[CodeGen][Documentation]

examples/codegen/README.md

@@ -14,17 +14,74 @@ 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
+TODO
+
+### 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.