[TPU][V1] Add support for top-logprobs

[NickLucche]

This PR implements top-logprobs support for TPU V1.

The main design decisions I've taken in this first version are:

• Returning logprobs is optional, so it has a separate graph that is executed only when needed.
  Akin to what is happening on GPU, when a single request in the batch requires logprobs, the prob tensor is gathered for all requests in the batch (but only streamed back to those that need it).
• To mitigate compilation issue and strike a balance between long compilation times and minimal computational waste at runtime, "logprobs" is a binary flag. Therefore
  a graph is generated for when the flag is off (no change from current) and another one when it's on.
• The value for which logprobs are gathered is static and fixed at startup with model_config.max_logprobs. Default is 20 as specified by the OpenAI API. Hence (when needed) this impl will gather the top 20 logprobs values, move the batched tensor to host and then slice off the needed ones with the same logic as in GPU.

Benchmark+Compile time highlight:

pre:
============ Serving Benchmark Result ============
Successful requests:                     1000      
Benchmark duration (s):                  133.86    
Total input tokens:                      1638796   
Total generated tokens:                  128000    
Request throughput (req/s):              7.47      
Output token throughput (tok/s):         956.22    
Total Token throughput (tok/s):          13198.78  
---------------Time to First Token----------------
Mean TTFT (ms):                          63739.75  
Median TTFT (ms):                        64401.80  
P99 TTFT (ms):                           129335.27 
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms):                          79.05     
Median TPOT (ms):                        81.04     
P99 TPOT (ms):                           94.23     
---------------Inter-token Latency----------------
Mean ITL (ms):                           79.05     
Median ITL (ms):                         41.94     
P99 ITL (ms):                            125.57    
==================================================

INFO 04-23 18:02:40 [tpu_model_runner.py:1033] Compiling sampling with different num_reqs.
INFO 04-23 18:02:48 [tpu_model_runner.py:1053]   -- num_seqs: 8
INFO 04-23 18:02:57 [tpu_model_runner.py:1053]   -- num_seqs: 16
INFO 04-23 18:03:07 [tpu_model_runner.py:1053]   -- num_seqs: 32
INFO 04-23 18:03:16 [tpu_model_runner.py:1053]   -- num_seqs: 64
INFO 04-23 18:03:24 [tpu_model_runner.py:1053]   -- num_seqs: 128
INFO 04-23 18:03:24 [tpu_model_runner.py:1056] Compilation finished in in 44.63 [secs].


post (I assume no logprobs on sonnet benchmark):
============ Serving Benchmark Result ============
Successful requests:                     1000      
Benchmark duration (s):                  133.84    
Total input tokens:                      1638796   
Total generated tokens:                  128000    
Request throughput (req/s):              7.47      
Output token throughput (tok/s):         956.38    
Total Token throughput (tok/s):          13201.06  
---------------Time to First Token----------------
Mean TTFT (ms):                          63713.64  
Median TTFT (ms):                        64372.12  
P99 TTFT (ms):                           129280.69 
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms):                          79.16     
Median TPOT (ms):                        81.05     
P99 TPOT (ms):                           93.32     
---------------Inter-token Latency----------------
Mean ITL (ms):                           79.16     
Median ITL (ms):                         41.98     
P99 ITL (ms):                            125.63    
==================================================

INFO 04-23 17:53:50 [tpu_model_runner.py:1097] Compiling sample_from_logits with different input shapes.
INFO 04-23 17:54:09 [tpu_model_runner.py:1119]   -- num_seqs: 8
INFO 04-23 17:54:29 [tpu_model_runner.py:1119]   -- num_seqs: 16
INFO 04-23 17:54:52 [tpu_model_runner.py:1119]   -- num_seqs: 32
INFO 04-23 17:55:15 [tpu_model_runner.py:1119]   -- num_seqs: 64
INFO 04-23 17:55:38 [tpu_model_runner.py:1119]   -- num_seqs: 128
INFO 04-23 17:55:38 [tpu_model_runner.py:1122] Compilation finished in 107.82 [secs].

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[Chenyaaang]

please update the function's return type.

[Chenyaaang]

dumb q: what's the reason of using logprobs given that we already use greedy sampling?