groq content

aiTestbeds/Groq/README.md

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+# Groq 
+
+## Connection to Groq
+
+![Groq connection diagram](./groqrack_system_diagram.png)
+
+Login to the Groq login node from your local machine.
+Once you are on the login node, ssh to one of the Groq nodes.
+
+```bash
+local > ssh [email protected]
+```
+```bash
+groq-login > ssh groq-r01-gn-01.ai.alcf.anl.gov
+# or
+groq-login > ssh groq-r01-gn-09.ai.alcf.anl.gov
+# or any node with hostname of form groq-r01-gn-0[1-9].ai.alcf.anl.gov
+```
+
+## Create Virtual Environment 
+
+### Install Miniconda
+
+```bash
+wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
+bash Miniconda3-latest-Linux-x86_64.sh
+```
+
+### PyTorch virtual environment
+
+```bash
+export PYTHON_VERSION=3.10.12
+conda create -n groqflow python=$PYTHON_VERSION
+conda activate groqflow
+```
+
+### Install Groqflow
+
+```bash
+git clone https://github.com/groq/groqflow.git
+cd groqflow
+pip install --upgrade pip
+pip install -e .
+pushd . 
+cd demo_helpers
+pip install -e .
+popd
+```
+
+
+## Job Queuing and Submission
+
+Groq jobs in the AI Testbed's groqrack are managed by the PBS job scheduler.
+
+* `qsub` : to submit a batch job using a script
+* `qstat`: to display queue information
+* `qdel`: to delete (cancel) a job:
+* `qhold`: to hold a job
+
+### Schedule batch Job
+
+<details>
+  <summary>Sample run_minilmv2.sh script</summary>
+
+    ```bash
+    #!/bin/bash
+    # >>> conda initialize >>>
+    # !! Contents within this block are managed by 'conda init' !!
+    __conda_setup="$(${HOME}'/miniconda3/bin/conda' 'shell.bash' 'hook' 2> /dev/null)"
+    if [ $? -eq 0 ]; then
+        eval "$__conda_setup"
+    else
+        if [ -f "${HOME}/miniconda3/etc/profile.d/conda.sh" ]; then
+            . "${HOME}/miniconda3/etc/profile.d/conda.sh"
+        else
+            export PATH="${HOME}/miniconda3/bin:$PATH"
+        fi
+    fi
+    unset __conda_setup
+    # <<< conda initialize <<<
+    conda activate groqflow
+    cd ~/groqflow/proof_points/natural_language_processing/minilm
+    pip install -r requirements.txt
+    python minilmv2.py
+
+    ```
+
+</details>
+
+Then run the script as a batch job with PBS:
+```bash
+qsub run_minilmv2.sh
+```
+
+
+### Schedule Interactive Job
+
+Following command gives a single Groq node interactively for 1 hour
+```bash
+qsub -I -l walltime=1:00:00 
+```
+Other flags that can be used
+```bash
+-l ncpus=1 
+-l groq_accelerator=1
+```
+
+## Run Examples
+
+Refer to respective instrcutions below 
+
+* [GPT2](./GPT2.md)
+
+
+## Next Steps 
+
+* [MiniLM](./minilm.md)
+* [ResNet50](./resnet50.md)
+
+* Explore other examples under [Proof Points](https://github.com/groq/groqflow/tree/main/proof_points)
+
+## Useful Resources 
+
+* [ALCF Groq Documenation](https://docs.alcf.anl.gov/ai-testbed/groq/system-overview/)
+* [Groq Documentation](https://support.groq.com/#/login)
+* [Groq Examples Repository](https://github.com/groq/groqflow/tree/main/proof_points)

aiTestbeds/Groq/gpt2.md

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+# GPT2 On Groq
+
+GPT-2 is a transformers model pretrained on a very large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it was trained to guess the next word in sentences.
+
+More precisely, inputs are sequences of continuous text of a certain length and the targets are the same sequence, shifted one token (word or piece of word) to the right. The model uses internally a mask-mechanism to make sure the predictions for the token i only uses the inputs from 1 to i but not the future tokens.
+
+This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks. The model is best at what it was pretrained for however, which is generating texts from a prompt.
+
+#### Get a Groq node interactively
+
+```bash
+qsub -I -l walltime=1:00:00
+```
+
+#### Go to directory with GPT2 example. 
+```bash
+git clone [email protected]:argonne-lcf/ALCF_Hands_on_HPC_Workshop.git
+cd aiTestbeds/Groq
+```
+
+#### Activate groqflow virtual Environment 
+```bash
+conda activate groqflow
+```
+
+#### Install Requirements 
+
+Install the python dependencies using the following command:
+```bash
+pip install transformers
+```
+
+#### Run Inference Job
+
+```bash
+python gpt2.py
+```
+
+<!-- #### Run end-to-end Inference Job with WikiText dataset
+
+```bash
+python GPT2-wiki.py
+``` -->

aiTestbeds/Groq/gpt2.py

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+import torch
+import torch.nn as nn
+import transformers
+try:
+    from groqflow import groqit
+except:
+    raise ImportError("GroqFlow module not found!")
+
+# Instantiate model from transformers library with the corresponding config
+model = transformers.GPT2Model(transformers.GPT2Config())
+
+# Create dummy inputs with static dimensions and specified data type
+inputs = {
+   "input_ids": torch.ones(1, 256, dtype=torch.long),
+   "attention_mask": torch.ones(1, 256, dtype=torch.float),
+}
+
+# Rock it with GroqIt to compile the model
+gmodel = groqit(model, inputs, rebuild="never")
+groq_output = gmodel(**inputs) # Run inference on the model on Groq with the GroqIt runtime
+
+print(groq_output) # print outputs in raw form, this should be decoded with a tokenizer in a real life example