Uh oh!
There was an error while loading. Please reload this page.
[TARGET] ONNX codegen - #5052
Merged
Merged
Conversation
maheshambule
commented
Mar 12, 2020
ContributorAuthor
CC: @yongwww, @zhiics, @kevinthesun |
kevinthesun
commented
Mar 12, 2020
Contributor
Since this is a new frontend module to TVM, @maheshambule could you open an RFC to discuss with community a bit about the purpose, overall design and constraints? From there, we can figure out the best way to support all similar use cases. |
maheshambule
commented
Mar 20, 2020
ContributorAuthor
Sorry for the late response. I will post the RFC may be in the next week. |
jroesch
commented
Mar 23, 2020
Member
cc @tqchen RE: naming I think we should probably refer to these as |
Member
I agree that target is possibly a better name, we can discuss in the RFC |
maheshambule
commented
Mar 26, 2020
ContributorAuthor
RFC created at https://discuss.tvm.ai/t/rfc-relay-to-onnx/6101 |
ContributorAuthor
maheshambule
commented
Jun 17, 2020
ContributorAuthor
@tqchen, @zhiics, @siju-samuel Could you please approve the PR if there are no further comments. |
tqchen
approved these changes
Jun 17, 2020
alexwong
suggested changes
Jun 17, 2020
| if node_entry['name'] in self._params: | ||
| self._add_params(node_entry, idx) | ||
| else: | ||
| type = node_entry['types'][0] |
Contributor
There was a problem hiding this comment.
I think it's probably preferable to rename type to something else to not overwrite Python's type function. Also, I am testing this with a very specific case and it seems like this function needs some extra logic to handle TupleType as an input (but it seems there's already some of that else where in the code). I may be able to provide an example (either script or relay graph) to illustrate if you'd like.
ContributorAuthor
There was a problem hiding this comment.
OK will change type to relay_type. Also please do share the test script. I will check if there is an issue.
Also, the test_tuple_types test case contains different test scenarios for Tuple type. You might want to take a look.
ContributorAuthor
There was a problem hiding this comment.
@alexwong, could you please provide the example so that I can test it?
I guess your concern is about accessing only the first element of node_entry and not all of them. Below is the test script which tests concat operator which works on TupleType and this script works fine. It works fine because even if concat operator accepts TupleType the wrapper function (relay.Function) does not accept relay.Tuple but a python list or tuple and converts it to relay.Tuple and passes it to the concat. So you will never get a relay.Tuple as an input to the relay graph and node_ entry will always have one element in it.
defverify_concatenate(shapes, axis, dtype="float32"):
in_vars= []
in_data= []
fori, shapeinenumerate(shapes):
in_vars.append(relay.var("x"+str(i), relay.ty.TensorType(shape, dtype)))
in_data.append(np.random.uniform(size=shape).astype(dtype))
y=relay.Tuple(in_vars)
out_tensor=relay.concatenate(y, axis)
func=relay.Function(in_vars, out_tensor)
verify_results(func, in_data, 'test_concatenate', rtol=1e-5, atol=1e-5)
verify_concatenate([(2,), (2,), (2,)], -1)Relay IR:
fn (%x0: Tensor[(2), float32], %x1: Tensor[(2), float32], %x2: Tensor[(2), float32]) {
%0 = (%x0, %x1, %x2);
concatenate(%0, axis=-1)
}Contributor
There was a problem hiding this comment.
Sorry for the late response. Is it possible for you to test with this Relay module? I serialized it to json and uploaded here, you can use load_json to get the Relay module from this. Let me know once you have it so I can remove it from S3.
File "tests/python/relay/temptest.py", line 218, in <module>
test_mxnet_mobilenet_ssd()
File "tests/python/relay/temptest.py", line 212, in test_mxnet_mobilenet_ssd
onnx_model = to_onnx(module, {}, name, path=onnx_path)
File "/tvm/python/tvm/contrib/target/onnx.py", line 853, in to_onnx
onnx_model = converter.convert_to_onnx(func)
File "/tvm/python/tvm/contrib/target/onnx.py", line 677, in convert_to_onnx
self.visit(func)
File "/tvm/python/tvm/contrib/target/onnx.py", line 685, in visit
super().visit(expr)
File "/tvm/python/tvm/relay/expr_functor.py", line 44, in visit
res = self.visit_function(expr)
File "/tvm/python/tvm/relay/expr_functor.py", line 153, in visit_function
[self.visit(f.body)]
File "/tvm/python/tvm/contrib/target/onnx.py", line 685, in visit
super().visit(expr)
File "/tvm/python/tvm/relay/expr_functor.py", line 56, in visit
res = self.visit_tuple(expr)
File "/tvm/python/tvm/contrib/target/onnx.py", line 707, in visit_tuple
self.visit(f)
File "/tvm/python/tvm/contrib/target/onnx.py", line 685, in visit
super().visit(expr)
File "/tvm/python/tvm/relay/expr_functor.py", line 46, in visit
res = self.visit_call(expr)
File "/tvm/python/tvm/contrib/target/onnx.py", line 735, in visit_call
self.visit(input_arg)
File "/tvm/python/tvm/contrib/target/onnx.py", line 685, in visit
super().visit(expr)
File "/tvm/python/tvm/relay/expr_functor.py", line 56, in visit
res = self.visit_tuple(expr)
File "/tvm/python/tvm/contrib/target/onnx.py", line 707, in visit_tuple
self.visit(f)
File "/tvm/python/tvm/contrib/target/onnx.py", line 685, in visit
super().visit(expr)
File "/tvm/python/tvm/relay/expr_functor.py", line 46, in visit
res = self.visit_call(expr)
File "/tvm/python/tvm/contrib/target/onnx.py", line 735, in visit_call
self.visit(input_arg)
File "/tvm/python/tvm/contrib/target/onnx.py", line 685, in visit
super().visit(expr)
File "/tvm/python/tvm/relay/expr_functor.py", line 46, in visit
res = self.visit_call(expr)
File "/tvm/python/tvm/contrib/target/onnx.py", line 747, in visit_call
self._add_node(node_entry, node_index)
File "/tvm/python/tvm/contrib/target/onnx.py", line 758, in _add_node
return converter.convert(node_entry, self._mc, self._node_dict)
File "/tvm/python/tvm/contrib/target/onnx.py", line 530, in convert
dtype = input_node['relay_node'].type_annotation.dtype
File "/tvm/python/tvm/runtime/object.py", line 59, in __getattr__
"%s has no attribute %s" % (str(type(self)), name))
AttributeError: <class 'tvm.relay.expr.Call'> has no attribute type_annotation
ContributorAuthor
There was a problem hiding this comment.
@alexwong, Thanks. I have downloaded the model, will try to reproduce the issue in a day or two, and then will provide a fix as well.
ContributorAuthor
There was a problem hiding this comment.
@alexwong, While converting the module I faced issue related to type inference for ConstantOfShapeZeros op, I have fixed that issue. After fixing the issue, found an issue with Relay module itself. It seems arguments provided to reshape op are not correct. Below is the error I am getting. Could you please look into it?
test_onnx_model.py:36: in func_to_onnx
onnx_model = to_onnx(mod, params, name, path=None)
../../../python/tvm/contrib/target/onnx.py:845: in to_onnx
onnx_model = converter.convert_to_onnx(func)
../../../python/tvm/contrib/target/onnx.py:678: in convert_to_onnx
self.visit(func)
../../../python/tvm/contrib/target/onnx.py:686: in visit
super().visit(expr)
../../../python/tvm/relay/expr_functor.py:44: in visit
res = self.visit_function(expr)
../../../python/tvm/relay/expr_functor.py:153: in visit_function
self.visit(f.body)
../../../python/tvm/contrib/target/onnx.py:686: in visit
super().visit(expr)
../../../python/tvm/relay/expr_functor.py:56: in visit
res = self.visit_tuple(expr)
../../../python/tvm/contrib/target/onnx.py:708: in visit_tuple
self.visit(f)
../../../python/tvm/contrib/target/onnx.py:686: in visit
super().visit(expr)
../../../python/tvm/relay/expr_functor.py:46: in visit
res = self.visit_call(expr)
../../../python/tvm/contrib/target/onnx.py:736: in visit_call
self.visit(input_arg)
../../../python/tvm/contrib/target/onnx.py:686: in visit
super().visit(expr)
../../../python/tvm/relay/expr_functor.py:46: in visit
res = self.visit_call(expr)
../../../python/tvm/contrib/target/onnx.py:736: in visit_call
self.visit(input_arg)
../../../python/tvm/contrib/target/onnx.py:686: in visit
super().visit(expr)
../../../python/tvm/relay/expr_functor.py:46: in visit
res = self.visit_call(expr)
../../../python/tvm/contrib/target/onnx.py:743: in visit_call
node_entry['types'] = call_node_infer_type(call)
../../../python/tvm/contrib/target/onnx.py:54: in call_node_infer_type
infer_out = infer_type(node)
../../../python/tvm/contrib/target/onnx.py:46: in infer_type
mod = tvm.IRModule.from_expr(node)
../../../python/tvm/ir/module.py:222: in from_expr
return _ffi_api.Module_FromExpr(expr, funcs, defs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = <tvm.runtime.packed_func.PackedFunc object at 0x118015780>
args = (CallNode(Op(reshape), [CallNode(Op(concatenate), [Tuple([CallNode(Op(nn.batch_flatten), [CallNode(Op(transpose), [Cal...1 21])], relay.attrs.ReshapeAttrs(0x7fc0c2626908), [TensorType([1, 128772], float32), TensorType([3], int32)]), {}, {})
temp_args = [{}, {}]
values = <tvm._ffi._ctypes.packed_func.TVMValue_Array_3 object at 0x11e962c40>
tcodes = <tvm._ffi._ctypes.packed_func.c_int_Array_3 object at 0x11e962040>
def __call__(self, *args):
"""Call the function with positional arguments
args : list
The positional arguments to the function call.
"""
temp_args = []
values, tcodes, num_args = _make_tvm_args(args, temp_args)
ret_val = TVMValue()
ret_tcode = ctypes.c_int()
if _LIB.TVMFuncCall(
self.handle, values, tcodes, ctypes.c_int(num_args),
ctypes.byref(ret_val), ctypes.byref(ret_tcode)) != 0:
> raise get_last_ffi_error()
E tvm._ffi.base.TVMError: Traceback (most recent call last):
E [bt] (8) 9 libtvm.dylib 0x00000001189cff09 tvm::relay::TypeInferencer::GetType(tvm::RelayExpr const&) + 297
E [bt] (7) 8 libtvm.dylib 0x00000001189d855a tvm::relay::ExprFunctor<tvm::Type (tvm::RelayExpr const&)>::VisitExpr(tvm::RelayExpr const&) + 138
E [bt] (6) 7 libtvm.dylib 0x00000001189de80f tvm::NodeFunctor<tvm::Type (tvm::runtime::ObjectRef const&, tvm::relay::ExprFunctor<tvm::Type (tvm::RelayExpr const&)>*)>::operator()(tvm::runtime::ObjectRef const&, tvm::relay::ExprFunctor<tvm::Type (tvm::RelayExpr const&)>*) const + 255
E [bt] (5) 6 libtvm.dylib 0x00000001189dfe58 tvm::relay::ExprFunctor<tvm::Type (tvm::RelayExpr const&)>::InitVTable()::'lambda4'(tvm::runtime::ObjectRef const&, tvm::relay::ExprFunctor<tvm::Type (tvm::RelayExpr const&)>*)::__invoke(tvm::runtime::ObjectRef const&, tvm::relay::ExprFunctor<tvm::Type (tvm::RelayExpr const&)>*) + 24
E [bt] (4) 5 libtvm.dylib 0x00000001189d977d tvm::relay::TypeInferencer::VisitExpr_(tvm::relay::CallNode const*) + 717
E [bt] (3) 4 libtvm.dylib 0x00000001189e23b2 tvm::relay::TypeInferencer::GeneralCall(tvm::relay::CallNode const*, tvm::runtime::Array<tvm::Type, void>) + 2866
E [bt] (2) 3 libtvm.dylib 0x00000001189e00ba tvm::relay::TypeInferencer::ReportFatalError(tvm::runtime::ObjectRef const&, tvm::Error const&) + 154
E [bt] (1) 2 libtvm.dylib 0x00000001181a9850 tvm::ErrorReporter::RenderErrors(tvm::IRModule const&, bool) + 5296
E [bt] (0) 1 libtvm.dylib 0x0000000118086781 dmlc::LogMessageFatal::~LogMessageFatal() + 113
E File "/Users/demo/git/tvm/src/ir/error.cc", line 132
E TVMError: E Error(s) have occurred. The program has been annotated with them:
E E In `main`: E v0.0.4
E fn (%cv22_0_i0: Tensor[(1, 3, 512, 512), float32]) {
E %0 = nn.conv2d(%cv22_0_i0, meta[relay.Constant][0], strides=[2, 2], padding=[1, 1, 1, 1], channels=32, kernel_size=[3, 3]);
E %1 = nn.batch_norm(%0, meta[relay.Constant][1], meta[relay.Constant][2], meta[relay.Constant][3], meta[relay.Constant][4]);
E %2 = %1.0;
E %3 = nn.relu(%2);
E %4 = nn.conv2d(%3, meta[relay.Constant][5], padding=[1, 1, 1, 1], groups=32, channels=32, kernel_size=[3, 3]);
E %5 = nn.batch_norm(%4, meta[relay.Constant][6], meta[relay.Constant][7], meta[relay.Constant][8], meta[relay.Constant][9]);
E %6 = %5.0;
E %7 = nn.relu(%6);
E %8 = nn.conv2d(%7, meta[relay.Constant][10], padding=[0, 0, 0, 0], channels=64, kernel_size=[1, 1]);
E %9 = nn.batch_norm(%8, meta[relay.Constant][11], meta[relay.Constant][12], meta[relay.Constant][13], meta[relay.Constant][14]);
E %10 = %9.0;
E %11 = nn.relu(%10);
E %12 = nn.conv2d(%11, meta[relay.Constant][15], strides=[2, 2], padding=[1, 1, 1, 1], groups=64, channels=64, kernel_size=[3, 3]);
E %13 = nn.batch_norm(%12, meta[relay.Constant][16], meta[relay.Constant][17], meta[relay.Constant][18], meta[relay.Constant][19]);
E %14 = %13.0;
E %15 = nn.relu(%14);
E %16 = nn.conv2d(%15, meta[relay.Constant][20], padding=[0, 0, 0, 0], channels=128, kernel_size=[1, 1]);
E %17 = nn.batch_norm(%16, meta[relay.Constant][21], meta[relay.Constant][22], meta[relay.Constant][23], meta[relay.Constant][24]);
E %18 = %17.0;
E %19 = nn.relu(%18);
E %20 = nn.conv2d(%19, meta[relay.Constant][25], padding=[1, 1, 1, 1], groups=128, channels=128, kernel_size=[3, 3]);
E %21 = nn.batch_norm(%20, meta[relay.Constant][26], meta[relay.Constant][27], meta[relay.Constant][28], meta[relay.Constant][29]);
E %22 = %21.0;
E %23 = nn.relu(%22);
E %24 = nn.conv2d(%23, meta[relay.Constant][30], padding=[0, 0, 0, 0], channels=128, kernel_size=[1, 1]);
E %25 = nn.batch_norm(%24, meta[relay.Constant][31], meta[relay.Constant][32], meta[relay.Constant][33], meta[relay.Constant][34]);
E %26 = %25.0;
E %27 = nn.relu(%26);
E %28 = nn.conv2d(%27, meta[relay.Constant][35], strides=[2, 2], padding=[1, 1, 1, 1], groups=128, channels=128, kernel_size=[3, 3]);
E %29 = nn.batch_norm(%28, meta[relay.Constant][36], meta[relay.Constant][37], meta[relay.Constant][38], meta[relay.Constant][39]);
E %30 = %29.0;
E %31 = nn.relu(%30);
E %32 = nn.conv2d(%31, meta[relay.Constant][40], padding=[0, 0, 0, 0], channels=256, kernel_size=[1, 1]);
E %33 = nn.batch_norm(%32, meta[relay.Constant][41], meta[relay.Constant][42], meta[relay.Constant][43], meta[relay.Constant][44]);
E %34 = %33.0;
E %35 = nn.relu(%34);
E %36 = nn.conv2d(%35, meta[relay.Constant][45], padding=[1, 1, 1, 1], groups=256, channels=256, kernel_size=[3, 3]);
E %37 = nn.batch_norm(%36, meta[relay.Constant][46], meta[relay.Constant][47], meta[relay.Constant][48], meta[relay.Constant][49]);
E %38 = %37.0;
E %39 = nn.relu(%38);
E %40 = nn.conv2d(%39, meta[relay.Constant][50], padding=[0, 0, 0, 0], channels=256, kernel_size=[1, 1]);
E %41 = nn.batch_norm(%40, meta[relay.Constant][51], meta[relay.Constant][52], meta[relay.Constant][53], meta[relay.Constant][54]);
E %42 = %41.0;
E %43 = nn.relu(%42);
E %44 = nn.conv2d(%43, meta[relay.Constant][55], strides=[2, 2], padding=[1, 1, 1, 1], groups=256, channels=256, kernel_size=[3, 3]);
E %45 = nn.batch_norm(%44, meta[relay.Constant][56], meta[relay.Constant][57], meta[relay.Constant][58], meta[relay.Constant][59]);
E %46 = %45.0;
E %47 = nn.relu(%46);
E %48 = nn.conv2d(%47, meta[relay.Constant][60], padding=[0, 0, 0, 0], channels=512, kernel_size=[1, 1]);
E %49 = nn.batch_norm(%48, meta[relay.Constant][61], meta[relay.Constant][62], meta[relay.Constant][63], meta[relay.Constant][64]);
E %50 = %49.0;
E %51 = nn.relu(%50);
E %52 = nn.conv2d(%51, meta[relay.Constant][65], padding=[1, 1, 1, 1], groups=512, channels=512, kernel_size=[3, 3]);
E %53 = nn.batch_norm(%52, meta[relay.Constant][66], meta[relay.Constant][67], meta[relay.Constant][68], meta[relay.Constant][69]);
E %54 = %53.0;
E %55 = nn.relu(%54);
E %56 = nn.conv2d(%55, meta[relay.Constant][70], padding=[0, 0, 0, 0], channels=512, kernel_size=[1, 1]);
E %57 = nn.batch_norm(%56, meta[relay.Constant][71], meta[relay.Constant][72], meta[relay.Constant][73], meta[relay.Constant][74]);
E %58 = %57.0;
E %59 = nn.relu(%58);
E %60 = nn.conv2d(%59, meta[relay.Constant][75], padding=[1, 1, 1, 1], groups=512, channels=512, kernel_size=[3, 3]);
E %61 = nn.batch_norm(%60, meta[relay.Constant][76], meta[relay.Constant][77], meta[relay.Constant][78], meta[relay.Constant][79]);
E %62 = %61.0;
E %63 = nn.relu(%62);
E %64 = nn.conv2d(%63, meta[relay.Constant][80], padding=[0, 0, 0, 0], channels=512, kernel_size=[1, 1]);
E %65 = nn.batch_norm(%64, meta[relay.Constant][81], meta[relay.Constant][82], meta[relay.Constant][83], meta[relay.Constant][84]);
E %66 = %65.0;
E %67 = nn.relu(%66);
E %68 = nn.conv2d(%67, meta[relay.Constant][85], padding=[1, 1, 1, 1], groups=512, channels=512, kernel_size=[3, 3]);
E %69 = nn.batch_norm(%68, meta[relay.Constant][86], meta[relay.Constant][87], meta[relay.Constant][88], meta[relay.Constant][89]);
E %70 = %69.0;
E %71 = nn.relu(%70);
E %72 = nn.conv2d(%71, meta[relay.Constant][90], padding=[0, 0, 0, 0], channels=512, kernel_size=[1, 1]);
E %73 = nn.batch_norm(%72, meta[relay.Constant][91], meta[relay.Constant][92], meta[relay.Constant][93], meta[relay.Constant][94]);
E %74 = %73.0;
E %75 = nn.relu(%74);
E %76 = nn.conv2d(%75, meta[relay.Constant][95], padding=[1, 1, 1, 1], groups=512, channels=512, kernel_size=[3, 3]);
E %77 = nn.batch_norm(%76, meta[relay.Constant][96], meta[relay.Constant][97], meta[relay.Constant][98], meta[relay.Constant][99]);
E %78 = %77.0;
E %79 = nn.relu(%78);
E %80 = nn.conv2d(%79, meta[relay.Constant][100], padding=[0, 0, 0, 0], channels=512, kernel_size=[1, 1]);
E %81 = nn.batch_norm(%80, meta[relay.Constant][101], meta[relay.Constant][102], meta[relay.Constant][103], meta[relay.Constant][104]);
E %82 = %81.0;
E %83 = nn.relu(%82);
E %84 = nn.conv2d(%83, meta[relay.Constant][105], padding=[1, 1, 1, 1], groups=512, channels=512, kernel_size=[3, 3]);
E %85 = nn.batch_norm(%84, meta[relay.Constant][106], meta[relay.Constant][107], meta[relay.Constant][108], meta[relay.Constant][109]);
E %86 = %85.0;
E %87 = nn.relu(%86);
E %88 = nn.conv2d(%87, meta[relay.Constant][110], padding=[0, 0, 0, 0], channels=512, kernel_size=[1, 1]);
E %89 = nn.batch_norm(%88, meta[relay.Constant][111], meta[relay.Constant][112], meta[relay.Constant][113], meta[relay.Constant][114]);
E %90 = %89.0;
E %91 = nn.relu(%90);
E %92 = nn.conv2d(%91, meta[relay.Constant][115], padding=[1, 1, 1, 1], channels=84, kernel_size=[3, 3]);
E %93 = nn.bias_add(%92, meta[relay.Constant][116]);
E %94 = transpose(%93, axes=[0, 2, 3, 1]);
E %95 = nn.batch_flatten(%94);
E %96 = nn.conv2d(%91, meta[relay.Constant][117], strides=[2, 2], padding=[1, 1, 1, 1], groups=512, channels=512, kernel_size=[3, 3]);
E %97 = nn.batch_norm(%96, meta[relay.Constant][118], meta[relay.Constant][119], meta[relay.Constant][120], meta[relay.Constant][121]);
E %98 = %97.0;
E %99 = nn.relu(%98);
E %100 = nn.conv2d(%99, meta[relay.Constant][122], padding=[0, 0, 0, 0], channels=1024, kernel_size=[1, 1]);
E %101 = nn.batch_norm(%100, meta[relay.Constant][123], meta[relay.Constant][124], meta[relay.Constant][125], meta[relay.Constant][126]);
E %102 = %101.0;
E %103 = nn.relu(%102);
E %104 = nn.conv2d(%103, meta[relay.Constant][127], padding=[1, 1, 1, 1], groups=1024, channels=1024, kernel_size=[3, 3]);
E %105 = nn.batch_norm(%104, meta[relay.Constant][128], meta[relay.Constant][129], meta[relay.Constant][130], meta[relay.Constant][131]);
E %106 = %105.0;
E %107 = nn.relu(%106);
E %108 = nn.conv2d(%107, meta[relay.Constant][132], padding=[0, 0, 0, 0], channels=1024, kernel_size=[1, 1]);
E %109 = nn.batch_norm(%108, meta[relay.Constant][133], meta[relay.Constant][134], meta[relay.Constant][135], meta[relay.Constant][136]);
E %110 = %109.0;
E %111 = nn.relu(%110);
E %112 = nn.conv2d(%111, meta[relay.Constant][137], padding=[1, 1, 1, 1], channels=126, kernel_size=[3, 3]);
E %113 = nn.bias_add(%112, meta[relay.Constant][138]);
E %114 = transpose(%113, axes=[0, 2, 3, 1]);
E %115 = nn.batch_flatten(%114);
E %116 = nn.conv2d(%111, meta[relay.Constant][139], padding=[0, 0, 0, 0], channels=512, kernel_size=[1, 1]);
E %117 = nn.batch_norm(%116, meta[relay.Constant][140], meta[relay.Constant][141], meta[relay.Constant][142], meta[relay.Constant][143], epsilon=0.001f, scale=False);
E %118 = %117.0;
E %119 = nn.relu(%118);
E %120 = nn.conv2d(%119, meta[relay.Constant][144], strides=[2, 2], padding=[1, 1, 1, 1], channels=512, kernel_size=[3, 3]);
E %121 = nn.batch_norm(%120, meta[relay.Constant][145], meta[relay.Constant][146], meta[relay.Constant][147], meta[relay.Constant][148], epsilon=0.001f, scale=False);
E %122 = %121.0;
E %123 = nn.relu(%122);
E %124 = nn.conv2d(%123, meta[relay.Constant][149], padding=[1, 1, 1, 1], channels=126, kernel_size=[3, 3]);
E %125 = nn.bias_add(%124, meta[relay.Constant][150]);
E %126 = transpose(%125, axes=[0, 2, 3, 1]);
E %127 = nn.batch_flatten(%126);
E %128 = nn.conv2d(%123, meta[relay.Constant][151], padding=[0, 0, 0, 0], channels=512, kernel_size=[1, 1]);
E %129 = nn.batch_norm(%128, meta[relay.Constant][152], meta[relay.Constant][153], meta[relay.Constant][154], meta[relay.Constant][155], epsilon=0.001f, scale=False);
E %130 = %129.0;
E %131 = nn.relu(%130);
E %132 = nn.conv2d(%131, meta[relay.Constant][156], strides=[2, 2], padding=[1, 1, 1, 1], channels=512, kernel_size=[3, 3]);
E %133 = nn.batch_norm(%132, meta[relay.Constant][157], meta[relay.Constant][158], meta[relay.Constant][159], meta[relay.Constant][160], epsilon=0.001f, scale=False);
E %134 = %133.0;
E %135 = nn.relu(%134);
E %136 = nn.conv2d(%135, meta[relay.Constant][161], padding=[1, 1, 1, 1], channels=126, kernel_size=[3, 3]);
E %137 = nn.bias_add(%136, meta[relay.Constant][162]);
E %138 = transpose(%137, axes=[0, 2, 3, 1]);
E %139 = nn.batch_flatten(%138);
E %140 = nn.conv2d(%135, meta[relay.Constant][163], padding=[0, 0, 0, 0], channels=256, kernel_size=[1, 1]);
E %141 = nn.batch_norm(%140, meta[relay.Constant][164], meta[relay.Constant][165], meta[relay.Constant][166], meta[relay.Constant][167], epsilon=0.001f, scale=False);
E %142 = %141.0;
E %143 = nn.relu(%142);
E %144 = nn.conv2d(%143, meta[relay.Constant][168], strides=[2, 2], padding=[1, 1, 1, 1], channels=256, kernel_size=[3, 3]);
E %145 = nn.batch_norm(%144, meta[relay.Constant][169], meta[relay.Constant][170], meta[relay.Constant][171], meta[relay.Constant][172], epsilon=0.001f, scale=False);
E %146 = %145.0;
E %147 = nn.relu(%146);
E %148 = nn.conv2d(%147, meta[relay.Constant][173], padding=[1, 1, 1, 1], channels=84, kernel_size=[3, 3]);
E %149 = nn.bias_add(%148, meta[relay.Constant][174]);
E %150 = transpose(%149, axes=[0, 2, 3, 1]);
E %151 = nn.batch_flatten(%150);
E %152 = nn.conv2d(%147, meta[relay.Constant][175], padding=[0, 0, 0, 0], channels=256, kernel_size=[1, 1]);
E %153 = nn.batch_norm(%152, meta[relay.Constant][176], meta[relay.Constant][177], meta[relay.Constant][178], meta[relay.Constant][179], epsilon=0.001f, scale=False);
E %154 = %153.0;
E %155 = nn.relu(%154);
E %156 = nn.conv2d(%155, meta[relay.Constant][180], strides=[2, 2], padding=[1, 1, 1, 1], channels=256, kernel_size=[3, 3]);
E %157 = nn.batch_norm(%156, meta[relay.Constant][181], meta[relay.Constant][182], meta[relay.Constant][183], meta[relay.Constant][184], epsilon=0.001f, scale=False);
E %158 = %157.0;
E %159 = nn.relu(%158);
E %160 = nn.conv2d(%159, meta[relay.Constant][185], padding=[1, 1, 1, 1], channels=84, kernel_size=[3, 3]);
E %161 = nn.bias_add(%160, meta[relay.Constant][186]);
E %162 = transpose(%161, axes=[0, 2, 3, 1]);
E %163 = nn.batch_flatten(%162);
E %164 = (%95, %115, %127, %139, %151, %163);
E %165 = concatenate(%164, axis=1);
E reshape(%165, meta[relay.Constant][187], newshape=[0, -1, 21]) the function is provided too many arguments expected 1, found 2; E }
E // meta data omitted. you can use show_meta_data=True to include meta data
kazum
commented
Jun 29, 2020
Contributor
@maheshambule Can you rebase this PR onto the latest master? I think you need to update your code to address the changes in #5770. |
…into relay_to_onnx
maheshambule
commented
Jul 12, 2020
ContributorAuthor
Uh oh!
There was an error while loading. Please reload this page.
tqchen
commented
Jul 14, 2020
Member
@srkreddy1238 please also update, @kazum feel free to make a call to dismiss outstanding reviews |
kazum
commented
Jul 14, 2020
Contributor
Thanks, looks good. I'll merge this after @srkreddy1238 update the review status. |
kazum
dismissed
srkreddy1238’s stale reviewJuly 15, 2020 20:22
The request changes are addressed and there are already approving reviews.
kazum
commented
Jul 15, 2020
Contributor
maheshambule
commented
Jul 15, 2020
ContributorAuthor
Thanks @kazum |
trevor-m pushed a commit
to trevor-m/tvm
that referenced
this pull request
Aug 26, 2020
* Relay to ONNX converter * Relay to ONNX op test cases * Relay to ONNX end to end model test cases * Add test cases to jenkins * CI CD fixes * ONNX codegen * ONNX codegen * ONNX codegen * onnx testcases * ONNX codegen * test onnx * ONNX codegen * shape calculation * move onnx codegen to contrib/target * review comments * ONNX target use visitor * onnx fixes * lint fixes * doc string changes * review comments * review comment fixes * review comment * pytest skip * rename type to node type * test * Fix for constantshpae, add exp, fix for metadatamodule * Fix cpplint * change error tol values
trevor-m pushed a commit
to trevor-m/tvm
that referenced
this pull request
Aug 26, 2020
* Relay to ONNX converter * Relay to ONNX op test cases * Relay to ONNX end to end model test cases * Add test cases to jenkins * CI CD fixes * ONNX codegen * ONNX codegen * ONNX codegen * onnx testcases * ONNX codegen * test onnx * ONNX codegen * shape calculation * move onnx codegen to contrib/target * review comments * ONNX target use visitor * onnx fixes * lint fixes * doc string changes * review comments * review comment fixes * review comment * pytest skip * rename type to node type * test * Fix for constantshpae, add exp, fix for metadatamodule * Fix cpplint * change error tol values
trevor-m pushed a commit
to trevor-m/tvm
that referenced
this pull request
Sep 2, 2020
* Relay to ONNX converter * Relay to ONNX op test cases * Relay to ONNX end to end model test cases * Add test cases to jenkins * CI CD fixes * ONNX codegen * ONNX codegen * ONNX codegen * onnx testcases * ONNX codegen * test onnx * ONNX codegen * shape calculation * move onnx codegen to contrib/target * review comments * ONNX target use visitor * onnx fixes * lint fixes * doc string changes * review comments * review comment fixes * review comment * pytest skip * rename type to node type * test * Fix for constantshpae, add exp, fix for metadatamodule * Fix cpplint * change error tol values
trevor-m pushed a commit
to neo-ai/tvm
that referenced
this pull request
Sep 3, 2020
* Relay to ONNX converter * Relay to ONNX op test cases * Relay to ONNX end to end model test cases * Add test cases to jenkins * CI CD fixes * ONNX codegen * ONNX codegen * ONNX codegen * onnx testcases * ONNX codegen * test onnx * ONNX codegen * shape calculation * move onnx codegen to contrib/target * review comments * ONNX target use visitor * onnx fixes * lint fixes * doc string changes * review comments * review comment fixes * review comment * pytest skip * rename type to node type * test * Fix for constantshpae, add exp, fix for metadatamodule * Fix cpplint * change error tol values
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Sign up for freeto join this conversation on GitHub.
Already have an account?
Sign in to comment
9 participants
Add this suggestion to a batch that can be applied as a single commit.This suggestion is invalid because no changes were made to the code.Suggestions cannot be applied while the pull request is closed.Suggestions cannot be applied while viewing a subset of changes.Only one suggestion per line can be applied in a batch.Add this suggestion to a batch that can be applied as a single commit.Applying suggestions on deleted lines is not supported.You must change the existing code in this line in order to create a valid suggestion.Outdated suggestions cannot be applied.This suggestion has been applied or marked resolved.Suggestions cannot be applied from pending reviews.Suggestions cannot be applied on multi-line comments.Suggestions cannot be applied while the pull request is queued to merge.Suggestion cannot be applied right now. Please check back later.
This PR adds support for ONNX codegen. Currently, only a subset of operators is supported. The conversion can be done on optimized Relay modules as well except for fused op passes.
ONNX Codegen Module is added. Runtime support is not added.
Operators supported:
Models tested:
TO-DO: