update sgl-kernel for EP: python part (#8550)
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@@ -54,7 +54,7 @@ runtime_common = [
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srt = [
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"sglang[runtime_common]",
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"sgl-kernel==0.2.7",
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"sgl-kernel==0.2.8",
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"torch==2.7.1",
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"torchaudio==2.7.1",
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"torchvision==0.22.1",
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@@ -648,7 +648,7 @@ def _set_envs_and_config(server_args: ServerArgs):
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if _is_cuda:
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assert_pkg_version(
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"sgl-kernel",
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"0.2.7",
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"0.2.8",
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"Please reinstall the latest version with `pip install sgl-kernel --force-reinstall`",
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)
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@@ -568,7 +568,7 @@ def moe_align_block_size(
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- The padding ensures that the total number of tokens is now divisible
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by block_size for proper block matrix operations.
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"""
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max_num_tokens_padded = topk_ids.numel() + num_experts * (block_size - 1)
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max_num_tokens_padded = topk_ids.numel() + (num_experts + 1) * (block_size - 1)
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sorted_ids = torch.empty(
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(max_num_tokens_padded,), dtype=torch.int32, device=topk_ids.device
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)
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@@ -578,13 +578,9 @@ def moe_align_block_size(
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)
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num_tokens_post_pad = torch.empty((1), dtype=torch.int32, device=topk_ids.device)
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# In EP, expert_ids for filtered experts are -1. We have num_experts + 1 ids in total.
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cumsum_buffer = torch.empty(
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(num_experts + 1,), dtype=torch.int32, device=topk_ids.device
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)
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token_cnts_buffer = torch.empty(
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(num_experts + 1) * num_experts,
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dtype=torch.int32,
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device=topk_ids.device,
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(num_experts + 2,), dtype=torch.int32, device=topk_ids.device
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)
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# Threshold based on benchmark results
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@@ -594,12 +590,11 @@ def moe_align_block_size(
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sgl_moe_align_block_size(
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topk_ids,
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num_experts,
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num_experts + 1,
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block_size,
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sorted_ids,
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expert_ids,
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num_tokens_post_pad,
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token_cnts_buffer,
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cumsum_buffer,
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fuse_sorted_ids_padding,
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)
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