Remove numpy dependencies in src/lightning/pytorch (#19841)

src/lightning/pytorch/loggers/logger.py

@@ -15,11 +15,11 @@
 
 import functools
 import operator
+import statistics
 from abc import ABC
 from collections import defaultdict
 from typing import Any, Callable, Dict, Mapping, Optional, Sequence
 
-import numpy as np
 from typing_extensions import override
 
 from lightning.fabric.loggers import Logger as FabricLogger
@@ -100,7 +100,7 @@ def method(*args: Any, **kwargs: Any) -> None:
 def merge_dicts(  # pragma: no cover
     dicts: Sequence[Mapping],
     agg_key_funcs: Optional[Mapping] = None,
-    default_func: Callable[[Sequence[float]], float] = np.mean,
+    default_func: Callable[[Sequence[float]], float] = statistics.mean,
 ) -> Dict:
     """Merge a sequence with dictionaries into one dictionary by aggregating the same keys with some given function.
 
@@ -126,7 +126,7 @@ def merge_dicts(  # pragma: no cover
         >>> d2 = {'a': 1.1, 'b': 2.2, 'v': 1, 'd': {'d1': 2, 'd2': 3}}
         >>> d3 = {'a': 1.1, 'v': 2.3, 'd': {'d3': 3, 'd4': {'d5': 1}}}
         >>> dflt_func = min
-        >>> agg_funcs = {'a': np.mean, 'v': max, 'd': {'d1': sum}}
+        >>> agg_funcs = {'a': statistics.mean, 'v': max, 'd': {'d1': sum}}
         >>> pprint.pprint(merge_dicts([d1, d2, d3], agg_funcs, dflt_func))
         {'a': 1.3,
          'b': 2.0,

src/lightning/pytorch/tuner/lr_finder.py

@@ -190,7 +190,7 @@ def suggestion(self, skip_begin: int = 10, skip_end: int = 1) -> Optional[float]
         losses = losses[torch.isfinite(losses)]
 
         if len(losses) < 2:
-            # computing np.gradient requires at least 2 points
+            # computing torch.gradient requires at least 2 points
             log.error(
                 "Failed to compute suggestion for learning rate because there are not enough points. Increase the loop"
                 " iteration limits or the size of your dataset/dataloader."

src/lightning/pytorch/utilities/__init__.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 """General utilities."""
 
-import numpy
+import torch
 
 from lightning.fabric.utilities import (
     LightningEnum,
@@ -55,6 +55,6 @@
     "suggested_max_num_workers",
 ]
 
-FLOAT16_EPSILON = numpy.finfo(numpy.float16).eps
-FLOAT32_EPSILON = numpy.finfo(numpy.float32).eps
-FLOAT64_EPSILON = numpy.finfo(numpy.float64).eps
+FLOAT16_EPSILON = torch.finfo(torch.float16).eps
+FLOAT32_EPSILON = torch.finfo(torch.float32).eps
+FLOAT64_EPSILON = torch.finfo(torch.float64).eps