Russell Bryant
e489ad7a21
- **Add SPDX license headers to python source files** - **Check for SPDX headers using pre-commit** commit 9d7ef44c3cfb72ca4c32e1c677d99259d10d4745 Author: Russell Bryant <rbryant@redhat.com> Date: Fri Jan 31 14:18:24 2025 -0500 Add SPDX license headers to python source files This commit adds SPDX license headers to python source files as recommended to the project by the Linux Foundation. These headers provide a concise way that is both human and machine readable for communicating license information for each source file. It helps avoid any ambiguity about the license of the code and can also be easily used by tools to help manage license compliance. The Linux Foundation runs license scans against the codebase to help ensure we are in compliance with the licenses of the code we use, including dependencies. Having these headers in place helps that tool do its job. More information can be found on the SPDX site: - https://spdx.dev/learn/handling-license-info/ Signed-off-by: Russell Bryant <rbryant@redhat.com> commit 5a1cf1cb3b80759131c73f6a9dddebccac039dea Author: Russell Bryant <rbryant@redhat.com> Date: Fri Jan 31 14:36:32 2025 -0500 Check for SPDX headers using pre-commit Signed-off-by: Russell Bryant <rbryant@redhat.com> --------- Signed-off-by: Russell Bryant <rbryant@redhat.com>
1001 lines
36 KiB
Python
1001 lines
36 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, List, 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, AttentionMetadata, 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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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 .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 = 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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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, 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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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, kv_cache, attn_metadata)
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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 = 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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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, 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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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, kv_cache, attn_metadata)
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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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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 = 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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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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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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# (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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attn_output = self.attn(q, k, v, kv_cache, attn_metadata)
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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, kv_cache: torch.Tensor,
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attn_metadata: AttentionMetadata) -> 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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kv_cache:
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Layer-wise list of KV cache tensors
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attn_metadata:
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vLLM Attention metadata structure
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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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kv_cache=kv_cache,
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attn_metadata=attn_metadata)
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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",
|
|
)
|
|
self.activation_fn = get_act_fn(config.activation_function)
|
|
|
|
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,
|
|
prefix=f"{prefix}.encoder_attn",
|
|
)
|
|
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:
|
|
vLLM 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,
|
|
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,
|
|
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:
|
|
vLLM Attention metadata structure
|
|
Returns:
|
|
Decoder output torch.Tensor
|
|
"""
|
|
# retrieve input_ids and inputs_embeds
|
|
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 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,
|
|
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],
|
|
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:
|
|
vLLM 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)
|
|
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, *, 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
|
|
|
|
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,
|
|
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, 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:
|
|
vLLM 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):
|
|
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,
|
|
kv_caches: List[torch.Tensor],
|
|
attn_metadata: AttentionMetadata,
|
|
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
|
|
kv_caches:
|
|
Layer-wise list of KV cache tensors
|
|
attn_metadata:
|
|
vLLM Attention metadata structure
|
|
Returns:
|
|
Output torch.Tensor
|
|
"""
|
|
return self.model(input_ids, positions, encoder_input_ids,
|
|
encoder_positions, kv_caches, attn_metadata)
|
|
|
|
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)
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