Add LLaMA end-to-end benchmarking (microsoft#19985)

### Description

This PR adds a benchmarking script to measure end-to-end performance and
saves the results in a CSV file.

### Motivation and Context

With this PR, end-to-end performance can be easily measured for many
large-language models such as LLaMA-2. The performance numbers for
LLaMA-2 are located
[here](https://github.com/microsoft/onnxruntime-inference-examples/tree/main/python/models/llama).

Author: kunal-vaishnavi

onnxruntime/python/tools/transformers/models/llama/README.md

@@ -1,7 +1,14 @@
 # Contents
  - [LLaMA-2](#llama-2)
+   - [Prerequisites](#prerequisites)
    - [Exporting LLaMA-2](#exporting-llama-2)
+   - [Examples of Exporting LLaMA-2](#examples-of-exporting-llama-2)
+   - [Parity Checking LLaMA-2](#parity-checking-llama-2)
    - [Benchmarking LLaMA-2](#benchmark-llama-2)
+     - [Variants](#variants)
+     - [Benchmark All](#benchmark-all)
+     - [Benchmark E2E](#benchmark-e2e)
+   - [E2E Inference with LLaMA-2](#e2e-inference-with-llama-2)
  - [Mistral](#mistral)
    - [Exporting Mistral](#exporting-mistral)
    - [Optimizing and Quantizing Mistral](#optimizing-and-quantizing-mistral)
@@ -229,6 +236,55 @@ $ ./build.sh --config Release --use_cuda --cuda_home /usr/local/cuda-12.2 --cudn
 $ CUDA_VISIBLE_DEVICES=0,1,2,3 bash convert_70b_model.sh 4 -m meta-llama/Llama-2-70b-hf --output llama2-70b-distributed --precision fp16 --execution_provider cuda --use_gqa
 ```
 
+## Parity Checking LLaMA-2
+
+Here are some examples of how you can use the parity checker to verify your LLaMA-2 ONNX model.
+
+1. Merged ONNX model, FP32 CPU
+```
+CUDA_VISIBLE_DEVICES=0 python3 -m models.llama.llama_parity \
+    --model_name meta-llama/Llama-2-7b-hf \
+    --onnx_model_path ./llama2-7b/rank_0_Llama-2-7b-hf_decoder_merged_model_fp32.onnx \
+    --merged \
+    --execution_provider cpu \
+    --precision fp32 \
+    --cache_dir ./model_cache \
+```
+
+2. Merged ONNX model, FP32 CUDA
+```
+CUDA_VISIBLE_DEVICES=0 python3 -m models.llama.llama_parity \
+    --model_name meta-llama/Llama-2-7b-hf \
+    --onnx_model_path ./llama2-7b/rank_0_Llama-2-7b-hf_decoder_merged_model_fp32.onnx \
+    --merged \
+    --execution_provider cuda \
+    --precision fp32 \
+    --cache_dir ./model_cache \
+```
+
+3. Merged ONNX model, FP16 CUDA
+```
+CUDA_VISIBLE_DEVICES=0 python3 -m models.llama.llama_parity \
+    --model_name meta-llama/Llama-2-7b-hf \
+    --onnx_model_path ./llama2-7b/rank_0_Llama-2-7b-hf_decoder_merged_model_fp32.onnx \
+    --merged \
+    --execution_provider cuda \
+    --precision fp16 \
+    --cache_dir ./model_cache \
+```
+
+4. Merged ONNX model, FP16 CUDA with GroupQueryAttention
+```
+CUDA_VISIBLE_DEVICES=0 python3 -m models.llama.llama_parity \
+    --model_name meta-llama/Llama-2-7b-hf \
+    --onnx_model_path ./llama2-7b/rank_0_Llama-2-7b-hf_decoder_merged_model_fp32.onnx \
+    --merged \
+    --use_gqa \
+    --execution_provider cuda \
+    --precision fp16 \
+    --cache_dir ./model_cache \
+```
+
 ## Benchmark LLaMA-2
 
 Here are some examples of how you can benchmark LLaMA-2.
@@ -240,6 +296,7 @@ Here are some examples of how you can benchmark LLaMA-2.
 CUDA_VISIBLE_DEVICES=0 python3 -m models.llama.benchmark \
     --benchmark-type hf-pt-eager \
     --model-name meta-llama/Llama-2-7b-hf \
+    --cache-dir ./model_cache \
     --precision fp32 \
     --batch-sizes "1 2" \
     --sequence-lengths "8 16" \
@@ -252,6 +309,7 @@ CUDA_VISIBLE_DEVICES=0 python3 -m models.llama.benchmark \
 CUDA_VISIBLE_DEVICES=0 python3 -m models.llama.benchmark \
     --benchmark-type hf-pt-compile \
     --model-name meta-llama/Llama-2-7b-hf \
+    --cache-dir ./model_cache \
     --precision fp16 \
     --batch-sizes "1 2" \
     --sequence-lengths "8 16" \
@@ -265,6 +323,7 @@ CUDA_VISIBLE_DEVICES=0 python3 -m models.llama.benchmark \
     --benchmark-type hf-ort \
     --hf-ort-dir-path ./Llama-2-7b-hf-onnx/ \
     --model-name meta-llama/Llama-2-7b-hf \
+    --cache-dir ./model_cache \
     --precision fp32 \
     --batch-sizes "1 2" \
     --sequence-lengths "8 16" \
@@ -278,6 +337,7 @@ CUDA_VISIBLE_DEVICES=0 python3 -m models.llama.benchmark \
     --benchmark-type hf-ort \
     --hf-ort-dir-path ./Llama-2-7b-hf-onnx/ \
     --model-name meta-llama/Llama-2-7b-hf \
+    --cache-dir ./model_cache \
     --precision fp16 \
     --batch-sizes "1 2" \
     --sequence-lengths "8 16" \
@@ -291,6 +351,7 @@ CUDA_VISIBLE_DEVICES=0 python3 -m models.llama.benchmark \
     --benchmark-type ort-msft \
     --ort-model-path ./llama-2-onnx/7B_float32/ONNX/LlamaV2_7B_float32.onnx \
     --model-name meta-llama/Llama-2-7b-hf \
+    --cache-dir ./model_cache \
     --precision fp32 \
     --batch-sizes "1 2" \
     --sequence-lengths "8 16" \
@@ -303,6 +364,7 @@ CUDA_VISIBLE_DEVICES=0 python3 -m models.llama.benchmark \
     --benchmark-type ort-msft \
     --ort-model-path ./llama-2-onnx/7B_float16/ONNX/LlamaV2_7B_float16.onnx \
     --model-name meta-llama/Llama-2-7b-hf \
+    --cache-dir ./model_cache \
     --precision fp16 \
     --batch-sizes "1 2" \
     --sequence-lengths "8 16" \
@@ -315,6 +377,7 @@ CUDA_VISIBLE_DEVICES=1 python3 -m models.llama.benchmark \
     --benchmark-type ort-convert-to-onnx \
     --ort-model-path ./llama2-7b/rank_0_Llama-2-7b-hf_decoder_merged_model_fp32.onnx \
     --model-name meta-llama/Llama-2-7b-hf \
+    --cache-dir ./model_cache \
     --precision fp32 \
     --batch-sizes "1 2" \
     --sequence-lengths "8 16" \
@@ -327,6 +390,7 @@ CUDA_VISIBLE_DEVICES=4 python3 -m models.llama.benchmark \
     --benchmark-type ort-convert-to-onnx \
     --ort-model-path ./llama2-7b/rank_0_Llama-2-7b-hf_decoder_merged_model_fp16.onnx \
     --model-name meta-llama/Llama-2-7b-hf \
+    --cache-dir ./model_cache \
     --precision fp16 \
     --batch-sizes "1 2" \
     --sequence-lengths "8 16" \
@@ -339,6 +403,7 @@ CUDA_VISIBLE_DEVICES=4,5,6,7 bash benchmark_70b_model.sh 4 \
     --benchmark-type ort-convert-to-onnx \
     --ort-model-path ./llama2-70b-dis/rank_{}_Llama-2-70b-hf_decoder_merged_model_fp16.onnx \
     --model-name meta-llama/Llama-2-70b-hf \
+    --cache-dir ./model_cache \
     --precision fp16 \
     --device cuda \
     --warmup-runs 5 \
@@ -357,6 +422,7 @@ CUDA_VISIBLE_DEVICES=0 python3 -m models.llama.benchmark_all \
     --ort-convert-to-onnx-model-path ./llama2-7b-fp16/Llama-2-7b-hf_decoder_merged_model_fp16.onnx \
     --ort-msft-model-path ./llama-2-onnx/7B_float16/ONNX/LlamaV2_7B_float16.onnx \
     --model-name meta-llama/Llama-2-7b-hf \
+    --cache-dir ./model_cache \
     --precision fp16 \
     --batch-sizes "1 2" \
     --sequence-lengths "8 16" \
@@ -366,6 +432,72 @@ CUDA_VISIBLE_DEVICES=0 python3 -m models.llama.benchmark_all \
     --timeout 60  # number of minutes before moving to the next benchmark
 ```
 
+### Benchmark E2E
+You can use `benchmark_e2e.py` to benchmark the full end-to-end scenario and automatically store the results in a CSV file. This tool uses `argmax` for sampling to standardize the benchmarking process.
+
+1. PyTorch without `torch.compile`, FP32
+```
+CUDA_VISIBLE_DEVICES=0 python3 -m models.llama.benchmark_e2e \
+    --benchmark-type pt-eager \
+    --model-name meta-llama/Llama-2-7b-hf \
+    --cache-dir ./model_cache \
+    --prompts-file ./models/llama/prompts.json \
+    --precision fp32 \
+    --batch-sizes "1 2" \
+    --prompt-lengths "16 64" \
+    --device cpu \
+    --auth
+```
+
+2. PyTorch with `torch.compile`, FP16
+```
+CUDA_VISIBLE_DEVICES=0 python3 -m models.llama.benchmark_e2e \
+    --benchmark-type pt-compile \
+    --model-name meta-llama/Llama-2-7b-hf \
+    --cache-dir ./model_cache \
+    --prompts-file ./models/llama/prompts.json \
+    --precision fp16 \
+    --batch-sizes "1 2" \
+    --prompt-lengths "16 64" \
+    --device cuda \
+    --auth
+```
+
+3. ONNX Runtime with `convert_to_onnx`, FP32
+```
+CUDA_VISIBLE_DEVICES=0 python3 -m models.llama.benchmark_e2e \
+    --benchmark-type ort \
+    --model-name meta-llama/Llama-2-7b-hf \
+    --cache-dir ./model_cache \
+    --onnx-model-path ./llama2-7b/rank_0_Llama-2-7b-hf_decoder_merged_model_fp32.onnx \
+    --prompts-file ./models/llama/prompts.json \
+    --precision fp32 \
+    --batch-sizes "1 2" \
+    --prompt-lengths "16 64" \
+    --device cpu \
+    --auth
+```
+
+4. ONNX Runtime with `convert_to_onnx`, FP16
+```
+CUDA_VISIBLE_DEVICES=0 python3 -m models.llama.benchmark_e2e \
+    --benchmark-type ort \
+    --model-name meta-llama/Llama-2-7b-hf \
+    --cache-dir ./model_cache \
+    --onnx-model-path ./llama2-7b/rank_0_Llama-2-7b-hf_decoder_merged_model_fp32.onnx \
+    --prompts-file ./models/llama/prompts.json \
+    --precision fp16 \
+    --batch-sizes "1 2" \
+    --prompt-lengths "16 64" \
+    --device cuda \
+    --use_buffer_share \
+    --auth
+```
+
+## E2E Inference with LLaMA-2
+
+For end-to-end inference, please visit the [ONNX Runtime Inference Examples folder](https://github.com/microsoft/onnxruntime-inference-examples/tree/main/python/models/llama) for a step-by-step walkthrough, code examples, and performance metrics.
+
 # Mistral
 
 ## Introduction

onnxruntime/python/tools/transformers/models/llama/benchmark.py

@@ -1,3 +1,8 @@
+# -------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation.  All rights reserved.
+# Licensed under the MIT License.  See License.txt in the project root for
+# license information.
+# --------------------------------------------------------------------------
 import argparse
 import datetime
 import gc
@@ -14,11 +19,12 @@
 from benchmark_helper import measure_memory, setup_logger
 from dist_settings import get_rank, get_size
 from llama_inputs import (
-    add_io_bindings,
+    add_io_bindings_as_ortvalues,
     get_merged_sample_with_past_kv_inputs,
     get_msft_sample_inputs,
     get_sample_inputs,
     get_sample_with_past_kv_inputs,
+    verify_ort_inputs,
 )
 from optimum.onnxruntime import ORTModelForCausalLM
 from torch.profiler import ProfilerActivity, profile, record_function
@@ -199,6 +205,7 @@ def get_model(args: argparse.Namespace):
             torch_dtype=torch.float16 if args.use_fp16 else torch.float32,
             use_auth_token=args.auth,
             use_cache=True,
+            cache_dir=args.cache_dir,
         ).to(args.target_device)
         end_time = time.time()
 
@@ -444,24 +451,12 @@ def get_logits(inputs):
 
 def run_ort_inference(args, init_inputs, iter_inputs, model):
     def prepare_ort_inputs(inputs, kv_cache_ortvalues):
-        # Check that all model inputs will be provided
-        model_inputs = set(map(lambda model_input: model_input.name, model.get_inputs()))
-        user_inputs = set(inputs.keys())
-        missing_inputs = model_inputs - user_inputs
-        if len(missing_inputs):
-            logger.error(f"The following model inputs are missing: {missing_inputs}")
-            raise Exception("There are missing inputs to the model. Please add them and try again.")
-
-        # Remove unnecessary inputs from model inputs
-        unnecessary_inputs = user_inputs - model_inputs
-        if len(unnecessary_inputs):
-            for unnecessary_input in unnecessary_inputs:
-                logger.info(f"Removing unnecessary input '{unnecessary_input}' from user provided inputs")
-                del inputs[unnecessary_input]
+        # Verify model inputs
+        inputs = verify_ort_inputs(model, inputs)
 
         # Add IO bindings for non-CPU execution providers
         if args.device != "cpu":
-            io_binding, kv_cache_ortvalues = add_io_bindings(
+            io_binding, kv_cache_ortvalues = add_io_bindings_as_ortvalues(
                 model, inputs, args.device, int(args.rank), args.use_gqa, kv_cache_ortvalues
             )
             setattr(args, "io_binding", io_binding)  # noqa: B010
@@ -612,6 +607,13 @@ def get_args(rank=0):
     parser.add_argument("--pt-num-rows", type=int, default=1000, help="Number of rows for PyTorch profiler to display")
     parser.add_argument("--verbose", default=False, action="store_true")
     parser.add_argument("--log-folder", type=str, default=os.path.join("."), help="Folder to cache log files")
+    parser.add_argument(
+        "--cache-dir",
+        type=str,
+        required=True,
+        default="./model_cache",
+        help="Cache dir where Hugging Face files are stored",
+    )
 
     args = parser.parse_args()
 
@@ -662,8 +664,8 @@ def main():
 
     args.rank = rank
     args.world_size = world_size
-    tokenizer = AutoTokenizer.from_pretrained(args.model_name)
-    config = AutoConfig.from_pretrained(args.model_name)
+    tokenizer = AutoTokenizer.from_pretrained(args.model_name, cache_dir=args.cache_dir)
+    config = AutoConfig.from_pretrained(args.model_name, cache_dir=args.cache_dir)
     target_device = f"cuda:{args.rank}" if args.device != "cpu" else args.device
     use_fp16 = args.precision == "fp16"
 

onnxruntime/python/tools/transformers/models/llama/benchmark_all.py

@@ -1,3 +1,8 @@
+# -------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation.  All rights reserved.
+# Licensed under the MIT License.  See License.txt in the project root for
+# license information.
+# --------------------------------------------------------------------------
 import argparse
 import datetime
 import json
@@ -78,6 +83,13 @@ def get_args():
         help="Path to ONNX model from convert_to_onnx",
     )
 
+    parser.add_argument(
+        "--cache-dir",
+        type=str,
+        default="./model_cache",
+        help="Cache dir where Hugging Face files are stored",
+    )
+
     parser.add_argument(
         "--model-name",
         type=str,
@@ -332,6 +344,8 @@ def main():
             str(args.num_runs),
             "--log-folder",
             args.log_folder,
+            "--cache-dir",
+            args.cache_dir,
             "--auth",
         ]
         logger.info("Benchmark PyTorch without torch.compile")
@@ -362,6 +376,8 @@ def main():
             str(args.num_runs),
             "--log-folder",
             args.log_folder,
+            "--cache-dir",
+            args.cache_dir,
             "--auth",
         ]
         logger.info("Benchmark PyTorch with torch.compile")
@@ -394,6 +410,8 @@ def main():
             str(args.num_runs),
             "--log-folder",
             args.log_folder,
+            "--cache-dir",
+            args.cache_dir,
             "--auth",
         ]
         logger.info("Benchmark Optimum + ONNX Runtime")
@@ -426,6 +444,8 @@ def main():
             str(args.num_runs),
             "--log-folder",
             args.log_folder,
+            "--cache-dir",
+            args.cache_dir,
         ]
         logger.info("Benchmark Microsoft model in ONNX Runtime")
         results = benchmark(args, benchmark_cmd, "ort-msft")
@@ -457,6 +477,8 @@ def main():
             str(args.num_runs),
             "--log-folder",
             args.log_folder,
+            "--cache-dir",
+            args.cache_dir,
         ]
         logger.info("Benchmark convert_to_onnx model in ONNX Runtime")
         results = benchmark(args, benchmark_cmd, "onnxruntime")