Update document

Author: glistening

runtime/ggma/examples/generate_text/README.md

@@ -0,0 +1,125 @@
+# TinyLlama Text Generation Example
+
+This document provides a step‑by‑step guide for generating and processing a TinyLlama text‑generation model.
+
+## Summary
+
+1. Set up the environment and install dependencies.
+2. Generate the initial `prefill` and `decode` Circle model files.
+3. Run the pipeline to optimize, reshape, and prune the model, producing a final `decode.circle` ready for inference.
+
+## Prerequisites
+
+### 1. Python virtual environment
+```bash
+cd runtime/ggma/examples/generate_text/
+python3 -m venv _
+source _/bin/activate
+```
+
+### 2. Install required Python packages
+```bash
+pip install -r requirements.txt
+```
+
+### 3. Install TICO (Torch IR to Circle ONE)
+```bash
+# Clone the repository
+git clone https://github.com/Samsung/TICO.git
+# Install it in editable mode
+pip install -e TICO
+```
+
+### 4. Get [o2o](https://github.com/Samsung/ONE/pull/16233) in PATH
+*Requires the GitHub CLI (`gh`).*
+```bash
+gh pr checkout 16233
+export PATH=../../../../tools/o2o:$PATH
+```
+
+## Generating Model Files
+
+### 1. Create the prefill and decode Circle model files
+```bash
+python prefill.py   # Generates prefill.circle
+python decode.py    # Generates decode_.circle
+```
+
+Verify the generated files:
+```bash
+ls -lh *.circle
+# -rw-rw-r-- 1 gyu gyu 18M Nov 14 14:09 decode_.circle
+# -rw-rw-r-- 1 gyu gyu 18M Nov 14 14:09 prefill.circle
+```
+
+### 2. Update `tinyllama.decode.circle`
+Fuse attention and normalize KV-cache inputs for the decode model.
+
+```bash
+# Fuse attention and reshape KV-cache for the decode model
+fuse.attention.py < decode_.circle \
+    | fuse.bmm_lhs_const.py \
+    | reshape.io.py input --by_shape [1,16,30,4] [1,16,32,4] \
+    | transpose.io.kvcache.py > decode.circle
+```
+
+### 3. Merge prefill and decode circles
+Merge the models, retype input IDs, and clean up.
+
+```bash
+merge.circles.py prefill.circle decode.circle \
+    | downcast.input_ids.py \
+    | gc.py > model.circle
+```
+
+Verify final model files:
+```bash
+ls -l {decode,prefill,model}.circle
+# -rw-rw-r-- 1 gyu gyu 18594868 Nov 22 17:26 decode.circle
+# -rw-rw-r-- 1 gyu gyu 18642052 Nov 22 07:53 prefill.circle
+# -rw-rw-r-- 1 gyu gyu 18629520 Nov 22 17:28 model.circle
+```
+
+## Create a GGMA package
+
+1. Create the package root directory and move `model.circle` there:
+```bash
+cd runtime/ggma/examples/generate_text
+mkdir tinyllama
+mv model.circle tinyllama/
+```
+
+2. Copy the tokenizer files (replace `{your_snapshot}` with the actual snapshot hash):
+```bash
+cp -L ~/.cache/huggingface/hub/models--Maykeye--TinyLLama-v0/snapshots/{your_snapshot}/tokenizer.* tinyllama/
+cp -L ~/.cache/huggingface/hub/models--Maykeye--TinyLLama-v0/snapshots/{your_snapshot}/config.json tinyllama/
+```
+
+```bash
+tree tinyllama/
+tinyllama/
+├── model.circle
+├── tokenizer.json
+└── tokenizer.model
+```
+
+## Build and run `ggma_run`
+
+```bash
+make -j$(nproc)
+make install
+```
+
+Check version:
+```bash
+Product/out/bin/ggma_run --version
+# ggma_run v0.1.0 (nnfw runtime: v1.31.0)
+```
+
+Run the model:
+```bash
+Product/out/bin/ggma_run tinyllama
+# prompt: Lily picked up a flower.
+# generated: { 1100, 7899, 289, 826, 351, 600, 2439, 288, 266, 3653, 31843, 1100, 7899, 289, 1261, 291, 5869, 291, 1261, 31843, 1100, 7899 }
+# detokenized: She liked to play with her friends in the park. She liked to run and jump and run. She liked
+```

runtime/ggma/examples/generate_text/decode.py

@@ -65,4 +65,4 @@
 model = AutoModelForCausalLM.from_pretrained(model_name)
 model.eval()
 circle_model = tico.convert(model, captured_input)
-circle_model.save(f"tinyllama.decode.circle")
+circle_model.save(f"decode_.circle")

runtime/ggma/examples/generate_text/prefill.py

@@ -72,4 +72,4 @@
 model = AutoModelForCausalLM.from_pretrained(model_name)
 model.eval()
 circle_model = tico.convert(model, captured_input)
-circle_model.save(f"tinyllama.prefill.circle")
+circle_model.save(f"prefill.circle")