From 5f1ede7f7e1e5d31e14c99d3fdfd5f2a94c2af16 Mon Sep 17 00:00:00 2001
From: chenyu <chenyu@fastmail.com>
Date: Wed, 7 Jan 2026 15:45:42 -0500
Subject: [PATCH] clean up test_dtype (#14055)

use less lambda
---
 test/test_dtype.py | 53 ++++++++++++++++++++++------------------------
 1 file changed, 25 insertions(+), 28 deletions(-)

diff --git a/test/test_dtype.py b/test/test_dtype.py
index 2104b67112..5b34d11989 100644
--- a/test/test_dtype.py
+++ b/test/test_dtype.py
@@ -48,11 +48,11 @@ def _test_cast(a:Tensor, target_dtype:DType):
     a = (a > 65504).where(65504, a)
 
   expected = list(a.numpy().astype(_to_np_dtype(target_dtype)))
-  if target_dtype in dtypes.fp8s: expected = list(map(lambda x: truncate[target_dtype](x), expected))
+  if target_dtype in dtypes.fp8s: expected = [truncate[target_dtype](x) for x in expected]
   _test_op(lambda: a.cast(target_dtype), target_dtype, expected)
 def _test_bitcast(a:Tensor, target_dtype:DType, target=None):
   expected = torch.tensor(a.tolist(), dtype=_to_torch_storage_type(a.dtype)).view(_to_torch_dtype(target_dtype)).tolist()
-  if target_dtype in dtypes.fp8s: expected = list(map(lambda x: fp8_to_float(x, target_dtype), expected))
+  if target_dtype in dtypes.fp8s: expected = [fp8_to_float(x, target_dtype) for x in expected]
   _test_op(lambda: a.bitcast(target_dtype), target_dtype, target or expected)
 
 class TestDType(unittest.TestCase):
@@ -68,37 +68,34 @@ class TestDType(unittest.TestCase):
   def test_to_np(self):
     _test_to_np(Tensor(self.DATA, dtype=self.DTYPE), _to_np_dtype(self.DTYPE), np.array(self.DATA, dtype=_to_np_dtype(self.DTYPE)))
 
-  def test_casts_to(self): list(map(
-    lambda dtype: _test_cast(Tensor(self.DATA, dtype=dtype), self.DTYPE),
-    get_available_cast_dtypes(self.DTYPE)
-  ))
-  def test_casts_from(self): list(map(
-    lambda dtype: _test_cast(Tensor(self.DATA, dtype=self.DTYPE), dtype),
-    get_available_cast_dtypes(self.DTYPE)
-  ))
+  def test_casts_to(self):
+    for dtype in get_available_cast_dtypes(self.DTYPE):
+      _test_cast(Tensor(self.DATA, dtype=dtype), self.DTYPE)
+
+  def test_casts_from(self):
+    for dtype in get_available_cast_dtypes(self.DTYPE):
+      _test_cast(Tensor(self.DATA, dtype=self.DTYPE), dtype)
 
   def test_same_size_ops(self):
-    list(map(
-      lambda dtype: _test_ops(a_dtype=self.DTYPE, b_dtype=dtype) if dtype.itemsize == self.DTYPE.itemsize else None,
-      get_available_cast_dtypes(self.DTYPE)
-    ))
+    for dtype in get_available_cast_dtypes(self.DTYPE):
+      if dtype.itemsize == self.DTYPE.itemsize:
+        _test_ops(a_dtype=self.DTYPE, b_dtype=dtype)
+
   def test_upcast_ops(self):
-    list(map(
-      lambda dtype: _test_ops(a_dtype=self.DTYPE, b_dtype=dtype) if dtype.itemsize > self.DTYPE.itemsize else None,
-      get_available_cast_dtypes(self.DTYPE)
-  ))
+    for dtype in get_available_cast_dtypes(self.DTYPE):
+      if dtype.itemsize > self.DTYPE.itemsize:
+        _test_ops(a_dtype=self.DTYPE, b_dtype=dtype)
+
   def test_upcast_to_ops(self):
-    list(map(
-      lambda dtype: _test_ops(a_dtype=dtype, b_dtype=self.DTYPE) if dtype.itemsize < self.DTYPE.itemsize else None,
-      get_available_cast_dtypes(self.DTYPE)
-  ))
+    for dtype in get_available_cast_dtypes(self.DTYPE):
+      if dtype.itemsize < self.DTYPE.itemsize:
+        _test_ops(a_dtype=dtype, b_dtype=self.DTYPE)
+
   def test_bitcast(self):
     if self.DTYPE == dtypes.bool: raise unittest.SkipTest("no bools in bitcast")
-    list(map(
-      lambda dtype:
-        _test_bitcast(Tensor(self.DATA[:8], dtype=self.DTYPE), dtype) if dtype != dtypes.bool else None,
-     get_available_cast_dtypes(self.DTYPE)
-    ))
+    for dtype in get_available_cast_dtypes(self.DTYPE):
+      if dtype != dtypes.bool:
+        _test_bitcast(Tensor(self.DATA[:8], dtype=self.DTYPE), dtype)
 
   @unittest.skipIf(Device.DEFAULT == "PYTHON", "skip for now")
   @unittest.skipIf(isinstance(Device[Device.DEFAULT].renderer, (PTXRenderer, NIRRenderer)), "skip for now")
@@ -307,7 +304,7 @@ class TestBitCast(unittest.TestCase):
     data = rand_for_dtype(dt1, 32).reshape(2, 2, 8)
     expected = torch.tensor(data.tolist(), dtype=_to_torch_storage_type(dt1)).view(_to_torch_dtype(dt2))
     if dt2 in dtypes.fp8s:
-      expected = torch.tensor(list(map(lambda x: fp8_to_float(x, dt2), expected.view(-1).tolist()))).view_as(expected)
+      expected = torch.tensor([fp8_to_float(x, dt2) for x in expected.view(-1).tolist()]).view_as(expected)
     _test_op(lambda: Tensor(data, dtype=dt1).bitcast(dt2), dt2, expected.tolist())
 
   def test_shape_change_bitcast_exceptions(self):