[NNVM][FRONTEND] Tensorflow frontend support #1188

srkreddy1238 · 2018-05-29T17:51:51Z

Tested for Inception V1 and V3

srkreddy1238 · 2018-05-29T17:57:57Z

@tqchen , @Huyuwei and @FrozenGene pls review.

srkreddy1238 · 2018-05-29T18:06:45Z

With respect to nnvm base it's all the same except tutorials/nnvm/ location changed now.

colin1988 · 2018-05-30T15:22:52Z

nnvm/python/nnvm/frontend/tensorflow.py

+            # Tensor flow doesn't have seperate list for params extraction.
+            # Operator name 'Const' is treated as a parameter to build NNVM params dict.
+            if node.op == "Placeholder":
+                # Assuming only one input graph with type 'Placeholder'


why only a Placeholder is supported?

Unlike other frameworks Tensorflow doesn't list graph inputs.
Hence I assumed first 'Placeholder' or 'Const' node as input node for the graph.

colin1988 · 2018-05-30T15:26:32Z

nnvm/python/nnvm/frontend/tensorflow.py

+
+                self._output_shapes[node.name] = \
+                     [tensor_util.TensorShapeProtoToList(shape) \
+                     for shape in self._parse_attr(node.attr)['_output_shapes']]


Does the code require a specific GraphDef version?
When I try your feature, a problem troubles me.
""
for shape in self._parse_attr(node.attr)['_output_shapes']]
KeyError: '_output_shapes'
""

Yes, you need to freeze the tensorflow graph with add_shapes=True like below.

output_graph_def = tf.graph_util.convert_variables_to_constants(
sess,
sess.graph.as_graph_def(add_shapes=True),
['InceptionV3/Predictions/Reshape_1'],
)

It was discussed earlier @ dmlc/nnvm#472

thanks! "add_shapes=True" can really avoid the error which happened on Placeholder, but the same error happens th on Const even if the fix is added.

_output_shapes should exist for all nodes with above option.

Can you share the dump of the Const node ? Or Attribute dump of Const node ?

"convert_variables_to_constants" method does not seem to add "output_shape" field

@srkreddy1238 I want to cry! Looking forward to your updates

I will try to share a patch for multi input later today :)

Can you share the protobuf file for reference ?

@colin1988
I just checked and multi input work fine. No need of any change.
Ref. added a test case for the same.

@srkreddy1238 thanks very much. I will try it right now.

@srkreddy1238 I found the method to fix it. More precisely, I found an error in my code.
609 output_graph_def = tf.graph_util.convert_variables_to_constants(
610 sess, sess.graph.as_graph_def(add_shapes=True), ["dnn/title_scoring/final_score/Sigmoid"])
611 tf.reset_default_graph()
612 graph = tf.import_graph_def(output_graph_def, return_elements=["dnn/title_scoring/final_score/Sigmoid"], name='')
613 output_graph_def = tf.graph_util.convert_variables_to_constants(
614 sess, tf.get_default_graph().as_graph_def(add_shapes=True), ["dnn/title_scoring/final_score/Sigmoid"])
615 tf.train.write_graph(output_graph_def, 'export_model', "asq_title_score.pb.txt", as_text=True)

Line 611 is essential !!

srkreddy1238 · 2018-05-30T16:57:02Z

@Huyuwei Can you help with a final review on this ?

kaishijeng · 2018-06-02T07:21:34Z

@srkreddy1238

I tried from_tensorflow.py with MobileNet_v1_1.0_224 from https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet_v1.md.
Unfortunately, it gives me errors below:

('x', (299, 299, 3))
Traceback (most recent call last):
File "from_tensorflow.py", line 154, in
sym, params = nnvm.frontend.from_tensorflow(graph_def)
File "/usr/local/lib/python2.7/dist-packages/nnvm-0.8.0-py2.7.egg/nnvm/frontend/tensorflow.py", line 650, in from_tensorflow
sym, params = g.from_tensorflow(graph)
File "/usr/local/lib/python2.7/dist-packages/nnvm-0.8.0-py2.7.egg/nnvm/frontend/tensorflow.py", line 463, in from_tensorflow
for shape in self._parse_attr(node.attr)['_output_shapes']]
KeyError: '_output_shapes'

Any idea?

Thanks

srkreddy1238 · 2018-06-02T08:02:16Z

Regenerate the tensor flow graph with add_shapes=True.

This is an assumption to have _output_shapes for all nodes.

kaishijeng · 2018-06-02T08:44:48Z

@srkreddy1238

Do you have a python example to convert checkpoint to frozen model with shapes=True?

Thanks,

srkreddy1238 · 2018-06-02T12:11:24Z

Ref tool for conversion: https://github.com/srkreddy1238/dmlc_data/blob/master/work/tf-to-nnvm.py

I checked Mobilenet and below operations missing in frontend now.
DepthwiseConv2dNative, FusedBatchNorm, Relu6.

I am working on mapping these operator to existing nnvm top.

Will share soon.

srkreddy1238 · 2018-06-02T12:13:31Z

@tqchen & @Huyuwei : Can you guys help to review this pull request soon?
Or
Should I continue to add additions on top of the same ?

kaishijeng · 2018-06-03T05:26:10Z

@srkreddy1238

I am able to convert mobilenet model with your python script. Waiting for your PR to support additional operators of mobilenetv1.

Thanks,

tqchen · 2018-06-03T16:38:25Z

await approval from @Huyuwei

Huyuwei · 2018-06-03T22:03:36Z

Sorry for late response due to travelling. Will do a final review Tuesday.

tqchen · 2018-06-04T16:49:27Z

tutorials/nnvm/from_tensorflow.py

+from tensorflow.python.framework import tensor_util
+
+
+repo_base = 'https://github.com/srkreddy1238/dmlc_data/raw/master/models/tensorflow/InceptionV1/'


put data into dmlc/web-data

dmlc/web-data#74
PR for support files @ web-data.

I will update the above paths after this merge.

data paths changed to dmlc/web-data.

tqchen · 2018-06-04T16:50:44Z

tutorials/nnvm/from_tensorflow.py

+
+######################################################################
+# Download processed tensorflow model
+# ---------------------------------------------


rst standard: make sure the line is the same as the title

tqchen · 2018-06-04T16:52:05Z

tutorials/nnvm/from_tensorflow.py

+    return graph_def
+
+class NodeLookup(object):
+    """Converts integer node ID's to human readable labels."""


This class is not very helpful in understanding in tutorials. Consider put it under nnvm.testiung

tqchen · 2018-06-04T16:52:24Z

tutorials/nnvm/from_tensorflow.py

+        self.node_lookup = self.load(label_lookup_path, uid_lookup_path)
+
+    def load(self, label_lookup_path, uid_lookup_path):
+        """Loads a human readable English name for each softmax node.


use numpydoc convention http://docs.tvm.ai/contribute/document.html#document-python

srkreddy1238 · 2018-06-04T18:34:55Z

@kaishijeng

I could get through FusedBatchNorm, Relu6 and Shape operators, but depthwise convolution don't have a direct support now.
I will work on depthwise conv later this week.

kaishijeng · 2018-06-05T06:36:12Z

@srkreddy1238

If there is no depthwise conv support, how from_mxnet.py can support mobilenetv1 without any issue?

Thanks,

srkreddy1238 · 2018-06-05T06:46:27Z

@kaishijeng
I referred mxnet too and tried some initial quick trials and failed 👎 .
I will attempt this again in detailed on Thursday & hope to PR new changes by this week end.

colin1988 · 2018-06-05T14:20:19Z

tensorflow use # Gather to implement # embedding_lookup. Is there corresponding op in tvm/nnvm?

I got the error below

Traceback (most recent call last):
File "asq_model.py", line 42, in
graph, params = nnvm.frontend.from_tensorflow(graph_def)
File "/home/disk2/liushengbing/dev/tvm/py_env/mms-hpc-devel/lib/python2.7/site-packages/nnvm-0.8.0-py2.7.egg/nnvm/frontend/tensorflow.py", line 650, in from_tensorflow
sym, params = g.from_tensorflow(graph)
File "/home/disk2/liushengbing/dev/tvm/py_env/mms-hpc-devel/lib/python2.7/site-packages/nnvm-0.8.0-py2.7.egg/nnvm/frontend/tensorflow.py", line 503, in from_tensorflow
op = self._convert_operator(node.op, inputs, attr)
File "/home/disk2/liushengbing/dev/tvm/py_env/mms-hpc-devel/lib/python2.7/site-packages/nnvm-0.8.0-py2.7.egg/nnvm/frontend/tensorflow.py", line 617, in _convert_operator
raise NotImplementedError("Operator {} not implemented.".format(op_name))
NotImplementedError: Operator Gather not implemented.

srkreddy1238 · 2018-06-05T15:58:57Z

@kaishijeng
srkreddy1238@c5e852e
I could compile and run mobilenet with this patch on top of this PR.
This patch need a bit of clean up before PR.

Can you help to validate the result first?

srkreddy1238 · 2018-06-05T16:03:00Z

@colin1988

Gather is not supported in tensorflow now.
I think we don't have support from nnvm/tvm too.

kaishijeng · 2018-06-06T00:33:54Z

Siva See below which has mobilenet with tvm/nnvm performance data https://github.com/merrymercy/tvm-mali Thanks,

…

On Tue, Jun 5, 2018 at 6:22 AM, Siva ***@***.***> wrote: @kaishijeng <https://github.com/kaishijeng> You said mxnet ?? I see some depthwise support in keras not in mxnet ? Am I missing something here ? — You are receiving this because you were mentioned. Reply to this email directly, view it on GitHub <#1188 (comment)>, or mute the thread <https://github.com/notifications/unsubscribe-auth/AMGg3tB7a9cqI8s-kErG9RQTNes5umbnks5t5oYPgaJpZM4UR8cu> .

colin1988 · 2018-06-06T03:35:51Z

@srkreddy1238 Tensorflow supported gather early， probably starting from 0.10

kaishijeng · 2018-06-06T03:45:57Z

@srkreddy1238

How do I pull this patch srkreddy1238/tvm@c5e852e in my code?

FC

Huyuwei · 2018-06-06T04:29:16Z

nnvm/tests/python/frontend/tensorflow/test_forward.py

+    test_forward_reshape()
+    test_forward_squeeze()
+    test_forward_concat_v2()
+    test_forward_multi_input()


Can you also test 2 or 3 end-to-end networks like Inception, ResNet?

@Huyuwei
Added Inception V1 & V3 end-to-end test cases.

I will add mobilenet as a separate PR soon (once the dependencies are merged).
Later I will check Resnet too.

srkreddy1238 · 2018-06-07T05:23:43Z

@colin1988

I mean our tensorflow frontend now doesn't support Gather now.

I see "take" operator is added to tvm already and nnvm operator for the same is pending.
We could use this to support Gather.

srkreddy1238 · 2018-06-07T05:29:19Z

@kaishijeng
We need to add ".patch" to get it as plain diff patch file.
Use the below link to download.
https://github.com/srkreddy1238/tvm/commit/c5e852e.patch

kaishijeng · 2018-06-07T07:04:09Z

@srkreddy1238

Apply patch and got the following error:

Traceback (most recent call last):
File "./from_tensorflow.py", line 162, in
graph, lib, params = nnvm.compiler.build(sym, target, shape_dict, dtype=dtype_dict, params=params)
File "/usr/local/lib/python2.7/dist-packages/nnvm-0.8.0-py2.7.egg/nnvm/compiler/build_module.py", line 272, in build
graph = optimize(graph, shape, dtype, layout)
File "/usr/local/lib/python2.7/dist-packages/nnvm-0.8.0-py2.7.egg/nnvm/compiler/build_module.py", line 175, in optimize
graph = graph.apply(["InferShape", "SimplifyInference"])
File "/usr/local/lib/python2.7/dist-packages/nnvm-0.8.0-py2.7.egg/nnvm/graph.py", line 234, in apply
check_call(_LIB.NNGraphApplyPasses(self.handle, npass, cpass, ctypes.byref(ghandle)))
File "/usr/local/lib/python2.7/dist-packages/nnvm-0.8.0-py2.7.egg/nnvm/_base.py", line 75, in check_call
raise NNVMError(py_str(_LIB.NNGetLastError()))
nnvm._base.NNVMError: [00:00:48] src/compiler/simplify_inference.cc:27: Check failed: dshape.ndim() != 0 (0 vs. 0)

Stack trace returned 10 entries:
[bt] (0) /usr/local/lib/python2.7/dist-packages/tvm-0.4.0-py2.7-linux-aarch64.egg/tvm/libtvm.so(dmlc::StackTraceabi:cxx11+0x58) [0x7f93f387c0]
[bt] (1) /usr/local/lib/python2.7/dist-packages/tvm-0.4.0-py2.7-linux-aarch64.egg/tvm/libtvm.so(dmlc::LogMessageFatal::~LogMessageFatal()+0x44) [0x7f93f3916c]
[bt] (2) /usr/local/lib/python2.7/dist-packages/nnvm-0.8.0-py2.7.egg/nnvm/libnnvm_compiler.so(nnvm::compiler::BatchNormToInferUnpack(nnvm::NodeAttrs const&, nnvm::NodeEntry, nnvm::NodeEntry, nnvm::NodeEntry, nnvm::NodeEntry, nnvm::NodeEntry, nnvm::TShape, nnvm::TShape)+0xf28) [0x7f91cb8658]
[bt] (3) /usr/local/lib/python2.7/dist-packages/nnvm-0.8.0-py2.7.egg/nnvm/libnnvm_compiler.so(+0x162fd8) [0x7f91cb8fd8]
[bt] (4) /usr/local/lib/python2.7/dist-packages/nnvm-0.8.0-py2.7.egg/nnvm/libnnvm_compiler.so(+0x163ec8) [0x7f91cb9ec8]
[bt] (5) /usr/local/lib/python2.7/dist-packages/nnvm-0.8.0-py2.7.egg/nnvm/libnnvm_compiler.so(nnvm::compiler::SimplifyInference(nnvm::Graph)+0x204) [0x7f91cba91c]
[bt] (6) /usr/local/lib/python2.7/dist-packages/nnvm-0.8.0-py2.7.egg/nnvm/libnnvm_compiler.so(std::_Function_handler<nnvm::Graph (nnvm::Graph), nnvm::Graph (*)(nnvm::Graph)>::_M_invoke(std::_Any_data const&, nnvm::Graph&&)+0xc8) [0x7f91cbb1b8]
[bt] (7) /usr/local/lib/python2.7/dist-packages/nnvm-0.8.0-py2.7.egg/nnvm/libnnvm_compiler.so(nnvm::ApplyPasses(nnvm::Graph, std::vector<std::__cxx11::basic_string<char, std::char_traits, std::allocator >, std::allocator<std::__cxx11::basic_string<char, std::char_traits, std::allocator > > > const&)+0x23c) [0x7f91d072b4]
[bt] (8) /usr/local/lib/python2.7/dist-packages/nnvm-0.8.0-py2.7.egg/nnvm/libnnvm_compiler.so(NNGraphApplyPasses+0x2a4) [0x7f91cff274]
[bt] (9) /usr/lib/aarch64-linux-gnu/libffi.so.6(ffi_call_SYSV+0x64) [0x7f9c794e60]

srkreddy1238 · 2018-06-07T07:23:31Z

@kaishijeng
Did you make corresponding changes to from_tensorflow utility ?

Mobilenet input node name is "input", hence

shape_dict = {'input': x.shape}
dtype_dict = {'input': x.dtype}

Ref. Below patch with changes to the utility for mobilenet. You just need to change tf model, image paths.
https://github.com/srkreddy1238/tvm/commit/edd72d6904b69ae266cf5067fddbb6487dc4d291.patch

srkreddy1238 · 2018-06-13T01:48:55Z

@tqchen & @Huyuwei
Done with review comments.

tqchen · 2018-06-13T03:01:21Z

nnvm/python/nnvm/compiler/build_module.py

+    params: dict of str to ndarray
+        The parameter dictionary
+    """
+    arg_list = graph.symbol.list_input_names()


efficiency: build a set

tqchen · 2018-06-13T05:38:14Z

Thanks to all the code reviewers and contributors for help improving this PR, this is now merged!

srkreddy1238 · 2018-06-13T06:47:32Z

Thanks for reviewing to improve the code quality.

kaishijeng · 2018-06-15T06:07:45Z

@srkreddy1238

What happens to below to support mobilenet?
https://github.com/srkreddy1238/tvm/tree/c5e852e9bc0cc763a69dbb3a7a8baf2c557e07ce
Will you create a PR for it? If not, how can I merge it to the latest TVM master?

Thanks,

srkreddy1238 · 2018-06-15T06:29:37Z

@kaishijeng
Ref. #1232 should go first before other changes to tensorflow frontend.

kaishijeng · 2018-06-15T06:53:17Z

@srkreddy1238

Which PR should I pull to add FusedBatchNorm operator?

Thanks

srkreddy1238 · 2018-06-15T06:58:08Z

for FusedBatchNorm only the below line should work.

srkreddy1238@c5e852e#diff-3845514369cc735d96dd63a274a21471R484

kaishijeng · 2018-06-15T07:02:58Z

Is there a good reason why you don't create a PR to support mobilenet? Thanks,

…

On Thu, Jun 14, 2018 at 11:58 PM, Siva ***@***.***> wrote: for only FusedBatchNorm only the below line should work. ***@***.***#diff-3845514369cc735d96dd63a274a21471R484 <srkreddy1238@c5e852e#diff-3845514369cc735d96dd63a274a21471R484> — You are receiving this because you were mentioned. Reply to this email directly, view it on GitHub <#1188 (comment)>, or mute the thread <https://github.com/notifications/unsubscribe-auth/AMGg3tLKeTQwdJLqnR27s_IRjij9fEGsks5t81sIgaJpZM4UR8cu> .

srkreddy1238 · 2018-06-15T07:43:27Z

:) no specific reason.

I understand you wanted mobilenet on latest code.

I will raise today for the frontend.

**You may need to take both patches to your local code for mobilenet.

srkreddy1238 · 2018-06-15T14:49:55Z

@kaishijeng
Ref. #1232 PR works for mobilenet.

kaishijeng · 2018-06-16T02:36:50Z

@srkreddy1238

Mobilenet works properly with 1232PR.
Now SSD is officially enabled in TVM. Do you have a plan to support tensorflow ssd_mobilenet?

Thanks,

FrozenGene · 2018-06-16T11:31:15Z

@kaishijeng in fact, I supported ssd-mobilenet tensorflow model before tvm support it. My way is transform tensorflow’s ssd-mobilenet to CoreML, then use NNVM/TVM support it. And I don’t need any special ops to support ssd-mobilenet.

srkreddy1238 · 2018-06-16T12:00:59Z

@kaishijeng
I will work on SSD addition to TF frontend probably end of next week.

FrozenGene · 2018-06-16T16:07:52Z

@srkreddy1238 if everything goes well, I think you will not use new ssd ops in tvm, which is for MXNet I think.

kaishijeng · 2018-06-16T18:06:33Z

@FrozenGene

Can you provide an instruction of your way to (via CoreML) enablessd-mobilenet? My platform is RK3399 which is arm-based linux?

Thanks,

FrozenGene · 2018-06-17T04:01:54Z

@kaishijeng see: https://github.com/tf-coreml/tf-coreml/tree/master/examples Maybe you will implement some ops in from_coreml.py in nnvm. I previous implemented some ops haven’t contributed back to nnvm.

madhavajay · 2018-10-09T17:30:39Z

@FrozenGene Are you able to share any of your code getting the CoreML converted Model to run on TVM?

FrozenGene · 2018-10-10T06:22:22Z

@madhavajay You could start from here: https://github.com/tf-coreml/tf-coreml/blob/master/examples/ssd_example.ipynb Then you could implement related ops to support this model. In my memory, there are not many ops to implement. Currently, my CoreML frontend has many different places compared than official TVM. (For example, my TVM support 4-D padding of Conv2D, but official TVM doesn't, we want to PR it soon). So I could not figure out what modification for SSD-Mobilenet model. We will also have plan PR our CoreML frontend, but doesn't have one detail time.

srkreddy1238 mentioned this pull request May 29, 2018

[FRONTEND] Tensorflow support #1186

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colin1988 reviewed May 30, 2018

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tqchen requested changes Jun 4, 2018

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srkreddy1238 force-pushed the master branch 2 times, most recently from f361f7f to d42ae4f Compare June 5, 2018 03:12

Huyuwei reviewed Jun 6, 2018

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[NNVM][FRONTEND] Frontend testcase.

2079485

srkreddy1238 force-pushed the master branch from bbb7dfc to 2079485 Compare June 13, 2018 01:32

tqchen requested changes Jun 13, 2018

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[NNVM][FRONTEND] Build set to be efficient.

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tqchen approved these changes Jun 13, 2018

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tqchen added status: accepted and removed status: review in progress labels Jun 13, 2018

tqchen merged commit 61d5303 into apache:master Jun 13, 2018

tqchen pushed a commit to tqchen/tvm that referenced this pull request Jul 6, 2018

[NNVM][FRONTEND] Tensorflow frontend support (apache#1188)

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mnuyens pushed a commit to mnuyens/tvm that referenced this pull request Jul 10, 2018

[NNVM][FRONTEND] Tensorflow frontend support (apache#1188)

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sergei-mironov pushed a commit to sergei-mironov/tvm that referenced this pull request Aug 8, 2018

[NNVM][FRONTEND] Tensorflow frontend support (apache#1188)

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daweili1226 mentioned this pull request Nov 14, 2018

Can you please support Split and Slice as well? #2099

Closed

		from tensorflow.python.framework import tensor_util


		repo_base = 'https://github.com/srkreddy1238/dmlc_data/raw/master/models/tensorflow/InceptionV1/'

[NNVM][FRONTEND] Tensorflow frontend support #1188

[NNVM][FRONTEND] Tensorflow frontend support #1188

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colin1988 commented Jun 5, 2018

I got the error below

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