liangfu commented on a change in pull request #4421: [VTA] Bringing group convolution support URL: https://github.com/apache/incubator-tvm/pull/4421#discussion_r351098420
########## File path: vta/tests/python/integration/test_benchmark_topi_group_conv2d.py ########## @@ -0,0 +1,242 @@ +# 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. + +"""Testing topi group conv2d operator for VTA""" + +import json +import os + +import numpy as np +from collections import namedtuple + +import tvm +from tvm import autotvm +from tvm.contrib import util +from tvm.contrib.pickle_memoize import memoize +import topi +import topi.testing +import vta +from vta import program_fpga, reconfig_runtime +import vta.testing +from vta.testing import simulator + + +Workload = namedtuple("GroupConv2DWorkload", + ['batch', 'height', 'width', 'in_filter', 'out_filter', 'groups', + 'hkernel', 'wkernel', 'hpad', 'wpad', 'hstride', 'wstride']) + +# Get batch info from env +env = vta.get_env() + +# Mobilenet (grouped variant) workloads +mobilenet_wkls = [ + ('mobilenet.D1', Workload(env.BATCH, 112, 112, 32, 32, 2, 3, 3, 1, 1, 1, 1)), + ('mobilenet.D2', Workload(env.BATCH, 112, 112, 64, 64, 4, 3, 3, 1, 1, 2, 2)), + ('mobilenet.D3', Workload(env.BATCH, 56, 56, 128, 128, 8, 3, 3, 1, 1, 1, 1)), + ('mobilenet.D4', Workload(env.BATCH, 56, 56, 128, 128, 8, 3, 3, 1, 1, 2, 2)), + ('mobilenet.D5', Workload(env.BATCH, 28, 28, 256, 256, 16, 3, 3, 1, 1, 1, 1)), + ('mobilenet.D6', Workload(env.BATCH, 28, 28, 256, 256, 16, 3, 3, 1, 1, 2, 2)), + ('mobilenet.D7', Workload(env.BATCH, 14, 14, 512, 512, 32, 3, 3, 1, 1, 1, 1)), + ('mobilenet.D8', Workload(env.BATCH, 14, 14, 512, 512, 32, 3, 3, 1, 1, 2, 2)), + ('mobilenet.D9', Workload(env.BATCH, 7, 7, 1024, 1024, 64, 3, 3, 1, 1, 1, 1)), +] + +# FIXME: we need a custom clip operator to circumvent a pattern detection limitation [email protected]_scope(tag=topi.tag.ELEMWISE) +def my_clip(x, a_min, a_max): + """Unlike topi's current clip, put min and max into two stages.""" + const_min = tvm.const(a_min, x.dtype) + const_max = tvm.const(a_max, x.dtype) + x = tvm.compute(x.shape, lambda *i: tvm.min(x(*i), const_max), name="clipA") + x = tvm.compute(x.shape, lambda *i: tvm.max(x(*i), const_min), name="clipB") + return x + +def run_group_conv2d(env, remote, wl, target, + check_correctness=True, print_ir=False, + samples=4): + + # Workload assertions + assert wl.hpad == wl.wpad + + # Perform packing only if we are targeting the accelerator + if "arm_cpu" in target.keys: + data_pack = False + layout = "NCHW" + elif "vta" in target.keys: + data_pack = True + layout = "NCHW%dn%dc" % (env.BATCH, env.BLOCK_IN) + + # Derive shapes depending upon packing + CI_G = wl.in_filter // wl.groups + a_shape = (wl.batch, wl.in_filter, wl.height, wl.width) + w_shape = (wl.out_filter, CI_G, wl.hkernel, wl.wkernel) + b_shape = (wl.batch, wl.out_filter, 1, 1) + if data_pack: + data_shape = (wl.batch//env.BATCH, wl.in_filter//env.BLOCK_IN, + wl.height, wl.width, env.BATCH, env.BLOCK_IN) + kernel_shape = (wl.out_filter//env.BLOCK_OUT, CI_G//env.BLOCK_IN, + wl.hkernel, wl.wkernel, env.BLOCK_OUT, env.BLOCK_IN) + bias_shape = (wl.batch//env.BATCH, wl.out_filter//env.BLOCK_OUT, + 1, 1, env.BATCH, env.BLOCK_OUT) + else: + data_shape = a_shape + kernel_shape = w_shape + bias_shape = b_shape + data = tvm.placeholder(data_shape, name="data", dtype=env.inp_dtype) + kernel = tvm.placeholder(kernel_shape, name="kernel", dtype=env.wgt_dtype) + bias = tvm.placeholder(bias_shape, name="bias", dtype=env.acc_dtype) + # Define base computation schedule + with target: + res = topi.nn.group_conv2d_nchw( + data, kernel, (wl.hstride, wl.wstride), (wl.hpad, wl.wpad), (1, 1), + wl.groups, env.acc_dtype) + res = topi.right_shift(res, 8) + res = topi.add(res, bias) + res = my_clip(res, 0, (1 << env.OUT_WIDTH - 1) - 1) + res = topi.cast(res, env.out_dtype) + # Derive base schedule + s = topi.generic.schedule_group_conv2d_nchw([res]) + if print_ir: + print(vta.lower(s, [data, kernel, bias, res], simple_mode=True)) + + # Derive number of ops + fout_height = (wl.height + 2 * wl.hpad - wl.hkernel) // wl.hstride + 1 + fout_width = (wl.width + 2 * wl.wpad - wl.wkernel) // wl.wstride + 1 + num_ops = 2 * wl.batch * fout_height * fout_width * wl.hkernel * wl.wkernel * \ + wl.out_filter * wl.in_filter // wl.groups + + # @memoize("vta.tests.test_benchmark_topi.conv2d.verify_nhwc") + def get_ref_data(): + # derive min max for act, wgt, and bias types (max non inclusive) + a_min, a_max = 0 - (1 << (env.INP_WIDTH - 1)), (1 << (env.INP_WIDTH - 1)) + w_min, w_max = 0 - (1 << (env.WGT_WIDTH - 1)), (1 << (env.WGT_WIDTH - 1)) + b_min, b_max = 0 - 1 << (env.INP_WIDTH + env.WGT_WIDTH - 2), 1 << (env.INP_WIDTH + env.WGT_WIDTH - 2) + a_np = np.random.randint(a_min, a_max, size=a_shape).astype(data.dtype) + w_np = np.random.randint(w_min, w_max, size=w_shape).astype(kernel.dtype) + b_np = np.random.randint(b_min, b_max, size=b_shape).astype(env.acc_dtype) + r_np = topi.testing.conv2d_nchw_python( + a_np.astype(env.acc_dtype), w_np.astype(env.acc_dtype), + (wl.hstride, wl.wstride), wl.hpad, wl.groups).astype(env.acc_dtype) + return a_np, w_np, b_np, r_np + + # Data in original format + data_np, kernel_np, bias_np, res_ref = get_ref_data() + if data_pack: + data_np = data_np.reshape( + wl.batch//env.BATCH, env.BATCH, + wl.in_filter//env.BLOCK_IN, env.BLOCK_IN, + wl.height, wl.width).transpose((0, 2, 4, 5, 1, 3)) + kernel_np = kernel_np.reshape( + wl.out_filter//env.BLOCK_OUT, env.BLOCK_OUT, + CI_G//env.BLOCK_IN, env.BLOCK_IN, + wl.hkernel, wl.wkernel).transpose((0, 2, 4, 5, 1, 3)) + bias_np = bias_np.reshape( + wl.batch//env.BATCH, wl.out_filter//env.BLOCK_OUT, + 1, 1, env.BATCH, env.BLOCK_OUT) + + # Build + if "vta" in target.keys: + mod = vta.build(s, [data, kernel, bias, res], + target=target, + target_host=env.target_host, + name="conv2d") + else: + mod = tvm.build(s, [data, kernel, bias, res], + target=target, + target_host=env.target_host, + name="conv2d") + temp = util.tempdir() + mod.save(temp.relpath("conv2d.o")) + remote.upload(temp.relpath("conv2d.o")) + f = remote.load_module("conv2d.o") + ctx = remote.context(str(target)) + + res_np = np.zeros(topi.util.get_const_tuple(res.shape)).astype(res.dtype) + data_arr = tvm.nd.array(data_np, ctx) + kernel_arr = tvm.nd.array(kernel_np, ctx) + bias_arr = tvm.nd.array(bias_np, ctx) + res_arr = tvm.nd.array(res_np, ctx) + time_f = f.time_evaluator("conv2d", ctx, number=samples) + + # In vta sim mode, collect simulator runtime statistics + stats = {} + cost = None + if env.TARGET in ["sim", "tsim"]: + # Check if we're in local RPC mode (allows us to rebuild the + # runtime on the fly when varying the VTA designs) + local_rpc = int(os.environ.get("VTA_LOCAL_SIM_RPC", "0")) + if local_rpc: + if env.TARGET == "sim": + remote.get_function("vta.simulator.profiler_clear")() + else: + remote.get_function("vta.tsim.profiler_clear")() + cost = time_f(data_arr, kernel_arr, bias_arr, res_arr) + if env.TARGET == "sim": + stats = json.loads(remote.get_function("vta.simulator.profiler_status")()) + else: + stats = json.loads(remote.get_function("vta.tsim.profiler_status")()) + else: + simulator.clear_stats() + cost = time_f(data_arr, kernel_arr, bias_arr, res_arr) + stats = simulator.stats() + else: + cost = time_f(data_arr, kernel_arr, bias_arr, res_arr) + + # Check correctness + correct = False + if check_correctness: + res_orig = res_arr.asnumpy() + if data_pack: + res_orig = res_orig.transpose( + (0, 4, 1, 5, 2, 3)).reshape(wl.batch, wl.out_filter, fout_height, fout_width) + bias_np = bias_np.transpose( + (0, 4, 1, 5, 2, 3)).reshape(wl.batch, wl.out_filter, 1, 1) + res_ref = res_ref >> env.WGT_WIDTH + res_ref += bias_np + res_ref = np.clip(res_ref, 0, (1 << env.OUT_WIDTH - 1) - 1) + res_ref = res_ref.astype(env.out_dtype) + correct = np.allclose(res_orig, res_ref) + + gops = (num_ops / cost.mean) / float(10 ** 9) + status = "PASSED" if correct else "FAILED" + if "arm_cpu" in target.keys: + device = "CPU" + elif "vta" in target.keys: + device = "VTA" + print("%s GROUP CONV2D TEST %s: Time cost = %g sec/op, %g GOPS" % (device, status, cost.mean, gops)) + + return correct, cost, stats + +def test_conv2d(device="vta"): + def _run(env, remote): + if device == "vta": + target = env.target + if env.TARGET not in ["sim", "tsim"]: + assert tvm.module.enabled("rpc") + program_fpga(remote, bitstream=None) + reconfig_runtime(remote) + elif device == "arm_cpu": + target = env.target_vta_cpu + with autotvm.tophub.context(target): # load pre-tuned schedule parameters + for _, wl in mobilenet_wkls: + print(wl) + run_group_conv2d(env, remote, wl, target) + vta.testing.run(_run) + +if __name__ == "__main__": + # test_conv2d(device="arm_cpu") Review comment: This is another line left to be removed, or it should be enabled for testing? ---------------------------------------------------------------- This is an automated message from the Apache Git Service. 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