biondizzle
f3b551956d
- Move dead dsv4/ modules to dsv4/_archive/ (52 files)
- model/{dsv4,mtp,layer,layer_schedule}
- layers/{embedding,attention,ffn,norm} (kept linear,mhc,router,moe,shared_expert,grouped_linear - live)
- cache/*, kernels/cache/*, kernels/indexer/{csa_indexer,score_topk,compute_valid_lens}
- kernels/router/{nvfp4_fused_router,dense_router_decode_kernel,dense_router_prefill}
- ops/{topk,topk_select,rope,router}, loader/{hf_checkpoint,layout_convert}
- reference/{attention,compressor,csa_attention,moe_pipeline}
- kernels/compressor/{compress_tail,csa_hca}
- Restore dsv4/ops/{router,custom_ops}.py (needed by live layers)
- Fix dsv4/kernels/{indexer,compressor,attention}/__init__.py (removed broken imports)
- Remove preload_all() from loader.py (dead, referenced nonexistent .cu file)
- Fix loader.py docstring (fused_amax_quantize_nvfp4 → quantize_nvfp4_from_buffer)
- Move broken tests to tests/e2e_archive/
- test_fused_router, production_values_test, e2e/{one_layer,model_construction,csa_hca}
- vLLM has 0 imports of dsv4 (Step 0 confirmed)
54 lines
1.9 KiB
Python
54 lines
1.9 KiB
Python
"""DSV4 FFN sub-block — routed MoE + shared expert.
|
|
|
|
The router instance encapsulates hash-vs-dense; this sub-block doesn't
|
|
have to care, it just calls router(x, token_ids) and feeds the result
|
|
to Nvfp4MoE. Shared expert runs in parallel (logically — kernels
|
|
can overlap).
|
|
"""
|
|
from __future__ import annotations
|
|
from typing import TYPE_CHECKING
|
|
import torch
|
|
|
|
from dsv4.layers.router import Router
|
|
from dsv4.layers.moe import Nvfp4MoE
|
|
from dsv4.layers.shared_expert import Nvfp4SharedExpert
|
|
from dsv4.model.layer_schedule import LayerSpec, RouterMode
|
|
|
|
if TYPE_CHECKING:
|
|
from dsv4.model.config import DSV4Config
|
|
|
|
|
|
class FFNSubBlock:
|
|
def __init__(self, config: "DSV4Config", spec: LayerSpec):
|
|
self.config = config
|
|
self.spec = spec
|
|
|
|
self.router = Router(
|
|
hidden_size=config.hidden_size,
|
|
num_experts=config.num_routed_experts,
|
|
top_k=config.num_experts_per_tok,
|
|
routed_scaling_factor=config.routed_scaling_factor,
|
|
mode="hash" if spec.router_mode == RouterMode.HASH else "dense",
|
|
vocab_size=config.vocab_size if spec.router_mode == RouterMode.HASH else None,
|
|
)
|
|
self.moe = Nvfp4MoE(
|
|
num_experts=config.num_routed_experts,
|
|
hidden_size=config.hidden_size,
|
|
intermediate_size=config.moe_intermediate_size,
|
|
top_k=config.num_experts_per_tok,
|
|
)
|
|
self.shared = Nvfp4SharedExpert(
|
|
hidden_size=config.hidden_size,
|
|
intermediate_size=config.moe_intermediate_size,
|
|
)
|
|
|
|
def forward(
|
|
self,
|
|
x: torch.Tensor, # (T, hidden_size) BF16, post-RMSNorm
|
|
token_ids: torch.Tensor, # (T,) int32 — needed only for hash routing
|
|
) -> torch.Tensor:
|
|
topk_w, topk_ids = self.router(x, token_ids=token_ids)
|
|
routed_out = self.moe.run(x, topk_w, topk_ids)
|
|
shared_out = self.shared.run(x)
|
|
return routed_out + shared_out
|