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"Example code to perform int8 GEMM" import logging import sys import numpy as np import tvm from tvm import autotvm from topi.cuda.tensor_intrin import dp4a DO_TUNING = True PRETUNED_INDEX = 75333 intrin_dp4a = dp4a('local', 'local', 'local') @autotvm.template def gemm_int8(n, m, l): A = tvm.placeholder((n, l), name='A', dtype='int8') B = tvm.placeholder((m, l), name='B', dtype='int8') k = tvm.reduce_axis((0, l), name='k') C = tvm.compute((n, m), lambda i, j: tvm.sum(A[i, k].astype('int32') * B[j, k].astype( 'int32'), axis=k), name='C') cfg = autotvm.get_config() s = tvm.create_schedule(C.op) y, x = C.op.axis 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') k = CC.op.reduce_axis[0] cfg.define_split('tile_k', cfg.axis(k), num_outputs=3, filter=lambda entity: entity.size[2] == 4 and \ entity.size[0] * 2 >= entity.size[1]) ko, kt, ki = cfg['tile_k'].apply(s, CC, k) s[CC].tensorize(ki, intrin_dp4a) block_x = tvm.thread_axis('blockIdx.x') block_y = tvm.thread_axis('blockIdx.y') thread_x = tvm.thread_axis('threadIdx.x') thread_y = tvm.thread_axis('threadIdx.y') def block_size_filter(entity): return entity.size[0] * 2 >= entity.size[1] * 2 and \ entity.size[1] <= 16 and entity.size[3] <= 4 cfg.define_split('tile_y', cfg.axis(y), num_outputs=4, filter=block_size_filter) cfg.define_split('tile_x', cfg.axis(x), num_outputs=4, filter=block_size_filter) by, tyz, ty, yi = cfg['tile_y'].apply(s, C, y) bx, txz, tx, xi = cfg['tile_x'].apply(s, C, x) s[C].bind(by, block_y) s[C].bind(bx, block_x) s[C].bind(tyz, tvm.thread_axis('vthread')) s[C].bind(txz, tvm.thread_axis('vthread')) s[C].bind(ty, thread_y) s[C].bind(tx, thread_x) s[C].reorder(by, bx, tyz, txz, ty, tx, yi, xi) s[CC].compute_at(s[C], tx) yo, xo = CC.op.axis s[CC].reorder(ko, kt, yo, xo, ki) s[CC].unroll(kt) for stage in [AL, BL]: s[stage].compute_at(s[CC], kt) _, xi = s[stage].split(stage.op.axis[1], factor=4) s[stage].vectorize(xi) s[stage].double_buffer() cfg.define_knob('storage_align', [16, 48]) for stage in [AA, BB]: s[stage].storage_align(s[stage].op.axis[0], cfg['storage_align'].val, 0) s[stage].compute_at(s[CC], ko) fused = s[stage].fuse(*s[stage].op.axis) ty, tx = s[stage].split(fused, nparts=cfg['tile_y'].size[2]) tx, xi = s[stage].split(tx, nparts=cfg['tile_x'].size[2]) _, xi = s[stage].split(xi, factor=16) s[stage].bind(ty, thread_y) s[stage].bind(tx, thread_x) s[stage].vectorize(xi) cfg.define_knob('auto_unroll_max_step', [512, 1500]) s[C].pragma(by, 'auto_unroll_max_step', cfg['auto_unroll_max_step'].val) s[C].pragma(by, 'unroll_explicit', False) cfg.add_flop(n*m*l*2) return s, [A, B, C] if __name__ == '__main__': N = 2048 n = m = l = N logging.basicConfig(level=logging.DEBUG, stream=sys.stdout) task = autotvm.task.create(gemm_int8, args=(n, m, l), target='cuda') print(task.config_space) measure_option = autotvm.measure_option( builder=autotvm.LocalBuilder(), runner=autotvm.LocalRunner(repeat=3, min_repeat_ms=100, timeout=4) ) log_name = 'gemm_int8.log' if DO_TUNING: tuner = autotvm.tuner.XGBTuner(task) tuner.tune(n_trial=1000, measure_option=measure_option, callbacks=[autotvm.callback.log_to_file(log_name)]) dispatch_context = autotvm.apply_history_best(log_name) best_config = dispatch_context.query(task.target, task.workload) print('\nBest config:') print(best_config) else: config = task.config_space.get(PRETUNED_INDEX) dispatch_context = autotvm.task.ApplyConfig(config) print("Using pretuned config:") print(config) with dispatch_context: with tvm.target.create('cuda'): s, arg_bufs = gemm_int8(n, m, l) f = tvm.build(s, arg_bufs, 'cuda', name='gemm_int8') ctx = tvm.context('cuda', 0) a_np = np.random.randint(size=(n, l), low=-128, high=127, dtype='int8') b_np = np.random.randint(size=(m, l), low=-128, high=127, dtype='int8') a = tvm.nd.array(a_np, ctx) b = tvm.nd.array(b_np, ctx) c = tvm.nd.array(np.zeros((n, m), dtype='int32'), ctx) f(a, b, c) tvm.testing.assert_allclose( c.asnumpy(), np.dot( a_np.astype('int32'), b_np.T.astype('int32')), rtol=1e-5) num_ops = 2 * l * m * n num_runs = 1000 timer_f = f.time_evaluator(f.entry_name, ctx, number=num_runs) t = timer_f(a, b, c).mean GOPS = num_ops / (t * 1e3) / 1e6 print("average time cost of %d runs = %g ms, %g GOPS." % (num_runs, t * 1e3, GOPS))