# 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
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 TaskType, register_module
from easydel.infra.loss_utils import auxiliary_load_balancing_loss_func
from easydel.infra.modeling_outputs import (
FlaxSequenceClassifierOutput,
MoeCausalLMOutput,
MoeModelOutput,
)
from easydel.infra.utils import (
ACT2FN,
auto_remat,
control_mlp_sharding,
get_dot_general_by_bits,
)
from easydel.layers.attention import FlaxAttentionModule, FlexibleAttentionModule
from easydel.layers.caching import TransformerCache, TransformerCacheView
from easydel.modules.dbrx.dbrx_configuration import (
DbrxAttentionConfig as DbrxAttentionConfig,
)
from easydel.modules.dbrx.dbrx_configuration import DbrxConfig as DbrxConfig
from easydel.modules.dbrx.dbrx_configuration import DbrxFFNConfig as DbrxFFNConfig
class DbrxAttention(FlaxAttentionModule):
def __init__(
self,
config: DbrxConfig,
dtype: jnp.dtype = jnp.float32,
param_dtype: jnp.dtype = jnp.float32,
precision: jax.lax.PrecisionLike = None,
*,
rngs: nn.Rngs,
):
super().__init__(config=config)
self.config = config
self.dtype = dtype
self.param_dtype = param_dtype
self.precision = precision
self.rngs = rngs
self.num_attention_heads = self.config.n_heads
self.num_key_value_heads = self.config.attn_config.kv_n_heads
config = self.config
self.hidden_size = config.hidden_size
self.head_dim = self.config.d_model // self.config.n_heads
self.num_key_value_groups = self.num_attention_heads // self.num_key_value_heads
if self.num_key_value_groups == 1:
assert self.num_attention_heads == self.config.attn_config.kv_n_heads
self.Wqkv = nn.Linear(
config.hidden_size,
self.hidden_size + 2 * self.num_key_value_heads * self.head_dim,
dtype=dtype,
param_dtype=param_dtype,
use_bias=False,
kernel_init=jax.nn.initializers.normal(config.initializer_range),
precision=self.precision,
rngs=rngs,
**get_dot_general_by_bits(config.bits, config.easy_method),
)
self.out_proj = nn.Linear(
config.hidden_size,
config.hidden_size,
dtype=dtype,
param_dtype=param_dtype,
use_bias=False,
kernel_init=jax.nn.initializers.normal(config.initializer_range),
precision=self.precision,
rngs=rngs,
**get_dot_general_by_bits(config.bits, config.easy_method),
)
self.rotary = self.config.get_basic_rope(
dtype=self.dtype,
rotary_dim=self.config.hidden_size // self.config.num_attention_heads,
head_size=self.config.hidden_size // self.config.num_attention_heads,
is_neox_style=True,
base=self.config.attn_config.rope_theta,
)
self.attention_performer = FlexibleAttentionModule(
base_config=config,
softmax_scale=self.head_dim**-0.5,
dropout_prob=0.0,
)
self.resid_dropout = nn.Dropout(rate=config.resid_pdrop)
def __call__(
self,
hidden_states: chex.Array,
attention_mask: chex.Array,
position_ids: chex.Array,
causal_mask: chex.Array,
cache_view: tp.Optional[TransformerCacheView] = None,
segment_ids: tp.Optional[chex.Array] = None,
output_attentions: bool = False,
fcm_mask: tp.Optional[chex.Array] = None,
frequencies: tp.Optional[chex.Array] = None,
):
"""
Forward pass of the attention module.
Args:
hidden_states (chex.Array): Input hidden states.
attention_mask (chex.Array): Mask to apply on the attention scores.
position_ids (chex.Array): Position indices for the tokens.
causal_mask (chex.Array): Causal mask for ensuring autoregressive behavior.
segment_ids (tp.Optional[chex.Array]): Segment IDs for segment-based attention (optional).
deterministic (bool): If True, disables dropout for deterministic behavior.
init_cache (bool): If True, initializes cache for caching keys and values.
output_attentions (bool): If True, outputs attention weights alongside the hidden states.
fcm_mask (tp.Optional[chex.Array]): fcm mask to be combined with attn mask and causal mask.
Returns:
tp.Tuple[chex.Array, chex.Array]: A tuple containing the attention output and the attention weights.
"""
batch_size, sequence_length = hidden_states.shape[:2]
qkv_states = self.Wqkv(hidden_states)
if self.config.attn_config.clip_qkv is not None:
qkv_states = qkv_states.clip(
min=-self.config.attn_config.clip_qkv,
max=self.config.attn_config.clip_qkv,
)
query_size = self.hidden_size
key_size = self.num_key_value_heads * self.head_dim
query_states, key_value_states = jnp.split(qkv_states, [query_size], axis=2)
key_states, value_states = jnp.split(key_value_states, [key_size], axis=2)
query_states = query_states.reshape(
batch_size,
sequence_length,
self.num_attention_heads,
self.head_dim,
)
key_states = key_states.reshape(
batch_size,
sequence_length,
self.num_key_value_heads,
self.head_dim,
)
value_states = value_states.reshape(
batch_size,
sequence_length,
self.num_key_value_heads,
self.head_dim,
)
query_states, key_states = self.rotary(
position_ids,
query=query_states,
key=key_states,
frequencies=frequencies,
)
(
key_states,
value_states,
attention_mask,
init_attention_bias,
) = self.concatenate(
query=query_states,
key=key_states,
cache_view=cache_view,
value=value_states,
attention_mask=attention_mask,
causal_mask=causal_mask,
fcm_mask=fcm_mask,
)
attentions = self.attention_performer.forward(
query_states=query_states,
key_states=key_states,
value_states=value_states,
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,)
if output_attentions:
outputs += (output_attentions,)
return outputs
class DbrxNormAttentionNorm(nn.Module):
def __init__(
self,
config: DbrxConfig,
dtype: jnp.dtype = jnp.float32,
param_dtype: jnp.dtype = jnp.float32,
precision: jax.lax.PrecisionLike = None,
*,
rngs: nn.Rngs,
):
super().__init__()
self.config = config
self.dtype = dtype
self.param_dtype = param_dtype
self.precision = precision
self.rngs = rngs
self.norm_1 = nn.LayerNorm(
self.config.hidden_size,
dtype=dtype,
param_dtype=param_dtype,
use_bias=False,
rngs=rngs,
)
self.attn = DbrxAttention( # statics 3,5,6,7
config=config,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
self.norm_2 = nn.LayerNorm(
self.config.hidden_size,
dtype=dtype,
param_dtype=param_dtype,
use_bias=False,
rngs=rngs,
)
self.dropout = nn.Dropout(
self.config.resid_pdrop,
rngs=rngs,
)
def __call__(
self,
hidden_states: chex.Array,
attention_mask: chex.Array,
position_ids: chex.Array,
causal_mask: chex.Array,
cache_view: tp.Optional[TransformerCacheView] = None,
segment_ids: tp.Optional[chex.Array] = None,
output_attentions: bool = False,
fcm_mask: tp.Optional[chex.Array] = None,
frequencies: tp.Optional[chex.Array] = None,
) -> tp.Tuple[chex.Array, chex.Array, tp.Optional[chex.Array]]:
"""
Forward pass of the attentionNrom module.
Args:
hidden_states (chex.Array): Input hidden states.
attention_mask (chex.Array): Mask to apply on the attention scores.
position_ids (chex.Array): Position indices for the tokens.
causal_mask (chex.Array): Causal mask for ensuring autoregressive behavior.
segment_ids (tp.Optional[chex.Array]): Segment IDs for segment-based attention (optional).
deterministic (bool): If True, disables dropout for deterministic behavior.
init_cache (bool): If True, initializes cache for caching keys and values.
output_attentions (bool): If True, outputs attention weights alongside the hidden states.
fcm_mask (tp.Optional[chex.Array]): fcm mask to be combined with attn mask and causal mask.
Returns:
tp.Tuple[chex.Array, chex.Array, tp.Optional[chex.Array]]: A tuple containing the residual_states, hidden states, and the attention weights.
"""
residual_states = hidden_states
hidden_states = self.norm_1(hidden_states)
attn_out = self.attn(
hidden_states=hidden_states,
attention_mask=attention_mask,
position_ids=position_ids,
output_attentions=output_attentions,
causal_mask=causal_mask,
segment_ids=segment_ids,
fcm_mask=fcm_mask,
frequencies=frequencies,
cache_view=cache_view,
)
hidden_states, attn_weights = attn_out if output_attentions else (attn_out[0], None)
hidden_states = self.dropout(hidden_states)
hidden_states = hidden_states + residual_states
residual_states = hidden_states
hidden_states = self.norm_2(hidden_states)
return residual_states, hidden_states, attn_weights
class DbrxExpertGLU(nn.Module):
def __init__(
self,
config: DbrxConfig,
dtype: jnp.dtype = jnp.float32,
param_dtype: jnp.dtype = jnp.float32,
precision: jax.lax.PrecisionLike = None,
*,
rngs: nn.Rngs,
):
super().__init__()
self.config = config
self.dtype = dtype
self.param_dtype = param_dtype
self.precision = precision
self.rngs = rngs
shape = (
self.config.ffn_config.moe_num_experts * self.config.ffn_config.ffn_hidden_size,
self.config.d_model,
)
init_fn = nn.initializers.normal(dtype=self.dtype)
self.w1 = nn.Param(init_fn(rngs.params(), shape, self.param_dtype))
self.v1 = nn.Param(init_fn(rngs.params(), shape, self.param_dtype))
self.w2 = nn.Param(init_fn(rngs.params(), shape, self.param_dtype))
self.activation_fn = ACT2FN[self.config.ffn_config.ffn_act_fn["name"]]
def __call__(self, x: chex.Array, expert_idx: int) -> chex.Array:
expert_shape = (
self.config.ffn_config.moe_num_experts,
self.config.ffn_config.ffn_hidden_size,
self.config.d_model,
)
expert_w1 = self.w1.value.reshape(expert_shape)[expert_idx]
expert_v1 = self.v1.value.reshape(expert_shape)[expert_idx]
expert_w2 = self.w2.value.reshape(expert_shape)[expert_idx]
x1 = jnp.matmul(
x,
jnp.expand_dims(expert_w1.T, 0),
precision=self.precision,
)
x2 = jnp.matmul(
x,
jnp.expand_dims(expert_v1.T, 0),
precision=self.precision,
)
x1 = self.activation_fn(x1)
x1 = x1 * x2
x1 = jnp.matmul(
x1,
jnp.expand_dims(expert_w2, 0),
precision=self.precision,
)
return x1
class DbrxExperts(nn.Module):
def __init__(
self,
config: DbrxConfig,
dtype: jnp.dtype = jnp.float32,
param_dtype: jnp.dtype = jnp.float32,
precision: jax.lax.PrecisionLike = None,
*,
rngs: nn.Rngs,
):
super().__init__()
self.config = config
self.dtype = dtype
self.param_dtype = param_dtype
self.precision = precision
self.rngs = rngs
self.mlp = DbrxExpertGLU(
config=config,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
def __call__(
self,
x: chex.Array,
weights: chex.Array,
top_weights: chex.Array,
top_experts: chex.Array,
):
final_hidden_state = jnp.zeros_like(x)
for index in range(self.config.ffn_config.moe_num_experts):
output_moe_layer = self.mlp(x, index)
final_hidden_state += (
jnp.sum(jnp.multiply(index == top_experts, top_weights), axis=-1)[:, :, None]
* output_moe_layer
)
return final_hidden_state
class DbrxRouter(nn.Module):
def __init__(
self,
config: DbrxConfig,
dtype: jnp.dtype = jnp.float32,
param_dtype: jnp.dtype = jnp.float32,
precision: jax.lax.PrecisionLike = None,
*,
rngs: nn.Rngs,
):
super().__init__()
self.config = config
self.dtype = dtype
self.param_dtype = param_dtype
self.precision = precision
self.rngs = rngs
self.hidden_size = self.config.d_model
self.moe_num_experts = self.config.ffn_config.moe_num_experts
self.moe_top_k = self.config.ffn_config.moe_top_k
self.moe_jitter_eps = self.config.ffn_config.moe_jitter_eps
self.moe_normalize_expert_weights = (
self.config.ffn_config.moe_normalize_expert_weights
)
self.uniform_expert_assignment = self.config.ffn_config.uniform_expert_assignment
self.layer = nn.Linear(
config.hidden_size,
self.moe_num_experts,
use_bias=False,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
def jitter(self, x: chex.Array) -> chex.Array:
if self.moe_jitter_eps is None:
raise RuntimeError("The router does not have moe_jitter_eps set.")
low = 1.0 - self.moe_jitter_eps
high = 1.0 + self.moe_jitter_eps
noise = jax.random.normal(self.make_rng("params"), x.shape, dtype=x.dtype)
return low + noise * (high - low)
def __call__(
self, x: chex.Array, deterministic: bool = True
) -> tp.Tuple[chex.Array, chex.Array, chex.Array]:
if not deterministic and self.moe_jitter_eps is not None:
x = x * self.jitter(x)
weights = self.layer(x.astype(jnp.promote_types(self.dtype, jnp.float32)))
weights = jax.nn.softmax(weights.astype(jnp.promote_types(self.dtype, jnp.float32)))
top_weights, top_experts = jax.lax.top_k(weights, self.moe_top_k)
if self.moe_normalize_expert_weights:
top_weights = top_weights / jnp.linalg.norm(
top_weights,
ord=int(self.moe_normalize_expert_weights),
axis=-1,
keepdims=True,
)
if self.uniform_expert_assignment:
top_experts = jax.lax.stop_gradient(
(
jnp.arange(
0,
jnp.prod(
jnp.asarray(top_experts.shape, dtype=jnp.int32),
dtype=jnp.int32,
),
dtype=top_experts.dtype,
)
% self.moe_num_experts
).reshape(top_experts.shape)
)
weights = weights.astype(x.dtype)
top_weights = top_weights.astype(x.dtype)
return weights, top_weights, top_experts
class DbrxFFN(nn.Module):
def __init__(
self,
config: DbrxConfig,
dtype: jnp.dtype = jnp.float32,
param_dtype: jnp.dtype = jnp.float32,
precision: jax.lax.PrecisionLike = None,
*,
rngs: nn.Rngs,
):
super().__init__()
self.config = config
self.dtype = dtype
self.param_dtype = param_dtype
self.precision = precision
self.rngs = rngs
self.router = DbrxRouter(
config=config,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
self.experts = DbrxExperts(
config=config,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
def __call__(self, x: chex.Array) -> tp.Tuple[chex.Array, chex.Array]:
x = control_mlp_sharding(x, self.config.partition_axis)
weights, top_weights, top_experts = self.router(x)
out = self.experts(x, weights, top_weights, top_experts)
return out, weights
class DbrxBlock(nn.Module):
def __init__(
self,
config: DbrxConfig,
dtype: jnp.dtype = jnp.float32,
param_dtype: jnp.dtype = jnp.float32,
precision: jax.lax.PrecisionLike = None,
*,
rngs: nn.Rngs,
):
super().__init__()
self.config = config
self.dtype = dtype
self.param_dtype = param_dtype
self.precision = precision
self.rngs = rngs
self.hidden_size = self.config.d_model
self.resid_pdrop = self.config.resid_pdrop
attn_block = DbrxNormAttentionNorm
ffn_block = DbrxFFN
attn_block, ffn_block = auto_remat(
attn_block,
ffn_block,
policy=config.gradient_checkpointing,
)
self.norm_attn_norm = attn_block(
config=config,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
self.ffn = ffn_block(
config=config,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
def __call__(
self,
hidden_states: chex.Array,
attention_mask: chex.Array,
position_ids: chex.Array,
causal_mask: chex.Array,
segment_ids: tp.Optional[chex.Array] = None,
cache_view: tp.Optional[TransformerCacheView] = None,
output_attentions: bool = False,
output_router_logits: bool = False,
fcm_mask: tp.Optional[chex.Array] = None,
frequencies: tp.Optional[chex.Array] = None,
) -> tp.Tuple[chex.Array, chex.Array, tp.Optional[chex.Array]]:
"""
Forward pass of the attentionNrom module.
Args:
hidden_states (chex.Array): Input hidden states.
attention_mask (chex.Array): Mask to apply on the attention scores.
position_ids (chex.Array): Position indices for the tokens.
causal_mask (chex.Array): Causal mask for ensuring autoregressive behavior.
segment_ids (tp.Optional[chex.Array]): Segment IDs for segment-based attention (optional).
deterministic (bool): If True, disables dropout for deterministic behavior.
init_cache (bool): If True, initializes cache for caching keys and values.
output_attentions (bool): If True, outputs attention weights.
output_router_logits (bool): If True, outputs router logits.
fcm_mask (tp.Optional[chex.Array]): fcm mask to be combined with attn mask and causal mask.
Returns:
tp.Tuple[chex.Array, chex.Array, tp.Optional[chex.Array]]: A tuple containing the residual_states, hidden states, and the attention weights.
"""
resid_states, hidden_states, self_attn_weights = self.norm_attn_norm(
hidden_states,
attention_mask,
position_ids,
causal_mask,
cache_view,
segment_ids,
output_attentions,
fcm_mask,
frequencies,
)
hidden_states, router_logits = self.ffn(hidden_states)
hidden_states = resid_states + hidden_states
outputs = (hidden_states,)
if output_attentions:
outputs += (self_attn_weights,)
if output_router_logits:
outputs += (router_logits,)
return outputs
[docs]@register_module(
TaskType.BASE_MODULE,
config=DbrxConfig,
model_type="dbrx",
)
class DbrxModel(EasyDeLBaseModule):
def __init__(
self,
config: DbrxConfig,
dtype: jnp.dtype = jnp.float32,
param_dtype: jnp.dtype = jnp.float32,
precision: jax.lax.PrecisionLike = None,
*,
rngs: nn.Rngs,
):
super().__init__(
config=config,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
self.padding_idx = self.config.pad_token_id
self.vocab_size = self.config.vocab_size
self.emb_pdrop = self.config.emb_pdrop
self.wte = nn.Embed(
self.config.vocab_size,
self.config.d_model,
dtype=dtype,
param_dtype=param_dtype,
rngs=rngs,
)
self.blocks = [
DbrxBlock(
config=config,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
for i in range(self.config.n_layers)
]
self.norm_f = nn.LayerNorm(
self.config.hidden_size,
use_bias=False,
dtype=dtype,
param_dtype=param_dtype,
rngs=rngs,
)
@cached_property
def frequencies(self):
return self.config.get_basic_frequencies(
rotary_dim=self.config.hidden_size // self.config.num_attention_heads,
head_size=self.config.hidden_size // self.config.num_attention_heads,
base=self.config.attn_config.rope_theta,
)
def __call__(
self,
input_ids: chex.Array,
attention_mask: tp.Optional[chex.Array] = None,
position_ids: tp.Optional[chex.Array] = None,
segment_ids: tp.Optional[chex.Array] = None,
inputs_embeds: tp.Optional[chex.Array] = None,
output_attentions: tp.Optional[bool] = None,
output_hidden_states: tp.Optional[bool] = None,
output_router_logits: tp.Optional[bool] = None,
past_key_values: tp.Optional[TransformerCache] = None,
return_dict: bool = True,
) -> MoeModelOutput | tp.Tuple:
if output_router_logits is None:
output_router_logits = self.config.output_router_logits
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[:2]
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)
output_attentions = (
output_attentions
if output_attentions is not None
else self.config.output_attentions
)
output_router_logits = (
output_router_logits
if output_router_logits is not None
else self.config.output_router_logits
)
output_hidden_states = (
output_hidden_states
if output_hidden_states is not None
else self.config.output_hidden_states
)
hidden_states = inputs_embeds
all_hidden_states = ()
all_router_logits = ()
all_attentions = ()
if past_key_values is None:
past_key_values = TransformerCache.init_empty(len(self.blocks))
for idx, block in enumerate(self.blocks):
if output_hidden_states:
all_hidden_states += (hidden_states,)
outputs = block(
hidden_states=hidden_states,
attention_mask=attention_mask,
position_ids=position_ids,
causal_mask=self.causal_mask,
segment_ids=segment_ids,
cache_view=past_key_values.views[idx],
output_attentions=output_attentions,
output_router_logits=output_router_logits,
frequencies=self.frequencies,
)
hidden_states = outputs[0]
if output_attentions:
all_attentions += (outputs[1],)
if output_router_logits:
all_router_logits += (outputs[-1],)
hidden_states = self.norm_f(hidden_states)
if output_hidden_states:
all_hidden_states += (hidden_states,)
if not return_dict:
return tuple(
v
for v in [
hidden_states,
all_hidden_states,
all_attentions,
all_router_logits,
]
if v is not None
)
return MoeModelOutput(
last_hidden_state=hidden_states,
hidden_states=all_hidden_states,
attentions=all_attentions,
router_logits=all_router_logits,
)
[docs]@register_module(
TaskType.CAUSAL_LM,
config=DbrxConfig,
model_type="dbrx",
)
class DbrxForCausalLM(EasyDeLBaseModule):
def __init__(
self,
config: DbrxConfig,
dtype: jnp.dtype = jnp.float32,
param_dtype: jnp.dtype = jnp.float32,
precision: jax.lax.PrecisionLike = None,
*,
rngs: nn.Rngs,
):
super().__init__(
config=config,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
self.transformer = DbrxModel(
config=config,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
self.lm_head = nn.Linear(
config.hidden_size,
config.vocab_size,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
use_bias=False,
rngs=rngs,
kernel_init=nn.initializers.normal(config.initializer_range),
**get_dot_general_by_bits(config.bits, config.easy_method),
)
def __call__(
self,
input_ids: chex.Array,
attention_mask: tp.Optional[chex.Array] = None,
position_ids: tp.Optional[chex.Array] = None,
segment_ids: tp.Optional[chex.Array] = None,
inputs_embeds: tp.Optional[chex.Array] = None,
output_attentions: tp.Optional[bool] = None,
output_hidden_states: tp.Optional[bool] = None,
output_router_logits: tp.Optional[bool] = None,
past_key_values: tp.Optional[TransformerCache] = None,
return_dict: bool = True,
) -> MoeCausalLMOutput | tp.Tuple:
if output_router_logits is None:
output_router_logits = self.config.output_router_logits
outputs = self.transformer(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
output_router_logits=output_router_logits,
past_key_values=past_key_values,
return_dict=True,
segment_ids=segment_ids,
)
logits = self.lm_head(outputs.last_hidden_state)
batch_size, seq_length, hd = logits.shape
aux_loss = None
if output_router_logits and outputs.router_logits is not None:
aux_loss = auxiliary_load_balancing_loss_func(
gate_logits=tuple( # type:ignore
[
logit.reshape(batch_size * seq_length, -1)
for logit in outputs.router_logits
] # type:ignore
),
num_experts=self.config.ffn_config.moe_num_experts,
top_k=self.config.ffn_config.moe_top_k,
attention_mask=attention_mask,
)
aux_loss = aux_loss * self.config.router_aux_loss_coef
if not return_dict:
outputs = (logits,) + tuple(
v
for v in [
aux_loss,
outputs.hidden_states,
outputs.attentions,
outputs.router_logits,
]
if v is not None
)
return outputs
return MoeCausalLMOutput(
aux_loss=aux_loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
router_logits=outputs.router_logits,
)
[docs]@register_module(
TaskType.SEQUENCE_CLASSIFICATION,
config=DbrxConfig,
model_type="dbrx",
)
class DbrxForSequenceClassification(EasyDeLBaseModule):
def __init__(
self,
config: DbrxConfig,
dtype: jnp.dtype = jnp.float32,
param_dtype: jnp.dtype = jnp.float32,
precision: jax.lax.PrecisionLike = None,
*,
rngs: nn.Rngs,
):
super().__init__(
config=config,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
self.transformer = DbrxModel(
config=config,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
assert hasattr(config, "num_labels"), (
"in order to use `SequenceClassification` Models in `EasyDeL` you first need to attach `num_labels` to model `config`"
)
self.score = nn.Linear(
config.hidden_size,
config.num_labels,
dtype=dtype,
param_dtype=param_dtype,
use_bias=False,
kernel_init=jax.nn.initializers.normal(stddev=config.initializer_range),
precision=precision,
rngs=rngs,
)
def __call__(
self,
input_ids: chex.Array,
attention_mask: tp.Optional[chex.Array] = None,
position_ids: tp.Optional[chex.Array] = None,
segment_ids: tp.Optional[chex.Array] = None,
inputs_embeds: tp.Optional[chex.Array] = None,
output_attentions: tp.Optional[bool] = None,
output_hidden_states: tp.Optional[bool] = None,
output_router_logits: tp.Optional[bool] = None,
past_key_values: tp.Optional[TransformerCache] = None,
return_dict: bool = True,
) -> FlaxSequenceClassifierOutput:
if output_router_logits is None:
output_router_logits = self.config.output_router_logits
transformer_outputs = self.transformer(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
output_router_logits=output_router_logits,
past_key_values=past_key_values,
return_dict=True,
segment_ids=segment_ids,
)
hidden_states = transformer_outputs[0]
logits = self.score(hidden_states)
if input_ids is not None:
batch_size = input_ids.shape[0]
else:
batch_size = inputs_embeds.shape[0]
if self.config.pad_token_id is None and batch_size != 1:
raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
if self.config.pad_token_id is None:
sequence_lengths = -1
else:
if input_ids is not None:
sequence_lengths = (
jnp.argmax(jnp.equal(input_ids, self.config.pad_token_id).astype("i4"), -1)
- 1
)
sequence_lengths = sequence_lengths % input_ids.shape[-1]
else:
sequence_lengths = -1
pooled_logits = logits[jnp.arange(batch_size), sequence_lengths]
aux_loss = None
if output_router_logits and transformer_outputs.router_logits is not None:
aux_loss = auxiliary_load_balancing_loss_func(
gate_logits=tuple(
[
logit.reshape(batch_size * sequence_lengths, -1)
for logit in transformer_outputs.router_logits
]
),
num_experts=self.config.ffn_config.moe_num_experts,
top_k=self.config.ffn_config.moe_top_k,
attention_mask=attention_mask,
)
aux_loss = aux_loss * self.config.router_aux_loss_coef
if not return_dict:
output = (pooled_logits,) + transformer_outputs[1:] + (aux_loss,)
return output
return FlaxSequenceClassifierOutput(
logits=pooled_logits,
past_key_values=past_key_values,
hidden_states=transformer_outputs.hidden_states,
attentions=transformer_outputs.attentions,
aux_loss=aux_loss,
)