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LLM: update benchmark_utils.py to handle do_sample=True (#8903)

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Ruonan Wang 2023-09-07 14:20:47 +08:00 committed by GitHub
parent 3d1c7e7082
commit 057e77e229
1 changed files with 42 additions and 0 deletions
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42
python/llm/dev/benchmark/benchmark_util.py
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@ -2662,8 +2662,12 @@ class BenchmarkWrapper:
unfinished_sequences = torch.ones(input_ids.shape[0], dtype=torch.long, device=input_ids.device) unfinished_sequences = torch.ones(input_ids.shape[0], dtype=torch.long, device=input_ids.device)
this_peer_finished = False # used by synced_gpus only this_peer_finished = False # used by synced_gpus only
first_token_time = None
last_token_time = []
# auto-regressive generation # auto-regressive generation
while True: while True:
st = time.perf_counter()
if synced_gpus: if synced_gpus:
# Under synced_gpus the `forward` call must continue until all gpus complete their sequence. # Under synced_gpus the `forward` call must continue until all gpus complete their sequence.
# The following logic allows an early break if all peers finished generating their sequence # The following logic allows an early break if all peers finished generating their sequence
@ -2740,6 +2744,14 @@ class BenchmarkWrapper:
if unfinished_sequences.max() == 0: if unfinished_sequences.max() == 0:
this_peer_finished = True this_peer_finished = True
if self.device.type == "xpu":
torch.xpu.synchronize()
end = time.perf_counter()
if first_token_time is None:
first_token_time = end - st
else:
last_token_time.append(end - st)
# stop if we exceed the maximum length # stop if we exceed the maximum length
if stopping_criteria(input_ids, scores): if stopping_criteria(input_ids, scores):
this_peer_finished = True this_peer_finished = True
@ -2747,6 +2759,15 @@ class BenchmarkWrapper:
if this_peer_finished and not synced_gpus: if this_peer_finished and not synced_gpus:
break break
if self.do_print:
print(f"=========First token cost {first_token_time:.4f} s=========")
if len(last_token_time) > 1:
self.first_cost = first_token_time
self.rest_cost_mean = np.mean(last_token_time)
if self.do_print:
print(f"=========Rest tokens cost average {self.rest_cost_mean:.4f} s ({len(last_token_time)}"
f" tokens in all)=========")
if streamer is not None: if streamer is not None:
streamer.end() streamer.end()
@ -3301,7 +3322,11 @@ class BenchmarkWrapper:
beam_scores = beam_scores.view((batch_size * num_beams,)) beam_scores = beam_scores.view((batch_size * num_beams,))
this_peer_finished = False # used by synced_gpus only this_peer_finished = False # used by synced_gpus only
first_token_time = None
last_token_time = []
while True: while True:
st = time.perf_counter()
if synced_gpus: if synced_gpus:
# Under synced_gpus the `forward` call must continue until all gpus complete their sequence. # Under synced_gpus the `forward` call must continue until all gpus complete their sequence.
# The following logic allows an early break if all peers finished generating their sequence # The following logic allows an early break if all peers finished generating their sequence
@ -3408,6 +3433,14 @@ class BenchmarkWrapper:
else: else:
this_peer_finished = True this_peer_finished = True
if self.device.type == "xpu":
torch.xpu.synchronize()
end = time.perf_counter()
if first_token_time is None:
first_token_time = end - st
else:
last_token_time.append(end - st)
sequence_outputs = beam_scorer.finalize( sequence_outputs = beam_scorer.finalize(
input_ids, input_ids,
beam_scores, beam_scores,
@ -3419,6 +3452,15 @@ class BenchmarkWrapper:
beam_indices=beam_indices, beam_indices=beam_indices,
) )
if self.do_print:
print(f"=========First token cost {first_token_time:.4f} s=========")
if len(last_token_time) > 1:
self.first_cost = first_token_time
self.rest_cost_mean = np.mean(last_token_time)
if self.do_print:
print(f"=========Rest tokens cost average {self.rest_cost_mean:.4f} s ({len(last_token_time)}"
f" tokens in all)=========")
if return_dict_in_generate: if return_dict_in_generate:
if not output_scores: if not output_scores:
sequence_outputs["sequence_scores"] = None sequence_outputs["sequence_scores"] = None