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

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

examples/offline_inference/profiling_tpu/README.md

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+# 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
+```