302 lines
12 KiB
Python
302 lines
12 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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import torch
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import torch.nn as nn
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import triton
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import triton.language as tl
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from vllm.config import VllmConfig, set_current_vllm_config
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from vllm.forward_context import set_forward_context
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from vllm.model_executor.model_loader.loader import get_model_loader
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from vllm.model_executor.model_loader.utils import set_default_torch_dtype
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from vllm.model_executor.models.llama_eagle import EagleLlamaForCausalLM
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from vllm.v1.attention.backends.flash_attn import FlashAttentionMetadata
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from vllm.v1.sample.metadata import SamplingMetadata
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PADDING_SLOT_ID = -1
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class EagleProposer:
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def __init__(
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self,
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vllm_config: VllmConfig,
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device: torch.device,
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):
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self.vllm_config = vllm_config
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self.num_speculative_tokens = (
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vllm_config.speculative_config.num_speculative_tokens)
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self.max_model_len = vllm_config.model_config.max_model_len
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self.block_size = vllm_config.cache_config.block_size
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# We need +1 here because the arange is used to set query_start_loc,
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# which has one more element than batch_size.
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self.arange = torch.arange(vllm_config.scheduler_config.max_num_seqs +
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1,
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device=device,
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dtype=torch.int32)
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def propose(
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self,
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# [num_tokens]
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target_token_ids: torch.Tensor,
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# [num_tokens]
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target_positions: torch.Tensor,
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# [num_tokens, hidden_size]
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target_hidden_states: torch.Tensor,
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# [num_tokens]
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target_slot_mapping: torch.Tensor,
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# [batch_size]
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next_token_ids: torch.Tensor,
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# [batch_size + 1] starting with 0
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cu_num_tokens: torch.Tensor,
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# [batch_size, max_num_blocks_per_req]
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block_table: torch.Tensor,
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sampling_metadata: SamplingMetadata,
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) -> torch.Tensor:
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num_tokens = target_token_ids.shape[0]
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batch_size = next_token_ids.shape[0]
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last_token_indices = cu_num_tokens[1:] - 1
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input_ids = torch.empty_like(target_token_ids)
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# Shift the input ids by one token.
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# E.g., [a1, b1, b2, c1, c2, c3] -> [b1, b2, c1, c2, c3, c3]
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input_ids[:-1] = target_token_ids[1:]
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# Replace the last token with the next token.
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# E.g., [b1, b2, c1, c2, c3, c3] -> [a2, b2, b3, c2, c3, c4]
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input_ids[last_token_indices] = next_token_ids
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# FA requires seq_len to have dtype int32.
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seq_lens = (target_positions[last_token_indices] + 1).int()
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# FIXME(woosuk): The below two ops cause synchronization. Optimize.
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max_seq_len = seq_lens.max().item()
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max_num_tokens = (cu_num_tokens[1:] - cu_num_tokens[:-1]).max().item()
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attn_metadata = FlashAttentionMetadata(
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num_actual_tokens=num_tokens,
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max_query_len=max_num_tokens,
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query_start_loc=cu_num_tokens,
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max_seq_len=max_seq_len,
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seq_lens=seq_lens,
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block_table=block_table,
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slot_mapping=target_slot_mapping,
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# TODO(woosuk): Support cascade attention.
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use_cascade=False,
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common_prefix_len=0,
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cu_prefix_query_lens=None,
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prefix_kv_lens=None,
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suffix_kv_lens=None,
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)
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with set_forward_context(attn_metadata, self.vllm_config):
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hidden_states = self.model(
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input_ids=input_ids,
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hidden_states=target_hidden_states,
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positions=target_positions,
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)
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sample_hidden_states = hidden_states[last_token_indices]
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logits = self.model.compute_logits(sample_hidden_states, None)
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draft_token_ids = logits.argmax(dim=-1)
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# Early exit if there is only one draft token to be generated.
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if self.num_speculative_tokens == 1:
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# [batch_size, 1]
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return draft_token_ids.view(-1, 1)
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# Generate the remaining draft tokens.
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draft_token_ids_list = [draft_token_ids]
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positions = target_positions[last_token_indices]
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hidden_states = sample_hidden_states
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attn_metadata.num_actual_tokens = batch_size
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attn_metadata.max_query_len = 1
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attn_metadata.query_start_loc = self.arange[:batch_size + 1]
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for _ in range(self.num_speculative_tokens - 1):
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# Update the inputs.
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input_ids = draft_token_ids_list[-1]
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positions += 1
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# NOTE(woosuk): We should handle the case where the draft model
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# generates tokens beyond the max model length. Since it is complex
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# to remove such requests from the batch, we keep them in the batch
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# but adjust the position ids and slot mappings to avoid the
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# out-of-range access during the model execution. The draft tokens
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# generated with this adjustment should be ignored.
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exceeds_max_model_len = positions >= self.max_model_len
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# Mask out the position ids that exceed the max model length.
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# Otherwise, we may get out-of-range error in RoPE.
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clamped_positions = torch.where(exceeds_max_model_len, 0,
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positions)
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# Increment the sequence lengths.
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attn_metadata.max_seq_len += 1
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attn_metadata.seq_lens += 1
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# Consider max model length.
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attn_metadata.max_seq_len = min(attn_metadata.max_seq_len,
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self.max_model_len)
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# For the requests that exceed the max model length, we set the
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# sequence length to 1 to minimize their overheads in attention.
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attn_metadata.seq_lens.masked_fill_(exceeds_max_model_len, 1)
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# Compute the slot mapping.
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block_numbers = clamped_positions // self.block_size
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block_ids = block_table.gather(dim=1,
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index=block_numbers.view(-1, 1))
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block_ids = block_ids.view(-1)
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attn_metadata.slot_mapping = (block_ids * self.block_size +
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clamped_positions % self.block_size)
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# Mask out the slot mappings that exceed the max model length.
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# Otherwise, the KV cache will be inadvertently updated with the
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# padding tokens.
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attn_metadata.slot_mapping.masked_fill_(exceeds_max_model_len,
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PADDING_SLOT_ID)
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# Run the model.
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with set_forward_context(attn_metadata, self.vllm_config):
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hidden_states = self.model(
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input_ids=input_ids,
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hidden_states=hidden_states,
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positions=clamped_positions,
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)
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logits = self.model.compute_logits(hidden_states, None)
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draft_token_ids = logits.argmax(dim=-1)
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draft_token_ids_list.append(draft_token_ids)
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# [batch_size, num_speculative_tokens]
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draft_token_ids = torch.stack(draft_token_ids_list, dim=1)
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return draft_token_ids
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@staticmethod
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def prepare_inputs(
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# [batch_size + 1]
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cu_target_query_lens: torch.Tensor,
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# [batch_size]
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num_rejected_tokens: torch.Tensor,
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) -> tuple[torch.Tensor, torch.Tensor]:
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# cu_target_query_lens: [0, a, a + b, a + b + c]
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# num_rejected_tokens: [n1, n2, n3]
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# num_tokens_per_req: [a - n1, b - n2, c - n3]
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# cu_num_tokens: [0, a - n1, a + b - n1 - n2, a + b + c - n1 - n2 - n3]
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# token_indices: [0, 1, ..., a - n1 - 1,
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# a, a + 1, ..., a + b - n2 - 1,
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# a + b, a + b + 1, ..., a + b + c - n3 - 1]
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# [0, a, a + b, a + b + c] -> [a, b, c]
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query_len_per_req = (cu_target_query_lens[1:] -
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cu_target_query_lens[:-1])
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# [a, b, c] -> [a - n1, b - n2, c - n3]
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num_tokens_per_req = query_len_per_req - num_rejected_tokens
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cu_num_tokens = torch.empty_like(cu_target_query_lens)
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torch.cumsum(num_tokens_per_req, dim=0, out=cu_num_tokens[1:])
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cu_num_tokens[0] = 0
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# FIXME(woosuk): Avoid synchronization.
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num_tokens = cu_num_tokens[-1].item()
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token_indices = torch.empty(
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num_tokens,
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dtype=torch.int32,
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device=cu_num_tokens.device,
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)
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batch_size = num_rejected_tokens.shape[0]
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BLOCK_SIZE = 1024
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prepare_input_kernel[(batch_size, )](
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token_indices,
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cu_target_query_lens,
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cu_num_tokens,
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BLOCK_SIZE=BLOCK_SIZE,
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)
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return cu_num_tokens, token_indices
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def load_model(self, target_model: nn.Module) -> None:
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loader = get_model_loader(self.vllm_config.load_config)
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target_layer_num = self.vllm_config.model_config.get_num_layers(
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self.vllm_config.parallel_config)
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draft_model_config = \
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self.vllm_config.speculative_config.draft_model_config
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# FIXME(lily): This does not handle with distributed inference.
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target_device = self.vllm_config.device_config.device
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# We need to set the vllm_config here to register attention
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# layers in the forward context.
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with set_default_torch_dtype(
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draft_model_config.dtype), set_current_vllm_config(
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self.vllm_config):
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self.model = EagleLlamaForCausalLM(
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model_config=draft_model_config,
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start_layer_id=target_layer_num).to(target_device)
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self.model.load_weights(
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loader.get_all_weights(
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self.vllm_config.speculative_config.draft_model_config,
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self.model))
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self.model.lm_head = target_model.lm_head
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# NOTE(woosuk): Currently, the below code is not used and we always use argmax
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# to sample the draft tokens. We will use this after we find a way to manage
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# the draft prob tensor.
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# Refer to https://github.com/vllm-project/vllm/pull/16899 for the details.
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# FIXME(woosuk): The logic here is duplicated with the main sampling code.
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# We should refactor this to reuse the same sampling implementation.
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def compute_probs_and_sample_next_token(
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logits: torch.Tensor,
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sampling_metadata: SamplingMetadata,
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) -> tuple[torch.Tensor, torch.Tensor]:
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if sampling_metadata.all_greedy:
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# For greedy requests, draft_probs is not used in rejection sampling.
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# Therefore, we can just return the logits.
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probs = logits
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next_token_ids = logits.argmax(dim=-1)
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return next_token_ids, probs
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is_greedy = sampling_metadata.temperature == -1
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temperature = torch.where(is_greedy, 1.0, sampling_metadata.temperature)
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logits.div_(temperature.view(-1, 1))
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probs = logits.softmax(dim=-1, dtype=torch.float32)
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# NOTE(woosuk): Currently, we ignore most of the sampling parameters in
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# generating the draft tokens. We only use the temperature. While this
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# could degrade the acceptance rate, it does not affect the distribution
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# of the generated tokens after rejection sampling.
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# TODO(woosuk): Consider seeds.
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q = torch.empty_like(probs)
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q.exponential_()
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# NOTE(woosuk): We shouldn't use `probs.div_(q)` because the draft_probs
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# will be used later for rejection sampling.
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next_token_ids = probs.div(q).argmax(dim=-1).view(-1)
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if not sampling_metadata.all_random:
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greedy_token_ids = probs.argmax(dim=-1)
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next_token_ids = torch.where(
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is_greedy,
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greedy_token_ids,
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next_token_ids,
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)
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return next_token_ids, probs
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@triton.jit
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def prepare_input_kernel(
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out_ptr,
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cu_query_lens_ptr,
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cu_num_tokens_ptr,
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BLOCK_SIZE: tl.constexpr,
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):
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pid = tl.program_id(0)
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# [start_pos, end_pos)
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start_pos = tl.load(cu_num_tokens_ptr + pid)
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end_pos = tl.load(cu_num_tokens_ptr + pid + 1)
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num_tokens = end_pos - start_pos
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index_start = tl.load(cu_query_lens_ptr + pid)
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num_blocks = tl.cdiv(num_tokens, BLOCK_SIZE)
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for i in tl.range(num_blocks):
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offset = i * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE)
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tl.store(
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out_ptr + start_pos + offset,
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index_start + offset,
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mask=offset < num_tokens,
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)
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