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init v0.11.0rc0

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luopingyi
2025-10-14 10:38:28 +08:00
parent 67afd0ea78
commit 66dc16f966
278 changed files with 28130 additions and 11708 deletions
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35
vllm_ascend/torchair/quantization/torchair_w8a8_dynamic.py
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@@ -23,7 +23,6 @@ import torch_npu
from vllm.distributed import GroupCoordinator, get_ep_group
from vllm.forward_context import get_forward_context
import vllm_ascend.envs as envs_ascend
from vllm_ascend.ascend_config import get_ascend_config
from vllm_ascend.ascend_forward_context import FusedMoEState
from vllm_ascend.distributed.parallel_state import get_mc2_group
@@ -417,6 +416,7 @@ def torchair_fused_experts_with_all2all(
num_experts = w1.shape[0]
if expert_map is not None:
assert ep_group is not None, "ep_group must be provided when expert_map is given"
global_num_experts = len(expert_map) + global_redundant_expert_num
if hasattr(torch_npu, "npu_moe_init_routing_quant"):
quantized_tokens, expanded_row_idx, global_expert_tokens, _, token_scales = torch_npu.npu_moe_init_routing_quant(
@@ -436,8 +436,9 @@ def torchair_fused_experts_with_all2all(
gather_sizes = global_expert_tokens.new_empty(
global_expert_tokens.shape[0])
dist.all_to_all_single(gather_sizes, global_expert_tokens)
dist.all_to_all_single(gather_sizes,
global_expert_tokens,
group=ep_group.device_group)
token_counts_combined = torch.stack(
[gather_sizes, global_expert_tokens], dim=0)
token_counts_combined = token_counts_combined.view(
@@ -452,10 +453,16 @@ def torchair_fused_experts_with_all2all(
gather_size_list = token_counts_combined_cpu[1]
scatter_size_list = token_counts_combined_cpu[0]
dist.all_to_all_single(gathered_tokens, quantized_tokens,
scatter_size_list, gather_size_list)
dist.all_to_all_single(dynamic_scale, token_scales, scatter_size_list,
gather_size_list)
dist.all_to_all_single(gathered_tokens,
quantized_tokens,
scatter_size_list,
gather_size_list,
group=ep_group.device_group)
dist.all_to_all_single(dynamic_scale,
token_scales,
scatter_size_list,
gather_size_list,
group=ep_group.device_group)
hidden_states, dynamic_scale, inverse_indices, expert_tokens = torch_npu.npu_moe_re_routing(
gathered_tokens,
@@ -503,9 +510,11 @@ def torchair_fused_experts_with_all2all(
index=inverse_indices.to(torch.float32).argsort().to(torch.int32))
hidden_states = reordered_outputs.new_empty(*quantized_tokens.shape)
dist.all_to_all_single(hidden_states, reordered_outputs,
gather_size_list, scatter_size_list)
dist.all_to_all_single(hidden_states,
reordered_outputs,
gather_size_list,
scatter_size_list,
group=ep_group.device_group)
final_hidden_states = torch_npu.npu_moe_finalize_routing(
hidden_states,
skip1=None,
@@ -824,6 +833,7 @@ class TorchairAscendW8A8DynamicFusedMoEMethod:
ascend_config = get_ascend_config()
self.torchair_graph_enabled = ascend_config.torchair_graph_config.enabled
self.enable_shared_expert_dp = ascend_config.enable_shared_expert_dp
try:
device_group = get_mc2_group().device_group
@@ -937,6 +947,8 @@ class TorchairAscendW8A8DynamicFusedMoEMethod:
)
fused_moe_state = get_forward_context().fused_moe_state
if self.enable_shared_expert_dp and fused_moe_state == FusedMoEState.MC2:
fused_moe_state = FusedMoEState.All2All
shared_gate_up, shared_dequant_scale = None, None
if shared_experts is not None and fused_moe_state == FusedMoEState.MC2:
with npu_stream_switch("moe_secondary", 0):
@@ -1021,8 +1033,7 @@ class TorchairAscendW8A8DynamicFusedMoEMethod:
1, 2).contiguous()
layer.w2_weight.data = layer.w2_weight.data.transpose(
1, 2).contiguous()
if envs_ascend.VLLM_ENABLE_FUSED_EXPERTS_ALLGATHER_EP:
torch_npu.npu_format_cast_(layer.w2_weight, ACL_FORMAT_FRACTAL_NZ)
torch_npu.npu_format_cast_(layer.w2_weight, ACL_FORMAT_FRACTAL_NZ)
layer.w13_weight_scale.data = layer.w13_weight_scale.data.view(
layer.w13_weight_scale.data.shape[0], -1)
layer.w13_weight_scale_fp32 = layer.w13_weight_scale.data.to(