455 lines
21 KiB
Python
455 lines
21 KiB
Python
#
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# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
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# This file is a part of the vllm-ascend project.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import torch
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from einops import rearrange
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from vllm.distributed import get_pcp_group
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from vllm.forward_context import get_forward_context
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from vllm.model_executor.layers.fla.ops.l2norm import l2norm_fwd
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from vllm.model_executor.layers.mamba.gdn.base import GatedDeltaNetAttention
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from vllm.model_executor.layers.mamba.mamba_utils import MambaStateShapeCalculator
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from vllm.triton_utils import triton
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from vllm.v1.attention.backend import AttentionBackend, AttentionMetadata # type: ignore
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from vllm.v1.attention.backends.gdn_attn import GDNAttentionMetadata
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from vllm.v1.attention.backends.utils import PAD_SLOT_ID
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from vllm_ascend.attention.utils import maybe_save_kv_layer_to_connector
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from vllm_ascend.device.device_op import DeviceOperator
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from vllm_ascend.ops.gdn_attn_builder import AscendGDNAttentionBackend
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from vllm_ascend.ops.triton.fla.chunk import chunk_gated_delta_rule
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from vllm_ascend.ops.triton.fla.fused_qkvzba_split_reshape import fused_qkvzba_split_reshape_cat
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from vllm_ascend.ops.triton.fla.utils import clear_ssm_states
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from vllm_ascend.ops.triton.mamba.causal_conv1d import extract_last_width
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class AscendGatedDeltaNetAttention(GatedDeltaNetAttention):
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def _split_ba_for_tp(self, ba: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
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if hasattr(self, "split_ba"):
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return self.split_ba(ba)
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return ba.chunk(2, dim=-1)
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def get_state_shape(
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self,
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) -> tuple[tuple[int, ...], tuple[int, ...], tuple[int, ...], tuple[int, ...]]:
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return MambaStateShapeCalculator.gated_delta_net_state_shape(
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self.tp_size,
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self.num_k_heads,
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self.num_v_heads,
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self.head_k_dim,
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self.head_v_dim,
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self.conv_kernel_size,
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self.num_spec,
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)
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def _warmup_prefill_kernels(self, qkv_or_qkvz: torch.Tensor, v_dim: int) -> None:
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return
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def _warmup_prefill_kernels_v0202(self, mixed_qkv: torch.Tensor) -> None:
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return
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def get_attn_backend(self) -> type[AttentionBackend]:
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return AscendGDNAttentionBackend
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def forward(
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self,
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hidden_states: torch.Tensor,
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output: torch.Tensor,
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):
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"""
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Forward pass with three parts:
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1. Input projection
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2. Core attention (custom op)
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3. Output projection
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"""
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num_tokens = hidden_states.size(0)
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if hasattr(self, "in_proj_qkv"):
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mixed_qkv, _ = self.in_proj_qkv(hidden_states)
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ba, _ = self.in_proj_ba(hidden_states)
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z, _ = self.in_proj_z(hidden_states)
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z = z.reshape(z.size(0), -1, self.head_v_dim)
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b, a = self._split_ba_for_tp(ba)
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b = b.contiguous()
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a = a.contiguous()
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else:
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if not self.gqa_interleaved_layout:
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mixed_qkvz, _ = self.in_proj_qkvz(hidden_states)
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num_tokens = mixed_qkvz.size(0)
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qkv_size = (self.key_dim * 2 + self.value_dim) // self.tp_size
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z_size = self.value_dim // self.tp_size
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mixed_qkv, z = mixed_qkvz.split([qkv_size, z_size], dim=-1)
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z = z.reshape(z.size(0), -1, self.head_v_dim)
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ba, _ = self.in_proj_ba(hidden_states)
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b, a = self._split_ba_for_tp(ba)
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b = b.contiguous()
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a = a.contiguous()
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else:
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projected_states_qkvz, _ = self.in_proj_qkvz(hidden_states)
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projected_states_ba, _ = self.in_proj_ba(hidden_states)
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num_tokens = projected_states_qkvz.size(0)
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mixed_qkv, z, b, a = fused_qkvzba_split_reshape_cat(
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projected_states_qkvz,
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projected_states_ba,
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triton.cdiv(self.num_k_heads, self.tp_size),
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triton.cdiv(self.num_v_heads, self.tp_size),
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self.head_k_dim,
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self.head_v_dim,
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)
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# ============================================================
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# Part 2: Core Attention (Custom Op)
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# ============================================================
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# Note: we should not use torch.empty here like other attention backends,
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# see discussions in https://github.com/vllm-project/vllm/pull/28182
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core_attn_out = torch.zeros(
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(num_tokens, self.num_v_heads // self.tp_size, self.head_v_dim),
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dtype=hidden_states.dtype,
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device=hidden_states.device,
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)
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torch.ops.vllm.qwen_gdn_attention_core(
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mixed_qkv,
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b,
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a,
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core_attn_out,
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self.prefix,
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False,
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)
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# ============================================================
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# Part 3: Output Projection
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# ============================================================
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maybe_save_kv_layer_to_connector("", [])
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z_shape_og = z.shape
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# Reshape input data into 2D tensor
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core_attn_out = core_attn_out.reshape(-1, core_attn_out.shape[-1])
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z = z.reshape(-1, z.shape[-1])
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core_attn_out = self.norm(core_attn_out, z)
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core_attn_out = core_attn_out.reshape(z_shape_og)
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core_attn_out = rearrange(core_attn_out, "... h d -> ... (h d)")
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output[:num_tokens], _ = self.out_proj(core_attn_out)
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def _forward_core(
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self,
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mixed_qkv: torch.Tensor,
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b: torch.Tensor,
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a: torch.Tensor,
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core_attn_out: torch.Tensor,
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):
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"""
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Core attention computation (called by custom op).
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"""
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forward_context = get_forward_context()
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attn_metadata: AttentionMetadata = forward_context.attn_metadata
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if attn_metadata is None:
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# V1 profile run
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return
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assert isinstance(attn_metadata, dict)
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attn_metadata = attn_metadata[self.prefix]
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assert isinstance(attn_metadata, GDNAttentionMetadata)
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spec_sequence_masks = attn_metadata.spec_sequence_masks
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spec_token_indx = attn_metadata.spec_token_indx
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non_spec_token_indx = attn_metadata.non_spec_token_indx
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spec_state_indices_tensor = attn_metadata.spec_state_indices_tensor # noqa: E501
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non_spec_state_indices_tensor = attn_metadata.non_spec_state_indices_tensor # noqa: E501
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self_kv_cache = self.kv_cache
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ssm_state = self_kv_cache[1]
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num_actual_tokens = attn_metadata.num_actual_tokens
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mixed_qkv = mixed_qkv[:num_actual_tokens]
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b = b[:num_actual_tokens]
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a = a[:num_actual_tokens]
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# 1. Convolution sequence transformation
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conv_weights = self.conv1d.weight.view(self.conv1d.weight.size(0), self.conv1d.weight.size(2))
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if spec_sequence_masks is not None:
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if attn_metadata.num_prefills == 0 and attn_metadata.num_decodes == 0:
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mixed_qkv_spec = mixed_qkv
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mixed_qkv_non_spec = None
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else:
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mixed_qkv_spec = mixed_qkv.index_select(0, spec_token_indx)
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mixed_qkv_non_spec = mixed_qkv.index_select(0, non_spec_token_indx)
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else:
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mixed_qkv_spec = None
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mixed_qkv_non_spec = mixed_qkv
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# 1.1: Process the multi-query part
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if spec_sequence_masks is not None:
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conv_weights_T = conv_weights.transpose(0, 1)
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activation_num = 1 if self.activation else 0
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spec_causal_conv1d_meta = attn_metadata.spec_decode_metadata.spec_causal_conv1d
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spec_query_start_loc_device = spec_causal_conv1d_meta.query_start_loc
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output_spec = torch.empty_like(mixed_qkv_spec)
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torch.ops._C_ascend.npu_causal_conv1d_custom(
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output_spec,
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mixed_qkv_spec,
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conv_weights_T,
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conv_state=self_kv_cache[0],
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bias_opt=self.conv1d.bias,
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query_start_loc_opt=spec_query_start_loc_device,
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cache_indices_opt=spec_causal_conv1d_meta.cache_indices,
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initial_state_mode_opt=None,
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num_accepted_tokens_opt=spec_causal_conv1d_meta.num_accepted_tokens,
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activation_mode=activation_num,
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pad_slot_id=PAD_SLOT_ID,
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run_mode=1,
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)
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mixed_qkv_spec = output_spec
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# 1.2: Process the remaining part
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if attn_metadata.num_prefills > 0:
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if mixed_qkv_non_spec is not None:
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non_spec_causal_conv1d_meta = attn_metadata.non_spec_prefill_metadata.causal_conv1d
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query_start_loc_opt = non_spec_causal_conv1d_meta.query_start_loc
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cache_indices_opt = non_spec_causal_conv1d_meta.cache_indices
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initial_state_mode_opt = non_spec_causal_conv1d_meta.initial_state_mode
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if get_pcp_group().world_size > 1:
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conv_weights_T = conv_weights.transpose(0, 1)
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activation_num = 1 if self.activation else 0
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non_spec_query_start_loc = attn_metadata.non_spec_query_start_loc
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assert non_spec_query_start_loc is not None
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non_spec_state_indices_tensor = attn_metadata.non_spec_state_indices_tensor
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width = conv_weights.shape[1]
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state_len = width - 1
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num_seqs = non_spec_query_start_loc.shape[0] - 1
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prefill_seq_offset = max(0, num_seqs - attn_metadata.num_prefills)
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prefill_cache_indices = non_spec_state_indices_tensor[prefill_seq_offset:]
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mixed_qkv_non_spec_T = mixed_qkv_non_spec.transpose(0, 1)
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last_width_prefill_x = extract_last_width(
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mixed_qkv_non_spec_T, non_spec_query_start_loc[prefill_seq_offset:], state_len
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)
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pcp_rank = get_pcp_group().rank_in_group
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all_last_width_prefill_x = get_pcp_group().all_gather(
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last_width_prefill_x.unsqueeze(0).contiguous(), 0
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)
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if pcp_rank > 0 and prefill_cache_indices.shape[0] > 0:
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self_kv_cache[0][prefill_cache_indices, :state_len, :] = all_last_width_prefill_x[
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pcp_rank - 1, ...
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].transpose(-1, -2)
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mixed_qkv_non_spec_output = torch.empty_like(mixed_qkv_non_spec)
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torch.ops._C_ascend.npu_causal_conv1d_custom(
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mixed_qkv_non_spec_output,
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mixed_qkv_non_spec,
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conv_weights_T,
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conv_state=self_kv_cache[0],
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bias_opt=self.conv1d.bias,
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query_start_loc_opt=query_start_loc_opt,
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cache_indices_opt=cache_indices_opt,
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initial_state_mode_opt=initial_state_mode_opt,
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num_accepted_tokens_opt=None,
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activation_mode=activation_num,
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pad_slot_id=PAD_SLOT_ID,
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run_mode=0,
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)
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mixed_qkv_non_spec = mixed_qkv_non_spec_output
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if prefill_cache_indices.shape[0] > 0:
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self_kv_cache[0][prefill_cache_indices, :state_len, :] = all_last_width_prefill_x[
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-1, ...
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].transpose(-1, -2)
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else:
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conv_weights_T = conv_weights.transpose(0, 1)
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activation_num = 1 if self.activation else 0
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mixed_qkv_non_spec_output = torch.empty_like(mixed_qkv_non_spec)
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torch.ops._C_ascend.npu_causal_conv1d_custom(
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mixed_qkv_non_spec_output,
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mixed_qkv_non_spec,
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conv_weights_T,
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conv_state=self_kv_cache[0],
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bias_opt=self.conv1d.bias,
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query_start_loc_opt=query_start_loc_opt,
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cache_indices_opt=cache_indices_opt,
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initial_state_mode_opt=initial_state_mode_opt,
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num_accepted_tokens_opt=None,
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activation_mode=activation_num,
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pad_slot_id=PAD_SLOT_ID,
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run_mode=0,
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)
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mixed_qkv_non_spec = mixed_qkv_non_spec_output
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elif attn_metadata.num_decodes > 0:
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conv_weights_T = conv_weights.transpose(0, 1)
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activation_num = 1 if self.activation else 0
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non_spec_causal_conv1d_meta = attn_metadata.non_spec_decode_metadata.causal_conv1d
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non_spec_query_start_loc_device = non_spec_causal_conv1d_meta.query_start_loc
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output_non_spec = torch.empty_like(mixed_qkv_non_spec)
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torch.ops._C_ascend.npu_causal_conv1d_custom(
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output_non_spec,
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mixed_qkv_non_spec,
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conv_weights_T,
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conv_state=self_kv_cache[0],
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bias_opt=self.conv1d.bias,
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query_start_loc_opt=non_spec_query_start_loc_device,
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cache_indices_opt=non_spec_causal_conv1d_meta.cache_indices,
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initial_state_mode_opt=None,
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num_accepted_tokens_opt=None,
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activation_mode=activation_num,
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pad_slot_id=PAD_SLOT_ID,
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run_mode=1,
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)
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mixed_qkv_non_spec = output_non_spec
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else:
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mixed_qkv_non_spec = None
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query_spec, key_spec, value_spec = self.rearrange_mixed_qkv(mixed_qkv_spec)
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query_non_spec, key_non_spec, value_non_spec = self.rearrange_mixed_qkv(mixed_qkv_non_spec)
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# 2. Recurrent attention
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g, beta = DeviceOperator.fused_gdn_gating(self.A_log, a, b, self.dt_bias)
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if spec_sequence_masks is not None:
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if attn_metadata.num_prefills == 0 and attn_metadata.num_decodes == 0:
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g_spec = g
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beta_spec = beta
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g_non_spec = None
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beta_non_spec = None
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else:
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g_spec = g.index_select(1, spec_token_indx)
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beta_spec = beta.index_select(1, spec_token_indx)
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g_non_spec = g.index_select(1, non_spec_token_indx)
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beta_non_spec = beta.index_select(1, non_spec_token_indx)
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else:
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g_spec = None
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beta_spec = None
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g_non_spec = g
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beta_non_spec = beta
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split_non_spec = (
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spec_sequence_masks is None and attn_metadata.num_prefills > 0 and attn_metadata.num_decodes > 0
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)
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num_decode_tokens = attn_metadata.num_decode_tokens
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# 2.1: Process the multi-query part
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if spec_sequence_masks is not None:
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actual_seq_lengths = attn_metadata.spec_decode_metadata.actual_seq_lengths
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query_spec = l2norm_fwd(query_spec)
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key_spec = l2norm_fwd(key_spec)
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# Dispatches to the vllm-ascend AscendC custom operator
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# (csrc/recurrent_gated_delta_rule), NOT the built-in CANN operator.
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# The custom op extends dtype support (e.g. float32 state) and is
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# loaded at runtime via ASCEND_CUSTOM_OPP_PATH.
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core_attn_out_spec = torch.ops._C_ascend.npu_recurrent_gated_delta_rule(
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query=query_spec.squeeze(0),
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key=key_spec.squeeze(0),
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value=value_spec.squeeze(0),
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g=g_spec.squeeze(0),
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beta=beta_spec.squeeze(0),
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state=ssm_state,
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scale=key_spec.shape[-1] ** -0.5,
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actual_seq_lengths=actual_seq_lengths,
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ssm_state_indices=spec_state_indices_tensor.flatten(),
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num_accepted_tokens=spec_causal_conv1d_meta.num_accepted_tokens.to(torch.int32),
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).unsqueeze(0)
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else:
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core_attn_out_spec, last_recurrent_state = None, None
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# 2.2: Process non-spec-decode part in mixed non-spec batches
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if split_non_spec:
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assert mixed_qkv_non_spec is not None
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assert g_non_spec is not None
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assert beta_non_spec is not None
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query_decode, key_decode, value_decode = self.rearrange_mixed_qkv(mixed_qkv_non_spec[:num_decode_tokens])
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actual_seq_lengths = attn_metadata.non_spec_decode_metadata.actual_seq_lengths
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query_decode = l2norm_fwd(query_decode)
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key_decode = l2norm_fwd(key_decode)
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core_attn_out_decode = torch.ops._C_ascend.npu_recurrent_gated_delta_rule(
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query=query_decode.squeeze(0),
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key=key_decode.squeeze(0),
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value=value_decode.squeeze(0),
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g=g_non_spec[:, :num_decode_tokens].squeeze(0),
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beta=beta_non_spec[:, :num_decode_tokens].squeeze(0),
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state=ssm_state,
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scale=key_decode.shape[-1] ** -0.5,
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actual_seq_lengths=actual_seq_lengths,
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ssm_state_indices=non_spec_state_indices_tensor[: attn_metadata.num_decodes],
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).unsqueeze(0)
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else:
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core_attn_out_decode = None
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# 2.3: Process the remaining part
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if attn_metadata.num_prefills > 0:
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prefill_query_start_loc = attn_metadata.prefill_query_start_loc
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prefill_state_indices = attn_metadata.prefill_state_indices
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prefill_has_initial_state = attn_metadata.prefill_has_initial_state
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assert prefill_query_start_loc is not None
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assert prefill_state_indices is not None
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assert prefill_has_initial_state is not None
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assert g_non_spec is not None
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assert beta_non_spec is not None
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if split_non_spec:
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query_non_spec = query_non_spec[:, num_decode_tokens:]
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key_non_spec = key_non_spec[:, num_decode_tokens:]
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value_non_spec = value_non_spec[:, num_decode_tokens:]
|
|
g_non_spec = g_non_spec[:, num_decode_tokens:]
|
|
beta_non_spec = beta_non_spec[:, num_decode_tokens:]
|
|
|
|
initial_state = ssm_state[prefill_state_indices].transpose(-1, -2).contiguous()
|
|
clear_ssm_states(initial_state, prefill_has_initial_state)
|
|
(core_attn_out_non_spec, last_recurrent_state) = chunk_gated_delta_rule(
|
|
q=query_non_spec,
|
|
k=key_non_spec,
|
|
v=value_non_spec,
|
|
g=g_non_spec,
|
|
beta=beta_non_spec,
|
|
initial_state=initial_state,
|
|
output_final_state=True,
|
|
cu_seqlens=prefill_query_start_loc,
|
|
prebuilt_meta=attn_metadata.non_spec_prefill_metadata.chunk,
|
|
head_first=False,
|
|
use_qk_l2norm_in_kernel=True,
|
|
)
|
|
ssm_state[prefill_state_indices] = last_recurrent_state.transpose(-1, -2).contiguous().to(ssm_state.dtype)
|
|
if split_non_spec:
|
|
core_attn_out_non_spec = torch.cat(
|
|
[core_attn_out_decode, core_attn_out_non_spec],
|
|
dim=1,
|
|
)
|
|
elif attn_metadata.num_decodes > 0:
|
|
actual_seq_lengths = attn_metadata.non_spec_decode_metadata.actual_seq_lengths
|
|
query_non_spec = l2norm_fwd(query_non_spec)
|
|
key_non_spec = l2norm_fwd(key_non_spec)
|
|
# Dispatches to the vllm-ascend AscendC custom operator
|
|
# (csrc/recurrent_gated_delta_rule), NOT the built-in CANN operator.
|
|
core_attn_out_non_spec = torch.ops._C_ascend.npu_recurrent_gated_delta_rule(
|
|
query=query_non_spec.squeeze(0),
|
|
key=key_non_spec.squeeze(0),
|
|
value=value_non_spec.squeeze(0),
|
|
g=g_non_spec.squeeze(0) if g_non_spec is not None else g_non_spec,
|
|
beta=beta_non_spec.squeeze(0) if beta_non_spec is not None else beta_non_spec,
|
|
state=ssm_state,
|
|
scale=key_non_spec.shape[-1] ** -0.5,
|
|
actual_seq_lengths=actual_seq_lengths,
|
|
ssm_state_indices=non_spec_state_indices_tensor,
|
|
).unsqueeze(0)
|
|
else:
|
|
core_attn_out_non_spec, last_recurrent_state = None, None
|
|
|
|
# 3. Merge core attention output
|
|
if spec_sequence_masks is not None and core_attn_out_non_spec is not None:
|
|
merged_out = torch.empty(
|
|
(1, num_actual_tokens, *core_attn_out_spec.shape[2:]),
|
|
dtype=core_attn_out_non_spec.dtype,
|
|
device=core_attn_out_non_spec.device,
|
|
)
|
|
merged_out.index_copy_(1, spec_token_indx, core_attn_out_spec)
|
|
merged_out.index_copy_(1, non_spec_token_indx, core_attn_out_non_spec)
|
|
core_attn_out[:num_actual_tokens] = merged_out.squeeze(0)
|
|
elif spec_sequence_masks is not None:
|
|
core_attn_out[:num_actual_tokens] = core_attn_out_spec.squeeze(0)
|
|
else:
|
|
core_attn_out[:num_actual_tokens] = core_attn_out_non_spec.squeeze(0)
|