# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
# Copyright 2018 The OpenAI Team Authors and HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# 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 __future__ import annotations
import collections
import contextlib
import math
from functools import partial
import numpy as np
import paddle
import paddle.incubate as incubate
import paddle.nn as nn
import paddle.nn.functional as F
import paddle.tensor as tensor
from paddle.distributed import fleet
from paddle.distributed.fleet.meta_parallel import get_rng_state_tracker
from paddle.distributed.fleet.utils import recompute
from paddle.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...utils.converter import StateDictNameMapping
from ...utils.log import logger
from .. import PretrainedModel, register_base_model
from ..model_outputs import (
BaseModelOutputWithPastAndCrossAttentions,
CausalLMOutputWithCrossAttentions,
SequenceClassifierOutputWithPast,
TokenClassifierOutput,
)
from ..model_utils import dy2st_nocheck_guard_context
from ..sequence_parallel_utils import (
ColumnSequenceParallelLinear,
GatherOp,
RowSequenceParallelLinear,
ScatterOp,
mark_as_sequence_parallel_parameter,
)
from .configuration import GPT_PRETRAINED_INIT_CONFIGURATION, GPTConfig
try:
from paddle.nn.functional.flash_attention import flash_attention
except:
flash_attention = None
try:
from paddle.incubate.nn.layer.fused_dropout_add import FusedDropoutAdd
except:
FusedDropoutAdd = None
__all__ = [
"GPTModel",
"GPTPretrainedModel",
"GPTPretrainingCriterion",
"GPTForGreedyGeneration",
"GPTLMHeadModel",
"GPTForTokenClassification",
"GPTForSequenceClassification",
"GPTForCausalLM",
"GPTEmbeddings",
"GPTDecoderLayer",
]
def get_triangle_upper_mask(x, mask=None):
if mask is not None:
return mask
if paddle.is_compiled_with_xpu():
# xpu does not support set constant to -np.inf
mask = paddle.full_like(x, -1e4)
else:
mask = paddle.full_like(x, -np.inf)
mask.stop_gradient = True
mask = paddle.triu(mask, diagonal=1)
mask.stop_gradient = True
return mask
def parallel_matmul(x: paddle.Tensor, y: paddle.Tensor, transpose_y=True, tensor_parallel_output=True):
is_fleet_init = True
tensor_parallel_degree = 1
try:
hcg = fleet.get_hybrid_communicate_group()
model_parallel_group = hcg.get_model_parallel_group()
tensor_parallel_degree = hcg.get_model_parallel_world_size()
except:
is_fleet_init = False
if is_fleet_init and tensor_parallel_degree > 1 and y.is_distributed:
# if not running under distributed.launch, it will raise AttributeError: 'Fleet' object has no attribute '_hcg'
input_parallel = paddle.distributed.collective._c_identity(x, group=model_parallel_group)
logits = paddle.matmul(input_parallel, y, transpose_y=transpose_y)
if tensor_parallel_output:
return logits
return paddle.distributed.collective._c_concat(logits, group=model_parallel_group)
else:
logits = paddle.matmul(x, y, transpose_y=transpose_y)
return logits
def seed_guard_context(name=None):
if name in get_rng_state_tracker().states_:
return get_rng_state_tracker().rng_state(name)
else:
return contextlib.nullcontext()
def _make_causal_mask(input_ids_shape, past_key_values_length):
"""
Make causal mask used for self-attention
"""
batch_size, target_length = input_ids_shape # target_length: seq_len
mask = paddle.tril(paddle.ones((target_length, target_length), dtype="bool"))
if past_key_values_length > 0:
# [tgt_len, tgt_len + past_len]
mask = paddle.concat([paddle.ones([target_length, past_key_values_length], dtype="bool"), mask], axis=-1)
# [bs, 1, tgt_len, tgt_len + past_len]
return mask[None, None, :, :].expand([batch_size, 1, target_length, target_length + past_key_values_length])
def _expand_2d_mask(mask, dtype, tgt_length):
"""
Expands attention_mask from `[batch_size, src_length]` to `[batch_size, 1, tgt_length, src_length]`.
"""
batch_size, src_length = mask.shape[0], mask.shape[-1]
tgt_length = tgt_length if tgt_length is not None else src_length
mask = mask[:, None, None, :].astype("bool")
mask.stop_gradient = True
expanded_mask = mask.expand([batch_size, 1, tgt_length, src_length])
return expanded_mask
class MultiHeadAttention(nn.Layer):
"""
Attention mapps queries and a set of key-value pairs to outputs, and
Multi-Head Attention performs multiple parallel attention to jointly attending
to information from different representation subspaces.
"""
Cache = collections.namedtuple("Cache", ["k", "v"])
def __init__(
self,
config,
):
super(MultiHeadAttention, self).__init__()
self.config = config
# Recompute defaults to False and is controlled by Trainer
self.enable_recompute = False
self.use_flash_attention = config.use_flash_attention if flash_attention else False
self.head_dim = config.hidden_size // config.num_attention_heads
assert (
self.head_dim * config.num_attention_heads == config.hidden_size
), "hidden_size must be divisible by num_attention_heads"
self.num_attention_heads = config.num_attention_heads # default, without tensor parallel
if config.sequence_parallel:
ColumnParallelLinear = ColumnSequenceParallelLinear
RowParallelLinear = RowSequenceParallelLinear
else:
ColumnParallelLinear = fleet.meta_parallel.ColumnParallelLinear
RowParallelLinear = fleet.meta_parallel.RowParallelLinear
if config.tensor_parallel_degree > 1:
assert config.num_attention_heads % config.tensor_parallel_degree == 0
self.num_attention_heads = config.num_attention_heads // config.tensor_parallel_degree
if config.fuse_attention_qkv:
self.qkv_proj = ColumnParallelLinear(
config.hidden_size,
3 * config.hidden_size,
has_bias=True,
gather_output=False,
fuse_matmul_bias=config.fused_linear,
)
else:
self.q_proj = ColumnParallelLinear(
config.hidden_size,
config.hidden_size,
has_bias=True,
gather_output=False,
fuse_matmul_bias=config.fused_linear,
)
self.k_proj = ColumnParallelLinear(
config.hidden_size,
config.hidden_size,
has_bias=True,
gather_output=False,
fuse_matmul_bias=config.fused_linear,
)
self.v_proj = ColumnParallelLinear(
config.hidden_size,
config.hidden_size,
has_bias=True,
gather_output=False,
fuse_matmul_bias=config.fused_linear,
)
self.out_proj = RowParallelLinear(
config.hidden_size,
config.hidden_size,
has_bias=True,
input_is_parallel=True,
fuse_matmul_bias=config.fused_linear,
)
else:
if self.config.fuse_attention_qkv:
self.qkv_proj = nn.Linear(config.hidden_size, 3 * config.hidden_size, bias_attr=True)
else:
self.q_proj = nn.Linear(config.hidden_size, config.hidden_size, bias_attr=True)
self.k_proj = nn.Linear(config.hidden_size, config.hidden_size, bias_attr=True)
self.v_proj = nn.Linear(config.hidden_size, config.hidden_size, bias_attr=True)
self.out_proj = nn.Linear(config.hidden_size, config.hidden_size, bias_attr=True)
def _fuse_prepare_qkv(self, query, use_cache=False, past_key_value=None):
if self.config.sequence_parallel:
# [bs, seq_len, num_head * head_dim] -> [bs / n, seq_len, num_head, head_dim] (n is model parallelism)
target_shape = [-1, self.config.seq_length, self.num_attention_heads, 3 * self.head_dim]
else:
target_shape = [0, 0, self.num_attention_heads, 3 * self.head_dim]
# bs, seq_len, num_head * 3*head_dim
mix_layer = self.qkv_proj(query)
# bs, seq_len, num_head, 3*head_dim
mix_layer = paddle.reshape_(mix_layer, target_shape)
# query_states, key_states, value_states => bs, seq_len, num_head, head_dim
query_states, key_states, value_states = paddle.split(mix_layer, num_or_sections=3, axis=-1)
# [bs, seq_len, num_head, head_dim]
if past_key_value is not None:
# reuse k, v, self_attention
# concat along seqlen dimension
key_states = paddle.concat([past_key_value[0], key_states], axis=1)
value_states = paddle.concat([past_key_value[1], value_states], axis=1)
past_key_value = (key_states, value_states) if use_cache else None
return query_states, key_states, value_states, past_key_value
def _prepare_qkv(self, query, key, value, use_cache=False, past_key_value=None):
r"""
Prapares linear projected queries, keys and values for usage of subsequnt
multiple parallel attention. If `cache` is not None, using cached results
to reduce redundant calculations.
"""
if self.config.sequence_parallel:
# [bs, seq_len, num_head * head_dim] -> [bs/n, seq_len, num_head * head_dim] (n is model parallelism)
target_shape = [-1, self.config.seq_length, self.num_attention_heads, self.head_dim]
else:
target_shape = [0, 0, self.num_attention_heads, self.head_dim]
query_states = self.q_proj(query)
# [bs, seq_len, num_head, head_dim]
query_states = tensor.reshape(x=query_states, shape=target_shape)
key_states = self.k_proj(key)
# [bs, seq_len, num_head, head_dim]
key_states = tensor.reshape(x=key_states, shape=target_shape)
value_states = self.v_proj(value)
# [bs, seq_len, num_head, head_dim]
value_states = tensor.reshape(x=value_states, shape=target_shape)
# [bs, seq_len, num_head, head_dim]
if past_key_value is not None:
# reuse k, v, self_attention
# concat along seqlen dimension
key_states = paddle.concat([past_key_value[0], key_states], axis=1)
value_states = paddle.concat([past_key_value[1], value_states], axis=1)
past_key_value = (key_states, value_states) if use_cache else None
return query_states, key_states, value_states, past_key_value
def _flash_attention(self, q, k, v, attention_mask=None, output_attentions=False):
with seed_guard_context("local_seed"):
out, weights = flash_attention(
query=q,
key=k,
value=v,
dropout=self.config.attention_probs_dropout_prob,
causal=q.shape[1] != 1,
return_softmax=output_attentions,
training=self.training,
)
# [bs, seq_len, num_head, head_dim] -> [bs, seq_len, num_head * head_dim]
out = tensor.reshape(x=out, shape=[0, 0, out.shape[2] * out.shape[3]])
return (out, weights) if output_attentions else out
def _core_attention(self, q, k, v, attention_mask=None, output_attentions=False):
# [bs, seq_len, num_head, head_dim] -> [bs, num_head, seq_len, head_dim]
perm = [0, 2, 1, 3]
q = tensor.transpose(x=q, perm=perm)
k = tensor.transpose(x=k, perm=perm)
v = tensor.transpose(x=v, perm=perm)
# scale dot product attention
product = paddle.matmul(x=q * ((self.config.scale_qk_coeff * self.head_dim) ** -0.5), y=k, transpose_y=True)
if self.config.scale_qk_coeff != 1.0:
product = product.scale(self.config.scale_qk_coeff)
# softmax_mask_fuse_upper_triangle is not supported sif paddle is not compiled with cuda/rocm
if not paddle.is_compiled_with_cuda():
attention_mask = get_triangle_upper_mask(product, attention_mask)
if attention_mask is not None:
product = product + attention_mask.astype(product.dtype)
weights = F.softmax(product)
else:
weights = incubate.softmax_mask_fuse_upper_triangle(product)
if self.config.attention_probs_dropout_prob:
with seed_guard_context("local_seed"):
weights = F.dropout(
weights, self.config.attention_probs_dropout_prob, training=self.training, mode="upscale_in_train"
)
out = paddle.matmul(weights, v)
# combine heads
out = tensor.transpose(out, perm=[0, 2, 1, 3]) # bs, seq_len, num_head, head_dim
out = tensor.reshape(x=out, shape=[0, 0, -1]) # bs, seq_len, dim
return (out, weights) if output_attentions else out
def forward(
self, query, key, value, attention_mask=None, use_cache=False, past_key_value=None, output_attentions=False
):
r"""
Applies multi-head attention to map queries and a set of key-value pairs
to outputs.
"""
key = query if key is None else key
value = query if value is None else value
if self.config.fuse_attention_qkv:
# [bs, seq_len, num_head, head_dim]
q, k, v, past_key_value = self._fuse_prepare_qkv(query, use_cache, past_key_value)
else:
# [bs, seq_len, num_head, head_dim]
q, k, v, past_key_value = self._prepare_qkv(query, key, value, use_cache, past_key_value)
if self.config.use_flash_attention:
# Flash Attention now ignore attention mask
# Current Flash Attention doesn't support attn maskt
# Paddle Flash Attention input [batch_size, seq_len, num_heads, head_dim]
# Torch Flash Attention input (batch_size, seqlen, nheads, headdim)
# bsz, q_len, num_heads, head_dim = q.shape
# TODO: Support attention mask for flash attention
attention_func = self._flash_attention
else:
# scale dot product attention
# [bs, seq_len, num_head,]
attention_func = self._core_attention
has_gradient = (not q.stop_gradient) or (not k.stop_gradient) or (not v.stop_gradient)
if self.enable_recompute and self.config.recompute_granularity == "core_attn" and has_gradient:
outputs = recompute(attention_func, q, k, v, attention_mask, output_attentions, use_reentrant=False)
else:
outputs = attention_func(q, k, v, attention_mask=attention_mask, output_attentions=output_attentions)
if output_attentions:
out, weights = outputs
else:
out = outputs
# if sequence_parallel is true, out shape are [bs, seq_len, num_head * head_dim / n]
# else their shape are [bs, q_len, num_head * head_dim / n], n is mp parallelism.
if self.config.sequence_parallel:
bs, seq_len, dim = out.shape
out = out.reshape([bs * seq_len, dim]) # [bs, seq_len, dim / n] => [bs * seq_len, dim / n]
# project to output
out = self.out_proj(out)
# if sequence_parallel is true, out shape are [bs * seq_len / n, dim]
# else their shape are [bs, seq_len, dim], n is mp parallelism.
outs = [out]
if output_attentions:
outs.append(weights)
if use_cache:
outs.append(past_key_value)
return out if len(outs) == 1 else tuple(outs)
class TransformerDecoder(nn.Layer):
"""
TransformerDecoder is a stack of N decoder layers.
"""
def __init__(self, config, decoder_layers, norm=None, hidden_size=None):
super(TransformerDecoder, self).__init__()
self.config = config
self.layers = decoder_layers
self.norm = nn.LayerNorm(config.hidden_size, epsilon=1e-5)
if config.sequence_parallel:
mark_as_sequence_parallel_parameter(self.norm.weight)
mark_as_sequence_parallel_parameter(self.norm.bias)
# Note that we will actually perform a recompute only if both enable_recompute and layerwise_recompute are set to True
# Enable_recompute defaults to False and is controlled by Trainer
self.enable_recompute = False
@paddle.jit.not_to_static
def recompute_training(
self,
layer_module: nn.Layer,
hidden_states: paddle.Tensor,
past_key_value: paddle.Tensor,
attention_mask: paddle.Tensor,
use_cache: bool,
output_attentions: paddle.Tensor,
):
def create_custom_forward(module):
def custom_forward(*inputs):
return module(*inputs, output_attentions)
return custom_forward
# GPTDecoderLayer
# def forward(
# self, hidden_states, attention_mask=None, use_cache=False, past_key_value=None, output_attentions=False
# ):
hidden_states = recompute(
create_custom_forward(layer_module),
hidden_states,
attention_mask,
use_cache,
past_key_value,
use_reentrant=self.config.recompute_use_reentrant,
)
return hidden_states
def forward(
self,
hidden_states,
attention_mask=None,
use_cache=False,
past_key_values=None,
output_attentions=False,
output_hidden_states=False,
return_dict=False,
):
r"""
Applies a stack of N Transformer decoder layers on inputs. If `norm` is
provided, also applies layer normalization on the output of last decoder
layer.
"""
# [bs * seq_len, embed_dim] -> [seq_len * bs / n, embed_dim] (sequence_parallel)
output = hidden_states
all_self_attentions = () if output_attentions else None
all_hidden_states = () if output_hidden_states else None
next_decoder_cache = () if use_cache else None
for i, mod in enumerate(self.layers):
has_gradient = not output.stop_gradient
# def forward(self, hidden_states, attention_mask=None, use_cache=False, past_key_value=None, output_attentions=False):
if self.enable_recompute and has_gradient and self.config.recompute_granularity == "full_attn":
outputs = self.recompute_training(
layer_module=mod,
hidden_states=output,
attention_mask=attention_mask,
use_cache=use_cache,
past_key_value=None,
output_attentions=output_attentions,
)
else:
outputs = mod(
output,
attention_mask=attention_mask,
use_cache=use_cache,
past_key_value=past_key_values[i] if past_key_values is not None else None,
output_attentions=output_attentions,
)
# outputs = hidden_states if both use_cache and output_attentions are False
# Otherwise, outputs = (hidden_states, attention if output_attentions, cache if use_cache)
output = outputs[0] if (use_cache or output_attentions) else outputs
all_self_attentions = all_self_attentions + (outputs[1],) if output_attentions else None
all_hidden_states = all_hidden_states + (output,) if output_hidden_states else None
next_decoder_cache = next_decoder_cache + (outputs[-1],) if use_cache else None
if self.norm is not None:
output = self.norm(output)
next_cache = next_decoder_cache if use_cache else None
if not return_dict:
temp_list = [output, next_cache, all_hidden_states, all_self_attentions]
if not (use_cache or output_attentions or output_hidden_states):
return output
return tuple(v for v in temp_list if v is not None)
return BaseModelOutputWithPastAndCrossAttentions(
last_hidden_state=output,
past_key_values=next_cache,
hidden_states=all_hidden_states,
attentions=all_self_attentions,
cross_attentions=None,
)
[docs]class GPTDecoderLayer(nn.Layer):
"""
The transformer decoder layer.
It contains multiheadattention and some linear layers.
"""
def __init__(self, config: GPTConfig):
super(GPTDecoderLayer, self).__init__()
self.config = config
# Recompute defaults to False and is controlled by Trainer
self.enable_recompute = False
if not FusedDropoutAdd:
config.use_fused_dropout_add = False
self.self_attn = MultiHeadAttention(config=config)
if config.sequence_parallel:
ColumnParallelLinear = ColumnSequenceParallelLinear
RowParallelLinear = RowSequenceParallelLinear
else:
ColumnParallelLinear = fleet.meta_parallel.ColumnParallelLinear
RowParallelLinear = fleet.meta_parallel.RowParallelLinear
# TODO:config.fuse_attention_ffn @DrownFish19
if config.tensor_parallel_degree > 1:
self.linear1 = ColumnParallelLinear(
config.hidden_size,
config.intermediate_size,
gather_output=False,
has_bias=True,
fuse_matmul_bias=self.config.fused_linear,
)
self.linear2 = RowParallelLinear(
config.intermediate_size,
config.hidden_size,
input_is_parallel=True,
has_bias=True,
fuse_matmul_bias=self.config.fused_linear,
)
else:
self.linear1 = nn.Linear(config.hidden_size, config.intermediate_size, bias_attr=True)
self.linear2 = nn.Linear(config.intermediate_size, config.hidden_size, bias_attr=True)
self.norm1 = nn.LayerNorm(config.hidden_size, epsilon=1e-5)
self.norm2 = nn.LayerNorm(config.hidden_size, epsilon=1e-5)
if config.sequence_parallel:
mark_as_sequence_parallel_parameter(self.norm1.weight)
mark_as_sequence_parallel_parameter(self.norm1.bias)
mark_as_sequence_parallel_parameter(self.norm2.weight)
mark_as_sequence_parallel_parameter(self.norm2.bias)
if config.use_fused_dropout_add:
self.fused_dropout_add1 = FusedDropoutAdd(config.attention_probs_dropout_prob, mode="upscale_in_train")
self.fused_dropout_add2 = FusedDropoutAdd(config.hidden_dropout_prob, mode="upscale_in_train")
else:
self.dropout1 = nn.Dropout(config.attention_probs_dropout_prob, mode="upscale_in_train")
self.dropout2 = nn.Dropout(config.hidden_dropout_prob, mode="upscale_in_train")
if config.hidden_activation == "gelu":
self.activation = F.gelu
else:
self.activation = getattr(F, config.hidden_activation)
[docs] def forward(
self, hidden_states, attention_mask=None, use_cache=False, past_key_value=None, output_attentions=False
):
# when sequence_parallel=True:
# hidden_states => [bs * seq_len / n, embed_dim]
residual = hidden_states
if self.config.normalize_before:
hidden_states = self.norm1(hidden_states)
# self.self_attn:
# def forward(
# self, query, key, value, attention_mask=None, use_cache=False, past_key_value=None, output_attentions=False
# ):
# self.self_attn(...) --> hidden_states, weights, (past_key_value)
has_gradient = not hidden_states.stop_gradient
if self.enable_recompute and has_gradient and self.config.recompute_granularity == "full_attn":
hidden_states = recompute(
self.self_attn,
hidden_states,
None,
None,
attention_mask,
use_cache,
past_key_value,
output_attentions,
use_reentrant=False,
)
else:
hidden_states = self.self_attn(
hidden_states, None, None, attention_mask, use_cache, past_key_value, output_attentions
)
# when sequence_parallel=True:
# hidden_states => [bs * seq_len / n, embed_dim]
incremental_cache = hidden_states[-1] if use_cache else None
attention_weights = hidden_states[1] if output_attentions else None
hidden_states = hidden_states[0] if (use_cache or output_attentions) else hidden_states
# Use a ternary operator for a more concise assignment of current_seed
current_seed = "local_seed" if self.config.sequence_parallel else "global_seed"
# The 'with' block ensures the correct seed context is used
with seed_guard_context(current_seed):
if self.config.use_fused_dropout_add:
hidden_states = self.fused_dropout_add1(hidden_states, residual)
else:
hidden_states = residual + self.dropout1(hidden_states)
if not self.config.normalize_before:
hidden_states = self.norm1(hidden_states)
residual = hidden_states
if self.config.normalize_before:
hidden_states = self.norm2(hidden_states)
# when sequence_parallel=True:
# hidden_states => [bs * seq_len / n, embed_dim]
with seed_guard_context(current_seed):
if not self.config.use_fused_dropout_add:
hidden_states = residual + self.dropout2(
self.linear2(self.activation(self.linear1(hidden_states), approximate=True))
)
else:
hidden_states = self.fused_dropout_add2(
self.linear2(self.activation(self.linear1(hidden_states), approximate=True)), residual
)
if not self.config.normalize_before:
hidden_states = self.norm2(hidden_states)
if not (output_attentions or use_cache):
return hidden_states
temp_list = [
hidden_states,
attention_weights,
incremental_cache,
]
return tuple(v for v in temp_list if v is not None)
[docs]class GPTEmbeddings(nn.Layer):
"""
Include embeddings from word and position embeddings.
"""
def __init__(
self,
config,
):
super(GPTEmbeddings, self).__init__()
self.config = config
if config.tensor_parallel_degree > 1:
self.word_embeddings = fleet.meta_parallel.VocabParallelEmbedding(
config.vocab_size,
config.hidden_size,
)
else:
self.word_embeddings = nn.Embedding(
config.vocab_size,
config.hidden_size,
)
self.position_embeddings = nn.Embedding(
config.max_position_embeddings,
config.hidden_size,
)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
[docs] def forward(self, input_ids, position_ids=None, inputs_embeddings=None):
if input_ids is not None:
input_shape = paddle.shape(input_ids)
inputs_embeddings = self.word_embeddings(input_ids)
else:
input_shape = paddle.shape(inputs_embeddings)[:-1]
if position_ids is None:
ones = paddle.ones(input_shape, dtype="int64")
seq_length = paddle.cumsum(ones, axis=-1)
position_ids = seq_length - ones
position_embeddings = self.position_embeddings(position_ids)
embeddings = inputs_embeddings + position_embeddings
if self.config.sequence_parallel:
bs, seq_len, hidden_size = embeddings.shape
# [bs, seq_len, dim] -> [bs * seq_len, dim]
embeddings = paddle.reshape_(embeddings, [bs * seq_len, hidden_size])
# [bs * seq_len / n, dim] (n is mp parallelism)
embeddings = ScatterOp.apply(embeddings)
# Use a ternary operator for a more concise assignment of current_seed
current_seed = "local_seed" if self.config.sequence_parallel else "global_seed"
# The 'with' block ensures the correct seed context is used
with seed_guard_context(current_seed):
embeddings = self.dropout(embeddings)
return embeddings
[docs]class GPTPretrainedModel(PretrainedModel):
"""
An abstract class for pretrained GPT models. It provides GPT related
`model_config_file`, `resource_files_names`, `pretrained_resource_files_map`,
`pretrained_init_configuration`, `base_model_prefix` for downloading and
loading pretrained models.
See :class:`~paddlenlp.transformers.model_utils.PretrainedModel` for more details.
"""
model_config_file = "model_config.json"
resource_files_names = {"model_state": "model_state.pdparams"}
base_model_prefix = "gpt"
config_class = GPTConfig
pretrained_init_configuration = GPT_PRETRAINED_INIT_CONFIGURATION
@classmethod
def _get_tensor_parallel_mappings(cls, config, is_split=True):
from paddlenlp.transformers.conversion_utils import split_or_merge_func
fn = split_or_merge_func(
is_split=is_split,
tensor_parallel_degree=config.tensor_parallel_degree,
tensor_parallel_rank=config.tensor_parallel_rank,
num_attention_heads=config.num_attention_heads,
)
def get_tensor_parallel_split_mappings(num_layers):
final_actions = {}
base_actions = {
# Column Linear
"layers.0.linear1.weight": partial(fn, is_column=True),
"layers.0.linear1.bias": partial(fn, is_column=True),
# Row Linear
"word_embeddings.weight": partial(fn, is_column=False),
"layers.0.self_attn.out_proj.weight": partial(fn, is_column=False),
"layers.0.linear2.weight": partial(fn, is_column=False),
}
if config.fuse_attention_qkv:
base_actions["layers.0.self_attn.qkv_proj.weight"] = partial(fn, is_column=True)
base_actions["layers.0.self_attn.qkv_proj.bias"] = partial(fn, is_column=True)
else:
base_actions["layers.0.self_attn.q_proj.weight"] = partial(fn, is_column=True)
base_actions["layers.0.self_attn.k_proj.weight"] = partial(fn, is_column=True)
base_actions["layers.0.self_attn.v_proj.weight"] = partial(fn, is_column=True)
base_actions["layers.0.self_attn.q_proj.bias"] = partial(fn, is_column=True)
base_actions["layers.0.self_attn.k_proj.bias"] = partial(fn, is_column=True)
base_actions["layers.0.self_attn.v_proj.bias"] = partial(fn, is_column=True)
for key, action in base_actions.items():
if "layers.0." in key:
for i in range(num_layers):
final_actions[key.replace("layers.0.", f"layers.{i}.")] = action
final_actions[key] = action
return final_actions
mappings = get_tensor_parallel_split_mappings(config.num_hidden_layers)
return mappings
@classmethod
def _get_name_mappings(cls, config: GPTConfig) -> list[StateDictNameMapping]:
mappings: list[StateDictNameMapping] = []
model_mappings = [
["wte.weight", "embeddings.word_embeddings.weight"],
["wpe.weight", "embeddings.position_embeddings.weight"],
["ln_f.weight", "decoder.norm.weight"],
["ln_f.bias", "decoder.norm.bias"],
]
for layer_index in range(config.num_hidden_layers):
layer_mappings = [
[f"h.{layer_index}.ln_1.weight", f"decoder.layers.{layer_index}.norm1.weight"],
[f"h.{layer_index}.ln_1.bias", f"decoder.layers.{layer_index}.norm1.bias"],
[f"h.{layer_index}.ln_2.weight", f"decoder.layers.{layer_index}.norm2.weight"],
[f"h.{layer_index}.ln_2.bias", f"decoder.layers.{layer_index}.norm2.bias"],
[f"h.{layer_index}.mlp.c_fc.weight", f"decoder.layers.{layer_index}.linear1.weight"],
[f"h.{layer_index}.mlp.c_fc.bias", f"decoder.layers.{layer_index}.linear1.bias"],
[f"h.{layer_index}.mlp.c_proj.weight", f"decoder.layers.{layer_index}.linear2.weight"],
[f"h.{layer_index}.mlp.c_proj.bias", f"decoder.layers.{layer_index}.linear2.bias"],
[f"h.{layer_index}.attn.c_proj.weight", f"decoder.layers.{layer_index}.self_attn.out_proj.weight"],
[f"h.{layer_index}.attn.c_proj.bias", f"decoder.layers.{layer_index}.self_attn.out_proj.bias"],
# attention
[
f"h.{layer_index}.attn.c_attn.weight",
f"decoder.layers.{layer_index}.self_attn.q_proj.weight",
"split",
0,
],
[
f"h.{layer_index}.attn.c_attn.bias",
f"decoder.layers.{layer_index}.self_attn.q_proj.bias",
"split",
0,
],
[
f"h.{layer_index}.attn.c_attn.weight",
f"decoder.layers.{layer_index}.self_attn.k_proj.weight",
"split",
1,
],
[
f"h.{layer_index}.attn.c_attn.bias",
f"decoder.layers.{layer_index}.self_attn.k_proj.bias",
"split",
1,
],
[
f"h.{layer_index}.attn.c_attn.weight",
f"decoder.layers.{layer_index}.self_attn.v_proj.weight",
"split",
2,
],
[
f"h.{layer_index}.attn.c_attn.bias",
f"decoder.layers.{layer_index}.self_attn.v_proj.bias",
"split",
2,
],
]
model_mappings.extend(layer_mappings)
# downstream mappings
if "GPT2Model" not in config.architectures:
for mapping in model_mappings:
mapping[0] = "transformer." + mapping[0]
mapping[1] = "gpt." + mapping[1]
if "GPT2ForTokenClassification" in config.architectures:
model_mappings.extend([["classifier.weight", "classifier.weight", "transpose"]])
if "GPT2ForSequenceClassification" in config.architectures:
model_mappings.extend([["score.weight", "score.weight", "transpose"]])
if "GPT2LMHeadModel" in config.architectures:
model_mappings.append(["lm_head.weight", "lm_head.decoder.weight"])
mappings = [StateDictNameMapping(*mapping) for mapping in model_mappings]
return mappings
def _init_weights(self, layer):
"""Initialization hook"""
if self.config.tensor_parallel_degree > 1:
rng_tracker = get_rng_state_tracker().rng_state
if isinstance(
layer,
(
nn.Linear,
nn.Embedding,
fleet.meta_parallel.VocabParallelEmbedding,
fleet.meta_parallel.ColumnParallelLinear,
fleet.meta_parallel.RowParallelLinear,
ColumnSequenceParallelLinear,
RowSequenceParallelLinear,
),
):
# In the dygraph mode, use the `set_value` to reset the parameter directly,
# and reset the `state_dict` to update parameter in static mode.
if isinstance(layer.weight, paddle.Tensor):
if layer.weight.is_distributed:
with rng_tracker():
layer.weight.set_value(
paddle.tensor.normal(
mean=0.0,
std=self.config.initializer_range,
shape=layer.weight.shape,
)
)
else:
layer.weight.set_value(
paddle.tensor.normal(
mean=0.0,
std=self.config.initializer_range,
shape=layer.weight.shape,
)
)
# Layer.apply is DFS https://github.com/PaddlePaddle/Paddle/blob/a6f5021fcc58b21f4414bae6bf4731ef6971582c/python/paddle/nn/layer/layers.py#L527-L530
# sublayer is init first
# scale RowParallelLinear weight
with paddle.no_grad():
if isinstance(layer, GPTDecoderLayer):
factor = 1 / math.sqrt(2 * self.config.num_hidden_layers)
layer.linear2.weight.scale_(factor)
if isinstance(layer, MultiHeadAttention):
factor = 1 / math.sqrt(2 * self.config.num_hidden_layers)
layer.out_proj.weight.scale_(factor)
[docs]@register_base_model
class GPTModel(GPTPretrainedModel):
r"""
The bare GPT Model transformer 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/zh/api/paddle/nn/Layer_cn.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:
vocab_size (int):
Vocabulary size of `inputs_ids` in `GPTModel`. Also is the vocab size of token embedding matrix.
Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling `GPTModel`.
hidden_size (int, optional):
Dimensionality of the embedding layer and decoder layer. Defaults to `768`.
num_hidden_layers (int, optional):
Number of hidden layers in the Transformer decoder. Defaults to `12`.
num_attention_heads (int, optional):
Number of attention heads for each attention layer in the Transformer decoder.
Defaults to `12`.
intermediate_size (int, optional):
Dimensionality of the feed-forward (ff) layer in the decoder. Input tensors
to ff layers are firstly projected from `hidden_size` to `intermediate_size`,
and then projected back to `hidden_size`. Typically `intermediate_size` is larger than `hidden_size`.
Defaults to `3072`.
hidden_act (str, optional):
The non-linear activation function in the feed-forward layer.
``"gelu"``, ``"relu"`` and any other paddle supported activation functions
are supported. Defaults to `"gelu"`.
hidden_dropout_prob (float, optional):
The dropout probability for all fully connected layers in the embeddings and decoder.
Defaults to `0.1`.
attention_probs_dropout_prob (float, optional):
The dropout probability used in MultiHeadAttention in all decoder layers to drop some attention target.
Defaults to `0.1`.
max_position_embeddings (int, optional):
The maximum value of the dimensionality of position encoding, which dictates the maximum supported length of an input
sequence. Defaults to `512`.
type_vocab_size (int, optional):
The vocabulary size of the `token_type_ids`. Defaults to `16`.
.. note::
Please NOT using `type_vocab_size`, for it will be obsolete in the future..
initializer_range (float, optional):
The standard deviation of the normal initializer. Default to `0.02`.
.. note::
A normal_initializer initializes weight matrices as normal distributions.
See :meth:`GPTPretrainedModel._init_weights()` for how weights are initialized in `GPTModel`.
pad_token_id(int, optional):
The index of padding token in the token vocabulary.
Defaults to `0`.
"""
def __init__(self, config: GPTConfig):
super(GPTModel, self).__init__(config)
self.config = config
self.pad_token_id = config.pad_token_id
self.eos_token_id = config.eos_token_id
self.bos_token_id = config.bos_token_id
self.eol_token_id = config.eol_token_id
self.vocab_size = config.vocab_size
self.bias = paddle.tril(
paddle.ones([1, 1, config.max_position_embeddings, config.max_position_embeddings], dtype="int64")
)
self.embeddings = GPTEmbeddings(config)
decoder_layers = nn.LayerList()
for i in range(config.num_hidden_layers):
decoder_layers.append(GPTDecoderLayer(config))
self.decoder = TransformerDecoder(
config,
decoder_layers,
)
@staticmethod
def _prepare_decoder_attention_mask(attention_mask, input_shape, past_key_values_length, dtype):
if attention_mask is not None:
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
if len(attention_mask.shape) == 2:
expanded_attn_mask = _expand_2d_mask(attention_mask, dtype, tgt_length=input_shape[-1])
# For decoding phase in generation, seq_length = 1, we don't need to add causal mask
if input_shape[-1] > 1:
combined_attention_mask = _make_causal_mask(
input_shape, past_key_values_length=past_key_values_length
)
expanded_attn_mask = expanded_attn_mask & combined_attention_mask
# [bsz, seq_len, seq_len] -> [bsz, 1, seq_len, seq_len]
elif len(attention_mask.shape) == 3:
expanded_attn_mask = attention_mask.unsqueeze(1).astype("bool")
# if attention_mask is already 4-D, do nothing
else:
expanded_attn_mask = attention_mask
else:
expanded_attn_mask = _make_causal_mask(input_shape, past_key_values_length=past_key_values_length)
# Convert bool attention_mask to float attention mask, which will be added to attention_scores later
expanded_attn_mask = paddle.where(expanded_attn_mask, 0.0, paddle.finfo(dtype).min).astype(dtype)
return expanded_attn_mask
[docs] def forward(
self,
input_ids=None,
position_ids=None,
attention_mask=None,
inputs_embeds=None,
use_cache=False,
past_key_values=None,
output_attentions=False,
output_hidden_states=False,
return_dict=False,
):
r"""
The GPTModel 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].
Defaults to None.
position_ids(Tensor, optional):
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range ``[0,
max_position_embeddings - 1]``.
Shape as `(batch_size, num_tokens)` and dtype as int64. Defaults to `None`.
attention_mask (Tensor, optional):
Mask used in self attention to avoid performing attention to some unwanted positions,
usually the subsequent positions.
It is a tensor with shape broadcasted to `[batch_size, num_attention_heads, sequence_length, sequence_length]`.
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].
Its data type should be int64.
The `masked` tokens have `0` values, and the `unmasked` tokens have `1` values.
Defaults to `None`, which means nothing needed to be prevented attention to.
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.
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.
past_key_values (list, optional):
It is only used for inference and should be None for training.
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`,
returns tensor `outputs` which is the output at the last layer of the model.
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 GPTModel, GPTTokenizer
tokenizer = GPTTokenizer.from_pretrained('gpt2-medium-en')
model = GPTModel.from_pretrained('gpt2-medium-en')
inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!", return_token_type_ids=False)
inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()}
output = model(**inputs)
"""
if self.config.sequence_parallel and use_cache:
raise ValueError("We currently only support sequence parallel without cache.")
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 = paddle.shape(input_ids)
input_ids = input_ids.reshape((-1, input_shape[-1]))
elif inputs_embeds is not None:
input_shape = paddle.shape(inputs_embeds)[:-1]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
# input_shape => bs, seq_len
if past_key_values is None:
past_key_values = tuple([None] * len(self.decoder.layers))
if position_ids is None:
past_length = 0
if past_key_values[0] is not None:
# bs, seq_len, num_head, head_dim
past_length = paddle.shape(past_key_values[0][0])[1]
position_ids = paddle.arange(past_length, input_shape[-1] + past_length, dtype="int64")
position_ids = position_ids.unsqueeze(0)
position_ids = paddle.expand(position_ids, input_shape)
embedding_output = self.embeddings(
input_ids=input_ids, position_ids=position_ids, inputs_embeddings=inputs_embeds
)
# TODO, use registered buffer
length = input_shape[-1]
if past_key_values[0] is not None:
cache_length = paddle.shape(past_key_values[0][0])[1]
length = length + cache_length
else:
cache_length = 0
causal_mask = self.bias[:, :, cache_length:length, :length]
if attention_mask is not None:
if attention_mask.dtype != paddle.int64:
attention_mask = paddle.cast(attention_mask, dtype=paddle.int64)
if len(attention_mask.shape) == 2:
attention_mask = attention_mask[:, None, None, :]
attention_mask = (1.0 - (attention_mask & causal_mask)) * -1e4
else:
attention_mask = (1.0 - causal_mask) * -1e4
# The tensor returned by triu not in static graph.
attention_mask.stop_gradient = True
outputs = self.decoder(
embedding_output,
attention_mask=attention_mask,
use_cache=use_cache,
past_key_values=past_key_values,
output_hidden_states=output_hidden_states,
output_attentions=output_attentions,
return_dict=return_dict,
)
if output_hidden_states:
if return_dict:
outputs.hidden_states = (embedding_output,) + outputs.hidden_states
else: # outputs is a tuple
idx = 2 if use_cache else 1
all_hidden_states = (embedding_output,) + outputs[idx]
outputs[idx] = all_hidden_states
return outputs
[docs]class GPTPretrainingCriterion(paddle.nn.Layer):
"""
Criterion for GPT. It calculates the final loss.
"""
def __init__(self, config):
super(GPTPretrainingCriterion, self).__init__()
self.config = config
if config.tensor_parallel_degree > 1 and config.tensor_parallel_output:
self.loss_func = fleet.meta_parallel.ParallelCrossEntropy(ignore_index=config.ignore_index)
else:
self.loss_func = paddle.nn.CrossEntropyLoss(reduction="none", ignore_index=config.ignore_index)
[docs] def forward(self, prediction_scores, masked_lm_labels, loss_mask=None):
"""
Args:
prediction_scores(Tensor):
The logits of masked token prediction. Its data type should be float32 and
its shape is [batch_size, sequence_length, vocab_size].
masked_lm_labels(Tensor):
The labels of the masked language modeling, the dimensionality of `masked_lm_labels`
is equal to `prediction_scores`. Its data type should be int64 and
its shape is [batch_size, sequence_length, 1].
loss_mask(Tensor):
Mask used for calculating the loss of the masked language modeling to avoid
calculating some unwanted tokens.
Its data type should be float32 and its shape is [batch_size, sequence_length, 1].
Returns:
Tensor: The pretraining loss. Its data type should be float32 and its shape is [1].
"""
with paddle.amp.auto_cast(False):
masked_lm_loss = self.loss_func(prediction_scores.astype("float32"), masked_lm_labels.unsqueeze(2))
# skip ignore_index which loss == 0
if loss_mask is None:
loss_mask = (masked_lm_loss > 0).astype("float32")
loss_mask = loss_mask.reshape([-1])
masked_lm_loss = paddle.sum(masked_lm_loss.reshape([-1]) * loss_mask)
loss = masked_lm_loss / loss_mask.sum()
return loss
[docs]class GPTForGreedyGeneration(GPTPretrainedModel):
"""
The generate model for GPT-2.
It use the greedy strategy and generate the output sequence with highest probability.
Args:
gpt (:class:`GPTModel`):
An instance of `paddlenlp.transformers.GPTModel`.
max_predict_len(int):
The max length of the prediction.
"""
def __init__(self, config: GPTConfig, max_predict_len: int = 32):
super(GPTForGreedyGeneration, self).__init__(config)
self.gpt = GPTModel(config)
self.max_predict_len = paddle.to_tensor(max_predict_len, dtype="int32")
self.eol_token_id = config.eol_token_id
[docs] def model(
self,
input_ids,
position_ids=None,
attention_mask=None,
masked_positions=None,
use_cache=False,
past_key_values=None,
):
r"""
Args:
input_ids (Tensor, optional):
See :class:`GPTModel`.
position_ids (Tensor, optional):
See :class:`GPTModel`.
attention_mask (Tensor, optional):
See :class:`GPTModel`.
use_cache (bool, optional):
See :class:`GPTModel`.
cache (Tensor, optional):
See :class:`GPTModel`.
Returns:
Tensor or tuple: Returns tensor `logits` or tuple `(logits, cached_kvs)`. If `use_cache` is True,
tuple (`logits, cached_kvs`) will be returned. Otherwise, tensor `logits` will be returned.
`logits` is the output of the gpt model.
`cache_kvs` is the cache output of gpt model if `use_cache` is True.
"""
outputs = self.gpt(
input_ids,
position_ids=position_ids,
attention_mask=attention_mask,
use_cache=use_cache,
past_key_values=past_key_values,
)
if use_cache:
encoder_outputs, cached_kvs = outputs[:2]
else:
encoder_outputs = outputs
logits = paddle.matmul(encoder_outputs, self.gpt.embeddings.word_embeddings.weight, transpose_y=True)
if use_cache:
return logits, cached_kvs
else:
return logits
[docs] def forward(self, input_ids):
"""
Args:
input_ids(Tensor):
See :class:`GPTModel`.
Returns:
Tensor: Returns tensor `src_ids`, which means the indices of output sequence tokens in the vocabulary.
They are numerical representations of tokens that build the output sequence.
"""
output, cached_kvs = self.model(input_ids, use_cache=True, past_key_values=None)
src_ids = input_ids
nid = paddle.argmax(output[:, -1, :], axis=-1).reshape([-1, 1])
src_ids = paddle.concat([src_ids, nid], axis=1)
cur_len = 0
with dy2st_nocheck_guard_context():
while cur_len < self.max_predict_len:
output, cached_kvs = self.model(nid, use_cache=True, past_key_values=cached_kvs)
nid = paddle.argmax(output[:, -1, :], axis=-1).reshape([-1, 1])
src_ids = paddle.concat([src_ids, nid], axis=1)
cur_len += 1
if paddle.max(nid) == self.eol_token_id:
break
return src_ids
class GPTLMHead(nn.Layer):
def __init__(self, config: GPTConfig, embedding_weights=None):
super(GPTLMHead, self).__init__()
self.config = config
self.transpose_y = True
if embedding_weights is not None:
self.transpose_y = True
self.weight = embedding_weights
else:
if config.tensor_parallel_degree > 1:
vocab_size = config.vocab_size // config.tensor_parallel_degree
else:
vocab_size = config.vocab_size
self.weight = self.create_parameter(
shape=[vocab_size, config.hidden_size],
dtype=paddle.get_default_dtype(),
)
# Must set distributed attr for Tensor Parallel !
self.weight.is_distributed = True if (vocab_size != config.vocab_size) else False
if self.weight.is_distributed:
self.weight.split_axis = 0
def forward(self, hidden_states, tensor_parallel_output=None):
if self.config.sequence_parallel:
hidden_states = GatherOp.apply(hidden_states)
hidden_states = paddle.reshape_(hidden_states, [-1, self.config.seq_length, self.config.hidden_size])
if tensor_parallel_output is None:
tensor_parallel_output = self.config.tensor_parallel_output
logits = parallel_matmul(
hidden_states, self.weight, transpose_y=self.transpose_y, tensor_parallel_output=tensor_parallel_output
)
return logits
[docs]class GPTForCausalLM(GPTPretrainedModel):
"""
The GPT Model with a `language modeling` head on top.
Args:
gpt (:class:`GPTModel`):
An instance of :class:`GPTModel`.
"""
def __init__(self, config: GPTConfig):
super(GPTForCausalLM, self).__init__(config)
self.gpt = GPTModel(config)
self.lm_head = GPTLMHead(config, embedding_weights=self.gpt.embeddings.word_embeddings.weight)
self.tie_weights()
self.criterion = GPTPretrainingCriterion(config)
[docs] def get_output_embeddings(self):
return self.lm_head
[docs] def forward(
self,
input_ids=None,
position_ids=None,
attention_mask=None,
inputs_embeds=None,
use_cache=False,
past_key_values=None,
labels=None,
output_attentions=False,
output_hidden_states=False,
return_dict=False,
):
r"""
Args:
input_ids (Tensor, optional):
See :class:`GPTModel`.
position_ids (Tensor, optional):
See :class:`GPTModel`.
attention_mask (Tensor, optional):
See :class:`GPTModel`.
inputs_embeds (Tensor, optional):
See :class:`GPTModel`.
use_cache (bool, optional):
See :class:`GPTModel`.
past_key_values (Tensor, optional):
See :class:`GPTModel`.
labels (paddle.Tensor, optional):
A Tensor of shape `(batch_size, sequence_length)`.
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]`
Defaults to None.
output_attentions (bool, optional):
See :class:`GPTModel`.
output_hidden_states (bool, optional):
See :class:`GPTModel`.
return_dict (bool, optional):
See :class:`GPTModel`.
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`.
Especialy, when `return_dict=use_cache=output_attentions=output_hidden_states=False`,
returns a tensor `logits` which is the output of the gpt model.
"""
input_type = type(input_ids) if input_ids is not None else type(inputs_embeds)
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
outputs = self.gpt(
input_ids,
position_ids=position_ids,
attention_mask=attention_mask,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
past_key_values=past_key_values,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
if isinstance(outputs, input_type):
hidden_states = outputs
else:
hidden_states = outputs[0]
logits = self.lm_head(hidden_states)
loss = None
if labels is not None:
loss = self.criterion(logits, labels)
# # Shift so that tokens < n predict n
# shift_logits = logits[:, :-1, :]
# shift_labels = labels[:, 1:]
# # Flatten the tokens
# loss_fct = CrossEntropyLoss()
# loss = loss_fct(shift_logits.reshape((-1, shift_logits.shape[-1])), shift_labels.reshape((-1,)))
# outputs = [output, all_hidden_states, new_caches, all_self_attentions]
if not return_dict:
if isinstance(outputs, input_type):
return (loss, logits) if loss is not None else logits
outputs = (logits,) + outputs[1:]
return ((loss,) + outputs) if loss is not None else outputs
return CausalLMOutputWithCrossAttentions(
loss=loss,
logits=logits,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
cross_attentions=outputs.cross_attentions,
)
def prepare_fast_entry(self, kwargs):
from paddlenlp.ops import FasterGPT
use_fp16_decoding = kwargs.get("use_fp16_decoding", False)
decode_strategy = kwargs.get("decode_strategy")
if decode_strategy == "beam_search":
raise AttributeError("'beam_search' is not supported yet in the fast version of GPT")
# Currently, FasterTransformer only support restricted size_per_head.
size_per_head = self.gpt.config["hidden_size"] // self.gpt.config["num_attention_heads"]
if size_per_head not in [32, 64, 80, 96, 128]:
raise AttributeError(
"'size_per_head = %d' is not supported yet in the fast version of GPT" % 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")
if kwargs["min_length"] != 0:
# not support for min_length yet in the fast version
raise AttributeError("'min_length != 0' is not supported yet in the fast version")
self._fast_entry = FasterGPT(self, use_fp16_decoding=use_fp16_decoding).forward
return self._fast_entry
def prepare_inputs_for_generation(self, input_ids, use_cache=False, past_key_values=None, **kwargs):
# only last token for inputs_ids if cache is defined in kwargs
position_ids = kwargs.get("position_ids", None)
# attention_mask = kwargs.get("attention_mask", None)
if past_key_values is not None:
input_ids = input_ids[:, -1].unsqueeze(-1)
if position_ids is not None:
position_ids = position_ids[:, -1].unsqueeze(-1)
return {
"input_ids": input_ids,
"position_ids": position_ids,
"attention_mask": None,
"use_cache": use_cache,
"past_key_values": past_key_values,
}
@staticmethod
def prepare_attention_mask_for_generation(input_ids, pad_token_id, eos_token_id):
is_pad_token_in_inputs_ids = (pad_token_id is not None) and float(paddle.any(input_ids == pad_token_id))
is_pad_token_not_equal_to_eos_token_id = (eos_token_id is None) or (
(eos_token_id is not None) and (pad_token_id != eos_token_id)
)
if is_pad_token_in_inputs_ids and is_pad_token_not_equal_to_eos_token_id:
attention_mask = (input_ids != pad_token_id).astype("int64")
else:
attention_mask = paddle.ones_like(input_ids, dtype="int64")
return paddle.unsqueeze(attention_mask, axis=[1, 2])
[docs]class GPTForTokenClassification(GPTPretrainedModel):
"""
GPT Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g.
for Named-Entity-Recognition (NER) tasks.
Args:
gpt (:class:`GPTModel`):
An instance of GPTModel.
num_labels (int, optional):
The number of classes. Defaults to `2`.
dropout (float, optional):
The dropout probability for output of GPT.
If None, use the same value as `hidden_dropout_prob` of `GPTModel`
instance `gpt`. Defaults to None.
"""
def __init__(self, config: GPTConfig):
super(GPTForTokenClassification, self).__init__(config)
self.num_labels = config.num_labels
self.gpt = GPTModel(config) # allow gpt to be config
dropout_p = config.hidden_dropout_prob if config.classifier_dropout is None else config.classifier_dropout
self.dropout = nn.Dropout(dropout_p)
self.classifier = nn.Linear(config.hidden_size, config.num_labels)
[docs] def forward(
self,
input_ids=None,
position_ids=None,
attention_mask=None,
inputs_embeds=None,
labels=None,
output_attentions=False,
output_hidden_states=False,
return_dict=False,
):
r"""
The GPTForTokenClassification forward method, overrides the __call__() special method.
Args:
input_ids (Tensor, optional):
See :class:`GPTModel`.
position_ids(Tensor, optional):
See :class:`GPTModel`.
attention_mask (list, optional):
See :class:`GPTModel`.
inputs_embeds (Tensor, optional):
See :class:`GPTModel`.
labels (Tensor, optional):
Labels of shape `(batch_size, sequence_length)` for computing the sequence classification/regression loss. Indices should be in
`[0, ..., num_labels - 1]`. If `num_labels == 1` a regression loss is computed (Mean-Square loss), If
`num_labels > 1` a classification loss is computed (Cross-Entropy). Defaults to None.
output_attentions (bool, optional):
See :class:`GPTModel`.
output_hidden_states (bool, optional):
See :class:`GPTModel`.
return_dict (bool, optional):
See :class:`GPTModel`.
Returns:
An instance of :class:`~paddlenlp.transformers.model_outputs.TokenClassifierOutput` 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.TokenClassifierOutput`.
Especialy, when `return_dict=output_attentions=output_hidden_states=False`,
returns tensor `logits`, a tensor of the input token classification logits.
Shape as `[batch_size, sequence_length, num_labels]` and dtype as `float32`.
Example:
.. code-block::
import paddle
from paddlenlp.transformers import GPTForTokenClassification, GPTTokenizer
tokenizer = GPTTokenizer.from_pretrained('gpt2-medium-en')
model = GPTForTokenClassification.from_pretrained('gpt2-medium-en')
inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!", return_token_type_ids=False)
inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()}
logits = model(**inputs)
"""
input_type = type(input_ids) if input_ids is not None else type(inputs_embeds)
sequence_output = self.gpt(
input_ids,
position_ids=position_ids,
attention_mask=attention_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
if isinstance(sequence_output, input_type):
hidden_states = sequence_output
else:
hidden_states = sequence_output[0]
hidden_states = self.dropout(hidden_states)
logits = self.classifier(hidden_states)
loss = None
if labels is not None:
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.reshape((-1, self.num_labels)), labels.reshape((-1,)))
if not return_dict:
if isinstance(sequence_output, input_type):
return (loss, logits) if loss is not None else logits
outputs = (logits,) + sequence_output[1:]
return ((loss,) + outputs) if loss is not None else outputs
return TokenClassifierOutput(
loss=loss,
logits=logits,
hidden_states=sequence_output.hidden_states,
attentions=sequence_output.attentions,
)
[docs]class GPTForSequenceClassification(GPTPretrainedModel):
"""
GPT Model with a sequence classification/regression head on top (a linear layer on top of the pooled output) e.g.
for GLUE tasks.
Args:
gpt (:class:`GPTModel`):
An instance of GPTModel.
num_labels (int, optional):
The number of classes. Defaults to `2`.
"""
def __init__(self, config: GPTConfig):
super(GPTForSequenceClassification, self).__init__(config)
self.num_labels = config.num_labels
self.gpt = GPTModel(config)
self.score = nn.Linear(config.hidden_size, config.num_labels, bias_attr=False)
[docs] def forward(
self,
input_ids=None,
position_ids=None,
attention_mask=None,
inputs_embeds=None,
labels=None,
use_cache=False,
output_attentions=False,
output_hidden_states=False,
return_dict=False,
):
r"""
The GPTForSequenceClassification forward method, overrides the __call__() special method.
Args:
input_ids (Tensor, optional):
See :class:`GPTModel`.
position_ids(Tensor, optional):
See :class:`GPTModel`.
attention_mask (list, optional):
See :class:`GPTModel`.
inputs_embeds (Tensor, optional):
See :class:`GPTModel`.
labels (Tensor, optional):
Labels of shape `(batch_size, sequence_length)` for computing the sequence classification/regression loss. Indices should be in
`[0, ..., num_labels - 1]`. If `num_labels == 1` a regression loss is computed (Mean-Square loss), If
`num_labels > 1` a classification loss is computed (Cross-Entropy). Defaults to None.
use_cache (bool, optional):
See :classL `GPTModel`.
output_attentions (bool, optional):
See :class:`GPTModel`.
output_hidden_states (bool, optional):
See :class:`GPTModel`.
return_dict (bool, optional):
See :class:`GPTModel`.
Returns:
An instance of :class:`~paddlenlp.transformers.model_outputs.SequenceClassifierOutputWithPast` 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.SequenceClassifierOutputWithPast`.
Especialy, when `return_dict=output_attentions=output_hidden_states=False`,
returns tensor `logits`, a tensor of the input text classification logits.
Shape as `[batch_size, num_labels]` and dtype as float32.
Example:
.. code-block::
import paddle
from paddlenlp.transformers import GPTForSequenceClassification, GPTTokenizer
tokenizer = GPTTokenizer.from_pretrained('gpt2-medium-en')
model = GPTForSequenceClassification.from_pretrained('gpt2-medium-en')
inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!", return_token_type_ids=False)
inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()}
logits = model(**inputs)
"""
input_type = type(input_ids) if input_ids is not None else type(inputs_embeds)
# sequence_output shape [bs, seq_len, hidden_size]
sequence_output = self.gpt(
input_ids,
position_ids=position_ids,
attention_mask=attention_mask,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
if isinstance(sequence_output, input_type):
hidden_states = sequence_output
else:
hidden_states = sequence_output[0]
# logits shape [bs, seq_len, num_class]
logits = self.score(hidden_states)
# padding index maybe 0
eos_token_id = self.gpt.config.eos_token_id or 0
# sequence_lengths shape [bs,]
if input_ids is not None:
sequence_lengths = (input_ids != eos_token_id).astype("int64").sum(axis=-1) - 1
else:
inputs_shape = paddle.shape(inputs_embeds)[:-1]
sequence_lengths = paddle.ones(inputs_shape[:-1], dtype="int64") * (inputs_shape[1] - 1)
logger.warning(
f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be "
"unexpected if using padding tokens in conjunction with `inputs_embeds.`"
)
pooled_logits = logits.gather_nd(
paddle.stack([paddle.arange(paddle.shape(logits)[0]), sequence_lengths], axis=-1)
)
loss = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
self.config.problem_type = "regression"
elif self.num_labels > 1 and (labels.dtype == paddle.int64 or labels.dtype == paddle.int32):
self.config.problem_type = "single_label_classification"
else:
self.config.problem_type = "multi_label_classification"
if self.config.problem_type == "regression":
loss_fct = MSELoss()
if self.num_labels == 1:
loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
else:
loss = loss_fct(pooled_logits, labels)
elif self.config.problem_type == "single_label_classification":
loss_fct = CrossEntropyLoss()
loss = loss_fct(pooled_logits.reshape((-1, self.num_labels)), labels.reshape((-1,)))
elif self.config.problem_type == "multi_label_classification":
loss_fct = BCEWithLogitsLoss()
loss = loss_fct(pooled_logits, labels)
if not return_dict:
if isinstance(sequence_output, input_type):
return (loss, pooled_logits) if loss is not None else pooled_logits
outputs = (pooled_logits,) + sequence_output[1:]
return ((loss,) + outputs) if loss is not None else outputs
return SequenceClassifierOutputWithPast(
loss=loss,
logits=pooled_logits,
past_key_values=sequence_output.past_key_values,
hidden_states=sequence_output.hidden_states,
attentions=sequence_output.attentions,
)
GPTLMHeadModel = GPTForCausalLM
