Qualcomm AI Engine Direct - Enable AR-N mode to process prompt in hybrid

mode

Summary:
- Add `max_seq_len` to refer to maximum number of tokens that the model can process & consider at once to generate predictions/responses.
- Add `prefill_ar_n` to determine the number of tokens to consume and the number of logits to produce for prompt processor in hybrid mode.
- Remove prefill mode

Author: shewu-quic

examples/qualcomm/oss_scripts/llama/README.md

@@ -8,11 +8,10 @@ This file provides you the instructions to run LLAMA model with different parame
 
 We offer the following modes to execute the model:
 
-Prefill Mode: This is also known as batch prefill mode, where the model takes in a list of tokens as input and generates the next token along with the key-value (KV) cache for all tokens. This mode is efficient for encoding the user's prompt.
-
 KV Cache Mode: In KV Cache mode, the model takes in a single previous token and generates the next predicted token along with its KV cache. It is efficient for generating subsequent tokens after the initial prompt.
 
-Hybrid Mode: Hybrid mode leverages the strengths of both batch prefill and KV cache modes to optimize token generation speed. Initially, it uses prefill mode to efficiently generate the prompt's key-value (KV) cache. Then, the mode switches to KV cache mode, which excels at generating subsequent tokens.
+Hybrid Mode: Hybrid mode leverages the strengths of both AR-N model and KV cache modes to optimize token generation speed. Initially, it uses AR-N model to efficiently generate the prompt's key-value (KV) cache. Then, the mode switches to KV cache mode, which excels at generating subsequent tokens.
+  - AR-N model: The auto-regression (AR) length determines the number of tokens to consume and the number of logits to produce. Use it to process the prompt and generate the key-value (kv) cache, which serves as a prompt processor in hybrid mode.
 
 
 ## Instructions
@@ -50,13 +49,13 @@ At the end of this step, users should have the following files ready: `consolida
 ### Step3: Run default examples using hybrid mode.
 #### LLAMA2
 ```bash
-python examples/qualcomm/oss_scripts/llama/llama.py -b build-android -s ${SERIAL_NUM} -m ${SOC_MODEL} --ptq 16a4w --checkpoint stories110M.pt --params params.json --tokenizer_model tokenizer.model --tokenizer_bin tokenizer.bin --llama_model stories110m --model_mode hybrid --prefill_seq_len 32 --kv_seq_len 128 --prompt "Once upon a time"
+python examples/qualcomm/oss_scripts/llama/llama.py -b build-android -s ${SERIAL_NUM} -m ${SOC_MODEL} --ptq 16a4w --checkpoint stories110M.pt --params params.json --tokenizer_model tokenizer.model --tokenizer_bin tokenizer.bin --llama_model stories110m --model_mode hybrid --prefill_ar_len 32 --max_seq_len 128 --prompt "Once upon a time"
 ```
 
 #### LLAMA3.2
 Default example using hybrid mode.
 ```bash
-python examples/qualcomm/oss_scripts/llama/llama.py -b build-android -s ${SERIAL_NUM} -m ${SOC_MODEL} --ptq 16a4w --checkpoint consolidated.00.pth --params params.json --tokenizer_model tokenizer.model --llama_model llama3_2 --model_mode hybrid --prefill_seq_len 32 --kv_seq_len 128 --prompt "what is 1+1"
+python examples/qualcomm/oss_scripts/llama/llama.py -b build-android -s ${SERIAL_NUM} -m ${SOC_MODEL} --ptq 16a4w --checkpoint consolidated.00.pth --params params.json --tokenizer_model tokenizer.model --llama_model llama3_2 --model_mode hybrid --prefill_ar_len 32 --max_seq_len 128 --prompt "what is 1+1"
 ```
 
 ### KV Cache update mechanism
@@ -109,16 +108,16 @@ We have two distinct mechanisms for updating the key-value (KV) cache, which can
 ### Additional Configs when running the script
 If you would like to compile the model only, we have provided the flag `--compile_only`. Taking LLAMA3.2 as an example:
 ```bash
-python examples/qualcomm/oss_scripts/llama/llama.py -b build-android -m ${SOC_MODEL} --ptq 16a4w --checkpoint consolidated.00.pth --params params.json --tokenizer_model tokenizer.model --llama_model llama3_2 --model_mode hybrid --prefill_seq_len 32 --kv_seq_len 128 --prompt "what is 1+1" --compile_only
+python examples/qualcomm/oss_scripts/llama/llama.py -b build-android -m ${SOC_MODEL} --ptq 16a4w --checkpoint consolidated.00.pth --params params.json --tokenizer_model tokenizer.model --llama_model llama3_2 --model_mode hybrid --prefill_ar_len 32 --max_seq_len 128 --prompt "what is 1+1" --compile_only
 ```
 
 On the other hand, if you already have a pre-compiled .pte model, you can perform inference by providing the flag `--pre_gen_pte` and specifying the folder that contains the .pte model. Taking LLAMA3.2 as an example:
 ```bash
-python examples/qualcomm/oss_scripts/llama/llama.py -b build-android -s ${SERIAL_NUM} -m ${SOC_MODEL} --ptq 16a4w --checkpoint consolidated.00.pth --params params.json --tokenizer_model tokenizer.model --llama_model llama3_2 --model_mode hybrid --prefill_seq_len 32 --kv_seq_len 128 --prompt "what is 1+1" --pre_gen_pte ${FOLDER_TO_PRE_GEN_PTE}
+python examples/qualcomm/oss_scripts/llama/llama.py -b build-android -s ${SERIAL_NUM} -m ${SOC_MODEL} --ptq 16a4w --checkpoint consolidated.00.pth --params params.json --tokenizer_model tokenizer.model --llama_model llama3_2 --model_mode hybrid --prefill_ar_len 32 --max_seq_len 128 --prompt "what is 1+1" --pre_gen_pte ${FOLDER_TO_PRE_GEN_PTE}
 ```
 
 You can select the KV Cache update mechanism at runtime by setting the `KV_UPDATER` variable to either "shift_pointer" or "smart_mask". By default, it is set to "smart_mask".
 `KV_UPDATER` = "shift_pointer"
 ```bash
-python examples/qualcomm/oss_scripts/llama/llama.py -b build-android -s ${SERIAL_NUM} -m ${SOC_MODEL} --ptq 16a4w --checkpoint consolidated.00.pth --params params.json --tokenizer_model tokenizer.model --llama_model llama3_2 --model_mode hybrid --prefill_seq_len 32 --kv_seq_len 128 --prompt "what is 1+1" --kv_updator ${KV_UPDATER}
+python examples/qualcomm/oss_scripts/llama/llama.py -b build-android -s ${SERIAL_NUM} -m ${SOC_MODEL} --ptq 16a4w --checkpoint consolidated.00.pth --params params.json --tokenizer_model tokenizer.model --llama_model llama3_2 --model_mode hybrid --prefill_ar_len 32 --max_seq_len 128 --prompt "what is 1+1" --kv_updator ${KV_UPDATER}
 ```