947 lines
33 KiB
Python
947 lines
33 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# Derived from BART implementation posted on HuggingFace; license below:
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#
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# coding=utf-8
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# Copyright 2021 The Fairseq Authors and The HuggingFace Inc. team.
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# All rights reserved.
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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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"""PyTorch BART model."""
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import math
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from typing import Iterable, Optional, Tuple
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import torch
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from torch import nn
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from transformers import BartConfig
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from transformers.utils import logging
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from vllm.attention import Attention, AttentionType
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from vllm.config import CacheConfig, LoRAConfig, VllmConfig
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from vllm.distributed import get_tensor_model_parallel_world_size
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from vllm.model_executor.layers.activation import get_act_fn
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from vllm.model_executor.layers.linear import (ColumnParallelLinear,
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QKVCrossParallelLinear,
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QKVParallelLinear,
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RowParallelLinear)
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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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from vllm.model_executor.layers.sampler import SamplerOutput, get_sampler
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from vllm.model_executor.layers.vocab_parallel_embedding import (
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ParallelLMHead, VocabParallelEmbedding)
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from vllm.model_executor.model_loader.weight_utils import default_weight_loader
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from vllm.model_executor.sampling_metadata import SamplingMetadata
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from vllm.sequence import IntermediateTensors
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from .interfaces import SupportsQuant, SupportsV0Only
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from .utils import maybe_prefix
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logger = logging.get_logger(__name__)
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def get_bsz_seq_len(input_ids):
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shp = input_ids.shape
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ndim = len(shp)
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if ndim == 1:
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return 1, input_ids.numel()
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else:
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return shp[:2]
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class BartLearnedPositionalEmbedding(VocabParallelEmbedding):
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"""
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This module learns positional embeddings up to a fixed maximum size.
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"""
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def __init__(self, num_embeddings: int, embedding_dim: int):
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# Bart is set up so that if padding_idx is
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# specified then offset the embedding ids by 2
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# and adjust num_embeddings appropriately.
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# Other models don't have this hack
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self.offset = 2
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super().__init__(num_embeddings + self.offset, embedding_dim)
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def forward(
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self,
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positions: torch.Tensor,
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) -> torch.Tensor:
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"""`input_ids' shape is expected to be [bsz x seqlen]."""
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return super().forward(positions + self.offset)
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class BartScaledWordEmbedding(VocabParallelEmbedding):
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"""
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This module overrides VocabParallelEmbedding's
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forward by multiplying with embeddings scale.
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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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embed_scale: float = 1.0):
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super().__init__(num_embeddings, embedding_dim)
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self.embed_scale = embed_scale
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def forward(self, input_ids: torch.Tensor) -> torch.Tensor:
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return super().forward(input_ids) * self.embed_scale
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class BartParallelLMHead(ParallelLMHead):
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"""
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This module overrides ParallelLMHead's
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forward by dividing by embeddings scale,
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yielding effectively the inverse of
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BartScaledWordEmbedding
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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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embed_scale: float = 1.0):
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super().__init__(num_embeddings, embedding_dim)
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self.embed_scale = embed_scale
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def forward(self, input_ids: torch.Tensor) -> torch.Tensor:
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return super().forward(input_ids) / self.embed_scale
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class BartEncoderAttention(nn.Module):
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def __init__(
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self,
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embed_dim: int,
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num_heads: int,
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bias: bool = True,
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config: Optional[BartConfig] = None,
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cache_config: Optional[CacheConfig] = None,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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):
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super().__init__()
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self.d_model = config.d_model
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self.embed_dim = embed_dim
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self.total_num_heads = num_heads
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self.total_num_kv_heads = self.total_num_heads
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self.head_dim = embed_dim // num_heads
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self.config = config
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if (self.head_dim * num_heads) != self.embed_dim:
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raise ValueError(f"embed_dim must be divisible by num_heads "
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f"(got `embed_dim`: {self.embed_dim}"
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f" and `num_heads`: {num_heads}).")
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self.scaling = self.head_dim**-0.5
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self.qkv_proj = QKVParallelLinear(
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self.d_model,
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self.d_model // self.total_num_heads,
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self.total_num_heads,
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self.total_num_kv_heads,
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bias=bias,
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quant_config=quant_config,
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)
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self.out_proj = RowParallelLinear(
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embed_dim,
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embed_dim,
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bias=bias,
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quant_config=quant_config,
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)
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tp_world_size = get_tensor_model_parallel_world_size()
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assert self.total_num_heads % tp_world_size == 0
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self.num_heads = self.total_num_heads // tp_world_size
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if self.total_num_kv_heads >= tp_world_size:
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# Number of KV heads is greater than TP size, so we partition
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# the KV heads across multiple tensor parallel GPUs.
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assert self.total_num_kv_heads % tp_world_size == 0
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else:
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# Number of KV heads is less than TP size, so we replicate
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# the KV heads across multiple tensor parallel GPUs.
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assert tp_world_size % self.total_num_kv_heads == 0
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self.num_kv_heads = self.num_heads
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self.q_size = self.num_heads * self.head_dim
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self.kv_size = self.num_kv_heads * self.head_dim
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self.attn = Attention(self.num_heads,
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self.head_dim,
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self.scaling,
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num_kv_heads=self.num_kv_heads,
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cache_config=cache_config,
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quant_config=quant_config,
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prefix=f"{prefix}.attn",
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attn_type=AttentionType.ENCODER)
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def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
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"""Input shape: Batch x Time x Channel"""
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qkv, _ = self.qkv_proj(hidden_states)
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q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
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attn_output = self.attn(q, k, v)
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output, _ = self.out_proj(attn_output)
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return output
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class BartDecoderSelfAttention(nn.Module):
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def __init__(
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self,
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embed_dim: int,
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num_heads: int,
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bias: bool = True,
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config: Optional[BartConfig] = None,
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cache_config: Optional[CacheConfig] = None,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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):
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super().__init__()
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self.d_model = config.d_model
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self.embed_dim = embed_dim
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self.total_num_heads = num_heads
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self.total_num_kv_heads = self.total_num_heads
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self.head_dim = embed_dim // num_heads
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self.config = config
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if (self.head_dim * num_heads) != self.embed_dim:
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raise ValueError(f"embed_dim must be divisible by num_heads "
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f"(got `embed_dim`: {self.embed_dim}"
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f" and `num_heads`: {num_heads}).")
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self.scaling = self.head_dim**-0.5
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self.qkv_proj = QKVParallelLinear(
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self.d_model,
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self.d_model // self.total_num_heads,
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self.total_num_heads,
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self.total_num_kv_heads,
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bias=bias,
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quant_config=quant_config,
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)
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self.out_proj = RowParallelLinear(
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embed_dim,
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embed_dim,
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bias=bias,
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quant_config=quant_config,
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)
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tp_world_size = get_tensor_model_parallel_world_size()
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assert self.total_num_heads % tp_world_size == 0
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self.num_heads = self.total_num_heads // tp_world_size
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if self.total_num_kv_heads >= tp_world_size:
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# Number of KV heads is greater than TP size, so we partition
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# the KV heads across multiple tensor parallel GPUs.
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assert self.total_num_kv_heads % tp_world_size == 0
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else:
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# Number of KV heads is less than TP size, so we replicate
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# the KV heads across multiple tensor parallel GPUs.
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assert tp_world_size % self.total_num_kv_heads == 0
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self.num_kv_heads = self.num_heads
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self.q_size = self.num_heads * self.head_dim
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self.kv_size = self.num_kv_heads * self.head_dim
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self.attn = Attention(self.num_heads,
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self.head_dim,
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self.scaling,
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num_kv_heads=self.num_kv_heads,
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cache_config=cache_config,
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quant_config=quant_config,
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prefix=f"{prefix}.attn",
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attn_type=AttentionType.DECODER)
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def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
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"""Input shape: Batch x Time x Channel"""
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qkv, _ = self.qkv_proj(hidden_states)
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q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
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attn_output = self.attn(q, k, v)
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output, _ = self.out_proj(attn_output)
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return output
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class BartCrossAttention(nn.Module):
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def __init__(
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self,
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embed_dim: int,
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num_heads: int,
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bias: bool = True,
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config: Optional[BartConfig] = None,
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cache_config: Optional[CacheConfig] = None,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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):
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super().__init__()
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self.d_model = config.d_model
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self.embed_dim = embed_dim
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self.total_num_heads = num_heads
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self.total_num_kv_heads = self.total_num_heads
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self.head_dim = embed_dim // num_heads
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self.config = config
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if (self.head_dim * num_heads) != self.embed_dim:
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raise ValueError(f"embed_dim must be divisible by num_heads "
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f"(got `embed_dim`: {self.embed_dim}"
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f" and `num_heads`: {num_heads}).")
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self.scaling = self.head_dim**-0.5
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# TP sharding sizes is accounted for within "*Parallel" layers.
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self.qkv_proj = QKVCrossParallelLinear(self.d_model,
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self.d_model //
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self.total_num_heads,
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self.total_num_heads,
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self.total_num_kv_heads,
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bias,
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quant_config=quant_config)
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self.out_proj = RowParallelLinear(
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embed_dim,
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embed_dim,
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bias=bias,
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quant_config=quant_config,
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)
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tp_world_size = get_tensor_model_parallel_world_size()
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assert self.total_num_heads % tp_world_size == 0
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self.num_heads = self.total_num_heads // tp_world_size
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if self.total_num_kv_heads >= tp_world_size:
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# Number of KV heads is greater than TP size, so we partition
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# the KV heads across multiple tensor parallel GPUs.
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assert self.total_num_kv_heads % tp_world_size == 0
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else:
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# Number of KV heads is less than TP size, so we replicate
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# the KV heads across multiple tensor parallel GPUs.
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assert tp_world_size % self.total_num_kv_heads == 0
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self.num_kv_heads = self.num_heads # No GQA in bart
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self.attn = Attention(self.num_heads,
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self.head_dim,
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self.scaling,
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num_kv_heads=self.num_kv_heads,
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cache_config=cache_config,
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quant_config=quant_config,
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prefix=f"{prefix}.attn",
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attn_type=AttentionType.ENCODER_DECODER)
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def forward(
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self,
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decoder_hidden_states: torch.Tensor,
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encoder_hidden_states: Optional[torch.Tensor] = None,
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) -> torch.Tensor:
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"""Input shape: Batch x Time x Channel"""
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q, k, v = self.qkv_proj(decoder_hidden_states, encoder_hidden_states)
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attn_output = self.attn(q, k, v)
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output, _ = self.out_proj(attn_output)
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return output
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class BartEncoderLayer(nn.Module):
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def __init__(
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self,
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config: BartConfig,
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cache_config: Optional[CacheConfig] = None,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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):
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super().__init__()
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self.embed_dim = config.d_model
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self.self_attn = BartEncoderAttention(
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embed_dim=self.embed_dim,
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num_heads=config.encoder_attention_heads,
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config=config,
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cache_config=cache_config,
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quant_config=quant_config,
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prefix=f"{prefix}.self_attn",
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)
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self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
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self.activation_fn = get_act_fn(config.activation_function)
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ffn_hidden_size = self.embed_dim
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ffn_intermediate_size = config.encoder_ffn_dim
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ffn_has_bias = True
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self.fc1 = ColumnParallelLinear(
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ffn_hidden_size,
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ffn_intermediate_size,
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bias=ffn_has_bias,
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quant_config=quant_config,
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)
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self.act = get_act_fn("gelu")
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self.fc2 = RowParallelLinear(
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ffn_intermediate_size,
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ffn_hidden_size,
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bias=ffn_has_bias,
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quant_config=quant_config,
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)
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self.final_layer_norm = nn.LayerNorm(self.embed_dim)
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def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
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r"""
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Args:
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hidden_states
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torch.Tensor of *encoder* input embeddings.
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Returns:
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Encoder layer output torch.Tensor
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"""
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residual = hidden_states
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hidden_states = self.self_attn(hidden_states=hidden_states)
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hidden_states = residual + hidden_states
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hidden_states = self.self_attn_layer_norm(hidden_states)
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residual = hidden_states
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fc1_out, _ = self.fc1(hidden_states)
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hidden_states = self.activation_fn(fc1_out)
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hidden_states, _ = self.fc2(hidden_states)
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hidden_states = residual + hidden_states
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hidden_states = self.final_layer_norm(hidden_states)
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if hidden_states.dtype == torch.float16 and (
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torch.isinf(hidden_states).any()
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or torch.isnan(hidden_states).any()):
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clamp_value = torch.finfo(hidden_states.dtype).max - 1000
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hidden_states = torch.clamp(hidden_states,
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min=-clamp_value,
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max=clamp_value)
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return hidden_states
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class BartDecoderLayer(nn.Module):
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def __init__(
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self,
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config: BartConfig,
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cache_config: Optional[CacheConfig] = None,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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):
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super().__init__()
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self.embed_dim = config.d_model
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self.self_attn = BartDecoderSelfAttention(
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embed_dim=self.embed_dim,
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num_heads=config.decoder_attention_heads,
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config=config,
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cache_config=cache_config,
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quant_config=quant_config,
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prefix=f"{prefix}.self_attn",
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)
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self.activation_fn = get_act_fn(config.activation_function)
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self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
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'''
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afeldman-nm: personally I would call this "cross-attention",
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however I left the name as "encoder_attn" to maintain consistency
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with the name of the pretrained weights.
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'''
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self.encoder_attn = BartCrossAttention(
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self.embed_dim,
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config.decoder_attention_heads,
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config=config,
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prefix=f"{prefix}.encoder_attn",
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)
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self.encoder_attn_layer_norm = nn.LayerNorm(self.embed_dim)
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ffn_hidden_size = self.embed_dim
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ffn_intermediate_size = config.encoder_ffn_dim
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ffn_has_bias = True
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self.fc1 = ColumnParallelLinear(
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ffn_hidden_size,
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ffn_intermediate_size,
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bias=ffn_has_bias,
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quant_config=quant_config,
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)
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self.fc2 = RowParallelLinear(
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ffn_intermediate_size,
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ffn_hidden_size,
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bias=ffn_has_bias,
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quant_config=quant_config,
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)
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self.final_layer_norm = nn.LayerNorm(self.embed_dim)
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def forward(
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self,
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decoder_hidden_states: torch.Tensor,
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|
encoder_hidden_states: Optional[torch.Tensor] = None,
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|
) -> torch.Tensor:
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|
r"""
|
|
Args:
|
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decoder_hidden_states
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|
torch.Tensor of *decoder* input embeddings.
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|
encoder_hidden_states
|
|
torch.Tensor of *encoder* input embeddings.
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|
Returns:
|
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Decoder layer output torch.Tensor
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"""
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residual = decoder_hidden_states
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# Self Attention
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hidden_states = self.self_attn(hidden_states=decoder_hidden_states)
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hidden_states = residual + hidden_states
|
|
hidden_states = self.self_attn_layer_norm(hidden_states)
|
|
|
|
# Cross-Attention Block
|
|
|
|
residual = hidden_states
|
|
|
|
hidden_states = self.encoder_attn(
|
|
decoder_hidden_states=hidden_states,
|
|
encoder_hidden_states=encoder_hidden_states,
|
|
)
|
|
|
|
hidden_states = residual + hidden_states
|
|
hidden_states = self.encoder_attn_layer_norm(hidden_states)
|
|
|
|
# Fully Connected
|
|
residual = hidden_states
|
|
fc1_out, _ = self.fc1(hidden_states)
|
|
hidden_states = self.activation_fn(fc1_out)
|
|
|
|
hidden_states, _ = self.fc2(hidden_states)
|
|
|
|
hidden_states = residual + hidden_states
|
|
hidden_states = self.final_layer_norm(hidden_states)
|
|
|
|
return hidden_states
|
|
|
|
|
|
class BartEncoder(nn.Module):
|
|
"""
|
|
Transformer encoder consisting of *config.encoder_layers*
|
|
self attention layers. Each layer is a [`BartEncoderLayer`].
|
|
Args:
|
|
config: BartConfig
|
|
embed_tokens (nn.Embedding): output embedding
|
|
"""
|
|
|
|
def __init__(self,
|
|
config: BartConfig,
|
|
cache_config: Optional[CacheConfig] = None,
|
|
quant_config: Optional[QuantizationConfig] = None,
|
|
lora_config: Optional[LoRAConfig] = None,
|
|
embed_tokens: Optional[nn.Embedding] = None,
|
|
prefix: str = ""):
|
|
super().__init__()
|
|
|
|
self.cache_config = cache_config
|
|
self.quant_config = quant_config
|
|
self.lora_config = lora_config
|
|
embed_dim = config.d_model
|
|
self.max_source_positions = config.max_position_embeddings
|
|
embed_scale = math.sqrt(embed_dim) if config.scale_embedding else 1.0
|
|
|
|
self.embed_tokens = BartScaledWordEmbedding(config.vocab_size,
|
|
embed_dim,
|
|
embed_scale=embed_scale)
|
|
|
|
if embed_tokens is not None:
|
|
self.embed_tokens.weight = embed_tokens.weight
|
|
|
|
self.embed_positions = BartLearnedPositionalEmbedding(
|
|
config.max_position_embeddings,
|
|
embed_dim,
|
|
)
|
|
self.layers = nn.ModuleList([
|
|
BartEncoderLayer(config,
|
|
cache_config,
|
|
quant_config,
|
|
prefix=f"{prefix}.layers.{layer_idx}")
|
|
for layer_idx in range(config.encoder_layers)
|
|
])
|
|
|
|
self.layernorm_embedding = nn.LayerNorm(embed_dim)
|
|
|
|
def forward(
|
|
self,
|
|
input_ids: torch.Tensor,
|
|
positions: torch.Tensor,
|
|
inputs_embeds: Optional[torch.Tensor] = None,
|
|
) -> torch.Tensor:
|
|
r"""
|
|
Args:
|
|
input_ids
|
|
Indices of *encoder* input sequence tokens in the vocabulary.
|
|
Padding will be ignored by default should you
|
|
provide it.
|
|
positions
|
|
Positions of *encoder* input sequence tokens.
|
|
Returns:
|
|
Decoder output torch.Tensor
|
|
"""
|
|
# retrieve input_ids and inputs_embeds
|
|
if inputs_embeds is None:
|
|
inputs_embeds = self.embed_tokens(input_ids)
|
|
|
|
embed_pos = self.embed_positions(positions)
|
|
embed_pos = embed_pos.to(inputs_embeds.device)
|
|
|
|
hidden_states = inputs_embeds + embed_pos
|
|
hidden_states = self.layernorm_embedding(hidden_states)
|
|
|
|
for encoder_layer in self.layers:
|
|
hidden_states = encoder_layer(hidden_states=hidden_states)
|
|
|
|
return hidden_states
|
|
|
|
|
|
class BartDecoder(nn.Module):
|
|
"""
|
|
Transformer decoder consisting of *config.decoder_layers* layers.
|
|
Each layer is a [`BartDecoderLayer`]
|
|
Args:
|
|
config: BartConfig
|
|
embed_tokens (nn.Embedding): output embedding
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
config: BartConfig,
|
|
cache_config: Optional[CacheConfig] = None,
|
|
quant_config: Optional[QuantizationConfig] = None,
|
|
lora_config: Optional[LoRAConfig] = None,
|
|
embed_tokens: Optional[nn.Embedding] = None,
|
|
prefix: str = "",
|
|
):
|
|
super().__init__()
|
|
self.cache_config = cache_config
|
|
self.quant_config = quant_config
|
|
self.lora_config = lora_config
|
|
self.max_target_positions = config.max_position_embeddings
|
|
embed_scale = math.sqrt(
|
|
config.d_model) if config.scale_embedding else 1.0
|
|
|
|
self.embed_tokens = BartScaledWordEmbedding(config.vocab_size,
|
|
config.d_model,
|
|
embed_scale=embed_scale)
|
|
|
|
if embed_tokens is not None:
|
|
self.embed_tokens.weight = embed_tokens.weight
|
|
|
|
self.embed_positions = BartLearnedPositionalEmbedding(
|
|
config.max_position_embeddings,
|
|
config.d_model,
|
|
)
|
|
|
|
self.layers = nn.ModuleList(
|
|
[BartDecoderLayer(config,cache_config,quant_config,
|
|
prefix=f"{prefix}.layers.{layer_idx}") \
|
|
for layer_idx in range(config.decoder_layers)])
|
|
|
|
self.layernorm_embedding = nn.LayerNorm(config.d_model)
|
|
|
|
def forward(
|
|
self,
|
|
decoder_input_ids: torch.Tensor,
|
|
decoder_positions: torch.Tensor,
|
|
encoder_hidden_states: Optional[torch.Tensor],
|
|
inputs_embeds: Optional[torch.Tensor] = None,
|
|
) -> torch.Tensor:
|
|
r"""
|
|
Args:
|
|
decoder_input_ids
|
|
Indices of *decoder* input sequence tokens in the vocabulary.
|
|
Padding will be ignored by default should you
|
|
provide it.
|
|
decoder_positions
|
|
Positions of *decoder* input sequence tokens.
|
|
encoder_hidden_states:
|
|
Tensor of encoder output embeddings
|
|
Returns:
|
|
Decoder output torch.Tensor
|
|
"""
|
|
if inputs_embeds is None:
|
|
inputs_embeds = self.embed_tokens(decoder_input_ids)
|
|
else:
|
|
decoder_positions = inputs_embeds[:, -1]
|
|
|
|
# embed positions
|
|
embed_pos = self.embed_positions(decoder_positions)
|
|
embed_pos = embed_pos.to(inputs_embeds.device)
|
|
|
|
hidden_states = inputs_embeds + embed_pos
|
|
hidden_states = self.layernorm_embedding(hidden_states)
|
|
|
|
# decoder layers
|
|
|
|
for decoder_layer in self.layers:
|
|
hidden_states = decoder_layer(
|
|
decoder_hidden_states=hidden_states,
|
|
encoder_hidden_states=encoder_hidden_states,
|
|
)
|
|
|
|
return hidden_states
|
|
|
|
|
|
class BartModel(nn.Module, SupportsQuant):
|
|
_tied_weights_keys = [
|
|
"encoder.embed_tokens.weight", "decoder.embed_tokens.weight"
|
|
]
|
|
|
|
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
|
|
super().__init__()
|
|
|
|
config = vllm_config.model_config.hf_config
|
|
cache_config = vllm_config.cache_config
|
|
quant_config = vllm_config.quant_config
|
|
lora_config = vllm_config.lora_config
|
|
|
|
self.config = config
|
|
|
|
lora_vocab = (lora_config.lora_extra_vocab_size *
|
|
(lora_config.max_loras or 1)) if lora_config else 0
|
|
self.vocab_size = config.vocab_size + lora_vocab
|
|
self.org_vocab_size = config.vocab_size
|
|
|
|
self.encoder = BartEncoder(config,
|
|
cache_config,
|
|
quant_config=quant_config,
|
|
prefix=f"{prefix}.encoder")
|
|
self.decoder = BartDecoder(config,
|
|
cache_config,
|
|
quant_config=quant_config,
|
|
prefix=f"{prefix}.decoder")
|
|
|
|
def forward(self, input_ids: torch.Tensor, positions: torch.Tensor,
|
|
encoder_input_ids: torch.Tensor,
|
|
encoder_positions: torch.Tensor) -> torch.Tensor:
|
|
r"""
|
|
Args:
|
|
input_ids
|
|
Indices of *decoder* input sequence tokens in the vocabulary.
|
|
Padding will be ignored by default should you
|
|
provide it.
|
|
positions
|
|
Positions of *decoder* input sequence tokens.
|
|
encoder_input_ids
|
|
Indices of *encoder* input sequence tokens in the vocabulary.
|
|
encoder_positions:
|
|
Positions of *encoder* input sequence tokens.
|
|
Returns:
|
|
Model output torch.Tensor
|
|
"""
|
|
|
|
encoder_hidden_states = None
|
|
|
|
if encoder_input_ids.numel() > 0:
|
|
# Run encoder attention if a non-zero number of encoder tokens
|
|
# are provided as input
|
|
encoder_hidden_states = self.encoder(input_ids=encoder_input_ids,
|
|
positions=encoder_positions)
|
|
|
|
# decoder outputs consists of
|
|
# (dec_features, past_key_value, dec_hidden, dec_attn)
|
|
decoder_outputs = self.decoder(
|
|
decoder_input_ids=input_ids,
|
|
decoder_positions=positions,
|
|
encoder_hidden_states=encoder_hidden_states)
|
|
|
|
return decoder_outputs
|
|
|
|
|
|
class BartForConditionalGeneration(nn.Module, SupportsV0Only, SupportsQuant):
|
|
packed_modules_mapping = {"qkv_proj": ["q_proj", "k_proj", "v_proj"]}
|
|
base_model_prefix = "model"
|
|
|
|
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
|
|
|
|
super().__init__()
|
|
config = vllm_config.model_config.hf_config
|
|
lora_config = vllm_config.lora_config
|
|
# currently all existing BART models have `tie_word_embeddings` enabled
|
|
assert config.tie_word_embeddings
|
|
self.config = config
|
|
self.model = BartModel(vllm_config=vllm_config,
|
|
prefix=maybe_prefix(prefix, "model"))
|
|
|
|
self.unpadded_vocab_size = config.vocab_size
|
|
if lora_config:
|
|
self.unpadded_vocab_size += lora_config.lora_extra_vocab_size
|
|
|
|
embed_scale = math.sqrt(
|
|
config.d_model) if config.scale_embedding else 1.0
|
|
|
|
self.lm_head = BartParallelLMHead(config.vocab_size,
|
|
config.d_model,
|
|
embed_scale=embed_scale)
|
|
|
|
self.logits_processor = LogitsProcessor(self.unpadded_vocab_size,
|
|
config.vocab_size)
|
|
self.sampler = get_sampler()
|
|
|
|
def forward(
|
|
self,
|
|
input_ids: torch.Tensor,
|
|
positions: torch.Tensor,
|
|
intermediate_tensors: Optional[IntermediateTensors] = None,
|
|
*,
|
|
encoder_input_ids: torch.Tensor,
|
|
encoder_positions: torch.Tensor,
|
|
**kwargs,
|
|
) -> torch.Tensor:
|
|
r"""
|
|
Args:
|
|
input_ids
|
|
torch.Tensor of *decoder* input token ids.
|
|
positions
|
|
torch.Tensor of *decoder* position indices.
|
|
encoder_input_ids
|
|
torch.Tensor of *encoder* input token ids.
|
|
encoder_positions
|
|
torch.Tensor of *encoder* position indices
|
|
Returns:
|
|
Output torch.Tensor
|
|
"""
|
|
return self.model(input_ids, positions, encoder_input_ids,
|
|
encoder_positions)
|
|
|
|
def compute_logits(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
sampling_metadata: SamplingMetadata,
|
|
) -> Optional[torch.Tensor]:
|
|
logits = self.logits_processor(self.lm_head, hidden_states,
|
|
sampling_metadata)
|
|
return logits
|
|
|
|
def sample(
|
|
self,
|
|
logits: Optional[torch.Tensor],
|
|
sampling_metadata: SamplingMetadata,
|
|
) -> Optional[SamplerOutput]:
|
|
next_tokens = self.sampler(logits, sampling_metadata)
|
|
return next_tokens
|
|
|
|
stacked_params_mapping = {
|
|
"q_proj": {
|
|
"param_name": "qkv_proj",
|
|
"shard_id": "q",
|
|
},
|
|
"k_proj": {
|
|
"param_name": "qkv_proj",
|
|
"shard_id": "k",
|
|
},
|
|
"v_proj": {
|
|
"param_name": "qkv_proj",
|
|
"shard_id": "v",
|
|
},
|
|
}
|
|
|
|
params_mapping = {
|
|
"beta": "bias",
|
|
"gamma": "weight",
|
|
"LayerNorm": "layernorm",
|
|
}
|
|
|
|
def _rename_key(self, key: str):
|
|
prefix = f"{self.base_model_prefix}."
|
|
key = key[len(prefix):] if key.startswith(prefix) else key
|
|
|
|
for src, dst in self.params_mapping.items():
|
|
key = key.replace(src, dst)
|
|
|
|
return key
|
|
|
|
def _rename_stacked_param(
|
|
self,
|
|
name: str,
|
|
) -> Tuple[str, Optional[str]]:
|
|
for key, mapping in self.stacked_params_mapping.items():
|
|
if key in name:
|
|
name = name.replace(key, mapping["param_name"])
|
|
return name, mapping["shard_id"]
|
|
return name, None
|
|
|
|
def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
|
|
|
|
model_params_dict = dict(self.model.named_parameters())
|
|
top_params_dict = dict(self.named_parameters())
|
|
|
|
weights_tuple_list = list(weights)
|
|
|
|
shared_embedding_weight = None
|
|
shared_embedding_shard_id = None
|
|
|
|
for name, loaded_weight in weights_tuple_list:
|
|
|
|
name = self._rename_key(name)
|
|
name, shard_id = self._rename_stacked_param(name)
|
|
|
|
if ('shared.weight' in name
|
|
or 'encoder.embed_tokens.weight' in name
|
|
or 'decoder.embed_tokens.weight' in name
|
|
or 'lm_head.weight' in name):
|
|
assert shared_embedding_weight is None, (
|
|
"Conflicting embedding weights.")
|
|
shared_embedding_weight = loaded_weight
|
|
shared_embedding_shard_id = shard_id
|
|
else:
|
|
# Skip the specific downstream task weight.
|
|
if name.startswith('cls.'):
|
|
continue
|
|
# use Pooler instead.
|
|
if name.startswith('pooler.'):
|
|
continue
|
|
# Skip loading extra bias for GPTQ models.
|
|
if name.endswith(".bias") and name not in model_params_dict:
|
|
continue
|
|
|
|
param = model_params_dict[name]
|
|
weight_loader = getattr(param, "weight_loader",
|
|
default_weight_loader)
|
|
if shard_id:
|
|
weight_loader(param, loaded_weight, shard_id)
|
|
else:
|
|
weight_loader(param, loaded_weight)
|
|
|
|
# Assign shared weight values
|
|
encoder_in_param = model_params_dict['encoder.embed_tokens.weight']
|
|
encoder_in_weight_loader = getattr(encoder_in_param, "weight_loader",
|
|
default_weight_loader)
|
|
|
|
decoder_in_param = model_params_dict['decoder.embed_tokens.weight']
|
|
decoder_in_weight_loader = getattr(decoder_in_param, "weight_loader",
|
|
default_weight_loader)
|
|
|
|
lm_head_in_param = top_params_dict['lm_head.weight']
|
|
lm_head_in_weight_loader = getattr(lm_head_in_param, "weight_loader",
|
|
default_weight_loader)
|
|
|
|
assert shared_embedding_weight is not None
|
|
|
|
if shared_embedding_shard_id:
|
|
encoder_in_weight_loader(encoder_in_param, shared_embedding_weight,
|
|
shared_embedding_shard_id)
|
|
decoder_in_weight_loader(decoder_in_param, shared_embedding_weight,
|
|
shared_embedding_shard_id)
|
|
lm_head_in_weight_loader(lm_head_in_param, shared_embedding_weight,
|
|
shared_embedding_shard_id)
|
|
else:
|
|
encoder_in_weight_loader(encoder_in_param, shared_embedding_weight)
|
|
decoder_in_weight_loader(decoder_in_param, shared_embedding_weight)
|
|
lm_head_in_weight_loader(lm_head_in_param, shared_embedding_weight)
|