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84 lines
3.0 KiB
Python
84 lines
3.0 KiB
Python
# Copyright (c) 2026 LightSeek Foundation
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#
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# Permission is hereby granted, free of charge, to any person obtaining a copy
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# of this software and associated documentation files (the "Software"), to deal
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# in the Software without restriction, including without limitation the rights
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# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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# copies of the Software, and to permit persons to whom the Software is
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# furnished to do so, subject to the following conditions:
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#
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# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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# SOFTWARE.
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"""FlashInfer rotary embedding kernels."""
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import torch
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from tokenspeed_kernel.platform import CapabilityRequirement, current_platform
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from tokenspeed_kernel.registry import Priority, register_kernel
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from tokenspeed_kernel.signature import format_signatures
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platform = current_platform()
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if platform.is_nvidia:
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from flashinfer.rope import mla_rope_quantize_fp8
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@register_kernel(
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"embedding",
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"rope_mla",
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name="flashinfer_embedding_rope_mla",
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solution="flashinfer",
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capability=CapabilityRequirement(vendors=frozenset({"nvidia"})),
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signatures=format_signatures(
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("q_rope", "k_rope", "q_nope", "k_nope"),
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"dense",
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{torch.float16, torch.bfloat16},
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),
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priority=Priority.SPECIALIZED,
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traits={
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"is_neox": frozenset({True, False}),
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"quantize_dtype": frozenset({torch.float8_e4m3fn}),
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"has_scale_q_tensor": frozenset({False}),
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"has_scale_kv_tensor": frozenset({False}),
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},
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)
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def flashinfer_embedding_rope_mla(
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*,
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positions: torch.Tensor,
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q_rope: torch.Tensor,
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k_rope: torch.Tensor,
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q_nope: torch.Tensor,
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k_nope: torch.Tensor,
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cos_sin_cache: torch.Tensor,
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q_rope_out: torch.Tensor,
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k_rope_out: torch.Tensor,
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q_nope_out: torch.Tensor,
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k_nope_out: torch.Tensor,
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is_neox: bool = True,
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quant_scale_q: float = 1.0,
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quant_scale_kv: float = 1.0,
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enable_pdl: bool = False,
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) -> None:
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mla_rope_quantize_fp8(
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q_rope=q_rope,
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k_rope=k_rope,
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q_nope=q_nope,
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k_nope=k_nope,
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cos_sin_cache=cos_sin_cache,
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pos_ids=positions,
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is_neox=is_neox,
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quantize_dtype=torch.float8_e4m3fn,
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q_rope_out=q_rope_out,
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k_rope_out=k_rope_out,
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q_nope_out=q_nope_out,
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k_nope_out=k_nope_out,
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quant_scale_q=quant_scale_q,
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quant_scale_kv=quant_scale_kv,
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enable_pdl=enable_pdl,
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)
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