# Copyright 2023-2024 SGLang Team # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== """Logits processing.""" import dataclasses import logging from typing import Any, Dict, List, Optional, Tuple, Union import torch import triton import triton.language as tl from torch import nn from sglang.srt.distributed import ( get_tensor_model_parallel_world_size, tensor_model_parallel_all_gather, ) from sglang.srt.environ import envs from sglang.srt.layers.dp_attention import ( DpPaddingMode, attn_tp_all_gather, attn_tp_all_gather_into_tensor, dp_gather_replicate, dp_scatter, get_attention_dp_rank, get_attention_dp_size, get_attention_tp_size, get_dp_device, get_dp_dtype, get_dp_hidden_size, ) from sglang.srt.layers.utils.logprob import ( InputLogprobsResult, compute_temp_top_p_normalized_logprobs, get_token_ids_logprobs_chunk, get_token_ids_logprobs_prefill, get_top_logprobs_chunk, get_top_logprobs_prefill, ) from sglang.srt.layers.vocab_parallel_embedding import VocabParallelEmbedding from sglang.srt.model_executor.forward_batch_info import ( CaptureHiddenMode, ForwardBatch, ForwardMode, ) from sglang.srt.server_args import get_global_server_args from sglang.srt.utils.common import is_npu, use_intel_amx_backend logger = logging.getLogger(__name__) _is_npu = is_npu() @dataclasses.dataclass class LogitsProcessorOutput: ## Part 1: This part will be assigned in python/sglang/srt/layers/logits_processor.py::LogitsProcessor # The logits of the next tokens. shape: [#seq, vocab_size] # Can be None for certain prefill-only requests (e.g., multi-item scoring) that don't need next token generation next_token_logits: Optional[torch.Tensor] # Used by speculative decoding (EAGLE) # The last hidden layers hidden_states: Optional[torch.Tensor] = None ## Part 2: This part will be assigned in python/sglang/srt/layers/sampler.py::Sampler # he log probs of output tokens, if SGLANG_RETURN_ORIGINAL_LOGPROB = True, will get the log probs before applying temperature. If False, will get the log probs before applying temperature. next_token_logprobs: Optional[torch.Tensor] = None # The logprobs and ids of the top-k tokens in output positions. shape: [#seq, k] next_token_top_logprobs_val: Optional[List] = None next_token_top_logprobs_idx: Optional[List] = None # The logprobs and ids of the requested token ids in output positions. shape: [#seq, n] (n is the number of requested token ids) # Can contain either lists or GPU tensors (for delayed copy optimization in prefill-only requests) next_token_token_ids_logprobs_val: Optional[ List[Union[List[float], torch.Tensor]] ] = None next_token_token_ids_logprobs_idx: Optional[List] = None ## Part 3: Prefill-only. This part will be assigned in python/sglang/srt/layers/logits_processor.py::LogitsProcessor # The logprobs of input tokens. shape: [#token] input_token_logprobs: Optional[torch.Tensor] = None # The logprobs and ids of the top-k tokens in input positions. shape: [#seq, #token, k] input_top_logprobs_val: Optional[List] = None input_top_logprobs_idx: Optional[List] = None # The logprobs and ids of the requested token ids in input positions. shape: [#seq, n] (n is the number of requested token ids) # Can contain either lists or GPU tensors (for delayed GPU-to-CPU transfer optimization) input_token_ids_logprobs_val: Optional[List[Union[List[float], torch.Tensor]]] = ( None ) input_token_ids_logprobs_idx: Optional[List] = None ## Part 4: Diffusion LLM only. full_logits: Optional[torch.Tensor] = None ## Part 5: Customized Info customized_info: Optional[Dict[str, List[Any]]] = None mm_input_embeds: Optional[torch.Tensor] = None @dataclasses.dataclass class LogitsMetadata: forward_mode: ForwardMode capture_hidden_mode: CaptureHiddenMode = CaptureHiddenMode.NULL next_token_logits_buffer: Optional[torch.Tensor] = None extend_return_logprob: bool = False extend_return_top_logprob: bool = False extend_token_ids_logprob: bool = False extend_seq_lens: Optional[torch.Tensor] = None extend_seq_lens_cpu: Optional[List[int]] = None extend_logprob_start_lens_cpu: Optional[List[int]] = None extend_logprob_pruned_lens_cpu: Optional[List[int]] = None top_logprobs_nums: Optional[List[int]] = None extend_input_logprob_token_ids_gpu: Optional[torch.Tensor] = None token_ids_logprobs: Optional[List[List[int]]] = None # logits and logprobs post processing temp_scaled_logprobs: bool = False temperature: torch.Tensor = None top_p_normalized_logprobs: bool = False top_p: torch.Tensor = None # DP attention metadata. Not needed when DP attention is not used. # Number of tokens in the request. global_num_tokens_gpu: Optional[torch.Tensor] = None # The start position of local hidden states. dp_local_start_pos: Optional[torch.Tensor] = None dp_local_num_tokens: Optional[torch.Tensor] = None global_dp_buffer_len: Optional[int] = None # Number of tokens to sample per DP rank global_num_tokens_for_logprob_cpu: Optional[torch.Tensor] = None global_num_tokens_for_logprob_gpu: Optional[torch.Tensor] = None # The gather mode for DP attention dp_padding_mode: Optional[DpPaddingMode] = None # for padding padded_static_len: int = -1 # Whether this batch is prefill-only (no token generation needed) is_prefill_only: bool = False mm_input_embeds: Optional[torch.Tensor] = None @classmethod def from_forward_batch(cls, forward_batch: ForwardBatch): if ( forward_batch.forward_mode.is_extend() and forward_batch.return_logprob and not forward_batch.forward_mode.is_target_verify() ): extend_return_top_logprob = any( x > 0 for x in forward_batch.top_logprobs_nums ) extend_token_ids_logprob = any( x is not None for x in forward_batch.token_ids_logprobs ) extend_return_logprob = False extend_logprob_pruned_lens_cpu = [] for extend_len, start_len in zip( forward_batch.extend_seq_lens_cpu, forward_batch.extend_logprob_start_lens_cpu, ): if extend_len - start_len > 0: extend_return_logprob = True extend_logprob_pruned_lens_cpu.append(extend_len - start_len) else: extend_return_logprob = extend_return_top_logprob = ( extend_token_ids_logprob ) = extend_logprob_pruned_lens_cpu = False return cls( forward_mode=forward_batch.forward_mode, capture_hidden_mode=forward_batch.capture_hidden_mode, next_token_logits_buffer=forward_batch.next_token_logits_buffer, extend_return_logprob=extend_return_logprob, extend_return_top_logprob=extend_return_top_logprob, extend_token_ids_logprob=extend_token_ids_logprob, extend_seq_lens=forward_batch.extend_seq_lens, extend_seq_lens_cpu=forward_batch.extend_seq_lens_cpu, extend_logprob_start_lens_cpu=forward_batch.extend_logprob_start_lens_cpu, extend_logprob_pruned_lens_cpu=extend_logprob_pruned_lens_cpu, top_logprobs_nums=forward_batch.top_logprobs_nums, token_ids_logprobs=forward_batch.token_ids_logprobs, extend_input_logprob_token_ids_gpu=forward_batch.extend_input_logprob_token_ids_gpu, padded_static_len=forward_batch.padded_static_len, is_prefill_only=forward_batch.is_prefill_only, global_num_tokens_gpu=forward_batch.global_num_tokens_gpu, dp_local_start_pos=forward_batch.dp_local_start_pos, dp_local_num_tokens=forward_batch.dp_local_num_tokens, global_dp_buffer_len=forward_batch.global_dp_buffer_len, global_num_tokens_for_logprob_cpu=forward_batch.global_num_tokens_for_logprob_cpu, global_num_tokens_for_logprob_gpu=forward_batch.global_num_tokens_for_logprob_gpu, dp_padding_mode=DpPaddingMode.SUM_LEN, mm_input_embeds=forward_batch.mm_input_embeds, ) def compute_dp_attention_metadata(self): cumtokens = torch.cumsum(self.global_num_tokens_for_logprob_gpu, dim=0) dp_rank = get_attention_dp_rank() if dp_rank == 0: dp_local_start_pos = torch.zeros_like( self.global_num_tokens_for_logprob_gpu[0] ) else: dp_local_start_pos = cumtokens[dp_rank - 1] self.dp_local_start_pos = dp_local_start_pos self.dp_local_num_tokens = self.global_num_tokens_for_logprob_gpu[dp_rank] hidden_size = get_dp_hidden_size() dtype = get_dp_dtype() device = get_dp_device() if self.global_num_tokens_for_logprob_cpu is not None: # create a smaller buffer to reduce peak memory usage self.global_dp_buffer_len = sum(self.global_num_tokens_for_logprob_cpu) else: self.global_dp_buffer_len = self.global_dp_buffer_len self.gathered_buffer = torch.empty( ( self.global_dp_buffer_len, hidden_size, ), dtype=dtype, device=device, ) class LogitsProcessor(nn.Module): def __init__( self, config, skip_all_gather: bool = False, logit_scale: Optional[float] = None, return_full_logits: bool = False, ): super().__init__() self.config = config self.vocab_size = config.vocab_size self.logit_scale = logit_scale self.use_attn_tp_group = get_global_server_args().enable_dp_lm_head self.use_fp32_lm_head = get_global_server_args().enable_fp32_lm_head if self.use_attn_tp_group: self.attn_tp_size = get_attention_tp_size() self.do_tensor_parallel_all_gather = ( not skip_all_gather and self.attn_tp_size > 1 ) self.do_tensor_parallel_all_gather_dp_attn = False else: self.do_tensor_parallel_all_gather = ( not skip_all_gather and get_tensor_model_parallel_world_size() > 1 ) self.do_tensor_parallel_all_gather_dp_attn = ( self.do_tensor_parallel_all_gather and get_attention_dp_size() != 1 ) self.final_logit_softcapping = getattr( self.config, "final_logit_softcapping", None ) if ( self.final_logit_softcapping is not None and self.final_logit_softcapping < 0 ): self.final_logit_softcapping = None self.return_full_logits = return_full_logits self.multi_item_delimiter = ( get_global_server_args().multi_item_scoring_delimiter ) # enable chunked logprobs processing self.enable_logprobs_chunk = envs.SGLANG_ENABLE_LOGITS_PROCESSER_CHUNK.get() # chunk size for logprobs processing self.logprobs_chunk_size = envs.SGLANG_LOGITS_PROCESSER_CHUNK_SIZE.get() def forward( self, input_ids, hidden_states, lm_head: VocabParallelEmbedding, logits_metadata: Union[LogitsMetadata, ForwardBatch], aux_hidden_states: Optional[torch.Tensor] = None, hidden_states_before_norm: Optional[torch.Tensor] = None, ) -> LogitsProcessorOutput: if isinstance(logits_metadata, ForwardBatch): logits_metadata = LogitsMetadata.from_forward_batch(logits_metadata) # Multi-item scoring only for prefill-only requests. if self.multi_item_delimiter is not None and logits_metadata.is_prefill_only: return self.compute_logprobs_for_multi_item_scoring( input_ids, hidden_states, lm_head, logits_metadata, self.multi_item_delimiter, ) # Diffusion LLM only. if logits_metadata.forward_mode.is_dllm_extend(): return self._get_dllm_logits(hidden_states, lm_head, logits_metadata) # Get the last hidden states and last logits for the next token prediction ( pruned_states, pruned_states_before_norm, aux_pruned_states, sample_indices, input_logprob_indices, token_to_seq_idx, ) = self._get_pruned_states( hidden_states, hidden_states_before_norm, aux_hidden_states, logits_metadata, ) hidden_states_to_store = self._get_hidden_states_to_store( hidden_states, hidden_states_before_norm, aux_hidden_states, pruned_states, pruned_states_before_norm, aux_pruned_states, sample_indices, logits_metadata, ) del hidden_states if not logits_metadata.extend_return_logprob: # Compute logits for both input and sampled tokens. logits = self._get_logits(pruned_states, lm_head, logits_metadata) sampled_logits = ( logits[sample_indices] if sample_indices is not None else logits ) # Decode mode or extend mode without return_logprob. return LogitsProcessorOutput( next_token_logits=sampled_logits, hidden_states=hidden_states_to_store, mm_input_embeds=logits_metadata.mm_input_embeds, ) # Start to process input logprobs # Normalize the logprob w/o temperature, top-p self._expand_metadata_for_logprobs(logits_metadata, pruned_states.device) # Determine whether to use chunked or non-chunked logits processing. # Skip chunking if: # 1. Chunking is disabled # 2. Total count is below chunk size threshold # 3. DP attention all-gather is enabled (can use "enable_dp_lm_head" to enable chunking) should_skip_chunking = ( not self.enable_logprobs_chunk or pruned_states.shape[0] <= self.logprobs_chunk_size or self.do_tensor_parallel_all_gather_dp_attn ) if should_skip_chunking: # Compute logits for both input and sampled tokens. logits = self._get_logits(pruned_states, lm_head, logits_metadata) sampled_logits = ( logits[sample_indices] if sample_indices is not None else logits ) input_logits = logits[input_logprob_indices] del logits logprobs_result = self.process_input_logprobs(input_logits, logits_metadata) else: logprobs_result, sampled_logits = self.process_input_logprobs_by_chunk( pruned_states, sample_indices, input_logprob_indices, token_to_seq_idx, lm_head, logits_metadata, ) return LogitsProcessorOutput( next_token_logits=sampled_logits, hidden_states=hidden_states_to_store, input_token_logprobs=logprobs_result.input_token_logprobs, input_top_logprobs_val=logprobs_result.input_top_logprobs_val, input_top_logprobs_idx=logprobs_result.input_top_logprobs_idx, input_token_ids_logprobs_val=logprobs_result.input_token_ids_logprobs_val, input_token_ids_logprobs_idx=logprobs_result.input_token_ids_logprobs_idx, mm_input_embeds=logits_metadata.mm_input_embeds, ) def _get_pruned_states( self, hidden_states: torch.Tensor, hidden_states_before_norm: Optional[torch.Tensor], aux_hidden_states: Optional[torch.Tensor], logits_metadata: LogitsMetadata, ): pruned_states_before_norm: Optional[torch.Tensor] = None aux_pruned_states = None token_to_seq_idx = [] if ( logits_metadata.forward_mode.is_decode_or_idle() or logits_metadata.forward_mode.is_target_verify() or logits_metadata.forward_mode.is_draft_extend_v2() ): pruned_states = hidden_states pruned_states_before_norm = hidden_states_before_norm if aux_hidden_states is not None: aux_pruned_states = [hidden for hidden in aux_hidden_states] sample_indices = None input_logprob_indices = None elif ( logits_metadata.forward_mode.is_extend() and not logits_metadata.extend_return_logprob ): # Prefill without input logprobs. if logits_metadata.padded_static_len < 0: last_index = torch.cumsum(logits_metadata.extend_seq_lens, dim=0) - 1 else: # If padding_static length is 5 and extended_seq_lens is [2, 3], # then our batch looks like [t00, t01, p, p, p, t10, t11, t12, p, p] # and this retrieves t01 and t12, which are the valid last tokens idx = torch.arange( len(logits_metadata.extend_seq_lens), device=logits_metadata.extend_seq_lens.device, ) last_index = ( idx * logits_metadata.padded_static_len + logits_metadata.extend_seq_lens - 1 ) pruned_states = hidden_states[last_index] if hidden_states_before_norm is not None: pruned_states_before_norm = hidden_states_before_norm[last_index] if aux_hidden_states is not None: aux_pruned_states = [hidden[last_index] for hidden in aux_hidden_states] sample_indices = None input_logprob_indices = None else: # Prefill with input logprobs. # Find 4 different indices. # 1. pruned_states: hidden states that we want logprobs from. # 2. sample_indices: Indices that have sampled tokens. # 3. input_logprob_indices: Indices that have input logprob tokens. # 4. token_to_seq_idx: map each token to its sequence index # # Example # ------- # Suppose a batch (flattened by sequence): # [t00, t01, t02, t03, t10, t11, t12, t13, t14, t20, t21, t22, t23, t24, t25] # extend_seq_lens_cpu = [4, 5, 6] # extend_logprob_start_lens_cpu = [0, 5, 3] # # Then, the indices are: # pruned_states -> [t00, t01, t02, t03, t14, t23, t24, t25] # sample_indices -> [3, 4, 7] # input_logprob_indices -> [0, 1, 2, 3, 5, 6, 7] # token_to_seq_idx -> [0, 0, 0, 0, 1, 2, 2, 2] # # If chunk is enabled and chunk_size = 3, the chunks will be computed in a chunked manner: # [t00, t01, t02], [t03, t14, t23], [t24, t25] sample_index_pt = -1 sample_indices = [] input_logprob_indices_pt = 0 input_logprob_indices = [] pt, pruned_states_list, pruned_states_before_norm_list = 0, [], [] for idx, (extend_logprob_start_len, extend_len) in enumerate( zip( logits_metadata.extend_logprob_start_lens_cpu, logits_metadata.extend_seq_lens_cpu, ) ): # It can happen in chunked prefill. We still need to sample 1 token, # But we don't want to include it in input logprob. if extend_len == extend_logprob_start_len: start_len = extend_logprob_start_len - 1 else: start_len = extend_logprob_start_len # We always need at least 1 token to sample because that's required # by a caller. assert extend_len > start_len pruned_states_list.append( hidden_states[pt + start_len : pt + extend_len] ) if hidden_states_before_norm is not None: pruned_states_before_norm_list.append( hidden_states_before_norm[pt + start_len : pt + extend_len] ) # Map each token to its sequence index, for chunked computation # of input logprobs token_to_seq_idx.extend([idx] * (extend_len - start_len)) pt += extend_len sample_index_pt += extend_len - start_len sample_indices.append(sample_index_pt) input_logprob_indices.extend( [ input_logprob_indices_pt + i for i in range(extend_len - extend_logprob_start_len) ] ) input_logprob_indices_pt += extend_len - start_len # Set the last token of the last sequence token_to_seq_idx.append(len(logits_metadata.extend_seq_lens_cpu) - 1) pruned_states = torch.cat(pruned_states_list) if hidden_states_before_norm is not None: pruned_states_before_norm = torch.cat(pruned_states_before_norm_list) sample_indices = torch.tensor( sample_indices, device=pruned_states.device, dtype=torch.int64 ) input_logprob_indices = torch.tensor( input_logprob_indices, device=pruned_states.device, dtype=torch.int64 ) return ( pruned_states, pruned_states_before_norm, aux_pruned_states, sample_indices, input_logprob_indices, token_to_seq_idx, ) def _get_hidden_states_to_store( self, hidden_states: torch.Tensor, hidden_states_before_norm: Optional[torch.Tensor], aux_hidden_states: Optional[List[torch.Tensor]], pruned_states: torch.Tensor, pruned_states_before_norm: Optional[torch.Tensor], aux_pruned_states: Optional[List[torch.Tensor]], sample_indices: Optional[torch.Tensor], logits_metadata: LogitsMetadata, ) -> Optional[torch.Tensor]: hidden_states_to_store: Optional[torch.Tensor] = None hidden_states_to_store_before_norm: Optional[torch.Tensor] = None if logits_metadata.capture_hidden_mode.need_capture(): if logits_metadata.capture_hidden_mode.is_full(): if aux_hidden_states is not None: aux_hidden_states = torch.cat(aux_hidden_states, dim=-1) hidden_states_to_store = aux_hidden_states else: hidden_states_to_store = hidden_states hidden_states_to_store_before_norm = hidden_states_before_norm elif logits_metadata.capture_hidden_mode.is_last(): # Get the last token hidden states. If sample_indices is None, # pruned states only contain the last tokens already. if aux_hidden_states is not None: aux_pruned_states = torch.cat(aux_pruned_states, dim=-1) hidden_states_to_store = ( aux_pruned_states[sample_indices] if sample_indices is not None else aux_pruned_states ) else: hidden_states_to_store = ( pruned_states[sample_indices] if sample_indices is not None else pruned_states ) if hidden_states_before_norm is not None: hidden_states_to_store_before_norm = ( pruned_states_before_norm[sample_indices] if sample_indices is not None else pruned_states_before_norm ) else: assert False, "Should never reach" if hidden_states_to_store_before_norm is not None: # NOTE: when hidden_states_before_norm is provided, we always # prefer to return it. hidden_states_to_store = hidden_states_to_store_before_norm return hidden_states_to_store def _expand_metadata_for_logprobs( self, logits_metadata: LogitsMetadata, device: torch.device ): pruned_lens = torch.tensor( logits_metadata.extend_logprob_pruned_lens_cpu, device=device, ) if logits_metadata.temp_scaled_logprobs: logits_metadata.temperature = torch.repeat_interleave( logits_metadata.temperature.view(-1), pruned_lens, ).view(-1, 1) if logits_metadata.top_p_normalized_logprobs: logits_metadata.top_p = torch.repeat_interleave( logits_metadata.top_p, pruned_lens, ) def process_input_logprobs(self, input_logits, logits_metadata: LogitsMetadata): input_logprobs = compute_temp_top_p_normalized_logprobs( input_logits, logits_metadata ) # Get the logprob of top-k tokens if logits_metadata.extend_return_top_logprob: ( input_top_logprobs_val, input_top_logprobs_idx, ) = get_top_logprobs_prefill(input_logprobs, logits_metadata) else: input_top_logprobs_val = input_top_logprobs_idx = None # Get the logprob of given token id if logits_metadata.extend_token_ids_logprob: ( input_token_ids_logprobs_val, input_token_ids_logprobs_idx, ) = get_token_ids_logprobs_prefill(input_logprobs, logits_metadata) else: input_token_ids_logprobs_val = input_token_ids_logprobs_idx = None input_token_logprobs = input_logprobs[ torch.arange(input_logprobs.shape[0], device=input_logprobs.device), logits_metadata.extend_input_logprob_token_ids_gpu, ] return InputLogprobsResult( input_token_logprobs=input_token_logprobs, input_top_logprobs_val=input_top_logprobs_val, input_top_logprobs_idx=input_top_logprobs_idx, input_token_ids_logprobs_val=input_token_ids_logprobs_val, input_token_ids_logprobs_idx=input_token_ids_logprobs_idx, ) def process_input_logprobs_by_chunk( self, pruned_states: torch.Tensor, sample_indices: torch.Tensor, input_logprob_indices: torch.Tensor, token_to_seq_idx: list[int], lm_head: VocabParallelEmbedding, logits_metadata: LogitsMetadata, ) -> Tuple[InputLogprobsResult, torch.Tensor]: """ compute logprobs for the output token from the hidden states. To avoid using too much memory, we split pruned_states into chunks of rows to compute input_logprobs separately, then concatenate the results. Returns: InputLogprobsResult: logprobs result torch.Tensor: sampled logits """ # The peak memory usage is proportional to the chunk size. chunk_size = self.logprobs_chunk_size total_size = pruned_states.shape[0] num_chunks = (total_size + chunk_size - 1) // chunk_size input_token_logprobs = [] if logits_metadata.extend_return_top_logprob: input_top_logprobs_val = [] input_top_logprobs_idx = [] else: input_top_logprobs_val = None input_top_logprobs_idx = None if logits_metadata.extend_token_ids_logprob: input_token_ids_logprobs_val = [] input_token_ids_logprobs_idx = [] else: input_token_ids_logprobs_val = None input_token_ids_logprobs_idx = None # If a single sequence is split into multiple chunks, we need to keep track # of the pruned length of the sequences in the previous chunks. split_len_topk = 0 split_len_token_ids = 0 for i in range(num_chunks): start_idx = i * chunk_size end_idx = min((i + 1) * chunk_size, total_size) # Get indices for this chunk chunk_mask = (input_logprob_indices >= start_idx) & ( input_logprob_indices < end_idx ) global_indices = input_logprob_indices[chunk_mask] chunk_indices = global_indices - start_idx # Get the positions in the original array where chunk_mask is True # This is needed to correctly index into extend_input_logprob_token_ids_gpu mask_indices = torch.nonzero(chunk_mask, as_tuple=True)[0] # Get the logits for this chunk chunk_states = pruned_states[start_idx:end_idx] chunk_logits = self._get_logits(chunk_states, lm_head, logits_metadata) # Initialize sampled_logits on first chunk if i == 0: sampled_logits = torch.empty( (sample_indices.shape[0], chunk_logits.shape[1]), dtype=chunk_logits.dtype, device=chunk_logits.device, ) # Handle sampled logits for the chunk if needed # This must be done before the continue statement to ensure all sampled_logits are filled chunk_sample_mask = (sample_indices >= start_idx) & ( sample_indices < end_idx ) if chunk_sample_mask.any(): chunk_sample_indices = sample_indices[chunk_sample_mask] - start_idx sampled_logits[chunk_sample_mask] = chunk_logits[chunk_sample_indices] # If there are no input logprobs in this chunk, skip the rest if chunk_indices.numel() == 0: continue # Compute the logprobs of the chunk chunk_input_logprobs = chunk_logits[chunk_indices] # Only index per-token arrays when the corresponding feature is active. # Otherwise these tensors can be per-sequence (or scalars), which can # cause out-of-bounds indexing on GPU. chunk_temperature = ( logits_metadata.temperature[global_indices] if logits_metadata.temp_scaled_logprobs and logits_metadata.temperature is not None else None ) chunk_top_p = ( logits_metadata.top_p[global_indices] if logits_metadata.top_p_normalized_logprobs and logits_metadata.top_p is not None else None ) chunk_input_logprobs = compute_temp_top_p_normalized_logprobs( chunk_input_logprobs, logits_metadata, chunk_top_p, chunk_temperature, ) # For each chunk, we need to get the slice of the token_to_seq_idx chunk_slice = slice( token_to_seq_idx[start_idx], token_to_seq_idx[end_idx] + 1 ) # Get the logprob of top-k tokens if logits_metadata.extend_return_top_logprob: top_k_nums = logits_metadata.top_logprobs_nums[chunk_slice] pruned_lens = logits_metadata.extend_logprob_pruned_lens_cpu[ chunk_slice ] split_len_topk = get_top_logprobs_chunk( chunk_input_logprobs, logits_metadata, top_k_nums, pruned_lens, input_top_logprobs_val, input_top_logprobs_idx, split_len_topk, ) # Get the logprob of given token id if logits_metadata.extend_token_ids_logprob: token_ids_logprobs = logits_metadata.token_ids_logprobs[chunk_slice] pruned_lens = logits_metadata.extend_logprob_pruned_lens_cpu[ chunk_slice ] split_len_token_ids = get_token_ids_logprobs_chunk( chunk_input_logprobs, token_ids_logprobs, pruned_lens, input_token_ids_logprobs_val, input_token_ids_logprobs_idx, split_len_token_ids, ) # Get the logprob of the requested token ids chunk_input_token_logprobs = chunk_input_logprobs[ torch.arange( chunk_input_logprobs.shape[0], device=chunk_input_logprobs.device ), logits_metadata.extend_input_logprob_token_ids_gpu[mask_indices], ] input_token_logprobs.append(chunk_input_token_logprobs) # Concatenate the results input_token_logprobs = torch.cat(input_token_logprobs, dim=0) return ( InputLogprobsResult( input_token_logprobs=input_token_logprobs, input_top_logprobs_val=input_top_logprobs_val, input_top_logprobs_idx=input_top_logprobs_idx, input_token_ids_logprobs_val=input_token_ids_logprobs_val, input_token_ids_logprobs_idx=input_token_ids_logprobs_idx, ), sampled_logits, ) def _get_logits( self, hidden_states: torch.Tensor, lm_head: VocabParallelEmbedding, logits_metadata: LogitsMetadata, embedding_bias: Optional[torch.Tensor] = None, ) -> torch.Tensor: """Get logits from hidden_states. If sampled_logits_only is True, it means hidden_states only contain the last position (e.g., extend without input logprobs). The caller should guarantee the given hidden_states follow this constraint. """ hidden_states, local_hidden_states = self._gather_dp_attn_hidden_states( hidden_states, logits_metadata ) logits = self._compute_lm_head(hidden_states, lm_head, embedding_bias) if self.logit_scale is not None: logits.mul_(self.logit_scale) if self.do_tensor_parallel_all_gather: if self.use_attn_tp_group: logits = self._gather_attn_tp_logits(logits) else: logits = tensor_model_parallel_all_gather(logits) logits = self._scatter_dp_attn_logits( logits, local_hidden_states, logits_metadata ) logits = self._copy_logits_to_buffer(logits, logits_metadata) if self.final_logit_softcapping: if not _is_npu: fused_softcap(logits, self.final_logit_softcapping) else: logits = self.final_logit_softcapping * torch.tanh( logits / self.final_logit_softcapping ) return logits def _compute_lm_head( self, hidden_states: torch.Tensor, lm_head: VocabParallelEmbedding, embedding_bias: Optional[torch.Tensor] = None, ) -> torch.Tensor: if hasattr(lm_head, "set_lora") and hasattr(lm_head, "apply_lora"): # This is a LoRA-wrapped module, use its forward method logits = lm_head(hidden_states) elif hasattr(lm_head, "weight"): # Normal linear layer if self.use_fp32_lm_head: logits = torch.matmul( hidden_states.to(torch.float32), lm_head.weight.to(torch.float32).T ) elif use_intel_amx_backend(lm_head): logits = torch.ops.sgl_kernel.weight_packed_linear( hidden_states.to(lm_head.weight.dtype), lm_head.weight, None, # bias True, # is_vnni ) elif get_global_server_args().rl_on_policy_target is not None: # Due to tie-weight, we may not be able to change lm_head's weight dtype logits = torch.matmul( hidden_states.bfloat16(), lm_head.weight.T.bfloat16() ) else: logits = torch.matmul( hidden_states.to(lm_head.weight.dtype), lm_head.weight.T ) else: # GGUF models # TODO: use weight_packed_linear for GGUF models if self.use_fp32_lm_head: with torch.cuda.amp.autocast(enabled=False): logits = lm_head.quant_method.apply( lm_head, hidden_states.to(torch.float32), embedding_bias ) else: logits = lm_head.quant_method.apply( lm_head, hidden_states, embedding_bias ) return logits def _gather_dp_attn_hidden_states( self, hidden_states: torch.Tensor, logits_metadata: LogitsMetadata ) -> Tuple[torch.Tensor, torch.Tensor]: if self.do_tensor_parallel_all_gather_dp_attn: logits_metadata.compute_dp_attention_metadata() local_hidden_states = hidden_states hidden_states = logits_metadata.gathered_buffer dp_gather_replicate(hidden_states, local_hidden_states, logits_metadata) return hidden_states, local_hidden_states return hidden_states, hidden_states def _gather_attn_tp_logits(self, logits: torch.Tensor) -> torch.Tensor: if self.vocab_size % self.attn_tp_size == 0: global_logits = torch.empty( ( self.attn_tp_size, logits.shape[0], self.vocab_size // self.attn_tp_size, ), device=logits.device, dtype=logits.dtype, ) attn_tp_all_gather_into_tensor(global_logits, logits) global_logits = global_logits.permute(1, 0, 2).reshape( logits.shape[0], self.vocab_size ) else: global_logits = torch.empty( (self.vocab_size, logits.shape[0]), device=logits.device, dtype=logits.dtype, ) global_logits = global_logits.T attn_tp_all_gather( list(global_logits.tensor_split(self.attn_tp_size, dim=-1)), logits, ) return global_logits def _scatter_dp_attn_logits( self, logits: torch.Tensor, local_hidden_states: torch.Tensor, logits_metadata: LogitsMetadata, ) -> torch.Tensor: if self.do_tensor_parallel_all_gather_dp_attn: global_logits = logits logits = torch.empty( (local_hidden_states.shape[0], global_logits.shape[1]), device=global_logits.device, dtype=global_logits.dtype, ) dp_scatter(logits, global_logits, logits_metadata) return logits def _copy_logits_to_buffer( self, logits: torch.Tensor, logits_metadata: LogitsMetadata ) -> torch.Tensor: if logits_metadata.next_token_logits_buffer is not None: logits_buffer = logits_metadata.next_token_logits_buffer assert logits_buffer.dtype == torch.float logits_buffer.copy_(logits[:, : self.vocab_size]) logits = logits_buffer else: logits = logits[:, : self.vocab_size].float() return logits def _get_dllm_logits( self, hidden_states: torch.Tensor, lm_head: VocabParallelEmbedding, logits_metadata: LogitsMetadata, ) -> LogitsProcessorOutput: assert self.return_full_logits full_logits = self._get_logits(hidden_states, lm_head, logits_metadata) return LogitsProcessorOutput( full_logits=full_logits, next_token_logits=None, ) def compute_logprobs_for_multi_item_scoring( self, input_ids, hidden_states, lm_head: VocabParallelEmbedding, logits_metadata: Union[LogitsMetadata, ForwardBatch], delimiter_token: int, ): """ Compute logprobs for multi-item scoring using delimiter-based token extraction. This method is designed for scenarios where you want to score multiple items/candidates against a single query by combining them into one sequence separated by delimiters. Sequence format: QueryItem1Item2... Scoring positions: Extracts logprobs at positions before each Args: input_ids (torch.Tensor): Input token IDs containing query and items separated by delimiters. Shape: [total_sequence_length] for single request or [batch_total_length] for batch. hidden_states (torch.Tensor): Hidden states from the model. Shape: [sequence_length, hidden_dim]. lm_head (VocabParallelEmbedding): Language model head for computing logits. logits_metadata (Union[LogitsMetadata, ForwardBatch]): Metadata containing batch info and token ID specifications for logprob extraction. delimiter_token (int): Token ID used as delimiter between query and items. Returns: LogitsProcessorOutput: Contains: - next_token_logits: None (not needed for scoring-only requests) - input_token_logprobs: Logprobs of delimiter tokens at scoring positions - input_top_logprobs_val: Top-k logprobs at delimiter positions (if requested) - input_top_logprobs_idx: Top-k token indices at delimiter positions (if requested) - input_token_ids_logprobs_val: Logprobs for user-requested token IDs (if any) - input_token_ids_logprobs_idx: Indices for user-requested token IDs (if any) """ multi_item_indices = (input_ids == delimiter_token).nonzero(as_tuple=True)[ 0 ] - 1 # Extract hidden states at delimiter positions for multi-item scoring sliced_hidden = hidden_states[multi_item_indices] sliced_logits = self._get_logits(sliced_hidden, lm_head, logits_metadata) sliced_logprobs = torch.nn.functional.log_softmax(sliced_logits, dim=-1) # Initialize return values input_token_ids_logprobs_val = [] input_token_ids_logprobs_idx = [] input_top_logprobs_val = None input_top_logprobs_idx = None # Recalculate extend_logprob_pruned_lens_cpu to match delimiter counts per request # Original contains sequence lengths, but we need delimiter counts for sliced_logprobs if ( logits_metadata.token_ids_logprobs or logits_metadata.extend_return_top_logprob ): logits_metadata.extend_logprob_pruned_lens_cpu = [] if logits_metadata.extend_seq_lens_cpu is not None: # Multi-request batch: count delimiters per request input_pt = 0 for req_seq_len in logits_metadata.extend_seq_lens_cpu: req_input_ids = input_ids[input_pt : input_pt + req_seq_len] delimiter_count = (req_input_ids == delimiter_token).sum().item() logits_metadata.extend_logprob_pruned_lens_cpu.append( delimiter_count ) input_pt += req_seq_len else: # Single request case: one request gets all delimiters total_delimiters = (input_ids == delimiter_token).sum().item() logits_metadata.extend_logprob_pruned_lens_cpu = [total_delimiters] # Get the logprobs of specified token ids if logits_metadata.extend_token_ids_logprob: ( input_token_ids_logprobs_val, input_token_ids_logprobs_idx, ) = get_token_ids_logprobs_prefill( sliced_logprobs, logits_metadata, delay_cpu_copy=True ) # Get the logprob of top-k tokens if logits_metadata.extend_return_top_logprob: ( input_top_logprobs_val, input_top_logprobs_idx, ) = get_top_logprobs_prefill(sliced_logprobs, logits_metadata) # For input_token_logprobs, use delimiter token logprobs input_token_logprobs = sliced_logprobs[:, delimiter_token] return LogitsProcessorOutput( next_token_logits=None, # Multi-item scoring doesn't need next token logits input_token_logprobs=input_token_logprobs, input_top_logprobs_val=input_top_logprobs_val, input_top_logprobs_idx=input_top_logprobs_idx, input_token_ids_logprobs_val=input_token_ids_logprobs_val, input_token_ids_logprobs_idx=input_token_ids_logprobs_idx, # FIXME: These fields are not logits-related but are passed through here as a # workaround since ForwardBatch is local to forward_batch_generation(). # They should be moved to GenerationBatchResult to keep this class clean. mm_input_embeds=logits_metadata.mm_input_embeds, ) @triton.jit def fused_softcap_kernel( full_logits_ptr, softcapping_value, n_elements, BLOCK_SIZE: tl.constexpr, ): pid = tl.program_id(0).to(tl.int64) block_start = pid * BLOCK_SIZE offsets = block_start + tl.arange(0, BLOCK_SIZE) mask = offsets < n_elements # Load values x = tl.load(full_logits_ptr + offsets, mask=mask) # Perform operations in-place x = x / softcapping_value # Manual tanh implementation using exp exp2x = tl.exp(2 * x) x = (exp2x - 1) / (exp2x + 1) x = x * softcapping_value # Store result tl.store(full_logits_ptr + offsets, x, mask=mask) def fused_softcap(full_logits, final_logit_softcapping): n_elements = full_logits.numel() BLOCK_SIZE = 1024 grid = ((n_elements + BLOCK_SIZE - 1) // BLOCK_SIZE, 1, 1) fused_softcap_kernel[grid]( full_logits_ptr=full_logits, softcapping_value=final_logit_softcapping, n_elements=n_elements, BLOCK_SIZE=BLOCK_SIZE, ) return full_logits