Gluon model with prelu was built successfully by nnvm.compiler but crashed when I tried relay.build. When I deleted all prelu or replace them with relu, everything went fine.
Environment
- system: Ubuntu 16.04
- python: 3.6
- tvm: 0.6.dev
Crash Infomation
[2019-04-01 21:50:25] Cannot find config for target=llvm -mcpu=core-avx2, workload=('conv2d', (1, 3, 224, 224, 'float32'), (10, 3, 3, 3, 'float32'), (1, 1), (0, 0), (1, 1), 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
[2019-04-01 21:50:25] Cannot find config for target=llvm -mcpu=core-avx2, workload=('conv2d', (1, 10, 111, 111, 'float32'), (16, 10, 3, 3, 'float32'), (1, 1), (0, 0), (1, 1), 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
[2019-04-01 21:50:25] Cannot find config for target=llvm -mcpu=core-avx2, workload=('conv2d', (1, 16, 109, 109, 'float32'), (32, 16, 3, 3, 'float32'), (1, 1), (0, 0), (1, 1), 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
[2019-04-01 21:50:25] Cannot find config for target=llvm -mcpu=core-avx2, workload=('conv2d', (1, 32, 107, 107, 'float32'), (6, 32, 1, 1, 'float32'), (1, 1), (0, 0), (1, 1), 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
Traceback (most recent call last):
File "tools/tvm/speed.py", line 108, in <module>
speed()
File "tools/tvm/speed.py", line 71, in speed
graph, lib, params = relay.build(func, 'llvm -mcpu=core-avx2', params=params)
File "/home/yf/.local/lib/python3.6/site-packages/tvm-0.6.dev0-py3.6-linux-x86_64.egg/tvm/relay/build_module.py", line 276, in build
func = optimize(func, target, params)
File "/home/yf/.local/lib/python3.6/site-packages/tvm-0.6.dev0-py3.6-linux-x86_64.egg/tvm/relay/build_module.py", line 209, in optimize
func = ir_pass.fold_constant(func)
File "/home/yf/.local/lib/python3.6/site-packages/tvm-0.6.dev0-py3.6-linux-x86_64.egg/tvm/relay/ir_pass.py", line 687, in fold_constant
return _ir_pass.FoldConstant(expr)
File "tvm/_ffi/_cython/./function.pxi", line 287, in tvm._ffi._cy3.core.FunctionBase.__call__
File "tvm/_ffi/_cython/./function.pxi", line 222, in tvm._ffi._cy3.core.FuncCall
File "tvm/_ffi/_cython/./function.pxi", line 211, in tvm._ffi._cy3.core.FuncCall3
File "tvm/_ffi/_cython/./base.pxi", line 151, in tvm._ffi._cy3.core.CALL
tvm._ffi.base.TVMError: Traceback (most recent call last):
[bt] (8) /home/yf/.local/lib/python3.6/site-packages/tvm-0.6.dev0-py3.6-linux-x86_64.egg/tvm/libtvm.so(+0xd57fde) [0x7f8533080fde]
[bt] (7) /home/yf/.local/lib/python3.6/site-packages/tvm-0.6.dev0-py3.6-linux-x86_64.egg/tvm/libtvm.so(+0xd56a6b) [0x7f853307fa6b]
[bt] (6) /home/yf/.local/lib/python3.6/site-packages/tvm-0.6.dev0-py3.6-linux-x86_64.egg/tvm/libtvm.so(tvm::relay::InferType(tvm::relay::Expr const&, tvm::relay::Module const&)+0x3fd) [0x7f85330dd37d]
[bt] (5) /home/yf/.local/lib/python3.6/site-packages/tvm-0.6.dev0-py3.6-linux-x86_64.egg/tvm/libtvm.so(+0xbdc316) [0x7f8532f05316]
[bt] (4) /home/yf/.local/lib/python3.6/site-packages/tvm-0.6.dev0-py3.6-linux-x86_64.egg/tvm/libtvm.so(+0xbdb890) [0x7f8532f04890]
[bt] (3) /home/yf/.local/lib/python3.6/site-packages/tvm-0.6.dev0-py3.6-linux-x86_64.egg/tvm/libtvm.so(tvm::relay::InferType(tvm::relay::Function const&, tvm::relay::Module const&, tvm::relay::GlobalVar const&)+0x325) [0x7f85330dda35]
[bt] (2) /home/yf/.local/lib/python3.6/site-packages/tvm-0.6.dev0-py3.6-linux-x86_64.egg/tvm/libtvm.so(+0xdb3f2a) [0x7f85330dcf2a]
[bt] (1) /home/yf/.local/lib/python3.6/site-packages/tvm-0.6.dev0-py3.6-linux-x86_64.egg/tvm/libtvm.so(+0xbaf473) [0x7f8532ed8473]
[bt] (0) /home/yf/.local/lib/python3.6/site-packages/tvm-0.6.dev0-py3.6-linux-x86_64.egg/tvm/libtvm.so(+0x829442) [0x7f8532b52442]
[bt] (8) /home/yf/.local/lib/python3.6/site-packages/tvm-0.6.dev0-py3.6-linux-x86_64.egg/tvm/libtvm.so(+0xbdb890) [0x7f8532f04890]
[bt] (7) /home/yf/.local/lib/python3.6/site-packages/tvm-0.6.dev0-py3.6-linux-x86_64.egg/tvm/libtvm.so(tvm::relay::InferType(tvm::relay::Function const&, tvm::relay::Module const&, tvm::relay::GlobalVar const&)+0x325) [0x7f85330dda35]
[bt] (6) /home/yf/.local/lib/python3.6/site-packages/tvm-0.6.dev0-py3.6-linux-x86_64.egg/tvm/libtvm.so(+0xdb3d03) [0x7f85330dcd03]
[bt] (5) /home/yf/.local/lib/python3.6/site-packages/tvm-0.6.dev0-py3.6-linux-x86_64.egg/tvm/libtvm.so(+0xdcc4c4) [0x7f85330f54c4]
[bt] (4) /home/yf/.local/lib/python3.6/site-packages/tvm-0.6.dev0-py3.6-linux-x86_64.egg/tvm/libtvm.so(+0xc1fc24) [0x7f8532f48c24]
[bt] (3) /home/yf/.local/lib/python3.6/site-packages/tvm-0.6.dev0-py3.6-linux-x86_64.egg/tvm/libtvm.so(+0xcdb00a) [0x7f853300400a]
[bt] (2) /home/yf/.local/lib/python3.6/site-packages/tvm-0.6.dev0-py3.6-linux-x86_64.egg/tvm/libtvm.so(tvm::BijectiveLayout::ForwardShape(tvm::Array<HalideIR::Expr, void> const&) const+0x6e) [0x7f8532cb333e]
[bt] (1) /home/yf/.local/lib/python3.6/site-packages/tvm-0.6.dev0-py3.6-linux-x86_64.egg/tvm/libtvm.so(+0x98cc66) [0x7f8532cb5c66]
[bt] (0) /home/yf/.local/lib/python3.6/site-packages/tvm-0.6.dev0-py3.6-linux-x86_64.egg/tvm/libtvm.so(+0x829442) [0x7f8532b52442]
File "/home/yf/.software/tvm/src/relay/ir/error.cc", line 112
TVMError:
Error(s) have occurred. We have annotated the program with them:
In `main`:
v0.0.1
%1 = fn () {
%0 = layout_transform(meta[relay.Constant][0] // , src_layout="NCHW", dst_layout="NCHW5c") // an internal invariant was violdated while typechecking your program [21:50:26] /home/yf/.software/tvm/src/lang/data_layout.cc:254: Check failed: src_shape.size() == src_axis.size() (1 vs. 4) :
;
%0
}
%1
// meta data omitted. you can use show_meta_data=True to include meta data
Code to reproduce
import os
import tvm
import mxnet as mx
import nnvm.compiler
import tvm.relay as relay
from tvm.contrib import graph_runtime
from mxnet import gluon
from mxnet.gluon import nn as gnn
class Pnet(gluon.HybridBlock):
"""Proposal Network"""
def __init__(self, size=12, **kwargs):
super(Pnet, self).__init__(**kwargs)
self.size = size
self.base = gnn.HybridSequential()
self.base.add(
gnn.Conv2D(10, 3), gnn.PReLU(10),
gnn.MaxPool2D(ceil_mode=True), # caffe default
gnn.Conv2D(16, 3), gnn.PReLU(16),
gnn.Conv2D(32, 3), gnn.PReLU(32))
self.conv4_1 = gnn.Conv2D(2, 1)
self.conv4_2 = gnn.Conv2D(4, 1)
def hybrid_forward(self, F, x):
x = self.base(x)
cls_pred = self.conv4_1(x)
bbx_pred = self.conv4_2(x)
if not mx.autograd.is_training():
cls_pred = F.softmax(cls_pred, axis=1)
return cls_pred, bbx_pred
def test(network='pnet'):
shape_dict = {'data': (1, 3, 224, 224)}
x = mx.nd.random.randn(*shape_dict['data']).asnumpy()
net = Pnet()
net.initialize()
net.hybridize()
net(mx.nd.array(x))
net.export(network, 0)
sym, arg_params, aux_params = mx.model.load_checkpoint(network, 0)
os.remove(network + '-symbol.json')
os.remove(network + '-0000.params')
func, params = relay.frontend.from_mxnet(sym, shape_dict, 'float32', arg_params, aux_params)
with relay.build_config(opt_level=3):
graph, lib, params = relay.build(func, 'llvm -mcpu=core-avx2', params=params)
mod = graph_runtime.create(graph, lib, tvm.cpu())
mod.load_params(bytearray(relay.save_param_dict(params)))
mod.run(data=x)
test()