[incubator-mxnet] branch master updated (3f7b6ee -> 2d86c70)
This is an automated email from the ASF dual-hosted git repository.
haoj pushed a change to branch master
in repository https://gitbox.apache.org/repos/asf/incubator-mxnet.git.
from 3f7b6ee Improve quantization flow (#15961)
add 2d86c70 Port ops from np branch (#16018)
No new revisions were added by this update.
Summary of changes:
python/mxnet/ndarray/numpy/_op.py | 187 +++-
python/mxnet/ndarray/numpy/random.py | 52 -
python/mxnet/numpy/multiarray.py | 187 +++-
python/mxnet/numpy/random.py | 38 +++-
python/mxnet/numpy_extension/__init__.py | 1 +
python/mxnet/numpy_extension/random.py | 74 +++
python/mxnet/symbol/numpy/_symbol.py | 131 ++-
python/mxnet/symbol/numpy/random.py| 52 -
src/operator/numpy/np_broadcast_reduce_op_index.cc | 61 ++
.../np_broadcast_reduce_op_index.cu} | 19 +-
src/operator/numpy/np_elemwise_broadcast_op.cc | 74 ---
src/operator/numpy/np_elemwise_broadcast_op.cu | 12 --
src/operator/tensor/elemwise_binary_broadcast_op.h | 4 +
.../elemwise_binary_broadcast_op_extended.cc | 2 +
.../tensor/elemwise_binary_scalar_op_extended.cc | 6 +-
tests/python/unittest/test_numpy_op.py | 239 +
16 files changed, 1022 insertions(+), 117 deletions(-)
create mode 100644 python/mxnet/numpy_extension/random.py
create mode 100644 src/operator/numpy/np_broadcast_reduce_op_index.cc
copy src/operator/{contrib/fft.cu => numpy/np_broadcast_reduce_op_index.cu}
(74%)
[GitHub] [incubator-mxnet] haojin2 merged pull request #16018: Port ops from np branch
haojin2 merged pull request #16018: Port ops from np branch URL: https://github.com/apache/incubator-mxnet/pull/16018 This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-mxnet] TaoLv commented on issue #16027: [v1.5.x] FP16 Support for C Predict API (#15245)
TaoLv commented on issue #16027: [v1.5.x] FP16 Support for C Predict API (#15245) URL: https://github.com/apache/incubator-mxnet/pull/16027#issuecomment-526044213 @samskalicky Could you please take a look at the CI failures? This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-mxnet] TaoLv commented on issue #15803: [v1.5.x] Fix _copy_to on MKLDNN backend (#15637)
TaoLv commented on issue #15803: [v1.5.x] Fix _copy_to on MKLDNN backend (#15637) URL: https://github.com/apache/incubator-mxnet/pull/15803#issuecomment-526044021 @shufan @ZhennanQin Could you please rebase to see if it can pass the CI? I notice there are other PRs passed the CI properly last night and got merged into the branch. This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-mxnet] mxnet-label-bot commented on issue #16033: Building MXNet C++ from Source with VS 2017 Error LNK1248
mxnet-label-bot commented on issue #16033: Building MXNet C++ from Source with VS 2017 Error LNK1248 URL: https://github.com/apache/incubator-mxnet/issues/16033#issuecomment-526042110 Hey, this is the MXNet Label Bot. Thank you for submitting the issue! I will try and suggest some labels so that the appropriate MXNet community members can help resolve it. Here are my recommended label(s): Installation, C++, Build This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-mxnet] DerHaddy opened a new issue #16033: Building MXNet C++ from Source with VS 2017 Error LNK1248
DerHaddy opened a new issue #16033: Building MXNet C++ from Source with VS 2017 Error LNK1248 URL: https://github.com/apache/incubator-mxnet/issues/16033 Hey all, I am currently trying to build MXNet C++ API. I have done this (https://mxnet.incubator.apache.org/versions/master/install/windows_setup.html) guide for VS 2017. After 4 hours of building time I got this Error: ``` "C:\testv1mxnet\incubator-mxnet\build1\mxnet.sln" (default target) (1) -> "C:\testv1mxnet\incubator-mxnet\build1\mxnet.vcxproj.metaproj" (default target) (8) -> "C:\testv1mxnet\incubator-mxnet\build1\mxnet.vcxproj" (default target) (15) -> (Link target) -> LINK : fatal error LNK1248: image size (8C9B8000) exceeds maximum allowable size (8000) [C:\testv1mxnet\in cubator-mxnet\build1\mxnet.vcxproj] ``` I just read that there is a maximum build size even from 32 bit even at 64 bit, but I'm not sure. Can someone help me with this issue? Thanks This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-mxnet] wkcn commented on a change in pull request #15921: [WIP] dynamic custom operator support
wkcn commented on a change in pull request #15921: [WIP] dynamic custom
operator support
URL: https://github.com/apache/incubator-mxnet/pull/15921#discussion_r316055342
##
File path: example/lib_ops/mylib.cc
##
@@ -0,0 +1,139 @@
+/*
+ * 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.
+ */
+
+/*!
+ * Copyright (c) 2015 by Contributors
+ * \file mylib.cc
+ * \brief Sample library file
+ */
+
+#include
+#include "lib_api.h"
+
+/*
+ * main matrix multiplication routine
+ */
+void gemm(float* A, float* B, float* C, unsigned n, unsigned k, unsigned m) {
+ unsigned i,j,kk;
+ for (i=0;i attrs,
+ std::vector inputs, std::vector outputs) {
+ //validate inputs
+ for(int i=0; i();
+ float* input2 = inputs[1].getData();
+ float* output = outputs[0].getData();
+ //set tensor shapes
+ unsigned n = inputs[0].shape[0];
+ unsigned k = inputs[0].shape[1];
+ unsigned m = inputs[1].shape[1];
+
+ gemm(input1, input2, output, n, k, m);
+
+ return 1; //no error
+}
+
+int parseAttrs(std::map attrs,
+ int* num_in, int* num_out) {
+ /*
+ if(attrs.find("myParam") == attrs.end()) {
+std::cout << "Missing param 'myParam'" << std::endl;
+return 0;
+ }
+ */
+ *num_in = 2;
+ *num_out = 1;
+
+ return 1; //no error
+}
+
+int inferType(std::map attrs, std::vector
&intypes,
+ std::vector &outtypes) {
+ outtypes[0] = intypes[0];
+
+ return 1; //no error
+}
+
+int inferShape(std::map attrs,
std::vector> &inshapes,
+ std::vector> &outshapes) {
+ //validate inputs
+ if(inshapes.size() != 2) {
+std::cout << "Expected 2 inputs to inferShape" << std::endl;
+return 0;
+ }
+
+ if(inshapes[0].size() != 2) {
+std::cout << "Expected 2D for first input to inferShape" << std::endl;
+return 0;
+ }
+
+ if(inshapes[1].size() != 2) {
+std::cout << "Expected 2D for second input to inferShape" << std::endl;
+return 0;
+ }
+
+ unsigned n = inshapes[0][0];
+ unsigned k = inshapes[0][1];
+ unsigned kk = inshapes[1][0];
+ unsigned m = inshapes[1][1];
+
+ std::cout << "inshapes[0][0]=" << n << " inshapes[0][1]=" << k << std::endl;
+ std::cout << "inshapes[1][0]=" << kk << " inshapes[1][1]=" << m <<
std::endl;
+
+ if(k != kk) return 0;
+
+ outshapes[0].push_back(n);
+ outshapes[0].push_back(m);
+
+ return 1; //no error
+}
+
+REGISTER_OP(sam)
+.setFCompute(myFCompute)
+.setParseAttrs(parseAttrs)
+.setInferType(inferType)
+.setInferShape(inferShape);
+
+int initialize(int version) {
Review comment:
I prefer that the argument is a structure.
e.g.
```c++
struct MXNetIdentity {
int magic_number; // we will update the magic_number if we update the
structure
int version; // `version` is the second element of the structure, and the
offset is 32 bits.
...
};
```
```c++
int initialize(struct MXNetIdentity* id) {
if (id->magic_number == 123) {
int version = id->version;
...
} else {
...
}
}
```
In this way, we can check the version firstly, then check other attributions.
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[GitHub] [incubator-mxnet] wkcn commented on a change in pull request #15921: [WIP] dynamic custom operator support
wkcn commented on a change in pull request #15921: [WIP] dynamic custom
operator support
URL: https://github.com/apache/incubator-mxnet/pull/15921#discussion_r316058905
##
File path: include/mxnet/lib_api.h
##
@@ -25,26 +25,329 @@
#ifndef MXNET_LIB_API_H_
#define MXNET_LIB_API_H_
+#include
+#include
+#include
+#include
+
+/*!
+ * \brief External Tensor data types
+ */
+enum MXDType {
+ kFloat32 = 0,
+ kFloat64 = 1,
+ kFloat16 = 2,
+ kUint8 = 3,
+ kInt32 = 4,
+ kInt8 = 5,
+ kInt64 = 6,
+};
+
+/*!
+ * \brief External Tensor data structure
+ */
+struct MXTensor {
+ MXTensor() { data = nullptr; }
+ MXTensor(void *data, const std::vector &shape, MXDType dtype)
+ : data{data}, shape{shape}, dtype{dtype} {}
+
+ /*!
+ * \brief helper function to cast data pointer
+ */
+ template
+ data_type* getData() {
+return reinterpret_cast(data);
+ }
+
+ void *data; // not owned
+ std::vector shape;
+ MXDType dtype;
+};
+
+/*!
+ * Custom Operator function templates
+ */
+typedef int (*fcomp_t)(std::map,
+ std::vector, std::vector);
+typedef int (*parseAttrs_t)(std::map,
+int*, int*);
+typedef int (*inferType_t)(std::map,
+ std::vector&, std::vector&);
+typedef int (*inferShape_t)(std::map,
+std::vector>&,
+std::vector>&);
+
+/*!
+ * \brief Class to hold custom operator registration
+ */
+class CustomOp {
+ public:
+ explicit CustomOp(const char* op_name) : name(op_name), fcompute(nullptr),
+parse_attrs(nullptr), infer_type(nullptr), infer_shape(nullptr) {}
+ ~CustomOp() {}
+ CustomOp& setFCompute(fcomp_t fcomp) {
+fcompute = fcomp;
+return *this;
+ }
+ CustomOp& setParseAttrs(parseAttrs_t func) {
+parse_attrs = func;
+return *this;
+ }
+ CustomOp& setInferType(inferType_t func) {
+infer_type = func;
+return *this;
+ }
+ CustomOp& setInferShape(inferShape_t func) {
+infer_shape = func;
+return *this;
+ }
+ /*! \brief operator name */
+ const char* name;
+ /*! \brief operator functions */
+ fcomp_t fcompute;
+ parseAttrs_t parse_attrs;
+ inferType_t infer_type;
+ inferShape_t infer_shape;
+};
+
+/*!
+ * \brief Registry class to registers things (ops, properties)
+ * Singleton class
+ */
+template
+class Registry {
+ public:
+ /*!
+ * \brief get singleton pointer to class
+ * \returns pointer to class
+ */
+ static Registry* get() {
+static Registry inst;
+return &inst;
+ }
+ /*!
+ * \brief add a new entry
+ * \returns new object associated with registered name
+ */
+ T& add(const char* name) {
+T *entry = new T(name);
+entries.push_back(entry);
+return *entry;
+ }
+ int size() {
+return entries.size();
+ }
+ T& get(int idx) {
+return *(entries[idx]);
+ }
+
+ private:
+ /*! \brief constructor */
+ Registry() {}
+ /*! \brief destructor */
+ ~Registry() {}
+ /*! \brief map of entries in registry */
+ std::vector entries;
+};
+
+/*
+ * Macros to help with string concat
+ * Annoyingly, the concat_ and concat macros are necessary to
+ * be able to use __COUNTER__ in an identifier name
+ */
+#define _STR_CONCAT_(__a, __b) __a ## __b
+#define _STR_CONCAT(__a, __b) _STR_CONCAT_(__a, __b)
+
+/*!
+ * \brief convert a token to a string
+ */
+#define STRINGIFY(x) #x
+#define TOSTRING(x) STRINGIFY(x)
+
+/*!
+ * \brief declare a variable with custom name
+ */
+#define _REGISTER_NAME_(Name) MXNet ## _CustomOp ## _
+#define _REGISTER_DEF_(Name) CustomOp _REGISTER_NAME_(Name)
+
+/*!
+ * \brief assign a var to a value
+ */
+#define REGISTER_OP(Name) _STR_CONCAT(_REGISTER_DEF_(Name), __COUNTER__) = \
+Registry::get()->add(TOSTRING(Name))
+
+
/*!
* \brief Following are the APIs implemented in the external library
* Each API has a #define string that is used to lookup the function in the
library
* Followed by the function declaration
*/
+
+
+#define MXLIB_OPREGSIZE_STR "_opRegSize"
+typedef int (*opRegSize_t)(void);
+
+#define MXLIB_OPREGGET_STR "_opRegGet"
+typedef int (*opRegGet_t)(int, const char**, fcomp_t*,
+ parseAttrs_t*, inferType_t*,
+ inferShape_t*);
+
+#define MXLIB_OPCALLFREE_STR "_opCallFree"
+typedef int (*opCallFree_t)(void*);
+
+#define MXLIB_OPCALLPARSEATTRS_STR "_opCallParseAttrs"
+typedef int (*opCallParseAttrs_t)(parseAttrs_t, const char* const*, const
char* const*, int,
+ int*, int*);
+
+#define MXLIB_OPCALLINFERSHAPE_STR "_opCallInferShape"
+typedef int (*opCallInferShape_t)(inferShape_t, const char* const*, const
char* const*, int,
+ unsigned int**, int*, int,
+ unsigned int***, int**, int);
+
+#define MXLIB_OPCALLFCOMP_STR "_opCallFCompute"
+typedef int (*opCallFComp_t)(fcomp_t, const char* const*, const char* const*,
int,
+ const
[GitHub] [incubator-mxnet] wkcn commented on a change in pull request #15921: [WIP] dynamic custom operator support
wkcn commented on a change in pull request #15921: [WIP] dynamic custom
operator support
URL: https://github.com/apache/incubator-mxnet/pull/15921#discussion_r316052812
##
File path: example/lib_ops/mylib.cc
##
@@ -0,0 +1,139 @@
+/*
+ * 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.
+ */
+
+/*!
+ * Copyright (c) 2015 by Contributors
+ * \file mylib.cc
+ * \brief Sample library file
+ */
+
+#include
+#include "lib_api.h"
+
+/*
+ * main matrix multiplication routine
+ */
+void gemm(float* A, float* B, float* C, unsigned n, unsigned k, unsigned m) {
+ unsigned i,j,kk;
+ for (i=0;i attrs,
+ std::vector inputs, std::vector outputs) {
+ //validate inputs
+ for(int i=0; i();
+ float* input2 = inputs[1].getData();
+ float* output = outputs[0].getData();
+ //set tensor shapes
+ unsigned n = inputs[0].shape[0];
+ unsigned k = inputs[0].shape[1];
+ unsigned m = inputs[1].shape[1];
+
+ gemm(input1, input2, output, n, k, m);
+
+ return 1; //no error
Review comment:
Thank @samskalicky ! It may be better to use an Enumeration type as the
return value.
e.g.
```c++
enum CustomOpState {
SUCCESS,
FAIL
};
```
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[GitHub] [incubator-mxnet] pengzhao-intel commented on issue #16017: Add RROIAlign
pengzhao-intel commented on issue #16017: Add RROIAlign URL: https://github.com/apache/incubator-mxnet/pull/16017#issuecomment-526039495 @ciyongch could you take a look as well? This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-mxnet] gyshi commented on a change in pull request #15973: Numpy . implement numpy op exp2 with tvm
gyshi commented on a change in pull request #15973: Numpy . implement numpy op exp2 with tvm URL: https://github.com/apache/incubator-mxnet/pull/15973#discussion_r318889459 ## File path: contrib/tvmop/basic/ufunc.py ## @@ -98,3 +99,71 @@ def backward_vadd_gpu(dtype, ndim, reduce1st, req): s[t].bind(bx, block_x) s[t].bind(tx, thread_x) return s, [X, in_grad_a, in_grad] + +def compute_exp2(dtype, ndim): Review comment: this day i am optimizing tvm op exp2, so i have no time to update this pr, today i will updata it. in my test, when input shape is vary large, the speed of tvm op exp2 is faster than mxnet exp2 This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-mxnet] ElaineBao commented on a change in pull request #16017: Add RROIAlign
ElaineBao commented on a change in pull request #16017: Add RROIAlign
URL: https://github.com/apache/incubator-mxnet/pull/16017#discussion_r318891412
##
File path: src/operator/contrib/rroi_align.cc
##
@@ -0,0 +1,316 @@
+/*
+ * 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.
+ */
+
+/*!
+ * Copyright (c) 2015 by Contributors
+ * \file rroi_align.cc
+ * \brief rroi align operator
+ * \author Yixin Bao
+ * Forward pass adapted from Caffe2
+ * link:
https://github.com/pytorch/pytorch/blob/master/caffe2/operators/roi_align_rotated_op.cc
+ */
+#include "./rroi_align-inl.h"
+#include
+#include "math.h"
+
+using std::max;
+using std::min;
+using std::floor;
+using std::ceil;
+
+namespace mxnet {
+namespace op {
+
+template
+struct position_for_bilinear_interpolate {
+ // 4 positions and corresponding weights for
+ // computing bilinear interpolation
+ int pos1, pos2, pos3, pos4;
+ DType w1, w2, w3, w4;
+};
+
+template
+void pre_calc_for_bilinear_interpolate(
+const int height, const int width, const int pooled_height, const int
pooled_width,
+const int iy_upper, const int ix_upper, DType roi_start_h, DType
roi_start_w,
+DType bin_size_h, DType bin_size_w, int roi_bin_grid_h, int roi_bin_grid_w,
+DType roi_center_h, DType roi_center_w, DType theta,
+std::vector> *pre_calc) {
+ int pre_calc_index = 0;
+ DType cosTheta = cos(theta);
+ DType sinTheta = sin(theta);
+ for (int ph = 0; ph < pooled_height; ph++) {
+for (int pw = 0; pw < pooled_width; pw++) {
+ // calc bin grid position (xx,yy)
+ for (int iy = 0; iy < iy_upper; iy++) {
+const DType yy = roi_start_h + ph * bin_size_h +
+static_cast(iy + .5f) * bin_size_h /
+static_cast(roi_bin_grid_h); // e.g., 0.5, 1.5
+for (int ix = 0; ix < ix_upper; ix++) {
+ const DType xx = roi_start_w + pw * bin_size_w +
+ static_cast(ix + .5f) * bin_size_w /
+ static_cast(roi_bin_grid_w);
+
+ // Rotate by theta around the center and translate
+ DType x = xx * cosTheta + yy * sinTheta + roi_center_w;
+ DType y = yy * cosTheta - xx * sinTheta + roi_center_h;
+
+ // deal with: inverse elements are out of feature map boundary
+ if (y < -1.0 || y > height || x < -1.0 || x > width) {
+// empty
+position_for_bilinear_interpolate pc;
+pc.pos1 = 0;
+pc.pos2 = 0;
+pc.pos3 = 0;
+pc.pos4 = 0;
+pc.w1 = 0;
+pc.w2 = 0;
+pc.w3 = 0;
+pc.w4 = 0;
+pre_calc->at(pre_calc_index) = pc;
+pre_calc_index += 1;
+continue;
+ }
+ if (y <= 0) {
+y = 0;
+ }
+ if (x <= 0) {
+x = 0;
+ }
+
+ // calc 4 points for interpolation
+ int y_low = static_cast(y);
+ int x_low = static_cast(x);
+ int y_high;
+ int x_high;
+ if (y_low >= height - 1) {
+y_high = y_low = height - 1;
+y = (DType)y_low;
+ } else {
+y_high = y_low + 1;
+ }
+ if (x_low >= width - 1) {
+x_high = x_low = width - 1;
+x = (DType)x_low;
+ } else {
+x_high = x_low + 1;
+ }
+ DType ly = y - y_low;
+ DType lx = x - x_low;
+ DType hy = 1. - ly, hx = 1. - lx;
+ DType w1 = hy * hx, w2 = hy * lx, w3 = ly * hx, w4 = ly * lx;
+
+ // Save weights and indices
+ position_for_bilinear_interpolate pc;
Review comment:
thank you for your advices, @wkcn.
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[GitHub] [incubator-mxnet] gyshi commented on a change in pull request #15973: Numpy . implement numpy op exp2 with tvm
gyshi commented on a change in pull request #15973: Numpy . implement numpy op exp2 with tvm URL: https://github.com/apache/incubator-mxnet/pull/15973#discussion_r318889459 ## File path: contrib/tvmop/basic/ufunc.py ## @@ -98,3 +99,71 @@ def backward_vadd_gpu(dtype, ndim, reduce1st, req): s[t].bind(bx, block_x) s[t].bind(tx, thread_x) return s, [X, in_grad_a, in_grad] + +def compute_exp2(dtype, ndim): Review comment: this day i am optimizing tvm op exp2, so i have no time to update this pr, today i will updata it. in my test, when input shape is vary large, the speed of tvm op exp2 is fast than mxnet exp2 This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-mxnet] zixuanweeei commented on issue #15741: MKL-DNN LBR-GRU Inference Integration (FP32 LBR-GRU)
zixuanweeei commented on issue #15741: MKL-DNN LBR-GRU Inference Integration (FP32 LBR-GRU) URL: https://github.com/apache/incubator-mxnet/pull/15741#issuecomment-526026698 @pengzhao-intel Sure. There are lots of refactor work both on MKL-DNN RNN and naive RNN. At present, MKL-DNN related stuff is under review. Perhaps, we can just drop this PR, and start a new one from current commit on master. This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-mxnet] haojin2 commented on a change in pull request #15826: Numpy add numpy op moveaxis
haojin2 commented on a change in pull request #15826: Numpy add numpy op
moveaxis
URL: https://github.com/apache/incubator-mxnet/pull/15826#discussion_r318877886
##
File path: src/operator/numpy/np_matrix_op.cc
##
@@ -345,5 +346,95 @@ Examples::
.add_argument("data", "NDArray-or-Symbol[]", "List of arrays to stack")
.add_arguments(StackParam::__FIELDS__());
+bool NumpyMoveaxisShape(const nnvm::NodeAttrs& attrs,
+mxnet::ShapeVector *in_attrs,
+mxnet::ShapeVector *out_attrs) {
+ const NumpyMoveaxisParam& param =
nnvm::get(attrs.parsed);
+ CHECK_EQ(in_attrs->size(), 1U);
+ CHECK_EQ(out_attrs->size(), 1U);
+ mxnet::TShape& shp = (*in_attrs)[0];
+ CHECK_LE(shp.ndim(), 6) << "Transpose support at most 6 dimensions";
+ CHECK_EQ(param.source.ndim(), param.destination.ndim())
+<< "source and destination not equal.";
+ mxnet::TShape ret(shp.ndim(), -1);
+ mxnet::TShape axes(shp.ndim(), -1);
+ std::vector state_axes(shp.ndim(), false);
+ mxnet::TShape real_src(param.source.ndim(), -1);
+ mxnet::TShape real_des(param.destination.ndim(), -1);
+ for (int i = 0; i < param.source.ndim(); ++i) {
+if (param.source[i] >= 0) {
+ CHECK_LT(static_cast(param.source[i]), shp.ndim());
+ real_src[i] = param.source[i];
+} else {
+ CHECK_LT(param.source[i] + shp.ndim(), shp.ndim());
+ real_src[i] = param.source[i] + shp.ndim();
+}
+if (param.destination[i] >= 0) {
+ CHECK_LT(static_cast(param.destination[i]), shp.ndim());
+ real_des[i] = param.destination[i];
+} else {
+ CHECK_LT(param.destination[i] + shp.ndim(), shp.ndim());
+ real_des[i] = param.destination[i] + shp.ndim();
+}
+ }
+ if (shp.ndim() > 1) {
+for (int i = 0; i < param.source.ndim() - 1; ++i) {
+ for (int j = i + 1; j < param.source.ndim(); ++j) {
+CHECK_NE(real_src[i], real_src[j])
+ << "repeated axis in `source` argument";
+CHECK_NE(real_des[i], real_des[j])
+ << "repeated axis in `destination` argument";
+ }
+}
+ }
+ for (int i = 0; i < param.source.ndim(); ++i) {
+axes[real_des[i]] = real_src[i];
+state_axes[real_src[i]] = true;
+ }
+ for (int i = 0; i < axes.ndim(); ++i) {
+if (axes[i] < 0) {
+ for (int j = 0; j < axes.ndim(); ++j) {
+if (state_axes[j] == false) {
+ axes[i] = j;
+ state_axes[j] = true;
+ break;
+}
+ }
+}
+ }
+ for (int i = 0; i < shp.ndim(); ++i) {
+CHECK(axes[i] < static_cast(shp.ndim()));
+ret[i] = shp[axes[i]];
+ }
+ SHAPE_ASSIGN_CHECK(*out_attrs, 0, ret);
+ return shape_is_known(ret);
+}
+
+NNVM_REGISTER_OP(_np_moveaxis)
+.describe(R"code(Move axes of an array to new positions.
+Other axes remain in their original order.
+)code" ADD_FILELINE)
+.set_num_inputs(1)
+.set_num_outputs(1)
+.set_attr_parser(ParamParser)
+.set_attr("FInferShape", NumpyMoveaxisShape)
+.set_attr("FInferType", ElemwiseType<1, 1>)
+.set_attr("FGradient",
+ [](const nnvm::NodePtr& n, const std::vector& ograds) {
+ const NumpyMoveaxisParam& param =
nnvm::get(n->attrs.parsed);
+std::ostringstream os1;
Review comment:
indentation.
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[GitHub] [incubator-mxnet] haojin2 commented on a change in pull request #15902: Numpy add numpy op roll
haojin2 commented on a change in pull request #15902: Numpy add numpy op roll
URL: https://github.com/apache/incubator-mxnet/pull/15902#discussion_r318875866
##
File path: src/operator/numpy/np_matrix_op.cc
##
@@ -345,5 +346,73 @@ Examples::
.add_argument("data", "NDArray-or-Symbol[]", "List of arrays to stack")
.add_arguments(StackParam::__FIELDS__());
+inline bool NumpyRollShape(const nnvm::NodeAttrs& attrs,
+ mxnet::ShapeVector *in_attrs,
+ mxnet::ShapeVector *out_attrs) {
+ using namespace mshadow;
+ const NumpyRollParam& param = nnvm::get(attrs.parsed);
+
+ if (!param.shift.has_value()) {
+LOG(FATAL) << "roll missing 1 required positional argument: 'shift'.";
+ }
+ if (param.shift.value().ndim() > 1 &&
+ param.axis.has_value() &&
+ param.axis.value().ndim() != param.shift.value().ndim()) {
+LOG(FATAL) << "shift and `axis` must be a tuple of the same size.";
+ }
+ if (!param.axis.has_value() && param.shift.has_value() &&
param.shift.value().ndim() > 1) {
+LOG(FATAL) << "shift must be an int.";
+ }
+ if (param.axis.has_value()) {
+mxnet::TShape axes(param.axis.value());
+const index_t ndim = (*in_attrs)[0].ndim();
+for (index_t i = 0; i < axes.ndim(); i++) {
+ if (axes[i] < 0) {
+axes[i] += ndim;
+ }
+}
+std::sort(axes.begin(), axes.end());
+for (index_t i = 1; i < axes.ndim(); i++) {
+ CHECK_LT(axes[i - 1], axes[i])
+<< "axes have duplicates " << axes;
+}
+CHECK_LT(axes[axes.ndim() - 1], ndim)
+ << "axis " << axes[axes.ndim() - 1]
+ << " Exceeds input dimensions " << (*in_attrs)[0];
+CHECK_GE(axes[0], 0)
+ << "Reduction axis " << param.axis.value()
+ << " Exceeds input dimensions " << (*in_attrs)[0];
+ }
+ return ElemwiseShape<1, 1>(attrs, in_attrs, out_attrs);
+}
+
+NNVM_REGISTER_OP(_np_roll)
+.set_num_inputs(1)
+.set_num_outputs(1)
+.set_attr_parser(ParamParser)
+.set_attr("FListInputNames",
+ [](const NodeAttrs& attrs) {
+ return std::vector{"data"};
+})
+.set_attr("FInferShape", NumpyRollShape)
+.set_attr("FInferType", ElemwiseType<1, 1>)
+.set_attr("FCompute", NumpyRollCompute)
+.set_attr("FGradient",
+ [](const nnvm::NodePtr& n, const std::vector& ograds) {
Review comment:
Indentation.
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[GitHub] [incubator-mxnet] haojin2 commented on a change in pull request #15902: Numpy add numpy op roll
haojin2 commented on a change in pull request #15902: Numpy add numpy op roll
URL: https://github.com/apache/incubator-mxnet/pull/15902#discussion_r318875757
##
File path: src/operator/numpy/np_matrix_op.cc
##
@@ -345,5 +346,73 @@ Examples::
.add_argument("data", "NDArray-or-Symbol[]", "List of arrays to stack")
.add_arguments(StackParam::__FIELDS__());
+inline bool NumpyRollShape(const nnvm::NodeAttrs& attrs,
+ mxnet::ShapeVector *in_attrs,
+ mxnet::ShapeVector *out_attrs) {
+ using namespace mshadow;
+ const NumpyRollParam& param = nnvm::get(attrs.parsed);
+
+ if (!param.shift.has_value()) {
+LOG(FATAL) << "roll missing 1 required positional argument: 'shift'.";
+ }
+ if (param.shift.value().ndim() > 1 &&
+ param.axis.has_value() &&
+ param.axis.value().ndim() != param.shift.value().ndim()) {
+LOG(FATAL) << "shift and `axis` must be a tuple of the same size.";
+ }
+ if (!param.axis.has_value() && param.shift.has_value() &&
param.shift.value().ndim() > 1) {
+LOG(FATAL) << "shift must be an int.";
+ }
+ if (param.axis.has_value()) {
+mxnet::TShape axes(param.axis.value());
+const index_t ndim = (*in_attrs)[0].ndim();
+for (index_t i = 0; i < axes.ndim(); i++) {
+ if (axes[i] < 0) {
+axes[i] += ndim;
+ }
+}
+std::sort(axes.begin(), axes.end());
+for (index_t i = 1; i < axes.ndim(); i++) {
+ CHECK_LT(axes[i - 1], axes[i])
+<< "axes have duplicates " << axes;
+}
+CHECK_LT(axes[axes.ndim() - 1], ndim)
+ << "axis " << axes[axes.ndim() - 1]
+ << " Exceeds input dimensions " << (*in_attrs)[0];
+CHECK_GE(axes[0], 0)
+ << "Reduction axis " << param.axis.value()
+ << " Exceeds input dimensions " << (*in_attrs)[0];
+ }
+ return ElemwiseShape<1, 1>(attrs, in_attrs, out_attrs);
+}
+
+NNVM_REGISTER_OP(_np_roll)
+.set_num_inputs(1)
+.set_num_outputs(1)
+.set_attr_parser(ParamParser)
+.set_attr("FListInputNames",
+ [](const NodeAttrs& attrs) {
+ return std::vector{"data"};
+})
+.set_attr("FInferShape", NumpyRollShape)
+.set_attr("FInferType", ElemwiseType<1, 1>)
+.set_attr("FCompute", NumpyRollCompute)
+.set_attr("FGradient",
+ [](const nnvm::NodePtr& n, const std::vector& ograds) {
+ const NumpyRollParam& param = nnvm::get(n->attrs.parsed);
+ std::ostringstream os1;
+ os1 << param.shift;
+ std::ostringstream os2;
+ os2 << param.axis;
+ return MakeNonlossGradNode("_np_roll", n, ograds, {},
+ {{"shift", os1.str()}, {"axis", os2.str()}});
+})
+.set_attr("FResourceRequest",
+[](const NodeAttrs& n) {
+ return std::vector{ResourceRequest::kTempSpace};
Review comment:
Indentation.
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[GitHub] [incubator-mxnet] pengzhao-intel commented on issue #15741: MKL-DNN LBR-GRU Inference Integration (FP32 LBR-GRU)
pengzhao-intel commented on issue #15741: MKL-DNN LBR-GRU Inference Integration (FP32 LBR-GRU) URL: https://github.com/apache/incubator-mxnet/pull/15741#issuecomment-526009302 If it still needs lots of efforts to pass ci, we can drop it and wait to our 1.0 upgrade. @zixuanweeei you can make a decision :) This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-mxnet] haojin2 commented on a change in pull request #15902: Numpy add numpy op roll
haojin2 commented on a change in pull request #15902: Numpy add numpy op roll URL: https://github.com/apache/incubator-mxnet/pull/15902#discussion_r318875806 ## File path: tests/python/unittest/test_numpy_op.py ## @@ -1126,6 +1126,54 @@ def test_np_randint(): verify_generator(generator=generator_mx_same_seed, buckets=buckets, probs=probs, nrepeat=100) +@with_seed() +@use_np +def test_np_roll(): +class TestRoll(HybridBlock): +def __init__(self, shift=None, axis=None): +super(TestRoll, self).__init__() +self._shift = shift +self._axis = axis + +def hybrid_forward(self, F, x): +return F.np.roll(x, shift=self._shift, axis=self._axis) + +dtypes = ['int32', 'int64', 'float16', 'float32', 'float64'] +configs = [ +((), (3,), None), +((1,), (-3,), None), +((20,), (-3,), None), +((3,), (2,), 0), +((2, 3, 4), (12,), (1,)), +((2, 3, 4), (10, -10), (0, 1)), +((2, 3, 4, 5), (0, 1), (-1, 2)), +((2, 3, 0, 1), (0, 1), (-1, 2)), +((2, 3, 4, 5), 10, (0, 2)), +] +for dtype in dtypes: +for config in configs: +for hybridize in [False, True]: +shape, shift, axis = config[0], config[1], config[2] +x = rand_ndarray(shape=shape, dtype=dtype).as_np_ndarray() +net = TestRoll(shift=shift, axis=axis) +np_out = _np.roll(x.asnumpy(), shift=shift, axis=axis) +if hybridize: +net.hybridize() +x.attach_grad() +with mx.autograd.record(): +mx_out = net(x) +assert mx_out.shape == np_out.shape +mx_out.backward() +assert same(mx_out.asnumpy(), np_out) +assert same(x.grad.shape, x.shape) +assert same(x.grad.asnumpy(), _np.ones(shape)) + +# test imperativen Review comment: Get rid of this line. This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[incubator-mxnet] branch master updated (649429d -> 3f7b6ee)
This is an automated email from the ASF dual-hosted git repository.
patriczhao pushed a change to branch master
in repository https://gitbox.apache.org/repos/asf/incubator-mxnet.git.
from 649429d Disable flaky test in test_amp_conversion (#16031)
add 3f7b6ee Improve quantization flow (#15961)
No new revisions were added by this update.
Summary of changes:
example/quantization/README.md | 23 +-
example/quantization/imagenet_gen_qsym.py | 17 +-
example/quantization/imagenet_gen_qsym_mkldnn.py | 3 +-
example/ssd/quantization.py| 11 +-
include/mxnet/c_api.h | 7 +-
include/mxnet/op_attr_types.h | 30 ++
python/mxnet/contrib/quantization.py | 285 ---
src/c_api/c_api_symbolic.cc| 17 +-
src/common/utils.h | 10 +
src/executor/graph_executor.cc | 15 +-
.../mkldnn/mkldnn_flatten-inl.h} | 30 +-
src/operator/nn/mkldnn/mkldnn_flatten.cc | 10 +-
.../np_init_op.cu => quantization/calibrate-inl.h} | 35 +--
src/operator/quantization/calibrate.cc | 215 ++
.../quantization/mkldnn/mkldnn_quantize_v2-inl.h | 6 +-
.../mkldnn/mkldnn_quantized_elemwise_add.cc| 4 +-
.../mkldnn/mkldnn_quantized_flatten.cc | 61
src/operator/quantization/quantization_utils.h | 4 +-
src/operator/quantization/quantize_graph_pass.cc | 315 +
src/operator/quantization/quantize_v2.cc | 3 +
src/operator/quantization/quantized_batch_norm.cc | 3 +
src/operator/quantization/quantized_conv.cc| 3 +
.../quantization/quantized_fully_connected.cc | 3 +
src/operator/quantization/requantize.cc| 3 +
src/operator/subgraph/mkldnn/mkldnn_conv.cc| 3 +
src/operator/subgraph/mkldnn/mkldnn_fc.cc | 3 +
tests/python/mkl/test_subgraph.py | 9 +-
tests/python/quantization/test_quantization.py | 53 +++-
tests/python/unittest/test_operator.py | 5 +-
29 files changed, 776 insertions(+), 410 deletions(-)
copy src/operator/{quantization/mkldnn/mkldnn_quantized_ops-inl.h =>
nn/mkldnn/mkldnn_flatten-inl.h} (54%)
copy src/operator/{numpy/np_init_op.cu => quantization/calibrate-inl.h} (60%)
create mode 100644 src/operator/quantization/calibrate.cc
create mode 100644 src/operator/quantization/mkldnn/mkldnn_quantized_flatten.cc
[GitHub] [incubator-mxnet] haojin2 commented on a change in pull request #15987: Numpy add numpy op rot90
haojin2 commented on a change in pull request #15987: Numpy add numpy op rot90
URL: https://github.com/apache/incubator-mxnet/pull/15987#discussion_r318874589
##
File path: src/operator/numpy/np_matrix_op-inl.h
##
@@ -60,6 +60,246 @@ void NumpyTranspose(const nnvm::NodeAttrs& attrs,
}
}
+struct NumpyRot90Param : public dmlc::Parameter {
+ int k;
+ dmlc::optional axes;
+ DMLC_DECLARE_PARAMETER(NumpyRot90Param) {
+DMLC_DECLARE_FIELD(k)
+.set_default(1)
+.describe("Number of times the array is rotated by 90 degrees.");
+DMLC_DECLARE_FIELD(axes)
+.set_default(dmlc::optional())
+.describe(" The array is rotated in the plane defined by the axes. Axes
must be different.");
+ }
+};
+
+struct rot90reverse {
+ MSHADOW_XINLINE static index_t ReverseIndex(index_t idx,
+ index_t nreversedim,
+ const index_t * stride_,
+ const index_t * trailing_) {
+index_t outputIndex = idx;
+for (index_t i = 0; i < nreversedim; ++i) {
+ const index_t low = outputIndex % trailing_[i];
+ index_t high = outputIndex / trailing_[i];
+ const index_t x = high % stride_[i];
+ high /= stride_[i];
+ outputIndex = (high * stride_[i] + stride_[i] - 1 - x) * trailing_[i] +
low;
+}
+return outputIndex;
+ }
+ template
+ MSHADOW_XINLINE static void Map(index_t index, index_t nreversedim, const
DType *src, DType *dst,
+ const index_t * stride_,
+ const index_t * trailing_) {
+index_t new_idx = ReverseIndex(index, nreversedim, stride_, trailing_);
+dst[new_idx] = src[index];
+ }
+};
+
+template
+void NumpyRot90ComputeFlipIml(const OpContext& ctx,
+ const std::vector& inputs,
+ const std::vector& req,
+ const std::vector& outputs,
+ const index_t axis0, const index_t axis1) {
+ using namespace mshadow;
+ using namespace mxnet_op;
+
+ const mxnet::TShape& ishape = inputs[0].shape_;
+ Stream *s = ctx.get_stream();
+
+ std::vector stride_(2);
+ std::vector trailing_(2);
+ index_t reverse_index = 0;
+ std::vector temp{axis0, axis1};
+ for (int axis : temp) {
+stride_[reverse_index] = ishape[axis];
+trailing_[reverse_index] = 1;
+for (int i2 = axis + 1; i2 < ishape.ndim(); ++i2) {
+ trailing_[reverse_index] *= ishape[i2];
+}
+reverse_index++;
+ }
+
+ index_t workspace_size = 2 * sizeof(index_t);
+ Tensor workspace =
+ ctx.requested[0].get_space_typed(Shape1(2 *
workspace_size), s);
+ Tensor stride_cpu_tensor(stride_.data(),
Shape1(stride_.size()));
+ Tensor stride_xpu_tensor(
+ reinterpret_cast(workspace.dptr_), Shape1(stride_.size()));
+ Tensor trailing_cpu_tensor(trailing_.data(),
Shape1(trailing_.size()));
+ Tensor trailing_xpu_tensor(
+ reinterpret_cast(workspace.dptr_ + workspace_size),
Shape1(trailing_.size()));
+
+ mshadow::Copy(stride_xpu_tensor, stride_cpu_tensor, s);
+ mshadow::Copy(trailing_xpu_tensor, trailing_cpu_tensor, s);
+ MSHADOW_TYPE_SWITCH(outputs[0].type_flag_, DType, {
+ Kernel::Launch(s, inputs[0].Size(),
reverse_index,
+ inputs[0].dptr(), outputs[0].dptr(),
+ stride_xpu_tensor.dptr_, trailing_xpu_tensor.dptr_);
+ });
+}
+
+struct rot90Transreverse {
+ MSHADOW_XINLINE static index_t ReverseIndex(index_t idx,
+ const index_t stride_,
+ const index_t trailing_) {
+index_t outputIndex = idx;
+const index_t low = outputIndex % trailing_;
+index_t high = outputIndex / trailing_;
+const index_t x = high % stride_;
+high /= stride_;
+outputIndex = (high * stride_ + stride_ - 1 - x) * trailing_ + low;
+
+return outputIndex;
+ }
+ template
+ MSHADOW_XINLINE static void Map(index_t index, const DType *src, DType *dst,
+ const index_t stride_,
+ const index_t trailing_) {
+index_t new_idx = ReverseIndex(index, stride_, trailing_);
+dst[new_idx] = src[index];
+ }
+};
+
+template
+void NumpyRot90ComputeFlipTransposeIml(const OpContext& ctx,
+ const std::vector& inputs,
+ const std::vector& req,
+ const std::vector& outputs,
+ const mxnet::TShape axes_list,
+ const index_t axis) {
+ using namespace mshadow;
+ using namespace mxnet_op;
+
+ const mxnet::TShape& ishape = inputs[0].shape_;
+ Stream *s = ctx.get_stream();
+
+ index_t stride_;
+ index_t trailing_;
+
+ stride_ = ishape[axis];
+ trailing_ = 1;
[GitHub] [incubator-mxnet] haojin2 commented on a change in pull request #15987: Numpy add numpy op rot90
haojin2 commented on a change in pull request #15987: Numpy add numpy op rot90
URL: https://github.com/apache/incubator-mxnet/pull/15987#discussion_r318874396
##
File path: src/operator/numpy/np_matrix_op-inl.h
##
@@ -60,6 +60,246 @@ void NumpyTranspose(const nnvm::NodeAttrs& attrs,
}
}
+struct NumpyRot90Param : public dmlc::Parameter {
+ int k;
+ dmlc::optional axes;
+ DMLC_DECLARE_PARAMETER(NumpyRot90Param) {
+DMLC_DECLARE_FIELD(k)
+.set_default(1)
+.describe("Number of times the array is rotated by 90 degrees.");
+DMLC_DECLARE_FIELD(axes)
+.set_default(dmlc::optional())
+.describe(" The array is rotated in the plane defined by the axes. Axes
must be different.");
+ }
+};
+
+struct rot90reverse {
+ MSHADOW_XINLINE static index_t ReverseIndex(index_t idx,
+ index_t nreversedim,
+ const index_t * stride_,
+ const index_t * trailing_) {
+index_t outputIndex = idx;
+for (index_t i = 0; i < nreversedim; ++i) {
+ const index_t low = outputIndex % trailing_[i];
+ index_t high = outputIndex / trailing_[i];
+ const index_t x = high % stride_[i];
+ high /= stride_[i];
+ outputIndex = (high * stride_[i] + stride_[i] - 1 - x) * trailing_[i] +
low;
+}
+return outputIndex;
+ }
+ template
+ MSHADOW_XINLINE static void Map(index_t index, index_t nreversedim, const
DType *src, DType *dst,
Review comment:
should have only 1 space after `MSHADOW_XINLINE`
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[GitHub] [incubator-mxnet] haojin2 commented on a change in pull request #15987: Numpy add numpy op rot90
haojin2 commented on a change in pull request #15987: Numpy add numpy op rot90
URL: https://github.com/apache/incubator-mxnet/pull/15987#discussion_r318874434
##
File path: src/operator/numpy/np_matrix_op-inl.h
##
@@ -60,6 +60,246 @@ void NumpyTranspose(const nnvm::NodeAttrs& attrs,
}
}
+struct NumpyRot90Param : public dmlc::Parameter {
+ int k;
+ dmlc::optional axes;
+ DMLC_DECLARE_PARAMETER(NumpyRot90Param) {
+DMLC_DECLARE_FIELD(k)
+.set_default(1)
+.describe("Number of times the array is rotated by 90 degrees.");
+DMLC_DECLARE_FIELD(axes)
+.set_default(dmlc::optional())
+.describe(" The array is rotated in the plane defined by the axes. Axes
must be different.");
+ }
+};
+
+struct rot90reverse {
+ MSHADOW_XINLINE static index_t ReverseIndex(index_t idx,
+ index_t nreversedim,
+ const index_t * stride_,
+ const index_t * trailing_) {
+index_t outputIndex = idx;
+for (index_t i = 0; i < nreversedim; ++i) {
+ const index_t low = outputIndex % trailing_[i];
+ index_t high = outputIndex / trailing_[i];
+ const index_t x = high % stride_[i];
+ high /= stride_[i];
+ outputIndex = (high * stride_[i] + stride_[i] - 1 - x) * trailing_[i] +
low;
+}
+return outputIndex;
+ }
+ template
+ MSHADOW_XINLINE static void Map(index_t index, index_t nreversedim, const
DType *src, DType *dst,
+ const index_t * stride_,
+ const index_t * trailing_) {
+index_t new_idx = ReverseIndex(index, nreversedim, stride_, trailing_);
+dst[new_idx] = src[index];
+ }
+};
+
+template
+void NumpyRot90ComputeFlipIml(const OpContext& ctx,
+ const std::vector& inputs,
+ const std::vector& req,
+ const std::vector& outputs,
+ const index_t axis0, const index_t axis1) {
+ using namespace mshadow;
+ using namespace mxnet_op;
+
+ const mxnet::TShape& ishape = inputs[0].shape_;
+ Stream *s = ctx.get_stream();
+
+ std::vector stride_(2);
+ std::vector trailing_(2);
+ index_t reverse_index = 0;
+ std::vector temp{axis0, axis1};
+ for (int axis : temp) {
+stride_[reverse_index] = ishape[axis];
+trailing_[reverse_index] = 1;
+for (int i2 = axis + 1; i2 < ishape.ndim(); ++i2) {
+ trailing_[reverse_index] *= ishape[i2];
+}
+reverse_index++;
+ }
+
+ index_t workspace_size = 2 * sizeof(index_t);
+ Tensor workspace =
+ ctx.requested[0].get_space_typed(Shape1(2 *
workspace_size), s);
+ Tensor stride_cpu_tensor(stride_.data(),
Shape1(stride_.size()));
+ Tensor stride_xpu_tensor(
+ reinterpret_cast(workspace.dptr_), Shape1(stride_.size()));
+ Tensor trailing_cpu_tensor(trailing_.data(),
Shape1(trailing_.size()));
+ Tensor trailing_xpu_tensor(
+ reinterpret_cast(workspace.dptr_ + workspace_size),
Shape1(trailing_.size()));
+
+ mshadow::Copy(stride_xpu_tensor, stride_cpu_tensor, s);
+ mshadow::Copy(trailing_xpu_tensor, trailing_cpu_tensor, s);
+ MSHADOW_TYPE_SWITCH(outputs[0].type_flag_, DType, {
+ Kernel::Launch(s, inputs[0].Size(),
reverse_index,
Review comment:
Pay attention to the indentation.
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[GitHub] [incubator-mxnet] pengzhao-intel merged pull request #15961: Improve quantization flow
pengzhao-intel merged pull request #15961: Improve quantization flow URL: https://github.com/apache/incubator-mxnet/pull/15961 This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-mxnet] haojin2 commented on a change in pull request #16025: Numpy add numpy op left_shift and right_shift
haojin2 commented on a change in pull request #16025: Numpy add numpy op
left_shift and right_shift
URL: https://github.com/apache/incubator-mxnet/pull/16025#discussion_r318873768
##
File path: src/operator/operator_tune.cc
##
@@ -84,6 +84,13 @@ struct static_init_var {
__macro$(__VA_ARGS__, int32_t); \
__macro$(__VA_ARGS__, int64_t);
+#define MSHADOW_MACRO_INT_FOREACH_TYPE(__macro$, ...) \
+ __macro$(__VA_ARGS__, uint8_t); \
+ __macro$(__VA_ARGS__, int8_t); \
+ __macro$(__VA_ARGS__, int32_t); \
+ __macro$(__VA_ARGS__, int64_t);
+
+
Review comment:
1 less blank line.
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[GitHub] [incubator-mxnet] haojin2 commented on a change in pull request #16025: Numpy add numpy op left_shift and right_shift
haojin2 commented on a change in pull request #16025: Numpy add numpy op left_shift and right_shift URL: https://github.com/apache/incubator-mxnet/pull/16025#discussion_r318873830 ## File path: tests/python/unittest/test_numpy_op.py ## @@ -1126,6 +1126,83 @@ def test_np_randint(): verify_generator(generator=generator_mx_same_seed, buckets=buckets, probs=probs, nrepeat=100) +@with_seed() +@use_np +def test_np_left_shift(): +class TestLeftShift(HybridBlock): +def __init__(self): +super(TestLeftShift, self).__init__() + +def hybrid_forward(self, F, a, b): +return F.np.left_shift(a, b) + +shapes = [ +((), ()), +((), (2,)), +((3,), ()), +((3, 4), (3, 4)), +((3, 4, 5, 6), (6,)), +((2, 0, 3), (3,)), +((2, 1, 3), (2, 3)) +] +dtypes = ['uint8', 'int8', 'int32', 'int64'] +for shape in shapes: +a, b = shape[0], shape[1] +for dtype in dtypes: +for hybridize in [True, False]: +net = TestLeftShift() +x1 = mx.nd.random.uniform(-10.0, 10.0, a).astype(dtype).as_np_ndarray() +x2 = mx.nd.random.uniform(0, 10.0, b).astype(dtype).as_np_ndarray() +if hybridize: +net.hybridize() + +mx_out = net(x1, x2) +np_out = _np.left_shift(x1.asnumpy(), x2.asnumpy()) +assert same(mx_out.asnumpy(), np_out) + +mx_out = np.left_shift(x1, x2) +np_out = _np.left_shift(x1.asnumpy(), x2.asnumpy()) +assert same(mx_out.asnumpy(), np_out) + + +@with_seed() +@use_np +def test_np_right_shift(): Review comment: Merge the 2 tests into one. This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-mxnet] haojin2 commented on a change in pull request #16025: Numpy add numpy op left_shift and right_shift
haojin2 commented on a change in pull request #16025: Numpy add numpy op
left_shift and right_shift
URL: https://github.com/apache/incubator-mxnet/pull/16025#discussion_r318873675
##
File path: src/operator/numpy/np_elemwise_binary_broadcast_op.h
##
@@ -0,0 +1,97 @@
+/*
+ * 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.
+ */
+
+/*!
+ * Copyright (c) 2019 by Contributors
+ * \file np_elemwise_binary_broadcast_op.h
+ * \brief Function definition of elementwise binary broadcast related operators
+ */
+#ifndef MXNET_OPERATOR_NUMPY_NP_ELEMWISE_BINARY_BROADCAST_OP_H_
+#define MXNET_OPERATOR_NUMPY_NP_ELEMWISE_BINARY_BROADCAST_OP_H_
+
+#include
+#include "../tensor/elemwise_binary_broadcast_op.h"
+#include "../tensor/elemwise_binary_scalar_op.h"
+
+namespace mxnet {
+namespace op {
+
+/*! \brief Minimum of three */
+static MSHADOW_XINLINE size_t minthree(const size_t a, const size_t b, const
size_t c) {
+ return a < b ? (a < c ? a : c) : (b < c ? b : c);
+}
+
+template
+static void BitCompute(const nnvm::NodeAttrs &attrs,
+ const OpContext &ctx,
+ const std::vector &inputs,
+ const std::vector &req,
+ const std::vector &outputs) {
+ using namespace mxnet_op;
+ if (req[0] != kNullOp) {
+Stream *s = ctx.get_stream();
+CHECK_EQ(inputs.size(), 2U);
+CHECK_EQ(outputs.size(), 1U);
+MXNET_ASSIGN_REQ_SWITCH(req[0], Req, {
+ MXNET_INT_TYPE_SWITCH(outputs[0].type_flag_, DType, {
+const size_t size = (minthree(outputs[0].Size(), inputs[0].Size(),
inputs[1].Size())
++ DataType::kLanes - 1) / DataType::kLanes;
+if (size != 0) {
+ Kernel, xpu>::Launch(s, size,
+ outputs[0].dptr(),
+ inputs[0].dptr(), inputs[1].dptr());
+}
+ });
+});
+ }
+}
+
+template
+void BitBinaryBroadcastCompute(const nnvm::NodeAttrs& attrs,
+ const OpContext& ctx,
+ const std::vector& inputs,
+ const std::vector& req,
+ const std::vector& outputs) {
+ if (outputs[0].shape_.Size() == 0U) return;
+ mxnet::TShape new_lshape, new_rshape, new_oshape;
+ int ndim = BinaryBroadcastShapeCompact(inputs[0].shape_, inputs[1].shape_,
outputs[0].shape_,
+ &new_lshape, &new_rshape,
&new_oshape);
+ if (!ndim) {
+BitCompute(attrs, ctx, inputs, req, outputs);
+ } else {
+if (req[0] != kNullOp) {
+ mshadow::Stream *s = ctx.get_stream();
+ MXNET_INT_TYPE_SWITCH(outputs[0].type_flag_, DType, {
+BROADCAST_NDIM_SWITCH(ndim, NDim, {
+mshadow::Shape oshape = new_oshape.get();
Review comment:
2-space indentation
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[GitHub] [incubator-mxnet] haojin2 commented on a change in pull request #16025: Numpy add numpy op left_shift and right_shift
haojin2 commented on a change in pull request #16025: Numpy add numpy op
left_shift and right_shift
URL: https://github.com/apache/incubator-mxnet/pull/16025#discussion_r318873637
##
File path: src/operator/numpy/np_elemwise_binary_broadcast_op.h
##
@@ -0,0 +1,97 @@
+/*
+ * 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.
+ */
+
+/*!
+ * Copyright (c) 2019 by Contributors
+ * \file np_elemwise_binary_broadcast_op.h
+ * \brief Function definition of elementwise binary broadcast related operators
+ */
+#ifndef MXNET_OPERATOR_NUMPY_NP_ELEMWISE_BINARY_BROADCAST_OP_H_
+#define MXNET_OPERATOR_NUMPY_NP_ELEMWISE_BINARY_BROADCAST_OP_H_
+
+#include
+#include "../tensor/elemwise_binary_broadcast_op.h"
+#include "../tensor/elemwise_binary_scalar_op.h"
+
+namespace mxnet {
+namespace op {
+
+/*! \brief Minimum of three */
+static MSHADOW_XINLINE size_t minthree(const size_t a, const size_t b, const
size_t c) {
+ return a < b ? (a < c ? a : c) : (b < c ? b : c);
+}
+
+template
+static void BitCompute(const nnvm::NodeAttrs &attrs,
+ const OpContext &ctx,
+ const std::vector &inputs,
+ const std::vector &req,
+ const std::vector &outputs) {
+ using namespace mxnet_op;
+ if (req[0] != kNullOp) {
+Stream *s = ctx.get_stream();
+CHECK_EQ(inputs.size(), 2U);
+CHECK_EQ(outputs.size(), 1U);
+MXNET_ASSIGN_REQ_SWITCH(req[0], Req, {
+ MXNET_INT_TYPE_SWITCH(outputs[0].type_flag_, DType, {
+const size_t size = (minthree(outputs[0].Size(), inputs[0].Size(),
inputs[1].Size())
++ DataType::kLanes - 1) / DataType::kLanes;
Review comment:
indentation here.
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[GitHub] [incubator-mxnet] yangwenhuan commented on issue #16020: Is there any communication between parameter servers?
yangwenhuan commented on issue #16020: Is there any communication between parameter servers? URL: https://github.com/apache/incubator-mxnet/issues/16020#issuecomment-526001104 > No, but there's all-to-all communication between workers and servers Could you show me the relative source code? This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-mxnet] ZhennanQin commented on a change in pull request #15886: Graph Partition API
ZhennanQin commented on a change in pull request #15886: Graph Partition API
URL: https://github.com/apache/incubator-mxnet/pull/15886#discussion_r318868501
##
File path: src/c_api/c_api_symbolic.cc
##
@@ -1199,3 +1200,73 @@ int MXShallowCopySymbol(SymbolHandle src, SymbolHandle*
out) {
*out = out_sym;
API_END_HANDLE_ERROR(delete out_sym);
}
+
+int MXOptimizeForBackend(SymbolHandle sym_handle,
+ const char* backend_name,
+ SymbolHandle* ret_sym_handle,
+ const mx_uint len,
+ NDArrayHandle* in_args_handle,
+ const mx_uint num_options,
+ const char** keys,
+ const char** vals) {
+ nnvm::Symbol *s = new nnvm::Symbol();
+ API_BEGIN();
+ nnvm::Symbol *sym = static_cast(sym_handle);
+ *s = sym->Copy();
+ nnvm::Graph g = Symbol2Graph(*s);
+ if (len) {
+NDArray **in_args_ptr = reinterpret_cast(in_args_handle);
+Context default_ctx = in_args_ptr[0]->ctx();
Review comment:
Finally you got my point. Thanks for your patients. Either solution is OK
for me, you can make the decision.
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[GitHub] [incubator-mxnet] samskalicky commented on a change in pull request #15886: Graph Partition API
samskalicky commented on a change in pull request #15886: Graph Partition API
URL: https://github.com/apache/incubator-mxnet/pull/15886#discussion_r318866580
##
File path: src/c_api/c_api_symbolic.cc
##
@@ -1199,3 +1200,73 @@ int MXShallowCopySymbol(SymbolHandle src, SymbolHandle*
out) {
*out = out_sym;
API_END_HANDLE_ERROR(delete out_sym);
}
+
+int MXOptimizeForBackend(SymbolHandle sym_handle,
+ const char* backend_name,
+ SymbolHandle* ret_sym_handle,
+ const mx_uint len,
+ NDArrayHandle* in_args_handle,
+ const mx_uint num_options,
+ const char** keys,
+ const char** vals) {
+ nnvm::Symbol *s = new nnvm::Symbol();
+ API_BEGIN();
+ nnvm::Symbol *sym = static_cast(sym_handle);
+ *s = sym->Copy();
+ nnvm::Graph g = Symbol2Graph(*s);
+ if (len) {
+NDArray **in_args_ptr = reinterpret_cast(in_args_handle);
+Context default_ctx = in_args_ptr[0]->ctx();
Review comment:
Good stuff, totally agree! We should accept context and not use it from
args.
We want EIA to be a backend like MKLDNN, not a context.
We could do as you suggest and query the supported context from the backend
subgraph property. But it will still error out correctly if you partition with
CPU and then bind with GPU when you finally call forward, since the subgraph op
you inserted at partition wont support GPU FCompute.
Since we set g.attrs["context"] and then pass the graph to the PrePartition
function on the subgraph property, the subgraph property can error out if the
context is not supported.
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[GitHub] [incubator-mxnet] xinyu-intel commented on issue #15796: Model Quantization with CUDNN
xinyu-intel commented on issue #15796: Model Quantization with CUDNN URL: https://github.com/apache/incubator-mxnet/issues/15796#issuecomment-525998897 @jackchinor try to set ctx to cpu This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-mxnet] samskalicky commented on a change in pull request #15886: Graph Partition API
samskalicky commented on a change in pull request #15886: Graph Partition API
URL: https://github.com/apache/incubator-mxnet/pull/15886#discussion_r318866580
##
File path: src/c_api/c_api_symbolic.cc
##
@@ -1199,3 +1200,73 @@ int MXShallowCopySymbol(SymbolHandle src, SymbolHandle*
out) {
*out = out_sym;
API_END_HANDLE_ERROR(delete out_sym);
}
+
+int MXOptimizeForBackend(SymbolHandle sym_handle,
+ const char* backend_name,
+ SymbolHandle* ret_sym_handle,
+ const mx_uint len,
+ NDArrayHandle* in_args_handle,
+ const mx_uint num_options,
+ const char** keys,
+ const char** vals) {
+ nnvm::Symbol *s = new nnvm::Symbol();
+ API_BEGIN();
+ nnvm::Symbol *sym = static_cast(sym_handle);
+ *s = sym->Copy();
+ nnvm::Graph g = Symbol2Graph(*s);
+ if (len) {
+NDArray **in_args_ptr = reinterpret_cast(in_args_handle);
+Context default_ctx = in_args_ptr[0]->ctx();
Review comment:
Good stuff, totally agree! We should accept context and not use it from
args.
We want EIA to be a backend like MKLDNN, not a context.
We could do as you suggest and query the supported context from the backend
subgraph property. But it will still error out correctly if you partition with
CPU and then bind with GPU when you finally call forward, since the subgraph op
you inserted at partition wont support GPU FCompute.
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[GitHub] [incubator-mxnet] pengzhao-intel commented on issue #15796: Model Quantization with CUDNN
pengzhao-intel commented on issue #15796: Model Quantization with CUDNN URL: https://github.com/apache/incubator-mxnet/issues/15796#issuecomment-525998486 @jackchinor could you paste your cmd and output? @xinyu-intel This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-mxnet] jackchinor commented on issue #15796: Model Quantization with CUDNN
jackchinor commented on issue #15796: Model Quantization with CUDNN URL: https://github.com/apache/incubator-mxnet/issues/15796#issuecomment-525997352 @pengzhao-intel when I run quantize example with 1.Model Quantization with Intel® MKL-DNN , there is a error :"quantize_model_mkldnn only support Intel cpu platform with MKL-DNN Backend", I didn't change the example code. I install mxnet-mkl through"pip install mxnet-mkl --pre".Could you tell me how to fix it ? This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[incubator-mxnet] branch master updated (9b906a5 -> 649429d)
This is an automated email from the ASF dual-hosted git repository. patriczhao pushed a change to branch master in repository https://gitbox.apache.org/repos/asf/incubator-mxnet.git. from 9b906a5 Improve diagnose.py to display environment variables (#15715) add 649429d Disable flaky test in test_amp_conversion (#16031) No new revisions were added by this update. Summary of changes: tests/python/gpu/test_contrib_amp.py | 3 +++ 1 file changed, 3 insertions(+)
[GitHub] [incubator-mxnet] pengzhao-intel merged pull request #16029: [v1.5.x] Benchmark doc fix (#15769)
pengzhao-intel merged pull request #16029: [v1.5.x] Benchmark doc fix (#15769) URL: https://github.com/apache/incubator-mxnet/pull/16029 This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[incubator-mxnet] branch v1.5.x updated: Benchmark doc fix (#15769) (#16029)
This is an automated email from the ASF dual-hosted git repository.
patriczhao pushed a commit to branch v1.5.x
in repository https://gitbox.apache.org/repos/asf/incubator-mxnet.git
The following commit(s) were added to refs/heads/v1.5.x by this push:
new 006486a Benchmark doc fix (#15769) (#16029)
006486a is described below
commit 006486af3c912b67b73ecee26a2fc73762e6e9ee
Author: Chaitanya Prakash Bapat
AuthorDate: Wed Aug 28 19:38:15 2019 -0700
Benchmark doc fix (#15769) (#16029)
* Update pre-req for opperf
* Update README.md
* correct command to import binary broadcast
* no such op called nd.sub, it is nd.subtract
* Trigger notification
* Trigger notification
---
benchmark/opperf/README.md | 9 +
1 file changed, 5 insertions(+), 4 deletions(-)
diff --git a/benchmark/opperf/README.md b/benchmark/opperf/README.md
index 99c75be..c73592d 100644
--- a/benchmark/opperf/README.md
+++ b/benchmark/opperf/README.md
@@ -46,9 +46,10 @@ Hence, in this utility, we will build the functionality to
allow users and devel
## Prerequisites
-This utility uses MXNet profiler under the hood to fetch compute and memory
metrics. Hence, you need to build MXNet with `USE_PROFILER=1` flag.
+Provided you have MXNet installed (any version >= 1.5.1), all you need to use
opperf utility is to add path to your cloned MXNet repository to the PYTHONPATH.
-Make sure to build the flavor of MXNet, for example - with/without MKL, with
CUDA 9 or 10.1 etc., on which you would like to measure operator performance.
Finally, you need to add path to your cloned MXNet repository to the PYTHONPATH.
+Note:
+To install MXNet, refer [Installing MXNet
page](https://mxnet.incubator.apache.org/versions/master/install/index.html)
```
export PYTHONPATH=$PYTHONPATH:/path/to/incubator-mxnet/
@@ -76,7 +77,7 @@ For example, you want to run benchmarks for all NDArray
Broadcast Binary Operato
```
#!/usr/bin/python
-from benchmark.opperf.tensor_operations.binary_broadcast_operators import
run_mx_binary_broadcast_operators_benchmarks
+from benchmark.opperf.nd_operations.binary_operators import
run_mx_binary_broadcast_operators_benchmarks
# Run all Binary Broadcast operations benchmarks with default input values
print(run_mx_binary_broadcast_operators_benchmarks())
@@ -137,7 +138,7 @@ from mxnet import nd
from benchmark.opperf.utils.benchmark_utils import run_performance_test
-add_res = run_performance_test([nd.add, nd.sub], run_backward=True,
dtype='float32', ctx=mx.cpu(),
+add_res = run_performance_test([nd.add, nd.subtract], run_backward=True,
dtype='float32', ctx=mx.cpu(),
inputs=[{"lhs": (1024, 1024),
"rhs": (1024, 1024)}],
warmup=10, runs=25)
[GitHub] [incubator-mxnet] pengzhao-intel merged pull request #16031: Disable flaky test in test_amp_conversion
pengzhao-intel merged pull request #16031: Disable flaky test in test_amp_conversion URL: https://github.com/apache/incubator-mxnet/pull/16031 This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-mxnet] kkurni commented on issue #15998: Build MXNET with GPU but fail to import mxnet on CPU machine
kkurni commented on issue #15998: Build MXNET with GPU but fail to import mxnet on CPU machine URL: https://github.com/apache/incubator-mxnet/issues/15998#issuecomment-525993478 Yes, I link it with cuda path. I am using nvidia docker image. During runtime in CPU, it has the cuda driver there inside /usr/local/cuda But it is still complaining about cudalib This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-mxnet] keetsky commented on issue #12018: onnx converter error
keetsky commented on issue #12018: onnx converter error
URL:
https://github.com/apache/incubator-mxnet/issues/12018#issuecomment-525993323
If i let `mx.sym.BatchNorm(data=fc1, fix_gamma=True, eps=2e-5,
momentum=bn_mom, name='bn3')`
and save to onnx model,but when i load onnx mode ,find that mxnet default
set fix_gamma to False in _op_translations.py `# in test mode "fix_gamma"
should be unset.
new_attrs['fix_gamma'] = not attrs.get('is_test', 1)` ,so my predict
results are diffrent, how to fix it?
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[GitHub] [incubator-mxnet] ZhennanQin opened a new pull request #16032: [Don't merge] Update dmlc-core
ZhennanQin opened a new pull request #16032: [Don't merge] Update dmlc-core URL: https://github.com/apache/incubator-mxnet/pull/16032 Update dmlc-core to private repo to demonstrate the dmlc-core change effect on MXNet. ## Description ## (Brief description on what this PR is about) ## Checklist ## ### Essentials ### Please feel free to remove inapplicable items for your PR. - [ ] The PR title starts with [MXNET-$JIRA_ID], where $JIRA_ID refers to the relevant [JIRA issue](https://issues.apache.org/jira/projects/MXNET/issues) created (except PRs with tiny changes) - [ ] Changes are complete (i.e. I finished coding on this PR) - [ ] All changes have test coverage: - Unit tests are added for small changes to verify correctness (e.g. adding a new operator) - Nightly tests are added for complicated/long-running ones (e.g. changing distributed kvstore) - Build tests will be added for build configuration changes (e.g. adding a new build option with NCCL) - [ ] Code is well-documented: - For user-facing API changes, API doc string has been updated. - For new C++ functions in header files, their functionalities and arguments are documented. - For new examples, README.md is added to explain the what the example does, the source of the dataset, expected performance on test set and reference to the original paper if applicable - Check the API doc at http://mxnet-ci-doc.s3-accelerate.dualstack.amazonaws.com/PR-$PR_ID/$BUILD_ID/index.html - [ ] To the my best knowledge, examples are either not affected by this change, or have been fixed to be compatible with this change ### Changes ### - [ ] Feature1, tests, (and when applicable, API doc) - [ ] Feature2, tests, (and when applicable, API doc) ## Comments ## - If this change is a backward incompatible change, why must this change be made. - Interesting edge cases to note here This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-mxnet] keetsky commented on issue #12018: onnx converter error
keetsky commented on issue #12018: onnx converter error URL: https://github.com/apache/incubator-mxnet/issues/12018#issuecomment-525988479 Have solved? I met the same issue. This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-mxnet] wkcn commented on issue #14253: [RFC] Introducing NumPy-compatible coding experience into MXNet
wkcn commented on issue #14253: [RFC] Introducing NumPy-compatible coding experience into MXNet URL: https://github.com/apache/incubator-mxnet/issues/14253#issuecomment-525985724 Hi @reminisce , I try to pass a numpy-compatible array into a legacy operator, and it raises this error. ```python >>> import mxnet.numpy as np >>> import mxnet as mx >>> import mxnet.numpy as np >>> a = np.array([1,2]) >>> b = np.array([3,4]) >>> mx.nd.broadcast_add(a,b) Traceback (most recent call last): File "", line 1, in File "", line 56, in broadcast_add File "/home/wkcn/proj/mxnet/python/mxnet/ndarray/register.py", line 99, in _verify_all_legacy_ndarrays .format(op_name, func_name)) TypeError: Operator `broadcast_add` registered in backend is known as `broadcast_add` in Python. This is a legacy operator which can only accept legacy ndarrays, while received an MXNet numpy ndarray. Please call `as_nd_ndarray()` upon the numpy ndarray to convert it to a legacy ndarray, and then feed the converted array to this operator. ``` I hope that the legacy operator is the subset of This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-mxnet] wkcn closed issue #14253: [RFC] Introducing NumPy-compatible coding experience into MXNet
wkcn closed issue #14253: [RFC] Introducing NumPy-compatible coding experience into MXNet URL: https://github.com/apache/incubator-mxnet/issues/14253 This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-mxnet] wkcn removed a comment on issue #14253: [RFC] Introducing NumPy-compatible coding experience into MXNet
wkcn removed a comment on issue #14253: [RFC] Introducing NumPy-compatible coding experience into MXNet URL: https://github.com/apache/incubator-mxnet/issues/14253#issuecomment-525985724 Hi @reminisce , I try to pass a numpy-compatible array into a legacy operator, and it raises this error. ```python >>> import mxnet.numpy as np >>> import mxnet as mx >>> import mxnet.numpy as np >>> a = np.array([1,2]) >>> b = np.array([3,4]) >>> mx.nd.broadcast_add(a,b) Traceback (most recent call last): File "", line 1, in File "", line 56, in broadcast_add File "/home/wkcn/proj/mxnet/python/mxnet/ndarray/register.py", line 99, in _verify_all_legacy_ndarrays .format(op_name, func_name)) TypeError: Operator `broadcast_add` registered in backend is known as `broadcast_add` in Python. This is a legacy operator which can only accept legacy ndarrays, while received an MXNet numpy ndarray. Please call `as_nd_ndarray()` upon the numpy ndarray to convert it to a legacy ndarray, and then feed the converted array to this operator. ``` I hope that the legacy operator is the subset of This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-mxnet] reminisce opened a new issue #14253: [RFC] Introducing NumPy-compatible coding experience into MXNet
reminisce opened a new issue #14253: [RFC] Introducing NumPy-compatible coding experience into MXNet URL: https://github.com/apache/incubator-mxnet/issues/14253 ## Motivation Today deep learning scientists spend majority of their time on data processing, debugging tensor algorithms, and tuning model parameters, instead of architecting models from scratch by themselves as a result from the abundant pre-trained models existing in many deep learning model zoos. This has highlighted the usability of tensor APIs as a key factor for a framework to be widely adopted. MXNet was firstly designed with the focus on memory efficiency, computation throughput and scalability. The usability problems begin to show up nowadays when more and more models demonstrate dynamic natures, e.g. unknown-shape tensors before runtime, control flow depending on a runtime result, etc. Here we highlight the most frequent complaints about usability from users. - Scalar tensors (aka zero-dim tensors) are not supported. For example, given `a = [0, 1, 2]`, `a[1]` will generate an `NDArray` of shape `(1,)`, instead of `()` as in NumPy. - Zero-size tensor is not supported. For example, a tensor of shape `(0, 16, 256)` cannot be passed to an operator, because our system currently treats 0, the first dimension size, as unknown, rather than a concrete number. - Many operators' signatures and functionality are not NumPy compatible, e.g. `nd.dot` vs. `np.dot`, `nd.concatenate` vs. `np.concatenate`, etc. - Many NumPy operators are missing. See the [reference link](https://github.com/apache/incubator-mxnet/issues?q=is%3Aissue+numpy+label%3ANumpy) to GitHub issues. - Operators whose outputs' shapes can only be determined at runtime are not supported, e.g. `data[data < 0]` cannot run. - Diverged programming experience due to the separation of imperative and symbolic operators registered under `mxnet.ndarray` and `mxnet.symbol`. - Control flow operators are hard to use. Users have to understand the complicated signatures of control flow operators, instead of writing native Python code using `for`, `while`, `if/else`, etc. For example, we have learned (in a hard way) that it does not make a lot of sense to ask users to write code like the following to perform a cumulative sum. ```python def sum(state, i): s = state + data[i] return s, [s, i + 1] def sum_cond(state, i): return i < 4 out, state = F.contrib.while_loop(sum_cond, sum, [F.zeros((1)), F.zeros((1))], max_iterations=5) ``` Instead, users should be able to just write native Python code as the following and if required, let the framework serialize it into a computation graph for optimization and deployment. ```python data = np.arange(5) out = 0 i = 0 while i < 5: out = out + data[i] ``` It is not hard to figure out that all of the above pain points can be summarized as a result from lack of NumPy-compatible coding experience in MXNet. While addressing the problems of better support of control flow operators and a consolidated coding style for writing imperative and symbolic code with more flexibility requires introducing fundamental changes into the codebase for building new infrastructures, such as a new graph IR and executor, which is extremely non-trivial and should be executed with a long-term plan, we can, at the moment, improve the usability by fixing the issue of zero-dim/size tensors and implementing NumPy operators in MXNet. Please allow us to discuss how to achieve these short-term goals in the following. ## Support of zero-dim and zero-size tensors ### What's the problem? Zero-dim and zero-size tensors are valid tensors in NumPy. The former, whose shapes are `()`, represent scalars in `numpy.ndarray` format. The latter, which have one or multiple zero dimension sizes in shapes, can be useful as a placeholder for many `ndarray` operations, such as concatenating a zero-size `ndarray` with another `ndarray`. MXNet does not support them due to the reserved semantics of empty shape `()` and shapes with zero dimension sizes indicating unknown shape information. Such information need to be filled out during the shape inference stage in order to move forward to tensor computations later. ### How to resolve the problem? We can first change the current semantics to comply with NumPy definition. 1. Change the definition of unknown shapes from `ndim = 0` to `ndim = -1` in `TShape` class. 2. Change the definition of unknown dimension sizes from `dim_size = 0` to `dim_size = -1` in `TShape` class. After this, we need to scan all over the codebase to modify the code accordingly where `shape.ndim() == 0` and `shape.Size() == 0` is used to perform unknown shape checks. Please note that although MXNet's shape is a type inheriting from `nnvm::Tuple`, which is
[incubator-mxnet-site] branch asf-site updated: Bump the publish timestamp.
This is an automated email from the ASF dual-hosted git repository. marcoabreu pushed a commit to branch asf-site in repository https://gitbox.apache.org/repos/asf/incubator-mxnet-site.git The following commit(s) were added to refs/heads/asf-site by this push: new ef553d4 Bump the publish timestamp. ef553d4 is described below commit ef553d47ac9bee0403a2f018630945894ac54f65 Author: mxnet-ci AuthorDate: Thu Aug 29 01:36:16 2019 + Bump the publish timestamp. --- date.txt | 1 + 1 file changed, 1 insertion(+) diff --git a/date.txt b/date.txt new file mode 100644 index 000..9a3780a --- /dev/null +++ b/date.txt @@ -0,0 +1 @@ +Thu Aug 29 01:36:16 UTC 2019
[GitHub] [incubator-mxnet] gyshi commented on issue #16025: Numpy add numpy op left_shift and right_shift
gyshi commented on issue #16025: Numpy add numpy op left_shift and right_shift URL: https://github.com/apache/incubator-mxnet/pull/16025#issuecomment-525984724 @haojin2 This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-mxnet] wkcn commented on a change in pull request #16017: Add RROIAlign
wkcn commented on a change in pull request #16017: Add RROIAlign
URL: https://github.com/apache/incubator-mxnet/pull/16017#discussion_r318850914
##
File path: src/operator/contrib/rroi_align-inl.h
##
@@ -0,0 +1,67 @@
+/*
+ * 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.
+ */
+
+/*!
+ * Copyright (c) 2015 by Contributors
+ * \file rroi_align-inl.h
+ * \brief rroi align operator and symbol
+ * \author Yixin Bao
+*/
+#ifndef MXNET_OPERATOR_CONTRIB_RROI_ALIGN_INL_H_
+#define MXNET_OPERATOR_CONTRIB_RROI_ALIGN_INL_H_
+
+#include
+#include
+#include
+#include
+#include
+#include
+#include
+#include "../mshadow_op.h"
+#include "../operator_common.h"
+
+namespace mxnet {
+namespace op {
+
+// Declare enumeration of input order to make code more intuitive.
+// These enums are only visible within this header
+namespace rroialign {
+enum RROIAlignOpInputs{kData, kBox};
+enum RROIAlignOpOutputs {kOut};
+} // rroialign
+
+struct RROIAlignParam : public dmlc::Parameter {
+ mxnet::TShape pooled_size;
+ float spatial_scale;
+ int sampling_ratio;
+ DMLC_DECLARE_PARAMETER(RROIAlignParam) {
+DMLC_DECLARE_FIELD(pooled_size)
+.set_expect_ndim(2).enforce_nonzero()
+.describe("RROI align output shape (h,w) ");
+DMLC_DECLARE_FIELD(spatial_scale).set_range(0.0, 1.0)
+.describe("Ratio of input feature map height (or w) to raw image height
(or w). "
Review comment:
It will be better to clarify what is 'or w', and I think it is 'or width'.
By the way, the copyright year can be changed to 2019 : )
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[GitHub] [incubator-mxnet] wkcn commented on a change in pull request #16017: Add RROIAlign
wkcn commented on a change in pull request #16017: Add RROIAlign
URL: https://github.com/apache/incubator-mxnet/pull/16017#discussion_r318853296
##
File path: src/operator/contrib/rroi_align.cc
##
@@ -0,0 +1,316 @@
+/*
+ * 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.
+ */
+
+/*!
+ * Copyright (c) 2015 by Contributors
+ * \file rroi_align.cc
+ * \brief rroi align operator
+ * \author Yixin Bao
+ * Forward pass adapted from Caffe2
+ * link:
https://github.com/pytorch/pytorch/blob/master/caffe2/operators/roi_align_rotated_op.cc
+ */
+#include "./rroi_align-inl.h"
+#include
+#include "math.h"
+
+using std::max;
+using std::min;
+using std::floor;
+using std::ceil;
+
+namespace mxnet {
+namespace op {
+
+template
+struct position_for_bilinear_interpolate {
+ // 4 positions and corresponding weights for
+ // computing bilinear interpolation
+ int pos1, pos2, pos3, pos4;
+ DType w1, w2, w3, w4;
+};
+
+template
+void pre_calc_for_bilinear_interpolate(
+const int height, const int width, const int pooled_height, const int
pooled_width,
+const int iy_upper, const int ix_upper, DType roi_start_h, DType
roi_start_w,
+DType bin_size_h, DType bin_size_w, int roi_bin_grid_h, int roi_bin_grid_w,
+DType roi_center_h, DType roi_center_w, DType theta,
+std::vector> *pre_calc) {
+ int pre_calc_index = 0;
+ DType cosTheta = cos(theta);
+ DType sinTheta = sin(theta);
+ for (int ph = 0; ph < pooled_height; ph++) {
+for (int pw = 0; pw < pooled_width; pw++) {
+ // calc bin grid position (xx,yy)
+ for (int iy = 0; iy < iy_upper; iy++) {
+const DType yy = roi_start_h + ph * bin_size_h +
+static_cast(iy + .5f) * bin_size_h /
+static_cast(roi_bin_grid_h); // e.g., 0.5, 1.5
+for (int ix = 0; ix < ix_upper; ix++) {
+ const DType xx = roi_start_w + pw * bin_size_w +
+ static_cast(ix + .5f) * bin_size_w /
+ static_cast(roi_bin_grid_w);
+
+ // Rotate by theta around the center and translate
+ DType x = xx * cosTheta + yy * sinTheta + roi_center_w;
+ DType y = yy * cosTheta - xx * sinTheta + roi_center_h;
+
+ // deal with: inverse elements are out of feature map boundary
+ if (y < -1.0 || y > height || x < -1.0 || x > width) {
+// empty
+position_for_bilinear_interpolate pc;
+pc.pos1 = 0;
+pc.pos2 = 0;
+pc.pos3 = 0;
+pc.pos4 = 0;
+pc.w1 = 0;
+pc.w2 = 0;
+pc.w3 = 0;
+pc.w4 = 0;
+pre_calc->at(pre_calc_index) = pc;
+pre_calc_index += 1;
+continue;
+ }
+ if (y <= 0) {
+y = 0;
+ }
+ if (x <= 0) {
+x = 0;
+ }
+
+ // calc 4 points for interpolation
+ int y_low = static_cast(y);
+ int x_low = static_cast(x);
+ int y_high;
+ int x_high;
+ if (y_low >= height - 1) {
+y_high = y_low = height - 1;
+y = (DType)y_low;
+ } else {
+y_high = y_low + 1;
+ }
+ if (x_low >= width - 1) {
+x_high = x_low = width - 1;
+x = (DType)x_low;
+ } else {
+x_high = x_low + 1;
+ }
+ DType ly = y - y_low;
+ DType lx = x - x_low;
+ DType hy = 1. - ly, hx = 1. - lx;
+ DType w1 = hy * hx, w2 = hy * lx, w3 = ly * hx, w4 = ly * lx;
+
+ // Save weights and indices
+ position_for_bilinear_interpolate pc;
Review comment:
`position_for_bilinear_interpolate &pc = (*pre_calc)[pre_calc_index];`
There will be no extra copy, and access by index is faster than .at()
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[GitHub] [incubator-mxnet] wkcn commented on a change in pull request #16017: Add RROIAlign
wkcn commented on a change in pull request #16017: Add RROIAlign
URL: https://github.com/apache/incubator-mxnet/pull/16017#discussion_r318850262
##
File path: src/operator/contrib/rroi_align.cc
##
@@ -0,0 +1,316 @@
+/*
+ * 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.
+ */
+
+/*!
+ * Copyright (c) 2015 by Contributors
+ * \file rroi_align.cc
+ * \brief rroi align operator
+ * \author Yixin Bao
+ * Forward pass adapted from Caffe2
+ * link:
https://github.com/pytorch/pytorch/blob/master/caffe2/operators/roi_align_rotated_op.cc
+ */
+#include "./rroi_align-inl.h"
+#include
+#include "math.h"
+
+using std::max;
+using std::min;
+using std::floor;
+using std::ceil;
+
+namespace mxnet {
+namespace op {
+
+template
+struct position_for_bilinear_interpolate {
+ // 4 positions and corresponding weights for
+ // computing bilinear interpolation
+ int pos1, pos2, pos3, pos4;
+ DType w1, w2, w3, w4;
+};
+
+template
+void pre_calc_for_bilinear_interpolate(
+const int height, const int width, const int pooled_height, const int
pooled_width,
+const int iy_upper, const int ix_upper, DType roi_start_h, DType
roi_start_w,
+DType bin_size_h, DType bin_size_w, int roi_bin_grid_h, int roi_bin_grid_w,
+DType roi_center_h, DType roi_center_w, DType theta,
+std::vector> *pre_calc) {
+ int pre_calc_index = 0;
+ DType cosTheta = cos(theta);
+ DType sinTheta = sin(theta);
+ for (int ph = 0; ph < pooled_height; ph++) {
+for (int pw = 0; pw < pooled_width; pw++) {
+ // calc bin grid position (xx,yy)
+ for (int iy = 0; iy < iy_upper; iy++) {
+const DType yy = roi_start_h + ph * bin_size_h +
+static_cast(iy + .5f) * bin_size_h /
+static_cast(roi_bin_grid_h); // e.g., 0.5, 1.5
+for (int ix = 0; ix < ix_upper; ix++) {
+ const DType xx = roi_start_w + pw * bin_size_w +
+ static_cast(ix + .5f) * bin_size_w /
+ static_cast(roi_bin_grid_w);
+
+ // Rotate by theta around the center and translate
+ DType x = xx * cosTheta + yy * sinTheta + roi_center_w;
+ DType y = yy * cosTheta - xx * sinTheta + roi_center_h;
+
+ // deal with: inverse elements are out of feature map boundary
+ if (y < -1.0 || y > height || x < -1.0 || x > width) {
+// empty
+position_for_bilinear_interpolate pc;
+pc.pos1 = 0;
+pc.pos2 = 0;
+pc.pos3 = 0;
+pc.pos4 = 0;
+pc.w1 = 0;
+pc.w2 = 0;
+pc.w3 = 0;
+pc.w4 = 0;
+pre_calc->at(pre_calc_index) = pc;
+pre_calc_index += 1;
+continue;
+ }
+ if (y <= 0) {
+y = 0;
+ }
+ if (x <= 0) {
+x = 0;
+ }
+
+ // calc 4 points for interpolation
+ int y_low = static_cast(y);
+ int x_low = static_cast(x);
+ int y_high;
+ int x_high;
+ if (y_low >= height - 1) {
+y_high = y_low = height - 1;
+y = (DType)y_low;
+ } else {
+y_high = y_low + 1;
+ }
+ if (x_low >= width - 1) {
+x_high = x_low = width - 1;
+x = (DType)x_low;
+ } else {
+x_high = x_low + 1;
+ }
+ DType ly = y - y_low;
+ DType lx = x - x_low;
+ DType hy = 1. - ly, hx = 1. - lx;
+ DType w1 = hy * hx, w2 = hy * lx, w3 = ly * hx, w4 = ly * lx;
+
+ // Save weights and indices
+ position_for_bilinear_interpolate pc;
+ pc.pos1 = y_low * width + x_low;
+ pc.pos2 = y_low * width + x_high;
+ pc.pos3 = y_high * width + x_low;
+ pc.pos4 = y_high * width + x_high;
+ pc.w1 = w1;
+ pc.w2 = w2;
+ pc.w3 = w3;
+ pc.w4 = w4;
+ pre_calc->at(pre_calc_index) = pc;
+
+ pre_calc_index += 1;
+}
+ }
+}
+ }
+}
+
+template
+inline void RROIAlignForward(const OpContext &ctx, const RROIAlignParam ¶m,
+ const std::vector &in_data, const
std::vector &req,
+ con
[GitHub] [incubator-mxnet] wkcn commented on a change in pull request #16017: Add RROIAlign
wkcn commented on a change in pull request #16017: Add RROIAlign
URL: https://github.com/apache/incubator-mxnet/pull/16017#discussion_r318853237
##
File path: src/operator/contrib/rroi_align.cc
##
@@ -0,0 +1,316 @@
+/*
+ * 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.
+ */
+
+/*!
+ * Copyright (c) 2015 by Contributors
+ * \file rroi_align.cc
+ * \brief rroi align operator
+ * \author Yixin Bao
+ * Forward pass adapted from Caffe2
+ * link:
https://github.com/pytorch/pytorch/blob/master/caffe2/operators/roi_align_rotated_op.cc
+ */
+#include "./rroi_align-inl.h"
+#include
+#include "math.h"
+
+using std::max;
+using std::min;
+using std::floor;
+using std::ceil;
+
+namespace mxnet {
+namespace op {
+
+template
+struct position_for_bilinear_interpolate {
+ // 4 positions and corresponding weights for
+ // computing bilinear interpolation
+ int pos1, pos2, pos3, pos4;
+ DType w1, w2, w3, w4;
+};
+
+template
+void pre_calc_for_bilinear_interpolate(
+const int height, const int width, const int pooled_height, const int
pooled_width,
+const int iy_upper, const int ix_upper, DType roi_start_h, DType
roi_start_w,
+DType bin_size_h, DType bin_size_w, int roi_bin_grid_h, int roi_bin_grid_w,
+DType roi_center_h, DType roi_center_w, DType theta,
+std::vector> *pre_calc) {
+ int pre_calc_index = 0;
+ DType cosTheta = cos(theta);
+ DType sinTheta = sin(theta);
+ for (int ph = 0; ph < pooled_height; ph++) {
+for (int pw = 0; pw < pooled_width; pw++) {
+ // calc bin grid position (xx,yy)
+ for (int iy = 0; iy < iy_upper; iy++) {
+const DType yy = roi_start_h + ph * bin_size_h +
+static_cast(iy + .5f) * bin_size_h /
+static_cast(roi_bin_grid_h); // e.g., 0.5, 1.5
+for (int ix = 0; ix < ix_upper; ix++) {
+ const DType xx = roi_start_w + pw * bin_size_w +
+ static_cast(ix + .5f) * bin_size_w /
+ static_cast(roi_bin_grid_w);
+
+ // Rotate by theta around the center and translate
+ DType x = xx * cosTheta + yy * sinTheta + roi_center_w;
+ DType y = yy * cosTheta - xx * sinTheta + roi_center_h;
+
+ // deal with: inverse elements are out of feature map boundary
+ if (y < -1.0 || y > height || x < -1.0 || x > width) {
+// empty
+position_for_bilinear_interpolate pc;
Review comment:
`position_for_bilinear_interpolate &pc = (*pre_calc)[pre_calc_index];`
There will be no extra copy, and access by index is faster than .at()
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[GitHub] [incubator-mxnet] ZhennanQin commented on a change in pull request #15886: Graph Partition API
ZhennanQin commented on a change in pull request #15886: Graph Partition API
URL: https://github.com/apache/incubator-mxnet/pull/15886#discussion_r318845381
##
File path: src/c_api/c_api_symbolic.cc
##
@@ -1199,3 +1200,73 @@ int MXShallowCopySymbol(SymbolHandle src, SymbolHandle*
out) {
*out = out_sym;
API_END_HANDLE_ERROR(delete out_sym);
}
+
+int MXOptimizeForBackend(SymbolHandle sym_handle,
+ const char* backend_name,
+ SymbolHandle* ret_sym_handle,
+ const mx_uint len,
+ NDArrayHandle* in_args_handle,
+ const mx_uint num_options,
+ const char** keys,
+ const char** vals) {
+ nnvm::Symbol *s = new nnvm::Symbol();
+ API_BEGIN();
+ nnvm::Symbol *sym = static_cast(sym_handle);
+ *s = sym->Copy();
+ nnvm::Graph g = Symbol2Graph(*s);
+ if (len) {
+NDArray **in_args_ptr = reinterpret_cast(in_args_handle);
+Context default_ctx = in_args_ptr[0]->ctx();
Review comment:
> Instead we'll pull the context from where the args reside.
args are normally loaded by
```
sym, args, aux = mx.model.load_checkpoint('resnet-50', 0)
```
No contexts are specified. That's why we need to pass the expected contexts
during bind. The contexts from args doesn't mean anything.
>We expect that users could call this more than once for the same model. For
example, users could first optimize for EIA (that would group supported nodes
into subgraphs) and then optimize for MKLDNN for the remaining nodes that were
not partitioned into subgraphs.
That's the comments you provided earlier. So I thought you're working to
support multi-context execution. If not, then you mean we only support running
multiple `optimize_for` for same contexts? Even for that, we need to pass
correct contexts for `optimize_for`, not the gathered one from args.
Now again, the correct contexts are important for `InferStorageTypes` for
MKLDNN backend, so we need to pass correct context(same as bind) to
`optimize_for`. If you don't want to introduce `ctx` for `optimize_for` API,
then I suggest to query the expected context from backend property, and use it
to pass `InferStorageTypes`. Or, you can figure out other solution to solve
this, I just want to ensure that subgraph partition can get same
`InferStorageTypes` results as bind.
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[incubator-mxnet-site] branch beta-site created (now a41bb1c)
This is an automated email from the ASF dual-hosted git repository. aaronmarkham pushed a change to branch beta-site in repository https://gitbox.apache.org/repos/asf/incubator-mxnet-site.git. at a41bb1c Nightly build This branch includes the following new commits: new 9399101 Nightly build new 042f077 Nightly build new d0df68a Bump the publish timestamp. new 0a689b8 Nightly build new 8763f3e Bump the publish timestamp. new 68e215d Nightly build new 8f2367f Bump the publish timestamp. new f3a5db0 Nightly build new 48db7f8 Bump the publish timestamp. new d10997b Nightly build new a975849 Bump the publish timestamp. new 640df3d Nightly build new 33f7334 Nightly build new f18c8f3 Bump the publish timestamp. new 9750706 Nightly build new 3232c84 Bump the publish timestamp. new c9e3e68 Nightly build new 0ffee7e Bump the publish timestamp. new 6cee844 Nightly build new e0dc4c0 Bump the publish timestamp. new 78d6f2a Nightly build new 3711dd4 Bump the publish timestamp. new 40e405b Nightly build new a86a8ca Bump the publish timestamp. new 832527e Nightly build new e0ce022 Bump the publish timestamp. new af8e89c Nightly build new b49410e Bump the publish timestamp. new c30ac37 Nightly build new a88895d Bump the publish timestamp. new 1de773b Nightly build new c24272a Bump the publish timestamp. new 5289ded Nightly build new bbc0359 Bump the publish timestamp. new dedbdb5 Nightly build new 2b983b1 Bump the publish timestamp. new 9ec240c Nightly build new d6d4e40 Bump the publish timestamp. new 265727a Nightly build new b6504d7 Bump the publish timestamp. new 409d11d Nightly build new d5f9413 Bump the publish timestamp. new 086340b Nightly build new 91eb702 Bump the publish timestamp. new bad227f Nightly build new 368b9c4 Bump the publish timestamp. new 7f98f17 Nightly build new 7ce5f01 Bump the publish timestamp. new 466f5ad Nightly build new 19b27d8 Bump the publish timestamp. new d9105f9 Nightly build new afbe32e Bump the publish timestamp. new 89c5e25 Nightly build new 98a3ec8 Bump the publish timestamp. new 25a16cf Nightly build new ff02aa6 Bump the publish timestamp. new 966a15b Nightly build new 6c738fe Bump the publish timestamp. new 763799f Nightly build new 003c4ef Bump the publish timestamp. new 7a30464 Nightly build new fdbabfd Bump the publish timestamp. new d3d1775 Nightly build new 5c43006 Bump the publish timestamp. new eea36db Nightly build new 29c8818 Bump the publish timestamp. new 107da96 Nightly build new 1fffe9e Bump the publish timestamp. new eb03af0 Nightly build new db11fa9 Bump the publish timestamp. new 06d379e Nightly build new 1b59243 Bump the publish timestamp. new 6ba692c Nightly build new 96faddb Bump the publish timestamp. new 657dcb3 Nightly build new 086a0b1 Bump the publish timestamp. new a320c67 Nightly build new 80d84a7 Bump the publish timestamp. new ac411b2 Nightly build new 95e4678 Bump the publish timestamp. new 1a46f06 Nightly build new bf79110 Bump the publish timestamp. new 73b3aaf Nightly build new 6b228c5 Bump the publish timestamp. new 9987af1 Nightly build new 2fe80ca Bump the publish timestamp. new 4bfa1d5 Nightly build new 0bee588 Bump the publish timestamp. new 51f12cd Nightly build new 24d3fa7 Bump the publish timestamp. new ed11ec2 Nightly build new 3cf886b Nightly build new a41bb1c Nightly build The 93 revisions listed above as "new" are entirely new to this repository and will be described in separate emails. The revisions listed as "add" were already present in the repository and have only been added to this reference.
[incubator-mxnet-site] branch beta-site updated: add staging trigger file
This is an automated email from the ASF dual-hosted git repository. aaronmarkham pushed a commit to branch beta-site in repository https://gitbox.apache.org/repos/asf/incubator-mxnet-site.git The following commit(s) were added to refs/heads/beta-site by this push: new 449d9d2 add staging trigger file 449d9d2 is described below commit 449d9d2cc9cbc2a179196a215a79d4a675893a98 Author: Aaron Markham AuthorDate: Wed Aug 28 17:03:22 2019 -0700 add staging trigger file --- .asf.yaml | 3 +++ 1 file changed, 3 insertions(+) diff --git a/.asf.yaml b/.asf.yaml new file mode 100644 index 000..968aa01 --- /dev/null +++ b/.asf.yaml @@ -0,0 +1,3 @@ +staging: + profile: beta +
[GitHub] [incubator-mxnet] apeforest commented on issue #16023: Revert "Refactor LibraryInitializer so it's thread safe. Fixes random sporadical concurrency crashes."
apeforest commented on issue #16023: Revert "Refactor LibraryInitializer so it's thread safe. Fixes random sporadical concurrency crashes." URL: https://github.com/apache/incubator-mxnet/pull/16023#issuecomment-525937670 Sure! @larroy could you please create a PR. I can merge it for you, This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-mxnet] reminisce commented on issue #16018: Port ops from np branch
reminisce commented on issue #16018: Port ops from np branch URL: https://github.com/apache/incubator-mxnet/pull/16018#issuecomment-525936882 @eric-haibin-lin I made a mistake by exposing `seed` directly in `npx`. Now it's moved to `npx.random`. The reason of putting it under `npx` instead of `np` is because our `seed` provides a different signature (with the extra parameter `ctx`) than the official NumPy seed function. In addition, we only accept integers, while numpy can also take `None` as seed. This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[incubator-mxnet] branch master updated (b2c0cbc -> 9b906a5)
This is an automated email from the ASF dual-hosted git repository. roywei pushed a change to branch master in repository https://gitbox.apache.org/repos/asf/incubator-mxnet.git. from b2c0cbc Windows cmake flags cleanup (#16013) add 9b906a5 Improve diagnose.py to display environment variables (#15715) No new revisions were added by this update. Summary of changes: tools/diagnose.py | 53 - 1 file changed, 36 insertions(+), 17 deletions(-)
[GitHub] [incubator-mxnet] roywei merged pull request #15715: Improve diagnose.py to display environment variables
roywei merged pull request #15715: Improve diagnose.py to display environment variables URL: https://github.com/apache/incubator-mxnet/pull/15715 This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-mxnet] larroy commented on a change in pull request #14836: Refactor AGInfo and Imperative
larroy commented on a change in pull request #14836: Refactor AGInfo and
Imperative
URL: https://github.com/apache/incubator-mxnet/pull/14836#discussion_r318802544
##
File path: src/imperative/imperative.cc
##
@@ -316,181 +308,220 @@ std::vector Imperative::Backward(
info.outputs.back() = static_cast(1.0);
}
}
+ return ograd_entries;
+}
- // Get gradient graph
- Symbol sym;
- sym.outputs = graph.outputs;
- std::vector xs;
- std::vector x_grads;
- std::vector x_reqs;
- if (variables.size()) {
-xs.reserve(variables.size());
-x_grads.reserve(variables.size());
-x_reqs.reserve(variables.size());
+struct Imperative::GradientVariableNodes {
+ std::vector variable_nodes;
+ std::vector gradients;
+ std::vector op_req_types;
+};
+
+Imperative::GradientVariableNodes Imperative::CreateGradientVariableNodes(
+const std::vector &variables,
+const std::vector &outputs) {
+ GradientVariableNodes var_nodes;
+ if (!variables.empty()) {
+var_nodes.variable_nodes.reserve(variables.size());
+var_nodes.gradients.reserve(variables.size());
+var_nodes.op_req_types.reserve(variables.size());
for (size_t i = 0; i < variables.size(); ++i) {
CHECK(!AGInfo::IsNone(*variables[i]) &&
-AGInfo::IsVariable(variables[i]->entry_.node))
+AGInfo::IsVariable(variables[i]->autograd_.node))
<< "Cannot differentiate with respect to the " << i+1 << "-th
variable"
- << " because it does not require gradient.";
- xs.emplace_back(variables[i]->entry_);
- x_grads.push_back(new NDArray());
- x_reqs.push_back(kWriteTo);
+ << " because it does not require gradient. Did you forget
attach_grad()?";
+ var_nodes.variable_nodes.emplace_back(variables[i]->autograd_);
+ var_nodes.gradients.push_back(new NDArray());
+ var_nodes.op_req_types.push_back(kWriteTo);
}
} else {
-std::vector args = sym.ListInputs(Symbol::kReadOnlyArgs);
-xs.reserve(args.size());
-x_grads.reserve(args.size());
-x_reqs.reserve(args.size());
-for (const auto& i : args) {
- AGInfo& info = AGInfo::Get(i);
- if (info.grad_req == kNullOp) continue;
- xs.emplace_back(NodeEntry{i, 0, 0});
- x_grads.push_back(&info.out_grads[0]);
- x_reqs.push_back(info.grad_req);
- info.fresh_out_grad = true;
+nnvm::Symbol s;
+s.outputs = outputs;
+std::vector input_ro_nodes =
s.ListInputs(Symbol::kReadOnlyArgs);
+var_nodes.variable_nodes.reserve(input_ro_nodes.size());
+var_nodes.gradients.reserve(input_ro_nodes.size());
+var_nodes.op_req_types.reserve(input_ro_nodes.size());
+for (const auto& node : input_ro_nodes) {
+ AGInfo& info = AGInfo::Get(node);
+ if (info.grad_req != kNullOp) {
+var_nodes.variable_nodes.emplace_back(node);
+var_nodes.gradients.push_back(&info.out_grads[0]);
+var_nodes.op_req_types.push_back(info.grad_req);
+info.fresh_out_grad = true;
+ }
}
-CHECK_GT(xs.size(), 0)
+CHECK_GT(var_nodes.variable_nodes.size(), 0)
<< "There are no inputs in computation graph that require gradients.";
}
+ return var_nodes;
+}
- Graph g_graph = pass::MXGradient(
- graph, graph.outputs, xs, ograd_entries,
+std::vector Imperative::Backward(
+const std::vector& outputs,
+const std::vector& ograds,
+const std::vector& variables,
+bool is_train, bool retain_graph,
+bool create_graph) {
+ using namespace nnvm;
+ using namespace imperative;
+ static const std::vector zero_ops{Op::Get("zeros_like"),
Op::Get("_zeros")};
+ static const Op* copy_op = Op::Get("_copy");
+
+ Graph graph = CreateGraph(outputs);
+
+ // Prepare head gradient nodes
+ std::vector ograd_entries = CreateHeadGradientNodes(outputs,
ograds);
+
+ // Get variable nodes
+ GradientVariableNodes gvars = CreateGradientVariableNodes(variables,
graph.outputs);
+
+ // Run backward on the graph
+ Graph gradient_graph = pass::MXGradient(
+ graph, graph.outputs, gvars.variable_nodes, ograd_entries,
exec::AggregateGradient, nullptr, nullptr,
zero_ops, "_copy");
- CHECK_EQ(g_graph.outputs.size(), xs.size());
- for (const auto& e : g_graph.outputs) {
-if (e.node->op() == nullptr) {
+
+ CHECK_EQ(gradient_graph.outputs.size(), gvars.variable_nodes.size());
+ std::vector forward_outputs = graph.outputs;
+ const size_t num_forward_outputs = graph.outputs.size();
+
+ // TODO(larroy): move inside pass::MXGradient
+ for (const auto& backward_node : gradient_graph.outputs) {
+if (backward_node.node->is_variable()) {
auto node = Node::Create();
node->attrs.op = copy_op;
- node->inputs.push_back(e);
+ node->inputs.push_back(backward_node);
graph.outputs.emplace_back(std::move(node));
} else {
- graph.outputs.push_back(e);
+ graph.outputs.push_back(backward_node);
}
}
- const auto& idx = graph.indexed_graph
[GitHub] [incubator-mxnet] larroy commented on issue #15715: Improve diagnose.py to display environment variables
larroy commented on issue #15715: Improve diagnose.py to display environment variables URL: https://github.com/apache/incubator-mxnet/pull/15715#issuecomment-525930415 @mxnet-label-bot add [pr-awaiting-merge] This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-mxnet] larroy commented on issue #15715: Improve diagnose.py to display environment variables
larroy commented on issue #15715: Improve diagnose.py to display environment variables URL: https://github.com/apache/incubator-mxnet/pull/15715#issuecomment-525930359 @mxnet-label-bot remove [pr-awaiting-review] This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-mxnet] samskalicky commented on a change in pull request #15886: Graph Partition API
samskalicky commented on a change in pull request #15886: Graph Partition API
URL: https://github.com/apache/incubator-mxnet/pull/15886#discussion_r318703729
##
File path: src/c_api/c_api_symbolic.cc
##
@@ -1199,3 +1200,73 @@ int MXShallowCopySymbol(SymbolHandle src, SymbolHandle*
out) {
*out = out_sym;
API_END_HANDLE_ERROR(delete out_sym);
}
+
+int MXOptimizeForBackend(SymbolHandle sym_handle,
+ const char* backend_name,
+ SymbolHandle* ret_sym_handle,
+ const mx_uint len,
+ NDArrayHandle* in_args_handle,
+ const mx_uint num_options,
+ const char** keys,
+ const char** vals) {
+ nnvm::Symbol *s = new nnvm::Symbol();
+ API_BEGIN();
+ nnvm::Symbol *sym = static_cast(sym_handle);
+ *s = sym->Copy();
+ nnvm::Graph g = Symbol2Graph(*s);
+ if (len) {
+NDArray **in_args_ptr = reinterpret_cast(in_args_handle);
+Context default_ctx = in_args_ptr[0]->ctx();
Review comment:
First, lets clarify that we already help users avoid this problem by not
explicitly accepting a ctx argument to optimize_for. Instead we'll pull the
context from where the args reside.
https://github.com/apache/incubator-mxnet/blob/9ccf6c60f38b1589595d4d5f6d31d00b846150dd/src/c_api/c_api_symbolic.cc#L1219-L1232
We're not trying to support multi-context execution yet.
We checked and theres no way to "mark" or set an attribute on the
partitioned symbol that we return. So for now we cannot enforce the requirement
that the same context be used for subsequent calls to optimize_for or calls to
bind.
But we expect to have optimization passes that could be context-agnostic.
And some that are context-specific. So the way MXNet currently enforces this is
by having context-specific FCompute functions for operators. This just means
that context-specific optimizations should insert operators that only have an
FCompute function registered for that context.
So multiple calls to optimize_for with different contexts would succeed. But
at runtime executing the subgraph operator on a different context would fail.
Thats the current and only way to enforce this. So the requirement will be that
the subgraph property inserts subgraph operators that only support that context
by only defining FCompute for the desired context.
This is already implemented:
https://github.com/apache/incubator-mxnet/blob/b2c0cbc8ea4defa0a8b53fb628a9679accca281a/src/operator/subgraph/mkldnn/mkldnn_conv.cc#L780
or
https://github.com/apache/incubator-mxnet/blob/b2c0cbc8ea4defa0a8b53fb628a9679accca281a/src/operator/subgraph/tensorrt/tensorrt.cu#L64
So if we ran the following example:
```
sym = sym.optimize_for('MKLDNN', ctx= mx.cpu(), args)
sym = sym.optimize_for('TensorRT', ctx= mx.gpu(), args)
```
the calls to optimize_for would succeed. Presumably there will be both
mkldnn and tensorrt subgraph ops inserted. But at runtime the user will see an
error about one or the other not being supported on that context.
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[incubator-mxnet] branch master updated (8df9469 -> b2c0cbc)
This is an automated email from the ASF dual-hosted git repository. yuxihu pushed a change to branch master in repository https://gitbox.apache.org/repos/asf/incubator-mxnet.git. from 8df9469 Refines NDArray indexing and adds numpy ndarray indexing [READY FOR REVIEW] (#15942) add b2c0cbc Windows cmake flags cleanup (#16013) No new revisions were added by this update. Summary of changes: ci/build_windows.py | 165 cmake/cmake_options.yml | 1 - 2 files changed, 83 insertions(+), 83 deletions(-)
[GitHub] [incubator-mxnet] samskalicky commented on a change in pull request #15886: Graph Partition API
samskalicky commented on a change in pull request #15886: Graph Partition API
URL: https://github.com/apache/incubator-mxnet/pull/15886#discussion_r318703729
##
File path: src/c_api/c_api_symbolic.cc
##
@@ -1199,3 +1200,73 @@ int MXShallowCopySymbol(SymbolHandle src, SymbolHandle*
out) {
*out = out_sym;
API_END_HANDLE_ERROR(delete out_sym);
}
+
+int MXOptimizeForBackend(SymbolHandle sym_handle,
+ const char* backend_name,
+ SymbolHandle* ret_sym_handle,
+ const mx_uint len,
+ NDArrayHandle* in_args_handle,
+ const mx_uint num_options,
+ const char** keys,
+ const char** vals) {
+ nnvm::Symbol *s = new nnvm::Symbol();
+ API_BEGIN();
+ nnvm::Symbol *sym = static_cast(sym_handle);
+ *s = sym->Copy();
+ nnvm::Graph g = Symbol2Graph(*s);
+ if (len) {
+NDArray **in_args_ptr = reinterpret_cast(in_args_handle);
+Context default_ctx = in_args_ptr[0]->ctx();
Review comment:
First, lets clarify that we already help users avoid this problem by not
explicitly accepting a ctx argument to optimize_for. Instead we'll pull the
context from where the args reside.
https://github.com/apache/incubator-mxnet/blob/9ccf6c60f38b1589595d4d5f6d31d00b846150dd/src/c_api/c_api_symbolic.cc#L1219-L1232
We're not trying to support multi-context execution yet.
We checked and theres no way to "mark" or set an attribute on the
partitioned symbol that we return. So for now we cannot enforce the requirement
that the same context be used for subsequent calls to optimize_for or calls to
bind.
But we expect to have optimization passes that could be context-agnostic.
And some that are context-specific. So the way MXNet currently enforces this is
by having context-specific FCompute functions for operators. This just means
that context-specific optimizations should insert operators that only have an
FCompute function registered for that context.
So multiple calls to optimize_for with different contexts would succeed. But
at runtime executing the subgraph operator on a different context would fail.
Thats the current and only way to enforce this. So the requirement will be that
the subgraph property inserts subgraph operators that only support that context
by only defining FCompute for the desired context.
This is already implemented:
https://github.com/apache/incubator-mxnet/blob/b2c0cbc8ea4defa0a8b53fb628a9679accca281a/src/operator/subgraph/mkldnn/mkldnn_conv.cc#L780
or
https://github.com/apache/incubator-mxnet/blob/master/src/operator/subgraph/tensorrt/tensorrt.cu#L64
So if we ran the following example:
```
sym = sym.optimize_for('MKLDNN', ctx= mx.cpu(), args)
sym = sym.optimize_for('TensorRT', ctx= mx.gpu(), args)
```
the calls to optimize_for would succeed. Presumably there will be both
mkldnn and tensorrt subgraph ops inserted. But at runtime the user will see an
error about one or the other not being supported on that context.
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[GitHub] [incubator-mxnet] yuxihu merged pull request #16013: Windows cmake flags cleanup
yuxihu merged pull request #16013: Windows cmake flags cleanup URL: https://github.com/apache/incubator-mxnet/pull/16013 This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-mxnet] larroy commented on issue #16023: Revert "Refactor LibraryInitializer so it's thread safe. Fixes random sporadical concurrency crashes."
larroy commented on issue #16023: Revert "Refactor LibraryInitializer so it's thread safe. Fixes random sporadical concurrency crashes." URL: https://github.com/apache/incubator-mxnet/pull/16023#issuecomment-525929544 @apeforest another option is not to revert the fix and just restore the CMakefile. This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-mxnet] samskalicky commented on a change in pull request #15886: Graph Partition API
samskalicky commented on a change in pull request #15886: Graph Partition API
URL: https://github.com/apache/incubator-mxnet/pull/15886#discussion_r318703729
##
File path: src/c_api/c_api_symbolic.cc
##
@@ -1199,3 +1200,73 @@ int MXShallowCopySymbol(SymbolHandle src, SymbolHandle*
out) {
*out = out_sym;
API_END_HANDLE_ERROR(delete out_sym);
}
+
+int MXOptimizeForBackend(SymbolHandle sym_handle,
+ const char* backend_name,
+ SymbolHandle* ret_sym_handle,
+ const mx_uint len,
+ NDArrayHandle* in_args_handle,
+ const mx_uint num_options,
+ const char** keys,
+ const char** vals) {
+ nnvm::Symbol *s = new nnvm::Symbol();
+ API_BEGIN();
+ nnvm::Symbol *sym = static_cast(sym_handle);
+ *s = sym->Copy();
+ nnvm::Graph g = Symbol2Graph(*s);
+ if (len) {
+NDArray **in_args_ptr = reinterpret_cast(in_args_handle);
+Context default_ctx = in_args_ptr[0]->ctx();
Review comment:
First, lets clarify that we already help users avoid this problem by not
explicitly accepting a ctx argument to optimize_for. Instead we'll pull the
context from where the args reside.
https://github.com/apache/incubator-mxnet/blob/9ccf6c60f38b1589595d4d5f6d31d00b846150dd/src/c_api/c_api_symbolic.cc#L1219-L1232
We're not trying to support multi-context execution yet.
We checked and theres no way to "mark" or set an attribute on the
partitioned symbol that we return. So for now we cannot enforce the requirement
that the same context be used for subsequent calls to optimize_for or calls to
bind.
But we expect to have optimization passes that could be context-agnostic.
And some that are context-specific. So the way MXNet currently enforces this is
by having context-specific FCompute functions for operators. This just means
that context-specific optimizations should insert operators that only have an
FCompute function registered for that context.
So multiple calls to optimize_for with different contexts would succeed. But
at runtime executing the subgraph operator on a different context would fail.
Thats the current and only way to enforce this. So the requirement will be that
the subgraph property inserts subgraph operators that only support that context
by only defining FCompute for the desired context.
This is already implemented:
https://github.com/apache/incubator-mxnet/blob/master/src/operator/subgraph/mkldnn/mkldnn_conv.cc#L780
or
https://github.com/apache/incubator-mxnet/blob/master/src/operator/subgraph/tensorrt/tensorrt.cu#L64
So if we ran the following example:
```
sym = sym.optimize_for('MKLDNN', ctx= mx.cpu(), args)
sym = sym.optimize_for('TensorRT', ctx= mx.gpu(), args)
```
the calls to optimize_for would succeed. Presumably there will be both
mkldnn and tensorrt subgraph ops inserted. But at runtime the user will see an
error about one or the other not being supported on that context.
This is an automated message from the Apache Git Service.
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[GitHub] [incubator-mxnet] larroy commented on issue #16013: Windows cmake flags cleanup
larroy commented on issue #16013: Windows cmake flags cleanup URL: https://github.com/apache/incubator-mxnet/pull/16013#issuecomment-525929174 USE_PROFILER was deprecated by @ChaiBapchya ON OFF is preferred as it was requested to have multi value flag for the openmp flag in a different PR. This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-mxnet] yuxihu commented on issue #16013: Windows cmake flags cleanup
yuxihu commented on issue #16013: Windows cmake flags cleanup URL: https://github.com/apache/incubator-mxnet/pull/16013#issuecomment-525928813 The changes look straightforward. Two questisons: 1. ON/OFF is the preferred/recommended way of setting cmake flags? 0/1 also worked, right? 2. Why removing USE_PROFILER? Is it ON in other platforms? I think we have profiler related tests. Would it be affected? This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[incubator-mxnet] branch v1.5.x updated (bf78959 -> 49c6ee2)
This is an automated email from the ASF dual-hosted git repository. roywei pushed a change to branch v1.5.x in repository https://gitbox.apache.org/repos/asf/incubator-mxnet.git. from bf78959 added check for empty params file and unknown param (not arg/aux) (#15917) add 49c6ee2 remove Julia cat image for license issue (#15964) (#16026) No new revisions were added by this update. Summary of changes: .../Prediction with Pre-trained Model.ipynb| 4 ++-- .../imagenet/ijulia-pretrained-predict/cat.png | Bin 123126 -> 0 bytes 2 files changed, 2 insertions(+), 2 deletions(-) delete mode 100644 julia/examples/imagenet/ijulia-pretrained-predict/cat.png
[GitHub] [incubator-mxnet] roywei merged pull request #16026: [v1.5.x] remove Julia cat image for license issue (#15964)
roywei merged pull request #16026: [v1.5.x] remove Julia cat image for license issue (#15964) URL: https://github.com/apache/incubator-mxnet/pull/16026 This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-mxnet] larroy commented on issue #15998: Build MXNET with GPU but fail to import mxnet on CPU machine
larroy commented on issue #15998: Build MXNET with GPU but fail to import mxnet on CPU machine URL: https://github.com/apache/incubator-mxnet/issues/15998#issuecomment-525924577 I think you need to figure out the paths for runtime libraries, if you link with cuda you need cuda or a cuda stub on runtime. This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-mxnet] larroy commented on issue #15998: Build MXNET with GPU but fail to import mxnet on CPU machine
larroy commented on issue #15998: Build MXNET with GPU but fail to import mxnet on CPU machine URL: https://github.com/apache/incubator-mxnet/issues/15998#issuecomment-525924358 @mxnet-label-bot add [Question] This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[incubator-mxnet-site] branch asf-site updated: Bump the publish timestamp.
This is an automated email from the ASF dual-hosted git repository. marcoabreu pushed a commit to branch asf-site in repository https://gitbox.apache.org/repos/asf/incubator-mxnet-site.git The following commit(s) were added to refs/heads/asf-site by this push: new d2d55c1 Bump the publish timestamp. d2d55c1 is described below commit d2d55c18ca4381a3ac307c7bc1f8475fca4bdf2d Author: mxnet-ci AuthorDate: Wed Aug 28 21:02:52 2019 + Bump the publish timestamp. --- date.txt | 1 + 1 file changed, 1 insertion(+) diff --git a/date.txt b/date.txt new file mode 100644 index 000..9a45687 --- /dev/null +++ b/date.txt @@ -0,0 +1 @@ +Wed Aug 28 21:02:52 UTC 2019
[incubator-mxnet-site] branch asf-site updated: Bump the publish timestamp.
This is an automated email from the ASF dual-hosted git repository. marcoabreu pushed a commit to branch asf-site in repository https://gitbox.apache.org/repos/asf/incubator-mxnet-site.git The following commit(s) were added to refs/heads/asf-site by this push: new 402fac1 Bump the publish timestamp. 402fac1 is described below commit 402fac1c8f19484c5cea0897963b3fb91add1a24 Author: mxnet-ci AuthorDate: Wed Aug 28 19:29:34 2019 + Bump the publish timestamp. --- date.txt | 1 + 1 file changed, 1 insertion(+) diff --git a/date.txt b/date.txt new file mode 100644 index 000..e730bdd --- /dev/null +++ b/date.txt @@ -0,0 +1 @@ +Wed Aug 28 19:29:34 UTC 2019
[GitHub] [incubator-mxnet] leleamol commented on issue #14203: [Bug]Cannot compile mxnet on windows
leleamol commented on issue #14203: [Bug]Cannot compile mxnet on windows URL: https://github.com/apache/incubator-mxnet/issues/14203#issuecomment-525876884 @mxnet-label-bot add [Pending Requester Info] This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-mxnet] anirudh2290 opened a new pull request #16031: Disable flaky test in test_amp_conversion
anirudh2290 opened a new pull request #16031: Disable flaky test in test_amp_conversion URL: https://github.com/apache/incubator-mxnet/pull/16031 ## Description ## Disable Flaky test, Please see : https://github.com/apache/incubator-mxnet/issues/16030 ## Checklist ## ### Essentials ### Please feel free to remove inapplicable items for your PR. - [ ] The PR title starts with [MXNET-$JIRA_ID], where $JIRA_ID refers to the relevant [JIRA issue](https://issues.apache.org/jira/projects/MXNET/issues) created (except PRs with tiny changes) - [ ] Changes are complete (i.e. I finished coding on this PR) - [ ] All changes have test coverage: - Unit tests are added for small changes to verify correctness (e.g. adding a new operator) - Nightly tests are added for complicated/long-running ones (e.g. changing distributed kvstore) - Build tests will be added for build configuration changes (e.g. adding a new build option with NCCL) - [ ] Code is well-documented: - For user-facing API changes, API doc string has been updated. - For new C++ functions in header files, their functionalities and arguments are documented. - For new examples, README.md is added to explain the what the example does, the source of the dataset, expected performance on test set and reference to the original paper if applicable - Check the API doc at http://mxnet-ci-doc.s3-accelerate.dualstack.amazonaws.com/PR-$PR_ID/$BUILD_ID/index.html - [ ] To the my best knowledge, examples are either not affected by this change, or have been fixed to be compatible with this change ### Changes ### - [ ] Feature1, tests, (and when applicable, API doc) - [ ] Feature2, tests, (and when applicable, API doc) ## Comments ## - If this change is a backward incompatible change, why must this change be made. - Interesting edge cases to note here This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-mxnet] mxnet-label-bot commented on issue #16030: Flaky test : test_amp_conversion
mxnet-label-bot commented on issue #16030: Flaky test : test_amp_conversion URL: https://github.com/apache/incubator-mxnet/issues/16030#issuecomment-525872074 Hey, this is the MXNet Label Bot. Thank you for submitting the issue! I will try and suggest some labels so that the appropriate MXNet community members can help resolve it. Here are my recommended label(s): Test, Flaky This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-mxnet] eric-haibin-lin commented on issue #16020: Is there any communication between parameter servers?
eric-haibin-lin commented on issue #16020: Is there any communication between parameter servers? URL: https://github.com/apache/incubator-mxnet/issues/16020#issuecomment-525872064 No, but there's all-to-all communication between workers and servers This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-mxnet] anirudh2290 opened a new issue #16030: Flaky test : test_amp_conversion
anirudh2290 opened a new issue #16030: Flaky test : test_amp_conversion URL: https://github.com/apache/incubator-mxnet/issues/16030 ### Description Flaky test, test_amp_conversion : http://jenkins.mxnet-ci.amazon-ml.com/blue/organizations/jenkins/mxnet-validation%2Funix-gpu/detail/PR-15943/26/pipeline/315 . This seems to be only happening when cast_optional_param=True. Will add a PR commenting out this part of the test for now and will investigate further. This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-mxnet] ChaiBapchya edited a comment on issue #15757: [Discussion] Unified performance tests and dashboard
ChaiBapchya edited a comment on issue #15757: [Discussion] Unified performance tests and dashboard URL: https://github.com/apache/incubator-mxnet/issues/15757#issuecomment-525860730 Yes. (This error is probably caused because incorrect file is being used. It was previously used for testing on my branch. But now with latest master, `opperf.py` file is good to use.) Few pointers - 1. Don't use separate file for testing large tensor `opperf_large_tensor.py`. Functionality has been merged into the original opperf.py file. 2. All the operators that have been benchmarked so far in the opperf utility (in the master branch) can be profiled with native/python. 3. Inclusion of python time module via flag 4. Adding more operators to improve coverage For current master branch, All you've to do now for the opperf utility is run `python opperf.py` with your desired flags `--ctx=cpu -p python` It will run all the ops supported without error. Let me know if that helps. This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-mxnet] ChaiBapchya edited a comment on issue #15757: [Discussion] Unified performance tests and dashboard
ChaiBapchya edited a comment on issue #15757: [Discussion] Unified performance tests and dashboard URL: https://github.com/apache/incubator-mxnet/issues/15757#issuecomment-525860730 Yes. (This error is probably caused because incorrect file is being used (it was used for testing previously on my branch. But now master's opperf file is good to use.) 1. Don't use separate file for testing large tensor `opperf_large_tensor.py`. Functionality has been merged into the original opperf.py file. 2. All the operators that have been benchmarked so far in the opperf utility (in the master branch) can be profiled with native/python. 3. Inclusion of python time module via flag 4. Adding more operators to improve coverage For current master branch, All you've to do now for the opperf utility is run `python opperf.py` with your desired flags `--ctx=cpu -p python` It will run all the ops supported without error. Let me know if that helps. This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-mxnet] ChaiBapchya edited a comment on issue #15757: [Discussion] Unified performance tests and dashboard
ChaiBapchya edited a comment on issue #15757: [Discussion] Unified performance tests and dashboard URL: https://github.com/apache/incubator-mxnet/issues/15757#issuecomment-525860730 Yes. (This error is probably caused because incorrect file is being used (it was used for testing previously on my branch. But now master's opperf file is good to use.) 1. Don't use separate file for testing large tensor `opperf_large_tensor.py`. Functionality has been merged into the original opperf.py file. 2. All the operators that have been benchmarked so far in the opperf utility (in the master branch) can be profiled with native/python. 3. Inclusion of python time module via flag 4. Adding more operators to improve coverage For current master branch, All you've to do now for the opperf utility is run python opperf.py with your desired flags `--ctx=cpu -p python` It will run all the ops supported without error. Let me know if that helps. This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-mxnet] ChaiBapchya commented on issue #15757: [Discussion] Unified performance tests and dashboard
ChaiBapchya commented on issue #15757: [Discussion] Unified performance tests and dashboard URL: https://github.com/apache/incubator-mxnet/issues/15757#issuecomment-525860730 Yes. (This error is probably caused because using the incorrect file being used) 1. Don't use separate file for testing large tensor `opperf_large_tensor.py`. Functionality has been merged into the original opperf.py file. 2. All the operators that have been benchmarked so far in the opperf utility (in the master branch) can be profiled with native/python. 3. Inclusion of python time module via flag 4. Adding more operators to improve coverage For current master branch, All you've to do now for the opperf utility is run python opperf.py with your desired flags `--ctx=cpu -p python` It will run all the ops supported without error. Let me know if that helps. This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[incubator-mxnet] branch v1.5.x updated (33f4de1 -> bf78959)
This is an automated email from the ASF dual-hosted git repository. anirudh2290 pushed a change to branch v1.5.x in repository https://gitbox.apache.org/repos/asf/incubator-mxnet.git. from 33f4de1 Revert "Fix a memory misalignment in topk operator" (#15999) add bf78959 added check for empty params file and unknown param (not arg/aux) (#15917) No new revisions were added by this update. Summary of changes: python/mxnet/model.py | 19 +-- 1 file changed, 13 insertions(+), 6 deletions(-)
[GitHub] [incubator-mxnet] anirudh2290 commented on issue #16028: added check for empty params file and unknown param (not arg/aux) (#1…
anirudh2290 commented on issue #16028: added check for empty params file and unknown param (not arg/aux) (#1… URL: https://github.com/apache/incubator-mxnet/pull/16028#issuecomment-525853819 Rebased and merged. This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-mxnet] anirudh2290 merged pull request #16028: added check for empty params file and unknown param (not arg/aux) (#1…
anirudh2290 merged pull request #16028: added check for empty params file and unknown param (not arg/aux) (#1… URL: https://github.com/apache/incubator-mxnet/pull/16028 This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-mxnet] samskalicky commented on a change in pull request #15886: Graph Partition API
samskalicky commented on a change in pull request #15886: Graph Partition API
URL: https://github.com/apache/incubator-mxnet/pull/15886#discussion_r318703729
##
File path: src/c_api/c_api_symbolic.cc
##
@@ -1199,3 +1200,73 @@ int MXShallowCopySymbol(SymbolHandle src, SymbolHandle*
out) {
*out = out_sym;
API_END_HANDLE_ERROR(delete out_sym);
}
+
+int MXOptimizeForBackend(SymbolHandle sym_handle,
+ const char* backend_name,
+ SymbolHandle* ret_sym_handle,
+ const mx_uint len,
+ NDArrayHandle* in_args_handle,
+ const mx_uint num_options,
+ const char** keys,
+ const char** vals) {
+ nnvm::Symbol *s = new nnvm::Symbol();
+ API_BEGIN();
+ nnvm::Symbol *sym = static_cast(sym_handle);
+ *s = sym->Copy();
+ nnvm::Graph g = Symbol2Graph(*s);
+ if (len) {
+NDArray **in_args_ptr = reinterpret_cast(in_args_handle);
+Context default_ctx = in_args_ptr[0]->ctx();
Review comment:
First, lets clarify that we already help users avoid this problem by not
explicitly accepting a ctx argument to optimize_for. Instead we'll pull the
context from where the args reside.
https://github.com/apache/incubator-mxnet/blob/9ccf6c60f38b1589595d4d5f6d31d00b846150dd/src/c_api/c_api_symbolic.cc#L1219-L1232
We're not trying to support multi-context execution yet. So I think we
should add some check/guard to prevent this situation for now. We can remove it
later when we actively support multi-context and auto-insertion of copy
operators from one context to another.
We already set a context on the graph here:
https://github.com/apache/incubator-mxnet/blob/9ccf6c60f38b1589595d4d5f6d31d00b846150dd/src/c_api/c_api_symbolic.cc#L1235-L1236
We can add a check if it already exists and validate that the same context
is used for each partitioning and bind.
That way we can make sure users have a good experience by enforcing what we
currently support.
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[GitHub] [incubator-mxnet] leleamol edited a comment on issue #15997: MxNet triggered Segmentation Fault when using together with Ray or PyTorch
leleamol edited a comment on issue #15997: MxNet triggered Segmentation Fault when using together with Ray or PyTorch URL: https://github.com/apache/incubator-mxnet/issues/15997#issuecomment-525479325 @mxnet-label-bot add [Bug, Memory, Pending Requester Info] This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-mxnet] samskalicky commented on a change in pull request #15886: Graph Partition API
samskalicky commented on a change in pull request #15886: Graph Partition API
URL: https://github.com/apache/incubator-mxnet/pull/15886#discussion_r318703729
##
File path: src/c_api/c_api_symbolic.cc
##
@@ -1199,3 +1200,73 @@ int MXShallowCopySymbol(SymbolHandle src, SymbolHandle*
out) {
*out = out_sym;
API_END_HANDLE_ERROR(delete out_sym);
}
+
+int MXOptimizeForBackend(SymbolHandle sym_handle,
+ const char* backend_name,
+ SymbolHandle* ret_sym_handle,
+ const mx_uint len,
+ NDArrayHandle* in_args_handle,
+ const mx_uint num_options,
+ const char** keys,
+ const char** vals) {
+ nnvm::Symbol *s = new nnvm::Symbol();
+ API_BEGIN();
+ nnvm::Symbol *sym = static_cast(sym_handle);
+ *s = sym->Copy();
+ nnvm::Graph g = Symbol2Graph(*s);
+ if (len) {
+NDArray **in_args_ptr = reinterpret_cast(in_args_handle);
+Context default_ctx = in_args_ptr[0]->ctx();
Review comment:
First, lets clarify that we already help users avoid this problem by not
explicitly accepting a ctx argument to optimize_for. Instead we'll pull the
context from where the args reside.
https://github.com/apache/incubator-mxnet/blob/9ccf6c60f38b1589595d4d5f6d31d00b846150dd/src/c_api/c_api_symbolic.cc#L1219-L1232
We're not trying to support multi-context execution yet. So I think we
should add some check/guard to prevent this situation for now. We can remove it
later when we actively support multi-context and auto-insertion of copy
operators from one context to another.
Lets set an attribute on the graph like "PartitionedContext" and assign it
the first time we do a partition. And we'll check it if it already exists and
validate that the same context is used for each partitioning and bind.
That way we can make sure users have a good experience by enforcing what we
currently support.
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[GitHub] [incubator-mxnet] samskalicky commented on a change in pull request #15886: Graph Partition API
samskalicky commented on a change in pull request #15886: Graph Partition API
URL: https://github.com/apache/incubator-mxnet/pull/15886#discussion_r318703729
##
File path: src/c_api/c_api_symbolic.cc
##
@@ -1199,3 +1200,73 @@ int MXShallowCopySymbol(SymbolHandle src, SymbolHandle*
out) {
*out = out_sym;
API_END_HANDLE_ERROR(delete out_sym);
}
+
+int MXOptimizeForBackend(SymbolHandle sym_handle,
+ const char* backend_name,
+ SymbolHandle* ret_sym_handle,
+ const mx_uint len,
+ NDArrayHandle* in_args_handle,
+ const mx_uint num_options,
+ const char** keys,
+ const char** vals) {
+ nnvm::Symbol *s = new nnvm::Symbol();
+ API_BEGIN();
+ nnvm::Symbol *sym = static_cast(sym_handle);
+ *s = sym->Copy();
+ nnvm::Graph g = Symbol2Graph(*s);
+ if (len) {
+NDArray **in_args_ptr = reinterpret_cast(in_args_handle);
+Context default_ctx = in_args_ptr[0]->ctx();
Review comment:
First, lets clarify that we already help users avoid this problem by not
explicitly accepting a ctx argument to optimize_for. Instead we'll pull the
context from where the args reside.
https://github.com/apache/incubator-mxnet/pull/15886/files#diff-48b64958e69c6674970a3ddbd1bd91e5R1219-R1232
We're not trying to support multi-context execution yet. So I think we
should add some check/guard to prevent this situation for now. We can remove it
later when we actively support multi-context and auto-insertion of copy
operators from one context to another.
Lets set an attribute on the graph like "PartitionedContext" and assign it
the first time we do a partition. And we'll check it if it already exists and
validate that the same context is used for each partitioning and bind.
That way we can make sure users have a good experience by enforcing what we
currently support.
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To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
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[GitHub] [incubator-mxnet] ChaiBapchya opened a new pull request #16029: Benchmark doc fix (#15769)
ChaiBapchya opened a new pull request #16029: Benchmark doc fix (#15769) URL: https://github.com/apache/incubator-mxnet/pull/16029 * Update pre-req for opperf * Update README.md * correct command to import binary broadcast * no such op called nd.sub, it is nd.subtract * Trigger notification * Trigger notification This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-mxnet] ChaiBapchya commented on issue #16029: Benchmark doc fix (#15769)
ChaiBapchya commented on issue #16029: Benchmark doc fix (#15769) URL: https://github.com/apache/incubator-mxnet/pull/16029#issuecomment-525840777 @TaoLv This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-mxnet] leleamol edited a comment on issue #15927: sym.Variable input init need init.dumps(), but NDarray is not JSON serializable
leleamol edited a comment on issue #15927: sym.Variable input init need init.dumps(), but NDarray is not JSON serializable URL: https://github.com/apache/incubator-mxnet/issues/15927#issuecomment-525834629 @mxnet-label-bot add [Pending Requester Info] This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-mxnet] leleamol commented on issue #15927: sym.Variable input init need init.dumps(), but NDarray is not JSON serializable
leleamol commented on issue #15927: sym.Variable input init need init.dumps(), but NDarray is not JSON serializable URL: https://github.com/apache/incubator-mxnet/issues/15927#issuecomment-525834629 @mxnet-label-bot add [Pending Requster Info] This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-mxnet] samskalicky commented on issue #16028: added check for empty params file and unknown param (not arg/aux) (#1…
samskalicky commented on issue #16028: added check for empty params file and unknown param (not arg/aux) (#1… URL: https://github.com/apache/incubator-mxnet/pull/16028#issuecomment-525832832 @TaoLv This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-mxnet] samskalicky opened a new pull request #16028: added check for empty params file and unknown param (not arg/aux) (#1…
samskalicky opened a new pull request #16028: added check for empty params file and unknown param (not arg/aux) (#1… URL: https://github.com/apache/incubator-mxnet/pull/16028 …5917) * added check for empty params file and unknown param (not arg/aux) * changed exception to warning for unknown params * removed unnecessary MXNetError import * added warning message is params is empty * fixed print * fixed formatting * missing paren ## Description ## (Brief description on what this PR is about) ## Checklist ## ### Essentials ### Please feel free to remove inapplicable items for your PR. - [ ] The PR title starts with [MXNET-$JIRA_ID], where $JIRA_ID refers to the relevant [JIRA issue](https://issues.apache.org/jira/projects/MXNET/issues) created (except PRs with tiny changes) - [ ] Changes are complete (i.e. I finished coding on this PR) - [ ] All changes have test coverage: - Unit tests are added for small changes to verify correctness (e.g. adding a new operator) - Nightly tests are added for complicated/long-running ones (e.g. changing distributed kvstore) - Build tests will be added for build configuration changes (e.g. adding a new build option with NCCL) - [ ] Code is well-documented: - For user-facing API changes, API doc string has been updated. - For new C++ functions in header files, their functionalities and arguments are documented. - For new examples, README.md is added to explain the what the example does, the source of the dataset, expected performance on test set and reference to the original paper if applicable - Check the API doc at http://mxnet-ci-doc.s3-accelerate.dualstack.amazonaws.com/PR-$PR_ID/$BUILD_ID/index.html - [ ] To the my best knowledge, examples are either not affected by this change, or have been fixed to be compatible with this change ### Changes ### - [ ] Feature1, tests, (and when applicable, API doc) - [ ] Feature2, tests, (and when applicable, API doc) ## Comments ## - If this change is a backward incompatible change, why must this change be made. - Interesting edge cases to note here This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-mxnet] leleamol commented on issue #15597: c_predict_api.h not working with mxnet amalgamation
leleamol commented on issue #15597: c_predict_api.h not working with mxnet amalgamation URL: https://github.com/apache/incubator-mxnet/issues/15597#issuecomment-525831884 @mxnet-label-bot add [C API] This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
