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DeepSeek V4: Support context parallelism with fused MoE (non-DeepEP) (#24947)

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This commit is contained in:
Yongfei Xu
2026-06-01 14:25:43 -07:00
committed by GitHub
parent 3bce192bd2
commit 5700790c05
17 changed files with 1982 additions and 24 deletions
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python/sglang/srt/layers/communicator_dsa_cp.py
+30 -15
View File
@@ -35,6 +35,10 @@ from sglang.srt.layers.dp_attention import (
attn_cp_all_gather_into_tensor,
attn_cp_reduce_scatter_tensor,
get_attention_cp_group,
get_attention_cp_rank,
get_attention_cp_size,
get_attention_dp_size,
get_attention_tp_size,
get_local_dp_buffer,
)
from sglang.srt.layers.utils.cp_utils import mla_use_prefill_cp
@@ -48,6 +52,30 @@ def dsa_enable_prefill_cp():
return is_dsa_enable_prefill_cp()
def dsa_cp_gather_hidden_states(hidden_states: torch.Tensor):
attn_dp_size = get_attention_dp_size()
attn_tp_size = get_attention_tp_size()
assert attn_dp_size == 1 and attn_tp_size == 1
hidden_states, local_hidden_states = (
get_local_dp_buffer(get_attention_cp_group()),
hidden_states,
)
attn_cp_all_gather_into_tensor(hidden_states, local_hidden_states)
return hidden_states
def dsa_cp_reduce_scatter_hidden_states(hidden_states: torch.Tensor):
attn_dp_size = get_attention_dp_size()
attn_tp_size = get_attention_tp_size()
assert attn_dp_size == 1 and attn_tp_size == 1
cp_size = get_attention_cp_size()
cp_rank = get_attention_cp_rank()
input_hidden_states = hidden_states
hidden_states = hidden_states.tensor_split(cp_size)[cp_rank]
attn_cp_reduce_scatter_tensor(hidden_states, input_hidden_states)
return hidden_states
class DSACPLayerCommunicator(LayerCommunicator):
def __init__(
self,
@@ -154,15 +182,7 @@ class DSACPCommunicateWithAllReduceAndLayerNormFn(
# for prefill: attn tp scattered -> full
# for decode: attn tp full -> full
if dsa_use_prefill_cp(forward_batch) or mla_use_prefill_cp(forward_batch):
assert context.attn_dp_size == 1
hidden_states, local_hidden_states = (
get_local_dp_buffer(get_attention_cp_group()),
hidden_states,
)
attn_cp_all_gather_into_tensor(
hidden_states,
local_hidden_states,
)
hidden_states = dsa_cp_gather_hidden_states(hidden_states)
return hidden_states, residual
@@ -207,10 +227,5 @@ class DSACPCommunicateSummableTensorPairFn(CommunicateSummableTensorPairFn):
# for prefill: full -> attn tp scattered
# for decode: full -> attn tp full
if dsa_use_prefill_cp(forward_batch) or mla_use_prefill_cp(forward_batch):
assert context.attn_dp_size == 1
input_hidden_states = hidden_states
hidden_states = hidden_states.tensor_split(context.attn_cp_size)[
context.attn_cp_rank
]
attn_cp_reduce_scatter_tensor(hidden_states, input_hidden_states)
hidden_states = dsa_cp_reduce_scatter_hidden_states(hidden_states)
return hidden_states, residual
python/sglang/srt/layers/moe/moe_runner/triton_utils/configs/triton_3_5_1/E=256,N=256,device_name=NVIDIA_H20,dtype=fp8_w8a8,block_shape=[128, 128].json
+146
View File
@@ -0,0 +1,146 @@
{
"1": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 64,
"num_warps": 4,
"num_stages": 5
},
"2": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 16,
"num_warps": 4,
"num_stages": 5
},
"4": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 5
},
"8": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 4
},
"16": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 64,
"num_warps": 4,
"num_stages": 4
},
"24": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 3
},
"32": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 3
},
"48": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 4
},
"64": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 16,
"num_warps": 4,
"num_stages": 4
},
"96": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 16,
"num_warps": 4,
"num_stages": 3
},
"128": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 3
},
"256": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 16,
"num_warps": 4,
"num_stages": 3
},
"512": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 3
},
"1024": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 16,
"num_warps": 4,
"num_stages": 3
},
"1536": {
"BLOCK_SIZE_M": 32,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 16,
"num_warps": 4,
"num_stages": 3
},
"2048": {
"BLOCK_SIZE_M": 32,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 3
},
"3072": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 8,
"num_stages": 3
},
"4096": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 64,
"num_warps": 8,
"num_stages": 3
}
}
python/sglang/srt/layers/moe/moe_runner/triton_utils/configs/triton_3_5_1/E=256,N=256,device_name=NVIDIA_H20,dtype=fp8_w8a8,block_shape=[128, 128]_down.json
+164
View File
@@ -0,0 +1,164 @@
{
"1": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 16,
"num_warps": 4,
"num_stages": 2,
"USE_TMA": true
},
"2": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 2,
"USE_TMA": true
},
"4": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 32,
"num_warps": 4,
"num_stages": 2,
"USE_TMA": true
},
"8": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 2,
"USE_TMA": true
},
"16": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 2,
"USE_TMA": true
},
"24": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 2,
"USE_TMA": true
},
"32": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 32,
"num_warps": 4,
"num_stages": 2,
"USE_TMA": true
},
"48": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 64,
"num_warps": 4,
"num_stages": 2,
"USE_TMA": true
},
"64": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 32,
"num_warps": 4,
"num_stages": 2,
"USE_TMA": true
},
"96": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 32,
"num_warps": 4,
"num_stages": 2,
"USE_TMA": true
},
"128": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 32,
"num_warps": 4,
"num_stages": 2,
"USE_TMA": true
},
"256": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 32,
"num_warps": 4,
"num_stages": 2,
"USE_TMA": true
},
"512": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 32,
"num_warps": 4,
"num_stages": 2,
"USE_TMA": true
},
"1024": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 32,
"num_warps": 4,
"num_stages": 2,
"USE_TMA": true
},
"1536": {
"BLOCK_SIZE_M": 32,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 16,
"num_warps": 4,
"num_stages": 2,
"USE_TMA": true
},
"2048": {
"BLOCK_SIZE_M": 32,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 16,
"num_warps": 4,
"num_stages": 2,
"USE_TMA": true
},
"3072": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 32,
"num_warps": 4,
"num_stages": 3,
"USE_TMA": true
},
"4096": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 32,
"num_warps": 4,
"num_stages": 3,
"USE_TMA": true
}
}
python/sglang/srt/layers/moe/moe_runner/triton_utils/configs/triton_3_5_1/E=256,N=256,device_name=NVIDIA_H20-3e,dtype=fp8_w8a8,block_shape=[128, 128].json
+146
View File
@@ -0,0 +1,146 @@
{
"1": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 64,
"num_warps": 4,
"num_stages": 5
},
"2": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 16,
"num_warps": 4,
"num_stages": 5
},
"4": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 5
},
"8": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 4
},
"16": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 64,
"num_warps": 4,
"num_stages": 4
},
"24": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 3
},
"32": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 3
},
"48": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 4
},
"64": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 16,
"num_warps": 4,
"num_stages": 4
},
"96": {
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"num_stages": 3
},
"128": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 3
},
"256": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 16,
"num_warps": 4,
"num_stages": 3
},
"512": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
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},
"1024": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
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"num_warps": 4,
"num_stages": 3
},
"1536": {
"BLOCK_SIZE_M": 32,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 16,
"num_warps": 4,
"num_stages": 3
},
"2048": {
"BLOCK_SIZE_M": 32,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 3
},
"3072": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 8,
"num_stages": 3
},
"4096": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 64,
"num_warps": 8,
"num_stages": 3
}
}
python/sglang/srt/layers/moe/moe_runner/triton_utils/configs/triton_3_5_1/E=256,N=256,device_name=NVIDIA_H20-3e,dtype=fp8_w8a8,block_shape=[128, 128]_down.json
+164
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@@ -0,0 +1,164 @@
{
"1": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 16,
"num_warps": 4,
"num_stages": 2,
"USE_TMA": true
},
"2": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 2,
"USE_TMA": true
},
"4": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 32,
"num_warps": 4,
"num_stages": 2,
"USE_TMA": true
},
"8": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 2,
"USE_TMA": true
},
"16": {
"BLOCK_SIZE_M": 16,
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"num_warps": 4,
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"USE_TMA": true
},
"24": {
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"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 2,
"USE_TMA": true
},
"32": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 32,
"num_warps": 4,
"num_stages": 2,
"USE_TMA": true
},
"48": {
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"USE_TMA": true
},
"64": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 32,
"num_warps": 4,
"num_stages": 2,
"USE_TMA": true
},
"96": {
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"USE_TMA": true
},
"128": {
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"GROUP_SIZE_M": 32,
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},
"256": {
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},
"512": {
"BLOCK_SIZE_M": 16,
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"GROUP_SIZE_M": 32,
"num_warps": 4,
"num_stages": 2,
"USE_TMA": true
},
"1024": {
"BLOCK_SIZE_M": 16,
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"GROUP_SIZE_M": 32,
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},
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"USE_TMA": true
},
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"USE_TMA": true
},
"3072": {
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"USE_TMA": true
},
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"USE_TMA": true
}
}
python/sglang/srt/layers/moe/moe_runner/triton_utils/configs/triton_3_5_1/E=256,N=512,device_name=NVIDIA_H20,dtype=fp8_w8a8,block_shape=[128, 128].json
+146
View File
@@ -0,0 +1,146 @@
{
"1": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 5
},
"2": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 5
},
"4": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 16,
"num_warps": 4,
"num_stages": 4
},
"8": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 3
},
"16": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 32,
"num_warps": 4,
"num_stages": 4
},
"24": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 16,
"num_warps": 4,
"num_stages": 3
},
"32": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 3
},
"48": {
"BLOCK_SIZE_M": 16,
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"num_warps": 4,
"num_stages": 3
},
"64": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 32,
"num_warps": 4,
"num_stages": 4
},
"96": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 16,
"num_warps": 4,
"num_stages": 3
},
"128": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 16,
"num_warps": 4,
"num_stages": 3
},
"256": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 4
},
"512": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 16,
"num_warps": 4,
"num_stages": 3
},
"1024": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
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python/sglang/srt/layers/moe/moe_runner/triton_utils/configs/triton_3_5_1/E=256,N=512,device_name=NVIDIA_H20,dtype=fp8_w8a8,block_shape=[128, 128]_down.json
+164
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python/sglang/srt/layers/moe/moe_runner/triton_utils/configs/triton_3_5_1/E=256,N=512,device_name=NVIDIA_H20-3e,dtype=fp8_w8a8,block_shape=[128, 128].json
+146
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python/sglang/srt/layers/moe/moe_runner/triton_utils/configs/triton_3_5_1/E=256,N=512,device_name=NVIDIA_H20-3e,dtype=fp8_w8a8,block_shape=[128, 128]_down.json
+164
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python/sglang/srt/layers/moe/moe_runner/triton_utils/configs/triton_3_5_1/E=384,N=384,device_name=NVIDIA_H20,dtype=fp8_w8a8,block_shape=[128, 128].json
+146
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python/sglang/srt/layers/moe/moe_runner/triton_utils/configs/triton_3_5_1/E=384,N=384,device_name=NVIDIA_H20,dtype=fp8_w8a8,block_shape=[128, 128]_down.json
+164
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python/sglang/srt/layers/moe/moe_runner/triton_utils/configs/triton_3_5_1/E=384,N=384,device_name=NVIDIA_H20-3e,dtype=fp8_w8a8,block_shape=[128, 128].json
+146
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"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 5
},
"8": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 3
},
"16": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 4
},
"24": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 4
},
"32": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 3
},
"48": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 4
},
"64": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 4
},
"96": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 4
},
"128": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 4
},
"256": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 4
},
"512": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 64,
"num_warps": 4,
"num_stages": 3
},
"1024": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 3
},
"1536": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 16,
"num_warps": 4,
"num_stages": 3
},
"2048": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 3
},
"3072": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 3
},
"4096": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 4
}
}
python/sglang/srt/layers/moe/moe_runner/triton_utils/configs/triton_3_5_1/E=384,N=384,device_name=NVIDIA_H20-3e,dtype=fp8_w8a8,block_shape=[128, 128]_down.json
+164
View File
@@ -0,0 +1,164 @@
{
"1": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 16,
"num_warps": 4,
"num_stages": 2,
"USE_TMA": true
},
"2": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 2,
"USE_TMA": true
},
"4": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 2,
"USE_TMA": true
},
"8": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 2,
"USE_TMA": true
},
"16": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 2,
"USE_TMA": true
},
"24": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 2,
"USE_TMA": true
},
"32": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 2,
"USE_TMA": true
},
"48": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 256,
"BLOCK_SIZE_K": 64,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 3,
"USE_TMA": true
},
"64": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 256,
"BLOCK_SIZE_K": 64,
"GROUP_SIZE_M": 32,
"num_warps": 4,
"num_stages": 3,
"USE_TMA": true
},
"96": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 256,
"BLOCK_SIZE_K": 64,
"GROUP_SIZE_M": 16,
"num_warps": 4,
"num_stages": 3,
"USE_TMA": true
},
"128": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 2,
"USE_TMA": true
},
"256": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 16,
"num_warps": 4,
"num_stages": 2,
"USE_TMA": true
},
"512": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 64,
"GROUP_SIZE_M": 64,
"num_warps": 4,
"num_stages": 2,
"USE_TMA": true
},
"1024": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 2,
"USE_TMA": true
},
"1536": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 16,
"num_warps": 4,
"num_stages": 2,
"USE_TMA": true
},
"2048": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 16,
"num_warps": 4,
"num_stages": 2,
"USE_TMA": true
},
"3072": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 2,
"USE_TMA": true
},
"4096": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 32,
"num_warps": 4,
"num_stages": 3,
"USE_TMA": true
}
}
python/sglang/srt/layers/utils/cp_utils.py
+26
View File
@@ -11,9 +11,11 @@ from sglang.srt.distributed.device_communicators.pynccl_allocator import (
from sglang.srt.layers.dp_attention import (
attn_cp_all_gather_into_tensor,
get_attention_cp_group,
get_attention_cp_rank,
get_attention_cp_size,
is_allocation_symmetric,
)
from sglang.srt.layers.moe import get_moe_a2a_backend
from sglang.srt.model_executor.forward_context import get_token_to_kv_pool
from sglang.srt.server_args import get_global_server_args
@@ -171,6 +173,30 @@ def cp_split_and_rebuild_position(forward_batch, positions: torch.Tensor):
return positions
def cp_round_robin_input_ids(input_ids):
"""
input input_ids:
rank0~7: 0,1,2,3,4,5,...
output input_ids:
a2a none:
rank0~7: 0,8,16,...,1,9,17,...,2,10,18,...
not a2a none:
rank0: 0,8,16,...
rank1: 1,9,17,...
rank2: 2,10,18,...
...
"""
cp_size = get_attention_cp_size()
cp_rank = get_attention_cp_rank()
if get_moe_a2a_backend().is_none():
input_ids = input_ids.reshape(-1, cp_size).T.flatten()
else:
input_ids = input_ids[cp_rank::cp_size].contiguous()
return input_ids
def cp_all_gather_reorganized_into_tensor(input_tensor, cp_size, forward_batch, stream):
"""
Allgather communication for context_parallel(kv_cache, index_k, hidden_states).
python/sglang/srt/models/deepseek_v4.py
+18 -9
View File
@@ -45,6 +45,10 @@ from sglang.srt.layers.attention.dsa.utils import (
from sglang.srt.layers.attention.dsv4.compressor import Compressor
from sglang.srt.layers.attention.dsv4.indexer import C4Indexer
from sglang.srt.layers.communicator import get_attn_tp_context
from sglang.srt.layers.communicator_dsa_cp import (
dsa_cp_gather_hidden_states,
dsa_cp_reduce_scatter_hidden_states,
)
from sglang.srt.layers.dp_attention import (
_DpGatheredBufferWrapper,
attn_tp_all_gather,
@@ -70,6 +74,7 @@ from sglang.srt.layers.rotary_embedding import get_rope_wrapper
from sglang.srt.layers.utils import PPMissingLayer, get_layer_id
from sglang.srt.layers.utils.cp_utils import (
cp_all_gather_rerange_output,
cp_round_robin_input_ids,
cp_split_and_rebuild_data,
cp_split_and_rebuild_position,
prepare_context_parallel_metadata,
@@ -1391,14 +1396,13 @@ class DeepseekV4DecoderLayer(nn.Module):
and not get_moe_a2a_backend().is_none()
)
if _use_cp:
assert get_moe_a2a_backend().is_deepep(), (
"CP requires DeepEP (moe_a2a_backend == deepep). "
"Only DeepEP is tested with CP's per-rank token split."
)
cp_rank = get_attention_cp_rank()
cp_size = get_attention_cp_size()
input_ids = input_ids[cp_rank::cp_size].contiguous()
input_ids_global = input_ids
if get_moe_a2a_backend().is_none():
hidden_states = dsa_cp_gather_hidden_states(hidden_states)
else:
assert get_moe_a2a_backend().is_deepep(), (
"CP requires DeepEP (moe_a2a_backend == deepep). "
"Only DeepEP is tested with CP's per-rank token split."
)
elif _use_tp_moe_gather:
hidden_states, local_hidden_states = (
get_global_dp_buffer(get_tp_group()),
@@ -1417,8 +1421,11 @@ class DeepseekV4DecoderLayer(nn.Module):
forward_batch,
input_ids=input_ids,
input_ids_global=input_ids_global,
use_reduce_scatter=_use_cp,
)
if _use_tp_moe_gather:
if _use_cp and get_moe_a2a_backend().is_none():
hidden_states = dsa_cp_reduce_scatter_hidden_states(hidden_states)
elif _use_tp_moe_gather:
hidden_states, global_hidden_states = (
get_local_dp_buffer(get_tp_group()),
hidden_states,
@@ -1568,6 +1575,8 @@ class DeepseekV4Model(nn.Module):
if self.pp_group.is_first_rank:
hidden_states = cp_split_and_rebuild_data(forward_batch, hidden_states)
positions = cp_split_and_rebuild_position(forward_batch, positions)
input_ids = cp_round_robin_input_ids(input_ids)
input_ids_global = input_ids
# Reset Compressor's per-step freqs_cis cache from any previous step.
for _attr in ("freqs_cis_c4", "freqs_cis_c128"):
python/sglang/srt/models/deepseek_v4_nextn.py
+3
View File
@@ -28,6 +28,7 @@ from sglang.srt.layers.moe.utils import get_moe_a2a_backend
from sglang.srt.layers.quantization.base_config import QuantizationConfig
from sglang.srt.layers.utils.cp_utils import (
cp_all_gather_rerange_output,
cp_round_robin_input_ids,
cp_split_and_rebuild_data,
cp_split_and_rebuild_position,
prepare_context_parallel_metadata,
@@ -169,6 +170,8 @@ class DeepseekV4ModelNextN(nn.Module):
if dsa_use_prefill_cp(forward_batch):
hidden_states = cp_split_and_rebuild_data(forward_batch, hidden_states)
positions = cp_split_and_rebuild_position(forward_batch, positions)
input_ids = cp_round_robin_input_ids(input_ids)
input_ids_global = input_ids
hidden_states, residual, post, comb = self.decoder(
positions=positions,