34cfdf5520
1. Remove useless logger
2. Fix logger bug, same problem as
https://github.com/vllm-project/vllm-ascend/pull/515
- vLLM version: v0.10.0
- vLLM main:
18cc33dd60
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
385 lines
17 KiB
Python
385 lines
17 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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import os
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import torch
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import torch.nn as nn
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from vllm.attention.layer import Attention
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from vllm.config import (CompilationLevel, VllmConfig,
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get_layers_from_vllm_config)
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from vllm.distributed.parallel_state import get_pp_group
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from vllm.logger import logger
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from vllm.model_executor.model_loader import get_model
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from vllm.model_executor.models import supports_multimodal
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from vllm.model_executor.models.llama_eagle3 import Eagle3LlamaForCausalLM
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from vllm.v1.sample.metadata import SamplingMetadata
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from vllm_ascend.ascend_forward_context import set_ascend_forward_context
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from vllm_ascend.attention.attention_mask import AttentionMaskBuilder
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from vllm_ascend.attention.attention_v1 import AscendAttentionState
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PADDING_SLOT_ID = -1
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class EagleProposer:
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def __init__(self,
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vllm_config: VllmConfig,
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device: torch.device,
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runner=None):
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self.vllm_config = vllm_config
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self.speculative_config = vllm_config.speculative_config
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self.draft_model_config = self.speculative_config.draft_model_config
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self.method = self.speculative_config.method
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self.runner = runner
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self.model_config = vllm_config.model_config
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self.dtype = vllm_config.model_config.dtype
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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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self.num_speculative_tokens = (
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self.speculative_config.num_speculative_tokens)
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self.max_num_tokens = (
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vllm_config.scheduler_config.max_num_batched_tokens)
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self.device = device
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# We need to get the hidden size from the draft model config because
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# the draft model's hidden size can be different from the target model's
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# hidden size (e.g., Llama 3.3 70B).
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self.hidden_size = self.draft_model_config.get_hidden_size()
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self.use_cuda_graph = (self.vllm_config.compilation_config.level
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== CompilationLevel.PIECEWISE and
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not self.vllm_config.model_config.enforce_eager)
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self.cudagraph_batch_sizes = list(
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reversed(
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self.vllm_config.compilation_config.cudagraph_capture_sizes))
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# persistent buffers for cuda graph
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self.input_ids = torch.zeros(self.max_num_tokens,
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dtype=torch.int32,
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device=device)
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self.positions = torch.zeros(self.max_num_tokens,
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dtype=torch.int64,
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device=device)
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self.hidden_states = torch.zeros(
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(self.max_num_tokens, self.hidden_size),
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dtype=self.dtype,
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device=device)
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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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mask_len = os.getenv("PAGED_ATTENTION_MASK_LEN", 10000)
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self.attn_mask_len = min(self.model_config.max_model_len,
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int(mask_len))
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self.attn_mask_builder = AttentionMaskBuilder(self.attn_mask_len,
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self.dtype)
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def _make_attention_mask(
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self,
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seq_lens,
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query_lens,
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position,
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) -> torch.Tensor:
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return self.attn_mask_builder.get_splitfuse_attn_mask(
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seq_lens, query_lens, position, self.dtype, self.device)
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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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device = cu_num_tokens.device
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cu_num_tokens = cu_num_tokens.cpu()
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block_table = block_table.cpu()
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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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target_positions = target_positions.cpu()
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if self.method == "eagle3":
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assert isinstance(self.model, Eagle3LlamaForCausalLM)
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target_hidden_states = self.model.combine_hidden_states(
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target_hidden_states)
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assert target_hidden_states.shape[-1] == self.hidden_size
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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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self.input_ids[:num_tokens - 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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self.input_ids[last_token_indices] = next_token_ids[0]
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query_lens = cu_num_tokens[1:] - cu_num_tokens[:-1]
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max_query_len = query_lens.max().item()
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# FIXME(woosuk): The below two ops cause synchronization. Optimize.
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attn_metadata = self.runner.attn_metadata_builder.build(
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num_reqs=batch_size,
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num_actual_tokens=num_tokens,
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max_query_len=max_query_len,
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)
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if self.use_cuda_graph and \
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num_tokens <= self.cudagraph_batch_sizes[-1]:
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num_input_tokens = self.vllm_config.pad_for_cudagraph(num_tokens)
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else:
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num_input_tokens = num_tokens
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# copy inputs to buffer for cudagraph
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self.positions[:num_tokens] = target_positions.to(device)
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self.hidden_states[:num_tokens] = target_hidden_states
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attn_metadata.block_tables = block_table.to(device)
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with set_ascend_forward_context(attn_metadata,
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self.vllm_config,
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num_tokens=num_input_tokens):
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last_hidden_states, hidden_states = self.model(
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input_ids=self.input_ids[:num_input_tokens],
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positions=self.positions[:num_input_tokens],
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hidden_states=self.hidden_states[:num_input_tokens],
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)
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sample_hidden_states = last_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_tensor = torch.zeros(
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(self.num_speculative_tokens, *draft_token_ids.shape),
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dtype=draft_token_ids.dtype)
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draft_token_ids_tensor[0] = draft_token_ids
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positions_cpu = target_positions[last_token_indices].cpu().to(
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torch.int64)
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hidden_states = hidden_states[last_token_indices]
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if self.use_cuda_graph and \
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batch_size <= self.cudagraph_batch_sizes[-1]:
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input_batch_size = self.vllm_config.pad_for_cudagraph(batch_size)
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else:
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input_batch_size = batch_size
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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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if self.num_speculative_tokens > 2:
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raise ValueError("Speculative tokens > 2 are not supported yet.")
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attn_metadata.attn_state = AscendAttentionState.ChunkedPrefill
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for now_speculative in range(self.num_speculative_tokens - 1):
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# Update the inputs.
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# cast to int32 is crucial when eagle model is compiled.
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# tensor.argmax() returns int64 by default.
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input_ids = draft_token_ids_tensor[now_speculative].to(device)
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positions_cpu += 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_cpu >= 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_cpu = torch.where(exceeds_max_model_len, 0,
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positions_cpu)
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clamped_positions = clamped_positions_cpu.to(device)
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# TODO: Increment the sequence lengths.
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attn_metadata.seq_lens += 1
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# TODO: 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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# TODO: sequence length to 1 to minimize their overheads in attention.
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# Compute the slot mapping.
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block_numbers = (clamped_positions_cpu // 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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slot_mapping_cpu = (block_ids * self.block_size +
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clamped_positions_cpu % 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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slot_mapping_cpu.masked_fill_(exceeds_max_model_len,
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PADDING_SLOT_ID)
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# NOTE: ASCEND slot_mapping must on cpu
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attn_metadata.slot_mapping = slot_mapping_cpu.to(
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torch.int32).to(device)
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# copy inputs to buffer for cudagraph
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self.input_ids[:batch_size] = input_ids
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self.positions[:batch_size] = clamped_positions
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self.hidden_states[:batch_size] = hidden_states
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positions = positions_cpu.to(device)
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attn_mask = self._make_attention_mask(
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seq_lens=attn_metadata.seq_lens,
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query_lens=attn_metadata.max_query_len,
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position=positions,
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)
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attn_metadata.attn_mask = attn_mask
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attn_metadata.block_tables = block_table.to(device)
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# Run the model.
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with set_ascend_forward_context(attn_metadata,
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self.vllm_config,
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num_tokens=input_batch_size):
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last_hidden_states, hidden_states = self.model(
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input_ids=self.input_ids[:input_batch_size],
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positions=self.positions[:input_batch_size],
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hidden_states=self.hidden_states[:input_batch_size],
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)
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hidden_states = hidden_states[:batch_size]
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logits = self.model.compute_logits(last_hidden_states[:batch_size],
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None)
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# TODO(wenlong): get more than one token for tree attention
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draft_token_ids = logits.argmax(dim=-1)
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draft_token_ids_tensor[now_speculative + 1] = draft_token_ids.cpu()
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# [batch_size, num_speculative_tokens]
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draft_token_ids = draft_token_ids_tensor.swapaxes(0, 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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num_tokens: int,
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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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# [a - n1, b - n2, c - n3] ->
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# [0, a - n1, a + b - n1 - n2, a + b + c - n1 - n2 - n3]
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cu_num_tokens = torch.zeros_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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token_indices = torch.empty(
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num_tokens,
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dtype=torch.int32,
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device=cu_target_query_lens.device,
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)
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BLOCK_SIZE = 1024
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prepare_eagle_input_sequential(
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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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draft_model_config = \
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self.vllm_config.speculative_config.draft_model_config
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target_attn_layer_names = set(
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get_layers_from_vllm_config(self.vllm_config, Attention).keys())
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self.model = get_model(vllm_config=self.vllm_config,
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model_config=draft_model_config)
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draft_attn_layer_names = (
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get_layers_from_vllm_config(self.vllm_config, Attention).keys() -
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target_attn_layer_names)
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self.attn_layer_names = list(draft_attn_layer_names)
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self.attn_layer_name = next(iter(draft_attn_layer_names))
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# share embed_tokens with the target model if needed
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if get_pp_group().world_size == 1:
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logger.info(
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"The EAGLE head shares the same vocab embedding" \
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" with the target model."
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)
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self.model.model.embed_tokens = target_model.model.embed_tokens
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else:
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logger.info(
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"Since PP > 1, the EAGLE head loaded its own vocab embedding" \
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" weights instead of sharing them with the target model."
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)
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# share lm_head with the target model if needed
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# some model definition do not define lm_head explicitly
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# and reuse embed_tokens for lm_head, e.g., CohereForCausalLM
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if self.vllm_config.speculative_config.method != "eagle3" and \
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hasattr(target_model, "lm_head"):
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logger.info("Loading EAGLE LM head weights from the target model.")
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if supports_multimodal(target_model):
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self.model.lm_head = target_model.get_language_model().lm_head
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else:
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self.model.lm_head = target_model.lm_head
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@torch.inference_mode()
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def dummy_run(
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self,
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num_tokens: int,
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) -> None:
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with set_ascend_forward_context(None,
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self.vllm_config,
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num_tokens=num_tokens):
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self.model(
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input_ids=self.input_ids[:num_tokens],
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positions=self.positions[:num_tokens],
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hidden_states=self.hidden_states[:num_tokens],
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)
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def prepare_eagle_input_sequential(out_tensor: torch.Tensor,
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cu_query_lens: torch.Tensor,
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cu_num_tokens: torch.Tensor,
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block_size: int):
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num_programs = len(cu_num_tokens) - 1
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for pid in range(num_programs):
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start_pos = cu_num_tokens[pid].item()
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end_pos = cu_num_tokens[pid + 1].item()
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num_tokens = end_pos - start_pos
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index_start = cu_query_lens[pid].item()
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num_blocks = int(
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torch.ceil(torch.tensor(num_tokens / block_size)).item())
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for i in range(num_blocks):
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offset_tensor = torch.arange(0,
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block_size,
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dtype=torch.int32,
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device=out_tensor.device)
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global_start_offset = i * block_size
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target_indices = torch.tensor(
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start_pos + global_start_offset,
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dtype=torch.int32,
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device=out_tensor.device) + offset_tensor
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values_to_store = torch.tensor(
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index_start, dtype=torch.int32,
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device=out_tensor.device) + offset_tensor
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mask = (target_indices >= start_pos) & \
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(target_indices < end_pos) & \
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(offset_tensor < num_tokens)
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out_tensor[target_indices[mask]] = values_to_store[mask]
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