0cc3fc357f
### What this PR does / why we need it?
qwen3_next add fused_sigmoid_gating_delta_rule_update op which fused
fused_gdn_gating+fused_recurrent_gated_delta_rule
- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c
Signed-off-by: wangxiaoxin-sherie <wangxiaoxin7@huawei.com>
Co-authored-by: wangxiaoxin-sherie <wangxiaoxin7@huawei.com>
66 lines
2.5 KiB
Python
66 lines
2.5 KiB
Python
import torch
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from vllm.model_executor.layers.fla.ops import fused_recurrent_gated_delta_rule
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from vllm.model_executor.models.qwen3_next import fused_gdn_gating
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from vllm_ascend.ops.triton.fla.sigmoid_gating import \
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fused_sigmoid_gating_delta_rule_update
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def test_triton_fusion_ops():
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q = torch.randn(1, 1, 4, 128, dtype=torch.bfloat16).npu()
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k = torch.randn(1, 1, 4, 128, dtype=torch.bfloat16).npu()
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v = torch.randn(1, 1, 8, 128, dtype=torch.bfloat16).npu()
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a = torch.tensor([[
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-2.6094, -0.2617, -0.3848, 2.2656, 3.6250, -0.7383, -1.0938, -0.0505
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]]).bfloat16().npu()
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b = torch.tensor(
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[[0.4277, 0.8906, 1.6875, 2.3750, 4.1562, 0.3809, 1.0625,
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3.6719]]).bfloat16().npu()
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ssm_state = torch.randn(1, 8, 128, 128, dtype=torch.bfloat16).npu()
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non_spec_state_indices_tensor = torch.tensor([2]).int().npu()
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non_spec_query_start_loc = torch.tensor([0, 1]).int().npu()
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a_log = torch.tensor([
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-2.6875, -3.2031, -3.3438, -2.7812, -3.0625, -4.0312, -5.3750, 5.7188
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]).bfloat16().npu()
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dt_bias = torch.tensor(
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[-4.7812, -5.0938, -5.5000, 9.4375, 7.6250, -4.3750, -3.0938,
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0.9688]).bfloat16().npu()
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core_attn_out_non_spec_fused = fused_sigmoid_gating_delta_rule_update(
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A_log=a_log.contiguous(),
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dt_bias=dt_bias.contiguous(),
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q=q.contiguous(),
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k=k.contiguous(),
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v=v.contiguous(),
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a=a.contiguous(),
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b=b.contiguous(),
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initial_state_source=ssm_state,
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initial_state_indices=non_spec_state_indices_tensor,
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cu_seqlens=non_spec_query_start_loc,
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use_qk_l2norm_in_kernel=True,
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softplus_beta=1.0,
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softplus_threshold=20.0,
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)
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g, beta = fused_gdn_gating(a_log, a, b, dt_bias)
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g_non_spec = g
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beta_non_spec = beta
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core_attn_out_non_spec_split, last_recurrent_state = (
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fused_recurrent_gated_delta_rule(
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q=q,
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k=k,
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v=v,
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g=g_non_spec,
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beta=beta_non_spec,
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initial_state=ssm_state,
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inplace_final_state=True,
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cu_seqlens=non_spec_query_start_loc,
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ssm_state_indices=non_spec_state_indices_tensor,
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use_qk_l2norm_in_kernel=True,
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))
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torch.testing.assert_close(core_attn_out_non_spec_fused,
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core_attn_out_non_spec_split,
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rtol=1e-02,
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atol=1e-02,
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equal_nan=True)
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