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Differential Revision: D62796663

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pytorch-bot bot commented Sep 17, 2024

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🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/5430

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@facebook-github-bot facebook-github-bot added the CLA Signed This label is managed by the Facebook bot. Authors need to sign the CLA before a PR can be reviewed. label Sep 17, 2024
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This pull request was exported from Phabricator. Differential Revision: D62796663

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This pull request was exported from Phabricator. Differential Revision: D62796663

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This pull request was exported from Phabricator. Differential Revision: D62796663

Olivia-liu added a commit to Olivia-liu/executorch-1 that referenced this pull request Sep 17, 2024
Summary:
Pull Request resolved: pytorch#5430

## Why
The memory planning inspection tool `generate_memory_trace()` has proved to be useful by users, and we will be presenting it in the executorch debugging and profiling lightning talk at PTC, so it's important to have documentation for it which users can refer to.

## What
A new page in the official doc that talks about how to use the memory planning inspection tool `generate_memory_trace()`.

Differential Revision: D62796663

## Debugging Tool

Please refer to [Memory Planing Inspection](./memory-planning-inspection.md) for a tool to inspect the result of memory planning.
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Missing 'n' in Planning (Planing)

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Please refer to [Memory Planing Inspection](./memory-planning-inspection.md) for a tool to inspect the result of memory planning.
Please refer to [Memory Planning Inspection](./memory-planning-inspection.md) for a tool to inspect the result of memory planning.

- **Delegate Integration** - Surfacing performance details from delegate backends
- Link back delegate operator execution to the nodes they represent in the edge dialect graph (and subsequently linking back to source code and module hierarchy)
- **Debugging** - Intermediate outputs and output quality analysis
- **Memory Inspection** - Understand memory allocation
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"memory inspection" implies that you're looking at the contents of memory, but that's not what's happening here. "Memory Allocation Tracking" or "Memory Allocation Insights", maybe.

And please expand this a bit to describe which allocations it covers. "Runtime Memory Allocation ...", for example, with a description that talks about debugging memory allocation behavior at runtime.


![Memory planning Chrome trace visualization](/_static/img/memory_planning_inspection.png)

Note that since we are repurposing the Chrome trace tool, the horizontal axis, despite being labeled in seconds (s), actually represents megabytes (MB).
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And the vertical axis, despite being labeled as process IDs, represents time.

Though "time" isn't exactly right; it's more like "iterations through the memory planning process"

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Would be more natural for the X axis label to use the plural "in megabytes"

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This pull request was exported from Phabricator. Differential Revision: D62796663

Olivia-liu added a commit to Olivia-liu/executorch-1 that referenced this pull request Sep 25, 2024
Summary:
Pull Request resolved: pytorch#5430

## Why
The memory planning inspection tool `generate_memory_trace()` has proved to be useful by users, and we will be presenting it in the executorch debugging and profiling lightning talk at PTC, so it's important to have documentation for it which users can refer to.

## What
A new page in the official doc that talks about how to use the memory planning inspection tool `generate_memory_trace()`.

Differential Revision: D62796663
Summary:
Pull Request resolved: pytorch#5430

## Why
The memory planning inspection tool `generate_memory_trace()` has proved to be useful by users, and we will be presenting it in the executorch debugging and profiling lightning talk at PTC, so it's important to have documentation for it which users can refer to.

## What
A new page in the official doc that talks about how to use the memory planning inspection tool `generate_memory_trace()`.

Differential Revision: D62796663
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This pull request was exported from Phabricator. Differential Revision: D62796663

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This pull request has been merged in 7127ea9.

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@pytorchbot cherry-pick --onto release/0.4 -c docs

pytorchbot pushed a commit that referenced this pull request Sep 27, 2024
Summary:
Pull Request resolved: #5430

## Why
The memory planning inspection tool `generate_memory_trace()` has proved to be useful by users, and we will be presenting it in the executorch debugging and profiling lightning talk at PTC, so it's important to have documentation for it which users can refer to.

## What
A new page in the official doc that talks about how to use the memory planning inspection tool `generate_memory_trace()`.

Reviewed By: dbort

Differential Revision: D62796663

fbshipit-source-id: 6c868590d805e9f21bccc7023131eb814b96e5b1
(cherry picked from commit 7127ea9)
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Cherry picking #5430

The cherry pick PR is at #5724 The following tracker issues are updated:

Details for Dev Infra team Raised by workflow job

jackzhxng pushed a commit that referenced this pull request Sep 27, 2024
New doc for the memory planning inspection util function (#5430)

Summary:
Pull Request resolved: #5430

## Why
The memory planning inspection tool `generate_memory_trace()` has proved to be useful by users, and we will be presenting it in the executorch debugging and profiling lightning talk at PTC, so it's important to have documentation for it which users can refer to.

## What
A new page in the official doc that talks about how to use the memory planning inspection tool `generate_memory_trace()`.

Reviewed By: dbort

Differential Revision: D62796663

fbshipit-source-id: 6c868590d805e9f21bccc7023131eb814b96e5b1
(cherry picked from commit 7127ea9)

Co-authored-by: Olivia Liu <olivialpx@meta.com>
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