biondizzle
4453d7475a
- Nvfp4GroupedLinear: (n_local_groups, heads_per_group, head_dim, o_lora_rank) - mHCLayer: hidden_dim, t_max_sinkhorn (not hidden_size, sinkhorn_iters) - RMSNorm: PyTorch reference implementation (BF16, cudagraph-safe) - Verified: all 43 Flash + 61 Pro layers construct cleanly - All projection shapes validated against architecture spec
246 lines
10 KiB
Python
246 lines
10 KiB
Python
"""DSV4 attention sub-block — CSA / HCA / SWA variants.
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The constructor decides which variant this instance is by reading the
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LayerSpec passed in. After construction, forward() is fixed — no runtime
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branching on attention type, so torch.compile can fold the dispatch
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completely.
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What this class is responsible for:
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- Holding the projection weights (Q-down/up, KV-down, output groups).
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- Calling Nvfp4Linear for the projections.
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- Running RoPE (partial, last rope_dim dimensions).
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- Calling the compressor (CSA or HCA) to write into the KV cache.
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- For CSA only: calling the indexer + top-k to select sparse entries.
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- Calling the FMHA kernel with the right K/V source.
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- Calling inverse RoPE on the output.
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- Calling Nvfp4GroupedLinear (wo_a) + Nvfp4Linear (wo_b).
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What this class does NOT touch:
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- mHC pre/post — done by TransformerLayer.
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- Pre-norm — done by TransformerLayer.
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- KV cache allocation / eviction — done by the cache handle.
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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.linear import Nvfp4Linear
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from dsv4.layers.grouped_linear import Nvfp4GroupedLinear
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from dsv4.model.layer_schedule import AttentionType, 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 AttentionSubBlock:
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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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self.attn_type = spec.attn # frozen for this layer's lifetime
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# ---- Projections (all NVFP4) ----
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# Query path is low-rank: hidden -> dc -> head_dim * num_heads.
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self.q_down = Nvfp4Linear(
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in_features=config.hidden_size,
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out_features=config.query_compression_dim,
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)
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self.q_up = Nvfp4Linear(
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in_features=config.query_compression_dim,
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out_features=config.head_dim * config.num_query_heads,
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)
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# KV projection — single head shared across queries (MQA).
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# CSA needs two streams (a, b) for the overlapping compression;
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# HCA needs one. SWA needs raw KV with no compression.
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if self.attn_type == AttentionType.CSA:
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# 2 * head_dim for (Ca, Cb), 2 * head_dim for compression weights (Za, Zb)
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self.kv_down = Nvfp4Linear(
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in_features=config.hidden_size,
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out_features=4 * config.head_dim,
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)
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elif self.attn_type == AttentionType.HCA:
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# head_dim for C, head_dim for Z
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self.kv_down = Nvfp4Linear(
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in_features=config.hidden_size,
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out_features=2 * config.head_dim,
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)
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else: # SWA
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self.kv_down = Nvfp4Linear(
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in_features=config.hidden_size,
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out_features=config.head_dim,
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)
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# ---- Indexer (CSA only) ----
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if self.attn_type == AttentionType.CSA:
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# Indexer queries share the q_down latent c_Q with main queries.
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# Up-projection: c_Q -> indexer_num_heads * indexer_head_dim.
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self.indexer_q_up = Nvfp4Linear(
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in_features=config.query_compression_dim,
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out_features=config.indexer_num_heads * config.indexer_head_dim,
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)
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# Per-head weight vector w_h: hidden -> indexer_num_heads.
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self.indexer_head_weights = Nvfp4Linear(
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in_features=config.hidden_size,
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out_features=config.indexer_num_heads,
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max_num_tokens=1, # scalar per head, not per-token projection
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)
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# ---- Output projection ----
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# wo_a: grouped, splits the n_heads outputs into n_groups and projects
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# each group from (heads_per_group * head_dim) to o_lora_rank.
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heads_per_group = config.num_query_heads // config.num_output_groups
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self.wo_a = Nvfp4GroupedLinear(
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n_local_groups=config.num_output_groups,
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heads_per_group=heads_per_group,
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head_dim=config.head_dim,
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o_lora_rank=config.output_group_dim,
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)
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# wo_b: dense, concatenated group outputs back to hidden_size.
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self.wo_b = Nvfp4Linear(
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in_features=config.output_group_dim * config.num_output_groups,
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out_features=config.hidden_size,
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)
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# Attention sink: per-head learnable logit added to the softmax
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# denominator (paper §2.3.3). One scalar per query head.
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self.sink_logits: torch.Tensor | None = None # set by load_weights
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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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cache: "LayerCacheHandle", # per-layer cache slice
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) -> torch.Tensor: # (T, hidden_size) BF16
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# Branch on attention type. Frozen at construction so this is a
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# constant in any traced graph.
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if self.attn_type == AttentionType.CSA:
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return self._forward_csa(x, cache)
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elif self.attn_type == AttentionType.HCA:
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return self._forward_hca(x, cache)
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else:
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return self._forward_swa(x, cache)
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# ------------------------------------------------------------------
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# CSA: compressed + sparse attention, with indexer + top-k.
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# ------------------------------------------------------------------
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def _forward_csa(self, x, cache):
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# 1) Low-rank query projection. c_Q is shared with the indexer.
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c_Q = self.q_down(x) # (T, dc)
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q = self.q_up(c_Q) # (T, n_h * head_dim)
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# 2) KV down-projection — produces (Ca, Cb, Za, Zb) interleaved.
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kv_raw = self.kv_down(x) # (T, 4 * head_dim)
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# 3) Apply partial RoPE to q and the to-be-compressed keys.
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q_roped = self._apply_rope(q, positions=cache.positions)
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# 4) Compress + write to cache. Returns nothing — the cache holds it.
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from dsv4.kernels.compressor import csa_compress_and_store
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csa_compress_and_store(
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kv_raw=kv_raw,
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cache=cache, # writes C_comp + Z + indexer keys
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positions=cache.positions,
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compression_ratio=self.config.csa_compression_ratio,
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)
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# 5) Indexer: compute index scores, select top-k compressed entries.
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q_indexer = self.indexer_q_up(c_Q) # (T, n_I_h * c_I)
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w_indexer = self.indexer_head_weights(x) # (T, n_I_h)
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from dsv4.kernels.indexer import compute_index_scores_topk
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topk_indices = compute_index_scores_topk(
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q_indexer=q_indexer,
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w_indexer=w_indexer,
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cache=cache, # provides compressed indexer keys
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top_k=self.config.csa_top_k,
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)
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# 6) FMHA over selected sparse entries + SWA branch, fused sink merge.
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from dsv4.kernels.attention import sparse_fmha_with_swa
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attn_out = sparse_fmha_with_swa(
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q=q_roped,
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cache=cache,
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selected_indices=topk_indices,
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sink_logits=self.sink_logits,
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sliding_window=self.config.sliding_window,
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) # (T, n_h * head_dim)
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# 7) Inverse RoPE on outputs (paper §2.3.3).
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from dsv4.ops.rope import inverse_rope_bf16
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attn_out = inverse_rope_bf16(attn_out, positions=cache.positions)
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# 8) Grouped output projection: wo_a then wo_b.
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grouped = self.wo_a(attn_out) # (T, n_groups * d_g)
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return self.wo_b(grouped) # (T, hidden_size)
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# ------------------------------------------------------------------
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# HCA: heavy compression, dense attention over compressed entries.
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# ------------------------------------------------------------------
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def _forward_hca(self, x, cache):
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c_Q = self.q_down(x)
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q = self.q_up(c_Q)
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kv_raw = self.kv_down(x) # (T, 2 * head_dim)
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q_roped = self._apply_rope(q, positions=cache.positions)
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from dsv4.kernels.compressor import hca_compress_and_store
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hca_compress_and_store(
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kv_raw=kv_raw,
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cache=cache,
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positions=cache.positions,
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compression_ratio=self.config.hca_compression_ratio,
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)
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# Dense FMHA over ALL compressed entries (not top-k) plus SWA.
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from dsv4.kernels.attention import dense_fmha_with_swa
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attn_out = dense_fmha_with_swa(
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q=q_roped,
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cache=cache,
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sink_logits=self.sink_logits,
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sliding_window=self.config.sliding_window,
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)
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from dsv4.ops.rope import inverse_rope_bf16
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attn_out = inverse_rope_bf16(attn_out, positions=cache.positions)
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grouped = self.wo_a(attn_out)
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return self.wo_b(grouped)
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# ------------------------------------------------------------------
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# SWA: sliding window only, no compression branch.
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# First 2 layers of Flash. Acts as the "local context" foundation.
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# ------------------------------------------------------------------
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def _forward_swa(self, x, cache):
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c_Q = self.q_down(x)
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q = self.q_up(c_Q)
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kv_raw = self.kv_down(x) # (T, head_dim)
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q_roped = self._apply_rope(q, positions=cache.positions)
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# Write raw KV to the SWA window in the cache. No compressor.
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cache.write_swa(kv_raw, positions=cache.positions)
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# Dense FMHA over the sliding window only.
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from dsv4.kernels.attention import swa_only_fmha
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attn_out = swa_only_fmha(
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q=q_roped,
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cache=cache,
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sink_logits=self.sink_logits,
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sliding_window=self.config.sliding_window,
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)
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from dsv4.ops.rope import inverse_rope_bf16
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attn_out = inverse_rope_bf16(attn_out, positions=cache.positions)
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grouped = self.wo_a(attn_out)
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return self.wo_b(grouped)
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# ------------------------------------------------------------------
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def _apply_rope(self, q: torch.Tensor, positions: torch.Tensor):
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"""Apply RoPE to the last `rope_dim` dimensions of each head."""
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from dsv4.ops.rope import forward_rope_partial
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return forward_rope_partial(
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q, positions=positions, rope_dim=self.config.rope_dim,
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)
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