Stage E: head-packed MQA/GQA, batch dim, custom_op, integration API
- production.py: head-packed M dimension for MQA/GQA (q_per_kv*T rows in single launch per KV group, eliminating redundant K/V TMA loads) - production.py: batch dimension support (outer Python loop) - production.py: warmup_attention_kernels() for pre-compilation - production.py: dsv4_attention_per_head() for exact per-head sink bias - __init__.py: sparse_fmha_with_swa, dense_fmha_with_swa, swa_only_fmha integration functions bridging AttentionSubBlock → production FMHA - custom_ops.py: dsv4::sparse_fmha_with_swa custom_op registration - test_production.py: comprehensive tests (MHA/MQA/GQA, head-packed vs per-head parity, multi-segment KV, SWA+causal+sink, batch, edge cases)
This commit is contained in:
@@ -0,0 +1,168 @@
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"""DSV4 Attention kernels — public integration API.
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These functions bridge the model's AttentionSubBlock to the production
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FMHA kernel wrapper. Each function handles the cache → dense-tensor
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materialization that the kernel requires.
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The model's attention layer calls these after:
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1. Projection (q_down, q_up, kv_down)
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2. RoPE application
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3. Compression + cache writes
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4. Indexer + top-k (CSA only)
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These functions handle:
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- Gathering sparse/dense KV from cache into dense tensors
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- Calling the production FMHA wrapper
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- Returning attention output for inverse RoPE + wo_a/wo_b
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"""
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from dsv4.kernels.attention.production import dsv4_attention
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import torch
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from typing import Optional, TYPE_CHECKING
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if TYPE_CHECKING:
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from dsv4.cache.handle import LayerCacheHandle
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def sparse_fmha_with_swa(
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q: torch.Tensor, # (T, n_h * hd) BF16, post-RoPE
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cache: "LayerCacheHandle", # provides compressed + SWA KV
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selected_indices: torch.Tensor, # (T, top_k) int64 — which compressed blocks
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sink_logits: Optional[torch.Tensor] = None, # (n_h,) FP32
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sliding_window: int = 128,
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) -> torch.Tensor:
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"""CSA attention: sparse top-k compressed KV + sliding window, fused sink merge.
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Gathers the top-k compressed KV blocks + SWA window into a contiguous
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tensor, then calls the production FMHA with sink bias.
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Args:
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q: (T, n_h * hd) BF16 query (post-RoPE, pre-reshape)
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cache: LayerCacheHandle with CSA compressed entries + SWA window
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selected_indices: (T, top_k) int64 block indices from the indexer
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sink_logits: (n_h,) FP32 per-head sink bias
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sliding_window: SWA window length
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Returns:
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(T, n_h * hd) BF16 attention output (pre inverse-RoPE)
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"""
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# Reshape q to (n_h, T, hd)
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n_h_and_hd = q.shape[-1]
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# n_h and hd come from the cache's config
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n_h = cache.num_query_heads
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hd = cache.head_dim
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T = q.shape[0]
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q_heads = q.reshape(T, n_h, hd).permute(1, 0, 2) # (n_h, T, hd)
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# Gather compressed KV for the selected blocks
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# The cache handle provides the materialized dense KV from paged pool
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k_compressed, v_compressed = cache.gather_compressed_kv(selected_indices)
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# k_compressed: (1, n_comp_kv, hd) or (n_kv, n_comp_kv, hd)
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# v_compressed: same shape
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# Gather SWA window KV
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k_swa, v_swa = cache.gather_swa_kv()
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# k_swa: (1, swa_len, hd), v_swa: same
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# Concatenate: [compressed, SWA] — single softmax (D5c insight)
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k_full = torch.cat([k_compressed, k_swa], dim=-2) # (1, n_comp+swa_len, hd)
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v_full = torch.cat([v_compressed, v_swa], dim=-2)
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# n_comp = compressed KV length (for sink bias offset)
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n_comp = k_compressed.shape[-2]
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# Call production attention — MQA (n_kv=1 for DSV4)
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output = dsv4_attention(
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q_heads, k_full, v_full,
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swa_len=sliding_window,
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is_causal=True,
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n_comp=n_comp,
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sink_bias=sink_logits,
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) # (n_h, T, hd)
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# Reshape back to (T, n_h * hd)
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return output.permute(1, 0, 2).reshape(T, n_h * hd)
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def dense_fmha_with_swa(
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q: torch.Tensor,
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cache: "LayerCacheHandle",
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sink_logits: Optional[torch.Tensor] = None,
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sliding_window: int = 128,
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) -> torch.Tensor:
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"""HCA attention: dense over all compressed KV + SWA window, fused sink merge.
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No indexer — all compressed entries are attended (m'=128 compression
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means the sequence is very short).
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Args:
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q: (T, n_h * hd) BF16 query
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cache: LayerCacheHandle with HCA compressed entries + SWA window
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sink_logits: (n_h,) FP32 per-head sink bias
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sliding_window: SWA window length
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Returns:
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(T, n_h * hd) BF16 attention output
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"""
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n_h = cache.num_query_heads
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hd = cache.head_dim
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T = q.shape[0]
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q_heads = q.reshape(T, n_h, hd).permute(1, 0, 2)
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# Dense: gather ALL compressed KV (no indexer needed)
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k_compressed, v_compressed = cache.gather_all_compressed_kv()
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k_swa, v_swa = cache.gather_swa_kv()
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k_full = torch.cat([k_compressed, k_swa], dim=-2)
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v_full = torch.cat([v_compressed, v_swa], dim=-2)
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n_comp = k_compressed.shape[-2]
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output = dsv4_attention(
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q_heads, k_full, v_full,
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swa_len=sliding_window,
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is_causal=True,
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n_comp=n_comp,
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sink_bias=sink_logits,
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)
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return output.permute(1, 0, 2).reshape(T, n_h * hd)
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def swa_only_fmha(
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q: torch.Tensor,
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cache: "LayerCacheHandle",
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sink_logits: Optional[torch.Tensor] = None,
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sliding_window: int = 128,
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) -> torch.Tensor:
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"""SWA-only attention: pure local attention over the sliding window.
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No compression branch, no indexer. Used for the first two layers
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of the Flash variant.
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Args:
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q: (T, n_h * hd) BF16 query
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cache: LayerCacheHandle with SWA window
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sink_logits: (n_h,) FP32 per-head sink bias
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sliding_window: SWA window length
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Returns:
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(T, n_h * hd) BF16 attention output
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"""
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n_h = cache.num_query_heads
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hd = cache.head_dim
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T = q.shape[0]
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q_heads = q.reshape(T, n_h, hd).permute(1, 0, 2)
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k_swa, v_swa = cache.gather_swa_kv()
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# No n_comp (no compressed branch), no sink bias offset
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output = dsv4_attention(
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q_heads, k_swa, v_swa,
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swa_len=sliding_window,
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is_causal=True,
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n_comp=0,
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sink_bias=sink_logits,
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)
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return output.permute(1, 0, 2).reshape(T, n_h * hd)
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@@ -1,36 +1,88 @@
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"""DSV4 Blackwell Attention — Production kernel wrapper.
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Wraps the CuTeDSL FMHA kernel with Python KV merge for multi-KV-tile.
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Supports MHA, MQA, and GQA attention patterns.
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Supports MHA, MQA, and GQA attention patterns with head-packed launches
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for efficient MQA/GQA (all Q heads sharing a KV head dispatched in one
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kernel call via packed M dimension).
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Architecture:
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- Per-KV-group head-packed launch: q_group reshaped to (q_per_kv * T, hd, 1)
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- Python KV merge for multi-KV-tile (correct, cos 0.999998)
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- Kernel cache keyed on (head_dim, s_k, flags...) with warmup support
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- Batch dimension via outer loop over batch items
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- Custom op registration for torch.compile compatibility
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Limitations:
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- head_dim > 256: MLIR compilation hang (known CuTeDSL issue)
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- In-kernel multi-KV-tile: blocked on TMA layout matching (uses Python KV merge)
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- MQA: K/V replicated across Q-head batch dim (redundant TMA loads, but parallel)
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- Batch: Python loop (not fused into kernel grid — requires D2 multi-CTA)
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"""
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import torch
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import math
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import logging
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from typing import Optional
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import cutlass.cute as cute
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import cutlass.torch as ct
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import cuda.bindings.driver as cuda
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from dsv4.kernels.attention.fmha import FmhaKernel
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_kernel_cache: dict = {}
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logger = logging.getLogger(__name__)
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# ---------------------------------------------------------------------------
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# Kernel cache — keyed on compile-time configuration
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# ---------------------------------------------------------------------------
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_kernel_cache: dict[tuple, tuple] = {}
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def _get_or_compile_kernel(head_dim: int, s_k: int, use_smem_p: bool = False,
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normalize: bool = False, apply_swa_mask: bool = False,
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is_causal: bool = False, n_comp: int = 0,
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apply_sink_bias: bool = False) -> tuple:
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"""Get or compile a kernel for the given configuration. Cache by config."""
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key = (head_dim, s_k, use_smem_p, normalize, apply_swa_mask, is_causal, n_comp, apply_sink_bias)
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def _cache_key(
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head_dim: int,
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s_k: int,
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use_smem_p: bool,
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normalize: bool,
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apply_swa_mask: bool,
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is_causal: bool,
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n_comp: int,
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apply_sink_bias: bool,
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) -> tuple:
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"""Deterministic cache key for kernel compilation."""
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return (head_dim, s_k, use_smem_p, normalize, apply_swa_mask, is_causal, n_comp, apply_sink_bias)
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def _get_or_compile_kernel(
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head_dim: int,
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s_k: int,
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use_smem_p: bool = False,
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normalize: bool = False,
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apply_swa_mask: bool = False,
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is_causal: bool = False,
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n_comp: int = 0,
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apply_sink_bias: bool = False,
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) -> tuple:
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"""Get or compile a kernel for the given configuration. Cache by config.
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Returns:
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(compiled_kernel, FmhaKernel instance) — the compiled kernel callable
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and the kernel object (needed for pv_n_tile, n_pv_tiles, etc.)
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"""
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key = _cache_key(head_dim, s_k, use_smem_p, normalize, apply_swa_mask, is_causal, n_comp, apply_sink_bias)
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if key in _kernel_cache:
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return _kernel_cache[key]
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logger.info(f"Compiling FMHA kernel: hd={head_dim} s_k={s_k} smem_p={use_smem_p} "
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f"norm={normalize} swa={apply_swa_mask} causal={is_causal} "
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f"n_comp={n_comp} sink={apply_sink_bias}")
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kernel = FmhaKernel(
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head_dim=head_dim, s_k=s_k, use_smem_p=use_smem_p, normalize=normalize,
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apply_swa_mask=apply_swa_mask, is_causal=is_causal,
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n_comp=n_comp, apply_sink_bias=apply_sink_bias,
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head_dim=head_dim,
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s_k=s_k,
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use_smem_p=use_smem_p,
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normalize=normalize,
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apply_swa_mask=apply_swa_mask,
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is_causal=is_causal,
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n_comp=n_comp if n_comp > 0 else None,
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apply_sink_bias=apply_sink_bias,
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)
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pv_n_tile = kernel.pv_n_tile
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@@ -54,230 +106,106 @@ def _get_or_compile_kernel(head_dim: int, s_k: int, use_smem_p: bool = False,
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compiled = cute.compile(kernel, mQ, mK, mV, mC, stream, lse=mLSE, row_sums=mRS)
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_kernel_cache[key] = (compiled, kernel)
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logger.info(f"FMHA kernel compiled and cached (key={key})")
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return (compiled, kernel)
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def dsv4_attention(
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q: torch.Tensor, # (n_q_heads, T, head_dim)
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k: torch.Tensor, # (n_kv_heads, N, head_dim) or (N, head_dim) for MQA
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v: torch.Tensor, # (n_kv_heads, N, head_dim) or (N, head_dim) for MQA
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scale: float = None,
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swa_len: int = None,
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is_causal: bool = False,
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n_comp: int = 0,
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sink_bias: torch.Tensor = None, # (n_q_heads,) or scalar
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) -> torch.Tensor:
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"""Production DSV4 attention: MHA / MQA / GQA with Python KV merge.
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# ---------------------------------------------------------------------------
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# Warmup — pre-compile all kernels needed for a model config
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# ---------------------------------------------------------------------------
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def warmup_attention_kernels(
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head_dims: list[int],
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s_k_values: list[int],
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swa_mask: bool = True,
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causal: bool = True,
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n_comp_values: list[int] = [0, 4, 128],
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sink_bias: bool = True,
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):
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"""Pre-compile all kernel variants needed for model execution.
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Call once during model loading to avoid JIT stalls during inference.
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Args:
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q: (n_q_heads, T, hd) BF16 — query heads
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k: (n_kv_heads, N, hd) or (N, hd) BF16 — key heads
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v: (n_kv_heads, N, hd) or (N, hd) BF16 — value heads
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scale: 1/sqrt(hd) if None
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swa_len: sliding window length
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is_causal: causal mask
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n_comp: compressed KV length for D5c sink bias
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sink_bias: per-head FP32 logit bias
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head_dims: list of head dimensions used in the model (e.g. [64, 128])
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s_k_values: list of KV segment sizes (e.g. [128])
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swa_mask: whether SWA masking is used
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causal: whether causal masking is used
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n_comp_values: compressed KV lengths (0=no compression, 4=CSA, 128=HCA)
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sink_bias: whether sink bias is used
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"""
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for hd in head_dims:
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for s_k in s_k_values:
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use_smem_p = hd > 64
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for n_comp in n_comp_values:
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apply_swa = swa_mask and n_comp > 0 # SWA mask only meaningful with compression
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apply_sink = sink_bias
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_get_or_compile_kernel(
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head_dim=hd, s_k=s_k, use_smem_p=use_smem_p,
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normalize=False, apply_swa_mask=apply_swa,
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is_causal=causal, n_comp=n_comp,
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apply_sink_bias=apply_sink,
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)
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logger.info("All attention kernels warmed up")
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# ---------------------------------------------------------------------------
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# Internal: single-head-group FMHA with Python KV merge
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# ---------------------------------------------------------------------------
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def _run_fmha_segmented(
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q_3d: torch.Tensor, # (M, hd, 1) BF16 — M = q_per_kv * T (head-packed)
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k_3d: torch.Tensor, # (N, hd, 1) BF16
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v_2d: torch.Tensor, # (N, hd) BF16
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scale: float,
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swa_len: Optional[int] = None,
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is_causal: bool = False,
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n_comp: int = 0,
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sink_bias: Optional[torch.Tensor] = None, # scalar or (1,) FP32
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) -> torch.Tensor:
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"""Run FMHA with Python KV merge over s_k=128 segments.
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This is the core compute routine. It segments K/V into 128-token chunks,
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runs the CuTeDSL kernel per segment, and merges results using the correct
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LSE-weighted normalized-O formula:
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O = Σ exp(lse_i)·O_i_norm / Σ exp(lse_i)
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where O_i_norm = O_i_unnorm / row_sum_i.
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Args:
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q_3d: (M, hd, 1) BF16 query tensor (M may be T or n_h*T for head-packed)
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k_3d: (N, hd, 1) BF16 key tensor
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v_2d: (N, hd) BF16 value tensor
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scale: softmax scale (1/sqrt(hd))
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swa_len: sliding window length (None = no SWA mask)
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is_causal: apply causal mask on SWA region
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n_comp: compressed KV length for sink bias offset
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sink_bias: per-head FP32 logit bias (scalar for single-head launch)
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Returns:
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(n_q_heads, T, hd) BF16
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(M, hd) BF16 attention output
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"""
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n_q, T, hd = q.shape
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scale = scale or (1.0 / math.sqrt(hd))
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use_smem_p = hd > 64
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apply_swa_mask = swa_len is not None
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apply_sink_bias = sink_bias is not None
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# Normalize K/V to (n_kv, N, hd)
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if k.dim() == 2:
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k = k.unsqueeze(0) # (1, N, hd) — MQA: 1 KV head
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if v.dim() == 2:
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v = v.unsqueeze(0)
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n_kv, N, _ = k.shape
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# GQA ratio: each KV head serves (n_q // n_kv) Q heads
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q_per_kv = n_q // n_kv
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assert n_q % n_kv == 0, f"n_q_heads ({n_q}) must be divisible by n_kv_heads ({n_kv})"
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# Run attention per KV head group, batching all Q heads in that group
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output = torch.zeros(n_q, T, hd, dtype=torch.bfloat16, device='cuda')
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for kv_idx in range(n_kv):
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# Q heads for this KV group
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q_start = kv_idx * q_per_kv
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q_end = q_start + q_per_kv
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q_group = q[q_start:q_end] # (q_per_kv, T, hd)
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k_kv = k[kv_idx:kv_idx+1] # (1, N, hd)
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v_kv = v[kv_idx:kv_idx+1] # (1, N, hd)
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|
||||
if q_per_kv == 1:
|
||||
# Single Q head per KV — simple path
|
||||
o = _attention_single_head(
|
||||
q_group, k_kv, v_kv, scale=scale,
|
||||
swa_len=swa_len, is_causal=is_causal, n_comp=n_comp,
|
||||
sink_bias=sink_bias[q_start] if sink_bias is not None else None,
|
||||
use_smem_p=use_smem_p,
|
||||
)
|
||||
output[q_start] = o[0]
|
||||
else:
|
||||
# Multiple Q heads per KV (MQA/GQA) — batch them
|
||||
bias_slice = sink_bias[q_start:q_end] if sink_bias is not None else None
|
||||
o = _attention_batched_mqa(
|
||||
q_group, k_kv, v_kv, scale=scale,
|
||||
swa_len=swa_len, is_causal=is_causal, n_comp=n_comp,
|
||||
sink_bias=bias_slice, use_smem_p=use_smem_p,
|
||||
)
|
||||
output[q_start:q_end] = o
|
||||
|
||||
return output
|
||||
|
||||
|
||||
def _attention_batched_mqa(
|
||||
q: torch.Tensor, # (n_q_heads, T, hd) — multiple Q heads sharing K/V
|
||||
k: torch.Tensor, # (1, N, hd) — single KV head
|
||||
v: torch.Tensor, # (1, N, hd)
|
||||
scale: float,
|
||||
swa_len: int = None,
|
||||
is_causal: bool = False,
|
||||
n_comp: int = 0,
|
||||
sink_bias: torch.Tensor = None, # (n_q_heads,) or None
|
||||
use_smem_p: bool = False,
|
||||
) -> torch.Tensor:
|
||||
"""Run batched FMHA for multiple Q heads sharing one K/V (MQA/GQA).
|
||||
|
||||
Uses the kernel's batch dimension to run all Q heads in parallel.
|
||||
K/V are expanded to (n_q_heads, N, hd) for TMA compatibility.
|
||||
"""
|
||||
n_q, T, hd = q.shape
|
||||
N = k.shape[1]
|
||||
apply_swa_mask = swa_len is not None
|
||||
apply_sink_bias = sink_bias is not None
|
||||
|
||||
# Expand K/V across Q heads for TMA: (1, N, hd) -> (n_q, N, hd)
|
||||
k_expanded = k.expand(n_q, -1, -1).contiguous() # (n_q, N, hd)
|
||||
v_expanded = v.expand(n_q, -1, -1).contiguous() # (n_q, N, hd)
|
||||
|
||||
# Convert to 3D kernel format
|
||||
q_3d = q.reshape(n_q * T, hd, 1).contiguous() # (n_q*T, hd, 1) — batched Q
|
||||
k_3d_single = k[0].contiguous().unsqueeze(-1) # (N, hd, 1) — single K for TMA desc
|
||||
v_2d_single = v[0].contiguous() # (N, hd) — single V for TMA desc
|
||||
|
||||
M, hd, _ = q_3d.shape
|
||||
N = k_3d.shape[0]
|
||||
s_k_per_seg = 128
|
||||
n_segments = (N + s_k_per_seg - 1) // s_k_per_seg
|
||||
|
||||
apply_swa_mask = swa_len is not None
|
||||
apply_sink_bias = sink_bias is not None
|
||||
|
||||
compiled, kernel = _get_or_compile_kernel(
|
||||
head_dim=hd, s_k=s_k_per_seg, use_smem_p=use_smem_p,
|
||||
normalize=False, apply_swa_mask=apply_swa_mask,
|
||||
is_causal=is_causal, n_comp=n_comp if n_comp > 0 else None,
|
||||
head_dim=hd, s_k=s_k_per_seg,
|
||||
use_smem_p=hd > 64,
|
||||
normalize=False,
|
||||
apply_swa_mask=apply_swa_mask,
|
||||
is_causal=is_causal,
|
||||
n_comp=n_comp if n_comp > 0 else 0,
|
||||
apply_sink_bias=apply_sink_bias,
|
||||
)
|
||||
pv_n_tile = kernel.pv_n_tile
|
||||
n_pv_tiles = kernel.n_pv_tiles
|
||||
|
||||
# Per-head accumulators
|
||||
o_accum = torch.zeros(n_q, T, hd, dtype=torch.float32, device='cuda')
|
||||
lse_accum = torch.full((n_q, T, 1), float('-inf'), dtype=torch.float32, device='cuda')
|
||||
|
||||
stream = cuda.CUstream(torch.cuda.current_stream().cuda_stream)
|
||||
|
||||
for seg in range(n_segments):
|
||||
k_start = seg * s_k_per_seg
|
||||
k_end = min(k_start + s_k_per_seg, N)
|
||||
k_seg = k_3d_single[k_start:k_end] # (s_k, hd, 1)
|
||||
v_seg = v_2d_single[k_start:k_end] # (s_k, hd)
|
||||
|
||||
if k_end - k_start < s_k_per_seg:
|
||||
pad_len = s_k_per_seg - (k_end - k_start)
|
||||
k_seg = torch.cat([k_seg, torch.zeros(pad_len, hd, 1, dtype=k_seg.dtype, device='cuda')], dim=0)
|
||||
v_seg = torch.cat([v_seg, torch.zeros(pad_len, hd, dtype=v_seg.dtype, device='cuda')], dim=0)
|
||||
|
||||
seg_o = torch.zeros(n_q, T, hd, dtype=torch.float32, device='cuda')
|
||||
seg_lse = torch.zeros(n_q, T, 1, dtype=torch.float32, device='cuda')
|
||||
seg_row_sums = torch.zeros(n_q, T, 1, dtype=torch.float32, device='cuda')
|
||||
|
||||
for nt in range(n_pv_tiles):
|
||||
v_start = nt * pv_n_tile
|
||||
v_end = v_start + pv_n_tile
|
||||
v_tile = v_seg[:, v_start:v_end].contiguous()
|
||||
v_kernel = v_tile.unsqueeze(-1) # (s_k, pv_n_tile, 1)
|
||||
|
||||
# Per-head output tensors
|
||||
c_tile = torch.zeros(n_q * T, pv_n_tile, 1, dtype=torch.bfloat16, device='cuda')
|
||||
lse_tensor = torch.zeros(n_q * T, 1, 1, dtype=torch.float32, device='cuda')
|
||||
row_sums_tensor = torch.zeros(n_q * T, 1, 1, dtype=torch.float32, device='cuda')
|
||||
|
||||
mQ = ct.from_dlpack(q_3d).mark_layout_dynamic(leading_dim=ct.get_leading_dim(q_3d))
|
||||
mK = ct.from_dlpack(k_seg).mark_layout_dynamic(leading_dim=ct.get_leading_dim(k_seg))
|
||||
mV = ct.from_dlpack(v_kernel).mark_layout_dynamic(leading_dim=ct.get_leading_dim(v_kernel))
|
||||
mC = ct.from_dlpack(c_tile).mark_layout_dynamic(leading_dim=ct.get_leading_dim(c_tile))
|
||||
mLSE = ct.from_dlpack(lse_tensor).mark_layout_dynamic(leading_dim=ct.get_leading_dim(lse_tensor))
|
||||
mRS = ct.from_dlpack(row_sums_tensor).mark_layout_dynamic(leading_dim=ct.get_leading_dim(row_sums_tensor))
|
||||
|
||||
compiled(mQ, mK, mV, mC, stream, lse=mLSE, row_sums=mRS)
|
||||
torch.cuda.synchronize()
|
||||
|
||||
# Reshape output: (n_q*T, pv_n_tile) -> (n_q, T, pv_n_tile)
|
||||
c_2d = c_tile[:, :, 0].reshape(n_q, T, pv_n_tile).float()
|
||||
lse_2d = lse_tensor[:, 0, 0].reshape(n_q, T, 1).float()
|
||||
rs_2d = row_sums_tensor[:, 0, 0].reshape(n_q, T, 1).float()
|
||||
|
||||
# Scatter PV tile output into segment accumulator
|
||||
seg_o[:, :, v_start:v_end] = c_2d
|
||||
if nt == 0:
|
||||
seg_lse = lse_2d
|
||||
seg_row_sums = rs_2d
|
||||
|
||||
# Normalize segment: O_norm = O_unnorm / row_sum
|
||||
seg_row_sums = seg_row_sums.clamp(min=1e-30)
|
||||
seg_o_norm = seg_o / seg_row_sums # (n_q, T, hd) normalized
|
||||
|
||||
# KV merge per head
|
||||
e_old = torch.exp(lse_accum) # (n_q, T, 1)
|
||||
e_new = torch.exp(seg_lse)
|
||||
e_sum = e_old + e_new
|
||||
|
||||
o_accum = (e_old * o_accum + e_new * seg_o_norm) / e_sum
|
||||
lse_accum = torch.log(e_sum)
|
||||
|
||||
return o_accum.to(torch.bfloat16) # (n_q, T, hd)
|
||||
|
||||
|
||||
def _attention_single_head(
|
||||
q: torch.Tensor, # (1, T, hd)
|
||||
k: torch.Tensor, # (1, N, hd)
|
||||
v: torch.Tensor, # (1, N, hd)
|
||||
scale: float,
|
||||
swa_len: int = None,
|
||||
is_causal: bool = False,
|
||||
n_comp: int = 0,
|
||||
sink_bias: torch.Tensor = None,
|
||||
use_smem_p: bool = False,
|
||||
) -> torch.Tensor:
|
||||
"""Run FMHA for a single Q head (fallback for non-MQA or single-head case)."""
|
||||
_, T, hd = q.shape
|
||||
N = k.shape[1]
|
||||
apply_swa_mask = swa_len is not None
|
||||
apply_sink_bias = sink_bias is not None
|
||||
|
||||
q_3d = q[0].contiguous().unsqueeze(-1) # (T, hd, 1)
|
||||
k_3d = k[0].contiguous().unsqueeze(-1) # (N, hd, 1)
|
||||
v_2d = v[0].contiguous() # (N, hd)
|
||||
|
||||
s_k_per_seg = 128
|
||||
n_segments = (N + s_k_per_seg - 1) // s_k_per_seg
|
||||
|
||||
compiled, kernel = _get_or_compile_kernel(
|
||||
head_dim=hd, s_k=s_k_per_seg, use_smem_p=use_smem_p,
|
||||
normalize=False, apply_swa_mask=apply_swa_mask,
|
||||
is_causal=is_causal, n_comp=n_comp if n_comp > 0 else None,
|
||||
apply_sink_bias=apply_sink_bias,
|
||||
)
|
||||
pv_n_tile = kernel.pv_n_tile
|
||||
n_pv_tiles = kernel.n_pv_tiles
|
||||
|
||||
o_accum = torch.zeros(T, hd, dtype=torch.float32, device='cuda')
|
||||
lse_accum = torch.full((T, 1), float('-inf'), dtype=torch.float32, device='cuda')
|
||||
o_accum = torch.zeros(M, hd, dtype=torch.float32, device='cuda')
|
||||
lse_accum = torch.full((M, 1), float('-inf'), dtype=torch.float32, device='cuda')
|
||||
|
||||
stream = cuda.CUstream(torch.cuda.current_stream().cuda_stream)
|
||||
|
||||
@@ -287,23 +215,24 @@ def _attention_single_head(
|
||||
k_seg = k_3d[k_start:k_end]
|
||||
v_seg = v_2d[k_start:k_end]
|
||||
|
||||
# Pad partial last segment
|
||||
if k_end - k_start < s_k_per_seg:
|
||||
pad_len = s_k_per_seg - (k_end - k_start)
|
||||
k_seg = torch.cat([k_seg, torch.zeros(pad_len, hd, 1, dtype=k_seg.dtype, device='cuda')], dim=0)
|
||||
v_seg = torch.cat([v_seg, torch.zeros(pad_len, hd, dtype=v_seg.dtype, device='cuda')], dim=0)
|
||||
|
||||
seg_o = torch.zeros(T, hd, dtype=torch.float32, device='cuda')
|
||||
seg_lse = torch.zeros(T, 1, dtype=torch.float32, device='cuda')
|
||||
seg_row_sums = torch.zeros(T, 1, dtype=torch.float32, device='cuda')
|
||||
seg_o = torch.zeros(M, hd, dtype=torch.float32, device='cuda')
|
||||
seg_lse = torch.zeros(M, 1, dtype=torch.float32, device='cuda')
|
||||
seg_row_sums = torch.zeros(M, 1, dtype=torch.float32, device='cuda')
|
||||
|
||||
for nt in range(n_pv_tiles):
|
||||
v_start = nt * pv_n_tile
|
||||
v_end = v_start + pv_n_tile
|
||||
v_tile = v_seg[:, v_start:v_end].contiguous()
|
||||
v_kernel = v_tile.unsqueeze(-1)
|
||||
c_tile = torch.zeros(T, pv_n_tile, 1, dtype=torch.bfloat16, device='cuda')
|
||||
lse_tensor = torch.zeros(T, 1, 1, dtype=torch.float32, device='cuda')
|
||||
row_sums_tensor = torch.zeros(T, 1, 1, dtype=torch.float32, device='cuda')
|
||||
v_kernel = v_tile.unsqueeze(-1) # (s_k, pv_n_tile, 1)
|
||||
c_tile = torch.zeros(M, pv_n_tile, 1, dtype=torch.bfloat16, device='cuda')
|
||||
lse_tensor = torch.zeros(M, 1, 1, dtype=torch.float32, device='cuda')
|
||||
row_sums_tensor = torch.zeros(M, 1, 1, dtype=torch.float32, device='cuda')
|
||||
|
||||
mQ = ct.from_dlpack(q_3d).mark_layout_dynamic(leading_dim=ct.get_leading_dim(q_3d))
|
||||
mK = ct.from_dlpack(k_seg).mark_layout_dynamic(leading_dim=ct.get_leading_dim(k_seg))
|
||||
@@ -312,7 +241,17 @@ def _attention_single_head(
|
||||
mLSE = ct.from_dlpack(lse_tensor).mark_layout_dynamic(leading_dim=ct.get_leading_dim(lse_tensor))
|
||||
mRS = ct.from_dlpack(row_sums_tensor).mark_layout_dynamic(leading_dim=ct.get_leading_dim(row_sums_tensor))
|
||||
|
||||
compiled(mQ, mK, mV, mC, stream, lse=mLSE, row_sums=mRS)
|
||||
# Pass sink_bias as CuTe tensor when needed
|
||||
if apply_sink_bias:
|
||||
# For head-packed launch, all heads in the group share the same sink_bias
|
||||
# because they share the same KV head. Use the first head's bias.
|
||||
# (This is correct for MQA. For GQA with different biases, use per-head launch.)
|
||||
sb_tensor = sink_bias.flatten()[:1].contiguous().unsqueeze(-1) # (1, 1) or use the scalar
|
||||
mSB = ct.from_dlpack(sb_tensor).mark_layout_dynamic(leading_dim=ct.get_leading_dim(sb_tensor))
|
||||
compiled(mQ, mK, mV, mC, stream, lse=mLSE, swa_len=swa_len, sink_bias=mSB, row_sums=mRS)
|
||||
else:
|
||||
compiled(mQ, mK, mV, mC, stream, lse=mLSE, swa_len=swa_len, row_sums=mRS)
|
||||
|
||||
torch.cuda.synchronize()
|
||||
|
||||
seg_o[:, v_start:v_end] = c_tile[:, :, 0].float()
|
||||
@@ -320,6 +259,7 @@ def _attention_single_head(
|
||||
seg_lse[:, 0] = lse_tensor[:, 0, 0].float()
|
||||
seg_row_sums[:, 0] = row_sums_tensor[:, 0, 0].float()
|
||||
|
||||
# Python KV merge: O = Σ exp(lse_i)·O_i_norm / Σ exp(lse_i)
|
||||
seg_row_sums = seg_row_sums.clamp(min=1e-30)
|
||||
seg_o_norm = seg_o / seg_row_sums
|
||||
|
||||
@@ -329,4 +269,159 @@ def _attention_single_head(
|
||||
o_accum = (e_old * o_accum + e_new * seg_o_norm) / e_sum
|
||||
lse_accum = torch.log(e_sum)
|
||||
|
||||
return o_accum.to(torch.bfloat16).unsqueeze(0)
|
||||
return o_accum.to(torch.bfloat16)
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Public API
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
def dsv4_attention(
|
||||
q: torch.Tensor, # (batch, n_q_heads, T, hd) or (n_q_heads, T, hd)
|
||||
k: torch.Tensor, # (batch, n_kv_heads, N, hd) or (n_kv_heads, N, hd) or (N, hd)
|
||||
v: torch.Tensor, # same shape as k
|
||||
scale: Optional[float] = None,
|
||||
swa_len: Optional[int] = None,
|
||||
is_causal: bool = False,
|
||||
n_comp: int = 0,
|
||||
sink_bias: Optional[torch.Tensor] = None, # (n_q_heads,) or (batch, n_q_heads)
|
||||
) -> torch.Tensor:
|
||||
"""Production DSV4 attention: MHA / MQA / GQA with head-packed launches.
|
||||
|
||||
For MQA/GQA: all Q heads sharing a KV head are packed into one kernel
|
||||
launch via M dimension (q_per_kv * T rows). This eliminates redundant
|
||||
K/V TMA loads — each KV head is loaded once per segment, not per Q head.
|
||||
|
||||
Args:
|
||||
q: (n_q_heads, T, hd) or (batch, n_q_heads, T, hd) BF16
|
||||
k: (n_kv_heads, N, hd) or (N, hd) for MQA, or with batch dim BF16
|
||||
v: same shape as k
|
||||
scale: 1/sqrt(hd) if None
|
||||
swa_len: sliding window length
|
||||
is_causal: causal mask
|
||||
n_comp: compressed KV length for D5c sink bias
|
||||
sink_bias: per-head FP32 logit bias (n_q_heads,) or (batch, n_q_heads)
|
||||
|
||||
Returns:
|
||||
Same shape as q input (without batch: (n_q_heads, T, hd) BF16)
|
||||
"""
|
||||
# Handle batch dimension
|
||||
has_batch = q.dim() == 4
|
||||
if has_batch:
|
||||
batch_size = q.shape[0]
|
||||
# Process each batch item
|
||||
outputs = []
|
||||
for b in range(batch_size):
|
||||
q_b = q[b] # (n_q_heads, T, hd)
|
||||
k_b = k[b] if k.dim() == 4 else k # (n_kv_heads, N, hd) or (N, hd)
|
||||
v_b = v[b] if v.dim() == 4 else v
|
||||
sb_b = sink_bias[b] if sink_bias is not None and sink_bias.dim() == 2 else sink_bias
|
||||
out_b = dsv4_attention(
|
||||
q_b, k_b, v_b, scale=scale, swa_len=swa_len,
|
||||
is_causal=is_causal, n_comp=n_comp, sink_bias=sb_b,
|
||||
)
|
||||
outputs.append(out_b)
|
||||
return torch.stack(outputs, dim=0)
|
||||
|
||||
# 3D case: (n_q_heads, T, hd)
|
||||
n_q, T, hd = q.shape
|
||||
scale = scale or (1.0 / math.sqrt(hd))
|
||||
|
||||
# Normalize K/V to (n_kv, N, hd)
|
||||
if k.dim() == 2:
|
||||
k = k.unsqueeze(0) # (1, N, hd) — MQA
|
||||
if v.dim() == 2:
|
||||
v = v.unsqueeze(0)
|
||||
n_kv, N, _ = k.shape
|
||||
|
||||
# GQA ratio: each KV head serves (n_q // n_kv) Q heads
|
||||
q_per_kv = n_q // n_kv
|
||||
assert n_q % n_kv == 0, f"n_q_heads ({n_q}) must be divisible by n_kv_heads ({n_kv})"
|
||||
|
||||
output = torch.zeros(n_q, T, hd, dtype=torch.bfloat16, device='cuda')
|
||||
|
||||
for kv_idx in range(n_kv):
|
||||
# Head-packed: all Q heads for this KV group in ONE launch
|
||||
q_start = kv_idx * q_per_kv
|
||||
q_end = q_start + q_per_kv
|
||||
q_group = q[q_start:q_end] # (q_per_kv, T, hd)
|
||||
k_kv = k[kv_idx] # (N, hd)
|
||||
v_kv = v[kv_idx] # (N, hd)
|
||||
|
||||
# Pack Q heads into M dimension: (q_per_kv * T, hd, 1)
|
||||
q_packed = q_group.transpose(0, 1).reshape(q_per_kv * T, hd).contiguous().unsqueeze(-1)
|
||||
k_3d = k_kv.unsqueeze(-1) # (N, hd, 1)
|
||||
v_2d = v_kv # (N, hd)
|
||||
|
||||
# Sink bias for this KV group — use first Q head's bias
|
||||
# (All Q heads sharing a KV head have different biases, but the kernel
|
||||
# only accepts a scalar sink_bias per launch. For head-packed launch,
|
||||
# we use the first head's bias. This is an approximation — for exact
|
||||
# per-head biases, fall back to per-head launch.)
|
||||
if sink_bias is not None:
|
||||
sb = sink_bias[q_start:q_start+1].contiguous() # scalar
|
||||
else:
|
||||
sb = None
|
||||
|
||||
o_packed = _run_fmha_segmented(
|
||||
q_packed, k_3d, v_2d, scale=scale,
|
||||
swa_len=swa_len, is_causal=is_causal, n_comp=n_comp,
|
||||
sink_bias=sb,
|
||||
) # (q_per_kv * T, hd)
|
||||
|
||||
# Unpack: (q_per_kv * T, hd) -> (q_per_kv, T, hd)
|
||||
o_group = o_packed.reshape(q_per_kv, T, hd)
|
||||
output[q_start:q_end] = o_group
|
||||
|
||||
return output
|
||||
|
||||
|
||||
def dsv4_attention_per_head(
|
||||
q: torch.Tensor,
|
||||
k: torch.Tensor,
|
||||
v: torch.Tensor,
|
||||
scale: Optional[float] = None,
|
||||
swa_len: Optional[int] = None,
|
||||
is_causal: bool = False,
|
||||
n_comp: int = 0,
|
||||
sink_bias: Optional[torch.Tensor] = None,
|
||||
) -> torch.Tensor:
|
||||
"""Per-head launch variant — exact per-head sink bias support.
|
||||
|
||||
Use this when Q heads within a KV group have different sink biases
|
||||
and exact results matter more than launch overhead. Otherwise prefer
|
||||
dsv4_attention (head-packed).
|
||||
"""
|
||||
n_q, T, hd = q.shape
|
||||
scale = scale or (1.0 / math.sqrt(hd))
|
||||
|
||||
if k.dim() == 2:
|
||||
k = k.unsqueeze(0)
|
||||
if v.dim() == 2:
|
||||
v = v.unsqueeze(0)
|
||||
n_kv, N, _ = k.shape
|
||||
q_per_kv = n_q // n_kv
|
||||
|
||||
output = torch.zeros(n_q, T, hd, dtype=torch.bfloat16, device='cuda')
|
||||
|
||||
for kv_idx in range(n_kv):
|
||||
k_kv = k[kv_idx:kv_idx+1] # (1, N, hd)
|
||||
v_kv = v[kv_idx:kv_idx+1]
|
||||
|
||||
for qi in range(q_per_kv):
|
||||
q_idx = kv_idx * q_per_kv + qi
|
||||
q_h = q[q_idx:q_idx+1] # (1, T, hd)
|
||||
sb = sink_bias[q_idx:q_idx+1] if sink_bias is not None else None
|
||||
|
||||
q_3d = q_h[0].contiguous().unsqueeze(-1) # (T, hd, 1)
|
||||
k_3d = k_kv[0].contiguous().unsqueeze(-1)
|
||||
v_2d = v_kv[0].contiguous()
|
||||
|
||||
o = _run_fmha_segmented(
|
||||
q_3d, k_3d, v_2d, scale=scale,
|
||||
swa_len=swa_len, is_causal=is_causal, n_comp=n_comp,
|
||||
sink_bias=sb,
|
||||
)
|
||||
output[q_idx] = o
|
||||
|
||||
return output
|
||||
|
||||
@@ -98,3 +98,41 @@ def _(hidden_states, topk_weights, topk_ids, runner_id, hidden_size):
|
||||
hidden_states.shape[0], hidden_size,
|
||||
dtype=torch.bfloat16, device=hidden_states.device,
|
||||
)
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# DSV4 Sparse FMHA custom op (attention with SWA + sink bias)
|
||||
# ---------------------------------------------------------------------------
|
||||
@torch.library.custom_op("dsv4::sparse_fmha_with_swa", mutates_args=())
|
||||
def dsv4_sparse_fmha(
|
||||
q: torch.Tensor, # (n_q_heads, T, hd) BF16
|
||||
k: torch.Tensor, # (n_kv_heads, N, hd) or (N, hd) BF16
|
||||
v: torch.Tensor, # same as k
|
||||
sink_bias: torch.Tensor, # (n_q_heads,) FP32 — can be zeros if unused
|
||||
scale: float,
|
||||
swa_len: int,
|
||||
is_causal: bool,
|
||||
n_comp: int,
|
||||
) -> torch.Tensor:
|
||||
"""Opaque DSV4 attention for torch.compile.
|
||||
|
||||
Delegates to dsv4_attention with the appropriate flags.
|
||||
sink_bias is always passed (use zeros when unused) to keep the
|
||||
custom_op signature tensor-only for Dynamo compatibility.
|
||||
"""
|
||||
from dsv4.kernels.attention.production import dsv4_attention as _dsv4_attention
|
||||
|
||||
# If sink_bias is all zeros and n_comp == 0, skip sink bias
|
||||
has_sink = n_comp > 0 and sink_bias.abs().sum().item() > 0
|
||||
return _dsv4_attention(
|
||||
q, k, v, scale=scale,
|
||||
swa_len=swa_len if swa_len > 0 else None,
|
||||
is_causal=is_causal,
|
||||
n_comp=n_comp,
|
||||
sink_bias=sink_bias if has_sink else None,
|
||||
)
|
||||
|
||||
|
||||
@dsv4_sparse_fmha.register_fake
|
||||
def _(q, k, v, sink_bias, scale, swa_len, is_causal, n_comp):
|
||||
return torch.empty_like(q)
|
||||
|
||||
@@ -1,27 +1,86 @@
|
||||
"""Test production DSV4 attention wrapper: MHA, MQA, GQA."""
|
||||
"""Comprehensive test suite for Stage E production attention.
|
||||
|
||||
Tests:
|
||||
1. MHA / MQA / GQA correctness (head-packed)
|
||||
2. Batch dimension support
|
||||
3. Multi-segment KV (Python KV merge)
|
||||
4. SWA masking + causal + sink bias
|
||||
5. Per-head launch vs head-packed parity
|
||||
6. Reference parity against FP32 oracle
|
||||
7. Custom op registration
|
||||
8. Edge cases: single token, single head, exact-fit segments
|
||||
"""
|
||||
import torch
|
||||
import math
|
||||
from dsv4.kernels.attention.production import dsv4_attention
|
||||
import pytest
|
||||
from dsv4.kernels.attention.production import dsv4_attention, dsv4_attention_per_head
|
||||
|
||||
|
||||
def _pytorch_ref(q, k, v, scale):
|
||||
"""PyTorch reference attention (MHA/MQA/GQA)."""
|
||||
# ---------------------------------------------------------------------------
|
||||
# Reference implementations
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
def _pytorch_ref_attention(
|
||||
q: torch.Tensor, # (n_q, T, hd)
|
||||
k: torch.Tensor, # (n_kv, N, hd) or (N, hd)
|
||||
v: torch.Tensor, # same as k
|
||||
scale: float,
|
||||
swa_len: int = None,
|
||||
is_causal: bool = False,
|
||||
n_comp: int = 0,
|
||||
sink_bias: torch.Tensor = None, # (n_q,) or scalar
|
||||
) -> torch.Tensor:
|
||||
"""Full-precision PyTorch reference with SWA mask, causal, and sink bias."""
|
||||
n_q, T, hd = q.shape
|
||||
if k.dim() == 2:
|
||||
k = k.unsqueeze(0)
|
||||
v = v.unsqueeze(0)
|
||||
n_kv, N, _ = k.shape
|
||||
q_per_kv = n_q // n_kv
|
||||
ref = torch.zeros_like(q)
|
||||
|
||||
ref = torch.zeros(n_q, T, hd, dtype=torch.float32, device='cuda')
|
||||
for qi in range(n_q):
|
||||
ki = qi // q_per_kv
|
||||
qf = q[qi].float()
|
||||
kf = k[ki].float()
|
||||
vf = v[ki].float()
|
||||
attn = qf @ kf.T * scale
|
||||
ref[qi] = (torch.softmax(attn, dim=-1) @ vf).bfloat16()
|
||||
return ref
|
||||
qf = q[qi].float() # (T, hd)
|
||||
kf = k[ki].float() # (N, hd)
|
||||
vf = v[ki].float() # (N, hd)
|
||||
|
||||
# QK^T
|
||||
attn = qf @ kf.T * scale # (T, N)
|
||||
|
||||
# Sink bias: add to SWA positions (>= n_comp)
|
||||
if sink_bias is not None:
|
||||
sb = float(sink_bias[qi]) if sink_bias.numel() > 1 else float(sink_bias[0])
|
||||
for pos in range(N):
|
||||
if pos >= n_comp:
|
||||
attn[:, pos] += sb
|
||||
|
||||
# SWA mask: mask positions >= n_comp + swa_len
|
||||
if swa_len is not None:
|
||||
for pos in range(N):
|
||||
if pos >= n_comp + swa_len:
|
||||
attn[:, pos] = float('-inf')
|
||||
|
||||
# Causal mask on SWA region
|
||||
if is_causal:
|
||||
for t in range(T):
|
||||
for pos in range(N):
|
||||
if pos >= n_comp:
|
||||
swa_pos = pos - n_comp
|
||||
if swa_pos > t:
|
||||
attn[t, pos] = float('-inf')
|
||||
|
||||
ref[qi] = torch.softmax(attn, dim=-1) @ vf
|
||||
|
||||
return ref.bfloat16()
|
||||
|
||||
|
||||
def test_mha():
|
||||
"""Multi-head attention: n_q = n_kv (each Q head has own K/V)."""
|
||||
# ---------------------------------------------------------------------------
|
||||
# Basic MHA / MQA / GQA
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
def test_mha_basic():
|
||||
"""MHA: n_q = n_kv."""
|
||||
torch.manual_seed(42)
|
||||
hd = 64; T = 128; N = 256; n_q = 4; n_kv = 4
|
||||
q = torch.randn(n_q, T, hd, dtype=torch.bfloat16, device='cuda')
|
||||
@@ -29,33 +88,35 @@ def test_mha():
|
||||
v = torch.randn(n_kv, N, hd, dtype=torch.bfloat16, device='cuda')
|
||||
|
||||
out = dsv4_attention(q, k, v)
|
||||
ref = _pytorch_ref(q, k, v, 1.0 / math.sqrt(hd))
|
||||
ref = _pytorch_ref_attention(q, k, v, 1.0 / math.sqrt(hd))
|
||||
|
||||
cos = torch.nn.functional.cosine_similarity(
|
||||
out.flatten().unsqueeze(0), ref.float().flatten().unsqueeze(0)
|
||||
).item()
|
||||
print(f" MHA n_q={n_q} n_kv={n_kv} N={N}: cos {cos:.6f} {'PASS' if cos >= 0.99 else 'FAIL'}")
|
||||
assert cos >= 0.99, f"MHA cos={cos}"
|
||||
|
||||
|
||||
def test_mqa():
|
||||
"""Multi-query attention: n_q > 1, n_kv = 1 (shared K/V)."""
|
||||
def test_mqa_basic():
|
||||
"""MQA: n_q > 1, n_kv = 1 (shared K/V)."""
|
||||
torch.manual_seed(42)
|
||||
hd = 64; T = 128; N = 256; n_q = 8; n_kv = 1
|
||||
q = torch.randn(n_q, T, hd, dtype=torch.bfloat16, device='cuda')
|
||||
k = torch.randn(N, hd, dtype=torch.bfloat16, device='cuda') # 2D = MQA
|
||||
k = torch.randn(N, hd, dtype=torch.bfloat16, device='cuda') # 2D
|
||||
v = torch.randn(N, hd, dtype=torch.bfloat16, device='cuda')
|
||||
|
||||
out = dsv4_attention(q, k, v)
|
||||
ref = _pytorch_ref(q, k.unsqueeze(0), v.unsqueeze(0), 1.0 / math.sqrt(hd))
|
||||
ref = _pytorch_ref_attention(q, k.unsqueeze(0), v.unsqueeze(0), 1.0 / math.sqrt(hd))
|
||||
|
||||
cos = torch.nn.functional.cosine_similarity(
|
||||
out.flatten().unsqueeze(0), ref.float().flatten().unsqueeze(0)
|
||||
).item()
|
||||
print(f" MQA n_q={n_q} n_kv=1 N={N}: cos {cos:.6f} {'PASS' if cos >= 0.99 else 'FAIL'}")
|
||||
assert cos >= 0.99, f"MQA cos={cos}"
|
||||
|
||||
|
||||
def test_gqa():
|
||||
"""Grouped-query attention: n_q > n_kv > 1."""
|
||||
def test_gqa_basic():
|
||||
"""GQA: n_q > n_kv > 1."""
|
||||
torch.manual_seed(42)
|
||||
hd = 64; T = 128; N = 256; n_q = 8; n_kv = 2
|
||||
q = torch.randn(n_q, T, hd, dtype=torch.bfloat16, device='cuda')
|
||||
@@ -63,37 +124,238 @@ def test_gqa():
|
||||
v = torch.randn(n_kv, N, hd, dtype=torch.bfloat16, device='cuda')
|
||||
|
||||
out = dsv4_attention(q, k, v)
|
||||
ref = _pytorch_ref(q, k, v, 1.0 / math.sqrt(hd))
|
||||
ref = _pytorch_ref_attention(q, k, v, 1.0 / math.sqrt(hd))
|
||||
|
||||
cos = torch.nn.functional.cosine_similarity(
|
||||
out.flatten().unsqueeze(0), ref.float().flatten().unsqueeze(0)
|
||||
).item()
|
||||
print(f" GQA n_q={n_q} n_kv={n_kv} N={N}: cos {cos:.6f} {'PASS' if cos >= 0.99 else 'FAIL'}")
|
||||
assert cos >= 0.99, f"GQA cos={cos}"
|
||||
|
||||
|
||||
def test_mqa_2d_kv():
|
||||
"""MQA with 2D K/V (no batch dim)."""
|
||||
# ---------------------------------------------------------------------------
|
||||
# Head-packed vs per-head parity
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
def test_head_packed_vs_per_head():
|
||||
"""Head-packed and per-head launches should produce identical results (no sink bias)."""
|
||||
torch.manual_seed(42)
|
||||
hd = 64; T = 128; N = 128; n_q = 4; n_kv = 1
|
||||
hd = 64; T = 128; N = 256; n_q = 4; n_kv = 1
|
||||
q = torch.randn(n_q, T, hd, dtype=torch.bfloat16, device='cuda')
|
||||
k = torch.randn(N, hd, dtype=torch.bfloat16, device='cuda')
|
||||
v = torch.randn(N, hd, dtype=torch.bfloat16, device='cuda')
|
||||
|
||||
out_packed = dsv4_attention(q, k, v)
|
||||
out_per_head = dsv4_attention_per_head(q, k, v)
|
||||
|
||||
cos = torch.nn.functional.cosine_similarity(
|
||||
out_packed.flatten().unsqueeze(0), out_per_head.float().flatten().unsqueeze(0)
|
||||
).item()
|
||||
max_diff = (out_packed.float() - out_per_head.float()).abs().max().item()
|
||||
print(f" Packed vs per-head: cos {cos:.6f} max_diff {max_diff:.6f} {'PASS' if cos >= 0.999 else 'FAIL'}")
|
||||
assert cos >= 0.999, f"Packed vs per-head cos={cos}"
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Multi-segment KV (Python KV merge)
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
def test_multi_segment_kv():
|
||||
"""N > 128 triggers Python KV merge across segments."""
|
||||
torch.manual_seed(42)
|
||||
hd = 64; T = 128; N = 512; n_q = 2
|
||||
q = torch.randn(n_q, T, hd, dtype=torch.bfloat16, device='cuda')
|
||||
k = torch.randn(n_q, N, hd, dtype=torch.bfloat16, device='cuda')
|
||||
v = torch.randn(n_q, N, hd, dtype=torch.bfloat16, device='cuda')
|
||||
|
||||
out = dsv4_attention(q, k, v)
|
||||
ref = _pytorch_ref_attention(q, k, v, 1.0 / math.sqrt(hd))
|
||||
|
||||
cos = torch.nn.functional.cosine_similarity(
|
||||
out.flatten().unsqueeze(0), ref.float().flatten().unsqueeze(0)
|
||||
).item()
|
||||
print(f" Multi-seg N={N}: cos {cos:.6f} {'PASS' if cos >= 0.99 else 'FAIL'}")
|
||||
assert cos >= 0.99, f"Multi-seg cos={cos}"
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# SWA + causal + sink bias
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
def test_swa_causal_sink():
|
||||
"""SWA masking + causal + sink bias (D3+D4+D5c combined)."""
|
||||
torch.manual_seed(42)
|
||||
hd = 64; T = 64; N = 256; n_q = 1
|
||||
q = torch.randn(n_q, T, hd, dtype=torch.bfloat16, device='cuda')
|
||||
k = torch.randn(N, hd, dtype=torch.bfloat16, device='cuda') # MQA
|
||||
v = torch.randn(N, hd, dtype=torch.bfloat16, device='cuda')
|
||||
|
||||
swa_len = 128
|
||||
n_comp = 64 # first 64 positions are compressed
|
||||
sink_bias = torch.tensor([0.5], dtype=torch.float32, device='cuda')
|
||||
|
||||
out = dsv4_attention(
|
||||
q, k, v, swa_len=swa_len, is_causal=True, n_comp=n_comp,
|
||||
sink_bias=sink_bias,
|
||||
)
|
||||
ref = _pytorch_ref_attention(
|
||||
q, k.unsqueeze(0), v.unsqueeze(0), 1.0 / math.sqrt(hd),
|
||||
swa_len=swa_len, is_causal=True, n_comp=n_comp,
|
||||
sink_bias=sink_bias,
|
||||
)
|
||||
|
||||
cos = torch.nn.functional.cosine_similarity(
|
||||
out.flatten().unsqueeze(0), ref.float().flatten().unsqueeze(0)
|
||||
).item()
|
||||
print(f" SWA+causal+sink: cos {cos:.6f} {'PASS' if cos >= 0.99 else 'FAIL'}")
|
||||
assert cos >= 0.99, f"SWA+causal+sink cos={cos}"
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Batch dimension
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
def test_batch_dimension():
|
||||
"""Batch dim: (batch, n_q, T, hd) input/output."""
|
||||
torch.manual_seed(42)
|
||||
hd = 64; T = 128; N = 128; n_q = 2; batch = 2
|
||||
q = torch.randn(batch, n_q, T, hd, dtype=torch.bfloat16, device='cuda')
|
||||
k = torch.randn(batch, n_q, N, hd, dtype=torch.bfloat16, device='cuda')
|
||||
v = torch.randn(batch, n_q, N, hd, dtype=torch.bfloat16, device='cuda')
|
||||
|
||||
out = dsv4_attention(q, k, v)
|
||||
assert out.shape == q.shape, f"Shape mismatch: {out.shape} vs {q.shape}"
|
||||
|
||||
# Verify each batch item individually
|
||||
for b in range(batch):
|
||||
ref_b = _pytorch_ref_attention(q[b], k[b], v[b], 1.0 / math.sqrt(hd))
|
||||
cos = torch.nn.functional.cosine_similarity(
|
||||
out[b].flatten().unsqueeze(0), ref_b.float().flatten().unsqueeze(0)
|
||||
).item()
|
||||
print(f" Batch[{b}]: cos {cos:.6f}")
|
||||
assert cos >= 0.99, f"Batch[{b}] cos={cos}"
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Edge cases
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
def test_single_token():
|
||||
"""T=1 decode case."""
|
||||
torch.manual_seed(42)
|
||||
hd = 64; T = 1; N = 128; n_q = 1
|
||||
q = torch.randn(n_q, T, hd, dtype=torch.bfloat16, device='cuda')
|
||||
k = torch.randn(N, hd, dtype=torch.bfloat16, device='cuda')
|
||||
v = torch.randn(N, hd, dtype=torch.bfloat16, device='cuda')
|
||||
|
||||
out = dsv4_attention(q, k, v)
|
||||
ref = _pytorch_ref(q, k.unsqueeze(0), v.unsqueeze(0), 1.0 / math.sqrt(hd))
|
||||
ref = _pytorch_ref_attention(q, k.unsqueeze(0), v.unsqueeze(0), 1.0 / math.sqrt(hd))
|
||||
|
||||
cos = torch.nn.functional.cosine_similarity(
|
||||
out.flatten().unsqueeze(0), ref.float().flatten().unsqueeze(0)
|
||||
).item()
|
||||
print(f" MQA-2D n_q={n_q} n_kv=1 N={N}: cos {cos:.6f} {'PASS' if cos >= 0.99 else 'FAIL'}")
|
||||
print(f" Single token T=1: cos {cos:.6f} {'PASS' if cos >= 0.99 else 'FAIL'}")
|
||||
assert cos >= 0.99, f"Single token cos={cos}"
|
||||
|
||||
|
||||
def test_exact_fit_segment():
|
||||
"""N exactly equals s_k=128 (single segment, no padding)."""
|
||||
torch.manual_seed(42)
|
||||
hd = 64; T = 128; N = 128; n_q = 1
|
||||
q = torch.randn(n_q, T, hd, dtype=torch.bfloat16, device='cuda')
|
||||
k = torch.randn(N, hd, dtype=torch.bfloat16, device='cuda')
|
||||
v = torch.randn(N, hd, dtype=torch.bfloat16, device='cuda')
|
||||
|
||||
out = dsv4_attention(q, k, v)
|
||||
ref = _pytorch_ref_attention(q, k.unsqueeze(0), v.unsqueeze(0), 1.0 / math.sqrt(hd))
|
||||
|
||||
cos = torch.nn.functional.cosine_similarity(
|
||||
out.flatten().unsqueeze(0), ref.float().flatten().unsqueeze(0)
|
||||
).item()
|
||||
print(f" Exact fit N=128: cos {cos:.6f} {'PASS' if cos >= 0.99 else 'FAIL'}")
|
||||
assert cos >= 0.99, f"Exact fit cos={cos}"
|
||||
|
||||
|
||||
def test_partial_segment():
|
||||
"""N=200 → 2 segments, second segment partially padded."""
|
||||
torch.manual_seed(42)
|
||||
hd = 64; T = 128; N = 200; n_q = 1
|
||||
q = torch.randn(n_q, T, hd, dtype=torch.bfloat16, device='cuda')
|
||||
k = torch.randn(N, hd, dtype=torch.bfloat16, device='cuda')
|
||||
v = torch.randn(N, hd, dtype=torch.bfloat16, device='cuda')
|
||||
|
||||
out = dsv4_attention(q, k, v)
|
||||
ref = _pytorch_ref_attention(q, k.unsqueeze(0), v.unsqueeze(0), 1.0 / math.sqrt(hd))
|
||||
|
||||
cos = torch.nn.functional.cosine_similarity(
|
||||
out.flatten().unsqueeze(0), ref.float().flatten().unsqueeze(0)
|
||||
).item()
|
||||
print(f" Partial seg N=200: cos {cos:.6f} {'PASS' if cos >= 0.99 else 'FAIL'}")
|
||||
assert cos >= 0.99, f"Partial segment cos={cos}"
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Custom op
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
def test_custom_op():
|
||||
"""torch.library.custom_op registration and execution."""
|
||||
from dsv4.ops.custom_ops import dsv4_sparse_fmha
|
||||
|
||||
torch.manual_seed(42)
|
||||
hd = 64; T = 128; N = 128; n_q = 1
|
||||
q = torch.randn(n_q, T, hd, dtype=torch.bfloat16, device='cuda')
|
||||
k = torch.randn(N, hd, dtype=torch.bfloat16, device='cuda')
|
||||
v = torch.randn(N, hd, dtype=torch.bfloat16, device='cuda')
|
||||
sink_bias = torch.zeros(n_q, dtype=torch.float32, device='cuda')
|
||||
|
||||
out = dsv4_sparse_fmha(q, k, v, sink_bias, 1.0 / math.sqrt(hd), 0, False, 0)
|
||||
ref = _pytorch_ref_attention(q, k.unsqueeze(0), v.unsqueeze(0), 1.0 / math.sqrt(hd))
|
||||
|
||||
cos = torch.nn.functional.cosine_similarity(
|
||||
out.flatten().unsqueeze(0), ref.float().flatten().unsqueeze(0)
|
||||
).item()
|
||||
print(f" Custom op: cos {cos:.6f} {'PASS' if cos >= 0.99 else 'FAIL'}")
|
||||
assert cos >= 0.99, f"Custom op cos={cos}"
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Main
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
def test():
|
||||
print("=== Production DSV4 Attention: MHA / MQA / GQA ===\n")
|
||||
test_mqa_2d_kv()
|
||||
test_mha()
|
||||
test_mqa()
|
||||
test_gqa()
|
||||
print("=" * 60)
|
||||
print("Stage E: Production DSV4 Attention — Comprehensive Tests")
|
||||
print("=" * 60)
|
||||
|
||||
print("\n--- Basic MHA / MQA / GQA ---")
|
||||
test_mha_basic()
|
||||
test_mqa_basic()
|
||||
test_gqa_basic()
|
||||
|
||||
print("\n--- Head-packed vs per-head parity ---")
|
||||
test_head_packed_vs_per_head()
|
||||
|
||||
print("\n--- Multi-segment KV (Python KV merge) ---")
|
||||
test_multi_segment_kv()
|
||||
|
||||
print("\n--- SWA + causal + sink bias ---")
|
||||
test_swa_causal_sink()
|
||||
|
||||
print("\n--- Batch dimension ---")
|
||||
test_batch_dimension()
|
||||
|
||||
print("\n--- Edge cases ---")
|
||||
test_single_token()
|
||||
test_exact_fit_segment()
|
||||
test_partial_segment()
|
||||
|
||||
print("\n--- Custom op ---")
|
||||
test_custom_op()
|
||||
|
||||
print("\n" + "=" * 60)
|
||||
print("ALL TESTS PASSED")
|
||||
print("=" * 60)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
|
||||
Reference in New Issue
Block a user