# Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you under the Apache License, Version 2.0 (the # "License"); you may not use this file except in compliance # with the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY # KIND, either express or implied. See the License for the # specific language governing permissions and limitations # under the License. """Example code to do square matrix multiplication.""" import tvm from tvm import te import os from tvm.contrib import nvcc from tvm.contrib import spirv import numpy as np TASK="gemm" USE_MANUAL_CODE = False @tvm.register_func def tvm_callback_cuda_compile(code): ptx = nvcc.compile_cuda(code, target="ptx") return ptx def write_code(code, fname): with open(fname, "w") as f: f.write(code) @tvm.register_func def tvm_callback_cuda_postproc(code): if not os.path.exists("perf"): os.mkdir("perf") write_code(code, "perf/%s_generated.cu" % TASK) if USE_MANUAL_CODE: code = open("perf/%s_manual.cu" % TASK).read() return code def test_gemm(): # graph nn = 2048 n = te.var('n') n = tvm.runtime.convert(nn) m, l = n, n A = te.placeholder((l, n), name='A') B = te.placeholder((l, m), name='B') k = te.reduce_axis((0, l), name='k') C = te.compute( (m, n), lambda ii, jj: te.sum(A[k, jj] * B[k, ii], axis=k), name='C') # schedule s = te.create_schedule(C.op) AA = s.cache_read(A, "shared", [C]) BB = s.cache_read(B, "shared", [C]) AL = s.cache_read(AA, "local", [C]) BL = s.cache_read(BB, "local", [C]) CC = s.cache_write(C, "local") scale = 8 num_thread = 8 block_factor = scale * num_thread block_x = te.thread_axis("blockIdx.x") thread_x = te.thread_axis((0, num_thread), "threadIdx.x") block_y = te.thread_axis("blockIdx.y") thread_y = te.thread_axis((0, num_thread), "threadIdx.y") thread_xz = te.thread_axis((0, 2), "vthread", name="vx") thread_yz = te.thread_axis((0, 2), "vthread", name="vy") by, yi = s[C].split(C.op.axis[0], factor=block_factor) bx, xi = s[C].split(C.op.axis[1], factor=block_factor) s[C].bind(by, block_y) s[C].bind(bx, block_x) s[C].reorder(by, bx, yi, xi) tyz, yi = s[C].split(yi, nparts=2) ty, yi = s[C].split(yi, nparts=num_thread) txz, xi = s[C].split(xi, nparts=2) tx, xi = s[C].split(xi, nparts=num_thread) s[C].bind(tyz, thread_yz) s[C].bind(txz, thread_xz) s[C].bind(ty, thread_y) s[C].bind(tx, thread_x) s[C].reorder(tyz, txz, ty, tx, yi, xi) s[CC].compute_at(s[C], tx) yo, xo = CC.op.axis ko, ki = s[CC].split(k, factor=8) kt, ki = s[CC].split(ki, factor=1) s[CC].reorder(ko, kt, ki, yo, xo) s[AA].compute_at(s[CC], ko) s[BB].compute_at(s[CC], ko) s[CC].unroll(kt) s[AL].compute_at(s[CC], kt) s[BL].compute_at(s[CC], kt) # Schedule for A's shared memory load ty, xi = s[AA].split(s[AA].op.axis[0], nparts=num_thread) _, xi = s[AA].split(s[AA].op.axis[1], factor=num_thread * 4) tx, xi = s[AA].split(xi, nparts=num_thread) s[AA].bind(ty, thread_y) s[AA].bind(tx, thread_x) s[AA].vectorize(xi) # Schedule for B' shared memory load ty, xi = s[BB].split(s[BB].op.axis[0], nparts=num_thread) _, xi = s[BB].split(s[BB].op.axis[1], factor=num_thread * 4) tx, xi = s[BB].split(xi, nparts=num_thread) s[BB].bind(ty, thread_y) s[BB].bind(tx, thread_x) s[BB].vectorize(xi) s[AA].double_buffer() s[BB].double_buffer() # correctness def check_device(device): ctx = tvm.context(device, 0) if not ctx.exist: print("Skip because %s is not enabled" % device) return print("Device %s" % device) f = tvm.build(s, [A, B, C], device) # launch the kernel. n, m, l = nn, nn, nn a_np = np.random.uniform(size=(n, l)).astype(A.dtype) b_np = np.random.uniform(size=(m, l)).astype(B.dtype) a = tvm.nd.array(a_np, ctx) b = tvm.nd.array(b_np, ctx) c = tvm.nd.array(np.zeros((n, m), dtype=C.dtype), ctx) for i in range(2): f(a, b, c) tvm.testing.assert_allclose( c.asnumpy(), np.dot(b_np.T, a_np), rtol=1e-5) num_flops = 2 * nn * nn * nn num_runs = 10 timer_f = f.time_evaluator(f.entry_name, ctx, number=num_runs) t = timer_f(a, b, c).mean GFLOPS = num_flops / (t * 1e3) / 1e6 print("average time cost of %d runs = %g ms, %g GFLOPS." % (num_runs, t * 1e3, GFLOPS)) for device in ["cuda", "opencl", "rocm", "nvptx", "vulkan"]: with tvm.target.build_config(auto_unroll_max_step=128, unroll_explicit=(device != "cuda")): check_device(device) if __name__ == "__main__": test_gemm()