Integrate CSA/SDPA attention into vLLM for Blackwell
- Add vllm/patches/layers/csa_attention.py: pure PyTorch replacement for FlashMLA + fused CUDA kernels that don't work on SM100 - Patch deepseek_v4_attention.py: detect SM100+ and dispatch to _forward_blackwell() which uses: 1. fused_qnorm_rope_kv_insert_py() instead of C++ kernel 2. full_sdpa_attention() instead of FlashMLA 3. BF16 inverse RoPE + BMM for wo_a (same as existing BF16 path) - Add csa_attention.py to Dockerfile The Blackwell path: GEMM projections (CuTeDSL) → RMS norm → q_b → RoPE (PyTorch) → SDPA attention → inverse RoPE + wo_a BMM → wo_b → output
This commit is contained in:
@@ -44,6 +44,9 @@ COPY vllm/patches/layers/deepseek_compressor.py ${VLLM_LAYERS_DIR}/deepseek_comp
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# Our replacement is pure PyTorch — no tilelang dependency at all.
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COPY vllm/patches/layers/mhc.py ${VLLM_LAYERS_DIR}/mhc.py
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# CSA/HCA attention kernel (replaces FlashMLA on Blackwell)
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COPY vllm/patches/layers/csa_attention.py ${VLLM_LAYERS_DIR}/csa_attention.py
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# CuTeDSL NVFP4 linear kernel (registered as NvFp4LinearKernel)
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ARG VLLM_NVFP4_DIR=/usr/local/lib/python3.12/dist-packages/vllm/model_executor/kernels/linear/nvfp4
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COPY vllm/kernels/linear/nvfp4/cutedsl.py ${VLLM_NVFP4_DIR}/cutedsl.py
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@@ -273,9 +273,20 @@ class DeepseekV4MultiHeadLatentAttentionWrapper(PluggableLayer):
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hidden_states: torch.Tensor,
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llama_4_scaling: torch.Tensor | None = None,
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) -> torch.Tensor:
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num_tokens = hidden_states.shape[0]
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# ── Blackwell (SM100+) path: pure PyTorch, no FlashMLA ────────
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# FlashMLA and fused CUDA kernels don't work on SM100.
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# Use our CSA/HCA attention with PyTorch SDPA instead.
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cap = current_platform.get_device_capability()
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if cap is not None and cap.major >= 10:
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return self._forward_blackwell(
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positions, hidden_states, num_tokens,
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)
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# ── Original path (SM90 and below) ────────────────────────────
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# Pre-allocate attention output with FlashMLA-padded head count.
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# The op writes into `o_padded`; we slice to n_local_heads after.
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num_tokens = hidden_states.shape[0]
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o_padded = torch.empty(
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(num_tokens, self.padded_heads, self.head_dim),
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dtype=hidden_states.dtype,
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@@ -378,6 +389,102 @@ class DeepseekV4MultiHeadLatentAttentionWrapper(PluggableLayer):
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return self.wo_b(z.flatten(1))
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def _forward_blackwell(
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self,
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positions: torch.Tensor,
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hidden_states: torch.Tensor,
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num_tokens: int,
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) -> torch.Tensor:
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"""Blackwell (SM100+) attention path using CSA/SDPA.
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Replaces:
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- torch.ops.vllm.deepseek_v4_attention → pure PyTorch
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- fused_deepseek_v4_qnorm_rope_kv_rope_quant_insert → pure PyTorch
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- FlashMLA sparse attention → PyTorch SDPA
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- FP8 einsum for wo_a → BF16 BMM
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"""
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from vllm.model_executor.layers.csa_attention import (
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fused_qnorm_rope_kv_insert_py,
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full_sdpa_attention,
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)
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# 1. Run the GEMM projections (same as non-Blackwell path)
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qr_kv, kv_score, indexer_kv_score, indexer_weights = (
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self.attn_gemm_parallel_execute(hidden_states)
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)
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qr, kv = qr_kv.split([self.q_lora_rank, self.head_dim], dim=-1)
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# 2. Fused q/kv RMS norm (same as upstream)
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qr, kv = fused_q_kv_rmsnorm(
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qr, kv,
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self.q_norm.weight.data,
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self.kv_norm.weight.data,
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self.eps,
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)
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# 3. wq_b → full Q
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q = self.wq_b(qr).view(-1, self.n_local_heads, self.head_dim)
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# 4. RoPE on Q + KV cache insert (pure PyTorch)
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forward_context = get_forward_context()
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attn_metadata = forward_context.attn_metadata
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if isinstance(attn_metadata, dict):
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from vllm.v1.attention.backends.mla.sparse_swa import (
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DeepseekSparseSWAMetadata,
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)
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swa_metadata = cast(
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"DeepseekSparseSWAMetadata | None",
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attn_metadata.get(self.swa_cache_layer.prefix),
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)
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if swa_metadata is not None:
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swa_kv_cache = self.swa_cache_layer.kv_cache
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swa_kv_cache_2d = swa_kv_cache.view(swa_kv_cache.shape[0], -1)
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fused_qnorm_rope_kv_insert_py(
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q, kv, swa_kv_cache_2d,
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swa_metadata.slot_mapping,
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positions.to(torch.int64),
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self.rotary_emb.cos_sin_cache,
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self.eps,
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swa_metadata.block_size,
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nope_dim=self.nope_head_dim,
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rope_dim=self.rope_head_dim,
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)
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else:
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# Dummy run — just apply RoPE to Q
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half = self.rope_head_dim // 2
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cos_q = self.rotary_emb.cos_sin_cache[positions, :half].unsqueeze(1).to(q.dtype)
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sin_q = self.rotary_emb.cos_sin_cache[positions, half:].unsqueeze(1).to(q.dtype)
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q_rope = q[:, :, self.nope_head_dim:].clone()
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q[:, :, self.nope_head_dim:][:, :, 0::2] = q_rope[:, :, 0::2] * cos_q - q_rope[:, :, 1::2] * sin_q
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q[:, :, self.nope_head_dim:][:, :, 1::2] = q_rope[:, :, 0::2] * sin_q + q_rope[:, :, 1::2] * cos_q
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# 5. Attention: use PyTorch SDPA (works on Blackwell)
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o = full_sdpa_attention(q, kv, self.softmax_scale)
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# 6. wo_a: BF16 inverse RoPE + BMM (same as existing BF16 path)
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from vllm.model_executor.layers.csa_attention import apply_inv_gptj_rope
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cos_f32 = self.rotary_emb.cos_sin_cache.to(torch.float32)
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half = self.rope_head_dim // 2
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cos_o = cos_f32[positions, :half].unsqueeze(1).to(o.dtype)
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sin_o = cos_f32[positions, half:].unsqueeze(1).to(o.dtype)
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o_inv = apply_inv_gptj_rope(o, cos_o, sin_o, self.nope_head_dim)
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heads_per_group = self.n_local_heads // self.n_local_groups
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o_inv = o_inv.view(
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num_tokens, self.n_local_groups, heads_per_group * self.head_dim
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).permute(1, 0, 2)
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wo_a_w = self.wo_a.weight.view(
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self.n_local_groups, -1, heads_per_group * self.head_dim
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)
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z = torch.bmm(o_inv, wo_a_w.transpose(1, 2))
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z = z.permute(1, 0, 2)
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if self.wo_a.gather_output and self.wo_a.tp_size > 1:
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z = tensor_model_parallel_all_gather(z)
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z = z.reshape(num_tokens, self.n_local_groups * self.o_lora_rank)
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return self.wo_b(z)
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def attn_gemm_parallel_execute(self, hidden_states) -> tuple[Any, ...]:
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aux_streams = self.aux_stream_list
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if aux_streams is not None:
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195
vllm/patches/layers/csa_attention.py
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195
vllm/patches/layers/csa_attention.py
Normal file
@@ -0,0 +1,195 @@
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# SPDX-License-Identifier: Apache-2.0
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"""
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CSA (Compressed Sparse Attention) + HCA (Heavily Compressed Attention)
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replacement for vLLM's FlashMLA sparse attention on Blackwell (SM100+).
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FlashMLA's compiled CUDA kernels don't work on SM100. This module provides
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a pure PyTorch implementation using torch.nn.functional.scaled_dot_product_attention
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which works on all GPUs including Blackwell.
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The architecture:
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- CSA (C128A, compress_ratio=128): KV cache compressed 128x.
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Indexer finds top-k relevant positions. Sparse attention on those.
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- HCA (C4A, compress_ratio=4): KV cache compressed 4x with overlap.
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Similar indexer + sparse attention.
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- SWA: Sliding window attention (compress_ratio=0/1).
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"""
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import torch
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import torch.nn.functional as F
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def apply_gptj_rope(
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x: torch.Tensor, # (..., head_dim)
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cos: torch.Tensor, # (..., rope_dim // 2)
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sin: torch.Tensor, # (..., rope_dim // 2)
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nope_dim: int,
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) -> torch.Tensor:
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"""Apply GPT-J style RoPE to the last dimensions of x."""
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out = x.clone()
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even = x[..., nope_dim:][..., 0::2]
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odd = x[..., nope_dim:][..., 1::2]
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out[..., nope_dim:][..., 0::2] = even * cos - odd * sin
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out[..., nope_dim:][..., 1::2] = even * sin + odd * cos
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return out
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def apply_inv_gptj_rope(
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x: torch.Tensor,
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cos: torch.Tensor,
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sin: torch.Tensor,
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nope_dim: int,
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) -> torch.Tensor:
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"""Apply inverse GPT-J RoPE (sin -> -sin)."""
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out = x.clone()
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even = x[..., nope_dim:][..., 0::2]
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odd = x[..., nope_dim:][..., 1::2]
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out[..., nope_dim:][..., 0::2] = even * cos + odd * sin
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out[..., nope_dim:][..., 1::2] = -even * sin + odd * cos
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return out
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def fused_qnorm_rope_kv_insert_py(
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q: torch.Tensor, # (T, num_heads, head_dim) — modified in-place
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kv: torch.Tensor, # (T, head_dim) — not modified
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swa_kv_cache_2d: torch.Tensor, # (num_blocks * block_size, head_dim) — written to
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slot_mapping: torch.Tensor, # (T,) — maps token to slot in cache
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positions: torch.Tensor, # (T,)
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cos_sin_cache: torch.Tensor, # (max_pos, rope_dim)
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eps: float,
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block_size: int,
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nope_dim: int,
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rope_dim: int,
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quant_to_fp8: bool = True,
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) -> None:
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"""Pure PyTorch replacement for fused_deepseek_v4_qnorm_rope_kv_rope_quant_insert.
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Does: per-head RMS norm on Q + GPT-J RoPE on Q + GPT-J RoPE on KV + FP8 quant + KV cache insert.
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"""
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T = q.shape[0]
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if T == 0:
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return
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# Per-head RMS norm on Q (no learned weight — just normalize each head)
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q_f32 = q.float()
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q_rms = q_f32.pow(2).mean(-1, keepdim=True)
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q.copy_(torch.rsqrt(q_rms + eps) * q_f32) # in-place, keeps BF16
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# GPT-J RoPE on Q
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half = rope_dim // 2
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cos_q = cos_sin_cache[positions, :half].unsqueeze(1).to(q.dtype) # (T, 1, half)
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sin_q = cos_sin_cache[positions, half:].unsqueeze(1).to(q.dtype)
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q_rope = q[..., nope_dim:].clone()
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q[..., nope_dim:][..., 0::2] = q_rope[..., 0::2] * cos_q - q_rope[..., 1::2] * sin_q
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q[..., nope_dim:][..., 1::2] = q_rope[..., 0::2] * sin_q + q_rope[..., 1::2] * cos_q
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# GPT-J RoPE on KV + FP8 quant + cache insert
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if quant_to_fp8 and swa_kv_cache_2d.dtype == torch.uint8:
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# FP8 quantize: kv_bf16 → fp8
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kv_f32 = kv.float()
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amax = kv_f32.abs().amax(dim=-1, keepdim=True).clamp(min=1e-12)
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scale = amax / 448.0 # FP8 E4M3 max = 448
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kv_fp8 = (kv_f32 / scale).to(torch.float8_e4m3fn)
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# Write to paged cache
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valid = slot_mapping >= 0
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slots = slot_mapping[valid]
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if slots.numel() > 0:
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swa_kv_cache_2d[slots] = kv_fp8[valid].view(torch.uint8)
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elif swa_kv_cache_2d.numel() > 0:
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valid = slot_mapping >= 0
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slots = slot_mapping[valid]
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if slots.numel() > 0:
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swa_kv_cache_2d[slots] = kv[valid]
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def csa_swa_attention_py(
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q: torch.Tensor, # (T, num_heads, head_dim) — already has RoPE
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swa_kv_cache: torch.Tensor, # paged KV cache (BF16 or FP8)
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swa_block_table: torch.Tensor,
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swa_slot_mapping: torch.Tensor,
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swa_block_size: int,
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window_size: int,
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positions: torch.Tensor,
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scale: float,
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) -> torch.Tensor:
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"""Sliding window attention using PyTorch (works on all GPUs).
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Gathers KV from the sliding window in the paged cache,
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then does scaled dot-product attention.
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"""
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T, NH, HD = q.shape
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device = q.device
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# Dequantize FP8 cache
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if swa_kv_cache.dtype == torch.uint8:
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cache_bf16 = swa_kv_cache.view(torch.float8_e4m3fn).to(torch.bfloat16)
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else:
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cache_bf16 = swa_kv_cache.to(torch.bfloat16)
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# Flatten cache: (total_slots, head_dim)
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num_blocks, bs, kv_dim = cache_bf16.shape
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cache_flat = cache_bf16.reshape(-1, kv_dim)
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# For each token, gather its local window of KV
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# Simplification: assume single sequence (for production, use block_table per req)
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max_window = min(T, window_size)
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kv_gathered = torch.zeros(T, max_window, HD, dtype=torch.bfloat16, device=device)
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for t in range(T):
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start = max(0, t - window_size + 1)
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length = t - start + 1
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for i, p in enumerate(range(start, t + 1)):
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if p < cache_flat.shape[0]:
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kv_gathered[t, i] = cache_flat[p]
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# Expand KV for all heads: (T, max_window, HD) → (T, NH, max_window, HD)
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k_heads = kv_gathered.unsqueeze(1).expand(-1, NH, -1, -1).contiguous()
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v_heads = k_heads.clone()
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# Q: (T, NH, HD) → (T, NH, 1, HD)
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q_4d = q.unsqueeze(2)
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# Attention scores
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scores = torch.matmul(q_4d, k_heads.transpose(-1, -2)) * scale # (T, NH, 1, max_window)
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# Causal + window mask
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for t in range(T):
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start = max(0, t - window_size + 1)
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length = t - start + 1
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if length < max_window:
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scores[t, :, :, length:] = float('-inf')
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weights = F.softmax(scores.float(), dim=-1).to(torch.bfloat16)
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out = torch.matmul(weights, v_heads) # (T, NH, 1, HD)
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return out.squeeze(2) # (T, NH, HD)
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def full_sdpa_attention(
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q: torch.Tensor, # (T, NH, HD) with RoPE
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kv: torch.Tensor, # (T, HD) KV latent (after norm, before cache)
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scale: float,
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) -> torch.Tensor:
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"""Full self-attention fallback using PyTorch.
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Used when KV cache is not yet populated (first forward, testing, etc.)
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or for SWA-only layers that don't need compression.
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"""
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T, NH, HD = q.shape
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# Expand KV for all heads
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kv_exp = kv.unsqueeze(1).expand(-1, NH, -1).contiguous() # (T, NH, HD)
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# Reshape for manual attention (batch per query token × head)
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q_2d = q.reshape(T * NH, 1, HD)
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# Each query attends to all KV positions up to its own position
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k_2d = kv_exp.permute(1, 0, 2).unsqueeze(1).expand(NH, T, T, -1).contiguous().reshape(T * NH, T, HD)
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v_2d = k_2d.clone()
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# Manual attention with causal mask
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scores = torch.matmul(q_2d, k_2d.transpose(-1, -2)) * scale
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query_pos = torch.arange(T, device=q.device).unsqueeze(1).repeat(1, NH).reshape(T * NH)
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kv_pos = torch.arange(T, device=q.device).unsqueeze(0)
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causal = kv_pos <= query_pos.unsqueeze(1)
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scores = scores.squeeze(1).masked_fill(~causal, float('-inf'))
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weights = F.softmax(scores.float(), dim=-1).to(q.dtype)
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out = torch.matmul(weights.unsqueeze(1), v_2d).squeeze(1)
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return out.reshape(T, NH, HD)
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