850 lines
31 KiB
Python
850 lines
31 KiB
Python
"""
|
|
Copyright 2023-2024 SGLang Team
|
|
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.
|
|
"""
|
|
|
|
# Adapted from:
|
|
# https://github.com/vllm-project/vllm/blob/fb6af8bc086328ca6659e72d11ffd4309ce4de22/vllm/model_executor/models/deepseek_v2.py
|
|
"""Inference-only DeepseekV2 model."""
|
|
from typing import Any, Dict, Iterable, Optional, Tuple
|
|
|
|
import torch
|
|
from torch import nn
|
|
from transformers import PretrainedConfig
|
|
from vllm.distributed import (
|
|
get_tensor_model_parallel_rank,
|
|
get_tensor_model_parallel_world_size,
|
|
get_tp_group,
|
|
tensor_model_parallel_all_reduce,
|
|
)
|
|
from vllm.model_executor.layers.fused_moe import FusedMoE
|
|
from vllm.model_executor.layers.rotary_embedding import get_rope
|
|
from vllm.model_executor.model_loader.weight_utils import default_weight_loader
|
|
|
|
from sglang.srt.layers.activation import SiluAndMul
|
|
from sglang.srt.layers.layernorm import RMSNorm
|
|
from sglang.srt.layers.linear import (
|
|
ColumnParallelLinear,
|
|
MergedColumnParallelLinear,
|
|
ReplicatedLinear,
|
|
RowParallelLinear,
|
|
)
|
|
from sglang.srt.layers.logits_processor import LogitsProcessor
|
|
from sglang.srt.layers.quantization.base_config import QuantizationConfig
|
|
from sglang.srt.layers.radix_attention import RadixAttention
|
|
from sglang.srt.layers.vocab_parallel_embedding import (
|
|
ParallelLMHead,
|
|
VocabParallelEmbedding,
|
|
)
|
|
from sglang.srt.managers.schedule_batch import global_server_args_dict
|
|
from sglang.srt.model_executor.forward_batch_info import ForwardBatch
|
|
from sglang.srt.utils import is_flashinfer_available
|
|
|
|
if is_flashinfer_available():
|
|
from flashinfer import bmm_fp8
|
|
|
|
|
|
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,
|
|
) -> None:
|
|
super().__init__()
|
|
self.gate_up_proj = MergedColumnParallelLinear(
|
|
hidden_size, [intermediate_size] * 2, bias=False, quant_config=quant_config
|
|
)
|
|
self.down_proj = RowParallelLinear(
|
|
intermediate_size,
|
|
hidden_size,
|
|
bias=False,
|
|
quant_config=quant_config,
|
|
reduce_results=reduce_results,
|
|
)
|
|
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: PretrainedConfig,
|
|
quant_config: Optional[QuantizationConfig] = None,
|
|
):
|
|
super().__init__()
|
|
self.tp_size = get_tensor_model_parallel_world_size()
|
|
self.routed_scaling_factor = config.routed_scaling_factor
|
|
self.n_shared_experts = config.n_shared_experts
|
|
self.routed_scaling_factor = config.routed_scaling_factor
|
|
if self.tp_size > config.n_routed_experts:
|
|
raise ValueError(
|
|
f"Tensor parallel size {self.tp_size} is greater than "
|
|
f"the number of experts {config.n_routed_experts}."
|
|
)
|
|
|
|
if config.hidden_act != "silu":
|
|
raise ValueError(
|
|
f"Unsupported activation: {config.hidden_act}. "
|
|
"Only silu is supported for now."
|
|
)
|
|
|
|
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,
|
|
)
|
|
|
|
self.gate = ReplicatedLinear(
|
|
config.hidden_size, config.n_routed_experts, bias=False, quant_config=None
|
|
)
|
|
if config.n_shared_experts is not None:
|
|
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,
|
|
reduce_results=False,
|
|
)
|
|
|
|
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
|
|
num_tokens, hidden_dim = hidden_states.shape
|
|
hidden_states = hidden_states.view(-1, hidden_dim)
|
|
if self.n_shared_experts is not None:
|
|
shared_output = self.shared_experts(hidden_states)
|
|
# router_logits: (num_tokens, n_experts)
|
|
router_logits, _ = self.gate(hidden_states)
|
|
final_hidden_states = (
|
|
self.experts(hidden_states=hidden_states, router_logits=router_logits)
|
|
* self.routed_scaling_factor
|
|
)
|
|
if shared_output is not None:
|
|
final_hidden_states = final_hidden_states + shared_output
|
|
if self.tp_size > 1:
|
|
final_hidden_states = tensor_model_parallel_all_reduce(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
|
|
|
|
|
|
def input_to_float8(x, dtype=torch.float8_e4m3fn):
|
|
finfo = torch.finfo(dtype)
|
|
min_val, max_val = x.aminmax()
|
|
amax = torch.maximum(min_val.abs(), max_val.abs()).clamp(min=1e-12)
|
|
scale = finfo.max / amax
|
|
x_scl_sat = (x * scale).clamp(min=finfo.min, max=finfo.max)
|
|
return x_scl_sat.to(dtype).contiguous(), scale.float().reciprocal()
|
|
|
|
|
|
class DeepseekV2Attention(nn.Module):
|
|
|
|
def __init__(
|
|
self,
|
|
config: PretrainedConfig,
|
|
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=None,
|
|
quant_config: Optional[QuantizationConfig] = None,
|
|
layer_id=None,
|
|
) -> None:
|
|
super().__init__()
|
|
self.layer_id = layer_id
|
|
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.q_a_proj = ReplicatedLinear(
|
|
self.hidden_size,
|
|
self.q_lora_rank,
|
|
bias=False,
|
|
quant_config=quant_config,
|
|
)
|
|
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,
|
|
)
|
|
else:
|
|
self.q_proj = ColumnParallelLinear(
|
|
self.hidden_size,
|
|
self.num_heads * self.qk_head_dim,
|
|
bias=False,
|
|
quant_config=quant_config,
|
|
)
|
|
|
|
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,
|
|
)
|
|
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,
|
|
)
|
|
# O projection.
|
|
self.o_proj = RowParallelLinear(
|
|
self.num_heads * self.v_head_dim,
|
|
self.hidden_size,
|
|
bias=False,
|
|
quant_config=quant_config,
|
|
)
|
|
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
|
|
|
|
# TODO, support head_size 192
|
|
self.attn = RadixAttention(
|
|
self.num_local_heads,
|
|
256,
|
|
self.scaling,
|
|
num_kv_heads=self.num_local_heads,
|
|
layer_id=layer_id,
|
|
)
|
|
|
|
def forward(
|
|
self,
|
|
positions: torch.Tensor,
|
|
hidden_states: torch.Tensor,
|
|
forward_batch: ForwardBatch,
|
|
) -> 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_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.contiguous())
|
|
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
|
|
q = torch.nn.functional.pad(q, [0, 256 - self.qk_head_dim], value=0).view(
|
|
-1, self.num_local_heads * 256
|
|
)
|
|
k = torch.nn.functional.pad(k, [0, 256 - self.qk_head_dim], value=0).view(
|
|
-1, self.num_local_heads * 256
|
|
)
|
|
v = torch.nn.functional.pad(v, [0, 256 - self.v_head_dim], value=0).view(
|
|
-1, self.num_local_heads * 256
|
|
)
|
|
attn_output = self.attn(q, k, v, forward_batch)
|
|
attn_output = attn_output.view(-1, self.num_local_heads, 256)[
|
|
..., : self.v_head_dim
|
|
].reshape(-1, self.num_local_heads * self.v_head_dim)
|
|
output, _ = self.o_proj(attn_output)
|
|
return output
|
|
|
|
|
|
class DeepseekV2AttentionMLA(nn.Module):
|
|
|
|
def __init__(
|
|
self,
|
|
config: PretrainedConfig,
|
|
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=None,
|
|
quant_config: Optional[QuantizationConfig] = None,
|
|
layer_id=None,
|
|
use_dp=False,
|
|
) -> None:
|
|
super().__init__()
|
|
self.layer_id = layer_id
|
|
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 if use_dp else 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 use_dp:
|
|
# For data parallel attention
|
|
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,
|
|
)
|
|
self.q_a_layernorm = RMSNorm(self.q_lora_rank, eps=config.rms_norm_eps)
|
|
self.q_b_proj = ReplicatedLinear(
|
|
q_lora_rank,
|
|
self.num_heads * self.qk_head_dim,
|
|
bias=False,
|
|
quant_config=quant_config,
|
|
)
|
|
else:
|
|
self.q_proj = ReplicatedLinear(
|
|
self.hidden_size,
|
|
self.num_heads * self.qk_head_dim,
|
|
bias=False,
|
|
quant_config=quant_config,
|
|
)
|
|
self.kv_b_proj = ReplicatedLinear(
|
|
self.kv_lora_rank,
|
|
self.num_heads * (self.qk_nope_head_dim + self.v_head_dim),
|
|
bias=False,
|
|
quant_config=quant_config,
|
|
)
|
|
# O projection.
|
|
self.o_proj = ReplicatedLinear(
|
|
self.num_heads * self.v_head_dim,
|
|
self.hidden_size,
|
|
bias=False,
|
|
quant_config=quant_config,
|
|
)
|
|
else:
|
|
# For tensor parallel attention
|
|
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,
|
|
)
|
|
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,
|
|
)
|
|
else:
|
|
self.q_proj = ColumnParallelLinear(
|
|
self.hidden_size,
|
|
self.num_heads * self.qk_head_dim,
|
|
bias=False,
|
|
quant_config=quant_config,
|
|
)
|
|
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,
|
|
)
|
|
# O projection.
|
|
self.o_proj = RowParallelLinear(
|
|
self.num_heads * self.v_head_dim,
|
|
self.hidden_size,
|
|
bias=False,
|
|
quant_config=quant_config,
|
|
)
|
|
|
|
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,
|
|
)
|
|
self.kv_a_layernorm = RMSNorm(self.kv_lora_rank, eps=config.rms_norm_eps)
|
|
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 = RadixAttention(
|
|
self.num_local_heads,
|
|
self.kv_lora_rank + self.qk_rope_head_dim,
|
|
self.scaling,
|
|
num_kv_heads=1,
|
|
layer_id=layer_id,
|
|
v_head_dim=self.kv_lora_rank,
|
|
)
|
|
|
|
self.w_kc = None
|
|
self.w_vc = None
|
|
self.w_scale = None
|
|
|
|
def forward(
|
|
self,
|
|
positions: torch.Tensor,
|
|
hidden_states: torch.Tensor,
|
|
forward_batch: ForwardBatch,
|
|
) -> torch.Tensor:
|
|
q_len = hidden_states.shape[0]
|
|
q_input = hidden_states.new_empty(
|
|
q_len, self.num_local_heads, self.kv_lora_rank + self.qk_rope_head_dim
|
|
)
|
|
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)
|
|
|
|
if self.w_kc.dtype == torch.float8_e4m3fn:
|
|
q_nope_val, q_nope_scale = input_to_float8(
|
|
q_nope.transpose(0, 1), torch.float8_e4m3fn
|
|
)
|
|
q_nope_out = bmm_fp8(
|
|
q_nope_val, self.w_kc, q_nope_scale, self.w_scale, torch.bfloat16
|
|
)
|
|
else:
|
|
q_nope_out = torch.bmm(q_nope.transpose(0, 1), self.w_kc)
|
|
q_input[..., : self.kv_lora_rank] = q_nope_out.transpose(0, 1)
|
|
|
|
latent_cache = self.kv_a_proj_with_mqa(hidden_states)[0]
|
|
v_input = latent_cache[..., : self.kv_lora_rank]
|
|
v_input = self.kv_a_layernorm(v_input.contiguous()).unsqueeze(1)
|
|
k_input = latent_cache.unsqueeze(1)
|
|
k_input[..., : self.kv_lora_rank] = v_input
|
|
k_pe = k_input[..., self.kv_lora_rank :]
|
|
|
|
q_pe, k_pe = self.rotary_emb(positions, q_pe, k_pe)
|
|
q_input[..., self.kv_lora_rank :] = q_pe
|
|
k_input[..., self.kv_lora_rank :] = k_pe
|
|
|
|
attn_output = self.attn(q_input, k_input, v_input, forward_batch)
|
|
attn_output = attn_output.view(-1, self.num_local_heads, self.kv_lora_rank)
|
|
|
|
if self.w_vc.dtype == torch.float8_e4m3fn:
|
|
attn_output_val, attn_output_scale = input_to_float8(
|
|
attn_output.transpose(0, 1), torch.float8_e4m3fn
|
|
)
|
|
attn_bmm_output = bmm_fp8(
|
|
attn_output_val,
|
|
self.w_vc,
|
|
attn_output_scale,
|
|
self.w_scale,
|
|
torch.bfloat16,
|
|
)
|
|
else:
|
|
attn_bmm_output = torch.bmm(attn_output.transpose(0, 1), self.w_vc)
|
|
attn_output = attn_bmm_output.transpose(0, 1).flatten(1, 2)
|
|
output, _ = self.o_proj(attn_output)
|
|
|
|
return output
|
|
|
|
|
|
def all_gather(
|
|
input_tensor: torch.Tensor, forward_batch: ForwardBatch, rank, world_size, group
|
|
):
|
|
if world_size == 1:
|
|
return input_tensor
|
|
|
|
all_lens = forward_batch.global_num_tokens
|
|
max_len = max(forward_batch.global_num_tokens)
|
|
|
|
padded_tensor = torch.nn.functional.pad(
|
|
input_tensor, (0, 0, 0, max_len - input_tensor.shape[0])
|
|
)
|
|
|
|
torch.distributed.all_gather_into_tensor(
|
|
forward_batch.gathered_buffer, padded_tensor, group=group
|
|
)
|
|
|
|
gathered_tensors = torch.concat(
|
|
[
|
|
forward_batch.gathered_buffer[i * max_len : i * max_len + all_lens[i]]
|
|
for i in range(world_size)
|
|
]
|
|
)
|
|
|
|
start_index = 0 if rank == 0 else sum(all_lens[:rank])
|
|
end_index = start_index + all_lens[rank]
|
|
|
|
return gathered_tensors, start_index, end_index
|
|
|
|
|
|
class DeepseekV2DecoderLayer(nn.Module):
|
|
|
|
def __init__(
|
|
self,
|
|
config: PretrainedConfig,
|
|
layer_id: int,
|
|
cache_config=None,
|
|
quant_config: Optional[QuantizationConfig] = None,
|
|
) -> None:
|
|
super().__init__()
|
|
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)
|
|
self.enable_dp_attention = (
|
|
not global_server_args_dict["disable_mla"]
|
|
and global_server_args_dict["enable_dp_attention"]
|
|
)
|
|
if self.enable_dp_attention:
|
|
self.tp_rank = get_tensor_model_parallel_rank()
|
|
self.tp_size = get_tensor_model_parallel_world_size()
|
|
self.tp_group = get_tp_group().device_group
|
|
if not global_server_args_dict["disable_mla"]:
|
|
self.self_attn = DeepseekV2AttentionMLA(
|
|
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,
|
|
layer_id=layer_id,
|
|
use_dp=self.enable_dp_attention,
|
|
)
|
|
else:
|
|
self.self_attn = DeepseekV2Attention(
|
|
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,
|
|
layer_id=layer_id,
|
|
)
|
|
if (
|
|
config.n_routed_experts is not None
|
|
and layer_id >= config.first_k_dense_replace
|
|
and layer_id % config.moe_layer_freq == 0
|
|
):
|
|
self.mlp = DeepseekV2MoE(config=config, quant_config=quant_config)
|
|
else:
|
|
self.mlp = DeepseekV2MLP(
|
|
hidden_size=config.hidden_size,
|
|
intermediate_size=config.intermediate_size,
|
|
hidden_act=config.hidden_act,
|
|
quant_config=quant_config,
|
|
)
|
|
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
|
|
)
|
|
|
|
def forward(
|
|
self,
|
|
positions: torch.Tensor,
|
|
hidden_states: torch.Tensor,
|
|
forward_batch: ForwardBatch,
|
|
residual: Optional[torch.Tensor],
|
|
) -> torch.Tensor:
|
|
# Self Attention
|
|
if not forward_batch.forward_mode.is_idle():
|
|
if residual is None:
|
|
residual = hidden_states
|
|
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,
|
|
forward_batch=forward_batch,
|
|
)
|
|
hidden_states, residual = self.post_attention_layernorm(
|
|
hidden_states, residual
|
|
)
|
|
|
|
# Fully Connected
|
|
if self.enable_dp_attention:
|
|
hidden_states, start_idx, end_idx = all_gather(
|
|
hidden_states, forward_batch, self.tp_rank, self.tp_size, self.tp_group
|
|
)
|
|
hidden_states = self.mlp(hidden_states)
|
|
hidden_states = hidden_states[start_idx:end_idx]
|
|
else:
|
|
hidden_states = self.mlp(hidden_states)
|
|
|
|
return hidden_states, residual
|
|
|
|
|
|
class DeepseekV2Model(nn.Module):
|
|
|
|
fall_back_to_pt_during_load = False
|
|
|
|
def __init__(
|
|
self,
|
|
config: PretrainedConfig,
|
|
cache_config=None,
|
|
quant_config: Optional[QuantizationConfig] = None,
|
|
) -> None:
|
|
super().__init__()
|
|
self.padding_id = config.pad_token_id
|
|
self.vocab_size = config.vocab_size
|
|
|
|
self.embed_tokens = VocabParallelEmbedding(
|
|
config.vocab_size,
|
|
config.hidden_size,
|
|
enable_tp=not global_server_args_dict["enable_dp_attention"],
|
|
)
|
|
self.layers = nn.ModuleList(
|
|
[
|
|
DeepseekV2DecoderLayer(
|
|
config,
|
|
layer_id,
|
|
cache_config=cache_config,
|
|
quant_config=quant_config,
|
|
)
|
|
for layer_id in range(config.num_hidden_layers)
|
|
]
|
|
)
|
|
self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
|
|
|
def forward(
|
|
self,
|
|
input_ids: torch.Tensor,
|
|
positions: torch.Tensor,
|
|
forward_batch: ForwardBatch,
|
|
) -> torch.Tensor:
|
|
hidden_states = self.embed_tokens(input_ids)
|
|
residual = None
|
|
for i in range(len(self.layers)):
|
|
layer = self.layers[i]
|
|
hidden_states, residual = layer(
|
|
positions, hidden_states, forward_batch, residual
|
|
)
|
|
if not forward_batch.forward_mode.is_idle():
|
|
hidden_states, _ = self.norm(hidden_states, residual)
|
|
return hidden_states
|
|
|
|
|
|
class DeepseekV2ForCausalLM(nn.Module):
|
|
|
|
def __init__(
|
|
self,
|
|
config: PretrainedConfig,
|
|
cache_config=None,
|
|
quant_config: Optional[QuantizationConfig] = None,
|
|
) -> None:
|
|
super().__init__()
|
|
self.config = config
|
|
self.quant_config = quant_config
|
|
self.model = DeepseekV2Model(config, cache_config, quant_config)
|
|
if global_server_args_dict["enable_dp_attention"]:
|
|
self.lm_head = ReplicatedLinear(
|
|
config.hidden_size,
|
|
config.vocab_size,
|
|
bias=False,
|
|
)
|
|
self.logits_processor = LogitsProcessor(config, skip_all_gather=True)
|
|
else:
|
|
self.lm_head = ParallelLMHead(
|
|
config.vocab_size, config.hidden_size, quant_config=quant_config
|
|
)
|
|
self.logits_processor = LogitsProcessor(config)
|
|
|
|
@torch.no_grad()
|
|
def forward(
|
|
self,
|
|
input_ids: torch.Tensor,
|
|
positions: torch.Tensor,
|
|
forward_batch: ForwardBatch,
|
|
) -> torch.Tensor:
|
|
hidden_states = self.model(input_ids, positions, forward_batch)
|
|
if not forward_batch.forward_mode.is_idle():
|
|
return self.logits_processor(
|
|
input_ids, hidden_states, self.lm_head.weight, forward_batch
|
|
)
|
|
|
|
def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
|
|
stacked_params_mapping = [
|
|
# (param_name, shard_name, shard_id)
|
|
("gate_up_proj", "gate_proj", 0),
|
|
("gate_up_proj", "up_proj", 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,
|
|
)
|
|
|
|
params_dict = dict(self.named_parameters())
|
|
for name, loaded_weight in weights:
|
|
if "rotary_emb.inv_freq" in name:
|
|
continue
|
|
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 = name.replace(weight_name, param_name)
|
|
# Skip loading extra bias for GPTQ models.
|
|
if name.endswith(".bias") and name not in params_dict:
|
|
continue
|
|
param = params_dict[name]
|
|
weight_loader = param.weight_loader
|
|
weight_loader(param, loaded_weight, shard_id)
|
|
break
|
|
else:
|
|
for mapping in expert_params_mapping:
|
|
param_name, weight_name, expert_id, shard_id = mapping
|
|
if weight_name not in name:
|
|
continue
|
|
name = name.replace(weight_name, param_name)
|
|
param = params_dict[name]
|
|
weight_loader = param.weight_loader
|
|
weight_loader(
|
|
param,
|
|
loaded_weight,
|
|
name,
|
|
shard_id=shard_id,
|
|
expert_id=expert_id,
|
|
)
|
|
break
|
|
else:
|
|
# Skip loading extra bias for GPTQ models.
|
|
if name.endswith(".bias") and name not in params_dict:
|
|
continue
|
|
|
|
param = params_dict[name]
|
|
weight_loader = getattr(
|
|
param, "weight_loader", default_weight_loader
|
|
)
|
|
weight_loader(param, loaded_weight)
|
|
|
|
if not global_server_args_dict["disable_mla"]:
|
|
for layer_id in range(self.config.num_hidden_layers):
|
|
self_attn = self.model.layers[layer_id].self_attn
|
|
w_kc, w_vc = self_attn.kv_b_proj.weight.unflatten(
|
|
0, (-1, self_attn.qk_nope_head_dim + self_attn.v_head_dim)
|
|
).split([self_attn.qk_nope_head_dim, self_attn.v_head_dim], dim=1)
|
|
self_attn.w_kc = w_kc.transpose(1, 2).contiguous().transpose(1, 2)
|
|
self_attn.w_vc = w_vc.contiguous().transpose(1, 2)
|
|
if hasattr(self_attn.kv_b_proj, "weight_scale"):
|
|
self_attn.w_scale = self_attn.kv_b_proj.weight_scale
|
|
del self_attn.kv_b_proj
|
|
|
|
|
|
EntryClass = DeepseekV2ForCausalLM
|