a86ece5e39
### What this PR does / why we need it?
Relying on #3044, this PR aims to further fix:
1. The forward error occured when `LogitsProcessorWithLoRA` calls
`AscendLogitsProcessor.forward`. Since `LogitsProcessorWithLoRA`
bypasses the MRO to call it, `super().forward(...)` in
`AscendLogitsProcessor.forward` will raise an error. This PR fixes it by
directly invoking `LogitsProcessor.forward(self, ...)`;
2. The shape mismatch in `add_lora_logits` in punica_npu.py. The
`lora_a_stacked` and `lora_b_stacked` are organized as [num_loras, 1,
lora_rank, hidden_size] and [num_loras, 1, vocab_size, lora_rank] shapes
respectively, but they are misunderstood in #1583---the last two
dimensions were assumed in reverse order, which causes errors in
`bgmv_shrink` and `bgmv_expand`. This PR fixes it by reverting it to the
previous version to align with the implementation in punica_cpu.py in
vllm.
### Dependencies
This PR depends on changes introduced by #3044 (LoRA support for
`AscendQKVParallelLinear` and `AscendMergedQKVParallelLinear` layers).
### Does this PR introduce _any_ user-facing change?
N/A
### How was this patch tested?
The LoRA-related tests, e.g., test_ilama_lora.py and
test_ilama_lora_tp2.py, use ilama-3.2-1B, and this model is regarded as
`TransformersForCausalLM`, where `embedding_modules` attribute lacks
`lm_head`. However, `LlamaForCausalLM` and most other models include
both `embed_tokens` and `lm_head` in `embedding_modules`. This attribute
contributes to `supported_lora_modules` when using LoRA in vllm.
Therefore, without `lm_head` in `embedding_modules`, current tests using
ilama-3.2-1B are unable to find the abve errors since
`LogitsProcessorWithLoRA` replacing `lm_head` is skipped. Simply using
Meta-Llama-3.1-8B-Instruct can reproduce the above errors and check
whether these fixes can work. What's more, it's necessary to add more
comprehensive tests for LoRA.
- vLLM version: v0.10.2
- vLLM main:
f225ea7dd9
Signed-off-by: Zetong Li <slippersss@126.com>
357 lines
12 KiB
Python
357 lines
12 KiB
Python
# SPDX-License-Identifier: Apache-2.0
|
|
|
|
from typing import Callable, Optional, Tuple, Union
|
|
|
|
import torch
|
|
|
|
from vllm_ascend.utils import is_310p
|
|
|
|
if is_310p():
|
|
from vllm.lora.ops.torch_ops import (bgmv_expand, bgmv_expand_slice,
|
|
bgmv_shrink, sgmv_expand,
|
|
sgmv_expand_slice, sgmv_shrink)
|
|
else:
|
|
from vllm_ascend.lora.lora_ops import (bgmv_expand, bgmv_expand_slice,
|
|
bgmv_shrink, sgmv_expand,
|
|
sgmv_expand_slice, sgmv_shrink)
|
|
|
|
from vllm.lora.punica_wrapper.punica_base import PunicaWrapperBase
|
|
|
|
from vllm_ascend.lora.utils import refresh_all_lora_classes
|
|
|
|
|
|
# The platforms that are compatible with the PyTorch-native implementation can
|
|
# inherit this class
|
|
class PunicaWrapperNPU(PunicaWrapperBase):
|
|
"""
|
|
PunicaWrapperNPU is designed to manage and provide metadata for the punica
|
|
kernel. The main function is to maintain the state information for
|
|
Multi-LoRA, and to provide the interface for the pytorch punica ops.
|
|
"""
|
|
|
|
def __init__(self, max_num_batched_tokens: int, max_batches: int,
|
|
device: Union[torch.device, str], **kwargs):
|
|
PunicaWrapperBase.__init__(self, max_num_batched_tokens, max_batches,
|
|
device)
|
|
refresh_all_lora_classes()
|
|
|
|
def _shrink_prefill(
|
|
self,
|
|
y: torch.Tensor,
|
|
x: torch.Tensor,
|
|
w_t_all: torch.Tensor,
|
|
scale: float,
|
|
):
|
|
#No LoRA request, so return directly
|
|
if self.no_lora:
|
|
return
|
|
sgmv_shrink(
|
|
x,
|
|
w_t_all,
|
|
y,
|
|
*self.prefill_metadata,
|
|
scale,
|
|
)
|
|
|
|
def _shrink_decode(
|
|
self,
|
|
y: torch.Tensor,
|
|
x: torch.Tensor,
|
|
w_t_all: torch.Tensor,
|
|
scale: float,
|
|
):
|
|
bgmv_shrink(x, w_t_all, y, self.token_lora_indices, scale)
|
|
|
|
def _expand_prefill(
|
|
self,
|
|
y: torch.Tensor,
|
|
x: torch.Tensor,
|
|
w_t_all: torch.Tensor,
|
|
add_inputs: bool,
|
|
):
|
|
#No LoRA request, so return directly
|
|
if self.no_lora:
|
|
return
|
|
sgmv_expand(
|
|
x,
|
|
w_t_all,
|
|
y,
|
|
*self.prefill_metadata,
|
|
add_inputs,
|
|
)
|
|
|
|
def _expand_decode(
|
|
self,
|
|
y: torch.Tensor,
|
|
x: torch.Tensor,
|
|
w_t_all: torch.Tensor,
|
|
add_inputs: bool,
|
|
):
|
|
bgmv_expand(x, w_t_all, y, self.token_lora_indices, add_inputs)
|
|
|
|
def _expand_slice_prefill(
|
|
self,
|
|
y: torch.Tensor,
|
|
x: torch.Tensor,
|
|
w_t_all: torch.Tensor,
|
|
y_offset: int,
|
|
y_slice_size: int,
|
|
add_inputs: bool,
|
|
):
|
|
#No LoRA request, so return directly
|
|
if self.no_lora:
|
|
return
|
|
sgmv_expand_slice(
|
|
x,
|
|
w_t_all,
|
|
y,
|
|
*self.prefill_metadata,
|
|
y_offset,
|
|
y_slice_size,
|
|
add_inputs,
|
|
)
|
|
|
|
def _expand_slice_decode(
|
|
self,
|
|
y: torch.Tensor,
|
|
x: torch.Tensor,
|
|
w_t_all: torch.Tensor,
|
|
y_offset: int,
|
|
y_slice_size: int,
|
|
add_inputs: bool,
|
|
):
|
|
bgmv_expand_slice(x, w_t_all, y, self.token_lora_indices, y_offset,
|
|
y_slice_size, add_inputs)
|
|
|
|
def _apply_expand(
|
|
self,
|
|
y: torch.Tensor,
|
|
x: torch.Tensor,
|
|
w_t_all: torch.Tensor,
|
|
y_offset: int,
|
|
y_slice_size: int,
|
|
add_inputs: bool = True,
|
|
):
|
|
"""
|
|
Perform the ` y[:,y_offset:y_offset+y_slice_size]+=x@w_t_all`
|
|
computation, which is suitable for the
|
|
GEMM of lora'b.
|
|
"""
|
|
|
|
expand_slice_fun: Callable = (self._expand_slice_prefill
|
|
if self.is_prefill else
|
|
self._expand_slice_decode)
|
|
expand_slice_fun(y, x, w_t_all, y_offset, y_slice_size, add_inputs)
|
|
|
|
def _apply_shrink(self, y: torch.Tensor, x: torch.Tensor,
|
|
w_t_all: torch.Tensor, scale: float):
|
|
"""
|
|
Perform the ` y+=x@w_t_all` computation, which is suitable for the
|
|
GEMM of lora'a.
|
|
When `is_prefill is` true, it indicates that it is currently the
|
|
prefill stage, and the `_shrink_prefill` function should be called.
|
|
Otherwise, it is the decode stage, and the _shrink_decode function
|
|
should be called.
|
|
"""
|
|
y_org = y
|
|
y = y.view(-1, y.shape[-1])
|
|
shrink_fun: Callable = (self._shrink_prefill
|
|
if self.is_prefill else self._shrink_decode)
|
|
shrink_fun(y, x, w_t_all, scale)
|
|
y = y.view_as(y_org)
|
|
|
|
def add_shrink(self, y: Union[Tuple[torch.Tensor, ...], torch.Tensor],
|
|
x: torch.Tensor, lora_a_stacked: Tuple[torch.Tensor, ...],
|
|
scale: float, **kwargs):
|
|
"""
|
|
Performs GEMM for multiple slices of lora_a.
|
|
When `is_prefill is` true, it indicates that it is currently the
|
|
prefill stage, and the `_shrink_prefill` function should be called.
|
|
Otherwise, it is the decode stage, and the _shrink_decode function
|
|
should be called.
|
|
|
|
Semantics:
|
|
for i in range(len(lora_a_stacked)):
|
|
y[i] += (x @ lora_a_stacked[i]) * scale
|
|
|
|
Args:
|
|
y (Union[Tuple[torch.Tensor, ...], torch.Tensor]): Output tensors
|
|
x (torch.Tensor): Input tensor
|
|
lora_a_stacked (Tuple[torch.Tensor, ...]): lora_a's weights
|
|
scale (float): Scaling factor for the operation
|
|
"""
|
|
|
|
x = x.view(-1, x.shape[-1])
|
|
# TODO fuse these kernels
|
|
for slice_idx in range(len(lora_a_stacked)):
|
|
self._apply_shrink(y[slice_idx], x, lora_a_stacked[slice_idx],
|
|
scale)
|
|
|
|
def add_expand(self,
|
|
y: torch.Tensor,
|
|
x: Union[Tuple[torch.Tensor, ...], torch.Tensor],
|
|
lora_b_stacked: Tuple[torch.Tensor, ...],
|
|
lora_bias_stacked: Optional[Tuple[torch.Tensor, ...]],
|
|
output_slices: Tuple[int, ...],
|
|
offset_start: int = 0,
|
|
add_inputs=True,
|
|
**kwargs) -> None:
|
|
"""
|
|
Performs GEMM and bias addition for multiple slices of lora_b.
|
|
|
|
Semantics:
|
|
for i in range(len(lora_b_stacked)):
|
|
slice = output_slices[i]
|
|
y[:, offset:offset+slice] += x[i] @ lora_b_stacked[i] +
|
|
lora_bias_stacked[i]
|
|
offset += slice
|
|
|
|
Args:
|
|
y (torch.Tensor): Output tensor.
|
|
x (Union[Tuple[torch.Tensor, ...], torch.Tensor]): Input tensors
|
|
lora_b_stacked (Tuple[torch.Tensor, ...]): lora_b's weight
|
|
lora_bias_stacked (Optional[Tuple[torch.Tensor, ...]]):
|
|
bias's weight
|
|
output_slices (Tuple[int, ...]): Every slice's size
|
|
add_inputs (bool): Defaults to True.
|
|
"""
|
|
y_org = y
|
|
y = y.view(-1, y.shape[-1])
|
|
offset_left = offset_start
|
|
if lora_bias_stacked is not None:
|
|
self._apply_bias(self.token_lora_indices, y, output_slices,
|
|
lora_bias_stacked)
|
|
for slice_idx in range(len(lora_b_stacked)):
|
|
self._apply_expand(
|
|
y,
|
|
x[slice_idx],
|
|
lora_b_stacked[slice_idx],
|
|
offset_left,
|
|
output_slices[slice_idx],
|
|
add_inputs=add_inputs,
|
|
)
|
|
offset_left += output_slices[slice_idx]
|
|
y = y.view_as(y_org)
|
|
|
|
def add_lora_embedding(self,
|
|
y: torch.Tensor,
|
|
x: torch.Tensor,
|
|
lora_b_stacked: torch.Tensor,
|
|
add_inputs: bool = True,
|
|
**kwargs) -> None:
|
|
"""
|
|
Applies lora specifically for VocabParallelEmbeddingWithLoRA.
|
|
|
|
Semantics:
|
|
y += x @ lora_b_stacked
|
|
|
|
Args:
|
|
y (torch.Tensor): Output tensor.
|
|
x (torch.Tensor): Input tensor.
|
|
lora_b_stacked (torch.Tensor): lora_b's weights.
|
|
add_inputs (bool): Default to True.
|
|
"""
|
|
|
|
# Embedding layer only need expand op
|
|
expand_fun: Callable = (self._expand_prefill
|
|
if self.is_prefill else self._expand_decode)
|
|
expand_fun(y, x, lora_b_stacked, add_inputs)
|
|
|
|
def add_lora_linear(self,
|
|
y: torch.Tensor,
|
|
x: torch.Tensor,
|
|
lora_a_stacked: Tuple[torch.Tensor, ...],
|
|
lora_b_stacked: Tuple[torch.Tensor, ...],
|
|
lora_bias_stacked: Optional[Tuple[torch.Tensor, ...]],
|
|
scale: float,
|
|
output_slices: Tuple[int, ...],
|
|
*,
|
|
buffer: Optional[Tuple[torch.Tensor, ...]] = None,
|
|
**kwargs) -> None:
|
|
"""
|
|
Applicable to linear-related lora.
|
|
|
|
Semantics:
|
|
for i in range(len(lora_a_stacked)):
|
|
y[i] += (
|
|
x[i].unsqueeze(0)
|
|
@ lora_a_stacked[indices[i], layer_idx, :, :]
|
|
@ lora_b_stacked[indices[i], layer_idx, :, :]
|
|
* scale
|
|
).squeeze(0)+lora_bias_stacked[i]
|
|
|
|
Args:
|
|
y (torch.Tensor): Output tensor. Will be changed in-place.
|
|
x (torch.Tensor): Input tensor
|
|
lora_a_stacked (Tuple[torch.Tensor, ...]): lora_a's weight.
|
|
lora_b_stacked (Tuple[torch.Tensor, ...]): lora_b's weight.
|
|
lora_bias_stacked (Optional[Tuple[torch.Tensor, ...]]): lora's bias.
|
|
scale (float): Scaling factor.
|
|
output_slices (Tuple[int, ...]): Every slice's size.
|
|
buffer (Optional[Tuple[torch.Tensor, ...]]): Defaults to None.
|
|
"""
|
|
|
|
assert len(lora_a_stacked) == len(lora_b_stacked) == len(output_slices)
|
|
if lora_bias_stacked is not None:
|
|
assert len(lora_bias_stacked) == len(output_slices)
|
|
y = self._apply_bias(self.token_lora_indices, y, output_slices,
|
|
lora_bias_stacked)
|
|
|
|
if buffer is None:
|
|
r = lora_b_stacked[0].size(-1)
|
|
# We set the buffer to be float32 by default, consistent with the
|
|
# triton op
|
|
buffer = tuple(
|
|
torch.zeros(
|
|
(x.size(0), r), dtype=torch.float32, device=x.device)
|
|
for _ in range(len(output_slices)))
|
|
self.add_shrink(buffer, x, lora_a_stacked, scale, **kwargs)
|
|
self.add_expand(y,
|
|
buffer,
|
|
lora_b_stacked,
|
|
None,
|
|
output_slices,
|
|
add_inputs=True,
|
|
**kwargs)
|
|
|
|
def add_lora_logits(self,
|
|
y: torch.Tensor,
|
|
x: torch.Tensor,
|
|
lora_a_stacked: torch.Tensor,
|
|
lora_b_stacked: torch.Tensor,
|
|
scale,
|
|
*,
|
|
buffer: Optional[torch.Tensor] = None,
|
|
**kwargs) -> None:
|
|
"""
|
|
Applies lora specifically for LogitsProcessorWithLoRA.
|
|
|
|
Semantics:
|
|
buffer = (x @ lora_a_stacked) * scale
|
|
y += buffer @ lora_b_stacked
|
|
|
|
Args:
|
|
y (torch.Tensor): Output tensor.
|
|
x (torch.Tensor): Input tensor.
|
|
lora_a_stacked (torch.Tensor): lora_a's weights.
|
|
lora_b_stacked (torch.Tensor):lora_b's weights.
|
|
scale (float): Scaling factor.
|
|
buffer (Optional[torch.Tensor]):Default to None.
|
|
"""
|
|
y_org = y
|
|
y = y.view(-1, y.shape[-1])
|
|
x = x.view(-1, x.shape[-1])
|
|
r = lora_b_stacked.size(-1)
|
|
|
|
if buffer is None:
|
|
buffer = torch.zeros((x.size(0), r),
|
|
dtype=torch.float32,
|
|
device=x.device)
|
|
|
|
indices = self.sampler_indices
|
|
|
|
bgmv_shrink(x, lora_a_stacked, buffer, indices, scale)
|
|
bgmv_expand(buffer, lora_b_stacked, y, indices, add_inputs=True)
|
|
|
|
y = y.view_as(y_org)
|