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[New Model] DeepSeek-V3.2 (Rebased to Main) (#25896)

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Signed-off-by: Chen Zhang <zhangch99@outlook.com>
Signed-off-by: youkaichao <youkaichao@gmail.com>
Signed-off-by: Lucas Wilkinson <lwilkins@redhat.com>
Signed-off-by: mgoin <mgoin64@gmail.com>
Signed-off-by: NickLucche <nlucches@redhat.com>
Signed-off-by: Yongye Zhu <zyy1102000@gmail.com>
Signed-off-by: Barry Kang <43644113+Barry-Delaney@users.noreply.github.com>
Signed-off-by: Lucia Fang <fanglu@meta.com>
Co-authored-by: Chen Zhang <zhangch99@outlook.com>
Co-authored-by: youkaichao <youkaichao@gmail.com>
Co-authored-by: Lucas Wilkinson <lwilkins@redhat.com>
Co-authored-by: Robert Shaw <114415538+robertgshaw2-redhat@users.noreply.github.com>
Co-authored-by: Lucas Wilkinson <LucasWilkinson@users.noreply.github.com>
Co-authored-by: yewentao256 <zhyanwentao@126.com>
Co-authored-by: Wentao Ye <44945378+yewentao256@users.noreply.github.com>
Co-authored-by: mgoin <mgoin64@gmail.com>
Co-authored-by: Lucia Fang <116399278+luccafong@users.noreply.github.com>
Co-authored-by: Lucia Fang <fanglu@meta.com>
Co-authored-by: NickLucche <nlucches@redhat.com>
Co-authored-by: Siyuan Fu <siyuanf@nvidia.com>
Co-authored-by: Matthew Bonanni <mbonanni@redhat.com>
Co-authored-by: Xiaozhu Meng <mxz297@gmail.com>
Co-authored-by: Barry Kang <43644113+Barry-Delaney@users.noreply.github.com>
This commit is contained in:
Yongye Zhu
2025-09-30 05:14:41 -04:00
committed by GitHub
parent e23cacda35
commit fa7e254a7f
71 changed files with 3915 additions and 218 deletions
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449
vllm/model_executor/models/deepseek_v2.py
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@@ -33,15 +33,21 @@ from torch import nn
from transformers import DeepseekV2Config, DeepseekV3Config
from vllm.attention import Attention
from vllm.attention.backends.abstract import AttentionBackend
from vllm.attention.ops.common import pack_seq_triton, unpack_seq_triton
from vllm.compilation.decorators import support_torch_compile
from vllm.config import CacheConfig, ParallelConfig, VllmConfig
from vllm.config import (CacheConfig, ParallelConfig, VllmConfig,
get_current_vllm_config)
from vllm.distributed import (get_ep_group, get_pp_group,
get_tensor_model_parallel_rank,
get_tensor_model_parallel_world_size,
tensor_model_parallel_all_gather)
from vllm.forward_context import get_forward_context
from vllm.logger import init_logger
from vllm.model_executor.layers.activation import SiluAndMul
from vllm.model_executor.layers.attention_layer_base import AttentionLayerBase
from vllm.model_executor.layers.fused_moe import FusedMoE
from vllm.model_executor.layers.layernorm import RMSNorm
from vllm.model_executor.layers.layernorm import LayerNorm, RMSNorm
from vllm.model_executor.layers.linear import (ColumnParallelLinear,
MergedColumnParallelLinear,
ReplicatedLinear,
@@ -49,6 +55,8 @@ from vllm.model_executor.layers.linear import (ColumnParallelLinear,
from vllm.model_executor.layers.logits_processor import LogitsProcessor
from vllm.model_executor.layers.mla import MLAModules, MultiHeadLatentAttention
from vllm.model_executor.layers.quantization import QuantizationConfig
from vllm.model_executor.layers.quantization.utils.fp8_utils import (
per_token_group_quant_fp8)
from vllm.model_executor.layers.rotary_embedding import get_rope
from vllm.model_executor.layers.shared_fused_moe import SharedFusedMoE
from vllm.model_executor.layers.vocab_parallel_embedding import (
@@ -56,13 +64,26 @@ from vllm.model_executor.layers.vocab_parallel_embedding import (
from vllm.model_executor.model_loader.weight_utils import (
default_weight_loader, maybe_remap_kv_scale_name)
from vllm.model_executor.models.utils import sequence_parallel_chunk
from vllm.platforms import current_platform
from vllm.sequence import IntermediateTensors
from vllm.utils import cdiv, direct_register_custom_op
from vllm.utils.deep_gemm import fp8_mqa_logits, fp8_paged_mqa_logits
from vllm.v1.attention.backends.mla.indexer import (DeepseekV32IndexerBackend,
DeepseekV32IndexerMetadata)
from vllm.v1.kv_cache_interface import KVCacheSpec, MLAAttentionSpec
from .interfaces import MixtureOfExperts, SupportsLoRA, SupportsPP
from .utils import (PPMissingLayer, is_pp_missing_parameter,
make_empty_intermediate_tensors_factory, make_layers,
maybe_prefix)
if current_platform.is_cuda_alike():
from vllm import _custom_ops as ops
elif current_platform.is_xpu():
from vllm._ipex_ops import ipex_ops as ops
logger = init_logger(__name__)
class DeepseekV2MLP(nn.Module):
@@ -276,6 +297,7 @@ class DeepseekV2Attention(nn.Module):
def __init__(
self,
vllm_config: VllmConfig,
config: Union[DeepseekV2Config, DeepseekV3Config],
hidden_size: int,
num_heads: int,
@@ -289,6 +311,7 @@ class DeepseekV2Attention(nn.Module):
max_position_embeddings: int = 8192,
cache_config: Optional[CacheConfig] = None,
quant_config: Optional[QuantizationConfig] = None,
topk_indices_buffer: Optional[torch.Tensor] = None,
prefix: str = "",
) -> None:
super().__init__()
@@ -306,6 +329,8 @@ class DeepseekV2Attention(nn.Module):
self.scaling = self.qk_head_dim**-0.5
self.rope_theta = rope_theta
self.max_position_embeddings = max_position_embeddings
assert topk_indices_buffer is None, "topk_indices_buffer is not \
supported for DeepseekV2Attention"
if self.q_lora_rank is not None:
self.q_a_proj = ReplicatedLinear(self.hidden_size,
@@ -418,6 +443,391 @@ class DeepseekV2Attention(nn.Module):
return output
class DeepseekV32IndexerCache(torch.nn.Module, AttentionLayerBase):
def __init__(self, head_dim: int, dtype: torch.dtype, prefix: str,
cache_config: CacheConfig):
super().__init__()
self.kv_cache = [torch.tensor([])]
self.head_dim = head_dim
self.prefix = prefix
self.cache_config = cache_config
self.dtype = dtype
compilation_config = get_current_vllm_config().compilation_config
if prefix in compilation_config.static_forward_context:
raise ValueError(f"Duplicate layer name: {prefix}")
compilation_config.static_forward_context[prefix] = self
def get_kv_cache_spec(self) -> KVCacheSpec:
return MLAAttentionSpec( # Only has one vector instead of K + V
block_size=self.cache_config.block_size,
num_kv_heads=1,
head_size=self.head_dim,
dtype=self.dtype,
)
def forward(self):
...
def get_attn_backend(self) -> AttentionBackend:
return DeepseekV32IndexerBackend
@torch.inference_mode()
def cp_gather_indexer_k_quant_cache(
kv_cache, # [num_blocks, block_size, head_dim + 1]
dst_value, # [cu_seq_lens[-1], head_dim]
dst_scale, # [cu_seq_lens[-1], 4]
block_table, # [batch_size, num_blocks]
cu_seq_lens, # [batch_size + 1, ]
batch_size,
):
num_blocks, block_size, _ = kv_cache.shape
head_dim = dst_value.shape[-1]
kv_cache = kv_cache.view(num_blocks, -1)
expected_value = []
expected_scale = []
for b in range(batch_size):
s = cu_seq_lens[b + 1] - cu_seq_lens[b]
if s == 0:
continue
tot = cdiv(s, block_size)
blocks = block_table[b, :tot]
value = []
scale = []
full_block = torch.arange(tot - 1,
device=kv_cache.device,
dtype=torch.int32)
non_remaining_value = kv_cache[blocks[full_block], :block_size *
head_dim].view(-1, head_dim)
non_remaining_scale = kv_cache[blocks[full_block],
block_size * head_dim:].view(-1, 4)
remaining = s - (tot - 1) * block_size
value = torch.cat([
non_remaining_value,
kv_cache[blocks[-1], :remaining * head_dim].view(-1, head_dim)
],
dim=0)
scale = torch.cat([
non_remaining_scale,
kv_cache[blocks[-1], block_size * head_dim:block_size * head_dim +
remaining * 4].view(-1, 4)
],
dim=0)
expected_value.append(value)
expected_scale.append(scale)
gather_value = torch.cat(expected_value, dim=0).view(-1, head_dim)
gather_scale = torch.cat(expected_scale, dim=0).view(-1, 4)
gather_value = gather_value.view(torch.float8_e4m3fn)
gather_scale = gather_scale.view(torch.float32)
dst_value.copy_(gather_value)
dst_scale.copy_(gather_scale)
def sparse_attn_indexer(
hidden_states: torch.Tensor,
k_cache_prefix: str,
kv_cache: torch.Tensor,
q_fp8: torch.Tensor,
k: torch.Tensor,
weights: torch.Tensor,
quant_block_size: int,
scale_fmt: Optional[str],
topk_tokens: int,
head_dim: int,
max_model_len: int,
total_seq_lens: int,
topk_indices_buffer: Optional[torch.Tensor],
) -> torch.Tensor:
# careful! this will be None in dummy run
attn_metadata = get_forward_context().attn_metadata
# assert isinstance(attn_metadata, dict)
if not isinstance(attn_metadata, dict):
return sparse_attn_indexer_fake(
hidden_states,
k_cache_prefix,
kv_cache,
q_fp8,
k,
weights,
quant_block_size,
scale_fmt,
topk_tokens,
head_dim,
max_model_len,
total_seq_lens,
topk_indices_buffer,
)
attn_metadata = attn_metadata[k_cache_prefix]
assert isinstance(attn_metadata, DeepseekV32IndexerMetadata)
slot_mapping = attn_metadata.slot_mapping
has_decode = attn_metadata.num_decodes > 0
has_prefill = attn_metadata.num_prefills > 0
num_decode_tokens = attn_metadata.num_decode_tokens
ops.indexer_k_quant_and_cache(
k,
kv_cache,
slot_mapping,
quant_block_size,
scale_fmt,
)
topk_indices_buffer[:hidden_states.shape[0]] = -1
if has_prefill:
prefill_metadata = attn_metadata.prefill
num_prefills = attn_metadata.num_prefills
k_fp8 = torch.empty([prefill_metadata.total_seq_lens, head_dim],
device=k.device,
dtype=torch.float8_e4m3fn)
k_scale = torch.empty([prefill_metadata.total_seq_lens, 1],
device=k.device,
dtype=torch.float32)
cp_gather_indexer_k_quant_cache(
kv_cache,
k_fp8,
k_scale,
prefill_metadata.block_table,
prefill_metadata.cu_seq_lens,
num_prefills,
)
cu_seqlen_ks = prefill_metadata.cu_seqlen_ks
cu_seqlen_ke = prefill_metadata.cu_seqlen_ke
num_tokens = attn_metadata.num_actual_tokens
logits = fp8_mqa_logits(
q_fp8[num_decode_tokens:num_tokens],
(k_fp8, k_scale),
weights[num_decode_tokens:num_tokens],
cu_seqlen_ks,
cu_seqlen_ke,
)
topk_indices = logits.topk(min(topk_tokens, logits.shape[-1]),
dim=-1)[1]
topk_indices -= cu_seqlen_ks[:, None]
mask_lo = topk_indices >= 0
mask_hi = topk_indices - (cu_seqlen_ke - cu_seqlen_ks)[:, None] < 0
mask = torch.full_like(topk_indices,
False,
dtype=torch.bool,
device=topk_indices.device)
mask = mask_lo & mask_hi
topk_indices = topk_indices.masked_fill(~mask, -1)
topk_indices_buffer[num_decode_tokens:num_tokens, :topk_indices.
shape[-1]] = topk_indices.to(dtype=torch.int32)
if has_decode:
decode_metadata = attn_metadata.decode
# kv_cache size requirement [num_block, block_size, n_head, head_dim],
# we only have [num_block, block_size, head_dim],
kv_cache = kv_cache.unsqueeze(-2)
decode_lens = decode_metadata.decode_lens
if decode_metadata.requires_padding:
# pad in edge case where we have short chunked prefill length <
# decode_threshold since we unstrictly split
# prefill and decode by decode_threshold
# (currently set to 1 + speculative tokens)
padded_q_fp8_decode_tokens = pack_seq_triton(
q_fp8[:num_decode_tokens], decode_lens)
else:
padded_q_fp8_decode_tokens = q_fp8[:num_decode_tokens].reshape(
decode_lens.shape[0], -1, *q_fp8.shape[1:])
# TODO: move and optimize below logic with triton kernels
batch_size = padded_q_fp8_decode_tokens.shape[0]
next_n = padded_q_fp8_decode_tokens.shape[1]
assert batch_size == decode_metadata.seq_lens.shape[0]
num_padded_tokens = batch_size * next_n
logits = fp8_paged_mqa_logits(
padded_q_fp8_decode_tokens,
kv_cache,
weights[:num_padded_tokens],
decode_metadata.seq_lens,
decode_metadata.block_table,
decode_metadata.schedule_metadata,
max_model_len=max_model_len,
)
# padded query len
current_device = padded_q_fp8_decode_tokens.device
padded_num_tokens = batch_size * next_n
positions = torch.arange(max_model_len,
device=current_device).unsqueeze(0).expand(
batch_size * next_n, -1)
row_indices = torch.arange(padded_num_tokens,
device=current_device) // next_n
next_n_offset = torch.arange(
padded_num_tokens,
device=padded_q_fp8_decode_tokens.device) % next_n
index_end_pos = (decode_metadata.seq_lens[row_indices] - next_n +
next_n_offset).unsqueeze(1)
# index_end_pos: [B * N, 1]
mask = positions <= index_end_pos
# mask: [B * N, L]
logits = logits.masked_fill(~mask, float('-inf'))
topk_indices = logits.topk(topk_tokens,
dim=-1)[1].to(torch.int32) # [B * N, K]
# ensure we don't set indices for the top k
# that is out of range(masked already)
# this will happen if context length is shorter than K
topk_indices[topk_indices > index_end_pos] = -1
if decode_metadata.requires_padding:
# if padded, we need to unpack
# the topk indices removing padded tokens
topk_indices = unpack_seq_triton(
topk_indices.reshape(batch_size, -1, topk_indices.shape[-1]),
decode_lens)
topk_indices_buffer[:num_decode_tokens, :topk_indices.
shape[-1]] = topk_indices.to(dtype=torch.int32)
return topk_indices_buffer
def sparse_attn_indexer_fake(
hidden_states: torch.Tensor,
k_cache_prefix: str,
kv_cache: torch.Tensor,
q_fp8: torch.Tensor,
k: torch.Tensor,
weights: torch.Tensor,
quant_block_size: int,
scale_fmt: Optional[str],
topk_tokens: int,
head_dim: int,
max_model_len: int,
total_seq_lens: int,
topk_indices_buffer: Optional[torch.Tensor],
) -> torch.Tensor:
# profile run
# NOTE(Chen): create the max possible flattened_kv. So that
# profile_run can get correct memory usage.
_flattened_kv = torch.empty([total_seq_lens, head_dim + 4],
device=k.device,
dtype=torch.uint8)
_k_fp8 = _flattened_kv[..., :head_dim].view(
torch.float8_e4m3fn).contiguous()
_k_scale = _flattened_kv[..., head_dim:].view(torch.float32).contiguous()
return topk_indices_buffer
direct_register_custom_op(
op_name="sparse_attn_indexer",
op_func=sparse_attn_indexer,
mutates_args=["topk_indices_buffer"],
fake_impl=sparse_attn_indexer_fake,
dispatch_key=current_platform.dispatch_key,
)
class Indexer(nn.Module):
def __init__(self,
vllm_config: VllmConfig,
config: Union[DeepseekV2Config, DeepseekV3Config],
hidden_size: int,
q_lora_rank: int,
quant_config: Optional[QuantizationConfig],
cache_config: Optional[CacheConfig],
topk_indices_buffer: Optional[torch.Tensor],
prefix: str = ""):
super().__init__()
self.vllm_config = vllm_config
self.config = config
# self.indexer_cfg = config.attn_module_list_cfg[0]["attn_index"]
self.topk_tokens = config.index_topk
self.n_head = config.index_n_heads # 64
self.head_dim = config.index_head_dim # 128
self.rope_dim = config.qk_rope_head_dim # 64
self.q_lora_rank = q_lora_rank # 1536
# no tensor parallel, just replicated
self.wq_b = ReplicatedLinear(self.q_lora_rank,
self.head_dim * self.n_head,
bias=False,
quant_config=quant_config,
prefix=f"{prefix}.wq_b")
self.wk = ReplicatedLinear(hidden_size,
self.head_dim,
bias=False,
quant_config=quant_config,
prefix=f"{prefix}.wk")
self.k_norm = LayerNorm(self.head_dim, eps=1e-6)
self.weights_proj = ReplicatedLinear(hidden_size,
self.n_head,
quant_config=None,
prefix=f"{prefix}.weights_proj")
self.softmax_scale = self.head_dim**-0.5
self.scale_fmt = "ue8m0"
self.quant_block_size = 128 # TODO: get from config
self.topk_indices_buffer = topk_indices_buffer
# NOTE: (zyongye) we use fp8 naive cache,
# where we store value in fp8 and scale in fp32
# per self.quant_block_size element
self.k_cache = DeepseekV32IndexerCache(
head_dim=self.head_dim +
self.head_dim // self.quant_block_size * 4,
dtype=torch.uint8,
prefix=f"{prefix}.k_cache",
cache_config=cache_config)
self.max_model_len = vllm_config.model_config.max_model_len
self.prefix = prefix
from vllm.v1.attention.backends.mla.indexer import (
get_max_prefill_buffer_size)
self.max_total_seq_len = get_max_prefill_buffer_size(vllm_config)
def forward(self, hidden_states: torch.Tensor, qr: torch.Tensor, positions,
rotary_emb) -> torch.Tensor:
q, _ = self.wq_b(qr)
q = q.view(-1, self.n_head, self.head_dim)
q_pe, q_nope = torch.split(
q, [self.rope_dim, self.head_dim - self.rope_dim], dim=-1)
k, _ = self.wk(hidden_states)
k = self.k_norm(k)
k_pe, k_nope = torch.split(
k, [self.rope_dim, self.head_dim - self.rope_dim], dim=-1)
q_pe, k_pe = rotary_emb(positions, q_pe, k_pe.unsqueeze(1))
q = torch.cat([q_pe, q_nope], dim=-1)
k = torch.cat([k_pe.squeeze(1), k_nope], dim=-1)
# we only quant q here since k quant is fused with cache insertion
q = q.view(-1, self.head_dim)
q_fp8, q_scale = per_token_group_quant_fp8(q,
self.quant_block_size,
column_major_scales=False,
use_ue8m0=self.scale_fmt
is not None)
q_fp8 = q_fp8.view(-1, self.n_head, self.head_dim)
q_scale = q_scale.view(-1, self.n_head, 1)
weights, _ = self.weights_proj(hidden_states)
weights = weights.unsqueeze(
-1) * q_scale * self.softmax_scale * self.n_head**-0.5
weights = weights.squeeze(-1)
return torch.ops.vllm.sparse_attn_indexer(
hidden_states,
self.k_cache.prefix,
self.k_cache.kv_cache[0],
q_fp8,
k,
weights,
self.quant_block_size,
self.scale_fmt,
self.topk_tokens,
self.head_dim,
self.max_model_len,
self.max_total_seq_len,
self.topk_indices_buffer,
)
class DeepseekV2MLAAttention(nn.Module):
"""
Main reference: DeepseekV2 paper, and FlashInfer Implementation
@@ -429,6 +839,7 @@ class DeepseekV2MLAAttention(nn.Module):
def __init__(
self,
vllm_config: VllmConfig,
config: Union[DeepseekV2Config, DeepseekV3Config],
hidden_size: int,
num_heads: int,
@@ -443,6 +854,7 @@ class DeepseekV2MLAAttention(nn.Module):
cache_config: Optional[CacheConfig] = None,
quant_config: Optional[QuantizationConfig] = None,
prefix: str = "",
topk_indices_buffer: Optional[torch.Tensor] = None,
) -> None:
super().__init__()
self.hidden_size = hidden_size
@@ -523,6 +935,15 @@ class DeepseekV2MLAAttention(nn.Module):
mscale = yarn_get_mscale(scaling_factor, float(mscale_all_dim))
self.scaling = self.scaling * mscale * mscale
self.is_v32 = hasattr(config, "index_topk")
if self.is_v32:
self.indexer = Indexer(vllm_config, config, hidden_size,
q_lora_rank, quant_config, cache_config,
topk_indices_buffer, f"{prefix}.indexer")
else:
self.indexer = None
mla_modules = MLAModules(
kv_a_layernorm=self.kv_a_layernorm,
kv_b_proj=self.kv_b_proj,
@@ -536,7 +957,11 @@ class DeepseekV2MLAAttention(nn.Module):
if self.q_lora_rank is not None else None,
q_b_proj=self.q_b_proj if self.q_lora_rank is not None else None,
q_proj=self.q_proj if self.q_lora_rank is None else None,
indexer=self.indexer,
is_sparse=self.is_v32,
topk_indices_buffer=topk_indices_buffer,
)
self.mla_attn = MultiHeadLatentAttention(
self.hidden_size,
self.num_local_heads,
@@ -562,7 +987,10 @@ class DeepseekV2MLAAttention(nn.Module):
class DeepseekV2DecoderLayer(nn.Module):
def __init__(self, vllm_config: VllmConfig, prefix: str) -> None:
def __init__(self,
vllm_config: VllmConfig,
prefix: str,
topk_indices_buffer: Optional[torch.Tensor] = None) -> None:
super().__init__()
config = vllm_config.model_config.hf_config
@@ -585,6 +1013,7 @@ class DeepseekV2DecoderLayer(nn.Module):
else:
attn_cls = DeepseekV2Attention
self.self_attn = attn_cls(
vllm_config=vllm_config,
config=config,
hidden_size=self.hidden_size,
num_heads=config.num_attention_heads,
@@ -600,6 +1029,7 @@ class DeepseekV2DecoderLayer(nn.Module):
cache_config=cache_config,
quant_config=quant_config,
prefix=f"{prefix}.self_attn",
topk_indices_buffer=topk_indices_buffer,
)
if (config.n_routed_experts is not None
@@ -683,6 +1113,16 @@ class DeepseekV2Model(nn.Module):
self.config = config
self.vocab_size = config.vocab_size
self.is_v32 = hasattr(config, "index_topk")
if self.is_v32:
topk_tokens = config.index_topk
topk_indices_buffer = torch.empty(
vllm_config.scheduler_config.max_num_batched_tokens,
topk_tokens,
dtype=torch.int32,
device="cuda")
else:
topk_indices_buffer = None
if get_pp_group().is_first_rank:
self.embed_tokens = VocabParallelEmbedding(
@@ -695,7 +1135,8 @@ class DeepseekV2Model(nn.Module):
self.start_layer, self.end_layer, self.layers = make_layers(
config.num_hidden_layers,
lambda prefix: DeepseekV2DecoderLayer(vllm_config, prefix),
lambda prefix: DeepseekV2DecoderLayer(vllm_config, prefix,
topk_indices_buffer),
prefix=f"{prefix}.layers")
if get_pp_group().is_last_rank: