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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, List, Optional, Tuple
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+
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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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+
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+from aphrodite.attention import Attention, AttentionMetadata, AttentionType
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+from aphrodite.common.config import CacheConfig, LoRAConfig
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+from aphrodite.common.sequence import IntermediateTensors, SamplerOutput
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+from aphrodite.distributed import get_tensor_model_parallel_world_size
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+from aphrodite.modeling.layers.activation import get_act_fn
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+from aphrodite.modeling.layers.linear import (ColumnParallelLinear,
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+ QKVParallelLinear,
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+ RowParallelLinear)
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+from aphrodite.modeling.layers.logits_processor import LogitsProcessor
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+from aphrodite.modeling.layers.sampler import Sampler
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+from aphrodite.modeling.layers.vocab_parallel_embedding import (
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+ ParallelLMHead, VocabParallelEmbedding)
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+from aphrodite.modeling.model_loader.weight_utils import default_weight_loader
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+from aphrodite.modeling.sampling_metadata import SamplingMetadata
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+from aphrodite.quantization.base_config import QuantizationConfig
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+
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+
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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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+
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+
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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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+
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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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+
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+ def forward(
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+ self,
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+ positions: torch.Tensor,
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+ attn_type: AttentionType,
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+ ) -> torch.Tensor:
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+ """`input_ids' shape is expected to be [bsz x seqlen]."""
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+
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+ assert attn_type != AttentionType.ENCODER_DECODER
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+
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+ return super().forward(positions + self.offset)
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+
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+
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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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+
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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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+
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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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+
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+
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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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+
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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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+
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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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+
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+
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+class BartEncoderAttention(nn.Module):
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+
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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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+ ):
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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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+
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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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+
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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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+
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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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+
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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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+
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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 = max(1, self.total_num_kv_heads // tp_world_size)
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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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+
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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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+
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+ def forward(self, hidden_states: torch.Tensor, kv_cache: torch.Tensor,
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+ attn_metadata: AttentionMetadata) -> torch.Tensor:
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+ """Input shape: Batch x Time x Channel"""
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+
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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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+
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+ attn_output = self.attn(q,
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+ k,
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+ v,
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+ kv_cache,
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+ attn_metadata,
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+ attn_type=AttentionType.ENCODER)
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+
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+ output, _ = self.out_proj(attn_output)
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+ return output
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+
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+
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+class BartDecoderSelfAttention(nn.Module):
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+
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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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+ ):
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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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+
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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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+
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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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+
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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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+
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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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+
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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 = max(1, self.total_num_kv_heads // tp_world_size)
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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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+
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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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+
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+ def forward(self, hidden_states: torch.Tensor, kv_cache: torch.Tensor,
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+ attn_metadata: AttentionMetadata) -> torch.Tensor:
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+ """Input shape: Batch x Time x Channel"""
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+
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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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+
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+ attn_output = self.attn(q,
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+ k,
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+ v,
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+ kv_cache,
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+ attn_metadata,
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+ attn_type=AttentionType.DECODER)
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+
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+ output, _ = self.out_proj(attn_output)
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+ return output
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+
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+
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+class BartCrossAttention(nn.Module):
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+
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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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+ ):
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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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+
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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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+
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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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+
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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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+
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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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+
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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 = max(1, self.total_num_kv_heads // tp_world_size)
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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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+
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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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+
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+ def forward(
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+ self,
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+ decoder_hidden_states: torch.Tensor,
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+ kv_cache: torch.Tensor,
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+ attn_metadata: AttentionMetadata,
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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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+
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+ # (afeldman-nm 2024/07/22) TODO:
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+ # Need a more efficient solution for q/k/v
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+ qkv_dec, _ = self.qkv_proj(decoder_hidden_states)
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+ q, _, _ = qkv_dec.split([self.q_size, self.kv_size, self.kv_size],
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+ dim=-1)
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+ if encoder_hidden_states is None:
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+ k = None
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+ v = None
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+ else:
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+ qkv_enc, _ = self.qkv_proj(encoder_hidden_states)
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+ _, k, v = qkv_enc.split([self.q_size, self.kv_size, self.kv_size],
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+ dim=-1)
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+
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+ attn_output = self.attn(q,
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+ k,
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+ v,
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+ kv_cache,
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+ attn_metadata,
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+ attn_type=AttentionType.ENCODER_DECODER)
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+
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+ output, _ = self.out_proj(attn_output)
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+ return output
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+
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+
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+class BartEncoderLayer(nn.Module):
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+
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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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+ ):
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+ super().__init__()
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+ self.embed_dim = config.d_model
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+
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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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+ self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
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|
+ self.activation_fn = get_act_fn(config.activation_function,
|
|
|
+ quant_config)
|
|
|
+
|
|
|
+ ffn_hidden_size = self.embed_dim
|
|
|
+ ffn_intermediate_size = config.encoder_ffn_dim
|
|
|
+ ffn_has_bias = True
|
|
|
+ self.fc1 = ColumnParallelLinear(
|
|
|
+ ffn_hidden_size,
|
|
|
+ ffn_intermediate_size,
|
|
|
+ bias=ffn_has_bias,
|
|
|
+ quant_config=quant_config,
|
|
|
+ )
|
|
|
+ self.act = get_act_fn("gelu", quant_config, ffn_intermediate_size)
|
|
|
+ self.fc2 = RowParallelLinear(
|
|
|
+ ffn_intermediate_size,
|
|
|
+ ffn_hidden_size,
|
|
|
+ bias=ffn_has_bias,
|
|
|
+ quant_config=quant_config,
|
|
|
+ )
|
|
|
+
|
|
|
+ self.final_layer_norm = nn.LayerNorm(self.embed_dim)
|
|
|
+
|
|
|
+ def forward(self, hidden_states: torch.Tensor, kv_cache: torch.Tensor,
|
|
|
+ attn_metadata: AttentionMetadata) -> torch.Tensor:
|
|
|
+ r"""
|
|
|
+ Args:
|
|
|
+ hidden_states
|
|
|
+ torch.Tensor of *encoder* input embeddings.
|
|
|
+ kv_cache:
|
|
|
+ Layer-wise list of KV cache tensors
|
|
|
+ attn_metadata:
|
|
|
+ Aphrodite Attention metadata structure
|
|
|
+ Returns:
|
|
|
+ Encoder layer output torch.Tensor
|
|
|
+ """
|
|
|
+ residual = hidden_states
|
|
|
+ hidden_states = self.self_attn(hidden_states=hidden_states,
|
|
|
+ kv_cache=kv_cache,
|
|
|
+ attn_metadata=attn_metadata)
|
|
|
+
|
|
|
+ hidden_states = residual + hidden_states
|
|
|
+ hidden_states = self.self_attn_layer_norm(hidden_states)
|
|
|
+
|
|
|
+ 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)
|
|
|
+
|
|
|
+ if hidden_states.dtype == torch.float16 and (
|
|
|
+ torch.isinf(hidden_states).any()
|
|
|
+ or torch.isnan(hidden_states).any()):
|
|
|
+ clamp_value = torch.finfo(hidden_states.dtype).max - 1000
|
|
|
+ hidden_states = torch.clamp(hidden_states,
|
|
|
+ min=-clamp_value,
|
|
|
+ max=clamp_value)
|
|
|
+
|
|
|
+ return hidden_states
|
|
|
+
|
|
|
+
|
|
|
+class BartDecoderLayer(nn.Module):
|
|
|
+
|
|
|
+ def __init__(
|
|
|
+ self,
|
|
|
+ config: BartConfig,
|
|
|
+ cache_config: Optional[CacheConfig] = None,
|
|
|
+ quant_config: Optional[QuantizationConfig] = None,
|
|
|
+ ):
|
|
|
+ super().__init__()
|
|
|
+ self.embed_dim = config.d_model
|
|
|
+
|
|
|
+ self.self_attn = BartDecoderSelfAttention(
|
|
|
+ embed_dim=self.embed_dim,
|
|
|
+ num_heads=config.decoder_attention_heads,
|
|
|
+ config=config,
|
|
|
+ cache_config=cache_config,
|
|
|
+ quant_config=quant_config)
|
|
|
+ self.activation_fn = get_act_fn(config.activation_function,
|
|
|
+ quant_config)
|
|
|
+
|
|
|
+ self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
|
|
|
+ '''
|
|
|
+ afeldman-nm: personally I would call this "cross-attention",
|
|
|
+ however I left the name as "encoder_attn" to maintain consistency
|
|
|
+ with the name of the pretrained weights.
|
|
|
+ '''
|
|
|
+ self.encoder_attn = BartCrossAttention(
|
|
|
+ self.embed_dim,
|
|
|
+ config.decoder_attention_heads,
|
|
|
+ config=config,
|
|
|
+ )
|
|
|
+ self.encoder_attn_layer_norm = nn.LayerNorm(self.embed_dim)
|
|
|
+
|
|
|
+ ffn_hidden_size = self.embed_dim
|
|
|
+ ffn_intermediate_size = config.encoder_ffn_dim
|
|
|
+ ffn_has_bias = True
|
|
|
+ self.fc1 = ColumnParallelLinear(
|
|
|
+ ffn_hidden_size,
|
|
|
+ ffn_intermediate_size,
|
|
|
+ bias=ffn_has_bias,
|
|
|
+ quant_config=quant_config,
|
|
|
+ )
|
|
|
+ self.fc2 = RowParallelLinear(
|
|
|
+ ffn_intermediate_size,
|
|
|
+ ffn_hidden_size,
|
|
|
+ bias=ffn_has_bias,
|
|
|
+ quant_config=quant_config,
|
|
|
+ )
|
|
|
+
|
|
|
+ self.final_layer_norm = nn.LayerNorm(self.embed_dim)
|
|
|
+
|
|
|
+ def forward(
|
|
|
+ self,
|
|
|
+ decoder_hidden_states: torch.Tensor,
|
|
|
+ kv_cache: torch.Tensor,
|
|
|
+ attn_metadata: AttentionMetadata,
|
|
|
+ encoder_hidden_states: Optional[torch.Tensor] = None,
|
|
|
+ ) -> torch.Tensor:
|
|
|
+ r"""
|
|
|
+ Args:
|
|
|
+ decoder_hidden_states
|
|
|
+ torch.Tensor of *decoder* input embeddings.
|
|
|
+ kv_cache:
|
|
|
+ KV cache tensor
|
|
|
+ attn_metadata:
|
|
|
+ Aphrodite Attention metadata structure
|
|
|
+ encoder_hidden_states
|
|
|
+ torch.Tensor of *encoder* input embeddings.
|
|
|
+ Returns:
|
|
|
+ Decoder layer output torch.Tensor
|
|
|
+ """
|
|
|
+ residual = decoder_hidden_states
|
|
|
+
|
|
|
+ # Self Attention
|
|
|
+ hidden_states = self.self_attn(hidden_states=decoder_hidden_states,
|
|
|
+ kv_cache=kv_cache,
|
|
|
+ attn_metadata=attn_metadata)
|
|
|
+
|
|
|
+ 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,
|
|
|
+ kv_cache=kv_cache,
|
|
|
+ attn_metadata=attn_metadata,
|
|
|
+ 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):
|
|
|
+ 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) \
|
|
|
+ for _ in range(config.encoder_layers)])
|
|
|
+
|
|
|
+ self.layernorm_embedding = nn.LayerNorm(embed_dim)
|
|
|
+
|
|
|
+ def forward(self, input_ids: torch.Tensor, positions: torch.Tensor,
|
|
|
+ kv_caches: List[torch.Tensor],
|
|
|
+ attn_metadata: AttentionMetadata) -> 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.
|
|
|
+ kv_caches:
|
|
|
+ Layer-wise list of KV cache tensors
|
|
|
+ attn_metadata:
|
|
|
+ Aphrodite Attention metadata structure
|
|
|
+ Returns:
|
|
|
+ Decoder output torch.Tensor
|
|
|
+ """
|
|
|
+ # retrieve input_ids and inputs_embeds
|
|
|
+
|
|
|
+ input_ids = input_ids.view(-1, input_ids.shape[-1])
|
|
|
+ inputs_embeds = self.embed_tokens(input_ids)
|
|
|
+
|
|
|
+ embed_pos = self.embed_positions(
|
|
|
+ positions,
|
|
|
+ AttentionType.ENCODER,
|
|
|
+ )
|
|
|
+ embed_pos = embed_pos.to(inputs_embeds.device)
|
|
|
+
|
|
|
+ hidden_states = inputs_embeds + embed_pos
|
|
|
+ hidden_states = self.layernorm_embedding(hidden_states)
|
|
|
+
|
|
|
+ for idx, encoder_layer in enumerate(self.layers):
|
|
|
+ hidden_states = encoder_layer(
|
|
|
+ hidden_states=hidden_states,
|
|
|
+ kv_cache=kv_caches[idx],
|
|
|
+ attn_metadata=attn_metadata,
|
|
|
+ )
|
|
|
+
|
|
|
+ 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,
|
|
|
+ ):
|
|
|
+ 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) \
|
|
|
+ for _ 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],
|
|
|
+ kv_caches: List[torch.Tensor],
|
|
|
+ attn_metadata: AttentionMetadata) -> 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
|
|
|
+ kv_caches:
|
|
|
+ Layer-wise list of KV cache tensors
|
|
|
+ attn_metadata:
|
|
|
+ Aphrodite Attention metadata structure
|
|
|
+ Returns:
|
|
|
+ Decoder output torch.Tensor
|
|
|
+ """
|
|
|
+
|
|
|
+ inputs_embeds = self.embed_tokens(decoder_input_ids)
|
|
|
+
|
|
|
+ # embed positions
|
|
|
+ embed_pos = self.embed_positions(
|
|
|
+ decoder_positions,
|
|
|
+ AttentionType.DECODER,
|
|
|
+ )
|
|
|
+ embed_pos = embed_pos.to(inputs_embeds.device)
|
|
|
+
|
|
|
+ hidden_states = inputs_embeds + embed_pos
|
|
|
+ hidden_states = self.layernorm_embedding(hidden_states)
|
|
|
+
|
|
|
+ # decoder layers
|
|
|
+
|
|
|
+ for idx, decoder_layer in enumerate(self.layers):
|
|
|
+ hidden_states = decoder_layer(
|
|
|
+ decoder_hidden_states=hidden_states,
|
|
|
+ kv_cache=kv_caches[idx],
|
|
|
+ attn_metadata=attn_metadata,
|
|
|
+ encoder_hidden_states=encoder_hidden_states,
|
|
|
+ )
|
|
|
+
|
|
|
+ return hidden_states
|
|
|
+
|
|
|
+
|
|
|
+class BartModel(nn.Module):
|
|
|
+ _tied_weights_keys = [
|
|
|
+ "encoder.embed_tokens.weight", "decoder.embed_tokens.weight"
|
|
|
+ ]
|
|
|
+
|
|
|
+ def __init__(self,
|
|
|
+ config: BartConfig,
|
|
|
+ cache_config: Optional[CacheConfig] = None,
|
|
|
+ quant_config: Optional[QuantizationConfig] = None,
|
|
|
+ lora_config: Optional[LoRAConfig] = None):
|
|
|
+ super().__init__()
|
|
|
+
|
|
|
+ self.config = config
|
|
|
+
|
|
|
+ self.padding_idx = config.pad_token_id
|
|
|
+ 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)
|
|
|
+ self.decoder = BartDecoder(config,
|
|
|
+ cache_config,
|
|
|
+ quant_config=quant_config)
|
|
|
+
|
|
|
+ def forward(self, input_ids: torch.Tensor, positions: torch.Tensor,
|
|
|
+ encoder_input_ids: torch.Tensor,
|
|
|
+ encoder_positions: torch.Tensor, kv_caches: List[torch.Tensor],
|
|
|
+ attn_metadata: AttentionMetadata) -> 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.
|
|
|
+ kv_caches:
|
|
|
+ Layer-wise list of KV cache tensors
|
|
|
+ attn_metadata:
|
|
|
+ Aphrodite Attention metadata structure
|
|
|
+ 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,
|
|
|
+ kv_caches=kv_caches,
|
|
|
+ attn_metadata=attn_metadata)
|
|
|
+
|
|
|
+ # 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,
|
|
|
+ kv_caches=kv_caches,
|
|
|
+ attn_metadata=attn_metadata)
|
|
|
+
|
|
|
+ return decoder_outputs
|
|
|
+
|
|
|
+
|
|
|
+class BartForConditionalGeneration(nn.Module):
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+ base_model_prefix = "model"
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+
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+ def __init__(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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+ lora_config: Optional[LoRAConfig] = None):
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+
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+ super().__init__()
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+ self.config = config
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+ self.model = BartModel(config,
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+ cache_config,
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+ quant_config,
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+ lora_config=lora_config)
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+
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+ self.unpadded_vocab_size = config.vocab_size
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+ if lora_config:
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+ self.unpadded_vocab_size += lora_config.lora_extra_vocab_size
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+
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+ embed_scale = math.sqrt(
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+ config.d_model) if config.scale_embedding else 1.0
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+
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+ self.lm_head = BartParallelLMHead(config.vocab_size,
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+ config.d_model,
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+ embed_scale=embed_scale)
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+
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+ self.logits_processor = LogitsProcessor(self.unpadded_vocab_size,
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+ config.vocab_size)
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+ self.sampler = Sampler()
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+
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+ def forward(
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+ self,
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+ input_ids: torch.Tensor,
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+ positions: torch.Tensor,
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+ encoder_input_ids: torch.Tensor,
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+ encoder_positions: torch.Tensor,
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+ kv_caches: List[torch.Tensor],
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+ attn_metadata: AttentionMetadata,
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+ intermediate_tensors: Optional[IntermediateTensors] = None,
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+ ) -> torch.Tensor:
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+ r"""
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+ Args:
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+ input_ids
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+ torch.Tensor of *decoder* input token ids.
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+ positions
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+ torch.Tensor of *decoder* position indices.
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+ encoder_input_ids
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+ torch.Tensor of *encoder* input token ids.
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+ encoder_positions
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+ torch.Tensor of *encoder* position indices
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+ kv_caches:
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+ Layer-wise list of KV cache tensors
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+ attn_metadata:
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+ Aphrodite Attention metadata structure
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+ Returns:
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+ Output torch.Tensor
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+ """
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+ return self.model(input_ids, positions, encoder_input_ids,
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+ encoder_positions, kv_caches, attn_metadata)
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+
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+ def compute_logits(self, hidden_states: torch.Tensor,
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+ sampling_metadata: SamplingMetadata) -> torch.Tensor:
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+ logits = self.logits_processor(self.lm_head, hidden_states,
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|
+ sampling_metadata)
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+ return logits
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+
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+ def sample(
|
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|
+ self,
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|
+ logits: Optional[torch.Tensor],
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|
+ sampling_metadata: SamplingMetadata,
|
|
|
+ ) -> Optional[SamplerOutput]:
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|
+ next_tokens = self.sampler(logits, sampling_metadata)
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|
+ return next_tokens
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|
+
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|
+ stacked_params_mapping = {
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|
+ "q_proj": {
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|
|
+ "param_name": "qkv_proj",
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|
|
+ "shard_id": "q",
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|
|
+ },
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|
+ "k_proj": {
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|
|
+ "param_name": "qkv_proj",
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|
|
+ "shard_id": "k",
|
|
|
+ },
|
|
|
+ "v_proj": {
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|
|
+ "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)
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|
|
+
|
|
|
+ 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)
|
AlpinDale