eagle.py

import torch
from vllm.forward_context import set_forward_context
from vllm.model_executor.models.llama_eagle3 import Eagle3LlamaForCausalLM
from vllm.v1.attention.backends.flash_attn import FlashAttentionMetadata
from vllm.v1.attention.backends.mla.common import MLACommonMetadata
from vllm.v1.attention.backends.utils import CommonAttentionMetadata
from vllm.v1.sample.metadata import SamplingMetadata
from vllm.v1.spec_decode.eagle import PADDING_SLOT_ID, EagleProposer


class V1ZeroEagleProposer(EagleProposer):
    def __init__(self, vllm_config, device, runner=None):
        super().__init__(vllm_config, device, runner)
        self.spec_scheduler_max_num_tokens = 0


    def propose(
        self,
        # [num_tokens]
        target_token_ids: torch.Tensor,
        # [num_tokens]
        target_positions: torch.Tensor,
        # [num_tokens, hidden_size]
        target_hidden_states: torch.Tensor,
        # [num_tokens]
        target_slot_mapping: torch.Tensor,
        # [batch_size]
        next_token_ids: torch.Tensor,
        # [batch_size + 1] starting with 0
        cu_num_tokens: torch.Tensor,
        # [batch_size, max_num_blocks_per_req]
        block_table: torch.Tensor,
        # [batch_size]
        sampling_metadata: SamplingMetadata,
        decoding: bool = False,
    ) -> torch.Tensor:
        num_tokens = target_token_ids.shape[0]
        batch_size = next_token_ids.shape[0]
        last_token_indices = cu_num_tokens[1:] - 1

        if self.method == "eagle3":
            assert isinstance(self.model, Eagle3LlamaForCausalLM)
            target_hidden_states = self.model.combine_hidden_states(
                target_hidden_states)
            assert target_hidden_states.shape[-1] == self.hidden_size

        # Shift the input ids by one token.
        # E.g., [a1, b1, b2, c1, c2, c3] -> [b1, b2, c1, c2, c3, c3]
        self.input_ids[:num_tokens - 1] = target_token_ids[1:]
        # Replace the last token with the next token.
        # E.g., [b1, b2, c1, c2, c3, c3] -> [a2, b2, b3, c2, c3, c4]
        self.input_ids[last_token_indices] = next_token_ids

        # FA requires seq_len to have dtype int32.
        seq_lens = (target_positions[last_token_indices] + 1).int()

        if self.method in ["eagle", "eagle3"]:
            # FIXME(woosuk): The below two ops cause synchronization. Optimize.
            max_seq_len = seq_lens.max().item()
            max_num_tokens = (cu_num_tokens[1:] -
                              cu_num_tokens[:-1]).max().item()
            attn_metadata = FlashAttentionMetadata(
                num_actual_tokens=num_tokens,
                max_query_len=max_num_tokens,
                query_start_loc=cu_num_tokens,
                max_seq_len=max_seq_len,
                seq_lens=seq_lens,
                block_table=block_table,
                slot_mapping=target_slot_mapping,
                # TODO(woosuk): Support cascade attention.
                use_cascade=False,
                common_prefix_len=0,
                cu_prefix_query_lens=None,
                prefix_kv_lens=None,
                suffix_kv_lens=None,
            )
        elif self.method == "deepseek_mtp":
            max_query_len = self.spec_scheduler_max_num_tokens
            common_attn_metadata = CommonAttentionMetadata(
                query_start_loc=cu_num_tokens,
                seq_lens=seq_lens,
                num_reqs=batch_size,
                num_actual_tokens=num_tokens,
                max_query_len=max_query_len,
                slot_mapping=target_slot_mapping,
                spec_layer_decoding=decoding
            )

            assert self.runner is not None

            # FIXME: need to consider multiple kv_cache_groups
            attn_metadata = self.runner.attn_metadata_builders[0].build(
                common_prefix_len=0,
                common_attn_metadata=common_attn_metadata
            )
        else:
            raise ValueError(f"Unsupported method: {self.method}")

        # At this moment, we assume all eagle layers belong to the same KV
        # cache group, thus using the same attention metadata.
        per_layer_attn_metadata = {}
        for layer_name in self.attn_layer_names:
            per_layer_attn_metadata[layer_name] = attn_metadata
        if self.use_cuda_graph and \
            num_tokens <= self.cudagraph_batch_sizes[-1]:
            num_input_tokens = self.vllm_config.pad_for_cudagraph(num_tokens)
        else:
            num_input_tokens = num_tokens
        # copy inputs to buffer for cudagraph
        self.positions[:num_tokens] = target_positions
        self.hidden_states[:num_tokens] = target_hidden_states

        if (decoding and self.use_full_cuda_graph
                and num_tokens <= self.cudagraph_batch_sizes[-1]):
            assert self.attn_metadata_cudagraph
            if self.method in ["eagle", "eagle3"]:
                self.attn_metadata_cudagraph.seq_lens[:batch_size] = (
                    attn_metadata.seq_lens)
                self.attn_metadata_cudagraph.slot_mapping[:num_tokens] = (
                    attn_metadata.slot_mapping)
                self.attn_metadata_cudagraph.query_start_loc[:batch_size + 1] = (
                    attn_metadata.query_start_loc)
                self.attn_metadata_cudagraph.block_table[:batch_size] = (
                    attn_metadata.block_table)
            elif self.method == "deepseek_mtp":
                self.attn_metadata_cudagraph.num_actual_tokens = (
                    attn_metadata.num_actual_tokens)
                self.attn_metadata_cudagraph.query_start_loc[:batch_size + 1] = (
                    attn_metadata.query_start_loc)
                self.attn_metadata_cudagraph.slot_mapping[:num_tokens] = (
                    attn_metadata.slot_mapping)
                self.attn_metadata_cudagraph.num_decodes = (
                    attn_metadata.num_decodes)
                self.attn_metadata_cudagraph.num_decode_tokens = (
                    attn_metadata.num_decode_tokens)
                self.attn_metadata_cudagraph.num_prefills = (
                    attn_metadata.num_prefills)

                if attn_metadata.decode is not None:
                    self.attn_metadata_cudagraph.decode.block_table[:attn_metadata.num_decode_tokens] = (
                            attn_metadata.decode.block_table)
                    self.attn_metadata_cudagraph.decode.seq_lens[:attn_metadata.num_decode_tokens] = (
                        attn_metadata.decode.seq_lens)

        with set_forward_context(per_layer_attn_metadata,
                                 self.vllm_config,
                                 num_tokens=num_input_tokens,
                                 skip_cuda_graphs=not decoding):
            ret_hidden_states = self.model(
                self.input_ids[:num_input_tokens],
                self.positions[:num_input_tokens],
                self.hidden_states[:num_input_tokens],
            )
            if self.method == "deepseek_mtp":
                last_hidden_states = ret_hidden_states
            else:
                last_hidden_states, hidden_states = ret_hidden_states
        sample_hidden_states = last_hidden_states[last_token_indices]
        logits = self.model.compute_logits(sample_hidden_states, None)

        draft_token_ids = logits.argmax(dim=-1)

        # Early exit if there is only one draft token to be generated.
        if self.num_speculative_tokens == 1:
            # [batch_size, 1]
            return draft_token_ids.view(-1, 1)

        # TODO: Currently, MTP module released by deepseek only has
        # one layer. Adapt this code to support multiple layers once
        # there's a multi-layer MTP module.

        # Generate the remaining draft tokens.
        draft_token_ids_list = [draft_token_ids]

        positions = target_positions[last_token_indices]

        if self.method == "deepseek_mtp":
            hidden_states = last_hidden_states[last_token_indices]
        else:
            hidden_states = hidden_states[last_token_indices]

        if self.use_cuda_graph and \
                batch_size <= self.cudagraph_batch_sizes[-1]:
            input_batch_size = self.vllm_config.pad_for_cudagraph(batch_size)
        else:
            input_batch_size = batch_size
        attn_metadata.num_actual_tokens = batch_size
        attn_metadata.max_query_len = 1
        attn_metadata.query_start_loc = self.arange[:batch_size + 1]

        if isinstance(attn_metadata, MLACommonMetadata):
            attn_metadata.num_decodes = batch_size
            attn_metadata.num_decode_tokens = batch_size
            attn_metadata.num_prefills = 0
            block_table = self.runner.attn_metadata_builders[0].block_table.get_device_tensor()[:batch_size, ...]
            attn_metadata.decode = self.runner.attn_metadata_builders[0]._build_decode(
                block_table_tensor=block_table,
                seq_lens=seq_lens,
            )

        for i in range(self.num_speculative_tokens - 1):
            # Update the inputs.
            # cast to int32 is crucial when eagle model is compiled.
            # tensor.argmax() returns int64 by default.
            input_ids = draft_token_ids_list[-1].int()
            positions += 1

            # NOTE(woosuk): We should handle the case where the draft model
            # generates tokens beyond the max model length. Since it is complex
            # to remove such requests from the batch, we keep them in the batch
            # but adjust the position ids and slot mappings to avoid the
            # out-of-range access during the model execution. The draft tokens
            # generated with this adjustment should be ignored.
            exceeds_max_model_len = positions >= self.max_model_len
            # Mask out the position ids that exceed the max model length.
            # Otherwise, we may get out-of-range error in RoPE.
            clamped_positions = torch.where(exceeds_max_model_len, 0,
                                            positions)

            if isinstance(attn_metadata, MLACommonMetadata):
                attn_metadata.decode.seq_lens += 1
            else:
                attn_metadata.seq_lens += 1

                # Increment the sequence lengths.
                attn_metadata.max_seq_len += 1
                # Consider max model length.
                attn_metadata.max_seq_len = min(attn_metadata.max_seq_len,
                                                self.max_model_len)

                # For the requests that exceed the max model length, we set the
                # sequence length to 1 to minimize their overheads in attention.
                attn_metadata.seq_lens.masked_fill_(exceeds_max_model_len, 1)

            # Compute the slot mapping.
            block_numbers = clamped_positions // self.block_size
            block_ids = block_table.gather(dim=1,
                                        index=block_numbers.view(-1, 1))
            block_ids = block_ids.view(-1)
            attn_metadata.slot_mapping = (block_ids * self.block_size +
                                        clamped_positions % self.block_size)
            # Mask out the slot mappings that exceed the max model length.
            # Otherwise, the KV cache will be inadvertently updated with the
            # padding tokens.
            attn_metadata.slot_mapping.masked_fill_(exceeds_max_model_len,
                                                    PADDING_SLOT_ID)

            # copy inputs to buffer for cudagraph
            self.input_ids[:batch_size] = input_ids
            self.positions[:batch_size] = clamped_positions
            self.hidden_states[:batch_size] = hidden_states

            if (self.use_full_cuda_graph
                    and batch_size <= self.cudagraph_batch_sizes[-1]):
                assert self.attn_metadata_cudagraph
                if self.method in ["eagle", "eagle3"]:
                    self.attn_metadata_cudagraph.seq_lens[:batch_size] = (
                        attn_metadata.seq_lens)
                    self.attn_metadata_cudagraph.slot_mapping[:batch_size] = (
                        attn_metadata.slot_mapping)
                    if i == 0:
                        self.attn_metadata_cudagraph.query_start_loc[:batch_size +
                                                                    1] = (
                                                                        attn_metadata
                                                                        .
                                                                        query_start_loc
                                                                    )
                        self.attn_metadata_cudagraph.block_table[:batch_size] = (
                            attn_metadata.block_table)
                elif self.method == "deepseek_mtp":
                    self.attn_metadata_cudagraph.num_actual_tokens = (
                        attn_metadata.num_actual_tokens)
                    self.attn_metadata_cudagraph.slot_mapping[:attn_metadata.num_decode_tokens] = (
                        attn_metadata.slot_mapping)
                    self.attn_metadata_cudagraph.num_decodes = (
                        attn_metadata.num_decodes)
                    self.attn_metadata_cudagraph.num_decode_tokens = (
                        attn_metadata.num_decode_tokens)
                    self.attn_metadata_cudagraph.num_prefills = (
                        attn_metadata.num_prefills)
                    self.attn_metadata_cudagraph.decode.seq_lens[:attn_metadata.num_decode_tokens] = (
                        attn_metadata.decode.seq_lens)

                    if i == 0:
                        self.attn_metadata_cudagraph.query_start_loc[:batch_size + 1] = (
                            attn_metadata.query_start_loc)
                        self.attn_metadata_cudagraph.decode.block_table[:attn_metadata.num_decode_tokens] = (
                            attn_metadata.decode.block_table)

            # Run the model.
            with set_forward_context(per_layer_attn_metadata,
                                     self.vllm_config,
                                     num_tokens=input_batch_size):
                ret_hidden_states = self.model(
                    self.input_ids[:input_batch_size],
                    self.positions[:input_batch_size],
                    self.hidden_states[:input_batch_size],
                )
                if self.method == "deepseek_mtp":
                    last_hidden_states = ret_hidden_states
                    hidden_states = last_hidden_states[:batch_size]
                else:
                    last_hidden_states, hidden_states = ret_hidden_states
                    hidden_states = hidden_states[:batch_size]

            logits = self.model.compute_logits(last_hidden_states[:batch_size],
                                               None)

            # TODO(wenlong): get more than one token for tree attention
            draft_token_ids = logits.argmax(dim=-1)
            draft_token_ids_list.append(draft_token_ids)

        # [batch_size, num_speculative_tokens]
        draft_token_ids = torch.stack(draft_token_ids_list, dim=1)

        return draft_token_ids