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Upstream Llama4 Support to Main (#16113)

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Signed-off-by: Aston Zhang <22279212+astonzhang@users.noreply.github.com>
Signed-off-by: Chris Thi <chris.c.thi@gmail.com>
Signed-off-by: drisspg <drisspguessous@gmail.com>
Signed-off-by: Jon Swenson <jmswen@gmail.com>
Signed-off-by: Keyun Tong <tongkeyun@gmail.com>
Signed-off-by: Lu Fang <fanglu@meta.com>
Signed-off-by: Xiaodong Wang <xdwang@meta.com>
Signed-off-by: Yang Chen <yangche@fb.com>
Signed-off-by: Ye (Charlotte) Qi <yeq@meta.com>
Signed-off-by: Yong Hoon Shin <yhshin@meta.com>
Signed-off-by: Zijing Liu <liuzijing2014@gmail.com>
Signed-off-by: Lu Fang <lufang@fb.com>
Signed-off-by: Lu Fang <fanglu@fb.com>
Signed-off-by: Lucia Fang <fanglu@fb.com>
Signed-off-by: Roger Wang <ywang@roblox.com>
Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>
Co-authored-by: Lu Fang <fanglu@fb.com>
Co-authored-by: Roger Wang <ywang@roblox.com>
Co-authored-by: DarkLight1337 <tlleungac@connect.ust.hk>
This commit is contained in:
Lu Fang
2025-04-07 08:06:27 -07:00
committed by GitHub
parent 8017c8db7f
commit 55dcce91df
43 changed files with 2436 additions and 155 deletions
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65
vllm/model_executor/layers/fused_moe/layer.py
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@@ -65,7 +65,9 @@ class FusedMoEMethodBase(QuantizeMethodBase):
expert_map: Optional[torch.Tensor] = None,
custom_routing_function: Optional[Callable] = None,
scoring_func: str = "softmax",
e_score_correction_bias: Optional[torch.Tensor] = None
e_score_correction_bias: Optional[torch.Tensor] = None,
apply_router_weight_on_input: bool = False,
activation: str = "silu",
) -> torch.Tensor:
raise NotImplementedError
@@ -156,22 +158,25 @@ class UnquantizedFusedMoEMethod(FusedMoEMethodBase, CustomOp):
custom_routing_function: Optional[Callable] = None,
scoring_func: str = "softmax",
e_score_correction_bias: Optional[torch.Tensor] = None,
apply_router_weight_on_input: bool = False,
activation: str = "silu",
) -> torch.Tensor:
return self.forward(x=x,
layer=layer,
router_logits=router_logits,
top_k=top_k,
renormalize=renormalize,
use_grouped_topk=use_grouped_topk,
topk_group=topk_group,
num_expert_group=num_expert_group,
global_num_experts=global_num_experts,
expert_map=expert_map,
custom_routing_function=custom_routing_function,
scoring_func=scoring_func,
e_score_correction_bias=e_score_correction_bias,
activation=activation)
return self.forward(
x=x,
layer=layer,
router_logits=router_logits,
top_k=top_k,
renormalize=renormalize,
use_grouped_topk=use_grouped_topk,
topk_group=topk_group,
num_expert_group=num_expert_group,
global_num_experts=global_num_experts,
expert_map=expert_map,
custom_routing_function=custom_routing_function,
scoring_func=scoring_func,
e_score_correction_bias=e_score_correction_bias,
activation=activation,
apply_router_weight_on_input=apply_router_weight_on_input)
def forward_cuda(
self,
@@ -188,6 +193,7 @@ class UnquantizedFusedMoEMethod(FusedMoEMethodBase, CustomOp):
custom_routing_function: Optional[Callable] = None,
scoring_func: str = "softmax",
e_score_correction_bias: Optional[torch.Tensor] = None,
apply_router_weight_on_input: bool = False,
activation: str = "silu",
) -> torch.Tensor:
topk_weights, topk_ids = FusedMoE.select_experts(
@@ -202,15 +208,17 @@ class UnquantizedFusedMoEMethod(FusedMoEMethodBase, CustomOp):
scoring_func=scoring_func,
e_score_correction_bias=e_score_correction_bias)
return fused_experts(hidden_states=x,
w1=layer.w13_weight,
w2=layer.w2_weight,
topk_weights=topk_weights,
topk_ids=topk_ids,
inplace=True,
activation=activation,
global_num_experts=global_num_experts,
expert_map=expert_map)
return fused_experts(
hidden_states=x,
w1=layer.w13_weight,
w2=layer.w2_weight,
topk_weights=topk_weights,
topk_ids=topk_ids,
inplace=True,
activation=activation,
apply_router_weight_on_input=apply_router_weight_on_input,
global_num_experts=global_num_experts,
expert_map=expert_map)
def forward_cpu(
self,
@@ -228,9 +236,11 @@ class UnquantizedFusedMoEMethod(FusedMoEMethodBase, CustomOp):
scoring_func: str = "softmax",
e_score_correction_bias: Optional[torch.Tensor] = None,
activation: str = "silu",
apply_router_weight_on_input: bool = False,
**kwargs,
):
assert activation == "silu", f"{activation} is not supported."
assert apply_router_weight_on_input is False
return layer.ipex_fusion(
x,
use_grouped_topk,
@@ -259,6 +269,7 @@ class UnquantizedFusedMoEMethod(FusedMoEMethodBase, CustomOp):
custom_routing_function: Optional[Callable] = None,
scoring_func: str = "softmax",
e_score_correction_bias: Optional[torch.Tensor] = None,
apply_router_weight_on_input: bool = False,
activation: str = "silu",
) -> torch.Tensor:
assert not use_grouped_topk
@@ -266,6 +277,7 @@ class UnquantizedFusedMoEMethod(FusedMoEMethodBase, CustomOp):
assert topk_group is None
assert custom_routing_function is None
assert layer is not None
assert apply_router_weight_on_input is False
if scoring_func != "softmax":
raise NotImplementedError(
"Only softmax scoring function is supported for HPU.")
@@ -290,12 +302,14 @@ class UnquantizedFusedMoEMethod(FusedMoEMethodBase, CustomOp):
custom_routing_function: Optional[Callable] = None,
scoring_func: str = "softmax",
e_score_correction_bias: Optional[torch.Tensor] = None,
apply_router_weight_on_input: bool = False,
activation: str = "silu",
) -> torch.Tensor:
assert not use_grouped_topk
assert num_expert_group is None
assert topk_group is None
assert custom_routing_function is None
assert apply_router_weight_on_input is False
if scoring_func != "softmax":
raise NotImplementedError(
"Only softmax scoring function is supported for TPU.")
@@ -401,6 +415,7 @@ class FusedMoE(torch.nn.Module):
custom_routing_function: Optional[Callable] = None,
scoring_func: str = "softmax",
e_score_correction_bias: Optional[torch.Tensor] = None,
apply_router_weight_on_input: bool = False,
activation: str = "silu",
):
super().__init__()
@@ -486,6 +501,7 @@ class FusedMoE(torch.nn.Module):
self.quant_method = quant_config.get_quant_method(self, prefix)
assert self.quant_method is not None
self.apply_router_weight_on_input = apply_router_weight_on_input
moe_quant_params = {
"num_experts": self.local_num_experts,
"hidden_size": hidden_size,
@@ -853,6 +869,7 @@ class FusedMoE(torch.nn.Module):
scoring_func=self.scoring_func,
e_score_correction_bias=self.e_score_correction_bias,
activation=self.activation,
apply_router_weight_on_input=self.apply_router_weight_on_input,
)
if self.dp_size > 1: