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 # GPTFast
 Accelerate your Hugging face Transformers 6-7x with GPTFast
+
+# Background
+[GPTFast](https://github.com/pytorch-labs/gpt-fast) was originally a set of techniques developed by the PyTorch Team to accelerate the inference speed of Llama-2-7b. This pip package generalizes those techniques to all Hugging Face models. 
+
+# Demo
+GPTFast Inference Time|Eager Inference Time
+--|--
+![](https://github.com/MDK8888/GPTFast/assets/79173446/4d7ed04e-ba3d-49c7-aeca-8f2b96ac45a8)|![](https://github.com/MDK8888/GPTFast/assets/79173446/1a4f2236-d2f4-42c7-a689-553482871905)
+
+# Getting Started
+* Make sure that your python version >= 3.10, and you are on a cuda enabled device.
+* Make a virtual environment on your machine and activate it.
+  ```bash
+  $python3 -m venv VENV_NAME
+  source VENV_NAME/bin/activate #./VENV_NAME/scripts/activate if you are on a Windows machine
+  ```
+* Call the following: ```pip install gptfast```
+* Copy the following code into a python file:
+  ```python
+  import os
+  import torch
+  from transformers import AutoModelForCausalLM, AutoTokenizer
+  from GPTFast.Core import gpt_fast
+  from GPTFast.Helpers import timed
+  
+  torch._dynamo.reset()
+  os.environ["TOKENIZERS_PARALLELISM"] = "false"
+  
+  device = "cuda" if torch.cuda.is_available() else "cpu"
+  
+  def argmax(self, probabilities):
+      # Use argmax to get the token with the maximum probability
+      max_prob_index = torch.argmax(probabilities, dim=-1)
+      return max_prob_index.unsqueeze(0)
+  
+  model_name = "gpt2-xl"
+  draft_model_name = "gpt2"
+  
+  tokenizer = AutoTokenizer.from_pretrained(model_name)
+  initial_string = "Write me a short story."
+  input_tokens = tokenizer.encode(initial_string, return_tensors="pt").to(device)
+  
+  N_ITERS=10
+  MAX_TOKENS=50
+  
+  attention_mask = torch.ones(input_tokens.shape, dtype=torch.long).to(device)
+  pad_token_id = 50256
+  
+  gpt_fast_model = gpt_fast(model_name, draft_model_name=draft_model_name, sample_function=argmax)
+  gpt_fast_model.to(device)
+  
+  fast_compile_times = []
+  for i in range(N_ITERS):
+      with torch.no_grad():
+          res, compile_time = timed(lambda: gpt_fast_model.generate(cur_tokens=input_tokens, max_tokens=MAX_TOKENS, speculate_k=6))
+      fast_compile_times.append(compile_time)
+      print(f"gpt fast eval time {i}: {compile_time}")
+  print("~" * 10)
+  ```
+  * Run it and watch the magic 🪄!
+
 # Documentation
-Coming today!
\ No newline at end of file
+At its core, this library provides a simple interface to LLM Inference acceleration techniques. All of the following functions can be imported from ```GPTFast.core```:
+
+* ```gpt_fast(model_name:str, draft_model_name:str, sample_function:Callable) -> torch.nn.Module```
+  * **Parameters**:
+      * ```model_name```: This is the name of the Hugging face model that you want to optimize.
+      * ```draft_model_name```: This is the name of the Hugging face draft model which is needed for [speculative decoding](https://arxiv.org/abs/2211.17192). Note that the model and the draft model must both use the same tokenizer, and the draft model must be **significantly** smaller to achieve inference acceleration.
+      * ```sample function(distribution, **kwargs)```: This is a function which is used to sample from the distribution generated by the main model. This function has a mandatory parameter which is a tensor of dimension ```(seq_len, vocab_size)``` and returns a tensor of shape ```(1, 1)```. 
+  * **Returns**:
+      * An accelerated model with one method:
+          * ```generate(self, cur_tokens:torch.Tensor, max_tokens:int, speculate_k:int, **sampling_kwargs) -> torch.Tensor```
+              * **Parameters**:
+                  * ```cur_tokens```: A PyTorch Tensor of size (1, seq_len).
+                  * ```max_tokens```: An int representing how many tokens you want to generate.
+                  * ```speculate_k```: An int specifying how far you want the draft model to speculate in speculative decoding.
+                  * ```**sampling_kwargs```: Additional parameters that are necessary for sampling from the distribution. Should match the ```**kwargs``` of the ```sample``` function above.
+              * **Returns**:
+                  * The generated tokens to your prompt, a tensor with dimensions ```(1, max_tokens)```.
+
+***
+
+* ```load_int8(model_name:str) -> torch.nn.Module```
+    * **Parameters**:
+        * ```model_name```: This is a string specifying the model that you are using.
+    * **Returns**:
+        * An ```int8``` quantized version of your model.
+
+***
+
+* ```add_kv_cache(model_name:str) -> KVCacheModel```
+    * **Parameters**:
+        * ```model_name```: This is a string specifying the model that you are using.
+    * **Returns**:
+        * An instance of the ```KVCacheModel``` class which is essentially just your model but with a key-value cache attached for accelerated inference.
+
+***
+
+* ```add_speculative_decode_kv_cache(model:KVCacheModel, draft_model:KVCacheModel, sample_function:Callable) -> torch.nn.Module```
+    * **Parameters**:
+        * ```model```: This is the KVCached version of your model.
+        * ```draft_model```: This is the KVCached version of your draft model.
+        * ```sample function(distribution, **kwargs)```: same as the documentation for ```gpt_fast```.
+    * **Returns**:
+        * An accelerated model with the ```generate``` method described above under the ```gpt_fast``` section.
+  
+  
+
+