fix: Simplified Chained Comparison

python/oneflow/framework/check_point_v2.py

@@ -728,7 +728,7 @@ def write_file():
             global_dst_rank, int
         ), f"global_dst_rank expected type int, but got {type(global_dst_rank)}."
         assert (
-            global_dst_rank >= 0 and global_dst_rank < flow.env.get_world_size()
+                0 <= global_dst_rank < flow.env.get_world_size()
         ), f"out of range (expected to be in range of [0, {flow.env.get_world_size()}), but got {global_dst_rank})."
         if flow.env.get_rank() == global_dst_rank:
             write_file()

python/oneflow/nn/graph/graph_config.py

@@ -123,11 +123,11 @@ def build(self, x):
         if not mode:
             self.proto.optimizer_placement_optimization_mode = "none"
             return
-        assert stage >= 1 and stage <= 3, "ZeRO stage must range from 1 to 3."
+        assert 1 <= stage <= 3, "ZeRO stage must range from 1 to 3."
         assert (
             shard_min_size > 0
         ), "ZeRO min size of a sharded optimizer state must > 0."
-        assert stage >= 1 and stage <= 3, "ZeRO stage must range from 1 to 3."
+        assert 1 <= stage <= 3, "ZeRO stage must range from 1 to 3."
         if stage >= 1:
             self.proto.optimizer_placement_optimization_mode = "distributed_split"
             self.proto.optimizer_placement_optimization_threshold = shard_min_size

python/oneflow/nn/optimizer/adam.py

@@ -128,10 +128,10 @@ def __init__(
         assert lr >= 0.0, f"Invalid learning rate: {lr}"
         assert eps >= 0.0, f"Invalid epsilon value: {eps}"
         assert (
-            betas[0] >= 0.0 and betas[0] < 1.0
+                0.0 <= betas[0] < 1.0
         ), f"Invalid beta parameter at index 0: {betas[0]}"
         assert (
-            betas[1] >= 0.0 and betas[1] < 1.0
+                0.0 <= betas[1] < 1.0
         ), f"Invalid beta parameter at index 1: {betas[1]}"
         assert weight_decay >= 0.0, f"Invalid weight_decay value: {weight_decay}"
         options = dict()

python/oneflow/nn/optimizer/adamw.py

@@ -130,10 +130,10 @@ def __init__(
         assert lr >= 0.0, f"Invalid learning rate: {lr}"
         assert eps >= 0.0, f"Invalid epsilon value: {eps}"
         assert (
-            betas[0] >= 0.0 and betas[0] < 1.0
+                0.0 <= betas[0] < 1.0
         ), f"Invalid beta parameter at index 0: {betas[0]}"
         assert (
-            betas[1] >= 0.0 and betas[1] < 1.0
+                0.0 <= betas[1] < 1.0
         ), f"Invalid beta parameter at index 1: {betas[1]}"
         assert weight_decay >= 0.0, f"Invalid weight_decay value: {weight_decay}"
         options = dict()

python/oneflow/nn/optimizer/lamb.py

@@ -124,10 +124,10 @@ def __init__(
         assert lr >= 0.0, f"Invalid learning rate: {lr}"
         assert eps >= 0.0, f"Invalid epsilon value: {eps}"
         assert (
-            betas[0] >= 0.0 and betas[0] < 1.0
+                0.0 <= betas[0] < 1.0
         ), f"Invalid beta parameter at index 0: {betas[0]}"
         assert (
-            betas[1] >= 0.0 and betas[1] < 1.0
+                0.0 <= betas[1] < 1.0
         ), f"Invalid beta parameter at index 1: {betas[1]}"
         assert weight_decay >= 0.0, f"Invalid weight_decay value: {weight_decay}"
 

python/oneflow/support/func_inspect_util.py

@@ -16,7 +16,7 @@
 import inspect
 import sys
 
-if sys.version_info > (2, 7) and sys.version_info < (3, 0):
+if (2, 7) < sys.version_info < (3, 0):
 
     def GetArgNameAndDefaultTuple(func):
         """

python/oneflow/test/graph/test_graph_clip_grad_norm.py

@@ -120,7 +120,7 @@ def __init__(
                 parallel_mode = [parallel_mode]
 
             assert all(p.upper() in ("DP", "MP", "PP") for p in parallel_mode)
-            assert len(parallel_mode) > 0 and len(parallel_mode) <= 2
+            assert 0 < len(parallel_mode) <= 2
 
             self.input_sbp = []
             self.target_sbp = []
@@ -254,7 +254,7 @@ def _compare_with_eager(
     # print(f"[rank{rank}] local_loss.numel(): {local_loss.numel()}")
     # print(f"[rank{rank}] local_loss: {local_loss}")
 
-    if acc > 1 and graph_loss.numel() == acc:
+    if 1 < acc == graph_loss.numel():
         graph_loss = graph_loss.mean()
 
     if parallel_mode is None:

python/oneflow/test/modules/test_reflection_pad.py

@@ -58,13 +58,13 @@ def _np_reflection_pad2d_grad(src, dest):
             ip_x = ip_y = 0
             if j < pad_left:
                 ip_x = pad_left * 2 - j
-            elif j >= pad_left and j < dx_width + pad_left:
+            elif pad_left <= j < dx_width + pad_left:
                 ip_x = j
             else:
                 ip_x = (dx_width + pad_left - 1) * 2 - j
             if i < pad_top:
                 ip_y = pad_top * 2 - i
-            elif i >= pad_top and i < dx_height + pad_top:
+            elif pad_top <= i < dx_height + pad_top:
                 ip_y = i
             else:
                 ip_y = (dx_height + pad_top - 1) * 2 - i

python/oneflow/test/modules/test_replication_pad.py

@@ -52,13 +52,13 @@ def _np_replication_pad2d_grad(src, dest, padding):
         ip_x = ip_y = 0
         if j < pad_left:
             ip_x = pad_left
-        elif j >= pad_left and j < dx_width + pad_left:
+        elif pad_left <= j < dx_width + pad_left:
             ip_x = j
         else:
             ip_x = dx_width + pad_left - 1
         if i < pad_top:
             ip_y = pad_top
-        elif i >= pad_top and i < dx_height + pad_top:
+        elif pad_top <= i < dx_height + pad_top:
             ip_y = i
         else:
             ip_y = dx_height + pad_top - 1

python/oneflow/test/modules/test_zeropad2d.py

@@ -49,9 +49,8 @@ def _np_zero_pad2d_grad(src, dest, padding):
         (n, c, i, j) = (coords[0], coords[c_idx], coords[h_idx], coords[w_idx])
         ip_x = ip_y = 0
         if (
-            j >= pad_left
-            and j < dx_width + pad_left
-            and (i >= pad_top)
+            pad_left <= j < dx_width + pad_left
+                and (i >= pad_top)
             and (i < dx_height + pad_top)
         ):
             ip_x = j - pad_left

python/oneflow/test/tensor/test_complex.py

@@ -92,9 +92,8 @@ def _np_zero_pad2d_grad(src, dest, padding):
         (n, c, i, j) = (coords[0], coords[c_idx], coords[h_idx], coords[w_idx])
         ip_x = ip_y = 0
         if (
-            j >= pad_left
-            and j < dx_width + pad_left
-            and (i >= pad_top)
+            pad_left <= j < dx_width + pad_left
+                and (i >= pad_top)
             and (i < dx_height + pad_top)
         ):
             ip_x = j - pad_left

tools/functional/generator.py

@@ -402,7 +402,7 @@ def to_string(self, to_cpp=False, drop_name=False):
             fmt = "{0} {1}(".format(self._ret.to_string(to_cpp=to_cpp), self._name)
         keyword_start = False
         for i, arg in enumerate(self._args):
-            if i > 0 and i < len(self._args):
+            if 0 < i < len(self._args):
                 fmt += ", "
             if not keyword_start and arg._keyword_only:
                 keyword_start = True