| # RUN: env SUPPORT_LIB=%mlir_c_runner_utils \ |
| # RUN: %PYTHON %s | FileCheck %s |
| |
| import ctypes |
| import numpy as np |
| import os |
| import sys |
| |
| from mlir import ir |
| from mlir import runtime as rt |
| |
| from mlir.dialects import sparse_tensor as st |
| from mlir.dialects import builtin |
| from mlir.dialects import func |
| from mlir.dialects.linalg.opdsl import lang as dsl |
| |
| _SCRIPT_PATH = os.path.dirname(os.path.abspath(__file__)) |
| sys.path.append(_SCRIPT_PATH) |
| from tools import sparse_compiler |
| |
| |
| @dsl.linalg_structured_op |
| def matmul_dsl( |
| A=dsl.TensorDef(dsl.T, dsl.S.M, dsl.S.K), |
| B=dsl.TensorDef(dsl.T, dsl.S.K, dsl.S.N), |
| C=dsl.TensorDef(dsl.T, dsl.S.M, dsl.S.N, output=True), |
| ): |
| C[dsl.D.m, dsl.D.n] += A[dsl.D.m, dsl.D.k] * B[dsl.D.k, dsl.D.n] |
| |
| |
| def build_SpMM(attr: st.EncodingAttr): |
| """Build SpMM kernel. |
| |
| This method generates a linalg op with for matrix multiplication using |
| just the Python API. Effectively, a generic linalg op is constructed |
| that computes C(i,j) += A(i,k) * B(k,j) for annotated matrix A. |
| """ |
| module = ir.Module.create() |
| f64 = ir.F64Type.get() |
| a = ir.RankedTensorType.get([3, 4], f64, attr) |
| b = ir.RankedTensorType.get([4, 2], f64) |
| c = ir.RankedTensorType.get([3, 2], f64) |
| arguments = [a, b, c] |
| with ir.InsertionPoint(module.body): |
| |
| @func.FuncOp.from_py_func(*arguments) |
| def spMxM(*args): |
| return matmul_dsl(args[0], args[1], outs=[args[2]]) |
| |
| return module |
| |
| |
| def boilerplate(attr: st.EncodingAttr): |
| """Returns boilerplate main method. |
| |
| This method sets up a boilerplate main method that takes three tensors |
| (a, b, c), converts the first tensor a into s sparse tensor, and then |
| calls the sparse kernel for matrix multiplication. For convenience, |
| this part is purely done as string input. |
| """ |
| return f""" |
| func.func @main(%ad: tensor<3x4xf64>, %b: tensor<4x2xf64>, %c: tensor<3x2xf64>) -> tensor<3x2xf64> |
| attributes {{ llvm.emit_c_interface }} {{ |
| %a = sparse_tensor.convert %ad : tensor<3x4xf64> to tensor<3x4xf64, {attr}> |
| %0 = call @spMxM(%a, %b, %c) : (tensor<3x4xf64, {attr}>, |
| tensor<4x2xf64>, |
| tensor<3x2xf64>) -> tensor<3x2xf64> |
| return %0 : tensor<3x2xf64> |
| }} |
| """ |
| |
| |
| def build_compile_and_run_SpMM(attr: st.EncodingAttr, compiler): |
| # Build. |
| module = build_SpMM(attr) |
| func = str(module.operation.regions[0].blocks[0].operations[0].operation) |
| module = ir.Module.parse(func + boilerplate(attr)) |
| |
| # Compile. |
| engine = compiler.compile_and_jit(module) |
| |
| # Set up numpy input and buffer for output. |
| a = np.array( |
| [[1.1, 0.0, 0.0, 1.4], [0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 3.3, 0.0]], np.float64 |
| ) |
| b = np.array([[1.0, 2.0], [4.0, 3.0], [5.0, 6.0], [8.0, 7.0]], np.float64) |
| c = np.zeros((3, 2), np.float64) |
| |
| mem_a = ctypes.pointer(ctypes.pointer(rt.get_ranked_memref_descriptor(a))) |
| mem_b = ctypes.pointer(ctypes.pointer(rt.get_ranked_memref_descriptor(b))) |
| mem_c = ctypes.pointer(ctypes.pointer(rt.get_ranked_memref_descriptor(c))) |
| # Allocate a MemRefDescriptor to receive the output tensor. |
| # The buffer itself is allocated inside the MLIR code generation. |
| ref_out = rt.make_nd_memref_descriptor(2, ctypes.c_double)() |
| mem_out = ctypes.pointer(ctypes.pointer(ref_out)) |
| |
| # Invoke the kernel and get numpy output. |
| # Built-in bufferization uses in-out buffers. |
| engine.invoke("main", mem_out, mem_a, mem_b, mem_c) |
| |
| # Sanity check on computed result. |
| expected = np.matmul(a, b) |
| c = rt.ranked_memref_to_numpy(mem_out[0]) |
| if np.allclose(c, expected): |
| pass |
| else: |
| quit(f"FAILURE") |
| |
| |
| def main(): |
| support_lib = os.getenv("SUPPORT_LIB") |
| assert support_lib is not None, "SUPPORT_LIB is undefined" |
| if not os.path.exists(support_lib): |
| raise FileNotFoundError(errno.ENOENT, os.strerror(errno.ENOENT), support_lib) |
| |
| # CHECK-LABEL: TEST: testSpMM |
| print("\nTEST: testSpMM") |
| count = 0 |
| with ir.Context() as ctx, ir.Location.unknown(): |
| # Loop over various ways to compile and annotate the SpMM kernel with |
| # a *single* sparse tensor. Note that we deliberate do not exhaustively |
| # search the full state space to reduce runtime of the test. It is |
| # straightforward to adapt the code below to explore more combinations. |
| # For these simple orderings, dim2lvl and lvl2dim are the same. |
| vl = 1 |
| e = False |
| opt = f"parallelization-strategy=none" |
| levels = [ |
| [st.DimLevelType.compressed_nu, st.DimLevelType.singleton], |
| [st.DimLevelType.dense, st.DimLevelType.dense], |
| [st.DimLevelType.dense, st.DimLevelType.compressed], |
| [st.DimLevelType.compressed, st.DimLevelType.dense], |
| [st.DimLevelType.compressed, st.DimLevelType.compressed], |
| ] |
| orderings = [ |
| ir.AffineMap.get_permutation([0, 1]), |
| ir.AffineMap.get_permutation([1, 0]), |
| ] |
| bitwidths = [0] |
| compiler = sparse_compiler.SparseCompiler( |
| options=opt, opt_level=0, shared_libs=[support_lib] |
| ) |
| for level in levels: |
| for ordering in orderings: |
| for pwidth in bitwidths: |
| for iwidth in bitwidths: |
| attr = st.EncodingAttr.get( |
| level, ordering, ordering, pwidth, iwidth |
| ) |
| build_compile_and_run_SpMM(attr, compiler) |
| count = count + 1 |
| # CHECK: Passed 10 tests |
| print("Passed ", count, "tests") |
| |
| |
| if __name__ == "__main__": |
| main() |