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"""Testing topi gemm operator for VTA"""
import os
import json
from collections import namedtuple
import numpy as np
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
# 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_gemm(env, remote, target,
batch_size, in_feat, out_feat,
check_correctness=True, print_ir=True,
samples=4):
# Perform packing only if we are targeting the accelerator
if "arm_cpu" in target.keys:
data_pack = False
elif "vta" in target.keys:
data_pack = True
# Derive shapes depending upon packing
a_shape = (batch_size, in_feat)
w_shape = (out_feat, in_feat)
if data_pack:
data_shape = (batch_size//env.BATCH, in_feat//env.BLOCK_IN,
env.BATCH, env.BLOCK_IN)
kernel_shape = (out_feat//env.BLOCK_OUT, in_feat//env.BLOCK_IN,
env.BLOCK_OUT, env.BLOCK_IN)
else:
data_shape = a_shape
kernel_shape = w_shape
data = tvm.placeholder(data_shape, name="data", dtype=env.inp_dtype)
kernel = tvm.placeholder(kernel_shape, name="kernel", dtype=env.wgt_dtype)
# Define base computation schedule
with target:
res = topi.nn.dense(
data, kernel, out_dtype=env.acc_dtype)
res = topi.right_shift(res, 8)
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_dense([res])
if print_ir:
print(vta.lower(s, [data, kernel, res], simple_mode=True))
# Derive number of ops
num_ops = 2 * batch_size * in_feat * out_feat
# @memoize("vta.tests.test_benchmark_topi.dense.verify")
def get_ref_data():
# derive min max for act, wgt 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))
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)
r_np = np.dot(a_np.astype(env.acc_dtype), w_np.T.astype(env.acc_dtype)).astype(env.acc_dtype)
return a_np, w_np, r_np
# Data in original format
data_np, kernel_np, res_ref = get_ref_data()
if data_pack:
data_np = data_np.reshape(
batch_size//env.BATCH, env.BATCH,
in_feat//env.BLOCK_IN, env.BLOCK_IN).transpose((0, 2, 1, 3))
kernel_np = kernel_np.reshape(
out_feat//env.BLOCK_OUT, env.BLOCK_OUT,
in_feat//env.BLOCK_IN, env.BLOCK_IN).transpose((0, 2, 1, 3))
# Build
if "vta" in target.keys:
mod = vta.build(s, [data, kernel, res],
target=target,
target_host=env.target_host,
name="dense")
else:
mod = tvm.build(s, [data, kernel, res],
target=target,
target_host=env.target_host,
name="dense")
temp = util.tempdir()
mod.save(temp.relpath("dense.o"))
remote.upload(temp.relpath("dense.o"))
f = remote.load_module("dense.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)
res_arr = tvm.nd.array(res_np, ctx)
time_f = f.time_evaluator("dense", 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, 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, res_arr)
stats = simulator.stats()
else:
cost = time_f(data_arr, kernel_arr, res_arr)
# Check correctness
correct = False
if check_correctness:
res_orig = res_arr.asnumpy()
if data_pack:
res_orig = res_orig.reshape(batch_size, out_feat)
res_ref = res_ref >> 8
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 DENSE TEST %s: Time cost = %g sec/op, %g GOPS" % (device, status, cost.mean, gops))
return correct, cost, stats
def test_gemm(device="vta", batch=128, in_feat=128, out_feat=128):
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
run_gemm(env, remote, target, batch, in_feat, out_feat)
vta.testing.run(_run)
if __name__ == "__main__":
test_gemm("vta", 16, 512, 1008)