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>
269 lines
12 KiB
Python
269 lines
12 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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from typing import Optional, Tuple
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import torch
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from torch import nn
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from torch.nn.parameter import Parameter
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from vllm.distributed import divide, tensor_model_parallel_all_reduce
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from vllm.distributed.parallel_state import get_tp_group
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from vllm.model_executor.layers.logits_processor import LogitsProcessor
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from vllm.model_executor.layers.quantization.base_config import (
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QuantizationConfig, QuantizeMethodBase, method_has_implemented_embedding)
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from vllm.model_executor.layers.vocab_parallel_embedding import (
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DEFAULT_VOCAB_PADDING_SIZE, ParallelLMHead, UnquantizedEmbeddingMethod,
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VocabParallelEmbedding, pad_vocab_size)
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from vllm.model_executor.utils import set_weight_attrs
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from vllm_ascend.distributed.parallel_state import get_lmhead_tp_group
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from vllm_ascend.utils import lmhead_tp_enable
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class AscendVocabParallelEmbedding(VocabParallelEmbedding):
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"""
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Register VocabParallelEmbedding as a custom op for Ascend.
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AscendVocabParallelEmbedding support different communication parallel groups
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Added the feature of lmheadTP in pure dp scenario
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"""
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def __init__(self,
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num_embeddings: int,
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embedding_dim: int,
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params_dtype: Optional[torch.dtype] = None,
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org_num_embeddings: Optional[int] = None,
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padding_size: int = DEFAULT_VOCAB_PADDING_SIZE,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = ""):
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nn.Module.__init__(self)
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if lmhead_tp_enable() and prefix.find("lm_head") != -1:
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self.comm_group = get_lmhead_tp_group()
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else:
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self.comm_group = get_tp_group()
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self.tp_size = self.comm_group.world_size
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self.tp_rank = self.comm_group.rank_in_group
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self.num_embeddings = num_embeddings
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self.padding_size = padding_size
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self.org_vocab_size = org_num_embeddings or num_embeddings
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num_added_embeddings = num_embeddings - self.org_vocab_size
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self.org_vocab_size_padded = pad_vocab_size(self.org_vocab_size,
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self.padding_size)
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self.num_embeddings_padded = pad_vocab_size(
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self.org_vocab_size_padded + num_added_embeddings,
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self.padding_size)
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assert self.org_vocab_size_padded <= self.num_embeddings_padded
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self.shard_indices = self._get_indices(self.num_embeddings_padded,
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self.org_vocab_size_padded,
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self.num_embeddings,
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self.org_vocab_size,
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self.tp_rank, self.tp_size)
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self.embedding_dim = embedding_dim
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quant_method = None
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if quant_config is not None:
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quant_method = quant_config.get_quant_method(self, prefix=prefix)
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if quant_method is None:
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quant_method = UnquantizedEmbeddingMethod()
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# If we are making an embedding layer, then our quantization linear
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# method must implement the embedding operation. If we are another
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# layer type like ParallelLMHead, this is not important.
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is_embedding_layer = type(self) is VocabParallelEmbedding
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quant_method_implements_embedding = method_has_implemented_embedding(
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type(quant_method))
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if is_embedding_layer and not quant_method_implements_embedding:
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raise NotImplementedError(
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f"The class {type(quant_method).__name__} must implement "
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"the 'embedding' method, see UnquantizedEmbeddingMethod.")
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self.quant_method: QuantizeMethodBase = quant_method
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if params_dtype is None:
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params_dtype = torch.get_default_dtype()
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self.params_dtype = params_dtype
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# Divide the weight matrix along the vocaburaly dimension.
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self.num_added_embeddings = self.num_embeddings - self.org_vocab_size
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self.num_embeddings_per_partition = divide(self.num_embeddings_padded,
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self.tp_size)
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assert (self.shard_indices.num_elements_padded ==
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self.num_embeddings_per_partition)
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self.num_org_embeddings_per_partition = (
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self.shard_indices.org_vocab_end_index -
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self.shard_indices.org_vocab_start_index)
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self.num_added_embeddings_per_partition = (
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self.shard_indices.added_vocab_end_index -
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self.shard_indices.added_vocab_start_index)
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self.quant_method.create_weights(self,
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self.embedding_dim,
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[self.num_embeddings_per_partition],
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self.embedding_dim,
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self.num_embeddings_padded,
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params_dtype=params_dtype,
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weight_loader=self.weight_loader)
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def _get_masked_input_and_mask(
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self, input_: torch.Tensor, org_vocab_start_index: int,
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org_vocab_end_index: int, num_org_vocab_padding: int,
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added_vocab_start_index: int,
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added_vocab_end_index: int) -> Tuple[torch.Tensor, torch.Tensor]:
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# torch.compile will fuse all of the pointwise ops below
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# into a single kernel, making it very fast
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org_vocab_mask = (input_ >= org_vocab_start_index) & (
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input_ < org_vocab_end_index)
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# Adapt: avoid create added_vocab_mask when added_vocab_start_index == added_vocab_end_index.
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if added_vocab_start_index == added_vocab_end_index:
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valid_offset = (org_vocab_start_index * org_vocab_mask)
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vocab_mask = org_vocab_mask
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else:
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added_vocab_mask = (input_ >= added_vocab_start_index) & (
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input_ < added_vocab_end_index)
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added_offset = added_vocab_start_index - (
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org_vocab_end_index -
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org_vocab_start_index) - num_org_vocab_padding
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valid_offset = (org_vocab_start_index *
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org_vocab_mask) + (added_offset * added_vocab_mask)
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vocab_mask = org_vocab_mask | added_vocab_mask
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# Adapt end.
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input_ = vocab_mask * (input_ - valid_offset)
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return input_, ~vocab_mask
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def forward(self, input_):
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if self.tp_size > 1:
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# Build the mask.
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masked_input, input_mask = self._get_masked_input_and_mask(
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input_, self.shard_indices.org_vocab_start_index,
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self.shard_indices.org_vocab_end_index,
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self.shard_indices.num_org_vocab_padding,
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self.shard_indices.added_vocab_start_index,
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self.shard_indices.added_vocab_end_index)
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else:
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masked_input = input_
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# Get the embeddings.
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output_parallel = self.quant_method.embedding(self,
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masked_input.long())
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# Mask the output embedding.
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if self.tp_size > 1:
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output_parallel.masked_fill_(input_mask.unsqueeze(-1), 0)
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# Reduce across all the model parallel GPUs.
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output = tensor_model_parallel_all_reduce(output_parallel)
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return output
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class AscendParallelLMHead(ParallelLMHead):
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"""
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Register ParallelLMHead as a custom op for Ascend."""
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def __init__(self,
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num_embeddings: int,
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embedding_dim: int,
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bias: bool = False,
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params_dtype: Optional[torch.dtype] = None,
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org_num_embeddings: Optional[int] = None,
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padding_size: int = DEFAULT_VOCAB_PADDING_SIZE,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = ""):
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AscendVocabParallelEmbedding.__init__(self, num_embeddings,
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embedding_dim, params_dtype,
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org_num_embeddings, padding_size,
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quant_config, prefix)
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self.quant_config = quant_config
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if bias:
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self.bias = Parameter(
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torch.empty(self.num_embeddings_per_partition,
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dtype=params_dtype))
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set_weight_attrs(self.bias, {
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"output_dim": 0,
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"weight_loader": self.weight_loader,
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})
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else:
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self.register_parameter("bias", None)
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class AscendLogitsProcessor(LogitsProcessor):
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"""
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Register LogitsProcessor as a custom op for Ascend.
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Added the feature of lmheadTP in pure dp scenario
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"""
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def _get_logits(
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self,
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hidden_states: torch.Tensor,
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lm_head: AscendParallelLMHead,
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embedding_bias: Optional[torch.Tensor] = None,
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) -> Optional[torch.Tensor]:
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if lmhead_tp_enable():
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return self._get_logits_lmheadtp(hidden_states, lm_head,
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embedding_bias)
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else:
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return self._get_logits_normal(hidden_states, lm_head,
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embedding_bias)
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def _get_logits_lmheadtp(
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self,
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hidden_states: torch.Tensor,
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lm_head: AscendParallelLMHead,
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embedding_bias: Optional[torch.Tensor],
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) -> Optional[torch.Tensor]:
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# Gather hidden states from all devices in tensor parallel group
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gathered_hidden_states = get_lmhead_tp_group().all_gather(
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hidden_states, dim=0)
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local_logits = lm_head.quant_method.apply(lm_head,
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gathered_hidden_states,
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bias=embedding_bias)
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# Gather logits for tensor parallel
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logits = get_lmhead_tp_group().all_to_all(local_logits)
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# Remove paddings in vocab (if any)
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if logits is not None:
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logits = logits[..., :self.org_vocab_size]
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return logits
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def _get_logits_normal(
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self,
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hidden_states: torch.Tensor,
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lm_head: AscendParallelLMHead,
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embedding_bias: Optional[torch.Tensor],
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) -> Optional[torch.Tensor]:
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local_logits = lm_head.quant_method.apply(lm_head,
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hidden_states,
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bias=embedding_bias)
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# Gather logits for tensor parallel
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logits = self._gather_logits(local_logits)
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# Remove paddings in vocab (if any)
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if logits is not None:
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logits = logits[..., :self.org_vocab_size]
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return logits
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def forward(
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self,
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lm_head: VocabParallelEmbedding,
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hidden_states: torch.Tensor,
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# keep this for version compatibility
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sampling_metadata=None, # type: ignore
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embedding_bias: Optional[torch.Tensor] = None,
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) -> Optional[torch.Tensor]:
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return LogitsProcessor.forward(self,
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lm_head,
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hidden_states,
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embedding_bias=embedding_bias)
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