26 Questions
Which tool can be used to visualize the timeline when profiling CUDA code with nvprof?
NVIDIA Visual Profiler (nvvp)
What is the purpose of using the emit_nvtx context manager?
To annotate nvprof traces for inspection
What information does emit_nvtx append to the ranges it generates?
Sequence number information
Which type of ranges are useful for correlating Function objects with the earlier forward pass?
Top-level ranges
When does each function's execution during backward become useful?
When a backward pass with create_graph=True is underway
What is the relationship between backward and double-backward?
Conceptually the same as the relationship between forward and backward
Which tool can be used to visualize the timeline when profiling CUDA code with nvprof?
NVIDIA Visual Profiler (nvvp)
What is the purpose of using the emit_nvtx context manager?
To annotate nvprof traces for inspection
What information does emit_nvtx append to the ranges it generates?
Sequence number
When does each function's execution during backward become useful?
During double-backward
What is the relationship between backward and double-backward?
backward is a subset of double-backward
What is the purpose of using torch.autograd.profiler.load_nvprof()?
To load the results in Python REPL
What does the emit_nvtx context manager do when running the program under nvprof?
Annotates nvprof traces for inspection
Which type of ranges are useful for correlating Function objects with the earlier forward pass?
Sequence number
What is the purpose of using create_graph=False during default backward?
To make sequence number information irrelevant
During CUDA profiling, why is it necessary to use the emit_nvtx context manager?
To annotate nvprof traces for inspection
Which type of ranges are useful for correlating Function objects with the earlier forward pass?
Top-level ranges
What is the purpose of using the emit_nvtx context manager?
To emit current-sequence-number-tagged ranges
When does each function's execution during backward become useful?
During double-backward
What is the relationship between backward and double-backward?
Conceptually the same
What information is given to each function's execution during backward if a backward pass with create_graph=True is underway?
A nonzero value
What can functions in the backward pass do during double-backward?
Create Function objects
What do Function objects' ranges in double-backward have?
Sequence numbers
What is the purpose of using create_graph=True during a backward pass?
Setting up for a double-backward
What is emitted by the functions during double-backward?
Current-sequence-number-tagged ranges
What can be compared to sequence numbers from the backward pass during double-backward?
Sequence numbers from Function objects' ranges
Study Notes
Profiling CUDA Code with Nvprof
- The tool used to visualize the timeline when profiling CUDA code with nvprof is Nsight Systems.
Purpose of Emit_Nvtx Context Manager
- The emit_nvtx context manager is used to generate ranges that correlate Function objects with the earlier forward pass.
- The emit_nvtx context manager appends sequence numbers and device/data transfer information to the ranges it generates.
Backward and Double-Backward
- Each function's execution during backward becomes useful when a backward pass with create_graph=True is underway.
- The relationship between backward and double-backward is that double-backward is a pass that occurs when the gradients of gradients are computed.
- During double-backward, functions can recreate the computation graph and compute the gradients of gradients.
- Function objects' ranges in double-backward have sequence numbers that can be compared to sequence numbers from the backward pass.
Creating Graphs and Profiling
- The purpose of using create_graph=False during default backward is to prevent the creation of a computation graph.
- The purpose of using create_graph=True during a backward pass is to create a computation graph.
- The emit_nvtx context manager is necessary during CUDA profiling to generate ranges that correlate Function objects with the earlier forward pass.
- torch.autograd.profiler.load_nvprof() is used to load the profiling results from nvprof.
Learn how to use a context manager to annotate and inspect nvprof traces in CUDA profiling with this informative quiz. Discover how to make every autograd operation emit an NVTX range and optimize your program under nvprof. Explore the benefits of using NVIDIA Visual Profiler (nvvp) for in-depth analysis of your traces. https://pytorch.org/docs/stable/autograd.html
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