[Lint] Phaseout Yapf format and embrace ruff format (#1417)

testing/python/jit/test_tilelang_jit_nullptr.py

@@ -7,16 +7,7 @@ from tilelang.utils import map_torch_type
 
 
 @tl.jit
-def tensor_null_test(M,
-                     N,
-                     K,
-                     block_M,
-                     block_N,
-                     block_K,
-                     dtype="float16",
-                     accum_dtype="float",
-                     with_bias=False):
-
+def tensor_null_test(M, N, K, block_M, block_N, block_K, dtype="float16", accum_dtype="float", with_bias=False):
     @T.prim_func
     def main(
         A: T.Tensor((M, K), dtype),
@@ -48,12 +39,10 @@ def tensor_null_test(M,
 
 
 def run_test(M, N, K, block_M, block_N, block_K, dtype="float16", accum_dtype="float"):
-
     a = torch.randn(M, K, device="cuda", dtype=map_torch_type(dtype))
     b = torch.randn(N, K, device="cuda", dtype=map_torch_type(dtype))
     c = torch.zeros(M, N, device="cuda", dtype=map_torch_type(accum_dtype))
-    kernel = tensor_null_test(
-        M, N, K, block_M, block_N, block_K, dtype, accum_dtype, with_bias=False)
+    kernel = tensor_null_test(M, N, K, block_M, block_N, block_K, dtype, accum_dtype, with_bias=False)
     kernel(a, b, c, None)
 
 

testing/python/jit/test_tilelang_jit_nvrtc.py

@@ -206,6 +206,7 @@ def run_gemm_jit_kernel(
 
     def ref_program(A, B):
         import torch
+
         C = torch.matmul(A.to(torch.float), B.to(torch.float))
         C = C.to(out_dtype)
         return C
@@ -233,19 +234,9 @@ def test_gemm_jit_kernel():
     )
 
 
-def run_nvrtc_kernel_do_bench(M,
-                              N,
-                              K,
-                              trans_A,
-                              trans_B,
-                              in_dtype,
-                              out_dtype,
-                              dtypeAccum,
-                              block_M,
-                              block_N,
-                              block_K,
-                              num_stages=3,
-                              num_threads=128):
+def run_nvrtc_kernel_do_bench(
+    M, N, K, trans_A, trans_B, in_dtype, out_dtype, dtypeAccum, block_M, block_N, block_K, num_stages=3, num_threads=128
+):
     program = matmul(
         M,
         N,
@@ -278,23 +269,12 @@ def run_nvrtc_kernel_do_bench(M,
 
 
 def test_nvrtc_kernel_do_bench():
-    run_nvrtc_kernel_do_bench(512, 1024, 768, False, False, "float16", "float16", "float16", 128,
-                              256, 32, 2)
+    run_nvrtc_kernel_do_bench(512, 1024, 768, False, False, "float16", "float16", "float16", 128, 256, 32, 2)
 
 
-def run_nvrtc_kernel_multi_stream(M,
-                                  N,
-                                  K,
-                                  trans_A,
-                                  trans_B,
-                                  in_dtype,
-                                  out_dtype,
-                                  dtypeAccum,
-                                  block_M,
-                                  block_N,
-                                  block_K,
-                                  num_stages=3,
-                                  num_threads=128):
+def run_nvrtc_kernel_multi_stream(
+    M, N, K, trans_A, trans_B, in_dtype, out_dtype, dtypeAccum, block_M, block_N, block_K, num_stages=3, num_threads=128
+):
     program = matmul(
         M,
         N,
@@ -331,23 +311,12 @@ def run_nvrtc_kernel_multi_stream(M,
 
 
 def test_nvrtc_kernel_multi_stream():
-    run_nvrtc_kernel_multi_stream(512, 1024, 768, False, False, "float16", "float16", "float16",
-                                  128, 256, 32, 2)
+    run_nvrtc_kernel_multi_stream(512, 1024, 768, False, False, "float16", "float16", "float16", 128, 256, 32, 2)
 
 
-def run_nvrtc_dynamic_shape(M,
-                            N,
-                            K,
-                            trans_A,
-                            trans_B,
-                            in_dtype,
-                            out_dtype,
-                            dtypeAccum,
-                            block_M,
-                            block_N,
-                            block_K,
-                            num_stages=3,
-                            num_threads=128):
+def run_nvrtc_dynamic_shape(
+    M, N, K, trans_A, trans_B, in_dtype, out_dtype, dtypeAccum, block_M, block_N, block_K, num_stages=3, num_threads=128
+):
     program = matmul(
         M,
         N,
@@ -387,21 +356,15 @@ def run_nvrtc_dynamic_shape(M,
     matmul_kernel(tensor_a, tensor_b, tensor_c)
 
     tensor_ref_c = torch.matmul(tensor_a.to(torch.float), tensor_b.to(torch.float)).to(out_dtype)
-    tilelang.testing.torch_assert_close(
-        tensor_c, tensor_ref_c, atol=1e-2, rtol=1e-2, max_mismatched_ratio=0.05)
+    tilelang.testing.torch_assert_close(tensor_c, tensor_ref_c, atol=1e-2, rtol=1e-2, max_mismatched_ratio=0.05)
 
 
 def test_nvrtc_dynamic_shape():
-    run_nvrtc_dynamic_shape(
-        T.dynamic("m"), 1024, 768, False, False, "float16", "float16", "float16", 128, 256, 32, 2)
+    run_nvrtc_dynamic_shape(T.dynamic("m"), 1024, 768, False, False, "float16", "float16", "float16", 128, 256, 32, 2)
 
-    run_nvrtc_dynamic_shape(
-        T.dynamic("m"), T.dynamic("n"), 768, False, False, "float16", "float16", "float16", 128,
-        256, 32, 2)
+    run_nvrtc_dynamic_shape(T.dynamic("m"), T.dynamic("n"), 768, False, False, "float16", "float16", "float16", 128, 256, 32, 2)
 
-    run_nvrtc_dynamic_shape(
-        T.dynamic("m"), T.dynamic("n"), T.dynamic("k"), False, False, "float16", "float16",
-        "float16", 128, 256, 32, 2)
+    run_nvrtc_dynamic_shape(T.dynamic("m"), T.dynamic("n"), T.dynamic("k"), False, False, "float16", "float16", "float16", 128, 256, 32, 2)
 
 
 def check_hopper():
@@ -412,22 +375,7 @@ def check_hopper():
     return compute_capability == (9, 0)
 
 
-def convolution_im2col(N,
-                       C,
-                       H,
-                       W,
-                       F,
-                       K,
-                       S,
-                       D,
-                       P,
-                       block_M,
-                       block_N,
-                       block_K,
-                       num_stages,
-                       threads,
-                       dtype="float16",
-                       accum_dtype="float"):
+def convolution_im2col(N, C, H, W, F, K, S, D, P, block_M, block_N, block_K, num_stages, threads, dtype="float16", accum_dtype="float"):
     KH, KW = K, K
     OH = (H + 2 * P - D * (K - 1) - 1) // S + 1
     OW = (W + 2 * P - D * (K - 1) - 1) // S + 1
@@ -438,9 +386,7 @@ def convolution_im2col(N,
         kernel: T.Tensor((KH, KW, C, F), dtype),
         out: T.Tensor((N, OH, OW, F), dtype),
     ):
-        with T.Kernel(
-                T.ceildiv(F, block_N), T.ceildiv(N * OH * OW, block_M),
-                threads=threads) as (bx, by):
+        with T.Kernel(T.ceildiv(F, block_N), T.ceildiv(N * OH * OW, block_M), threads=threads) as (bx, by):
             data_shared = T.alloc_shared((block_M, block_K), dtype)
             kernel_shared = T.alloc_shared((block_K, block_N), dtype)
             out_local = T.alloc_fragment((block_M, block_N), accum_dtype)
@@ -449,11 +395,13 @@ def convolution_im2col(N,
             kernel_flat = T.Tensor((KH * KW * C, F), dtype, kernel.data)
             out_flat = T.Tensor((N * OH * OW, F), dtype, out.data)
 
-            T.annotate_layout({
+            T.annotate_layout(
+                {
                     out_shared: tilelang.layout.make_swizzled_layout(out_shared),
                     data_shared: tilelang.layout.make_swizzled_layout(data_shared),
                     kernel_shared: tilelang.layout.make_swizzled_layout(kernel_shared),
-            })
+                }
+            )
 
             T.clear(out_local)
             for k_iter in T.Pipelined(T.ceildiv(KH * KW * C, block_K), num_stages=num_stages):
@@ -467,23 +415,9 @@ def convolution_im2col(N,
     return main
 
 
-def run_nvrtc_im2col_tma_desc(N,
-                              C,
-                              H,
-                              W,
-                              F,
-                              K,
-                              S,
-                              D,
-                              P,
-                              block_M,
-                              block_N,
-                              block_K,
-                              num_stages=3,
-                              num_threads=256):
+def run_nvrtc_im2col_tma_desc(N, C, H, W, F, K, S, D, P, block_M, block_N, block_K, num_stages=3, num_threads=256):
     """Test im2col TMA descriptor functionality in NVRTC backend."""
-    program = convolution_im2col(N, C, H, W, F, K, S, D, P, block_M, block_N, block_K, num_stages,
-                                 num_threads)
+    program = convolution_im2col(N, C, H, W, F, K, S, D, P, block_M, block_N, block_K, num_stages, num_threads)
 
     conv_kernel = tilelang.compile(program, out_idx=-1, execution_backend="nvrtc")
 
@@ -501,32 +435,20 @@ def run_nvrtc_im2col_tma_desc(N,
         return C
 
     ref_c = ref_program(a, b)
-    tilelang.testing.torch_assert_close(
-        out_c, ref_c, atol=1e-2, rtol=1e-2, max_mismatched_ratio=0.05)
+    tilelang.testing.torch_assert_close(out_c, ref_c, atol=1e-2, rtol=1e-2, max_mismatched_ratio=0.05)
 
 
 def test_nvrtc_im2col_tma_desc():
     """Test im2col TMA descriptor with NVRTC backend."""
     if not check_hopper():
         import pytest
+
         pytest.skip("Test requires Hopper GPU (compute capability 9.0)")
 
     # Small test case for im2col TMA descriptor
     run_nvrtc_im2col_tma_desc(
-        N=4,
-        C=64,
-        H=32,
-        W=32,
-        F=64,
-        K=3,
-        S=1,
-        D=1,
-        P=1,
-        block_M=64,
-        block_N=128,
-        block_K=32,
-        num_stages=3,
-        num_threads=256)
+        N=4, C=64, H=32, W=32, F=64, K=3, S=1, D=1, P=1, block_M=64, block_N=128, block_K=32, num_stages=3, num_threads=256
+    )
 
 
 def test_nvrtc_l2_persistent_map():
@@ -543,7 +465,6 @@ def test_nvrtc_l2_persistent_map():
         block_size=256,
         dtype="float32",
     ):
-
         @T.prim_func
         def kernel(
             A: T.Tensor((M, N), dtype),

testing/python/jit/test_tilelang_jit_parcompile.py

@@ -16,9 +16,9 @@ def matmul_kernel_jit(
     block_K,
     trans_A=False,
     trans_B=True,
-    in_dtype='float16',
-    out_dtype='float32',
-    accum_dtype='float32',
+    in_dtype="float16",
+    out_dtype="float32",
+    accum_dtype="float32",
     num_stages=2,
     threads=128,
 ):

testing/python/jit/test_tilelang_jit_tvm_ffi.py

@@ -144,6 +144,7 @@ def run_gemm_jit_kernel(
 
     def ref_program(A, B):
         import torch
+
         C = torch.matmul(A.to(torch.float), B.to(torch.float))
         C = C.to(out_dtype)
         return C
@@ -171,19 +172,9 @@ def test_gemm_jit_kernel():
     )
 
 
-def run_tvm_ffi_kernel_do_bench(M,
-                                N,
-                                K,
-                                trans_A,
-                                trans_B,
-                                in_dtype,
-                                out_dtype,
-                                dtypeAccum,
-                                block_M,
-                                block_N,
-                                block_K,
-                                num_stages=3,
-                                num_threads=128):
+def run_tvm_ffi_kernel_do_bench(
+    M, N, K, trans_A, trans_B, in_dtype, out_dtype, dtypeAccum, block_M, block_N, block_K, num_stages=3, num_threads=128
+):
     program = matmul(
         M,
         N,
@@ -216,23 +207,12 @@ def run_tvm_ffi_kernel_do_bench(M,
 
 
 def test_tvm_ffi_kernel_do_bench():
-    run_tvm_ffi_kernel_do_bench(512, 1024, 768, False, False, "float16", "float16", "float16", 128,
-                                256, 32, 2)
+    run_tvm_ffi_kernel_do_bench(512, 1024, 768, False, False, "float16", "float16", "float16", 128, 256, 32, 2)
 
 
-def run_tvm_ffi_kernel_multi_stream(M,
-                                    N,
-                                    K,
-                                    trans_A,
-                                    trans_B,
-                                    in_dtype,
-                                    out_dtype,
-                                    dtypeAccum,
-                                    block_M,
-                                    block_N,
-                                    block_K,
-                                    num_stages=3,
-                                    num_threads=128):
+def run_tvm_ffi_kernel_multi_stream(
+    M, N, K, trans_A, trans_B, in_dtype, out_dtype, dtypeAccum, block_M, block_N, block_K, num_stages=3, num_threads=128
+):
     program = matmul(
         M,
         N,
@@ -269,23 +249,12 @@ def run_tvm_ffi_kernel_multi_stream(M,
 
 
 def test_tvm_ffi_kernel_multi_stream():
-    run_tvm_ffi_kernel_multi_stream(512, 1024, 768, False, False, "float16", "float16", "float16",
-                                    128, 256, 32, 2)
+    run_tvm_ffi_kernel_multi_stream(512, 1024, 768, False, False, "float16", "float16", "float16", 128, 256, 32, 2)
 
 
-def run_tvm_ffi_dynamic_shape(M,
-                              N,
-                              K,
-                              trans_A,
-                              trans_B,
-                              in_dtype,
-                              out_dtype,
-                              dtypeAccum,
-                              block_M,
-                              block_N,
-                              block_K,
-                              num_stages=3,
-                              num_threads=128):
+def run_tvm_ffi_dynamic_shape(
+    M, N, K, trans_A, trans_B, in_dtype, out_dtype, dtypeAccum, block_M, block_N, block_K, num_stages=3, num_threads=128
+):
     program = matmul(
         M,
         N,
@@ -325,21 +294,17 @@ def run_tvm_ffi_dynamic_shape(M,
     matmul_kernel(tensor_a, tensor_b, tensor_c)
 
     tensor_ref_c = torch.matmul(tensor_a.to(torch.float), tensor_b.to(torch.float)).to(out_dtype)
-    tilelang.testing.torch_assert_close(
-        tensor_c, tensor_ref_c, atol=1e-2, rtol=1e-2, max_mismatched_ratio=0.05)
+    tilelang.testing.torch_assert_close(tensor_c, tensor_ref_c, atol=1e-2, rtol=1e-2, max_mismatched_ratio=0.05)
 
 
 def test_tvm_ffi_dynamic_shape():
-    run_tvm_ffi_dynamic_shape(
-        T.dynamic("m"), 1024, 768, False, False, "float16", "float16", "float16", 128, 256, 32, 2)
+    run_tvm_ffi_dynamic_shape(T.dynamic("m"), 1024, 768, False, False, "float16", "float16", "float16", 128, 256, 32, 2)
 
-    run_tvm_ffi_dynamic_shape(
-        T.dynamic("m"), T.dynamic("n"), 768, False, False, "float16", "float16", "float16", 128,
-        256, 32, 2)
+    run_tvm_ffi_dynamic_shape(T.dynamic("m"), T.dynamic("n"), 768, False, False, "float16", "float16", "float16", 128, 256, 32, 2)
 
     run_tvm_ffi_dynamic_shape(
-        T.dynamic("m"), T.dynamic("n"), T.dynamic("k"), False, False, "float16", "float16",
-        "float16", 128, 256, 32, 2)
+        T.dynamic("m"), T.dynamic("n"), T.dynamic("k"), False, False, "float16", "float16", "float16", 128, 256, 32, 2
+    )
 
 
 def check_hopper():
@@ -350,22 +315,7 @@ def check_hopper():
     return compute_capability == (9, 0)
 
 
-def convolution_im2col(N,
-                       C,
-                       H,
-                       W,
-                       F,
-                       K,
-                       S,
-                       D,
-                       P,
-                       block_M,
-                       block_N,
-                       block_K,
-                       num_stages,
-                       threads,
-                       dtype="float16",
-                       accum_dtype="float"):
+def convolution_im2col(N, C, H, W, F, K, S, D, P, block_M, block_N, block_K, num_stages, threads, dtype="float16", accum_dtype="float"):
     KH, KW = K, K
     OH = (H + 2 * P - D * (K - 1) - 1) // S + 1
     OW = (W + 2 * P - D * (K - 1) - 1) // S + 1
@@ -376,9 +326,7 @@ def convolution_im2col(N,
         kernel: T.Tensor((KH, KW, C, F), dtype),
         out: T.Tensor((N, OH, OW, F), dtype),
     ):
-        with T.Kernel(
-                T.ceildiv(F, block_N), T.ceildiv(N * OH * OW, block_M),
-                threads=threads) as (bx, by):
+        with T.Kernel(T.ceildiv(F, block_N), T.ceildiv(N * OH * OW, block_M), threads=threads) as (bx, by):
             data_shared = T.alloc_shared((block_M, block_K), dtype)
             kernel_shared = T.alloc_shared((block_K, block_N), dtype)
             out_local = T.alloc_fragment((block_M, block_N), accum_dtype)
@@ -387,11 +335,13 @@ def convolution_im2col(N,
             kernel_flat = T.Tensor((KH * KW * C, F), dtype, kernel.data)
             out_flat = T.Tensor((N * OH * OW, F), dtype, out.data)
 
-            T.annotate_layout({
+            T.annotate_layout(
+                {
                     out_shared: tilelang.layout.make_swizzled_layout(out_shared),
                     data_shared: tilelang.layout.make_swizzled_layout(data_shared),
                     kernel_shared: tilelang.layout.make_swizzled_layout(kernel_shared),
-            })
+                }
+            )
 
             T.clear(out_local)
             for k_iter in T.Pipelined(T.ceildiv(KH * KW * C, block_K), num_stages=num_stages):
@@ -405,23 +355,9 @@ def convolution_im2col(N,
     return main
 
 
-def run_tvm_ffi_im2col_tma_desc(N,
-                                C,
-                                H,
-                                W,
-                                F,
-                                K,
-                                S,
-                                D,
-                                P,
-                                block_M,
-                                block_N,
-                                block_K,
-                                num_stages=3,
-                                num_threads=256):
+def run_tvm_ffi_im2col_tma_desc(N, C, H, W, F, K, S, D, P, block_M, block_N, block_K, num_stages=3, num_threads=256):
     """Test im2col TMA descriptor functionality in tvm_ffi backend."""
-    program = convolution_im2col(N, C, H, W, F, K, S, D, P, block_M, block_N, block_K, num_stages,
-                                 num_threads)
+    program = convolution_im2col(N, C, H, W, F, K, S, D, P, block_M, block_N, block_K, num_stages, num_threads)
 
     conv_kernel = tilelang.compile(program, out_idx=-1, execution_backend="tvm_ffi")
 
@@ -439,32 +375,20 @@ def run_tvm_ffi_im2col_tma_desc(N,
         return C
 
     ref_c = ref_program(a, b)
-    tilelang.testing.torch_assert_close(
-        out_c, ref_c, atol=1e-2, rtol=1e-2, max_mismatched_ratio=0.05)
+    tilelang.testing.torch_assert_close(out_c, ref_c, atol=1e-2, rtol=1e-2, max_mismatched_ratio=0.05)
 
 
 def test_tvm_ffi_im2col_tma_desc():
     """Test im2col TMA descriptor with tvm_ffi backend."""
     if not check_hopper():
         import pytest
+
         pytest.skip("Test requires Hopper GPU (compute capability 9.0)")
 
     # Small test case for im2col TMA descriptor
     run_tvm_ffi_im2col_tma_desc(
-        N=4,
-        C=64,
-        H=32,
-        W=32,
-        F=64,
-        K=3,
-        S=1,
-        D=1,
-        P=1,
-        block_M=64,
-        block_N=128,
-        block_K=32,
-        num_stages=3,
-        num_threads=256)
+        N=4, C=64, H=32, W=32, F=64, K=3, S=1, D=1, P=1, block_M=64, block_N=128, block_K=32, num_stages=3, num_threads=256
+    )
 
 
 def test_tvm_ffi_l2_persistent_map():
@@ -481,7 +405,6 @@ def test_tvm_ffi_l2_persistent_map():
         block_size=256,
         dtype="float32",
     ):
-
         @T.prim_func
         def kernel(
             A: T.Tensor((M, N), dtype),
@@ -506,8 +429,12 @@ def test_tvm_ffi_l2_persistent_map():
     kernel = elementwise_add_with_l2_cache(M, N)
 
     source = kernel.get_host_source()
-    assert "__tvm_cuda_stream_set_access_policy_window_packed" in source, "Expected __tvm_cuda_stream_set_access_policy_window_packed in the kernel source"
-    assert "__tvm_cuda_stream_reset_access_policy_window_packed" in source, "Expected __tvm_cuda_stream_reset_access_policy_window_packed in the kernel source"
+    assert "__tvm_cuda_stream_set_access_policy_window_packed" in source, (
+        "Expected __tvm_cuda_stream_set_access_policy_window_packed in the kernel source"
+    )
+    assert "__tvm_cuda_stream_reset_access_policy_window_packed" in source, (
+        "Expected __tvm_cuda_stream_reset_access_policy_window_packed in the kernel source"
+    )
 
     # Create test tensors
     a = torch.randn(M, N, dtype=torch.float32).cuda()

testing/python/kernel/test_tilelang_kernel_bf16_gemm_mma.py

@@ -6,7 +6,8 @@ from tvm import DataType
 import tilelang.language as T
 from tilelang.intrinsics import get_swizzle_layout
 from tilelang.intrinsics.mma_macro_generator import (
-    TensorCoreIntrinEmitter,)
+    TensorCoreIntrinEmitter,
+)
 from tilelang.transform import simplify_prim_func
 from tilelang.utils.tensor import map_torch_type
 
@@ -116,7 +117,6 @@ def tl_matmul(
         C: T.Tensor((M, N), out_dtype),
     ):
         with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=threads) as (bx, by):
-
             A_shared = T.alloc_shared(A_shared_shape, in_dtype, scope=shared_scope)
             B_shared = T.alloc_shared(B_shared_shape, in_dtype, scope=shared_scope)
             C_shared = T.alloc_shared(C_shared_shape, out_dtype, scope=shared_scope)
@@ -124,10 +124,12 @@ def tl_matmul(
             B_local = T.alloc_local((warp_cols * local_size_b), in_dtype)
             C_local = T.alloc_local((warp_rows * warp_cols * local_size_c), accum_dtype)
 
-            T.annotate_layout({
+            T.annotate_layout(
+                {
                     A_shared: make_swizzle_layout(A_shared),
                     B_shared: make_swizzle_layout(B_shared),
-            })
+                }
+            )
 
             # Improve L2 Cache
             T.use_swizzle(panel_size=10)
@@ -135,7 +137,6 @@ def tl_matmul(
             T.clear(C_local)
 
             for ko in T.Pipelined((K // block_K), num_stages=stage):
-
                 # Load A into shared memory
                 for i, k in T.Parallel(block_M, block_K):
                     A_shared[i, k] = A[by * block_M + i, ko * block_K + k]
@@ -145,7 +146,6 @@ def tl_matmul(
                     B_shared[j, k] = B[bx * block_N + j, ko * block_K + k]
 
                 for ki in T.serial(0, (block_K // micro_size_k)):
-
                     # Load A into fragment
                     mma_emitter.ldmatrix_a(
                         A_local,

testing/python/kernel/test_tilelang_kernel_element_wise_add.py

@@ -24,7 +24,7 @@ def elementwise_add(
             start_x = bx * block_N
             start_y = by * block_M
 
-            for (local_y, local_x) in T.Parallel(block_M, block_N):
+            for local_y, local_x in T.Parallel(block_M, block_N):
                 y = start_y + local_y
                 x = start_x + local_x
 

testing/python/kernel/test_tilelang_kernel_fp8_gemm.py

@@ -12,7 +12,6 @@ def calc_diff(x, y):
 
 
 def matmul_nt(M, N, K, bM, bN, bK, in_dtype, out_dtype, accum_dtype):
-
     @T.prim_func
     def main(
         A: T.Tensor((M, K), in_dtype),
@@ -44,8 +43,7 @@ def assert_matmul_correctness(M, N, K, block_M, block_N, block_K, in_dtype, out_
 
     C = kernel(A, B)
 
-    ref_c = torch.matmul(A.to(map_torch_type(accum_dtype)),
-                         B.T.to(map_torch_type(accum_dtype))).to(map_torch_type(out_dtype))
+    ref_c = torch.matmul(A.to(map_torch_type(accum_dtype)), B.T.to(map_torch_type(accum_dtype))).to(map_torch_type(out_dtype))
     print(C)
     print(ref_c)
     diff = calc_diff(C, ref_c)

testing/python/kernel/test_tilelang_kernel_fp8_gemm_mma.py

@@ -6,7 +6,8 @@ from tvm import DataType
 import tilelang.language as T
 from tilelang.intrinsics import get_swizzle_layout
 from tilelang.intrinsics.mma_macro_generator import (
-    TensorCoreIntrinEmitter,)
+    TensorCoreIntrinEmitter,
+)
 from tilelang.transform import simplify_prim_func
 from tilelang.utils.tensor import map_torch_type
 
@@ -115,7 +116,6 @@ def tl_matmul(
         C: T.Tensor((M, N), out_dtype),
     ):
         with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=threads) as (bx, by):
-
             A_shared = T.alloc_shared(A_shared_shape, in_dtype, scope=shared_scope)
             B_shared = T.alloc_shared(B_shared_shape, in_dtype, scope=shared_scope)
             C_shared = T.alloc_shared(C_shared_shape, out_dtype, scope=shared_scope)
@@ -123,10 +123,12 @@ def tl_matmul(
             B_local = T.alloc_local((warp_cols * local_size_b), in_dtype)
             C_local = T.alloc_local((warp_rows * warp_cols * local_size_c), accum_dtype)
 
-            T.annotate_layout({
+            T.annotate_layout(
+                {
                     A_shared: make_swizzle_layout(A_shared),
                     B_shared: make_swizzle_layout(B_shared),
-            })
+                }
+            )
 
             # Improve L2 Cache
             T.use_swizzle(panel_size=10)
@@ -134,7 +136,6 @@ def tl_matmul(
             T.clear(C_local)
 
             for ko in T.Pipelined((K // block_K), num_stages=stage):
-
                 # Load A into shared memory
                 for i, k in T.Parallel(block_M, block_K):
                     A_shared[i, k] = A[by * block_M + i, ko * block_K + k]
@@ -144,7 +145,6 @@ def tl_matmul(
                     B_shared[j, k] = B[bx * block_N + j, ko * block_K + k]
 
                 for ki in T.serial(0, (block_K // micro_size_k)):
-
                     # Load A into fragment
                     mma_emitter.ldmatrix_a(
                         A_local,

testing/python/kernel/test_tilelang_kernel_fp8_gemv_simt.py

@@ -27,8 +27,8 @@ def gemv_simt(
 ):
     assert n_partition is not None, "n_partition must be provided"
     assert reduce_thread is not None, (
-        "reduce_thread must be provided currently, as related bitblas.gpu.gemv.GEMV"
-        "sch_outer_reduction_with_config is not implemented")
+        "reduce_thread must be provided currently, as related bitblas.gpu.gemv.GEMVsch_outer_reduction_with_config is not implemented"
+    )
 
     assert isinstance(N, int) and isinstance(K, int), "Do not support dynamic N and K Currently"
 
@@ -55,8 +55,7 @@ def gemv_simt(
         Bias: T.Tensor(Bias_shape, out_dtype),
         C: T.Tensor(C_shape, out_dtype),
     ):
-        with T.Kernel(
-                T.ceildiv(N, n_partition), M, threads=(reduce_thread, n_partition)) as (
+        with T.Kernel(T.ceildiv(N, n_partition), M, threads=(reduce_thread, n_partition)) as (
             bx,
             by,
         ):
@@ -88,8 +87,7 @@ def gemv_simt(
                         )
                 else:
                     for ki in T.serial(micro_size_k):
-                        accum_res[0] += A_local[ki].astype(accum_dtype) * B_local[ki].astype(
-                            accum_dtype)
+                        accum_res[0] += A_local[ki].astype(accum_dtype) * B_local[ki].astype(accum_dtype)
 
             with T.attr(
                 T.comm_reducer(lambda x, y: x + y, [T.Cast(accum_dtype, 0)]),
@@ -104,11 +102,11 @@ def gemv_simt(
                         reduced_accum_res[0],
                         kr,
                         dtype="handle",
-                    ))
+                    )
+                )
             if kr == 0:
                 if with_bias:
-                    C[by,
-                      bx * n_partition + ni] = reduced_accum_res[0] + Bias[bx * n_partition + ni]
+                    C[by, bx * n_partition + ni] = reduced_accum_res[0] + Bias[bx * n_partition + ni]
                 else:
                     C[by, bx * n_partition + ni] = reduced_accum_res[0]
 

testing/python/kernel/test_tilelang_kernel_gemm.py

@@ -95,8 +95,8 @@ def run_gemm(
         if in_dtype == "float32":
             # Convert float32 to tfloat32 because tfloat32 mma cannot truncate
             # float32 automatically, -0x1000 meas
-            A = ((A.view(torch.int32) - 0x1000)).view(torch.float32)
-            B = ((B.view(torch.int32) - 0x1000)).view(torch.float32)
+            A = (A.view(torch.int32) - 0x1000).view(torch.float32)
+            B = (B.view(torch.int32) - 0x1000).view(torch.float32)
         C = torch.matmul(A.to(torch.float), B.to(torch.float))
         C = C.to(torch.__getattribute__(out_dtype))
         return C

testing/python/kernel/test_tilelang_kernel_gemm_mma_intrinsic.py

@@ -6,7 +6,8 @@ from tvm import DataType
 import tilelang.language as T
 from tilelang.intrinsics import get_swizzle_layout
 from tilelang.intrinsics.mma_macro_generator import (
-    TensorCoreIntrinEmitter,)
+    TensorCoreIntrinEmitter,
+)
 from tilelang.transform import simplify_prim_func
 from tilelang.utils.tensor import map_torch_type
 
@@ -116,7 +117,6 @@ def tl_matmul(
         C: T.Tensor((M, N), out_dtype),
     ):
         with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=threads) as (bx, by):
-
             A_shared = T.alloc_shared(A_shared_shape, in_dtype, scope=shared_scope)
             B_shared = T.alloc_shared(B_shared_shape, in_dtype, scope=shared_scope)
             C_shared = T.alloc_shared(C_shared_shape, out_dtype, scope=shared_scope)
@@ -124,10 +124,12 @@ def tl_matmul(
             B_local = T.alloc_local((warp_cols * local_size_b), in_dtype)
             C_local = T.alloc_local((warp_rows * warp_cols * local_size_c), accum_dtype)
 
-            T.annotate_layout({
+            T.annotate_layout(
+                {
                     A_shared: make_swizzle_layout(A_shared),
                     B_shared: make_swizzle_layout(B_shared),
-            })
+                }
+            )
 
             # Improve L2 Cache
             T.use_swizzle(panel_size=10)
@@ -135,7 +137,6 @@ def tl_matmul(
             T.clear(C_local)
 
             for ko in T.Pipelined((K // block_K), num_stages=stage):
-
                 # Load A into shared memory
                 for i, k in T.Parallel(block_M, block_K):
                     A_shared[i, k] = A[by * block_M + i, ko * block_K + k]
@@ -145,7 +146,6 @@ def tl_matmul(
                     B_shared[j, k] = B[bx * block_N + j, ko * block_K + k]
 
                 for ki in T.serial(0, (block_K // micro_size_k)):
-
                     # Load A into fragment
                     mma_emitter.ldmatrix_a(
                         A_local,

testing/python/kernel/test_tilelang_kernel_gemm_simt.py

@@ -81,7 +81,6 @@ def tl_matmul_simt(
         C: T.Tensor(C_shape, out_dtype),
     ):
         with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=threads) as (bx, by):
-
             A_shared = T.alloc_shared(A_shared_shape, in_dtype, scope=shared_scope)
             B_shared = T.alloc_shared(B_shared_shape, in_dtype, scope=shared_scope)
 
@@ -97,7 +96,6 @@ def tl_matmul_simt(
             T.clear(C_local)
 
             for ko in T.serial(K // block_K):
-
                 # Load A into shared memory
                 for i, k in T.Parallel(block_M, block_K):
                     A_shared[i, k] = A[by * block_M + i, ko * block_K + k]
@@ -109,29 +107,24 @@ def tl_matmul_simt(
                 for ki in T.serial((block_K // micro_size_k)):
                     for i in T.serial(local_size_a):
                         for mk in T.vectorized(micro_size_k):
-                            A_local[i, mk] = A_shared[warp_m * local_size_a + i,
-                                                      ki * micro_size_k + mk]
+                            A_local[i, mk] = A_shared[warp_m * local_size_a + i, ki * micro_size_k + mk]
 
                     for i in T.serial(local_size_b):
                         for mk in T.vectorized(micro_size_k):
-                            B_local[i, mk] = B_shared[warp_n * local_size_b + i,
-                                                      ki * micro_size_k + mk]
+                            B_local[i, mk] = B_shared[warp_n * local_size_b + i, ki * micro_size_k + mk]
 
                     for i, j in T.grid(local_size_a, local_size_b):
                         for mk in T.serial(micro_size_k // dp4a_size):
                             if use_dp4a:
-                                T.dp4a(A_local[i, mk * dp4a_size], B_local[j, mk * dp4a_size],
-                                       C_local[i * local_size_b + j])
+                                T.dp4a(A_local[i, mk * dp4a_size], B_local[j, mk * dp4a_size], C_local[i * local_size_b + j])
                             else:
                                 for dp4a_idx in T.serial(dp4a_size):
-                                    C_local[i * local_size_b +
-                                            j] += A_local[i, mk * dp4a_size +
-                                                          dp4a_idx] * B_local[j, mk * dp4a_size +
-                                                                              dp4a_idx]
+                                    C_local[i * local_size_b + j] += (
+                                        A_local[i, mk * dp4a_size + dp4a_idx] * B_local[j, mk * dp4a_size + dp4a_idx]
+                                    )
 
             for i, j in T.grid(local_size_a, local_size_b):
-                C[by * block_M + warp_m * local_size_a + i,
-                  bx * block_N + warp_n * local_size_b + j] = C_local[i * local_size_b + j]
+                C[by * block_M + warp_m * local_size_a + i, bx * block_N + warp_n * local_size_b + j] = C_local[i * local_size_b + j]
 
     return main
 

testing/python/kernel/test_tilelang_kernel_gemm_with_stride.py

@@ -5,7 +5,6 @@ import torch
 
 
 def matmul(M, N, K, block_M, block_N, block_K, dtype="float16", accum_dtype="float"):
-
     @T.prim_func
     def main(
         A: T.Tensor((M, K), dtype),
@@ -59,7 +58,8 @@ def run_gemm_with_stride_ss(M: int, N: int, K: int, block_M: int, block_N: int,
         pass_configs={
             tilelang.PassConfigKey.TL_DISABLE_TMA_LOWER: True,
             tilelang.PassConfigKey.TL_DISABLE_WARP_SPECIALIZED: True,
-        })
+        },
+    )
     # Create random input tensors on the GPU
     a = torch.randn(M, K, device="cuda", dtype=torch.float16)
     b = torch.randn(K, N, device="cuda", dtype=torch.float16)

testing/python/kernel/test_tilelang_kernel_gemv_simt.py

@@ -27,8 +27,8 @@ def gemv_simt(
 ):
     assert n_partition is not None, "n_partition must be provided"
     assert reduce_thread is not None, (
-        "reduce_thread must be provided currently, as related bitblas.gpu.gemv.GEMV"
-        "sch_outer_reduction_with_config is not implemented")
+        "reduce_thread must be provided currently, as related bitblas.gpu.gemv.GEMVsch_outer_reduction_with_config is not implemented"
+    )
 
     assert isinstance(N, int) and isinstance(K, int), "Do not support dynamic N and K Currently"
 
@@ -55,8 +55,7 @@ def gemv_simt(
         Bias: T.Tensor(Bias_shape, out_dtype),
         C: T.Tensor(C_shape, out_dtype),
     ):
-        with T.Kernel(
-                T.ceildiv(N, n_partition), M, threads=(reduce_thread, n_partition)) as (
+        with T.Kernel(T.ceildiv(N, n_partition), M, threads=(reduce_thread, n_partition)) as (
             bx,
             by,
         ):
@@ -88,8 +87,7 @@ def gemv_simt(
                         )
                 else:
                     for ki in T.serial(micro_size_k):
-                        accum_res[0] += A_local[ki].astype(accum_dtype) * B_local[ki].astype(
-                            accum_dtype)
+                        accum_res[0] += A_local[ki].astype(accum_dtype) * B_local[ki].astype(accum_dtype)
 
             with T.attr(
                 T.comm_reducer(lambda x, y: x + y, [T.Cast(accum_dtype, 0)]),
@@ -104,11 +102,11 @@ def gemv_simt(
                         reduced_accum_res[0],
                         kr,
                         dtype="handle",
-                    ))
+                    )
+                )
             if kr == 0:
                 if with_bias:
-                    C[by,
-                      bx * n_partition + ni] = reduced_accum_res[0] + Bias[bx * n_partition + ni]
+                    C[by, bx * n_partition + ni] = reduced_accum_res[0] + Bias[bx * n_partition + ni]
                 else:
                     C[by, bx * n_partition + ni] = reduced_accum_res[0]
 

testing/python/kernel/test_tilelang_kernel_int4_gemm_mma.py

@@ -4,7 +4,8 @@ from tilelang import tvm as tvm
 import tilelang.testing
 import tilelang.language as T
 from tilelang.intrinsics import (
-    make_mma_swizzle_layout as make_swizzle_layout,)
+    make_mma_swizzle_layout as make_swizzle_layout,
+)
 
 from tilelang.intrinsics.mma_macro_generator import (
     INT4TensorCoreIntrinEmitter,
@@ -96,7 +97,6 @@ def tl_matmul(
         C: T.Tensor((M, N), out_dtype),
     ):
         with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=threads) as (bx, by):
-
             A_shared = T.alloc_shared(A_shared_shape, in_dtype, scope=shared_scope)
             B_shared = T.alloc_shared(B_shared_shape, in_dtype, scope=shared_scope)
             C_shared = T.alloc_shared(C_shared_shape, out_dtype, scope=shared_scope)
@@ -104,10 +104,12 @@ def tl_matmul(
             B_local = T.alloc_local((warp_cols * local_size_b), in_dtype)
             C_local = T.alloc_local((warp_rows * warp_cols * local_size_c), accum_dtype)
 
-            T.annotate_layout({
+            T.annotate_layout(
+                {
                     A_shared: make_swizzle_layout(A_shared),
                     B_shared: make_swizzle_layout(B_shared),
-            })
+                }
+            )
 
             # Improve L2 Cache
             T.use_swizzle(panel_size=10)
@@ -115,7 +117,6 @@ def tl_matmul(
             T.clear(C_local)
 
             for ko in T.Pipelined(T.ceildiv(K, block_K), num_stages=stage):
-
                 # Load A into shared memory
                 for i, k in T.Parallel(block_M, block_K):
                     A_shared[i, k] = A[by * block_M + i, ko * block_K + k]
@@ -125,7 +126,6 @@ def tl_matmul(
                     B_shared[j, k] = B[bx * block_N + j, ko * block_K + k]
 
                 for ki in T.serial(0, (block_K // micro_size_k)):
-
                     # Load A into fragment
                     mma_emitter.ldmatrix_a(
                         A_local,
@@ -168,7 +168,8 @@ def assert_tl_matmul_correctness(M, N, K, in_dtype, out_dtype, accum_dtype):
         out_idx=[2],
         pass_configs={
             tilelang.PassConfigKey.TL_DEBUG_MERGE_SHARED_MEMORY_ALLOCATIONS: True,
-        })
+        },
+    )
     print(kernel.get_kernel_source())
     profiler = kernel.get_profiler()
 
@@ -290,7 +291,6 @@ def tl_matmul_weight_only_transform(
         C: T.Tensor((M, N), out_dtype),
     ):
         with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=threads) as (bx, by):
-
             A_shared = T.alloc_shared(A_shared_shape, in_dtype, scope=shared_scope)
             B_shared = T.alloc_shared(B_shared_shape, in_dtype, scope=shared_scope)
             C_shared = T.alloc_shared(C_shared_shape, out_dtype, scope=shared_scope)
@@ -298,10 +298,12 @@ def tl_matmul_weight_only_transform(
             B_local = T.alloc_local((warp_cols * local_size_b), in_dtype)
             C_local = T.alloc_local((warp_rows * warp_cols * local_size_c), accum_dtype)
 
-            T.annotate_layout({
+            T.annotate_layout(
+                {
                     A_shared: make_swizzle_layout(A_shared),
                     B_shared: make_swizzle_layout(B_shared),
-            })
+                }
+            )
 
             # Improve L2 Cache
             T.use_swizzle(panel_size=10)
@@ -309,19 +311,15 @@ def tl_matmul_weight_only_transform(
             T.clear(C_local)
 
             for ko in T.Pipelined(T.ceildiv(K, block_K), num_stages=stage):
-
                 # Load A into shared memory
                 for i, k in T.Parallel(block_M, block_K):
                     A_shared[i, k] = A[by * block_M + i, ko * block_K + k]
 
                 # Load B into shared memory
-                for j, k, jj, kk in T.Parallel(block_N // micro_size_y, block_K // micro_size_k,
-                                               micro_size_y, micro_size_k):
-                    B_shared[j, k, jj, kk] = B[bx * (block_N // micro_size_y) + j,
-                                               ko * (block_K // micro_size_k) + k, jj, kk]
+                for j, k, jj, kk in T.Parallel(block_N // micro_size_y, block_K // micro_size_k, micro_size_y, micro_size_k):
+                    B_shared[j, k, jj, kk] = B[bx * (block_N // micro_size_y) + j, ko * (block_K // micro_size_k) + k, jj, kk]
 
                 for ki in T.serial(0, (block_K // micro_size_k)):
-
                     # Load A into fragment
                     mma_emitter.ldmatrix_a(
                         A_local,
@@ -359,6 +357,7 @@ def tl_matmul_weight_only_transform(
 
 def assert_tl_matmul_weight_only_transform_correctness(M, N, K, in_dtype, out_dtype, accum_dtype):
     import bitblas
+
     matmul = tl_matmul_weight_only_transform(M, N, K, in_dtype, out_dtype, accum_dtype)
     kernel = tilelang.compile(matmul, out_idx=[2])
     profiler = kernel.get_profiler()

testing/python/language/test_tilelang_capture.py

@@ -6,16 +6,17 @@ import gc
 
 
 def test_tilelang_capture():
-
     @tilelang.jit(
         pass_configs={
             tilelang.PassConfigKey.TL_DISABLE_TMA_LOWER: True,
             tilelang.PassConfigKey.TL_DISABLE_WARP_SPECIALIZED: True,
-        },)
+        },
+    )
     def get_dummy_kernel():
-
         @T.prim_func
-        def dummy_kernel(a: T.Tensor[(1,), T.float32],):
+        def dummy_kernel(
+            a: T.Tensor[(1,), T.float32],
+        ):
             with T.Kernel(1) as _:
                 a[0] = 1
 
@@ -36,5 +37,5 @@ def test_tilelang_capture():
     #     objgraph.show_backrefs([a_upgrade], max_depth=5)
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     tilelang.testing.main()

testing/python/language/test_tilelang_intimm.py

@@ -4,25 +4,25 @@ import tilelang.language as T
 
 
 def test_tilelang_intimm():
-    T.int32(0x7fffffff)
-    T.int32(-0x7fffffff - 1)
-    T.uint32(0xffffffff)
-    T.int64(0x7fffffffffffffff)
-    T.int64(-0x7fffffffffffffff - 1)
-    T.uint64(0xffffffffffffffff)
+    T.int32(0x7FFFFFFF)
+    T.int32(-0x7FFFFFFF - 1)
+    T.uint32(0xFFFFFFFF)
+    T.int64(0x7FFFFFFFFFFFFFFF)
+    T.int64(-0x7FFFFFFFFFFFFFFF - 1)
+    T.uint64(0xFFFFFFFFFFFFFFFF)
 
     a = T.int32()
-    a & 0x7fffffff
+    a & 0x7FFFFFFF
 
     a = T.uint32()
-    a & 0xffffffff
+    a & 0xFFFFFFFF
 
     a = T.int64()
-    a & 0x7fffffffffffffff
+    a & 0x7FFFFFFFFFFFFFFF
 
     a = T.uint64()
-    a & T.uint64(0xffffffffffffffff)
+    a & T.uint64(0xFFFFFFFFFFFFFFFF)
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     tilelang.testing.main()

testing/python/language/test_tilelang_language_alias.py

@@ -5,7 +5,6 @@ import tilelang.language as T
 # add decorator @tilelang.jit if you want to return a torch function
 # @tilelang.jit
 def matmul(M, N, K, block_M, block_N, block_K, dtype="float16", accum_dtype="float"):
-
     @T.prim_func
     def main(
         A: T.Tensor((M, K), dtype),

testing/python/language/test_tilelang_language_all_of.py

@@ -13,11 +13,10 @@ def ref_program(A, B, BlockMask, block_M, block_N, block_K):
             accu = torch.zeros((block_M, block_N), dtype=torch.float32, device=A.device)
             for k in range(K // block_K):
                 if torch.all(BlockMask[i, j, k]):
-                    accu += A[i * block_M:(i + 1) * block_M, k * block_K:(k + 1) * block_K].to(
-                        torch.float32) @ B[k * block_K:(k + 1) * block_K,
-                                           j * block_N:(j + 1) * block_N].to(torch.float32)
-            ref_c[i * block_M:(i + 1) * block_M, j * block_N:(j + 1) * block_N] = (
-                accu.to(torch.float16))
+                    accu += A[i * block_M : (i + 1) * block_M, k * block_K : (k + 1) * block_K].to(torch.float32) @ B[
+                        k * block_K : (k + 1) * block_K, j * block_N : (j + 1) * block_N
+                    ].to(torch.float32)
+            ref_c[i * block_M : (i + 1) * block_M, j * block_N : (j + 1) * block_N] = accu.to(torch.float16)
     return ref_c
 
 
@@ -35,7 +34,6 @@ def blocksparse_matmul_global(
     dtype="float16",
     accum_dtype="float",
 ):
-
     block_mask_shape = (M // block_M, N // block_N, K // block_K, condition_dim)
 
     @T.prim_func
@@ -80,7 +78,6 @@ def blocksparse_matmul_shared(
     dtype="float16",
     accum_dtype="float",
 ):
-
     block_mask_shape = (M // block_M, N // block_N, K // block_K, condition_dim)
 
     @T.prim_func
@@ -130,7 +127,6 @@ def blocksparse_matmul_local(
     dtype="float16",
     accum_dtype="float",
 ):
-
     block_mask_shape = (M // block_M, N // block_N, K // block_K, condition_dim)
 
     @T.prim_func
@@ -237,7 +233,8 @@ def run_block_sparse_matmul_shared(M=1024, N=1024, K=1024, sparsity=0.5, conditi
         pass_configs={
             tilelang.PassConfigKey.TL_DISABLE_TMA_LOWER: True,
             tilelang.PassConfigKey.TL_DISABLE_WARP_SPECIALIZED: True,
-        })
+        },
+    )
     # Create block mask with desired sparsity
     mask_shape = (M // block_M, N // block_N, K // block_K)
     block_mask = torch.rand(mask_shape).cuda() > sparsity
@@ -284,7 +281,8 @@ def run_block_sparse_matmul_local(M=1024, N=1024, K=1024, sparsity=0.5, conditio
         pass_configs={
             tilelang.PassConfigKey.TL_DISABLE_TMA_LOWER: True,
             tilelang.PassConfigKey.TL_DISABLE_WARP_SPECIALIZED: True,
-        })
+        },
+    )
     # Create block mask with desired sparsity
     mask_shape = (M // block_M, N // block_N, K // block_K)
     block_mask = torch.rand(mask_shape).cuda() > sparsity