When running fold_scale_axis pass, if in_scale is type of relay.const, I encounter a error like this:
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tvm._ffi.base.TVMError: Traceback (most recent call last):
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[bt] (8) /home/yin/.local/lib/python3.5/site-packages/tvm-0.6.dev0-py3.5-linux-x86_64.egg/tvm/libtvm.so(tvm::relay::ScheduleGetter::VisitExpr_(tvm::relay::CallNode const*)+0x24e) [0x7f9ed02e51b8]
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[bt] (7) /home/yin/.local/lib/python3.5/site-packages/tvm-0.6.dev0-py3.5-linux-x86_64.egg/tvm/libtvm.so(tvm::relay::ScheduleGetter::VisitExpr(tvm::relay::Expr const&)+0x98) [0x7f9ed02e46e2]
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[bt] (6) /home/yin/.local/lib/python3.5/site-packages/tvm-0.6.dev0-py3.5-linux-x86_64.egg/tvm/libtvm.so(tvm::relay::ExprFunctor<tvm::Array<tvm::Tensor, void> (tvm::relay::Expr const&)>::VisitExpr(tvm::relay::Expr const&)+0x12d) [0x7f9ed02ec35b]
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[bt] (5) /home/yin/.local/lib/python3.5/site-packages/tvm-0.6.dev0-py3.5-linux-x86_64.egg/tvm/libtvm.so(tvm::IRFunctor<tvm::Array<tvm::Tensor, void> (tvm::NodeRef const&, tvm::relay::ExprFunctor<tvm::Array<tvm::Tensor, void> (tvm::relay::Expr const&)>)>::operator()(tvm::NodeRef const&, tvm::relay::ExprFunctor<tvm::Array<tvm::Tensor, void> (tvm::relay::Expr const&)>) const+0x16d) [0x7f9ed02ee9fb]
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[bt] (4) /home/yin/.local/lib/python3.5/site-packages/tvm-0.6.dev0-py3.5-linux-x86_64.egg/tvm/libtvm.so(std::function<tvm::Array<tvm::Tensor, void> (tvm::NodeRef const&, tvm::relay::ExprFunctor<tvm::Array<tvm::Tensor, void> (tvm::relay::Expr const&)>)>::operator()(tvm::NodeRef const&, tvm::relay::ExprFunctor<tvm::Array<tvm::Tensor, void> (tvm::relay::Expr const&)>) const+0x66) [0x7f9ed02f35ba]
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[bt] (3) /home/yin/.local/lib/python3.5/site-packages/tvm-0.6.dev0-py3.5-linux-x86_64.egg/tvm/libtvm.so(std::_Function_handler<tvm::Array<tvm::Tensor, void> (tvm::NodeRef const&, tvm::relay::ExprFunctor<tvm::Array<tvm::Tensor, void> (tvm::relay::Expr const&)>), tvm::relay::ExprFunctor<tvm::Array<tvm::Tensor, void> (tvm::relay::Expr const&)>::InitVTable()::{lambda(tvm::NodeRef const&, tvm::relay::ExprFunctor<tvm::Array<tvm::Tensor, void> (tvm::relay::Expr const&)>)#1}>::_M_invoke(std::_Any_data const&, tvm::NodeRef const&, tvm::relay::ExprFunctor<tvm::Array<tvm::Tensor, void> (tvm::relay::Expr const&)>*)+0x5a) [0x7f9ed02f5739]
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[bt] (2) /home/yin/.local/lib/python3.5/site-packages/tvm-0.6.dev0-py3.5-linux-x86_64.egg/tvm/libtvm.so(tvm::relay::ExprFunctor<tvm::Array<tvm::Tensor, void> (tvm::relay::Expr const&)>::InitVTable()::{lambda(tvm::NodeRef const&, tvm::relay::ExprFunctor<tvm::Array<tvm::Tensor, void> (tvm::relay::Expr const&)>)#1}::operator()(tvm::NodeRef const&, tvm::relay::ExprFunctor<tvm::Array<tvm::Tensor, void> (tvm::relay::Expr const&)>) const+0x46) [0x7f9ed02edb22]
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[bt] (1) /home/yin/.local/lib/python3.5/site-packages/tvm-0.6.dev0-py3.5-linux-x86_64.egg/tvm/libtvm.so(tvm::relay::ScheduleGetter::VisitExpr_(tvm::relay::ConstantNode const*)+0xa5) [0x7f9ed02e4bbf]
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[bt] (0) /home/yin/.local/lib/python3.5/site-packages/tvm-0.6.dev0-py3.5-linux-x86_64.egg/tvm/libtvm.so(dmlc::LogMessageFatal::~LogMessageFatal()+0x25) [0x7f9ecfa79049]
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File “/home/yin/program_files/tvm/src/relay/backend/compile_engine.cc”, line 179
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TVMError: Check failed: op->is_scalar():
the code is below
def test_fold_fwd_simple():
“”“Simple testcase.”""
def before(x, conv_weight, in_bias, in_scale, channels):
args = [x, conv_weight, in_bias]
in_bias = relay.expand_dims(in_bias, axis=1, num_newaxis=2)
x = relay.multiply(x, in_scale)
x = relay.nn.relu(x)
x = relay.add(x, in_bias)
y = relay.nn.conv2d(x, conv_weight,
channels=channels,
kernel_size=(3, 3),
padding=(1, 1))return relay.Function(args, y)def expected(x, conv_weight, in_bias, in_scale, channels):
# use a fixed order of args so alpha equal check can pass
args = [x, conv_weight, in_bias]
in_bias = relay.expand_dims(in_bias, axis=1, num_newaxis=2)
squeezed_scale = relay.squeeze(in_scale, axis=[1,2])
x = relay.nn.relu(x)
in_bias = relay.divide(in_bias, relay.expand_dims(squeezed_scale, axis=1, num_newaxis=2))
x = relay.add(x, in_bias)
conv_weight = relay.multiply(
conv_weight , relay.expand_dims(squeezed_scale, axis=1, num_newaxis=2))
y = relay.nn.conv2d(x, conv_weight,
channels=channels,
kernel_size=(3, 3),
padding=(1, 1))
return relay.Function(args, y)def check(shape, channels):
x = relay.var(“x”, shape=shape)
in_channels = shape[1]
weight = relay.var(“weight”)
in_bias = relay.var(“in_bias”, shape=(in_channels,))
#in_scale = relay.var(“in_scale”, shape=(in_channels, 1, 1))####can run safely, but the pass is useless.
in_scale = relay.const(_get_positive_scale((in_channels, 1, 1)))####if use this type, the pass is useful, but cannot be compiled.
y1 = before(x, weight, in_bias, in_scale, channels)
y1 = run_opt_pass(y1, transform.InferType())type_dict = {x.name_hint:x.checked_type for x in y1.params} weight = relay.var("weight", type_dict["weight"]) y1_folded = run_opt_pass(y1, transform.FoldScaleAxis()) y1_folded = run_opt_pass(y1_folded, transform.ForwardFoldScaleAxis()) y1_expected = expected(x, weight, in_bias, in_scale, channels) # y1_folded = run_opt_pass(y1_folded, transform.InferType()) # y1_folded = run_opt_pass(y1_folded, transform.FuseOps(1)) y1_expected = run_opt_pass(y1_expected, transform.InferType()) run_opt_pass(y1, transform.PrintIR()) run_opt_pass(y1_folded, transform.PrintIR()) run_opt_pass(y1_expected, transform.PrintIR()) engine = relay.backend.compile_engine.get() f = engine.jit(y1_expected, "llvm")#### if in_scale is const type, cannot pass compiling. a = tvm.nd.array(np.ones(shape=(2,4,10,10)).astype(A.dtype),tvm.cpu(0)) w = tvm.nd.array(np.ones(shape=(2,4,3,3)).astype(A.dtype),tvm.cpu(0)) b = tvm.nd.array(np.asarray([1,1,1,1]).astype(A.dtype),tvm.cpu(0)) in_s = tvm.nd.array(np.asarray([[[2]],[[2]],[[2]],[[2]]]).astype(A.dtype),tvm.cpu(0)) o = tvm.nd.array(np.zeros(shape=(2,2,10,10)).astype(A.dtype),tvm.cpu(0)) f(a,w,b,in_s,o)