318 lines
15 KiB
Python
318 lines
15 KiB
Python
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import torch
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from vllm.forward_context import set_forward_context
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from vllm.model_executor.models.llama_eagle3 import Eagle3LlamaForCausalLM
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from vllm.v1.attention.backends.flash_attn import FlashAttentionMetadata
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from vllm.v1.attention.backends.mla.common import MLACommonMetadata
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from vllm.v1.attention.backends.utils import CommonAttentionMetadata
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from vllm.v1.sample.metadata import SamplingMetadata
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from vllm.v1.spec_decode.eagle import PADDING_SLOT_ID, EagleProposer
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class V1ZeroEagleProposer(EagleProposer):
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def __init__(self, vllm_config, device, runner=None):
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super().__init__(vllm_config, device, runner)
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self.spec_scheduler_max_num_tokens = 0
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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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# [batch_size]
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sampling_metadata: SamplingMetadata,
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decoding: bool = False,
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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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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
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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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if self.method in ["eagle", "eagle3"]:
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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:] -
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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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elif self.method == "deepseek_mtp":
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max_query_len = self.spec_scheduler_max_num_tokens
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common_attn_metadata = CommonAttentionMetadata(
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query_start_loc=cu_num_tokens,
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seq_lens=seq_lens,
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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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slot_mapping=target_slot_mapping,
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spec_layer_decoding=decoding
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)
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assert self.runner is not None
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# FIXME: need to consider multiple kv_cache_groups
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attn_metadata = self.runner.attn_metadata_builders[0].build(
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common_prefix_len=0,
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common_attn_metadata=common_attn_metadata
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)
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else:
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raise ValueError(f"Unsupported method: {self.method}")
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# At this moment, we assume all eagle layers belong to the same KV
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# cache group, thus using the same attention metadata.
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per_layer_attn_metadata = {}
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for layer_name in self.attn_layer_names:
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per_layer_attn_metadata[layer_name] = attn_metadata
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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
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self.hidden_states[:num_tokens] = target_hidden_states
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if (decoding and self.use_full_cuda_graph
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and num_tokens <= self.cudagraph_batch_sizes[-1]):
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assert self.attn_metadata_cudagraph
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if self.method in ["eagle", "eagle3"]:
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self.attn_metadata_cudagraph.seq_lens[:batch_size] = (
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attn_metadata.seq_lens)
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self.attn_metadata_cudagraph.slot_mapping[:num_tokens] = (
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attn_metadata.slot_mapping)
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self.attn_metadata_cudagraph.query_start_loc[:batch_size + 1] = (
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attn_metadata.query_start_loc)
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self.attn_metadata_cudagraph.block_table[:batch_size] = (
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attn_metadata.block_table)
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elif self.method == "deepseek_mtp":
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self.attn_metadata_cudagraph.num_actual_tokens = (
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attn_metadata.num_actual_tokens)
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self.attn_metadata_cudagraph.query_start_loc[:batch_size + 1] = (
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attn_metadata.query_start_loc)
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self.attn_metadata_cudagraph.slot_mapping[:num_tokens] = (
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attn_metadata.slot_mapping)
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self.attn_metadata_cudagraph.num_decodes = (
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attn_metadata.num_decodes)
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self.attn_metadata_cudagraph.num_decode_tokens = (
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attn_metadata.num_decode_tokens)
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self.attn_metadata_cudagraph.num_prefills = (
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attn_metadata.num_prefills)
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if attn_metadata.decode is not None:
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self.attn_metadata_cudagraph.decode.block_table[:attn_metadata.num_decode_tokens] = (
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attn_metadata.decode.block_table)
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self.attn_metadata_cudagraph.decode.seq_lens[:attn_metadata.num_decode_tokens] = (
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attn_metadata.decode.seq_lens)
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with set_forward_context(per_layer_attn_metadata,
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self.vllm_config,
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num_tokens=num_input_tokens,
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skip_cuda_graphs=not decoding):
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ret_hidden_states = self.model(
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self.input_ids[:num_input_tokens],
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self.positions[:num_input_tokens],
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self.hidden_states[:num_input_tokens],
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)
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if self.method == "deepseek_mtp":
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last_hidden_states = ret_hidden_states
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else:
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last_hidden_states, hidden_states = ret_hidden_states
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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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# TODO: Currently, MTP module released by deepseek only has
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# one layer. Adapt this code to support multiple layers once
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# there's a multi-layer MTP module.
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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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if self.method == "deepseek_mtp":
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hidden_states = last_hidden_states[last_token_indices]
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else:
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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 isinstance(attn_metadata, MLACommonMetadata):
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attn_metadata.num_decodes = batch_size
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attn_metadata.num_decode_tokens = batch_size
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attn_metadata.num_prefills = 0
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block_table = self.runner.attn_metadata_builders[0].block_table.get_device_tensor()[:batch_size, ...]
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attn_metadata.decode = self.runner.attn_metadata_builders[0]._build_decode(
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block_table_tensor=block_table,
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seq_lens=seq_lens,
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)
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for i 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_list[-1].int()
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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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if isinstance(attn_metadata, MLACommonMetadata):
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attn_metadata.decode.seq_lens += 1
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else:
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attn_metadata.seq_lens += 1
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# Increment the sequence lengths.
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attn_metadata.max_seq_len += 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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# 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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if (self.use_full_cuda_graph
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and batch_size <= self.cudagraph_batch_sizes[-1]):
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assert self.attn_metadata_cudagraph
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if self.method in ["eagle", "eagle3"]:
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self.attn_metadata_cudagraph.seq_lens[:batch_size] = (
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attn_metadata.seq_lens)
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self.attn_metadata_cudagraph.slot_mapping[:batch_size] = (
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attn_metadata.slot_mapping)
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if i == 0:
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self.attn_metadata_cudagraph.query_start_loc[:batch_size +
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1] = (
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attn_metadata
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.
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query_start_loc
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)
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self.attn_metadata_cudagraph.block_table[:batch_size] = (
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attn_metadata.block_table)
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elif self.method == "deepseek_mtp":
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self.attn_metadata_cudagraph.num_actual_tokens = (
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attn_metadata.num_actual_tokens)
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self.attn_metadata_cudagraph.slot_mapping[:attn_metadata.num_decode_tokens] = (
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attn_metadata.slot_mapping)
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self.attn_metadata_cudagraph.num_decodes = (
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attn_metadata.num_decodes)
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self.attn_metadata_cudagraph.num_decode_tokens = (
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attn_metadata.num_decode_tokens)
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self.attn_metadata_cudagraph.num_prefills = (
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attn_metadata.num_prefills)
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self.attn_metadata_cudagraph.decode.seq_lens[:attn_metadata.num_decode_tokens] = (
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attn_metadata.decode.seq_lens)
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if i == 0:
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self.attn_metadata_cudagraph.query_start_loc[:batch_size + 1] = (
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attn_metadata.query_start_loc)
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self.attn_metadata_cudagraph.decode.block_table[:attn_metadata.num_decode_tokens] = (
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attn_metadata.decode.block_table)
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# Run the model.
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with set_forward_context(per_layer_attn_metadata,
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self.vllm_config,
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num_tokens=input_batch_size):
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ret_hidden_states = self.model(
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self.input_ids[:input_batch_size],
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self.positions[:input_batch_size],
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self.hidden_states[:input_batch_size],
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)
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if self.method == "deepseek_mtp":
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last_hidden_states = ret_hidden_states
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hidden_states = last_hidden_states[:batch_size]
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else:
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last_hidden_states, hidden_states = ret_hidden_states
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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_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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