[GitHub] [incubator-mxnet] ChaiBapchya commented on a change in pull request #15517: [WIP]Tensor Inspector Tutorial
ChaiBapchya commented on a change in pull request #15517: [WIP]Tensor Inspector Tutorial URL: https://github.com/apache/incubator-mxnet/pull/15517#discussion_r303174181 ## File path: docs/faq/tensor_inspector_tutorial.md ## @@ -0,0 +1,164 @@ + + + + + + + + + + + + + + + +# Use TensorInspector to Help Debug Operators + +## Introduction + +When developing new operators, developers need to deal with tensor objects extensively. This new utility, Tensor Inspector, mainly aims to help developers debug by providing unified interfaces to print, check, and dump the tensor value. To developers' convenience, This utility works for all the three data types: Tensors, TBlobs, and NDArrays. Also, it supports both CPU and GPU tensors. + + +## Usage + +This utility locates in `src/common/tensor_inspector.h`. To use it in any operator code, just include `tensor_inspector`, construct an `TensorInspector` object, and call the APIs on that object. You can run any script that uses the operator you just modified then. + +The screenshot below shows a sample usage in `src/operator/nn/convolution-inl.h`. + + Review comment: Would be great if you could give the image a recognizable name 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 a change in pull request #15517: [WIP]Tensor Inspector Tutorial
ChaiBapchya commented on a change in pull request #15517: [WIP]Tensor Inspector
Tutorial
URL: https://github.com/apache/incubator-mxnet/pull/15517#discussion_r303174854
##
File path: docs/faq/tensor_inspector_tutorial.md
##
@@ -0,0 +1,164 @@
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+# Use TensorInspector to Help Debug Operators
+
+## Introduction
+
+When developing new operators, developers need to deal with tensor objects
extensively. This new utility, Tensor Inspector, mainly aims to help developers
debug by providing unified interfaces to print, check, and dump the tensor
value. To developers' convenience, This utility works for all the three data
types: Tensors, TBlobs, and NDArrays. Also, it supports both CPU and GPU
tensors.
+
+
+## Usage
+
+This utility locates in `src/common/tensor_inspector.h`. To use it in any
operator code, just include `tensor_inspector`, construct an `TensorInspector`
object, and call the APIs on that object. You can run any script that uses the
operator you just modified then.
+
+The screenshot below shows a sample usage in
`src/operator/nn/convolution-inl.h`.
+
+
+
+
+## Functionalities/APIs
+
+### Create a TensorInspector Object from Tensor, TBlob, and NDArray Objects
+
+You can create a `TensorInspector` object by passing in two things: 1) an
object of type `Tensor`, `Tbob`, or `NDArray`, and 2) an `RunContext` object.
+
+Essentially, `TensorInspector` can be understood as a wrapper class around
`TBlob`. Internally, the `Tensor`, `Tbob`, or `NDArray` object that you passed
in will all be converted to a `TBlob` object. The `RunContext` object is used
when the the tensor is a GPU tensor; in such case, we need to use the context
information to copy the data from GPU memory to CPU/main memory.
+
+Below are the three constructors:
+
+```c++
+// Construct from Tensor object
+template
+TensorInspector(const mshadow::Tensor& ts, const
RunContext& ctx);
+
+// Construct from TBlob object
+TensorInspector(const TBlob& tb, const RunContext& ctx);
+
+// Construct from NDArray object
+TensorInspector(const NDArray& arr, const RunContext& ctx):
+```
+
+### Print Tensor Value (Static)
+
+To print out the tensor value in a nicely structured way, you can use this
API:
+
+```c++
+void print_string();
+```
+
+This API will print the entire tensor to `std::cout` and preserve the shape
(it supports all dimensions from 1 and up). You can copy the output and
interpret it with any `JSON` loader. Also, on the last line of the output you
can find some useful information about the tensor. Refer to the case below, we
are able to know that this is a float-typed tensor with shape 20x1x5x5.
+
+
+
+If instead of printing the tensor to `std::cout`, you just need a `string`,
you can use this API:
+```c++
+std::string void to_string();
+```
+
+### Interactively Print Tensor Value (Dynamic)
+
+When debugging, situations might occur that at compilation time, you do not
know which part of a tensor to inspect. Also, sometimes, it would be nice to
pause the operator control flow to “zoom into” a specific, erroneous part of a
tensor multiple times until you are satisfied. In this regard, you can use this
API to interactively inspect the tensor:
+
+```c++
+void interactive_print(std::string tag = "") {
+```
+
+This API will set a "break point" in your code, so that you will enter a loop
that will keep asking you for further command. In the API call, `tag` is an
optional parameter to give the call a name, so that you can identify it when
you have multiple `interactive_print()` calls in different parts of your code.
A visit count will tell you for how many times have you stepped into this
particular "break point", should this operator be called more than once. Note
that all `interactive_print()` calls are properly locked, so you can use it in
many different places without issues.
+
+
+
+Refer the screenshot above, there are many useful commands available: you can
type "e" to print out the entire tensor, ''d" to dump the tensor to file (see
below), "b" to break from this command loop, and "s" to skip all future
`interactive_print()`. Most importantly, in this screen, you can specify a part
of the tensor that you are particularly interested in and want to print out.
For example, for this 20x1x5x5 tensor, you can type in "0, 0" and presss enter
to check the sub-tensor with shape 5x5 at coordinate (0, 0).
+
+### Check Tensor Value
+
+Sometimes, developers might want to check if the tensor contains unexpected
values which could be negative
[GitHub] [incubator-mxnet] ChaiBapchya commented on a change in pull request #15517: [WIP]Tensor Inspector Tutorial
ChaiBapchya commented on a change in pull request #15517: [WIP]Tensor Inspector
Tutorial
URL: https://github.com/apache/incubator-mxnet/pull/15517#discussion_r303174924
##
File path: docs/faq/tensor_inspector_tutorial.md
##
@@ -0,0 +1,164 @@
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+# Use TensorInspector to Help Debug Operators
+
+## Introduction
+
+When developing new operators, developers need to deal with tensor objects
extensively. This new utility, Tensor Inspector, mainly aims to help developers
debug by providing unified interfaces to print, check, and dump the tensor
value. To developers' convenience, This utility works for all the three data
types: Tensors, TBlobs, and NDArrays. Also, it supports both CPU and GPU
tensors.
+
+
+## Usage
+
+This utility locates in `src/common/tensor_inspector.h`. To use it in any
operator code, just include `tensor_inspector`, construct an `TensorInspector`
object, and call the APIs on that object. You can run any script that uses the
operator you just modified then.
+
+The screenshot below shows a sample usage in
`src/operator/nn/convolution-inl.h`.
+
+
+
+
+## Functionalities/APIs
+
+### Create a TensorInspector Object from Tensor, TBlob, and NDArray Objects
+
+You can create a `TensorInspector` object by passing in two things: 1) an
object of type `Tensor`, `Tbob`, or `NDArray`, and 2) an `RunContext` object.
+
+Essentially, `TensorInspector` can be understood as a wrapper class around
`TBlob`. Internally, the `Tensor`, `Tbob`, or `NDArray` object that you passed
in will all be converted to a `TBlob` object. The `RunContext` object is used
when the the tensor is a GPU tensor; in such case, we need to use the context
information to copy the data from GPU memory to CPU/main memory.
+
+Below are the three constructors:
+
+```c++
+// Construct from Tensor object
+template
+TensorInspector(const mshadow::Tensor& ts, const
RunContext& ctx);
+
+// Construct from TBlob object
+TensorInspector(const TBlob& tb, const RunContext& ctx);
+
+// Construct from NDArray object
+TensorInspector(const NDArray& arr, const RunContext& ctx):
+```
+
+### Print Tensor Value (Static)
+
+To print out the tensor value in a nicely structured way, you can use this
API:
+
+```c++
+void print_string();
+```
+
+This API will print the entire tensor to `std::cout` and preserve the shape
(it supports all dimensions from 1 and up). You can copy the output and
interpret it with any `JSON` loader. Also, on the last line of the output you
can find some useful information about the tensor. Refer to the case below, we
are able to know that this is a float-typed tensor with shape 20x1x5x5.
+
+
+
+If instead of printing the tensor to `std::cout`, you just need a `string`,
you can use this API:
+```c++
+std::string void to_string();
+```
+
+### Interactively Print Tensor Value (Dynamic)
+
+When debugging, situations might occur that at compilation time, you do not
know which part of a tensor to inspect. Also, sometimes, it would be nice to
pause the operator control flow to “zoom into” a specific, erroneous part of a
tensor multiple times until you are satisfied. In this regard, you can use this
API to interactively inspect the tensor:
+
+```c++
+void interactive_print(std::string tag = "") {
+```
+
+This API will set a "break point" in your code, so that you will enter a loop
that will keep asking you for further command. In the API call, `tag` is an
optional parameter to give the call a name, so that you can identify it when
you have multiple `interactive_print()` calls in different parts of your code.
A visit count will tell you for how many times have you stepped into this
particular "break point", should this operator be called more than once. Note
that all `interactive_print()` calls are properly locked, so you can use it in
many different places without issues.
+
+
+
+Refer the screenshot above, there are many useful commands available: you can
type "e" to print out the entire tensor, ''d" to dump the tensor to file (see
below), "b" to break from this command loop, and "s" to skip all future
`interactive_print()`. Most importantly, in this screen, you can specify a part
of the tensor that you are particularly interested in and want to print out.
For example, for this 20x1x5x5 tensor, you can type in "0, 0" and presss enter
to check the sub-tensor with shape 5x5 at coordinate (0, 0).
+
+### Check Tensor Value
+
+Sometimes, developers might want to check if the tensor contains unexpected
values which could be negative
[GitHub] [incubator-mxnet] ChaiBapchya commented on a change in pull request #15517: [WIP]Tensor Inspector Tutorial
ChaiBapchya commented on a change in pull request #15517: [WIP]Tensor Inspector Tutorial URL: https://github.com/apache/incubator-mxnet/pull/15517#discussion_r303174371 ## File path: docs/faq/tensor_inspector_tutorial.md ## @@ -0,0 +1,164 @@ + + + + + + + + + + + + + + + +# Use TensorInspector to Help Debug Operators + +## Introduction + +When developing new operators, developers need to deal with tensor objects extensively. This new utility, Tensor Inspector, mainly aims to help developers debug by providing unified interfaces to print, check, and dump the tensor value. To developers' convenience, This utility works for all the three data types: Tensors, TBlobs, and NDArrays. Also, it supports both CPU and GPU tensors. + + +## Usage + +This utility locates in `src/common/tensor_inspector.h`. To use it in any operator code, just include `tensor_inspector`, construct an `TensorInspector` object, and call the APIs on that object. You can run any script that uses the operator you just modified then. + +The screenshot below shows a sample usage in `src/operator/nn/convolution-inl.h`. + + + + +## Functionalities/APIs + +### Create a TensorInspector Object from Tensor, TBlob, and NDArray Objects + +You can create a `TensorInspector` object by passing in two things: 1) an object of type `Tensor`, `Tbob`, or `NDArray`, and 2) an `RunContext` object. + +Essentially, `TensorInspector` can be understood as a wrapper class around `TBlob`. Internally, the `Tensor`, `Tbob`, or `NDArray` object that you passed in will all be converted to a `TBlob` object. The `RunContext` object is used when the the tensor is a GPU tensor; in such case, we need to use the context information to copy the data from GPU memory to CPU/main memory. Review comment: ```suggestion Essentially, `TensorInspector` can be understood as a wrapper class around `TBlob`. Internally, the `Tensor`, `Tbob`, or `NDArray` object that you passed in will be converted to a `TBlob` object. The `RunContext` object is used when the the tensor is a GPU tensor; in such a case, we need to use the context information to copy the data from GPU memory to CPU/main memory. ``` 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 a change in pull request #15517: [WIP]Tensor Inspector Tutorial
ChaiBapchya commented on a change in pull request #15517: [WIP]Tensor Inspector Tutorial URL: https://github.com/apache/incubator-mxnet/pull/15517#discussion_r303173725 ## File path: docs/faq/tensor_inspector_tutorial.md ## @@ -0,0 +1,164 @@ + + + + + + + + + + + + + + + +# Use TensorInspector to Help Debug Operators + +## Introduction + +When developing new operators, developers need to deal with tensor objects extensively. This new utility, Tensor Inspector, mainly aims to help developers debug by providing unified interfaces to print, check, and dump the tensor value. To developers' convenience, This utility works for all the three data types: Tensors, TBlobs, and NDArrays. Also, it supports both CPU and GPU tensors. + + +## Usage + +This utility locates in `src/common/tensor_inspector.h`. To use it in any operator code, just include `tensor_inspector`, construct an `TensorInspector` object, and call the APIs on that object. You can run any script that uses the operator you just modified then. Review comment: ```suggestion This utility is located in `src/common/tensor_inspector.h`. To use it in any operator code, just include `tensor_inspector`, construct an `TensorInspector` object, and call the APIs on that object. You can run any script that uses the operator you just modified then. ``` 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 a change in pull request #15517: [WIP]Tensor Inspector Tutorial
ChaiBapchya commented on a change in pull request #15517: [WIP]Tensor Inspector Tutorial URL: https://github.com/apache/incubator-mxnet/pull/15517#discussion_r303173914 ## File path: docs/faq/tensor_inspector_tutorial.md ## @@ -0,0 +1,164 @@ + + + + + + + + + + + + + + + +# Use TensorInspector to Help Debug Operators + +## Introduction + +When developing new operators, developers need to deal with tensor objects extensively. This new utility, Tensor Inspector, mainly aims to help developers debug by providing unified interfaces to print, check, and dump the tensor value. To developers' convenience, This utility works for all the three data types: Tensors, TBlobs, and NDArrays. Also, it supports both CPU and GPU tensors. Review comment: ```suggestion When developing new operators, developers need to deal with tensor objects extensively. This new utility, Tensor Inspector, mainly aims to help developers debug by providing unified interfaces to print, check, and dump the tensor value. To developers' convenience, this utility works for all the three data types: Tensors, TBlobs, and NDArrays. Also, it supports both CPU and GPU tensors. ``` 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 a change in pull request #15517: [WIP]Tensor Inspector Tutorial
ChaiBapchya commented on a change in pull request #15517: [WIP]Tensor Inspector Tutorial URL: https://github.com/apache/incubator-mxnet/pull/15517#discussion_r303174576 ## File path: docs/faq/tensor_inspector_tutorial.md ## @@ -0,0 +1,164 @@ + + + + + + + + + + + + + + + +# Use TensorInspector to Help Debug Operators + +## Introduction + +When developing new operators, developers need to deal with tensor objects extensively. This new utility, Tensor Inspector, mainly aims to help developers debug by providing unified interfaces to print, check, and dump the tensor value. To developers' convenience, This utility works for all the three data types: Tensors, TBlobs, and NDArrays. Also, it supports both CPU and GPU tensors. + + +## Usage + +This utility locates in `src/common/tensor_inspector.h`. To use it in any operator code, just include `tensor_inspector`, construct an `TensorInspector` object, and call the APIs on that object. You can run any script that uses the operator you just modified then. + +The screenshot below shows a sample usage in `src/operator/nn/convolution-inl.h`. + + + + +## Functionalities/APIs + +### Create a TensorInspector Object from Tensor, TBlob, and NDArray Objects + +You can create a `TensorInspector` object by passing in two things: 1) an object of type `Tensor`, `Tbob`, or `NDArray`, and 2) an `RunContext` object. + +Essentially, `TensorInspector` can be understood as a wrapper class around `TBlob`. Internally, the `Tensor`, `Tbob`, or `NDArray` object that you passed in will all be converted to a `TBlob` object. The `RunContext` object is used when the the tensor is a GPU tensor; in such case, we need to use the context information to copy the data from GPU memory to CPU/main memory. + +Below are the three constructors: + +```c++ +// Construct from Tensor object +template +TensorInspector(const mshadow::Tensor& ts, const RunContext& ctx); + +// Construct from TBlob object +TensorInspector(const TBlob& tb, const RunContext& ctx); + +// Construct from NDArray object +TensorInspector(const NDArray& arr, const RunContext& ctx): +``` + +### Print Tensor Value (Static) + +To print out the tensor value in a nicely structured way, you can use this API: + +```c++ +void print_string(); +``` + +This API will print the entire tensor to `std::cout` and preserve the shape (it supports all dimensions from 1 and up). You can copy the output and interpret it with any `JSON` loader. Also, on the last line of the output you can find some useful information about the tensor. Refer to the case below, we are able to know that this is a float-typed tensor with shape 20x1x5x5. + + Review comment: likewise 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
