biondizzle
5a63604d6a
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).
83 lines
2.8 KiB
Python
83 lines
2.8 KiB
Python
"""A single DSV4 transformer layer.
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Structure (paper Figure 2):
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X_l ─→ mHC.pre_block ─→ RMSNorm ─→ Attention ─→ mHC.post_block (using F_attn)
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│
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▼
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mHC.pre_block ─→ RMSNorm ─→ FFN ─→ mHC.post_block (using F_ffn)
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│
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▼
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X_{l+1}
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Each layer owns:
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- One LayerSpec (from build_schedule).
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- Two mHC instances (one per sub-block).
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- One AttentionSubBlock (type fixed by spec.attn).
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- One FFNSubBlock (router mode fixed by spec.router_mode).
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- Two RMSNorm weight tensors.
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The layer is otherwise pure orchestration: no learned params live
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directly on TransformerLayer, only on its components.
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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.mhc import mHCLayer
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from dsv4.layers.attention import AttentionSubBlock
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from dsv4.layers.ffn import FFNSubBlock
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from dsv4.layers.norm import RMSNorm # PyTorch ref for now, fused later
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from dsv4.model.layer_schedule import LayerSpec
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if TYPE_CHECKING:
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from dsv4.model.config import DSV4Config
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from dsv4.cache.paged_cache import LayerCacheHandle
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class TransformerLayer:
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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.layer_idx = spec.layer_idx
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# Two mHC wrappers — one per sub-block. mHCLayer holds its own
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# projection weights (W_pre, W_res, W_post) and static biases.
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self.mhc_attn = mHCLayer(
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hidden_size=config.hidden_size,
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n_hc=config.n_hc,
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sinkhorn_iters=config.sinkhorn_iters,
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)
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self.mhc_ffn = mHCLayer(
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hidden_size=config.hidden_size,
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n_hc=config.n_hc,
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sinkhorn_iters=config.sinkhorn_iters,
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)
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# Pre-block norms (one per sub-block).
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self.norm_attn = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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self.norm_ffn = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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# Sub-blocks — type-frozen at construction.
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self.attn = AttentionSubBlock(config, spec)
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self.ffn = FFNSubBlock(config, spec)
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def forward(
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self,
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X: torch.Tensor, # (T, n_hc, hidden_size) BF16 — residual streams
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token_ids: torch.Tensor, # (T,) int32 — for hash routing
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cache: "LayerCacheHandle",
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) -> torch.Tensor:
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# ---- Attention sub-block ----
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x_attn_in, ctx_attn = self.mhc_attn.pre_block(X)
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x_attn_in = self.norm_attn(x_attn_in)
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F_attn = self.attn.forward(x_attn_in, cache)
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X = self.mhc_attn.post_block(X, F_attn, ctx_attn)
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# ---- FFN sub-block ----
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x_ffn_in, ctx_ffn = self.mhc_ffn.pre_block(X)
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x_ffn_in = self.norm_ffn(x_ffn_in)
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F_ffn = self.ffn.forward(x_ffn_in, token_ids)
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X = self.mhc_ffn.post_block(X, F_ffn, ctx_ffn)
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return X
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