forked from EngineX-MetaX/enginex-c_series-vllm
406 lines
16 KiB
Python
406 lines
16 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import math
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from typing import TYPE_CHECKING, Optional, Union
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import torch
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import torch.nn.functional as F
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import torch_xla.core.xla_model as xm
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from vllm.lora.ops.xla_ops import bgmv_expand, bgmv_expand_slice, bgmv_shrink
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from vllm.lora.punica_wrapper.utils import convert_mapping
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if TYPE_CHECKING:
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# avoid circuit import
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from vllm.lora.layers import LoRAMapping
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from vllm.lora.models import LongContextLoRAContext
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from .punica_base import PunicaWrapperBase
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class PunicaWrapperTPU(PunicaWrapperBase):
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"""
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PunicaWrapperTPU is designed to manage and provide metadata for the punica
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kernel. The main function is to maintain the state information for
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Multi-LoRA, and to provide the interface for the pytorch punica ops.
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"""
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def __init__(self, max_num_batched_tokens: int, max_batches: int,
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device: Union[torch.device, str], **kwargs):
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PunicaWrapperBase.__init__(self, max_num_batched_tokens, max_batches,
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device)
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# PunicaWrapperBase defines some tensors with dtype=torch.int64, which
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# isn't supported by the TPU. So convert those tensors to int32.
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# Not all of them are used by the TPU so only convert the useful ones.
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self._token_lora_indices = self._token_lora_indices.to(
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dtype=torch.int32)
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self._sampler_indices = self._sampler_indices.to(dtype=torch.int32)
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self._sampler_indices_padded = self._sampler_indices_padded.to(
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dtype=torch.int32)
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torch.ops.xla.dynamo_set_buffer_donor_(self._token_lora_indices, True)
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torch.ops.xla.dynamo_set_buffer_donor_(self._sampler_indices, True)
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torch.ops.xla.dynamo_set_buffer_donor_(self._sampler_indices_padded,
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True)
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torch.ops.xla.dynamo_set_buffer_donor_(self._embeddings_indices, True)
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torch.ops.xla.dynamo_set_buffer_donor_(self._long_lora_indices, True)
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torch.ops.xla.dynamo_set_buffer_donor_(self._lora_indices_per_batch,
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True)
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torch._dynamo.mark_dynamic(self._token_lora_indices, 0)
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torch._dynamo.mark_dynamic(self._embeddings_indices, 1)
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torch._dynamo.mark_dynamic(self._sampler_indices_padded, 0)
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def _get_token_lora_indices(self, x: torch.Tensor) -> torch.IntTensor:
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return torch.narrow(self._token_lora_indices, 0, 0, x.size(0))
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@property
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def embeddings_indices(self) -> torch.Tensor:
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"""
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This property provides access to the indices used for lora embeddings,
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specifically for VocabParallelEmbeddingWithLoRA.
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"""
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return self._embeddings_indices[:]
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@property
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def sampler_indices_padded(self) -> torch.Tensor:
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"""
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This property provides access to padded sampler indices.
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"""
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return self._sampler_indices_padded[:]
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def shrink(
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self,
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x: torch.Tensor,
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w_t_all: torch.Tensor,
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scale: float,
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):
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return bgmv_shrink(x, w_t_all, self._get_token_lora_indices(x), scale)
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def expand(self, y: torch.Tensor, x: torch.Tensor, w_t_all: torch.Tensor,
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add_inputs: bool):
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return bgmv_expand(x, w_t_all, y, self._get_token_lora_indices(x),
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add_inputs)
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def expand_slice(self, y: torch.Tensor, x: torch.Tensor,
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w_t_all: torch.Tensor, y_offset: int, y_slice_size: int,
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add_inputs: bool) -> torch.Tensor:
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return bgmv_expand_slice(x, w_t_all, y,
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self._get_token_lora_indices(x), y_offset,
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y_slice_size, add_inputs)
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def add_shrink(self, y: Union[tuple[torch.Tensor, ...], torch.Tensor],
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x: torch.Tensor, lora_a_stacked: tuple[torch.Tensor, ...],
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scale: float, **kwargs) -> Optional[torch.Tensor]:
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"""
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Performs GEMM for multiple slices of lora_a.
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Semantics:
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for i in range(len(lora_a_stacked)):
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y[i] += (x @ lora_a_stacked[i]) * scale
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Args:
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y (Union[tuple[torch.Tensor, ...], torch.Tensor]): Output tensors
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x (torch.Tensor): Input tensor
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lora_a_stacked (tuple[torch.Tensor, ...]): lora_a's weights
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scale (float): Scaling factor for the operation
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"""
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torch.ops.xla.dynamo_set_buffer_donor_(y, True)
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x = x.view(-1, x.shape[-1])
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for slice_idx in range(len(lora_a_stacked)):
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lora_s = lora_a_stacked[slice_idx]
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y_s = self.shrink(x, lora_s, scale)
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y[slice_idx, :, :] = y_s # type: ignore[index]
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return y
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def add_expand(self,
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y: torch.Tensor,
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x: Union[tuple[torch.Tensor, ...], torch.Tensor],
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lora_b_stacked: tuple[torch.Tensor, ...],
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lora_bias_stacked: Optional[tuple[torch.Tensor, ...]],
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output_slices: tuple[int, ...],
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offset_start: int = 0,
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add_inputs=True,
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**kwargs) -> torch.Tensor:
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"""
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Performs GEMM and bias addition for multiple slices of lora_b.
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Semantics:
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for i in range(len(lora_b_stacked)):
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slice = output_slices[i]
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y[:, offset:offset+slice] += x[i] @ lora_b_stacked[i] +
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lora_bias_stacked[i]
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offset += slice
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Args:
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y (torch.Tensor): Output tensor.
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x (Union[tuple[torch.Tensor, ...], torch.Tensor]): Input tensors
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lora_b_stacked (tuple[torch.Tensor, ...]): lora_b's weight
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lora_bias_stacked (Optional[tuple[torch.Tensor, ...]]):
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bias's weight
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output_slices (tuple[int, ...]): Every slice's size
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add_inputs (bool): Defaults to True.
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"""
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y_org = y
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y = y.view(-1, y.shape[-1])
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offset_left = 0
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if lora_bias_stacked is not None:
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y = self._apply_bias(self._get_token_lora_indices(y), y,
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output_slices, lora_bias_stacked)
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for slice_idx in range(len(lora_b_stacked)):
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y = self.expand_slice(y,
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x[slice_idx],
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lora_b_stacked[slice_idx],
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offset_left,
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output_slices[slice_idx],
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add_inputs=add_inputs)
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offset_left += output_slices[slice_idx]
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return y.view_as(y_org)
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def add_lora_embedding(self,
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y: torch.Tensor,
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x: torch.Tensor,
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lora_b_stacked: torch.Tensor,
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add_inputs: bool = True,
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**kwargs) -> torch.Tensor:
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"""
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Applies lora specifically for VocabParallelEmbeddingWithLoRA.
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Semantics:
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y += x @ lora_b_stacked
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Args:
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y (torch.Tensor): Output tensor.
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x (torch.Tensor): Input tensor.
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lora_b_stacked (torch.Tensor): lora_b's weights.
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add_inputs (bool): Default to True.
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"""
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# Embedding layer only needs the expand op
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return self.expand(y, x, lora_b_stacked, add_inputs)
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def add_lora_linear(self,
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y: torch.Tensor,
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x: torch.Tensor,
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lora_a_stacked: tuple[torch.Tensor, ...],
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lora_b_stacked: tuple[torch.Tensor, ...],
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lora_bias_stacked: Optional[tuple[torch.Tensor, ...]],
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scale: float,
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output_slices: tuple[int, ...],
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*,
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buffer: Optional[tuple[torch.Tensor, ...]] = None,
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**kwargs) -> torch.Tensor:
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"""
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Applicable to linear-related lora.
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Semantics:
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for i in range(len(lora_a_stacked)):
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y[i] += (
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x[i].unsqueeze(0)
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@ lora_a_stacked[indices[i], layer_idx, :, :]
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@ lora_b_stacked[indices[i], layer_idx, :, :]
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* scale
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).squeeze(0)+lora_bias_stacked[i]
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Args:
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y (torch.Tensor): Output tensor. Will not be changed in-place.
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x (torch.Tensor): Input tensor (T, E)
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lora_a_stacked (tuple[torch.Tensor, ...]): lora_a's weight.
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lora_b_stacked (tuple[torch.Tensor, ...]): lora_b's weight.
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lora_bias_stacked (Optional[tuple[torch.Tensor, ...]]): lora's bias.
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scale (float): Scaling factor.
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output_slices (tuple[int, ...]): Every slice's size.
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buffer (Optional[tuple[torch.Tensor, ...]]): Defaults to None.
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"""
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assert len(lora_a_stacked) == len(lora_b_stacked) == len(output_slices)
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if lora_bias_stacked is not None:
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assert len(lora_bias_stacked) == len(output_slices)
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y = self._apply_bias(self._get_token_lora_indices(y), y,
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output_slices, lora_bias_stacked)
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if buffer is None:
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r = lora_b_stacked[0].size(-1)
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T = x.size(0)
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buffer = torch.zeros(
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(len(output_slices), T, r),
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dtype=x.dtype,
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device=x.device,
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)
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buffer = self.add_shrink(buffer, x, lora_a_stacked, scale, **kwargs)
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return self.add_expand(y,
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buffer,
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lora_b_stacked,
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None,
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output_slices,
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add_inputs=True,
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**kwargs)
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def add_lora_logits(self,
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y: torch.Tensor,
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x: torch.Tensor,
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lora_a_stacked: torch.Tensor,
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lora_b_stacked: torch.Tensor,
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scale,
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*,
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buffer: Optional[torch.Tensor] = None,
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**kwargs) -> torch.Tensor:
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"""
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Applies lora specifically for LogitsProcessorWithLoRA.
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Semantics:
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buffer = (x @ lora_a_stacked) * scale
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y += buffer @ lora_b_stacked
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Args:
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y (torch.Tensor): Output tensor.
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x (torch.Tensor): Input tensor.
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lora_a_stacked (torch.Tensor): lora_a's weights.
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lora_b_stacked (torch.Tensor):lora_b's weights.
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scale (float): Scaling factor.
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buffer (Optional[torch.Tensor]):Default to None.
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"""
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y_org = y
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y = y.view(-1, y.shape[-1])
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x = x.view(-1, x.shape[-1])
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sampler_indices = torch.narrow(self._sampler_indices, 0, 0, x.size(0))
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buffer = bgmv_shrink(x, lora_a_stacked, sampler_indices, scale)
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y = bgmv_expand(buffer,
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lora_b_stacked,
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y,
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sampler_indices,
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add_inputs=True)
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return y.view_as(y_org)
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def _apply_bias(
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self,
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indices: torch.Tensor,
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output: torch.Tensor,
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output_slices: tuple[int, ...],
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lora_bias_stacked: tuple[Optional[torch.Tensor], ...],
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):
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"""Applies bias to output
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Input shapes:
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lora_bias_stacked: 3 element tuple of (num_loras, output_dim)
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indices: (batch_size)
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output: (batch_size, q_slice_size + 2*kv_slice_size)
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output_slices: n-1 element tuple of (slice_size...),
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where n is number of slices
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"""
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org_output = output
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output = output.view(-1, output.shape[-1])
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indices = indices.view(-1)
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offset_left = 0
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for slice_idx, slice in enumerate(output_slices):
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bias = lora_bias_stacked[slice_idx]
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if bias is not None:
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bias = bias.view(-1, bias.shape[-1])
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bias = bias[indices]
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bias = torch.where(indices[:, None] == -1, 0, bias)
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bias = F.pad(bias, (offset_left, output.shape[1] -
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(offset_left + slice), 0, 0))
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output += bias
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offset_left += slice
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return output.view_as(org_output)
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# This performs the same tensor ops as the base method, except it does them
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# on the CPU then transfers the results to the TPU
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def _update_base_metadata(
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self,
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mapping: "LoRAMapping",
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lora_index_to_id: list[Optional[int]],
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max_loras: int,
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vocab_size: int,
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extra_vocab_size: int,
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long_lora_context: Optional["LongContextLoRAContext"] = None,
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):
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# Make sure we don't accidentally collect outside operations
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xm.mark_step()
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# Pad the prompt mapping to avoid running into recompiles on the TPU
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# TODO: Should this happen inside mapping internally? If so how can we
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# avoid having backend specific LoRAMapping classes?
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mapping.prompt_mapping = self._pad_prompt_mapping(
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mapping.prompt_mapping)
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(
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base_indices,
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sampler_indices,
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sampler_indices_padded,
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embeddings_indices,
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long_lora_offsets_tensor,
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indices_len,
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) = convert_mapping(
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mapping,
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lora_index_to_id,
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max_loras,
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vocab_size,
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extra_vocab_size,
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"cpu",
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long_lora_context,
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)
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self._token_lora_indices = self._pad_to_shape(
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base_indices, self._token_lora_indices.shape,
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dims=1).to(self.device)
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self._sampler_indices = self._pad_to_shape(sampler_indices,
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self._sampler_indices.shape,
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dims=1).to(self.device)
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self._sampler_indices_padded = self._pad_to_shape(
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sampler_indices_padded, self._sampler_indices_padded.shape,
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dims=1).to(self.device)
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self._embeddings_indices = self._pad_to_shape(
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embeddings_indices, self._embeddings_indices.shape,
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dims=2).to(self.device)
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if long_lora_offsets_tensor is not None:
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self._long_lora_indices = self._pad_to_shape(
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long_lora_offsets_tensor,
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self._long_lora_indices.shape,
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dims=1).to(self.device)
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else:
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zeroed = torch.zeros_like(self._long_lora_indices.cpu(),
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dtype=torch.int32)
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self._long_lora_indices = zeroed.to(self.device)
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self.indices_len[:] = indices_len
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def _update_prefill_metadata(self,
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token_lora_tensor: torch.Tensor) -> None:
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self.batch_size = 1
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self._lora_indices_per_batch[:self.
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batch_size] = token_lora_tensor[:self.
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batch_size]
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def _pad_prompt_mapping(
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self, prompt_mapping: tuple[int, ...]) -> tuple[int, ...]:
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num_reqs = len(prompt_mapping)
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# From vllm/v1/worker/tpu_model_runner:51, but need to avoid a circular
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# import
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MIN_NUM_SEQS = 8
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padded_num_reqs = max(2**math.ceil(math.log2(num_reqs)), MIN_NUM_SEQS)
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pad_len = padded_num_reqs - num_reqs
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padding = [-1] * pad_len
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return tuple(list(prompt_mapping) + padding)
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def _pad_to_shape(self, src, target_shape, dims=1):
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if dims == 1:
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pad_len = target_shape[0] - src.shape[0]
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return F.pad(src, (0, pad_len), value=0).to(torch.int32)
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else:
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pad_rows = target_shape[0] - src.shape[0]
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pad_cols = target_shape[1] - src.shape[1]
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return F.pad(src, (0, pad_cols, 0, pad_rows),
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value=0).to(torch.int32)
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