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import torch
import torch.nn as nn
from einops import rearrange
from diffulex.attention import Attention
from diffulex.layer.layernorm import RMSNorm
from diffulex.layer.activation import SiluAndMul
from diffulex.layer.rotary_embedding import get_rope
from diffulex.layer.linear import RowParallelLinear, ColumnParallelLinear
from diffulex.layer.embed_head import VocabParallelEmbedding, ParallelLMHead
from diffulex.model.auto_model import AutoModelForDiffusionLM
from diffulex.model.config.sdar.configuration_sdar import SDARConfig
from diffulex.distributed.parallel_state import fetch_parallel_state
if os.environ.get("TRITON_INTERPRET", None) == "1":
torch._dynamo.reset()
torch._dynamo.config.suppress_errors = True
torch.backends.optimized_mode = False
class SDARAttention(nn.Module):
"""SDAR attention (Diffulex native KV cache path).
Compatible with Diffulex runner KV cache injection:
runner sets `self.attn.k_cache` / `self.attn.v_cache` by assigning to modules
that expose these attributes (see `diffulex/attention/attn_impl.py`).
"""
def __init__(self, config: SDARConfig) -> None:
super().__init__()
parallel_state = fetch_parallel_state()
tp_size = parallel_state.get_tp_world_size()
self.total_num_heads = config.num_attention_heads
assert self.total_num_heads % tp_size == 0
self.num_heads = self.total_num_heads // tp_size
self.total_num_kv_heads = config.num_key_value_heads
assert self.total_num_kv_heads % tp_size == 0
self.num_kv_heads = self.total_num_kv_heads // tp_size
head_dim = getattr(config, "head_dim", None)
self.head_dim = head_dim or (config.hidden_size // self.total_num_heads)
self.q_size = self.num_heads * self.head_dim
self.kv_size = self.num_kv_heads * self.head_dim
self.scaling = self.head_dim**-0.5
bias = getattr(config, "attention_bias", False)
self.q_proj = ColumnParallelLinear(
config.hidden_size,
self.total_num_heads * self.head_dim,
bias=bias,
)
self.k_proj = ColumnParallelLinear(
config.hidden_size,
self.total_num_kv_heads * self.head_dim,
bias=bias,
)
self.v_proj = ColumnParallelLinear(
config.hidden_size,
self.total_num_kv_heads * self.head_dim,
bias=bias,
)
self.o_proj = RowParallelLinear(
self.total_num_heads * self.head_dim,
config.hidden_size,
bias=bias,
)
# SDAR uses q/k per-head RMSNorm.
self.q_norm = RMSNorm(self.head_dim, eps=config.rms_norm_eps)
self.k_norm = RMSNorm(self.head_dim, eps=config.rms_norm_eps)
self.rotary_emb = get_rope(
self.head_dim,
rotary_dim=self.head_dim,
max_position=config.max_position_embeddings,
base=getattr(config, "rope_theta", 10000),
rope_scaling=getattr(config, "rope_scaling", None),
)
# Diffulex Attention implements KV cache store/load via injected k_cache/v_cache.
self.attn = Attention(
self.num_heads,
self.head_dim,
self.scaling,
self.num_kv_heads,
attn_impl=getattr(config, "attn_impl", "triton"),
)
def forward(
self,
positions: torch.Tensor,
hidden_states: torch.Tensor,
mask: torch.Tensor | None = None,
) -> torch.Tensor:
q = self.q_proj(hidden_states)
k = self.k_proj(hidden_states)
v = self.v_proj(hidden_states)
q = rearrange(
self.q_norm(
rearrange(q, "token (head head_dim) -> token head head_dim", head=self.num_heads)
),
"token head head_dim -> token (head head_dim)",
)
k = rearrange(
self.k_norm(
rearrange(k, "token (head head_dim) -> token head head_dim", head=self.num_kv_heads)
),
"token head head_dim -> token (head head_dim)",
)
q, k = self.rotary_emb(positions, q, k)
o = self.attn(q, k, v, mask)
return self.o_proj(o)
class SDARMLP(nn.Module):
"""SDAR MLP: SiLU(gate) * up -> down."""
def __init__(self, config: SDARConfig) -> None:
super().__init__()
self.gate_proj = ColumnParallelLinear(
config.hidden_size,
config.intermediate_size,
bias=False,
)
self.up_proj = ColumnParallelLinear(
config.hidden_size,
config.intermediate_size,
bias=False,
)
self.down_proj = RowParallelLinear(
config.intermediate_size,
config.hidden_size,
bias=False,
)
assert getattr(config, "hidden_act", "silu") == "silu"
self.act_fn = SiluAndMul()
def forward(self, x: torch.Tensor) -> torch.Tensor:
gate = self.gate_proj(x)
up = self.up_proj(x)
x = self.act_fn(torch.cat([gate, up], dim=-1))
return self.down_proj(x)
class SDARDecoderLayer(nn.Module):
def __init__(self, config: SDARConfig) -> None:
super().__init__()
self.self_attn = SDARAttention(config)
self.mlp = SDARMLP(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,
residual: torch.Tensor | None,
mask: torch.Tensor | None = None,
) -> tuple[torch.Tensor, torch.Tensor]:
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, hidden_states, mask)
hidden_states, residual = self.post_attention_layernorm(hidden_states, residual)
hidden_states = self.mlp(hidden_states)
return hidden_states, residual
class SDARModel(nn.Module):
def __init__(self, config: SDARConfig) -> None:
super().__init__()
self.embed_tokens = VocabParallelEmbedding(config.vocab_size, config.hidden_size)
self.layers = nn.ModuleList([SDARDecoderLayer(config) for _ 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,
mask: torch.Tensor | None = None,
) -> torch.Tensor:
hidden_states = self.embed_tokens(input_ids)
residual = None
for layer in self.layers:
hidden_states, residual = layer(positions, hidden_states, residual, mask)
hidden_states, _ = self.norm(hidden_states, residual)
return hidden_states
@AutoModelForDiffusionLM.register("sdar")
class SDARForDiffusionLM(nn.Module):
packed_modules_mapping = {}
def __init__(self, config: SDARConfig) -> None:
super().__init__()
self.model = SDARModel(config)
self.lm_head = ParallelLMHead(config.vocab_size, config.hidden_size)
if getattr(config, "tie_word_embeddings", False):
self.lm_head.weight.data = self.model.embed_tokens.weight.data
def forward(
self,
input_ids: torch.Tensor,
positions: torch.Tensor,
mask: torch.Tensor | None = None,
) -> torch.Tensor:
return self.model(input_ids, positions, mask)
def compute_logits(self, hidden_states: torch.Tensor) -> torch.Tensor:
return self.lm_head(hidden_states)
__all__ = [
"SDARConfig",
"SDARAttention",
"SDARMLP",
"SDARDecoderLayer",
"SDARModel",
"SDARForDiffusionLM",
]
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