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Async output process for HPU #341
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### Issue:
torch.compile recompiles after warmup because `tensor 'L['input_ids']'
dispatch key set mismatch. expected DispatchKeySet(HPU, BackendSelect),
actual DispatchKeySet(HPU, BackendSelect, ADInplaceOrView). `
### Detail:
Run script with `TORCH_LOGS="guards"` and get different dispatch key set
info:
- warmup:
```
TENSOR_MATCH: check_tensor(L['input_ids'], Tensor, DispatchKeySet(HPU, BackendSelect), torch.int64, device=0, requires_grad=False, size=[2, 1], stride=[1, 1]) # masked_input = input_ # ome/zyuwen/workspace/vllm/habana_main_g3_v2/vllm/model_executor/layers/vocab_parallel_embedding.py:358 in forward
```
- after warmup:
```
TENSOR_MATCH: check_tensor(L['input_ids'], Tensor, DispatchKeySet(HPU, BackendSelect, ADInplaceOrView), torch.int64, device=0, requires_grad=False, size=[2, 1], stride=[1, 1]) # masked_input = input_ # ome/zyuwen/workspace/vllm/habana_main_g3_v2/vllm/model_executor/layers/vocab_parallel_embedding.py:358 in forward
```
### Solution:
The difference in dispatch key set is caused by the
'torch.inference_mode()' decoration, and here is a simple example:
```python
import torch
import habana_frameworks.torch as htorch
@torch.inference_mode()
def func():
x = torch.rand(3, 3).to("hpu")
print(torch._C._dispatch_key_set(x))
func()
# output: DispatchKeySet(HPU, AutocastHPU)
```
```python
import torch
import habana_frameworks.torch as htorch
def func():
x = torch.rand(3, 3).to("hpu")
print(torch._C._dispatch_key_set(x))
func()
# output: DispatchKeySet(HPU, ADInplaceOrView, AutogradHPU, AutocastHPU)
```
In vllm-fork, the warmup phase is decorated with
`torch.inference_mode()` in
[habana_model_runner.py#L1487-L1488](https://github.com/HabanaAI/vllm-fork/blob/b62fba85ac03326e9f466d8d37e91ae1b14a6511/vllm/worker/habana_model_runner.py#L1487-L1488),
but the after-warmup phase is not.
So in this PR I add the decorator to `prepare_input_tensors` function to
keep the dispatch key set the same.
---
<details>
<!-- inside this <details> section, markdown rendering does not work, so
we use raw html here. -->
<summary><b> PR Checklist (Click to Expand) </b></summary>
<p>Thank you for your contribution to vLLM! Before submitting the pull
request, please ensure the PR meets the following criteria. This helps
vLLM maintain the code quality and improve the efficiency of the review
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<h3>PR Title and Classification</h3>
<p>Only specific types of PRs will be reviewed. The PR title is prefixed
appropriately to indicate the type of change. Please use one of the
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<li><code>[Bugfix]</code> for bug fixes.</li>
<li><code>[CI/Build]</code> for build or continuous integration
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</ul>
<p><strong>Note:</strong> If the PR spans more than one category, please
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<h3>Code Quality</h3>
<p>The PR need to meet the following code quality standards:</p>
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<h3>Notes for Large Changes</h3>
<p>Please keep the changes as concise as possible. For major
architectural changes (>500 LOC excluding kernel/data/config/test), we
would expect a GitHub issue (RFC) discussing the technical design and
justification. Otherwise, we will tag it with <code>rfc-required</code>
and might not go through the PR.</p>
<h3>What to Expect for the Reviews</h3>
<p>The goal of the vLLM team is to be a <i>transparent reviewing
machine</i>. We would like to make the review process transparent and
efficient and make sure no contributor feel confused or frustrated.
However, the vLLM team is small, so we need to prioritize some PRs over
others. Here is what you can expect from the review process: </p>
<ul>
<li> After the PR is submitted, the PR will be assigned to a reviewer.
Every reviewer will pick up the PRs based on their expertise and
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free to ping the reviewer or the vLLM team.</li>
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action-required</code> label on the PR if there are changes required.
The contributor should address the comments and ping the reviewer to
re-review the PR.</li>
<li> Please respond to all comments within a reasonable time frame. If a
comment isn't clear or you disagree with a suggestion, feel free to ask
for clarification or discuss the suggestion.
</li>
</ul>
<h3>Thank You</h3>
<p> Finally, thank you for taking the time to read these guidelines and
for your interest in contributing to vLLM. Your contributions make vLLM
a great tool for everyone! </p>
</details>
Signed-off-by: yuwenzho <yuwen.zhou@intel.com>
HabanaAI#289) Re-implements following PRs for current habana_main: HabanaAI#102 (Removing div_i32 operations from each layer) HabanaAI#115 (removing scatter for reshape&cache in case of prompt) Accuracy (GSM8K on Llama3.1-8B-Instruct): | Tasks |Version| Filter |n-shot| Metric | |Value | |Stderr| |---------------|------:|----------------|-----:|-----------|---|-----:|---|-----:| |gsm8k_cot_llama| 3|flexible-extract| 8|exact_match|↑ |0.8415|± |0.0101| | | |strict-match | 8|exact_match|↑ |0.8400|± |0.0101| I've benchmarked this change on Llama3.1-8B-Instruct and on average, +2.50% throughput gain (+558.14 tok/s, ~21594 tok/s -> ~22152 tok/s) can be observed across all prefill buckets on G2, with up to +4.40% (+956.79 tok/s, ~25031 -> ~25988 tok/s) throughput increase in compute-bound scenarios.
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FILL IN THE PR DESCRIPTION HERE
This PR refer to #7049 to implement Asynchronous Output Processor on HPU. It is open by default, to disable it, please pass the
--disable_async_output_procflag.From my local test on latest habana_main branch(commit 29fb5ed), the throughput improves from 3847 TPS to 4011 TPS.
BEFORE SUBMITTING, PLEASE READ THE CHECKLIST BELOW AND FILL IN THE DESCRIPTION ABOVE
PR Checklist (Click to Expand)
Thank you for your contribution to vLLM! Before submitting the pull request, please ensure the PR meets the following criteria. This helps vLLM maintain the code quality and improve the efficiency of the review process.
PR Title and Classification
Only specific types of PRs will be reviewed. The PR title is prefixed appropriately to indicate the type of change. Please use one of the following:
[Bugfix]for bug fixes.[CI/Build]for build or continuous integration improvements.[Doc]for documentation fixes and improvements.[Model]for adding a new model or improving an existing model. Model name should appear in the title.[Frontend]For changes on the vLLM frontend (e.g., OpenAI API server,LLMclass, etc.)[Kernel]for changes affecting CUDA kernels or other compute kernels.[Core]for changes in the core vLLM logic (e.g.,LLMEngine,AsyncLLMEngine,Scheduler, etc.)[Hardware][Vendor]for hardware-specific changes. Vendor name should appear in the prefix (e.g.,[Hardware][AMD]).[Misc]for PRs that do not fit the above categories. Please use this sparingly.Note: If the PR spans more than one category, please include all relevant prefixes.
Code Quality
The PR need to meet the following code quality standards:
format.shto format your code.docs/source/if the PR modifies the user-facing behaviors of vLLM. It helps vLLM user understand and utilize the new features or changes.Adding or changing kernels
Each custom kernel needs a schema and one or more implementations to be registered with PyTorch.
Tensorsrequire meta-functions. Meta-functions should be implemented and registered in python so that dynamic dims can be handled automatically. See above documents for a description of meta-functions.torch.libary.opcheck()to test the function registration and meta-function for any registered ops. Seetests/kernelsfor examples.Notes for Large Changes
Please keep the changes as concise as possible. For major architectural changes (>500 LOC excluding kernel/data/config/test), we would expect a GitHub issue (RFC) discussing the technical design and justification. Otherwise, we will tag it with
rfc-requiredand might not go through the PR.What to Expect for the Reviews
The goal of the vLLM team is to be a transparent reviewing machine. We would like to make the review process transparent and efficient and make sure no contributor feel confused or frustrated. However, the vLLM team is small, so we need to prioritize some PRs over others. Here is what you can expect from the review process:
action-requiredlabel on the PR if there are changes required. The contributor should address the comments and ping the reviewer to re-review the PR.Thank You
Finally, thank you for taking the time to read these guidelines and for your interest in contributing to vLLM. Your contributions make vLLM a great tool for everyone!