c869eb995a
Wires the new mtlgemm fused sparse attention kernel through the ATTN backend selector. Dispatches to the fused Metal kernel when max(max_q, max_kv) <= 256 (where the naive per-thread-serial-KV kernel beats SDPA-padded on M3 Max), and falls through to an inline SDPA-padded path for larger max_seqlen. Opt in via ATTN_BACKEND=flex_gemm_sparse_attn or SPARSE_ATTN_BACKEND=flex_gemm_sparse_attn. Default on Darwin stays 'sdpa' — the threshold-based fallback doesn't yet prove a universal win across the pipeline's attention shape distribution. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
103 lines
3.3 KiB
Python
103 lines
3.3 KiB
Python
from typing import *
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import platform
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import sys
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CONV = 'flex_gemm'
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DEBUG = False
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ATTN = 'flash_attn'
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def __detect_defaults():
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"""Auto-detect best backends for current platform."""
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global CONV, ATTN
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if platform.system() == 'Darwin':
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ATTN = 'sdpa'
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if __flex_gemm_works_on_mps():
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CONV = 'flex_gemm'
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else:
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CONV = 'pytorch'
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elif not __has_cuda():
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CONV = 'pytorch'
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ATTN = 'sdpa'
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def __flex_gemm_works_on_mps():
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"""Probe flex_gemm with tiny MPS convs covering both IMPLICIT_GEMM and
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MASKED_IMPLICIT_GEMM. If the install pre-dates the device-routing fix
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(or pre-dates the real masked kernel in round 2), one of these returns
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a CPU tensor — fall back to the pure-PyTorch backend rather than
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crashing inside the model on the first LayerNorm. Build tensors on CPU
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and move to MPS because some PyTorch builds lack int/fp16 torch.zeros
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kernels on MPS."""
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try:
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import torch
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if not torch.backends.mps.is_available():
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return False
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import flex_gemm
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from flex_gemm.ops.spconv import sparse_submanifold_conv3d, Algorithm, set_algorithm
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# Exercise both algorithms — masked carries its own cache/dispatch path
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# distinct from dense. A stale install may have one working and the
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# other broken (e.g. the pre-round-2 aliased-to-dense fallback).
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coords = torch.tensor([[0, 0, 0, 0]], dtype=torch.int32).to('mps')
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feats = torch.zeros((1, 4), dtype=torch.float16).to('mps')
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weight = torch.zeros((4, 1, 1, 1, 4), dtype=torch.float16).to('mps')
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shape = torch.Size([1, 4, 1, 1, 1])
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for algo in (Algorithm.IMPLICIT_GEMM, Algorithm.MASKED_IMPLICIT_GEMM):
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set_algorithm(algo)
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out, _ = sparse_submanifold_conv3d(feats, coords, shape, weight)
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if out.device.type != 'mps':
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return False
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return True
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except Exception:
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return False
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def __has_cuda():
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try:
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import torch
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return torch.cuda.is_available()
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except Exception:
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return False
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def __from_env():
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import os
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global CONV
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global DEBUG
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global ATTN
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__detect_defaults()
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env_sparse_conv_backend = os.environ.get('SPARSE_CONV_BACKEND')
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env_sparse_debug = os.environ.get('SPARSE_DEBUG')
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env_sparse_attn_backend = os.environ.get('SPARSE_ATTN_BACKEND')
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if env_sparse_attn_backend is None:
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env_sparse_attn_backend = os.environ.get('ATTN_BACKEND')
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if env_sparse_conv_backend is not None and env_sparse_conv_backend in ['none', 'spconv', 'torchsparse', 'flex_gemm', 'pytorch']:
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CONV = env_sparse_conv_backend
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if env_sparse_debug is not None:
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DEBUG = env_sparse_debug == '1'
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if env_sparse_attn_backend is not None and env_sparse_attn_backend in [
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'xformers', 'flash_attn', 'flash_attn_3', 'sdpa', 'flex_gemm_sparse_attn',
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]:
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ATTN = env_sparse_attn_backend
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print(f"[SPARSE] Conv backend: {CONV}; Attention backend: {ATTN}")
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__from_env()
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def set_conv_backend(backend: Literal['none', 'spconv', 'torchsparse', 'flex_gemm', 'pytorch']):
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global CONV
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CONV = backend
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def set_debug(debug: bool):
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global DEBUG
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DEBUG = debug
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def set_attn_backend(backend: Literal['xformers', 'flash_attn', 'sdpa']):
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global ATTN
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ATTN = backend
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