ViperEkura
7540acb43e
- replace the per-shape auto tables in the linear backend with an M-banded rule (M in [2,4] on compute capability 8.0+) that measured at the HBM bandwidth floor across every family, and fold the capability check into the capable guard - drop the unreachable swiglu auto shape-table machinery so both backends share one env-mode ladder via the new dispatch.env_mode helper - add __all__ across extension modules, name the rotary registration records, and unify typing to the typing-module style - rewrite test_linear_dispatch.py around behavioral routing assertions and document the M-banded policy in the developer docs - Benchmark: L20 SM89, Python dispatch overhead 2.9us to 1.5us, auto now covers every projection shape at M in [2,4].
63 lines
2.2 KiB
Python
63 lines
2.2 KiB
Python
"""Inference-only fused SwiGLU selection for dense MLP layers."""
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import torch
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import torch.nn.functional as F
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from torch import Tensor
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from astrai.extension.backend.linear import linear
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from astrai.extension.dispatch import env_mode
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from astrai.extension.loader import is_available
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from astrai.extension.ops.swiglu import bf16_swiglu
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def _unfused_swiglu(x: Tensor, up_weight: Tensor, gate_weight: Tensor) -> Tensor:
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# Keep the existing linear backend in the fallback chain. This preserves
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# any independently qualified GEMV batches instead of making the fusion
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# decision suppress linear-level optimizations.
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return linear(x, up_weight) * F.silu(linear(x, gate_weight))
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def _fused_swiglu(x: Tensor, up_weight: Tensor, gate_weight: Tensor) -> Tensor:
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return bf16_swiglu(x.detach(), up_weight.detach(), gate_weight.detach())
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def _swiglu_capable(x: Tensor, up_weight: Tensor, gate_weight: Tensor) -> bool:
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return not (
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torch.is_grad_enabled()
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or not x.is_cuda
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or x.dtype != torch.bfloat16
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or up_weight.dtype != torch.bfloat16
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or gate_weight.dtype != torch.bfloat16
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or x.ndim not in (1, 2)
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or up_weight.ndim != 2
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or gate_weight.ndim != 2
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or (x.ndim == 2 and not 1 <= x.shape[0] <= 8)
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or up_weight.shape != gate_weight.shape
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or x.shape[-1] != up_weight.shape[1]
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or x.shape[-1] % 8 != 0
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or x.device != up_weight.device
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or x.device != gate_weight.device
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or not x.is_contiguous()
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or not up_weight.is_contiguous()
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or not gate_weight.is_contiguous()
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or not is_available("bf16_swiglu")
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)
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def swiglu(x: Tensor, up_weight: Tensor, gate_weight: Tensor) -> Tensor:
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"""Apply the dense-MLP SwiGLU projection with a safe torch fallback.
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``ASTRAI_SWIGLU=0`` and ``auto`` keep the unfused linear-backend chain;
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``1`` forces the fused primitive for supported inputs. Auto will adopt
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an M-banded rule mirroring the linear backend once end-to-end evidence
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qualifies one.
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"""
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if env_mode("ASTRAI_SWIGLU") != "1" or not _swiglu_capable(
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x, up_weight, gate_weight
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):
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return _unfused_swiglu(x, up_weight, gate_weight)
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return _fused_swiglu(x, up_weight, gate_weight)
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__all__ = ["swiglu"]
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