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[Core] Use numpy to speed up padded token processing #6442

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merged 3 commits into from
Jul 16, 2024
Merged

[Core] Use numpy to speed up padded token processing #6442

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550753b
Use numpy to speed up padded token processing
peng1999 Jul 15, 2024
9320301
Add pin_memory() call
peng1999 Jul 16, 2024
e7f3c9d
call pin_memory() only if available
peng1999 Jul 16, 2024
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35 changes: 15 additions & 20 deletions vllm/model_executor/sampling_metadata.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2,6 +2,7 @@
from dataclasses import dataclass
from typing import Dict, List, Optional, Tuple

import numpy as np
import torch

from vllm.model_executor.layers.ops.sample import get_num_triton_sampler_splits
Expand Down Expand Up @@ -457,16 +458,20 @@ def from_lists(cls, temperatures: List[float], top_ps: List[float],
if do_penalties:
prompt_max_len = max([len(tokens) for tokens in prompt_tokens],
default=0)
prompt_padded_tokens = [
tokens + [vocab_size] * (prompt_max_len - len(tokens))
for tokens in prompt_tokens
]
prompt_padded_tokens = np.full(
(len(prompt_tokens), prompt_max_len),
vocab_size,
dtype=np.int64)
for i, tokens in enumerate(prompt_tokens):
prompt_padded_tokens[i, :len(tokens)] = tokens
output_max_len = max([len(tokens) for tokens in output_tokens],
default=0)
output_padded_tokens = [
tokens + [vocab_size] * (output_max_len - len(tokens))
for tokens in output_tokens
]
output_padded_tokens = np.full(
(len(output_tokens), output_max_len),
vocab_size,
dtype=np.int64)
for i, tokens in enumerate(output_tokens):
output_padded_tokens[i, :len(tokens)] = tokens

temperatures_t = torch.tensor(
temperatures,
Expand Down Expand Up @@ -517,18 +522,8 @@ def from_lists(cls, temperatures: List[float], top_ps: List[float],
pin_memory=pin_memory,
)
if do_penalties:
prompt_tensor = torch.tensor(
prompt_padded_tokens,
device="cpu",
dtype=torch.long,
pin_memory=pin_memory,
)
output_tensor = torch.tensor(
output_padded_tokens,
device="cpu",
dtype=torch.long,
pin_memory=pin_memory,
)
prompt_tensor = torch.from_numpy(prompt_padded_tokens)
output_tensor = torch.from_numpy(output_padded_tokens)
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else:
prompt_tensor = None
output_tensor = None
Expand Down
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