biondizzle
3fb3c925af
- Split bridge.py -> ops/quantize.py, ops/layouts.py, ops/gemm_runner.py - Renamed classes: CuTeDSLNvfp4Linear -> Nvfp4Linear, etc. - Moved kernel code to dsv4/kernels/ (gemm, attention, compressor, decode, cuda) - Moved PyTorch bridges to dsv4/ops/ - Moved nn.Module layers to dsv4layers/ - Moved reference implementations to dsv4/reference/ - Moved vendored CUTLASS code to vendored/ - Archived ~190 debug tests to tests/archive/ - Kept ~15 canonical tests in tests/unit/ - Updated all import paths - Added stubs for future components (model/, cache/, loader/) - Updated pyproject.toml: dsv4-inference package name
90 lines
2.5 KiB
Python
90 lines
2.5 KiB
Python
"""
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Sparse topk metadata kernels for DeepSeek-V4 Blackwell decode attention.
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Own kernels — no FlashMLA, no Triton from vLLM.
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C128A: position-based compressed KV slot lookup via block table.
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C4A: local topk index to global slot ID mapping via block table.
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"""
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import os
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import torch
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from typing import Optional
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_kernel_module = None
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def _get_kernel_module():
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"""Lazy-load the CUDA extension."""
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global _kernel_module
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if _kernel_module is not None:
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return _kernel_module
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from torch.utils.cpp_extension import load
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kernel_dir = os.path.join(os.path.dirname(__file__), "kernels")
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_kernel_module = load(
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name="sparse_topk_metadata",
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sources=[os.path.join(kernel_dir, "sparse_topk_metadata.cu")],
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extra_cuda_cflags=["-O3", "--generate-code=arch=compute_100a,code=[sm_100a]"],
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verbose=False,
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)
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return _kernel_module
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def build_c128a_topk_metadata(
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positions: torch.Tensor,
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compress_ratio: int,
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num_decode_tokens: int,
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token_to_req: torch.Tensor,
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block_table: torch.Tensor,
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block_size: int,
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slot_mapping: torch.Tensor,
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global_decode_buffer: torch.Tensor,
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decode_lens_buffer: torch.Tensor,
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prefill_buffer: torch.Tensor,
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max_compressed_tokens: int = 8192,
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) -> tuple:
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"""Build C128A topk metadata for decode and prefill tokens.
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For decode tokens: maps compressed KV positions to global slot IDs
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via block table lookup. Returns (global_decode, decode_lens, prefill_local).
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"""
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mod = _get_kernel_module()
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return mod.build_c128a_topk_metadata(
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positions,
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compress_ratio,
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num_decode_tokens,
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token_to_req,
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block_table,
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block_size,
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slot_mapping,
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global_decode_buffer,
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decode_lens_buffer,
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prefill_buffer,
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max_compressed_tokens,
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)
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def compute_c4a_global_topk(
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local_topk: torch.Tensor,
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token_to_req: torch.Tensor,
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block_table: torch.Tensor,
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block_size: int,
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is_valid_token: torch.Tensor,
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) -> tuple:
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"""Map local C4A topk indices to global KV cache slots.
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For each token, takes local compressed indices (from the indexer)
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and maps them to global slot IDs via block table lookup.
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Returns (global_topk_indices, topk_lens).
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"""
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mod = _get_kernel_module()
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if is_valid_token.dtype == torch.bool:
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is_valid_token = is_valid_token.to(torch.int32)
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return mod.compute_c4a_global_topk(
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local_topk,
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token_to_req,
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block_table,
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block_size,
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is_valid_token,
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)
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