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performance optimization, usability optimization and API compatibility adjustments for deepseek with npu graph mode (#731)

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### What this PR does / why we need it?
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- Fixes #
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1. Improve inference speed and usability for deepsek models with NPU
graph mode.
2. Modify some codes to adapt to CANN 8.1.RC1.beta1.
3. Add a switch for NPU graph mode and its cache.

### Does this PR introduce _any_ user-facing change?
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Note that it means *any* user-facing change including all aspects such
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This PR provides an experimental configuration to enable NPU graph mode
for Deepseek models. User can set
additional_config={'enable_graph_mode': True} to try this feature. Note
that this feature currently only supports for V0 engine.


### How was this patch tested?
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This patch was tested with the newest torch_npu 2.5.1
(https://pypi.org/project/torch-npu/#files) and CANN 8.1.RC1.beta1
toolkit&nnal&kernels
(https://www.hiascend.com/developer/download/community/result?module=cann)
released in 25/30 April.

Signed-off-by: linfeng-yuan <1102311262@qq.com>
This commit is contained in:
linfeng-yuan
2025-05-01 13:51:42 +08:00
committed by GitHub
parent 399b03830d
commit 84e2ed898b
6 changed files with 163 additions and 51 deletions
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20
vllm_ascend/attention/attention.py
View File

@@ -590,14 +590,14 @@ class AscendMetadataBuilder(CommonMetadataBuilder[AscendMetadata]):
self.input_builder.chunked_prefill_enabled)
device = self.runner.device
use_torchair_graph = graph_pad_size != -1
use_npu_graph = graph_pad_size != -1
max_query_len = max(query_lens)
max_prefill_seq_len = max(self.prefill_seq_lens, default=0)
max_decode_seq_len = max(self.curr_seq_lens, default=0)
num_decode_tokens = self.num_decode_tokens
if self.num_prefills == 0 and use_torchair_graph:
if self.num_prefills == 0 and use_npu_graph:
num_seqs = len(seq_lens)
self.slot_mapping.extend([PAD_SLOT_ID] * graph_pad_size)
self.block_tables.extend([[]] * graph_pad_size)
@@ -915,7 +915,7 @@ class AscendMLAAttentionBackendImpl(MLAAttentionImpl):
# npu_kv_rmsnorm_rope_cache needs [B, N, S, D]
kv = kv.view(B, N, S, self.kv_lora_rank + self.qk_rope_head_dim)
k_pe, k_nope = torch.ops.npu_inference.npu_kv_rmsnorm_rope_cache(
k_pe, k_nope, _, _ = torch.ops.npu_inference.npu_kv_rmsnorm_rope_cache(
kv,
self.kv_a_layernorm.weight,
cos,
@@ -1123,9 +1123,17 @@ class AscendMLAAttentionBackendImpl(MLAAttentionImpl):
elif attn_metadata.decode_metadata:
assert kv_cache is not None
if self.enable_graph_mode:
# TorchAir's shape is [bs, num_heads_per_rank, seq_len, dim]
# shape of query for npu graph mode should be:
# [bs, num_heads_per_rank, seq_len, dim]
q_nope = q_nope.view(num_tokens, self.num_heads, 1, -1)
q_pe = q_pe.view(num_tokens, self.num_heads, 1, -1)
# shape of knope/k_pe for npu graph mode should be:
# [num_blocks, num_kv_heads, block_size, self.kv_lora_rank/self.qk_rope_head_dim]
block_size = kv_cache[0].shape[1]
k_nope = k_nope.view(-1, self.num_kv_heads, block_size,
self.kv_lora_rank)
k_pe = k_pe.view(-1, self.num_kv_heads, block_size,
self.qk_rope_head_dim)
attn_output, _ = torch.ops.npu.npu_fused_infer_attention_score(
q_nope,
k_nope,
@@ -1133,14 +1141,14 @@ class AscendMLAAttentionBackendImpl(MLAAttentionImpl):
query_rope=q_pe,
key_rope=k_pe,
num_heads=self.num_heads,
num_key_value_heads=1,
num_key_value_heads=self.num_kv_heads,
input_layout="BNSD",
atten_mask=attn_metadata.attn_mask,
scale=self.scale,
antiquant_mode=0,
antiquant_scale=None,
block_table=attn_metadata.block_tables,
block_size=kv_cache[0].shape[1],
block_size=block_size,
actual_seq_lengths_kv=attn_metadata.seq_lens,
)
attn_output = attn_output.view(num_tokens, -1,

92
vllm_ascend/models/deepseek_v2.py
View File

@@ -30,6 +30,7 @@ from typing import Any, Dict, List, Optional, Union
import torch
import torch.distributed as dist
import torch_npu
from torch import nn
from transformers import PretrainedConfig
from vllm.attention import Attention, AttentionMetadata
@@ -39,10 +40,13 @@ from vllm.distributed import (get_dp_group, get_pp_group,
get_tensor_model_parallel_world_size,
get_tp_group, tensor_model_parallel_all_reduce)
from vllm.forward_context import get_forward_context
from vllm.model_executor.layers.activation import SiluAndMul
from vllm.model_executor.layers.layernorm import RMSNorm
from vllm.model_executor.layers.linear import (ColumnParallelLinear,
MergedColumnParallelLinear,
ReplicatedLinear,
RowParallelLinear)
RowParallelLinear,
UnquantizedLinearMethod)
from vllm.model_executor.layers.logits_processor import LogitsProcessor
from vllm.model_executor.layers.quantization import QuantizationConfig
from vllm.model_executor.layers.rotary_embedding import get_rope
@@ -55,15 +59,84 @@ from vllm.model_executor.models.deepseek_v2 import \
yarn_get_mscale # ruff: noqa: E501
from vllm.model_executor.models.deepseek_v2 import (DeepseekV2Attention,
DeepseekV2DecoderLayer,
DeepseekV2MLAAttention,
DeepseekV2MLP)
DeepseekV2MLAAttention)
from vllm.model_executor.models.utils import (
PPMissingLayer, make_empty_intermediate_tensors_factory, make_layers,
maybe_prefix)
# >>>>>>> dcd5c73 (Feat: Graph mode for deepseek v2/v3.)
from vllm.sequence import IntermediateTensors
from vllm_ascend.ops.fused_moe import AscendFusedMoE
from vllm_ascend.quantization.w8a8_dynamic import AscendW8A8DynamicLinearMethod
class CustomDeepseekV2MLP(nn.Module):
def __init__(
self,
hidden_size: int,
intermediate_size: int,
hidden_act: str,
quant_config: Optional[QuantizationConfig] = None,
reduce_results: bool = True,
prefix: str = "",
) -> None:
super().__init__()
self.gate_up_proj = MergedColumnParallelLinear(
hidden_size, [intermediate_size] * 2,
bias=False,
quant_config=quant_config,
prefix=f"{prefix}.gate_up_proj")
self.down_proj = RowParallelLinear(intermediate_size,
hidden_size,
bias=False,
quant_config=quant_config,
reduce_results=reduce_results,
prefix=f"{prefix}.down_proj")
if hidden_act != "silu":
raise ValueError(f"Unsupported activation: {hidden_act}. "
"Only silu is supported for now.")
self.act_fn = SiluAndMul()
# NOTE: `torch_npu.npu_dequant_swiglu_quant` can only be enabled in dynamic quant
self.is_dynamic_quant = not isinstance(
self.gate_up_proj.quant_method,
UnquantizedLinearMethod) and isinstance(
self.gate_up_proj.quant_method.quant_method,
AscendW8A8DynamicLinearMethod)
def forward(self, x):
if self.is_dynamic_quant:
x, dynamic_scale = torch_npu.npu_dynamic_quant(x)
x = torch_npu.npu_quant_matmul(
x,
self.gate_up_proj.weight,
self.gate_up_proj.weight_scale,
output_dtype=torch.int32,
)
x, dynamic_scale = torch_npu.npu_dequant_swiglu_quant(
x=x,
weight_scale=self.gate_up_proj.weight_scale_fp32,
activation_scale=dynamic_scale,
bias=None,
quant_scale=None,
quant_offset=None,
group_index=None,
activate_left=True,
quant_mode=1)
x = torch_npu.npu_quant_matmul(
x,
self.down_proj.weight,
self.down_proj.weight_scale,
pertoken_scale=dynamic_scale,
output_dtype=torch.bfloat16,
)
if self.down_proj.reduce_results and self.down_proj.tp_size > 1:
x = tensor_model_parallel_all_reduce(x)
return x
gate_up, _ = self.gate_up_proj(x)
x = self.act_fn(gate_up)
x, _ = self.down_proj(x)
return x
class CustomDeepseekV2MoE(nn.Module):
@@ -119,7 +192,7 @@ class CustomDeepseekV2MoE(nn.Module):
if config.n_shared_experts is not None:
intermediate_size = (config.moe_intermediate_size *
config.n_shared_experts)
self.shared_experts = DeepseekV2MLP(
self.shared_experts = CustomDeepseekV2MLP(
hidden_size=config.hidden_size,
intermediate_size=intermediate_size,
hidden_act=config.hidden_act,
@@ -392,7 +465,7 @@ class CustomDeepseekV2DecoderLayer(DeepseekV2DecoderLayer):
prefix=f"{prefix}.mlp",
)
else:
self.mlp = DeepseekV2MLP(
self.mlp = CustomDeepseekV2MLP(
hidden_size=config.hidden_size,
intermediate_size=config.intermediate_size,
hidden_act=config.hidden_act,
@@ -442,8 +515,9 @@ class CustomDeepseekV2DecoderLayer(DeepseekV2DecoderLayer):
hidden_states, residual)
hidden_states = self.mlp(hidden_states)
if isinstance(self.mlp,
DeepseekV2MLP) and hidden_states.dtype == torch.float16:
if isinstance(
self.mlp,
CustomDeepseekV2MLP) and hidden_states.dtype == torch.float16:
# Fix FP16 overflow
# Scaling the DeepseekV2MLP output, it is the input of
# input_layernorm of next decoder layer.
@@ -582,4 +656,4 @@ class CustomDeepseekV2ForCausalLM(DeepseekV2ForCausalLM):
class CustomDeepseekV3ForCausalLM(CustomDeepseekV2ForCausalLM):
pass
pass

6
vllm_ascend/ops/rotary_embedding.py
View File

@@ -221,10 +221,16 @@ def _set_cos_sin_cache(self, seq_len, device, dtype):
t = torch.arange(seq_len, device=device, dtype=torch.float32)
freqs = torch.outer(t, inv_freq)
cos_cached = torch.cat([freqs, freqs], dim=-1).cos() * self.mscale
sin_cached = torch.cat([freqs, freqs], dim=-1).sin() * self.mscale
cos_cached = cos_cached.to(dtype)
sin_cached = sin_cached.to(dtype)
cache = torch.cat([freqs.cos() * self.mscale,
freqs.sin() * self.mscale],
dim=-1).to(dtype)
self.register_buffer("cos_sin_cache", cache, persistent=False)
self.register_buffer("cos_cached", cos_cached, persistent=False)
self.register_buffer("sin_cached", sin_cached, persistent=False)
def deepseek_rope_init_func(

10
vllm_ascend/platform.py
View File

@@ -124,7 +124,10 @@ class NPUPlatform(Platform):
enforce_eager = True
logger.warning(
"NPU compilation support pending. Will be available in future CANN and "
"torch_npu releases. Using default: enforce_eager=True")
"torch_npu releases. NPU graph mode is currently experimental and disabled "
"by default. You can just adopt additional_config={'enable_graph_mode': True} "
"to serve deepseek models with NPU graph mode on vllm-ascend with V0 engine. "
)
if enforce_eager or compilation_config.level == CompilationLevel.NO_COMPILATION:
logger.info("Compilation disabled, using eager mode by default")
@@ -150,6 +153,11 @@ class NPUPlatform(Platform):
"enable_graph_mode is not supported because the version of torch is too low, forcing close enable_graph_mode"
)
vllm_config.additional_config["enable_graph_mode"] = False
if enable_graph_mode and envs.VLLM_USE_V1:
logger.warning(
"NPU graph mode is still experimental and not supported for V1 currently, "
"it has been disabled automatically.")
vllm_config.additional_config["enable_graph_mode"] = False
parallel_config = vllm_config.parallel_config
if parallel_config and parallel_config.worker_cls == "auto":

61
vllm_ascend/quantization/w8a8_dynamic.py
View File

@@ -62,38 +62,38 @@ def apply_mlp(x: torch.Tensor,
h = x
pertoken_scale = dynamic_scale
output_dtype = torch.bfloat16 if w1_scale.dtype == torch.bfloat16 else \
torch.float16
# gmm1: gate_up_proj
gate_up_out_list = torch_npu.npu_grouped_matmul(
x=[h],
weight=[w1],
scale=[w1_scale],
per_token_scale=[pertoken_scale],
split_item=3,
group_list_type=group_list_type,
group_type=0,
group_list=group_list,
output_dtype=output_dtype)
gate_up_out = gate_up_out_list[0]
gate_up_out = torch_npu.npu_grouped_matmul(x=[h],
weight=[w1],
split_item=3,
group_list_type=group_list_type,
group_type=0,
group_list=group_list,
output_dtype=torch.int32)[0]
# swiglu
swiglu_out = torch_npu.npu_swiglu(gate_up_out)
swiglu_out, swiglu_out_scale = torch_npu.npu_dynamic_quant(swiglu_out)
swiglu_out, swiglu_out_scale = torch_npu.npu_dequant_swiglu_quant(
x=gate_up_out,
weight_scale=w1_scale,
activation_scale=pertoken_scale,
bias=None,
quant_scale=None,
quant_offset=None,
group_index=group_list,
activate_left=True,
quant_mode=1,
)
# down_proj
down_out_list = torch_npu.npu_grouped_matmul(
x=[swiglu_out],
weight=[w2],
scale=[w2_scale],
per_token_scale=[swiglu_out_scale],
split_item=3,
group_list_type=group_list_type,
group_type=0,
group_list=group_list,
output_dtype=output_dtype)
return down_out_list[0]
down_out = torch_npu.npu_grouped_matmul(x=[swiglu_out],
weight=[w2],
scale=[w2_scale],
per_token_scale=[swiglu_out_scale],
split_item=2,
group_list_type=group_list_type,
group_type=0,
group_list=group_list,
output_dtype=w2_scale.dtype)[0]
return down_out
def fused_experts_with_mc2(
@@ -363,7 +363,10 @@ class AscendW8A8DynamicLinearMethod:
def process_weights_after_loading(self, layer):
if self.transpose_weight:
layer.weight.data = layer.weight.data.transpose(0, 1).contiguous()
# cast quantized weight tensors in NZ format (29) for higher inference speed
layer.weight.data = torch_npu.npu_format_cast(layer.weight.data, 29)
layer.weight_scale.data = layer.weight_scale.data.flatten()
layer.weight_scale_fp32 = layer.weight_scale.data.to(torch.float32)
layer.weight_offset.data = layer.weight_offset.data.flatten()
@@ -508,7 +511,7 @@ class AscendW8A8DynamicFusedMoEMethod:
layer.w2_weight.data = layer.w2_weight.data.transpose(
1, 2).contiguous()
layer.w13_weight_scale.data = layer.w13_weight_scale.data.view(
layer.w13_weight_scale.data.shape[0], -1)
layer.w13_weight_scale.data.shape[0], -1).to(torch.float32)
layer.w13_weight_offset.data = layer.w13_weight_offset.data.view(
layer.w13_weight_offset.data.shape[0], -1)
layer.w2_weight_scale.data = layer.w2_weight_scale.data.view(

25
vllm_ascend/worker/model_runner.py
View File

@@ -69,6 +69,8 @@ if TYPE_CHECKING:
TModelInputForNPU = TypeVar('TModelInputForNPU', bound="ModelInputForNPU")
ENCODER_NUM = 0
# if True, allow tensor initialization and casting with internal format (e.g., NZ)
torch.npu.config.allow_internal_format = True
@dataclass(frozen=True)
@@ -864,10 +866,13 @@ class NPUModelRunnerBase(ModelRunnerBase[TModelInputForNPU]):
self.vllm_config.compilation_config.max_capture_size
self.enable_graph_mode = False
self.use_cached_npu_graph = False
additional_config = vllm_config.additional_config
if additional_config:
self.enable_graph_mode = additional_config.get(
"enable_graph_mode", False)
self.use_cached_npu_graph = additional_config.get(
"use_cached_npu_graph", False)
self.has_inner_state = model_config.has_inner_state
@@ -981,12 +986,20 @@ class NPUModelRunnerBase(ModelRunnerBase[TModelInputForNPU]):
config.experimental_config.frozen_parameter = True
config.experimental_config.tiling_schedule_optimize = True
torch.npu.set_compile_mode(jit_compile=False)
self.compile_model = torchair.inference.cache_compile(
self.model.forward,
dynamic=True,
fullgraph=envs.VLLM_TEST_DYNAMO_FULLGRAPH_CAPTURE,
config=config,
ge_cache=False)
if not self.use_cached_npu_graph:
npu_backend = torchair.get_npu_backend(compiler_config=config)
self.compile_model = torch.compile(
self.model,
dynamic=True,
fullgraph=envs.VLLM_TEST_DYNAMO_FULLGRAPH_CAPTURE,
backend=npu_backend)
else:
self.compile_model = torchair.inference.cache_compile(
self.model.forward,
dynamic=True,
fullgraph=envs.VLLM_TEST_DYNAMO_FULLGRAPH_CAPTURE,
config=config,
ge_cache=False)
def save_sharded_state(
self,