[TextGeneration] Add new text_generation.md with examples and tables with generation_config attributes

src/deepsparse/transformers/README.md

@@ -354,6 +354,7 @@ deepsparse.benchmark zoo:nlp/question_answering/bert-base/pytorch/huggingface/sq
 To learn more about benchmarking, refer to the appropriate documentation.
 Also, check out our [Benchmarking tutorial](https://github.com/neuralmagic/deepsparse/tree/main/src/deepsparse/benchmark)!
 
+
 ## Tutorials:
 For a deeper dive into using transformers within the Neural Magic ecosystem, refer to the detailed tutorials on our [website](https://neuralmagic.com/):
 - [Token Classification: Named Entity Recognition](https://neuralmagic.com/use-cases/sparse-named-entity-recognition/)

src/deepsparse/transformers/text_generation.md

@@ -0,0 +1,201 @@
+# Pipeline Creation
+
+```python
+from deepsparse import TextGeneration
+
+MODEL_PATH = "path/to/model/or/zoostub"
+text_pipeline = TextGeneration(model_path=MODEL_PATH)
+
+```
+
+# Inference Runs
+
+```python
+PROMPT = "how are you?"
+SECOND_PROMPT = "what book is really popular right now?"
+```
+
+### All defaults 
+```python
+text_result = text_pipeline(prompt=PROMPT)
+```
+
+### Enable Streaming
+```python
+generations = text_pipeline(prompt=PROMPT, streaming=True)
+for text_generation in generations:
+    print(text_generation)
+```
+
+### Multiple Inputs
+```python
+PROMPTS = [PROMPT, SECOND_PROMPT]
+text_output = text_pipeline(prompt=PROMPTS)
+
+prompt_output = text_output.generations[0]
+second_prompt_output = text_output.generations[1]
+```
+
+### Use `generation_config` to control the generated results
+- Limit the generated output size using the `max_length` property
+- For a complete list of supported attributes, see the tables below
+
+```python
+generation_config = {"max_length": 10}
+generations = text_pipeline(prompt=PROMPT, generation_config=generation_config)
+```
+
+### Use the transformers `GenerationConfig` object for the `generation_config`
+
+```python
+from transformers import GenerationConfig
+
+generation_config = GenerationConfig()
+generation_config.max_length = 10
+
+generations = text_pipeline(prompt=PROMPT, generation_config=generation_config)
+```
+
+### Use just `kwargs`
+- The attributes supported through the `generation_config` are also supported through
+`kwargs`
+
+```python
+generations = text_pipeline(prompt=PROMPT, max_length=10)
+```
+### Use the GenerationConfig during pipeline creation
+- Every inference run with this pipeline will apply this generation config, unless
+also provided during inference
+
+```python
+MODEL_PATH = "path/to/model/or/zoostub"
+generation_config = {"max_length": 10}
+text_pipeline = TextGeneration(model_path=MODEL_PATH, generation_config=generation_config)
+
+generations = text_pipeline(prompt=PROMPT)
+
+# Override the generation config by providing a config during inference time
+generation_config = {"max_length": 25}
+generations = text_pipeline(prompt=PROMPT, generation_config=generation_config)
+```
+
+### Get more then one response for a given prompt
+
+```python
+generation_config = {"num_return_sequences": 2}
+generations = text_pipeline(prompt=PROMPT, generation_config=generation_config)
+```
+
+### Get more than one unique response
+
+```python
+generation_config = {"num_return_sequences": 2, "do_sample": True}
+generations = text_pipeline(prompt=PROMPT, generation_config=generation_config)
+```
+
+### Use multiple prompts and generate multiple outputs for each prompt
+
+```python
+PROMPTS = [PROMPT, SECOND_PROMPT]
+
+generations = text_pipeline(prompt=PROMPTS, num_return_sequences=2, do_sample=True, max_length=100)
+prompt_outputs = text_output.generations[0]
+second_prompt_outputs = text_output.generations[1]
+
+print("Outputs from the first prompt: ")
+for output in prompt_outputs:
+   print(output)
+   print("\n")
+
+print("Outputs from the second prompt: ")
+for output in second_prompt_outputs:
+   print(output)
+   print("\n")
+```
+
+Output:
+```
+Outputs from the first prompt:
+text="  are you coping better with holidays?\nI'm been reall getting good friends and helping friends as much as i can so it's all good." score=None finished=True finished_reason='stop'
+
+text="\nI'm good... minor panic attacks but aside from that I'm good." score=None finished=True finished_reason='stop'
+
+Outputs from the second prompt: 
+text='\nHAVING A GOOD TIME by Maya Angelou; How to Be a Winner by Peter Enns; BE CAREFUL WHAT YOU WHORE FOR by Sarah Bergman; 18: The Basic Ingredients of a Good Life by Jack Canfield.\nI think you might also read The Sympathy of the earth by Charles Darwin, if you are not interested in reading books. Do you write? I think it will help you to refine your own writing.' score=None finished=True finished_reason='stop'
+
+text='  every school or publication I have looked at has said the same two books.\nIt depends on the school/master. AIS was the New York Times Bestseller forever, kicked an ass in the teen fiction genre for many reasons, a lot of fiction picks like that have been around a while hence popularity. And most science fiction and fantasy titles (but not romance or thriller) are still popular.' score=None finished=True finished_reason='stop'
+```
+
+
+### Output scores
+
+```python
+generations = text_pipeline(prompt=PROMPT, output_score=True)
+```
+
+<h1><summary>Text Generation GenerationConfig Features Supported </h1></summary>
+
+
+<h2> Parameters controlling the output length: </h2>
+
+| Feature | Description | Deepsparse Default | HuggingFace Default | Supported |
+| :---    |      :----: |         :----:     |        :----:       |       ---:|
+| max_length | Maximum length of generated tokens. Equal to input_prompt + max_new_tokens. Overridden by max_new_tokens | 1024 | 20 | Yes|
+| max_new_tokens | Maximum number of tokens to generate, ignoring prompt tokens. | None | None | Yes |
+| min_length | Minimum length of generated tokens. Equal to input_prompt + min_new_tokens. Overridden by min_new_tokens | - | 0 | No
+| min_new_tokens | Minomum number of tokens to generate, ignoring prompt tokens. | - | None | No |
+| max_time | - | - | - | No |
+
+<br/>
+<h2> Parameters for manipulation of the model output logits </h2>
+
+| Feature | Description | Deepsparse Default | HuggingFace Default | Supported |
+| :---    |      :----: |         :----:     |        :----:       |       ---:|
+| top_k | The number of highest probability vocabulary tokens to keep for top-k-filtering | 0 | 50 | Yes
+| top_p | Keep the generated tokens where its cumulative probability is >= top_p | 0.0 | 1.0 | Yes
+| repetition_penalty | Penalty applied for generating new token. Existing token frequencies summed to subtraction the logit of its corresponding logit value | 0.0 | 1.0 | Yes |
+| temperature | The temperature to use when sampling from the probability distribution computed from the logits. Higher values will result in more random samples. Should be greater than 0.0 | 1.0 | 1.0 | Yes |
+| typical_p | - | - | - | No |
+| epsilon_cutoff | - | - | - | No |
+| eta_cutoff | - | - | - | No |
+| diversity_penalty | - | - | - | No |
+| length_penalty | - | - | - | No |
+| bad_words_ids | - | - | - | No |
+| force_words_ids | - | - | - | No |
+| renormalize_logits | - | - | - | No |
+| constraints | - | - | - | No |
+| forced_bos_token_id | - | - | - | No |
+| forced_eos_token_id | - | - | - | No |
+| remove_invalid_values | - | - | - | No |
+| exponential_decay_length_penalty | - | - | - | No |
+| suppress_tokens | - | - | - | No |
+| begin_suppress_tokens | - | - | - | No |
+| forced_decoder_ids | - | - | - | No |
+
+<br/>
+<h2> Parameters that control the generation strategy used </h2>
+
+| Feature | Description | Deepsparse Default | HuggingFace Default | Supported |
+| :---    |      :----: |         :----:     |        :----:       |       ---:|
+| do_sample | If True, will apply sampling from the probability distribution computed from the logits | False | False | Yes |
+
+<br/>
+<h2> Parameters for output variables: </h2>
+
+| Feature | Description | Deepsparse Default | HuggingFace Default | Supported |
+| :---    |      :----: |         :----:     |        :----:       |       ---:|
+| num_return_sequences | The number of sequences generated for each prompt | 1 | 1 | Yes |
+| output_scores | Whether to return the generated logits | False | False | Yes |
+| return_dict_generate | - | - | - | No |
+
+<br/>
+<h2> Special Tokens: </h2>
+
+| Feature | Description | Deepsparse Default | HuggingFace Default | Supported |
+| :---    |      :----: |         :----:     |        :----:       |       ---:|
+| pad_token_id | - | - | - | No |
+| bos_token_id | - | - | - | No |
+| eos_token_id | - | - | - | No |
+
+
+<br/>