[1/N][Feat] Xlite Qwen3 MoE Support (#5951)
### What this PR does / why we need it? This patch adds support for the Qwen3-MoE model in Xlite. For more details about Xlite, please refer to the following link:https://atomgit.com/openeuler/GVirt/blob/master/xlite/README.md. Qwen3-MoE TODO List: - [ ] Qwen3-235B-A22B support - [ ] Qwen3-MoE weights NZ support - [ ] Qwen3-MoE data parallel support ## Qwen3-30B-A3B-Instruct-2507 910B3(A2) Online Inference Performance Comparison - aclgraph: main(69b170b8b5) - xlite-full: main + xlite-full - xlite-decode-only: main + xlite-decode-only - diff1: Performance comparison between xlite-full and aclgraph - diff2: Performance comparison between xlite-decode-only and aclgraph | maxconcurrency | item | TTFT(ms) | | TPOT(ms) | | QPS (req/s) | OutputSpeed (token/s) | | --- | --- | --- | --- | --- | --- | --- | --- | | | | Avg | P99 | Avg | P99 | | | | 1 | baseline-aclgraph | 205.07 | 287.29 | 12.34 | 12.65 | 0.14 | 78.81 | | 1 | xlite-full | 66.40 | 113.69 | 11.71 | 12.40 | 0.15 | 84.73 | | 1 | xlite-decode-only | 221.15 | 316.40 | 12.16 | 12.91 | 0.14 | 79.70 | | 1 | diff1 | -67.62% | -60.43% | -5.11% | -1.98% | 7.14% | 7.51% | | 1 | diff2 | 7.84% | 10.13% | -1.46% | 2.06% | 0.00% | 1.13% | | | | | | | | | | | 16 | baseline-aclgraph | 1892.16 | 13916.86 | 22.78 | 39.28 | 1.15 | 589.89 | | 16 | xlite-full | 1355.40 | 8907.45 | 15.96 | 25.15 | 1.65 | 850.21 | | 16 | xlite-decode-only | 1519.42 | 8711.64 | 19.23 | 29.73 | 1.38 | 711.60 | | 16 | diff1 | -28.37% | -36.00% | -29.94% | -35.97% | 43.48% | 44.13% | | 16 | diff2 | -19.70% | -37.40% | -15.58% | -24.31% | 20.00% | 20.63% | | | | | | | | | | | 32 | baseline-aclgraph | 673.80 | 3914.90 | 32.20 | 37.95 | 1.80 | 928.54 | | 32 | xlite-full | 481.65 | 2710.50 | 19.95 | 25.35 | 2.91 | 1506.67 | | 32 | xlite-decode-only | 372.22 | 1095.25 | 25.19 | 28.47 | 2.33 | 1202.82 | | 32 | diff1 | -28.52% | -30.76% | -38.04% | -33.20% | 61.67% | 62.26% | | 32 | diff2 | -44.76% | -72.02% | -21.77% | -24.98% | 29.44% | 29.54% | | | | | | | | | | | 48 | baseline-aclgraph | 583.18 | 3277.65 | 41.02 | 46.05 | 2.17 | 1115.08 | | 48 | xlite-full | 973.42 | 8237.33 | 23.29 | 30.50 | 3.71 | 1908.09 | | 48 | xlite-decode-only | 480.79 | 2026.98 | 31.48 | 35.41 | 2.83 | 1453.75 | | 48 | diff1 | 66.92% | 151.32% | -43.22% | -33.77% | 70.97% | 71.12% | | 48 | diff2 | -17.56% | -38.16% | -23.26% | -23.11% | 30.41% | 30.37% | | | | | | | | | | | 64 | baseline-aclgraph | 742.74 | 5953.39 | 47.79 | 53.15 | 2.48 | 1272.37 | | 64 | xlite-full | 545.22 | 3941.34 | 25.09 | 30.41 | 4.64 | 2376.44 | | 64 | xlite-decode-only | 752.40 | 4534.29 | 38.67 | 43.28 | 3.06 | 1567.94 | | 64 | diff1 | -26.59% | -33.80% | -47.50% | -42.78% | 87.10% | 86.77% | | 64 | diff2 | 1.30% | -23.84% | -19.08% | -18.57% | 23.39% | 23.23% | | | | | | | | | | | 100 | baseline-aclgraph | 565.52 | 1716.81 | 60.89 | 68.69 | 3.08 | 1580.64 | | 100 | xlite-full | 398.14 | 2328.88 | 30.70 | 32.45 | 6.01 | 3086.42 | | 100 | xlite-decode-only | 712.53 | 4875.94 | 52.71 | 60.78 | 3.53 | 1813.58 | | 100 | diff1 | -29.60% | 35.65% | -49.58% | -52.76% | 95.13% | 95.26% | | 100 | diff2 | 26.00% | 184.01% | -13.43% | -11.52% | 14.61% | 14.74% | | | | | | | | | | | 150 | baseline-aclgraph | 842.42 | 5175.01 | 73.60 | 88.18 | 3.80 | 1952.26 | | 150 | xlite-full | 568.52 | 4204.33 | 37.90 | 40.01 | 7.27 | 3734.72 | | 150 | xlite-decode-only | 654.43 | 2504.06 | 67.40 | 77.00 | 4.18 | 2145.11 | | 150 | diff1 | -32.51% | -18.76% | -48.51% | -54.63% | 91.32% | 91.30% | | 150 | diff2 | -22.32% | -51.61% | -8.42% | -12.68% | 10.00% | 9.88% | | | | | | | | | | | 200 | baseline-aclgraph | 750.63 | 3049.91 | 88.26 | 101.95 | 4.28 | 2189.72 | | 200 | xlite-full | 558.48 | 3791.98 | 45.54 | 49.04 | 8.17 | 4175.52 | | 200 | xlite-decode-only | 807.09 | 4254.95 | 85.18 | 101.79 | 4.44 | 2271.52 | | 200 | diff1 | -25.60% | 24.33% | -48.40% | -51.90% | 90.89% | 90.69% | | 200 | diff2 | 7.52% | 39.51% | -3.49% | -0.16% | 3.74% | 3.74% | | | | | | | | | | ### How was this patch tested? - vLLM version: v0.13.0 - vLLM main:2c24bc6996--------- Signed-off-by: changdawei1 <changdawei3@huawei.com> Co-authored-by: LVYANGGUO <275926687@qq.com> Co-authored-by: lulina <lina.lulina@huawei.com>
This commit is contained in:
Magnus
@@ -19,12 +19,14 @@ from typing import Any, Callable, Tuple
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import torch
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import torch.nn as nn
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from vllm.config import VllmConfig
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from vllm.distributed import (get_tensor_model_parallel_world_size,
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from vllm.distributed import (get_ep_group,
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get_tensor_model_parallel_world_size,
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get_world_group)
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from vllm.forward_context import get_forward_context
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from vllm.logger import logger
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from vllm.sequence import IntermediateTensors
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from xlite._C import AttnMHA, Model, ModelAttnMeta, ModelConfig, Runtime
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from xlite._C import (AttnMHA, Model, ModelAttnMeta, ModelConfig, Runtime,
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ScoringFuncSoftmax)
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import vllm_ascend.envs as envs_ascend
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from vllm_ascend.ascend_config import get_ascend_config
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@@ -47,67 +49,8 @@ class LlamaXliteModel(XliteModel):
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self, runnable: nn.Module,
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vllm_config: VllmConfig) -> Tuple[Model, int, int, torch.dtype]:
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dtype = vllm_config.model_config.dtype
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params_dict = dict(runnable.named_parameters())
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if hasattr(runnable, "language_model"):
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layers = runnable.language_model.model.layers
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model_prefix = "language_model."
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else:
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layers = runnable.model.layers
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model_prefix = ""
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config = self._build_model_config(vllm_config)
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xlite_model = Model()
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xlite_model.embed = params_dict.get(model_prefix +
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"model.embed_tokens.weight")
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xlite_model.norm = params_dict.get(model_prefix + "model.norm.weight")
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if vllm_config.model_config.hf_text_config.tie_word_embeddings:
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xlite_model.head = xlite_model.embed
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else:
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xlite_model.head = params_dict.get(model_prefix + "lm_head.weight")
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xlite_model.attn_norm = [
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layer.input_layernorm.weight for layer in layers
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]
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xlite_model.attn_out = [
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layer.self_attn.o_proj.weight for layer in layers
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]
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xlite_model.mha_qkv = [
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layer.self_attn.qkv_proj.weight for layer in layers
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]
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xlite_model.mlp_norm = [
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layer.post_attention_layernorm.weight for layer in layers
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]
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xlite_model.mlp_up_gate = [
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layer.mlp.gate_up_proj.weight for layer in layers
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]
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xlite_model.mlp_down = [layer.mlp.down_proj.weight for layer in layers]
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mha_qkv_bias = [
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layer.self_attn.qkv_proj.bias for layer in layers
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if hasattr(layer.self_attn.qkv_proj, "bias")
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and layer.self_attn.qkv_proj.bias is not None
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]
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q_norm = [
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layer.self_attn.q_norm.weight for layer in layers
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if hasattr(layer.self_attn, "q_norm")
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]
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k_norm = [
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layer.self_attn.k_norm.weight for layer in layers
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if hasattr(layer.self_attn, "k_norm")
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]
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if len(mha_qkv_bias) != config.n_layers:
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config.qkv_bias = False
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else:
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config.qkv_bias = True
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xlite_model.mha_qkv_bias = mha_qkv_bias
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if (len(q_norm) != config.n_layers or len(k_norm) != config.n_layers):
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config.qk_norm = False
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else:
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config.qk_norm = True
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xlite_model.mha_q_norm = q_norm
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xlite_model.mha_k_norm = k_norm
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xlite_model = self._build_model(runnable, vllm_config, config)
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rank = torch.distributed.get_rank()
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xlite_model.init(config, rank)
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@@ -153,6 +96,76 @@ class LlamaXliteModel(XliteModel):
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config.block_size = vllm_config.cache_config.block_size
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return config
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def _build_model(self, runnable: nn.Module, vllm_config: VllmConfig,
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config: ModelConfig) -> Model:
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params_dict = dict(runnable.named_parameters())
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if hasattr(runnable, "language_model"):
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layers = runnable.language_model.model.layers
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model_prefix = "language_model."
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else:
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layers = runnable.model.layers
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model_prefix = ""
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xlite_model = Model()
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xlite_model.embed = params_dict.get(model_prefix +
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"model.embed_tokens.weight")
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xlite_model.norm = params_dict.get(model_prefix + "model.norm.weight")
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if vllm_config.model_config.hf_text_config.tie_word_embeddings:
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xlite_model.head = xlite_model.embed
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else:
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xlite_model.head = params_dict.get(model_prefix + "lm_head.weight")
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xlite_model.attn_norm = [
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layer.input_layernorm.weight for layer in layers
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]
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xlite_model.attn_out = [
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layer.self_attn.o_proj.weight for layer in layers
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]
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xlite_model.mha_qkv = [
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layer.self_attn.qkv_proj.weight for layer in layers
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]
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xlite_model.mlp_norm = [
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layer.post_attention_layernorm.weight for layer in layers
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]
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xlite_model.mlp_up_gate = [
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layer.mlp.gate_up_proj.weight for layer in layers
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if hasattr(layer.mlp, "gate_up_proj")
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and layer.mlp.gate_up_proj.weight is not None
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]
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xlite_model.mlp_down = [
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layer.mlp.down_proj.weight for layer in layers
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if hasattr(layer.mlp, "down_proj")
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and layer.mlp.down_proj.weight is not None
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]
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mha_qkv_bias = [
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layer.self_attn.qkv_proj.bias for layer in layers
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if hasattr(layer.self_attn.qkv_proj, "bias")
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and layer.self_attn.qkv_proj.bias is not None
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]
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q_norm = [
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layer.self_attn.q_norm.weight for layer in layers
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if hasattr(layer.self_attn, "q_norm")
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]
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k_norm = [
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layer.self_attn.k_norm.weight for layer in layers
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if hasattr(layer.self_attn, "k_norm")
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]
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if len(mha_qkv_bias) != config.n_layers:
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config.qkv_bias = False
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else:
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config.qkv_bias = True
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xlite_model.mha_qkv_bias = mha_qkv_bias
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if (len(q_norm) != config.n_layers or len(k_norm) != config.n_layers):
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config.qk_norm = False
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else:
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config.qk_norm = True
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xlite_model.mha_q_norm = q_norm
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xlite_model.mha_k_norm = k_norm
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return xlite_model
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def _precompute_freqs_cis(self,
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dim: int,
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end: int,
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@@ -168,6 +181,62 @@ class LlamaXliteModel(XliteModel):
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return freq_cis.to(device='npu')
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class QwenMoeXliteModel(LlamaXliteModel):
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def initialize(
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self, runnable: nn.Module,
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vllm_config: VllmConfig) -> Tuple[Model, int, int, torch.dtype]:
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if envs_ascend.VLLM_ASCEND_ENABLE_NZ == 2:
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architecture = vllm_config.model_config.architectures[0]
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raise ValueError(
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f"{architecture} not support VLLM_ASCEND_ENABLE_NZ = 2!")
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dtype = vllm_config.model_config.dtype
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config = self._build_model_config(vllm_config)
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xlite_model = self._build_model(runnable, vllm_config, config)
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rank = torch.distributed.get_rank()
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xlite_model.init(config, rank)
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freq_cis = super()._precompute_freqs_cis(config.head_dim,
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config.max_seq_len, dtype,
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config.rope_theta)
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return (xlite_model, freq_cis, config.hidden_size, dtype)
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def _build_model_config(self, vllm_config: VllmConfig) -> ModelConfig:
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config = super()._build_model_config(vllm_config)
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hf_config = vllm_config.model_config.hf_text_config
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ep_group = get_ep_group()
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config.n_layers = hf_config.max_window_layers
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config.n_dense_layers = 0
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config.n_routed_experts = hf_config.num_experts
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config.n_shared_experts = 0
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config.n_act_experts = hf_config.num_experts_per_tok
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config.def_dp_size = vllm_config.parallel_config.data_parallel_size
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config.moe_ep_size = ep_group.world_size if vllm_config.parallel_config.enable_expert_parallel else 1
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config.moe_tp_size = 1 if vllm_config.parallel_config.enable_expert_parallel else ep_group.world_size
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config.experts_weight_transpose = True
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config.moe_intermediate_size = hf_config.moe_intermediate_size
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config.norm_topk_prob = hf_config.norm_topk_prob
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config.scoring_func = ScoringFuncSoftmax
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return config
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def _build_model(self, runnable: nn.Module, vllm_config: VllmConfig,
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config: ModelConfig) -> Model:
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xlite_model = super()._build_model(runnable, vllm_config, config)
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layers = runnable.model.layers
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xlite_model.gate = [layer.mlp.gate.weight for layer in layers]
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xlite_model.re_up_gate = [
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layer.mlp.experts.w13_weight[i] for layer in layers
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for i in range(layer.mlp.experts.local_num_experts)
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]
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xlite_model.re_down = [
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layer.mlp.experts.w2_weight[i] for layer in layers
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for i in range(layer.mlp.experts.local_num_experts)
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]
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return xlite_model
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def xlite_model_init(
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runnable: nn.Module,
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vllm_config: VllmConfig) -> Tuple[Model, int, int, torch.dtype]:
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@@ -176,6 +245,7 @@ def xlite_model_init(
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"Qwen2ForCausalLM": LlamaXliteModel,
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"Qwen3ForCausalLM": LlamaXliteModel,
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"Qwen3VLForConditionalGeneration": LlamaXliteModel,
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"Qwen3MoeForCausalLM": QwenMoeXliteModel,
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}
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architecture = vllm_config.model_config.architectures[0]
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@@ -197,7 +267,8 @@ class XliteWrapper:
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rank = torch.distributed.get_rank()
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local_rank = get_world_group().local_rank
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self.xlite_rt = Runtime(local_rank, 0, rank,
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get_tensor_model_parallel_world_size())
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get_tensor_model_parallel_world_size(),
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vllm_config.parallel_config.data_parallel_size)
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(self.xlite_model, self.freq_cis, hidden_size,
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dtype) = xlite_model_init(runnable, vllm_config)
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