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# Copied from transformers.models.bart.modeling_tf_bart.TFBartForConditionalGeneration.serving_output
def serving_output(self, output):
pkv = tf.tuple(output.past_key_values)[1] if self.config.use_cache else None
dec_hs = tf.convert_to_tensor(output.decoder_hidden_states) if self.config.output_hidde... | 10,095 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_tf_mbart.py |
return TFSeq2SeqLMOutput(
logits=output.logits,
past_key_values=pkv,
decoder_hidden_states=dec_hs,
decoder_attentions=dec_attns,
cross_attentions=cross_attns,
encoder_last_hidden_state=output.encoder_last_hidden_state,
encoder_hidden_st... | 10,095 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_tf_mbart.py |
if decoder_attention_mask is not None: # xla
decoder_position_ids = tf.math.cumsum(decoder_attention_mask, axis=-1, exclusive=True)[:, -1:]
elif past_key_values is not None: # no xla + past_key_values
decoder_position_ids = past_key_values[0][0].shape[2]
else: # no xla + no pa... | 10,095 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_tf_mbart.py |
return {
"input_ids": None, # encoder_outputs is defined. input_ids not needed
"encoder_outputs": encoder_outputs,
"past_key_values": past_key_values,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mas... | 10,095 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_tf_mbart.py |
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "model", None) is not None:
with tf.name_scope(self.model.name):
self.model.build(None)
if getattr(self, "bias_layer", None) is not None:
with t... | 10,095 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_tf_mbart.py |
class GraphormerDataCollator:
def __init__(self, spatial_pos_max=20, on_the_fly_processing=False):
if not is_cython_available():
raise ImportError("Graphormer preprocessing needs Cython (pyximport)")
self.spatial_pos_max = spatial_pos_max
self.on_the_fly_processing = on_the_fly_... | 10,096 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/graphormer/collating_graphormer.py |
batch["attn_bias"] = torch.zeros(batch_size, max_node_num + 1, max_node_num + 1, dtype=torch.float)
batch["attn_edge_type"] = torch.zeros(batch_size, max_node_num, max_node_num, edge_feat_size, dtype=torch.long)
batch["spatial_pos"] = torch.zeros(batch_size, max_node_num, max_node_num, dtype=torch.long)... | 10,096 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/graphormer/collating_graphormer.py |
if len(f["attn_bias"][1:, 1:][f["spatial_pos"] >= self.spatial_pos_max]) > 0:
f["attn_bias"][1:, 1:][f["spatial_pos"] >= self.spatial_pos_max] = float("-inf")
batch["attn_bias"][ix, : f["attn_bias"].shape[0], : f["attn_bias"].shape[1]] = f["attn_bias"]
batch["attn_edge_type"][ix... | 10,096 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/graphormer/collating_graphormer.py |
sample = features[0]["labels"]
if len(sample) == 1: # one task
if isinstance(sample[0], float): # regression
batch["labels"] = torch.from_numpy(np.concatenate([i["labels"] for i in features]))
else: # binary classification
batch["labels"] = torch.from_n... | 10,096 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/graphormer/collating_graphormer.py |
class GraphormerConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`~GraphormerModel`]. It is used to instantiate an
Graphormer model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will y... | 10,097 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/graphormer/configuration_graphormer.py |
Args:
num_classes (`int`, *optional*, defaults to 1):
Number of target classes or labels, set to n for binary classification of n tasks.
num_atoms (`int`, *optional*, defaults to 512*9):
Number of node types in the graphs.
num_edges (`int`, *optional*, defaults to 512*3):... | 10,097 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/graphormer/configuration_graphormer.py |
Maximum distance between nodes in the graph attention bias matrices, used during preprocessing and
collation.
edge_type (`str`, *optional*, defaults to multihop):
Type of edge relation chosen.
max_nodes (`int`, *optional*, defaults to 512):
Maximum number of nodes whi... | 10,097 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/graphormer/configuration_graphormer.py |
Number of attention heads in the encoder.
self_attention (`bool`, *optional*, defaults to `True`):
Model is self attentive (False not implemented).
activation_function (`str` or `function`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string)... | 10,097 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/graphormer/configuration_graphormer.py |
The LayerDrop probability for the encoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556)
for more details.
bias (`bool`, *optional*, defaults to `True`):
Uses bias in the attention module - unsupported at the moment.
embed_scale(`float`, *optional*, defaults to ... | 10,097 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/graphormer/configuration_graphormer.py |
freeze_embeddings (`bool`, *optional*, defaults to `False`):
Freeze the embedding layer, or train it along the model.
encoder_normalize_before (`bool`, *optional*, defaults to `False`):
Apply the layer norm before each encoder block.
q_noise (`float`, *optional*, defaults to 0.0)... | 10,097 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/graphormer/configuration_graphormer.py |
Whether or not the model should return the last key/values attentions (not used by all models).
traceable (`bool`, *optional*, defaults to `False`):
Changes return value of the encoder's inner_state to stacked tensors. | 10,097 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/graphormer/configuration_graphormer.py |
Example:
```python
>>> from transformers import GraphormerForGraphClassification, GraphormerConfig
>>> # Initializing a Graphormer graphormer-base-pcqm4mv2 style configuration
>>> configuration = GraphormerConfig()
>>> # Initializing a model from the graphor... | 10,097 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/graphormer/configuration_graphormer.py |
def __init__(
self,
num_classes: int = 1,
num_atoms: int = 512 * 9,
num_edges: int = 512 * 3,
num_in_degree: int = 512,
num_out_degree: int = 512,
num_spatial: int = 512,
num_edge_dis: int = 128,
multi_hop_max_dist: int = 5, # sometimes is 20
... | 10,097 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/graphormer/configuration_graphormer.py |
num_trans_layers_to_freeze: int = 0,
traceable: bool = False,
q_noise: float = 0.0,
qn_block_size: int = 8,
kdim: int = None,
vdim: int = None,
bias: bool = True,
self_attention: bool = True,
pad_token_id=0,
bos_token_id=1,
eos_token_id=2,
... | 10,097 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/graphormer/configuration_graphormer.py |
self.num_attention_heads = num_attention_heads
self.dropout = dropout
self.attention_dropout = attention_dropout
self.activation_dropout = activation_dropout
self.layerdrop = layerdrop
self.encoder_normalize_before = encoder_normalize_before
self.pre_layernorm = pre_layer... | 10,097 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/graphormer/configuration_graphormer.py |
# These parameters are here for future extensions
# atm, the model only supports self attention
self.kdim = kdim
self.vdim = vdim
self.self_attention = self_attention
self.bias = bias
super().__init__(
pad_token_id=pad_token_id,
bos_token_id=bos_t... | 10,097 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/graphormer/configuration_graphormer.py |
class LayerDropModuleList(nn.ModuleList):
"""
From:
https://github.com/facebookresearch/fairseq/blob/dd0079bde7f678b0cd0715cbd0ae68d661b7226d/fairseq/modules/layer_drop.py
A LayerDrop implementation based on [`torch.nn.ModuleList`]. LayerDrop as described in
https://arxiv.org/abs/1909.11556.
We... | 10,098 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/graphormer/modeling_graphormer.py |
def __init__(self, p: float, modules: Optional[Iterable[nn.Module]] = None):
super().__init__(modules)
self.p = p
def __iter__(self) -> Iterator[nn.Module]:
dropout_probs = torch.empty(len(self)).uniform_()
for i, m in enumerate(super().__iter__()):
if not self.training ... | 10,098 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/graphormer/modeling_graphormer.py |
class GraphormerGraphNodeFeature(nn.Module):
"""
Compute node features for each node in the graph.
"""
def __init__(self, config: GraphormerConfig):
super().__init__()
self.num_heads = config.num_attention_heads
self.num_atoms = config.num_atoms
self.atom_encoder = nn.E... | 10,099 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/graphormer/modeling_graphormer.py |
node_feature = ( # node feature + graph token
self.atom_encoder(input_nodes).sum(dim=-2) # [n_graph, n_node, n_hidden]
+ self.in_degree_encoder(in_degree)
+ self.out_degree_encoder(out_degree)
)
graph_token_feature = self.graph_token.weight.unsqueeze(0).repeat(n_gr... | 10,099 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/graphormer/modeling_graphormer.py |
class GraphormerGraphAttnBias(nn.Module):
"""
Compute attention bias for each head.
"""
def __init__(self, config: GraphormerConfig):
super().__init__()
self.num_heads = config.num_attention_heads
self.multi_hop_max_dist = config.multi_hop_max_dist
# We do not change ed... | 10,100 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/graphormer/modeling_graphormer.py |
def forward(
self,
input_nodes: torch.LongTensor,
attn_bias: torch.Tensor,
spatial_pos: torch.LongTensor,
input_edges: torch.LongTensor,
attn_edge_type: torch.LongTensor,
) -> torch.Tensor:
n_graph, n_node = input_nodes.size()[:2]
graph_attn_bias = att... | 10,100 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/graphormer/modeling_graphormer.py |
# edge feature
if self.edge_type == "multi_hop":
spatial_pos_ = spatial_pos.clone()
spatial_pos_[spatial_pos_ == 0] = 1 # set pad to 1
# set 1 to 1, input_nodes > 1 to input_nodes - 1
spatial_pos_ = torch.where(spatial_pos_ > 1, spatial_pos_ - 1, spatial_pos_)
... | 10,100 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/graphormer/modeling_graphormer.py |
input_edges = self.edge_encoder(input_edges).mean(-2)
max_dist = input_edges.size(-2)
edge_input_flat = input_edges.permute(3, 0, 1, 2, 4).reshape(max_dist, -1, self.num_heads)
edge_input_flat = torch.bmm(
edge_input_flat,
self.edge_dis_encoder.weight.... | 10,100 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/graphormer/modeling_graphormer.py |
return graph_attn_bias | 10,100 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/graphormer/modeling_graphormer.py |
class GraphormerMultiheadAttention(nn.Module):
"""Multi-headed attention.
See "Attention Is All You Need" for more details.
"""
def __init__(self, config: GraphormerConfig):
super().__init__()
self.embedding_dim = config.embedding_dim
self.kdim = config.kdim if config.kdim is n... | 10,101 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/graphormer/modeling_graphormer.py |
self.self_attention = True # config.self_attention
if not (self.self_attention):
raise NotImplementedError("The Graphormer model only supports self attention for now.")
if self.self_attention and not self.qkv_same_dim:
raise AssertionError("Self-attention requires query, key and... | 10,101 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/graphormer/modeling_graphormer.py |
self.out_proj = quant_noise(
nn.Linear(config.embedding_dim, config.embedding_dim, bias=config.bias),
config.q_noise,
config.qn_block_size,
)
self.onnx_trace = False
def reset_parameters(self):
if self.qkv_same_dim:
# Empirically observed the... | 10,101 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/graphormer/modeling_graphormer.py |
def forward(
self,
query: torch.LongTensor,
key: Optional[torch.Tensor],
value: Optional[torch.Tensor],
attn_bias: Optional[torch.Tensor],
key_padding_mask: Optional[torch.Tensor] = None,
need_weights: bool = True,
attn_mask: Optional[torch.Tensor] = None,... | 10,101 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/graphormer/modeling_graphormer.py |
before_softmax (bool, optional): return the raw attention
weights and values before the attention softmax.
need_head_weights (bool, optional): return the attention
weights for each head. Implies *need_weights*. Default: return the average attention weights over all
... | 10,101 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/graphormer/modeling_graphormer.py |
tgt_len, bsz, embedding_dim = query.size()
src_len = tgt_len
if not (embedding_dim == self.embedding_dim):
raise AssertionError(
f"The query embedding dimension {embedding_dim} is not equal to the expected embedding_dim"
f" {self.embedding_dim}."
)... | 10,101 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/graphormer/modeling_graphormer.py |
q = q.contiguous().view(tgt_len, bsz * self.num_heads, self.head_dim).transpose(0, 1)
if k is not None:
k = k.contiguous().view(-1, bsz * self.num_heads, self.head_dim).transpose(0, 1)
if v is not None:
v = v.contiguous().view(-1, bsz * self.num_heads, self.head_dim).transpose(0,... | 10,101 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/graphormer/modeling_graphormer.py |
if key_padding_mask is not None:
if key_padding_mask.size(0) != bsz or key_padding_mask.size(1) != src_len:
raise AssertionError(
"The shape of the generated padding mask for the key does not match expected dimensions."
)
attn_weights = torch.bmm(q... | 10,101 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/graphormer/modeling_graphormer.py |
if key_padding_mask is not None:
# don't attend to padding symbols
attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
attn_weights = attn_weights.masked_fill(
key_padding_mask.unsqueeze(1).unsqueeze(2).to(torch.bool), float("-inf")
)
... | 10,101 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/graphormer/modeling_graphormer.py |
attn = attn.transpose(0, 1).contiguous().view(tgt_len, bsz, embedding_dim)
attn: torch.Tensor = self.out_proj(attn)
attn_weights = None
if need_weights:
attn_weights = attn_weights_float.contiguous().view(bsz, self.num_heads, tgt_len, src_len).transpose(1, 0)
if not need... | 10,101 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/graphormer/modeling_graphormer.py |
class GraphormerGraphEncoderLayer(nn.Module):
def __init__(self, config: GraphormerConfig) -> None:
super().__init__()
# Initialize parameters
self.embedding_dim = config.embedding_dim
self.num_attention_heads = config.num_attention_heads
self.q_noise = config.q_noise
... | 10,102 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/graphormer/modeling_graphormer.py |
self.fc1 = self.build_fc(
self.embedding_dim,
config.ffn_embedding_dim,
q_noise=config.q_noise,
qn_block_size=config.qn_block_size,
)
self.fc2 = self.build_fc(
config.ffn_embedding_dim,
self.embedding_dim,
q_noise=config... | 10,102 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/graphormer/modeling_graphormer.py |
def forward(
self,
input_nodes: torch.Tensor,
self_attn_bias: Optional[torch.Tensor] = None,
self_attn_mask: Optional[torch.Tensor] = None,
self_attn_padding_mask: Optional[torch.Tensor] = None,
) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
"""
nn.LayerNor... | 10,102 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/graphormer/modeling_graphormer.py |
input_nodes, attn = self.self_attn(
query=input_nodes,
key=input_nodes,
value=input_nodes,
attn_bias=self_attn_bias,
key_padding_mask=self_attn_padding_mask,
need_weights=False,
attn_mask=self_attn_mask,
)
input_nodes = ... | 10,102 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/graphormer/modeling_graphormer.py |
return input_nodes, attn | 10,102 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/graphormer/modeling_graphormer.py |
class GraphormerGraphEncoder(nn.Module):
def __init__(self, config: GraphormerConfig):
super().__init__()
self.dropout_module = torch.nn.Dropout(p=config.dropout, inplace=False)
self.layerdrop = config.layerdrop
self.embedding_dim = config.embedding_dim
self.apply_graphormer... | 10,103 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/graphormer/modeling_graphormer.py |
if config.encoder_normalize_before:
self.emb_layer_norm = nn.LayerNorm(self.embedding_dim)
else:
self.emb_layer_norm = None
if config.pre_layernorm:
self.final_layer_norm = nn.LayerNorm(self.embedding_dim)
if self.layerdrop > 0.0:
self.layers = L... | 10,103 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/graphormer/modeling_graphormer.py |
def forward(
self,
input_nodes: torch.LongTensor,
input_edges: torch.LongTensor,
attn_bias: torch.Tensor,
in_degree: torch.LongTensor,
out_degree: torch.LongTensor,
spatial_pos: torch.LongTensor,
attn_edge_type: torch.LongTensor,
perturb=None,
... | 10,103 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/graphormer/modeling_graphormer.py |
if token_embeddings is not None:
input_nodes = token_embeddings
else:
input_nodes = self.graph_node_feature(input_nodes, in_degree, out_degree)
if perturb is not None:
input_nodes[:, 1:, :] += perturb
if self.embed_scale is not None:
input_nodes ... | 10,103 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/graphormer/modeling_graphormer.py |
for layer in self.layers:
input_nodes, _ = layer(
input_nodes,
self_attn_padding_mask=padding_mask,
self_attn_mask=attn_mask,
self_attn_bias=attn_bias,
)
if not last_state_only:
inner_states.append(input_... | 10,103 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/graphormer/modeling_graphormer.py |
class GraphormerDecoderHead(nn.Module):
def __init__(self, embedding_dim: int, num_classes: int):
super().__init__()
"""num_classes should be 1 for regression, or the number of classes for classification"""
self.lm_output_learned_bias = nn.Parameter(torch.zeros(1))
self.classifier = ... | 10,104 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/graphormer/modeling_graphormer.py |
class GraphormerPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = GraphormerConfig
base_model_prefix = "graphormer"
main_input_name_nodes = "input_nodes"
main_... | 10,105 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/graphormer/modeling_graphormer.py |
def init_graphormer_params(self, module: Union[nn.Linear, nn.Embedding, GraphormerMultiheadAttention]):
"""
Initialize the weights specific to the Graphormer Model.
"""
if isinstance(module, nn.Linear):
self.normal_(module.weight.data)
if module.bias is not None:
... | 10,105 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/graphormer/modeling_graphormer.py |
def _init_weights(
self,
module: Union[
nn.Linear, nn.Conv2d, nn.Embedding, nn.LayerNorm, GraphormerMultiheadAttention, GraphormerGraphEncoder
],
):
"""
Initialize the weights
"""
if isinstance(module, (nn.Linear, nn.Conv2d)):
# We migh... | 10,105 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/graphormer/modeling_graphormer.py |
module.reset_parameters()
elif isinstance(module, nn.LayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
elif isinstance(module, GraphormerGraphEncoder):
if module.apply_graphormer_init:
module.apply(self.init_graphormer_params) | 10,105 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/graphormer/modeling_graphormer.py |
elif isinstance(module, nn.LayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0) | 10,105 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/graphormer/modeling_graphormer.py |
class GraphormerModel(GraphormerPreTrainedModel):
"""The Graphormer model is a graph-encoder model.
It goes from a graph to its representation. If you want to use the model for a downstream classification task, use
GraphormerForGraphClassification instead. For any other downstream task, feel free to add a ... | 10,106 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/graphormer/modeling_graphormer.py |
self.lm_head_transform_weight = nn.Linear(config.embedding_dim, config.embedding_dim)
self.activation_fn = ACT2FN[config.activation_fn]
self.layer_norm = nn.LayerNorm(config.embedding_dim)
self.post_init()
def reset_output_layer_parameters(self):
self.lm_output_learned_bias = nn.Pa... | 10,106 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/graphormer/modeling_graphormer.py |
inner_states, graph_rep = self.graph_encoder(
input_nodes, input_edges, attn_bias, in_degree, out_degree, spatial_pos, attn_edge_type, perturb=perturb
)
# last inner state, then revert Batch and Graph len
input_nodes = inner_states[-1].transpose(0, 1)
# project masked token... | 10,106 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/graphormer/modeling_graphormer.py |
def max_nodes(self):
"""Maximum output length supported by the encoder."""
return self.max_nodes | 10,106 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/graphormer/modeling_graphormer.py |
class GraphormerForGraphClassification(GraphormerPreTrainedModel):
"""
This model can be used for graph-level classification or regression tasks.
It can be trained on
- regression (by setting config.num_classes to 1); there should be one float-type label per graph
- one task classification (by sett... | 10,107 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/graphormer/modeling_graphormer.py |
def forward(
self,
input_nodes: torch.LongTensor,
input_edges: torch.LongTensor,
attn_bias: torch.Tensor,
in_degree: torch.LongTensor,
out_degree: torch.LongTensor,
spatial_pos: torch.LongTensor,
attn_edge_type: torch.LongTensor,
labels: Optional[t... | 10,107 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/graphormer/modeling_graphormer.py |
head_outputs = self.classifier(outputs)
logits = head_outputs[:, 0, :].contiguous()
loss = None
if labels is not None:
mask = ~torch.isnan(labels)
if self.num_classes == 1: # regression
loss_fct = MSELoss()
loss = loss_fct(logits[mask].s... | 10,107 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/graphormer/modeling_graphormer.py |
class Parser(utils.Parser):
dataset: str = "halfcheetah-medium-expert-v2"
config: str = "config.offline" | 10,108 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/trajectory_transformer/convert_trajectory_transformer_original_pytorch_checkpoint_to_pytorch.py |
class TrajectoryTransformerOutput(ModelOutput):
"""
Base class for model's outputs that also contains a pooling of the last hidden states. | 10,109 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/trajectory_transformer/modeling_trajectory_transformer.py |
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Language modeling loss.
logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
Prediction scores of the language modeling head (scores for each voc... | 10,109 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/trajectory_transformer/modeling_trajectory_transformer.py |
Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer
plus the initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`,... | 10,109 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/trajectory_transformer/modeling_trajectory_transformer.py |
loss: Optional[torch.FloatTensor] = None
logits: torch.FloatTensor = None
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None | 10,109 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/trajectory_transformer/modeling_trajectory_transformer.py |
class TrajectoryTransformerPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = TrajectoryTransformerConfig
load_tf_weights = load_tf_weights_in_trajectory_transformer
... | 10,110 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/trajectory_transformer/modeling_trajectory_transformer.py |
def _init_weights(self, module):
if isinstance(module, (nn.Linear, nn.Embedding)):
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if isinstance(module, nn.Linear) and module.bias is not None:
module.bias.data.zero_()
elif isinstance(module... | 10,110 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/trajectory_transformer/modeling_trajectory_transformer.py |
class EinLinear(nn.Module):
def __init__(self, n_models, in_features, out_features, bias):
super().__init__()
self.n_models = n_models
self.out_features = out_features
self.in_features = in_features
self.weight = nn.Parameter(torch.Tensor(n_models, out_features, in_features))... | 10,111 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/trajectory_transformer/modeling_trajectory_transformer.py |
def forward(self, input):
"""
Args:
input (`torch.FloatTensor` of shape `(B, n_models, input_dim)`):
The input to the layer.
"""
# [ batch_size x n_models x output_dim ]
output = torch.einsum("eoi,bei->beo", self.weight, input)
if self.bias is ... | 10,111 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/trajectory_transformer/modeling_trajectory_transformer.py |
class CausalSelfAttention(nn.Module):
def __init__(self, config):
super().__init__()
if config.n_embd % config.n_head != 0:
raise ValueError(f"n_head ({config.n_head}) should be a divisor of n_embd ({config.n_embd})")
# key, query, value projections for all heads
self.k... | 10,112 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/trajectory_transformer/modeling_trajectory_transformer.py |
# causal mask to ensure that attention is only applied to the left in the input sequence
self.register_buffer(
"mask",
torch.tril(torch.ones(config.block_size, config.block_size)).view(
1, 1, config.block_size, config.block_size
),
persistent=False... | 10,112 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/trajectory_transformer/modeling_trajectory_transformer.py |
# calculate query, key, values for all heads in batch and move head forward to be the batch dim
# [ batch_size x n_heads x sequence_length x head_dim ]
key = (
self.key(hidden_states)
.view(batch_size, sequence_length, self.n_head, embedding_dim // self.n_head)
.trans... | 10,112 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/trajectory_transformer/modeling_trajectory_transformer.py |
# causal self-attention
# [ batch_size x n_heads x sequence_length x sequence_length ]
attn_weights = (torch.matmul(query, key.transpose(-2, -1))) * (1.0 / math.sqrt(key.size(-1)))
attn_weights = attn_weights.masked_fill(
self.mask[:, :, :sequence_length, :sequence_length] == 0, torc... | 10,112 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/trajectory_transformer/modeling_trajectory_transformer.py |
class Block(nn.Module):
def __init__(self, config):
super().__init__()
self.ln1 = nn.LayerNorm(config.n_embd)
self.ln2 = nn.LayerNorm(config.n_embd)
self.attn = CausalSelfAttention(config)
# MLP
self.l1 = nn.Linear(config.n_embd, 4 * config.n_embd)
self.act =... | 10,113 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/trajectory_transformer/modeling_trajectory_transformer.py |
attn_outputs = self.attn(
hidden_states, layer_past=layer_past, use_cache=use_cache, output_attentions=output_attentions
)
attn_output = attn_outputs[0]
outputs = attn_outputs[1:]
hidden_states = attn_output + residual
residual = hidden_states
hidden_states =... | 10,113 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/trajectory_transformer/modeling_trajectory_transformer.py |
class TrajectoryTransformerModel(TrajectoryTransformerPreTrainedModel):
"""the full GPT language model, with a context size of block_size"""
def __init__(self, config):
super().__init__(config)
# input embedding stem (+1 for stop token)
self.tok_emb = nn.Embedding(config.vocab_size * c... | 10,114 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/trajectory_transformer/modeling_trajectory_transformer.py |
self.observation_dim = config.observation_dim
self.action_dim = config.action_dim
self.transition_dim = config.transition_dim
self.embedding_dim = config.n_embd
self.action_weight = config.action_weight
self.reward_weight = config.reward_weight
self.value_weight = config... | 10,114 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/trajectory_transformer/modeling_trajectory_transformer.py |
def pad_to_full_observation(self, hidden_states):
batch_size, sequence_length, _ = hidden_states.shape
n_pad = (self.transition_dim - sequence_length % self.transition_dim) % self.transition_dim
padding = torch.zeros(batch_size, n_pad, self.embedding_dim, device=hidden_states.device)
#... | 10,114 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/trajectory_transformer/modeling_trajectory_transformer.py |
@add_start_docstrings_to_model_forward(
TRAJECTORY_TRANSFORMER_INPUTS_DOCSTRING.format("batch_size, sequence_length")
)
@replace_return_docstrings(output_type=TrajectoryTransformerOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
trajectories: Optional[torch.LongTensor] = None... | 10,114 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/trajectory_transformer/modeling_trajectory_transformer.py |
>>> model = TrajectoryTransformerModel.from_pretrained(
... "CarlCochet/trajectory-transformer-halfcheetah-medium-v2"
... )
>>> model.to(device)
>>> model.eval()
>>> observations_dim, action_dim, batch_size = 17, 6, 256
>>> seq_length = observations_dim + action_dim ... | 10,114 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/trajectory_transformer/modeling_trajectory_transformer.py |
>>> outputs = model(
... trajectories,
... targets=targets,
... use_cache=True,
... output_attentions=True,
... output_hidden_states=True,
... return_dict=True,
... )
```
"""
output_attentions = output_attentions if ... | 10,114 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/trajectory_transformer/modeling_trajectory_transformer.py |
offset_trajectories = self.offset_tokens(trajectories)
# [ batch_size x sequence_length x embedding_dim ]
# forward the GPT model
token_embeddings = self.tok_emb(offset_trajectories) # each index maps to a (learnable) vector
position_embeddings = self.pos_emb[:, :sequence_length, :] # ... | 10,114 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/trajectory_transformer/modeling_trajectory_transformer.py |
for i, (block, layer_past) in enumerate(zip(self.blocks, past_key_values)):
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if self.gradient_checkpointing and self.training:
outputs = self._gradient_checkpointing_func(
... | 10,114 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/trajectory_transformer/modeling_trajectory_transformer.py |
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
hidden_states_pad, n_pad = self.pad_to_full_observation(hidden_state)
logits = self.head(hidden_states_pad)
logits = logits.reshape(batch_size, sequence_length + n_pad, self.vocab_size + 1)
lo... | 10,114 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/trajectory_transformer/modeling_trajectory_transformer.py |
# if we are given some desired targets also calculate the loss
if targets is not None:
loss = F.cross_entropy(logits.reshape(-1, logits.size(-1)), targets.view(-1), reduction="none")
if self.action_weight != 1 or self.reward_weight != 1 or self.value_weight != 1:
# make w... | 10,114 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/trajectory_transformer/modeling_trajectory_transformer.py |
loss = loss * weights.view(-1)
loss = (loss * attention_mask.view(-1)).mean()
else:
loss = None | 10,114 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/trajectory_transformer/modeling_trajectory_transformer.py |
if not return_dict:
return tuple(v for v in [loss, logits, presents, all_hidden_states, all_self_attentions] if v is not None)
return TrajectoryTransformerOutput(
loss=loss,
logits=logits,
past_key_values=presents,
hidden_states=all_hidden_states,
... | 10,114 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/trajectory_transformer/modeling_trajectory_transformer.py |
class TrajectoryTransformerConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`TrajectoryTransformerModel`]. It is used to
instantiate an TrajectoryTransformer model according to the specified arguments, defining the model architecture.
Instantiating a config... | 10,115 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/trajectory_transformer/configuration_trajectory_transformer.py |
Args:
vocab_size (`int`, *optional*, defaults to 100):
Vocabulary size of the TrajectoryTransformer model. Defines the number of different tokens that can be
represented by the `trajectories` passed when calling [`TrajectoryTransformerModel`]
action_weight (`int`, *optional*, def... | 10,115 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/trajectory_transformer/configuration_trajectory_transformer.py |
Dimension of the transition space.
n_layer (`int`, *optional*, defaults to 4):
Number of hidden layers in the Transformer encoder.
n_head (`int`, *optional*, defaults to 4):
Number of attention heads for each attention layer in the Transformer encoder.
n_embd (`int`, *opt... | 10,115 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/trajectory_transformer/configuration_trajectory_transformer.py |
`"relu"`, `"selu"` and `"gelu_new"` are supported.
max_position_embeddings (`int`, *optional*, defaults to 512):
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).
initializer_range ... | 10,115 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/trajectory_transformer/configuration_trajectory_transformer.py |
Example: | 10,115 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/trajectory_transformer/configuration_trajectory_transformer.py |
```python
>>> from transformers import TrajectoryTransformerConfig, TrajectoryTransformerModel
>>> # Initializing a TrajectoryTransformer CarlCochet/trajectory-transformer-halfcheetah-medium-v2 style configuration
>>> configuration = TrajectoryTransformerConfig()
>>> # Initializing a model (with rando... | 10,115 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/trajectory_transformer/configuration_trajectory_transformer.py |
def __init__(
self,
vocab_size=100,
action_weight=5,
reward_weight=1,
value_weight=1,
block_size=249,
action_dim=6,
observation_dim=17,
transition_dim=25,
n_layer=4,
n_head=4,
n_embd=128,
embd_pdrop=0.1,
attn... | 10,115 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/trajectory_transformer/configuration_trajectory_transformer.py |
self.transition_dim = transition_dim
self.learning_rate = learning_rate
self.n_layer = n_layer
self.n_head = n_head
self.n_embd = n_embd
self.embd_pdrop = embd_pdrop
self.attn_pdrop = attn_pdrop
self.resid_pdrop = resid_pdrop
self.initializer_range = initi... | 10,115 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/trajectory_transformer/configuration_trajectory_transformer.py |
class XLMProphetNetSeq2SeqLMOutput(ModelOutput):
"""
Base class for sequence-to-sequence language models outputs. | 10,116 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_prophetnet.py |
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Language modeling loss.
logits (`torch.FloatTensor` of shape `(batch_size, decoder_sequence_length, config.vocab_size)`):
Prediction scores of the main stream language modeling head ... | 10,116 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_prophetnet.py |
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