test_benchmark_topi_conv2d.py

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"""Testing topi 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("Conv2DWorkload",
                      ['batch', 'height', 'width', 'in_filter', 'out_filter',
                       'hkernel', 'wkernel', 'hpad', 'wpad', 'hstride', 'wstride'])

# Get batch info from env
env = vta.get_env()

# ResNet18 workloads
resnet_wkls = [
    # Workloads of resnet18 on imagenet
    # ('resnet-18.C1',  Workload(env.BATCH, 224, 224, 3,   64,  7, 7, 3, 3, 2, 2)),
    ('resnet-18.C2',  Workload(env.BATCH,  56,  56, 64,  64,  3, 3, 1, 1, 1, 1)),
    ('resnet-18.C3',  Workload(env.BATCH,  56,  56, 64,  128, 3, 3, 1, 1, 2, 2)),
    ('resnet-18.C4',  Workload(env.BATCH,  56,  56, 64,  128, 1, 1, 0, 0, 2, 2)),
    ('resnet-18.C5',  Workload(env.BATCH,  28,  28, 128, 128, 3, 3, 1, 1, 1, 1)),
    ('resnet-18.C6',  Workload(env.BATCH,  28,  28, 128, 256, 3, 3, 1, 1, 2, 2)),
    ('resnet-18.C7',  Workload(env.BATCH,  28,  28, 128, 256, 1, 1, 0, 0, 2, 2)),
    ('resnet-18.C8',  Workload(env.BATCH,  14,  14, 256, 256, 3, 3, 1, 1, 1, 1)),
    ('resnet-18.C9',  Workload(env.BATCH,  14,  14, 256, 512, 3, 3, 1, 1, 2, 2)),
    ('resnet-18.C10', Workload(env.BATCH,  14,  14, 256, 512, 1, 1, 0, 0, 2, 2)),
    ('resnet-18.C11', Workload(env.BATCH,   7,   7, 512, 512, 3, 3, 1, 1, 1, 1)),
]

# FIXME: we need a custom clip operator to circumvent a pattern detection limitation
@tvm.tag_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_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
    a_shape = (wl.batch, wl.in_filter, wl.height, wl.width)
    w_shape = (wl.out_filter, wl.in_filter, 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, wl.in_filter//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.conv2d(
            data, kernel, (wl.hstride, wl.wstride), (wl.hpad, wl.wpad), (1, 1),
            layout, 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_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

    # @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).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,
            wl.in_filter//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 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 resnet_wkls:
                print(wl)
                run_conv2d(env, remote, wl, target)
    vta.testing.run(_run)

if __name__ == "__main__":
    test_conv2d(device="arm_cpu")
    test_conv2d(device="vta")