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import torch
import torch.nn as nn
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.model.auto_model import AutoModelForDiffusionLM
from diffulex.layer.linear import RowParallelLinear, ColumnParallelLinear
from diffulex.layer.embed_head import VocabParallelEmbedding, ParallelLMHead
from diffulex.model.config.fast_dllm_v2.configuration_fast_dllm_v2 import (
FastdLLMV2Config,
)
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 FastdLLMV2RMSNorm(RMSNorm):
def __init__(self, hidden_size, eps=1e-6):
super().__init__(hidden_size, eps)
class FastdLLMV2Attention(nn.Module):
"""FastdLLM V2 attention mechanism."""
def __init__(
self,
hidden_size: int,
num_heads: int,
num_kv_heads: int,
max_position: int = 32768,
head_dim: int | None = None,
rms_norm_eps: float = 1e-6,
qkv_bias: bool = True,
rope_theta: float = 10000,
rope_scaling: tuple | None = None,
attn_impl: str = "triton",
) -> None:
super().__init__()
parallel_state = fetch_parallel_state()
tp_size = parallel_state.get_tp_world_size()
self.total_num_heads = num_heads
assert self.total_num_heads % tp_size == 0
self.num_heads = self.total_num_heads // tp_size
self.total_num_kv_heads = num_kv_heads
assert self.total_num_kv_heads % tp_size == 0
self.num_kv_heads = self.total_num_kv_heads // tp_size
self.head_dim = head_dim or 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
self.q_proj = ColumnParallelLinear(
hidden_size,
self.total_num_heads * self.head_dim,
bias=qkv_bias,
)
self.k_proj = ColumnParallelLinear(
hidden_size,
self.total_num_kv_heads * self.head_dim,
bias=qkv_bias,
)
self.v_proj = ColumnParallelLinear(
hidden_size,
self.total_num_kv_heads * self.head_dim,
bias=qkv_bias,
)
self.o_proj = RowParallelLinear(
self.total_num_heads * self.head_dim,
hidden_size,
bias=False,
)
self.rotary_emb = get_rope(
self.head_dim,
rotary_dim=self.head_dim,
max_position=max_position,
base=rope_theta,
rope_scaling=rope_scaling,
)
self.attn = Attention(
self.num_heads,
self.head_dim,
self.scaling,
self.num_kv_heads,
attn_impl=attn_impl,
)
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, k = self.rotary_emb(positions, q, k)
o = self.attn(q, k, v, mask)
output = self.o_proj(o)
return output
class FastdLLMV2MLP(nn.Module):
"""FastdLLM V2 MLP with SiLU activation."""
def __init__(
self,
hidden_size: int,
intermediate_size: int,
hidden_act: str,
) -> None:
super().__init__()
self.gate_proj = ColumnParallelLinear(
hidden_size,
intermediate_size,
bias=False,
)
self.up_proj = ColumnParallelLinear(
hidden_size,
intermediate_size,
bias=False,
)
self.down_proj = RowParallelLinear(
intermediate_size,
hidden_size,
bias=False,
)
assert hidden_act == "silu"
self.act_fn = SiluAndMul()
def forward(self, x):
gate = self.gate_proj(x)
up = self.up_proj(x)
x = self.act_fn(torch.cat([gate, up], dim=-1))
x = self.down_proj(x)
return x
class FastdLLMV2DecoderLayer(nn.Module):
"""FastdLLM V2 transformer decoder layer."""
def __init__(
self,
config: FastdLLMV2Config,
) -> None:
super().__init__()
self.self_attn = FastdLLMV2Attention(
hidden_size=config.hidden_size,
num_heads=config.num_attention_heads,
num_kv_heads=config.num_key_value_heads,
max_position=config.max_position_embeddings,
rms_norm_eps=config.rms_norm_eps,
qkv_bias=True, # Dream uses bias in attention
head_dim=getattr(config, "head_dim", None),
rope_theta=getattr(config, "rope_theta", 10000),
rope_scaling=getattr(config, "rope_scaling", None),
attn_impl=getattr(config, "attn_impl", "triton"),
)
self.mlp = FastdLLMV2MLP(
hidden_size=config.hidden_size,
intermediate_size=config.intermediate_size,
hidden_act=config.hidden_act,
)
self.input_layernorm = FastdLLMV2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.post_attention_layernorm = FastdLLMV2RMSNorm(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 FastdLLMV2Model(nn.Module):
"""FastdLLM V2 model for diffusion language modeling."""
def __init__(
self,
config: FastdLLMV2Config,
) -> None:
super().__init__()
self.embed_tokens = VocabParallelEmbedding(config.vocab_size, config.hidden_size)
self.layers = nn.ModuleList([FastdLLMV2DecoderLayer(config) for _ in range(config.num_hidden_layers)])
self.norm = FastdLLMV2RMSNorm(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 enumerate(self.layers):
hidden_states, residual = layer(positions, hidden_states, residual, mask)
hidden_states, _ = self.norm(hidden_states, residual)
return hidden_states
@AutoModelForDiffusionLM.register("fast_dllm_v2")
class FastdLLMV2ForDiffusionLM(nn.Module):
"""FastdLLM V2 model for diffusion language modeling with LM head."""
packed_modules_mapping = {}
def __init__(
self,
config: FastdLLMV2Config,
) -> None:
super().__init__()
self.model = FastdLLMV2Model(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:
hidden_states = self.model(input_ids, positions, mask)
return hidden_states
def compute_logits(
self,
hidden_states: torch.Tensor,
) -> torch.Tensor:
logits = self.lm_head(hidden_states)
return logits
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