[GitHub] [incubator-tvm] tqchen merged pull request #5658: Upgrade XGBoost to latest

2020-05-22 Thread GitBox


tqchen merged pull request #5658:
URL: https://github.com/apache/incubator-tvm/pull/5658


   



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[GitHub] [incubator-tvm] tqchen merged pull request #5660: [uTVM] Increase bss section size.

2020-05-22 Thread GitBox


tqchen merged pull request #5660:
URL: https://github.com/apache/incubator-tvm/pull/5660


   



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[incubator-tvm] branch master updated: Increase bss section size. (#5660)

2020-05-22 Thread tqchen
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tqchen pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/incubator-tvm.git


The following commit(s) were added to refs/heads/master by this push:
 new e2d66e1  Increase bss section size. (#5660)
e2d66e1 is described below

commit e2d66e1688c0622cce50747ee847872ff60c0389
Author: Andrew Reusch 
AuthorDate: Fri May 22 19:41:14 2020 -0700

Increase bss section size. (#5660)

* Likely broken in PR 5590.
---
 python/tvm/micro/device/arm/stm32f746xx.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/python/tvm/micro/device/arm/stm32f746xx.py 
b/python/tvm/micro/device/arm/stm32f746xx.py
index 7469585..997093b 100644
--- a/python/tvm/micro/device/arm/stm32f746xx.py
+++ b/python/tvm/micro/device/arm/stm32f746xx.py
@@ -32,7 +32,7 @@ DEFAULT_SECTION_CONSTRAINTS = {
 "text": (18000, MemConstraint.ABSOLUTE_BYTES),
 "rodata": (100, MemConstraint.ABSOLUTE_BYTES),
 "data": (100, MemConstraint.ABSOLUTE_BYTES),
-"bss": (600, MemConstraint.ABSOLUTE_BYTES),
+"bss": (640, MemConstraint.ABSOLUTE_BYTES),
 "args": (4096, MemConstraint.ABSOLUTE_BYTES),
 "heap": (100.0, MemConstraint.WEIGHT),
 "workspace": (64000, MemConstraint.ABSOLUTE_BYTES),



[incubator-tvm] branch master updated: Upgrade XGBoost to latest (#5658)

2020-05-22 Thread tqchen
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tqchen pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/incubator-tvm.git


The following commit(s) were added to refs/heads/master by this push:
 new 3827cd0  Upgrade XGBoost to latest (#5658)
3827cd0 is described below

commit 3827cd044d843006dc49bb28a058275598c52c29
Author: Philip Hyunsu Cho 
AuthorDate: Fri May 22 19:40:46 2020 -0700

Upgrade XGBoost to latest (#5658)
---
 python/setup.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/python/setup.py b/python/setup.py
index fb126ec..682589e 100644
--- a/python/setup.py
+++ b/python/setup.py
@@ -165,7 +165,7 @@ setup(name='tvm',
'matplotlib'],
   'extra_feature': ['tornado',
 'psutil',
-'xgboost==0.90',
+'xgboost>=1.1.0',
 'mypy',
 'orderedset',
 'antlr4-python3-runtime']},



[incubator-tvm] branch master updated: [PatternLang] Convert PatternGrouper to do pre-order, non-recursive analysis (#5653)

2020-05-22 Thread masahi
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masahi pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/incubator-tvm.git


The following commit(s) were added to refs/heads/master by this push:
 new 53251c8  [PatternLang] Convert PatternGrouper to do pre-order, 
non-recursive analysis (#5653)
53251c8 is described below

commit 53251c87b2a7be53d00a968629bfc688585d8e4e
Author: Matthew Brookhart 
AuthorDate: Fri May 22 20:17:39 2020 -0700

[PatternLang] Convert PatternGrouper to do pre-order, non-recursive 
analysis (#5653)

* make the PatternGrouper iterate over the input Expr in a non-recursive 
pre-order fasion

* add a comment
---
 src/relay/ir/dataflow_matcher.cc| 43 +++--
 tests/python/relay/test_dataflow_pattern.py | 32 -
 2 files changed, 59 insertions(+), 16 deletions(-)

diff --git a/src/relay/ir/dataflow_matcher.cc b/src/relay/ir/dataflow_matcher.cc
index 980935c..2f25733 100644
--- a/src/relay/ir/dataflow_matcher.cc
+++ b/src/relay/ir/dataflow_matcher.cc
@@ -43,6 +43,7 @@ class DFPatternMatcher : public DFPatternFunctor> GetMemo() { return Map>(memo_); }
+  const IndexedGraph expr_graph_;
 
  protected:
   bool VisitDFPattern(const DFPattern& pattern, const Expr& expr) override;
@@ -63,7 +64,6 @@ class DFPatternMatcher : public DFPatternFunctor, ObjectHash, ObjectEqual> memo_;
   std::vector matched_nodes_;
-  IndexedGraph expr_graph_;
   bool memoize_ = true;
 };
 
@@ -291,7 +291,7 @@ bool DFPatternMatcher::VisitDFPattern_(const 
CallPatternNode* op, const Expr& ex
 // Recursively find the Dominator parent along all inputs paths.
 bool DFPatternMatcher::MatchesPath(const DominatorPatternNode* op, const Expr& 
expr) {
   auto call_node = expr.as();
-  for (auto node : expr_graph_.node_map_[expr]->inputs_) {
+  for (auto node : expr_graph_.node_map_.at(expr)->inputs_) {
 if (!(call_node && node->ref_ == call_node->op)) {
   memoize_ = true;
   if (VisitDFPattern(op->parent, node->ref_)) {
@@ -315,7 +315,7 @@ bool DFPatternMatcher::DominatesParent(const 
DominatorPatternNode* op, const Exp
   while (!stack.empty()) {
 Expr current = stack.top();
 stack.pop();
-for (auto node : expr_graph_.node_map_[current]->dominator_children_) {
+for (auto node : expr_graph_.node_map_.at(current)->dominator_children_) {
   if (visited.count(node->ref_) == 0) {
 if (VisitDFPattern(op->parent, node->ref_)) {
   return true;
@@ -412,7 +412,7 @@ 
TVM_REGISTER_GLOBAL("relay.dataflow_pattern.match").set_body_typed(MatchPattern)
  * This is primarily needed to support the post-dominator analysis required 
for dominator pattern
  * matching.
  */
-class PatternGrouper : protected MixedModeVisitor {
+class PatternGrouper {
  public:
   /* \brief Internal Group class for storing analysis */
   struct Group {
@@ -432,26 +432,39 @@ class PatternGrouper : protected MixedModeVisitor {
   const std::vector& GroupMatches(const DFPattern& pattern, const Expr& 
pre) {
 groups_ = {Group()};
 gid_assignments_.clear();
-visit_counter_.clear();
 
 pattern_ = pattern;
 pattern_graph_ = CreateIndexedGraph(pattern_);
 auto matcher = DFPatternMatcher(pre);
 matcher_ = 
-this->VisitExpr(pre);
+this->VisitExprs();
 return this->groups_;
   }
 
  protected:
-  using ExprVisitor::VisitExpr_;
-  void VisitLeaf(const Expr& pre) override {
-if (matcher_->Match(pattern_, pre)) {
-  CreateGroup(pre);
-}
-  }
-  void VisitExpr_(const FunctionNode* op) override {
-if (op->attrs->dict.count(attr::kPartitionedFromPattern) == 0) {
-  ExprVisitor::VisitExpr_(op);
+  /* \brief Iteratively traverse the Expression in pre-order to find subgraphs
+   *
+   * If we traverse the graph in post-order, we can run into situtations where 
a small subgraph will
+   * match the pattern. Due to options like AltPattern, a larger subgraph with 
more nodes later in
+   * the graph may also match the pattern. With post-order traversal, we mark 
the smaller subgraph
+   * as matched and fail to catch the larger subgraph. This problem is fixed 
by using pre-order
+   * traversal.
+   */
+  void VisitExprs() {
+std::unordered_set pre_partitioned;
+for (size_t i = matcher_->expr_graph_.topological_order_.size(); i != 0; 
--i) {
+  size_t index = i - 1;
+  Expr current = matcher_->expr_graph_.topological_order_.at(index)->ref_;
+  if (auto op = current.as()) {
+if (op->attrs->dict.count(attr::kPartitionedFromPattern) != 0) {
+  pre_partitioned.insert(current);
+  PostOrderVisit(op->body,
+ [_partitioned](const Expr& expr) { 
pre_partitioned.insert(expr); });
+}
+  }
+  if (pre_partitioned.count(current) == 0 && matcher_->Match(pattern_, 
current)) {
+CreateGroup(current);
+  }
 }
   }
   /* \brief Creates a new set of nodes based on Group 

[GitHub] [incubator-tvm] masahi merged pull request #5653: [PatternLang] Convert PatternGrouper to do pre-order, non-recursive analysis

2020-05-22 Thread GitBox


masahi merged pull request #5653:
URL: https://github.com/apache/incubator-tvm/pull/5653


   



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[GitHub] [incubator-tvm] masahi commented on pull request #5653: [PatternLang] Convert PatternGrouper to do pre-order, non-recursive analysis

2020-05-22 Thread GitBox


masahi commented on pull request #5653:
URL: https://github.com/apache/incubator-tvm/pull/5653#issuecomment-632977181


   Thanks @mbrookhart @comaniac 



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[incubator-tvm] branch master updated: [Relay, Topi][OP] affine_grid and grid_sample (#5657)

2020-05-22 Thread masahi
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masahi pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/incubator-tvm.git


The following commit(s) were added to refs/heads/master by this push:
 new e369c5a  [Relay,Topi][OP] affine_grid and grid_sample (#5657)
e369c5a is described below

commit e369c5a9cbacb926ca7b95ebc4ae01a6de33c6cd
Author: Wuwei Lin 
AuthorDate: Sat May 23 00:57:58 2020 -0400

[Relay,Topi][OP] affine_grid and grid_sample (#5657)

* [Relay,Topi][OP] affine_grid and grid_sample

* lint
---
 include/tvm/relay/attrs/image.h|  28 +
 python/tvm/relay/frontend/mxnet.py |  22 
 python/tvm/relay/op/image/_image.py|  20 +++
 python/tvm/relay/op/image/image.py |  64 ++
 src/relay/op/image/grid_sample.cc  | 168 +
 tests/python/frontend/mxnet/test_forward.py|  38 ++
 tests/python/relay/test_op_level5.py   |  52 
 topi/python/topi/image/__init__.py |   1 +
 topi/python/topi/image/grid_sample.py  | 124 ++
 topi/python/topi/testing/__init__.py   |   1 +
 topi/python/topi/testing/grid_sample_python.py |  65 ++
 topi/tests/python/test_topi_image.py   |  83 
 12 files changed, 666 insertions(+)

diff --git a/include/tvm/relay/attrs/image.h b/include/tvm/relay/attrs/image.h
index 58fd44b..cf5a6ef 100644
--- a/include/tvm/relay/attrs/image.h
+++ b/include/tvm/relay/attrs/image.h
@@ -167,6 +167,34 @@ struct Dilation2DAttrs : public 
tvm::AttrsNode {
   }
 };
 
+/*! \brief Attributes used in image affine_grid operator */
+struct AffineGridAttrs : public tvm::AttrsNode {
+  Array target_shape;
+
+  TVM_DECLARE_ATTRS(AffineGridAttrs, "relay.attrs.AffineGridAttrs") {
+TVM_ATTR_FIELD(target_shape).describe("Specifies the output shape (H, 
W).");
+  }
+};
+
+/*! \brief Attributes used in image grid_sample operator */
+struct GridSampleAttrs : public tvm::AttrsNode {
+  String method;
+  String layout;
+
+  TVM_DECLARE_ATTRS(GridSampleAttrs, "relay.attrs.GridSampleAttrs") {
+TVM_ATTR_FIELD(method)
+.set_default("bilinear")
+.describe(
+"Specify the mode to use for scaling."
+"bilinear - Bilinear Interpolation");
+TVM_ATTR_FIELD(layout).set_default("NCHW").describe(
+"Dimension ordering of input data. Can be 'NCHW', 'NHWC', etc."
+"'N', 'C', 'H', 'W' stands for batch, channel, height, and width"
+"dimensions respectively. Resize is applied on the 'H' and"
+"'W' dimensions.");
+  }
+};
+
 }  // namespace relay
 }  // namespace tvm
 #endif  // TVM_RELAY_ATTRS_IMAGE_H_
diff --git a/python/tvm/relay/frontend/mxnet.py 
b/python/tvm/relay/frontend/mxnet.py
index 9f97ee9..c75612d 100644
--- a/python/tvm/relay/frontend/mxnet.py
+++ b/python/tvm/relay/frontend/mxnet.py
@@ -757,6 +757,26 @@ def _mx_resize(inputs, attrs):
 return _op.image.resize(inputs[0], size,
 coordinate_transformation_mode="align_corners")
 
+def _mx_grid_generator(inputs, attrs):
+transform_type = attrs.get_str("transform_type")
+if transform_type == 'affine':
+target_shape = attrs.get_int_tuple("target_shape")
+return _op.image.affine_grid(_op.reshape(inputs[0], (0, 2, 3)), 
target_shape)
+if transform_type == 'warp':
+checked_type = _infer_type(inputs[0]).checked_type
+batch, _, height, width = get_const_tuple(checked_type.shape)
+dtype = checked_type.dtype
+identity_affine = relay.const(np.array([[[1.0, 0.0, 0.0], [0.0, 1.0, 
0.0]]], dtype=dtype))
+identity_affine = _op.broadcast_to(identity_affine, (batch, 2, 3))
+normalizer = (2.0 / np.array([width - 1, height - 1])).reshape(1, -1, 
1, 1).astype(dtype)
+normalized_flow = inputs[0] * relay.const(normalizer)
+grid = _op.image.affine_grid(identity_affine, (height, width))
+return grid + normalized_flow
+raise ValueError("unknown transform type" + transform_type)
+
+def _mx_bilinear_sampler(inputs, attrs):
+return _op.image.grid_sample(inputs[0], inputs[1], 'bilinear', 'NCHW')
+
 def _mx_roi_pooling(inputs, attrs):
 new_attrs = {}
 new_attrs["pooled_size"] = attrs.get_int_tuple("pooled_size")
@@ -1996,6 +2016,8 @@ _convert_map = {
 "_contrib_box_nms" : _mx_box_nms,
 "_contrib_DeformableConvolution" : _mx_deformable_convolution,
 "_contrib_AdaptiveAvgPooling2D" : _mx_adaptive_avg_pooling,
+"GridGenerator" : _mx_grid_generator,
+"BilinearSampler"   : _mx_bilinear_sampler,
 # NLP
 "RNN"   : _mx_rnn_layer,
 "_rnn_param_concat" : _mx_rnn_param_concat,
diff --git a/python/tvm/relay/op/image/_image.py 
b/python/tvm/relay/op/image/_image.py
index 290c0a2..bcb110f 100644
--- a/python/tvm/relay/op/image/_image.py
+++ 

[GitHub] [incubator-tvm] masahi commented on pull request #5657: [Relay,Topi][OP] affine_grid and grid_sample

2020-05-22 Thread GitBox


masahi commented on pull request #5657:
URL: https://github.com/apache/incubator-tvm/pull/5657#issuecomment-632985707


   Thanks @vinx13 



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[GitHub] [incubator-tvm] masahi merged pull request #5657: [Relay,Topi][OP] affine_grid and grid_sample

2020-05-22 Thread GitBox


masahi merged pull request #5657:
URL: https://github.com/apache/incubator-tvm/pull/5657


   



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[GitHub] [incubator-tvm] mei-ye commented on pull request #5659: [TARGET] enable amd_apu device on vulkan target

2020-05-22 Thread GitBox


mei-ye commented on pull request #5659:
URL: https://github.com/apache/incubator-tvm/pull/5659#issuecomment-632981824


   yes, I will check in log to  tophub after this.  This also fixes two bugs in 
vulkan runtime:
   maxComputeWorkGroupInvocations:  the maximum total number of compute shader 
invocations in a single local workgroup.
   maxComputeWorkGroupSize[3]: the maximum size of a local compute workgroup, 
per dimension.



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[incubator-tvm] branch master updated: [Relay, Topi][OP] Correlation (#5628)

2020-05-22 Thread masahi
This is an automated email from the ASF dual-hosted git repository.

masahi pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/incubator-tvm.git


The following commit(s) were added to refs/heads/master by this push:
 new e55f9ff  [Relay, Topi][OP] Correlation (#5628)
e55f9ff is described below

commit e55f9ff115e2e9364e8ed8b3eeb44dbbc1894eb1
Author: Wuwei Lin 
AuthorDate: Fri May 22 02:00:33 2020 -0400

[Relay, Topi][OP] Correlation (#5628)

* [Relay,Topi] Correlation

* fix

* move

* typo

* Update test_topi_correlation.py
---
 include/tvm/relay/attrs/nn.h   |  30 
 python/tvm/relay/frontend/mxnet.py |  14 ++
 python/tvm/relay/op/nn/_nn.py  |   5 +
 python/tvm/relay/op/nn/nn.py   |  83 ++
 python/tvm/relay/op/op_attrs.py|   5 +
 python/tvm/relay/op/strategy/cuda.py   |  12 ++
 python/tvm/relay/op/strategy/generic.py|  27 
 src/relay/op/nn/correlation.cc | 136 
 tests/python/frontend/mxnet/test_forward.py|  35 +++-
 tests/python/relay/test_op_level2.py   |  40 +
 topi/python/topi/cuda/__init__.py  |   1 +
 topi/python/topi/cuda/correlation.py   | 176 +
 topi/python/topi/generic/nn.py |  17 ++
 topi/python/topi/nn/__init__.py|   1 +
 topi/python/topi/nn/correlation.py | 116 ++
 topi/python/topi/testing/__init__.py   |   1 +
 .../python/topi/testing/correlation_nchw_python.py | 103 
 topi/tests/python/test_topi_correlation.py |  93 +++
 18 files changed, 894 insertions(+), 1 deletion(-)

diff --git a/include/tvm/relay/attrs/nn.h b/include/tvm/relay/attrs/nn.h
index a9c3059..dcb4cb6 100644
--- a/include/tvm/relay/attrs/nn.h
+++ b/include/tvm/relay/attrs/nn.h
@@ -1203,6 +1203,36 @@ struct SubPixelAttrs : public 
tvm::AttrsNode {
   }
 };  // struct SubPixelAttrs
 
+/*! \brief Attributes used in correlation operators */
+struct CorrelationAttrs : public tvm::AttrsNode {
+  int kernel_size;
+  int max_displacement;
+  int stride1;
+  int stride2;
+  Array padding;
+  bool is_multiply;
+  String layout;
+
+  TVM_DECLARE_ATTRS(CorrelationAttrs, "relay.attrs.CorrelationAttrs") {
+TVM_ATTR_FIELD(kernel_size)
+.describe("Kernel size for correlation, must be an odd number.")
+.set_default(1);
+TVM_ATTR_FIELD(max_displacement).describe("Max displacement of 
Correlation.").set_default(1);
+TVM_ATTR_FIELD(stride1).describe("Stride for data1.").set_default(1);
+TVM_ATTR_FIELD(stride2).describe("Stride for data2.").set_default(1);
+TVM_ATTR_FIELD(padding)
+.describe("Padding for data1 and data2.")
+.set_default(Array{0, 0});
+TVM_ATTR_FIELD(is_multiply)
+.describe("Operation type is either multiplication or substraction.")
+.set_default(true);
+TVM_ATTR_FIELD(layout).set_default("NCHW").describe(
+"Dimension ordering of input data. Can be 'NCHW', 'NHWC', etc."
+"'N', 'C', 'H', 'W' stands for batch, channel, height, and width"
+"dimensions respectively.");
+  }
+};  // struct CorrelationAttrs
+
 }  // namespace relay
 }  // namespace tvm
 #endif  // TVM_RELAY_ATTRS_NN_H_
diff --git a/python/tvm/relay/frontend/mxnet.py 
b/python/tvm/relay/frontend/mxnet.py
index edf6680..9f97ee9 100644
--- a/python/tvm/relay/frontend/mxnet.py
+++ b/python/tvm/relay/frontend/mxnet.py
@@ -1133,6 +1133,19 @@ def _mx_space_to_depth(inputs, attrs):
 return _op.nn.space_to_depth(*inputs, **new_attrs)
 
 
+def _mx_correlation(inputs, attrs):
+assert len(inputs) == 2
+new_attrs = {}
+new_attrs["kernel_size"] = attrs.get_int("kernel_size", 1)
+new_attrs["max_displacement"] = attrs.get_int("max_displacement", 1)
+new_attrs["stride1"] = attrs.get_int("stride1", 1)
+new_attrs["stride2"] = attrs.get_int("stride2", 1)
+new_attrs["padding"] = attrs.get_int("pad_size", 0)
+new_attrs["is_multiply"] = attrs.get_bool("is_multiply", True)
+new_attrs["layout"] = "NCHW"
+return _op.nn.correlation(*inputs, **new_attrs)
+
+
 def _mx_contrib_fifo_buffer(inputs, attrs):
 new_attrs = {}
 new_attrs['axis'] = attrs.get_int('axis')
@@ -1971,6 +1984,7 @@ _convert_map = {
 "one_hot"   : _mx_one_hot,
 "depth_to_space": _mx_depth_to_space,
 "space_to_depth": _mx_space_to_depth,
+"Correlation"   : _mx_correlation,
 # vision
 "_contrib_BilinearResize2D" : _mx_resize,
 "_contrib_MultiBoxPrior" : _mx_multibox_prior,
diff --git a/python/tvm/relay/op/nn/_nn.py b/python/tvm/relay/op/nn/_nn.py
index 9a9bfe0..0633451 100644
--- a/python/tvm/relay/op/nn/_nn.py
+++ b/python/tvm/relay/op/nn/_nn.py
@@ -563,6 +563,11 @@ 

[GitHub] [incubator-tvm] masahi commented on pull request #5628: [Relay, Topi][OP] Correlation

2020-05-22 Thread GitBox


masahi commented on pull request #5628:
URL: https://github.com/apache/incubator-tvm/pull/5628#issuecomment-632501657


   thanks @vinx13 @tqchen 



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[GitHub] [incubator-tvm] masahi merged pull request #5628: [Relay, Topi][OP] Correlation

2020-05-22 Thread GitBox


masahi merged pull request #5628:
URL: https://github.com/apache/incubator-tvm/pull/5628


   



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[GitHub] [incubator-tvm] kazum commented on a change in pull request #5052: [TARGET] ONNX codegen

2020-05-22 Thread GitBox


kazum commented on a change in pull request #5052:
URL: https://github.com/apache/incubator-tvm/pull/5052#discussion_r429168772



##
File path: src/runtime/contrib/onnx/onnx_module.cc
##
@@ -0,0 +1,65 @@
+/*
+ * 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 onnx_module.cc
+ * \brief ONNX Module without runtime support
+ */
+#include 
+#include 
+#include 
+
+namespace tvm {
+namespace codegen {
+using namespace tvm::runtime;
+
+class ONNXSourceModuleNode : public runtime::ModuleNode {
+ public:
+  explicit ONNXSourceModuleNode(String code) : code_(code) {}
+
+  const char* type_key() const { return "onnx"; }
+
+  PackedFunc GetFunction(const std::string& name, const ObjectPtr& 
sptr_to_self) final {
+LOG(FATAL) << "ONNX Source module cannot execute, to get executable module"
+   << " build TVM with 'onnx' runtime support";
+return PackedFunc();
+  }
+
+  std::string GetSource(const std::string& format) final { return code_; }

Review comment:
   The content of `code_` is the source code of this module?  If no, I 
think it's better to return an empty string.

##
File path: tests/python/contrib/test_onnx.py
##
@@ -0,0 +1,406 @@
+# 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.
+
+"""Relay to ONNX serialization test cases"""
+import pytest
+pytest.importorskip('onnx')
+pytest.importorskip('onnxruntime')
+
+import numpy as np
+import tvm
+from tvm import relay
+from tvm.contrib.target.onnx import to_onnx
+import onnxruntime as rt
+
+
+def func_to_onnx(func, name):
+mod = tvm.IRModule()
+mod['main'] = func
+onnx_model = to_onnx(mod, {}, name, path=None)
+return onnx_model.SerializeToString()
+
+
+def run_onnx(onnx_model, input_data):
+sess = rt.InferenceSession(onnx_model)
+input_names = {}
+for input, data in zip(sess.get_inputs(), input_data):
+input_names[input.name] = data
+output_name = sess.get_outputs()[0].name
+res = sess.run([output_name], input_names)
+return res[0]
+
+
+def run_relay(func, data_tuple):
+target = 'llvm'
+ctx = tvm.context('llvm', 0)
+intrp = relay.create_executor("graph", ctx=ctx, target=target)
+relay_res = intrp.evaluate(func)(*data_tuple)
+return relay_res.asnumpy()
+
+
+def verify_results(relay_func, indata, test_name, rtol=1e-7, atol=0):
+relay_res = run_relay(relay_func, indata)
+onnx_res = run_onnx(func_to_onnx(relay_func, test_name), indata)
+np.testing.assert_allclose(relay_res, onnx_res, rtol=rtol, atol=atol)
+
+
+def test_add():
+dtype = 'float32'
+t1 = relay.TensorType((5, 10, 5))
+t2 = relay.TensorType((5, 10, 5))
+x = relay.var("x", t1, dtype=dtype)
+y = relay.var("y", t2, dtype=dtype)
+z = relay.add(x, y)
+func = relay.Function([x, y], z)
+
+x_data = np.random.rand(5, 10, 5).astype(dtype)
+y_data = np.random.rand(5, 10, 5).astype(dtype)
+
+verify_results(func, [x_data, y_data], 'test_add')
+
+
+def test_bias_add():
+for dtype in ['float16', 'float32']:
+xshape = (10, 2, 3, 4)
+bshape = (2,)
+rtol = 1e-2 if dtype == 'float16' else 1e-5
+x = relay.var("x", shape=xshape, dtype=dtype)
+bias = relay.var("bias", dtype=dtype)
+z = relay.nn.bias_add(x, bias)
+func = relay.Function([x, bias], z)
+
+x_data = np.random.uniform(size=xshape).astype(dtype)
+y_data = np.random.uniform(size=bshape).astype(dtype)
+
+

[GitHub] [incubator-tvm] tqchen commented on pull request #5645: [TARGET] add amd_gpu target

2020-05-22 Thread GitBox


tqchen commented on pull request #5645:
URL: https://github.com/apache/incubator-tvm/pull/5645#issuecomment-632731324


   See the example case above(the link to mali log) which uses the opencl, but 
have a separate log by itself



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[GitHub] [incubator-tvm] tqchen opened a new pull request #5649: [AutoTVM] Update XGBoost verbosity option

2020-05-22 Thread GitBox


tqchen opened a new pull request #5649:
URL: https://github.com/apache/incubator-tvm/pull/5649


   cc @liangfu @hcho3 @merrymercy 



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[GitHub] [incubator-tvm] maheshambule commented on a change in pull request #5052: [TARGET] ONNX codegen

2020-05-22 Thread GitBox


maheshambule commented on a change in pull request #5052:
URL: https://github.com/apache/incubator-tvm/pull/5052#discussion_r429260135



##
File path: tests/python/contrib/test_onnx.py
##
@@ -0,0 +1,406 @@
+# 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.
+
+"""Relay to ONNX serialization test cases"""
+import pytest
+pytest.importorskip('onnx')
+pytest.importorskip('onnxruntime')
+
+import numpy as np
+import tvm
+from tvm import relay
+from tvm.contrib.target.onnx import to_onnx
+import onnxruntime as rt
+
+
+def func_to_onnx(func, name):
+mod = tvm.IRModule()
+mod['main'] = func
+onnx_model = to_onnx(mod, {}, name, path=None)
+return onnx_model.SerializeToString()
+
+
+def run_onnx(onnx_model, input_data):
+sess = rt.InferenceSession(onnx_model)
+input_names = {}
+for input, data in zip(sess.get_inputs(), input_data):
+input_names[input.name] = data
+output_name = sess.get_outputs()[0].name
+res = sess.run([output_name], input_names)
+return res[0]
+
+
+def run_relay(func, data_tuple):
+target = 'llvm'
+ctx = tvm.context('llvm', 0)
+intrp = relay.create_executor("graph", ctx=ctx, target=target)
+relay_res = intrp.evaluate(func)(*data_tuple)
+return relay_res.asnumpy()
+
+
+def verify_results(relay_func, indata, test_name, rtol=1e-7, atol=0):
+relay_res = run_relay(relay_func, indata)
+onnx_res = run_onnx(func_to_onnx(relay_func, test_name), indata)
+np.testing.assert_allclose(relay_res, onnx_res, rtol=rtol, atol=atol)
+
+
+def test_add():
+dtype = 'float32'
+t1 = relay.TensorType((5, 10, 5))
+t2 = relay.TensorType((5, 10, 5))
+x = relay.var("x", t1, dtype=dtype)
+y = relay.var("y", t2, dtype=dtype)
+z = relay.add(x, y)
+func = relay.Function([x, y], z)
+
+x_data = np.random.rand(5, 10, 5).astype(dtype)
+y_data = np.random.rand(5, 10, 5).astype(dtype)
+
+verify_results(func, [x_data, y_data], 'test_add')
+
+
+def test_bias_add():
+for dtype in ['float16', 'float32']:
+xshape = (10, 2, 3, 4)
+bshape = (2,)
+rtol = 1e-2 if dtype == 'float16' else 1e-5
+x = relay.var("x", shape=xshape, dtype=dtype)
+bias = relay.var("bias", dtype=dtype)
+z = relay.nn.bias_add(x, bias)
+func = relay.Function([x, bias], z)
+
+x_data = np.random.uniform(size=xshape).astype(dtype)
+y_data = np.random.uniform(size=bshape).astype(dtype)
+
+verify_results(func, [x_data, y_data], 'test_bias_add', rtol=rtol)
+
+
+def test_conv2d():
+def verify_conv2d(dtype, scale, dshape, kshape,
+  padding=(1, 1),
+  groups=1,
+  dilation=(1, 1),
+  **attrs):
+x = relay.var("x", shape=dshape, dtype=dtype)
+w = relay.var("w", shape=kshape, dtype=dtype)
+y = relay.nn.conv2d(x, w,
+padding=padding,
+dilation=dilation,
+groups=groups,
+**attrs)
+func = relay.Function([x, w], y)
+data = np.random.uniform(-scale, scale, size=dshape).astype(dtype)
+kernel = np.random.uniform(-scale, scale, size=kshape).astype(dtype)
+verify_results(func, [data, kernel], 'test_conv2d', rtol=1e-5, 
atol=1e-5)
+
+dshape = (1, 32, 18, 18)
+kshape = (32, 1, 3, 3)
+verify_conv2d("float32", 1, dshape, kshape,
+  padding=(1, 1), channels=32, groups=32, kernel_size=(3, 3))
+
+dshape = (1, 32, 18, 18)
+kshape = (32, 4, 3, 3)
+verify_conv2d("float32", 1, dshape, kshape,
+  padding=(1, 1), channels=32, groups=8, kernel_size=(3, 3))
+
+# also group conv2d
+dshape = (1, 32, 18, 18)
+kshape = (64, 1, 3, 3)
+verify_conv2d("float32", 1, dshape, kshape,
+  padding=(1, 1), channels=64, groups=32, kernel_size=(3, 3))
+
+# normal conv2d
+dshape = (1, 3, 224, 224)
+kshape = (10, 3, 3, 3)
+verify_conv2d("float32", 1, dshape, kshape,
+  padding=(1, 1), channels=10, kernel_size=(3, 3))
+
+dshape = (1, 3, 224, 224)
+kshape = (10, 3, 3, 3)
+verify_conv2d("float32", 

[GitHub] [incubator-tvm] maheshambule commented on a change in pull request #5052: [TARGET] ONNX codegen

2020-05-22 Thread GitBox


maheshambule commented on a change in pull request #5052:
URL: https://github.com/apache/incubator-tvm/pull/5052#discussion_r429260948



##
File path: python/tvm/contrib/codegen_onnx.py
##
@@ -0,0 +1,752 @@
+# 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
+#

Review comment:
   done





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[GitHub] [incubator-tvm] tqchen edited a comment on issue #5650: [REFACTOR] PassContext.fallback_device -> PassConfig.config

2020-05-22 Thread GitBox


tqchen edited a comment on issue #5650:
URL: https://github.com/apache/incubator-tvm/issues/5650#issuecomment-632740723


   cc @zhiics @comaniac 



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[GitHub] [incubator-tvm] areusch commented on pull request #5648: µtvm debug improvements

2020-05-22 Thread GitBox


areusch commented on pull request #5648:
URL: https://github.com/apache/incubator-tvm/pull/5648#issuecomment-632754557


   @tqchen @tmoreau89 @liangfu @weberlo @u99127 @tom-gall



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[GitHub] [incubator-tvm] srkreddy1238 commented on a change in pull request #5052: [TARGET] ONNX codegen

2020-05-22 Thread GitBox


srkreddy1238 commented on a change in pull request #5052:
URL: https://github.com/apache/incubator-tvm/pull/5052#discussion_r429299616



##
File path: tests/python/contrib/test_onnx.py
##
@@ -0,0 +1,393 @@
+# 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.
+
+"""Relay to ONNX serialization test cases"""
+import pytest
+pytest.importorskip('onnx')
+pytest.importorskip('onnxruntime')
+
+import numpy as np
+import tvm
+from tvm import relay
+from tvm.contrib.target.onnx import to_onnx
+import onnxruntime as rt
+
+
+def func_to_onnx(func, name):
+mod = tvm.IRModule()
+mod['main'] = func
+onnx_model = to_onnx(mod, {}, name, path=None)
+return onnx_model.SerializeToString()
+
+
+def run_onnx(onnx_model, input_data):
+sess = rt.InferenceSession(onnx_model)
+input_names = {}
+for input, data in zip(sess.get_inputs(), input_data):
+input_names[input.name] = data
+output_name = sess.get_outputs()[0].name
+res = sess.run([output_name], input_names)
+return res[0]
+
+
+def run_relay(func, data_tuple):
+target = 'llvm'
+ctx = tvm.context('llvm', 0)
+intrp = relay.create_executor("graph", ctx=ctx, target=target)
+relay_res = intrp.evaluate(func)(*data_tuple)
+return relay_res.asnumpy()
+
+
+def verify_results(relay_func, indata, test_name, rtol=1e-7, atol=0):
+relay_res = run_relay(relay_func, indata)
+onnx_res = run_onnx(func_to_onnx(relay_func, test_name), indata)
+np.testing.assert_allclose(relay_res, onnx_res, rtol=rtol, atol=atol)
+
+
+def test_add():
+dtype = 'float32'
+t1 = relay.TensorType((5, 10, 5))
+t2 = relay.TensorType((5, 10, 5))
+x = relay.var("x", t1, dtype=dtype)
+y = relay.var("y", t2, dtype=dtype)
+z = relay.add(x, y)
+func = relay.Function([x, y], z)
+
+x_data = np.random.rand(5, 10, 5).astype(dtype)
+y_data = np.random.rand(5, 10, 5).astype(dtype)
+
+verify_results(func, [x_data, y_data], 'test_add')
+
+
+def test_bias_add():
+for dtype in ['float16', 'float32']:
+xshape = (10, 2, 3, 4)
+bshape = (2,)
+rtol = 1e-2 if dtype == 'float16' else 1e-5
+x = relay.var("x", shape=xshape, dtype=dtype)
+bias = relay.var("bias", dtype=dtype)
+z = relay.nn.bias_add(x, bias)
+func = relay.Function([x, bias], z)
+
+x_data = np.random.uniform(size=xshape).astype(dtype)
+y_data = np.random.uniform(size=bshape).astype(dtype)
+
+verify_results(func, [x_data, y_data], 'test_bias_add', rtol=rtol)
+
+
+def test_conv2d():
+def verify_conv2d(dtype, scale, dshape, kshape,
+  padding=(1, 1),
+  groups=1,
+  dilation=(1, 1),
+  **attrs):
+x = relay.var("x", shape=dshape, dtype=dtype)
+w = relay.var("w", shape=kshape, dtype=dtype)
+y = relay.nn.conv2d(x, w,
+padding=padding,
+dilation=dilation,
+groups=groups,
+**attrs)
+func = relay.Function([x, w], y)
+data = np.random.uniform(-scale, scale, size=dshape).astype(dtype)
+kernel = np.random.uniform(-scale, scale, size=kshape).astype(dtype)
+verify_results(func, [data, kernel], 'test_conv2d', rtol=1e-5, 
atol=1e-5)
+
+dshape = (1, 32, 18, 18)
+kshape = (32, 1, 3, 3)
+verify_conv2d("float32", 1, dshape, kshape,
+  padding=(1, 1), channels=32, groups=32, kernel_size=(3, 3))
+
+dshape = (1, 32, 18, 18)
+kshape = (32, 4, 3, 3)
+verify_conv2d("float32", 1, dshape, kshape,
+  padding=(1, 1), channels=32, groups=8, kernel_size=(3, 3))
+
+# also group conv2d
+dshape = (1, 32, 18, 18)
+kshape = (64, 1, 3, 3)
+verify_conv2d("float32", 1, dshape, kshape,
+  padding=(1, 1), channels=64, groups=32, kernel_size=(3, 3))
+
+# normal conv2d
+dshape = (1, 3, 224, 224)
+kshape = (10, 3, 3, 3)
+verify_conv2d("float32", 1, dshape, kshape,
+  padding=(1, 1), channels=10, kernel_size=(3, 3))
+
+dshape = (1, 3, 224, 224)
+kshape = (10, 3, 3, 3)
+verify_conv2d("float32", 

[GitHub] [incubator-tvm] maheshambule commented on a change in pull request #5052: [TARGET] ONNX codegen

2020-05-22 Thread GitBox


maheshambule commented on a change in pull request #5052:
URL: https://github.com/apache/incubator-tvm/pull/5052#discussion_r429260012



##
File path: src/runtime/contrib/onnx/onnx_module.cc
##
@@ -0,0 +1,65 @@
+/*
+ * 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 onnx_module.cc
+ * \brief ONNX Module without runtime support
+ */
+#include 
+#include 
+#include 
+
+namespace tvm {
+namespace codegen {
+using namespace tvm::runtime;
+
+class ONNXSourceModuleNode : public runtime::ModuleNode {
+ public:
+  explicit ONNXSourceModuleNode(String code) : code_(code) {}
+
+  const char* type_key() const { return "onnx"; }
+
+  PackedFunc GetFunction(const std::string& name, const ObjectPtr& 
sptr_to_self) final {
+LOG(FATAL) << "ONNX Source module cannot execute, to get executable module"
+   << " build TVM with 'onnx' runtime support";
+return PackedFunc();
+  }
+
+  std::string GetSource(const std::string& format) final { return code_; }

Review comment:
   The ```code_``` contains hex string of subgraph's ModelProto protobuf 
which can be converted back to ModelProto.  





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[GitHub] [incubator-tvm] tqchen commented on issue #5650: [REFACTOR] PassContext.fallback_device -> PassConfig.config

2020-05-22 Thread GitBox


tqchen commented on issue #5650:
URL: https://github.com/apache/incubator-tvm/issues/5650#issuecomment-632740723


   cc @zhiics 



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[GitHub] [incubator-tvm] tqchen opened a new issue #5650: [REFACTOR] PassContext.fallback_device -> PassConfig.config

2020-05-22 Thread GitBox


tqchen opened a new issue #5650:
URL: https://github.com/apache/incubator-tvm/issues/5650


   https://github.com/apache/incubator-tvm/pull/5631 Introduces a generic 
config mechanism to store the pass configuration options. Given that 
fallback_device is a pas related config, We should probably also migrate 
PassContext->fallback_device to a special config. e.g.` 
relay.fallback_device_type` with IntImmNode as its node type



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[GitHub] [incubator-tvm] tqchen commented on issue #5373: [REFACTOR][TIR] Migrate HoistIfThenElse to the unified pass manager

2020-05-22 Thread GitBox


tqchen commented on issue #5373:
URL: https://github.com/apache/incubator-tvm/issues/5373#issuecomment-632750012


   ping @kevinthesun 



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[GitHub] [incubator-tvm] maheshambule commented on a change in pull request #5052: [TARGET] ONNX codegen

2020-05-22 Thread GitBox


maheshambule commented on a change in pull request #5052:
URL: https://github.com/apache/incubator-tvm/pull/5052#discussion_r429256951



##
File path: python/tvm/contrib/target/onnx.py
##
@@ -0,0 +1,752 @@
+# 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=invalid-name, import-self, len-as-condition, 
unused-argument, too-many-lines, redefined-builtin
+"""Relay to ONNX codegen """
+
+import os
+import struct
+import numpy
+import onnx
+import onnx.utils
+from onnx import numpy_helper, OperatorSetIdProto, defs
+import tvm
+import tvm._ffi
+from tvm.autotvm.graph_tuner.utils.traverse_graph import _expr2graph_impl
+from tvm.relay.expr import Call, TupleGetItem, Var, Constant, Tuple
+
+ONNX_OPSET_VERSONS_SUPPORTED = [11]
+
+
+def tvm_array_to_list(arr):
+return tuple(x.value for x in arr)
+
+
+def get_onnx_version():
+return onnx.__version__
+
+
+def add_input(data, name, model_container):
+dtype = onnx.mapping.NP_TYPE_TO_TENSOR_TYPE[data.dtype]
+tensor_value_info = onnx.helper.make_tensor_value_info(name, dtype, 
shape=data.shape)
+model_container.add_inputs([tensor_value_info])
+data_tensor = numpy_helper.from_array(data, name)
+model_container.add_initializers([data_tensor])
+
+
+class OpConverter(object):
+""" Operator converter Base Class.
+"""
+
+@classmethod
+def convert_attributes(cls, attrs):
+"""convert Relay attributes to ONNX attributes.
+   The derived classes should implement this method
+   if attributes are required by the operator
+   otherwise by default no attributes are passed
+"""
+return {}
+
+@classmethod
+def convert(cls, node, model_container, node_list):
+attrs = cls.convert_attributes(node['node'].attrs)
+node = onnx.helper.make_node(cls.__name__,
+ node['input_names'],
+ node['output_names'],
+ **attrs)
+model_container.add_nodes([node])
+
+
+def rename(op_name):
+""" This method creates dynamic operator of name op_name with empty 
attributes
+"""
+return type(op_name, (OpConverter,), {})
+
+
+class Reshape(object):
+""" Operator converter for Reshape.
+"""
+
+@classmethod
+def convert(cls, node, model_container, node_list):
+"""Converts Relay operator Reshape to ONNX operator.
+   Relay operator accepts shape as attribute but ONNX operator
+   accepts it as a input.
+"""
+
+shape = numpy.asarray([a.value for a in node['node'].attrs.newshape],
+  dtype=numpy.int64)
+input_name = 'shape{}'.format(node['output_names'][0])
+node = onnx.helper.make_node(cls.__name__, [node['input_names'][0], 
input_name],
+ node['output_names'])
+model_container.add_nodes([node])
+add_input(shape, input_name, model_container)
+
+
+class Conv(OpConverter):
+""" Operator converter for Conv.
+"""
+
+@classmethod
+def convert_attributes(cls, attrs):
+return {
+'group': attrs.get_int("groups"),
+'pads': attrs.get_int_tuple("padding"),
+'strides': attrs.get_int_tuple("strides"),
+'dilations': attrs.get_int_tuple("dilation"),
+'kernel_shape': attrs.get_int_tuple("kernel_size"),
+}
+
+
+class MaxPool(OpConverter):
+""" Operator converter for MaxPool.
+"""
+
+@classmethod
+def convert_attributes(cls, attrs):
+return {
+'pads': attrs.get_int_tuple("padding"),
+'strides': attrs.get_int_tuple("strides"),
+'kernel_shape': attrs.get_int_tuple("pool_size"),
+}
+
+
+class Transpose(OpConverter):
+""" Operator converter for Transpose.
+"""
+
+@classmethod
+def convert_attributes(cls, attrs):
+return {'perm': attrs.get_int_tuple("axes")} if attrs["axes"] else {}
+
+
+class MatMul(OpConverter):
+""" Operator converter for MatMul.
+"""
+
+@classmethod
+def convert(cls, node, model_container, node_list):
+output_name = 

[GitHub] [incubator-tvm] maheshambule commented on a change in pull request #5052: [TARGET] ONNX codegen

2020-05-22 Thread GitBox


maheshambule commented on a change in pull request #5052:
URL: https://github.com/apache/incubator-tvm/pull/5052#discussion_r429256748



##
File path: python/tvm/contrib/target/onnx.py
##
@@ -0,0 +1,752 @@
+# 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=invalid-name, import-self, len-as-condition, 
unused-argument, too-many-lines, redefined-builtin
+"""Relay to ONNX codegen """
+
+import os
+import struct
+import numpy
+import onnx
+import onnx.utils
+from onnx import numpy_helper, OperatorSetIdProto, defs
+import tvm
+import tvm._ffi
+from tvm.autotvm.graph_tuner.utils.traverse_graph import _expr2graph_impl
+from tvm.relay.expr import Call, TupleGetItem, Var, Constant, Tuple
+
+ONNX_OPSET_VERSONS_SUPPORTED = [11]
+
+
+def tvm_array_to_list(arr):
+return tuple(x.value for x in arr)
+
+
+def get_onnx_version():
+return onnx.__version__
+
+
+def add_input(data, name, model_container):
+dtype = onnx.mapping.NP_TYPE_TO_TENSOR_TYPE[data.dtype]
+tensor_value_info = onnx.helper.make_tensor_value_info(name, dtype, 
shape=data.shape)
+model_container.add_inputs([tensor_value_info])
+data_tensor = numpy_helper.from_array(data, name)
+model_container.add_initializers([data_tensor])
+
+
+class OpConverter(object):
+""" Operator converter Base Class.
+"""
+
+@classmethod
+def convert_attributes(cls, attrs):
+"""convert Relay attributes to ONNX attributes.
+   The derived classes should implement this method
+   if attributes are required by the operator
+   otherwise by default no attributes are passed
+"""
+return {}
+
+@classmethod
+def convert(cls, node, model_container, node_list):
+attrs = cls.convert_attributes(node['node'].attrs)
+node = onnx.helper.make_node(cls.__name__,
+ node['input_names'],
+ node['output_names'],
+ **attrs)
+model_container.add_nodes([node])
+
+
+def rename(op_name):
+""" This method creates dynamic operator of name op_name with empty 
attributes
+"""
+return type(op_name, (OpConverter,), {})
+
+
+class Reshape(object):
+""" Operator converter for Reshape.
+"""
+
+@classmethod
+def convert(cls, node, model_container, node_list):
+"""Converts Relay operator Reshape to ONNX operator.
+   Relay operator accepts shape as attribute but ONNX operator
+   accepts it as a input.
+"""
+
+shape = numpy.asarray([a.value for a in node['node'].attrs.newshape],
+  dtype=numpy.int64)
+input_name = 'shape{}'.format(node['output_names'][0])
+node = onnx.helper.make_node(cls.__name__, [node['input_names'][0], 
input_name],
+ node['output_names'])
+model_container.add_nodes([node])
+add_input(shape, input_name, model_container)
+
+
+class Conv(OpConverter):
+""" Operator converter for Conv.
+"""
+
+@classmethod
+def convert_attributes(cls, attrs):
+return {
+'group': attrs.get_int("groups"),
+'pads': attrs.get_int_tuple("padding"),
+'strides': attrs.get_int_tuple("strides"),
+'dilations': attrs.get_int_tuple("dilation"),
+'kernel_shape': attrs.get_int_tuple("kernel_size"),
+}
+
+
+class MaxPool(OpConverter):
+""" Operator converter for MaxPool.
+"""
+
+@classmethod
+def convert_attributes(cls, attrs):
+return {
+'pads': attrs.get_int_tuple("padding"),
+'strides': attrs.get_int_tuple("strides"),
+'kernel_shape': attrs.get_int_tuple("pool_size"),
+}
+
+
+class Transpose(OpConverter):
+""" Operator converter for Transpose.
+"""
+
+@classmethod
+def convert_attributes(cls, attrs):
+return {'perm': attrs.get_int_tuple("axes")} if attrs["axes"] else {}
+
+
+class MatMul(OpConverter):
+""" Operator converter for MatMul.
+"""
+
+@classmethod
+def convert(cls, node, model_container, node_list):
+output_name = 

[GitHub] [incubator-tvm] zhiics commented on issue #5650: [REFACTOR] PassContext.fallback_device -> PassConfig.config

2020-05-22 Thread GitBox


zhiics commented on issue #5650:
URL: https://github.com/apache/incubator-tvm/issues/5650#issuecomment-632751624


   @tqchen Thanks for reminding. I will take a stab next week. 



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[GitHub] [incubator-tvm] areusch opened a new pull request #5661: add tvm.micro pydoc to sphinx

2020-05-22 Thread GitBox


areusch opened a new pull request #5661:
URL: https://github.com/apache/incubator-tvm/pull/5661


   Just adding API docs for now



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[GitHub] [incubator-tvm] comaniac opened a new issue #5662: [PatternLang] Lift constant nodes to partitioned function arguments

2020-05-22 Thread GitBox


comaniac opened a new issue #5662:
URL: https://github.com/apache/incubator-tvm/issues/5662


   In #5656, we found that `pattern.partition` will not lift the bind constant 
nodes to the partitioned function arguments. This results in argument mismatch 
and could be a potential problem when applying to op fusion.
   
   Here is an illustration example:
   ```python
   import tvm
   from tvm import relay
   from tvm.relay.dataflow_pattern import *
   from tvm.relay.build_module import bind_params_by_name
   import numpy as np
   
   x = relay.var('x', shape=(1, 3, 224, 224))
   w = relay.var('w', shape=(3, 3, 3, 3))
   b = relay.var('b', shape=(3,))
   
   conv2d = relay.op.nn.conv2d(x, w)
   out = relay.op.nn.bias_add(conv2d, b)
   func = relay.Function([x, w, b], out)
   mod = tvm.IRModule.from_expr(func)
   
   mod["main"] = bind_params_by_name(mod["main"],
 {'w': tvm.nd.array(np.ones(shape=(3, 3, 3, 
3)))})
   print('=== Fuse ')
   print(relay.transform.FuseOps()(mod))
   
   conv2d = is_op('nn.conv2d')(wildcard(), wildcard())
   pattern = is_op('nn.bias_add')(conv2d, wildcard())
   print('=== Partition ===')
   print(pattern.partition(mod['main'].body, {'Composite': 'aa'}))
   ```
   
   Output:
   ```
   === Fuse 
   def @main(%x: Tensor[(1, 3, 224, 224), float32], %b: Tensor[(3), float32]) 
-> Tensor[(1, 3, 222, 222), float32] {
 %1 = fn (%p0: Tensor[(1, 3, 224, 224), float32], %p1: Tensor[(3, 3, 3, 3), 
float64], %p2: Tensor[(3), float32], Primitive=1) -> Tensor[(1, 3, 222, 222), 
float32] {
   %0 = nn.conv2d(%p0, %p1, padding=[0, 0, 0, 0]) /* ty=Tensor[(1, 3, 222, 
222), float32] */;
   nn.bias_add(%0, %p2) /* ty=Tensor[(1, 3, 222, 222), float32] */
 };
 %1(%x, meta[relay.Constant][0] /* ty=Tensor[(3, 3, 3, 3), float64] */ /* 
ty=Tensor[(3, 3, 3, 3), float64] */, %b) /* ty=Tensor[(1, 3, 222, 222), 
float32] */
   }
   
   // meta data omitted. you can use show_meta_data=True to include meta data
   === Partition ===
   free_var %x: Tensor[(1, 3, 224, 224), float32]
   free_var %b: Tensor[(3), float32]
   %1 = fn (%FunctionVar_0_0, %FunctionVar_0_1, Composite="aa", 
PartitionedFromPattern="nn.conv2d_nn.bias_add_") {
 %0 = nn.conv2d(%FunctionVar_0_0, meta[relay.Constant][0] /* ty=Tensor[(3, 
3, 3, 3), float64] */ /* ty=Tensor[(3, 3, 3, 3), float64] */, padding=[0, 0, 0, 
0]);
 nn.bias_add(%0, %FunctionVar_0_1)
   };
   %1(%x, %b)
   // meta data omitted. you can use show_meta_data=True to include meta data
   ```
   
   We can see that the function generated by the op fusion keeps the original 
arguments and refers to the constant node in the function call. However, the 
partitioned function directly accesses the constant node from inside of the 
function body. Ideally, the partitioned should be same as the fused function.
   
   cc @mbrookhart @masahi @zhiics 



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[GitHub] [incubator-tvm] tmoreau89 commented on pull request #5659: [TARGET] enable amd_apu device on vulkan target

2020-05-22 Thread GitBox


tmoreau89 commented on pull request #5659:
URL: https://github.com/apache/incubator-tvm/pull/5659#issuecomment-632969425


   Never mind the other PR was already closed.



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[GitHub] [incubator-tvm] tmoreau89 commented on pull request #5659: [TARGET] enable amd_apu device on vulkan target

2020-05-22 Thread GitBox


tmoreau89 commented on pull request #5659:
URL: https://github.com/apache/incubator-tvm/pull/5659#issuecomment-632969349


   Thanks you @mei-ye I'll close your other PR since it's a duplicate of this 
one.
   
   One question: do you also intend on uploading logs into TopHub for the v1000?



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[GitHub] [incubator-tvm] tmoreau89 commented on pull request #5659: [TARGET] enable amd_apu device on vulkan target

2020-05-22 Thread GitBox


tmoreau89 commented on pull request #5659:
URL: https://github.com/apache/incubator-tvm/pull/5659#issuecomment-632969624


   Could you also add in the comments that there are a couple fixes applied to 
the Vulkan runtime? Because this PR could be technically split into two (bug 
fixes to Vulkan runtime for GPUs, and adding the V1000 target)



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[GitHub] [incubator-tvm] tqchen commented on pull request #5651: [RUNTIME] Resolve constexpr issue in debug mode.

2020-05-22 Thread GitBox


tqchen commented on pull request #5651:
URL: https://github.com/apache/incubator-tvm/pull/5651#issuecomment-632776222


   cc @junrushao1994 @ZihengJiang @jroesch 



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[GitHub] [incubator-tvm] tqchen opened a new pull request #5651: [RUNTIME] Resolve constexpr issue in debug mode.

2020-05-22 Thread GitBox


tqchen opened a new pull request #5651:
URL: https://github.com/apache/incubator-tvm/pull/5651


   static constexpr is a bit weird before c++17.
   They are not inlined by default and does not have symbols after compilation.
   It usually isn't a problem when they are inlined(in c++17 they are inlined 
by default).
   But will create compilation error when passed to functions that take 
(const)references.
   This PR fixes the problem so that we can compile on debugmode.
   
   



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[GitHub] [incubator-tvm] junrushao1994 commented on pull request #5651: [RUNTIME] Resolve constexpr issue in debug mode.

2020-05-22 Thread GitBox


junrushao1994 commented on pull request #5651:
URL: https://github.com/apache/incubator-tvm/pull/5651#issuecomment-632786346


   Interesting. I think I met this before but didn’t know the root cause. Thank 
you for the explanation!!



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[GitHub] [incubator-tvm] zhiics commented on pull request #5622: [Relay] Move compiler_begin/end_op to local static objects

2020-05-22 Thread GitBox


zhiics commented on pull request #5622:
URL: https://github.com/apache/incubator-tvm/pull/5622#issuecomment-632791073


   Thanks @hlu1 @tqchen 



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[GitHub] [incubator-tvm] tqchen commented on pull request #5652: [TIR][BUILD] Remove buffer params from pass config.

2020-05-22 Thread GitBox


tqchen commented on pull request #5652:
URL: https://github.com/apache/incubator-tvm/pull/5652#issuecomment-632791420


   cc @ZihengJiang @zhiics 



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[GitHub] [incubator-tvm] tqchen opened a new pull request #5652: [TIR][BUILD] Remove buffer params from pass config.

2020-05-22 Thread GitBox


tqchen opened a new pull request #5652:
URL: https://github.com/apache/incubator-tvm/pull/5652


   Buffer configurations can be passed during construction
   and does not need to be part of the build config.
   
   This is a refactor step to simplify the buildconfig for the PassContext 
migration.
   
   



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[GitHub] [incubator-tvm] ANSHUMAN87 commented on pull request #5625: [Arith] ExtendedEuclidean merge impl to int_operator

2020-05-22 Thread GitBox


ANSHUMAN87 commented on pull request #5625:
URL: https://github.com/apache/incubator-tvm/pull/5625#issuecomment-632827740


   @yzhliu : Gentle ping!



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[GitHub] [incubator-tvm] ANSHUMAN87 commented on pull request #5654: [REFACTOR][IR] Migrate IRModule ObjectRef to not-null

2020-05-22 Thread GitBox


ANSHUMAN87 commented on pull request #5654:
URL: https://github.com/apache/incubator-tvm/pull/5654#issuecomment-632830825


   There are 2 approaches followed here to solve Optional issue in exposed APIs.
   A0: Using Optional way as mentioned in the reference link. (Ref 
src/relay/analysis/feature.cc)
   A1: Making API user provide Empty IRModule. (Ref 
python/tvm/relay/transform/transform.py)
   
   Please let me know, if we should follow only A0 strictly. TIA!



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[GitHub] [incubator-tvm] srkreddy1238 commented on a change in pull request #5617: [TENSORFLOW]StatefulPartitionedCall/PartitionedCall Ops support added

2020-05-22 Thread GitBox


srkreddy1238 commented on a change in pull request #5617:
URL: https://github.com/apache/incubator-tvm/pull/5617#discussion_r429331590



##
File path: python/tvm/relay/frontend/tensorflow.py
##
@@ -2896,15 +2903,29 @@ def _parse_import_prerequisites(self, graph):
 """
 missing_operators = set()
 for node in graph.node:
+try:
+from tensorflow.python.framework import op_def_registry

Review comment:
   Can you confirm is op_def_registry is not part of all TF versions ? pls 
confirn.
   If this is not in 1.x we shouldn't error as the front end is compatible to 
TF 1.x now.

##
File path: tests/python/frontend/tensorflow/test_forward.py
##
@@ -3179,10 +3183,342 @@ def test_forward_isfinite():
 _verify_infiniteness_ops(tf.is_finite, "isfinite")
 
 
+def _test_spop_placeholder_one():
+with tf.Graph().as_default():

Review comment:
   Advice to use appropriate name instead of _one / _two ...etc.
   

##
File path: python/tvm/relay/frontend/tensorflow.py
##
@@ -3155,6 +3176,91 @@ def _convert_control_flow_operator(self, node, inputs, 
attrs, control_flow_node_
 
 return op
 
+def _partition_call_operator(self, inputs, attr):
+"""
+Convert the Relay Partition call ops into Relay Function calls and
+function definitions from Tensorflow graph library attribute to Relay 
global
+functions
+
+Parameters
+--
+node: TensorFlow graph node object.
+A TensorFlow graph node object.
+
+inputs : List[tvm.relay.Expr]
+List of input symbols.
+
+attrs : Dict[tvm.Attrs]
+Dict of operator attributes.
+
+Returns
+---
+op : tvm.relay.Expr
+Converted relay expression.
+"""
+
+try:
+from tensorflow.python.framework import function_def_to_graph
+except ImportError as e:
+raise ImportError(
+"Unable to import tensorflow which is required {}".format(e))
+
+main_graph_proto = self._main_graph_proto
+outer_graph_def = main_graph_proto._graph
+
+node_func_name = attr.get('f').name
+func = next((f for f in outer_graph_def.library.function
+ if f.signature.name == node_func_name), None)
+if func:
+devices = set(node.device for node in func.node_def)
+if len(devices) > 1:
+raise Exception("Found inconsistent Device assignment in the "\
+"Stateful Partitioned SubGraph. Rejecting "\
+"the subgraph ")
+# Convert function definition to graph
+func_input_shapes = func.attr["_input_shapes"].list.shape
+subgraph, _ = function_def_to_graph.\
+function_def_to_graph_def(func, func_input_shapes)
+
+# Computing subgraph's input shape dictionary
+subgraph_shape_dict, input_expr_dict = {}, {}
+for f_arg, input in zip(func.signature.input_arg, inputs):
+input_expr_dict[f_arg.name] = input
+subgraph_shape_dict[f_arg.name] = _infer_shape(input, 
main_graph_proto._mod)
+
+func_name = 'func_{}'.format(func.signature.name)
+try:
+global_func = main_graph_proto._mod[func_name]

Review comment:
   Is this the case where the function is called multiple times with in a 
graph ?

##
File path: python/tvm/relay/frontend/tensorflow.py
##
@@ -2896,15 +2903,29 @@ def _parse_import_prerequisites(self, graph):
 """
 missing_operators = set()
 for node in graph.node:
+try:
+from tensorflow.python.framework import op_def_registry
+except ImportError as e:
+raise ImportError(
+"Unable to import tensorflow which is required 
{}".format(e))
+getOpDef = op_def_registry._registered_ops.get if 
hasattr(op_def_registry,\
+"_registered_ops") else op_def_registry.get
+op_def = getOpDef(node.op)
 if node.op == "Placeholder" or node.op == 'PlaceholderWithDefault':
 pass
 elif node.op == "Const":
 pass
+elif node.op in ["PartitionedCall", "StatefulPartitionedCall"]:
+pass
 else:
 if any([node.op in t for t in [_identity_list, _convert_map,
_convert_map_rnn,
_control_flow_nodes]]):
 pass
+elif op_def is not None and op_def.is_stateful:
+raise Exception("Found a stateful operator in this graph 
{}. "\

Review comment:
   Better to add this to missing op list (with extended info if needed) 
still 

[GitHub] [incubator-tvm] mbrookhart opened a new pull request #5653: Convert PatternGrouper to do pre-order, non-recursive analysis

2020-05-22 Thread GitBox


mbrookhart opened a new pull request #5653:
URL: https://github.com/apache/incubator-tvm/pull/5653


   Fixes https://github.com/apache/incubator-tvm/issues/5647
   
   @comaniac @masahi Thanks!



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[GitHub] [incubator-tvm] u99127 commented on a change in pull request #5648: µtvm debug improvements

2020-05-22 Thread GitBox


u99127 commented on a change in pull request #5648:
URL: https://github.com/apache/incubator-tvm/pull/5648#discussion_r429357376



##
File path: src/runtime/micro/host_driven/utvm_runtime.c
##
@@ -101,7 +101,7 @@ void UTVMMain() {
 
 // We use a dummy function to signal execution is finished for device
 // backends which require breakpoints.
-void __attribute__((noinline)) UTVMDone() { utvm_done = 1; }
+void __attribute__((noinline,noreturn)) UTVMDone() { utvm_done = 1; for (;;) ; 
}

Review comment:
   Would we want this to trap ever in which case a __builtin_trap might be 
a trick to play ? 
   
   





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[GitHub] [incubator-tvm] tom-gall commented on pull request #5648: µtvm debug improvements

2020-05-22 Thread GitBox


tom-gall commented on pull request #5648:
URL: https://github.com/apache/incubator-tvm/pull/5648#issuecomment-632824516


   LGTM



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[incubator-tvm] branch master updated: HG: Commit message of changeset 6281661. (#5622)

2020-05-22 Thread zhic
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zhic pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/incubator-tvm.git


The following commit(s) were added to refs/heads/master by this push:
 new dbb8be7  HG: Commit message of changeset 6281661. (#5622)
dbb8be7 is described below

commit dbb8be79908a1e15ccf2028f634b9c6c170b19d7
Author: hlu1 <14827759+h...@users.noreply.github.com>
AuthorDate: Fri May 22 09:38:30 2020 -0700

HG: Commit message of changeset 6281661. (#5622)

[Relay] Move compiler_begin/end_op to local static objects
---
 src/relay/analysis/util.cc | 12 ++--
 src/relay/transforms/annotate_target.cc| 15 +++
 src/relay/transforms/merge_compiler_regions.cc | 18 +++---
 src/relay/transforms/partition_graph.cc| 17 ++---
 src/relay/transforms/pass_util.h   | 20 
 src/runtime/object.cc  |  1 +
 6 files changed, 47 insertions(+), 36 deletions(-)

diff --git a/src/relay/analysis/util.cc b/src/relay/analysis/util.cc
index 6d246c0..a05bb8f 100644
--- a/src/relay/analysis/util.cc
+++ b/src/relay/analysis/util.cc
@@ -338,13 +338,13 @@ bool IsNDArrayAllGreaterEqual(const runtime::NDArray& 
tensor, T value) {
   return true;
 }
 
-// Cache the operators that are checked recursively to reduce lookup overhead.
-static const auto& expand_dims_op = Op::Get("expand_dims");
-static const auto& reshape_op = Op::Get("reshape");
-static const auto& transpose_op = Op::Get("transpose");
-static const auto& squeeze_op = Op::Get("squeeze");
-
 bool IsAllPositiveConstant(const Expr& expr) {
+  // Cache the operators that are checked recursively to reduce lookup 
overhead.
+  static const auto& expand_dims_op = Op::Get("expand_dims");
+  static const auto& reshape_op = Op::Get("reshape");
+  static const auto& transpose_op = Op::Get("transpose");
+  static const auto& squeeze_op = Op::Get("squeeze");
+
   // peel through a few common transform ops.
   if (const auto* constant = expr.as()) {
 const auto& tensor = constant->data;
diff --git a/src/relay/transforms/annotate_target.cc 
b/src/relay/transforms/annotate_target.cc
index 0d97005..bf2788f 100644
--- a/src/relay/transforms/annotate_target.cc
+++ b/src/relay/transforms/annotate_target.cc
@@ -29,13 +29,12 @@
 #include 
 #include 
 
+#include "pass_util.h"
+
 namespace tvm {
 namespace relay {
 namespace annotate_target {
 
-static const Op& compiler_begin_op = Op::Get("annotation.compiler_begin");
-static const Op& compiler_end_op = Op::Get("annotation.compiler_end");
-
 const PackedFunc* make_begin_op =
 runtime::Registry::Get("relay.op.annotation._make.compiler_begin");
 const PackedFunc* make_end_op = 
runtime::Registry::Get("relay.op.annotation._make.compiler_end");
@@ -66,12 +65,12 @@ class AnnotateTargetRewriter : public ExprRewriter {
   std::string arg_target = "default";
   const CallNode* call = arg.as();
 
-  if (call && call->op == compiler_begin_op) {
+  if (call && call->op == CompilerBeginOp()) {
 // Argument is already compiler begin node meaning that this is not 
the first time
 // running this pass, so we simply remove it and will add a new one 
later.
 CHECK_EQ(call->args.size(), 1U);
 const CallNode* end = call->args[0].as();
-if (end->op == compiler_end_op) {
+if (end->op == CompilerEndOp()) {
   arg_target = end->attrs.as()->compiler;
 }
 compiler_ends.push_back(call->args[0]);
@@ -115,12 +114,12 @@ class AnnotateTargetRewriter : public ExprRewriter {
 auto op_node = pre->op.as();
 
 // This graph has annotations, meaning that this is not the first time 
running this pass.
-if (op_node && pre->op == compiler_begin_op) {
+if (op_node && pre->op == CompilerBeginOp()) {
   // Bypass compiler begin due to lack of target information. It will be 
processed
   // when the following op handling arguments.
   CHECK_EQ(pre->args.size(), 1U);
   return post.as()->args[0];
-} else if (op_node && pre->op == compiler_end_op) {
+} else if (op_node && pre->op == CompilerEndOp()) {
   // Override compiler end with the new target.
   CHECK_EQ(pre->args.size(), 1U);
   auto input_expr = post.as()->args[0];
@@ -131,7 +130,7 @@ class AnnotateTargetRewriter : public ExprRewriter {
 // Peek the first argument. If it is compiler begin then this node had 
annotated by
 // another target before, so we also consider that target as a supported 
target.
 const CallNode* first_arg_call = pre->args[0].as();
-if (first_arg_call && first_arg_call->op == compiler_begin_op) {
+if (first_arg_call && first_arg_call->op == CompilerBeginOp()) {
   std::string arg_target = 
first_arg_call->attrs.as()->compiler;
   if (arg_target != "default") {
 supported_targets.push_back(arg_target);
diff --git 

[GitHub] [incubator-tvm] zhiics merged pull request #5622: [Relay] Move compiler_begin/end_op to local static objects

2020-05-22 Thread GitBox


zhiics merged pull request #5622:
URL: https://github.com/apache/incubator-tvm/pull/5622


   



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[incubator-tvm] branch master updated (dbb8be7 -> b26f988)

2020-05-22 Thread tqchen
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 dbb8be7  HG: Commit message of changeset 6281661. (#5622)
 add b26f988  [AutoTVM] Update XGBoost verbosity option (#5649)

No new revisions were added by this update.

Summary of changes:
 python/tvm/autotvm/tuner/xgboost_cost_model.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)



[GitHub] [incubator-tvm] tqchen merged pull request #5649: [AutoTVM] Update XGBoost verbosity option

2020-05-22 Thread GitBox


tqchen merged pull request #5649:
URL: https://github.com/apache/incubator-tvm/pull/5649


   



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[GitHub] [incubator-tvm] ANSHUMAN87 opened a new pull request #5654: [REFACTOR][IR] Migrate IRModule ObjectRef to not-null

2020-05-22 Thread GitBox


ANSHUMAN87 opened a new pull request #5654:
URL: https://github.com/apache/incubator-tvm/pull/5654


   Refer #5318
   
   @tqchen : Please help review, Thanks!



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[GitHub] [incubator-tvm] mei-ye closed pull request #5645: [TARGET] add amd_gpu target

2020-05-22 Thread GitBox


mei-ye closed pull request #5645:
URL: https://github.com/apache/incubator-tvm/pull/5645


   



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[GitHub] [incubator-tvm] tom-gall opened a new pull request #5655: Add MicroTVM tutorial using the STM32F746 discovery board

2020-05-22 Thread GitBox


tom-gall opened a new pull request #5655:
URL: https://github.com/apache/incubator-tvm/pull/5655


   Add MicroTVM tutorial using the STM32F746 discovery board
   with a sample tflite model
   
   Signed-off-by: Tom Gall 
   
   Thanks for contributing to TVM!   Please refer to guideline 
https://tvm.apache.org/docs/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.
   



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[GitHub] [incubator-tvm] jroesch merged pull request #5651: [RUNTIME] Resolve constexpr issue in debug mode.

2020-05-22 Thread GitBox


jroesch merged pull request #5651:
URL: https://github.com/apache/incubator-tvm/pull/5651


   



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[incubator-tvm] branch master updated: [RUNTIME] Resolve constexpr issue in debug mode. (#5651)

2020-05-22 Thread jroesch
This is an automated email from the ASF dual-hosted git repository.

jroesch pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/incubator-tvm.git


The following commit(s) were added to refs/heads/master by this push:
 new 896557d  [RUNTIME] Resolve constexpr issue in debug mode. (#5651)
896557d is described below

commit 896557d935999391dcc8bda001fb67d79e2f9ef8
Author: Tianqi Chen 
AuthorDate: Fri May 22 13:36:43 2020 -0700

[RUNTIME] Resolve constexpr issue in debug mode. (#5651)

static constexpr is a bit weird before c++17.
They are not inlined by default and does not have symbols after compilation.
It usually isn't a problem when they are inlined(in c++17 they are inlined 
by default).
But will create compilation error when passed to functions that take 
(const)references.
This PR fixes the problem so that we can compile on debugmode.
---
 include/tvm/runtime/container.h | 8 +---
 1 file changed, 5 insertions(+), 3 deletions(-)

diff --git a/include/tvm/runtime/container.h b/include/tvm/runtime/container.h
index a52e997..1569c67 100644
--- a/include/tvm/runtime/container.h
+++ b/include/tvm/runtime/container.h
@@ -491,10 +491,10 @@ class ArrayNode : public Object, public 
InplaceArrayBase {
   int64_t capacity_;
 
   /*! \brief Initial size of ArrayNode */
-  static const constexpr int64_t kInitSize = 4;
+  static constexpr int64_t kInitSize = 4;
 
   /*! \brief Expansion factor of the Array */
-  static const constexpr int64_t kIncFactor = 2;
+  static constexpr int64_t kIncFactor = 2;
 
   // CRTP parent class
   friend InplaceArrayBase;
@@ -929,7 +929,9 @@ class Array : public ObjectRef {
   ArrayNode* CopyOnWrite(int64_t reserve_extra) {
 ArrayNode* p = GetArrayNode();
 if (p == nullptr) {
-  return SwitchContainer(std::max(ArrayNode::kInitSize, reserve_extra));
+  // necessary to get around the constexpr address issue before c++17
+  const int64_t kInitSize = ArrayNode::kInitSize;
+  return SwitchContainer(std::max(kInitSize, reserve_extra));
 }
 if (p->capacity_ >= p->size_ + reserve_extra) {
   return CopyOnWrite();



[GitHub] [incubator-tvm] comaniac opened a new pull request #5656: [BYOC] Pattern Language MergeComposite

2020-05-22 Thread GitBox


comaniac opened a new pull request #5656:
URL: https://github.com/apache/incubator-tvm/pull/5656


   This PR uses the new Relay pattern language to simplify the `MergeComposite` 
pass implementation. Here are some highlights and discussions:
   
   - **After this PR, the patterns for composite functions must be rewritten.**
   - All patterns in the unit tests are rewritten to be DFPattern.
   - All unit tests are passed with the simplified implementation.
   - The `MergeComposite` pass implementation becomes a simple wrapper to call 
`PartitionPattern`.
   - I'll add a test case for a pattern with `optional` after #5653 is merged.
   
   cc @mbrookhart @mbaret @masahi @zhiics 



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[GitHub] [incubator-tvm] areusch commented on a change in pull request #5648: µtvm debug improvements

2020-05-22 Thread GitBox


areusch commented on a change in pull request #5648:
URL: https://github.com/apache/incubator-tvm/pull/5648#discussion_r429446488



##
File path: src/runtime/micro/host_driven/utvm_runtime.c
##
@@ -101,7 +101,7 @@ void UTVMMain() {
 
 // We use a dummy function to signal execution is finished for device
 // backends which require breakpoints.
-void __attribute__((noinline)) UTVMDone() { utvm_done = 1; }
+void __attribute__((noinline,noreturn)) UTVMDone() { utvm_done = 1; for (;;) ; 
}

Review comment:
   I think at the moment, since we aren't installing a predictable ISR 
vector table, we should avoid triggering those. but we can revisit approaches 
like this when we switch to on-device runtime.





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[GitHub] [incubator-tvm] comaniac commented on a change in pull request #5656: [BYOC] Pattern Language MergeComposite

2020-05-22 Thread GitBox


comaniac commented on a change in pull request #5656:
URL: https://github.com/apache/incubator-tvm/pull/5656#discussion_r429484128



##
File path: tests/python/relay/test_pass_merge_composite.py
##
@@ -64,55 +65,53 @@
 
 
 def make_add_sub_mul_pattern():
-"""Create a pattern to match the following graph.
+r"""Create a pattern to match the following graph.
 
 add  sub
  \   /
   \ /
   mul
 """
-x = relay.var('x')
-y = relay.var('y')
-add_node = relay.add(x, y)
-sub_node = relay.subtract(x, y)
-mul_node = relay.multiply(add_node, sub_node)
+x = wildcard()
+y = wildcard()
+add_node = is_op('add')(x, y)
+sub_node = is_op('subtract')(x, y)
+mul_node = is_op('multiply')(add_node, sub_node)

Review comment:
   Wow that's nice to know. I'll change some patterns using this syntax to 
demonstrate the different ways of making patterns.





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[GitHub] [incubator-tvm] comaniac commented on pull request #5656: [BYOC] Pattern Language MergeComposite

2020-05-22 Thread GitBox


comaniac commented on pull request #5656:
URL: https://github.com/apache/incubator-tvm/pull/5656#issuecomment-632938583


   @masahi I found that `test_dnnl_fuse` in `test_pass_partition_graph` is 
actually failed at the DNNL code compilation, but it won't fail the CI because 
we don't have DNNL enabled (we should do it soon).
   
   The failure in that case is not related to this PR but 
`bind_params_by_name`. The DNNL function template such as 
dnnl_fused_conv2d_bias_relu expecting 3 inputs, but you generate the function 
based on the call node argument, which would be just 1 after 
`bind_params_by_name`.



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[GitHub] [incubator-tvm] comaniac edited a comment on pull request #5656: [BYOC] Pattern Language MergeComposite

2020-05-22 Thread GitBox


comaniac edited a comment on pull request #5656:
URL: https://github.com/apache/incubator-tvm/pull/5656#issuecomment-632938583


   @masahi I found that `test_dnnl_fuse` in `test_pass_partition_graph` is 
actually failed at the DNNL code compilation, but it won't fail the CI because 
we don't have DNNL enabled (we should do it soon).
   
   The failure in that case is not related to this PR but 
`bind_params_by_name`. The DNNL function template such as 
`dnnl_fused_conv2d_bias_relu` expecting 3 inputs, but we generate the function 
call based on the call node argument, which would be just 1 after 
`bind_params_by_name`.



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[GitHub] [incubator-tvm] comaniac edited a comment on pull request #5656: [BYOC] Pattern Language MergeComposite

2020-05-22 Thread GitBox


comaniac edited a comment on pull request #5656:
URL: https://github.com/apache/incubator-tvm/pull/5656#issuecomment-632938583


   @masahi I found that `test_dnnl_fuse` in `test_pass_partition_graph` is 
actually failed at the DNNL code compilation, but it won't fail the CI because 
we don't have DNNL enabled (we should do it soon).
   
   The failure in that case is not related to this PR but 
`bind_params_by_name`. The DNNL function template such as 
`dnnl_fused_conv2d_bias_relu` expecting 3 inputs, but you generate the function 
based on the call node argument, which would be just 1 after 
`bind_params_by_name`.



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[GitHub] [incubator-tvm] masahi commented on pull request #5656: [BYOC] Pattern Language MergeComposite

2020-05-22 Thread GitBox


masahi commented on pull request #5656:
URL: https://github.com/apache/incubator-tvm/pull/5656#issuecomment-632940481


   @comaniac I just ran this test. The two fused layer test at 
https://github.com/apache/incubator-tvm/blob/master/tests/python/relay/test_pass_partition_graph.py#L932
 works, but the full mobilenet test 
https://github.com/apache/incubator-tvm/blob/master/tests/python/relay/test_pass_partition_graph.py#L936
 seems to stuck during compilation. Is the the error you are seeing?
   
   I remember this test worked no problem when @zhiics enabled compilation with 
big constants. I don't know when regression was introduced.



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[GitHub] [incubator-tvm] masahi edited a comment on pull request #5656: [BYOC] Pattern Language MergeComposite

2020-05-22 Thread GitBox


masahi edited a comment on pull request #5656:
URL: https://github.com/apache/incubator-tvm/pull/5656#issuecomment-632940481


   @comaniac I just ran this test. The two fused layer test at 
https://github.com/apache/incubator-tvm/blob/master/tests/python/relay/test_pass_partition_graph.py#L932
 works, but the full mobilenet test 
https://github.com/apache/incubator-tvm/blob/master/tests/python/relay/test_pass_partition_graph.py#L936
 seems to stuck during compilation. Is this the error you are seeing?
   
   I remember this test worked no problem when @zhiics enabled compilation with 
big constants. I don't know when regression was introduced.



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[GitHub] [incubator-tvm] comaniac commented on pull request #5656: [BYOC] Pattern Language MergeComposite

2020-05-22 Thread GitBox


comaniac commented on pull request #5656:
URL: https://github.com/apache/incubator-tvm/pull/5656#issuecomment-632944262


   > @comaniac I just ran this test. The two fused layer test at 
https://github.com/apache/incubator-tvm/blob/master/tests/python/relay/test_pass_partition_graph.py#L932
 works, but the full mobilenet test 
https://github.com/apache/incubator-tvm/blob/master/tests/python/relay/test_pass_partition_graph.py#L936
 seems to stuck during compilation. Is this the error you are seeing?
   > 
   > I remember this test worked no problem when @zhiics enabled compilation 
with big constants. I don't know when regression was introduced.
   
   Sorry my bad. The original `MergeComposite` pass will recover bind 
parameters so the composite functions are guaranteed to have the same number of 
inputs as before. However, composite functions generated by `pattern.partition` 
still accesses the metadata for constant nodes. That's why the input number 
doesn't match. I'll try to solve this problem.



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[GitHub] [incubator-tvm] tom-gall commented on pull request #5660: [uTVM] Increase bss section size.

2020-05-22 Thread GitBox


tom-gall commented on pull request #5660:
URL: https://github.com/apache/incubator-tvm/pull/5660#issuecomment-632953040


   Tested on my stm32F746. Back to working. Thanks @areusch 



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[incubator-tvm] branch master updated (896557d -> 3e074c5)

2020-05-22 Thread moreau
This is an automated email from the ASF dual-hosted git repository.

moreau pushed a change to branch master
in repository https://gitbox.apache.org/repos/asf/incubator-tvm.git.


from 896557d  [RUNTIME] Resolve constexpr issue in debug mode. (#5651)
 add 3e074c5  µtvm debug improvements (#5648)

No new revisions were added by this update.

Summary of changes:
 python/tvm/micro/base.py |  3 +-
 python/tvm/micro/device/host.py  |  1 +
 src/runtime/micro/host_driven/utvm_runtime.c |  8 -
 src/runtime/micro/micro_session.cc   | 53 
 src/runtime/micro/micro_session.h|  4 ++-
 5 files changed, 44 insertions(+), 25 deletions(-)



[GitHub] [incubator-tvm] masahi commented on pull request #5657: [Relay,Topi][OP] affine_grid and grid_sample

2020-05-22 Thread GitBox


masahi commented on pull request #5657:
URL: https://github.com/apache/incubator-tvm/pull/5657#issuecomment-632907557


   interesting.



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[GitHub] [incubator-tvm] comaniac closed issue #5647: [PatternLang] pattern.partition doesn't include the last optional op.

2020-05-22 Thread GitBox


comaniac closed issue #5647:
URL: https://github.com/apache/incubator-tvm/issues/5647


   



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[GitHub] [incubator-tvm] comaniac commented on issue #5647: [PatternLang] pattern.partition doesn't include the last optional op.

2020-05-22 Thread GitBox


comaniac commented on issue #5647:
URL: https://github.com/apache/incubator-tvm/issues/5647#issuecomment-632907849


   Thanks @mbrookhart for the quick fix :)



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[incubator-tvm] branch master updated (3e074c5 -> 9fa8341)

2020-05-22 Thread tqchen
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 3e074c5  µtvm debug improvements (#5648)
 add 9fa8341  [REFACTOR][IR] Migrate IRModule ObjectRef to not-null (#5654)

No new revisions were added by this update.

Summary of changes:
 include/tvm/ir/module.h| 12 +---
 python/tvm/relay/transform/transform.py|  3 ++-
 src/relay/analysis/feature.cc  |  7 +--
 src/relay/analysis/match_exhaustion.cc |  7 ++-
 src/relay/backend/compile_engine.h |  2 +-
 src/relay/backend/graph_runtime_codegen.cc |  4 ++--
 src/relay/transforms/gradient.cc   | 14 --
 src/tir/transforms/split_host_device.cc|  2 +-
 8 files changed, 26 insertions(+), 25 deletions(-)



[GitHub] [incubator-tvm] tqchen commented on pull request #5654: [REFACTOR][IR] Migrate IRModule ObjectRef to not-null

2020-05-22 Thread GitBox


tqchen commented on pull request #5654:
URL: https://github.com/apache/incubator-tvm/pull/5654#issuecomment-632916889


   Thanks @ANSHUMAN87 @jroesch !



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[GitHub] [incubator-tvm] tqchen merged pull request #5654: [REFACTOR][IR] Migrate IRModule ObjectRef to not-null

2020-05-22 Thread GitBox


tqchen merged pull request #5654:
URL: https://github.com/apache/incubator-tvm/pull/5654


   



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[GitHub] [incubator-tvm] tom-gall edited a comment on pull request #5655: Add MicroTVM tutorial using the STM32F746 discovery board

2020-05-22 Thread GitBox


tom-gall edited a comment on pull request #5655:
URL: https://github.com/apache/incubator-tvm/pull/5655#issuecomment-632915897


   @areusch @u99127 



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[GitHub] [incubator-tvm] hcho3 opened a new pull request #5658: Upgrade XGBoost to latest

2020-05-22 Thread GitBox


hcho3 opened a new pull request #5658:
URL: https://github.com/apache/incubator-tvm/pull/5658


   Now that #4953 is fixed in latest XGBoost release (1.1.0), upgrade XGBoost.



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[GitHub] [incubator-tvm] mbrookhart commented on a change in pull request #5653: [PatternLang] Convert PatternGrouper to do pre-order, non-recursive analysis

2020-05-22 Thread GitBox


mbrookhart commented on a change in pull request #5653:
URL: https://github.com/apache/incubator-tvm/pull/5653#discussion_r429468918



##
File path: src/relay/ir/dataflow_matcher.cc
##
@@ -432,26 +432,39 @@ class PatternGrouper : protected MixedModeVisitor {
   const std::vector& GroupMatches(const DFPattern& pattern, const Expr& 
pre) {
 groups_ = {Group()};
 gid_assignments_.clear();
-visit_counter_.clear();
 
 pattern_ = pattern;
 pattern_graph_ = CreateIndexedGraph(pattern_);
 auto matcher = DFPatternMatcher(pre);
 matcher_ = 
-this->VisitExpr(pre);
+this->VisitExprs();
 return this->groups_;
   }
 
  protected:
-  using ExprVisitor::VisitExpr_;
-  void VisitLeaf(const Expr& pre) override {
-if (matcher_->Match(pattern_, pre)) {
-  CreateGroup(pre);
-}
-  }
-  void VisitExpr_(const FunctionNode* op) override {
-if (op->attrs->dict.count(attr::kPartitionedFromPattern) == 0) {
-  ExprVisitor::VisitExpr_(op);
+  /* \brief Iteratively traverse the Expression in pre-order to find subgraphs
+   *
+   * If we traverse the graph in post-order, we can run into situtations where 
a small subgraph will
+   * match the pattern. Due to options like AltPattern, a larger subgraph with 
more nodes later in
+   * the graph may also match the pattern. With post-order traversal, we mark 
the smaller subgraph
+   * as matched and fail to catch the larger subgraph. This problem is fixed 
by using pre-order
+   * traversal.
+   */
+  void VisitExprs() {
+std::unordered_set pre_partitioned;
+for (size_t i = matcher_->expr_graph_.topological_order_.size(); i != 0; 
--i) {
+  size_t index = i - 1;

Review comment:
   This is something I was too lazy to debug.
   
   If I iterate over something of size 4 with the constraint `i > 0`, I get 3 
iterations with i = 3, 2, 1
   
   If I use the line you gave me, I get 5 iterations, with i = 3, 2, 1, 0, 
18446744073709551615 as the size_t underflows.
   
   I can't figure out _why_ that line screws up





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[GitHub] [incubator-tvm] mbrookhart commented on a change in pull request #5653: [PatternLang] Convert PatternGrouper to do pre-order, non-recursive analysis

2020-05-22 Thread GitBox


mbrookhart commented on a change in pull request #5653:
URL: https://github.com/apache/incubator-tvm/pull/5653#discussion_r429468918



##
File path: src/relay/ir/dataflow_matcher.cc
##
@@ -432,26 +432,39 @@ class PatternGrouper : protected MixedModeVisitor {
   const std::vector& GroupMatches(const DFPattern& pattern, const Expr& 
pre) {
 groups_ = {Group()};
 gid_assignments_.clear();
-visit_counter_.clear();
 
 pattern_ = pattern;
 pattern_graph_ = CreateIndexedGraph(pattern_);
 auto matcher = DFPatternMatcher(pre);
 matcher_ = 
-this->VisitExpr(pre);
+this->VisitExprs();
 return this->groups_;
   }
 
  protected:
-  using ExprVisitor::VisitExpr_;
-  void VisitLeaf(const Expr& pre) override {
-if (matcher_->Match(pattern_, pre)) {
-  CreateGroup(pre);
-}
-  }
-  void VisitExpr_(const FunctionNode* op) override {
-if (op->attrs->dict.count(attr::kPartitionedFromPattern) == 0) {
-  ExprVisitor::VisitExpr_(op);
+  /* \brief Iteratively traverse the Expression in pre-order to find subgraphs
+   *
+   * If we traverse the graph in post-order, we can run into situtations where 
a small subgraph will
+   * match the pattern. Due to options like AltPattern, a larger subgraph with 
more nodes later in
+   * the graph may also match the pattern. With post-order traversal, we mark 
the smaller subgraph
+   * as matched and fail to catch the larger subgraph. This problem is fixed 
by using pre-order
+   * traversal.
+   */
+  void VisitExprs() {
+std::unordered_set pre_partitioned;
+for (size_t i = matcher_->expr_graph_.topological_order_.size(); i != 0; 
--i) {
+  size_t index = i - 1;

Review comment:
   This is something I was too lazy to debug.
   
   If I iterate over something of size 4 with the constraint `i > 0`, I get 3 
iterations with i = 3, 2, 1
   
   If I use the line you gave me, I get 5 iterations, with i = 3, 2, 1, 0, 
18446744073709551615 as the size_t underflows.





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[GitHub] [incubator-tvm] mei-ye opened a new pull request #5659: enable amd_apu device on vulkan target

2020-05-22 Thread GitBox


mei-ye opened a new pull request #5659:
URL: https://github.com/apache/incubator-tvm/pull/5659


   Thanks for contributing to TVM!   Please refer to guideline 
https://tvm.apache.org/docs/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.
   
   enable amd_apu device on vulkan target.
   @tqchen, @tmoreau89 



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[GitHub] [incubator-tvm] areusch opened a new pull request #5660: Increase bss section size.

2020-05-22 Thread GitBox


areusch opened a new pull request #5660:
URL: https://github.com/apache/incubator-tvm/pull/5660


* Likely broken in PR 5590.
   
   This fix unbreaks `tests/micro/test_runtime_micro_on_arm.py` for me. Let's 
bump the global default, since this is a minimal test case.



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[GitHub] [incubator-tvm] areusch commented on pull request #5660: Increase bss section size.

2020-05-22 Thread GitBox


areusch commented on pull request #5660:
URL: https://github.com/apache/incubator-tvm/pull/5660#issuecomment-632930007


   @tqchen @tmoreau89 @liangfu @weberlo @u99127 @tom-gall



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[GitHub] [incubator-tvm] tqchen commented on pull request #5659: enable amd_apu device on vulkan target

2020-05-22 Thread GitBox


tqchen commented on pull request #5659:
URL: https://github.com/apache/incubator-tvm/pull/5659#issuecomment-632929975


   Thanks @mei-ye , assigning @tmoreau89  BTW, you do not have to reopen 
another PR for the updates, you can just force push to your original branch and 
the PR will be updated.



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[GitHub] [incubator-tvm] mbrookhart commented on a change in pull request #5653: [PatternLang] Convert PatternGrouper to do pre-order, non-recursive analysis

2020-05-22 Thread GitBox


mbrookhart commented on a change in pull request #5653:
URL: https://github.com/apache/incubator-tvm/pull/5653#discussion_r429477690



##
File path: src/relay/ir/dataflow_matcher.cc
##
@@ -432,26 +432,39 @@ class PatternGrouper : protected MixedModeVisitor {
   const std::vector& GroupMatches(const DFPattern& pattern, const Expr& 
pre) {
 groups_ = {Group()};
 gid_assignments_.clear();
-visit_counter_.clear();
 
 pattern_ = pattern;
 pattern_graph_ = CreateIndexedGraph(pattern_);
 auto matcher = DFPatternMatcher(pre);
 matcher_ = 
-this->VisitExpr(pre);
+this->VisitExprs();
 return this->groups_;
   }
 
  protected:
-  using ExprVisitor::VisitExpr_;
-  void VisitLeaf(const Expr& pre) override {
-if (matcher_->Match(pattern_, pre)) {
-  CreateGroup(pre);
-}
-  }
-  void VisitExpr_(const FunctionNode* op) override {
-if (op->attrs->dict.count(attr::kPartitionedFromPattern) == 0) {
-  ExprVisitor::VisitExpr_(op);
+  /* \brief Iteratively traverse the Expression in pre-order to find subgraphs
+   *
+   * If we traverse the graph in post-order, we can run into situtations where 
a small subgraph will
+   * match the pattern. Due to options like AltPattern, a larger subgraph with 
more nodes later in
+   * the graph may also match the pattern. With post-order traversal, we mark 
the smaller subgraph
+   * as matched and fail to catch the larger subgraph. This problem is fixed 
by using pre-order
+   * traversal.
+   */
+  void VisitExprs() {
+std::unordered_set pre_partitioned;
+for (size_t i = matcher_->expr_graph_.topological_order_.size(); i != 0; 
--i) {
+  size_t index = i - 1;

Review comment:
   No worries! Undid it.





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[GitHub] [incubator-tvm] masahi commented on a change in pull request #5653: [PatternLang] Convert PatternGrouper to do pre-order, non-recursive analysis

2020-05-22 Thread GitBox


masahi commented on a change in pull request #5653:
URL: https://github.com/apache/incubator-tvm/pull/5653#discussion_r429478715



##
File path: src/relay/ir/dataflow_matcher.cc
##
@@ -432,26 +432,39 @@ class PatternGrouper : protected MixedModeVisitor {
   const std::vector& GroupMatches(const DFPattern& pattern, const Expr& 
pre) {
 groups_ = {Group()};
 gid_assignments_.clear();
-visit_counter_.clear();
 
 pattern_ = pattern;
 pattern_graph_ = CreateIndexedGraph(pattern_);
 auto matcher = DFPatternMatcher(pre);
 matcher_ = 
-this->VisitExpr(pre);
+this->VisitExprs();
 return this->groups_;
   }
 
  protected:
-  using ExprVisitor::VisitExpr_;
-  void VisitLeaf(const Expr& pre) override {
-if (matcher_->Match(pattern_, pre)) {
-  CreateGroup(pre);
-}
-  }
-  void VisitExpr_(const FunctionNode* op) override {
-if (op->attrs->dict.count(attr::kPartitionedFromPattern) == 0) {
-  ExprVisitor::VisitExpr_(op);
+  /* \brief Iteratively traverse the Expression in pre-order to find subgraphs
+   *
+   * If we traverse the graph in post-order, we can run into situtations where 
a small subgraph will
+   * match the pattern. Due to options like AltPattern, a larger subgraph with 
more nodes later in
+   * the graph may also match the pattern. With post-order traversal, we mark 
the smaller subgraph
+   * as matched and fail to catch the larger subgraph. This problem is fixed 
by using pre-order
+   * traversal.
+   */
+  void VisitExprs() {
+std::unordered_set pre_partitioned;
+for (size_t i = matcher_->expr_graph_.topological_order_.size(); i != 0; 
--i) {
+  size_t index = i - 1;

Review comment:
   Thanks. Grepping the code base with `i - 1` finds many instances of 
this, and your original code seems to be the common solution :)





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[GitHub] [incubator-tvm] mbrookhart commented on a change in pull request #5656: [BYOC] Pattern Language MergeComposite

2020-05-22 Thread GitBox


mbrookhart commented on a change in pull request #5656:
URL: https://github.com/apache/incubator-tvm/pull/5656#discussion_r429476254



##
File path: tests/python/relay/test_pass_merge_composite.py
##
@@ -64,55 +65,53 @@
 
 
 def make_add_sub_mul_pattern():
-"""Create a pattern to match the following graph.
+r"""Create a pattern to match the following graph.
 
 add  sub
  \   /
   \ /
   mul
 """
-x = relay.var('x')
-y = relay.var('y')
-add_node = relay.add(x, y)
-sub_node = relay.subtract(x, y)
-mul_node = relay.multiply(add_node, sub_node)
+x = wildcard()
+y = wildcard()
+add_node = is_op('add')(x, y)
+sub_node = is_op('subtract')(x, y)
+mul_node = is_op('multiply')(add_node, sub_node)

Review comment:
   Just FYI, if you think it would be easier, the DFPattern objects on the 
python side have syntatic sugar for +, -, *, / , |





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[GitHub] [incubator-tvm] masahi commented on a change in pull request #5653: [PatternLang] Convert PatternGrouper to do pre-order, non-recursive analysis

2020-05-22 Thread GitBox


masahi commented on a change in pull request #5653:
URL: https://github.com/apache/incubator-tvm/pull/5653#discussion_r429449194



##
File path: src/relay/ir/dataflow_matcher.cc
##
@@ -432,26 +432,39 @@ class PatternGrouper : protected MixedModeVisitor {
   const std::vector& GroupMatches(const DFPattern& pattern, const Expr& 
pre) {
 groups_ = {Group()};
 gid_assignments_.clear();
-visit_counter_.clear();
 
 pattern_ = pattern;
 pattern_graph_ = CreateIndexedGraph(pattern_);
 auto matcher = DFPatternMatcher(pre);
 matcher_ = 
-this->VisitExpr(pre);
+this->VisitExprs();
 return this->groups_;
   }
 
  protected:
-  using ExprVisitor::VisitExpr_;
-  void VisitLeaf(const Expr& pre) override {
-if (matcher_->Match(pattern_, pre)) {
-  CreateGroup(pre);
-}
-  }
-  void VisitExpr_(const FunctionNode* op) override {
-if (op->attrs->dict.count(attr::kPartitionedFromPattern) == 0) {
-  ExprVisitor::VisitExpr_(op);
+  /* \brief Iteratively traverse the Expression in pre-order to find subgraphs
+   *
+   * If we traverse the graph in post-order, we can run into situtations where 
a small subgraph will
+   * match the pattern. Due to options like AltPattern, a larger subgraph with 
more nodes later in
+   * the graph may also match the pattern. With post-order traversal, we mark 
the smaller subgraph
+   * as matched and fail to catch the larger subgraph. This problem is fixed 
by using pre-order
+   * traversal.
+   */
+  void VisitExprs() {
+std::unordered_set pre_partitioned;
+for (size_t i = matcher_->expr_graph_.topological_order_.size(); i != 0; 
--i) {
+  size_t index = i - 1;

Review comment:
   Having both `i` and `index` seems redundant.





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[GitHub] [incubator-tvm] vinx13 opened a new pull request #5657: [Relay,Topi][OP] affine_grid and grid_sample

2020-05-22 Thread GitBox


vinx13 opened a new pull request #5657:
URL: https://github.com/apache/incubator-tvm/pull/5657


   This PR added the `affine_grid` and `grid_sample` operator introduced in 
[Spatial Transformer Networks](https://arxiv.org/abs/1506.02025).
   
   API reference:
   MXNet: 
[GridGenerator](https://beta.mxnet.io/api/ndarray/_autogen/mxnet.ndarray.GridGenerator.html),
 
[BilinearSampler](https://beta.mxnet.io/api/ndarray/_autogen/mxnet.ndarray.BilinearSampler.html)
   Pytorch: 
[affine_grid](https://pytorch.org/docs/stable/nn.functional.html?highlight=affine_grid#torch.nn.functional.affine_grid),
 
[grid_sample](https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.grid_sample)
   
   
   cc @masahi @icemelon9 



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[GitHub] [incubator-tvm] masahi commented on a change in pull request #5653: [PatternLang] Convert PatternGrouper to do pre-order, non-recursive analysis

2020-05-22 Thread GitBox


masahi commented on a change in pull request #5653:
URL: https://github.com/apache/incubator-tvm/pull/5653#discussion_r429449194



##
File path: src/relay/ir/dataflow_matcher.cc
##
@@ -432,26 +432,39 @@ class PatternGrouper : protected MixedModeVisitor {
   const std::vector& GroupMatches(const DFPattern& pattern, const Expr& 
pre) {
 groups_ = {Group()};
 gid_assignments_.clear();
-visit_counter_.clear();
 
 pattern_ = pattern;
 pattern_graph_ = CreateIndexedGraph(pattern_);
 auto matcher = DFPatternMatcher(pre);
 matcher_ = 
-this->VisitExpr(pre);
+this->VisitExprs();
 return this->groups_;
   }
 
  protected:
-  using ExprVisitor::VisitExpr_;
-  void VisitLeaf(const Expr& pre) override {
-if (matcher_->Match(pattern_, pre)) {
-  CreateGroup(pre);
-}
-  }
-  void VisitExpr_(const FunctionNode* op) override {
-if (op->attrs->dict.count(attr::kPartitionedFromPattern) == 0) {
-  ExprVisitor::VisitExpr_(op);
+  /* \brief Iteratively traverse the Expression in pre-order to find subgraphs
+   *
+   * If we traverse the graph in post-order, we can run into situtations where 
a small subgraph will
+   * match the pattern. Due to options like AltPattern, a larger subgraph with 
more nodes later in
+   * the graph may also match the pattern. With post-order traversal, we mark 
the smaller subgraph
+   * as matched and fail to catch the larger subgraph. This problem is fixed 
by using pre-order
+   * traversal.
+   */
+  void VisitExprs() {
+std::unordered_set pre_partitioned;
+for (size_t i = matcher_->expr_graph_.topological_order_.size(); i != 0; 
--i) {
+  size_t index = i - 1;

Review comment:
   Having both `i` and `index` seems redundant. Why not
   
   ```
   for (size_t i = matcher_->expr_graph_.topological_order_.size() - 1; i >= 0; 
--i)
   ```





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[GitHub] [incubator-tvm] tmoreau89 merged pull request #5648: µtvm debug improvements

2020-05-22 Thread GitBox


tmoreau89 merged pull request #5648:
URL: https://github.com/apache/incubator-tvm/pull/5648


   



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[GitHub] [incubator-tvm] tom-gall commented on pull request #5655: Add MicroTVM tutorial using the STM32F746 discovery board

2020-05-22 Thread GitBox


tom-gall commented on pull request #5655:
URL: https://github.com/apache/incubator-tvm/pull/5655#issuecomment-632915897


   @areusch 



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[GitHub] [incubator-tvm] mbrookhart commented on a change in pull request #5653: [PatternLang] Convert PatternGrouper to do pre-order, non-recursive analysis

2020-05-22 Thread GitBox


mbrookhart commented on a change in pull request #5653:
URL: https://github.com/apache/incubator-tvm/pull/5653#discussion_r429471161



##
File path: src/relay/ir/dataflow_matcher.cc
##
@@ -432,26 +432,39 @@ class PatternGrouper : protected MixedModeVisitor {
   const std::vector& GroupMatches(const DFPattern& pattern, const Expr& 
pre) {
 groups_ = {Group()};
 gid_assignments_.clear();
-visit_counter_.clear();
 
 pattern_ = pattern;
 pattern_graph_ = CreateIndexedGraph(pattern_);
 auto matcher = DFPatternMatcher(pre);
 matcher_ = 
-this->VisitExpr(pre);
+this->VisitExprs();
 return this->groups_;
   }
 
  protected:
-  using ExprVisitor::VisitExpr_;
-  void VisitLeaf(const Expr& pre) override {
-if (matcher_->Match(pattern_, pre)) {
-  CreateGroup(pre);
-}
-  }
-  void VisitExpr_(const FunctionNode* op) override {
-if (op->attrs->dict.count(attr::kPartitionedFromPattern) == 0) {
-  ExprVisitor::VisitExpr_(op);
+  /* \brief Iteratively traverse the Expression in pre-order to find subgraphs
+   *
+   * If we traverse the graph in post-order, we can run into situtations where 
a small subgraph will
+   * match the pattern. Due to options like AltPattern, a larger subgraph with 
more nodes later in
+   * the graph may also match the pattern. With post-order traversal, we mark 
the smaller subgraph
+   * as matched and fail to catch the larger subgraph. This problem is fixed 
by using pre-order
+   * traversal.
+   */
+  void VisitExprs() {
+std::unordered_set pre_partitioned;
+for (size_t i = matcher_->expr_graph_.topological_order_.size(); i != 0; 
--i) {
+  size_t index = i - 1;

Review comment:
   Ah, nvm, I see. Of course i >=0, it's a size_t. I need to use an int.





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[GitHub] [incubator-tvm] masahi commented on a change in pull request #5653: [PatternLang] Convert PatternGrouper to do pre-order, non-recursive analysis

2020-05-22 Thread GitBox


masahi commented on a change in pull request #5653:
URL: https://github.com/apache/incubator-tvm/pull/5653#discussion_r429477179



##
File path: src/relay/ir/dataflow_matcher.cc
##
@@ -432,26 +432,39 @@ class PatternGrouper : protected MixedModeVisitor {
   const std::vector& GroupMatches(const DFPattern& pattern, const Expr& 
pre) {
 groups_ = {Group()};
 gid_assignments_.clear();
-visit_counter_.clear();
 
 pattern_ = pattern;
 pattern_graph_ = CreateIndexedGraph(pattern_);
 auto matcher = DFPatternMatcher(pre);
 matcher_ = 
-this->VisitExpr(pre);
+this->VisitExprs();
 return this->groups_;
   }
 
  protected:
-  using ExprVisitor::VisitExpr_;
-  void VisitLeaf(const Expr& pre) override {
-if (matcher_->Match(pattern_, pre)) {
-  CreateGroup(pre);
-}
-  }
-  void VisitExpr_(const FunctionNode* op) override {
-if (op->attrs->dict.count(attr::kPartitionedFromPattern) == 0) {
-  ExprVisitor::VisitExpr_(op);
+  /* \brief Iteratively traverse the Expression in pre-order to find subgraphs
+   *
+   * If we traverse the graph in post-order, we can run into situtations where 
a small subgraph will
+   * match the pattern. Due to options like AltPattern, a larger subgraph with 
more nodes later in
+   * the graph may also match the pattern. With post-order traversal, we mark 
the smaller subgraph
+   * as matched and fail to catch the larger subgraph. This problem is fixed 
by using pre-order
+   * traversal.
+   */
+  void VisitExprs() {
+std::unordered_set pre_partitioned;
+for (size_t i = matcher_->expr_graph_.topological_order_.size(); i != 0; 
--i) {
+  size_t index = i - 1;

Review comment:
   Ah sorry you are right. What I suggested may get a warning from compiler 
I think (size_t to int conversion). Moreover, 
`matcher_->expr_graph_.topological_order_.size() - 1` would wrap around if 
`matcher_->expr_graph_.topological_order_` is empty (not sure if this is 
possible).  
   
   So I think your original code is safer. Can you undo the last commit? Sorry 
about this.





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[GitHub] [incubator-tvm] masahi commented on a change in pull request #5653: [PatternLang] Convert PatternGrouper to do pre-order, non-recursive analysis

2020-05-22 Thread GitBox


masahi commented on a change in pull request #5653:
URL: https://github.com/apache/incubator-tvm/pull/5653#discussion_r429477179



##
File path: src/relay/ir/dataflow_matcher.cc
##
@@ -432,26 +432,39 @@ class PatternGrouper : protected MixedModeVisitor {
   const std::vector& GroupMatches(const DFPattern& pattern, const Expr& 
pre) {
 groups_ = {Group()};
 gid_assignments_.clear();
-visit_counter_.clear();
 
 pattern_ = pattern;
 pattern_graph_ = CreateIndexedGraph(pattern_);
 auto matcher = DFPatternMatcher(pre);
 matcher_ = 
-this->VisitExpr(pre);
+this->VisitExprs();
 return this->groups_;
   }
 
  protected:
-  using ExprVisitor::VisitExpr_;
-  void VisitLeaf(const Expr& pre) override {
-if (matcher_->Match(pattern_, pre)) {
-  CreateGroup(pre);
-}
-  }
-  void VisitExpr_(const FunctionNode* op) override {
-if (op->attrs->dict.count(attr::kPartitionedFromPattern) == 0) {
-  ExprVisitor::VisitExpr_(op);
+  /* \brief Iteratively traverse the Expression in pre-order to find subgraphs
+   *
+   * If we traverse the graph in post-order, we can run into situtations where 
a small subgraph will
+   * match the pattern. Due to options like AltPattern, a larger subgraph with 
more nodes later in
+   * the graph may also match the pattern. With post-order traversal, we mark 
the smaller subgraph
+   * as matched and fail to catch the larger subgraph. This problem is fixed 
by using pre-order
+   * traversal.
+   */
+  void VisitExprs() {
+std::unordered_set pre_partitioned;
+for (size_t i = matcher_->expr_graph_.topological_order_.size(); i != 0; 
--i) {
+  size_t index = i - 1;

Review comment:
   Ah sorry you are right. What I suggested may get a warning from compiler 
I think (size_t to int conversion). Moreover, 
`matcher_->expr_graph_.topological_order_.size() - 1` would wrap around if 
`matcher_->expr_graph_.topological_order_` is empty.  
   
   So I think your original code is safer. Can you undo the last commit? Sorry 
about this.





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[GitHub] [incubator-tvm] masahi commented on a change in pull request #5653: [PatternLang] Convert PatternGrouper to do pre-order, non-recursive analysis

2020-05-22 Thread GitBox


masahi commented on a change in pull request #5653:
URL: https://github.com/apache/incubator-tvm/pull/5653#discussion_r429478715



##
File path: src/relay/ir/dataflow_matcher.cc
##
@@ -432,26 +432,39 @@ class PatternGrouper : protected MixedModeVisitor {
   const std::vector& GroupMatches(const DFPattern& pattern, const Expr& 
pre) {
 groups_ = {Group()};
 gid_assignments_.clear();
-visit_counter_.clear();
 
 pattern_ = pattern;
 pattern_graph_ = CreateIndexedGraph(pattern_);
 auto matcher = DFPatternMatcher(pre);
 matcher_ = 
-this->VisitExpr(pre);
+this->VisitExprs();
 return this->groups_;
   }
 
  protected:
-  using ExprVisitor::VisitExpr_;
-  void VisitLeaf(const Expr& pre) override {
-if (matcher_->Match(pattern_, pre)) {
-  CreateGroup(pre);
-}
-  }
-  void VisitExpr_(const FunctionNode* op) override {
-if (op->attrs->dict.count(attr::kPartitionedFromPattern) == 0) {
-  ExprVisitor::VisitExpr_(op);
+  /* \brief Iteratively traverse the Expression in pre-order to find subgraphs
+   *
+   * If we traverse the graph in post-order, we can run into situtations where 
a small subgraph will
+   * match the pattern. Due to options like AltPattern, a larger subgraph with 
more nodes later in
+   * the graph may also match the pattern. With post-order traversal, we mark 
the smaller subgraph
+   * as matched and fail to catch the larger subgraph. This problem is fixed 
by using pre-order
+   * traversal.
+   */
+  void VisitExprs() {
+std::unordered_set pre_partitioned;
+for (size_t i = matcher_->expr_graph_.topological_order_.size(); i != 0; 
--i) {
+  size_t index = i - 1;

Review comment:
   Thanks. Grepping the code base with `i - 1` finds many instances of 
this, and your original code seems to be the solution :)





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