1c0eee919d
Signed-off-by: Jack Hu <Jack.Hu@amd.com> Signed-off-by: Jack Hu <jack.hu@amd.com> Signed-off-by: Douglas Lehr <Doug.Lehr@amd.com> Co-authored-by: James E T Smith <jamesETsmith@users.noreply.github.com> Co-authored-by: Douglas Lehr <91553416+dllehr-amd@users.noreply.github.com> Co-authored-by: Douglas Lehr <Doug.Lehr@amd.com>
40 lines
1.6 KiB
Python
40 lines
1.6 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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"""DeepSeek V3.2 (``deepseek_v32``) platform entry point.
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DeepSeek V3.2 introduced the DeepSeek Sparse Attention (DSA) architecture:
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MLA + a "lightning indexer" that selects the top-k tokens for a sparse MLA
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attend. The same model code serves any DSA checkpoint, including GLM-5.2
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(``glm_moe_dsa``), which reuses this architecture.
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The CUDA and ROCm implementations select capability-specific kernels
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internally and fall back when an optimization is unavailable. Other platforms
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use the generic implementation by default.
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"""
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from vllm.platforms import current_platform
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if current_platform.is_cuda():
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# GLM-5.2 (glm_moe_dsa) reuses the CUDA DSA module. Individual optimized
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# kernels remain gated on the device capabilities they support.
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from .nvidia.model import DeepseekV32ForCausalLM
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from .nvidia.model import DeepseekV32ForCausalLM as GlmMoeDsaForCausalLM
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from .nvidia.mtp import DeepseekV32MTP
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elif current_platform.is_rocm():
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from .amd.model import DeepseekV32ForCausalLM
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from .amd.model import DeepseekV32ForCausalLM as GlmMoeDsaForCausalLM
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from .amd.mtp import DeepseekV32MTP
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else:
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# XPU and CPU keep the generic implementation.
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from vllm.model_executor.models.deepseek_mtp import DeepSeekMTP as DeepseekV32MTP
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from vllm.model_executor.models.deepseek_v2 import (
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DeepseekV3ForCausalLM as DeepseekV32ForCausalLM,
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)
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from vllm.model_executor.models.deepseek_v2 import GlmMoeDsaForCausalLM
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__all__ = [
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"DeepseekV32ForCausalLM",
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"DeepseekV32MTP",
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"GlmMoeDsaForCausalLM",
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]
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