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# ------------------------------------------------------------------------
# Modified from OFA (https://github.com/OFA-Sys/OFA)
# Copyright 2022 The OFA-Sys Team.
# All rights reserved.
# This source code is licensed under the Apache 2.0 license
# found in the LICENSE file in the root directory.
# ------------------------------------------------------------------------
# Modifications Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
# SPDX-License-Identifier: Apache-2.0
"""
PolyFormer
"""
from typing import Optional
import logging
import torch
import torch.nn as nn
from fairseq import utils
from fairseq.models import register_model, register_model_architecture
from fairseq.modules.transformer_sentence_encoder import init_bert_params
from .unify_transformer import TransformerModel
logger = logging.getLogger(__name__)
@register_model("polyformer")
class PolyFormerModel(TransformerModel):
__jit_unused_properties__ = ["supported_targets"]
def __init__(self, args, encoder, decoder):
super().__init__(args, encoder, decoder)
# We follow BERT's random weight initialization
self.apply(init_bert_params)
self.classification_heads = nn.ModuleDict()
if hasattr(self.encoder, "dictionary"):
self.eos: int = self.encoder.dictionary.eos()
@staticmethod
def add_args(parser):
super(PolyFormerModel, PolyFormerModel).add_args(parser)
parser.add_argument(
"--pooler-dropout",
type=float,
metavar="D",
help="dropout probability in the masked_lm pooler layers",
)
parser.add_argument(
"--pooler-classifier",
type=str,
choices=['mlp', 'linear'],
help="type of pooler classifier",
)
parser.add_argument(
"--pooler-activation-fn",
choices=utils.get_available_activation_fns(),
help="activation function to use for pooler layer",
)
parser.add_argument(
"--spectral-norm-classification-head",
action="store_true",
help="Apply spectral normalization on the classification head",
)
@property
def supported_targets(self):
return {"self"}
def forward(
self,
src_tokens,
src_lengths,
att_masks,
prev_output_tokens_11,
prev_output_tokens_12,
prev_output_tokens_21,
prev_output_tokens_22,
delta_x1,
delta_y1,
delta_x2,
delta_y2,
patch_images: Optional[torch.Tensor] = None,
patch_masks: Optional[torch.Tensor] = None,
code_masks: Optional[torch.Tensor] = None,
sample_patch_num: Optional[int] = None,
features_only: bool = False,
classification_head_name: Optional[str] = None,
token_embeddings: Optional[torch.Tensor] = None,
return_all_hiddens: bool = False,
alignment_layer: Optional[int] = None,
alignment_heads: Optional[int] = None,
):
if classification_head_name is not None:
features_only = True
encoder_out = self.encoder(
src_tokens,
src_lengths=src_lengths,
att_masks=att_masks,
patch_images=patch_images,
patch_masks=patch_masks,
token_embeddings=token_embeddings,
return_all_hiddens=return_all_hiddens,
sample_patch_num=sample_patch_num
)
x_cls, x_reg, extra = self.decoder(
prev_output_tokens_11,
prev_output_tokens_12,
prev_output_tokens_21,
prev_output_tokens_22,
delta_x1,
delta_y1,
delta_x2,
delta_y2,
code_masks=code_masks,
encoder_out=encoder_out,
features_only=features_only,
alignment_layer=alignment_layer,
alignment_heads=alignment_heads,
src_lengths=src_lengths,
return_all_hiddens=return_all_hiddens,
)
return x_cls, x_reg, extra
def upgrade_state_dict_named(self, state_dict, name):
pass
@register_model_architecture("polyformer", "polyformer_l")
def polyformer_l_architecture(args):
args.encoder_embed_path = getattr(args, "encoder_embed_path", None)
args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 768)
args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 4 * 1024)
args.encoder_layers = getattr(args, "encoder_layers", 12)
args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 16)
args.encoder_normalize_before = getattr(args, "encoder_normalize_before", True)
args.encoder_learned_pos = getattr(args, "encoder_learned_pos", True)
args.decoder_embed_path = getattr(args, "decoder_embed_path", None)
args.decoder_embed_dim = getattr(args, "decoder_embed_dim", args.encoder_embed_dim)
args.decoder_ffn_embed_dim = getattr(
args, "decoder_ffn_embed_dim", args.encoder_ffn_embed_dim
)
args.decoder_layers = getattr(args, "decoder_layers", 12)
args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 16)
args.decoder_normalize_before = getattr(args, "decoder_normalize_before", True)
args.decoder_learned_pos = getattr(args, "decoder_learned_pos", True)
args.attention_dropout = getattr(args, "attention_dropout", 0.0)
args.relu_dropout = getattr(args, "relu_dropout", 0.0)
args.dropout = getattr(args, "dropout", 0.0)
args.max_target_positions = getattr(args, "max_target_positions", 1024)
args.max_source_positions = getattr(args, "max_source_positions", 1024)
args.adaptive_softmax_cutoff = getattr(args, "adaptive_softmax_cutoff", None)
args.adaptive_softmax_dropout = getattr(args, "adaptive_softmax_dropout", 0)
args.share_decoder_input_output_embed = getattr(
args, "share_decoder_input_output_embed", True
)
args.share_all_embeddings = getattr(args, "share_all_embeddings", True)
args.decoder_output_dim = getattr(
args, "decoder_output_dim", args.decoder_embed_dim
)
args.decoder_input_dim = getattr(args, "decoder_input_dim", args.decoder_embed_dim)
args.no_scale_embedding = getattr(args, "no_scale_embedding", True)
args.layernorm_embedding = getattr(args, "layernorm_embedding", True)
args.activation_fn = getattr(args, "activation_fn", "gelu")
args.pooler_activation_fn = getattr(args, "pooler_activation_fn", "tanh")
args.pooler_dropout = getattr(args, "pooler_dropout", 0.0)
args.pooler_classifier = getattr(args, "pooler_classifier", "mlp")
args.resnet_drop_path_rate = getattr(args, "resnet_drop_path_rate", 0.0)
args.encoder_drop_path_rate = getattr(args, "encoder_drop_path_rate", 0.0)
args.decoder_drop_path_rate = getattr(args, "decoder_drop_path_rate", 0.0)
args.vis_encoder_type = getattr(args, "vis_encoder_type", "swin-large")
args.out_index = getattr(args, "out_index", 3)
args.token_bucket_size = getattr(args, "token_bucket_size", 256)
args.image_bucket_size = getattr(args, "image_bucket_size", 42)
args.freeze_encoder_embedding = getattr(args, "freeze_encoder_embedding", False)
args.freeze_decoder_embedding = getattr(args, "freeze_decoder_embedding", False)
args.add_type_embedding = getattr(args, "add_type_embedding", True)
args.attn_scale_factor = getattr(args, "attn_scale_factor", 2)
args.code_image_size = getattr(args, "code_image_size", 128)
args.patch_layernorm_embedding = getattr(args, "patch_layernorm_embedding", True)
args.code_layernorm_embedding = getattr(args, "code_layernorm_embedding", True)
args.entangle_position_embedding = getattr(args, "entangle_position_embedding", False)
args.disable_entangle = getattr(args, "disable_entangle", False)
args.sync_bn = getattr(args, "sync_bn", False)
args.scale_attn = getattr(args, "scale_attn", False)
args.scale_fc = getattr(args, "scale_fc", False)
args.scale_heads = getattr(args, "scale_heads", False)
args.scale_resids = getattr(args, "scale_resids", False)
@register_model_architecture("polyformer", "polyformer_b")
def polyformer_b_architecture(args):
args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 768)
args.out_index = getattr(args, "out_index", 3)
args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 4 * 768)
args.encoder_layers = getattr(args, "encoder_layers", 6)
args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 12)
args.decoder_layers = getattr(args, "decoder_layers", 6)
args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 12)
args.vis_encoder_type = getattr(args, "vis_encoder_type", "swin-base")
polyformer_l_architecture(args) |