biondizzle
66a89859ed
DSV4Config: frozen dataclass with .flash() / .pro() classmethods. All architectural constants (dims, heads, MoE params, mHC) in one place. LayerSchedule: pure-data per-layer-index -> (attn_type, ffn_type, router_mode). Flash: SWA, SWA, CSA, HCA, CSA, HCA, ... (43 layers) Pro: HCA, HCA, CSA, HCA, CSA, HCA, ... (61 layers) Both: first 3 MoE layers = hash routing, rest = dense validate_schedule() enforces correctness at construction. AttentionSubBlock: CSA / HCA / SWA variants. - Low-rank Q projection (q_down -> q_up) - KV down-projection (varies by attn type: 4h/2h/1h) - CSA: indexer_q_up + indexer_head_weights - Grouped output projection (wo_a + wo_b) - Kernel calls are imports (NotImplementedError until kernel lands) - No PyTorch fallback paths FFNSubBlock: MoE + shared expert. - Router (hash/dense) mode from LayerSpec - Nvfp4MoE + Nvfp4SharedExpert TransformerLayer: composition of mHC + norm + attention + FFN. - Two mHC wrappers (attn + ffn sub-blocks) - Two RMSNorm (one per sub-block) - Pure orchestration, no learned params on the layer itself Tests: schedule construction + validation for both variants. No forward tests yet (depends on FMHA kernel + KV cache).
54 lines
1.9 KiB
Python
54 lines
1.9 KiB
Python
"""DSV4 FFN sub-block — routed MoE + shared expert.
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The router instance encapsulates hash-vs-dense; this sub-block doesn't
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have to care, it just calls router(x, token_ids) and feeds the result
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to Nvfp4MoE. Shared expert runs in parallel (logically — kernels
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can overlap).
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"""
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from __future__ import annotations
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from typing import TYPE_CHECKING
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import torch
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from dsv4.layers.router import Router
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from dsv4.layers.moe import Nvfp4MoE
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from dsv4.layers.shared_expert import Nvfp4SharedExpert
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from dsv4.model.layer_schedule import LayerSpec, RouterMode
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if TYPE_CHECKING:
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from dsv4.model.config import DSV4Config
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class FFNSubBlock:
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def __init__(self, config: "DSV4Config", spec: LayerSpec):
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self.config = config
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self.spec = spec
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self.router = Router(
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hidden_size=config.hidden_size,
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num_experts=config.num_routed_experts,
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top_k=config.num_experts_per_tok,
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routed_scaling_factor=config.routed_scaling_factor,
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mode="hash" if spec.router_mode == RouterMode.HASH else "dense",
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vocab_size=config.vocab_size if spec.router_mode == RouterMode.HASH else None,
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)
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self.moe = Nvfp4MoE(
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num_experts=config.num_routed_experts,
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hidden_size=config.hidden_size,
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intermediate_size=config.moe_intermediate_size,
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top_k=config.num_experts_per_tok,
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)
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self.shared = Nvfp4SharedExpert(
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hidden_size=config.hidden_size,
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intermediate_size=config.moe_intermediate_size,
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)
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def forward(
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self,
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x: torch.Tensor, # (T, hidden_size) BF16, post-RMSNorm
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token_ids: torch.Tensor, # (T,) int32 — needed only for hash routing
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) -> torch.Tensor:
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topk_w, topk_ids = self.router(x, token_ids=token_ids)
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routed_out = self.moe.run(x, topk_w, topk_ids)
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shared_out = self.shared.run(x)
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return routed_out + shared_out
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