342 lines
14 KiB
Python
342 lines
14 KiB
Python
#
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# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
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# This file is a part of the vllm-ascend project.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import torch
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import torch.distributed as dist
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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
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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,
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QuantizeMethodBase,
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method_has_implemented_embedding,
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)
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from vllm.model_executor.layers.vocab_parallel_embedding import (
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DEFAULT_VOCAB_PADDING_SIZE,
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ParallelLMHead,
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UnquantizedEmbeddingMethod,
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VocabParallelEmbedding,
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pad_vocab_size,
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)
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from vllm.model_executor.utils import set_weight_attrs
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from vllm_ascend.ascend_config import get_ascend_config
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from vllm_ascend.distributed.parallel_state import get_embed_tp_group, get_lmhead_tp_group
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from vllm_ascend.utils import embedding_tp_enable, get_potential_max_tokens, 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__(
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self,
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num_embeddings: int,
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embedding_dim: int,
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params_dtype: torch.dtype | None = None,
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org_num_embeddings: int | None = None,
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padding_size: int = DEFAULT_VOCAB_PADDING_SIZE,
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quant_config: QuantizationConfig | None = None,
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prefix: str = "",
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):
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nn.Module.__init__(self)
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self.forward_type = None
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if lmhead_tp_enable() and "head" in prefix:
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self.comm_group = get_lmhead_tp_group()
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elif embedding_tp_enable() and "embed_tokens" in prefix:
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self.comm_group = get_embed_tp_group()
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self.forward_type = "embed_tp"
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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, 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, self.padding_size
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)
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assert self.org_vocab_size_padded <= self.num_embeddings_padded
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self.shard_indices = self._get_indices(
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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,
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self.tp_size,
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)
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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(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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)
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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, self.tp_size)
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assert self.shard_indices.num_elements_padded == 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 - self.shard_indices.org_vocab_start_index
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)
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self.num_added_embeddings_per_partition = (
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self.shard_indices.added_vocab_end_index - self.shard_indices.added_vocab_start_index
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)
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self.quant_method.create_weights(
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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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)
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def _get_masked_input_and_mask(
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self,
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input_: torch.Tensor,
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org_vocab_start_index: int,
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org_vocab_end_index: int,
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num_org_vocab_padding: int,
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added_vocab_start_index: int,
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added_vocab_end_index: int,
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) -> 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) & (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) & (input_ < added_vocab_end_index)
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added_offset = (
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added_vocab_start_index - (org_vocab_end_index - org_vocab_start_index) - num_org_vocab_padding
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)
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valid_offset = (org_vocab_start_index * 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.forward_type == "embed_tp":
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return self._forward_embed_tp(input_)
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else:
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return self._forward_origin(input_)
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def _forward_embed_tp(self, input_):
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num_tokens = input_.shape[0]
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# potential_max_tokens is computed once in the model runner __init__, so
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# reading it here is a cheap global lookup. Validate before allocating so
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# an oversized batch fails fast.
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capacity = get_potential_max_tokens()
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if num_tokens > capacity:
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raise ValueError(
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f"embedding_tp static capacity {capacity} < num_tokens "
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f"{num_tokens}; increase max_cudagraph_capture_size or "
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f"max_num_batched_tokens."
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)
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# Lazy init on first call (profiling run, which precedes ACL graph
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# capture). Static buffers keep a stable device address across all
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# later capture/replay cycles — graph replay requires the same
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# address that was recorded at capture (comm_group.all_gather and
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# reduce_scatter internally torch.empty() per call, which would
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# desync the HCCL operator recorded at capture).
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# Mirrors the OTP v13 fix in dsa_v1.py:_forward_o_proj.
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if not hasattr(self, "_embed_ag_in_buf"):
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device = input_.device
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# all_gather buffers carry token IDs (int64).
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self._embed_ag_in_buf = torch.zeros((capacity,), dtype=input_.dtype, device=device)
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self._embed_ag_out_buf = torch.empty((self.tp_size * capacity,), dtype=input_.dtype, device=device)
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# reduce_scatter buffers carry bf16 embeddings.
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self._embed_rs_in_buf = torch.empty(
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(self.tp_size * capacity, self.embedding_dim), dtype=self.params_dtype, device=device
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)
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self._embed_rs_out_buf = torch.empty((capacity, self.embedding_dim), dtype=self.params_dtype, device=device)
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# Pad input into the address-stable all_gather input buffer.
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self._embed_ag_in_buf.zero_()
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self._embed_ag_in_buf[:num_tokens].copy_(input_)
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dist.all_gather_into_tensor(self._embed_ag_out_buf, self._embed_ag_in_buf, group=self.comm_group.device_group)
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complete_input = self._embed_ag_out_buf
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# Masking unchanged; padding rows map to OOB and get masked to 0
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# via masked_fill_ below (token_id=0 stays in-range after shift).
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masked_input, input_mask = self._get_masked_input_and_mask(
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complete_input,
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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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)
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# Embedding lookup is a local op (F.embedding); its fresh allocation
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# does not affect ACL graph replay. Copy into the static rs_in
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# buffer so reduce_scatter reads from a stable address.
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output_parallel = self.quant_method.embedding(self, masked_input.long())
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self._embed_rs_in_buf.copy_(output_parallel)
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self._embed_rs_in_buf.masked_fill_(input_mask.unsqueeze(-1), 0)
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dist.reduce_scatter_tensor(self._embed_rs_out_buf, self._embed_rs_in_buf, group=self.comm_group.device_group)
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# Strip padding rows; preserve the original return shape.
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return self._embed_rs_out_buf[:num_tokens].view(num_tokens, -1)
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def _forward_origin(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_,
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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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)
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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, 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 = torch.ops.vllm.maybe_pad_and_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__(
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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: torch.dtype | None = None,
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org_num_embeddings: int | None = None,
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padding_size: int = DEFAULT_VOCAB_PADDING_SIZE,
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quant_config: QuantizationConfig | None = None,
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prefix: str = "",
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):
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AscendVocabParallelEmbedding.__init__(
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self, num_embeddings, embedding_dim, params_dtype, org_num_embeddings, padding_size, quant_config, prefix
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)
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self.quant_config = quant_config
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if bias:
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self.bias = Parameter(torch.empty(self.num_embeddings_per_partition, dtype=params_dtype))
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set_weight_attrs(
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self.bias,
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{
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"output_dim": 0,
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"weight_loader": self.weight_loader,
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},
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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: torch.Tensor | None = None,
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) -> torch.Tensor | None:
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if lmhead_tp_enable():
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return self._get_logits_lmheadtp(hidden_states, lm_head, embedding_bias)
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else:
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return self._get_logits_normal(hidden_states, lm_head, 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: torch.Tensor | None,
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) -> torch.Tensor | None:
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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(hidden_states, dim=0)
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logits = lm_head.quant_method.apply(lm_head, gathered_hidden_states, bias=embedding_bias)
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# Gather logits for tensor parallel
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if not get_ascend_config().enable_reduce_sample:
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logits = get_lmhead_tp_group().all_to_all(logits)
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# Remove paddings in vocab (if any)
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if logits is not None:
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if not get_ascend_config().enable_reduce_sample:
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logits = logits[..., : self.org_vocab_size]
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else:
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logits = logits[..., : lm_head.num_org_embeddings_per_partition]
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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: torch.Tensor | None,
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) -> torch.Tensor | None:
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logits = lm_head.quant_method.apply(lm_head, hidden_states, bias=embedding_bias)
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# Gather logits for tensor parallel
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if not get_ascend_config().enable_reduce_sample:
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logits = self._gather_logits(logits)
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# Remove paddings in vocab (if any)
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if logits is not None:
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if not get_ascend_config().enable_reduce_sample:
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logits = logits[..., : self.org_vocab_size]
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else:
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logits = logits[..., : lm_head.num_org_embeddings_per_partition]
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return logits
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