[CUDNN] Format and lint

hidet-org · Jun 27, 2024 · 2f8fdaa · 2f8fdaa
1 parent 5d5d8a6
commit 2f8fdaa
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Showing 3 changed files with 15 additions and 15 deletions.
diff --git a/python/hidet/cuda/cudnn/benchmark.py b/python/hidet/cuda/cudnn/benchmark.py
@@ -101,7 +101,6 @@ def benchmark_cudnn_conv2d_gemm(dtype, compute_type, n, c, h, w, k, p, q, r, s,
     print("-------------------------------------------------")
 
 
-
 def benchmark_torch_conv2d(dtype, compute_type, n, c, h, w, k, p, q, r, s, padding, stride, dilations):
     # Native PyTorch Eager-mode Execution
     data = np.array(np.random.randn(n, c, h, w)).astype(dtype)
@@ -129,7 +128,7 @@ def benchmark_torch_conv2d(dtype, compute_type, n, c, h, w, k, p, q, r, s, paddi
 
 def benchmark_hidet_conv2d(dtype, compute_type, n, c, h, w, k, p, q, r, s, padding, stride, dilations):
     # Uses optimized Hidet Graph implementation
-    tx = tw = ty = dtype
+    tx = tw = dtype
     pad_dim1, pad_dim2 = padding
     str_dim1, str_dim2 = stride
     dil_dim1, dil_dim2 = dilations

diff --git a/python/hidet/cuda/cudnn/kernels.py b/python/hidet/cuda/cudnn/kernels.py
@@ -209,4 +209,3 @@ def conv2d_gemm(
         dil_dim1,
         dil_dim2,
     )
-
diff --git a/tests/cuda/test_cudnn.py b/tests/cuda/test_cudnn.py
@@ -21,16 +21,17 @@
     [
         [1, 3, 32, 32, 12, 30, 30, 3, 3, [0, 0], [1, 1], [1, 1]],  # kernel 3,
         [2, 3, 32, 32, 12, 11, 6, 7, 7, [1, 2], [2, 3], [2, 3]],  # kernel 7, batch size 2
-        [1, 3, 224, 224, 64, 112, 112, 7, 7, [3, 3], [2, 2], [1, 1]], # resnet layer 1
-        [1, 64, 56, 56, 128, 56, 56, 1, 1, [0, 0], [1, 1], [1, 1]], # resnet layer 2 - kernel size 1
+        [1, 3, 224, 224, 64, 112, 112, 7, 7, [3, 3], [2, 2], [1, 1]],  # resnet layer 1
+        [1, 64, 56, 56, 128, 56, 56, 1, 1, [0, 0], [1, 1], [1, 1]],  # resnet layer 2 - kernel size 1
     ],
 )
 @pytest.mark.parametrize(
     'dtype, compute_type, tol',
-    [(hidet.float16, cudnnDataType.CUDNN_DATA_HALF, 1e-2),
-     (hidet.float32, cudnnDataType.CUDNN_DATA_FLOAT, 1e-5), 
-     (hidet.float64, cudnnDataType.CUDNN_DATA_DOUBLE, 1e-8),
-     ]
+    [
+        (hidet.float16, cudnnDataType.CUDNN_DATA_HALF, 1e-2),
+        (hidet.float32, cudnnDataType.CUDNN_DATA_FLOAT, 1e-5),
+        (hidet.float64, cudnnDataType.CUDNN_DATA_DOUBLE, 1e-8),
+    ],
 )
 def test_cudnn_conv2d(n, c, h, w, k, p, q, r, s, dtype, compute_type, padding, stride, dilations, tol):
     tx = tw = ty = dtype
@@ -78,16 +79,17 @@ def test_cudnn_conv2d(n, c, h, w, k, p, q, r, s, dtype, compute_type, padding, s
     [
         [1, 3, 32, 32, 12, 30, 30, 3, 3, [0, 0], [1, 1], [1, 1]],  # kernel 3,
         [2, 3, 32, 32, 12, 11, 6, 7, 7, [1, 2], [2, 3], [2, 3]],  # kernel 7, batch size 2
-        [1, 3, 224, 224, 64, 112, 112, 7, 7, [3, 3], [2, 2], [1, 1]], # resnet layer 1
-        [1, 64, 56, 56, 128, 56, 56, 1, 1, [0, 0], [1, 1], [1, 1]], # resnet layer 2 - kernel size 1
+        [1, 3, 224, 224, 64, 112, 112, 7, 7, [3, 3], [2, 2], [1, 1]],  # resnet layer 1
+        [1, 64, 56, 56, 128, 56, 56, 1, 1, [0, 0], [1, 1], [1, 1]],  # resnet layer 2 - kernel size 1
     ],
 )
 @pytest.mark.parametrize(
     'dtype, compute_type, tol',
-    [(hidet.float16, cudnnDataType.CUDNN_DATA_HALF, 1e-2),
-     (hidet.float32, cudnnDataType.CUDNN_DATA_FLOAT, 1e-5), 
-     (hidet.float64, cudnnDataType.CUDNN_DATA_DOUBLE, 1e-8),
-     ]
+    [
+        (hidet.float16, cudnnDataType.CUDNN_DATA_HALF, 1e-2),
+        (hidet.float32, cudnnDataType.CUDNN_DATA_FLOAT, 1e-5),
+        (hidet.float64, cudnnDataType.CUDNN_DATA_DOUBLE, 1e-8),
+    ],
 )
 def test_cudnn_conv2d_gemm(n, c, h, w, k, p, q, r, s, dtype, compute_type, padding, stride, dilations, tol):
     tx = tw = ty = dtype