[TPU] Add example for profiling TPU inference (#12531)

Signed-off-by: mgoin <mgoin@redhat.com>

examples/offline_inference/profiling_tpu/README.md

@@ -0,0 +1,67 @@
+# vLLM TPU Profiling
+
+This script is used to profile the TPU performance of vLLM for specific prefill or decode token shapes.
+
+Note: an actual running server is a mix of both prefill of many shapes and decode of many shapes.
+
+We assume you are on a TPU already (this was tested on TPU v6e) and have installed vLLM according to the [installation guide](https://docs.vllm.ai/en/latest/getting_started/installation/ai_accelerator/index.html).
+
+> In all examples below, we run several warmups before (so `--enforce-eager` is okay)
+
+## Profile Examples
+
+### Generate Prefill Trace
+
+This example runs Qwen/Qwen2.5-7B-Instruct with a single request of 1024 input tokens. This is set up in attempt to profile just the prefill time and operations.
+
+```bash
+export XLA_HLO_DEBUG=1
+export MODEL=Qwen/Qwen2.5-7B-Instruct
+export VLLM_TPU_PROFILE_DURATION_MS=3000
+export VLLM_TPU_PROFILE_DELAY_MS=0
+
+python3 profiling.py \
+    --model $MODEL \
+    --input-len 1024 --output-len 1 \
+    --batch-size 1 --enforce-eager \
+    --max-model-len 2048 \
+    --tensor-parallel-size 1 \
+    --profile-result-dir profiles
+```
+
+
+### Generate Decode Trace
+
+This example runs Llama 3.1 70B with a batch of 32 requests where each has 1 input token and 128 output tokens. This is set up in attempt to profile just the 32 decodes running in parallel by having an extremely small prefill of 1 token and setting `VLLM_TPU_PROFILE_DELAY_MS=1000` to skip the first second of inference (hopefully prefill).
+
+```bash
+export XLA_HLO_DEBUG=1
+export MODEL=meta-llama/Llama-3.1-70B-Instruct
+export VLLM_TPU_PROFILE_DURATION_MS=2000
+export VLLM_TPU_PROFILE_DELAY_MS=1000
+
+rm -rf ~/.cache/vllm/xla_cache
+python3 profiling.py \
+    --model $MODEL \
+    --input-len 1 \
+    --output-len 128 \
+    --batch-size 32 \
+    --enforce-eager \
+    --profile-result-dir profiles \
+    --max-model-len 2048 --tensor-parallel-size 8
+```
+
+
+## Visualizing the profiles
+
+Once you have collected your profiles with this script, you can visualize them using [TensorBoard](https://cloud.google.com/tpu/docs/pytorch-xla-performance-profiling-tpu-vm).
+
+Here are most likely the dependencies you need to install:
+```bash
+pip install tensorflow-cpu tensorboard-plugin-profile etils importlib_resources
+```
+
+Then you just need to point TensorBoard to the directory where you saved the profiles and visit `http://localhost:6006/` in your browser:
+```bash
+tensorboard --logdir profiles/ --port 6006
+```

examples/offline_inference/profiling_tpu/profiling.py

@@ -0,0 +1,101 @@
+import argparse
+import dataclasses
+import os
+import time
+from typing import List
+
+import numpy as np
+import torch_xla.debug.profiler as xp
+from tqdm import tqdm
+
+from vllm import LLM, SamplingParams
+from vllm.engine.arg_utils import EngineArgs
+from vllm.inputs import PromptType
+from vllm.utils import FlexibleArgumentParser
+
+DURATION_MS = int(os.getenv("VLLM_TPU_PROFILE_DURATION_MS", 3000))
+DELAY_MS = int(os.getenv("VLLM_TPU_PROFILE_DELAY_MS", 0))
+
+
+def main(args: argparse.Namespace):
+    print(args)
+
+    engine_args = EngineArgs.from_cli_args(args)
+    llm = LLM(**dataclasses.asdict(engine_args))
+    _ = xp.start_server(9012)
+
+    sampling_params = SamplingParams(
+        temperature=0.0,
+        ignore_eos=True,
+        max_tokens=args.output_len,
+    )
+    print(sampling_params)
+    dummy_prompt_token_ids = np.random.randint(10000,
+                                               size=(args.batch_size,
+                                                     args.input_len))
+    dummy_prompts: List[PromptType] = [{
+        "prompt_token_ids": batch
+    } for batch in dummy_prompt_token_ids.tolist()]
+
+    def run_to_completion():
+        start_time = time.perf_counter()
+        llm.generate(dummy_prompts,
+                     sampling_params=sampling_params,
+                     use_tqdm=False)
+        end_time = time.perf_counter()
+        latency = end_time - start_time
+        return latency
+
+    # Warmup
+    print("Warming up...")
+    warmup_latencies = []
+    for _ in tqdm(range(args.num_iters_warmup), desc="Warmup iterations"):
+        warmup_latencies.append(run_to_completion())
+    print(f"Average warmup latency: {np.mean(warmup_latencies):.4f}s")
+
+    # Profile
+    profile_dir = args.profile_result_dir
+    print(f"Profiling (results will be saved to '{profile_dir}')...")
+    # Enable tracing on server
+    xp.trace_detached("localhost:9012",
+                      profile_dir,
+                      delay_ms=DELAY_MS,
+                      duration_ms=DURATION_MS)
+    if DELAY_MS == 0:
+        time.sleep(1.0)
+    profile_latencies = []
+    for _ in tqdm(range(args.num_iters), desc="Profile iterations"):
+        profile_latencies.append(run_to_completion())
+    print(f"Average profile latency: {np.mean(profile_latencies):.4f}s")
+
+    return
+
+
+if __name__ == '__main__':
+    parser = FlexibleArgumentParser(
+        description='Benchmark the latency of processing a single batch of '
+        'requests till completion.')
+    parser.add_argument('--input-len', type=int, default=32)
+    parser.add_argument('--output-len', type=int, default=128)
+    parser.add_argument('--batch-size', type=int, default=8)
+    parser.add_argument('--num-iters-warmup',
+                        type=int,
+                        default=5,
+                        help='Number of iterations to run for warmup.')
+    parser.add_argument('--num-iters',
+                        type=int,
+                        default=1,
+                        help='Number of iterations to run for profiling.')
+    parser.add_argument(
+        '--profile-result-dir',
+        type=str,
+        default="profiles",
+        help=
+        ('path to save the pytorch profiler output. Can be visualized '
+         'with ui.perfetto.dev or Tensorboard '
+         '(https://cloud.google.com/tpu/docs/pytorch-xla-performance-profiling-tpu-vm).'
+         ))
+
+    parser = EngineArgs.add_cli_args(parser)
+    args = parser.parse_args()
+    main(args)