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Hugging Face Transformers Compatible Modeling Implementation for Mark-Engineer-V2-8B
Self-contained causal language model architecture with Multi-Head Latent Attention (MLA),
DeepSeekMoE (1 Shared + 24 Routed Experts, Top-2 active), and Multi-Token Prediction (MTP).
"""
import math
from typing import List, Optional, Tuple, Union
import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers.modeling_outputs import CausalLMOutputWithPast
from transformers.modeling_utils import PreTrainedModel
try:
from .configuration_mark_engineer_v2_8b import MarkEngineerV2_8BConfig
except (ImportError, ValueError):
from configuration_mark_engineer_v2_8b import MarkEngineerV2_8BConfig
class MarkRMSNorm(nn.Module):
def __init__(self, dim: int, eps: float = 1e-5):
super().__init__()
self.eps = eps
self.weight = nn.Parameter(torch.ones(dim))
def forward(self, x: torch.Tensor) -> torch.Tensor:
variance = x.pow(2).mean(-1, keepdim=True)
return x * torch.rsqrt(variance + self.eps) * self.weight
def precompute_rope_freqs(head_dim: int, max_seq_len: int, theta: float = 500000.0) -> Tuple[torch.Tensor, torch.Tensor]:
freqs = 1.0 / (theta ** (torch.arange(0, head_dim, 2).float() / head_dim))
t = torch.arange(max_seq_len, dtype=torch.float)
freqs = torch.outer(t, freqs)
cos = freqs.cos()
sin = freqs.sin()
return cos, sin
def apply_rotary_emb(x: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor) -> torch.Tensor:
d = x.shape[-1]
x1 = x[..., : d // 2]
x2 = x[..., d // 2 :]
rotated = torch.cat((-x2, x1), dim=-1)
cos = cos.unsqueeze(0).unsqueeze(2)
sin = sin.unsqueeze(0).unsqueeze(2)
cos = torch.cat([cos, cos], dim=-1)
sin = torch.cat([sin, sin], dim=-1)
return (x * cos) + (rotated * sin)
class MarkSwiGLUFFN(nn.Module):
def __init__(self, dim: int, intermediate_size: int):
super().__init__()
self.w_gate = nn.Linear(dim, intermediate_size, bias=False)
self.w_up = nn.Linear(dim, intermediate_size, bias=False)
self.w_down = nn.Linear(intermediate_size, dim, bias=False)
def forward(self, x: torch.Tensor) -> torch.Tensor:
return self.w_down(F.silu(self.w_gate(x)) * self.w_up(x))
class MarkMLAAttention(nn.Module):
def __init__(self, config: MarkEngineerV2_8BConfig, layer_idx: int):
super().__init__()
self.layer_idx = layer_idx
self.dim = config.hidden_size
self.n_heads = config.num_attention_heads
self.q_lora_rank = config.q_lora_rank
self.kv_lora_rank = config.kv_lora_rank
self.qk_nope_head_dim = config.qk_nope_head_dim
self.qk_rope_head_dim = config.qk_rope_head_dim
self.v_head_dim = config.v_head_dim
self.qk_head_dim = self.qk_nope_head_dim + self.qk_rope_head_dim
self.wq_a = nn.Linear(self.dim, self.q_lora_rank, bias=False)
self.q_norm = MarkRMSNorm(self.q_lora_rank, eps=config.rms_norm_eps)
self.wq_b = nn.Linear(self.q_lora_rank, self.n_heads * self.qk_head_dim, bias=False)
self.wkv_a = nn.Linear(self.dim, self.kv_lora_rank + self.qk_rope_head_dim, bias=False)
self.kv_norm = MarkRMSNorm(self.kv_lora_rank, eps=config.rms_norm_eps)
self.wkv_b = nn.Linear(
self.kv_lora_rank,
self.n_heads * (self.qk_nope_head_dim + self.v_head_dim),
bias=False,
)
self.wo = nn.Linear(self.n_heads * self.v_head_dim, self.dim, bias=False)
def forward(
self,
x: torch.Tensor,
cos: torch.Tensor,
sin: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
) -> torch.Tensor:
bsz, seqlen, _ = x.shape
q_latent = self.q_norm(self.wq_a(x))
q = self.wq_b(q_latent).view(bsz, seqlen, self.n_heads, self.qk_head_dim)
q_nope = q[..., : self.qk_nope_head_dim]
q_pe = q[..., self.qk_nope_head_dim :]
kv_combined = self.wkv_a(x)
kv_latent = self.kv_norm(kv_combined[..., : self.kv_lora_rank])
k_pe = kv_combined[..., self.kv_lora_rank :].view(bsz, seqlen, 1, self.qk_rope_head_dim)
kv = self.wkv_b(kv_latent).view(bsz, seqlen, self.n_heads, self.qk_nope_head_dim + self.v_head_dim)
k_nope = kv[..., : self.qk_nope_head_dim]
v = kv[..., self.qk_nope_head_dim :]
q_pe = apply_rotary_emb(q_pe, cos[:seqlen], sin[:seqlen])
k_pe = apply_rotary_emb(k_pe.expand(-1, -1, self.n_heads, -1), cos[:seqlen], sin[:seqlen])
k = torch.cat([k_nope, k_pe], dim=-1)
q = torch.cat([q_nope, q_pe], dim=-1)
q = q.transpose(1, 2)
k = k.transpose(1, 2)
v = v.transpose(1, 2)
scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(self.qk_head_dim)
if attention_mask is not None:
scores = scores + attention_mask
attn_weights = F.softmax(scores, dim=-1)
output = torch.matmul(attn_weights, v)
output = output.transpose(1, 2).contiguous().view(bsz, seqlen, -1)
return self.wo(output)
class MarkMoELayer(nn.Module):
def __init__(self, config: MarkEngineerV2_8BConfig):
super().__init__()
self.n_routed = config.n_routed_experts
self.n_shared = config.n_shared_experts
self.top_k = config.num_experts_per_tok
self.dim = config.hidden_size
self.moe_intermediate = config.moe_intermediate_size
self.gate = nn.Linear(self.dim, self.n_routed, bias=False)
self.register_buffer("router_bias", torch.zeros(self.n_routed))
self.router_bias_rate = config.router_bias_update_rate
self.routed_experts = nn.ModuleList([
MarkSwiGLUFFN(self.dim, self.moe_intermediate) for _ in range(self.n_routed)
])
if self.n_shared > 0:
self.shared_experts = MarkSwiGLUFFN(self.dim, self.moe_intermediate * self.n_shared)
else:
self.shared_experts = None
self.last_router_z_loss = None
def forward(self, x: torch.Tensor) -> torch.Tensor:
bsz, seqlen, dim = x.shape
flat_x = x.view(-1, dim)
logits = self.gate(flat_x)
if self.training:
self.last_router_z_loss = torch.logsumexp(logits, dim=-1).pow(2).mean()
biased_logits = logits + self.router_bias
topk_weights, topk_indices = torch.topk(F.softmax(biased_logits, dim=-1), self.top_k, dim=-1)
topk_weights = topk_weights / topk_weights.sum(dim=-1, keepdim=True)
out = torch.zeros_like(flat_x)
for i, expert in enumerate(self.routed_experts):
mask = (topk_indices == i).any(dim=-1)
if mask.any():
idx_in_topk = (topk_indices[mask] == i).nonzero(as_tuple=True)[1]
w = topk_weights[mask, idx_in_topk].unsqueeze(-1)
out[mask] += w * expert(flat_x[mask])
if self.shared_experts is not None:
out += self.shared_experts(flat_x)
return out.view(bsz, seqlen, dim)
class MarkEngineerV2Block(nn.Module):
def __init__(self, config: MarkEngineerV2_8BConfig, layer_idx: int):
super().__init__()
self.attn_norm = MarkRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.attn = MarkMLAAttention(config, layer_idx)
self.ffn_norm = MarkRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
if layer_idx < config.n_dense_layers:
self.ffn = MarkSwiGLUFFN(config.hidden_size, config.intermediate_size)
self.is_moe = False
else:
self.ffn = MarkMoELayer(config)
self.is_moe = True
def forward(
self,
x: torch.Tensor,
cos: torch.Tensor,
sin: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
) -> torch.Tensor:
h = x + self.attn(self.attn_norm(x), cos, sin, attention_mask)
out = h + self.ffn(self.ffn_norm(h))
return out
class MarkEngineerV2ForCausalLM(PreTrainedModel):
config_class = MarkEngineerV2_8BConfig
base_model_prefix = "model"
_supports_flash_attn_2 = False
def __init__(self, config: MarkEngineerV2_8BConfig):
super().__init__(config)
self.config = config
self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size)
self.layers = nn.ModuleList([
MarkEngineerV2Block(config, i) for i in range(config.num_hidden_layers)
])
self.norm = MarkRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
if config.mtp_depth > 0:
self.mtp_proj = nn.Linear(config.hidden_size * 2, config.hidden_size, bias=False)
self.mtp_norm = MarkRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.mtp_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
else:
self.mtp_proj = None
if config.tie_word_embeddings:
self.lm_head.weight = self.embed_tokens.weight
cos, sin = precompute_rope_freqs(
config.qk_rope_head_dim,
config.max_position_embeddings,
theta=config.rope_theta,
)
self.register_buffer("rope_cos", cos, persistent=False)
self.register_buffer("rope_sin", sin, persistent=False)
self.post_init()
def get_input_embeddings(self):
return self.embed_tokens
def set_input_embeddings(self, value):
self.embed_tokens = value
def get_output_embeddings(self):
return self.lm_head
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
labels: Optional[torch.LongTensor] = None,
return_mtp: bool = False,
**kwargs,
) -> Union[Tuple, CausalLMOutputWithPast]:
bsz, seqlen = input_ids.shape
x = self.embed_tokens(input_ids)
if attention_mask is None:
causal_mask = torch.triu(
torch.full((seqlen, seqlen), float("-inf"), device=input_ids.device),
diagonal=1,
).unsqueeze(0).unsqueeze(0)
else:
causal_mask = attention_mask
for layer in self.layers:
x = layer(x, self.rope_cos, self.rope_sin, causal_mask)
h = self.norm(x)
logits = self.lm_head(h)
mtp_logits = None
if return_mtp and self.mtp_proj is not None and seqlen > 1:
next_emb = self.embed_tokens(input_ids[:, 1:])
h_prev = h[:, :-1]
cat_h = torch.cat([h_prev, next_emb], dim=-1)
h_mtp = self.mtp_norm(self.mtp_proj(cat_h))
mtp_logits = self.mtp_head(h_mtp)
loss = None
if labels is not None:
shift_logits = logits[..., :-1, :].contiguous()
shift_labels = labels[..., 1:].contiguous()
loss_fct = nn.CrossEntropyLoss()
loss = loss_fct(shift_logits.view(-1, self.config.vocab_size), shift_labels.view(-1))
if mtp_logits is not None and seqlen > 2:
shift_mtp_logits = mtp_logits[..., :-1, :].contiguous()
shift_mtp_labels = labels[..., 2:].contiguous()
loss_mtp = loss_fct(shift_mtp_logits.view(-1, self.config.vocab_size), shift_mtp_labels.view(-1))
loss = loss + (self.config.mtp_loss_weight * loss_mtp)
return CausalLMOutputWithPast(
loss=loss,
logits=logits,
)
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