266 lines
10 KiB
Python
266 lines
10 KiB
Python
"""Logits processing."""
|
|
|
|
import dataclasses
|
|
from typing import List, Optional, Union
|
|
|
|
import torch
|
|
from torch import nn
|
|
from vllm.distributed import (
|
|
get_tensor_model_parallel_world_size,
|
|
tensor_model_parallel_all_gather,
|
|
)
|
|
|
|
from sglang.srt.managers.controller.model_runner import ForwardMode, InputMetadata
|
|
|
|
|
|
@dataclasses.dataclass
|
|
class LogitProcessorOutput:
|
|
# The logits of the next tokens. shape: [#seq, vocab_size]
|
|
next_token_logits: torch.Tensor
|
|
# The logprobs of the next tokens. shape: [#seq, vocab_size]
|
|
next_token_logprobs: torch.Tensor
|
|
|
|
# The normlaized logprobs of prompts. shape: [#seq]
|
|
normalized_prompt_logprobs: torch.Tensor
|
|
# The logprobs of input tokens. shape: [#token, vocab_size]
|
|
input_token_logprobs: torch.Tensor
|
|
|
|
# The logprob and id of the top-k tokens in input positions. shape [#seq, #token, k] of Tuple(logprob, token_id)
|
|
input_top_logprobs: List
|
|
# The logprob and id of the top-k tokens in output positions. shape [#seq, #token, k] of Tuple(logprob, token_id)
|
|
output_top_logprobs: List
|
|
|
|
|
|
@dataclasses.dataclass
|
|
class LogitsMetadata:
|
|
forward_mode: ForwardMode
|
|
return_logprob: bool = False
|
|
|
|
extend_seq_lens: Optional[torch.Tensor] = None
|
|
extend_start_loc: Optional[torch.Tensor] = None
|
|
top_logprobs_nums: Optional[List[int]] = None
|
|
|
|
@classmethod
|
|
def from_input_metadata(cls, input_metadata: InputMetadata):
|
|
return cls(
|
|
forward_mode=input_metadata.forward_mode,
|
|
extend_seq_lens=input_metadata.extend_seq_lens,
|
|
extend_start_loc=input_metadata.extend_start_loc,
|
|
return_logprob=input_metadata.return_logprob,
|
|
top_logprobs_nums=input_metadata.top_logprobs_nums,
|
|
)
|
|
|
|
|
|
class LogitsProcessor(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.config = config
|
|
self.tp_size = get_tensor_model_parallel_world_size()
|
|
|
|
def _get_normalized_prompt_logprobs(
|
|
self, input_token_logprobs, logits_metadata: LogitsMetadata
|
|
):
|
|
logprobs_cumsum = torch.cumsum(input_token_logprobs, dim=0, dtype=torch.float32)
|
|
|
|
start = logits_metadata.extend_start_loc.clone()
|
|
end = start + logits_metadata.extend_seq_lens - 2
|
|
start.clamp_(min=0, max=input_token_logprobs.shape[0] - 1)
|
|
end.clamp_(min=0, max=input_token_logprobs.shape[0] - 1)
|
|
sum_logp = (
|
|
logprobs_cumsum[end] - logprobs_cumsum[start] + input_token_logprobs[start]
|
|
)
|
|
normalized_prompt_logprobs = sum_logp / (
|
|
(logits_metadata.extend_seq_lens - 1).clamp(min=1)
|
|
)
|
|
|
|
return normalized_prompt_logprobs
|
|
|
|
@staticmethod
|
|
def get_top_logprobs(all_logprobs, logits_metadata: LogitsMetadata):
|
|
if logits_metadata.forward_mode == ForwardMode.DECODE:
|
|
output_top_logprobs = []
|
|
max_k = max(logits_metadata.top_logprobs_nums)
|
|
ret = all_logprobs.topk(max_k, dim=1)
|
|
values = ret.values.tolist()
|
|
indices = ret.indices.tolist()
|
|
for i, k in enumerate(logits_metadata.top_logprobs_nums):
|
|
output_top_logprobs.append(list(zip(values[i][:k], indices[i][:k])))
|
|
return None, output_top_logprobs
|
|
else:
|
|
# TODO: vectorize the code below
|
|
input_top_logprobs, output_top_logprobs = [], []
|
|
pt = 0
|
|
extend_seq_lens_cpu = logits_metadata.extend_seq_lens.tolist()
|
|
|
|
max_k = max(logits_metadata.top_logprobs_nums)
|
|
ret = all_logprobs.topk(max_k, dim=1)
|
|
values = ret.values.tolist()
|
|
indices = ret.indices.tolist()
|
|
|
|
for i, extend_seq_len in enumerate(extend_seq_lens_cpu):
|
|
if extend_seq_len == 0:
|
|
input_top_logprobs.append([])
|
|
output_top_logprobs.append([])
|
|
continue
|
|
k = logits_metadata.top_logprobs_nums[i]
|
|
input_top_logprobs.append(
|
|
[
|
|
list(zip(values[pt + j][:k], indices[pt + j][:k]))
|
|
for j in range(extend_seq_len - 1)
|
|
]
|
|
)
|
|
output_top_logprobs.append(
|
|
list(
|
|
zip(
|
|
values[pt + extend_seq_len - 1][:k],
|
|
indices[pt + extend_seq_len - 1][:k],
|
|
)
|
|
)
|
|
)
|
|
pt += extend_seq_len
|
|
|
|
return input_top_logprobs, output_top_logprobs
|
|
|
|
def forward(
|
|
self,
|
|
input_ids,
|
|
hidden_states,
|
|
weight,
|
|
logits_metadata: Union[LogitsMetadata, InputMetadata],
|
|
):
|
|
if isinstance(logits_metadata, InputMetadata):
|
|
logits_metadata = LogitsMetadata.from_input_metadata(logits_metadata)
|
|
assert isinstance(logits_metadata, LogitsMetadata)
|
|
|
|
# Get the last hidden states and last logits for the next token prediction
|
|
if logits_metadata.forward_mode == ForwardMode.DECODE:
|
|
last_index = None
|
|
last_hidden = hidden_states
|
|
else:
|
|
last_index = (
|
|
torch.cumsum(logits_metadata.extend_seq_lens, dim=0, dtype=torch.long)
|
|
- 1
|
|
)
|
|
last_hidden = hidden_states[last_index]
|
|
|
|
last_logits = torch.matmul(last_hidden, weight.T)
|
|
if self.tp_size > 1:
|
|
last_logits = tensor_model_parallel_all_gather(last_logits)
|
|
last_logits = last_logits[:, : self.config.vocab_size].float()
|
|
|
|
if hasattr(self.config, "final_logit_softcapping"):
|
|
last_logits /= self.config.final_logit_softcapping
|
|
last_logits = torch.tanh(last_logits)
|
|
last_logits *= self.config.final_logit_softcapping
|
|
|
|
# Return only last_logits if logprob is not requested
|
|
if not logits_metadata.return_logprob:
|
|
return LogitProcessorOutput(
|
|
next_token_logits=last_logits,
|
|
next_token_logprobs=None,
|
|
normalized_prompt_logprobs=None,
|
|
input_token_logprobs=None,
|
|
input_top_logprobs=None,
|
|
output_top_logprobs=None,
|
|
)
|
|
else:
|
|
# When logprob is requested, compute the logits for all tokens.
|
|
if logits_metadata.forward_mode == ForwardMode.DECODE:
|
|
last_logprobs = torch.nn.functional.log_softmax(last_logits, dim=-1)
|
|
|
|
# Get the logprob of top-k tokens
|
|
return_top_logprob = any(
|
|
x > 0 for x in logits_metadata.top_logprobs_nums
|
|
)
|
|
if return_top_logprob:
|
|
output_top_logprobs = self.get_top_logprobs(
|
|
last_logprobs, logits_metadata
|
|
)[1]
|
|
else:
|
|
output_top_logprobs = None
|
|
|
|
return LogitProcessorOutput(
|
|
next_token_logits=last_logits,
|
|
next_token_logprobs=last_logprobs,
|
|
normalized_prompt_logprobs=None,
|
|
input_token_logprobs=None,
|
|
input_top_logprobs=None,
|
|
output_top_logprobs=output_top_logprobs,
|
|
)
|
|
else:
|
|
all_logits = torch.matmul(hidden_states, weight.T)
|
|
if self.tp_size > 1:
|
|
all_logits = tensor_model_parallel_all_gather(all_logits)
|
|
all_logits = all_logits[:, : self.config.vocab_size].float()
|
|
|
|
all_logprobs = all_logits
|
|
del all_logits
|
|
all_logprobs[:] = torch.nn.functional.log_softmax(all_logprobs, dim=-1)
|
|
|
|
# Get the logprob of top-k tokens
|
|
return_top_logprob = any(
|
|
x > 0 for x in logits_metadata.top_logprobs_nums
|
|
)
|
|
if return_top_logprob:
|
|
input_top_logprobs, output_top_logprobs = self.get_top_logprobs(
|
|
all_logprobs, logits_metadata
|
|
)
|
|
else:
|
|
input_top_logprobs = output_top_logprobs = None
|
|
|
|
last_logprobs = all_logprobs[last_index]
|
|
|
|
# Compute the logprobs and normalized logprobs for the prefill tokens.
|
|
# Note that we pad a zero at the end of each sequence for easy computation.
|
|
input_token_logprobs = all_logprobs[
|
|
torch.arange(all_logprobs.shape[0], device="cuda"),
|
|
torch.cat([input_ids[1:], torch.tensor([0], device="cuda")]),
|
|
]
|
|
|
|
normalized_prompt_logprobs = self._get_normalized_prompt_logprobs(
|
|
input_token_logprobs, logits_metadata
|
|
)
|
|
|
|
return LogitProcessorOutput(
|
|
next_token_logits=last_logits,
|
|
next_token_logprobs=last_logprobs,
|
|
normalized_prompt_logprobs=normalized_prompt_logprobs,
|
|
input_token_logprobs=input_token_logprobs,
|
|
input_top_logprobs=input_top_logprobs,
|
|
output_top_logprobs=output_top_logprobs,
|
|
)
|
|
|
|
|
|
def test():
|
|
all_logprobs = torch.tensor(
|
|
# s s s
|
|
[[0, 1, 2, 3], [1, 2, 3, 4], [2, 3, 4, 5], [3, 4, 5, 6], [4, 5, 6, 7]],
|
|
dtype=torch.float32,
|
|
device="cuda",
|
|
)
|
|
seq_lens = torch.tensor([2, 0, 3, 0], dtype=torch.int32, device="cuda")
|
|
input_ids = torch.tensor([1, 2, 3, 0, 1], dtype=torch.int32, device="cuda")
|
|
|
|
token_logprobs = all_logprobs[
|
|
torch.arange(all_logprobs.shape[0], device="cuda"),
|
|
torch.cat([input_ids[1:], torch.tensor([0], device="cuda")]),
|
|
]
|
|
logprobs_cumsum = torch.cumsum(token_logprobs, dim=0, dtype=torch.float32)
|
|
|
|
len_cumsum = torch.cumsum(seq_lens, dim=0)
|
|
start = torch.cat((torch.tensor([0], device="cuda"), len_cumsum[:-1]), 0)
|
|
end = start + seq_lens - 2
|
|
start.clamp_(min=0, max=token_logprobs.shape[0] - 1)
|
|
end.clamp_(min=0, max=token_logprobs.shape[0] - 1)
|
|
sum_logp = logprobs_cumsum[end] - logprobs_cumsum[start] + token_logprobs[start]
|
|
|
|
# assert logprobs == [2, _, 2, 4, _]
|
|
print("token logprobs", token_logprobs)
|
|
print("start", start)
|
|
print("end", end)
|
|
print("sum_logp", sum_logp)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
test()
|