Added logging and made dtype adjustments in histogram modules

- Introduced `logging` in `fate/arch/histogram/histogram.py` to provide better traceability.
- Added a warning log when the update value data type does not match the histogram data type.
- Adjusted the data type of "cnt" from `torch.float32` to `torch.int32` in `fate/ml/ensemble/learner/decision_tree/tree_core/hist.py` for better type consistency.

Signed-off-by: sagewe <wbwmat@gmail.com>

python/fate/arch/histogram/histogram.py

@@ -1,11 +1,14 @@
 import typing
+import logging
 from typing import List, MutableMapping, Tuple
 
 import torch
 
 # from fate_utils.histogram import HistogramIndexer, Shuffler
 from .indexer import HistogramIndexer, Shuffler
 
+loggger = logging.getLogger(__name__)
+
 
 class HistogramValues:
     def iadd_slice(self, value, sa, sb, size):
@@ -192,10 +195,10 @@ def i_update(self, value, positions):
         index = index.flatten()
 
         if self.data.dtype != value.dtype:
+            loggger.warning(f"update value dtype {value.dtype} is not equal to data dtype {self.data.dtype}")
             value = value.to(self.data.dtype)
         self.data.scatter_add_(0, index, value)
 
-
     def i_shuffle(self, shuffler: "Shuffler", reverse=False):
         indices = shuffler.get_shuffle_index(step=self.stride, reverse=reverse)
         self.data = self.data[indices]

python/fate/ml/ensemble/learner/decision_tree/tree_core/hist.py

@@ -113,14 +113,14 @@ def _get_plain_text_schema(self):
         return {
                 "g": {"type": "tensor", "stride": 1, "dtype": torch.float32},
                 "h": {"type": "tensor", "stride": 1, "dtype": torch.float32},
-                "cnt": {"type": "tensor", "stride": 1, "dtype": torch.float32},
+                "cnt": {"type": "tensor", "stride": 1, "dtype": torch.int32},
             }
 
     def _get_enc_hist_schema(self, pk, evaluator):
         return {
                 "g":{"type": "paillier", "stride": 1, "pk": pk, "evaluator": evaluator},
                 "h":{"type": "paillier", "stride": 1, "pk": pk, "evaluator": evaluator},
-                "cnt": {"type": "tensor", "stride": 1, "dtype": torch.float32},
+                "cnt": {"type": "tensor", "stride": 1, "dtype": torch.int32},
             }
 
     def compute_hist(self, ctx: Context, nodes: List[Node], bin_train_data: DataFrame, gh: DataFrame, sample_pos: DataFrame = None, node_map={}, pk=None, evaluator=None):