cd2f14a1b3
Adapts deepseek mtp with torch air graph mode in v1. --------- Signed-off-by: whx-sjtu <2952154980@qq.com>
226 lines
8.5 KiB
Python
226 lines
8.5 KiB
Python
import torch
|
|
from vllm.attention.layer import Attention
|
|
from vllm.config import (VllmConfig, get_layers_from_vllm_config,
|
|
set_current_vllm_config)
|
|
from vllm.forward_context import set_forward_context
|
|
from vllm.model_executor.model_loader import get_model_loader
|
|
from vllm.model_executor.model_loader.utils import (
|
|
process_weights_after_loading, set_default_torch_dtype)
|
|
from vllm.v1.sample.metadata import SamplingMetadata
|
|
|
|
from vllm_ascend.attention.mla_v1 import CommonAttentionMetadata
|
|
from vllm_ascend.models.deepseek_mtp import CustomDeepSeekMTP
|
|
|
|
|
|
# FIXME(woosuk): The logic here is duplicated with the main sampling code.
|
|
# We should refactor this to reuse the same sampling implementation.
|
|
def compute_probs_and_sample_next_token(
|
|
logits: torch.Tensor,
|
|
sampling_metadata: SamplingMetadata,
|
|
) -> tuple[torch.Tensor, torch.Tensor]:
|
|
if sampling_metadata.all_greedy:
|
|
# For greedy requests, draft_probs is not used in rejection sampling.
|
|
# Therefore, we can just return the logits.
|
|
probs = logits
|
|
next_token_ids = logits.argmax(dim=-1)
|
|
return next_token_ids, probs
|
|
|
|
is_greedy = sampling_metadata.temperature == -1
|
|
temperature = torch.where(is_greedy, 1.0, sampling_metadata.temperature)
|
|
logits.div_(temperature.view(-1, 1))
|
|
probs = logits.softmax(dim=-1, dtype=torch.float32)
|
|
|
|
# NOTE(woosuk): Currently, we ignore most of the sampling parameters in
|
|
# generating the draft tokens. We only use the temperature. While this
|
|
# could degrade the acceptance rate, it does not affect the distribution
|
|
# of the generated tokens after rejection sampling.
|
|
|
|
# TODO(woosuk): Consider seeds.
|
|
q = torch.empty_like(probs)
|
|
q.exponential_()
|
|
next_token_ids = probs.div_(q).argmax(dim=-1).view(-1)
|
|
if not sampling_metadata.all_random:
|
|
greedy_token_ids = probs.argmax(dim=-1)
|
|
next_token_ids = torch.where(
|
|
is_greedy,
|
|
greedy_token_ids,
|
|
next_token_ids,
|
|
)
|
|
return next_token_ids, probs
|
|
|
|
|
|
class MtpProposer:
|
|
|
|
def __init__(
|
|
self,
|
|
vllm_config: VllmConfig,
|
|
runner,
|
|
):
|
|
self.vllm_config = vllm_config
|
|
self.num_speculative_tokens = (
|
|
vllm_config.speculative_config.num_speculative_tokens)
|
|
self.block_size = vllm_config.cache_config.block_size
|
|
self.runner = runner
|
|
|
|
@staticmethod
|
|
def prepare_inputs(
|
|
# [batch_size + 1]
|
|
cu_target_query_lens: torch.Tensor,
|
|
# [batch_size]
|
|
num_rejected_tokens: torch.Tensor,
|
|
) -> tuple[torch.Tensor, torch.Tensor]:
|
|
# cu_target_query_lens: [0, a, a + b, a + b + c]
|
|
# num_rejected_tokens: [n1, n2, n3]
|
|
# num_tokens_per_req: [a - n1, b - n2, c - n3]
|
|
# cu_num_tokens: [0, a - n1, a + b - n1 - n2, a + b + c - n1 - n2 - n3]
|
|
# token_indices: [0, 1, ..., a - n1 - 1,
|
|
# a, a + 1, ..., a + b - n2 - 1,
|
|
# a + b, a + b + 1, ..., a + b + c - n3 - 1]
|
|
|
|
# [0, a, a + b, a + b + c] -> [a, b, c]
|
|
query_len_per_req = (cu_target_query_lens[1:] -
|
|
cu_target_query_lens[:-1])
|
|
# [a, b, c] -> [a - n1, b - n2, c - n3]
|
|
num_tokens_per_req = query_len_per_req - num_rejected_tokens
|
|
|
|
cu_num_tokens = torch.empty_like(cu_target_query_lens)
|
|
torch.cumsum(num_tokens_per_req, dim=0, out=cu_num_tokens[1:])
|
|
cu_num_tokens[0] = 0
|
|
|
|
# FIXME(woosuk): Avoid synchronization.
|
|
num_tokens = cu_num_tokens[-1].item()
|
|
token_indices = torch.empty(
|
|
num_tokens,
|
|
dtype=torch.int32,
|
|
device=cu_num_tokens.device,
|
|
)
|
|
|
|
BLOCK_SIZE = 1024
|
|
prepare_input_kernel(
|
|
token_indices,
|
|
cu_target_query_lens,
|
|
cu_num_tokens,
|
|
block_size=BLOCK_SIZE,
|
|
)
|
|
return cu_num_tokens, token_indices
|
|
|
|
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,
|
|
sampling_metadata: SamplingMetadata,
|
|
) -> tuple[torch.Tensor, torch.Tensor]:
|
|
num_tokens = target_token_ids.shape[0]
|
|
batch_size = next_token_ids.shape[0]
|
|
last_token_indices = cu_num_tokens[1:] - 1
|
|
|
|
input_ids = torch.empty_like(target_token_ids)
|
|
# Shift the input ids by one token.
|
|
# E.g., [a1, b1, b2, c1, c2, c3] -> [b1, b2, c1, c2, c3, c3]
|
|
input_ids[:-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]
|
|
input_ids[last_token_indices] = next_token_ids
|
|
|
|
query_lens = cu_num_tokens[1:] - cu_num_tokens[:-1]
|
|
max_query_len = query_lens.max().item()
|
|
|
|
seq_lens = (target_positions[last_token_indices] + 1)
|
|
|
|
common_attn_metadata = CommonAttentionMetadata(
|
|
query_start_loc=cu_num_tokens, seq_lens=seq_lens)
|
|
|
|
# FIXME: reorder_batch() needs to be called before build()
|
|
# because fields of attn_metadata_builder needs to be updated.
|
|
# However, currently reorder_batch() takes input_batch and
|
|
# scheduler_output as arguments, we should probably refactor
|
|
# the method to use new data structures which are independent
|
|
# from input_batch and scheduler_output.
|
|
# self.runner.attn_metadata_builder.reorder_batch(
|
|
# input_batch=self.runner.input_batch,
|
|
# scheduler_output=self.runner.scheduler_output,
|
|
# )
|
|
|
|
attn_metadata = self.runner.attn_metadata_builder.build(
|
|
num_reqs=batch_size,
|
|
num_actual_tokens=num_tokens,
|
|
max_query_len=max_query_len,
|
|
common_prefix_len=0,
|
|
common_attn_metadata=common_attn_metadata,
|
|
)
|
|
|
|
with set_forward_context(attn_metadata, self.vllm_config):
|
|
hidden_states = self.model(
|
|
input_ids=input_ids,
|
|
positions=target_positions,
|
|
previous_hidden_states=target_hidden_states,
|
|
)
|
|
sample_hidden_states = hidden_states[last_token_indices]
|
|
logits = self.model.compute_logits(sample_hidden_states, None)
|
|
draft_token_ids = logits.argmax(dim=-1)
|
|
|
|
# [batch_size, 1]
|
|
return draft_token_ids.view(-1, 1)
|
|
|
|
def load_model(self) -> None:
|
|
loader = get_model_loader(self.vllm_config.load_config)
|
|
|
|
target_attn_layer_names = set(
|
|
get_layers_from_vllm_config(self.vllm_config, Attention).keys())
|
|
draft_model_config = \
|
|
self.vllm_config.speculative_config.draft_model_config
|
|
target_device = self.vllm_config.device_config.device
|
|
|
|
with set_default_torch_dtype(
|
|
draft_model_config.dtype), set_current_vllm_config(
|
|
self.vllm_config):
|
|
self.model = CustomDeepSeekMTP(
|
|
vllm_config=self.vllm_config).to(target_device)
|
|
|
|
draft_attn_layer_names = (
|
|
get_layers_from_vllm_config(self.vllm_config, Attention).keys() -
|
|
target_attn_layer_names)
|
|
|
|
assert len(draft_attn_layer_names) == 1
|
|
self.attn_layer_name = next(iter(draft_attn_layer_names))
|
|
|
|
self.model.load_weights(
|
|
loader.get_all_weights(
|
|
self.vllm_config.speculative_config.draft_model_config,
|
|
self.model))
|
|
process_weights_after_loading(self.model, draft_model_config,
|
|
target_device)
|
|
|
|
|
|
# TODO Using torch instead of triton may result in poor performance
|
|
def prepare_input_kernel(out_ptr: torch.Tensor, cu_query_lens: torch.Tensor,
|
|
cu_num_tokens: torch.Tensor, block_size: int):
|
|
device = cu_query_lens.device
|
|
dtype = out_ptr.dtype
|
|
|
|
offsets = torch.arange(block_size, device=device, dtype=dtype)
|
|
start_pos = cu_num_tokens[:-1]
|
|
end_pos = cu_num_tokens[1:]
|
|
num_tokens = end_pos - start_pos
|
|
|
|
global_indices = (start_pos.view(-1, 1) + offsets.view(1, -1))
|
|
values = (cu_query_lens[:-1].view(-1, 1) + offsets.view(1, -1))
|
|
|
|
mask = (offsets.view(1, -1) < num_tokens.view(-1, 1))
|
|
|
|
global_indices_flat = global_indices[mask]
|
|
values_flat = values[mask]
|
|
out_ptr[global_indices_flat] = values_flat
|