limit sgl-kernel causal conv1d to cuda only (#10648)
Co-authored-by: Jinyan Chen <jinyanc@nvidia.com>
This commit is contained in:
@@ -13,9 +13,6 @@ from sglang.srt.layers.attention.fla.fused_recurrent import (
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from sglang.srt.layers.attention.fla.fused_sigmoid_gating_recurrent import (
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from sglang.srt.layers.attention.fla.fused_sigmoid_gating_recurrent import (
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fused_sigmoid_gating_delta_rule_update,
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fused_sigmoid_gating_delta_rule_update,
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)
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)
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from sglang.srt.layers.attention.mamba.causal_conv1d import (
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causal_conv1d_fn as causal_conv1d_fn_sgl,
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)
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from sglang.srt.layers.attention.mamba.causal_conv1d_triton import (
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from sglang.srt.layers.attention.mamba.causal_conv1d_triton import (
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causal_conv1d_fn,
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causal_conv1d_fn,
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causal_conv1d_update,
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causal_conv1d_update,
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@@ -26,9 +23,15 @@ from sglang.srt.model_executor.forward_batch_info import ForwardBatch, ForwardMo
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from sglang.srt.model_executor.model_runner import ModelRunner
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from sglang.srt.model_executor.model_runner import ModelRunner
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from sglang.srt.models.qwen3_next import Qwen3HybridLinearDecoderLayer, fused_gdn_gating
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from sglang.srt.models.qwen3_next import Qwen3HybridLinearDecoderLayer, fused_gdn_gating
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from sglang.srt.speculative.eagle_utils import EagleDraftInput, EagleVerifyInput
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from sglang.srt.speculative.eagle_utils import EagleDraftInput, EagleVerifyInput
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from sglang.srt.utils import is_npu
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from sglang.srt.utils import is_cuda, is_npu
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if is_npu():
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if is_cuda():
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from sglang.srt.layers.attention.mamba.causal_conv1d import (
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causal_conv1d_fn as causal_conv1d_fn_cuda,
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)
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causal_conv1d_fn = causal_conv1d_fn_cuda
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elif is_npu():
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from sgl_kernel_npu.fla.chunk import chunk_gated_delta_rule_npu
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from sgl_kernel_npu.fla.chunk import chunk_gated_delta_rule_npu
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from sgl_kernel_npu.fla.fused_sigmoid_gating_recurrent import (
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from sgl_kernel_npu.fla.fused_sigmoid_gating_recurrent import (
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fused_sigmoid_gating_delta_rule_update_npu,
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fused_sigmoid_gating_delta_rule_update_npu,
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@@ -350,7 +353,7 @@ class MambaAttnBackend(AttentionBackend):
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mixed_qkv_processed.transpose(1, 2).contiguous().view(seq_len, -1)
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mixed_qkv_processed.transpose(1, 2).contiguous().view(seq_len, -1)
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)
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)
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else:
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else:
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mixed_qkv = causal_conv1d_fn_sgl(
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mixed_qkv = causal_conv1d_fn(
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mixed_qkv.transpose(0, 1),
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mixed_qkv.transpose(0, 1),
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conv_weights,
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conv_weights,
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bias,
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bias,
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