model: mse err from 0.02-> 0.000056 (#23891)
* mse err from 0.028070712 -> 5.8073703e-05 * build with weights fixup * need thneed lib also * don't break for binaries * static analysis says i need init * check the bias * load_dlc_weights * nicer scons * tested scons * fix static * pylint issue * new ref * a few more asserts Co-authored-by: Harald Schafer <harald.the.engineer@gmail.com>pull/23899/head
parent
77fd64ee30
commit
8d6f49aecf
|
@ -424,6 +424,7 @@ selfdrive/modeld/transforms/transform.cc
|
|||
selfdrive/modeld/transforms/transform.h
|
||||
selfdrive/modeld/transforms/transform.cl
|
||||
|
||||
selfdrive/modeld/thneed/*.py
|
||||
selfdrive/modeld/thneed/thneed.*
|
||||
selfdrive/modeld/thneed/serialize.cc
|
||||
selfdrive/modeld/thneed/compile.cc
|
||||
|
|
|
@ -67,14 +67,22 @@ common_model = lenv.Object(common_src)
|
|||
if use_thneed and arch in ("aarch64", "larch64"):
|
||||
fn = File("#models/supercombo").abspath
|
||||
compiler = lenv.Program('thneed/compile', ["thneed/compile.cc"]+common_model, LIBS=libs)
|
||||
cmd = f"cd {Dir('.').abspath} && {compiler[0].abspath} {fn}.dlc {fn}.thneed --binary"
|
||||
cmd = f"cd {Dir('.').abspath} && {compiler[0].abspath} {fn}.dlc {fn}_badweights.thneed --binary"
|
||||
|
||||
lib_paths = ':'.join(Dir(p).abspath for p in lenv["LIBPATH"])
|
||||
kernel_path = os.path.join(Dir('.').abspath, "thneed", "kernels")
|
||||
cenv = Environment(ENV={'LD_LIBRARY_PATH': f"{lib_paths}:{lenv['ENV']['LD_LIBRARY_PATH']}", 'KERNEL_PATH': kernel_path})
|
||||
|
||||
kernels = [os.path.join(kernel_path, x) for x in os.listdir(kernel_path) if x.endswith(".cl")]
|
||||
cenv.Command(fn + ".thneed", [fn + ".dlc", kernels, compiler], cmd)
|
||||
cenv.Command(fn + "_badweights.thneed", [fn + ".dlc", kernels, compiler], cmd)
|
||||
|
||||
from selfdrive.modeld.thneed.weights_fixup import weights_fixup
|
||||
def weights_fixup_action(target, source, env):
|
||||
weights_fixup(target[0].abspath, source[0].abspath, source[1].abspath)
|
||||
|
||||
env = Environment(BUILDERS = {'WeightFixup' : Builder(action = weights_fixup_action)})
|
||||
env.WeightFixup(target=fn + ".thneed", source=[fn+"_badweights.thneed", fn+".dlc"])
|
||||
|
||||
|
||||
lenv.Program('_dmonitoringmodeld', [
|
||||
"dmonitoringmodeld.cc",
|
||||
|
|
|
@ -0,0 +1,31 @@
|
|||
import struct, json
|
||||
|
||||
def load_thneed(fn):
|
||||
with open(fn, "rb") as f:
|
||||
json_len = struct.unpack("I", f.read(4))[0]
|
||||
jdat = json.loads(f.read(json_len).decode('latin_1'))
|
||||
weights = f.read()
|
||||
ptr = 0
|
||||
for o in jdat['objects']:
|
||||
if o['needs_load']:
|
||||
nptr = ptr + o['size']
|
||||
o['data'] = weights[ptr:nptr]
|
||||
ptr = nptr
|
||||
for o in jdat['binaries']:
|
||||
nptr = ptr + o['length']
|
||||
o['data'] = weights[ptr:nptr]
|
||||
ptr = nptr
|
||||
return jdat
|
||||
|
||||
def save_thneed(jdat, fn):
|
||||
new_weights = []
|
||||
for o in jdat['objects'] + jdat['binaries']:
|
||||
if 'data' in o:
|
||||
new_weights.append(o['data'])
|
||||
del o['data']
|
||||
new_weights = b''.join(new_weights)
|
||||
with open(fn, "wb") as f:
|
||||
j = json.dumps(jdat, ensure_ascii=False).encode('latin_1')
|
||||
f.write(struct.pack("I", len(j)))
|
||||
f.write(j)
|
||||
f.write(new_weights)
|
|
@ -0,0 +1,145 @@
|
|||
#!/usr/bin/env python3
|
||||
import os
|
||||
import struct
|
||||
import zipfile
|
||||
import numpy as np
|
||||
from tqdm import tqdm
|
||||
|
||||
from common.basedir import BASEDIR
|
||||
from selfdrive.modeld.thneed.lib import load_thneed, save_thneed
|
||||
|
||||
# this is junk code, but it doesn't have deps
|
||||
def load_dlc_weights(fn):
|
||||
archive = zipfile.ZipFile(fn, 'r')
|
||||
dlc_params = archive.read("model.params")
|
||||
|
||||
def extract(rdat):
|
||||
idx = rdat.find(b"\x00\x00\x00\x09\x04\x00\x00\x00")
|
||||
rdat = rdat[idx+8:]
|
||||
ll = struct.unpack("I", rdat[0:4])[0]
|
||||
buf = np.frombuffer(rdat[4:4+ll*4], dtype=np.float32)
|
||||
rdat = rdat[4+ll*4:]
|
||||
dims = struct.unpack("I", rdat[0:4])[0]
|
||||
buf = buf.reshape(struct.unpack("I"*dims, rdat[4:4+dims*4]))
|
||||
if len(buf.shape) == 4:
|
||||
buf = np.transpose(buf, (3,2,0,1))
|
||||
return buf
|
||||
|
||||
def parse(tdat):
|
||||
ll = struct.unpack("I", tdat[0:4])[0] + 4
|
||||
return (None, [extract(tdat[0:]), extract(tdat[ll:])])
|
||||
|
||||
ptr = 0x20
|
||||
def r4():
|
||||
nonlocal ptr
|
||||
ret = struct.unpack("I", dlc_params[ptr:ptr+4])[0]
|
||||
ptr += 4
|
||||
return ret
|
||||
ranges = []
|
||||
cnt = r4()
|
||||
for _ in range(cnt):
|
||||
o = r4() + ptr
|
||||
# the header is 0xC
|
||||
plen, is_4, is_2 = struct.unpack("III", dlc_params[o:o+0xC])
|
||||
assert is_4 == 4 and is_2 == 2
|
||||
ranges.append((o+0xC, o+plen+0xC))
|
||||
ranges = sorted(ranges, reverse=True)
|
||||
|
||||
return [parse(dlc_params[s:e]) for s,e in ranges]
|
||||
|
||||
# this won't run on device without onnx
|
||||
def load_onnx_weights(fn):
|
||||
import onnx
|
||||
from onnx import numpy_helper
|
||||
|
||||
model = onnx.load(fn)
|
||||
graph = model.graph # pylint: disable=maybe-no-member
|
||||
init = {x.name:x for x in graph.initializer}
|
||||
|
||||
onnx_layers = []
|
||||
for node in graph.node:
|
||||
#print(node.name, node.op_type, node.input, node.output)
|
||||
vals = []
|
||||
for inp in node.input:
|
||||
if inp in init:
|
||||
vals.append(numpy_helper.to_array(init[inp]))
|
||||
if len(vals) > 0:
|
||||
onnx_layers.append((node.name, vals))
|
||||
return onnx_layers
|
||||
|
||||
def weights_fixup(target, source_thneed, dlc):
|
||||
#onnx_layers = load_onnx_weights(os.path.join(BASEDIR, "models/supercombo.onnx"))
|
||||
onnx_layers = load_dlc_weights(dlc)
|
||||
jdat = load_thneed(source_thneed)
|
||||
|
||||
bufs = {}
|
||||
for o in jdat['objects']:
|
||||
bufs[o['id']] = o
|
||||
|
||||
thneed_layers = []
|
||||
for k in jdat['kernels']:
|
||||
#print(k['name'])
|
||||
vals = []
|
||||
for a in k['args']:
|
||||
if a in bufs:
|
||||
o = bufs[a]
|
||||
if o['needs_load'] or ('buffer_id' in o and bufs[o['buffer_id']]['needs_load']):
|
||||
#print(" ", o['arg_type'])
|
||||
vals.append(o)
|
||||
if len(vals) > 0:
|
||||
thneed_layers.append((k['name'], vals))
|
||||
|
||||
assert len(thneed_layers) == len(onnx_layers)
|
||||
|
||||
# fix up weights
|
||||
for tl, ol in tqdm(zip(thneed_layers, onnx_layers), total=len(thneed_layers)):
|
||||
#print(tl[0], ol[0])
|
||||
assert len(tl[1]) == len(ol[1])
|
||||
for o, onnx_weight in zip(tl[1], ol[1]):
|
||||
if o['arg_type'] == "image2d_t":
|
||||
obuf = bufs[o['buffer_id']]
|
||||
saved_weights = np.frombuffer(obuf['data'], dtype=np.float16).reshape(o['height'], o['row_pitch']//2)
|
||||
|
||||
if len(onnx_weight.shape) == 4:
|
||||
# convolution
|
||||
oc,ic,ch,cw = onnx_weight.shape
|
||||
|
||||
if 'depthwise' in tl[0]:
|
||||
assert ic == 1
|
||||
weights = np.transpose(onnx_weight.reshape(oc//4,4,ch,cw), (0,2,3,1)).reshape(o['height'], o['width']*4)
|
||||
else:
|
||||
weights = np.transpose(onnx_weight.reshape(oc//4,4,ic//4,4,ch,cw), (0,4,2,5,1,3)).reshape(o['height'], o['width']*4)
|
||||
else:
|
||||
# fc_Wtx
|
||||
weights = onnx_weight
|
||||
|
||||
new_weights = np.zeros((o['height'], o['row_pitch']//2), dtype=np.float32)
|
||||
new_weights[:, :weights.shape[1]] = weights
|
||||
|
||||
# weights shouldn't be too far off
|
||||
err = np.mean((saved_weights.astype(np.float32) - new_weights)**2)
|
||||
assert err < 1e-3
|
||||
rerr = np.mean(np.abs((saved_weights.astype(np.float32) - new_weights)/(new_weights+1e-12)))
|
||||
assert rerr < 0.5
|
||||
|
||||
# fix should improve things
|
||||
fixed_err = np.mean((new_weights.astype(np.float16).astype(np.float32) - new_weights)**2)
|
||||
assert (err/fixed_err) >= 1
|
||||
|
||||
#print(" ", o['size'], onnx_weight.shape, o['row_pitch'], o['width'], o['height'], "err %.2fx better" % (err/fixed_err))
|
||||
|
||||
obuf['data'] = new_weights.astype(np.float16).tobytes()
|
||||
|
||||
elif o['arg_type'] == "float*":
|
||||
# unconverted floats are correct
|
||||
new_weights = np.zeros(o['size']//4, dtype=np.float32)
|
||||
new_weights[:onnx_weight.shape[0]] = onnx_weight
|
||||
assert new_weights.tobytes() == o['data']
|
||||
#print(" ", o['size'], onnx_weight.shape)
|
||||
|
||||
save_thneed(jdat, target)
|
||||
|
||||
if __name__ == "__main__":
|
||||
weights_fixup(os.path.join(BASEDIR, "models/supercombo_fixed.thneed"),
|
||||
os.path.join(BASEDIR, "models/supercombo.thneed"),
|
||||
os.path.join(BASEDIR, "models/supercombo.dlc"))
|
|
@ -1 +1 @@
|
|||
19720e79b1c5136a882efd689651d9044e2e2007
|
||||
15821a7f867f6b497a17e8a36c9d42ad548acacd
|
||||
|
|
Loading…
Reference in New Issue