# Copyright 2023 The EASYDEL Author @erfanzar (Erfan Zare Chavoshi).
#
# 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
#
# https://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.
import typing as tp
from functools import cached_property, partial
import chex
import jax
import jax.numpy as jnp
from flax import nnx as nn
from easydel.infra.base_module import EasyDeLBaseModule
from easydel.infra.factory import register_module
from easydel.infra.modeling_outputs import (
FlaxBaseModelOutput,
FlaxCausalLMOutput,
)
from easydel.infra.utils import (
ACT2FN,
auto_remat,
block_wise_ffn,
get_dot_general_by_bits,
)
from easydel.layers.attention import FlaxAttentionModule, FlexibleAttentionModule
from easydel.layers.caching import TransformerCache, TransformerCacheView
from easydel.modules.gpt_j.gpt_j_configuration import GPTJConfig as GPTJConfig
from easydel.utils.helpers import get_logger
logger = get_logger(__name__)
[docs]class GPTJAttention(FlaxAttentionModule):
def __init__(
self,
config: GPTJConfig,
dtype: jnp.dtype = jnp.float32,
param_dtype: jnp.dtype = jnp.float32,
precision: jax.lax.PrecisionLike = None,
causal: bool = True,
is_cross_attention: bool = False,
*,
rngs: nn.Rngs,
):
super().__init__(config=config)
self.precision = precision
self.dtype = dtype
self.rngs = rngs
self.is_cross_attention = is_cross_attention
self.causal = causal
self.embed_dim = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
self.rotary_dim = config.rotary_dim
linear = partial(
nn.Linear,
self.embed_dim,
self.embed_dim,
use_bias=False,
dtype=dtype,
kernel_init=nn.initializers.normal(config.initializer_range),
param_dtype=param_dtype,
precision=precision,
**get_dot_general_by_bits(config.bits, config.easy_method),
)
self.q_proj, self.k_proj, self.v_proj = (
linear(rngs=rngs),
linear(rngs=rngs),
linear(rngs=rngs),
)
self.out_proj = linear(rngs=rngs)
self.resid_dropout = nn.Dropout(rate=config.resid_pdrop, rngs=rngs)
self.rotary = self.config.get_basic_rope(
self.dtype,
head_size=self.embed_dim,
rotary_dim=self.rotary_dim,
base=10000,
is_neox_style=False,
)
self.attention_performer = FlexibleAttentionModule(
dropout_prob=config.attn_pdrop,
base_config=config,
softmax_scale=self.head_dim**-0.5,
)
def _split_heads(self, hidden_states):
return hidden_states.reshape(
hidden_states.shape[:2] + (self.num_heads, self.head_dim)
)
def __call__(
self,
hidden_states: chex.Array,
attention_mask: chex.Array,
position_ids: chex.Array,
causal_mask: tp.Optional[chex.Array] = None,
segment_ids: tp.Optional[chex.Array] = None,
cache_view: tp.Optional[TransformerCacheView] = None,
output_attentions: bool = False,
frequencies: tp.Optional[chex.Array] = None,
):
query = self.q_proj(hidden_states)
key = self.k_proj(hidden_states)
value = self.v_proj(hidden_states)
query = self._split_heads(query)
key = self._split_heads(key)
value = self._split_heads(value)
query, key = self.rotary(
positions=position_ids,
query=query,
key=key,
frequencies=frequencies,
)
(
key,
value,
attention_mask,
init_attention_bias,
) = self.concatenate(
query=query,
key=key,
cache_view=cache_view,
value=value,
attention_mask=attention_mask,
causal_mask=causal_mask,
fcm_mask=None,
)
attentions = self.attention_performer.forward(
query_states=query,
key_states=key,
value_states=value,
bias=None,
init_bias=init_attention_bias,
attention_mask=attention_mask,
segment_ids=segment_ids,
causal=True,
dropout_rng=self.rngs.params(),
)
attn_output = self.shard_attention_prod(
self._merge_heads(attentions.attention_outputs)
)
attn_output = self.out_proj(attn_output)
attn_output = self.resid_dropout(attn_output)
outputs = (
(attn_output, attentions.attention_weights)
if output_attentions
else (attn_output, None)
)
return outputs
[docs]class GPTJMLP(nn.Module):
def __init__(
self,
config: GPTJConfig,
intermediate_size: int,
dtype: jnp.dtype = jnp.float32,
param_dtype: jnp.dtype = jnp.float32,
precision: tp.Optional[tp.Union[str, jax.lax.Precision]] = None,
*,
rngs: nn.Rngs,
):
self.config: GPTJConfig = config
self.dtype = dtype
self.param_dtype = param_dtype
self.precision = precision
self.intermediate_size = intermediate_size
embed_dim = config.hidden_size
kernel_init = nn.initializers.normal(config.initializer_range)
self.fc_in = nn.Linear(
embed_dim,
intermediate_size,
dtype=dtype,
param_dtype=dtype,
precision=precision,
kernel_init=kernel_init,
rngs=rngs,
**get_dot_general_by_bits(config.bits, config.easy_method),
)
self.fc_out = nn.Linear(
intermediate_size,
embed_dim,
dtype=dtype,
param_dtype=dtype,
precision=precision,
kernel_init=kernel_init,
rngs=rngs,
**get_dot_general_by_bits(config.bits, config.easy_method),
)
self.act = ACT2FN[config.activation_function]
self.dropout = nn.Dropout(rate=config.resid_pdrop)
def __call__(self, hidden_states):
hidden_states = self.dropout(self.fc_out(self.act(self.fc_in(hidden_states))))
return hidden_states
[docs]class GPTJBlock(nn.Module):
def __init__(
self,
config: GPTJConfig,
dtype: jnp.dtype = jnp.float32,
param_dtype: jnp.dtype = jnp.float32,
precision: tp.Optional[tp.Union[str, jax.lax.Precision]] = None,
*,
rngs: nn.Rngs,
):
self.config: GPTJConfig = config
self.dtype = dtype
self.param_dtype = param_dtype
self.precision = precision
hidden_size = self.config.hidden_size
inner_dim = config.n_inner if config.n_inner is not None else 4 * hidden_size
attn_block = GPTJAttention
mlp_block = GPTJMLP
attn_block, mlp_block = auto_remat(
attn_block,
mlp_block,
policy=config.gradient_checkpointing,
)
self.ln_1 = nn.LayerNorm(
self.config.hidden_size,
epsilon=config.layer_norm_epsilon,
dtype=dtype,
param_dtype=dtype,
rngs=rngs,
)
self.attn = attn_block(
config,
dtype=dtype,
param_dtype=dtype,
precision=precision,
rngs=rngs,
)
self.mlp = mlp_block(
config,
inner_dim,
dtype=dtype,
param_dtype=dtype,
precision=precision,
rngs=rngs,
)
def __call__(
self,
hidden_states: chex.Array,
attention_mask: chex.Array,
position_ids: chex.Array,
causal_mask: tp.Optional[chex.Array] = None,
segment_ids: tp.Optional[chex.Array] = None,
cache_view: tp.Optional[TransformerCacheView] = None,
output_attentions: bool = False,
frequencies: tp.Optional[chex.Array] = None,
):
residual = hidden_states
hidden_states = self.ln_1(hidden_states)
attn_outputs = self.attn(
hidden_states,
attention_mask,
position_ids,
causal_mask,
segment_ids,
cache_view,
output_attentions,
frequencies,
)
attn_output = attn_outputs[0]
if self.config.use_scan_mlp:
feed_forward_hidden_states = block_wise_ffn(
self.mlp,
hidden_states,
self.config.scan_mlp_chunk_size,
)
else:
feed_forward_hidden_states = self.mlp(hidden_states)
# residual connection
hidden_states = attn_output + feed_forward_hidden_states + residual
return (hidden_states,) + attn_outputs[1:]
[docs]@register_module(
"base-module",
config=GPTJConfig,
model_type="gptj",
embedding_layer_names=["wte"],
layernorm_names=["ln_1", "ln_2", "ln_f"],
)
class GPTJModel(EasyDeLBaseModule):
def __init__(
self,
config: GPTJConfig,
dtype: jnp.dtype = jnp.float32,
param_dtype: jnp.dtype = jnp.float32,
precision: tp.Optional[tp.Union[str, jax.lax.Precision]] = None,
*,
rngs: nn.Rngs,
):
super().__init__(
config=config,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
self.embed_dim = config.hidden_size
self.wte = nn.Embed(
self.config.vocab_size,
self.embed_dim,
embedding_init=nn.initializers.normal(stddev=config.initializer_range),
dtype=dtype,
param_dtype=param_dtype,
rngs=rngs,
)
self.dropout = nn.Dropout(
rate=self.config.embd_pdrop,
rngs=rngs,
)
self.h = [
GPTJBlock(
config,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
for i in range(self.config.num_hidden_layers)
]
self.ln_f = nn.LayerNorm(
self.config.hidden_size,
epsilon=self.config.layer_norm_epsilon,
dtype=dtype,
param_dtype=param_dtype,
rngs=rngs,
)
@cached_property
def frequencies(self):
embed_dim = self.config.hidden_size
num_heads = self.config.num_attention_heads
head_dim = embed_dim // num_heads
rotary_dim = self.config.rotary_dim
return self.config.get_basic_frequencies(
rotary_dim=rotary_dim,
head_size=head_dim,
base=10000,
)
def __call__(
self,
input_ids: tp.Optional[chex.Array] = None,
attention_mask: tp.Optional[chex.Array] = None,
position_ids: tp.Optional[chex.Array] = None,
past_key_values: tp.Optional[TransformerCache] = None,
inputs_embeds: tp.Optional[chex.Array] = None,
segment_ids: tp.Optional[chex.Array] = None,
extra_embedding: tp.Optional[chex.Array] = None,
output_attentions: bool = False,
output_hidden_states: bool = False,
return_dict: bool = True,
):
all_attentions = () if output_attentions else None
all_hidden_states = () if output_hidden_states else None
if (input_ids is None) ^ (inputs_embeds is not None):
raise ValueError(
"You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one"
)
if inputs_embeds is None:
inputs_embeds = self.wte(input_ids.astype("i4"))
batch_size, sequence_length, _ = inputs_embeds.shape
if attention_mask is None:
attention_mask = jnp.ones((batch_size, sequence_length), "b1")
else:
if attention_mask.dtype != jnp.bool:
attention_mask = jnp.astype(attention_mask == 1, "b1")
if position_ids is None:
position_ids = jnp.broadcast_to(
jnp.clip(jnp.cumsum(attention_mask, axis=-1) - 1, a_min=0),
(batch_size, sequence_length),
).astype(jnp.int32)
assert sequence_length <= self.config.max_position_embeddings, (
f"Maximum Position Embedding Reached ! (Excepted <= {self.config.max_position_embeddings} got {sequence_length})"
)
hidden_states = (
inputs_embeds + extra_embedding if extra_embedding is not None else inputs_embeds
)
if past_key_values is None:
past_key_values = TransformerCache.init_empty(len(self.h))
hidden_states = self.dropout(inputs_embeds)
for idx, block in enumerate(self.h):
if output_hidden_states:
all_hidden_states += (hidden_states,)
hidden_states, attn_weight = block(
hidden_states=hidden_states,
attention_mask=attention_mask,
cache_view=past_key_values.views[idx],
position_ids=position_ids,
output_attentions=output_attentions,
segment_ids=segment_ids,
frequencies=self.frequencies,
causal_mask=self.causal_mask,
)
if output_attentions:
all_attentions += (attn_weight,)
hidden_states = self.ln_f(hidden_states)
if output_hidden_states:
all_hidden_states += (hidden_states,)
outputs = (hidden_states, all_hidden_states, all_attentions, past_key_values)
if not return_dict:
return tuple(v for v in outputs if v is not None)
return FlaxBaseModelOutput(
last_hidden_state=hidden_states,
hidden_states=all_hidden_states,
attentions=all_attentions,
past_key_values=past_key_values,
)
[docs]@register_module(
"causal-language-model",
config=GPTJConfig,
model_type="gptj",
embedding_layer_names=["wte"],
layernorm_names=["ln_1", "ln_2", "ln_f"],
)
class GPTJForCausalLM(EasyDeLBaseModule):
def __init__(
self,
config: GPTJConfig,
dtype: jnp.dtype = jnp.float32,
param_dtype: jnp.dtype = jnp.float32,
precision: tp.Optional[tp.Union[str, jax.lax.Precision]] = None,
*,
rngs: nn.Rngs,
):
super().__init__(
config=config,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
self.transformer = GPTJModel(
self.config,
dtype=self.dtype,
param_dtype=self.dtype,
precision=self.precision,
rngs=rngs,
)
self.lm_head = nn.Linear(
config.hidden_size,
config.vocab_size,
rngs=rngs,
dtype=self.dtype,
kernel_init=jax.nn.initializers.normal(stddev=config.initializer_range),
param_dtype=self.dtype,
precision=self.precision,
**get_dot_general_by_bits(config.bits, config.easy_method),
)
def __call__(
self,
input_ids: tp.Optional[chex.Array] = None,
attention_mask: tp.Optional[chex.Array] = None,
position_ids: tp.Optional[chex.Array] = None,
past_key_values: tp.Optional[TransformerCache] = None,
inputs_embeds: tp.Optional[chex.Array] = None,
segment_ids: tp.Optional[chex.Array] = None,
extra_embedding: tp.Optional[chex.Array] = None,
output_attentions: bool = False,
output_hidden_states: bool = False,
return_dict: bool = True,
):
outputs = self.transformer(
input_ids=input_ids,
extra_embedding=extra_embedding,
segment_ids=segment_ids,
attention_mask=attention_mask,
inputs_embeds=inputs_embeds,
past_key_values=past_key_values,
position_ids=position_ids,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
hidden_states = outputs[0]
if self.config.tie_word_embeddings:
lm_logits = jax.lax.dot_general(
hidden_states,
self.transformer.wte.embedding.value.T,
(((hidden_states.ndim - 1), (0,)), ((), ())),
)
else:
lm_logits = self.lm_head(hidden_states)
if not return_dict:
return (lm_logits,) + outputs[1:]
return FlaxCausalLMOutput(
logits=lm_logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
past_key_values=outputs.past_key_values,
)