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xc-llm-kunlun/vllm_kunlun/models/deepseek_v2.py

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[Feature] support deepseek v3/r1/v3.2 (#78) * [Feature] support deepseek v3/r1/v3.2 * fix gpt_oss * update readme * update readme --------- Co-authored-by: hanhaowen <hanhaowen@baidu.com>
2026-01-05 22:55:35 +08:00
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
# Adapted from
# https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/llama/modeling_llama.py
# Copyright 2023 The vLLM team.
# Copyright 2023 DeepSeek-AI and the HuggingFace Inc. team. All rights reserved.
#
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
# and OPT implementations in this library. It has been modified from its
# original forms to accommodate minor architectural differences compared
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Inference-only DeepseekV2/DeepseekV3 model."""
import typing
from collections.abc import Callable, Iterable
from itertools import islice
from typing import Any, Optional, Union
import torch
from torch.library import custom_op
from torch import nn
from transformers import DeepseekV2Config, DeepseekV3Config
from vllm_kunlun.ops.attention.layer 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,
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_kunlun.ops.activation import SiluAndMul
from vllm.model_executor.layers.fused_moe import FusedMoE
from vllm.model_executor.layers.attention_layer_base import AttentionLayerBase
from vllm.model_executor.layers.layernorm import LayerNorm, RMSNorm
from vllm.model_executor.layers.linear import (ColumnParallelLinear,
MergedColumnParallelLinear,
RowParallelLinear)
from vllm_kunlun.ops.linear import ReplicatedLinear
from vllm.model_executor.layers.logits_processor import LogitsProcessor
from vllm_kunlun.ops.attention.mla import MLAModules, MultiHeadLatentAttention
from vllm.model_executor.layers.quantization import QuantizationConfig
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 (
ParallelLMHead, VocabParallelEmbedding)
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_kunlun.ops.deep_gemm import int8_mqa_logits, int8_paged_mqa_logits
from vllm.v1.attention.backends.mla.indexer import DeepseekV32IndexerBackend
from vllm_kunlun.v1.attention.backends.mla.indexer import DeepseekV32IndexerMetadata
from vllm.v1.kv_cache_interface import KVCacheSpec, MLAAttentionSpec
from vllm.model_executor.models.interfaces import MixtureOfExperts, SupportsLoRA, SupportsPP
from vllm.model_executor.models.utils import (PPMissingLayer, is_pp_missing_parameter,
make_empty_intermediate_tensors_factory, make_layers,
maybe_prefix)
from vllm.model_executor.models.deepseek_v2 import DeepseekV32IndexerCache
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
import xspeedgate_ops
_is_kunlun = True
logger = init_logger(__name__)
class DeepseekV2MLP(nn.Module):
def __init__(
self,
hidden_size: int,
intermediate_size: int,
hidden_act: str,
quant_config: Optional[QuantizationConfig] = None,
reduce_results: bool = True,
is_sequence_parallel=False,
prefix: str = "",
) -> None:
super().__init__()
# If is_sequence_parallel, the input and output tensors are sharded
# across the ranks within the tp_group. In this case the weights are
# replicated and no collective ops are needed.
# Otherwise we use standard TP with an allreduce at the end.
self.gate_up_proj = MergedColumnParallelLinear(
hidden_size, [intermediate_size] * 2,
bias=False,
quant_config=quant_config,
disable_tp=is_sequence_parallel,
prefix=f"{prefix}.gate_up_proj")
self.down_proj = RowParallelLinear(intermediate_size,
hidden_size,
bias=False,
quant_config=quant_config,
reduce_results=reduce_results,
disable_tp=is_sequence_parallel,
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()
def forward(self, x):
gate_up, _ = self.gate_up_proj(x)
x = self.act_fn(gate_up)
x, _ = self.down_proj(x)
return x
class DeepseekV2MoE(nn.Module):
def __init__(
self,
config: Union[DeepseekV2Config, DeepseekV3Config],
parallel_config: ParallelConfig,
quant_config: Optional[QuantizationConfig] = None,
prefix: str = "",
):
super().__init__()
self.tp_size = get_tensor_model_parallel_world_size()
self.tp_rank = get_tensor_model_parallel_rank()
self.routed_scaling_factor = config.routed_scaling_factor
self.ep_group = get_ep_group().device_group
self.ep_rank = self.ep_group.rank()
self.ep_size = self.ep_group.size()
self.n_routed_experts: int = config.n_routed_experts
self.n_shared_experts: int = config.n_shared_experts
self.is_sequence_parallel = parallel_config.use_sequence_parallel_moe
if config.hidden_act != "silu":
raise ValueError(f"Unsupported activation: {config.hidden_act}. "
"Only silu is supported for now.")
self.gate = ReplicatedLinear(config.hidden_size,
config.n_routed_experts,
bias=False,
quant_config=None,
prefix=f"{prefix}.gate")
if config.topk_method == "noaux_tc":
self.gate.e_score_correction_bias = nn.Parameter(
torch.empty(config.n_routed_experts, dtype=torch.float32))
else:
self.gate.e_score_correction_bias = None
# Load balancing settings.
eplb_config = parallel_config.eplb_config
self.enable_eplb = parallel_config.enable_eplb
self.n_redundant_experts = eplb_config.num_redundant_experts
self.n_logical_experts = self.n_routed_experts
self.n_physical_experts = (self.n_logical_experts +
self.n_redundant_experts)
self.n_local_physical_experts = self.n_physical_experts // self.ep_size
self.physical_expert_start = (self.ep_rank *
self.n_local_physical_experts)
self.physical_expert_end = (self.physical_expert_start +
self.n_local_physical_experts)
if config.n_shared_experts is None:
self.experts = FusedMoE(
num_experts=config.n_routed_experts,
top_k=config.num_experts_per_tok,
hidden_size=config.hidden_size,
intermediate_size=config.moe_intermediate_size,
reduce_results=False,
renormalize=config.norm_topk_prob,
quant_config=quant_config,
use_grouped_topk=True,
num_expert_group=config.n_group,
topk_group=config.topk_group,
prefix=f"{prefix}.experts",
scoring_func=config.scoring_func,
# we do scaling outside, set factor to 1.0 to avoid double mul
routed_scaling_factor=1.0,
e_score_correction_bias=self.gate.e_score_correction_bias,
enable_eplb=self.enable_eplb,
num_redundant_experts=self.n_redundant_experts,
is_sequence_parallel=self.is_sequence_parallel,
)
self.shared_experts = None
else:
intermediate_size = (config.moe_intermediate_size *
config.n_shared_experts)
self.shared_experts = DeepseekV2MLP(
hidden_size=config.hidden_size,
intermediate_size=intermediate_size,
hidden_act=config.hidden_act,
quant_config=quant_config,
is_sequence_parallel=self.is_sequence_parallel,
reduce_results=False,
prefix=f"{prefix}.shared_experts",
)
self.experts = SharedFusedMoE(
shared_experts=self.shared_experts,
num_experts=config.n_routed_experts,
top_k=config.num_experts_per_tok,
hidden_size=config.hidden_size,
intermediate_size=config.moe_intermediate_size,
reduce_results=False,
renormalize=config.norm_topk_prob,
quant_config=quant_config,
use_grouped_topk=True,
num_expert_group=config.n_group,
topk_group=config.topk_group,
prefix=f"{prefix}.experts",
scoring_func=config.scoring_func,
# we do scaling outside, set factor to 1.0 to avoid double mul
routed_scaling_factor=1.0,
e_score_correction_bias=self.gate.e_score_correction_bias,
enable_eplb=self.enable_eplb,
num_redundant_experts=self.n_redundant_experts,
is_sequence_parallel=self.is_sequence_parallel,
)
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
num_tokens, hidden_dim = hidden_states.shape
hidden_states = hidden_states.view(-1, hidden_dim)
# Chunk the hidden states so they aren't replicated across TP ranks.
# This avoids duplicate computation in self.experts.
# TODO: We can replace the all_reduce at the end of attn with a
# reduce_scatter instead of chunking here.
if self.is_sequence_parallel:
hidden_states = sequence_parallel_chunk(hidden_states)
# router_logits: (num_tokens, n_experts)
router_logits, _ = self.gate(hidden_states)
fused_moe_out = self.experts(hidden_states=hidden_states,
router_logits=router_logits)
if self.shared_experts is not None:
shared_output, final_hidden_states = fused_moe_out
else:
shared_output = None
final_hidden_states = fused_moe_out
# Fix FP16 overflow
# See DeepseekV2DecoderLayer for more details.
if hidden_states.dtype != torch.float16:
final_hidden_states *= self.routed_scaling_factor
elif self.shared_experts is not None:
assert shared_output is not None
shared_output *= (1. / self.routed_scaling_factor)
if self.shared_experts is not None:
assert shared_output is not None
final_hidden_states += shared_output
if self.is_sequence_parallel:
final_hidden_states = tensor_model_parallel_all_gather(
final_hidden_states, 0)
final_hidden_states = final_hidden_states[:num_tokens]
elif self.tp_size > 1:
final_hidden_states = (
self.experts.maybe_all_reduce_tensor_model_parallel(
final_hidden_states))
return final_hidden_states.view(num_tokens, hidden_dim)
def yarn_get_mscale(scale: float = 1, mscale: float = 1) -> float:
import math
if scale <= 1:
return 1.0
return 0.1 * mscale * math.log(scale) + 1.0
class DeepseekV2Attention(nn.Module):
def __init__(
self,
vllm_config: VllmConfig,
config: Union[DeepseekV2Config, DeepseekV3Config],
hidden_size: int,
num_heads: int,
qk_nope_head_dim: int,
qk_rope_head_dim: int,
v_head_dim: int,
q_lora_rank: int,
kv_lora_rank: int,
rope_theta: float = 10000,
rope_scaling: Optional[dict[str, Any]] = None,
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__()
self.hidden_size = hidden_size
self.qk_nope_head_dim = qk_nope_head_dim
self.qk_rope_head_dim = qk_rope_head_dim
self.qk_head_dim = qk_nope_head_dim + qk_rope_head_dim
self.v_head_dim = v_head_dim
self.q_lora_rank = q_lora_rank
self.kv_lora_rank = kv_lora_rank
self.num_heads = num_heads
tp_size = get_tensor_model_parallel_world_size()
assert num_heads % tp_size == 0
self.num_local_heads = num_heads // tp_size
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,
self.q_lora_rank,
bias=False,
quant_config=quant_config,
prefix=f"{prefix}.q_a_proj")
self.q_a_layernorm = RMSNorm(self.q_lora_rank,
eps=config.rms_norm_eps)
self.q_b_proj = ColumnParallelLinear(q_lora_rank,
self.num_heads *
self.qk_head_dim,
bias=False,
quant_config=quant_config,
prefix=f"{prefix}.q_b_proj")
else:
self.q_proj = ColumnParallelLinear(self.hidden_size,
self.num_heads *
self.qk_head_dim,
bias=False,
quant_config=quant_config,
prefix=f"{prefix}.q_proj")
self.kv_a_proj_with_mqa = ReplicatedLinear(
self.hidden_size,
self.kv_lora_rank + self.qk_rope_head_dim,
bias=False,
quant_config=quant_config,
prefix=f"{prefix}.kv_a_proj_with_mqa")
self.kv_a_layernorm = RMSNorm(self.kv_lora_rank,
eps=config.rms_norm_eps)
self.kv_b_proj = ColumnParallelLinear(
self.kv_lora_rank,
self.num_heads * (self.qk_nope_head_dim + self.v_head_dim),
bias=False,
quant_config=quant_config,
prefix=f"{prefix}.kv_b_proj")
# O projection.
self.o_proj = RowParallelLinear(self.num_heads * self.v_head_dim,
self.hidden_size,
bias=False,
quant_config=quant_config,
prefix=f"{prefix}.o_proj")
if rope_scaling:
rope_scaling["rope_type"] = 'deepseek_yarn'
self.rotary_emb = get_rope(qk_rope_head_dim,
rotary_dim=qk_rope_head_dim,
max_position=max_position_embeddings,
base=rope_theta,
rope_scaling=rope_scaling,
is_neox_style=False)
if rope_scaling:
mscale_all_dim = rope_scaling.get("mscale_all_dim", False)
scaling_factor = rope_scaling["factor"]
mscale = yarn_get_mscale(scaling_factor, float(mscale_all_dim))
self.scaling = self.scaling * mscale * mscale
self.attn = Attention(self.num_local_heads,
self.qk_head_dim,
self.scaling,
num_kv_heads=self.num_local_heads,
cache_config=cache_config,
quant_config=quant_config,
prefix=f"{prefix}.attn")
def forward(
self,
positions: torch.Tensor,
hidden_states: torch.Tensor,
) -> torch.Tensor:
if self.q_lora_rank is not None:
q = self.q_a_proj(hidden_states)[0]
q = self.q_a_layernorm(q)
q = self.q_b_proj(q)[0].view(-1, self.num_local_heads,
self.qk_head_dim)
else:
q = self.q_proj(hidden_states)[0].view(-1, self.num_local_heads,
self.qk_head_dim)
q_nope, q_pe = q.split([self.qk_nope_head_dim, self.qk_rope_head_dim],
dim=-1)
latent_cache = self.kv_a_proj_with_mqa(hidden_states)[0]
kv_a, _ = latent_cache.split(
[self.kv_lora_rank, self.qk_rope_head_dim], dim=-1)
latent_cache = latent_cache.unsqueeze(1)
kv_a = self.kv_a_layernorm(kv_a)
kv = self.kv_b_proj(kv_a)[0]
kv = kv.view(-1, self.num_local_heads,
self.qk_nope_head_dim + self.v_head_dim)
k_nope, v = kv.split([self.qk_nope_head_dim, self.v_head_dim], dim=-1)
k_pe = latent_cache[:, :, self.kv_lora_rank:]
q_pe, k_pe = self.rotary_emb(positions, q_pe, k_pe)
q[..., self.qk_nope_head_dim:] = q_pe
k = torch.empty_like(q)
k[..., :self.qk_nope_head_dim] = k_nope
k[..., self.qk_nope_head_dim:] = k_pe
# padding value to qk_head_dim for alignment
v = torch.nn.functional.pad(
v, [0, self.qk_head_dim - self.v_head_dim],
value=0).view(-1, self.num_local_heads * self.qk_head_dim)
attn_output = self.attn(q, k, v)
attn_output = attn_output.view(
-1, self.num_local_heads,
self.qk_head_dim)[..., :self.v_head_dim].reshape(
-1, self.num_local_heads * self.v_head_dim)
output, _ = self.o_proj(attn_output)
return output
@torch.inference_mode()
def cp_gather_indexer_k_quant_cache(
kv_cache, # [num_blocks, block_size, head_dim + 1]
block_table, # [batch_size, num_blocks]
cu_seq_lens, # [batch_size + 1, ]
batch_size,
head_dim,
):
num_blocks, block_size, _ = kv_cache.shape
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.int8)
gather_scale = gather_scale.view(torch.float32)
return gather_value, gather_scale
@torch.inference_mode()
def kunlun_indexer_k_quant_cache(
k, #[num_tokens, head_dim]
kv_cache, # [num_blocks, cache_block_size, head_dim + 1]
slot_mapping, # [num_tokens]
quant_block_size,
):
num_blocks, cache_block_size, cache_stride = kv_cache.shape
# num_tokens, head_dim = k.shape
head_dim = k.shape[1]
num_tokens = slot_mapping.shape[0]
assert head_dim % quant_block_size == 0
kv_cache = kv_cache.view(num_blocks, -1)
k_fp8 = torch.empty(
k.shape,
device=k.device,
dtype=torch.int8,
)
k_scale = torch.empty(
[k.shape[0], 1],
device=k.device,
dtype=torch.float32,
)
torch.ops._C.quant2d(k, k_fp8, k_scale, force_sdnn=True)
k_scale /= 127
for token_idx in range(num_tokens):
slot_idx = slot_mapping[token_idx]
if slot_idx < 0:
continue
block_idx = slot_idx // cache_block_size
block_offset = slot_idx % cache_block_size
v_offset = block_offset * head_dim
kv_cache[block_idx, v_offset:v_offset + head_dim] = k_fp8[token_idx, :].view(torch.uint8).contiguous()
s_offset = cache_block_size * head_dim + block_offset * 4
kv_cache[block_idx, s_offset:s_offset + 4] = k_scale[token_idx, :].view(torch.uint8).contiguous()
kv_cache = kv_cache.view(num_blocks, cache_block_size, cache_stride)
@custom_op("vllm::sparse_attn_indexer_vllm_kunlun", mutates_args=())
def sparse_attn_indexer_vllm_kunlun(
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],
) -> None:
# 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):
sparse_attn_indexer_vllm_kunlun_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,
)
return
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
# kunlun_indexer_k_quant_cache(
# k,
# kv_cache,
# slot_mapping,
# quant_block_size,
# )
torch.ops.xspeedgate_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
for chunk in prefill_metadata.chunks:
k_fp8, k_scale = cp_gather_indexer_k_quant_cache(
kv_cache,
chunk.block_table,
chunk.cu_seq_lens,
chunk.num_reqs,
head_dim,
)
logits = int8_mqa_logits(
q_fp8[chunk.token_start:chunk.token_end],
(k_fp8, k_scale),
weights[chunk.token_start:chunk.token_end],
chunk.cu_seqlen_ks,
chunk.cu_seqlen_ke,
)
topk_indices = logits.topk(min(topk_tokens, logits.shape[-1]),
dim=-1)[1]
topk_indices -= chunk.cu_seqlen_ks[:, None]
mask_lo = topk_indices >= 0
mask_hi = topk_indices - (chunk.cu_seqlen_ke -
chunk.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[
chunk.token_start:chunk.token_end, :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 = int8_paged_mqa_logits(
padded_q_fp8_decode_tokens,
kv_cache,
weights[:num_padded_tokens],
decode_metadata.seq_lens,
decode_metadata.seq_lens_cpu,
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_vllm_kunlun_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],
) -> None:
return
sparse_attn_indexer_vllm_kunlun.register_fake(sparse_attn_indexer_vllm_kunlun_fake)
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,
bias=False,
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
if self.max_model_len % cache_config.block_size != 0: #由于I8_paged_mqa_logits输入参数的限制，最大长度必须为block_zise的整数倍
self.max_model_len = self.max_model_len + cache_config.block_size - (self.max_model_len % cache_config.block_size)
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 = torch.empty(
q.shape,
device=q.device,
dtype=torch.int8,
)
q_scale = torch.empty(
[q.shape[0], 1],
device=q.device,
dtype=torch.float32,
)
torch.ops._C.quant2d(q, q_fp8, q_scale, force_sdnn=True)
q_scale /= 127
q_fp8 = q_fp8.view(-1, self.n_head, self.head_dim)
q_scale = q_scale.view(-1, self.n_head)
weights, _ = self.weights_proj(hidden_states)
weights = weights * self.n_head**-0.5
weights = weights * q_scale * self.softmax_scale
torch.ops.vllm.sparse_attn_indexer_vllm_kunlun(
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,
)
return self.topk_indices_buffer
class DeepseekV2MLAAttention(nn.Module):
"""
Main reference: DeepseekV2 paper, and FlashInfer Implementation
(https://arxiv.org/abs/2405.04434 and https://github.com/flashinfer-ai/flashinfer/pull/551).
For more info see MLACommonImpl in:
vllm/v1/attention/backends/mla/utils.py
"""
def __init__(
self,
vllm_config: VllmConfig,
config: Union[DeepseekV2Config, DeepseekV3Config],
hidden_size: int,
num_heads: int,
qk_nope_head_dim: int,
qk_rope_head_dim: int,
v_head_dim: int,
q_lora_rank: Optional[int],
kv_lora_rank: int,
rope_theta: float = 10000,
rope_scaling: Optional[dict[str, Any]] = None,
max_position_embeddings: int = 8192,
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
self.qk_nope_head_dim = qk_nope_head_dim
self.qk_rope_head_dim = qk_rope_head_dim
self.qk_head_dim = qk_nope_head_dim + qk_rope_head_dim
self.v_head_dim = v_head_dim
self.q_lora_rank = q_lora_rank
self.kv_lora_rank = kv_lora_rank
self.num_heads = num_heads
tp_size = get_tensor_model_parallel_world_size()
assert num_heads % tp_size == 0
self.num_local_heads = num_heads // tp_size
self.scaling = self.qk_head_dim**-0.5
self.rope_theta = rope_theta
self.max_position_embeddings = max_position_embeddings
if self.q_lora_rank is not None:
self.fused_qkv_a_proj = MergedColumnParallelLinear(
self.hidden_size,
[self.q_lora_rank, self.kv_lora_rank + self.qk_rope_head_dim],
bias=False,
quant_config=quant_config,
prefix=f"{prefix}.fused_qkv_a_proj",
disable_tp=True)
else:
self.kv_a_proj_with_mqa = ReplicatedLinear(
self.hidden_size,
self.kv_lora_rank + self.qk_rope_head_dim,
bias=False,
quant_config=quant_config,
prefix=f"{prefix}.kv_a_proj_with_mqa")
if self.q_lora_rank is not None:
self.q_a_layernorm = RMSNorm(self.q_lora_rank,
eps=config.rms_norm_eps)
self.q_b_proj = ColumnParallelLinear(self.q_lora_rank,
self.num_heads *
self.qk_head_dim,
bias=False,
quant_config=quant_config,
prefix=f"{prefix}.q_b_proj")
else:
self.q_proj = ColumnParallelLinear(self.hidden_size,
self.num_heads *
self.qk_head_dim,
bias=False,
quant_config=quant_config,
prefix=f"{prefix}.q_proj")
self.kv_a_layernorm = RMSNorm(self.kv_lora_rank,
eps=config.rms_norm_eps)
self.kv_b_proj = ColumnParallelLinear(
self.kv_lora_rank,
self.num_heads * (self.qk_nope_head_dim + self.v_head_dim),
bias=False,
quant_config=quant_config,
prefix=f"{prefix}.kv_b_proj")
self.o_proj = RowParallelLinear(self.num_heads * self.v_head_dim,
self.hidden_size,
bias=False,
quant_config=quant_config,
prefix=f"{prefix}.o_proj")
if rope_scaling:
rope_scaling["rope_type"] = 'deepseek_yarn'
self.rotary_emb = get_rope(qk_rope_head_dim,
rotary_dim=qk_rope_head_dim,
max_position=max_position_embeddings,
base=rope_theta,
rope_scaling=rope_scaling,
is_neox_style=False)
if rope_scaling:
mscale_all_dim = rope_scaling.get("mscale_all_dim", False)
scaling_factor = rope_scaling["factor"]
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,
rotary_emb=self.rotary_emb,
o_proj=self.o_proj,
fused_qkv_a_proj=self.fused_qkv_a_proj
if self.q_lora_rank is not None else None,
kv_a_proj_with_mqa=self.kv_a_proj_with_mqa
if self.q_lora_rank is None else None,
q_a_layernorm=self.q_a_layernorm
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,
self.scaling,
self.qk_nope_head_dim,
self.qk_rope_head_dim,
self.v_head_dim,
self.q_lora_rank,
self.kv_lora_rank,
mla_modules,
cache_config,
quant_config,
prefix,
)
def forward(
self,
positions: torch.Tensor,
hidden_states: torch.Tensor,
) -> torch.Tensor:
return self.mla_attn(positions, hidden_states)
class DeepseekV2DecoderLayer(nn.Module):
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
model_config = vllm_config.model_config
cache_config = vllm_config.cache_config
quant_config = vllm_config.quant_config
parallel_config = vllm_config.parallel_config
self.hidden_size = config.hidden_size
rope_theta = getattr(config, "rope_theta", 10000)
rope_scaling = getattr(config, "rope_scaling", None)
max_position_embeddings = getattr(config, "max_position_embeddings",
8192)
# DecoderLayers are created with `make_layers` which passes the prefix
# with the layer's index.
layer_idx = int(prefix.split(sep='.')[-1])
self.layer_idx = layer_idx
if model_config.use_mla:
attn_cls = DeepseekV2MLAAttention
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,
qk_nope_head_dim=config.qk_nope_head_dim,
qk_rope_head_dim=config.qk_rope_head_dim,
v_head_dim=config.v_head_dim,
q_lora_rank=config.q_lora_rank
if hasattr(config, "q_lora_rank") else None,
kv_lora_rank=config.kv_lora_rank,
rope_theta=rope_theta,
rope_scaling=rope_scaling,
max_position_embeddings=max_position_embeddings,
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
and layer_idx >= config.first_k_dense_replace
and layer_idx % config.moe_layer_freq == 0):
self.mlp = DeepseekV2MoE(
config=config,
parallel_config=parallel_config,
quant_config=quant_config,
prefix=f"{prefix}.mlp",
)
else:
self.mlp = DeepseekV2MLP(
hidden_size=config.hidden_size,
intermediate_size=config.intermediate_size,
hidden_act=config.hidden_act,
quant_config=quant_config,
prefix=f"{prefix}.mlp",
)
self.input_layernorm = RMSNorm(config.hidden_size,
eps=config.rms_norm_eps)
self.post_attention_layernorm = RMSNorm(config.hidden_size,
eps=config.rms_norm_eps)
self.routed_scaling_factor = config.routed_scaling_factor
def forward(
self,
positions: torch.Tensor,
hidden_states: torch.Tensor,
residual: Optional[torch.Tensor],
) -> torch.Tensor:
# Self Attention
if residual is None:
residual = hidden_states.clone()
hidden_states = self.input_layernorm(hidden_states)
else:
hidden_states, residual = self.input_layernorm(
hidden_states, residual)
hidden_states = self.self_attn(
positions=positions,
hidden_states=hidden_states,
)
if hidden_states.dtype == torch.float16:
# Fix FP16 overflow
# We scale both hidden_states and residual before
# rmsnorm, and rmsnorm result would not affect by scale.
hidden_states *= 1. / self.routed_scaling_factor
if self.layer_idx == 0:
# The residual is shared by all layers, we only scale it on
# first layer.
residual *= 1. / self.routed_scaling_factor
# Fully Connected
hidden_states, residual = self.post_attention_layernorm(
hidden_states, residual)
hidden_states = self.mlp(hidden_states)
if isinstance(self.mlp,
DeepseekV2MLP) and hidden_states.dtype == torch.float16:
# Fix FP16 overflow
# Scaling the DeepseekV2MLP output, it is the input of
# input_layernorm of next decoder layer.
# The scaling of DeepseekV2MOE output would be done in the forward
# of DeepseekV2MOE
hidden_states *= 1. / self.routed_scaling_factor
return hidden_states, residual
@support_torch_compile
class DeepseekV2Model(nn.Module):
fall_back_to_pt_during_load = False
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
super().__init__()
config = vllm_config.model_config.hf_config
quant_config = vllm_config.quant_config
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(
config.vocab_size,
config.hidden_size,
quant_config=quant_config,
prefix=f"{prefix}.embed_tokens")
else:
self.embed_tokens = PPMissingLayer()
self.start_layer, self.end_layer, self.layers = make_layers(
config.num_hidden_layers,
lambda prefix: DeepseekV2DecoderLayer(vllm_config, prefix,
topk_indices_buffer),
prefix=f"{prefix}.layers")
if get_pp_group().is_last_rank:
self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
else:
self.norm = PPMissingLayer()
self.make_empty_intermediate_tensors = (
make_empty_intermediate_tensors_factory(
["hidden_states", "residual"], config.hidden_size))
def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
return self.embed_tokens(input_ids)
def forward(
self,
input_ids: torch.Tensor,
positions: torch.Tensor,
intermediate_tensors: Optional[IntermediateTensors],
inputs_embeds: Optional[torch.Tensor] = None,
) -> Union[torch.Tensor, IntermediateTensors]:
if get_pp_group().is_first_rank:
if inputs_embeds is not None:
hidden_states = inputs_embeds
else:
hidden_states = self.get_input_embeddings(input_ids)
residual = None
else:
assert intermediate_tensors is not None
hidden_states = intermediate_tensors["hidden_states"]
residual = intermediate_tensors["residual"]
for layer in islice(self.layers, self.start_layer, self.end_layer):
hidden_states, residual = layer(positions, hidden_states, residual)
if not get_pp_group().is_last_rank:
return IntermediateTensors({
"hidden_states": hidden_states,
"residual": residual
})
hidden_states, _ = self.norm(hidden_states, residual)
return hidden_states
class DeepseekV2ForCausalLM(nn.Module, SupportsPP, MixtureOfExperts,
SupportsLoRA):
packed_modules_mapping = {
"gate_up_proj": ["gate_proj", "up_proj"],
}
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
super().__init__()
config = vllm_config.model_config.hf_config
quant_config = vllm_config.quant_config
self.config = config
self.quant_config = quant_config
# `packed_modules_mapping` needs to be modified before
# initializing DeepseekV2Model, as it is passed inplace to
# quantization config init and may be used to select the
# quant_method for relevant layers during initialization.
self.fuse_qkv_a_proj = hasattr(
config, "q_lora_rank") and config.q_lora_rank is not None
if self.fuse_qkv_a_proj:
self.packed_modules_mapping["fused_qkv_a_proj"] = [
"q_a_proj",
"kv_a_proj_with_mqa",
]
self.model = DeepseekV2Model(vllm_config=vllm_config,
prefix=maybe_prefix(prefix, "model"))
if get_pp_group().is_last_rank:
self.lm_head = ParallelLMHead(
config.vocab_size,
config.hidden_size,
quant_config=quant_config,
prefix=maybe_prefix(prefix, "lm_head"),
)
else:
self.lm_head = PPMissingLayer()
self.logits_processor = LogitsProcessor(config.vocab_size)
self.make_empty_intermediate_tensors = (
self.model.make_empty_intermediate_tensors)
self.expert_weights = []
# Set MoE hyperparameters
self.num_moe_layers = (config.num_hidden_layers -
config.first_k_dense_replace)
self.num_expert_groups = config.n_group
self.moe_layers: list[FusedMoE] = []
example_moe = None
for layer in self.model.layers:
if isinstance(layer, PPMissingLayer):
continue
assert isinstance(layer, DeepseekV2DecoderLayer)
if isinstance(layer.mlp, DeepseekV2MoE):
# Pick last one layer since the first ones may be dense layers.
example_moe = layer.mlp
self.moe_layers.append(layer.mlp.experts)
if example_moe is None:
raise RuntimeError("No DeepseekV2MoE layer found in model.layers.")
self.num_logical_experts = example_moe.n_logical_experts
self.num_physical_experts = example_moe.n_physical_experts
self.num_local_physical_experts = example_moe.n_local_physical_experts
self.num_routed_experts = example_moe.n_routed_experts
self.num_shared_experts = example_moe.n_shared_experts
self.num_redundant_experts = example_moe.n_redundant_experts
def set_eplb_state(
self,
expert_load_view: torch.Tensor,
logical_to_physical_map: torch.Tensor,
logical_replica_count: torch.Tensor,
) -> None:
for layer_idx, layer in enumerate(self.moe_layers):
# Register the expert weights.
self.expert_weights.append(layer.get_expert_weights())
layer.set_eplb_state(
moe_layer_idx=layer_idx,
expert_load_view=expert_load_view,
logical_to_physical_map=logical_to_physical_map,
logical_replica_count=logical_replica_count,
)
def update_physical_experts_metadata(
self,
num_physical_experts: int,
num_local_physical_experts: int,
) -> None:
assert self.num_local_physical_experts == num_local_physical_experts
self.num_physical_experts = num_physical_experts
self.num_local_physical_experts = num_local_physical_experts
self.num_redundant_experts = (num_physical_experts -
self.num_logical_experts)
for layer in self.model.layers:
if isinstance(layer.mlp, DeepseekV2MoE):
moe = layer.mlp
moe.n_local_physical_experts = num_local_physical_experts
moe.n_physical_experts = num_physical_experts
moe.n_redundant_experts = self.num_redundant_experts
moe.experts.update_expert_map()
def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
return self.model.get_input_embeddings(input_ids)
def forward(
self,
input_ids: torch.Tensor,
positions: torch.Tensor,
intermediate_tensors: Optional[IntermediateTensors] = None,
inputs_embeds: Optional[torch.Tensor] = None,
) -> Union[torch.Tensor, IntermediateTensors]:
hidden_states = self.model(input_ids, positions, intermediate_tensors,
inputs_embeds)
return hidden_states
def compute_logits(
self,
hidden_states: torch.Tensor,
) -> Optional[torch.Tensor]:
logits = self.logits_processor(self.lm_head, hidden_states)
return logits
def load_weights(self, weights: Iterable[tuple[str,
torch.Tensor]]) -> set[str]:
stacked_params_mapping = [
# (param_name, shard_name, shard_id)
("gate_up_proj", "gate_proj", 0),
("gate_up_proj", "up_proj", 1),
("fused_qkv_a_proj", "q_a_proj", 0),
("fused_qkv_a_proj", "kv_a_proj_with_mqa", 1),
]
# Params for weights, fp8 weight scales, fp8 activation scales
# (param_name, weight_name, expert_id, shard_id)
expert_params_mapping = FusedMoE.make_expert_params_mapping(
ckpt_gate_proj_name="gate_proj",
ckpt_down_proj_name="down_proj",
ckpt_up_proj_name="up_proj",
num_experts=self.config.n_routed_experts,
num_redundant_experts=self.num_redundant_experts)
params_dict = dict(self.named_parameters())
loaded_params: set[str] = set()
for name, loaded_weight in weights:
if "rotary_emb.inv_freq" in name:
continue
spec_layer = get_spec_layer_idx_from_weight_name(self.config, name)
if spec_layer is not None:
continue # skip spec decode layers for main model
for (param_name, weight_name, shard_id) in stacked_params_mapping:
# Skip non-stacked layers and experts (experts handled below).
if weight_name not in name:
continue
# We have mlp.experts[0].gate_proj in the checkpoint.
# Since we handle the experts below in expert_params_mapping,
# we need to skip here BEFORE we update the name, otherwise
# name will be updated to mlp.experts[0].gate_up_proj, which
# will then be updated below in expert_params_mapping
# for mlp.experts[0].gate_gate_up_proj, which breaks load.
if (("mlp.experts." in name) and name not in params_dict):
continue
name_mapped = name.replace(weight_name, param_name)
# QKV fusion is optional, fall back to normal
# weight loading if it's not enabled
# if go with fusion option, then update name
if ((param_name == "fused_qkv_a_proj")
and name_mapped not in params_dict):
continue
else:
name = name_mapped
# Skip loading extra bias for GPTQ models.
if name.endswith(".bias") and name not in params_dict:
continue
if is_pp_missing_parameter(name, self):
continue
if name not in params_dict:
continue
param = params_dict[name]
weight_loader = param.weight_loader
weight_loader(param, loaded_weight, shard_id)
break
else:
is_expert_weight = False
for mapping in expert_params_mapping:
param_name, weight_name, expert_id, shard_id = mapping
if weight_name not in name:
continue
# Anyway, this is an expert weight and should not be
# attempted to load as other weights later
is_expert_weight = True
# Do not modify `name` since the loop may continue here
# Instead, create a new variable
name_mapped = name.replace(weight_name, param_name)
if is_pp_missing_parameter(name_mapped, self):
continue
if name_mapped not in params_dict:
continue
param = params_dict[name_mapped]
# We should ask the weight loader to return success or not
# here since otherwise we may skip experts with other
# available replicas.
weight_loader = typing.cast(Callable[..., bool],
param.weight_loader)
success = weight_loader(param,
loaded_weight,
name_mapped,
shard_id=shard_id,
expert_id=expert_id,
return_success=True)
if success:
name = name_mapped
break
else:
if is_expert_weight:
# We've checked that this is an expert weight
# However it's not mapped locally to this rank
# So we simply skip it
continue
# Skip loading extra bias for GPTQ models.
if name.endswith(".bias") and name not in params_dict:
continue
# Remapping the name of FP8 kv-scale.
name = maybe_remap_kv_scale_name(name, params_dict)
if name is None:
continue
if is_pp_missing_parameter(name, self):
continue
if name not in params_dict:
continue
param = params_dict[name]
weight_loader = getattr(param, "weight_loader",
default_weight_loader)
weight_loader(param, loaded_weight)
loaded_params.add(name)
return loaded_params
class DeepseekV3ForCausalLM(DeepseekV2ForCausalLM):
pass
# Compatibility with
# https://huggingface.co/deepseek-ai/DeepSeek-V3-Base/blob/main/configuration_deepseek.py
def get_spec_layer_idx_from_weight_name(config: Union[DeepseekV2Config,
DeepseekV3Config],
weight_name: str) -> Optional[int]:
if (hasattr(config, "num_nextn_predict_layers")
and config.num_nextn_predict_layers > 0):
layer_idx = config.num_hidden_layers
for i in range(config.num_nextn_predict_layers):
if weight_name.startswith(f"model.layers.{layer_idx+i}."):
return layer_idx + i
return None
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