Apply dsv3_fused_a_gemm kernel (#7635)
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@@ -96,6 +96,7 @@ from sglang.srt.utils import (
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bind_or_assign,
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cpu_has_amx_support,
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get_bool_env_var,
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get_device_sm,
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get_int_env_var,
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is_cpu,
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is_cuda,
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@@ -112,7 +113,7 @@ _is_cpu_amx_available = cpu_has_amx_support()
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_is_cpu = is_cpu()
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if _is_cuda:
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from sgl_kernel import awq_dequantize, bmm_fp8, merge_state_v2
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from sgl_kernel import awq_dequantize, bmm_fp8, dsv3_fused_a_gemm, merge_state_v2
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elif _is_cpu and _is_cpu_amx_available:
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pass
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else:
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@@ -875,6 +876,15 @@ class DeepseekV2AttentionMLA(nn.Module):
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weight_names=["w_kc", "w_vc"], transpose_dims=[[1, 2], [1, 2]]
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)
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self.use_min_latency_fused_a_gemm = (
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hasattr(self, "fused_qkv_a_proj_with_mqa")
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and self.fused_qkv_a_proj_with_mqa.weight.dtype == torch.bfloat16
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and self.fused_qkv_a_proj_with_mqa.weight.shape[0] == 2112
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and self.fused_qkv_a_proj_with_mqa.weight.shape[1] == 7168
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and is_cuda
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and get_device_sm() >= 90
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)
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self.qkv_proj_with_rope_is_int8 = (
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hasattr(self, "fused_qkv_a_proj_with_mqa")
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and self.fused_qkv_a_proj_with_mqa.weight.dtype == torch.int8
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@@ -1114,7 +1124,13 @@ class DeepseekV2AttentionMLA(nn.Module):
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from sglang.srt.model_executor.cuda_graph_runner import get_is_capture_mode
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if self.q_lora_rank is not None:
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q, latent_cache = self.fused_qkv_a_proj_with_mqa(hidden_states)[0].split(
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if hidden_states.shape[0] <= 16 and self.use_min_latency_fused_a_gemm:
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fused_qkv_a_proj_out = dsv3_fused_a_gemm(
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hidden_states, self.fused_qkv_a_proj_with_mqa.weight.T
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)
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else:
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fused_qkv_a_proj_out = self.fused_qkv_a_proj_with_mqa(hidden_states)[0]
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q, latent_cache = fused_qkv_a_proj_out.split(
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[self.q_lora_rank, self.kv_lora_rank + self.qk_rope_head_dim], dim=-1
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)
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k_nope = latent_cache[..., : self.kv_lora_rank]
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