From 236b69602b563febcb1b51fbfbdcd2f6182ff2d1 Mon Sep 17 00:00:00 2001
From: George Tamer <gtamer2@gmail.com>
Date: Tue, 12 Dec 2023 21:38:54 -0500
Subject: [PATCH 01/64] Add inference benchmark, delete extra files

---
 .github/ISSUE_TEMPLATE/bug_report.md |  38 ---------
 CODE_OF_CONDUCT.md                   |  80 ------------------
 CONTRIBUTING.md                      |  31 -------
 Responsible-Use-Guide.pdf            | Bin 1253223 -> 0 bytes
 UPDATES.md                           |  21 -----
 USE_POLICY.md                        |  50 ------------
 inference_benchmark.py               | 116 +++++++++++++++++++++++++++
 7 files changed, 116 insertions(+), 220 deletions(-)
 delete mode 100644 .github/ISSUE_TEMPLATE/bug_report.md
 delete mode 100644 CODE_OF_CONDUCT.md
 delete mode 100644 CONTRIBUTING.md
 delete mode 100644 Responsible-Use-Guide.pdf
 delete mode 100644 UPDATES.md
 delete mode 100644 USE_POLICY.md
 create mode 100644 inference_benchmark.py

diff --git a/.github/ISSUE_TEMPLATE/bug_report.md b/.github/ISSUE_TEMPLATE/bug_report.md
deleted file mode 100644
index 428351ed0..000000000
--- a/.github/ISSUE_TEMPLATE/bug_report.md
+++ /dev/null
@@ -1,38 +0,0 @@
----
-name: Bug report
-about: Create a report to help us reproduce and fix the issue
-title: ''
-labels: ''
-assignees: ''
-
----
-
-**Before submitting a bug, please make sure the issue hasn't been already addressed by searching through the [FAQs](https://ai.meta.com/llama/faq/) and [existing/past issues](https://github.com/facebookresearch/llama/issues)**
-
-## Describe the bug
-<Please provide a clear and concise description of what the bug is. If relevant, please include a _minimal_ (least lines of code necessary) _reproducible_ (running this will give us the same result as you get) code snippet. Make sure to include the relevant imports.>
-
-### Minimal reproducible example
-<Remember to wrap the code in ```` ```triple-quotes blocks``` ````>
-
-```python
-# sample code to repro the bug
-```
-
-### Output
-<Remember to wrap the output in ```` ```triple-quotes blocks``` ````>
-
-```
-<paste stacktrace and other outputs here>
-```
-
-## Runtime Environment
-- Model: [eg: `llama-2-7b-chat`]
-- Using via huggingface?: [yes/no]
-- OS: [eg. Linux/Ubuntu, Windows]
-- GPU VRAM: 
-- Number of GPUs:
-- GPU Make: [eg: Nvidia, AMD, Intel]
-
-**Additional context**
-Add any other context about the problem or environment here.
diff --git a/CODE_OF_CONDUCT.md b/CODE_OF_CONDUCT.md
deleted file mode 100644
index cf9dc2448..000000000
--- a/CODE_OF_CONDUCT.md
+++ /dev/null
@@ -1,80 +0,0 @@
-# Code of Conduct
-
-## Our Pledge
-
-In the interest of fostering an open and welcoming environment, we as
-contributors and maintainers pledge to make participation in our project and
-our community a harassment-free experience for everyone, regardless of age, body
-size, disability, ethnicity, sex characteristics, gender identity and expression,
-level of experience, education, socio-economic status, nationality, personal
-appearance, race, religion, or sexual identity and orientation.
-
-## Our Standards
-
-Examples of behavior that contributes to creating a positive environment
-include:
-
-* Using welcoming and inclusive language
-* Being respectful of differing viewpoints and experiences
-* Gracefully accepting constructive criticism
-* Focusing on what is best for the community
-* Showing empathy towards other community members
-
-Examples of unacceptable behavior by participants include:
-
-* The use of sexualized language or imagery and unwelcome sexual attention or
-advances
-* Trolling, insulting/derogatory comments, and personal or political attacks
-* Public or private harassment
-* Publishing others' private information, such as a physical or electronic
-address, without explicit permission
-* Other conduct which could reasonably be considered inappropriate in a
-professional setting
-
-## Our Responsibilities
-
-Project maintainers are responsible for clarifying the standards of acceptable
-behavior and are expected to take appropriate and fair corrective action in
-response to any instances of unacceptable behavior.
-
-Project maintainers have the right and responsibility to remove, edit, or
-reject comments, commits, code, wiki edits, issues, and other contributions
-that are not aligned to this Code of Conduct, or to ban temporarily or
-permanently any contributor for other behaviors that they deem inappropriate,
-threatening, offensive, or harmful.
-
-## Scope
-
-This Code of Conduct applies within all project spaces, and it also applies when
-an individual is representing the project or its community in public spaces.
-Examples of representing a project or community include using an official
-project e-mail address, posting via an official social media account, or acting
-as an appointed representative at an online or offline event. Representation of
-a project may be further defined and clarified by project maintainers.
-
-This Code of Conduct also applies outside the project spaces when there is a
-reasonable belief that an individual's behavior may have a negative impact on
-the project or its community.
-
-## Enforcement
-
-Instances of abusive, harassing, or otherwise unacceptable behavior may be
-reported by contacting the project team at <opensource-conduct@meta.com>. All
-complaints will be reviewed and investigated and will result in a response that
-is deemed necessary and appropriate to the circumstances. The project team is
-obligated to maintain confidentiality with regard to the reporter of an incident.
-Further details of specific enforcement policies may be posted separately.
-
-Project maintainers who do not follow or enforce the Code of Conduct in good
-faith may face temporary or permanent repercussions as determined by other
-members of the project's leadership.
-
-## Attribution
-
-This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4,
-available at https://www.contributor-covenant.org/version/1/4/code-of-conduct.html
-
-[homepage]: https://www.contributor-covenant.org
-
-For answers to common questions about this code of conduct, see
-https://www.contributor-covenant.org/faq
\ No newline at end of file
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
deleted file mode 100644
index 5eb507d67..000000000
--- a/CONTRIBUTING.md
+++ /dev/null
@@ -1,31 +0,0 @@
-# Contributing to Llama
-We want to make contributing to this project as easy and transparent as
-possible.
-
-## Pull Requests
-We actively welcome your pull requests.
-
-1. Fork the repo and create your branch from `main`.
-2. If you've added code that should be tested, add tests.
-3. If you've changed APIs, update the documentation.
-4. Ensure the test suite passes.
-5. Make sure your code lints.
-6. If you haven't already, complete the Contributor License Agreement ("CLA").
-
-## Contributor License Agreement ("CLA")
-In order to accept your pull request, we need you to submit a CLA. You only need
-to do this once to work on any of Meta's open source projects.
-
-Complete your CLA here: <https://code.facebook.com/cla>
-
-## Issues
-We use GitHub issues to track public bugs. Please ensure your description is
-clear and has sufficient instructions to be able to reproduce the issue.
-
-Meta has a [bounty program](https://www.facebook.com/whitehat/) for the safe
-disclosure of security bugs. In those cases, please go through the process
-outlined on that page and do not file a public issue.
-
-## License
-By contributing to Llama, you agree that your contributions will be licensed
-under the LICENSE file in the root directory of this source tree.
\ No newline at end of file
diff --git a/Responsible-Use-Guide.pdf b/Responsible-Use-Guide.pdf
deleted file mode 100644
index e65e5d1c8100ed329c0ec844772e6a470e817a67..0000000000000000000000000000000000000000
GIT binary patch
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diff --git a/UPDATES.md b/UPDATES.md
deleted file mode 100644
index f3429d838..000000000
--- a/UPDATES.md
+++ /dev/null
@@ -1,21 +0,0 @@
-# 8/7/23 Updates
-
-## System Prompt Update
-
-### Observed Issue
-We received feedback from the community on our prompt template and we are providing an update to reduce the false refusal rates seen. False refusals occur when the model incorrectly refuses to answer a question that it should, for example due to overly broad instructions to be cautious in how it provides responses. 
-
-### Updated approach
-Based on evaluation and analysis, we recommend the removal of the system prompt as the default setting.  Pull request [#626](https://github.com/facebookresearch/llama/pull/626) removes the system prompt as the default option, but still provides an example to help enable experimentation for those using it. 
-
-## Token Sanitization Update
-
-### Observed Issue
-The PyTorch scripts currently provided for tokenization and model inference allow for direct prompt injection via string concatenation. Prompt injections allow for the addition of special system and instruction prompt strings from user-provided prompts. 
-
-As noted in the documentation, these strings are required to use the fine-tuned chat models. However, prompt injections have also been used for manipulating or abusing models by bypassing their safeguards, allowing for the creation of content or behaviors otherwise outside the bounds of acceptable use. 
-
-### Updated approach
-We recommend sanitizing [these strings](https://github.com/facebookresearch/llama#fine-tuned-chat-models) from any user provided prompts. Sanitization of user prompts mitigates malicious or accidental abuse of these strings. The provided scripts have been updated to do this. 
-
-Note: even with this update safety classifiers should still be applied to catch unsafe behaviors or content produced by the model. An [example](https://github.com/facebookresearch/llama-recipes/blob/main/inference/inference.py) of how to deploy such a classifier can be found in the llama-recipes repository.
diff --git a/USE_POLICY.md b/USE_POLICY.md
deleted file mode 100644
index abbcc199b..000000000
--- a/USE_POLICY.md
+++ /dev/null
@@ -1,50 +0,0 @@
-# Llama 2 Acceptable Use Policy
-
-Meta is committed to promoting safe and fair use of its tools and features, including Llama 2. If you access or use Llama 2, you agree to this Acceptable Use Policy (“Policy”). The most recent copy of this policy can be found at [ai.meta.com/llama/use-policy](http://ai.meta.com/llama/use-policy).
-
-## Prohibited Uses
-We want everyone to use Llama 2 safely and responsibly. You agree you will not use, or allow others to use, Llama 2 to: 
-
-1. Violate the law or others’ rights, including to:
-    1. Engage in, promote, generate, contribute to, encourage, plan, incite, or further illegal or unlawful activity or content, such as: 
-        1. Violence or terrorism 
-        2. Exploitation or harm to children, including the solicitation, creation, acquisition, or dissemination of child exploitative content or failure to report Child Sexual Abuse Material
-        3. Human trafficking, exploitation, and sexual violence
-        4. The illegal distribution of information or materials to minors, including obscene materials, or failure to employ legally required age-gating in connection with such information or materials.
-        5. Sexual solicitation
-        6. Any other criminal activity
-    2. Engage in, promote, incite, or facilitate the harassment, abuse, threatening, or bullying of individuals or groups of individuals
-    3. Engage in, promote, incite, or facilitate discrimination or other unlawful or harmful conduct in the provision of employment, employment benefits, credit, housing, other economic benefits, or other essential goods and services
-    4. Engage in the unauthorized or unlicensed practice of any profession including, but not limited to, financial, legal, medical/health, or related professional practices 
-    5. Collect, process, disclose, generate, or infer health, demographic, or other sensitive personal or private information about individuals without rights and consents required by applicable laws
-    6. Engage in or facilitate any action or generate any content that infringes, misappropriates, or otherwise violates any third-party rights, including the outputs or results of any products or services using the Llama 2 Materials
-    7. Create, generate, or facilitate the creation of malicious code, malware, computer viruses or do anything else that could disable, overburden, interfere with or impair the proper working, integrity, operation or appearance of a website or computer system 
-
-
-
-2. Engage in, promote, incite, facilitate, or assist in the planning or development of activities that present a risk of death or bodily harm to individuals, including use of Llama 2 related to the following:
-    1. Military, warfare, nuclear industries or applications, espionage, use for materials or activities that are subject to the International Traffic Arms Regulations (ITAR) maintained by the United States Department of State
-    2. Guns and illegal weapons (including weapon development)
-    3. Illegal drugs and regulated/controlled substances
-    4. Operation of critical infrastructure, transportation technologies, or heavy machinery
-    5. Self-harm or harm to others, including suicide, cutting, and eating disorders
-    6. Any content intended to incite or promote violence, abuse, or any infliction of bodily harm to an individual
-
-
-
-3. Intentionally deceive or mislead others, including use of Llama 2 related to the following:
-    1. Generating, promoting, or furthering fraud or the creation or promotion of disinformation
-    2. Generating, promoting, or furthering defamatory content, including the creation of defamatory statements, images, or other content
-    3. Generating, promoting, or further distributing spam
-    4. Impersonating another individual without consent, authorization, or legal right
-    5. Representing that the use of Llama 2 or outputs are human-generated
-    6. Generating or facilitating false online engagement, including fake reviews and other means of fake online engagement 
-4. Fail to appropriately disclose to end users any known dangers of your AI system 
-
-Please report any violation of this Policy, software “bug,” or other problems that could lead to a violation of this Policy through one of the following means:
-
-* Reporting issues with the model: [github.com/facebookresearch/llama](http://github.com/facebookresearch/llama)
-* Reporting risky content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback)
-* Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info)
-* Reporting violations of the Acceptable Use Policy or unlicensed uses of Llama: [LlamaUseReport@meta.com](mailto:LlamaUseReport@meta.com)
-
diff --git a/inference_benchmark.py b/inference_benchmark.py
new file mode 100644
index 000000000..88f1b43fe
--- /dev/null
+++ b/inference_benchmark.py
@@ -0,0 +1,116 @@
+import torch
+from torch.utils.data import DataLoader
+import time
+import cotracker.models.build_cotracker
+from cotracker.datasets.tap_vid_datasets import TapVidDataset
+import os 
+from cotracker.datasets.utils import collate_fn
+from datasets import load_dataset
+
+### Setup ###
+BATCH_SIZE = 1
+BATCH_COUNT = 5
+NUM_WORKERS = 1
+
+# Manual seed for reproducatibility
+SEED = 42
+torch.manual_seed(SEED)
+torch.cuda.manual_seed(SEED)
+
+DEVICE_CUDA = 'cuda'
+DEVICE_CPU = 'cpu'
+
+
+def get_data_loader(num_workers=1):
+    dataset = load_dataset("HuggingFaceH4/no_robots")
+    dataloader = DataLoader(
+        dataset,
+        batch_size=BATCH_SIZE,
+        shuffle=False,
+        num_workers=num_workers,
+        collate_fn=collate_fn,
+    )
+    return dataloader
+
+
+def get_model(checkpoint_path=CHECKPOINT_S4_W12):
+    return cotracker.models.build_cotracker.build_cotracker(checkpoint_path)
+
+
+def run_inference(dataloader, model, cuda=True):
+    load_time_per_batch = torch.zeros(BATCH_COUNT)
+    inference_time_per_batch = torch.zeros(BATCH_COUNT)
+    total_time_per_batch = torch.zeros(BATCH_COUNT)
+    
+    device = DEVICE_CUDA if cuda else DEVICE_CPU
+    print("Working on device: {}".format(device))
+    model.to(device)
+    
+    for batch_idx in range(BATCH_COUNT):
+        print("Starting BATCHs {} of {}".format(batch_idx + 1, BATCH_COUNT))
+        (output, load_time, train_time), batch_time = measure_runtime(run_batch_inference,
+                                                              dataloader,
+                                                              model,
+                                                              cuda)
+        load_time_per_batch[batch_idx] = load_time
+        inference_time_per_batch[batch_idx] = train_time
+        total_time_per_batch[batch_idx] = batch_time
+
+        print("Finished Batch {} of {}".format(batch_idx + 1, BATCH_COUNT))
+        print("Batch load time: {}".format(load_time))
+        print("Batch inference time: {}".format(train_time))
+        print("Batch total time: {}".format(batch_time))
+    return model, load_time_per_batch, inference_time_per_batch, total_time_per_batch
+
+
+def measure_runtime(func, *func_args):
+    start = time.perf_counter()
+    result = func(*func_args)
+    end = time.perf_counter()
+    elapsed = end - start
+    return result, elapsed
+
+
+def run_batch_inference(dataloader, model, cuda=True):
+    (x, y), load_time = measure_runtime(
+        __get_next_batch, dataloader)
+
+    if cuda:
+        x = x.to(DEVICE_CUDA)
+        y = y.to(DEVICE_CUDA)
+
+    output, train_time = measure_runtime(
+        model,
+        x)
+    
+    return output, load_time, train_time
+
+def __get_next_batch(dataloader):
+    return next(iter(dataloader))
+
+
+def benchmark():
+    print("Starting up...")
+
+    print("Building data loaders...")
+    data_loader = get_data_loader()
+
+    print("Initializing Model...")
+    net = get_model()
+
+    print("Running inference benchmark...\n")
+    _, load, inference, total = run_batch_inference(data_loader, net)
+
+    print("Results...")
+    print("C2.1: Data-loading times")
+    print("> per epoch: ", load)
+    print("> average: ", torch.mean(load))
+    print("C2.2: Training time for each epoch")
+    print("> per epoch", inference)
+    print("> average", torch.mean(inference))
+    print("C2.3: Total time for each epoch")
+    print("> per epoch", total)
+    print("> average", torch.mean(total))
+
+if __name__ == "__main__":
+    benchmark()
\ No newline at end of file

From ed186f52c4518495fbbf5b73ba45dc7c01312535 Mon Sep 17 00:00:00 2001
From: George Tamer <gtamer2@gmail.com>
Date: Tue, 12 Dec 2023 21:39:55 -0500
Subject: [PATCH 02/64] more deletions

---
 LICENSE       | 126 --------------------------------------------------
 MODEL_CARD.md | 100 ---------------------------------------
 README.md     |  41 +++++++++-------
 3 files changed, 24 insertions(+), 243 deletions(-)
 delete mode 100644 LICENSE
 delete mode 100644 MODEL_CARD.md

diff --git a/LICENSE b/LICENSE
deleted file mode 100644
index 28c98e84d..000000000
--- a/LICENSE
+++ /dev/null
@@ -1,126 +0,0 @@
-LLAMA 2 COMMUNITY LICENSE AGREEMENT	
-Llama 2 Version Release Date: July 18, 2023
-
-"Agreement" means the terms and conditions for use, reproduction, distribution and 
-modification of the Llama Materials set forth herein.
-
-"Documentation" means the specifications, manuals and documentation 
-accompanying Llama 2 distributed by Meta at ai.meta.com/resources/models-and-
-libraries/llama-downloads/.
-
-"Licensee" or "you" means you, or your employer or any other person or entity (if 
-you are entering into this Agreement on such person or entity's behalf), of the age 
-required under applicable laws, rules or regulations to provide legal consent and that 
-has legal authority to bind your employer or such other person or entity if you are 
-entering in this Agreement on their behalf.
-
-"Llama 2" means the foundational large language models and software and 
-algorithms, including machine-learning model code, trained model weights, 
-inference-enabling code, training-enabling code, fine-tuning enabling code and other 
-elements of the foregoing distributed by Meta at ai.meta.com/resources/models-and-
-libraries/llama-downloads/.
-
-"Llama Materials" means, collectively, Meta's proprietary Llama 2 and 
-Documentation (and any portion thereof) made available under this Agreement.
-
-"Meta" or "we" means Meta Platforms Ireland Limited (if you are located in or, if you 
-are an entity, your principal place of business is in the EEA or Switzerland) and Meta 
-Platforms, Inc. (if you are located outside of the EEA or Switzerland). 
-
-By clicking "I Accept" below or by using or distributing any portion or element of the 
-Llama Materials, you agree to be bound by this Agreement.
-
-1. License Rights and Redistribution. 
-
-      a. Grant of Rights. You are granted a non-exclusive, worldwide, non-
-transferable and royalty-free limited license under Meta's intellectual property or 
-other rights owned by Meta embodied in the Llama Materials to use, reproduce, 
-distribute, copy, create derivative works of, and make modifications to the Llama 
-Materials.  
-      
-      b. Redistribution and Use.  
-
-            i. If you distribute or make the Llama Materials, or any derivative works 
-thereof, available to a third party, you shall provide a copy of this Agreement to such 
-third party. 
-            ii.  If you receive Llama Materials, or any derivative works thereof, from 
-a Licensee as part of an integrated end user product, then Section 2 of this 
-Agreement will not apply to you. 
-
-            iii. You must retain in all copies of the Llama Materials that you 
-distribute the following attribution notice within a "Notice" text file distributed as a 
-part of such copies: "Llama 2 is licensed under the LLAMA 2 Community License, 
-Copyright (c) Meta Platforms, Inc. All Rights Reserved."
-
-            iv. Your use of the Llama Materials must comply with applicable laws 
-and regulations (including trade compliance laws and regulations) and adhere to the 
-Acceptable Use Policy for the Llama Materials (available at 
-https://ai.meta.com/llama/use-policy), which is hereby incorporated by reference into 
-this Agreement.
-
-            v. You will not use the Llama Materials or any output or results of the 
-Llama Materials to improve any other large language model (excluding Llama 2 or 
-derivative works thereof).  
-
-2. Additional Commercial Terms. If, on the Llama 2 version release date, the 
-monthly active users of the products or services made available by or for Licensee, 
-or Licensee's affiliates, is greater than 700 million monthly active users in the 
-preceding calendar month, you must request a license from Meta, which Meta may 
-grant to you in its sole discretion, and you are not authorized to exercise any of the 
-rights under this Agreement unless or until Meta otherwise expressly grants you 
-such rights.
-            
-3. Disclaimer of Warranty. UNLESS REQUIRED BY APPLICABLE LAW, THE 
-LLAMA MATERIALS AND ANY OUTPUT AND RESULTS THEREFROM ARE 
-PROVIDED ON AN "AS IS" BASIS, WITHOUT WARRANTIES OF ANY KIND, 
-EITHER EXPRESS OR IMPLIED, INCLUDING, WITHOUT LIMITATION, ANY 
-WARRANTIES OF TITLE, NON-INFRINGEMENT, MERCHANTABILITY, OR 
-FITNESS FOR A PARTICULAR PURPOSE. YOU ARE SOLELY RESPONSIBLE 
-FOR DETERMINING THE APPROPRIATENESS OF USING OR REDISTRIBUTING 
-THE LLAMA MATERIALS AND ASSUME ANY RISKS ASSOCIATED WITH YOUR 
-USE OF THE LLAMA MATERIALS AND ANY OUTPUT AND RESULTS.
-
-4. Limitation of Liability. IN NO EVENT WILL META OR ITS AFFILIATES BE 
-LIABLE UNDER ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, TORT, 
-NEGLIGENCE, PRODUCTS LIABILITY, OR OTHERWISE, ARISING OUT OF THIS 
-AGREEMENT, FOR ANY LOST PROFITS OR ANY INDIRECT, SPECIAL, 
-CONSEQUENTIAL, INCIDENTAL, EXEMPLARY OR PUNITIVE DAMAGES, EVEN 
-IF META OR ITS AFFILIATES HAVE BEEN ADVISED OF THE POSSIBILITY OF 
-ANY OF THE FOREGOING.
- 
-5. Intellectual Property.
-
-      a. No trademark licenses are granted under this Agreement, and in 
-connection with the Llama Materials, neither Meta nor Licensee may use any name 
-or mark owned by or associated with the other or any of its affiliates, except as 
-required for reasonable and customary use in describing and redistributing the 
-Llama Materials.
-
-      b. Subject to Meta's ownership of Llama Materials and derivatives made by or 
-for Meta, with respect to any derivative works and modifications of the Llama 
-Materials that are made by you, as between you and Meta, you are and will be the 
-owner of such derivative works and modifications.
-
-      c. If you institute litigation or other proceedings against Meta or any entity 
-(including a cross-claim or counterclaim in a lawsuit) alleging that the Llama 
-Materials or Llama 2 outputs or results, or any portion of any of the foregoing, 
-constitutes an infringement of intellectual property or other rights owned or licensable 
-by you, then any licenses granted to you under this Agreement shall terminate as of 
-the date such litigation or claim is filed or instituted. You will indemnify and hold 
-harmless Meta from and against any claim by any third party arising out of or related 
-to your use or distribution of the Llama Materials.
-
-6. Term and Termination. The term of this Agreement will commence upon your 
-acceptance of this Agreement or access to the Llama Materials and will continue in 
-full force and effect until terminated in accordance with the terms and conditions 
-herein. Meta may terminate this Agreement if you are in breach of any term or 
-condition of this Agreement. Upon termination of this Agreement, you shall delete 
-and cease use of the Llama Materials. Sections 3, 4 and 7 shall survive the 
-termination of this Agreement. 
-
-7. Governing Law and Jurisdiction. This Agreement will be governed and 
-construed under the laws of the State of California without regard to choice of law 
-principles, and the UN Convention on Contracts for the International Sale of Goods 
-does not apply to this Agreement. The courts of California shall have exclusive 
-jurisdiction of any dispute arising out of this Agreement. 
-
diff --git a/MODEL_CARD.md b/MODEL_CARD.md
deleted file mode 100644
index 370807880..000000000
--- a/MODEL_CARD.md
+++ /dev/null
@@ -1,100 +0,0 @@
-# **Model Details**
-
-Meta developed and released the Llama 2 family of large language models (LLMs), a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. Our fine-tuned LLMs, called Llama-2-Chat, are optimized for dialogue use cases. Llama-2-Chat models outperform open-source chat models on most benchmarks we tested, and in our human evaluations for helpfulness and safety, are on par with some popular closed-source models like ChatGPT and PaLM.
-
-**Model Developers** Meta
-
-**Variations** Llama 2 comes in a range of parameter sizes — 7B, 13B, and 70B — as well as pretrained and fine-tuned variations.
-
-**Input** Models input text only.
-
-**Output** Models generate text only.
-
-**Model Architecture** Llama 2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety.
-
-||Training Data|Params|Context Length|GQA|Tokens|LR|
-|---|---|---|---|---|---|---|
-Llama 2|*A new mix of publicly available online data*|7B|4k|&#10007;|2.0T|3.0 x 10<sup>-4</sup>
-Llama 2|*A new mix of publicly available online data*|13B|4k|&#10007;|2.0T|3.0 x 10<sup>-4</sup>
-Llama 2|*A new mix of publicly available online data*|70B|4k|&#10004;|2.0T|1.5 x 10<sup>-4</sup>
-
-**Llama 2 family of models.** Token counts refer to pretraining data only. All models are trained with a global batch-size of 4M tokens. The 70B version uses Grouped-Query Attention (GQA) for improved inference scalability.
-
-**Model Dates** Llama 2 was trained between January 2023 and July 2023.
-
-**Status** This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback.
-
-**License** A custom commercial license is available at: [https://ai.meta.com/resources/models-and-libraries/llama-downloads/](https://ai.meta.com/resources/models-and-libraries/llama-downloads/)
-
-**Research Paper** More information can be found in the paper "Llama-2: Open Foundation and Fine-tuned Chat Models", available at https://ai.meta.com/research/publications/llama-2-open-foundation-and-fine-tuned-chat-models/.
-
-**Where to send questions or comments about the model** Instructions on how to provide feedback or comments on the model can be found in the model [README](README.md).
-
-# **Intended Use**
-**Intended Use Cases** Llama 2 is intended for commercial and research use in English. Tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks.
-
-**Out-of-scope** Uses Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 2 Community License. Use in languages other than English**. 
-
-**Note: Developers may fine-tune Llama 2 models for languages beyond English provided they comply with the Llama 2 Community License and the Acceptable Use Policy.
-
-# **Hardware and Software**
-**Training Factors** We used custom training libraries, Meta's Research Super Cluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute.
-
-**Carbon Footprint** Pretraining utilized a cumulative 3.3M GPU hours of computation on hardware of type A100-80GB (TDP of 350-400W). Estimated total emissions were 539 tCO2eq, 100% of which were offset by Meta’s sustainability program.
-
-||Time (GPU hours)|Power Consumption (W)|Carbon Emitted(tCO<sub>2</sub>eq)|
-|---|---|---|---|
-|Llama 2 7B|184320|400|31.22|
-|Llama 2 13B|368640|400|62.44|
-|Llama 2 70B|1720320|400|291.42|
-|Total|3311616||539.00|
-
-**CO<sub>2</sub> emissions during pretraining.** Time: total GPU time required for training each model. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others.
-
-# **Training Data**
-**Overview** Llama 2 was pretrained on 2 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over one million new human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data.
-
-**Data Freshness** The pretraining data has a cutoff of September 2022, but some tuning data is more recent, up to July 2023.
-
-# **Evaluation Results**
-
-In this section, we report the results for the Llama 1 and Llama 2 models on standard academic benchmarks.
-For all the evaluations, we use our internal evaluations library.
-
-|Model|Size|Code|Commonsense Reasoning|World Knowledge|Reading Comprehension|Math|MMLU|BBH|AGI Eval|
-|---|---|---|---|---|---|---|---|---|---|
-|Llama 1|7B|14.1|60.8|46.2|58.5|6.95|35.1|30.3|23.9|
-|Llama 1|13B|18.9|66.1|52.6|62.3|10.9|46.9|37.0|33.9|
-|Llama 1|33B|26.0|70.0|58.4|67.6|21.4|57.8|39.8|41.7|
-|Llama 1|65B|30.7|70.7|60.5|68.6|30.8|63.4|43.5|47.6|
-|Llama 2|7B|16.8|63.9|48.9|61.3|14.6|45.3|32.6|29.3|
-|Llama 2|13B|24.5|66.9|55.4|65.8|28.7|54.8|39.4|39.1|
-|Llama 2|70B|**37.5**|**71.9**|**63.6**|**69.4**|**35.2**|**68.9**|**51.2**|**54.2**|
-
-**Overall performance on grouped academic benchmarks.** *Code:* We report the average pass@1 scores of our models on HumanEval and MBPP. *Commonsense Reasoning:* We report the average of PIQA, SIQA, HellaSwag, WinoGrande, ARC easy and challenge, OpenBookQA, and CommonsenseQA. We report 7-shot results for CommonSenseQA and 0-shot results for all other benchmarks. *World Knowledge:* We evaluate the 5-shot performance on NaturalQuestions and TriviaQA and report the average. *Reading Comprehension:* For reading comprehension, we report the 0-shot average on SQuAD, QuAC, and BoolQ. *MATH:* We report the average of the GSM8K (8 shot) and MATH (4 shot) benchmarks at the top 1.
-
-|||TruthfulQA|Toxigen|
-|---|---|---|---|
-|Llama 1|7B|27.42|23.00|
-|Llama 1|13B|41.74|23.08|
-|Llama 1|33B|44.19|22.57|
-|Llama 1|65B|48.71|21.77|
-|Llama 2|7B|33.29|**21.25**|
-|Llama 2|13B|41.86|26.10|
-|Llama 2|70B|**50.18**|24.60|
-
-**Evaluation of pretrained LLMs on automatic safety benchmarks.** For TruthfulQA, we present the percentage of generations that are both truthful and informative (the higher the better). For ToxiGen, we present the percentage of toxic generations (the smaller the better).
-
-
-|||TruthfulQA|Toxigen|
-|---|---|---|---|
-|Llama-2-Chat|7B|57.04|**0.00**|
-|Llama-2-Chat|13B|62.18|**0.00**|
-|Llama-2-Chat|70B|**64.14**|0.01|
-
-**Evaluation of fine-tuned LLMs on different safety datasets.** Same metric definitions as above.
-
-# **Ethical Considerations and Limitations**
-Llama 2 is a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Llama 2’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 2, developers should perform safety testing and tuning tailored to their specific applications of the model.
-
-Please see the Responsible Use Guide available at [https://ai.meta.com/llama/responsible-use-guide/](https://ai.meta.com/llama/responsible-use-guide/)
diff --git a/README.md b/README.md
index 96ae58f01..22e8a7d8e 100755
--- a/README.md
+++ b/README.md
@@ -1,6 +1,6 @@
 # Llama 2
 
-We are unlocking the power of large language models. Our latest version of Llama is now accessible to individuals, creators, researchers and businesses of all sizes so that they can experiment, innovate and scale their ideas responsibly. 
+We are unlocking the power of large language models. Our latest version of Llama is now accessible to individuals, creators, researchers and businesses of all sizes so that they can experiment, innovate and scale their ideas responsibly.
 
 This release includes model weights and starting code for pretrained and fine-tuned Llama language models — ranging from 7B to 70B parameters.
 
@@ -28,32 +28,36 @@ We are also providing downloads on [Hugging Face](https://huggingface.co/meta-ll
 
 ## Quick Start
 
-You can follow the steps below to quickly get up and running with Llama 2 models. These steps will let you run quick inference locally. For more examples, see the [Llama 2 recipes repository](https://github.com/facebookresearch/llama-recipes). 
+You can follow the steps below to quickly get up and running with Llama 2 models. These steps will let you run quick inference locally. For more examples, see the [Llama 2 recipes repository](https://github.com/facebookresearch/llama-recipes).
 
 1. In a conda env with PyTorch / CUDA available clone and download this repository.
 
 2. In the top level directory run:
-    ```bash
-    pip install -e .
-    ```
+   ```bash
+   pip install -e .
+   ```
 3. Visit the [Meta website](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) and register to download the model/s.
 
 4. Once registered, you will get an email with a URL to download the models. You will need this URL when you run the download.sh script.
 
-5. Once you get the email, navigate to your downloaded llama repository and run the download.sh script. 
-    - Make sure to grant execution permissions to the download.sh script
-    - During this process, you will be prompted to enter the URL from the email. 
-    - Do not use the “Copy Link” option but rather make sure to manually copy the link from the email.
+5. Once you get the email, navigate to your downloaded llama repository and run the download.sh script.
+
+   - Make sure to grant execution permissions to the download.sh script
+   - During this process, you will be prompted to enter the URL from the email.
+   - Do not use the “Copy Link” option but rather make sure to manually copy the link from the email.
 
 6. Once the model/s you want have been downloaded, you can run the model locally using the command below:
+
 ```bash
 torchrun --nproc_per_node 1 example_chat_completion.py \
     --ckpt_dir llama-2-7b-chat/ \
     --tokenizer_path tokenizer.model \
     --max_seq_len 512 --max_batch_size 6
 ```
+
 **Note**
-- Replace  `llama-2-7b-chat/` with the path to your checkpoint directory and `tokenizer.model` with the path to your tokenizer model.
+
+- Replace `llama-2-7b-chat/` with the path to your checkpoint directory and `tokenizer.model` with the path to your tokenizer model.
 - The `–nproc_per_node` should be set to the [MP](#inference) value for the model you are using.
 - Adjust the `max_seq_len` and `max_batch_size` parameters as needed.
 - This example runs the [example_chat_completion.py](example_chat_completion.py) found in this repository but you can change that to a different .py file.
@@ -62,11 +66,11 @@ torchrun --nproc_per_node 1 example_chat_completion.py \
 
 Different models require different model-parallel (MP) values:
 
-|  Model | MP |
-|--------|----|
-| 7B     | 1  |
-| 13B    | 2  |
-| 70B    | 8  |
+| Model | MP  |
+| ----- | --- |
+| 7B    | 1   |
+| 13B   | 2   |
+| 70B   | 8   |
 
 All models support sequence length up to 4096 tokens, but we pre-allocate the cache according to `max_seq_len` and `max_batch_size` values. So set those according to your hardware.
 
@@ -105,16 +109,18 @@ In order to help developers address these risks, we have created the [Responsibl
 ## Issues
 
 Please report any software “bug”, or other problems with the models through one of the following means:
+
 - Reporting issues with the model: [github.com/facebookresearch/llama](http://github.com/facebookresearch/llama)
 - Reporting risky content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback)
 - Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info)
 
 ## Model Card
+
 See [MODEL_CARD.md](MODEL_CARD.md).
 
 ## License
 
-Our model and weights are licensed for both researchers and commercial entities, upholding the principles of openness. Our mission is to empower individuals, and industry through this opportunity, while fostering an environment of discovery and ethical AI advancements. 
+Our model and weights are licensed for both researchers and commercial entities, upholding the principles of openness. Our mission is to empower individuals, and industry through this opportunity, while fostering an environment of discovery and ethical AI advancements.
 
 See the [LICENSE](LICENSE) file, as well as our accompanying [Acceptable Use Policy](USE_POLICY.md)
 
@@ -124,7 +130,8 @@ See the [LICENSE](LICENSE) file, as well as our accompanying [Acceptable Use Pol
 2. [Llama 2 technical overview](https://ai.meta.com/resources/models-and-libraries/llama)
 3. [Open Innovation AI Research Community](https://ai.meta.com/llama/open-innovation-ai-research-community/)
 
-For common questions, the FAQ can be found [here](https://ai.meta.com/llama/faq/) which will be kept up to date over time as new questions arise. 
+For common questions, the FAQ can be found [here](https://ai.meta.com/llama/faq/) which will be kept up to date over time as new questions arise.
 
 ## Original LLaMA
+
 The repo for the original llama release is in the [`llama_v1`](https://github.com/facebookresearch/llama/tree/llama_v1) branch.

From 62f242dc8055b239fea134d140a225be55175f01 Mon Sep 17 00:00:00 2001
From: George Tamer <gtamer2@gmail.com>
Date: Tue, 12 Dec 2023 22:13:36 -0500
Subject: [PATCH 03/64] Changes

---
 inference_benchmark.py | 99 ++++++++++++++++++++++++++++++------------
 1 file changed, 71 insertions(+), 28 deletions(-)

diff --git a/inference_benchmark.py b/inference_benchmark.py
index 88f1b43fe..50e0ed1f2 100644
--- a/inference_benchmark.py
+++ b/inference_benchmark.py
@@ -1,10 +1,6 @@
 import torch
 from torch.utils.data import DataLoader
 import time
-import cotracker.models.build_cotracker
-from cotracker.datasets.tap_vid_datasets import TapVidDataset
-import os 
-from cotracker.datasets.utils import collate_fn
 from datasets import load_dataset
 
 ### Setup ###
@@ -20,6 +16,29 @@
 DEVICE_CUDA = 'cuda'
 DEVICE_CPU = 'cpu'
 
+from llama import Llama
+from typing import List
+
+prompts: List[str] = [
+    # For these prompts, the expected answer is the natural continuation of the prompt
+    "I believe the meaning of life is",
+    "Simply put, the theory of relativity states that ",
+    """A brief message congratulating the team on the launch:
+
+    Hi everyone,
+    
+    I just """,
+    # Few shot prompt (providing a few examples before asking model to complete more);
+    """Translate English to French:
+    
+    sea otter => loutre de mer
+    peppermint => menthe poivrée
+    plush girafe => girafe peluche
+    cheese =>""",
+]
+
+def get_device():
+    return torch.device(DEVICE_CUDA if torch.cuda.is_available() else DEVICE_CPU)
 
 def get_data_loader(num_workers=1):
     dataset = load_dataset("HuggingFaceH4/no_robots")
@@ -27,38 +46,43 @@ def get_data_loader(num_workers=1):
         dataset,
         batch_size=BATCH_SIZE,
         shuffle=False,
-        num_workers=num_workers,
-        collate_fn=collate_fn,
+        num_workers=num_workers
     )
     return dataloader
 
 
-def get_model(checkpoint_path=CHECKPOINT_S4_W12):
-    return cotracker.models.build_cotracker.build_cotracker(checkpoint_path)
+def get_model(ckpt_dir, tokenizer_path, max_seq_len, max_batch_size):
+    generator = Llama.build(
+        ckpt_dir=ckpt_dir,
+        tokenizer_path=tokenizer_path,
+        max_seq_len=max_seq_len,
+        max_batch_size=max_batch_size,
+    )
+    return generator
 
 
-def run_inference(dataloader, model, cuda=True):
+def run_benchmark(dataloader, model):
     load_time_per_batch = torch.zeros(BATCH_COUNT)
     inference_time_per_batch = torch.zeros(BATCH_COUNT)
     total_time_per_batch = torch.zeros(BATCH_COUNT)
     
-    device = DEVICE_CUDA if cuda else DEVICE_CPU
-    print("Working on device: {}".format(device))
+    device = get_device()
     model.to(device)
+    print("Working on device: {}".format(device))
+    
     
     for batch_idx in range(BATCH_COUNT):
-        print("Starting BATCHs {} of {}".format(batch_idx + 1, BATCH_COUNT))
-        (output, load_time, train_time), batch_time = measure_runtime(run_batch_inference,
+        print("Starting BATCH {} of {}".format(batch_idx + 1, BATCH_COUNT))
+        (output, load_time, inference_time), batch_time = measure_runtime(run_batch_inference,
                                                               dataloader,
-                                                              model,
-                                                              cuda)
+                                                              model)
         load_time_per_batch[batch_idx] = load_time
-        inference_time_per_batch[batch_idx] = train_time
+        inference_time_per_batch[batch_idx] = inference_time
         total_time_per_batch[batch_idx] = batch_time
 
         print("Finished Batch {} of {}".format(batch_idx + 1, BATCH_COUNT))
         print("Batch load time: {}".format(load_time))
-        print("Batch inference time: {}".format(train_time))
+        print("Batch inference time: {}".format(inference_time))
         print("Batch total time: {}".format(batch_time))
     return model, load_time_per_batch, inference_time_per_batch, total_time_per_batch
 
@@ -71,19 +95,38 @@ def measure_runtime(func, *func_args):
     return result, elapsed
 
 
-def run_batch_inference(dataloader, model, cuda=True):
-    (x, y), load_time = measure_runtime(
+def run_batch_inference(dataloader, model):
+    x, load_time = measure_runtime(
         __get_next_batch, dataloader)
 
-    if cuda:
-        x = x.to(DEVICE_CUDA)
-        y = y.to(DEVICE_CUDA)
+    device = get_device()
+    x = x.to(device)
+    # y = y.to(device)
 
-    output, train_time = measure_runtime(
+    output, inference_time = measure_runtime(
+        inference,
         model,
         x)
     
-    return output, load_time, train_time
+    return output, load_time, inference_time
+
+def inference(
+    generator: Llama,
+    prompts: List[str],
+    temperature: float = 0.6,
+    top_p: float = 0.9,
+    max_gen_len: int = 64,
+):
+    results = generator.text_completion(
+        prompts,
+        max_gen_len=max_gen_len,
+        temperature=temperature,
+        top_p=top_p,
+    )
+    for prompt, result in zip(prompts, results):
+        print(prompt)
+        print(f"> {result['generation']}")
+        print("\n==================================\n")
 
 def __get_next_batch(dataloader):
     return next(iter(dataloader))
@@ -99,16 +142,16 @@ def benchmark():
     net = get_model()
 
     print("Running inference benchmark...\n")
-    _, load, inference, total = run_batch_inference(data_loader, net)
+    _, load, inference, total = run_benchmark(data_loader, net)
 
     print("Results...")
-    print("C2.1: Data-loading times")
+    print("Data-loading times")
     print("> per epoch: ", load)
     print("> average: ", torch.mean(load))
-    print("C2.2: Training time for each epoch")
+    print("\nInference time for each epoch")
     print("> per epoch", inference)
     print("> average", torch.mean(inference))
-    print("C2.3: Total time for each epoch")
+    print("\nTotal time for each epoch")
     print("> per epoch", total)
     print("> average", torch.mean(total))
 

From 257962fc7b67ee5e1c235df593ed891a99553e18 Mon Sep 17 00:00:00 2001
From: George Tamer <gtamer2@gmail.com>
Date: Tue, 12 Dec 2023 22:16:34 -0500
Subject: [PATCH 04/64] import fire

---
 inference_benchmark.py | 10 +++++++---
 1 file changed, 7 insertions(+), 3 deletions(-)

diff --git a/inference_benchmark.py b/inference_benchmark.py
index 50e0ed1f2..c4d5fa31b 100644
--- a/inference_benchmark.py
+++ b/inference_benchmark.py
@@ -2,6 +2,7 @@
 from torch.utils.data import DataLoader
 import time
 from datasets import load_dataset
+import fire
 
 ### Setup ###
 BATCH_SIZE = 1
@@ -132,14 +133,17 @@ def __get_next_batch(dataloader):
     return next(iter(dataloader))
 
 
-def benchmark():
+def benchmark(ckpt_dir, 
+              tokenizer_path, 
+              max_seq_len, 
+              max_batch_size):
     print("Starting up...")
 
     print("Building data loaders...")
     data_loader = get_data_loader()
 
     print("Initializing Model...")
-    net = get_model()
+    net = get_model(ckpt_dir, tokenizer_path, max_seq_len, max_batch_size)
 
     print("Running inference benchmark...\n")
     _, load, inference, total = run_benchmark(data_loader, net)
@@ -156,4 +160,4 @@ def benchmark():
     print("> average", torch.mean(total))
 
 if __name__ == "__main__":
-    benchmark()
\ No newline at end of file
+    fire.Fire(benchmark)
\ No newline at end of file

From a89488ad11ad2c7073d4d8004602b93db05ba34f Mon Sep 17 00:00:00 2001
From: George Tamer <gtamer2@gmail.com>
Date: Tue, 12 Dec 2023 22:40:41 -0500
Subject: [PATCH 05/64] git ignore

---
 .gitignore | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/.gitignore b/.gitignore
index 6769e21d9..da2cdaa50 100755
--- a/.gitignore
+++ b/.gitignore
@@ -1,3 +1,7 @@
+llama-2-7b/
+tokenizer.model
+tokenizer_checklist.chk
+
 # Byte-compiled / optimized / DLL files
 __pycache__/
 *.py[cod]

From 5a84145d83326adc53b4a5d87d83b71e4d72af7b Mon Sep 17 00:00:00 2001
From: George Tamer <gtamer2@gmail.com>
Date: Tue, 12 Dec 2023 22:43:04 -0500
Subject: [PATCH 06/64] fixes

---
 inference_benchmark.py | 8 ++++++++
 1 file changed, 8 insertions(+)

diff --git a/inference_benchmark.py b/inference_benchmark.py
index c4d5fa31b..9994540f0 100644
--- a/inference_benchmark.py
+++ b/inference_benchmark.py
@@ -3,6 +3,14 @@
 import time
 from datasets import load_dataset
 import fire
+import os
+import torch.distributed as dist
+
+### Enabling parallel
+os.environ['RANK'] = '0'  # Set the rank of the process
+os.environ['WORLD_SIZE'] = '1'  # Set the total number of processes
+
+dist.init_process_group(backend='nccl')  # Initialize the process group
 
 ### Setup ###
 BATCH_SIZE = 1

From cf2b65245db978b878e47a44b1dc3bef636bebe3 Mon Sep 17 00:00:00 2001
From: George Tamer <gtamer2@gmail.com>
Date: Wed, 13 Dec 2023 17:17:20 -0500
Subject: [PATCH 07/64] Push changes

---
 inference_benchmark.py | 26 +-------------------------
 1 file changed, 1 insertion(+), 25 deletions(-)

diff --git a/inference_benchmark.py b/inference_benchmark.py
index 9994540f0..a5d57a69d 100644
--- a/inference_benchmark.py
+++ b/inference_benchmark.py
@@ -6,12 +6,6 @@
 import os
 import torch.distributed as dist
 
-### Enabling parallel
-os.environ['RANK'] = '0'  # Set the rank of the process
-os.environ['WORLD_SIZE'] = '1'  # Set the total number of processes
-
-dist.init_process_group(backend='nccl')  # Initialize the process group
-
 ### Setup ###
 BATCH_SIZE = 1
 BATCH_COUNT = 5
@@ -28,29 +22,11 @@
 from llama import Llama
 from typing import List
 
-prompts: List[str] = [
-    # For these prompts, the expected answer is the natural continuation of the prompt
-    "I believe the meaning of life is",
-    "Simply put, the theory of relativity states that ",
-    """A brief message congratulating the team on the launch:
-
-    Hi everyone,
-    
-    I just """,
-    # Few shot prompt (providing a few examples before asking model to complete more);
-    """Translate English to French:
-    
-    sea otter => loutre de mer
-    peppermint => menthe poivrée
-    plush girafe => girafe peluche
-    cheese =>""",
-]
-
 def get_device():
     return torch.device(DEVICE_CUDA if torch.cuda.is_available() else DEVICE_CPU)
 
 def get_data_loader(num_workers=1):
-    dataset = load_dataset("HuggingFaceH4/no_robots")
+    dataset = load_dataset("HuggingFaceH4/no_robots")['test_sft']
     dataloader = DataLoader(
         dataset,
         batch_size=BATCH_SIZE,

From 47400da268e0a0ce72a666960ef9821f9b4f6b8b Mon Sep 17 00:00:00 2001
From: George Tamer <gtamer2@gmail.com>
Date: Wed, 13 Dec 2023 17:18:56 -0500
Subject: [PATCH 08/64] comment out omdel.to

---
 inference_benchmark.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/inference_benchmark.py b/inference_benchmark.py
index a5d57a69d..47b778850 100644
--- a/inference_benchmark.py
+++ b/inference_benchmark.py
@@ -52,7 +52,7 @@ def run_benchmark(dataloader, model):
     total_time_per_batch = torch.zeros(BATCH_COUNT)
     
     device = get_device()
-    model.to(device)
+    # model.to(device)
     print("Working on device: {}".format(device))
     
     

From 4c4d439324d120df335a3098cdd50fa33ee8e88b Mon Sep 17 00:00:00 2001
From: George Tamer <gtamer2@gmail.com>
Date: Wed, 13 Dec 2023 17:20:16 -0500
Subject: [PATCH 09/64] print x for debugging

---
 inference_benchmark.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/inference_benchmark.py b/inference_benchmark.py
index 47b778850..282acd7cd 100644
--- a/inference_benchmark.py
+++ b/inference_benchmark.py
@@ -85,6 +85,7 @@ def run_batch_inference(dataloader, model):
         __get_next_batch, dataloader)
 
     device = get_device()
+    print("x: ", x)
     x = x.to(device)
     # y = y.to(device)
 

From 207153997e5dff775af3d153590b058f0cc944e7 Mon Sep 17 00:00:00 2001
From: George Tamer <gtamer2@gmail.com>
Date: Wed, 13 Dec 2023 17:27:04 -0500
Subject: [PATCH 10/64] inference_benchmamrk

---
 inference_benchmark.py | 14 ++++++++------
 1 file changed, 8 insertions(+), 6 deletions(-)

diff --git a/inference_benchmark.py b/inference_benchmark.py
index 282acd7cd..079b266f4 100644
--- a/inference_benchmark.py
+++ b/inference_benchmark.py
@@ -10,6 +10,7 @@
 BATCH_SIZE = 1
 BATCH_COUNT = 5
 NUM_WORKERS = 1
+HUGGING_FACE_GSMK_DATASET_ID = "gsm8k"
 
 # Manual seed for reproducatibility
 SEED = 42
@@ -26,7 +27,7 @@ def get_device():
     return torch.device(DEVICE_CUDA if torch.cuda.is_available() else DEVICE_CPU)
 
 def get_data_loader(num_workers=1):
-    dataset = load_dataset("HuggingFaceH4/no_robots")['test_sft']
+    dataset = load_dataset(HUGGING_FACE_GSMK_DATASET_ID)['train']
     dataloader = DataLoader(
         dataset,
         batch_size=BATCH_SIZE,
@@ -81,18 +82,19 @@ def measure_runtime(func, *func_args):
 
 
 def run_batch_inference(dataloader, model):
-    x, load_time = measure_runtime(
+    (question, answer), load_time = measure_runtime(
         __get_next_batch, dataloader)
 
-    device = get_device()
-    print("x: ", x)
-    x = x.to(device)
+    
+    print("question: ", question, "answer", answer)
+    # device = get_device()
+    # x = x.to(device)
     # y = y.to(device)
 
     output, inference_time = measure_runtime(
         inference,
         model,
-        x)
+        question)
     
     return output, load_time, inference_time
 

From 58eee149f50f71a659606c0d6dc646f9a21e0f9b Mon Sep 17 00:00:00 2001
From: George Tamer <gtamer2@gmail.com>
Date: Wed, 13 Dec 2023 17:28:22 -0500
Subject: [PATCH 11/64] inference_benchmamrk

---
 inference_benchmark.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/inference_benchmark.py b/inference_benchmark.py
index 079b266f4..b983cb422 100644
--- a/inference_benchmark.py
+++ b/inference_benchmark.py
@@ -27,7 +27,7 @@ def get_device():
     return torch.device(DEVICE_CUDA if torch.cuda.is_available() else DEVICE_CPU)
 
 def get_data_loader(num_workers=1):
-    dataset = load_dataset(HUGGING_FACE_GSMK_DATASET_ID)['train']
+    dataset = load_dataset(HUGGING_FACE_GSMK_DATASET_ID, 'main')['train']
     dataloader = DataLoader(
         dataset,
         batch_size=BATCH_SIZE,

From 86ca1c72f259ef105f0aace961d622574184e9d7 Mon Sep 17 00:00:00 2001
From: George Tamer <gtamer2@gmail.com>
Date: Wed, 13 Dec 2023 17:36:34 -0500
Subject: [PATCH 12/64] Try large batch

---
 inference_benchmark.py | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/inference_benchmark.py b/inference_benchmark.py
index b983cb422..cc7cac84e 100644
--- a/inference_benchmark.py
+++ b/inference_benchmark.py
@@ -7,9 +7,11 @@
 import torch.distributed as dist
 
 ### Setup ###
-BATCH_SIZE = 1
+BATCH_SIZE = 3
 BATCH_COUNT = 5
 NUM_WORKERS = 1
+
+# https://huggingface.co/datasets/gsm8k
 HUGGING_FACE_GSMK_DATASET_ID = "gsm8k"
 
 # Manual seed for reproducatibility
@@ -87,6 +89,8 @@ def run_batch_inference(dataloader, model):
 
     
     print("question: ", question, "answer", answer)
+    print("question type: ", type(question), "answer type", type(answer))
+    print("question shape: ", len(question), "answer shape", answer.shape)
     # device = get_device()
     # x = x.to(device)
     # y = y.to(device)

From f14689530337128f4554ae7b8f97b0e1750d02ff Mon Sep 17 00:00:00 2001
From: George Tamer <gtamer2@gmail.com>
Date: Wed, 13 Dec 2023 17:49:16 -0500
Subject: [PATCH 13/64] Get workign for 1 batch

---
 inference_benchmark.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/inference_benchmark.py b/inference_benchmark.py
index cc7cac84e..bbce30be4 100644
--- a/inference_benchmark.py
+++ b/inference_benchmark.py
@@ -7,7 +7,7 @@
 import torch.distributed as dist
 
 ### Setup ###
-BATCH_SIZE = 3
+BATCH_SIZE = 1
 BATCH_COUNT = 5
 NUM_WORKERS = 1
 
@@ -88,9 +88,9 @@ def run_batch_inference(dataloader, model):
         __get_next_batch, dataloader)
 
     
-    print("question: ", question, "answer", answer)
-    print("question type: ", type(question), "answer type", type(answer))
-    print("question shape: ", len(question), "answer shape", answer.shape)
+    # print("question: ", question, "\nanswer: ", answer)
+    # print("question type: ", type(question), "answer type", type(answer))
+    # print("question shape: ", len(question), "answer shape", len(answer))
     # device = get_device()
     # x = x.to(device)
     # y = y.to(device)

From fd8a6cf3e03eb76644b5c083677bdc27200945c2 Mon Sep 17 00:00:00 2001
From: George Tamer <gtamer2@gmail.com>
Date: Wed, 13 Dec 2023 17:50:13 -0500
Subject: [PATCH 14/64] Get workign for 1 batch

---
 inference_benchmark.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/inference_benchmark.py b/inference_benchmark.py
index bbce30be4..29cfa988a 100644
--- a/inference_benchmark.py
+++ b/inference_benchmark.py
@@ -98,7 +98,7 @@ def run_batch_inference(dataloader, model):
     output, inference_time = measure_runtime(
         inference,
         model,
-        question)
+        [question])
     
     return output, load_time, inference_time
 

From 04cb36328398edb17693446f7a5358dd75da34f4 Mon Sep 17 00:00:00 2001
From: George Tamer <gtamer2@gmail.com>
Date: Thu, 14 Dec 2023 08:10:39 -0500
Subject: [PATCH 15/64] Add torch profiler

---
 inference_benchmark.py | 10 +++++++++-
 1 file changed, 9 insertions(+), 1 deletion(-)

diff --git a/inference_benchmark.py b/inference_benchmark.py
index 29cfa988a..bb2aef7c3 100644
--- a/inference_benchmark.py
+++ b/inference_benchmark.py
@@ -5,6 +5,7 @@
 import fire
 import os
 import torch.distributed as dist
+from torch.profiler import profile, record_function, ProfilerActivity
 
 ### Setup ###
 BATCH_SIZE = 1
@@ -151,4 +152,11 @@ def benchmark(ckpt_dir,
     print("> average", torch.mean(total))
 
 if __name__ == "__main__":
-    fire.Fire(benchmark)
\ No newline at end of file
+    with profile(activities=[ProfilerActivity.CUDA], record_shapes=True, profile_memory=True) as prof:
+        with record_function("benchmark"):
+            fire.Fire(benchmark)
+    print("Profiling sorted by CUDA time total")
+    print(prof.key_averages().table(sort_by="cuda_time_total", row_limit=10))
+    print("\n\n")
+    print("Profiling sorted by CUDA memory usage")
+    print(prof.key_averages().table(sort_by="self_cuda_memory_usage", row_limit=10))

From 40abad23da8fe96919a76bc2e8242ee22df5f016 Mon Sep 17 00:00:00 2001
From: George Tamer <gtamer2@gmail.com>
Date: Thu, 14 Dec 2023 08:36:37 -0500
Subject: [PATCH 16/64] Indent

---
 inference_benchmark.py | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/inference_benchmark.py b/inference_benchmark.py
index bb2aef7c3..264457144 100644
--- a/inference_benchmark.py
+++ b/inference_benchmark.py
@@ -155,8 +155,8 @@ def benchmark(ckpt_dir,
     with profile(activities=[ProfilerActivity.CUDA], record_shapes=True, profile_memory=True) as prof:
         with record_function("benchmark"):
             fire.Fire(benchmark)
-    print("Profiling sorted by CUDA time total")
-    print(prof.key_averages().table(sort_by="cuda_time_total", row_limit=10))
-    print("\n\n")
-    print("Profiling sorted by CUDA memory usage")
-    print(prof.key_averages().table(sort_by="self_cuda_memory_usage", row_limit=10))
+        print("Profiling sorted by CUDA time total")
+        print(prof.key_averages().table(sort_by="cuda_time_total", row_limit=10))
+        print("\n\n")
+        print("Profiling sorted by CUDA memory usage")
+        print(prof.key_averages().table(sort_by="self_cuda_memory_usage", row_limit=10))

From d6336fc1f64018d40fa96675e20bd739d1c0d2fb Mon Sep 17 00:00:00 2001
From: George Tamer <gtamer2@gmail.com>
Date: Thu, 14 Dec 2023 08:42:11 -0500
Subject: [PATCH 17/64] Indent

---
 inference_benchmark.py | 14 +++++++++-----
 1 file changed, 9 insertions(+), 5 deletions(-)

diff --git a/inference_benchmark.py b/inference_benchmark.py
index 264457144..ab18e11f3 100644
--- a/inference_benchmark.py
+++ b/inference_benchmark.py
@@ -155,8 +155,12 @@ def benchmark(ckpt_dir,
     with profile(activities=[ProfilerActivity.CUDA], record_shapes=True, profile_memory=True) as prof:
         with record_function("benchmark"):
             fire.Fire(benchmark)
-        print("Profiling sorted by CUDA time total")
-        print(prof.key_averages().table(sort_by="cuda_time_total", row_limit=10))
-        print("\n\n")
-        print("Profiling sorted by CUDA memory usage")
-        print(prof.key_averages().table(sort_by="self_cuda_memory_usage", row_limit=10))
+        profile_cuda_time = prof.key_averages().table(sort_by="cuda_time_total", row_limit=10)
+        profile_cuda_mem = prof.key_averages().table(sort_by="self_cuda_memory_usage", row_limit=10)
+
+    print("\n\n")
+    print("Profiling sorted by CUDA time total")
+    print(profile_cuda_time)
+    print("\n\n")
+    print("Profiling sorted by CUDA memory usage")
+    print(profile_cuda_mem)

From f80cf22ba34052b166f01641f76a737b8c787631 Mon Sep 17 00:00:00 2001
From: George Tamer <gtamer2@gmail.com>
Date: Thu, 14 Dec 2023 08:53:54 -0500
Subject: [PATCH 18/64] Profiel cpu and cuda

---
 inference_benchmark.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/inference_benchmark.py b/inference_benchmark.py
index ab18e11f3..58406ffa1 100644
--- a/inference_benchmark.py
+++ b/inference_benchmark.py
@@ -152,7 +152,7 @@ def benchmark(ckpt_dir,
     print("> average", torch.mean(total))
 
 if __name__ == "__main__":
-    with profile(activities=[ProfilerActivity.CUDA], record_shapes=True, profile_memory=True) as prof:
+    with profile(activities=[ProfilerActivity.CPU, ProfilerActivity.CUDA], record_shapes=True, profile_memory=True) as prof:
         with record_function("benchmark"):
             fire.Fire(benchmark)
         profile_cuda_time = prof.key_averages().table(sort_by="cuda_time_total", row_limit=10)

From 8436d96efb73aedf8329aeab546518eb91104627 Mon Sep 17 00:00:00 2001
From: George Tamer <gtamer2@gmail.com>
Date: Thu, 14 Dec 2023 09:06:44 -0500
Subject: [PATCH 19/64] move profiler down

---
 inference_benchmark.py | 23 +++++++++++++----------
 1 file changed, 13 insertions(+), 10 deletions(-)

diff --git a/inference_benchmark.py b/inference_benchmark.py
index 58406ffa1..6469001d7 100644
--- a/inference_benchmark.py
+++ b/inference_benchmark.py
@@ -11,6 +11,7 @@
 BATCH_SIZE = 1
 BATCH_COUNT = 5
 NUM_WORKERS = 1
+PROFILE_MEMORY = False
 
 # https://huggingface.co/datasets/gsm8k
 HUGGING_FACE_GSMK_DATASET_ID = "gsm8k"
@@ -138,9 +139,14 @@ def benchmark(ckpt_dir,
     net = get_model(ckpt_dir, tokenizer_path, max_seq_len, max_batch_size)
 
     print("Running inference benchmark...\n")
-    _, load, inference, total = run_benchmark(data_loader, net)
-
-    print("Results...")
+    
+    with profile(activities=[ProfilerActivity.CPU, ProfilerActivity.CUDA], record_shapes=True, profile_memory=PROFILE_MEMORY) as prof:
+        with record_function("run_benchmark"):
+            _, load, inference, total = run_benchmark(data_loader, net)
+        profile_cuda_time = prof.key_averages().table(sort_by="cuda_time_total", row_limit=10)
+        profile_cuda_mem = prof.key_averages().table(sort_by="self_cuda_memory_usage", row_limit=10)
+    
+    print("\n\n Manual Profile Results...")
     print("Data-loading times")
     print("> per epoch: ", load)
     print("> average: ", torch.mean(load))
@@ -151,16 +157,13 @@ def benchmark(ckpt_dir,
     print("> per epoch", total)
     print("> average", torch.mean(total))
 
-if __name__ == "__main__":
-    with profile(activities=[ProfilerActivity.CPU, ProfilerActivity.CUDA], record_shapes=True, profile_memory=True) as prof:
-        with record_function("benchmark"):
-            fire.Fire(benchmark)
-        profile_cuda_time = prof.key_averages().table(sort_by="cuda_time_total", row_limit=10)
-        profile_cuda_mem = prof.key_averages().table(sort_by="self_cuda_memory_usage", row_limit=10)
-
     print("\n\n")
     print("Profiling sorted by CUDA time total")
     print(profile_cuda_time)
     print("\n\n")
     print("Profiling sorted by CUDA memory usage")
     print(profile_cuda_mem)
+
+
+if __name__ == "__main__":
+    fire.Fire(benchmark)

From 60bc86863277883fb9666d2b9d0e600b9d29e756 Mon Sep 17 00:00:00 2001
From: George Tamer <gtamer2@gmail.com>
Date: Thu, 14 Dec 2023 10:12:55 -0500
Subject: [PATCH 20/64] Try using simplified llama

---
 inference_benchmark.py         |   2 -
 llama/__init__.py              |   2 +-
 llama/generation_simplified.py | 401 +++++++++++++++++++++++++++++++++
 3 files changed, 402 insertions(+), 3 deletions(-)
 create mode 100755 llama/generation_simplified.py

diff --git a/inference_benchmark.py b/inference_benchmark.py
index 6469001d7..ff71eebab 100644
--- a/inference_benchmark.py
+++ b/inference_benchmark.py
@@ -3,8 +3,6 @@
 import time
 from datasets import load_dataset
 import fire
-import os
-import torch.distributed as dist
 from torch.profiler import profile, record_function, ProfilerActivity
 
 ### Setup ###
diff --git a/llama/__init__.py b/llama/__init__.py
index 0bd1f8635..8ffe64fdb 100755
--- a/llama/__init__.py
+++ b/llama/__init__.py
@@ -1,6 +1,6 @@
 # Copyright (c) Meta Platforms, Inc. and affiliates.
 # This software may be used and distributed according to the terms of the Llama 2 Community License Agreement.
 
-from .generation import Llama, Dialog
+from .generation_simplified import Llama, Dialog
 from .model import ModelArgs, Transformer
 from .tokenizer import Tokenizer
diff --git a/llama/generation_simplified.py b/llama/generation_simplified.py
new file mode 100755
index 000000000..120b91eb0
--- /dev/null
+++ b/llama/generation_simplified.py
@@ -0,0 +1,401 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# This software may be used and distributed according to the terms of the Llama 2 Community License Agreement.
+
+import json
+import os
+import sys
+import time
+from pathlib import Path
+from typing import List, Literal, Optional, Tuple, TypedDict
+
+import torch
+import torch.nn.functional as F
+
+from llama.model import ModelArgs, Transformer
+from llama.tokenizer import Tokenizer
+
+Role = Literal["system", "user", "assistant"]
+
+
+class Message(TypedDict):
+    role: Role
+    content: str
+
+
+class CompletionPrediction(TypedDict, total=False):
+    generation: str
+    tokens: List[str]  # not required
+    logprobs: List[float]  # not required
+
+
+class ChatPrediction(TypedDict, total=False):
+    generation: Message
+    tokens: List[str]  # not required
+    logprobs: List[float]  # not required
+
+
+Dialog = List[Message]
+
+B_INST, E_INST = "[INST]", "[/INST]"
+B_SYS, E_SYS = "<<SYS>>\n", "\n<</SYS>>\n\n"
+
+SPECIAL_TAGS = [B_INST, E_INST, "<<SYS>>", "<</SYS>>"]
+UNSAFE_ERROR = "Error: special tags are not allowed as part of the prompt."
+
+
+class Llama:
+    @staticmethod
+    def build(
+        ckpt_dir: str,
+        tokenizer_path: str,
+        max_seq_len: int,
+        max_batch_size: int,
+        seed: int = 1,
+    ) -> "Llama":
+        """
+        Build a Llama instance by initializing and loading a pre-trained model.
+
+        Args:
+            ckpt_dir (str): Path to the directory containing checkpoint files.
+            tokenizer_path (str): Path to the tokenizer file.
+            max_seq_len (int): Maximum sequence length for input text.
+            max_batch_size (int): Maximum batch size for inference.
+                If not provided, it's determined from the environment. Defaults to None.
+
+        Returns:
+            Llama: An instance of the Llama class with the loaded model and tokenizer.
+
+        Note:
+            This method initializes the distributed process group, sets the device to CUDA,
+            and loads the pre-trained model and tokenizer.
+
+        """
+
+        local_rank = int(os.environ.get("LOCAL_RANK", 0))
+        torch.cuda.set_device(local_rank)
+
+        # seed must be the same in all processes
+        torch.manual_seed(seed)
+
+        if local_rank > 0:
+            sys.stdout = open(os.devnull, "w")
+
+        start_time = time.time()
+        checkpoints = sorted(Path(ckpt_dir).glob("*.pth"))
+        assert len(checkpoints) > 0, f"no checkpoint files found in {ckpt_dir}"
+        ckpt_path = checkpoints[0]
+        checkpoint = torch.load(ckpt_path, map_location="cpu")
+        with open(Path(ckpt_dir) / "params.json", "r") as f:
+            params = json.loads(f.read())
+
+        model_args: ModelArgs = ModelArgs(
+            max_seq_len=max_seq_len,
+            max_batch_size=max_batch_size,
+            **params,
+        )
+        tokenizer = Tokenizer(model_path=tokenizer_path)
+        model_args.vocab_size = tokenizer.n_words
+        torch.set_default_tensor_type(torch.cuda.HalfTensor)
+        model = Transformer(model_args)
+        model.load_state_dict(checkpoint, strict=False)
+        print(f"Loaded in {time.time() - start_time:.2f} seconds")
+
+        return Llama(model, tokenizer)
+
+    def __init__(self, model: Transformer, tokenizer: Tokenizer):
+        self.model = model
+        self.tokenizer = tokenizer
+
+    @torch.inference_mode()
+    def generate(
+        self,
+        prompt_tokens: List[List[int]],
+        max_gen_len: int,
+        temperature: float = 0.6,
+        top_p: float = 0.9,
+        logprobs: bool = False,
+        echo: bool = False,
+    ) -> Tuple[List[List[int]], Optional[List[List[float]]]]:
+        """
+        Generate text sequences based on provided prompts using the language generation model.
+
+        Args:
+            prompt_tokens (List[List[int]]): List of tokenized prompts, where each prompt is represented as a list of integers.
+            max_gen_len (int): Maximum length of the generated text sequence.
+            temperature (float, optional): Temperature value for controlling randomness in sampling. Defaults to 0.6.
+            top_p (float, optional): Top-p probability threshold for nucleus sampling. Defaults to 0.9.
+            logprobs (bool, optional): Flag indicating whether to compute token log probabilities. Defaults to False.
+            echo (bool, optional): Flag indicating whether to include prompt tokens in the generated output. Defaults to False.
+
+        Returns:
+            Tuple[List[List[int]], Optional[List[List[float]]]]: A tuple containing generated token sequences and, if logprobs is True, corresponding token log probabilities.
+
+        Note:
+            This method uses the provided prompts as a basis for generating text. It employs nucleus sampling to produce text with controlled randomness.
+            If logprobs is True, token log probabilities are computed for each generated token.
+
+        """
+        params = self.model.params
+        bsz = len(prompt_tokens)
+        assert bsz <= params.max_batch_size, (bsz, params.max_batch_size)
+
+        min_prompt_len = min(len(t) for t in prompt_tokens)
+        max_prompt_len = max(len(t) for t in prompt_tokens)
+        assert max_prompt_len <= params.max_seq_len
+        total_len = min(params.max_seq_len, max_gen_len + max_prompt_len)
+
+        pad_id = self.tokenizer.pad_id
+        tokens = torch.full((bsz, total_len), pad_id, dtype=torch.long, device="cuda")
+        for k, t in enumerate(prompt_tokens):
+            tokens[k, : len(t)] = torch.tensor(t, dtype=torch.long, device="cuda")
+        if logprobs:
+            token_logprobs = torch.zeros_like(tokens, dtype=torch.float)
+
+        prev_pos = 0
+        eos_reached = torch.tensor([False] * bsz, device="cuda")
+        input_text_mask = tokens != pad_id
+        if min_prompt_len == total_len:
+            logits = self.model.forward(tokens, prev_pos)
+            token_logprobs = -F.cross_entropy(
+                input=logits.transpose(1, 2),
+                target=tokens,
+                reduction="none",
+                ignore_index=pad_id,
+            )
+
+        for cur_pos in range(min_prompt_len, total_len):
+            logits = self.model.forward(tokens[:, prev_pos:cur_pos], prev_pos)
+            if temperature > 0:
+                probs = torch.softmax(logits[:, -1] / temperature, dim=-1)
+                next_token = sample_top_p(probs, top_p)
+            else:
+                next_token = torch.argmax(logits[:, -1], dim=-1)
+
+            next_token = next_token.reshape(-1)
+            # only replace token if prompt has already been generated
+            next_token = torch.where(
+                input_text_mask[:, cur_pos], tokens[:, cur_pos], next_token
+            )
+            tokens[:, cur_pos] = next_token
+            if logprobs:
+                token_logprobs[:, prev_pos + 1 : cur_pos + 1] = -F.cross_entropy(
+                    input=logits.transpose(1, 2),
+                    target=tokens[:, prev_pos + 1 : cur_pos + 1],
+                    reduction="none",
+                    ignore_index=pad_id,
+                )
+            eos_reached |= (~input_text_mask[:, cur_pos]) & (
+                next_token == self.tokenizer.eos_id
+            )
+            prev_pos = cur_pos
+            if all(eos_reached):
+                break
+
+        if logprobs:
+            token_logprobs = token_logprobs.tolist()
+        out_tokens, out_logprobs = [], []
+        for i, toks in enumerate(tokens.tolist()):
+            # cut to max gen len
+            start = 0 if echo else len(prompt_tokens[i])
+            toks = toks[start : len(prompt_tokens[i]) + max_gen_len]
+            probs = None
+            if logprobs:
+                probs = token_logprobs[i][start : len(prompt_tokens[i]) + max_gen_len]
+            # cut to eos tok if any
+            if self.tokenizer.eos_id in toks:
+                eos_idx = toks.index(self.tokenizer.eos_id)
+                toks = toks[:eos_idx]
+                probs = probs[:eos_idx] if logprobs else None
+            out_tokens.append(toks)
+            out_logprobs.append(probs)
+        return (out_tokens, out_logprobs if logprobs else None)
+
+    def text_completion(
+        self,
+        prompts: List[str],
+        temperature: float = 0.6,
+        top_p: float = 0.9,
+        max_gen_len: Optional[int] = None,
+        logprobs: bool = False,
+        echo: bool = False,
+    ) -> List[CompletionPrediction]:
+        """
+        Perform text completion for a list of prompts using the language generation model.
+
+        Args:
+            prompts (List[str]): List of text prompts for completion.
+            temperature (float, optional): Temperature value for controlling randomness in sampling. Defaults to 0.6.
+            top_p (float, optional): Top-p probability threshold for nucleus sampling. Defaults to 0.9.
+            max_gen_len (Optional[int], optional): Maximum length of the generated completion sequence.
+                If not provided, it's set to the model's maximum sequence length minus 1.
+            logprobs (bool, optional): Flag indicating whether to compute token log probabilities. Defaults to False.
+            echo (bool, optional): Flag indicating whether to include prompt tokens in the generated output. Defaults to False.
+
+        Returns:
+            List[CompletionPrediction]: List of completion predictions, each containing the generated text completion.
+
+        Note:
+            This method generates text completions for the provided prompts, employing nucleus sampling to introduce controlled randomness.
+            If logprobs is True, token log probabilities are computed for each generated token.
+
+        """
+        if max_gen_len is None:
+            max_gen_len = self.model.params.max_seq_len - 1
+        prompt_tokens = [self.tokenizer.encode(x, bos=True, eos=False) for x in prompts]
+        generation_tokens, generation_logprobs = self.generate(
+            prompt_tokens=prompt_tokens,
+            max_gen_len=max_gen_len,
+            temperature=temperature,
+            top_p=top_p,
+            logprobs=logprobs,
+            echo=echo,
+        )
+        if logprobs:
+            return [
+                {
+                    "generation": self.tokenizer.decode(t),
+                    "tokens": [self.tokenizer.decode(x) for x in t],
+                    "logprobs": logprobs_i,
+                }
+                for t, logprobs_i in zip(generation_tokens, generation_logprobs)
+            ]
+        return [{"generation": self.tokenizer.decode(t)} for t in generation_tokens]
+
+    def chat_completion(
+        self,
+        dialogs: List[Dialog],
+        temperature: float = 0.6,
+        top_p: float = 0.9,
+        max_gen_len: Optional[int] = None,
+        logprobs: bool = False,
+    ) -> List[ChatPrediction]:
+        """
+        Generate assistant responses for a list of conversational dialogs using the language generation model.
+
+        Args:
+            dialogs (List[Dialog]): List of conversational dialogs, where each dialog is a list of messages.
+            temperature (float, optional): Temperature value for controlling randomness in sampling. Defaults to 0.6.
+            top_p (float, optional): Top-p probability threshold for nucleus sampling. Defaults to 0.9.
+            max_gen_len (Optional[int], optional): Maximum length of the generated response sequence.
+                If not provided, it's set to the model's maximum sequence length minus 1.
+            logprobs (bool, optional): Flag indicating whether to compute token log probabilities. Defaults to False.
+
+        Returns:
+            List[ChatPrediction]: List of chat predictions, each containing the assistant's generated response.
+
+        Raises:
+            AssertionError: If the last message in a dialog is not from the user.
+            AssertionError: If the dialog roles are not in the required 'user', 'assistant', and optional 'system' order.
+
+        Note:
+            This method generates assistant responses for the provided conversational dialogs.
+            It employs nucleus sampling to introduce controlled randomness in text generation.
+            If logprobs is True, token log probabilities are computed for each generated token.
+
+        """
+        if max_gen_len is None:
+            max_gen_len = self.model.params.max_seq_len - 1
+        prompt_tokens = []
+        unsafe_requests = []
+        for dialog in dialogs:
+            unsafe_requests.append(
+                any([tag in msg["content"] for tag in SPECIAL_TAGS for msg in dialog])
+            )
+            if dialog[0]["role"] == "system":
+                dialog = [
+                    {
+                        "role": dialog[1]["role"],
+                        "content": B_SYS
+                        + dialog[0]["content"]
+                        + E_SYS
+                        + dialog[1]["content"],
+                    }
+                ] + dialog[2:]
+            assert all([msg["role"] == "user" for msg in dialog[::2]]) and all(
+                [msg["role"] == "assistant" for msg in dialog[1::2]]
+            ), (
+                "model only supports 'system', 'user' and 'assistant' roles, "
+                "starting with 'system', then 'user' and alternating (u/a/u/a/u...)"
+            )
+            dialog_tokens: List[int] = sum(
+                [
+                    self.tokenizer.encode(
+                        f"{B_INST} {(prompt['content']).strip()} {E_INST} {(answer['content']).strip()} ",
+                        bos=True,
+                        eos=True,
+                    )
+                    for prompt, answer in zip(
+                        dialog[::2],
+                        dialog[1::2],
+                    )
+                ],
+                [],
+            )
+            assert (
+                dialog[-1]["role"] == "user"
+            ), f"Last message must be from user, got {dialog[-1]['role']}"
+            dialog_tokens += self.tokenizer.encode(
+                f"{B_INST} {(dialog[-1]['content']).strip()} {E_INST}",
+                bos=True,
+                eos=False,
+            )
+            prompt_tokens.append(dialog_tokens)
+
+        generation_tokens, generation_logprobs = self.generate(
+            prompt_tokens=prompt_tokens,
+            max_gen_len=max_gen_len,
+            temperature=temperature,
+            top_p=top_p,
+            logprobs=logprobs,
+        )
+        if logprobs:
+            return [
+                {
+                    "generation": {
+                        "role": "assistant",
+                        "content": self.tokenizer.decode(t)
+                        if not unsafe
+                        else UNSAFE_ERROR,
+                    },
+                    "tokens": [self.tokenizer.decode(x) for x in t],
+                    "logprobs": logprobs_i,
+                }
+                for t, logprobs_i, unsafe in zip(
+                    generation_tokens, generation_logprobs, unsafe_requests
+                )
+            ]
+        return [
+            {
+                "generation": {
+                    "role": "assistant",
+                    "content": self.tokenizer.decode(t) if not unsafe else UNSAFE_ERROR,
+                }
+            }
+            for t, unsafe in zip(generation_tokens, unsafe_requests)
+        ]
+
+
+def sample_top_p(probs, p):
+    """
+    Perform top-p (nucleus) sampling on a probability distribution.
+
+    Args:
+        probs (torch.Tensor): Probability distribution tensor.
+        p (float): Probability threshold for top-p sampling.
+
+    Returns:
+        torch.Tensor: Sampled token indices.
+
+    Note:
+        Top-p sampling selects the smallest set of tokens whose cumulative probability mass
+        exceeds the threshold p. The distribution is renormalized based on the selected tokens.
+
+    """
+    probs_sort, probs_idx = torch.sort(probs, dim=-1, descending=True)
+    probs_sum = torch.cumsum(probs_sort, dim=-1)
+    mask = probs_sum - probs_sort > p
+    probs_sort[mask] = 0.0
+    probs_sort.div_(probs_sort.sum(dim=-1, keepdim=True))
+    next_token = torch.multinomial(probs_sort, num_samples=1)
+    next_token = torch.gather(probs_idx, -1, next_token)
+    return next_token

From 84416a48c9b3575f48be8a79238434fec77e78c2 Mon Sep 17 00:00:00 2001
From: George Tamer <gtamer2@gmail.com>
Date: Thu, 14 Dec 2023 10:36:21 -0500
Subject: [PATCH 21/64] revert

---
 llama/__init__.py              |   2 +-
 llama/generation_simplified.py | 401 ---------------------------------
 2 files changed, 1 insertion(+), 402 deletions(-)
 delete mode 100755 llama/generation_simplified.py

diff --git a/llama/__init__.py b/llama/__init__.py
index 8ffe64fdb..0bd1f8635 100755
--- a/llama/__init__.py
+++ b/llama/__init__.py
@@ -1,6 +1,6 @@
 # Copyright (c) Meta Platforms, Inc. and affiliates.
 # This software may be used and distributed according to the terms of the Llama 2 Community License Agreement.
 
-from .generation_simplified import Llama, Dialog
+from .generation import Llama, Dialog
 from .model import ModelArgs, Transformer
 from .tokenizer import Tokenizer
diff --git a/llama/generation_simplified.py b/llama/generation_simplified.py
deleted file mode 100755
index 120b91eb0..000000000
--- a/llama/generation_simplified.py
+++ /dev/null
@@ -1,401 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# This software may be used and distributed according to the terms of the Llama 2 Community License Agreement.
-
-import json
-import os
-import sys
-import time
-from pathlib import Path
-from typing import List, Literal, Optional, Tuple, TypedDict
-
-import torch
-import torch.nn.functional as F
-
-from llama.model import ModelArgs, Transformer
-from llama.tokenizer import Tokenizer
-
-Role = Literal["system", "user", "assistant"]
-
-
-class Message(TypedDict):
-    role: Role
-    content: str
-
-
-class CompletionPrediction(TypedDict, total=False):
-    generation: str
-    tokens: List[str]  # not required
-    logprobs: List[float]  # not required
-
-
-class ChatPrediction(TypedDict, total=False):
-    generation: Message
-    tokens: List[str]  # not required
-    logprobs: List[float]  # not required
-
-
-Dialog = List[Message]
-
-B_INST, E_INST = "[INST]", "[/INST]"
-B_SYS, E_SYS = "<<SYS>>\n", "\n<</SYS>>\n\n"
-
-SPECIAL_TAGS = [B_INST, E_INST, "<<SYS>>", "<</SYS>>"]
-UNSAFE_ERROR = "Error: special tags are not allowed as part of the prompt."
-
-
-class Llama:
-    @staticmethod
-    def build(
-        ckpt_dir: str,
-        tokenizer_path: str,
-        max_seq_len: int,
-        max_batch_size: int,
-        seed: int = 1,
-    ) -> "Llama":
-        """
-        Build a Llama instance by initializing and loading a pre-trained model.
-
-        Args:
-            ckpt_dir (str): Path to the directory containing checkpoint files.
-            tokenizer_path (str): Path to the tokenizer file.
-            max_seq_len (int): Maximum sequence length for input text.
-            max_batch_size (int): Maximum batch size for inference.
-                If not provided, it's determined from the environment. Defaults to None.
-
-        Returns:
-            Llama: An instance of the Llama class with the loaded model and tokenizer.
-
-        Note:
-            This method initializes the distributed process group, sets the device to CUDA,
-            and loads the pre-trained model and tokenizer.
-
-        """
-
-        local_rank = int(os.environ.get("LOCAL_RANK", 0))
-        torch.cuda.set_device(local_rank)
-
-        # seed must be the same in all processes
-        torch.manual_seed(seed)
-
-        if local_rank > 0:
-            sys.stdout = open(os.devnull, "w")
-
-        start_time = time.time()
-        checkpoints = sorted(Path(ckpt_dir).glob("*.pth"))
-        assert len(checkpoints) > 0, f"no checkpoint files found in {ckpt_dir}"
-        ckpt_path = checkpoints[0]
-        checkpoint = torch.load(ckpt_path, map_location="cpu")
-        with open(Path(ckpt_dir) / "params.json", "r") as f:
-            params = json.loads(f.read())
-
-        model_args: ModelArgs = ModelArgs(
-            max_seq_len=max_seq_len,
-            max_batch_size=max_batch_size,
-            **params,
-        )
-        tokenizer = Tokenizer(model_path=tokenizer_path)
-        model_args.vocab_size = tokenizer.n_words
-        torch.set_default_tensor_type(torch.cuda.HalfTensor)
-        model = Transformer(model_args)
-        model.load_state_dict(checkpoint, strict=False)
-        print(f"Loaded in {time.time() - start_time:.2f} seconds")
-
-        return Llama(model, tokenizer)
-
-    def __init__(self, model: Transformer, tokenizer: Tokenizer):
-        self.model = model
-        self.tokenizer = tokenizer
-
-    @torch.inference_mode()
-    def generate(
-        self,
-        prompt_tokens: List[List[int]],
-        max_gen_len: int,
-        temperature: float = 0.6,
-        top_p: float = 0.9,
-        logprobs: bool = False,
-        echo: bool = False,
-    ) -> Tuple[List[List[int]], Optional[List[List[float]]]]:
-        """
-        Generate text sequences based on provided prompts using the language generation model.
-
-        Args:
-            prompt_tokens (List[List[int]]): List of tokenized prompts, where each prompt is represented as a list of integers.
-            max_gen_len (int): Maximum length of the generated text sequence.
-            temperature (float, optional): Temperature value for controlling randomness in sampling. Defaults to 0.6.
-            top_p (float, optional): Top-p probability threshold for nucleus sampling. Defaults to 0.9.
-            logprobs (bool, optional): Flag indicating whether to compute token log probabilities. Defaults to False.
-            echo (bool, optional): Flag indicating whether to include prompt tokens in the generated output. Defaults to False.
-
-        Returns:
-            Tuple[List[List[int]], Optional[List[List[float]]]]: A tuple containing generated token sequences and, if logprobs is True, corresponding token log probabilities.
-
-        Note:
-            This method uses the provided prompts as a basis for generating text. It employs nucleus sampling to produce text with controlled randomness.
-            If logprobs is True, token log probabilities are computed for each generated token.
-
-        """
-        params = self.model.params
-        bsz = len(prompt_tokens)
-        assert bsz <= params.max_batch_size, (bsz, params.max_batch_size)
-
-        min_prompt_len = min(len(t) for t in prompt_tokens)
-        max_prompt_len = max(len(t) for t in prompt_tokens)
-        assert max_prompt_len <= params.max_seq_len
-        total_len = min(params.max_seq_len, max_gen_len + max_prompt_len)
-
-        pad_id = self.tokenizer.pad_id
-        tokens = torch.full((bsz, total_len), pad_id, dtype=torch.long, device="cuda")
-        for k, t in enumerate(prompt_tokens):
-            tokens[k, : len(t)] = torch.tensor(t, dtype=torch.long, device="cuda")
-        if logprobs:
-            token_logprobs = torch.zeros_like(tokens, dtype=torch.float)
-
-        prev_pos = 0
-        eos_reached = torch.tensor([False] * bsz, device="cuda")
-        input_text_mask = tokens != pad_id
-        if min_prompt_len == total_len:
-            logits = self.model.forward(tokens, prev_pos)
-            token_logprobs = -F.cross_entropy(
-                input=logits.transpose(1, 2),
-                target=tokens,
-                reduction="none",
-                ignore_index=pad_id,
-            )
-
-        for cur_pos in range(min_prompt_len, total_len):
-            logits = self.model.forward(tokens[:, prev_pos:cur_pos], prev_pos)
-            if temperature > 0:
-                probs = torch.softmax(logits[:, -1] / temperature, dim=-1)
-                next_token = sample_top_p(probs, top_p)
-            else:
-                next_token = torch.argmax(logits[:, -1], dim=-1)
-
-            next_token = next_token.reshape(-1)
-            # only replace token if prompt has already been generated
-            next_token = torch.where(
-                input_text_mask[:, cur_pos], tokens[:, cur_pos], next_token
-            )
-            tokens[:, cur_pos] = next_token
-            if logprobs:
-                token_logprobs[:, prev_pos + 1 : cur_pos + 1] = -F.cross_entropy(
-                    input=logits.transpose(1, 2),
-                    target=tokens[:, prev_pos + 1 : cur_pos + 1],
-                    reduction="none",
-                    ignore_index=pad_id,
-                )
-            eos_reached |= (~input_text_mask[:, cur_pos]) & (
-                next_token == self.tokenizer.eos_id
-            )
-            prev_pos = cur_pos
-            if all(eos_reached):
-                break
-
-        if logprobs:
-            token_logprobs = token_logprobs.tolist()
-        out_tokens, out_logprobs = [], []
-        for i, toks in enumerate(tokens.tolist()):
-            # cut to max gen len
-            start = 0 if echo else len(prompt_tokens[i])
-            toks = toks[start : len(prompt_tokens[i]) + max_gen_len]
-            probs = None
-            if logprobs:
-                probs = token_logprobs[i][start : len(prompt_tokens[i]) + max_gen_len]
-            # cut to eos tok if any
-            if self.tokenizer.eos_id in toks:
-                eos_idx = toks.index(self.tokenizer.eos_id)
-                toks = toks[:eos_idx]
-                probs = probs[:eos_idx] if logprobs else None
-            out_tokens.append(toks)
-            out_logprobs.append(probs)
-        return (out_tokens, out_logprobs if logprobs else None)
-
-    def text_completion(
-        self,
-        prompts: List[str],
-        temperature: float = 0.6,
-        top_p: float = 0.9,
-        max_gen_len: Optional[int] = None,
-        logprobs: bool = False,
-        echo: bool = False,
-    ) -> List[CompletionPrediction]:
-        """
-        Perform text completion for a list of prompts using the language generation model.
-
-        Args:
-            prompts (List[str]): List of text prompts for completion.
-            temperature (float, optional): Temperature value for controlling randomness in sampling. Defaults to 0.6.
-            top_p (float, optional): Top-p probability threshold for nucleus sampling. Defaults to 0.9.
-            max_gen_len (Optional[int], optional): Maximum length of the generated completion sequence.
-                If not provided, it's set to the model's maximum sequence length minus 1.
-            logprobs (bool, optional): Flag indicating whether to compute token log probabilities. Defaults to False.
-            echo (bool, optional): Flag indicating whether to include prompt tokens in the generated output. Defaults to False.
-
-        Returns:
-            List[CompletionPrediction]: List of completion predictions, each containing the generated text completion.
-
-        Note:
-            This method generates text completions for the provided prompts, employing nucleus sampling to introduce controlled randomness.
-            If logprobs is True, token log probabilities are computed for each generated token.
-
-        """
-        if max_gen_len is None:
-            max_gen_len = self.model.params.max_seq_len - 1
-        prompt_tokens = [self.tokenizer.encode(x, bos=True, eos=False) for x in prompts]
-        generation_tokens, generation_logprobs = self.generate(
-            prompt_tokens=prompt_tokens,
-            max_gen_len=max_gen_len,
-            temperature=temperature,
-            top_p=top_p,
-            logprobs=logprobs,
-            echo=echo,
-        )
-        if logprobs:
-            return [
-                {
-                    "generation": self.tokenizer.decode(t),
-                    "tokens": [self.tokenizer.decode(x) for x in t],
-                    "logprobs": logprobs_i,
-                }
-                for t, logprobs_i in zip(generation_tokens, generation_logprobs)
-            ]
-        return [{"generation": self.tokenizer.decode(t)} for t in generation_tokens]
-
-    def chat_completion(
-        self,
-        dialogs: List[Dialog],
-        temperature: float = 0.6,
-        top_p: float = 0.9,
-        max_gen_len: Optional[int] = None,
-        logprobs: bool = False,
-    ) -> List[ChatPrediction]:
-        """
-        Generate assistant responses for a list of conversational dialogs using the language generation model.
-
-        Args:
-            dialogs (List[Dialog]): List of conversational dialogs, where each dialog is a list of messages.
-            temperature (float, optional): Temperature value for controlling randomness in sampling. Defaults to 0.6.
-            top_p (float, optional): Top-p probability threshold for nucleus sampling. Defaults to 0.9.
-            max_gen_len (Optional[int], optional): Maximum length of the generated response sequence.
-                If not provided, it's set to the model's maximum sequence length minus 1.
-            logprobs (bool, optional): Flag indicating whether to compute token log probabilities. Defaults to False.
-
-        Returns:
-            List[ChatPrediction]: List of chat predictions, each containing the assistant's generated response.
-
-        Raises:
-            AssertionError: If the last message in a dialog is not from the user.
-            AssertionError: If the dialog roles are not in the required 'user', 'assistant', and optional 'system' order.
-
-        Note:
-            This method generates assistant responses for the provided conversational dialogs.
-            It employs nucleus sampling to introduce controlled randomness in text generation.
-            If logprobs is True, token log probabilities are computed for each generated token.
-
-        """
-        if max_gen_len is None:
-            max_gen_len = self.model.params.max_seq_len - 1
-        prompt_tokens = []
-        unsafe_requests = []
-        for dialog in dialogs:
-            unsafe_requests.append(
-                any([tag in msg["content"] for tag in SPECIAL_TAGS for msg in dialog])
-            )
-            if dialog[0]["role"] == "system":
-                dialog = [
-                    {
-                        "role": dialog[1]["role"],
-                        "content": B_SYS
-                        + dialog[0]["content"]
-                        + E_SYS
-                        + dialog[1]["content"],
-                    }
-                ] + dialog[2:]
-            assert all([msg["role"] == "user" for msg in dialog[::2]]) and all(
-                [msg["role"] == "assistant" for msg in dialog[1::2]]
-            ), (
-                "model only supports 'system', 'user' and 'assistant' roles, "
-                "starting with 'system', then 'user' and alternating (u/a/u/a/u...)"
-            )
-            dialog_tokens: List[int] = sum(
-                [
-                    self.tokenizer.encode(
-                        f"{B_INST} {(prompt['content']).strip()} {E_INST} {(answer['content']).strip()} ",
-                        bos=True,
-                        eos=True,
-                    )
-                    for prompt, answer in zip(
-                        dialog[::2],
-                        dialog[1::2],
-                    )
-                ],
-                [],
-            )
-            assert (
-                dialog[-1]["role"] == "user"
-            ), f"Last message must be from user, got {dialog[-1]['role']}"
-            dialog_tokens += self.tokenizer.encode(
-                f"{B_INST} {(dialog[-1]['content']).strip()} {E_INST}",
-                bos=True,
-                eos=False,
-            )
-            prompt_tokens.append(dialog_tokens)
-
-        generation_tokens, generation_logprobs = self.generate(
-            prompt_tokens=prompt_tokens,
-            max_gen_len=max_gen_len,
-            temperature=temperature,
-            top_p=top_p,
-            logprobs=logprobs,
-        )
-        if logprobs:
-            return [
-                {
-                    "generation": {
-                        "role": "assistant",
-                        "content": self.tokenizer.decode(t)
-                        if not unsafe
-                        else UNSAFE_ERROR,
-                    },
-                    "tokens": [self.tokenizer.decode(x) for x in t],
-                    "logprobs": logprobs_i,
-                }
-                for t, logprobs_i, unsafe in zip(
-                    generation_tokens, generation_logprobs, unsafe_requests
-                )
-            ]
-        return [
-            {
-                "generation": {
-                    "role": "assistant",
-                    "content": self.tokenizer.decode(t) if not unsafe else UNSAFE_ERROR,
-                }
-            }
-            for t, unsafe in zip(generation_tokens, unsafe_requests)
-        ]
-
-
-def sample_top_p(probs, p):
-    """
-    Perform top-p (nucleus) sampling on a probability distribution.
-
-    Args:
-        probs (torch.Tensor): Probability distribution tensor.
-        p (float): Probability threshold for top-p sampling.
-
-    Returns:
-        torch.Tensor: Sampled token indices.
-
-    Note:
-        Top-p sampling selects the smallest set of tokens whose cumulative probability mass
-        exceeds the threshold p. The distribution is renormalized based on the selected tokens.
-
-    """
-    probs_sort, probs_idx = torch.sort(probs, dim=-1, descending=True)
-    probs_sum = torch.cumsum(probs_sort, dim=-1)
-    mask = probs_sum - probs_sort > p
-    probs_sort[mask] = 0.0
-    probs_sort.div_(probs_sort.sum(dim=-1, keepdim=True))
-    next_token = torch.multinomial(probs_sort, num_samples=1)
-    next_token = torch.gather(probs_idx, -1, next_token)
-    return next_token

From 7dfe9e99bf019292a61f4941d68d010287018acf Mon Sep 17 00:00:00 2001
From: George Tamer <gtamer2@gmail.com>
Date: Thu, 14 Dec 2023 10:43:36 -0500
Subject: [PATCH 22/64] Move outside

---
 inference_benchmark.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/inference_benchmark.py b/inference_benchmark.py
index ff71eebab..4336a66d7 100644
--- a/inference_benchmark.py
+++ b/inference_benchmark.py
@@ -141,8 +141,8 @@ def benchmark(ckpt_dir,
     with profile(activities=[ProfilerActivity.CPU, ProfilerActivity.CUDA], record_shapes=True, profile_memory=PROFILE_MEMORY) as prof:
         with record_function("run_benchmark"):
             _, load, inference, total = run_benchmark(data_loader, net)
-        profile_cuda_time = prof.key_averages().table(sort_by="cuda_time_total", row_limit=10)
-        profile_cuda_mem = prof.key_averages().table(sort_by="self_cuda_memory_usage", row_limit=10)
+    profile_cuda_time = prof.key_averages().table(sort_by="cuda_time_total", row_limit=10)
+    profile_cuda_mem = prof.key_averages().table(sort_by="self_cuda_memory_usage", row_limit=10)
     
     print("\n\n Manual Profile Results...")
     print("Data-loading times")

From e5f04089aed8e83eb73cc87a37dc5c0f6b0c129f Mon Sep 17 00:00:00 2001
From: George Tamer <gtamer2@gmail.com>
Date: Thu, 14 Dec 2023 10:51:53 -0500
Subject: [PATCH 23/64] one more yolo

---
 inference_benchmark.py | 23 +++++++++++------------
 1 file changed, 11 insertions(+), 12 deletions(-)

diff --git a/inference_benchmark.py b/inference_benchmark.py
index 4336a66d7..703546cef 100644
--- a/inference_benchmark.py
+++ b/inference_benchmark.py
@@ -109,16 +109,14 @@ def inference(
     top_p: float = 0.9,
     max_gen_len: int = 64,
 ):
-    results = generator.text_completion(
-        prompts,
-        max_gen_len=max_gen_len,
-        temperature=temperature,
-        top_p=top_p,
-    )
-    for prompt, result in zip(prompts, results):
-        print(prompt)
-        print(f"> {result['generation']}")
-        print("\n==================================\n")
+    with torch.no_grad():
+        results = generator.text_completion(
+            prompts,
+            max_gen_len=max_gen_len,
+            temperature=temperature,
+            top_p=top_p,
+        )
+        return zip(prompts, results)
 
 def __get_next_batch(dataloader):
     return next(iter(dataloader))
@@ -139,8 +137,9 @@ def benchmark(ckpt_dir,
     print("Running inference benchmark...\n")
     
     with profile(activities=[ProfilerActivity.CPU, ProfilerActivity.CUDA], record_shapes=True, profile_memory=PROFILE_MEMORY) as prof:
-        with record_function("run_benchmark"):
-            _, load, inference, total = run_benchmark(data_loader, net)
+        # with record_function("run_benchmark"):
+        #     _, load, inference, total = run_benchmark(data_loader, net)
+        _, load, inference, total = run_benchmark(data_loader, net)
     profile_cuda_time = prof.key_averages().table(sort_by="cuda_time_total", row_limit=10)
     profile_cuda_mem = prof.key_averages().table(sort_by="self_cuda_memory_usage", row_limit=10)
     

From d7da6e1e820039103d5418e621d18e0c52e4fa14 Mon Sep 17 00:00:00 2001
From: George Tamer <gtamer2@gmail.com>
Date: Thu, 14 Dec 2023 10:54:17 -0500
Subject: [PATCH 24/64] rerecord mem

---
 inference_benchmark.py | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/inference_benchmark.py b/inference_benchmark.py
index 703546cef..b5d093b28 100644
--- a/inference_benchmark.py
+++ b/inference_benchmark.py
@@ -9,7 +9,7 @@
 BATCH_SIZE = 1
 BATCH_COUNT = 5
 NUM_WORKERS = 1
-PROFILE_MEMORY = False
+PROFILE_MEMORY = True
 
 # https://huggingface.co/datasets/gsm8k
 HUGGING_FACE_GSMK_DATASET_ID = "gsm8k"
@@ -140,8 +140,6 @@ def benchmark(ckpt_dir,
         # with record_function("run_benchmark"):
         #     _, load, inference, total = run_benchmark(data_loader, net)
         _, load, inference, total = run_benchmark(data_loader, net)
-    profile_cuda_time = prof.key_averages().table(sort_by="cuda_time_total", row_limit=10)
-    profile_cuda_mem = prof.key_averages().table(sort_by="self_cuda_memory_usage", row_limit=10)
     
     print("\n\n Manual Profile Results...")
     print("Data-loading times")
@@ -156,9 +154,12 @@ def benchmark(ckpt_dir,
 
     print("\n\n")
     print("Profiling sorted by CUDA time total")
+    profile_cuda_time = prof.key_averages().table(sort_by="cuda_time_total", row_limit=10)
     print(profile_cuda_time)
+
     print("\n\n")
     print("Profiling sorted by CUDA memory usage")
+    profile_cuda_mem = prof.key_averages().table(sort_by="self_cuda_memory_usage", row_limit=10)
     print(profile_cuda_mem)
 
 

From 431ebab7b31c01232f4dc7e08ed8da9e2e6dac43 Mon Sep 17 00:00:00 2001
From: Guru Sharma <guru@gururams-air.lan>
Date: Thu, 14 Dec 2023 11:40:09 -0500
Subject: [PATCH 25/64] added torch.jit.script to model in generation.py

---
 llama/generation.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/llama/generation.py b/llama/generation.py
index 5f8faf9f3..4ed79ea01 100755
--- a/llama/generation.py
+++ b/llama/generation.py
@@ -116,7 +116,7 @@ def build(
         tokenizer = Tokenizer(model_path=tokenizer_path)
         model_args.vocab_size = tokenizer.n_words
         torch.set_default_tensor_type(torch.cuda.HalfTensor)
-        model = Transformer(model_args)
+        model = torch.jit.script(Transformer(model_args))
         model.load_state_dict(checkpoint, strict=False)
         print(f"Loaded in {time.time() - start_time:.2f} seconds")
 

From 67d5a14677ceb40f039d012ee71cd90123f1a465 Mon Sep 17 00:00:00 2001
From: George Tamer <gtamer2@gmail.com>
Date: Thu, 14 Dec 2023 11:45:34 -0500
Subject: [PATCH 26/64] Script to run benchmarks

---
 inference_benchmark.py      | 12 ++++----
 run_inference_benchmarks.sh | 55 +++++++++++++++++++++++++++++++++++++
 2 files changed, 62 insertions(+), 5 deletions(-)
 create mode 100644 run_inference_benchmarks.sh

diff --git a/inference_benchmark.py b/inference_benchmark.py
index b5d093b28..1422a8b7a 100644
--- a/inference_benchmark.py
+++ b/inference_benchmark.py
@@ -6,7 +6,7 @@
 from torch.profiler import profile, record_function, ProfilerActivity
 
 ### Setup ###
-BATCH_SIZE = 1
+BATCH_SIZE = 16
 BATCH_COUNT = 5
 NUM_WORKERS = 1
 PROFILE_MEMORY = True
@@ -28,11 +28,11 @@
 def get_device():
     return torch.device(DEVICE_CUDA if torch.cuda.is_available() else DEVICE_CPU)
 
-def get_data_loader(num_workers=1):
+def get_data_loader(num_workers, batch_size):
     dataset = load_dataset(HUGGING_FACE_GSMK_DATASET_ID, 'main')['train']
     dataloader = DataLoader(
         dataset,
-        batch_size=BATCH_SIZE,
+        batch_size=batch_size,
         shuffle=False,
         num_workers=num_workers
     )
@@ -125,11 +125,13 @@ def __get_next_batch(dataloader):
 def benchmark(ckpt_dir, 
               tokenizer_path, 
               max_seq_len, 
-              max_batch_size):
+              max_batch_size,
+              batch_size=BATCH_SIZE,
+              num_workers=NUM_WORKERS):
     print("Starting up...")
 
     print("Building data loaders...")
-    data_loader = get_data_loader()
+    data_loader = get_data_loader(num_workers)
 
     print("Initializing Model...")
     net = get_model(ckpt_dir, tokenizer_path, max_seq_len, max_batch_size)
diff --git a/run_inference_benchmarks.sh b/run_inference_benchmarks.sh
new file mode 100644
index 000000000..5f8129e6a
--- /dev/null
+++ b/run_inference_benchmarks.sh
@@ -0,0 +1,55 @@
+# TODO: parameterize. this works for now.
+
+echo "Running inference benchmarks"
+
+if [ ! -d "benchmark_outputs" ]; then
+  echo "Creating benchmark_outputs directory"
+  mkdir benchmark_outputs
+fi
+
+echo "Batch size 1, num workers 0"
+torchrun inference_benchmark.py --ckpt_dir llama-2-7b/ --tokenizer_path tokenizer.model --max_seq_len 512 --max_batch_size 200 --batch_size 1 --num_workers 0 > benchmark_outputs/batch_size_1_num_workers_0.txt
+echo "Batch size 16, num workers 0"
+torchrun inference_benchmark.py --ckpt_dir llama-2-7b/ --tokenizer_path tokenizer.model --max_seq_len 512 --max_batch_size 200 --batch_size 16 --num_workers 0 > benchmark_outputs/batch_size_16_num_workers_0.txt
+echo "Batch size 32, num workers 0"
+torchrun inference_benchmark.py --ckpt_dir llama-2-7b/ --tokenizer_path tokenizer.model --max_seq_len 512 --max_batch_size 200 --batch_size 32 --num_workers 0 > benchmark_outputs/batch_size_32_num_workers_0.txt
+echo "Batch size 128, num workers 0"
+torchrun inference_benchmark.py --ckpt_dir llama-2-7b/ --tokenizer_path tokenizer.model --max_seq_len 512 --max_batch_size 200 --batch_size 128 --num_workers 0 > benchmark_outputs/batch_size_128_num_workers_0.txt
+
+echo "Batch size 1, num workers 1"
+torchrun inference_benchmark.py --ckpt_dir llama-2-7b/ --tokenizer_path tokenizer.model --max_seq_len 512 --max_batch_size 200 --batch_size 1 --num_workers 1 > benchmark_outputs/batch_size_1_num_workers_1.txt
+echo "Batch size 16, num workers 1"
+torchrun inference_benchmark.py --ckpt_dir llama-2-7b/ --tokenizer_path tokenizer.model --max_seq_len 512 --max_batch_size 200 --batch_size 16 --num_workers 1 > benchmark_outputs/batch_size_16_num_workers_1.txt
+echo "Batch size 32, num workers 1"
+torchrun inference_benchmark.py --ckpt_dir llama-2-7b/ --tokenizer_path tokenizer.model --max_seq_len 512 --max_batch_size 200 --batch_size 32 --num_workers 1 > benchmark_outputs/batch_size_32_num_workers_1.txt
+echo "Batch size 128, num workers 1"
+torchrun inference_benchmark.py --ckpt_dir llama-2-7b/ --tokenizer_path tokenizer.model --max_seq_len 512 --max_batch_size 200 --batch_size 128 --num_workers 1 > benchmark_outputs/batch_size_128_num_workers_1.txt
+
+echo "Batch size 1, num workers 2"
+torchrun inference_benchmark.py --ckpt_dir llama-2-7b/ --tokenizer_path tokenizer.model --max_seq_len 512 --max_batch_size 200 --batch_size 1 --num_workers 2 > benchmark_outputs/batch_size_1_num_workers_2.txt
+echo "Batch size 16, num workers 2"
+torchrun inference_benchmark.py --ckpt_dir llama-2-7b/ --tokenizer_path tokenizer.model --max_seq_len 512 --max_batch_size 200 --batch_size 16 --num_workers 2 > benchmark_outputs/batch_size_16_num_workers_2.txt
+echo "Batch size 32, num workers 2"
+torchrun inference_benchmark.py --ckpt_dir llama-2-7b/ --tokenizer_path tokenizer.model --max_seq_len 512 --max_batch_size 200 --batch_size 32 --num_workers 2 > benchmark_outputs/batch_size_32_num_workers_2.txt
+echo "Batch size 128, num workers 2"
+torchrun inference_benchmark.py --ckpt_dir llama-2-7b/ --tokenizer_path tokenizer.model --max_seq_len 512 --max_batch_size 200 --batch_size 128 --num_workers 2 > benchmark_outputs/batch_size_128_num_workers_2.txt
+
+echo "Batch size 1, num workers 4"
+torchrun inference_benchmark.py --ckpt_dir llama-2-7b/ --tokenizer_path tokenizer.model --max_seq_len 512 --max_batch_size 200 --batch_size 1 --num_workers 4 > benchmark_outputs/batch_size_1_num_workers_4.txt
+echo "Batch size 16, num workers 4"
+torchrun inference_benchmark.py --ckpt_dir llama-2-7b/ --tokenizer_path tokenizer.model --max_seq_len 512 --max_batch_size 200 --batch_size 16 --num_workers 4 > benchmark_outputs/batch_size_16_num_workers_4.txt
+echo "Batch size 32, num workers 4"
+torchrun inference_benchmark.py --ckpt_dir llama-2-7b/ --tokenizer_path tokenizer.model --max_seq_len 512 --max_batch_size 200 --batch_size 32 --num_workers 4 > benchmark_outputs/batch_size_32_num_workers_4.txt
+echo "Batch size 128, num workers 4"
+torchrun inference_benchmark.py --ckpt_dir llama-2-7b/ --tokenizer_path tokenizer.model --max_seq_len 512 --max_batch_size 200 --batch_size 128 --num_workers 4 > benchmark_outputs/batch_size_128_num_workers_4.txt
+
+echo "Batch size 1, num workers 8"
+torchrun inference_benchmark.py --ckpt_dir llama-2-7b/ --tokenizer_path tokenizer.model --max_seq_len 512 --max_batch_size 200 --batch_size 1 --num_workers 8 > benchmark_outputs/batch_size_1_num_workers_8.txt
+echo "Batch size 16, num workers 8"
+torchrun inference_benchmark.py --ckpt_dir llama-2-7b/ --tokenizer_path tokenizer.model --max_seq_len 512 --max_batch_size 200 --batch_size 16 --num_workers 8 > benchmark_outputs/batch_size_16_num_workers_8.txt
+echo "Batch size 32, num workers 8"
+torchrun inference_benchmark.py --ckpt_dir llama-2-7b/ --tokenizer_path tokenizer.model --max_seq_len 512 --max_batch_size 200 --batch_size 32 --num_workers 8 > benchmark_outputs/batch_size_32_num_workers_8.txt
+echo "Batch size 128, num workers 8"
+torchrun inference_benchmark.py --ckpt_dir llama-2-7b/ --tokenizer_path tokenizer.model --max_seq_len 512 --max_batch_size 200 --batch_size 128 --num_workers 8 > benchmark_outputs/batch_size_128_num_workers_8.txt
+
+echo "DONE. Exiting."
\ No newline at end of file

From dcda8a18ef14e1a3e9257ac37aa0471a8011c312 Mon Sep 17 00:00:00 2001
From: George Tamer <gtamer2@gmail.com>
Date: Thu, 14 Dec 2023 11:47:34 -0500
Subject: [PATCH 27/64] Fix missing param

---
 inference_benchmark.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/inference_benchmark.py b/inference_benchmark.py
index 1422a8b7a..db14fcef3 100644
--- a/inference_benchmark.py
+++ b/inference_benchmark.py
@@ -131,7 +131,7 @@ def benchmark(ckpt_dir,
     print("Starting up...")
 
     print("Building data loaders...")
-    data_loader = get_data_loader(num_workers)
+    data_loader = get_data_loader(num_workers, batch_size)
 
     print("Initializing Model...")
     net = get_model(ckpt_dir, tokenizer_path, max_seq_len, max_batch_size)

From 816b895f08de777161d9b2582e53840c8407df83 Mon Sep 17 00:00:00 2001
From: Guru Sharma <guru@gururams-air.lan>
Date: Thu, 14 Dec 2023 11:59:36 -0500
Subject: [PATCH 28/64] tried changing .jit.script to be around the llama
 object call in inference benchmark

---
 inference_benchmark.py | 4 ++--
 llama/generation.py    | 2 +-
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/inference_benchmark.py b/inference_benchmark.py
index b5d093b28..c23bb7f3e 100644
--- a/inference_benchmark.py
+++ b/inference_benchmark.py
@@ -40,12 +40,12 @@ def get_data_loader(num_workers=1):
 
 
 def get_model(ckpt_dir, tokenizer_path, max_seq_len, max_batch_size):
-    generator = Llama.build(
+    generator = torch.jit.script(Llama.build(
         ckpt_dir=ckpt_dir,
         tokenizer_path=tokenizer_path,
         max_seq_len=max_seq_len,
         max_batch_size=max_batch_size,
-    )
+    ))
     return generator
 
 
diff --git a/llama/generation.py b/llama/generation.py
index 4ed79ea01..5f8faf9f3 100755
--- a/llama/generation.py
+++ b/llama/generation.py
@@ -116,7 +116,7 @@ def build(
         tokenizer = Tokenizer(model_path=tokenizer_path)
         model_args.vocab_size = tokenizer.n_words
         torch.set_default_tensor_type(torch.cuda.HalfTensor)
-        model = torch.jit.script(Transformer(model_args))
+        model = Transformer(model_args)
         model.load_state_dict(checkpoint, strict=False)
         print(f"Loaded in {time.time() - start_time:.2f} seconds")
 

From 078aa9c09d755fcd07fd602fe90cde95209f10ae Mon Sep 17 00:00:00 2001
From: George Tamer <gtamer2@gmail.com>
Date: Thu, 14 Dec 2023 12:01:48 -0500
Subject: [PATCH 29/64] Empty cuda cache

---
 inference_benchmark.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/inference_benchmark.py b/inference_benchmark.py
index db14fcef3..03ee9699e 100644
--- a/inference_benchmark.py
+++ b/inference_benchmark.py
@@ -166,4 +166,5 @@ def benchmark(ckpt_dir,
 
 
 if __name__ == "__main__":
+    torch.cuda.empty_cache()
     fire.Fire(benchmark)

From 47aaa2994ad192c9f55e1ec897078cc8faca4f8b Mon Sep 17 00:00:00 2001
From: Guru Sharma <guru@gururams-air.lan>
Date: Thu, 14 Dec 2023 12:04:57 -0500
Subject: [PATCH 30/64] changed to trace from script

---
 inference_benchmark.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/inference_benchmark.py b/inference_benchmark.py
index c23bb7f3e..23fb9bc5a 100644
--- a/inference_benchmark.py
+++ b/inference_benchmark.py
@@ -40,7 +40,7 @@ def get_data_loader(num_workers=1):
 
 
 def get_model(ckpt_dir, tokenizer_path, max_seq_len, max_batch_size):
-    generator = torch.jit.script(Llama.build(
+    generator = torch.jit.trace(Llama.build(
         ckpt_dir=ckpt_dir,
         tokenizer_path=tokenizer_path,
         max_seq_len=max_seq_len,

From e8f2d142c4a5a05a941b9fa5822d2f2d56daa291 Mon Sep 17 00:00:00 2001
From: Guru Sharma <guru@gururams-air.lan>
Date: Thu, 14 Dec 2023 12:10:04 -0500
Subject: [PATCH 31/64] .trace on generation.py

---
 inference_benchmark.py | 4 ++--
 llama/generation.py    | 2 +-
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/inference_benchmark.py b/inference_benchmark.py
index 23fb9bc5a..b5d093b28 100644
--- a/inference_benchmark.py
+++ b/inference_benchmark.py
@@ -40,12 +40,12 @@ def get_data_loader(num_workers=1):
 
 
 def get_model(ckpt_dir, tokenizer_path, max_seq_len, max_batch_size):
-    generator = torch.jit.trace(Llama.build(
+    generator = Llama.build(
         ckpt_dir=ckpt_dir,
         tokenizer_path=tokenizer_path,
         max_seq_len=max_seq_len,
         max_batch_size=max_batch_size,
-    ))
+    )
     return generator
 
 
diff --git a/llama/generation.py b/llama/generation.py
index 5f8faf9f3..d60df0734 100755
--- a/llama/generation.py
+++ b/llama/generation.py
@@ -116,7 +116,7 @@ def build(
         tokenizer = Tokenizer(model_path=tokenizer_path)
         model_args.vocab_size = tokenizer.n_words
         torch.set_default_tensor_type(torch.cuda.HalfTensor)
-        model = Transformer(model_args)
+        model = torch.jit.trace(Transformer(model_args))
         model.load_state_dict(checkpoint, strict=False)
         print(f"Loaded in {time.time() - start_time:.2f} seconds")
 

From c18ee45ecb197e363ec357523c0f8a6131207385 Mon Sep 17 00:00:00 2001
From: Guru Sharma <guru@gururams-air.lan>
Date: Thu, 14 Dec 2023 12:11:23 -0500
Subject: [PATCH 32/64] get rid of trace entirely to see if it is causing issue

---
 llama/generation.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/llama/generation.py b/llama/generation.py
index d60df0734..5f8faf9f3 100755
--- a/llama/generation.py
+++ b/llama/generation.py
@@ -116,7 +116,7 @@ def build(
         tokenizer = Tokenizer(model_path=tokenizer_path)
         model_args.vocab_size = tokenizer.n_words
         torch.set_default_tensor_type(torch.cuda.HalfTensor)
-        model = torch.jit.trace(Transformer(model_args))
+        model = Transformer(model_args)
         model.load_state_dict(checkpoint, strict=False)
         print(f"Loaded in {time.time() - start_time:.2f} seconds")
 

From a79d31a85810ccf87b86f215ace7f44a6f2e1010 Mon Sep 17 00:00:00 2001
From: George Tamer <gtamer2@gmail.com>
Date: Thu, 14 Dec 2023 13:28:41 -0500
Subject: [PATCH 33/64] Add torchscript python script

---
 script_model.py | 72 +++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 72 insertions(+)
 create mode 100644 script_model.py

diff --git a/script_model.py b/script_model.py
new file mode 100644
index 000000000..ff79ae70f
--- /dev/null
+++ b/script_model.py
@@ -0,0 +1,72 @@
+import torch
+from torch.utils.data import DataLoader
+import time
+from datasets import load_dataset
+import fire
+from torch.profiler import profile, record_function, ProfilerActivity
+
+### Setup ###
+BATCH_SIZE = 1
+BATCH_COUNT = 5
+NUM_WORKERS = 1
+PROFILE_MEMORY = True
+
+# https://huggingface.co/datasets/gsm8k
+HUGGING_FACE_GSMK_DATASET_ID = "gsm8k"
+
+# Manual seed for reproducatibility
+SEED = 42
+torch.manual_seed(SEED)
+torch.cuda.manual_seed(SEED)
+
+DEVICE_CUDA = 'cuda'
+DEVICE_CPU = 'cpu'
+
+from llama import Llama
+from typing import List
+
+def get_model(ckpt_dir, tokenizer_path, max_seq_len, max_batch_size):
+    generator = Llama.build(
+        ckpt_dir=ckpt_dir,
+        tokenizer_path=tokenizer_path,
+        max_seq_len=max_seq_len,
+        max_batch_size=max_batch_size,
+    )
+    return generator
+
+def inference(
+    generator: Llama,
+    prompts: List[str],
+    temperature: float = 0.6,
+    top_p: float = 0.9,
+    max_gen_len: int = 64,
+):
+    with torch.no_grad():
+        results = generator.text_completion(
+            prompts,
+            max_gen_len=max_gen_len,
+            temperature=temperature,
+            top_p=top_p,
+        )
+        return zip(prompts, results)
+
+
+
+def script_model(ckpt_dir, 
+              tokenizer_path, 
+              max_seq_len, 
+              max_batch_size):
+    print("Starting up...")
+    print("Initializing Model...")
+    llama = get_model(ckpt_dir, tokenizer_path, max_seq_len, max_batch_size)
+
+    print("Attempting to script model...")
+    scripted_llama = torch.jit.script(llama)
+    print("Successfully scripted model!")
+    
+    print("Saving scripted model...")
+    scripted_llama.save("scripted_llama.pt")
+
+
+if __name__ == "__main__":
+    fire.Fire(script_model)

From 680d2a67256c0d054361259b2b75b80c7d966494 Mon Sep 17 00:00:00 2001
From: George Tamer <gtamer2@gmail.com>
Date: Thu, 14 Dec 2023 13:43:18 -0500
Subject: [PATCH 34/64] reset

---
 script_model.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/script_model.py b/script_model.py
index ff79ae70f..8eb082305 100644
--- a/script_model.py
+++ b/script_model.py
@@ -57,6 +57,8 @@ def script_model(ckpt_dir,
               max_seq_len, 
               max_batch_size):
     print("Starting up...")
+    torch.cuda.reset()
+    torch.cuda.empty_cache()
     print("Initializing Model...")
     llama = get_model(ckpt_dir, tokenizer_path, max_seq_len, max_batch_size)
 

From aeab877ccc88bd598275f5bf1e97a630b5530b60 Mon Sep 17 00:00:00 2001
From: George Tamer <gtamer2@gmail.com>
Date: Thu, 14 Dec 2023 13:45:37 -0500
Subject: [PATCH 35/64] remove bard hallucination

---
 script_model.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/script_model.py b/script_model.py
index 8eb082305..ba89e000b 100644
--- a/script_model.py
+++ b/script_model.py
@@ -57,7 +57,7 @@ def script_model(ckpt_dir,
               max_seq_len, 
               max_batch_size):
     print("Starting up...")
-    torch.cuda.reset()
+    # torch.cuda.reset()
     torch.cuda.empty_cache()
     print("Initializing Model...")
     llama = get_model(ckpt_dir, tokenizer_path, max_seq_len, max_batch_size)

From c588e99672ecb519085c48e827d1b0b4602e410b Mon Sep 17 00:00:00 2001
From: George Tamer <gtamer2@gmail.com>
Date: Thu, 14 Dec 2023 14:47:34 -0500
Subject: [PATCH 36/64] revisions

---
 inference_benchmark.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/inference_benchmark.py b/inference_benchmark.py
index 03ee9699e..f3b9b96a9 100644
--- a/inference_benchmark.py
+++ b/inference_benchmark.py
@@ -139,9 +139,9 @@ def benchmark(ckpt_dir,
     print("Running inference benchmark...\n")
     
     with profile(activities=[ProfilerActivity.CPU, ProfilerActivity.CUDA], record_shapes=True, profile_memory=PROFILE_MEMORY) as prof:
-        # with record_function("run_benchmark"):
+        with record_function("run_benchmark"):
         #     _, load, inference, total = run_benchmark(data_loader, net)
-        _, load, inference, total = run_benchmark(data_loader, net)
+            _, load, inference, total = run_benchmark(data_loader, net)
     
     print("\n\n Manual Profile Results...")
     print("Data-loading times")
@@ -166,5 +166,5 @@ def benchmark(ckpt_dir,
 
 
 if __name__ == "__main__":
-    torch.cuda.empty_cache()
+    # torch.cuda.empty_cache()
     fire.Fire(benchmark)

From 714dd1985c8551f200ba1fe1dcff852208a42db4 Mon Sep 17 00:00:00 2001
From: George Tamer <gtamer2@gmail.com>
Date: Thu, 14 Dec 2023 14:49:16 -0500
Subject: [PATCH 37/64] revisions

---
 run_inference_benchmarks.sh | 50 ++++++++++++++++++-------------------
 1 file changed, 25 insertions(+), 25 deletions(-)

diff --git a/run_inference_benchmarks.sh b/run_inference_benchmarks.sh
index 5f8129e6a..4e63be3b2 100644
--- a/run_inference_benchmarks.sh
+++ b/run_inference_benchmarks.sh
@@ -8,48 +8,48 @@ if [ ! -d "benchmark_outputs" ]; then
 fi
 
 echo "Batch size 1, num workers 0"
-torchrun inference_benchmark.py --ckpt_dir llama-2-7b/ --tokenizer_path tokenizer.model --max_seq_len 512 --max_batch_size 200 --batch_size 1 --num_workers 0 > benchmark_outputs/batch_size_1_num_workers_0.txt
+torchrun inference_benchmark.py --ckpt_dir llama-2-7b/ --tokenizer_path tokenizer.model --max_seq_len 512 --max_batch_size 64 --batch_size 1 --num_workers 0 > benchmark_outputs/batch_size_1_num_workers_0.txt
 echo "Batch size 16, num workers 0"
-torchrun inference_benchmark.py --ckpt_dir llama-2-7b/ --tokenizer_path tokenizer.model --max_seq_len 512 --max_batch_size 200 --batch_size 16 --num_workers 0 > benchmark_outputs/batch_size_16_num_workers_0.txt
+torchrun inference_benchmark.py --ckpt_dir llama-2-7b/ --tokenizer_path tokenizer.model --max_seq_len 512 --max_batch_size 64 --batch_size 16 --num_workers 0 > benchmark_outputs/batch_size_16_num_workers_0.txt
 echo "Batch size 32, num workers 0"
-torchrun inference_benchmark.py --ckpt_dir llama-2-7b/ --tokenizer_path tokenizer.model --max_seq_len 512 --max_batch_size 200 --batch_size 32 --num_workers 0 > benchmark_outputs/batch_size_32_num_workers_0.txt
-echo "Batch size 128, num workers 0"
-torchrun inference_benchmark.py --ckpt_dir llama-2-7b/ --tokenizer_path tokenizer.model --max_seq_len 512 --max_batch_size 200 --batch_size 128 --num_workers 0 > benchmark_outputs/batch_size_128_num_workers_0.txt
+torchrun inference_benchmark.py --ckpt_dir llama-2-7b/ --tokenizer_path tokenizer.model --max_seq_len 512 --max_batch_size 64 --batch_size 32 --num_workers 0 > benchmark_outputs/batch_size_32_num_workers_0.txt
+echo "Batch size 64, num workers 0"
+torchrun inference_benchmark.py --ckpt_dir llama-2-7b/ --tokenizer_path tokenizer.model --max_seq_len 512 --max_batch_size 64 --batch_size 64 --num_workers 0 > benchmark_outputs/batch_size_64_num_workers_0.txt
 
 echo "Batch size 1, num workers 1"
-torchrun inference_benchmark.py --ckpt_dir llama-2-7b/ --tokenizer_path tokenizer.model --max_seq_len 512 --max_batch_size 200 --batch_size 1 --num_workers 1 > benchmark_outputs/batch_size_1_num_workers_1.txt
+torchrun inference_benchmark.py --ckpt_dir llama-2-7b/ --tokenizer_path tokenizer.model --max_seq_len 512 --max_batch_size 64 --batch_size 1 --num_workers 1 > benchmark_outputs/batch_size_1_num_workers_1.txt
 echo "Batch size 16, num workers 1"
-torchrun inference_benchmark.py --ckpt_dir llama-2-7b/ --tokenizer_path tokenizer.model --max_seq_len 512 --max_batch_size 200 --batch_size 16 --num_workers 1 > benchmark_outputs/batch_size_16_num_workers_1.txt
+torchrun inference_benchmark.py --ckpt_dir llama-2-7b/ --tokenizer_path tokenizer.model --max_seq_len 512 --max_batch_size 64 --batch_size 16 --num_workers 1 > benchmark_outputs/batch_size_16_num_workers_1.txt
 echo "Batch size 32, num workers 1"
-torchrun inference_benchmark.py --ckpt_dir llama-2-7b/ --tokenizer_path tokenizer.model --max_seq_len 512 --max_batch_size 200 --batch_size 32 --num_workers 1 > benchmark_outputs/batch_size_32_num_workers_1.txt
-echo "Batch size 128, num workers 1"
-torchrun inference_benchmark.py --ckpt_dir llama-2-7b/ --tokenizer_path tokenizer.model --max_seq_len 512 --max_batch_size 200 --batch_size 128 --num_workers 1 > benchmark_outputs/batch_size_128_num_workers_1.txt
+torchrun inference_benchmark.py --ckpt_dir llama-2-7b/ --tokenizer_path tokenizer.model --max_seq_len 512 --max_batch_size 64 --batch_size 32 --num_workers 1 > benchmark_outputs/batch_size_32_num_workers_1.txt
+echo "Batch size 64, num workers 1"
+torchrun inference_benchmark.py --ckpt_dir llama-2-7b/ --tokenizer_path tokenizer.model --max_seq_len 512 --max_batch_size 64 --batch_size 64 --num_workers 1 > benchmark_outputs/batch_size_64_num_workers_1.txt
 
 echo "Batch size 1, num workers 2"
-torchrun inference_benchmark.py --ckpt_dir llama-2-7b/ --tokenizer_path tokenizer.model --max_seq_len 512 --max_batch_size 200 --batch_size 1 --num_workers 2 > benchmark_outputs/batch_size_1_num_workers_2.txt
+torchrun inference_benchmark.py --ckpt_dir llama-2-7b/ --tokenizer_path tokenizer.model --max_seq_len 512 --max_batch_size 64 --batch_size 1 --num_workers 2 > benchmark_outputs/batch_size_1_num_workers_2.txt
 echo "Batch size 16, num workers 2"
-torchrun inference_benchmark.py --ckpt_dir llama-2-7b/ --tokenizer_path tokenizer.model --max_seq_len 512 --max_batch_size 200 --batch_size 16 --num_workers 2 > benchmark_outputs/batch_size_16_num_workers_2.txt
+torchrun inference_benchmark.py --ckpt_dir llama-2-7b/ --tokenizer_path tokenizer.model --max_seq_len 512 --max_batch_size 64 --batch_size 16 --num_workers 2 > benchmark_outputs/batch_size_16_num_workers_2.txt
 echo "Batch size 32, num workers 2"
-torchrun inference_benchmark.py --ckpt_dir llama-2-7b/ --tokenizer_path tokenizer.model --max_seq_len 512 --max_batch_size 200 --batch_size 32 --num_workers 2 > benchmark_outputs/batch_size_32_num_workers_2.txt
-echo "Batch size 128, num workers 2"
-torchrun inference_benchmark.py --ckpt_dir llama-2-7b/ --tokenizer_path tokenizer.model --max_seq_len 512 --max_batch_size 200 --batch_size 128 --num_workers 2 > benchmark_outputs/batch_size_128_num_workers_2.txt
+torchrun inference_benchmark.py --ckpt_dir llama-2-7b/ --tokenizer_path tokenizer.model --max_seq_len 512 --max_batch_size 64 --batch_size 32 --num_workers 2 > benchmark_outputs/batch_size_32_num_workers_2.txt
+echo "Batch size 64, num workers 2"
+torchrun inference_benchmark.py --ckpt_dir llama-2-7b/ --tokenizer_path tokenizer.model --max_seq_len 512 --max_batch_size 64 --batch_size 64 --num_workers 2 > benchmark_outputs/batch_size_64_num_workers_2.txt
 
 echo "Batch size 1, num workers 4"
-torchrun inference_benchmark.py --ckpt_dir llama-2-7b/ --tokenizer_path tokenizer.model --max_seq_len 512 --max_batch_size 200 --batch_size 1 --num_workers 4 > benchmark_outputs/batch_size_1_num_workers_4.txt
+torchrun inference_benchmark.py --ckpt_dir llama-2-7b/ --tokenizer_path tokenizer.model --max_seq_len 512 --max_batch_size 64 --batch_size 1 --num_workers 4 > benchmark_outputs/batch_size_1_num_workers_4.txt
 echo "Batch size 16, num workers 4"
-torchrun inference_benchmark.py --ckpt_dir llama-2-7b/ --tokenizer_path tokenizer.model --max_seq_len 512 --max_batch_size 200 --batch_size 16 --num_workers 4 > benchmark_outputs/batch_size_16_num_workers_4.txt
+torchrun inference_benchmark.py --ckpt_dir llama-2-7b/ --tokenizer_path tokenizer.model --max_seq_len 512 --max_batch_size 64 --batch_size 16 --num_workers 4 > benchmark_outputs/batch_size_16_num_workers_4.txt
 echo "Batch size 32, num workers 4"
-torchrun inference_benchmark.py --ckpt_dir llama-2-7b/ --tokenizer_path tokenizer.model --max_seq_len 512 --max_batch_size 200 --batch_size 32 --num_workers 4 > benchmark_outputs/batch_size_32_num_workers_4.txt
-echo "Batch size 128, num workers 4"
-torchrun inference_benchmark.py --ckpt_dir llama-2-7b/ --tokenizer_path tokenizer.model --max_seq_len 512 --max_batch_size 200 --batch_size 128 --num_workers 4 > benchmark_outputs/batch_size_128_num_workers_4.txt
+torchrun inference_benchmark.py --ckpt_dir llama-2-7b/ --tokenizer_path tokenizer.model --max_seq_len 512 --max_batch_size 64 --batch_size 32 --num_workers 4 > benchmark_outputs/batch_size_32_num_workers_4.txt
+echo "Batch size 64, num workers 4"
+torchrun inference_benchmark.py --ckpt_dir llama-2-7b/ --tokenizer_path tokenizer.model --max_seq_len 512 --max_batch_size 64 --batch_size 64 --num_workers 4 > benchmark_outputs/batch_size_64_num_workers_4.txt
 
 echo "Batch size 1, num workers 8"
-torchrun inference_benchmark.py --ckpt_dir llama-2-7b/ --tokenizer_path tokenizer.model --max_seq_len 512 --max_batch_size 200 --batch_size 1 --num_workers 8 > benchmark_outputs/batch_size_1_num_workers_8.txt
+torchrun inference_benchmark.py --ckpt_dir llama-2-7b/ --tokenizer_path tokenizer.model --max_seq_len 512 --max_batch_size 64 --batch_size 1 --num_workers 8 > benchmark_outputs/batch_size_1_num_workers_8.txt
 echo "Batch size 16, num workers 8"
-torchrun inference_benchmark.py --ckpt_dir llama-2-7b/ --tokenizer_path tokenizer.model --max_seq_len 512 --max_batch_size 200 --batch_size 16 --num_workers 8 > benchmark_outputs/batch_size_16_num_workers_8.txt
+torchrun inference_benchmark.py --ckpt_dir llama-2-7b/ --tokenizer_path tokenizer.model --max_seq_len 512 --max_batch_size 64 --batch_size 16 --num_workers 8 > benchmark_outputs/batch_size_16_num_workers_8.txt
 echo "Batch size 32, num workers 8"
-torchrun inference_benchmark.py --ckpt_dir llama-2-7b/ --tokenizer_path tokenizer.model --max_seq_len 512 --max_batch_size 200 --batch_size 32 --num_workers 8 > benchmark_outputs/batch_size_32_num_workers_8.txt
-echo "Batch size 128, num workers 8"
-torchrun inference_benchmark.py --ckpt_dir llama-2-7b/ --tokenizer_path tokenizer.model --max_seq_len 512 --max_batch_size 200 --batch_size 128 --num_workers 8 > benchmark_outputs/batch_size_128_num_workers_8.txt
+torchrun inference_benchmark.py --ckpt_dir llama-2-7b/ --tokenizer_path tokenizer.model --max_seq_len 512 --max_batch_size 64 --batch_size 32 --num_workers 8 > benchmark_outputs/batch_size_32_num_workers_8.txt
+echo "Batch size 64, num workers 8"
+torchrun inference_benchmark.py --ckpt_dir llama-2-7b/ --tokenizer_path tokenizer.model --max_seq_len 512 --max_batch_size 64 --batch_size 64 --num_workers 8 > benchmark_outputs/batch_size_64_num_workers_8.txt
 
 echo "DONE. Exiting."
\ No newline at end of file

From 0abe652f709dca62981f652a9d0951ab48543fb6 Mon Sep 17 00:00:00 2001
From: George Tamer <gtamer2@gmail.com>
Date: Thu, 14 Dec 2023 14:53:05 -0500
Subject: [PATCH 38/64] revisions

---
 run_inference_benchmarks.sh | 70 ++++++++++++++++++-------------------
 1 file changed, 35 insertions(+), 35 deletions(-)

diff --git a/run_inference_benchmarks.sh b/run_inference_benchmarks.sh
index 4e63be3b2..a1763fb2c 100644
--- a/run_inference_benchmarks.sh
+++ b/run_inference_benchmarks.sh
@@ -8,48 +8,48 @@ if [ ! -d "benchmark_outputs" ]; then
 fi
 
 echo "Batch size 1, num workers 0"
-torchrun inference_benchmark.py --ckpt_dir llama-2-7b/ --tokenizer_path tokenizer.model --max_seq_len 512 --max_batch_size 64 --batch_size 1 --num_workers 0 > benchmark_outputs/batch_size_1_num_workers_0.txt
-echo "Batch size 16, num workers 0"
-torchrun inference_benchmark.py --ckpt_dir llama-2-7b/ --tokenizer_path tokenizer.model --max_seq_len 512 --max_batch_size 64 --batch_size 16 --num_workers 0 > benchmark_outputs/batch_size_16_num_workers_0.txt
-echo "Batch size 32, num workers 0"
-torchrun inference_benchmark.py --ckpt_dir llama-2-7b/ --tokenizer_path tokenizer.model --max_seq_len 512 --max_batch_size 64 --batch_size 32 --num_workers 0 > benchmark_outputs/batch_size_32_num_workers_0.txt
-echo "Batch size 64, num workers 0"
-torchrun inference_benchmark.py --ckpt_dir llama-2-7b/ --tokenizer_path tokenizer.model --max_seq_len 512 --max_batch_size 64 --batch_size 64 --num_workers 0 > benchmark_outputs/batch_size_64_num_workers_0.txt
+torchrun inference_benchmark.py --ckpt_dir llama-2-7b/ --tokenizer_path tokenizer.model --max_seq_len 512 --max_batch_size 8 --batch_size 1 --num_workers 0 > benchmark_outputs/batch_size_1_num_workers_0.txt
+echo "Batch size 2, num workers 0"
+torchrun inference_benchmark.py --ckpt_dir llama-2-7b/ --tokenizer_path tokenizer.model --max_seq_len 512 --max_batch_size 8 --batch_size 2 --num_workers 0 > benchmark_outputs/batch_size_2_num_workers_0.txt
+echo "Batch size 4, num workers 0"
+torchrun inference_benchmark.py --ckpt_dir llama-2-7b/ --tokenizer_path tokenizer.model --max_seq_len 512 --max_batch_size 8 --batch_size 4 --num_workers 0 > benchmark_outputs/batch_size_4_num_workers_0.txt
+echo "Batch size 8, num workers 0"
+torchrun inference_benchmark.py --ckpt_dir llama-2-7b/ --tokenizer_path tokenizer.model --max_seq_len 512 --max_batch_size 8 --batch_size 8 --num_workers 0 > benchmark_outputs/batch_size_8_num_workers_0.txt
 
 echo "Batch size 1, num workers 1"
-torchrun inference_benchmark.py --ckpt_dir llama-2-7b/ --tokenizer_path tokenizer.model --max_seq_len 512 --max_batch_size 64 --batch_size 1 --num_workers 1 > benchmark_outputs/batch_size_1_num_workers_1.txt
-echo "Batch size 16, num workers 1"
-torchrun inference_benchmark.py --ckpt_dir llama-2-7b/ --tokenizer_path tokenizer.model --max_seq_len 512 --max_batch_size 64 --batch_size 16 --num_workers 1 > benchmark_outputs/batch_size_16_num_workers_1.txt
-echo "Batch size 32, num workers 1"
-torchrun inference_benchmark.py --ckpt_dir llama-2-7b/ --tokenizer_path tokenizer.model --max_seq_len 512 --max_batch_size 64 --batch_size 32 --num_workers 1 > benchmark_outputs/batch_size_32_num_workers_1.txt
-echo "Batch size 64, num workers 1"
-torchrun inference_benchmark.py --ckpt_dir llama-2-7b/ --tokenizer_path tokenizer.model --max_seq_len 512 --max_batch_size 64 --batch_size 64 --num_workers 1 > benchmark_outputs/batch_size_64_num_workers_1.txt
+torchrun inference_benchmark.py --ckpt_dir llama-2-7b/ --tokenizer_path tokenizer.model --max_seq_len 512 --max_batch_size 8 --batch_size 1 --num_workers 1 > benchmark_outputs/batch_size_1_num_workers_1.txt
+echo "Batch size 2, num workers 1"
+torchrun inference_benchmark.py --ckpt_dir llama-2-7b/ --tokenizer_path tokenizer.model --max_seq_len 512 --max_batch_size 8 --batch_size 2 --num_workers 1 > benchmark_outputs/batch_size_2_num_workers_1.txt
+echo "Batch size 4, num workers 1"
+torchrun inference_benchmark.py --ckpt_dir llama-2-7b/ --tokenizer_path tokenizer.model --max_seq_len 512 --max_batch_size 8 --batch_size 4 --num_workers 1 > benchmark_outputs/batch_size_4_num_workers_1.txt
+echo "Batch size 8, num workers 1"
+torchrun inference_benchmark.py --ckpt_dir llama-2-7b/ --tokenizer_path tokenizer.model --max_seq_len 512 --max_batch_size 8 --batch_size 8 --num_workers 1 > benchmark_outputs/batch_size_8_num_workers_1.txt
 
 echo "Batch size 1, num workers 2"
-torchrun inference_benchmark.py --ckpt_dir llama-2-7b/ --tokenizer_path tokenizer.model --max_seq_len 512 --max_batch_size 64 --batch_size 1 --num_workers 2 > benchmark_outputs/batch_size_1_num_workers_2.txt
-echo "Batch size 16, num workers 2"
-torchrun inference_benchmark.py --ckpt_dir llama-2-7b/ --tokenizer_path tokenizer.model --max_seq_len 512 --max_batch_size 64 --batch_size 16 --num_workers 2 > benchmark_outputs/batch_size_16_num_workers_2.txt
-echo "Batch size 32, num workers 2"
-torchrun inference_benchmark.py --ckpt_dir llama-2-7b/ --tokenizer_path tokenizer.model --max_seq_len 512 --max_batch_size 64 --batch_size 32 --num_workers 2 > benchmark_outputs/batch_size_32_num_workers_2.txt
-echo "Batch size 64, num workers 2"
-torchrun inference_benchmark.py --ckpt_dir llama-2-7b/ --tokenizer_path tokenizer.model --max_seq_len 512 --max_batch_size 64 --batch_size 64 --num_workers 2 > benchmark_outputs/batch_size_64_num_workers_2.txt
+torchrun inference_benchmark.py --ckpt_dir llama-2-7b/ --tokenizer_path tokenizer.model --max_seq_len 512 --max_batch_size 8 --batch_size 1 --num_workers 2 > benchmark_outputs/batch_size_1_num_workers_2.txt
+echo "Batch size 2, num workers 2"
+torchrun inference_benchmark.py --ckpt_dir llama-2-7b/ --tokenizer_path tokenizer.model --max_seq_len 512 --max_batch_size 8 --batch_size 2 --num_workers 2 > benchmark_outputs/batch_size_2_num_workers_2.txt
+echo "Batch size 4, num workers 2"
+torchrun inference_benchmark.py --ckpt_dir llama-2-7b/ --tokenizer_path tokenizer.model --max_seq_len 512 --max_batch_size 8 --batch_size 4 --num_workers 2 > benchmark_outputs/batch_size_4_num_workers_2.txt
+echo "Batch size 8, num workers 2"
+torchrun inference_benchmark.py --ckpt_dir llama-2-7b/ --tokenizer_path tokenizer.model --max_seq_len 512 --max_batch_size 8 --batch_size 8 --num_workers 2 > benchmark_outputs/batch_size_8_num_workers_2.txt
 
 echo "Batch size 1, num workers 4"
-torchrun inference_benchmark.py --ckpt_dir llama-2-7b/ --tokenizer_path tokenizer.model --max_seq_len 512 --max_batch_size 64 --batch_size 1 --num_workers 4 > benchmark_outputs/batch_size_1_num_workers_4.txt
-echo "Batch size 16, num workers 4"
-torchrun inference_benchmark.py --ckpt_dir llama-2-7b/ --tokenizer_path tokenizer.model --max_seq_len 512 --max_batch_size 64 --batch_size 16 --num_workers 4 > benchmark_outputs/batch_size_16_num_workers_4.txt
-echo "Batch size 32, num workers 4"
-torchrun inference_benchmark.py --ckpt_dir llama-2-7b/ --tokenizer_path tokenizer.model --max_seq_len 512 --max_batch_size 64 --batch_size 32 --num_workers 4 > benchmark_outputs/batch_size_32_num_workers_4.txt
-echo "Batch size 64, num workers 4"
-torchrun inference_benchmark.py --ckpt_dir llama-2-7b/ --tokenizer_path tokenizer.model --max_seq_len 512 --max_batch_size 64 --batch_size 64 --num_workers 4 > benchmark_outputs/batch_size_64_num_workers_4.txt
+torchrun inference_benchmark.py --ckpt_dir llama-2-7b/ --tokenizer_path tokenizer.model --max_seq_len 512 --max_batch_size 8 --batch_size 1 --num_workers 4 > benchmark_outputs/batch_size_1_num_workers_4.txt
+echo "Batch size 2, num workers 4"
+torchrun inference_benchmark.py --ckpt_dir llama-2-7b/ --tokenizer_path tokenizer.model --max_seq_len 512 --max_batch_size 8 --batch_size 2 --num_workers 4 > benchmark_outputs/batch_size_2_num_workers_4.txt
+echo "Batch size 4, num workers 4"
+torchrun inference_benchmark.py --ckpt_dir llama-2-7b/ --tokenizer_path tokenizer.model --max_seq_len 512 --max_batch_size 8 --batch_size 4 --num_workers 4 > benchmark_outputs/batch_size_4_num_workers_4.txt
+echo "Batch size 8, num workers 4"
+torchrun inference_benchmark.py --ckpt_dir llama-2-7b/ --tokenizer_path tokenizer.model --max_seq_len 512 --max_batch_size 8 --batch_size 8 --num_workers 4 > benchmark_outputs/batch_size_8_num_workers_4.txt
 
 echo "Batch size 1, num workers 8"
-torchrun inference_benchmark.py --ckpt_dir llama-2-7b/ --tokenizer_path tokenizer.model --max_seq_len 512 --max_batch_size 64 --batch_size 1 --num_workers 8 > benchmark_outputs/batch_size_1_num_workers_8.txt
-echo "Batch size 16, num workers 8"
-torchrun inference_benchmark.py --ckpt_dir llama-2-7b/ --tokenizer_path tokenizer.model --max_seq_len 512 --max_batch_size 64 --batch_size 16 --num_workers 8 > benchmark_outputs/batch_size_16_num_workers_8.txt
-echo "Batch size 32, num workers 8"
-torchrun inference_benchmark.py --ckpt_dir llama-2-7b/ --tokenizer_path tokenizer.model --max_seq_len 512 --max_batch_size 64 --batch_size 32 --num_workers 8 > benchmark_outputs/batch_size_32_num_workers_8.txt
-echo "Batch size 64, num workers 8"
-torchrun inference_benchmark.py --ckpt_dir llama-2-7b/ --tokenizer_path tokenizer.model --max_seq_len 512 --max_batch_size 64 --batch_size 64 --num_workers 8 > benchmark_outputs/batch_size_64_num_workers_8.txt
+torchrun inference_benchmark.py --ckpt_dir llama-2-7b/ --tokenizer_path tokenizer.model --max_seq_len 512 --max_batch_size 8 --batch_size 1 --num_workers 8 > benchmark_outputs/batch_size_1_num_workers_8.txt
+echo "Batch size 2, num workers 8"
+torchrun inference_benchmark.py --ckpt_dir llama-2-7b/ --tokenizer_path tokenizer.model --max_seq_len 512 --max_batch_size 8 --batch_size 2 --num_workers 8 > benchmark_outputs/batch_size_2_num_workers_8.txt
+echo "Batch size 4, num workers 8"
+torchrun inference_benchmark.py --ckpt_dir llama-2-7b/ --tokenizer_path tokenizer.model --max_seq_len 512 --max_batch_size 8 --batch_size 4 --num_workers 8 > benchmark_outputs/batch_size_4_num_workers_8.txt
+echo "Batch size 8, num workers 8"
+torchrun inference_benchmark.py --ckpt_dir llama-2-7b/ --tokenizer_path tokenizer.model --max_seq_len 512 --max_batch_size 8 --batch_size 8 --num_workers 8 > benchmark_outputs/batch_size_8_num_workers_8.txt
 
 echo "DONE. Exiting."
\ No newline at end of file

From 5534bbf84c68108ea7329db6e2f809aa1aabff0c Mon Sep 17 00:00:00 2001
From: George Tamer <gtamer2@gmail.com>
Date: Thu, 14 Dec 2023 16:22:30 -0500
Subject: [PATCH 39/64] Add benchmarks

---
 .../batch_size_1_num_workers_0.txt            | 58 +++++++++++++++++++
 .../batch_size_1_num_workers_1.txt            | 58 +++++++++++++++++++
 .../batch_size_2_num_workers_0.txt            | 58 +++++++++++++++++++
 .../batch_size_2_num_workers_1.txt            | 58 +++++++++++++++++++
 .../batch_size_4_num_workers_0.txt            | 58 +++++++++++++++++++
 .../batch_size_4_num_workers_1.txt            | 58 +++++++++++++++++++
 .../batch_size_8_num_workers_0.txt            | 58 +++++++++++++++++++
 .../batch_size_8_num_workers_1.txt            | 30 ++++++++++
 8 files changed, 436 insertions(+)
 create mode 100644 benchmark_outputs/batch_size_1_num_workers_0.txt
 create mode 100644 benchmark_outputs/batch_size_1_num_workers_1.txt
 create mode 100644 benchmark_outputs/batch_size_2_num_workers_0.txt
 create mode 100644 benchmark_outputs/batch_size_2_num_workers_1.txt
 create mode 100644 benchmark_outputs/batch_size_4_num_workers_0.txt
 create mode 100644 benchmark_outputs/batch_size_4_num_workers_1.txt
 create mode 100644 benchmark_outputs/batch_size_8_num_workers_0.txt
 create mode 100644 benchmark_outputs/batch_size_8_num_workers_1.txt

diff --git a/benchmark_outputs/batch_size_1_num_workers_0.txt b/benchmark_outputs/batch_size_1_num_workers_0.txt
new file mode 100644
index 000000000..b768df38b
--- /dev/null
+++ b/benchmark_outputs/batch_size_1_num_workers_0.txt
@@ -0,0 +1,58 @@
+Starting up...
+Building data loaders...
+Initializing Model...
+> initializing model parallel with size 1
+> initializing ddp with size 1
+> initializing pipeline with size 1
+Loaded in 12.05 seconds
+Running inference benchmark...
+
+Working on device: cuda
+Starting BATCH 1 of 5
+Finished Batch 1 of 5
+Batch load time: 0.0010531009902479127
+Batch inference time: 5.044861934002256
+Batch total time: 5.045921023993287
+Starting BATCH 2 of 5
+Finished Batch 2 of 5
+Batch load time: 0.000634292999166064
+Batch inference time: 4.504916821009829
+Batch total time: 4.505557062002481
+Starting BATCH 3 of 5
+Finished Batch 3 of 5
+Batch load time: 0.0007395810098387301
+Batch inference time: 4.533521624005516
+Batch total time: 4.534268278002855
+Starting BATCH 4 of 5
+Finished Batch 4 of 5
+Batch load time: 0.0006648830021731555
+Batch inference time: 4.495368515010341
+Batch total time: 4.496039069999824
+Starting BATCH 5 of 5
+Finished Batch 5 of 5
+Batch load time: 0.0006519919988932088
+Batch inference time: 4.496985145000508
+Batch total time: 4.497643199007143
+
+
+ Manual Profile Results...
+Data-loading times
+> per epoch:  tensor([0.0011, 0.0006, 0.0007, 0.0007, 0.0007])
+> average:  tensor(0.0007)
+
+Inference time for each epoch
+> per epoch tensor([5.0430, 4.5039, 4.5352, 4.4961, 4.4961])
+> average tensor(4.6133)
+
+Total time for each epoch
+> per epoch tensor([5.0469, 4.5039, 4.5352, 4.4961, 4.4961])
+> average tensor(4.6172)
+
+
+
+Profiling sorted by CUDA time total
+-------------------------------------------------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------
+                                                   Name    Self CPU %      Self CPU   CPU total %     CPU total  CPU time avg     Self CUDA   Self CUDA %    CUDA total  CUDA time avg       CPU Mem  Self CPU Mem      CUDA Mem  Self CUDA Mem    # of Calls
+-------------------------------------------------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------
+                                          run_benchmark        27.07%        6.249s       100.00%       23.083s       23.083s       0.000us         0.00%       15.070s       15.070s           0 b      -2.66 Kb       8.13 Mb      -4.18 Gb             1
+"benchmark_outputs/batch_size_1_num_workers_0.txt" 91L, 8729B                       53,1          Top
\ No newline at end of file
diff --git a/benchmark_outputs/batch_size_1_num_workers_1.txt b/benchmark_outputs/batch_size_1_num_workers_1.txt
new file mode 100644
index 000000000..92517ddd6
--- /dev/null
+++ b/benchmark_outputs/batch_size_1_num_workers_1.txt
@@ -0,0 +1,58 @@
+Starting up...
+Building data loaders...
+Initializing Model...
+> initializing model parallel with size 1
+> initializing ddp with size 1
+> initializing pipeline with size 1
+Loaded in 65.44 seconds
+Running inference benchmark...
+
+Working on device: cuda
+Starting BATCH 1 of 5
+Finished Batch 1 of 5
+Batch load time: 0.17542113500530832
+Batch inference time: 5.1042240419919835
+Batch total time: 5.27965795599448
+Starting BATCH 2 of 5
+Finished Batch 2 of 5
+Batch load time: 0.11129688000073656
+Batch inference time: 4.523343688008026
+Batch total time: 4.634657599002821
+Starting BATCH 3 of 5
+Finished Batch 3 of 5
+Batch load time: 0.0730158430087613
+Batch inference time: 4.516634412007988
+Batch total time: 4.589664141007233
+Starting BATCH 4 of 5
+Finished Batch 4 of 5
+Batch load time: 0.07771697499265429
+Batch inference time: 4.432252533995779
+Batch total time: 4.509983689000364
+Starting BATCH 5 of 5
+Finished Batch 5 of 5
+Batch load time: 0.0820326890097931
+Batch inference time: 4.4701670890062815
+Batch total time: 4.552215193005395
+
+
+ Manual Profile Results...
+Data-loading times
+> per epoch:  tensor([0.1754, 0.1113, 0.0730, 0.0777, 0.0820])
+> average:  tensor(0.1039)
+
+Inference time for each epoch
+> per epoch tensor([5.1055, 4.5234, 4.5156, 4.4336, 4.4688])
+> average tensor(4.6094)
+
+Total time for each epoch
+> per epoch tensor([5.2812, 4.6328, 4.5898, 4.5117, 4.5508])
+> average tensor(4.7148)
+
+
+
+Profiling sorted by CUDA time total
+-------------------------------------------------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------
+                                                   Name    Self CPU %      Self CPU   CPU total %     CPU total  CPU time avg     Self CUDA   Self CUDA %    CUDA total  CUDA time avg       CPU Mem  Self CPU Mem      CUDA Mem  Self CUDA Mem    # of Calls
+-------------------------------------------------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------
+                                          run_benchmark        27.94%        6.585s       100.00%       23.570s       23.570s       0.000us         0.00%       15.065s       15.065s           0 b      -2.66 Kb       8.13 Mb      -4.16 Gb             1
+"benchmark_outputs/batch_size_1_num_workers_1.txt" 91L, 8719B                       1,1           Top
\ No newline at end of file
diff --git a/benchmark_outputs/batch_size_2_num_workers_0.txt b/benchmark_outputs/batch_size_2_num_workers_0.txt
new file mode 100644
index 000000000..7a9420d43
--- /dev/null
+++ b/benchmark_outputs/batch_size_2_num_workers_0.txt
@@ -0,0 +1,58 @@
+Starting up...
+Building data loaders...
+Initializing Model...
+> initializing model parallel with size 1
+> initializing ddp with size 1
+> initializing pipeline with size 1
+Loaded in 67.71 seconds
+Running inference benchmark...
+
+Working on device: cuda
+Starting BATCH 1 of 5
+Finished Batch 1 of 5
+Batch load time: 0.0012337989901425317
+Batch inference time: 4.979560280000442
+Batch total time: 4.980800386008923
+Starting BATCH 2 of 5
+Finished Batch 2 of 5
+Batch load time: 0.0006314070051303133
+Batch inference time: 4.436402372986777
+Batch total time: 4.4370395870064385
+Starting BATCH 3 of 5
+Finished Batch 3 of 5
+Batch load time: 0.0006100949976826087
+Batch inference time: 4.489002016998711
+Batch total time: 4.489618256004178
+Starting BATCH 4 of 5
+Finished Batch 4 of 5
+Batch load time: 0.000657053999020718
+Batch inference time: 4.481591136995121
+Batch total time: 4.482254397997167
+Starting BATCH 5 of 5
+Finished Batch 5 of 5
+Batch load time: 0.0006241549999685958
+Batch inference time: 4.433049211991602
+Batch total time: 4.43367860399303
+
+
+ Manual Profile Results...
+Data-loading times
+> per epoch:  tensor([0.0012, 0.0006, 0.0006, 0.0007, 0.0006])
+> average:  tensor(0.0008)
+
+Inference time for each epoch
+> per epoch tensor([4.9805, 4.4375, 4.4883, 4.4805, 4.4336])
+> average tensor(4.5625)
+
+Total time for each epoch
+> per epoch tensor([4.9805, 4.4375, 4.4883, 4.4805, 4.4336])
+> average tensor(4.5625)
+
+
+
+Profiling sorted by CUDA time total
+-------------------------------------------------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------
+                                                   Name    Self CPU %      Self CPU   CPU total %     CPU total  CPU time avg     Self CUDA   Self CUDA %    CUDA total  CUDA time avg       CPU Mem  Self CPU Mem      CUDA Mem  Self CUDA Mem    # of Calls
+-------------------------------------------------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------
+                                          run_benchmark        26.66%        6.085s       100.00%       22.827s       22.827s       0.000us         0.00%       15.085s       15.085s           0 b      -2.66 Kb       8.13 Mb      -4.18 Gb             1
+"benchmark_outputs/batch_size_2_num_workers_0.txt" 91L, 8729B                       52,0-1        Top
\ No newline at end of file
diff --git a/benchmark_outputs/batch_size_2_num_workers_1.txt b/benchmark_outputs/batch_size_2_num_workers_1.txt
new file mode 100644
index 000000000..36a617ade
--- /dev/null
+++ b/benchmark_outputs/batch_size_2_num_workers_1.txt
@@ -0,0 +1,58 @@
+Starting up...
+Building data loaders...
+Initializing Model...
+> initializing model parallel with size 1
+> initializing ddp with size 1
+> initializing pipeline with size 1
+Loaded in 111.55 seconds
+Running inference benchmark...
+
+Working on device: cuda
+Starting BATCH 1 of 5
+Finished Batch 1 of 5
+Batch load time: 0.0553153660002863
+Batch inference time: 4.988452916993992
+Batch total time: 5.043779415995232
+Starting BATCH 2 of 5
+Finished Batch 2 of 5
+Batch load time: 0.06645661000220571
+Batch inference time: 4.431401344001642
+Batch total time: 4.49787032698805
+Starting BATCH 3 of 5
+Finished Batch 3 of 5
+Batch load time: 0.06894606399873737
+Batch inference time: 4.5093786460056435
+Batch total time: 4.5783342299982905
+Starting BATCH 4 of 5
+Finished Batch 4 of 5
+Batch load time: 0.10248679800133687
+Batch inference time: 4.488342932003434
+Batch total time: 4.590840438992018
+Starting BATCH 5 of 5
+Finished Batch 5 of 5
+Batch load time: 0.07949267400545068
+Batch inference time: 4.5397761540079955
+Batch total time: 4.619280054001138
+
+
+ Manual Profile Results...
+Data-loading times
+> per epoch:  tensor([0.0553, 0.0665, 0.0690, 0.1025, 0.0795])
+> average:  tensor(0.0745)
+
+Inference time for each epoch
+> per epoch tensor([4.9883, 4.4297, 4.5078, 4.4883, 4.5391])
+> average tensor(4.5898)
+
+Total time for each epoch
+> per epoch tensor([5.0430, 4.4961, 4.5781, 4.5898, 4.6211])
+> average tensor(4.6641)
+
+
+
+Profiling sorted by CUDA time total
+-------------------------------------------------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------
+                                                   Name    Self CPU %      Self CPU   CPU total %     CPU total  CPU time avg     Self CUDA   Self CUDA %    CUDA total  CUDA time avg       CPU Mem  Self CPU Mem      CUDA Mem  Self CUDA Mem    # of Calls
+-------------------------------------------------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------
+                                          run_benchmark        27.47%        6.410s       100.00%       23.334s       23.334s       0.000us         0.00%       15.079s       15.079s           0 b      -2.66 Kb       8.13 Mb      -4.18 Gb             1
+"benchmark_outputs/batch_size_2_num_workers_1.txt" 91L, 8722B                       1,1           Top
\ No newline at end of file
diff --git a/benchmark_outputs/batch_size_4_num_workers_0.txt b/benchmark_outputs/batch_size_4_num_workers_0.txt
new file mode 100644
index 000000000..1d7aa9616
--- /dev/null
+++ b/benchmark_outputs/batch_size_4_num_workers_0.txt
@@ -0,0 +1,58 @@
+Starting up...
+Building data loaders...
+Initializing Model...
+> initializing model parallel with size 1
+> initializing ddp with size 1
+> initializing pipeline with size 1
+Loaded in 182.83 seconds
+Running inference benchmark...
+
+Working on device: cuda
+Starting BATCH 1 of 5
+Finished Batch 1 of 5
+Batch load time: 0.0010882980132009834
+Batch inference time: 5.046935585996835
+Batch total time: 5.048030566002126
+Starting BATCH 2 of 5
+Finished Batch 2 of 5
+Batch load time: 0.0006488210055977106
+Batch inference time: 4.47928033999051
+Batch total time: 4.479962162004085
+Starting BATCH 3 of 5
+Finished Batch 3 of 5
+Batch load time: 0.0006499989976873621
+Batch inference time: 4.4514494110044325
+Batch total time: 4.452105590986321
+Starting BATCH 4 of 5
+Finished Batch 4 of 5
+Batch load time: 0.0006679049984086305
+Batch inference time: 4.445740676994319
+Batch total time: 4.446414493009797
+Starting BATCH 5 of 5
+Finished Batch 5 of 5
+Batch load time: 0.0006229520076885819
+Batch inference time: 4.457714663003571
+Batch total time: 4.458343616002821
+
+
+ Manual Profile Results...
+Data-loading times
+> per epoch:  tensor([0.0011, 0.0006, 0.0006, 0.0007, 0.0006])
+> average:  tensor(0.0007)
+
+Inference time for each epoch
+> per epoch tensor([5.0469, 4.4805, 4.4531, 4.4453, 4.4570])
+> average tensor(4.5781)
+
+Total time for each epoch
+> per epoch tensor([5.0469, 4.4805, 4.4531, 4.4453, 4.4570])
+> average tensor(4.5781)
+
+
+
+Profiling sorted by CUDA time total
+-------------------------------------------------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------
+                                                   Name    Self CPU %      Self CPU   CPU total %     CPU total  CPU time avg     Self CUDA   Self CUDA %    CUDA total  CUDA time avg       CPU Mem  Self CPU Mem      CUDA Mem  Self CUDA Mem    # of Calls
+-------------------------------------------------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------
+                                          run_benchmark        26.98%        6.176s       100.00%       22.888s       22.888s       0.000us         0.00%       15.065s       15.065s           0 b      -2.66 Kb       8.13 Mb      -4.19 Gb             1
+"benchmark_outputs/batch_size_4_num_workers_0.txt" 91L, 8731B                       1,1           Top
\ No newline at end of file
diff --git a/benchmark_outputs/batch_size_4_num_workers_1.txt b/benchmark_outputs/batch_size_4_num_workers_1.txt
new file mode 100644
index 000000000..8178362f9
--- /dev/null
+++ b/benchmark_outputs/batch_size_4_num_workers_1.txt
@@ -0,0 +1,58 @@
+Starting up...
+Building data loaders...
+Initializing Model...
+> initializing model parallel with size 1
+> initializing ddp with size 1
+> initializing pipeline with size 1
+Loaded in 100.42 seconds
+Running inference benchmark...
+
+Working on device: cuda
+Starting BATCH 1 of 5
+Finished Batch 1 of 5
+Batch load time: 0.1019776799948886
+Batch inference time: 5.030931850997149
+Batch total time: 5.132919580995804
+Starting BATCH 2 of 5
+Finished Batch 2 of 5
+Batch load time: 0.0671316090010805
+Batch inference time: 4.438084541005082
+Batch total time: 4.505228757989244
+Starting BATCH 3 of 5
+Finished Batch 3 of 5
+Batch load time: 0.06837264500791207
+Batch inference time: 4.474854444008088
+Batch total time: 4.543237587000476
+Starting BATCH 4 of 5
+Finished Batch 4 of 5
+Batch load time: 0.07436333999794442
+Batch inference time: 4.4623387989995535
+Batch total time: 4.53671289801423
+Starting BATCH 5 of 5
+Finished Batch 5 of 5
+Batch load time: 0.07757725499686785
+Batch inference time: 4.4232901810028125
+Batch total time: 4.500878610997461
+
+
+ Manual Profile Results...
+Data-loading times
+> per epoch:  tensor([0.1020, 0.0671, 0.0684, 0.0743, 0.0776])
+> average:  tensor(0.0779)
+
+Inference time for each epoch
+> per epoch tensor([5.0312, 4.4375, 4.4766, 4.4609, 4.4219])
+> average tensor(4.5664)
+
+Total time for each epoch
+> per epoch tensor([5.1328, 4.5039, 4.5430, 4.5352, 4.5000])
+> average tensor(4.6445)
+
+
+
+Profiling sorted by CUDA time total
+-------------------------------------------------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------
+                                                   Name    Self CPU %      Self CPU   CPU total %     CPU total  CPU time avg     Self CUDA   Self CUDA %    CUDA total  CUDA time avg       CPU Mem  Self CPU Mem      CUDA Mem  Self CUDA Mem    # of Calls
+-------------------------------------------------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------
+                                          run_benchmark        27.55%        6.399s       100.00%       23.222s       23.222s       0.000us         0.00%       15.063s       15.063s           0 b      -3.18 Kb       8.13 Mb      -4.17 Gb             1
+"benchmark_outputs/batch_size_4_num_workers_1.txt" 91L, 8720B                       1,1           Top
\ No newline at end of file
diff --git a/benchmark_outputs/batch_size_8_num_workers_0.txt b/benchmark_outputs/batch_size_8_num_workers_0.txt
new file mode 100644
index 000000000..a2a91117a
--- /dev/null
+++ b/benchmark_outputs/batch_size_8_num_workers_0.txt
@@ -0,0 +1,58 @@
+Starting up...
+Building data loaders...
+Initializing Model...
+> initializing model parallel with size 1
+> initializing ddp with size 1
+> initializing pipeline with size 1
+Loaded in 182.13 seconds
+Running inference benchmark...
+
+Working on device: cuda
+Starting BATCH 1 of 5
+Finished Batch 1 of 5
+Batch load time: 0.001075489999493584
+Batch inference time: 4.895436988997972
+Batch total time: 4.896518624998862
+Starting BATCH 2 of 5
+Finished Batch 2 of 5
+Batch load time: 0.0006358410028042272
+Batch inference time: 4.359566414990695
+Batch total time: 4.360208049998619
+Starting BATCH 3 of 5
+Finished Batch 3 of 5
+Batch load time: 0.0006404919986380264
+Batch inference time: 4.369482416004757
+Batch total time: 4.370128884998849
+Starting BATCH 4 of 5
+Finished Batch 4 of 5
+Batch load time: 0.0006534309941343963
+Batch inference time: 4.313248220991227
+Batch total time: 4.313907664007274
+Starting BATCH 5 of 5
+Finished Batch 5 of 5
+Batch load time: 0.0006494239933090284
+Batch inference time: 4.3910173189942725
+Batch total time: 4.391673523001373
+
+
+ Manual Profile Results...
+Data-loading times
+> per epoch:  tensor([0.0011, 0.0006, 0.0006, 0.0007, 0.0006])
+> average:  tensor(0.0007)
+
+Inference time for each epoch
+> per epoch tensor([4.8945, 4.3594, 4.3711, 4.3125, 4.3906])
+> average tensor(4.4648)
+
+Total time for each epoch
+> per epoch tensor([4.8984, 4.3594, 4.3711, 4.3125, 4.3906])
+> average tensor(4.4648)
+
+
+
+Profiling sorted by CUDA time total
+-------------------------------------------------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------
+                                                   Name    Self CPU %      Self CPU   CPU total %     CPU total  CPU time avg     Self CUDA   Self CUDA %    CUDA total  CUDA time avg       CPU Mem  Self CPU Mem      CUDA Mem  Self CUDA Mem    # of Calls
+-------------------------------------------------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------
+                                          run_benchmark        26.27%        5.868s       100.00%       22.336s       22.336s       0.000us         0.00%       15.074s       15.074s           0 b      -2.66 Kb       8.13 Mb      -4.21 Gb             1
+"benchmark_outputs/batch_size_8_num_workers_0.txt" 91L, 8731B                       1,1           Top
\ No newline at end of file
diff --git a/benchmark_outputs/batch_size_8_num_workers_1.txt b/benchmark_outputs/batch_size_8_num_workers_1.txt
new file mode 100644
index 000000000..fc77342a7
--- /dev/null
+++ b/benchmark_outputs/batch_size_8_num_workers_1.txt
@@ -0,0 +1,30 @@
+                                            aten::copy_         4.45%        1.043s         8.63%        2.024s      16.191us     416.322ms         2.99%     743.074ms       5.945us           0 b           0 b           0 b           0 b        124995
+                                         aten::_to_copy         2.84%     666.198ms        12.83%        3.008s      29.012us       0.000us         0.00%     646.904ms       6.239us       2.58 Kb           0 b       1.06 Gb           0 b        103695
+                                               aten::to         1.22%     286.543ms        13.80%        3.236s      22.222us       0.000us         0.00%     641.483ms       4.405us       2.58 Kb           0 b       1.06 Gb      25.83 Mb        145620
+-------------------------------------------------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------
+Self CPU time total: 23.446s
+Self CUDA time total: 13.901s
+
+
+
+
+Profiling sorted by CUDA memory usage
+-------------------------------------------------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------
+                                                   Name    Self CPU %      Self CPU   CPU total %     CPU total  CPU time avg     Self CUDA   Self CUDA %    CUDA total  CUDA time avg       CPU Mem  Self CPU Mem      CUDA Mem  Self CUDA Mem    # of Calls
+-------------------------------------------------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------
+                                    aten::empty_strided         3.32%     777.328ms         3.32%     777.328ms       7.473us       0.000us         0.00%       0.000us       0.000us       2.58 Kb       2.58 Kb       1.10 Gb       1.10 Gb        104015
+                                              aten::mul         4.20%     983.674ms         6.09%        1.427s      19.729us     128.721ms         0.93%     311.740ms       4.311us           0 b           0 b       1.01 Gb       1.01 Gb         72320
+                                               aten::mm         9.76%        2.288s        12.53%        2.937s      40.787us       12.947s        93.14%       13.155s     182.704us           0 b           0 b     870.93 Mb     870.93 Mb         72000
+                                              aten::pow         1.51%     353.068ms         2.04%     477.868ms      22.974us      20.913ms         0.15%      79.482ms       3.821us           0 b           0 b     330.08 Mb     330.08 Mb         20800
+                                             aten::silu         0.65%     151.408ms         0.96%     225.273ms      21.999us      26.142ms         0.19%      50.783ms       4.959us           0 b           0 b     218.36 Mb     218.36 Mb         10240
+                                              aten::add         2.81%     657.946ms         4.07%     953.898ms      23.019us      62.703ms         0.45%     174.723ms       4.216us           0 b           0 b     172.73 Mb     172.73 Mb         41440
+                                             aten::sort         0.10%      23.128ms         0.22%      51.926ms     161.763us      26.676ms         0.19%      48.079ms     149.779us           0 b           0 b     117.55 Mb     117.55 Mb           321
+                                              aten::bmm         2.47%     579.866ms         3.22%     756.074ms      36.918us      82.598ms         0.59%     135.565ms       6.619us           0 b           0 b     104.38 Mb     104.38 Mb         20480
+                                         aten::_softmax         0.60%     140.315ms         0.88%     206.046ms      19.512us      32.235ms         0.23%      60.789ms       5.757us           0 b           0 b      82.81 Mb      82.81 Mb         10560
+                                              aten::div         0.85%     199.026ms         1.19%     279.037ms      25.647us      15.933ms         0.11%      42.943ms       3.947us           0 b           0 b      62.19 Mb      62.19 Mb         10880
+-------------------------------------------------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------
+Self CPU time total: 23.446s
+Self CUDA time total: 13.901s
+
+~                                                                                                     
+"benchmark_outputs/batch_size_8_num_workers_1.txt" 91L, 8721B                       91,0-1        Bot
\ No newline at end of file

From 44feb1172e16e02b0e898f36d852cf5c1e5f4232 Mon Sep 17 00:00:00 2001
From: Guru Sharma <guru@gururams-air.lan>
Date: Thu, 14 Dec 2023 16:24:21 -0500
Subject: [PATCH 40/64] get quantization working

---
 llama/generation.py | 2 +-
 llama/model.py      | 7 +++++++
 2 files changed, 8 insertions(+), 1 deletion(-)

diff --git a/llama/generation.py b/llama/generation.py
index 5f8faf9f3..4dcb2ae24 100755
--- a/llama/generation.py
+++ b/llama/generation.py
@@ -116,7 +116,7 @@ def build(
         tokenizer = Tokenizer(model_path=tokenizer_path)
         model_args.vocab_size = tokenizer.n_words
         torch.set_default_tensor_type(torch.cuda.HalfTensor)
-        model = Transformer(model_args)
+        model = Transformer(model_args).eval()
         model.load_state_dict(checkpoint, strict=False)
         print(f"Loaded in {time.time() - start_time:.2f} seconds")
 
diff --git a/llama/model.py b/llama/model.py
index c78570f68..6aac275e8 100755
--- a/llama/model.py
+++ b/llama/model.py
@@ -430,6 +430,8 @@ def __init__(self, params: ModelArgs):
 
         """
         super().__init__()
+        # quantization
+        self.quant = torch.ao.quantization.QuantStub()
         self.params = params
         self.vocab_size = params.vocab_size
         self.n_layers = params.n_layers
@@ -452,6 +454,9 @@ def __init__(self, params: ModelArgs):
             # Adding this multiplier instead of using 4096 directly allows for dynamism of token lengths while training or fine-tuning.
             self.params.dim // self.params.n_heads, self.params.max_seq_len * 2
         )
+        # quantization
+        self.dequant = torch.ao.quantization.DeQuantStub()
+
 
     @torch.inference_mode()
     def forward(self, tokens: torch.Tensor, start_pos: int):
@@ -492,4 +497,6 @@ def forward(self, tokens: torch.Tensor, start_pos: int):
             h = layer(h, start_pos, freqs_cis, mask)
         h = self.norm(h)
         output = self.output(h).float()
+        # quantization
+        output = self.dequant(output)
         return output

From 8e3426610639912eaf52e43fc8218e66bd7ca8e5 Mon Sep 17 00:00:00 2001
From: Guru Sharma <guru@gururams-air.lan>
Date: Thu, 14 Dec 2023 16:47:03 -0500
Subject: [PATCH 41/64] add some more quantization lines

---
 llama/generation.py | 8 +++++++-
 1 file changed, 7 insertions(+), 1 deletion(-)

diff --git a/llama/generation.py b/llama/generation.py
index 4dcb2ae24..90ad583da 100755
--- a/llama/generation.py
+++ b/llama/generation.py
@@ -116,7 +116,13 @@ def build(
         tokenizer = Tokenizer(model_path=tokenizer_path)
         model_args.vocab_size = tokenizer.n_words
         torch.set_default_tensor_type(torch.cuda.HalfTensor)
-        model = Transformer(model_args).eval()
+        model = Transformer(model_args).eval() # quantization
+        model.fuse_model() # quantization
+        
+        model.qconfig = torch.ao.quantization.default_qconfig # quantization
+        torch.ao.quantization.prepare(model, inplace=True)
+
+        
         model.load_state_dict(checkpoint, strict=False)
         print(f"Loaded in {time.time() - start_time:.2f} seconds")
 

From 94c70fb164da805c89b66741a709b094a5d3b88a Mon Sep 17 00:00:00 2001
From: Guru Sharma <guru@gururams-air.lan>
Date: Thu, 14 Dec 2023 16:50:13 -0500
Subject: [PATCH 42/64] got rid of fuse_model()

---
 llama/generation.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/llama/generation.py b/llama/generation.py
index 90ad583da..025dc69f1 100755
--- a/llama/generation.py
+++ b/llama/generation.py
@@ -117,7 +117,7 @@ def build(
         model_args.vocab_size = tokenizer.n_words
         torch.set_default_tensor_type(torch.cuda.HalfTensor)
         model = Transformer(model_args).eval() # quantization
-        model.fuse_model() # quantization
+        #model.fuse_model() # quantization
         
         model.qconfig = torch.ao.quantization.default_qconfig # quantization
         torch.ao.quantization.prepare(model, inplace=True)

From 3920ee573975ec507abb584725d465b41c4785b7 Mon Sep 17 00:00:00 2001
From: Guru Sharma <guru@gururams-air.lan>
Date: Thu, 14 Dec 2023 17:00:36 -0500
Subject: [PATCH 43/64] adding convert to quantization model

---
 llama/generation.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/llama/generation.py b/llama/generation.py
index 025dc69f1..a16d31f1a 100755
--- a/llama/generation.py
+++ b/llama/generation.py
@@ -121,7 +121,7 @@ def build(
         
         model.qconfig = torch.ao.quantization.default_qconfig # quantization
         torch.ao.quantization.prepare(model, inplace=True)
-
+        torch.ao.quantization.convert(model, inplace=True)
         
         model.load_state_dict(checkpoint, strict=False)
         print(f"Loaded in {time.time() - start_time:.2f} seconds")

From adb0190190eff5d01740c933dd1d751d1a4e6cd9 Mon Sep 17 00:00:00 2001
From: George Tamer <gtamer2@gmail.com>
Date: Thu, 14 Dec 2023 18:57:37 -0500
Subject: [PATCH 44/64] quantize script

---
 inference_benchmark.py |  6 ++++--
 quantize_model.py      | 40 ++++++++++++++++++++++++++++++++++++++++
 2 files changed, 44 insertions(+), 2 deletions(-)
 create mode 100644 quantize_model.py

diff --git a/inference_benchmark.py b/inference_benchmark.py
index b5d093b28..a862886db 100644
--- a/inference_benchmark.py
+++ b/inference_benchmark.py
@@ -5,6 +5,10 @@
 import fire
 from torch.profiler import profile, record_function, ProfilerActivity
 
+
+from llama import Llama
+from typing import List
+
 ### Setup ###
 BATCH_SIZE = 1
 BATCH_COUNT = 5
@@ -22,8 +26,6 @@
 DEVICE_CUDA = 'cuda'
 DEVICE_CPU = 'cpu'
 
-from llama import Llama
-from typing import List
 
 def get_device():
     return torch.device(DEVICE_CUDA if torch.cuda.is_available() else DEVICE_CPU)
diff --git a/quantize_model.py b/quantize_model.py
new file mode 100644
index 000000000..59f7217cd
--- /dev/null
+++ b/quantize_model.py
@@ -0,0 +1,40 @@
+import os
+import torch
+from llama import Llama
+
+def print_model_size(mdl):
+    torch.save(mdl.state_dict(), "tmp.pt")
+    print("%.2f MB" %(os.path.getsize("tmp.pt")/1e6))
+    os.remove('tmp.pt')
+    
+backend = "qnnpack"
+
+
+def get_model(ckpt_dir, tokenizer_path, max_seq_len, max_batch_size):
+    generator = Llama.build(
+        ckpt_dir=ckpt_dir,
+        tokenizer_path=tokenizer_path,
+        max_seq_len=max_seq_len,
+        max_batch_size=max_batch_size,
+    )
+    return generator
+
+def quantize_model(model):
+    model.qconfig = torch.ao.quantization.default_qconfig
+    torch.backends.quantized.engine = backend
+    model_static_quantized = torch.quantization.prepare(model, inplace=False)
+    model_static_quantized = torch.quantization.convert(model_static_quantized, inplace=False)
+    return model_static_quantized
+
+
+def main():
+    model = get_model("llama-2-7b/", "tokenizer.model", 512, 1)
+    q_model = quantize_model(model)
+    print_model_size(model)
+    print_model_size(q_model)
+
+    # save the quantized model
+    torch.save(q_model.state_dict(), "quantized_model.pt")
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file

From d71ffa0450eb31b94ae820d0ad67b5a0dd7627fe Mon Sep 17 00:00:00 2001
From: George Tamer <gtamer2@gmail.com>
Date: Thu, 14 Dec 2023 19:00:26 -0500
Subject: [PATCH 45/64] fire launch

---
 quantize_model.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/quantize_model.py b/quantize_model.py
index 59f7217cd..2bcf77b42 100644
--- a/quantize_model.py
+++ b/quantize_model.py
@@ -1,6 +1,7 @@
 import os
 import torch
 from llama import Llama
+import fire
 
 def print_model_size(mdl):
     torch.save(mdl.state_dict(), "tmp.pt")
@@ -37,4 +38,4 @@ def main():
     torch.save(q_model.state_dict(), "quantized_model.pt")
 
 if __name__ == "__main__":
-    main()
\ No newline at end of file
+    fire.Fire(main)
\ No newline at end of file

From 748bc32ac8e38b1e47674fc3acc5c70016b923ac Mon Sep 17 00:00:00 2001
From: George Tamer <gtamer2@gmail.com>
Date: Thu, 14 Dec 2023 19:09:07 -0500
Subject: [PATCH 46/64] Inplace

---
 quantize_model.py | 14 ++++++++------
 1 file changed, 8 insertions(+), 6 deletions(-)

diff --git a/quantize_model.py b/quantize_model.py
index 2bcf77b42..b4dd5f54b 100644
--- a/quantize_model.py
+++ b/quantize_model.py
@@ -23,19 +23,21 @@ def get_model(ckpt_dir, tokenizer_path, max_seq_len, max_batch_size):
 def quantize_model(model):
     model.qconfig = torch.ao.quantization.default_qconfig
     torch.backends.quantized.engine = backend
-    model_static_quantized = torch.quantization.prepare(model, inplace=False)
-    model_static_quantized = torch.quantization.convert(model_static_quantized, inplace=False)
-    return model_static_quantized
+    torch.quantization.prepare(model, inplace=True)
+    torch.quantization.convert(model, inplace=True)
 
 
 def main():
     model = get_model("llama-2-7b/", "tokenizer.model", 512, 1)
-    q_model = quantize_model(model)
+    print("model size before in-place quantization")
+    print_model_size(model)
+
+    quantize_model(model)
+    print("model size after in-place quantization")
     print_model_size(model)
-    print_model_size(q_model)
 
     # save the quantized model
-    torch.save(q_model.state_dict(), "quantized_model.pt")
+    torch.save(model.state_dict(), "quantized_model.pt")
 
 if __name__ == "__main__":
     fire.Fire(main)
\ No newline at end of file

From 775475a0651983455ef5ee38b2517984aeb98a03 Mon Sep 17 00:00:00 2001
From: George Tamer <gtamer2@gmail.com>
Date: Thu, 14 Dec 2023 19:14:23 -0500
Subject: [PATCH 47/64] access the transformer

---
 quantize_model.py | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/quantize_model.py b/quantize_model.py
index b4dd5f54b..f2cd77918 100644
--- a/quantize_model.py
+++ b/quantize_model.py
@@ -28,16 +28,16 @@ def quantize_model(model):
 
 
 def main():
-    model = get_model("llama-2-7b/", "tokenizer.model", 512, 1)
+    llama = get_model("llama-2-7b/", "tokenizer.model", 512, 1)
     print("model size before in-place quantization")
-    print_model_size(model)
+    print_model_size(llama.model)
 
-    quantize_model(model)
+    quantize_model(llama.model)
     print("model size after in-place quantization")
-    print_model_size(model)
+    print_model_size(llama.model)
 
     # save the quantized model
-    torch.save(model.state_dict(), "quantized_model.pt")
+    torch.save(llama.model.state_dict(), "quantized_model.pt")
 
 if __name__ == "__main__":
     fire.Fire(main)
\ No newline at end of file

From 165cd541670145d4fd5c4b0dde0c7cce476f2b18 Mon Sep 17 00:00:00 2001
From: George Tamer <gtamer2@gmail.com>
Date: Thu, 14 Dec 2023 19:43:42 -0500
Subject: [PATCH 48/64] New quant logic

---
 gsmk_dataset.py        | 18 ++++++++++++++++++
 inference_benchmark.py | 14 +-------------
 llama/model.py         |  1 +
 quantize_model.py      | 18 +++++++++++++++---
 4 files changed, 35 insertions(+), 16 deletions(-)
 create mode 100644 gsmk_dataset.py

diff --git a/gsmk_dataset.py b/gsmk_dataset.py
new file mode 100644
index 000000000..4f25d5884
--- /dev/null
+++ b/gsmk_dataset.py
@@ -0,0 +1,18 @@
+from torch.utils.data import DataLoader
+import time
+from datasets import load_dataset
+
+# https://huggingface.co/datasets/gsm8k
+HUGGING_FACE_GSMK_DATASET_ID = "gsm8k"
+
+# Manual seed for reproducatibility
+
+def get_data_loader(batch_size, num_workers):
+    dataset = load_dataset(HUGGING_FACE_GSMK_DATASET_ID, 'main')['train']
+    dataloader = DataLoader(
+        dataset,
+        batch_size=batch_size,
+        shuffle=False,
+        num_workers=num_workers
+    )
+    return dataloader
diff --git a/inference_benchmark.py b/inference_benchmark.py
index a862886db..f57305a4a 100644
--- a/inference_benchmark.py
+++ b/inference_benchmark.py
@@ -1,7 +1,6 @@
 import torch
-from torch.utils.data import DataLoader
+from gsmk_dataset import get_data_loader
 import time
-from datasets import load_dataset
 import fire
 from torch.profiler import profile, record_function, ProfilerActivity
 
@@ -30,17 +29,6 @@
 def get_device():
     return torch.device(DEVICE_CUDA if torch.cuda.is_available() else DEVICE_CPU)
 
-def get_data_loader(num_workers=1):
-    dataset = load_dataset(HUGGING_FACE_GSMK_DATASET_ID, 'main')['train']
-    dataloader = DataLoader(
-        dataset,
-        batch_size=BATCH_SIZE,
-        shuffle=False,
-        num_workers=num_workers
-    )
-    return dataloader
-
-
 def get_model(ckpt_dir, tokenizer_path, max_seq_len, max_batch_size):
     generator = Llama.build(
         ckpt_dir=ckpt_dir,
diff --git a/llama/model.py b/llama/model.py
index 6aac275e8..4d6d60f2e 100755
--- a/llama/model.py
+++ b/llama/model.py
@@ -493,6 +493,7 @@ def forward(self, tokens: torch.Tensor, start_pos: int):
                 mask
             ]).type_as(h)
 
+        h = self.quant(h)
         for layer in self.layers:
             h = layer(h, start_pos, freqs_cis, mask)
         h = self.norm(h)
diff --git a/quantize_model.py b/quantize_model.py
index f2cd77918..f45c0ecb5 100644
--- a/quantize_model.py
+++ b/quantize_model.py
@@ -2,13 +2,15 @@
 import torch
 from llama import Llama
 import fire
+from gsmk_dataset import get_data_loader
+    
+backend = "qnnpack"
 
 def print_model_size(mdl):
     torch.save(mdl.state_dict(), "tmp.pt")
     print("%.2f MB" %(os.path.getsize("tmp.pt")/1e6))
     os.remove('tmp.pt')
-    
-backend = "qnnpack"
+
 
 
 def get_model(ckpt_dir, tokenizer_path, max_seq_len, max_batch_size):
@@ -21,9 +23,19 @@ def get_model(ckpt_dir, tokenizer_path, max_seq_len, max_batch_size):
     return generator
 
 def quantize_model(model):
-    model.qconfig = torch.ao.quantization.default_qconfig
+    # setup quantization
+    model.eval()
+    model.qconfig = torch.ao.quantization.get_default_qconfig('x86')
     torch.backends.quantized.engine = backend
     torch.quantization.prepare(model, inplace=True)
+
+    # calibrate model to real world data
+    dataloader = get_data_loader(5, 0)
+    for i in range(10):
+        batch = next(iter(dataloader))
+        model(batch)
+
+    # convert in place
     torch.quantization.convert(model, inplace=True)
 
 

From c995e43484cd0fb92da26e5bad2945faefa20405 Mon Sep 17 00:00:00 2001
From: George Tamer <gtamer2@gmail.com>
Date: Thu, 14 Dec 2023 19:49:43 -0500
Subject: [PATCH 49/64] fix quant sample inf

---
 quantize_model.py | 14 ++++++++++----
 1 file changed, 10 insertions(+), 4 deletions(-)

diff --git a/quantize_model.py b/quantize_model.py
index f45c0ecb5..ccc1fe766 100644
--- a/quantize_model.py
+++ b/quantize_model.py
@@ -22,7 +22,8 @@ def get_model(ckpt_dir, tokenizer_path, max_seq_len, max_batch_size):
     )
     return generator
 
-def quantize_model(model):
+def quantize_model(llama):
+    model = llama.model
     # setup quantization
     model.eval()
     model.qconfig = torch.ao.quantization.get_default_qconfig('x86')
@@ -31,9 +32,14 @@ def quantize_model(model):
 
     # calibrate model to real world data
     dataloader = get_data_loader(5, 0)
-    for i in range(10):
+    for _ in range(10):
         batch = next(iter(dataloader))
-        model(batch)
+        llama.text_completion(
+            batch,
+            max_gen_len=512,
+            temperature=0.6,
+            top_p=0.9,
+        )
 
     # convert in place
     torch.quantization.convert(model, inplace=True)
@@ -44,7 +50,7 @@ def main():
     print("model size before in-place quantization")
     print_model_size(llama.model)
 
-    quantize_model(llama.model)
+    quantize_model(llama)
     print("model size after in-place quantization")
     print_model_size(llama.model)
 

From 575c197d1b70ade79f7d575d7205678a08052460 Mon Sep 17 00:00:00 2001
From: George Tamer <gtamer2@gmail.com>
Date: Thu, 14 Dec 2023 19:52:20 -0500
Subject: [PATCH 50/64] Fix args

---
 quantize_model.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/quantize_model.py b/quantize_model.py
index ccc1fe766..d12bd556a 100644
--- a/quantize_model.py
+++ b/quantize_model.py
@@ -31,7 +31,7 @@ def quantize_model(llama):
     torch.quantization.prepare(model, inplace=True)
 
     # calibrate model to real world data
-    dataloader = get_data_loader(5, 0)
+    dataloader = get_data_loader(3, 0)
     for _ in range(10):
         batch = next(iter(dataloader))
         llama.text_completion(
@@ -46,7 +46,7 @@ def quantize_model(llama):
 
 
 def main():
-    llama = get_model("llama-2-7b/", "tokenizer.model", 512, 1)
+    llama = get_model("llama-2-7b/", "tokenizer.model", 512, 6)
     print("model size before in-place quantization")
     print_model_size(llama.model)
 

From 787efd58a9230b5084af57ca40b70a9e6203f4f6 Mon Sep 17 00:00:00 2001
From: George Tamer <gtamer2@gmail.com>
Date: Thu, 14 Dec 2023 19:59:16 -0500
Subject: [PATCH 51/64] move h=quant(h) to after firist layer. It was operating
 on tokens whicch are Half, not float

---
 llama/model.py | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/llama/model.py b/llama/model.py
index 4d6d60f2e..7bf1bbeea 100755
--- a/llama/model.py
+++ b/llama/model.py
@@ -492,10 +492,11 @@ def forward(self, tokens: torch.Tensor, start_pos: int):
                 torch.zeros((seqlen, start_pos), device=tokens.device),
                 mask
             ]).type_as(h)
-
-        h = self.quant(h)
-        for layer in self.layers:
+        
+        for layer_idx, layer in enumerate(self.layers):
             h = layer(h, start_pos, freqs_cis, mask)
+            if layer_idx == 0:
+                h = self.quant(h)
         h = self.norm(h)
         output = self.output(h).float()
         # quantization

From d0b8b3975fe87914d299e8d59e7ffe1d612d4844 Mon Sep 17 00:00:00 2001
From: Guru Sharma <guru@gururams-air.lan>
Date: Fri, 15 Dec 2023 09:23:35 -0500
Subject: [PATCH 52/64] added a sample for pruning model

---
 prune_model.py | 68 ++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 68 insertions(+)
 create mode 100644 prune_model.py

diff --git a/prune_model.py b/prune_model.py
new file mode 100644
index 000000000..6d58b4dc6
--- /dev/null
+++ b/prune_model.py
@@ -0,0 +1,68 @@
+import os
+import torch
+from llama import Llama
+import fire
+from gsmk_dataset import get_data_loader
+    
+backend = "qnnpack"
+
+def print_model_size(mdl):
+    torch.save(mdl.state_dict(), "tmp.pt")
+    print("%.2f MB" %(os.path.getsize("tmp.pt")/1e6))
+    os.remove('tmp.pt')
+
+
+
+def get_model(ckpt_dir, tokenizer_path, max_seq_len, max_batch_size):
+    generator = Llama.build(
+        ckpt_dir=ckpt_dir,
+        tokenizer_path=tokenizer_path,
+        max_seq_len=max_seq_len,
+        max_batch_size=max_batch_size,
+    )
+    return generator
+
+def prune_model(llama):
+    model = llama.model
+    
+    print(f'model type = {type(model)}')
+    
+    # set up pruning:
+    enc = model.encoder
+    dec = model.decoder
+    
+    # setup quantization
+    model.eval()
+    model.qconfig = torch.ao.quantization.get_default_qconfig('x86')
+    torch.backends.quantized.engine = backend
+    torch.quantization.prepare(model, inplace=True)
+
+    # calibrate model to real world data
+    dataloader = get_data_loader(3, 0)
+    for _ in range(10):
+        batch = next(iter(dataloader))
+        llama.text_completion(
+            batch,
+            max_gen_len=512,
+            temperature=0.6,
+            top_p=0.9,
+        )
+
+    # convert in place
+    torch.quantization.convert(model, inplace=True)
+
+
+def main():
+    llama = get_model("/home/gyt2107/hpml_llama/llama-2-7b/", "tokenizer.model", 512, 6)
+    print("model size before in-place pruning")
+    print_model_size(llama.model)
+
+    prune_model(llama)
+    print("model size after in-place pruning")
+    print_model_size(llama.model)
+
+    # save the quantized model
+    torch.save(llama.model.state_dict(), "quantized_model.pt")
+
+if __name__ == "__main__":
+    fire.Fire(main)
\ No newline at end of file

From fab7b444079827c172b4f3a16e91343a511e4336 Mon Sep 17 00:00:00 2001
From: Guru Sharma <guru@gururams-air.lan>
Date: Fri, 15 Dec 2023 09:51:42 -0500
Subject: [PATCH 53/64] maybe it prunes now?

---
 prune_model.py | 18 +++++++++++++-----
 1 file changed, 13 insertions(+), 5 deletions(-)

diff --git a/prune_model.py b/prune_model.py
index 6d58b4dc6..8ea7ee12a 100644
--- a/prune_model.py
+++ b/prune_model.py
@@ -3,6 +3,7 @@
 from llama import Llama
 import fire
 from gsmk_dataset import get_data_loader
+import torch.nn.utils.prune as prune
     
 backend = "qnnpack"
 
@@ -28,15 +29,22 @@ def prune_model(llama):
     print(f'model type = {type(model)}')
     
     # set up pruning:
-    enc = model.encoder
-    dec = model.decoder
+    for layer in model.layers:
+        prune.random_unstructured(layer, name="weight", amount=0.3)
+        prune.l1_unstructured(layer, name="bias", amount=3)
+        
+    
+    
+    #enc = model.encoder
+    #dec = model.decoder
     
     # setup quantization
-    model.eval()
+    """model.eval()
     model.qconfig = torch.ao.quantization.get_default_qconfig('x86')
     torch.backends.quantized.engine = backend
     torch.quantization.prepare(model, inplace=True)
-
+    """
+    
     # calibrate model to real world data
     dataloader = get_data_loader(3, 0)
     for _ in range(10):
@@ -49,7 +57,7 @@ def prune_model(llama):
         )
 
     # convert in place
-    torch.quantization.convert(model, inplace=True)
+    # torch.quantization.convert(model, inplace=True)
 
 
 def main():

From 1a85f360e15d091468bd20d93d07ea9b26c872cf Mon Sep 17 00:00:00 2001
From: Guru Sharma <guru@gururams-air.lan>
Date: Fri, 15 Dec 2023 09:57:44 -0500
Subject: [PATCH 54/64] added attention

---
 prune_model.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/prune_model.py b/prune_model.py
index 8ea7ee12a..762478299 100644
--- a/prune_model.py
+++ b/prune_model.py
@@ -30,8 +30,8 @@ def prune_model(llama):
     
     # set up pruning:
     for layer in model.layers:
-        prune.random_unstructured(layer, name="weight", amount=0.3)
-        prune.l1_unstructured(layer, name="bias", amount=3)
+        prune.random_unstructured(layer, name="attention", amount=0.3)
+        #prune.l1_unstructured(layer, name="bias", amount=3)
         
     
     

From 0bf73c401bf4b70fc30a96cfcacaf3493131d10f Mon Sep 17 00:00:00 2001
From: Guru Sharma <guru@gururams-air.lan>
Date: Fri, 15 Dec 2023 10:09:48 -0500
Subject: [PATCH 55/64] add torch.nn.parameter to this

---
 prune_model.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/prune_model.py b/prune_model.py
index 762478299..04bfdba07 100644
--- a/prune_model.py
+++ b/prune_model.py
@@ -30,7 +30,7 @@ def prune_model(llama):
     
     # set up pruning:
     for layer in model.layers:
-        prune.random_unstructured(layer, name="attention", amount=0.3)
+        prune.random_unstructured(layer, name="attention_norm.weight", amount=0.3) # name is a torch.nn.Parameter
         #prune.l1_unstructured(layer, name="bias", amount=3)
         
     

From ac4fb5163cb60611711bc7d1ba40106b48f4edba Mon Sep 17 00:00:00 2001
From: Guru Sharma <guru@gururams-air.lan>
Date: Fri, 15 Dec 2023 10:17:34 -0500
Subject: [PATCH 56/64] added some changes for including torch.nn.Parameter
 into the pruning, created my own attribute

---
 llama/model.py | 1 +
 prune_model.py | 5 +++--
 2 files changed, 4 insertions(+), 2 deletions(-)

diff --git a/llama/model.py b/llama/model.py
index 7bf1bbeea..7d073ebd4 100755
--- a/llama/model.py
+++ b/llama/model.py
@@ -381,6 +381,7 @@ def __init__(self, layer_id: int, args: ModelArgs):
         )
         self.layer_id = layer_id
         self.attention_norm = RMSNorm(args.dim, eps=args.norm_eps)
+        self.attn_norm_w = self.attention_norm.weight
         self.ffn_norm = RMSNorm(args.dim, eps=args.norm_eps)
 
     def forward(
diff --git a/prune_model.py b/prune_model.py
index 04bfdba07..593ef5180 100644
--- a/prune_model.py
+++ b/prune_model.py
@@ -29,8 +29,9 @@ def prune_model(llama):
     print(f'model type = {type(model)}')
     
     # set up pruning:
-    for layer in model.layers:
-        prune.random_unstructured(layer, name="attention_norm.weight", amount=0.3) # name is a torch.nn.Parameter
+    for layer in model.layers: # each layer is a TransformerBlock
+        # we only have nn.Parameter objects in RMSNorm class
+        prune.random_unstructured(layer, name="attn_norm_w", amount=0.3) # name is a torch.nn.Parameter
         #prune.l1_unstructured(layer, name="bias", amount=3)
         
     

From caf74829ddc61eda76c4b23a540824dd67dded28 Mon Sep 17 00:00:00 2001
From: Guru Sharma <guru@gururams-air.lan>
Date: Fri, 15 Dec 2023 10:23:02 -0500
Subject: [PATCH 57/64] get rid of quantization modifications in model.py

---
 llama/model.py | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/llama/model.py b/llama/model.py
index 7d073ebd4..c0c14aa96 100755
--- a/llama/model.py
+++ b/llama/model.py
@@ -432,7 +432,7 @@ def __init__(self, params: ModelArgs):
         """
         super().__init__()
         # quantization
-        self.quant = torch.ao.quantization.QuantStub()
+        # self.quant = torch.ao.quantization.QuantStub()
         self.params = params
         self.vocab_size = params.vocab_size
         self.n_layers = params.n_layers
@@ -456,7 +456,7 @@ def __init__(self, params: ModelArgs):
             self.params.dim // self.params.n_heads, self.params.max_seq_len * 2
         )
         # quantization
-        self.dequant = torch.ao.quantization.DeQuantStub()
+        # self.dequant = torch.ao.quantization.DeQuantStub()
 
 
     @torch.inference_mode()
@@ -496,10 +496,10 @@ def forward(self, tokens: torch.Tensor, start_pos: int):
         
         for layer_idx, layer in enumerate(self.layers):
             h = layer(h, start_pos, freqs_cis, mask)
-            if layer_idx == 0:
-                h = self.quant(h)
+            """if layer_idx == 0:
+                h = self.quant(h)"""
         h = self.norm(h)
         output = self.output(h).float()
         # quantization
-        output = self.dequant(output)
+        # output = self.dequant(output)
         return output

From b790b373af2afd56bb2128e66006e21e0dab8c75 Mon Sep 17 00:00:00 2001
From: Guru Sharma <guru@gururams-air.lan>
Date: Fri, 15 Dec 2023 11:38:33 -0500
Subject: [PATCH 58/64] checking sparsity

---
 prune_model.py | 18 ++++++++++++++++--
 1 file changed, 16 insertions(+), 2 deletions(-)

diff --git a/prune_model.py b/prune_model.py
index 593ef5180..25ae4d1b4 100644
--- a/prune_model.py
+++ b/prune_model.py
@@ -31,8 +31,22 @@ def prune_model(llama):
     # set up pruning:
     for layer in model.layers: # each layer is a TransformerBlock
         # we only have nn.Parameter objects in RMSNorm class
-        prune.random_unstructured(layer, name="attn_norm_w", amount=0.3) # name is a torch.nn.Parameter
-        #prune.l1_unstructured(layer, name="bias", amount=3)
+        #import torch.nn.utils.prune as prune
+
+        # Assuming you have a TransformerBlock object named 'layer'
+        sparsity = prune.sparsity(layer)
+        print(f"Sparsity of the TransformerBlock (before pruning): {sparsity}")
+        model.layer = prune.random_unstructured(layer, name="attn_norm_w", amount=0.3) # name is a torch.nn.Parameter
+        print(f"Sparsity of the TransformerBlock (after pruning): {sparsity}")
+        # prune.l1_unstructured(layer, name="bias", amount=3)
+    
+    
+    """for i in range(len(model.layers)): # each layer is a TransformerBlock
+        # we only have nn.Parameter objects in RMSNorm class
+        model.layers[i] = prune.random_unstructured(model.layers[i], name="attn_norm_w", amount=0.3) # name is a torch.nn.Parameter
+        # model.layer = prune.random_unstructured(layer, name="attn_norm_w", amount=0.3) # name is a torch.nn.Parameter
+        # prune.l1_unstructured(layer, name="bias", amount=3)
+    """
         
     
     

From 1c13854620ef995d8a9165dd6a4c5d7bc9214014 Mon Sep 17 00:00:00 2001
From: Guru Sharma <guru@gururams-air.lan>
Date: Fri, 15 Dec 2023 11:44:21 -0500
Subject: [PATCH 59/64] trying to find sparsity again

---
 prune_model.py | 7 ++++++-
 1 file changed, 6 insertions(+), 1 deletion(-)

diff --git a/prune_model.py b/prune_model.py
index 25ae4d1b4..6272bbd02 100644
--- a/prune_model.py
+++ b/prune_model.py
@@ -3,6 +3,7 @@
 from llama import Llama
 import fire
 from gsmk_dataset import get_data_loader
+import torch
 import torch.nn.utils.prune as prune
     
 backend = "qnnpack"
@@ -34,9 +35,13 @@ def prune_model(llama):
         #import torch.nn.utils.prune as prune
 
         # Assuming you have a TransformerBlock object named 'layer'
-        sparsity = prune.sparsity(layer)
+        num_zeros = torch.sum(layer.weight == 0).item()
+        total_params = layer.weight.numel()
+        sparsity = num_zeros / total_params
         print(f"Sparsity of the TransformerBlock (before pruning): {sparsity}")
         model.layer = prune.random_unstructured(layer, name="attn_norm_w", amount=0.3) # name is a torch.nn.Parameter
+        num_zeros = torch.sum(layer.weight == 0).item()
+        sparsity = num_zeros / total_params
         print(f"Sparsity of the TransformerBlock (after pruning): {sparsity}")
         # prune.l1_unstructured(layer, name="bias", amount=3)
     

From bf0ebe036ac2d221be5ed27d583ef7b7d1ee1ac0 Mon Sep 17 00:00:00 2001
From: Guru Sharma <guru@gururams-air.lan>
Date: Fri, 15 Dec 2023 11:48:23 -0500
Subject: [PATCH 60/64] we got rid of sparsity

---
 prune_model.py | 18 +++++++++++-------
 1 file changed, 11 insertions(+), 7 deletions(-)

diff --git a/prune_model.py b/prune_model.py
index 6272bbd02..a06dce7c6 100644
--- a/prune_model.py
+++ b/prune_model.py
@@ -30,20 +30,24 @@ def prune_model(llama):
     print(f'model type = {type(model)}')
     
     # set up pruning:
+    count = 0
     for layer in model.layers: # each layer is a TransformerBlock
         # we only have nn.Parameter objects in RMSNorm class
         #import torch.nn.utils.prune as prune
 
         # Assuming you have a TransformerBlock object named 'layer'
-        num_zeros = torch.sum(layer.weight == 0).item()
-        total_params = layer.weight.numel()
-        sparsity = num_zeros / total_params
-        print(f"Sparsity of the TransformerBlock (before pruning): {sparsity}")
+        #num_zeros = torch.sum(layer.weight == 0).item()
+        #total_params = layer.weight.numel()
+        #sparsity = num_zeros / total_params
+        #print(f"Sparsity of the TransformerBlock (before pruning): {sparsity}")
         model.layer = prune.random_unstructured(layer, name="attn_norm_w", amount=0.3) # name is a torch.nn.Parameter
-        num_zeros = torch.sum(layer.weight == 0).item()
-        sparsity = num_zeros / total_params
-        print(f"Sparsity of the TransformerBlock (after pruning): {sparsity}")
+        #num_zeros = torch.sum(layer.weight == 0).item()
+        #sparsity = num_zeros / total_params
+        #print(f"Sparsity of the TransformerBlock (after pruning): {sparsity}")
         # prune.l1_unstructured(layer, name="bias", amount=3)
+        count += 1
+        if count % 10**6 == 0:
+            print(f'we are {count} layers in')
     
     
     """for i in range(len(model.layers)): # each layer is a TransformerBlock

From cb431516feaf561aa9b55a0e370c36c1fdf2ba49 Mon Sep 17 00:00:00 2001
From: Guru Sharma <guru@gururams-air.lan>
Date: Fri, 15 Dec 2023 14:19:23 -0500
Subject: [PATCH 61/64] giving sparsity another try

---
 prune_model.py | 10 ++++++++++
 1 file changed, 10 insertions(+)

diff --git a/prune_model.py b/prune_model.py
index a06dce7c6..6114c3622 100644
--- a/prune_model.py
+++ b/prune_model.py
@@ -40,6 +40,16 @@ def prune_model(llama):
         #total_params = layer.weight.numel()
         #sparsity = num_zeros / total_params
         #print(f"Sparsity of the TransformerBlock (before pruning): {sparsity}")
+        attention_w = layer.attention.wq + layer.attention.wk + layer.attention.wv
+        att_norm_w = layer.attention_norm.weight
+        ffn_norm_w = layer.ffn_norm.weight
+        total_weights = attention_w + att_norm_w + ffn_norm_w
+        num_zeros = torch.sum(total_weights == 0).item()
+        total_params = total_weights.numel()
+        sparsity = num_zeros / total_params
+        print(f'Sparsity of the TransformerBlock (before pruning): {sparsity}')
+        
+        
         model.layer = prune.random_unstructured(layer, name="attn_norm_w", amount=0.3) # name is a torch.nn.Parameter
         #num_zeros = torch.sum(layer.weight == 0).item()
         #sparsity = num_zeros / total_params

From 6779933fccf775fa0925c9eb5606783dc4725efc Mon Sep 17 00:00:00 2001
From: Guru Sharma <guru@gururams-air.lan>
Date: Fri, 15 Dec 2023 14:24:15 -0500
Subject: [PATCH 62/64] trying newer things with prune_model.py

---
 prune_model.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/prune_model.py b/prune_model.py
index 6114c3622..3fa9173f3 100644
--- a/prune_model.py
+++ b/prune_model.py
@@ -40,7 +40,7 @@ def prune_model(llama):
         #total_params = layer.weight.numel()
         #sparsity = num_zeros / total_params
         #print(f"Sparsity of the TransformerBlock (before pruning): {sparsity}")
-        attention_w = layer.attention.wq + layer.attention.wk + layer.attention.wv
+        attention_w = layer.attention.wq.weight + layer.attention.wk.weight + layer.attention.wv.weight
         att_norm_w = layer.attention_norm.weight
         ffn_norm_w = layer.ffn_norm.weight
         total_weights = attention_w + att_norm_w + ffn_norm_w

From 660e231cf13accb1a0bff7c3a161fb01b0f3460a Mon Sep 17 00:00:00 2001
From: Guru Sharma <guru@gururams-air.lan>
Date: Fri, 15 Dec 2023 14:35:24 -0500
Subject: [PATCH 63/64] got rid of print statements, left them until after it
 was done

---
 prune_model.py | 26 ++++++++++++++++++--------
 1 file changed, 18 insertions(+), 8 deletions(-)

diff --git a/prune_model.py b/prune_model.py
index 3fa9173f3..2c63a0f39 100644
--- a/prune_model.py
+++ b/prune_model.py
@@ -31,6 +31,9 @@ def prune_model(llama):
     
     # set up pruning:
     count = 0
+    total_params = 0
+    before_num_zeros = 0
+    after_num_zeros = 0
     for layer in model.layers: # each layer is a TransformerBlock
         # we only have nn.Parameter objects in RMSNorm class
         #import torch.nn.utils.prune as prune
@@ -40,17 +43,20 @@ def prune_model(llama):
         #total_params = layer.weight.numel()
         #sparsity = num_zeros / total_params
         #print(f"Sparsity of the TransformerBlock (before pruning): {sparsity}")
-        attention_w = layer.attention.wq.weight + layer.attention.wk.weight + layer.attention.wv.weight
-        att_norm_w = layer.attention_norm.weight
-        ffn_norm_w = layer.ffn_norm.weight
-        total_weights = attention_w + att_norm_w + ffn_norm_w
-        num_zeros = torch.sum(total_weights == 0).item()
-        total_params = total_weights.numel()
-        sparsity = num_zeros / total_params
-        print(f'Sparsity of the TransformerBlock (before pruning): {sparsity}')
+        before_total_weights = layer.attention.wq.weight + layer.attention.wk.weight + layer.attention.wv.weight + layer.attention_norm.weight + layer.ffn_norm.weight
+        #att_norm_w = layer.attention_norm.weight
+        #ffn_norm_w = layer.ffn_norm.weight
+        #total_weights = attention_w + att_norm_w + ffn_norm_w
+        before_num_zeros += torch.sum(before_total_weights == 0).item()        
+        total_params += before_total_weights.numel()
+        #print(f'Sparsity of the TransformerBlock (before pruning): {sparsity}')
         
         
         model.layer = prune.random_unstructured(layer, name="attn_norm_w", amount=0.3) # name is a torch.nn.Parameter
+        
+        after_total_weights = layer.attention.wq.weight + layer.attention.wk.weight + layer.attention.wv.weight + layer.attention_norm.weight + layer.ffn_norm.weight
+        after_num_zeros += torch.sum(after_total_weights == 0).item()        
+        
         #num_zeros = torch.sum(layer.weight == 0).item()
         #sparsity = num_zeros / total_params
         #print(f"Sparsity of the TransformerBlock (after pruning): {sparsity}")
@@ -59,6 +65,10 @@ def prune_model(llama):
         if count % 10**6 == 0:
             print(f'we are {count} layers in')
     
+    print(f'Sparsity of the TransformerBlock (before pruning): {before_num_zeros/total_params}')
+    print(f'Sparsity of the TransformerBlock (after pruning): {after_num_zeros/total_params}')
+    
+    
     
     """for i in range(len(model.layers)): # each layer is a TransformerBlock
         # we only have nn.Parameter objects in RMSNorm class

From 651f8fb8531c0062cdb2b5eb3e6e768e6abaae47 Mon Sep 17 00:00:00 2001
From: Guru Sharma <guru@gururams-air.lan>
Date: Fri, 15 Dec 2023 14:39:40 -0500
Subject: [PATCH 64/64] trying to make sure layer is being modified in place

---
 prune_model.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/prune_model.py b/prune_model.py
index 2c63a0f39..d78d57fdc 100644
--- a/prune_model.py
+++ b/prune_model.py
@@ -52,7 +52,7 @@ def prune_model(llama):
         #print(f'Sparsity of the TransformerBlock (before pruning): {sparsity}')
         
         
-        model.layer = prune.random_unstructured(layer, name="attn_norm_w", amount=0.3) # name is a torch.nn.Parameter
+        layer=prune.random_unstructured(layer, name="attn_norm_w", amount=0.3) # name is a torch.nn.Parameter
         
         after_total_weights = layer.attention.wq.weight + layer.attention.wk.weight + layer.attention.wv.weight + layer.attention_norm.weight + layer.ffn_norm.weight
         after_num_zeros += torch.sum(after_total_weights == 0).item()