_cls="pipeline_patchtst_fm.PatchTSTFMPipeline",
+        repo_id="ibm-research/patchtst-fm-r1",
+        auto_map={
+        "AutoConfig": "configuration_patchtst_fm.PatchTSTFMConfig",
+        "AutoModelForCausalLM": "modeling_patchtst_fm.PatchTSTFMForPrediction",
+    },
+    ),
 }

iotdb-core/ainode/iotdb/ainode/core/model/patchtst_fm/configuration_patchtst_fm.py

@@ -0,0 +1,54 @@
+# Copyright contributors to the TSFM project
+#
+"""PatchTST-FM model configuration"""
+
+from transformers.configuration_utils import PretrainedConfig
+from transformers.utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+PATCHTSTFM_PRETRAINED_CONFIG_ARCHIVE_MAP = {}
+
+
+class PatchTSTFMConfig(PretrainedConfig):
+    model_type = "patchtst_fm"
+    attribute_map = {
+        "hidden_size": "d_model",
+        "num_hidden_layers": "n_layer",
+    }
+
+    # has_no_defaults_at_init = True
+    def __init__(
+        self,
+        context_length: int = 8192,
+        prediction_length: int = 64,
+        d_patch: int = 16,
+        d_model: int = 384,
+        n_head: int = 6,
+        n_layer: int = 6,
+        norm_first: bool = True,
+        pretrain_mask_ratio: float = 0.4,
+        pretrain_mask_cont: int = 8,
+        num_quantile: int = 99,
+        **kwargs,
+    ):
+        self.context_length = context_length
+        self.prediction_length = prediction_length
+        self.d_patch = d_patch
+        self.n_patch = int(context_length // d_patch)
+        self.d_model = d_model
+        self.n_head = n_head
+        self.n_layer = n_layer
+        self.norm_first = norm_first
+        self.pretrain_mask_ratio = pretrain_mask_ratio
+        self.pretrain_mask_cont = pretrain_mask_cont
+        self.num_quantile = num_quantile
+
+        if num_quantile % 9 == 0:
+            quantiles = [i / (self.num_quantile + 1) for i in range(1, self.num_quantile + 1)]
+        else:
+            quantiles = [i / (self.num_quantile - 1) for i in range(1, self.num_quantile - 1)]
+            quantiles = [0.01] + quantiles + [0.99]
+        self.quantile_levels = quantiles
+        super().__init__(**kwargs)