From bee122b33fe83c1673ba03eaa66d5abb5e972305 Mon Sep 17 00:00:00 2001
From: Yifan Zhao <yifanz16@illinois.edu>
Date: Tue, 6 Apr 2021 03:18:11 -0500
Subject: [PATCH] Fixed TODO in dyn-approx doc
---
.../developerdocs/dynamic-approximation.rst | 34 ++++++++++++++-----
1 file changed, 25 insertions(+), 9 deletions(-)
diff --git a/hpvm/docs/developerdocs/dynamic-approximation.rst b/hpvm/docs/developerdocs/dynamic-approximation.rst
index a2b96bd66a..ee2d52f734 100644
--- a/hpvm/docs/developerdocs/dynamic-approximation.rst
+++ b/hpvm/docs/developerdocs/dynamic-approximation.rst
@@ -1,20 +1,36 @@
Dynamic Approximation Control
---------------------------------------
-HPVM includes support for dynamically updating the approximation configuration used for processing an input Batch. The Keras and PyTorch frontends
-generate batched loops (each processes a batch of inputs) that load and apply a particular configuration. In the main batched loop the frontends generate a call to `llvm_hpvm_invokeRtControl` which invokes the dynamic approximation control. In the default mode, the runtime controller does not switch configuration - it loads the first configuration in the configuration file. The configuration file is described here [TODO: add link]. The configurations loaded from the configuration file are organized into a Pareto curve that is used for dynamic tuning. The code for the runtime controller exists under `projects/hpvm-tensor-rt/tensor_runtime/src/hpvm-rt-controller.cpp`.
-
-The runtime can be configured to switch configurations to respond to system slowdowns (e.g., caused by reduced frequency).
-This release does not include a demo that shows the dynamic approximation capability in HPVM but users can use/repurpose the code to cater to custom use cases.
+HPVM includes support for dynamically updating the approximation configuration used for processing an input Batch.
+The Keras and PyTorch frontends generate batched loops (each processes a batch of inputs)
+that load and apply a particular configuration.
+In the main batched loop the frontends generate a call to `llvm_hpvm_invokeRtControl`
+which invokes the dynamic approximation control.
+In the default mode, the runtime controller does not switch configuration -- it loads the first configuration in the configuration file.
+The configuration file is described here in :doc:`configuration-format`.
+The configurations loaded from the configuration file are organized into a Pareto curve
+that is used for dynamic tuning.
+The code for the runtime controller exists under
+``projects/hpvm-tensor-rt/tensor_runtime/src/hpvm-rt-controller.cpp``.
+
+The runtime can be configured to switch configurations to respond to system slowdowns
+(e.g., caused by reduced frequency).
+This release does not include a demo that shows the dynamic approximation capability in HPVM
+but users can use/repurpose the code to cater to custom use cases.
To use this mode, users must do the following steps:
-* The first line in the configuration line must include the target batch processing time (in milliseconds). Users can use this to specify any (soft) time constraints for processing a single batch of inputs. The HPVM runtime controller uses this target time and the measured time to compute the required speedup, and uses it to select a configuration that provides the required speedup.
+* The first line in the configuration line must include the target batch processing time (in milliseconds).
+ Users can use this to specify any (soft) time constraints for processing a single batch of inputs.
+ The HPVM runtime controller uses this target time and the measured time to compute the required speedup,
+ and uses it to select a configuration that provides the required speedup.
-* Modify `projects/hpvm-tensor-rt/tensor_runtime/src/hpvm-rt-controller.cpp` to update the `llvm_hpvm_invokeRtControl` macro at the top of the file. Uncomment this line:
+* Modify ``projects/hpvm-tensor-rt/tensor_runtime/src/hpvm-rt-controller.cpp`` to update the
+ `llvm_hpvm_invokeRtControl` macro at the top of the file. Uncomment this line:
.. code-block:: C++
#define llvm_hpvm_invokeRtControl_ADJUST_PR llvm_hpvm_invokeRtControl
-This enables the probabilistic configuration selection mode in HPVM. The probalistic selection is needed since often no configuration on the Pareto curve offers the exact required speedup. The probabilistic selection mode probabilistically picks among 2 configurations so as to provide the target speedup over time.
-
+This enables the probabilistic configuration selection mode in HPVM.
+The probalistic selection is needed since often no configuration on the Pareto curve offers the exact required speedup.
+The probabilistic selection mode probabilistically picks among 2 configurations so as to provide the target speedup over time.
--
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