class EagleProposer:
def __init__(
self,
vllm_config: VllmConfig,
device: torch.device,
runner=None,
):
self.vllm_config = vllm_config
self.speculative_config = vllm_config.speculative_config
self.draft_model_config = self.speculative_config.draft_model_config
self.method = self.speculative_config.method
self.runner = runner
self.dtype = vllm_config.model_config.dtype
self.max_model_len = vllm_config.model_config.max_model_len
self.block_size = vllm_config.cache_config.block_size
self.num_speculative_tokens = (
self.speculative_config.num_speculative_tokens)
self.max_num_tokens = (
vllm_config.scheduler_config.max_num_batched_tokens)
self.token_arange_np = np.arange(self.max_num_tokens)
# We need to get the hidden size from the draft model config because
# the draft model's hidden size can be different from the target model's
# hidden size (e.g., Llama 3.3 70B).
self.hidden_size = self.draft_model_config.get_hidden_size()
self.is_multimodal_model = vllm_config.model_config \
.is_multimodal_model
self.use_cuda_graph = (self.vllm_config.compilation_config.level
== CompilationLevel.PIECEWISE and
not self.vllm_config.model_config.enforce_eager)
self.cudagraph_batch_sizes = list(
reversed(
self.vllm_config.compilation_config.cudagraph_capture_sizes))
# persistent buffers for cuda graph
self.input_ids = torch.zeros(self.max_num_tokens,
dtype=torch.int32,
device=device)
self.positions = torch.zeros(self.max_num_tokens,
dtype=torch.int64,
device=device)
self.hidden_states = torch.zeros(
(self.max_num_tokens, self.hidden_size),
dtype=self.dtype,
device=device)
max_batch_size = vllm_config.scheduler_config.max_num_seqs
self.arange = torch.arange(
# We need +1 here because the arange is used to set query_start_loc,
# which has one more element than batch_size.
max_batch_size + 1,
device=device,
dtype=torch.int32,
)
self.inputs_embeds = torch.zeros(
(self.max_num_tokens, self.hidden_size),
dtype=self.dtype,
device=device)
# Parse the speculative token tree.
spec_token_tree = self.speculative_config.speculative_token_tree
self.tree_choices: list[tuple[int,
...]] = ast.literal_eval(spec_token_tree)
tree_depth = len(self.tree_choices[-1])
# Precompute per-level properties of the tree.
num_drafts_per_level = [0] * tree_depth
for node in self.tree_choices:
num_drafts_per_level[len(node) - 1] += 1
self.cu_drafts_per_level = [num_drafts_per_level[0]]
self.child_drafts_per_level = [num_drafts_per_level[0]]
for level in range(1, tree_depth):
self.cu_drafts_per_level.append(self.cu_drafts_per_level[-1] +
num_drafts_per_level[level])
self.child_drafts_per_level.append(num_drafts_per_level[level] //
num_drafts_per_level[level - 1])
# Find the first level where the tree branches off into one or more
# children.
self.first_branching_level = None
for level in range(tree_depth):
if self.cu_drafts_per_level[level] > level + 1:
self.first_branching_level = level
break
# Precompute draft position offsets in flattened tree.
self.tree_draft_pos_offsets = torch.arange(
1,
len(self.tree_choices) + 1,
device=device,
dtype=torch.int32,
).repeat(max_batch_size, 1)
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,
# [batch_size]
next_token_ids: torch.Tensor,
common_attn_metadata: CommonAttentionMetadata,
sampling_metadata: SamplingMetadata,
mm_embeds: Optional[list[torch.Tensor]] = None,
) -> torch.Tensor:
num_tokens = target_token_ids.shape[0]
batch_size = next_token_ids.shape[0]
last_token_indices = common_attn_metadata.query_start_loc[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
assert self.runner is not None
# FIXME: need to consider multiple kv_cache_groups
attn_metadata = self.runner.attn_groups[0][0].metadata_builder\
.build_for_drafting(common_attn_metadata=common_attn_metadata,
draft_index=0)
# 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 self.is_multimodal_model:
input_ids = self.input_ids[:num_tokens]
inputs_embeds = self.model.get_input_embeddings(
input_ids,
multimodal_embeddings=mm_embeds or None,
)
self.inputs_embeds[:num_tokens] = inputs_embeds
inputs_embeds = self.inputs_embeds[:num_input_tokens]
input_ids = None
else:
inputs_embeds = None
input_ids = self.input_ids[:num_input_tokens]
with set_forward_context(per_layer_attn_metadata,
self.vllm_config,
num_tokens=num_input_tokens):
ret_hidden_states = self.model(
input_ids=input_ids,
positions=self.positions[:num_input_tokens],
hidden_states=self.hidden_states[:num_input_tokens],
inputs_embeds=inputs_embeds,
)
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)
positions = target_positions[last_token_indices]
hidden_states = hidden_states[last_token_indices]
if self.first_branching_level == 0:
# Branching has occurred at the root level. Draft using tree
# attention.
draft_token_ids_list = self.propose_tree(
tree_root_level=0,
batch_size=batch_size,
logits=logits,
positions=positions,
hidden_states=hidden_states,
common_attn_metadata=common_attn_metadata,
)
# [batch_size, num_tree_tokens]
return torch.cat(draft_token_ids_list, dim=1)
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.
# On ROCm, both AiterFlashAttention and TritonAttention
# support multi-token eagle spec decode.
if current_platform.is_rocm():
assert isinstance(
attn_metadata,
(TritonAttentionMetadata, AiterFlashAttentionMetadata,
FlashAttentionMetadata))
else:
# Currently, only FlashAttention and TreeAttention support
# multi-token eagle spec decode. This is because the code below
# makes assumptions about attn_metadata attributes available.
assert isinstance(attn_metadata,
(FlashAttentionMetadata, TreeAttentionMetadata))
# Generate the remaining draft tokens.
draft_token_ids_list = [draft_token_ids]
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]
for token_index 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)
# Increment the sequence lengths.
attn_metadata.max_seq_len += 1
attn_metadata.seq_lens += 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 = attn_metadata.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.is_multimodal_model:
inputs_embeds = self.model.get_input_embeddings(input_ids)
self.inputs_embeds[:batch_size] = inputs_embeds
inputs_embeds = self.inputs_embeds[:input_batch_size]
input_ids = None
else:
inputs_embeds = None
input_ids = self.input_ids[:input_batch_size]
# Run the model.
with set_forward_context(per_layer_attn_metadata,
self.vllm_config,
num_tokens=input_batch_size):
last_hidden_states, hidden_states = self.model(
input_ids=input_ids,
positions=self.positions[:input_batch_size],
hidden_states=self.hidden_states[:input_batch_size],
inputs_embeds=inputs_embeds,
)
hidden_states = hidden_states[:batch_size]
logits = self.model.compute_logits(last_hidden_states[:batch_size],
None)
if self.first_branching_level == token_index + 1:
# Branching has occurred. The remaining tokens are drafted
# using tree attention.
draft_token_ids_list += self.propose_tree(
tree_root_level=token_index + 1,
batch_size=batch_size,
logits=logits,
positions=positions,
hidden_states=hidden_states,
common_attn_metadata=common_attn_metadata,
)
# [batch_size, num_tree_tokens]
return torch.cat(draft_token_ids_list, dim=1)
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
def propose_tree(
self,
tree_root_level: int,
batch_size: int,
# [num_tokens, vocab_size]
logits: torch.Tensor,
# [num_tokens]
positions: torch.Tensor,
# [num_tokens, hidden_size]
hidden_states: torch.Tensor,
common_attn_metadata: CommonAttentionMetadata,
) -> list[torch.Tensor]:
tree_attn_metadata_builder = \
self.runner.attn_groups[0][0].metadata_builder
assert isinstance(tree_attn_metadata_builder,
TreeAttentionMetadataBuilder)
total_num_drafts = self.cu_drafts_per_level[tree_root_level]
level_num_drafts = total_num_drafts
# Sample a draft token for each child at the tree root level.
num_children = self.child_drafts_per_level[tree_root_level]
if num_children == 1:
draft_token_ids = logits.argmax(dim=-1).view(batch_size, -1)
else:
draft_token_ids = torch.topk(logits, num_children,
dim=-1).indices.view(batch_size, -1)
draft_token_ids_list = [draft_token_ids]
draft_hidden_states = hidden_states.view(batch_size, 1, -1)
# Initialize empty tensors for concatenation with the level outputs.
tree_input_ids = torch.empty(0,
device=self.input_ids.device,
dtype=self.input_ids.dtype)
tree_positions = torch.empty(0,
device=self.positions.device,
dtype=self.positions.dtype)
tree_hidden_states = torch.empty(0,
device=self.hidden_states.device,
dtype=self.hidden_states.dtype)
# Precompute the draft token positions.
flattened_draft_positions = (
positions.view(batch_size, -1) +
self.tree_draft_pos_offsets[:batch_size, :])
tree_depth = len(self.cu_drafts_per_level)
for level in range(tree_root_level, tree_depth - 1):
# Get draft positions for RoPE.
draft_positions = positions + (level + 1)
exceeds_max_model_len = (positions +
total_num_drafts) >= 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_draft_positions = torch.where(
exceeds_max_model_len,
0,
draft_positions,
)
if level_num_drafts > 1:
# Repeat the positions for each draft at this level.
draft_positions = clamped_draft_positions.repeat_interleave(
level_num_drafts).reshape(batch_size, -1)
if num_children > 1:
# Repeat draft hidden states for each child.
draft_hidden_states = draft_hidden_states.repeat_interleave(
num_children, dim=1)
# Concatenate the draft tokens, positions, and hidden states.
tree_input_ids = torch.cat([tree_input_ids, draft_token_ids],
dim=1)
tree_positions = torch.cat([tree_positions, draft_positions],
dim=1)
tree_hidden_states = torch.cat(
[tree_hidden_states, draft_hidden_states], dim=1)
# Build new attention metadata for the next level of drafts.
# This is necessary to support tree attention.
query_len = total_num_drafts - tree_root_level
common_attn_metadata = replace(
common_attn_metadata,
query_start_loc=query_len * self.arange[:batch_size + 1],
seq_lens=common_attn_metadata.seq_lens + level_num_drafts,
num_actual_tokens=batch_size * query_len,
max_query_len=query_len,
)
attn_metadata = tree_attn_metadata_builder.build_for_drafting(
common_attn_metadata=common_attn_metadata,
draft_index=tree_root_level + 1,
)
# Apply new attention metadata to all layers.
per_layer_attn_metadata = {}
for layer_name in self.attn_layer_names:
per_layer_attn_metadata[layer_name] = attn_metadata
# 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.
query_positions = flattened_draft_positions[:, level:level +
query_len]
block_numbers = query_positions // self.block_size
block_ids = attn_metadata.block_table.gather(dim=1,
index=block_numbers)
slot_mapping = (block_ids * self.block_size +
query_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.
slot_mapping[exceeds_max_model_len] = PADDING_SLOT_ID
attn_metadata.slot_mapping = slot_mapping.view(-1)
# Copy inputs to buffer for cudagraph.
num_tokens = attn_metadata.num_actual_tokens
input_ids = tree_input_ids.view(-1)
self.input_ids[:num_tokens] = input_ids
self.positions[:num_tokens] = tree_positions.view(-1)
self.hidden_states[:num_tokens] = tree_hidden_states.view(
num_tokens, -1)
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
# Run the model.
with set_forward_context(per_layer_attn_metadata,
self.vllm_config,
num_tokens=num_input_tokens):
last_hidden_states, hidden_states = self.model(
input_ids=self.input_ids[:num_input_tokens],
positions=self.positions[:num_input_tokens],
hidden_states=self.hidden_states[:num_input_tokens],
inputs_embeds=None,
)
# Get the output hidden states for the draft tokens.
draft_hidden_states = hidden_states[:num_tokens].view(
batch_size, query_len, -1)[:, -level_num_drafts:]
draft_last_hidden_states = last_hidden_states[:num_tokens].view(
batch_size, query_len, -1)[:, -level_num_drafts:]
# Get the output logits for the draft tokens.
logits = self.model.compute_logits(
draft_last_hidden_states.reshape(batch_size * level_num_drafts,
-1),
None,
)
# Sample a draft token for each child at the next tree level.
num_children = self.child_drafts_per_level[level + 1]
if num_children == 1:
draft_token_ids = logits.argmax(dim=-1).view(batch_size, -1)
else:
draft_token_ids = torch.topk(logits, num_children,
dim=-1).indices.view(
batch_size, -1)
draft_token_ids_list.append(draft_token_ids)
# Update the # drafts counters for the next tree level.
level_num_drafts = self.cu_drafts_per_level[level +
1] - total_num_drafts
total_num_drafts = self.cu_drafts_per_level[level + 1]
return draft_token_ids_list
def prepare_inputs(
self,
common_attn_metadata: CommonAttentionMetadata,
# [batch_size]
num_rejected_tokens: torch.Tensor
) -> tuple[CommonAttentionMetadata, torch.Tensor]:
"""
This function is used to prepare the inputs for the spec decode.
It updates to the common_attn_metadata to account for the rejected
tokens (and newly sampled tokens). It also returns the token indices
of the tokens that should be fed to the speculator.
"""
# E.g.
# common_attn_metadata.query_start_loc{_cpu}:
# [0, q1, q1 + q2, q1 + q2 + q3]
# common_attn_metadata.seq_lens{_cpu}: [s1, s2, s3]
# num_rejected_tokens: [n1, n2, n3]
# This function computes the intermediate values:
# num_tokens_per_req: [q1 - n1, q2 - n2, q3 - n3]
# And returns:
# common_attn_metadata.query_start_loc{_cpu}:
# [0, q1 - n1, q1 + q2 - n1 - n2, q1 + q2 + q3 - n1 - n2 - n3]
# common_attn_metadata.seq_lens{_cpu}:
# [s1 - n1 + 1, s2 - n2 + 1, s3 - n3 + 1]
# token_indices: [0, 1, ..., q1 - n1 - 1,
# q1, q1 + 1, ..., q1 + q2 - n2 - 1,
# q1 + q2, q1 + q2 + 1, ..., q1 + q2 + q3 - n3 - 1]
device = common_attn_metadata.query_start_loc.device
query_start_loc_cpu = common_attn_metadata.query_start_loc_cpu
new_seq_lens_cpu = common_attn_metadata.seq_lens_cpu \
- num_rejected_tokens
# [0, q1, q1 + q2, q1 + q2 + q3] -> [q1, q2, q3]
new_query_len_per_req = (query_start_loc_cpu[1:] -
query_start_loc_cpu[:-1])
# [q1, q2, q3] -> [q1 - n1, q2 - n2, q3 - n3]
new_num_tokens_per_req = new_query_len_per_req - num_rejected_tokens
new_num_tokens_per_req_np = new_num_tokens_per_req.numpy()
# [q1 - n1, q2 - n2, q3 - n3] ->
# [0, q1 - n1, q1 + q2 - n1 - n2, q1 + q2 + q3 - n1 - n2 - n3]
new_query_start_loc_cpu = torch.zeros(
query_start_loc_cpu.shape,
dtype=torch.int32,
pin_memory=is_pin_memory_available())
new_query_start_loc_np = new_query_start_loc_cpu.numpy()
np.cumsum(new_num_tokens_per_req_np, out=new_query_start_loc_np[1:])
total_num_tokens = new_query_start_loc_np[-1]
# Example assuming num_tokens_per_req_np = [2, 4, 3]
# this implies that `new_query_start_locs` is:
# [0, 2, 6, 9] ->
# [0, 0, 2, 2, 2, 2, 6, 6, 6]
# _r1_ ____r2____ ___r3__
new_query_start_locs_expanded = np.repeat(new_query_start_loc_np[:-1],
new_num_tokens_per_req_np)
# [0, 1, 2, 3, 4, 5, 6, 7, 8] ->
# [0, 1, 0, 1, 2, 3, 0, 1, 2]
# _r1_ ____r2____ ___r3__
token_offests = self.token_arange_np[:total_num_tokens] \
- new_query_start_locs_expanded
# Expand starting positions to match token pattern
# [0, q1, q1 + q2] ->
# [0, 0, q1, q1, q1, q1, q1 + q2, q1 + q2, q1 + q2]
# _r1_ _____r2_______ ___________r3____________
old_query_start_locs_expanded = np.repeat(
query_start_loc_cpu[:-1].numpy(), new_num_tokens_per_req_np)
# Final token indices are:
# [0, 1, // req 1
# q1 + 0, q1 + 1, q1 + 2, q1 + 3, // req 2
# q1 + q2 + 0, q1 + q2 + 1, q1 + q2 + 2] // req 3
token_indices_np = token_offests + old_query_start_locs_expanded
token_indices = torch.from_numpy(token_indices_np).to(
device, non_blocking=True)
spec_common_attn_metadata = CommonAttentionMetadata(
query_start_loc=new_query_start_loc_cpu.to(device,
non_blocking=True),
seq_lens=new_seq_lens_cpu.to(device, non_blocking=True),
query_start_loc_cpu=new_query_start_loc_cpu,
seq_lens_cpu=new_seq_lens_cpu,
num_computed_tokens_cpu=common_attn_metadata.
num_computed_tokens_cpu,
num_reqs=common_attn_metadata.num_reqs,
num_actual_tokens=total_num_tokens,
max_query_len=new_query_len_per_req.max().item(),
block_table_tensor=common_attn_metadata.block_table_tensor,
slot_mapping=common_attn_metadata.slot_mapping[token_indices],
causal=True,
)
return spec_common_attn_metadata, token_indices
def load_model(self, target_model: nn.Module) -> None:
draft_model_config = \
self.vllm_config.speculative_config.draft_model_config
target_attn_layer_names = set(
get_layers_from_vllm_config(self.vllm_config, Attention).keys())
from vllm.compilation.backends import set_model_tag
with set_model_tag("eagle_head"):
self.model = get_model(vllm_config=self.vllm_config,
model_config=draft_model_config)
draft_attn_layer_names = (
get_layers_from_vllm_config(self.vllm_config, Attention).keys() -
target_attn_layer_names)
self.attn_layer_names = list(draft_attn_layer_names)
if supports_multimodal(target_model):
# handle multimodality
self.model.config.image_token_index = (
target_model.config.image_token_index)
target_language_model = target_model.get_language_model()
else:
target_language_model = target_model
# share embed_tokens with the target model if needed
if get_pp_group().world_size == 1 \
and self.model.model.embed_tokens.weight.shape \
== target_language_model.model.embed_tokens.weight.shape:
logger.info(
"Assuming the EAGLE head shares the same vocab embedding" \
" with the target model."
)
del self.model.model.embed_tokens
self.model.model.embed_tokens = (
target_language_model.model.embed_tokens)
else:
logger.info(
"The EAGLE head's vocab embedding will be loaded separately" \
" from the target model."
)
# share lm_head with the target model if needed
# some model definition do not define lm_head explicitly
# and reuse embed_tokens for lm_head, e.g., CohereForCausalLM
if self.vllm_config.speculative_config.method != "eagle3" and \
hasattr(target_language_model, "lm_head"):
logger.info("Loading EAGLE LM head weights from the target model.")
self.model.lm_head = target_language_model.lm_head
@torch.inference_mode()
def dummy_run(
self,
num_tokens: int,
) -> None:
with set_forward_context(None, self.vllm_config,
num_tokens=num_tokens):
if self.is_multimodal_model:
input_ids = None
inputs_embeds = self.inputs_embeds[:num_tokens]
else:
input_ids = self.input_ids[:num_tokens]
inputs_embeds = None
self.model(
input_ids=input_ids,
positions=self.positions[:num_tokens],
hidden_states=self.hidden_states[:num_tokens],
inputs_embeds=inputs_embeds,
)
def validate_same_kv_cache_group(self,
kv_cache_config: KVCacheConfig) -> None:
"""
Validate that all eagle layers belong to the same KVCacheGroup.
Need this assumption to ensure all eagle layers can use the
same AttentionMetadata.
May extend to multiple AttentionMetadata in the future.
"""
kv_cache_groups: dict[str, int] = {}
for id, kv_cache_group in enumerate(kv_cache_config.kv_cache_groups):
for layer_name in kv_cache_group.layer_names:
kv_cache_groups[layer_name] = id
assert len(
set([
kv_cache_groups[layer_name]
for layer_name in self.attn_layer_names
])
) == 1, "All eagle layers should belong to the same kv cache group"