Files
vllm-ascend/vllm_ascend/worker/mtp_proposer_v1.py
Mengqing Cao 1327f9be1c Fix some ci issue and refactor modelrunner (#2445)
### What this PR does / why we need it?
Fix some ci issue and refactor modelrunner

### Does this PR introduce _any_ user-facing change?
N/A

### How was this patch tested?
CI passed with existing test.

- vLLM version: v0.10.0
- vLLM main:
4d9c61993a

---------

Signed-off-by: wangli <wangli858794774@gmail.com>
Signed-off-by: MengqingCao <cmq0113@163.com>
Signed-off-by: weiguihua2 <weiguihua2@huawei.com>
Co-authored-by: wangli <wangli858794774@gmail.com>
Co-authored-by: weiguihua2 <weiguihua2@huawei.com>
2025-08-20 09:01:04 +08:00

421 lines
19 KiB
Python

import types
import torch
import torch.nn as nn
import torchair
import vllm.envs as envs_vllm
from torchair import patch_for_hcom
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 get_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.ascend_config import get_ascend_config
from vllm_ascend.ascend_forward_context import set_ascend_forward_context
from vllm_ascend.attention.utils import AscendCommonAttentionMetadata
from vllm_ascend.models.deepseek_mtp import CustomDeepSeekMTP
from vllm_ascend.torchair.utils import TorchairCommonAttentionMetadata
from vllm_ascend.utils import ProfileExecuteDuration
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.hidden_size = vllm_config.model_config.get_hidden_size()
self.runner = runner
# persistent buffers for graph
self.input_ids = torch.zeros(self.runner.max_num_tokens,
dtype=torch.int32,
device=self.runner.device)
self.positions = torch.zeros(self.runner.max_num_tokens,
dtype=torch.int64,
device=self.runner.device)
self.hidden_states = torch.zeros(
(self.runner.max_num_tokens, self.hidden_size),
dtype=self.runner.dtype,
device=self.runner.device)
self.torchair_compiled_model = None # type: ignore
self.torchair_compiled_models = {} # type: ignore
@staticmethod
def prepare_inputs(
# [batch_size + 1]
cu_target_query_lens: torch.Tensor,
# [batch_size]
num_rejected_tokens: torch.Tensor,
token_ids: torch.Tensor,
positions: torch.Tensor,
hidden_states: torch.Tensor,
slot_mapping: torch.Tensor,
is_torchair_graph: bool = False
) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor,
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
if is_torchair_graph:
cu_num_tokens = cu_target_query_lens
relative_index = query_len_per_req - num_rejected_tokens - 1
token_indices = cu_num_tokens[:-1] + relative_index
# the seq len of each bath is padded to 1+num_speculative_tokens, thus input is same as the main model
target_token_ids = token_ids
target_positions = positions
target_hidden_states = hidden_states
target_slot_mapping = slot_mapping
else:
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.zeros(
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,
)
target_token_ids = token_ids[token_indices]
target_positions = positions[token_indices]
target_hidden_states = hidden_states[token_indices]
target_slot_mapping = slot_mapping[token_indices]
return cu_num_tokens, token_indices, target_token_ids, target_positions, target_hidden_states, target_slot_mapping
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,
token_indices=None) -> torch.Tensor:
num_tokens = target_token_ids.shape[0]
batch_size = next_token_ids.shape[0]
last_token_indices = cu_num_tokens[1:] - 1
# 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]
if token_indices is not None and self.runner.torchair_graph_enabled:
last_token_indices = token_indices
self.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()
# 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,
# )
is_running_torchair = self.runner.torchair_graph_enabled and \
not self.runner.with_prefill
if is_running_torchair:
num_input_tokens = self.runner.graph_pad_size
else:
num_input_tokens = num_tokens
seq_lens = target_positions[last_token_indices] + 1
seq_lens = seq_lens.int()
common_attn_metadata = AscendCommonAttentionMetadata(
query_start_loc=cu_num_tokens[:batch_size + 1],
query_start_loc_cpu=cu_num_tokens[:batch_size + 1].cpu(),
seq_lens_cpu=seq_lens.cpu(),
num_reqs=batch_size,
num_actual_tokens=num_tokens,
max_query_len=max_query_len,
actual_seq_lengths_q=self.runner.actual_seq_lengths_q,
block_table_tensor=self.runner.input_batch.block_table[0].
get_device_tensor(),
slot_mapping_cpu=target_slot_mapping,
positions=target_positions,
attn_mask=self.runner.attn_mask,
spec_attn_mask=self.runner.spec_attn_mask,
attn_state=self.runner.attn_state,
graph_pad_size=self.runner.graph_pad_size,
decode_token_per_req=self.runner.decode_token_per_req,
)
attn_metadata = self.runner.attn_metadata_builder.build(
common_attn_metadata, self.runner.get_model())
self.positions[:num_tokens] = target_positions
self.hidden_states[:num_tokens] = target_hidden_states
if attn_metadata.prefill is not None:
attn_metadata.prefill.query_lens = query_lens.cpu()
attn_metadata.prefill.input_positions = target_positions
attn_metadata.prefill.seq_lens = seq_lens
if not self.runner.torchair_graph_enabled:
# torch mode need to update num_tokens_across_dp
# TODO: adapt enable_dbo later
(num_input_tokens, num_tokens_across_dp, with_prefill,
_) = self.runner._get_forward_metadata_across_dp_and_pad(
num_tokens, self.runner.with_prefill, False)
attn_metadata.slot_mapping = target_slot_mapping
else:
# torchair mode can reuse self.runner.num_tokens_across_dp
num_tokens_across_dp = self.runner.num_tokens_across_dp
with_prefill = self.runner.with_prefill
with set_ascend_forward_context(
attn_metadata,
self.vllm_config,
num_tokens=num_input_tokens,
with_prefill=with_prefill,
num_tokens_across_dp=num_tokens_across_dp,
in_profile_run=self.runner.in_profile_run,
num_actual_tokens=num_tokens):
with ProfileExecuteDuration().capture_async('mtp_forward'):
model_kwargs = {}
model_kwargs["attn_metadata"] = attn_metadata
if self.runner.torchair_graph_enabled:
model_kwargs["kv_caches"] = self.runner.kv_caches[-1:]
if is_running_torchair:
torchair_compiled_model = self._get_torchair_lazy_compiled_model(
num_input_tokens)
hidden_states = torchair_compiled_model(
input_ids=self.input_ids[:num_input_tokens],
positions=self.positions[:num_input_tokens],
previous_hidden_states=self.
hidden_states[:num_input_tokens],
inputs_embeds=None,
intermediate_tensors=None,
spec_step_idx=0,
**model_kwargs)
else:
hidden_states = self.model(
input_ids=self.input_ids[:num_input_tokens],
positions=self.positions[:num_input_tokens],
previous_hidden_states=self.
hidden_states[:num_input_tokens],
kv_caches=self.runner.kv_caches[-1:])
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)
@torch.inference_mode()
def dummy_run(self,
num_tokens: int,
with_prefill: bool = False,
skip_attn: bool = False,
num_reqs: int = 0,
num_tokens_across_dp=None) -> None:
if not self.runner.torchair_graph_enabled:
# TODO: adapt enable_dbo later
(num_tokens, num_tokens_across_dp, with_prefill,
_) = self.runner._get_forward_metadata_across_dp_and_pad(
num_tokens, with_prefill, False)
is_running_torchair = self.runner.torchair_graph_enabled and \
not with_prefill
if is_running_torchair:
skip_attn = False
if skip_attn:
attn_metadata = None
else:
common_attn_metadata = TorchairCommonAttentionMetadata(
num_reqs=num_reqs,
num_actual_tokens=1,
actual_seq_lengths_q=self.runner.actual_seq_lengths_q,
attn_mask=self.runner.attn_mask,
spec_attn_mask=self.runner.spec_attn_mask,
decode_token_per_req=self.runner.decode_token_per_req,
)
attn_metadata = self.runner.attn_metadata_builder.build_torchair_graph_dummy(
common_attn_metadata)
input_ids = self.input_ids[:num_tokens]
positions = self.positions[:num_tokens]
previous_hidden_states = self.hidden_states[:num_tokens]
with set_ascend_forward_context(
attn_metadata,
self.vllm_config,
num_tokens=num_tokens,
with_prefill=with_prefill,
num_tokens_across_dp=num_tokens_across_dp,
in_profile_run=self.runner.in_profile_run,
num_actual_tokens=0):
if is_running_torchair:
assert attn_metadata is not None
torch._dynamo.mark_static(input_ids)
torch._dynamo.mark_static(positions)
torch._dynamo.mark_static(previous_hidden_states)
torch._dynamo.mark_static(attn_metadata.decode.block_table)
torch._dynamo.mark_static(attn_metadata.decode.input_positions)
if hasattr(attn_metadata.decode, "sin"):
torch._dynamo.mark_static(attn_metadata.decode.sin)
torch._dynamo.mark_static(attn_metadata.decode.cos)
torch._dynamo.mark_static(get_forward_context().mc2_mask)
torch._dynamo.mark_static(attn_metadata.slot_mapping)
torch._dynamo.mark_static(attn_metadata.decode.attn_mask)
torchair_compiled_model = self._get_torchair_lazy_compiled_model(
num_tokens)
torchair_compiled_model(
input_ids=input_ids,
positions=positions,
previous_hidden_states=previous_hidden_states,
inputs_embeds=None,
intermediate_tensors=None,
attn_metadata=attn_metadata,
kv_caches=self.runner.kv_caches[-1:],
spec_step_idx=0)
else:
self.model(input_ids=input_ids,
positions=positions,
previous_hidden_states=previous_hidden_states)
def _get_torchair_lazy_compiled_model(self, batch_size: int):
if batch_size < 0 or batch_size > self.runner.torchair_graph_batch_sizes[
-1]:
raise ValueError(
f"Bad graph batch size:{batch_size}! max_graph_batch_sizes:{self.runner.torchair_graph_batch_sizes[-1]}"
)
compiled_model = self.torchair_compiled_models.get(
batch_size
) if self.runner.use_cached_npu_graph else self.torchair_compiled_model
if compiled_model:
return compiled_model
patch_for_hcom()
config = torchair.CompilerConfig()
config.experimental_config.frozen_parameter = True
config.experimental_config.tiling_schedule_optimize = True
config.experimental_config.enable_view_optimize = \
get_ascend_config().torchair_graph_config.enable_view_optimize
torch.npu.set_compile_mode(jit_compile=False)
if not self.runner.use_cached_npu_graph:
npu_backend = torchair.get_npu_backend(compiler_config=config)
self.torchair_compiled_model = torch.compile(
self.model,
dynamic=True,
fullgraph=envs_vllm.VLLM_TEST_DYNAMO_FULLGRAPH_CAPTURE,
backend=npu_backend)
return self.torchair_compiled_model
else:
# Generate a new forward proxy code object to prevent the invalidation of
# compilation cache caused by dynamo retracing
forward_proxy_name = f"{self.model.__class__.__name__}_forward_with_batch_size_{batch_size}"
forward_fn = self.model.forward
code = forward_fn.__code__
# Mark code object with a new proxy name
modified_code = code.replace(co_name=forward_proxy_name, )
modified_func = types.FunctionType(modified_code,
forward_fn.__globals__,
name=forward_proxy_name,
argdefs=forward_fn.__defaults__)
self.model.__dict__[forward_proxy_name] = modified_func.__get__(
self.model, nn.Module)
self.torchair_compiled_models[
batch_size] = torchair.inference.cache_compile(
self.model.__dict__[forward_proxy_name],
dynamic=True,
fullgraph=envs_vllm.VLLM_TEST_DYNAMO_FULLGRAPH_CAPTURE,
config=config,
ge_cache=False)
return self.torchair_compiled_models[batch_size]
# 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