[GitHub] [incubator-tvm] leandron commented on issue #4756: [Docker] Update torch version to 1.4
leandron commented on issue #4756: [Docker] Update torch version to 1.4 URL: https://github.com/apache/incubator-tvm/pull/4756#issuecomment-578681838 Sorry @masahi , I don't know how that error is related to the Pillow dependency and couldn't find any related issue on sphinx. This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] hcho3 commented on issue #4412: Python binding: No module named 'topi'
hcho3 commented on issue #4412: Python binding: No module named 'topi' URL: https://github.com/apache/incubator-tvm/issues/4412#issuecomment-578689513 I've had a similar problem before, and the solution is to run `python setup.py install` from `topi/python` directory. This command installs the Python package named `topi`. This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] mbarrett97 commented on a change in pull request #4543: [FRONTEND][TFLITE] Add support for TFLite_Detection_PostProcess
mbarrett97 commented on a change in pull request #4543: [FRONTEND][TFLITE] Add support for TFLite_Detection_PostProcess URL: https://github.com/apache/incubator-tvm/pull/4543#discussion_r371170921 ## File path: tests/python/frontend/tflite/test_forward.py ## @@ -1113,6 +1113,49 @@ def test_forward_fully_connected(): _test_fully_connected([5, 1, 1, 150], [150, 100], [100]) +### +# Custom Operators +# --- + +def test_detection_postprocess(): +tf_model_file = tf_testing.get_workload_official( +"http://download.tensorflow.org/models/object_detection/"; +"ssd_mobilenet_v2_quantized_300x300_coco_2019_01_03.tar.gz", Review comment: Apologies for the delayed response. Yes, that's probably the source of the error. Normally that could be worked around just by increasing the error tolerances. But that doesn't work in this case because of the sorting and clipping that occurs. This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] FrozenGene commented on a change in pull request #4543: [FRONTEND][TFLITE] Add support for TFLite_Detection_PostProcess
FrozenGene commented on a change in pull request #4543: [FRONTEND][TFLITE] Add support for TFLite_Detection_PostProcess URL: https://github.com/apache/incubator-tvm/pull/4543#discussion_r371193247 ## File path: tests/python/frontend/tflite/test_forward.py ## @@ -1113,6 +1113,49 @@ def test_forward_fully_connected(): _test_fully_connected([5, 1, 1, 150], [150, 100], [100]) +### +# Custom Operators +# --- + +def test_detection_postprocess(): +tf_model_file = tf_testing.get_workload_official( +"http://download.tensorflow.org/models/object_detection/"; +"ssd_mobilenet_v2_quantized_300x300_coco_2019_01_03.tar.gz", Review comment: I think we should resolve the issue of rounding in TVM. Would you mind opening an RFC to describe it? We could discuss and resolve it. This case is one good candidate why we need to keep the same as the rounding behavior of TFLite when we parse TFLite quantized model. This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] inadob commented on a change in pull request #4695: [Relay][Frontend][TFlite] Add parser support for relational ops
inadob commented on a change in pull request #4695: [Relay][Frontend][TFlite] Add parser support for relational ops URL: https://github.com/apache/incubator-tvm/pull/4695#discussion_r371203978 ## File path: python/tvm/relay/frontend/tflite.py ## @@ -705,47 +710,77 @@ def convert_div(self, op): # Check if the input tensor is quantized, call QNN op if self.is_quantized(op): raise tvm.error.OpNotImplemented( -'TFlite quantized div operator is not supported yet.') +'TFlite quantized DIV operator is not supported yet.') return self._convert_elemwise(_op.divide, op) def convert_pow(self, op): # Check if the input tensor is quantized, call QNN op if self.is_quantized(op): raise tvm.error.OpNotImplemented( -'TFlite quantized pow operator is not supported yet.') +'TFlite quantized POW operator is not supported yet.') return self._convert_elemwise(_op.power, op) +def convert_squared_difference(self, op): +# Check if the input tensor is quantized, call QNN op +if self.is_quantized(op): +raise tvm.error.OpNotImplemented( +'TFlite quantized SQUARED_DIFFERENCE operator is not supported yet.') +difference = self._convert_elemwise(_op.subtract, op) +# _convert_elemwise has guaranteed only have one output tensor +exp_type = self.get_tensor_type_str(self.get_output_tensors(op)[0].tensor.Type()) +out = _op.power(difference, relay.const(2, exp_type)) +return out + def convert_maximum(self, op): # Check if the input tensor is quantized, call QNN op if self.is_quantized(op): raise tvm.error.OpNotImplemented( -'TFlite quantized maximum operator is not supported yet.') +'TFlite quantized MAXIMUM operator is not supported yet.') return self._convert_elemwise(_op.maximum, op) def convert_minimum(self, op): # Check if the input tensor is quantized, call QNN op if self.is_quantized(op): raise tvm.error.OpNotImplemented( -'TFlite quantized minimum operator is not supported yet.') +'TFlite quantized MINIMUM operator is not supported yet.') return self._convert_elemwise(_op.minimum, op) def convert_greater(self, op): # Check if the input tensor is quantized, call QNN op if self.is_quantized(op): raise tvm.error.OpNotImplemented( -'TFlite quantized greater operator is not supported yet.') +'TFlite quantized GREATER operator is not supported yet.') return self._convert_elemwise(_op.greater, op) -def convert_squared_difference(self, op): -# Check if the input tensor is quantized, call QNN op +def convert_greater_equal(self, op): Review comment: so do you want me to add such a doc string to every elemwise function or to remove it from 'add', 'sub', 'mul' and 'div' This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] mbarrett97 commented on a change in pull request #4543: [FRONTEND][TFLITE] Add support for TFLite_Detection_PostProcess
mbarrett97 commented on a change in pull request #4543: [FRONTEND][TFLITE] Add support for TFLite_Detection_PostProcess URL: https://github.com/apache/incubator-tvm/pull/4543#discussion_r371205476 ## File path: tests/python/frontend/tflite/test_forward.py ## @@ -1113,6 +1113,49 @@ def test_forward_fully_connected(): _test_fully_connected([5, 1, 1, 150], [150, 100], [100]) +### +# Custom Operators +# --- + +def test_detection_postprocess(): +tf_model_file = tf_testing.get_workload_official( +"http://download.tensorflow.org/models/object_detection/"; +"ssd_mobilenet_v2_quantized_300x300_coco_2019_01_03.tar.gz", Review comment: I can do, but will we continue that as an orthogonal conversation? I'm just clarifying as I don't think that issue affects the correctness of this operator which is already tested by 'test_detection_postprocess'. This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] FrozenGene commented on a change in pull request #4543: [FRONTEND][TFLITE] Add support for TFLite_Detection_PostProcess
FrozenGene commented on a change in pull request #4543: [FRONTEND][TFLITE] Add support for TFLite_Detection_PostProcess URL: https://github.com/apache/incubator-tvm/pull/4543#discussion_r371269023 ## File path: tests/python/frontend/tflite/test_forward.py ## @@ -1113,6 +1113,49 @@ def test_forward_fully_connected(): _test_fully_connected([5, 1, 1, 150], [150, 100], [100]) +### +# Custom Operators +# --- + +def test_detection_postprocess(): +tf_model_file = tf_testing.get_workload_official( +"http://download.tensorflow.org/models/object_detection/"; +"ssd_mobilenet_v2_quantized_300x300_coco_2019_01_03.tar.gz", Review comment: Alright, we could remove ssd mobilenet model because of this limitation, but we should still keep the unit testing of detection postprocess. After we resolve the limitation, we could add ssd mobilenet testing back. Morever, we could remove the atol=1 of test_qconv2d and so on. Because we could get the same result completely compared with the tflite. Does it make sense to you? This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] FrozenGene commented on a change in pull request #4695: [Relay][Frontend][TFlite] Add parser support for relational ops
FrozenGene commented on a change in pull request #4695: [Relay][Frontend][TFlite] Add parser support for relational ops URL: https://github.com/apache/incubator-tvm/pull/4695#discussion_r371276409 ## File path: python/tvm/relay/frontend/tflite.py ## @@ -705,47 +710,77 @@ def convert_div(self, op): # Check if the input tensor is quantized, call QNN op if self.is_quantized(op): raise tvm.error.OpNotImplemented( -'TFlite quantized div operator is not supported yet.') +'TFlite quantized DIV operator is not supported yet.') return self._convert_elemwise(_op.divide, op) def convert_pow(self, op): # Check if the input tensor is quantized, call QNN op if self.is_quantized(op): raise tvm.error.OpNotImplemented( -'TFlite quantized pow operator is not supported yet.') +'TFlite quantized POW operator is not supported yet.') return self._convert_elemwise(_op.power, op) +def convert_squared_difference(self, op): +# Check if the input tensor is quantized, call QNN op +if self.is_quantized(op): +raise tvm.error.OpNotImplemented( +'TFlite quantized SQUARED_DIFFERENCE operator is not supported yet.') +difference = self._convert_elemwise(_op.subtract, op) +# _convert_elemwise has guaranteed only have one output tensor +exp_type = self.get_tensor_type_str(self.get_output_tensors(op)[0].tensor.Type()) +out = _op.power(difference, relay.const(2, exp_type)) +return out + def convert_maximum(self, op): # Check if the input tensor is quantized, call QNN op if self.is_quantized(op): raise tvm.error.OpNotImplemented( -'TFlite quantized maximum operator is not supported yet.') +'TFlite quantized MAXIMUM operator is not supported yet.') return self._convert_elemwise(_op.maximum, op) def convert_minimum(self, op): # Check if the input tensor is quantized, call QNN op if self.is_quantized(op): raise tvm.error.OpNotImplemented( -'TFlite quantized minimum operator is not supported yet.') +'TFlite quantized MINIMUM operator is not supported yet.') return self._convert_elemwise(_op.minimum, op) def convert_greater(self, op): # Check if the input tensor is quantized, call QNN op if self.is_quantized(op): raise tvm.error.OpNotImplemented( -'TFlite quantized greater operator is not supported yet.') +'TFlite quantized GREATER operator is not supported yet.') return self._convert_elemwise(_op.greater, op) -def convert_squared_difference(self, op): -# Check if the input tensor is quantized, call QNN op +def convert_greater_equal(self, op): Review comment: add such a doc string to every elemwise function. This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] mbarrett97 commented on a change in pull request #4543: [FRONTEND][TFLITE] Add support for TFLite_Detection_PostProcess
mbarrett97 commented on a change in pull request #4543: [FRONTEND][TFLITE] Add support for TFLite_Detection_PostProcess URL: https://github.com/apache/incubator-tvm/pull/4543#discussion_r371276909 ## File path: tests/python/frontend/tflite/test_forward.py ## @@ -1113,6 +1113,49 @@ def test_forward_fully_connected(): _test_fully_connected([5, 1, 1, 150], [150, 100], [100]) +### +# Custom Operators +# --- + +def test_detection_postprocess(): +tf_model_file = tf_testing.get_workload_official( +"http://download.tensorflow.org/models/object_detection/"; +"ssd_mobilenet_v2_quantized_300x300_coco_2019_01_03.tar.gz", Review comment: This test is a bit misleading because it doesn't actually run ssd mobilenet, it just test the postprocess op. I couldn't find a way to create the op using the tflite python API, so what I did instead was take a model that has it and then run it through the tflite converter but with the converter inputs set to the inputs of the postprocess op rather than the input to the network. This has the net effect of producing a single postprocess op, so this should already be a unit test (and it passes). I can add the end-to-end tests if/when we resolve the QNN accuracy issue. I'll open an RFC shortly to describe why rounding is a particularly significant in the case of this operator. This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] FrozenGene commented on a change in pull request #4543: [FRONTEND][TFLITE] Add support for TFLite_Detection_PostProcess
FrozenGene commented on a change in pull request #4543: [FRONTEND][TFLITE] Add support for TFLite_Detection_PostProcess URL: https://github.com/apache/incubator-tvm/pull/4543#discussion_r371281677 ## File path: tests/python/frontend/tflite/test_forward.py ## @@ -1113,6 +1113,49 @@ def test_forward_fully_connected(): _test_fully_connected([5, 1, 1, 150], [150, 100], [100]) +### +# Custom Operators +# --- + +def test_detection_postprocess(): +tf_model_file = tf_testing.get_workload_official( +"http://download.tensorflow.org/models/object_detection/"; +"ssd_mobilenet_v2_quantized_300x300_coco_2019_01_03.tar.gz", Review comment: I think if we could view the TOCO source code, maybe we could find how to construct detection_postprocess. Please refer our `test_prelu` comment. I ever write what the pattern tflite could produce prelu. However, current way is acceptable too in my opinion. This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] FrozenGene commented on a change in pull request #4543: [FRONTEND][TFLITE] Add support for TFLite_Detection_PostProcess
FrozenGene commented on a change in pull request #4543: [FRONTEND][TFLITE] Add support for TFLite_Detection_PostProcess URL: https://github.com/apache/incubator-tvm/pull/4543#discussion_r371281677 ## File path: tests/python/frontend/tflite/test_forward.py ## @@ -1113,6 +1113,49 @@ def test_forward_fully_connected(): _test_fully_connected([5, 1, 1, 150], [150, 100], [100]) +### +# Custom Operators +# --- + +def test_detection_postprocess(): +tf_model_file = tf_testing.get_workload_official( +"http://download.tensorflow.org/models/object_detection/"; +"ssd_mobilenet_v2_quantized_300x300_coco_2019_01_03.tar.gz", Review comment: I think if we could view the TOCO source code, maybe we could find how to construct detection_postprocess. Please refer our `_test_prelu` comment. I ever write what the pattern tflite could produce prelu. However, current way is acceptable too in my opinion. This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] yongfeng-nv commented on issue #4651: Tensor Expression Debug Display (TEDD)
yongfeng-nv commented on issue #4651: Tensor Expression Debug Display (TEDD) URL: https://github.com/apache/incubator-tvm/pull/4651#issuecomment-578845333 > > Thanks @yongfeng-nv . One thing that I think worth considering, as in many viz tools, is the separation of visualization data source specification(in this case the perhaps a dom tree or similar kind) from the visualization(graphviz). > > We can have a tool that extracts the spec into json, then have a tool to take that spec and visualize it > > @tqchen, I see your point. Let me spec the data source spec. @tqchen, @Hzfengsy, I have posted proposal DOM tree in the RFC thread: https://discuss.tvm.ai/t/visualize-tensor-expression/5174/6. Please leave your comments and suggestions. This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] wpan11nv opened a new pull request #4779: [AUTOTVM] Fix a bug in generating the search space
wpan11nv opened a new pull request #4779: [AUTOTVM] Fix a bug in generating the search space URL: https://github.com/apache/incubator-tvm/pull/4779 - Do not use numpy.prod which ignores integer (64 bits) overflows. This leads to an incorrect number of points in the search space. Thanks for contributing to TVM! Please refer to guideline https://docs.tvm.ai/contribute/ for useful information and tips. After the pull request is submitted, please request code reviews from [Reviewers](https://github.com/apache/incubator-tvm/blob/master/CONTRIBUTORS.md#reviewers) by @ them in the pull request thread. This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] comaniac commented on a change in pull request #4779: [AUTOTVM] Fix a bug in generating the search space
comaniac commented on a change in pull request #4779: [AUTOTVM] Fix a bug in generating the search space URL: https://github.com/apache/incubator-tvm/pull/4779#discussion_r371397521 ## File path: python/tvm/autotvm/task/space.py ## @@ -226,7 +226,13 @@ def __init__(self, axes, policy, **kwargs): def _generate_space(self, now, tmp_stack, enforce_no_tail=False): """Generate space by DFS""" if now == self.num_output - 1: -prod = np.prod(tmp_stack, dtype=np.int64) +prod = 1 Review comment: It seems to me that manually implementing a classic array production is not necessary in any case. Since limited types are only enforced in numpy and Python's types are unlimited, it would be more concise to use Python builtins to calculate the product: ```python import functools import operator prod = functools.reduce(operator.mul, tmp_stack, 1) ``` Note that the length of `tmp_stack` is always small (currently 4 at most, and I don't think it would be longer than 10), so this won't hurt the performance. This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] wpan11nv commented on a change in pull request #4779: [AUTOTVM] Fix a bug in generating the search space
wpan11nv commented on a change in pull request #4779: [AUTOTVM] Fix a bug in generating the search space URL: https://github.com/apache/incubator-tvm/pull/4779#discussion_r371417185 ## File path: python/tvm/autotvm/task/space.py ## @@ -226,7 +226,13 @@ def __init__(self, axes, policy, **kwargs): def _generate_space(self, now, tmp_stack, enforce_no_tail=False): """Generate space by DFS""" if now == self.num_output - 1: -prod = np.prod(tmp_stack, dtype=np.int64) +prod = 1 Review comment: It looks more pythonic. The number of lines will be similar. Here is one debate on this: :) https://stackoverflow.com/questions/9474412/python-alternative-to-reduce This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] comaniac commented on a change in pull request #4779: [AUTOTVM] Fix a bug in generating the search space
comaniac commented on a change in pull request #4779: [AUTOTVM] Fix a bug in generating the search space URL: https://github.com/apache/incubator-tvm/pull/4779#discussion_r371422539 ## File path: python/tvm/autotvm/task/space.py ## @@ -226,7 +226,13 @@ def __init__(self, axes, policy, **kwargs): def _generate_space(self, now, tmp_stack, enforce_no_tail=False): """Generate space by DFS""" if now == self.num_output - 1: -prod = np.prod(tmp_stack, dtype=np.int64) +prod = 1 Review comment: Well, I know that some people prevent `reduce` and other patterns like `map` from being used in Python 3, although I personally think not like `map` and `filter`, `reduce` is hard to be replaced by other pythonic representation. I am fine with that if you do not want to introduce `reduce` to the code. In this case I would suggest making this logic a standalone function in `autotvm/util.py`. This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] comaniac commented on a change in pull request #4779: [AUTOTVM] Fix a bug in generating the search space
comaniac commented on a change in pull request #4779: [AUTOTVM] Fix a bug in generating the search space URL: https://github.com/apache/incubator-tvm/pull/4779#discussion_r371422539 ## File path: python/tvm/autotvm/task/space.py ## @@ -226,7 +226,13 @@ def __init__(self, axes, policy, **kwargs): def _generate_space(self, now, tmp_stack, enforce_no_tail=False): """Generate space by DFS""" if now == self.num_output - 1: -prod = np.prod(tmp_stack, dtype=np.int64) +prod = 1 Review comment: Well, I know that some people prevent `reduce` and other patterns like `map` from being used in Python 3, although I personally think not like `map` and `filter`, `reduce` is hard to be replaced by other pythonic representations. I am fine with that if you do not want to introduce `reduce` to the code. In this case I would suggest making this logic a standalone function in `autotvm/util.py`. This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] soiferj commented on issue #4776: [Build] Explicitly link to cublasLt if it exists
soiferj commented on issue #4776: [Build] Explicitly link to cublasLt if it exists URL: https://github.com/apache/incubator-tvm/pull/4776#issuecomment-578902685 @masahi fixed. Would you mind taking another look? This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] soiferj opened a new pull request #4780: [FFI][Windows] Extract Python error type from Windows error log
soiferj opened a new pull request #4780: [FFI][Windows] Extract Python error type from Windows error log URL: https://github.com/apache/incubator-tvm/pull/4780 As discussed [here](https://discuss.tvm.ai/t/cross-compilation-ubuntu-for-tvm-generation-and-windows-for-executing-in-tvm-runtime/159/5), `hasattr` is currently broken for Windows. When a `Expr` does not have an attribute, a `TVMError` is thrown rather than an `AttributeError`. This is because of how we extract the error type from the logged message. On Unix, DMLC will log the stack trace when `LOG(FATAL)` is called. However, DMLC will not log the stack trace on Windows. This causes the errors to look different. Unix: ``` AttributeError: relay.Call object has no attributed name_hint Stack trace: File "/home/jonso/dev/TVM/src/node/reflection.cc", line 109 [bt] (0) /home/jonso/dev/TVM/build/libtvm.so(dmlc::LogMessageFatal::~LogMessageFatal()+0x32) [0x7f0970787a62] [bt] (1) /home/jonso/dev/TVM/build/libtvm.so(tvm::ReflectionVTable::GetAttr(tvm::runtime::Object*, std::__cxx11::basic_string, std::allocator > const&) const+0x2c5) [0x7f0970af27f5] [bt] (2) /home/jonso/dev/TVM/build/libtvm.so(tvm::NodeGetAttr(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)+0x15c) [0x7f0970af2c4c] [bt] (3) /home/jonso/dev/TVM/build/libtvm.so(std::_Function_handler::_M_invoke(std::_Any_data const&, tvm::runtime::TVMArgs&&, tvm::runtime::TVMRetValue*&&)+0x14) [0x7f0970af6f14] [bt] (4) /home/jonso/dev/TVM/build/libtvm.so(TVMFuncCall+0x61) [0x7f0970fb68a1] ``` Windows: ``` [11:19:42] D:\_work\1\s\TVM\src\node\reflection.cc:109: AttributeError: relay.Call object has no attributed name_hint ``` This change fixes the error string parsing logic in base.py to try to find the error type in the Windows error message. @jmorrill @tqchen would you be able to take a look? This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] abergeron commented on issue #4758: [relay] ADT match becomes not well formed during VM optimization
abergeron commented on issue #4758: [relay] ADT match becomes not well formed
during VM optimization
URL: https://github.com/apache/incubator-tvm/issues/4758#issuecomment-578918024
After some experimentation, if I comment out InlinePrimitives at line 921 in
compiler.cc
(https://github.com/apache/incubator-tvm/blob/master/src/relay/backend/vm/compiler.cc#L921),
then the compilation works. It seems this is the line that introduces the
weird change.
If I add an override like this:
```
Expr VisitExpr_(const MatchNode* m) {
std::vector clauses;
for (const Clause& p : m->clauses) {
clauses.push_back(VisitClause(p));
}
return GetRef(m);
}
```
to the PrimitiveInliner class (in
https://github.com/apache/incubator-tvm/blob/master/src/relay/backend/vm/inline_primitives.cc#L54),
it appears to make the compilation work (at least in my limited example), but
this may have some other consequences that I am not aware of (I suspect this
means that no transformations will be applied to the body of match clauses,
which might be bad).
I'll keep working on that for a bit trying to find an acceptable solution,
but I will gladly take any help/hints that I can get.
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[GitHub] [incubator-tvm] wpan11nv commented on a change in pull request #4779: [AUTOTVM] Fix a bug in generating the search space
wpan11nv commented on a change in pull request #4779: [AUTOTVM] Fix a bug in generating the search space URL: https://github.com/apache/incubator-tvm/pull/4779#discussion_r371466338 ## File path: python/tvm/autotvm/task/space.py ## @@ -226,7 +226,13 @@ def __init__(self, axes, policy, **kwargs): def _generate_space(self, now, tmp_stack, enforce_no_tail=False): """Generate space by DFS""" if now == self.num_output - 1: -prod = np.prod(tmp_stack, dtype=np.int64) +prod = 1 Review comment: Utility function looks like a overkill. A reduce call is fine. Updated. Thanks! This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] comaniac opened a new pull request #4781: [AutoTVM] Ignore error when removing tmpdir
comaniac opened a new pull request #4781: [AutoTVM] Ignore error when removing tmpdir URL: https://github.com/apache/incubator-tvm/pull/4781 Sometimes the tmpdir drops for some reasons. For example, /tmp is cleaned by someone or similar situations. Anyhow, it seems to me that the use of `rmtree` should be error-free. @merrymercy could you help review? Thanks. This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] comaniac commented on a change in pull request #4771: [Relay] Added Merge Composite pass
comaniac commented on a change in pull request #4771: [Relay] Added Merge
Composite pass
URL: https://github.com/apache/incubator-tvm/pull/4771#discussion_r371504533
##
File path: src/relay/pass/merge_composite.cc
##
@@ -0,0 +1,192 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied. See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * \file src/relay/pass/merge_composite.cc
+ * \brief Merges expressions matching patterns into functions marked
+ * as 'composite'.
+ */
+
+#include
+#include
+#include
+#include
+#include
+
+namespace tvm {
+namespace relay {
+namespace merge_composite {
+
+
+class MergeCompositeWrapper : public ExprMutator {
+ public:
+ explicit MergeCompositeWrapper(const tvm::Map&
pattern_map)
+: pattern_map_(pattern_map) {}
+
+ bool MatchPattern(const Call& pattern, const Call& root) {
+if (!pattern->op->IsInstance() || !root->op->IsInstance())
+ return false;
+if (pattern->op.as()->name != root->op.as()->name)
+ return false;
+if (pattern->args.size() != root->args.size())
+ return false;
+
+unsigned int i = 0;
+for (const auto& arg : pattern->args) {
+ if (arg->IsInstance()) {
+if (!root->args[i]->IsInstance())
+ return false;
+if (!MatchPattern(Downcast(arg), Downcast(root->args[i])))
+ return false;
+ }
+ i++;
+}
+return true;
+ }
+
+ Expr ExtractPattern(const Var& pattern, const Expr& root,
+ Map>* var_map) {
+if (var_map->find(pattern->name_hint()) == var_map->end()) {
+ auto free_var = VarNode::make(pattern->name_hint(), Type());
+ var_map->Set(pattern->name_hint(), Array({free_var, root}));
+ return free_var;
+} else {
+ return (*var_map)[pattern->name_hint()][0];
+}
+ }
+
+ Expr ExtractPattern(const Constant& pattern, const Expr& root,
+ Map>* var_map) {
+return root;
+ }
+
+ Expr ExtractPattern(const Call& pattern, const Call& root,
+ Map>* var_map) {
+Expr expr;
+Expr empty_expr;
+if (!pattern->op->IsInstance() || !root->op->IsInstance())
+ return empty_expr;
+if (pattern->op.as()->name != root->op.as()->name)
+ return empty_expr;
+if (pattern->args.size() != root->args.size())
+ return empty_expr;
+
+unsigned int i = 0;
+Array new_args;
+for (const auto& arg : pattern->args) {
+ if (arg->IsInstance()) {
+new_args.push_back(ExtractPattern(Downcast(arg),
+ Downcast(root->args[i]),
+ var_map));
+ }
+ if (arg->IsInstance()) {
+new_args.push_back(ExtractPattern(Downcast(arg),
+ root->args[i],
+ var_map));
+ }
+ if (arg->IsInstance()) {
+new_args.push_back(ExtractPattern(Downcast(arg),
+ root->args[i],
+ var_map));
+ }
+ i++;
+}
+
+auto new_call = CallNode::make(root->op, new_args, root->attrs);
+return new_call;
+ }
+
+ Expr VisitExpr_(const CallNode* cn) {
+Call call = GetRef(cn);
+if (call->op->IsInstance()) {
+ Function func = Downcast(call->op);
+ CHECK(func.defined());
+ const auto name_node = FunctionGetAttr(func,
attr::kComposite).as();
+ if (name_node->value != "") {
+tvm::Array new_args;
+for (const auto& arg : call->args) {
+ auto new_e = this->Mutate(arg);
+ new_args.push_back(new_e);
+}
+return CallNode::make(call->op, new_args, call->attrs);
+ }
+}
+
+Expr expr = ExprMutator::VisitExpr_(cn);
+call = Downcast(expr);
+if (!call->op->IsInstance())
+ return call;
+
+Op op = Downcast(call->op);
+CHECK(op.defined());
+for (const auto& x : pattern_map_) {
+ Call pattern = Downcast(x.second);
+ if (Downcast(pattern->op)->name != op->name)
+continue;
+
+ if (MatchPattern(pattern, call)) {
+Map> args_map;
+auto extract = ExtractPattern(pattern, call, &args_map);
+auto free_vars = FreeVars(extract);
+Function new_func = FunctionNode::mak
[GitHub] [incubator-tvm] mbarrett97 commented on a change in pull request #4771: [Relay] Added Merge Composite pass
mbarrett97 commented on a change in pull request #4771: [Relay] Added Merge
Composite pass
URL: https://github.com/apache/incubator-tvm/pull/4771#discussion_r371508067
##
File path: src/relay/pass/merge_composite.cc
##
@@ -0,0 +1,192 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied. See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * \file src/relay/pass/merge_composite.cc
+ * \brief Merges expressions matching patterns into functions marked
+ * as 'composite'.
+ */
+
+#include
+#include
+#include
+#include
+#include
+
+namespace tvm {
+namespace relay {
+namespace merge_composite {
+
+
+class MergeCompositeWrapper : public ExprMutator {
+ public:
+ explicit MergeCompositeWrapper(const tvm::Map&
pattern_map)
+: pattern_map_(pattern_map) {}
+
+ bool MatchPattern(const Call& pattern, const Call& root) {
+if (!pattern->op->IsInstance() || !root->op->IsInstance())
+ return false;
+if (pattern->op.as()->name != root->op.as()->name)
+ return false;
+if (pattern->args.size() != root->args.size())
+ return false;
+
+unsigned int i = 0;
+for (const auto& arg : pattern->args) {
+ if (arg->IsInstance()) {
+if (!root->args[i]->IsInstance())
+ return false;
+if (!MatchPattern(Downcast(arg), Downcast(root->args[i])))
+ return false;
+ }
+ i++;
+}
+return true;
+ }
+
+ Expr ExtractPattern(const Var& pattern, const Expr& root,
+ Map>* var_map) {
+if (var_map->find(pattern->name_hint()) == var_map->end()) {
+ auto free_var = VarNode::make(pattern->name_hint(), Type());
+ var_map->Set(pattern->name_hint(), Array({free_var, root}));
+ return free_var;
+} else {
+ return (*var_map)[pattern->name_hint()][0];
+}
+ }
+
+ Expr ExtractPattern(const Constant& pattern, const Expr& root,
+ Map>* var_map) {
+return root;
+ }
+
+ Expr ExtractPattern(const Call& pattern, const Call& root,
+ Map>* var_map) {
+Expr expr;
+Expr empty_expr;
+if (!pattern->op->IsInstance() || !root->op->IsInstance())
+ return empty_expr;
+if (pattern->op.as()->name != root->op.as()->name)
+ return empty_expr;
+if (pattern->args.size() != root->args.size())
+ return empty_expr;
+
+unsigned int i = 0;
+Array new_args;
+for (const auto& arg : pattern->args) {
+ if (arg->IsInstance()) {
+new_args.push_back(ExtractPattern(Downcast(arg),
+ Downcast(root->args[i]),
+ var_map));
+ }
+ if (arg->IsInstance()) {
+new_args.push_back(ExtractPattern(Downcast(arg),
+ root->args[i],
+ var_map));
+ }
+ if (arg->IsInstance()) {
+new_args.push_back(ExtractPattern(Downcast(arg),
+ root->args[i],
+ var_map));
+ }
+ i++;
+}
+
+auto new_call = CallNode::make(root->op, new_args, root->attrs);
+return new_call;
+ }
+
+ Expr VisitExpr_(const CallNode* cn) {
+Call call = GetRef(cn);
+if (call->op->IsInstance()) {
+ Function func = Downcast(call->op);
+ CHECK(func.defined());
+ const auto name_node = FunctionGetAttr(func,
attr::kComposite).as();
+ if (name_node->value != "") {
+tvm::Array new_args;
+for (const auto& arg : call->args) {
+ auto new_e = this->Mutate(arg);
+ new_args.push_back(new_e);
+}
+return CallNode::make(call->op, new_args, call->attrs);
+ }
+}
+
+Expr expr = ExprMutator::VisitExpr_(cn);
+call = Downcast(expr);
+if (!call->op->IsInstance())
+ return call;
+
+Op op = Downcast(call->op);
+CHECK(op.defined());
+for (const auto& x : pattern_map_) {
+ Call pattern = Downcast(x.second);
+ if (Downcast(pattern->op)->name != op->name)
+continue;
+
+ if (MatchPattern(pattern, call)) {
+Map> args_map;
+auto extract = ExtractPattern(pattern, call, &args_map);
+auto free_vars = FreeVars(extract);
+Function new_func = FunctionNode::m
[GitHub] [incubator-tvm] mbarrett97 closed issue #4150: [RFC] [AutoTVM] Implementing an auto-tuning library/cache
mbarrett97 closed issue #4150: [RFC] [AutoTVM] Implementing an auto-tuning library/cache URL: https://github.com/apache/incubator-tvm/issues/4150 This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] mbarrett97 commented on issue #4771: [Relay] Added Merge Composite pass
mbarrett97 commented on issue #4771: [Relay] Added Merge Composite pass URL: https://github.com/apache/incubator-tvm/pull/4771#issuecomment-578979321 It's the intention that this can be called on the entire Relay graph so that it can be used to help implement a generic annotation pass (one that is aware of composite functions). That way we can define functions similar to the 'Is Supported?' mechanism in the original annotation PR (since taken down) where you could declare in Python whether an operator was supported. That could be extended to say whether a composite function is supported without having to add pattern matching code to the annotator. The problem there is the case where composite functions do not end up in an external function after partitioning. My thinking is to have some legalize pass after the partitioning that removes the composite functions from sections of the graph not marked as external. This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] masahi merged pull request #4776: [Build] Explicitly link to cublasLt if it exists
masahi merged pull request #4776: [Build] Explicitly link to cublasLt if it exists URL: https://github.com/apache/incubator-tvm/pull/4776 This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] masahi commented on issue #4776: [Build] Explicitly link to cublasLt if it exists
masahi commented on issue #4776: [Build] Explicitly link to cublasLt if it exists URL: https://github.com/apache/incubator-tvm/pull/4776#issuecomment-578985825 thanks @soiferj This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[incubator-tvm] branch master updated (9056fc4 -> 00ec7f9)
This is an automated email from the ASF dual-hosted git repository. masahi pushed a change to branch master in repository https://gitbox.apache.org/repos/asf/incubator-tvm.git. from 9056fc4 Update tune_simple_template.py (#4778) add 00ec7f9 [Build] Explicitly link to cublasLt if it exists (#4776) No new revisions were added by this update. Summary of changes: cmake/modules/CUDA.cmake | 3 +++ cmake/util/FindCUDA.cmake | 7 +++ 2 files changed, 10 insertions(+)
[GitHub] [incubator-tvm] tqchen merged pull request #4780: [FFI][Windows] Fix hasattr by extracting Python error type from Windows error message
tqchen merged pull request #4780: [FFI][Windows] Fix hasattr by extracting Python error type from Windows error message URL: https://github.com/apache/incubator-tvm/pull/4780 This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] tqchen commented on issue #4780: [FFI][Windows] Fix hasattr by extracting Python error type from Windows error message
tqchen commented on issue #4780: [FFI][Windows] Fix hasattr by extracting Python error type from Windows error message URL: https://github.com/apache/incubator-tvm/pull/4780#issuecomment-578996008 Thanks @jonso4 ! This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[incubator-tvm] branch master updated (00ec7f9 -> f71a10c)
This is an automated email from the ASF dual-hosted git repository. tqchen pushed a change to branch master in repository https://gitbox.apache.org/repos/asf/incubator-tvm.git. from 00ec7f9 [Build] Explicitly link to cublasLt if it exists (#4776) add f71a10c properly extract error type from windows error message (#4780) No new revisions were added by this update. Summary of changes: python/tvm/_ffi/base.py | 29 +++-- 1 file changed, 23 insertions(+), 6 deletions(-)
[GitHub] [incubator-tvm] jroesch merged pull request #4774: [Relay][Frontend][ONNX] Broadcast condition, x, and y for Where op
jroesch merged pull request #4774: [Relay][Frontend][ONNX] Broadcast condition, x, and y for Where op URL: https://github.com/apache/incubator-tvm/pull/4774 This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[incubator-tvm] branch master updated (f71a10c -> de919cb)
This is an automated email from the ASF dual-hosted git repository. jroesch pushed a change to branch master in repository https://gitbox.apache.org/repos/asf/incubator-tvm.git. from f71a10c properly extract error type from windows error message (#4780) add de919cb [Relay][Frontend][ONNX] Broadcast condition, x, and y for Where op (#4774) No new revisions were added by this update. Summary of changes: python/tvm/relay/frontend/onnx.py | 29 - tests/python/frontend/onnx/test_forward.py | 16 2 files changed, 40 insertions(+), 5 deletions(-)
[GitHub] [incubator-tvm] comaniac commented on issue #4771: [Relay] Added Merge Composite pass
comaniac commented on issue #4771: [Relay] Added Merge Composite pass URL: https://github.com/apache/incubator-tvm/pull/4771#issuecomment-579014540 So you want the flow to be: ``` CompositeMerge -> Annotation -> Partitioning. ``` I agree that this would make annotation generic and straightforward, although it seems like we don't need annotation anymore if we specify all patterns including single ops. While there are lots of approaches to do so, maybe we could accept this solution first and consider the further steps. What you do think? @zhiics Also, please help clarify the question asked by @masahi and me about multiple matching. Thanks. This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] merrymercy merged pull request #4781: [AutoTVM] Ignore error when removing tmpdir
merrymercy merged pull request #4781: [AutoTVM] Ignore error when removing tmpdir URL: https://github.com/apache/incubator-tvm/pull/4781 This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[incubator-tvm] branch master updated (de919cb -> d54036a)
This is an automated email from the ASF dual-hosted git repository. lmzheng pushed a change to branch master in repository https://gitbox.apache.org/repos/asf/incubator-tvm.git. from de919cb [Relay][Frontend][ONNX] Broadcast condition, x, and y for Where op (#4774) add d54036a Safe remove tmpdir (#4781) No new revisions were added by this update. Summary of changes: python/tvm/autotvm/measure/measure_methods.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-)
[incubator-tvm] branch pass_callback_via_cx created (now 00ec7f9)
This is an automated email from the ASF dual-hosted git repository. jroesch pushed a change to branch pass_callback_via_cx in repository https://gitbox.apache.org/repos/asf/incubator-tvm.git. at 00ec7f9 [Build] Explicitly link to cublasLt if it exists (#4776) No new revisions were added by this update.
[incubator-tvm] branch pass_callback_via_cx updated: Implement pass tracing API
This is an automated email from the ASF dual-hosted git repository.
jroesch pushed a commit to branch pass_callback_via_cx
in repository https://gitbox.apache.org/repos/asf/incubator-tvm.git
The following commit(s) were added to refs/heads/pass_callback_via_cx by this
push:
new 1f38493 Implement pass tracing API
1f38493 is described below
commit 1f3849378884be54c16efcd23c78f168c5c99ecd
Author: Jared Roesch
AuthorDate: Mon Jan 27 16:35:45 2020 -0800
Implement pass tracing API
---
include/tvm/ir/transform.h | 19 +++
python/tvm/relay/transform.py | 13 +
src/ir/transform.cc | 8
src/relay/ir/transform.cc | 3 ++-
tests/python/relay/test_pass_manager.py | 30 ++
5 files changed, 68 insertions(+), 5 deletions(-)
diff --git a/include/tvm/ir/transform.h b/include/tvm/ir/transform.h
index c606b34..03aba40 100644
--- a/include/tvm/ir/transform.h
+++ b/include/tvm/ir/transform.h
@@ -65,6 +65,14 @@
namespace tvm {
namespace transform {
+// Forward declare for TraceFunc.
+class PassInfo;
+
+/*! \brief A callback for tracing passes, useful for debugging and logging.
+ *
+ */
+using TraceFunc = runtime::TypedPackedFunc;
+
/*!
* \brief PassContextNode contains the information that a pass can rely on,
* such as analysis results.
@@ -88,6 +96,8 @@ class PassContextNode : public Object {
/*! \brief The list of disabled passes. */
Array disabled_pass;
+ TraceFunc trace_func;
+
PassContextNode() = default;
void VisitAttrs(AttrVisitor* v) {
@@ -101,6 +111,7 @@ class PassContextNode : public Object {
TVM_DECLARE_FINAL_OBJECT_INFO(PassContextNode, Object);
};
+
/*!
* \brief PassContext that is used to configure the pass behavior.
*
@@ -146,6 +157,14 @@ class PassContext : public ObjectRef {
*/
TVM_DLL static PassContext Current();
+ /*!
+ * \brief Apply the tracing functions of the context to the module, with the
info.
+ * \param module The IRModule to trace.
+ * \param info The pass information.
+ * \param is_before Indicated whether the tracing is before or after a pass.
+ */
+ TVM_DLL void Trace(const IRModule& module, const PassInfo& info, bool
is_before) const;
+
// accessor.
using ContainerType = PassContextNode;
class Internal;
diff --git a/python/tvm/relay/transform.py b/python/tvm/relay/transform.py
index c4fbde6..26b20e0 100644
--- a/python/tvm/relay/transform.py
+++ b/python/tvm/relay/transform.py
@@ -78,7 +78,8 @@ class PassContext(RelayNode):
opt_level=2,
fallback_device=_nd.cpu(),
required_pass=None,
- disabled_pass=None):
+ disabled_pass=None,
+ trace=None):
if isinstance(fallback_device, str):
fallback_device = _nd.context(fallback_device).device_type
elif isinstance(fallback_device, TVMContext):
@@ -99,7 +100,7 @@ class PassContext(RelayNode):
self.__init_handle_by_constructor__(_transform.PassContext, opt_level,
fallback_device, required,
-disabled)
+disabled, trace)
def __enter__(self):
_transform.EnterPassContext(self)
@@ -117,7 +118,8 @@ class PassContext(RelayNode):
def build_config(opt_level=2,
fallback_device=_nd.cpu(),
required_pass=None,
- disabled_pass=None):
+ disabled_pass=None,
+ trace=None):
"""Configure the build behavior by setting config variables.
Parameters
@@ -151,13 +153,16 @@ def build_config(opt_level=2,
disabled_pass: set of str, optional
Optimization passes to be disabled during optimization.
+trace: Callable[[IRModule, PassInfo, bool], None]
+A tracing function for debugging or introspection.
+
Returns
---
pass_context: PassContext
The pass context for optimizations.
"""
return PassContext(opt_level, fallback_device, required_pass,
- disabled_pass)
+ disabled_pass, trace)
@register_relay_node
diff --git a/src/ir/transform.cc b/src/ir/transform.cc
index 1da010c..d14a5b4 100644
--- a/src/ir/transform.cc
+++ b/src/ir/transform.cc
@@ -84,6 +84,10 @@ PassContext PassContext::Create() {
return PassContext(make_object());
}
+void PassContext::Trace(const IRModule& module, const PassInfo& info, bool
is_before) const {
+this->operator->()->trace_func(module, info, is_before);
+}
+
class ModulePass;
/*!
@@ -231,8 +235,10 @@ IRModule ModulePassNode::operator()(const IRModule& mod,
<< " with opt level: "
<< pass_info->opt_level;
CHECK(mod.defined());
+ pass_ctx.Trace(mod, pass_info, true);
IRModule updated_mod = pass_func(mod, pass_ctx);
CHEC
[GitHub] [incubator-tvm] jroesch opened a new pull request #4782: [PassManager] Implement pass manager tracing API
jroesch opened a new pull request #4782: [PassManager] Implement pass manager tracing API URL: https://github.com/apache/incubator-tvm/pull/4782 I am working on some pass infrastructure for TVM and needed the previously discussed tracing [feature](https://discuss.tvm.ai/t/rfc-printing-ir-and-parameters-to-pass-pipelines/4239/5) This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] jroesch commented on issue #4782: [PassManager] Implement pass manager tracing API
jroesch commented on issue #4782: [PassManager] Implement pass manager tracing API URL: https://github.com/apache/incubator-tvm/pull/4782#issuecomment-579026027 cc @zhiics @tmoreau89 This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] jroesch commented on issue #4782: [PassManager] Implement pass manager tracing API
jroesch commented on issue #4782: [PassManager] Implement pass manager tracing API URL: https://github.com/apache/incubator-tvm/pull/4782#issuecomment-579028693 @tmoreau89 I added to the pass docs with an example. This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] alexwong commented on issue #4497: [WIP] [Relay] Add a PyTorch to Relay Parser
alexwong commented on issue #4497: [WIP] [Relay] Add a PyTorch to Relay Parser
URL: https://github.com/apache/incubator-tvm/pull/4497#issuecomment-579028784
> @alexwong I had a big success in refactoring, the parser itself (except op
conversion) is about 150 lines and the "main loop" is just:
>
> ```python
> def get_op_inputs(op_node, outputs, name_map):
> inputs = []
> for i in op_node.inputs():
> inode_name = name_map[i.debugName()]
> inputs.append(outputs[inode_name])
> return inputs
>
> outputs = list(input_vars.values())
> node_name_to_nid = dict(zip(input_vars.keys(), range(len(outputs
>
> for node_name, op_node in ops.items():
> operator = op_node.kind()
> if operator == "prim::Constant":
> node_name_to_nid[node_name] = len(outputs)
> outputs.append(consts[node_name])
> elif operator != 'prim::ListConstruct':
> node_name_to_nid[node_name] = len(outputs)
> inputs = get_op_inputs(op_node, outputs, node_name_to_nid)
> call = convert_map[operator](inputs, op_in_types[node_name])
> outputs.append(call)
>
> body = outputs[-1]
> func = tvm.relay.Function(_analysis.free_vars(body), body)
> param = {k: tvm.nd.array(v) for k, v in param_tensors.items()}
> ```
>
> My updated version is
[here](https://gist.github.com/masahi/7704856919563c4b8a74bf085686b519)
>
> Maybe this is a too much change for you, I'm happy to send my change as a
follow up after this PR. We can merge this after you fix the CI issue.
It is a lot of feedback but I think I can manage, just have been sidetracked
with other things that keep pulling me away from this. I'm not sure about today
but I should be able to work on this tomorrow. I'm all for simpler code though,
would you prefer I pull in the changes above in this PR or just try and make
all of the simpler changes to get this merged first?
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[GitHub] [incubator-tvm] masahi commented on issue #4497: [WIP] [Relay] Add a PyTorch to Relay Parser
masahi commented on issue #4497: [WIP] [Relay] Add a PyTorch to Relay Parser URL: https://github.com/apache/incubator-tvm/pull/4497#issuecomment-579038361 @alexwong I'm 100% for "make all of the simpler changes to get this merged first". I don't want you to spend your time redoing the refactoring work I did. Of course, if you want to improve your PR and have time to do so, go ahead and do that as you like. In particular, good simplification can be done around GetAttr node parsing logic and inputs/outputs handling inside main loop (my gist above have my take on this). Anyway, the must TODOs for this PR before we merge are, * Make CI green * Make it work with PyTorch 1.4 (just add `torch._C._jit_pass_inline(graph)`) * Replace all the string hacks with PyTorch's Python interface for JIT data structures After we merge this PR, I'll send a refactoring PR (only about 200 lines change, all op conversion and test cases will be used as is). I've finished refactoring on my branch and currently working on quantized model support. I'm also looking at dynamic models with control flow nodes. This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] masahi commented on a change in pull request #4497: [WIP] [Relay] Add a PyTorch to Relay Parser
masahi commented on a change in pull request #4497: [WIP] [Relay] Add a PyTorch
to Relay Parser
URL: https://github.com/apache/incubator-tvm/pull/4497#discussion_r369430546
##
File path: python/tvm/relay/frontend/pytorch.py
##
@@ -0,0 +1,1138 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+# pylint: disable=import-self, too-many-lines, len-as-condition,
no-else-return, unused-variable, too-many-nested-blocks
+# pylint: disable=consider-iterating-dictionary, invalid-name,
unused-argument, unused-variable, broad-except
+"""PT: PyTorch frontend."""
+import numpy as np
+
+import tvm
+
+from .. import analysis as _analysis
+from .. import expr as _expr
+from .. import module as _module
+from .. import op as _op
+from .common import get_relay_op
+from .common import infer_shape as _infer_shape
+
+__all__ = ['from_pytorch']
+
+# operator implementation
+def _elemwise(name):
+def _impl(inputs, input_types):
+data0 = convert_input(inputs[0])
+data1 = convert_input(inputs[1])
+
+if not isinstance(data0, (_expr.Call, _expr.TupleGetItem, _expr.Var)):
+temp = data0
+data0 = data1
+data1 = temp
+
+return get_relay_op(name)(data0, data1)
+return _impl
+
+def _unsqueeze():
+def _impl(inputs, input_types):
+data = inputs[0]
+axis = inputs[1]
+
+return _op.transform.expand_dims(data, int(axis), 1)
+return _impl
+
+def _concatenate():
+def _impl(inputs, input_types):
+data = inputs[0]
+axis = inputs[1]
+
+if isinstance(data, (_expr.Call, _expr.TupleGetItem, _expr.Var)):
+data = [data]
+
+return _op.tensor.concatenate(data, int(axis))
+return _impl
+
+def _slice():
+def _impl(inputs, input_types):
+data = inputs[0]
+strides = []
+
+inferred_shape = _infer_shape(data)
+end = []
+for infer in inferred_shape:
+end.append(int(infer))
+if isinstance(data, _expr.Var):
+end = _infer_shape(data)
+end = list(end)
+
+begin = [0]*len(end)
+dim = int(inputs[1])
+begin[dim] = int(inputs[2])
+
+if inputs[3].isdigit():
+end[dim] = min(end[dim], int(inputs[3]))
+
+strides.append(int(inputs[4]))
+return _op.transform.strided_slice(data, begin, end, strides)
+return _impl
+
+def _select():
+def _impl(inputs, input_types):
+data = inputs[0]
+inferred_shape = _infer_shape(data)
+end = []
+
+for infer in inferred_shape:
+end.append(int(infer))
+
+begin = [0]*len(end)
+dim = int(inputs[1])
+index = int(inputs[2])
+
+end[dim] = index+1
+begin[dim] = index
+
+strides = [1]*len(end)
+
+sym = _op.transform.strided_slice(data, begin, end, strides)
+axis = [dim]
+
+return _op.transform.squeeze(sym, axis)
+return _impl
+
+def _convert_data_type(input_type):
+if input_type == 'double' or input_type == 'torch.float64':
+return 'float64'
+elif input_type == 'float' or input_type == 'torch.float32':
+return 'float32'
+elif input_type == 'half' or input_type == 'torch.float16':
+return 'float16'
+elif input_type == 'long' or input_type == 'torch.int64':
+return 'int64'
+elif input_type == 'int' or input_type == 'torch.int32':
+return 'int32'
+elif input_type == 'short' or input_type == 'torch.int16':
+return 'int16'
+elif input_type == 'char' or input_type == 'torch.int8':
+return 'int8'
+elif input_type == 'byte' or input_type == 'torch.uint8':
+return 'uint8'
+else:
+return input_type
+
+def _ones():
+def _impl(inputs, input_types):
+if isinstance(inputs[0], _expr.Var):
+shape = _infer_shape(inputs[0])
+elif isinstance(inputs[0], (_expr.Call, _expr.TupleGetItem)):
+shape = _infer_shape(inputs[0])
+else:
+shape = inputs[0].shape
+
+fill_value = _get_fill_value(input_types)
+
+return get_relay_op('full')(fill_value, shape,
dtype=_convert_data_type(input_types[0]))
+return
[GitHub] [incubator-tvm] masahi commented on a change in pull request #4497: [WIP] [Relay] Add a PyTorch to Relay Parser
masahi commented on a change in pull request #4497: [WIP] [Relay] Add a PyTorch
to Relay Parser
URL: https://github.com/apache/incubator-tvm/pull/4497#discussion_r369428504
##
File path: python/tvm/relay/frontend/pytorch.py
##
@@ -0,0 +1,1138 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+# pylint: disable=import-self, too-many-lines, len-as-condition,
no-else-return, unused-variable, too-many-nested-blocks
+# pylint: disable=consider-iterating-dictionary, invalid-name,
unused-argument, unused-variable, broad-except
+"""PT: PyTorch frontend."""
+import numpy as np
+
+import tvm
+
+from .. import analysis as _analysis
+from .. import expr as _expr
+from .. import module as _module
+from .. import op as _op
+from .common import get_relay_op
+from .common import infer_shape as _infer_shape
+
+__all__ = ['from_pytorch']
+
+# operator implementation
+def _elemwise(name):
+def _impl(inputs, input_types):
+data0 = convert_input(inputs[0])
+data1 = convert_input(inputs[1])
+
+if not isinstance(data0, (_expr.Call, _expr.TupleGetItem, _expr.Var)):
+temp = data0
+data0 = data1
+data1 = temp
+
+return get_relay_op(name)(data0, data1)
+return _impl
+
+def _unsqueeze():
+def _impl(inputs, input_types):
+data = inputs[0]
+axis = inputs[1]
+
+return _op.transform.expand_dims(data, int(axis), 1)
+return _impl
+
+def _concatenate():
+def _impl(inputs, input_types):
+data = inputs[0]
+axis = inputs[1]
+
+if isinstance(data, (_expr.Call, _expr.TupleGetItem, _expr.Var)):
+data = [data]
+
+return _op.tensor.concatenate(data, int(axis))
+return _impl
+
+def _slice():
+def _impl(inputs, input_types):
+data = inputs[0]
+strides = []
+
+inferred_shape = _infer_shape(data)
+end = []
+for infer in inferred_shape:
+end.append(int(infer))
+if isinstance(data, _expr.Var):
+end = _infer_shape(data)
+end = list(end)
+
+begin = [0]*len(end)
+dim = int(inputs[1])
+begin[dim] = int(inputs[2])
+
+if inputs[3].isdigit():
+end[dim] = min(end[dim], int(inputs[3]))
+
+strides.append(int(inputs[4]))
+return _op.transform.strided_slice(data, begin, end, strides)
+return _impl
+
+def _select():
+def _impl(inputs, input_types):
+data = inputs[0]
+inferred_shape = _infer_shape(data)
+end = []
+
+for infer in inferred_shape:
+end.append(int(infer))
+
+begin = [0]*len(end)
+dim = int(inputs[1])
+index = int(inputs[2])
+
+end[dim] = index+1
+begin[dim] = index
+
+strides = [1]*len(end)
+
+sym = _op.transform.strided_slice(data, begin, end, strides)
+axis = [dim]
+
+return _op.transform.squeeze(sym, axis)
+return _impl
+
+def _convert_data_type(input_type):
+if input_type == 'double' or input_type == 'torch.float64':
+return 'float64'
+elif input_type == 'float' or input_type == 'torch.float32':
+return 'float32'
+elif input_type == 'half' or input_type == 'torch.float16':
+return 'float16'
+elif input_type == 'long' or input_type == 'torch.int64':
+return 'int64'
+elif input_type == 'int' or input_type == 'torch.int32':
+return 'int32'
+elif input_type == 'short' or input_type == 'torch.int16':
+return 'int16'
+elif input_type == 'char' or input_type == 'torch.int8':
+return 'int8'
+elif input_type == 'byte' or input_type == 'torch.uint8':
+return 'uint8'
+else:
+return input_type
+
+def _ones():
+def _impl(inputs, input_types):
+if isinstance(inputs[0], _expr.Var):
+shape = _infer_shape(inputs[0])
+elif isinstance(inputs[0], (_expr.Call, _expr.TupleGetItem)):
+shape = _infer_shape(inputs[0])
+else:
+shape = inputs[0].shape
+
+fill_value = _get_fill_value(input_types)
+
+return get_relay_op('full')(fill_value, shape,
dtype=_convert_data_type(input_types[0]))
+return
