140 lines
5.4 KiB
Python
140 lines
5.4 KiB
Python
################################################################################
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# Copyright(c)2020-2025 Shanghai Biren Technology Co., Ltd. All rights reserved.
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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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################################################################################
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from typing import Optional, Tuple
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import torch
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import torch.nn.functional as F
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import torch_br
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import torch_br.supa._debug as supa_debug
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from fastcore.basics import patch_to
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import vllm
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from vllm.distributed import tensor_model_parallel_all_reduce
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from vllm.model_executor.layers.vocab_parallel_embedding import (
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UnquantizedEmbeddingMethod, VocabParallelEmbedding)
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from vllm_br import envs
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@patch_to(UnquantizedEmbeddingMethod)
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def process_weights_after_loading(self, module):
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if envs.VLLM_BR_EMBEDDING_S0B:
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ori_weight = module.weight.data.cpu()
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module.weight.data = torch_br._empty_ut_only(module.weight.shape,
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"colmajor",
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False,
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0,
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dtype=module.weight.dtype,
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sbp='SB')
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module.weight.data.copy_(ori_weight)
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@patch_to(UnquantizedEmbeddingMethod)
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def embedding(self, layer: torch.nn.Module,
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input_: torch.Tensor) -> torch.Tensor:
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if envs.VLLM_BR_EMBEDDING_S0B:
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y_supa = torch_br._empty_ut_only(
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[1, input_.shape[0], layer.weight.shape[-1]],
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is_numa=False,
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dtype=layer.weight.dtype,
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sbp='BB',
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tensor_type="colmajor",
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)
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torch_br.out_embedding(y_supa, layer.weight.data, input_, -1, -1)
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y_supa.squeeze_(0)
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return y_supa
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return F.embedding(input_, layer.weight)
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@torch.jit.script
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def get_masked_input_and_mask(
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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.jit.script will fuse all of the pointwise ops below
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# into a single kernel, making it very fast
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spc_num = envs.VLLM_BR_DEVICE_SPC_NUM
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if spc_num > 16:
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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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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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input_ = vocab_mask * (input_ - valid_offset)
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return input_, ~vocab_mask
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else:
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input_, inv_vocab_mask = torch_br.supa_embedding_mask_infer(
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input_, org_vocab_start_index, org_vocab_end_index,
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num_org_vocab_padding, added_vocab_start_index,
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added_vocab_end_index)
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return input_, inv_vocab_mask
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vllm.model_executor.layers.vocab_parallel_embedding.get_masked_input_and_mask = get_masked_input_and_mask
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def vocab_parallel_embedding_forward(self, input_) -> torch.Tensor:
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if self.tp_size > 1:
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# Build the mask.
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masked_input, input_mask = 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),
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0) # type: ignore
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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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def apply(self,
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layer: torch.nn.Module,
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x: torch.Tensor,
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bias: Optional[torch.Tensor] = None) -> torch.Tensor:
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# numa weight is 3-dims
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if len(layer.weight.shape) == 3:
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output_size = (layer.output_size_per_partition if hasattr(
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layer, "output_size_per_partition") else layer.output_size)
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return torch_br.br_fused_mlp_infer(
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x, [layer.weight],
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output_w=output_size,
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bias=[bias] if bias is not None else None)
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supa_debug.set_enable_sublas_api(True)
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output = F.linear(x, layer.weight, bias)
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supa_debug.set_enable_sublas_api(False)
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return output
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UnquantizedEmbeddingMethod.apply = apply
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VocabParallelEmbedding.forward = vocab_parallel_embedding_forward
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