BugFix: Modify TPS metric calculation. Add default cpu threads for hybrid CPU system.

[hebangwen]

Summary

This PR introduces two minor fix for Llama runner:

1. Modify TPS metric calculation. Currently, TPS metric uses all token as the numerator, including the tokens from prefilling, not generated tokens. So we need to get the generated tokens by decoding, i.e. len(tokens) - len(prompt_tokens) - 1.
2. Use a default CPU threads setting. In hybrid CPU system, using all CPU threads will make a pretty high inference latency due to high workloads in efficient cores. So I set num_threads to 4 as a general setting.

Release notes: examples

Test plan

CPU: Intel Core-14900KF. 8 performance cores and 16 efficient cores, with total 32 CPU threads.

System: Ubuntu 22.04

Testing command:

python -m examples.models.llama.runner.native \
--model qwen3-4b \
--pte qwen3-4b.pte \
--tokenizer ~/.cache/huggingface/hub/models--Qwen--Qwen3-4B/snapshots/531c80e289d6cff3a7cd8c0db8110231d23a6f7a/tokenizer.json \
--tokenizer_config ~/.cache/huggingface/hub/models--Qwen--Qwen3-4B/snapshots/531c80e289d6cff3a7cd8c0db8110231d23a6f7a/tokenizer_config.json \
--prompt "Write a hello-world programme in c++. Code is:" \
--params examples/models/qwen3/4b_config.json \
--max_len 20 \
-kv \
--temperature 0.8

without this PR

INFO:root:Looking for libcustom_ops_aot_lib.so in executorch/extension/llm/custom_ops
INFO:root:Loading custom ops library: executorch/extension/llm/custom_ops/libcustom_ops_aot_lib.so
Warning - given vocab_size in params is unequal to tokenizer vocab size.
[program.cpp:134] InternalConsistency verification requested but not available
 #include <iostream> using namespace std;

Prefill time: 0.20537757873535156
Generation tok/s: 0.19834848596001395
Response: [671, 997, 366, 9665, 29, 1667, 4473, 1460, 26]

with this PR

INFO:root:Looking for libcustom_ops_aot_lib.so in executorch/extension/llm/custom_ops
INFO:root:Loading custom ops library: executorch/extension/llm/custom_ops/libcustom_ops_aot_lib.so
Warning - given vocab_size in params is unequal to tokenizer vocab size.
[program.cpp:134] InternalConsistency verification requested but not available
 #include <iostream> using namespace std;

Prefill time: 0.29981303215026855
Generation tok/s: 15.131355739212273
Generation tok/s: 37.82838934803068
Response: [671, 997, 366, 9665, 29, 1667, 4473, 1460, 26]

[pytorch-bot]

🔗 Helpful Links

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As of commit f2f59a9 with merge base d8c26ee ():
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[hebangwen]

@pytorchbot label "release notes: examples"