c3c265648f
What this PR does / why we need it?
When using a draft model (e.g., in MTP speculative decoding) with shared
expert data parallelism (enabled via flashcomm), a shape mismatch error
occurs in the rotary embedding calculation for models like GLM-4.7. This
is because the positions tensor has an incorrect shape for this specific
configuration.
This PR fixes the issue by adding a check in
AscendRotaryEmbedding.forward_oot. If the model is a draft model and
shared expert DP is enabled, it processes the positions tensor using
torch.ops.vllm.maybe_all_gather_and_maybe_unpad to ensure its shape is
correct before applying the rotary embedding. This resolves the shape
mismatch error.
- vLLM version: v0.16.0
- vLLM main:
15d76f74e2
---------
Signed-off-by: Zhu Jiyang <zhujiyang2@huawei.com>
586 lines
22 KiB
Python
586 lines
22 KiB
Python
#
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# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
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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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# This file is a part of the vllm-ascend project.
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#
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import math
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import os
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import torch
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import torch_npu
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from vllm.config import get_current_vllm_config
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from vllm.forward_context import get_forward_context
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from vllm.model_executor.layers.rotary_embedding import (
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DeepseekScalingRotaryEmbedding,
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MRotaryEmbedding,
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RotaryEmbedding,
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YaRNScalingRotaryEmbedding,
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)
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from vllm.model_executor.layers.rotary_embedding.common import ApplyRotaryEmb
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from vllm.triton_utils import HAS_TRITON
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from vllm_ascend.platform import NPUPlatform
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from vllm_ascend.utils import AscendDeviceType, get_ascend_device_type, has_rope, is_vl_model
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if HAS_TRITON:
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from vllm.model_executor.layers.rotary_embedding.mrope import triton_mrope
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from vllm_ascend.ops.triton.rope import rope_forward_triton
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# Currently, rope ops used on npu requires detached cos && sin as inputs.
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# However, RotaryEmbedding in vllm use cos_sin_cache as a whole variable.
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# So we have to preprocess cos_sin_cache int cos && sin. In the future,
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# we shall implement a new rope ops which accept cos_sin_cache as inputs.
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# NOTE(Angazenn): MLA && SFA models uses attn_metadata to pass cos && sin
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# to rope in AscendMLA(SFA)Impl. However, since rope is isolated from
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# AscendAttentionBackendImpl for GQA models, we cannot pass cos && sin by
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# attn_metadata. This causes that rope in GQA models must pass cos && sin
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# by different approaches.
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_cos_mla: torch.Tensor = None
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_sin_mla: torch.Tensor = None
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_cos_cache: torch.Tensor = None
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_sin_cache: torch.Tensor = None
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_cos_sin_cache: torch.Tensor = None
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_cos: torch.Tensor = None
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_sin: torch.Tensor = None
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_cos_slice: torch.Tensor = None
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_sin_slice: torch.Tensor = None
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def set_cos_and_sin(vllm_config, max_num_reqs, decode_token_per_req, dtype, device):
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global _cos_mla
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global _sin_mla
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global _cos
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global _sin
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if _cos_mla is not None or _sin_mla is not None or _cos is not None or _sin is not None:
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return
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model_config = vllm_config.model_config
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max_num_batched_tokens = vllm_config.scheduler_config.max_num_batched_tokens
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if model_config.use_mla:
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rope_dim = model_config.hf_text_config.qk_rope_head_dim
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_cos_mla = torch.ones(max_num_batched_tokens, 1, 1, rope_dim, dtype=dtype, device=device)
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_sin_mla = torch.zeros(max_num_batched_tokens, 1, 1, rope_dim, dtype=dtype, device=device)
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elif not is_vl_model(vllm_config) and has_rope(vllm_config):
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rope_dim = model_config.get_head_size()
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# For models using partial rope like Qwen3-Next.
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if hasattr(model_config.hf_text_config, "partial_rotary_factor"):
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rope_dim = int(rope_dim * model_config.hf_text_config.partial_rotary_factor)
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elif hasattr(model_config.hf_text_config, "rotary_dim"):
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rope_dim = int(model_config.hf_text_config.rotary_dim)
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_cos = torch.ones(1, max_num_batched_tokens, 1, rope_dim, dtype=dtype, device=device)
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_sin = torch.zeros(1, max_num_batched_tokens, 1, rope_dim, dtype=dtype, device=device)
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def get_cos_and_sin_mla(positions, use_cache=False):
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global _cos_cache
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global _sin_cache
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cos = _cos_cache[positions].unsqueeze(1).unsqueeze(2)
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sin = _sin_cache[positions].unsqueeze(1).unsqueeze(2)
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if not use_cache:
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return cos, sin
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global _cos_mla
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global _sin_mla
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num_tokens = positions.size(0)
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_cos_mla[:num_tokens, ...] = cos
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_sin_mla[:num_tokens, ...] = sin
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return _cos_mla[:num_tokens, ...], _sin_mla[:num_tokens, ...]
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def _record_cos_sin_cache(cos_sin_cache):
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global _cos_sin_cache
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if _cos_sin_cache is not None:
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return
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_cos_sin_cache = cos_sin_cache
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def _record_cos_and_sin_cache(cos_cache, sin_cache):
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global _cos_cache
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global _sin_cache
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_cos_cache = cos_cache
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_sin_cache = sin_cache
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def _record_cos_and_sin_cache_interleaved(cos_sin_cache):
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global _cos_cache
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global _sin_cache
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if _cos_cache is not None or _sin_cache is not None:
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return
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hidden_dim = cos_sin_cache.shape[-1] // 2
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cos_cache, sin_cache = cos_sin_cache.view(-1, 2, hidden_dim).repeat(1, 1, 2).chunk(2, dim=1)
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_cos_cache = cos_cache.squeeze(1)
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_sin_cache = sin_cache.squeeze(1)
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def update_cos_sin(positions):
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global _cos
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global _sin
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global _cos_slice
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global _sin_slice
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if _cos_sin_cache is None or _cos is None or _sin is None:
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return
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num_tokens = positions.size(0)
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_cos[:, :num_tokens] = (
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_cos_sin_cache.index_select(0, positions).view(num_tokens, 2, -1).repeat(1, 1, 2).chunk(2, dim=-2)[0]
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)
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_sin[:, :num_tokens] = (
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_cos_sin_cache.index_select(0, positions).view(num_tokens, 2, -1).repeat(1, 1, 2).chunk(2, dim=-2)[1]
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)
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_cos_slice = _cos[:, :num_tokens]
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_sin_slice = _sin[:, :num_tokens]
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def get_cos_and_sin_slice():
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return _cos_slice, _sin_slice
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def rope_forward_oot(
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positions: torch.Tensor,
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query: torch.Tensor,
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key: torch.Tensor,
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cos_sin_cache: torch.Tensor,
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head_size: int,
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rotary_dim: int,
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is_neox_style: bool,
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offsets: torch.Tensor | None = None,
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) -> tuple[torch.Tensor, torch.Tensor]:
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query_shape, key_shape = query.shape, key.shape
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if offsets is not None:
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raise NotImplementedError("Batched rotary embedding is currently not supported on NPU.")
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if HAS_TRITON:
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num_tokens = query.shape[0]
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query, key = rope_forward_triton(
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query.view(num_tokens, -1, head_size),
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key.view(num_tokens, -1, head_size),
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cos_sin_cache=cos_sin_cache,
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positions=positions,
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rope_dim=rotary_dim,
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is_neox_style=is_neox_style,
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)
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else:
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if rotary_dim < head_size:
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num_tokens = query.shape[0]
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query = query.view(num_tokens, -1, head_size)
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key = key.view(num_tokens, -1, head_size)
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q_rot = query[..., :rotary_dim]
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q_pass = query[..., rotary_dim:]
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k_rot = key[..., :rotary_dim]
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k_pass = key[..., rotary_dim:]
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q_rot = q_rot.contiguous().view(num_tokens, -1)
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k_rot = k_rot.contiguous().view(num_tokens, -1)
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# only the rotary part is processed here,
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# the dimension should be rotary_dim
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torch_npu._npu_rotary_embedding(
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positions,
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q_rot,
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k_rot,
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rotary_dim,
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cos_sin_cache,
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is_neox_style,
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)
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q_rot = q_rot.view(num_tokens, -1, rotary_dim)
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k_rot = k_rot.view(num_tokens, -1, rotary_dim)
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query = torch.cat((q_rot, q_pass), dim=-1).reshape(query_shape)
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key = torch.cat((k_rot, k_pass), dim=-1).reshape(key_shape)
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else:
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# TODO: Remove the contiguous in the future.
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query = query.contiguous().view(query.shape[0], -1)
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key = key.contiguous().view(key.shape[0], -1)
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torch_npu._npu_rotary_embedding(
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positions,
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query,
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key,
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head_size,
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cos_sin_cache,
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is_neox_style,
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)
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return query.view(query_shape), key.view(key_shape)
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class AscendRotaryEmbedding(RotaryEmbedding):
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def __init__(
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self,
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head_size: int,
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rotary_dim: int,
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max_position_embeddings: int,
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base: float,
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is_neox_style: bool,
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dtype: torch.dtype,
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) -> None:
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super().__init__(head_size, rotary_dim, max_position_embeddings, base, is_neox_style, dtype)
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vllm_config = get_current_vllm_config()
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self.use_mtp = vllm_config.speculative_config and vllm_config.speculative_config.method == "mtp"
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_record_cos_sin_cache(self.cos_sin_cache)
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_record_cos_and_sin_cache_interleaved(self.cos_sin_cache)
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def forward_oot(
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self,
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positions: torch.Tensor,
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query: torch.Tensor,
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key: torch.Tensor,
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offsets: torch.Tensor | None = None,
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is_neox_style_override: bool | None = None,
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):
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is_neox_style = self.is_neox_style
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if is_neox_style_override is not None:
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is_neox_style = is_neox_style_override
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is_draft_model = get_forward_context().is_draft_model
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flash_comm_v1_enabled = get_forward_context().flash_comm_v1_enabled
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if is_draft_model and self.use_mtp and flash_comm_v1_enabled:
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positions = torch.ops.vllm.maybe_all_gather_and_maybe_unpad(positions.contiguous(), True)
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return torch.ops.vllm.npu_rotary_embedding(
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positions, query, key, self.cos_sin_cache, self.head_size, self.rotary_dim, is_neox_style
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)
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class AscendYaRNRotaryEmbedding(YaRNScalingRotaryEmbedding):
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def __init__(
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self,
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head_size: int,
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rotary_dim: int,
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max_position_embeddings: int,
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base: float,
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is_neox_style: bool,
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scaling_factor: float,
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dtype: torch.dtype,
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*,
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extrapolation_factor: float = 1,
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attn_factor: float = 1,
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beta_fast: int = 32,
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beta_slow: int = 1,
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truncate: bool = False,
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) -> None:
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extra_kwargs = {
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"extrapolation_factor": extrapolation_factor,
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"attn_factor": attn_factor,
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"beta_fast": beta_fast,
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"beta_slow": beta_slow,
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# TODO: current not support actual truncate，adaptation for extra parameters to be compatible with vllm
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"truncate": truncate,
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}
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super().__init__(
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head_size, rotary_dim, max_position_embeddings, base, is_neox_style, scaling_factor, dtype, **extra_kwargs
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)
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_record_cos_sin_cache(self.cos_sin_cache)
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def forward_oot(
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self,
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positions: torch.Tensor,
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query: torch.Tensor,
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key: torch.Tensor,
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offsets: torch.Tensor | None = None,
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is_neox_style_override: bool | None = None,
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):
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return AscendRotaryEmbedding.forward_oot(self, positions, query, key, offsets, is_neox_style_override)
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class AscendDeepseekScalingRotaryEmbedding(DeepseekScalingRotaryEmbedding):
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def __init__(
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self,
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head_size: int,
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rotary_dim: int,
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max_position_embeddings: int,
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base: int,
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is_neox_style: bool,
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scaling_factor: float,
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dtype: torch.dtype,
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*,
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extrapolation_factor: float = 1,
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attn_factor: float = 1,
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beta_fast: int = 32,
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beta_slow: int = 1,
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mscale: float = 1,
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mscale_all_dim: float = 0,
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) -> None:
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# Note: we adopt the native huggingface deepseek rope initialization code from
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# https://huggingface.co/deepseek-ai/DeepSeek-V3-0324/blob/main/modeling_deepseek.py for
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# its more ascend compute friendly
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self.scaling_factor = scaling_factor
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self.extrapolation_factor = extrapolation_factor
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self.attn_factor = attn_factor
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self.beta_fast = beta_fast
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self.beta_slow = beta_slow
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# Get n-d magnitude scaling corrected for interpolation.
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self.mscale = float(
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self._yarn_get_mscale(self.scaling_factor, float(mscale))
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/ self._yarn_get_mscale(self.scaling_factor, float(mscale_all_dim))
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* attn_factor
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)
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super(DeepseekScalingRotaryEmbedding, self).__init__(
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head_size, rotary_dim, max_position_embeddings, base, is_neox_style, dtype
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)
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# NOTE: For ascend friendly computing, reorder sin and cos cache
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self.max_seq_len = math.ceil(max_position_embeddings * scaling_factor)
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self._set_cos_sin_cache(self.max_seq_len, device=NPUPlatform.device_type, dtype=dtype)
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def _yarn_get_mscale(self, scale: float = 1, mscale: float = 1) -> float:
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if scale <= 1:
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return 1.0
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return 0.1 * mscale * math.log(scale) + 1.0
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def _rotate_half(self, x):
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"""Rotates half the hidden dims of the input."""
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x1 = x[..., : x.shape[-1] // 2]
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x2 = x[..., x.shape[-1] // 2 :]
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return torch.cat((-x2, x1), dim=-1)
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def _yarn_linear_ramp_mask(self, min_value, max_value, dim):
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# Note: The if conditional branch is not used here
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# to solve MTP compilation error.
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max_value += (min_value == max_value).float() * 0.001
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linear_func = (torch.arange(dim, dtype=torch.float32) - min_value) / (max_value - min_value)
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ramp_func = torch.clamp(linear_func, 0, 1)
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return ramp_func
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# Inverse dim formula to find dim based on number of rotations
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def _yarn_find_correction_dim(self, num_rotations, dim, base=10000, max_position_embeddings=2048):
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# Note: use torch instead of math to solve MTP compilation error.
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return (dim * torch.log(torch.tensor(max_position_embeddings) / (num_rotations * 2 * torch.pi))) / (
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2 * torch.log(torch.tensor(base))
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)
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# Find dim range bounds based on rotations
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def _yarn_find_correction_range(self, low_rot, high_rot, dim, base=10000, max_position_embeddings=2048):
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# Note: use torch instead of math to solve MTP compilation error.
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low = torch.floor(self._yarn_find_correction_dim(low_rot, dim, base, max_position_embeddings))
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high = torch.ceil(self._yarn_find_correction_dim(high_rot, dim, base, max_position_embeddings))
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# Note: use torch instead of max/min to solve MTP compilation error.
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return torch.clamp(low, min=0), torch.clamp(high, max=dim - 1)
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# Copied from transformers.models.llama.modeling_llama.apply_rotary_pos_emb
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def _apply_rotary_pos_emb(self, q, k, cos, sin, position_ids, unsqueeze_dim=1):
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"""Applies Rotary Position Embedding to the query and key tensors.
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Args:
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q (`torch.Tensor`): The query tensor.
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k (`torch.Tensor`): The key tensor.
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cos (`torch.Tensor`): The cosine part of the rotary embedding.
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sin (`torch.Tensor`): The sine part of the rotary embedding.
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position_ids (`torch.Tensor`):
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The position indices of the tokens corresponding to the query and key tensors. For example, this can be
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used to pass offsetted position ids when working with a KV-cache.
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unsqueeze_dim (`int`, *optional*, defaults to 1):
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The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
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sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example,
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note that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim].
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Then, if q and k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1
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makes cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly,
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if q and k have the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
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Returns:
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`tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
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"""
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cos = cos[position_ids]
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sin = sin[position_ids]
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cos = cos[:, None, None, :]
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sin = sin[:, None, None, :]
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if len(q.shape) == 3:
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q = q[:, :, None, :]
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if len(k.shape) == 2:
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k = k[:, None, None, :]
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elif len(k.shape) == 3:
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k = k[:, :, None, :]
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b, h_q, s, d = q.shape
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q = q.view(b, h_q, s, d // 2, 2).transpose(4, 3).reshape(b, h_q, s, d)
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b, h_k, s, d = k.shape
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k = k.view(b, h_k, s, d // 2, 2).transpose(4, 3).reshape(b, h_k, s, d)
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q_embed = (q * cos) + (self._rotate_half(q) * sin)
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k_embed = (k * cos) + (self._rotate_half(k) * sin)
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q_embed = q_embed.view(b, h_q, d)
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k_embed = k_embed.view(b, h_k, d)
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|
||
return q_embed, k_embed
|
||
|
||
def _set_cos_sin_cache(self, max_seq_len, device, dtype):
|
||
dim = self.rotary_dim
|
||
|
||
freq_extra = 1.0 / (self.base ** (torch.arange(0, dim, 2, dtype=torch.float32, device=device) / dim))
|
||
freq_inter = 1.0 / (
|
||
self.scaling_factor * self.base ** (torch.arange(0, dim, 2, dtype=torch.float32, device=device) / dim)
|
||
)
|
||
|
||
low, high = self._yarn_find_correction_range(
|
||
self.beta_fast,
|
||
self.beta_slow,
|
||
dim,
|
||
self.base,
|
||
self.max_position_embeddings,
|
||
)
|
||
inv_freq_mask = 1.0 - self._yarn_linear_ramp_mask(low, high, dim // 2).to(device=device, dtype=torch.float32)
|
||
inv_freq = freq_inter * (1 - inv_freq_mask) + freq_extra * inv_freq_mask
|
||
self.register_buffer("inv_freq", inv_freq, persistent=False)
|
||
|
||
t = torch.arange(max_seq_len, device=device, dtype=torch.float32)
|
||
|
||
freqs = torch.outer(t, inv_freq)
|
||
cos_cached = torch.cat([freqs, freqs], dim=-1).cos() * self.mscale
|
||
sin_cached = torch.cat([freqs, freqs], dim=-1).sin() * self.mscale
|
||
cos_cached = cos_cached.to(dtype)
|
||
sin_cached = sin_cached.to(dtype)
|
||
cache = torch.cat([freqs.cos() * self.mscale, freqs.sin() * self.mscale], dim=-1).to(dtype)
|
||
self.register_buffer("cos_sin_cache", cache, persistent=False)
|
||
self.register_buffer("cos_cached", cos_cached, persistent=False)
|
||
self.register_buffer("sin_cached", sin_cached, persistent=False)
|
||
_record_cos_sin_cache(cache)
|
||
_record_cos_and_sin_cache(cos_cached, sin_cached)
|
||
|
||
def forward(
|
||
self, positions: torch.Tensor, query: torch.Tensor, key: torch.Tensor, offsets: torch.Tensor | None = None
|
||
):
|
||
if len(key.shape) == 2:
|
||
key = key[:, None, :]
|
||
# Note: we implement the non neox_style method with shuffle the last dim and neox style
|
||
# calculation method which is also more compute friendly to the ascend machine
|
||
# https://huggingface.co/deepseek-ai/DeepSeek-V3-0324/blob/main/modeling_deepseek.py
|
||
is_neox_style = True
|
||
if self.is_neox_style is False:
|
||
b, h_q, d = query.shape
|
||
query = query.view(b, h_q, d // 2, 2).transpose(3, 2).reshape(b, h_q, d)
|
||
b, h_k, d = key.shape
|
||
key = key.view(b, h_k, d // 2, 2).transpose(3, 2).reshape(b, h_k, d)
|
||
q_pe, k_pe = torch.ops.vllm.npu_rotary_embedding(
|
||
positions, query, key, self.cos_sin_cache, self.head_size, self.rotary_dim, is_neox_style
|
||
)
|
||
return q_pe, k_pe
|
||
|
||
|
||
class AscendMRotaryEmbedding(MRotaryEmbedding):
|
||
# Empirical safety threshold for large Triton grids on Ascend NPU
|
||
_ASCEND_TRITON_GRID_LIMIT = 65535
|
||
|
||
def forward_triton(
|
||
self,
|
||
positions: torch.Tensor,
|
||
query: torch.Tensor,
|
||
key: torch.Tensor | None = None,
|
||
offsets: torch.Tensor | None = None,
|
||
):
|
||
assert positions.ndim == 2
|
||
assert key is not None
|
||
|
||
self._match_cos_sin_cache_dtype(query)
|
||
self.cos = None
|
||
self.sin = None
|
||
if self.cos is None and self.sin is None:
|
||
cos_sin = self.cos_sin_cache[positions] # type: ignore
|
||
cos, sin = cos_sin.chunk(2, dim=-1)
|
||
self.cos = cos.contiguous()
|
||
self.sin = sin.contiguous()
|
||
query_shape = query.shape
|
||
key_shape = key.shape
|
||
|
||
assert self.mrope_section
|
||
|
||
# When the grid becomes large, enable TRITON_ALL_BLOCKS_PARALLEL
|
||
# to avoid scheduler/runtime failures.
|
||
if query_shape[0] > self._ASCEND_TRITON_GRID_LIMIT and os.environ.get("TRITON_ALL_BLOCKS_PARALLEL") != "1":
|
||
os.environ["TRITON_ALL_BLOCKS_PARALLEL"] = "1"
|
||
|
||
q, k = triton_mrope(
|
||
query,
|
||
key,
|
||
self.cos,
|
||
self.sin,
|
||
self.mrope_section,
|
||
self.head_size,
|
||
self.rotary_dim,
|
||
self.mrope_interleaved,
|
||
)
|
||
|
||
return q.reshape(query_shape), k.reshape(key_shape)
|
||
|
||
def forward_oot(
|
||
self,
|
||
positions: torch.Tensor,
|
||
query: torch.Tensor,
|
||
key: torch.Tensor,
|
||
):
|
||
if HAS_TRITON and positions.ndim == 2 and self.mrope_interleaved:
|
||
# todo: need cann update in 8.5.0
|
||
return self.forward_triton(positions, query, key)
|
||
|
||
if self.mrope_section != [16, 24, 24] or get_ascend_device_type() == AscendDeviceType.A5:
|
||
return super().forward_oot(positions, query, key)
|
||
|
||
import torch_npu
|
||
|
||
mrope_section = [0, 0, 0] if positions.ndim == 1 else self.mrope_section
|
||
|
||
if self.cos_sin_cache.device != query.device: # type: ignore
|
||
self.cos_sin_cache = self.cos_sin_cache.to( # type: ignore
|
||
query.device
|
||
) # type: ignore
|
||
|
||
if self.cos_sin_cache.dtype != query.dtype: # type: ignore
|
||
self.cos_sin_cache = self.cos_sin_cache.to( # type: ignore
|
||
query.dtype
|
||
) # type: ignore
|
||
|
||
query, key = torch_npu.npu_mrope(
|
||
positions.contiguous(),
|
||
query.contiguous(),
|
||
key.contiguous(),
|
||
self.cos_sin_cache.contiguous(),
|
||
self.head_size,
|
||
mrope_section=mrope_section,
|
||
rotary_mode="half",
|
||
)
|
||
|
||
return query, key
|
||
|
||
|
||
class AscendApplyRotaryEmb(ApplyRotaryEmb):
|
||
def __init__(
|
||
self,
|
||
enforce_enable: bool = False,
|
||
is_neox_style: bool = True,
|
||
enable_fp32_compute: bool = False,
|
||
) -> None:
|
||
super().__init__(
|
||
enforce_enable=enforce_enable,
|
||
is_neox_style=is_neox_style,
|
||
enable_fp32_compute=enable_fp32_compute,
|
||
)
|
||
|
||
def forward_oot(
|
||
self,
|
||
x: torch.Tensor,
|
||
cos: torch.Tensor,
|
||
sin: torch.Tensor,
|
||
) -> torch.Tensor:
|
||
x, cos, sin, origin_shape, origin_dtype = self._pre_process(x, cos, sin)
|
||
|
||
head_dim = x.shape[-1]
|
||
# cos, sin: [seq_len, head_dim // 2]
|
||
cos = torch.cat((cos, cos), dim=-1)
|
||
sin = torch.cat((sin, sin), dim=-1)
|
||
# cos, sin: [1, seq_len, 1, head_dim]
|
||
cos = cos.reshape(1, -1, 1, head_dim)
|
||
sin = sin.reshape(1, -1, 1, head_dim)
|
||
|
||
output = torch_npu.npu_rotary_mul(x, cos, sin)
|
||
|
||
output = self._post_process(output, origin_shape, origin_dtype)
|
||
|
||
return output
|