102 lines
3.9 KiB
Python
102 lines
3.9 KiB
Python
import torch
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from sglang.srt.managers.router.model_runner import ForwardMode, InputMetadata
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from torch import nn
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from vllm.model_executor.parallel_utils.communication_op import (
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get_tensor_model_parallel_world_size,
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tensor_model_parallel_all_gather,
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)
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class LogitsProcessor(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.config = config
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self.tp_size = get_tensor_model_parallel_world_size()
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def forward(self, input_ids, hidden_states, weight, input_metadata):
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if not input_metadata.return_logprob:
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if input_metadata.forward_mode == ForwardMode.DECODE:
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last_hidden = hidden_states
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else:
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last_index = (
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torch.cumsum(
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input_metadata.seq_lens - input_metadata.prefix_lens,
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dim=0,
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dtype=torch.long,
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)
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- 1
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)
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last_hidden = hidden_states[last_index]
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hidden_states = None
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last_logits = torch.matmul(last_hidden, weight.T)
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if self.tp_size > 1:
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last_logits = tensor_model_parallel_all_gather(last_logits)
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last_logits = last_logits[:, : self.config.vocab_size]
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return last_logits, (None, None)
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else:
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assert input_metadata.forward_mode != ForwardMode.DECODE
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last_index = (
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torch.cumsum(
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input_metadata.seq_lens - input_metadata.prefix_lens,
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dim=0,
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dtype=torch.long,
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)
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- 1
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)
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logits = torch.matmul(hidden_states, weight.T)
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if self.tp_size > 1:
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logits = tensor_model_parallel_all_gather(logits)
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logits = logits[:, : self.config.vocab_size]
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all_logprobs = torch.log(torch.softmax(logits.float(), dim=-1) + 1e-6)
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logprobs = all_logprobs[
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torch.arange(all_logprobs.shape[0], device="cuda"),
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torch.cat([input_ids[1:], torch.tensor([0], device="cuda")]),
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]
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logprobs_cumsum = torch.cumsum(logprobs, dim=0, dtype=torch.float32)
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start = input_metadata.extend_start_loc.clone()
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end = start + input_metadata.extend_seq_lens - 2
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start.clamp_(min=0, max=logprobs.shape[0] - 1)
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end.clamp_(min=0, max=logprobs.shape[0] - 1)
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sum_logp = logprobs_cumsum[end] - logprobs_cumsum[start] + logprobs[start]
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normalized_logprobs = sum_logp / (
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(input_metadata.extend_seq_lens - 1).clamp(min=1)
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)
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last_logits = logits[last_index]
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return last_logits, (logprobs, normalized_logprobs)
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if __name__ == "__main__":
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all_logprobs = torch.tensor(
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# s s s
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[[0, 1, 2, 3], [1, 2, 3, 4], [2, 3, 4, 5], [3, 4, 5, 6], [4, 5, 6, 7]],
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dtype=torch.float32,
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device="cuda",
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)
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seq_lens = torch.tensor([2, 0, 3, 0], dtype=torch.int32, device="cuda")
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input_ids = torch.tensor([1, 2, 3, 0, 1], dtype=torch.int32, device="cuda")
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logprobs = torch.zeros(5, dtype=torch.float32, device="cuda")
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logprobs = all_logprobs[
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torch.arange(all_logprobs.shape[0], device="cuda"),
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torch.cat([input_ids[1:], torch.tensor([0], device="cuda")]),
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]
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logprobs_cumsum = torch.cumsum(logprobs, dim=0, dtype=torch.float32)
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len_cumsum = torch.cumsum(seq_lens, dim=0)
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start = torch.cat((torch.tensor([0], device="cuda"), len_cumsum[:-1]), 0)
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end = start + seq_lens - 2
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start.clamp_(min=0, max=logprobs.shape[0] - 1)
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end.clamp_(min=0, max=logprobs.shape[0] - 1)
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sum_logp = logprobs_cumsum[end] - logprobs_cumsum[start] + logprobs[start]
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# assert logprobs == [2, _, 2, 4, _]
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print("logprobs", logprobs)
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print("start", start)
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print("end", end)
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print("sum_logp", sum_logp)
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