# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.

# Copyright 2022 The Salesforce authors, The Open AI Team Authors and The HuggingFace Inc. team

#

# Licensed under the Apache License, Version 2.0 (the "License");

# you may not use this file except in compliance with the License.

# You may obtain a copy of the License at

#

# http://www.apache.org/licenses/LICENSE-2.0

#

# Unless required by applicable law or agreed to in writing, software

# distributed under the License is distributed on an "AS IS" BASIS,

# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

# See the License for the specific language governing permissions and

# limitations under the License.

from typing import List, Optional, Tuple, Union

import paddle

import paddle.nn as nn

import paddle.nn.functional as F

from paddle import Tensor

from paddle.nn import Layer

from ...utils.env import CONFIG_NAME

from ...utils.log import logger

from .. import PretrainedModel, register_base_model

from ..activations import ACT2FN

from ..model_outputs import (

BaseModelOutputWithPastAndCrossAttentions,

CausalLMOutputWithCrossAttentions,

)

from .configuration import (

CODEGEN_PRETRAINED_INIT_CONFIGURATION,

CODEGEN_PRETRAINED_RESOURCE_FILES_MAP,

CodeGenConfig,

)

CODEGEN_PRETRAINED_MODEL_ARCHIVE_LIST = [

"Salesforce/codegen-350M-nl",

"Salesforce/codegen-350M-multi",

"Salesforce/codegen-350M-mono",

"Salesforce/codegen-2B-nl",

"Salesforce/codegen-2B-multi",

"Salesforce/codegen-2B-mono",

"Salesforce/codegen-6B-nl",

"Salesforce/codegen-6B-multi",

"Salesforce/codegen-6B-mono",

"Salesforce/codegen-16B-nl",

"Salesforce/codegen-16B-multi",

"Salesforce/codegen-16B-mono",

]

def fixed_pos_embedding(x, seq_dim=1, seq_len=None):

dim = x.shape[-1]

if seq_len is None:

seq_len = x.shape[seq_dim]

inv_freq = 1.0 / (10000 ** (paddle.arange(0, dim, 2) / dim))

sinusoid_inp = paddle.einsum("i,j->ij", paddle.arange(seq_len, dtype="float32"), inv_freq)

return paddle.sin(sinusoid_inp), paddle.cos(sinusoid_inp)

def rotate_every_two(x):

x1 = x[:, :, :, ::2]

x2 = x[:, :, :, 1::2]

x = paddle.stack((-x2, x1), axis=-1)

# In einsum notation: rearrange(x, '... d j -> ... (d j)')

return x.flatten(-2)

def duplicate_interleave(m):

return paddle.repeat_interleave(m, 2, axis=1)

def apply_rotary_pos_emb(x, sincos, offset=0):

sin, cos = map(lambda t: duplicate_interleave(t)[None, offset : x.shape[1] + offset, None, :], sincos)

# einsum notation for lambda t: repeat(t[offset:x.shape[1]+offset,:], "n d -> () n () (d j)", j=2)

return (x * cos) + (rotate_every_two(x) * sin)

class CodeGenAttention(Layer):

def __init__(self, config: CodeGenConfig):

super().__init__()

self.causal_mask = paddle.tril(

paddle.ones((config.n_positions, config.n_positions), dtype=paddle.get_default_dtype())

).reshape((1, 1, config.n_positions, config.n_positions))

self.attn_dropout = nn.Dropout(config.attn_pdrop)

self.resid_dropout = nn.Dropout(config.resid_pdrop)

self.embed_dim = config.n_embd

self.num_attention_heads = config.n_head

self.head_dim = self.embed_dim // self.num_attention_heads

if self.head_dim * self.num_attention_heads != self.embed_dim:

raise ValueError(

f"embed_dim must be divisible by num_attention_heads (got `embed_dim`: {self.embed_dim} and"

f" `num_attention_heads`: {self.num_attention_heads})."

)

self.scale_attn = paddle.sqrt(paddle.to_tensor(self.head_dim, dtype="float32"))

self.qkv_proj = nn.Linear(self.embed_dim, self.embed_dim * 3, bias_attr=False)

self.out_proj = nn.Linear(self.embed_dim, self.embed_dim, bias_attr=False)

self.rotary_dim = config.rotary_dim

def _split_heads(self, x, n_head, dim_head, mp_num):

reshaped = x.reshape(x.shape[:-1] + [n_head // mp_num, dim_head])

reshaped = reshaped.reshape(x.shape[:-2] + [-1] + reshaped.shape[-1:])

return reshaped

def _merge_heads(self, tensor, num_attention_heads, attn_head_size):

"""

Merges attn_head_size dim and num_attn_heads dim into n_ctx

"""

if len(tensor.shape) == 5:

tensor = tensor.transpose([0, 1, 3, 2, 4])

elif len(tensor.shape) == 4:

tensor = tensor.transpose([0, 2, 1, 3])

else:

raise ValueError(f"Input tensor rank should be one of [4, 5], but is: {len(tensor.shape)}")

new_shape = tensor.shape[:-2] + [num_attention_heads * attn_head_size]

return tensor.reshape(new_shape)

def _attn(self, query, key, value, attention_mask=None):

# compute causal mask from causal mask buffer

query_length, key_length = query.shape[-2], key.shape[-2]

causal_mask = self.causal_mask[:, :, key_length - query_length : key_length, :key_length]

# Keep the attention weights computation in fp32 to avoid overflow issues

query = paddle.cast(query, "float32")

key = paddle.cast(key, "float32")

attn_weights = paddle.matmul(query, key, transpose_y=True)

attn_weights = attn_weights / self.scale_attn

mask_value = paddle.to_tensor(-1e4, dtype=attn_weights.dtype)

# Need to be a tensor, otherwise we get error: `RuntimeError: expected scalar type float but found double`.

attn_weights = paddle.where(causal_mask, attn_weights, mask_value)

if attention_mask is not None:

# Apply the attention mask

attn_weights = attn_weights + attention_mask

attn_weights = F.softmax(attn_weights, axis=-1, dtype=value.dtype)

attn_weights = self.attn_dropout(attn_weights)

attn_output = paddle.matmul(attn_weights, value)

return attn_output, attn_weights

def forward(

self,

hidden_states: Tensor,

attention_mask: Optional[Tensor] = None,

use_cache: Optional[bool] = False,

cache: Optional[Tuple[Tensor]] = None,

output_attentions: Optional[bool] = False,

) -> Tuple:

qkv = self.qkv_proj(hidden_states)

mp_num = 4

qkv_split = qkv.reshape(qkv.shape[:-1] + [mp_num, -1])

local_dim = qkv_split.shape[-1] // (self.head_dim * self.num_attention_heads // mp_num)

query, value, key = paddle.split(qkv_split, local_dim, axis=-1)

query = self._split_heads(query, self.num_attention_heads, self.head_dim, mp_num=mp_num)

key = self._split_heads(key, self.num_attention_heads, self.head_dim, mp_num=mp_num)

value = self._split_heads(value, self.num_attention_heads, self.head_dim, mp_num=mp_num)

value = value.transpose([0, 2, 1, 3])

seq_len = key.shape[1]

offset = 0

if cache is not None:

offset = cache[0].shape[-2]

seq_len += offset

if self.rotary_dim is not None:

k_rot = key[:, :, :, : self.rotary_dim]

k_pass = key[:, :, :, self.rotary_dim :]

q_rot = query[:, :, :, : self.rotary_dim]

q_pass = query[:, :, :, self.rotary_dim :]

sincos = fixed_pos_embedding(k_rot, 1, seq_len=seq_len)

k_rot = apply_rotary_pos_emb(k_rot, sincos, offset=offset)

q_rot = apply_rotary_pos_emb(q_rot, sincos, offset=offset)

key = paddle.concat([k_rot, k_pass], axis=-1)

query = paddle.concat([q_rot, q_pass], axis=-1)

else:

sincos = fixed_pos_embedding(key, 1, seq_len=seq_len)

key = apply_rotary_pos_emb(key, sincos, offset=offset)

query = apply_rotary_pos_emb(query, sincos, offset=offset)

key = key.transpose([0, 2, 1, 3])

query = query.transpose([0, 2, 1, 3])

if cache is not None:

past_key = cache[0]

past_value = cache[1]

key = paddle.concat((past_key, key), axis=-2)

value = paddle.concat((past_value, value), axis=-2)

if use_cache is True:

present = (key, value)

else:

present = None

# compute self-attention: V x Softmax(QK^T)

attn_output, attn_weights = self._attn(query, key, value, attention_mask)

attn_output = self._merge_heads(attn_output, self.num_attention_heads, self.head_dim)

attn_output = self.out_proj(attn_output)

attn_output = self.resid_dropout(attn_output)

if output_attentions:

return attn_output, present, attn_weights

return attn_output, present

class CodeGenMLP(Layer):

def __init__(self, config: CodeGenConfig):

super().__init__()

inner_dim = config.n_inner if config.n_inner is not None else 4 * config.n_embd

self.fc_in = nn.Linear(config.n_embd, inner_dim)

self.fc_out = nn.Linear(inner_dim, config.n_embd)

self.act = ACT2FN[config.activation_function]

self.dropout = nn.Dropout(config.resid_pdrop)

def forward(self, hidden_states: Tensor) -> Tensor:

hidden_states = self.fc_in(hidden_states)

hidden_states = self.act(hidden_states)

hidden_states = self.fc_out(hidden_states)

hidden_states = self.dropout(hidden_states)

return hidden_states

class CodeGenBlock(Layer):

def __init__(self, config: CodeGenConfig):

super().__init__()

self.ln_1 = nn.LayerNorm(config.n_embd, epsilon=config.layer_norm_epsilon)

self.attn = CodeGenAttention(config)

self.mlp = CodeGenMLP(config)

def forward(

self,

hidden_states: Tensor,

attention_mask: Optional[Tensor] = None,

use_cache: Optional[bool] = False,

cache: Optional[Tuple[Tensor]] = None,

output_attentions: Optional[bool] = False,

) -> Tuple:

residual = hidden_states

hidden_states = self.ln_1(hidden_states)

attn_outputs = self.attn(

hidden_states,

attention_mask=attention_mask,

cache=cache,

use_cache=use_cache,

output_attentions=output_attentions,

)

attn_output = attn_outputs[0] # output_attn: a, present, (attentions)

outputs = attn_outputs[1:]

feed_forward_hidden_states = self.mlp(hidden_states)

hidden_states = attn_output + feed_forward_hidden_states + residual

if use_cache:

outputs = (hidden_states,) + outputs

else:

outputs = (hidden_states,) + outputs[1:]

return outputs # hidden_states, (present, attentions) outputs is a tuple

class CodeGenPreTrainedModel(PretrainedModel):

"""

An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained

models.

"""

model_config_file = CONFIG_NAME

pretrained_init_configuration = CODEGEN_PRETRAINED_INIT_CONFIGURATION

pretrained_resource_files_map = CODEGEN_PRETRAINED_RESOURCE_FILES_MAP

config_class = CodeGenConfig

base_model_prefix = "transformer"

def init_weights(self, layer):

"""Initialize the weights."""

if isinstance(layer, (nn.Linear, nn.Embedding)):

if isinstance(layer.weight, paddle.Tensor) and paddle.get_default_dtype() == "float32":

layer.weight.set_value(

paddle.tensor.normal(

mean=0.0,

std=self.config.initializer_range,

shape=layer.weight.shape,

)

)

elif isinstance(layer, nn.LayerNorm):

layer.bias.set_value(paddle.zeros_like(layer.bias))

layer.weight.set_value(paddle.full_like(layer.weight, 1.0))

layer._epsilon = self.config.layer_norm_epsilon

if isinstance(layer, nn.Linear) and layer.bias is not None:

layer.bias.set_value(paddle.zeros_like(layer.bias))

@register_base_model

class CodeGenModel(CodeGenPreTrainedModel):

r"""

The bare CodeGen Model outputting raw hidden-states.

This model inherits from :class:`~paddlenlp.transformers.model_utils.PretrainedModel`.

Refer to the superclass documentation for the generic methods.

This model is also a Paddle `paddle.nn.Layer <https://www.paddlepaddle.org.cn/documentation

/docs/en/api/paddle/fluid/dygraph/layers/Layer_en.html>`__ subclass. Use it as a regular Paddle Layer

and refer to the Paddle documentation for all matter related to general usage and behavior.

Args:

config (:class:`CodeGenConfig`):

An instance of CodeGenConfig used to construct CodeGenModel.

"""

def __init__(self, config: CodeGenConfig):

super().__init__(config)

self.vocab_size = config.vocab_size

self.bos_token_id = config.bos_token_id

self.pad_token_id = config.pad_token_id

self.eos_token_id = config.eos_token_id

self.embed_dim = config.n_embd

self.initializer_range = config.initializer_range

self.wte = nn.Embedding(config.vocab_size, self.embed_dim)

self.drop = nn.Dropout(config.embd_pdrop)

self.h = nn.LayerList([CodeGenBlock(config) for _ in range(config.n_layer)])

self.ln_f = nn.LayerNorm(self.embed_dim, epsilon=config.layer_norm_epsilon)

self.rotary_dim = min(config.rotary_dim, config.n_ctx // config.n_head)

# Initialize weights and apply final processing

self.apply(self.init_weights)

def get_input_embeddings(self):

return self.wte

def set_input_embeddings(self, new_embeddings):

self.wte = new_embeddings

def forward(

self,

input_ids: Optional[Tensor] = None,

attention_mask: Optional[Tensor] = None,

token_type_ids: Optional[Tensor] = None,

use_cache: Optional[bool] = None,

cache: Optional[List[Tuple[Tensor]]] = None,

inputs_embeds: Optional[Tensor] = None,

output_attentions: Optional[bool] = None,

output_hidden_states: Optional[bool] = None,

return_dict: Optional[bool] = None,

) -> Union[Tuple, BaseModelOutputWithPastAndCrossAttentions]:

r"""

The CodeGenModel forward method, overrides the `__call__()` special method.

Args:

input_ids (Tensor, optional):

Indices of input sequence tokens in the vocabulary. They are

numerical representations of tokens that build the input sequence.

Its data type should be `int64` and it has a shape of [batch_size, sequence_length].

attention_mask (Tensor, optional):

Mask used in multi-head attention to avoid performing attention to some unwanted positions,

usually the paddings or the subsequent positions.

Its data type can be int, float and bool.

When the data type is bool, the `masked` tokens have `False` values and the others have `True` values.

When the data type is int, the `masked` tokens have `0` values and the others have `1` values.

When the data type is float, the `masked` tokens have `-INF` values and the others have `0` values.

It is a tensor with shape broadcasted to `[batch_size, num_attention_heads, sequence_length, sequence_length]`.

For example, its shape can be [batch_size, sequence_length], [batch_size, sequence_length, sequence_length],

[batch_size, num_attention_heads, sequence_length, sequence_length].

Defaults to `None`, which means nothing needed to be prevented attention to.

use_cache (bool, optional):

Whether or not to use cache. Defaults to `False`. If set to `True`, key value states will be returned and

can be used to speed up decoding.

cache (list, optional):

It is a list, and each element in the list is a tuple `(incremental_cache, static_cache)`.

See `TransformerDecoder.gen_cache <https://github.com/PaddlePaddle/Paddle/blob/release/2.1/python/paddle/nn/layer/transformer.py#L1060>`__ for more details.

It is only used for inference and should be None for training.

Default to `None`.

inputs_embeds (Tensor, optional):

Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation

of shape `(batch_size, sequence_length, hidden_size)`. This is useful if you want more control over

how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix.

Default to None.

output_attentions (bool, optional):

Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned

tensors for more detail. Defaults to `False`.

output_hidden_states (bool, optional):

Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for

more detail. Defaults to `False`.

return_dict (bool, optional):

Whether to return a :class:`~paddlenlp.transformers.model_outputs.BaseModelOutputWithPastAndCrossAttentions` object.

If `False`, the output will be a tuple of tensors. Defaults to `False`.

Returns:

An instance of :class:`~paddlenlp.transformers.model_outputs.BaseModelOutputWithPastAndCrossAttentions` if

`return_dict=True`. Otherwise it returns a tuple of tensors corresponding

to ordered and not None (depending on the input arguments) fields of

:class:`~paddlenlp.transformers.model_outputs.BaseModelOutputWithPastAndCrossAttentions`.

Especially, When `return_dict=output_hidden_states=output_attentions=False` and `cache=None`,

returns a tensor representing the output of :class:`CodeGenModel`.

Its data type should be float32 and has a shape of [batch_size, sequence_length, hidden_size].

Example:

.. code-block::

import paddle

from paddlenlp.transformers import CodeGenModel, CodeGenTokenizer

tokenizer = CodeGenTokenizer.from_pretrained('Salesforce/codegen-350M-mono')

model = CodeGenModel.from_pretrained('Salesforce/codegen-350M-mono')

inputs = tokenizer("def hello_world():", return_token_type_ids=False)

inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()}

output = model(**inputs)

"""

output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions

output_hidden_states = (

output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states

)

use_cache = use_cache if use_cache is not None else self.config.use_cache

return_dict = return_dict if return_dict is not None else self.config.use_return_dict

if input_ids is not None and inputs_embeds is not None:

raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")

elif input_ids is not None:

input_shape = input_ids.shape

input_ids = input_ids.reshape((-1, input_shape[-1]))

batch_size = input_ids.shape[0]

elif inputs_embeds is not None:

input_shape = inputs_embeds.shape[:-1]

batch_size = inputs_embeds.shape[0]

else:

raise ValueError("You have to specify either input_ids or inputs_embeds")

if cache is None:

past_length = 0

cache = tuple([None] * len(self.h))

else:

past_length = cache[0][0].shape[-2]

# Attention mask.

if attention_mask is None:

if input_ids is not None:

if batch_size == 1 and past_length != 0:

batch_size, seq_len = input_shape

attention_mask = paddle.zeros(

[batch_size, 1, 1, seq_len + past_length], dtype=paddle.get_default_dtype()

)

else:

attention_mask = (

paddle.cast(input_ids == self.pad_token_id, dtype=paddle.get_default_dtype()).unsqueeze([1, 2])

* -1e4

)

else:

logger.warning(

"Provided inputs_embeds while attention_mask is None, attention weights will not be masked during forwarding."

)

# For 2D attention_mask from tokenizer

elif attention_mask.ndim == 2:

attention_mask = paddle.unsqueeze(attention_mask, axis=[1, 2]).astype(paddle.get_default_dtype())

attention_mask = (1.0 - attention_mask) * -1e4

if attention_mask is not None:

attention_mask.stop_gradient = True

# TODO: CodeGen Attention Mask is TOO confusion.

# When it's 2D, it must be int and it's denoted by 1/0.

# When using model.generate() without providing attention mask

# or using 4D attention mask,

# the attention mask's dtype must be float and it's denoted by 0/-inf.

# Moreover, cannot support 3D attention mask.

if inputs_embeds is None:

inputs_embeds = self.wte(input_ids)

if token_type_ids is not None:

token_type_embeds = self.wte(token_type_ids)

inputs_embeds = inputs_embeds + token_type_embeds

hidden_states = self.drop(inputs_embeds)

output_shape = input_shape[:] + [hidden_states.shape[-1]]

presents = () if use_cache else None

all_hidden_states = () if output_hidden_states else None

all_self_attentions = () if output_attentions else None

for i, (block, old_cache) in enumerate(zip(self.h, cache)):

if output_hidden_states:

all_hidden_states += (hidden_states,)

outputs = block(

hidden_states,

attention_mask=attention_mask,

use_cache=use_cache,

cache=old_cache,

output_attentions=output_attentions,

)

hidden_states = outputs[0]

if use_cache:

presents = presents + (outputs[1],)

if output_attentions:

all_self_attentions += (outputs[-1],)

hidden_states = self.ln_f(hidden_states)

hidden_states = hidden_states.reshape(shape=output_shape)

if output_hidden_states:

all_hidden_states += (hidden_states,)

last_hidden_state = hidden_states

new_cache = presents

if not return_dict:

temp_list = [

last_hidden_state,

new_cache,

all_hidden_states,

all_self_attentions,

]

return tuple(v for v in temp_list if v is not None)

return BaseModelOutputWithPastAndCrossAttentions(

last_hidden_state=last_hidden_state,

past_key_values=new_cache,

hidden_states=all_hidden_states,

attentions=all_self_attentions,

cross_attentions=None,

)

class CodeGenForCausalLM(CodeGenPreTrainedModel):

r"""

CodeGen Model with a `language modeling` head on top.

Args:

config (:class:`CodeGenConfig`):

An instance of CodeGenConfig used to construct CodeGenForCausalLM.

"""

_keys_to_ignore_on_load_missing = [r"h\.\d+\.attn\.masked_bias", r"h\.\d+\.attn\.bias"]

def __init__(self, config: CodeGenConfig):

super().__init__(config)

self.transformer = CodeGenModel(config)

self.lm_head = nn.Linear(config.n_embd, config.vocab_size)

# Initialize weights and apply final processing

self.apply(self.init_weights)

def get_output_embeddings(self):

return self.lm_head

def set_output_embeddings(self, new_embeddings):

self.lm_head = new_embeddings

def prepare_fast_entry(self, kwargs):

from paddlenlp.ops import FasterCodeGen

use_fp16_decoding = kwargs.get("use_fp16_decoding", False)

decoding_lib = kwargs.get("decoding_lib", None)

decode_strategy = kwargs.get("decode_strategy")

if decode_strategy == "beam_search":

raise AttributeError("'beam_search' is not supported yet in the fast version of GPTJ")

# Currently, FasterTransformer only support restricted size_per_head.

size_per_head = self.transformer.config.n_embd // self.transformer.config.n_head

if size_per_head not in [32, 64, 80, 96, 128, 160, 192, 224, 256]:

raise AttributeError(

"'size_per_head = %d' is not supported yet in the fast version of GPTJ" % size_per_head

)

if kwargs["forced_bos_token_id"] is not None:

# not support for min_length yet in the fast version

raise AttributeError("'forced_bos_token_id != None' is not supported yet in the fast version")

self._fast_entry = FasterCodeGen(self, decoding_lib=decoding_lib, use_fp16_decoding=use_fp16_decoding).forward

return self._fast_entry

def prepare_inputs_for_generation(self, input_ids, cache=None, **kwargs):

# only last token for inputs_ids if past is defined in kwargs

token_type_ids = kwargs.get("token_type_ids", None)

if cache:

input_ids = input_ids[:, -1].unsqueeze(-1)

if token_type_ids is not None:

token_type_ids = token_type_ids[:, -1].unsqueeze(-1)

attention_mask = kwargs.get("attention_mask", None)

if attention_mask is not None:

if len(attention_mask.shape) == 4:

attention_mask = attention_mask[:, :, -1:, :]

return {

"input_ids": input_ids,

"cache": cache,

"use_cache": kwargs.get("use_cache"),

"attention_mask": attention_mask,

"token_type_ids": token_type_ids,

}

def forward(

self,

input_ids: Optional[Tensor] = None,

attention_mask: Optional[Tensor] = None,

token_type_ids: Optional[Tensor] = None,

use_cache: Optional[bool] = None,

cache: Optional[List[Tuple[Tensor]]] = None,

labels: Optional[Tensor] = None,

inputs_embeds: Optional[Tensor] = None,

output_attentions: Optional[bool] = None,

output_hidden_states: Optional[bool] = None,

return_dict: Optional[bool] = None,

) -> Union[Tuple, CausalLMOutputWithCrossAttentions]:

r"""

The CodeGenForCausalLM forward method, overrides the __call__() special method.

Args:

input_ids (Tensor, optional):

See :class:`CodeGenModel`.

attention_mask (Tensor, optional):

See :class:`CodeGenModel`.

use_cache (bool, optional):

See :class:`CodeGenModel`.

cache (Tensor, optional):

See :class:`CodeGenModel`.

labels: (Tensor, optional):

Labels for language modeling. Note that the labels are shifted inside the model, i.e. you can set

`labels = input_ids` Indices are selected in `[-100, 0, ..., vocab_size]` All labels set to `-100`

are ignored (masked), the loss is only computed for labels in `[0, ..., vocab_size]`

inputs_embeds (Tensor, optional):

See :class:`CodeGenModel`.

output_attentions (bool, optional):

See :class: `CodeGenModel`.

output_hidden_states (bool, optional):

See :class: `CodeGenModel`.

return_dict (bool, optional):

See :class: `CodeGenModel`.

Returns:

An instance of :class:`~paddlenlp.transformers.model_outputs.CausalLMOutputWithPastAndCrossAttentions` if

`return_dict=True`. Otherwise it returns a tuple of tensors corresponding

to ordered and not None (depending on the input arguments) fields of

:class:`~paddlenlp.transformers.model_outputs.CausalLMOutputWithPastAndCrossAttentions`.

Especially, When `return_dict=output_hidden_states=output_attentions=False` and `cache=labels=None`,

returns tensor `lm_logits` of shape [batch_size, sequence_length, vocab_size],

Example:

.. code-block::

import paddle

from paddlenlp.transformers import CodeGenForCausalLM, CodeGenTokenizer

tokenizer = CodeGenTokenizer.from_pretrained('Salesforce/codegen-350M-mono')

model = CodeGenForCausalLM.from_pretrained('Salesforce/codegen-350M-mono')

inputs = tokenizer("def hello_world():", return_token_type_ids=False)

inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()}

outputs = model(**inputs)

"""

return_dict = return_dict if return_dict is not None else self.config.use_return_dict

transformer_outputs = self.transformer(

input_ids,

attention_mask=attention_mask,

token_type_ids=token_type_ids,

use_cache=use_cache,

cache=cache,

inputs_embeds=inputs_embeds,

output_attentions=output_attentions,

output_hidden_states=output_hidden_states,

return_dict=return_dict,

)

hidden_states = transformer_outputs[0]

# make sure sampling in fp16 works correctly and

# compute loss in fp32 to match with mesh-tf version

# https://github.com/EleutherAI/gpt-neo/blob/89ce74164da2fb16179106f54e2269b5da8db333/models/gpt2/gpt2.py#L179

lm_logits = paddle.cast(self.lm_head(hidden_states), "float32")

loss = None

if labels is not None:

# Shift so that tokens < n predict n

shift_logits = lm_logits[:, :-1, :]

shift_labels = labels[:, 1:]

# Flatten the tokens

loss_fct = nn.CrossEntropyLoss()

loss = loss_fct(shift_logits.reshape((-1, shift_logits.shape[-1])), shift_labels.reshape((-1,)))

if not return_dict:

# if isinstance(transformer_outputs, type(input_ids)):

# return (loss, lm_logits) if loss is not None else lm_logits

outputs = (lm_logits,) + transformer_outputs[1:]

return ((loss,) + outputs) if loss is not None else outputs

return CausalLMOutputWithCrossAttentions(

loss=loss,

logits=lm_logits,

past_key_values=transformer_outputs.past_key_values,

hidden_states=transformer_outputs.hidden_states,

attentions=transformer_outputs.attentions,

)

def __getattr__(self, name):

try:

return super().__getattr__(name)

except AttributeError:

return getattr(getattr(self, self.base_model_prefix), name)