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[V1] Adding min tokens/repetition/presence/frequence penalties to V1 sampler (#10681)

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Signed-off-by: Sourashis Roy <sroy@roblox.com>
Signed-off-by: Woosuk Kwon <woosuk.kwon@berkeley.edu>
Co-authored-by: Woosuk Kwon <woosuk.kwon@berkeley.edu>
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sroy745
2024-12-26 02:02:58 -08:00
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parent 7492a36207
commit dcb1a944d4
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tests/v1/sample/test_sampler.py Normal file
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from typing import List, Set, Tuple
import numpy as np
import pytest
import torch
from vllm.utils import make_tensor_with_pad
from vllm.v1.sample.metadata import SamplingMetadata
from vllm.v1.sample.sampler import Sampler
VOCAB_SIZE = 1024
NUM_OUTPUT_TOKENS = 20
CUDA_DEVICES = [
f"cuda:{i}" for i in range(1 if torch.cuda.device_count() == 1 else 2)
]
MAX_NUM_PROMPT_TOKENS = 64
def _create_fake_logits(batch_size: int, vocab_size: int) -> torch.Tensor:
fake_logits = torch.full((batch_size, vocab_size), 1e-2, dtype=torch.float)
return fake_logits
def _create_penalty_tensor(batch_size: int, penalty_value: float,
device: torch.device) -> torch.Tensor:
return torch.full((batch_size, ),
fill_value=penalty_value,
dtype=torch.float,
device=device)
def _create_prompt_tokens_tensor(
prompt_token_ids: List[List[int]],
vocab_size: int,
device: torch.device,
) -> torch.Tensor:
return make_tensor_with_pad(
prompt_token_ids,
pad=vocab_size,
device=device,
dtype=torch.int64,
pin_memory=False,
)
def _create_default_sampling_metadata(
num_output_tokens: int,
batch_size: int,
vocab_size: int,
device: torch.device,
) -> SamplingMetadata:
output_token_ids: List[List[int]] = []
prompt_token_ids: List[List[int]] = []
for _ in range(batch_size):
output_token_ids.append(
np.random.randint(0, vocab_size, size=num_output_tokens).tolist())
prompt_token_ids.append(
np.random.randint(0,
vocab_size,
size=np.random.randint(
1, MAX_NUM_PROMPT_TOKENS)).tolist())
fake_sampling_metadata = SamplingMetadata(
temperature=torch.full((batch_size, ), 0.0),
all_greedy=True,
all_random=False,
top_p=torch.empty(batch_size, ),
top_k=torch.empty(batch_size, ),
no_top_p=True,
no_top_k=True,
generators={},
max_num_logprobs=VOCAB_SIZE,
prompt_token_ids=_create_prompt_tokens_tensor(prompt_token_ids,
vocab_size, device),
output_token_ids=output_token_ids,
frequency_penalties=_create_penalty_tensor(batch_size, 0.0, device),
presence_penalties=_create_penalty_tensor(batch_size, 0.0, device),
repetition_penalties=_create_penalty_tensor(batch_size, 1.0, device),
no_penalties=True,
min_tokens=[],
stop_token_ids=[],
)
return fake_sampling_metadata
def _generate_min_token_penalties_and_stop_tokens(
num_output_tokens: int, batch_size: int, vocab_size: int,
batch_indices_for_min_token_penalty: List[int]
) -> Tuple[List[int], List[Set[int]]]:
"""
Generates and returns a list of minimum token penalties (`min_tokens`)
and a corresponding list of stop token IDs (`stop_token_ids`) for each
batch.
If a batch index is included in `batch_indices_for_min_token_penalty`,
a higher `min_tokens` value is assigned (within a randomized range),
and a random set of stop token IDs is created. Otherwise, a lower
`min_tokens` value is assigned, and the stop token IDs set is empty.
"""
stop_token_ids: List[Set[int]] = []
min_tokens: List[int] = []
for index in range(batch_size):
if index in batch_indices_for_min_token_penalty:
min_tokens.append(
np.random.randint(num_output_tokens + 1,
2 * num_output_tokens))
stop_token_ids.append(
set(
np.random.randint(0, vocab_size - 1)
for _ in range(np.random.randint(0, vocab_size))))
else:
min_tokens.append(np.random.randint(0, num_output_tokens))
stop_token_ids.append(set())
return (min_tokens, stop_token_ids)
def _create_weighted_output_token_list(
batch_size: int,
vocab_size: int) -> Tuple[List[List[int]], List[List[int]]]:
"""
Creates an output token list where each token occurs a distinct
number of times.
For each batch, a random subset of token IDs is selected from the
vocabulary. The selected tokens are then added to the output token
list, each with a different frequency.
Returns:
Tuple[List[List[int]], List[List[int]]]:
- The first element is the output token list, where each sublist
corresponds to a batch and contains tokens with weighted
frequencies.
- The second element is a list of distinct token IDs for each
batch, ordered by their frequency in the corresponding output
list.
"""
output_token_ids: List[List[int]] = []
sorted_token_ids_in_output: List[List[int]] = []
for _ in range(batch_size):
distinct_token_ids = np.random.choice(vocab_size,
size=np.random.randint(1, 10),
replace=False).tolist()
sorted_token_ids_in_output.append(distinct_token_ids)
output_token_ids_for_batch = []
for index, token_id in enumerate(distinct_token_ids):
output_token_ids_for_batch.extend(
[token_id for _ in range(index + 1)])
output_token_ids.append(output_token_ids_for_batch)
return (output_token_ids, sorted_token_ids_in_output)
@pytest.mark.parametrize("device", CUDA_DEVICES)
@pytest.mark.parametrize("batch_size", [1, 2, 32])
def test_sampler_min_tokens_penalty(device: str, batch_size: int):
"""
Tests that if the number of output tokens is less than
SamplingParams.min_tokens then we will set the logits for
the stop token ids to -inf.
"""
torch.set_default_device(device)
fake_logits = _create_fake_logits(batch_size, VOCAB_SIZE)
sampling_metadata = _create_default_sampling_metadata(
NUM_OUTPUT_TOKENS, batch_size, VOCAB_SIZE, torch.device(device))
batch_indices_for_min_token_penalty = np.random.randint(
0, batch_size - 1, size=np.random.randint(0, batch_size)).tolist()
min_tokens, stop_token_ids = _generate_min_token_penalties_and_stop_tokens(
NUM_OUTPUT_TOKENS, batch_size, VOCAB_SIZE,
batch_indices_for_min_token_penalty)
sampling_metadata.min_tokens = min_tokens
sampling_metadata.stop_token_ids = stop_token_ids
sampler = Sampler()
sampler_output = sampler(fake_logits, sampling_metadata)
for batch_idx in range(batch_size):
for vocab in range(VOCAB_SIZE):
# Verify that the logprobs for stop token ids is set
# to -inf.
logprob_index = torch.where(
sampler_output.logprob_token_ids[batch_idx] ==
vocab)[0].item()
if vocab in stop_token_ids[batch_idx]:
assert sampler_output.logprobs[batch_idx][
logprob_index] == -float("inf")
else:
assert sampler_output.logprobs[batch_idx][
logprob_index] != -float("inf")
@pytest.mark.parametrize("device", CUDA_DEVICES)
@pytest.mark.parametrize("batch_size", [1, 2, 32])
@pytest.mark.parametrize("presence_penalty", [-2.0, 2.0])
def test_sampler_presence_penalty(device: str, batch_size: int,
presence_penalty: float):
"""
Test to verify that if presence penalty is enabled then tokens
are penalized as per their presence in the existing output.
"""
torch.set_default_device(device)
# Create fake logits where each token is assigned the same
# logit value.
fake_logits = _create_fake_logits(batch_size, VOCAB_SIZE)
sampling_metadata = _create_default_sampling_metadata(
NUM_OUTPUT_TOKENS, batch_size, VOCAB_SIZE, torch.device(device))
output_token_ids = sampling_metadata.output_token_ids
sampling_metadata.presence_penalties = _create_penalty_tensor(
batch_size, presence_penalty, torch.device(device))
sampling_metadata.no_penalties = False
sampler = Sampler()
sampler_output = sampler(fake_logits, sampling_metadata)
for batch_idx in range(batch_size):
# The logprobs in the SamplerOutput are arranged in descending order.
# Since all tokens initially have the same logprobs, the non-penalized
# tokens will appear at the beginning, while the penalized tokens
# will appear at the end of the list.
penalized_token_id = sampler_output.logprob_token_ids[batch_idx][
VOCAB_SIZE - 1]
penalized_log_prod = sampler_output.logprobs[batch_idx][VOCAB_SIZE - 1]
non_penalized_token_id = sampler_output.logprob_token_ids[batch_idx][0]
non_penalized_log_prod = sampler_output.logprobs[batch_idx][0]
assert non_penalized_log_prod > penalized_log_prod
if presence_penalty > 0:
# If `presence_penalty` is set to a value greater than 0, it
# indicates a preference for new tokens over those already
# present in the output.
# Verify that the penalized token ID exists in the output, while the
# non-penalized token ID does not.
assert penalized_token_id in output_token_ids[batch_idx]
assert non_penalized_token_id not in output_token_ids[batch_idx]
elif presence_penalty < 0:
# If `presence_penalty` is set to a value less than 0, it indicates
# a preference for existing tokens over new ones. Verify that the
# non-penalized token ID exists in the output, while the penalized
# token ID does not.
assert non_penalized_token_id in output_token_ids[batch_idx]
assert penalized_token_id not in output_token_ids[batch_idx]
@pytest.mark.parametrize("device", CUDA_DEVICES)
@pytest.mark.parametrize("batch_size", [1, 2, 32])
@pytest.mark.parametrize("frequency_penalty", [-2.0, 2.0])
def test_sampler_frequency_penalty(device: str, batch_size: int,
frequency_penalty: float):
"""
Test to verify that if frequency penalty is enabled then tokens are
penalized as per their frequency of occurrence.
"""
torch.set_default_device(device)
# Create fake logits where each token is assigned the same
# logit value.
fake_logits = _create_fake_logits(batch_size, VOCAB_SIZE)
sampling_metadata = _create_default_sampling_metadata(
NUM_OUTPUT_TOKENS, batch_size, VOCAB_SIZE, torch.device(device))
sampling_metadata.frequency_penalties = _create_penalty_tensor(
batch_size, frequency_penalty, torch.device(device))
output_token_ids, sorted_token_ids_in_output = \
_create_weighted_output_token_list(batch_size, VOCAB_SIZE)
sampling_metadata.output_token_ids = output_token_ids
sampling_metadata.no_penalties = False
sampler = Sampler()
sampler_output = sampler(fake_logits, sampling_metadata)
for batch_idx in range(batch_size):
logprobs_token_ids = sampler_output.logprob_token_ids[batch_idx]
non_penalized_token_id = logprobs_token_ids[0]
penalized_token_id = logprobs_token_ids[VOCAB_SIZE - 1]
distinct_sorted_token_ids_in_output = \
sorted_token_ids_in_output[batch_idx]
most_frequent_token_id = distinct_sorted_token_ids_in_output[
len(distinct_sorted_token_ids_in_output) - 1]
if frequency_penalty > 0:
# If `frequency_penalty` is set to > 0, it indicates
# a preference for new tokens over existing ones. Verify that the
# non-penalized token ID is not present in the output, while the
# most penalized token is the one that occurs most frequently in
# the output.
assert non_penalized_token_id \
not in distinct_sorted_token_ids_in_output
assert penalized_token_id == most_frequent_token_id
elif frequency_penalty < 0:
# If `frequency_penalty` is set to < 0, it indicates
# a preference for existing tokens over new ones. Verify that the
# non-penalized token ID is the one that occurs most frequently
# in the output, while the penalized token ID is one that has not
# yet appeared.
assert non_penalized_token_id == most_frequent_token_id
assert penalized_token_id \
not in distinct_sorted_token_ids_in_output
@pytest.mark.parametrize("device", CUDA_DEVICES)
@pytest.mark.parametrize("batch_size", [1, 2, 32])
@pytest.mark.parametrize("repetition_penalty", [0.1, 1.9])
def test_sampler_repetition_penalty(device: str, batch_size: int,
repetition_penalty: float):
"""
Test to verify that when the repetition penalty is enabled, tokens
are penalized based on their presence in the prompt or the existing
output.
"""
torch.set_default_device(device)
# Create fake logits where each token is assigned the same
# logit value.
fake_logits = _create_fake_logits(batch_size, VOCAB_SIZE)
sampling_metadata = _create_default_sampling_metadata(
NUM_OUTPUT_TOKENS, batch_size, VOCAB_SIZE, torch.device(device))
sampling_metadata.repetition_penalties = _create_penalty_tensor(
batch_size, repetition_penalty, torch.device(device))
sampling_metadata.no_penalties = False
sampler = Sampler()
sampler_output = sampler(fake_logits, sampling_metadata)
for batch_idx in range(batch_size):
logprobs_token_ids = sampler_output.logprob_token_ids[batch_idx]
non_penalized_token_id = logprobs_token_ids[0]
penalized_token_id = logprobs_token_ids[VOCAB_SIZE - 1]
prompt_tokens = sampling_metadata.prompt_token_ids[
batch_idx][:].tolist()
output_tokens = sampling_metadata.output_token_ids[batch_idx]
if repetition_penalty > 1.0:
# If `repetition_penalty` > 1.0, verify that the non-penalized
# token ID has not been seen before, while the penalized token ID
# exists either in the prompt or the output.
assert (non_penalized_token_id not in prompt_tokens and \
non_penalized_token_id not in output_tokens)
assert (penalized_token_id in prompt_tokens or \
penalized_token_id in output_tokens)
elif repetition_penalty < 1.0:
# If `repetition_penalty` < 1.0, verify that the penalized
# token ID has not been seen before, while the non-penalized
# token ID exists either in the prompt or the output.
assert (penalized_token_id not in prompt_tokens and \
penalized_token_id not in output_tokens)
assert (non_penalized_token_id in prompt_tokens or \
non_penalized_token_id in output_tokens)