[Dev] Append CUTLASS submodule

.gitmodules

@@ -2,3 +2,7 @@
 	path = 3rdparty/tvm
 	url = https://github.com/LeiWang1999/tvm
 	branch = bitblas_tl
+[submodule "3rdparty/cutlass"]
+	path = 3rdparty/cutlass
+	url = https://github.com/NVIDIA/cutlass.git
+	branch = v3.2.2

bitblas/__init__.py

@@ -5,13 +5,19 @@
 
 # installing tvm
 install_tvm_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "3rdparty", "tvm")
+install_cutlass_path = os.path.join(
+    os.path.dirname(os.path.abspath(__file__)), "3rdparty", "cutlass")
 if os.path.exists(install_tvm_path) and install_tvm_path not in sys.path:
     os.environ["PYTHONPATH"] = install_tvm_path + "/python:" + os.environ.get("PYTHONPATH", "")
+    os.environ["TL_CUTLASS_PATH"] = install_cutlass_path + "/include"
     sys.path.insert(0, install_tvm_path + "/python")
 
 develop_tvm_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "..", "3rdparty", "tvm")
+develop_cutlass_path = os.path.join(
+    os.path.dirname(os.path.abspath(__file__)), "..", "3rdparty", "cutlass")
 if os.path.exists(develop_tvm_path) and develop_tvm_path not in sys.path:
     os.environ["PYTHONPATH"] = develop_tvm_path + "/python:" + os.environ.get("PYTHONPATH", "")
+    os.environ["TL_CUTLASS_PATH"] = develop_cutlass_path + "/include"
     sys.path.insert(0, develop_tvm_path + "/python")
 
 import tvm as tvm  # noqa: E402

setup.py

@@ -239,6 +239,7 @@ def run(self):
             "3rdparty/tvm/mypy.ini",
             "3rdparty/tvm/pyproject.toml",
             "3rdparty/tvm/version.py",
+            "3rdparty/tvm/src/tl/tl_templates",
         ]
         for item in TVM_PREBUILD_ITEMS:
             source_dir = os.path.join(ROOT_DIR, item)
@@ -252,6 +253,22 @@ def run(self):
                     os.makedirs(target_dir)
                 shutil.copy2(source_dir, target_dir)
 
+        # Copy CUTLASS to the package directory
+        CUTLASS_PREBUILD_ITEMS = [
+            "3rdparty/cutlass",
+        ]
+        for item in CUTLASS_PREBUILD_ITEMS:
+            source_dir = os.path.join(ROOT_DIR, item)
+            target_dir = os.path.join(self.build_lib, PACKAGE_NAME, item)
+            if os.path.isdir(source_dir):
+                self.mkpath(target_dir)
+                distutils.dir_util.copy_tree(source_dir, target_dir)
+            else:
+                target_dir = os.path.dirname(target_dir)
+                if not os.path.exists(target_dir):
+                    os.makedirs(target_dir)
+                shutil.copy2(source_dir, target_dir)
+
 
 class BitBLASSdistCommand(sdist):
     """Customized setuptools sdist command - includes the pyproject.toml file."""

testing/python/tilelang/test_tilelang_gemm.py

@@ -0,0 +1,173 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+
+from bitblas import tvm as tvm
+import bitblas.testing
+from tvm import tl
+
+
+def matmul(
+    M,
+    N,
+    K,
+    block_M,
+    block_N,
+    block_K,
+    trans_A,
+    trans_B,
+    dtypeAB,
+    dtypeC,
+    accum_dtype,
+    num_stages,
+    threads,
+):
+    A_shape = (K, M) if trans_A else (M, K)
+    B_shape = (N, K) if trans_B else (K, N)
+    A_shared_shape = (block_K, block_M) if trans_A else (block_M, block_K)
+    B_shared_shape = (block_N, block_K) if trans_B else (block_K, block_N)
+
+    import tvm.tl.language as T
+
+    @T.prim_func
+    def main(A: T.Buffer(A_shape, dtypeAB), B: T.Buffer(B_shape, dtypeAB), C: T.Buffer((M, N),
+                                                                                       dtypeC)):
+        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, dtypeAB)
+            B_shared = T.alloc_shared(B_shared_shape, dtypeAB)
+            C_local = T.alloc_fragment((block_M, block_N), accum_dtype)
+            T.clear(C_local)
+            for k in T.Pipelined(T.ceildiv(K, block_K), num_stages=num_stages):
+                if trans_A:
+                    T.copy(A[k * block_K, by * block_M], A_shared)
+                else:
+                    T.copy(A[by * block_M, k * block_K], A_shared)
+                if trans_B:
+                    T.copy(B[bx * block_N, k * block_K], B_shared)
+                else:
+                    T.copy(B[k * block_K, bx * block_N], B_shared)
+                T.gemm(A_shared, B_shared, C_local, trans_A, trans_B)
+            T.copy(C_local, C[by * block_M, bx * block_N])
+
+    return main
+
+
+def run_gemm(
+    M,
+    N,
+    K,
+    trans_A,
+    trans_B,
+    dtypeAB,
+    dtypeC,
+    dtypeAccum,
+    block_M,
+    block_N,
+    block_K,
+    num_stages=3,
+    num_threads=128,
+):
+    program = matmul(
+        M,
+        N,
+        K,
+        block_M,
+        block_N,
+        block_K,
+        trans_A,
+        trans_B,
+        dtypeAB,
+        dtypeC,
+        dtypeAccum,
+        num_stages,
+        num_threads,
+    )
+    mod, params = tl.lower(program)
+    mod = tl.Profiler(mod, params, [2], tl.TensorSupplyType.Integer)
+
+    def ref_program(A, B):
+        import torch
+
+        if trans_A:
+            A = A.T
+        if trans_B:
+            B = B.T
+        C = torch.matmul(A.to(torch.float), B.to(torch.float))
+        C = C.to(torch.__getattribute__(dtypeC))
+        return C
+
+    mod.assert_allclose(ref_program)
+
+
+def test_gemm_f16f16f16_nn():
+    run_gemm(512, 1024, 768, False, False, "float16", "float16", "float16", 128, 256, 32, 2)
+
+
+def test_gemm_f16f16f32_nn():
+    run_gemm(512, 1024, 768, False, False, "float16", "float16", "float32", 128, 128, 32)
+
+
+def test_gemm_bf16bf16f32_nn():
+    run_gemm(512, 1024, 768, False, False, "bfloat16", "bfloat16", "float32", 128, 128, 32)
+
+
+def test_gemm_f32f32f32_nn():
+    run_gemm(512, 1024, 768, False, False, "float32", "float32", "float32", 64, 128, 32)
+
+
+def test_gemm_f64f64f64_nn():
+    run_gemm(512, 1024, 768, False, False, "float64", "float64", "float64", 64, 64, 16)
+
+
+def test_gemm_i8i8i32_nn():
+    run_gemm(512, 1024, 768, False, False, "int8", "int8", "int32", 128, 128, 64)
+
+
+def test_gemm_f16f16f16_tn():
+    run_gemm(512, 1024, 768, True, False, "float16", "float16", "float16", 128, 256, 32, 2)
+
+
+def test_gemm_f16f16f16_nt():
+    run_gemm(512, 1024, 768, False, True, "float16", "float16", "float16", 128, 256, 32, 2)
+
+
+def test_gemm_i8i8i32_nt():
+    run_gemm(512, 1024, 768, False, True, "int8", "int8", "int32", 128, 128, 64)
+
+
+def test_gemm_i8i8i32_tn():
+    run_gemm(512, 1024, 768, True, False, "int8", "int8", "int32", 128, 128, 64)
+
+
+def test_gemm_f64f64f64_nt():
+    run_gemm(512, 1024, 768, False, True, "float64", "float64", "float64", 64, 32, 16)
+
+
+def test_gemm_f64f64f64_tn():
+    run_gemm(512, 1024, 768, True, False, "float64", "float64", "float64", 64, 32, 16)
+
+
+def test_gemm_f32f32f32_nt():
+    run_gemm(512, 1024, 768, False, True, "float32", "float32", "float32", 64, 128, 32)
+
+
+def test_gemm_f32f32f32_tn():
+    run_gemm(512, 1024, 768, True, False, "float32", "float32", "float32", 64, 128, 32)
+
+
+def test_pad_aligned_f16f16f16_nn():
+    run_gemm(512 - 8, 1024 - 32, 768 - 24, False, False, "float16", "float16", "float16", 128, 256,
+             32, 2)
+
+
+def test_pad_f16f16f16_nn():
+    run_gemm(512 - 9, 1024 - 7, 768 - 5, False, False, "float16", "float16", "float16", 128, 256,
+             32, 2)
+
+
+def test_pad_f16f16f32_nn():
+    run_gemm(512 + 19, 1024 + 17, 768 + 15, False, False, "float16", "float16", "float32", 128, 64,
+             32)
+
+
+if __name__ == "__main__":
+    bitblas.testing.main()