[CodeGen][Documentation] (#956)

* initial commit

* coreys simplifications

* finishing the second model static

* ready, time for beautification

* ready for review

* moved the code to examples

* fix eos logic

* add argument num_tokens_to_generate

* initial commit

* change order

* Update examples/codegen/README.md

Co-authored-by: corey-nm <109536191+corey-nm@users.noreply.github.com>

---------

Co-authored-by: corey-nm <109536191+corey-nm@users.noreply.github.com>

examples/codegen/README.md

@@ -14,17 +14,86 @@ See the License for the specific language governing permissions and
 limitations under the License.
 -->
 
-Example of how to run the pipeline:
+## ONNX Export
+Firstly, we need to install HuggingFace optimum library
+```bash
+pip install optimum
+```
+
+### Patch the original PyTorch Model
+First apply the following modification to this file in your transformers installation:
+https://github.com/huggingface/transformers/blob/main/src/transformers/models/codegen/modeling_codegen.py#L212
+
+\```diff
+-offset = layer_past[0].shape[-2]
++offset = (attention_mask[0] == 0.0).sum() - 1.0
+\```
+
+We need to do this because the existing with_past implementations assume there is no padding in the inputs. With deepsparse, we need to use static sequence length, which means our offset for the embeddings will depend on how many non-padded inputs we receive.
+
+The new line checks this with the attention_mask. At this point in the code, attention_mask has been transformed from a tensor with 0s and 1s, to a tensor of `float.min` and `0.0`. So when we compare `attention_mask == 0.0` we are actually saying everywhere the attention_mask is 1.
+
+We also need to subtract 1 from this count, because the attention mask is applied AFTER the kv cache is concatenated to the new token, which means the attention mask will actually be sequence length + 1 items. So we subtract 1 to get the current sequence length.
+
+### Export the model to ONNX
+
+```bash
+optimum-cli export onnx --model Salesforce/codegen-350M-multi codegen-350M-multi
+```
+This saves the model to directory `codegen-350-multi`
+
+### Updating Model's Inputs Outputs Dimension Sizes 
+TODO
+
+## Running in the DeepSparse Pipeline
+
+First, we need to rename `decoder_with_past_model.onnx` to `model.onnx` inside
+the `static-codegen-350-multi`, to abide the naming convention
+
+Finally, run the pipeline:
 
 ```python
 from examples.codegen.text_generation import TextGenerationPipeline
 
 codegen = TextGenerationPipeline(
     model_path="/network/damian/static-codegen-350M-multi",
     engine_type="onnxruntime",
-    sequence_length=128, )
+    sequence_length=128)
+
+out = codegen(sequences="def hello_world():")
+print(out.sequences[0])
+```
+
+```bash
+def hello_world():
+    return 'Hello World!'
+
+def hello_world_2():
+    return 'Hello World!'
+
+def hello_world_3():
+    return 'Hello World!'
+
+def hello_world_4():
+    return 'Hello World!'
+
+def hello_world_5():
+    return 'Hello World!'
+
+def hello_world_6():
+    return 'Hello World!'
+
+def hello_world_7():
+    return 'Hello World!'
+
+def hello_world_8():
+    return 'Hello World!'
+
+def hello
+```
 
-out = codegen(sequences=["def hello_world():", "def fibonacci(x):"])
-for seq in out.sequences:
-    print(seq)
-```
+Modifying pipeline behaviour:
+1. By adding argument `deterministic=False`, the next token of the sequence will not be chosen deterministically (using argmax), but will be
+sampled from the probablility distribution.
+2. By setting `sampling_temperature` when `deterministic=False`, we are allowing more or less randomness in the sampling method (https://towardsdatascience.com/how-to-sample-from-language-models-682bceb97277)
+3. By setting `num_tokens_to_generate`, we strictly specify how many tokens we want to generate per input.