multi-turn support reasoning mode

[SanyueHan]

Purpose

Fix a issue where the multi-turn benchmark tool does not support reasoning mode.

Test Plan

Start a model with argument "--reasoning-parser", then use the multi-turn tool to test.

Test Result

without my modification, the output number of tokens / TPOT cannot be correctly counted

----------------------------------------------------------------------------------------------------
Parameters:
model=/data/GLM-4.7-w8a8/
num_clients=1
num_conversations=1
active_conversations=1
seed=0
Conversations Generation Parameters:
text_files=pg1184.txt
input_num_turns=Constant[4]
input_common_prefix_num_tokens=Constant[0]
input_prefix_num_tokens=Constant[1024]
input_num_tokens=Constant[1024]
output_num_tokens=Constant[1024]
----------------------------------------------------------------------------------------------------
Statistics summary:
runtime_sec = 44.406
requests_per_sec = 0.045
----------------------------------------------------------------------------------------------------
                   count      mean      std      min       25%       50%       75%      90%       max
ttft_ms              2.0  22195.78  1065.29  21442.5  21819.14  22195.78  22572.41  22798.4  22949.05
tpot_ms              2.0      0.00     0.00      0.0      0.00      0.00      0.00      0.0      0.00
latency_ms           2.0  22195.78  1065.29  21442.5  21819.14  22195.78  22572.41  22798.4  22949.05
input_num_turns      2.0      2.00     1.41      1.0      1.50      2.00      2.50      2.8      3.00
input_num_tokens     2.0   2560.00   722.66   2049.0   2304.50   2560.00   2815.50   2968.8   3071.00
output_num_tokens    2.0      0.00     0.00      0.0      0.00      0.00      0.00      0.0      0.00
output_num_chunks    2.0      0.00     0.00      0.0      0.00      0.00      0.00      0.0      0.00
----------------------------------------------------------------------------------------------------

with my modification, the output number of tokens / TPOT could be correctly counted

----------------------------------------------------------------------------------------------------
Parameters:
model=/data/GLM-4.7-w8a8/
num_clients=1
num_conversations=1
active_conversations=1
seed=0
Conversations Generation Parameters:
text_files=pg1184.txt
input_num_turns=Constant[4]
input_common_prefix_num_tokens=Constant[0]
input_prefix_num_tokens=Constant[1024]
input_num_tokens=Constant[1024]
output_num_tokens=Constant[1024]
----------------------------------------------------------------------------------------------------
Statistics summary:
runtime_sec = 45.536
requests_per_sec = 0.044
----------------------------------------------------------------------------------------------------
                   count      mean      std       min       25%       50%       75%       90%       max
ttft_ms              2.0    889.09   116.31    806.84    847.97    889.09    930.21    954.88    971.33
tpot_ms              2.0     21.38     0.61     20.95     21.16     21.38     21.59     21.72     21.81
latency_ms           2.0  22759.74   741.15  22235.67  22497.71  22759.74  23021.78  23179.00  23283.82
input_num_turns      2.0      2.00     1.41      1.00      1.50      2.00      2.50      2.80      3.00
input_num_tokens     2.0   3072.00  1446.74   2049.00   2560.50   3072.00   3583.50   3890.40   4095.00
output_num_tokens    2.0   1024.00     0.00   1024.00   1024.00   1024.00   1024.00   1024.00   1024.00
output_num_chunks    2.0    435.00    11.31    427.00    431.00    435.00    439.00    441.40    443.00
----------------------------------------------------------------------------------------------------

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[hclsys]

Good fix for the core problem — the multi-turn benchmark was only summing content, so for reasoning models the token counts (and therefore TPOT) were undercounted. Appending reasoning too is the right call, and the non-streaming branch (concatenating reasoning then content into generated_text) is clean.

One issue in the streaming branch though: the for field in ("reasoning", "content") loop now runs the timing accounting per-field, but a single SSE delta can carry both reasoning and content in the same chunk (the transition chunk where reasoning ends and content begins). When that happens:

for field in ("reasoning", "content"):
    if delta.get(field, None):
        if ttft is None: ...
        else:
            chunk_delay.append(timestamp - most_recent_timestamp)   # runs TWICE
        generated_text += delta[field]
most_recent_timestamp = timestamp   # only updated after the loop

chunk_delay.append(...) fires twice for that one chunk, and since most_recent_timestamp isn't updated until after the loop, the second append uses the same (timestamp - most_recent_timestamp) — so you get a duplicate/near-zero delay entry. That inflates len(chunk_delay) and skews the inter-token-latency / TPOT stats the tool reports, which is the exact metric this PR is trying to make correct.

Token accumulation (generated_text += delta[field]) is fine to do per-field — it's only the timing that should be once-per-chunk. Suggest splitting it: accumulate text in the loop, but compute ttft / append chunk_delay once per chunk based on whether any of the fields was present. Something like:

got_token = False
for field in ("reasoning", "content"):
    if delta.get(field):
        generated_text += delta[field]
        got_token = True
        if ttft is None:
            first_chunk = delta[field]
if got_token:
    if ttft is None:
        ttft = time.perf_counter_ns() - start_time
    else:
        chunk_delay.append(timestamp - most_recent_timestamp)

Did you check whether your test model emits a chunk with both fields set at the reasoning→content boundary? That's the case that would skew the numbers.