# 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 math
import typing as tp
from functools import partial
import chex
import jax
import jax.numpy as jnp
from eformer import common_types
from eformer.escale import apply_logical_sharding
from eformer.pytree import auto_pytree
from flax import nnx as nn
from easydel.infra.base_module import EasyDeLBaseModule
from easydel.infra.factory import TaskType, register_module
from easydel.infra.modeling_outputs import (
AttentionLayerOutput,
BaseModelOutput,
CausalLMOutput,
DecoderLayerOutput,
EncoderLayerOutput,
ModelOutput,
SequenceClassifierOutput,
)
from easydel.infra.utils import (
ACT2FN,
auto_remat,
get_dot_general_by_bits,
)
from easydel.layers.attention import AttentionModule, FlexibleAttentionModule
from easydel.layers.caching import (
PagedAttentionCache,
PagedAttentionCacheView,
PagedAttentionMetadata,
TransformerCache,
TransformerCacheView,
TransformerMetadata,
)
from easydel.layers.linear import ParallelLinear
from easydel.layers.norms import RMSNorm as Llama4TextRMSNorm
from .llama4_configuration import Llama4Config, Llama4TextConfig, Llama4VisionConfig
@auto_pytree
class Llama4CausalLMOutputWithPast(ModelOutput):
"""
Base class for Llama4Vision causal language model (or autoregressive) outputs.
Args:
loss (`chex.Array` of shape `(1,)`, *optional*, returned when `labels` is provided):
Language modeling loss (for next-token prediction).
logits (`chex.Array` of shape `(batch_size, sequence_length, config.vocab_size)`):
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
past_key_values (`tuple(tuple(chex.Array))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
Tuple of `tuple(chex.Array)` of length `config.n_layers`, with each tuple having 2 tensors of shape
`(batch_size, num_heads, sequence_length, embed_size_per_head)`)
Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
`past_key_values` input) to speed up sequential decoding.
hidden_states (`tuple(chex.Array)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `chex.Array` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (`tuple(chex.Array)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `chex.Array` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
image_hidden_states (`chex.Array`, *optional*):
A `chex.Array` of size (batch_size * num_patches, num_images, sequence_length, hidden_size)`.
image_hidden_states of the model produced by the vision encoder and after projecting the last hidden state.
"""
loss: tp.Optional[chex.Array] = None
logits: chex.Array = None
past_key_values: tp.Optional[TransformerCache] = None
hidden_states: tp.Optional[tp.Tuple[chex.Array]] = None
attentions: tp.Optional[tp.Tuple[chex.Array]] = None
image_hidden_states: tp.Optional[chex.Array] = None
def bmm(inputs, kernel, precision):
subscript = "...ik,...kj->...ij" if inputs.ndim > 1 else "...k,...kj->...j"
return jnp.einsum(
subscript,
inputs,
kernel,
precision=precision,
optimize=True,
)
@partial(jax.jit, static_argnums=(0, 1, 2, 3))
def _vision_freqs(idx, hidden_size, num_attention_heads, rope_theta):
img_idx = jnp.arange(idx**2, dtype="i4").reshape(idx**2, 1)
img_idx = jnp.concatenate([img_idx, img_idx[:1]], axis=0)
img_idx = img_idx.at[-1, -1].set(-2)
frequencies_x = img_idx % idx
frequencies_y = img_idx // idx
freq_dim = hidden_size // num_attention_heads // 2
rope_arange = jnp.arange(0, freq_dim, 2)
rope_arange_sliced = rope_arange[: (freq_dim // 2)]
rope_freq = 1.0 / (rope_theta ** (rope_arange_sliced.astype("f4") / freq_dim))
rope_freq_broadcast = rope_freq[None, None, :]
freqs_x = jnp.repeat(
(frequencies_x + 1).astype("f4")[..., None] * rope_freq_broadcast, 2, axis=-1
)
freqs_y = jnp.repeat(
(frequencies_y + 1).astype("f4")[..., None] * rope_freq_broadcast, 2, axis=-1
)
freqs = jnp.concatenate([freqs_x, freqs_y], axis=-1)[..., ::2]
freqs = jnp.where(img_idx.reshape(-1, 1, 1) < 0, 0.0, freqs)
return jnp.exp(1j * freqs)
def _create_chunked_attention_mask(
attention_chunk_size: int,
start: int,
end: int,
):
blcok_position = jnp.abs(
(jnp.arange(start, end)[None, :] // attention_chunk_size)
- jnp.arange(start, end)[:, None] // attention_chunk_size
)
token_position = jnp.arange(start, end)[None, :] - jnp.arange(start, end)[:, None]
return ((blcok_position == 0) & (token_position <= 0)).astype("b1")
class Llama4TextExperts(nn.Module):
def __init__(
self,
config: Llama4Config,
dtype: jnp.dtype = jnp.float32,
param_dtype: jnp.dtype = jnp.float32,
precision: jax.lax.PrecisionLike = None,
*,
rngs: nn.Rngs,
):
self.config = config
self.dtype = dtype
self.param_dtype = param_dtype
self.precision = precision
self.num_experts = config.num_local_experts
self.intermediate_size = config.intermediate_size
self.hidden_size = config.hidden_size
self.expert_dim = self.intermediate_size
kernel_init = jax.nn.initializers.normal(config.initializer_range)
self.gate_up_proj = nn.Param(
kernel_init(
rngs.params(),
(self.num_experts, self.hidden_size, 2 * self.expert_dim),
self.param_dtype,
)
)
self.down_proj = nn.Param(
kernel_init(
rngs.params(),
(self.num_experts, self.expert_dim, self.hidden_size),
self.param_dtype,
)
)
self.activation_fn = ACT2FN[self.config.hidden_act]
def __call__(self, hidden_states: jnp.ndarray) -> jnp.ndarray:
hidden_states = hidden_states.reshape(self.num_experts, -1, self.hidden_size)
gate_up = bmm(hidden_states, self.gate_up_proj, self.precision)
gate, up = jnp.split(gate_up, 2, axis=-1)
next_states = bmm((up * self.activation_fn(gate)), self.down_proj, self.precision)
return next_states.reshape(-1, self.hidden_size)
class Llama4TextL2Norm(nn.Module):
def __init__(self, eps: float = 1e-6) -> None:
self.eps = eps
def _norm(self, x: jnp.ndarray) -> jnp.ndarray:
return x * jax.lax.rsqrt(jnp.square(x).mean(-1, keepdims=True) + self.eps)
@jax.named_scope("easydel-L2norm")
def __call__(self, x: jnp.ndarray) -> jnp.ndarray:
return self._norm(x.astype(jnp.float32)).astype(x.dtype)
class Llama4TextMLP(nn.Module):
def __init__(
self,
config: Llama4Config,
intermediate_size=None,
dtype: jnp.dtype = jnp.float32,
param_dtype: jnp.dtype = jnp.float32,
precision: jax.lax.PrecisionLike = None,
*,
rngs: nn.Rngs,
):
self.config = config
self.dtype = dtype
self.param_dtype = param_dtype
self.precision = precision
if intermediate_size is None:
intermediate_size = config.intermediate_size
linear_class = partial(
ParallelLinear,
dtype=dtype,
param_dtype=param_dtype,
use_bias=False,
kernel_init=jax.nn.initializers.normal(config.initializer_range),
precision=precision,
rngs=rngs,
**get_dot_general_by_bits(config.bits, config.easy_method),
)
self.gate_proj = linear_class(config.hidden_size, intermediate_size)
self.down_proj = linear_class(intermediate_size, config.hidden_size)
self.up_proj = linear_class(config.hidden_size, intermediate_size)
self.activation_fn = ACT2FN[self.config.hidden_act]
def __call__(self, hidden_states: jnp.ndarray) -> jnp.ndarray:
gate = self.activation_fn(self.gate_proj(hidden_states))
up = self.up_proj(hidden_states)
hidden_states = self.down_proj(gate * up)
return hidden_states
class Llama4TextMoe(nn.Module):
def __init__(
self,
config: Llama4Config,
dtype: jnp.dtype = jnp.float32,
param_dtype: jnp.dtype = jnp.float32,
precision: jax.lax.PrecisionLike = None,
*,
rngs: nn.Rngs,
):
self.config = config
self.dtype = dtype
self.param_dtype = param_dtype
self.precision = precision
self.rngs = rngs
self.top_k = config.num_experts_per_tok
self.hidden_dim = config.hidden_size
self.num_experts = config.num_local_experts
self.experts = Llama4TextExperts(
config=config,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
self.router = ParallelLinear(
config.hidden_size,
config.num_local_experts,
use_bias=False,
precision=precision,
rngs=rngs,
dtype=dtype,
param_dtype=param_dtype,
kernel_init=jax.nn.initializers.normal(config.initializer_range),
**get_dot_general_by_bits(config.bits, config.easy_method),
)
self.shared_expert = Llama4TextMLP(
config=config,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
def __call__(self, hidden_states):
batch, seq_len, hidden_dim = hidden_states.shape
assert hidden_dim == self.hidden_dim, "Input hidden_dim mismatch"
# Reshape to [batch*seq_len, hidden_dim]
flattened_hidden_states = hidden_states.reshape(-1, self.hidden_dim)
tokens_per_expert = flattened_hidden_states.shape[0]
router_logits = self.router(flattened_hidden_states)
router_top_value, router_indices_topk = jax.lax.top_k(router_logits, self.top_k)
scores_base = jnp.full_like(router_logits, -jnp.inf)
token_idx = jnp.arange(tokens_per_expert)[:, None]
expert_idx = router_indices_topk
scores_scattered = scores_base.at[token_idx, expert_idx].set(router_top_value)
router_scores = jax.nn.sigmoid(scores_scattered.astype(jnp.float32)).astype(
hidden_states.dtype
)
out = self.shared_expert(flattened_hidden_states)
expert_outputs = jnp.zeros_like(out)
for expert_idx in range(self.num_experts):
expert_mask = router_scores[:, expert_idx : expert_idx + 1]
expert_inputs = flattened_hidden_states * expert_mask
expert_output = self.experts(expert_inputs)
expert_outputs = expert_outputs + expert_output
final_output = out + expert_outputs
final_output = final_output.reshape(batch, seq_len, hidden_dim)
router_scores_transposed = router_scores.T
return final_output, router_scores_transposed
class Llama4TextAttention(AttentionModule):
def __init__(
self,
config: Llama4TextConfig,
layer_idx: int,
dtype: jnp.dtype = jnp.float32,
param_dtype: jnp.dtype = jnp.float32,
precision: jax.lax.PrecisionLike = None,
*,
rngs: nn.Rngs,
):
super().__init__(config=config)
self.dtype = dtype
self.param_dtype = param_dtype
self.precision = precision
self.rngs = rngs
self.hidden_size = config.hidden_size
head_dim = config.hidden_size // config.num_attention_heads
self.head_dim = getattr(config, "head_dim", head_dim)
self.num_key_value_groups = (
self.config.num_attention_heads // self.config.num_key_value_heads
)
if self.num_key_value_groups == 1:
assert self.config.num_attention_heads == self.config.num_key_value_heads
linear_class = partial(
ParallelLinear,
dtype=dtype,
param_dtype=param_dtype,
use_bias=config.attention_bias,
kernel_init=jax.nn.initializers.normal(config.initializer_range),
precision=precision,
**get_dot_general_by_bits(config.bits, config.easy_method),
)
self.q_proj = linear_class(
config.hidden_size,
config.num_attention_heads * self.head_dim,
rngs=rngs,
)
self.k_proj = linear_class(
config.hidden_size,
config.num_key_value_heads * self.head_dim,
rngs=rngs,
)
self.v_proj = linear_class(
config.hidden_size,
config.num_key_value_heads * self.head_dim,
rngs=rngs,
)
self.o_proj = linear_class(
config.num_attention_heads * self.head_dim,
config.hidden_size,
rngs=rngs,
)
self.use_rope = int((layer_idx + 1) % 4 != 0)
if self.use_rope:
self.rotary = self.config.get_basic_rope(
self.dtype,
self.head_dim,
self.head_dim,
True,
)
self.scaling = self.head_dim**-0.5
self.attn_scale = config.attn_scale
self.floor_scale = config.floor_scale
self.attn_temperature_tuning = config.attn_temperature_tuning
self.attention_dropout = config.attention_dropout
self.is_causal = True
self.attention_performer = FlexibleAttentionModule(
base_config=self.config,
softmax_scale=self.scaling,
dropout_prob=0.0,
)
if self.config.use_qk_norm and self.use_rope:
self.qk_norm = Llama4TextL2Norm()
def __call__(
self,
hidden_states: chex.Array,
attention_mask: chex.Array,
position_ids: chex.Array,
causal_mask: tp.Optional[chex.Array | bool],
mode: common_types.RUNTIME_MODE_TYPES, # type:ignore
cache_view: tp.Optional[TransformerCacheView | PagedAttentionCacheView] = None,
cache_metadata: tp.Optional[TransformerMetadata | PagedAttentionMetadata] = 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,
) -> AttentionLayerOutput:
batch_size, sequence_length = hidden_states.shape[:2]
input_shape = hidden_states.shape[:-1]
query_states, key_states, value_states = (
self.q_proj(hidden_states),
self.k_proj(hidden_states),
self.v_proj(hidden_states),
)
qshape = (
batch_size,
sequence_length,
self.config.num_attention_heads,
self.head_dim,
)
kv_shape = (
batch_size,
sequence_length,
self.config.num_key_value_heads,
self.head_dim,
)
query_states = query_states.reshape(qshape)
key_states = key_states.reshape(kv_shape)
value_states = value_states.reshape(kv_shape)
if self.use_rope:
query_states, key_states = self.apply_complex_rotary(
query_states,
key_states,
frequencies,
)
if hasattr(self, "qk_norm"):
query_states = self.qk_norm(query_states)
key_states = self.qk_norm(key_states)
if self.attn_temperature_tuning and not self.use_rope:
attn_scales = (
jnp.log(jnp.floor((position_ids.astype("f4") + 1.0) / self.floor_scale) + 1.0)
* self.attn_scale
+ 1.0
)
attn_scales = attn_scales.reshape((*input_shape, 1, 1))
query_states = (query_states * attn_scales).astype(query_states.dtype)
(
key_states,
value_states,
attention_mask,
init_attention_bias,
cache_view,
) = self.concatenate(
query=query_states,
key=key_states,
value=value_states,
cache_view=cache_view,
cache_metadata=cache_metadata,
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,
mode=mode,
bias=None,
cache_metadata=cache_metadata,
cache_view=cache_view,
init_bias=init_attention_bias,
attention_mask=attention_mask,
segment_ids=segment_ids,
causal=False,
dropout_rng=self.rngs.params(),
)
attn_output = self._merge_heads(attentions.attention_outputs)
attn_output = self.shard_attention_prod(attn_output)
attn_output = self.o_proj(attn_output)
return AttentionLayerOutput(
attention_output=attn_output,
attention_weight=attentions.attention_weights if output_attentions else None,
cache_view=cache_view,
)
class Llama4TextDecoderLayer(nn.Module):
def __init__(
self,
config: Llama4TextConfig,
layer_idx: int,
dtype: jnp.dtype = jnp.float32,
param_dtype: jnp.dtype = jnp.float32,
precision: jax.lax.PrecisionLike = None,
*,
rngs: nn.Rngs,
):
self.config = config
self.dtype = dtype
self.param_dtype = param_dtype
self.precision = precision
attn_block = Llama4TextAttention
mlp_block = Llama4TextMLP
moe_block = Llama4TextMoe
attn_block, mlp_block, moe_block = auto_remat(
attn_block,
mlp_block,
moe_block,
policy=config.gradient_checkpointing,
)
self.self_attn = attn_block(
config=config,
layer_idx=layer_idx,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
self.use_chunked_attention = int((layer_idx + 1) % 4 != 0) # <=> use rope
self.is_moe_layer = layer_idx in config.moe_layers
if self.is_moe_layer: # the 128E model interleaves dense / sparse
self.feed_forward = moe_block(
config=config,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
else:
self.feed_forward = mlp_block(
config=config,
intermediate_size=config.intermediate_size_mlp,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
self.input_layernorm = Llama4TextRMSNorm(
dim=config.hidden_size,
eps=config.rms_norm_eps,
dtype=dtype,
param_dtype=param_dtype,
rngs=rngs,
)
self.post_attention_layernorm = Llama4TextRMSNorm(
dim=config.hidden_size,
eps=config.rms_norm_eps,
dtype=dtype,
param_dtype=param_dtype,
rngs=rngs,
)
self.layer_idx = layer_idx
def __call__(
self,
hidden_states: chex.Array,
attention_mask: chex.Array,
position_ids: chex.Array,
causal_mask: tp.Optional[chex.Array | bool],
mode: common_types.RUNTIME_MODE_TYPES, # type:ignore
cache_view: tp.Optional[TransformerCacheView | PagedAttentionCacheView] = None,
cache_metadata: tp.Optional[TransformerMetadata | PagedAttentionMetadata] = None,
segment_ids: tp.Optional[chex.Array] = None,
output_attentions: bool = False,
output_router_logits: bool = False,
fcm_mask: tp.Optional[chex.Array] = None,
frequencies: tp.Optional[chex.Array] = None,
):
attn_outputs = self.self_attn(
self.input_layernorm(hidden_states),
attention_mask,
position_ids,
causal_mask,
mode,
cache_view,
cache_metadata,
segment_ids,
output_attentions,
fcm_mask,
frequencies,
)
hidden_states = hidden_states + attn_outputs.attention_output
feed_forward_input = self.post_attention_layernorm(hidden_states)
# TODO: Support Chunked MLP for LLaMA4
# if self.config.use_scan_mlp:
# feed_forward_hidden_states = block_wise_ffn(
# self.feed_forward,
# feed_forward_input,
# self.config.scan_mlp_chunk_size,
# )
# else:
feed_forward_hidden_states = self.feed_forward(feed_forward_input)
if self.is_moe_layer:
feed_forward_hidden_states, router_logits = feed_forward_hidden_states
else:
router_logits = None
hidden_states = hidden_states + feed_forward_hidden_states.reshape(
feed_forward_input.shape
)
return DecoderLayerOutput(
hidden_states=hidden_states,
attention_weight=attn_outputs.attention_weight,
router_logits=router_logits if output_router_logits else None,
cache_view=attn_outputs.cache_view,
)
[docs]@register_module(
TaskType.BASE_MODULE,
config=Llama4TextConfig,
model_type="llama4_text",
)
class Llama4TextModel(EasyDeLBaseModule):
def __init__(
self,
config: Llama4TextConfig,
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.embed_tokens = nn.Embed(
num_embeddings=self.config.vocab_size,
features=self.config.hidden_size,
dtype=dtype,
param_dtype=param_dtype,
embedding_init=jax.nn.initializers.normal(stddev=self.config.initializer_range),
rngs=rngs,
)
self.layers = [
Llama4TextDecoderLayer(
config=config,
layer_idx=layer_idx,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
for layer_idx in range(self.config.num_hidden_layers)
]
self.norm = Llama4TextRMSNorm(
dim=self.config.hidden_size,
eps=self.config.rms_norm_eps,
dtype=dtype,
param_dtype=param_dtype,
rngs=rngs,
)
def __call__(
self,
input_ids: tp.Optional[chex.Array] = None,
inputs_embeds: tp.Optional[chex.Array] = None,
attention_mask: tp.Optional[chex.Array] = None,
position_ids: tp.Optional[chex.Array] = None,
segment_ids: tp.Optional[chex.Array] = None,
mode: tp.Optional[common_types.RUNTIME_MODE_TYPES] = None, # type:ignore
past_key_values: tp.Optional[TransformerCache | PagedAttentionCache] = None,
cache_metadata: tp.Optional[TransformerMetadata | PagedAttentionMetadata] = None,
output_attentions: tp.Optional[bool] = None,
output_hidden_states: tp.Optional[bool] = None,
) -> BaseModelOutput:
"""Forward pass through the Llama model.
Args:
input_ids (chex.Array, optional): Input token IDs, shape (batch_size, sequence_length).
inputs_embeds (chex.Array, optional): Input embeddings, shape (batch_size, sequence_length, hidden_size).
attention_mask (chex.Array, optional): Mask to avoid attention on padding tokens.
position_ids (chex.Array, optional): Indices of positions of each input sequence token.
segment_ids (chex.Array, optional): Segment token indices for segment embeddings.
past_key_values (TransformerCache | PagedAttentionCache, optional): Cache containing precomputed key/value states.
cache_metadata (TransformerMetadata | PagedAttentionMetadata, optional): Metadata for cache handling.
output_attentions (bool, optional): Whether to return attention weights.
output_hidden_states (bool, optional): Whether to return hidden states of all layers.
Returns:
Union[BaseModelOutput, Tuple]: Model outputs (last hidden state, optional hidden states, optional attentions)
"""
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.embed_tokens(input_ids.astype("i4"))
batch_size, sequence_length, _ = inputs_embeds.shape
all_attentions = () if output_attentions else None
all_hidden_states = () if output_hidden_states else None
assert sequence_length <= self.config.max_position_embeddings, (
f"Maximum Position Embedding Reached ! (Excepted <= {self.config.max_position_embeddings} got {sequence_length})"
)
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)
hidden_states = inputs_embeds
if mode is None:
mode = (
common_types.MODE_DECODE
if sequence_length == 1 and past_key_values is not None
else common_types.MODE_TRAIN
)
if past_key_values is None:
past_key_values = TransformerCache.init_empty(len(self.layers))
hidden_states = apply_logical_sharding(
hidden_states,
dynamic_axes=common_types.HiddenStateSharding,
partition_manager=self.config.partition_manager,
)
causal_mask = jnp.expand_dims(
_create_chunked_attention_mask(
self.config.attention_chunk_size,
0,
sequence_length,
),
(0, 1),
)
frequencies = self.compute_complex_rotary(position_ids)
for idx, block in enumerate(self.layers):
if output_hidden_states:
all_hidden_states += (hidden_states,)
layer_outputs = block(
hidden_states=hidden_states,
attention_mask=attention_mask,
position_ids=position_ids,
mode=mode,
cache_view=past_key_values.views[idx],
cache_metadata=cache_metadata,
causal_mask=causal_mask,
output_attentions=output_attentions,
segment_ids=segment_ids,
frequencies=frequencies,
)
hidden_states = layer_outputs.hidden_states
if output_attentions:
all_attentions += (layer_outputs.attention_weight,)
past_key_values[idx] = layer_outputs.cache_view
hidden_states = self.norm(hidden_states)
if output_hidden_states:
all_hidden_states += (hidden_states,)
return BaseModelOutput(
last_hidden_state=hidden_states,
hidden_states=all_hidden_states,
attentions=all_attentions,
past_key_values=past_key_values,
)
[docs]@register_module(
TaskType.CAUSAL_LM,
config=Llama4TextConfig,
model_type="llama4_text",
)
class Llama4ForCausalLM(EasyDeLBaseModule):
def __init__(
self,
config: Llama4TextConfig,
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.model = Llama4TextModel(
config=config,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
self.lm_head = ParallelLinear(
config.hidden_size,
config.vocab_size,
dtype=dtype,
param_dtype=param_dtype,
use_bias=False,
kernel_init=jax.nn.initializers.normal(stddev=config.initializer_range),
precision=self.precision,
rngs=rngs,
**get_dot_general_by_bits(config.bits, config.easy_method),
)
def __call__(
self,
input_ids: tp.Optional[chex.Array] = None,
inputs_embeds: tp.Optional[chex.Array] = None,
attention_mask: tp.Optional[chex.Array] = None,
position_ids: tp.Optional[chex.Array] = None,
segment_ids: tp.Optional[chex.Array] = None,
mode: tp.Optional[common_types.RUNTIME_MODE_TYPES] = None, # type:ignore
past_key_values: tp.Optional[TransformerCache | PagedAttentionCache] = None,
cache_metadata: tp.Optional[TransformerMetadata | PagedAttentionMetadata] = None,
output_attentions: tp.Optional[bool] = None,
output_hidden_states: tp.Optional[bool] = None,
) -> CausalLMOutput:
"""Forward pass through the Llama model for causal language modeling.
Args:
input_ids (chex.Array, optional): Input token IDs, shape (batch_size, sequence_length).
inputs_embeds (chex.Array, optional): Input embeddings, shape (batch_size, sequence_length, hidden_size).
attention_mask (chex.Array, optional): Mask to avoid attention on padding tokens.
position_ids (chex.Array, optional): Indices of positions of each input sequence token.
segment_ids (chex.Array, optional): Segment token indices for segment embeddings.
past_key_values (TransformerCache | PagedAttentionCache, optional): Cache containing precomputed key/value states.
cache_metadata (TransformerMetadata | PagedAttentionMetadata, optional): Metadata for cache handling.
output_attentions (bool, optional): Whether to return attention weights.
output_hidden_states (bool, optional): Whether to return hidden states of all layers.
Returns:
Union[CausalLMOutput, Tuple]: Model outputs (logits, optional hidden states, optional attentions)
"""
outputs = self.model(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
mode=mode,
past_key_values=past_key_values,
cache_metadata=cache_metadata,
inputs_embeds=inputs_embeds,
segment_ids=segment_ids,
)
hidden_states = outputs.last_hidden_state
hidden_states = apply_logical_sharding(
hidden_states,
dynamic_axes=common_types.HiddenStateSharding,
partition_manager=self.config.partition_manager,
)
if self.config.tie_word_embeddings:
lm_logits = jax.lax.dot_general(
hidden_states,
self.model.embed_tokens.embedding.value.T,
(((hidden_states.ndim - 1), (0,)), ((), ())),
)
else:
lm_logits = self.lm_head(hidden_states)
return CausalLMOutput(
logits=lm_logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
past_key_values=outputs.past_key_values,
)
[docs]@register_module(
TaskType.SEQUENCE_CLASSIFICATION,
config=Llama4TextConfig,
model_type="llama4_text",
)
class Llama4ForSequenceClassification(EasyDeLBaseModule):
"""Llama model for sequence classification tasks.
This class extends the base Llama model by adding a linear classification head
to perform sequence classification tasks such as sentiment analysis or text classification.
Attributes:
config (LlamaConfig): Configuration for the model.
dtype (jnp.dtype): Data type for computations.
param_dtype (jnp.dtype): Data type for parameters.
precision: Precision setting for JAX operations.
"""
def __init__(
self,
config: Llama4TextConfig,
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.model = Llama4TextModel(
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 = ParallelLinear(
self.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=self.precision,
rngs=rngs,
)
def __call__(
self,
input_ids: tp.Optional[chex.Array] = None,
inputs_embeds: tp.Optional[chex.Array] = None,
attention_mask: tp.Optional[chex.Array] = None,
position_ids: tp.Optional[chex.Array] = None,
segment_ids: tp.Optional[chex.Array] = None,
mode: tp.Optional[common_types.RUNTIME_MODE_TYPES] = None, # type:ignore
past_key_values: tp.Optional[TransformerCache | PagedAttentionCache] = None,
cache_metadata: tp.Optional[TransformerMetadata | PagedAttentionMetadata] = None,
output_attentions: tp.Optional[bool] = None,
output_hidden_states: tp.Optional[bool] = None,
) -> SequenceClassifierOutput:
"""Forward pass through the Llama model for sequence classification.
This method processes input sequences through the Llama model and applies
a classification head to the output.
Args:
input_ids (chex.Array, optional): Input token IDs, shape (batch_size, sequence_length).
inputs_embeds (chex.Array, optional): Input embeddings, shape (batch_size, sequence_length, hidden_size).
attention_mask (chex.Array, optional): Mask to avoid attention on padding tokens.
position_ids (chex.Array, optional): Indices of positions of each input sequence token.
segment_ids (chex.Array, optional): Segment token indices for segment embeddings.
past_key_values (TransformerCache | PagedAttentionCache, optional): Cache containing precomputed key/value states.
cache_metadata (TransformerMetadata | PagedAttentionMetadata, optional): Metadata for cache handling.
output_attentions (bool, optional): Whether to return attention weights.
output_hidden_states (bool, optional): Whether to return hidden states of all layers.
Returns:
Union[SequenceClassifierOutput, Tuple]: Classification outputs including logits and optional model outputs
"""
transformer_outputs = self.model(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
mode=mode,
past_key_values=past_key_values,
cache_metadata=cache_metadata,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
inputs_embeds=inputs_embeds,
segment_ids=segment_ids,
)
hidden_states = transformer_outputs.last_hidden_state
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]
return SequenceClassifierOutput(
logits=pooled_logits,
past_key_values=past_key_values,
hidden_states=transformer_outputs.hidden_states,
attentions=transformer_outputs.attentions,
)
class Llama4VisionMLP2(nn.Module):
def __init__(
self,
config,
dtype: jnp.dtype = jnp.float32,
param_dtype: jnp.dtype = jnp.float32,
precision: jax.lax.PrecisionLike = None,
*,
rngs: nn.Rngs,
):
self.config = config
self.dtype = dtype
self.param_dtype = param_dtype
self.precision = precision
self.rngs = rngs
self.activation_fn = ACT2FN["gelu"]
linear_class = partial(
ParallelLinear,
use_bias=False,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
kernel_init=jax.nn.initializers.normal(0.01),
)
self.hidden_size = config.hidden_size
self.intermediate_size = config.intermediate_size
self.fc1 = linear_class(self.intermediate_size, config.projector_input_dim)
self.fc2 = linear_class(config.projector_output_dim, config.projector_output_dim)
def __call__(self, hidden_states: chex.Array) -> chex.Array:
hidden_states = self.fc2(self.activation_fn(self.fc1(hidden_states)))
return self.activation_fn(hidden_states)
class Llama4MultiModalProjector(nn.Module):
def __init__(
self,
config,
dtype: jnp.dtype = jnp.float32,
param_dtype: jnp.dtype = jnp.float32,
precision: jax.lax.PrecisionLike = None,
*,
rngs: nn.Rngs,
):
self.config = config
self.dtype = dtype
self.param_dtype = param_dtype
self.precision = precision
self.rngs = rngs
self.linear_1 = ParallelLinear(
config.vision_config.vision_output_dim,
config.text_config.hidden_size,
use_bias=False,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
kernel_init=jax.nn.initializers.normal(0.01),
)
def __call__(self, hidden_states: chex.Array) -> chex.Array:
return self.linear_1(hidden_states)
def pixel_shuffle(input_tensor, shuffle_ratio):
batch_size, num_patches, channels = input_tensor.shape
patch_size = int(math.sqrt(num_patches))
input_tensor = input_tensor.reshape(batch_size, patch_size, patch_size, -1)
batch_size, height, width, channels = input_tensor.shape
reshaped_tensor = input_tensor.reshape(
batch_size,
height,
int(width * shuffle_ratio),
int(channels / shuffle_ratio),
)
reshaped_tensor = jnp.transpose(reshaped_tensor, (0, 2, 1, 3))
reshaped_tensor = reshaped_tensor.reshape(
batch_size,
int(height * shuffle_ratio),
int(width * shuffle_ratio),
int(channels / (shuffle_ratio**2)),
)
reshaped_tensor = jnp.transpose(reshaped_tensor, (0, 2, 1, 3))
output_tensor = reshaped_tensor.reshape(batch_size, -1, reshaped_tensor.shape[-1])
return output_tensor
class Llama4VisionPixelShuffleMLP(nn.Module):
def __init__(
self,
config,
dtype: jnp.dtype = jnp.float32,
param_dtype: jnp.dtype = jnp.float32,
precision: jax.lax.PrecisionLike = None,
*,
rngs: nn.Rngs,
):
self.config = config
self.dtype = dtype
self.param_dtype = param_dtype
self.precision = precision
self.rngs = rngs
self.pixel_shuffle_ratio = config.pixel_shuffle_ratio
self.inner_dim = int(config.projector_input_dim // (self.pixel_shuffle_ratio**2))
self.output_dim = config.projector_output_dim
self.mlp = Llama4VisionMLP2(
config=config,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
def __call__(self, encoded_patches: chex.Array) -> chex.Array:
encoded_patches = pixel_shuffle(encoded_patches, self.pixel_shuffle_ratio)
return self.mlp(encoded_patches)
def reshape_for_broadcast(frequencies: jax.Array, query: jax.Array) -> jax.Array:
ndim = query.ndim
return jnp.reshape(
frequencies,
[d if i == 1 or i == ndim - 1 else 1 for i, d in enumerate(query.shape)],
)
def vision_apply_rotary_emb(
query: jax.Array,
key: jax.Array,
frequencies: jax.Array,
) -> tp.Tuple[jax.Array, jax.Array]:
query_dtype = query.dtype
key_dtype = key.dtype
query_reshaped = query.astype(jnp.float32).reshape(query.shape[:-1] + (-1, 2))
key_reshaped = key.astype(jnp.float32).reshape(key.shape[:-1] + (-1, 2))
query_complex = jax.lax.complex(query_reshaped[..., 0], query_reshaped[..., 1])
key_complex = jax.lax.complex(key_reshaped[..., 0], key_reshaped[..., 1])
frequencies_broadcast = reshape_for_broadcast(frequencies, query_complex)
query_rotated = query_complex * frequencies_broadcast
key_rotated = key_complex * frequencies_broadcast
query_out_real_imag = jnp.stack(
[jnp.real(query_rotated), jnp.imag(query_rotated)],
axis=-1,
)
key_out_real_imag = jnp.stack([jnp.real(key_rotated), jnp.imag(key_rotated)], axis=-1)
query_out = query_out_real_imag.reshape(query.shape)
key_out = key_out_real_imag.reshape(key.shape)
return query_out.astype(query_dtype), key_out.astype(key_dtype)
class Llama4VisionAttention(AttentionModule):
def __init__(
self,
config: Llama4VisionConfig,
layer_idx: int,
dtype: jnp.dtype = jnp.float32,
param_dtype: jnp.dtype = jnp.float32,
precision: jax.lax.PrecisionLike = None,
*,
rngs: nn.Rngs,
):
super().__init__(config=config)
self.layer_idx = layer_idx
self.dtype = dtype
self.param_dtype = param_dtype
self.precision = precision
self.rngs = rngs
self.embed_dim = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = config.hidden_size // config.num_attention_heads
self.num_key_value_groups = 1
self.attention_dropout = config.attention_dropout
linear_class = partial(
ParallelLinear,
dtype=dtype,
param_dtype=param_dtype,
use_bias=True,
kernel_init=jax.nn.initializers.normal(config.initializer_range),
precision=precision,
rngs=rngs,
**get_dot_general_by_bits(config.bits, config.easy_method),
)
self.q_proj = linear_class(self.embed_dim, self.num_heads * self.head_dim)
self.k_proj = linear_class(self.embed_dim, self.num_heads * self.head_dim)
self.v_proj = linear_class(self.embed_dim, self.num_heads * self.head_dim)
self.o_proj = linear_class(self.num_heads * self.head_dim, self.embed_dim)
self.attention_performer = FlexibleAttentionModule(
base_config=self.config,
softmax_scale=self.head_dim**-0.5,
dropout_prob=0.0,
)
def __call__(
self,
hidden_states: chex.Array,
frequencies: tp.Optional[chex.Array] = None,
output_attentions: bool = False,
) -> AttentionLayerOutput:
input_shape = hidden_states.shape[:-1]
hidden_shape = (*input_shape, -1, self.head_dim)
query_states, key_states, value_states = (
self.q_proj(hidden_states),
self.k_proj(hidden_states),
self.v_proj(hidden_states),
)
query_states = query_states.reshape(*hidden_shape)
key_states = key_states.reshape(*hidden_shape)
value_states = value_states.reshape(*hidden_shape)
query_states, key_states = vision_apply_rotary_emb(
query_states,
key_states,
frequencies=frequencies,
)
attentions = self.attention_performer.forward(
query_states=query_states,
key_states=key_states,
value_states=value_states,
mode=common_types.MODE_TRAIN,
bias=None,
cache_metadata=None,
cache_view=None,
init_bias=None,
attention_mask=None,
segment_ids=None,
causal=False,
)
attn_output = attentions.attention_outputs.reshape(*input_shape, -1)
attn_output = self.shard_attention_prod(attn_output)
attn_output = self.o_proj(attn_output)
return AttentionLayerOutput(
attention_output=attn_output,
attention_weight=attentions.attention_weights if output_attentions else None,
)
class Llama4VisionMLP(nn.Module):
def __init__(
self,
config,
dtype: jnp.dtype = jnp.float32,
param_dtype: jnp.dtype = jnp.float32,
precision: jax.lax.PrecisionLike = None,
*,
rngs: nn.Rngs,
):
self.config = config
self.dtype = dtype
self.param_dtype = param_dtype
self.precision = precision
self.rngs = rngs
linear_class = partial(
ParallelLinear,
use_bias=True,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
kernel_init=jax.nn.initializers.normal(0.01),
)
self.fc1 = linear_class(config.hidden_size, config.intermediate_size)
self.fc2 = linear_class(config.intermediate_size, config.hidden_size)
self.activation_fn = ACT2FN["gelu"]
def __call__(self, hidden_states: chex.Array) -> chex.Array:
hidden_states = self.fc1(hidden_states)
hidden_states = self.activation_fn(hidden_states)
hidden_states = self.fc2(hidden_states)
return hidden_states
class Llama4VisionEncoderLayer(nn.Module):
def __init__(
self,
config: Llama4VisionConfig,
layer_idx: int,
dtype: jnp.dtype = jnp.float32,
param_dtype: jnp.dtype = jnp.float32,
precision: jax.lax.PrecisionLike = None,
*,
rngs: nn.Rngs,
):
self.config = config
self.layer_idx = layer_idx
self.dtype = dtype
self.param_dtype = param_dtype
self.precision = precision
attn_block = Llama4VisionAttention
mlp_block = Llama4VisionMLP
attn_block, mlp_block = auto_remat(
attn_block,
mlp_block,
policy=config.gradient_checkpointing,
)
self.self_attn = attn_block(
config=config,
layer_idx=layer_idx,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
self.mlp = mlp_block(
config=config,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
self.input_layernorm = nn.LayerNorm(
num_features=config.hidden_size,
epsilon=0.00001,
dtype=dtype,
param_dtype=param_dtype,
rngs=rngs,
)
self.post_attention_layernorm = nn.LayerNorm(
num_features=config.hidden_size,
epsilon=0.00001,
dtype=dtype,
param_dtype=param_dtype,
rngs=rngs,
)
self.layer_idx = layer_idx
def __call__(
self,
hidden_states: chex.Array,
output_attentions: bool = False,
frequencies: tp.Optional[chex.Array] = None,
):
residual = hidden_states
hidden_states = self.input_layernorm(hidden_states)
hidden_states = apply_logical_sharding(
hidden_states,
dynamic_axes=common_types.HiddenStateSharding,
partition_manager=self.config.partition_manager,
)
attn_outputs = self.self_attn(
hidden_states,
frequencies,
output_attentions,
)
hidden_states = residual + attn_outputs.attention_output
residual = hidden_states
hidden_states = self.post_attention_layernorm(hidden_states)
hidden_states = self.mlp(hidden_states)
hidden_states = residual + hidden_states
hidden_states = apply_logical_sharding(
hidden_states,
dynamic_axes=common_types.HiddenStateSharding,
partition_manager=self.config.partition_manager,
)
return EncoderLayerOutput(
hidden_states=hidden_states,
attention_weight=attn_outputs.attention_weight,
)
class Llama4VisionEncoder(nn.Module):
def __init__(
self,
config: Llama4VisionConfig,
dtype: jnp.dtype = jnp.float32,
param_dtype: jnp.dtype = jnp.float32,
precision: jax.lax.PrecisionLike = None,
*,
rngs: nn.Rngs,
):
self.config = config
self.dtype = dtype
self.param_dtype = param_dtype
self.precision = precision
self.layers = [
Llama4VisionEncoderLayer(
config=config,
layer_idx=layer_idx,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
for layer_idx in range(self.config.num_hidden_layers)
]
def __call__(
self,
hidden_states: jax.Array,
frequencies: jax.Array,
attention_mask: tp.Optional[jax.Array] = None,
output_attentions: tp.Optional[bool] = None,
output_hidden_states: tp.Optional[bool] = None,
) -> BaseModelOutput:
output_attentions = (
output_attentions
if output_attentions is not None
else self.config.output_attentions
)
output_hidden_states = (
output_hidden_states
if output_hidden_states is not None
else self.config.output_hidden_states
)
encoder_states = () if output_hidden_states else None
all_attentions = () if output_attentions else None
for encoder_layer in self.layers:
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
layer_outputs = encoder_layer(
hidden_states=hidden_states,
output_attentions=output_attentions,
frequencies=frequencies,
)
if output_attentions:
all_attentions = all_attentions + (layer_outputs.attention_weight,)
hidden_states = layer_outputs.hidden_states
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
return BaseModelOutput(
last_hidden_state=hidden_states,
hidden_states=encoder_states,
attentions=all_attentions,
)
class Llama4UnfoldConvolution(nn.Module):
def __init__(
self,
config: Llama4VisionConfig,
dtype: jnp.dtype = jnp.float32,
param_dtype: jnp.dtype = jnp.float32,
precision: jax.lax.PrecisionLike = None,
*,
rngs: nn.Rngs,
):
patch_size_val = config.patch_size
if isinstance(patch_size_val, int):
self.kernel_size: tp.Tuple[int, int] = (patch_size_val, patch_size_val)
else:
self.kernel_size: tp.Tuple[int, int] = patch_size_val
self.stride = config.patch_size
if isinstance(self.stride, int):
self.stride = (self.stride, self.stride)
self.num_channels: int = config.num_channels
self.hidden_size: int = config.hidden_size
# Linear layer similar to PyTorch's version
in_features = self.num_channels * self.kernel_size[0] * self.kernel_size[1]
self.linear = ParallelLinear(
in_features=in_features,
out_features=self.hidden_size,
use_bias=False,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
def __call__(self, hidden_states: jax.Array) -> jax.Array:
batch_size = hidden_states.shape[0]
hidden_states_nhwc = jnp.transpose(hidden_states, (0, 2, 3, 1))
patches = jax.lax.conv_general_dilated_patches(
lhs=hidden_states_nhwc,
filter_shape=self.kernel_size,
window_strides=self.stride,
padding="VALID",
dimension_numbers=("NHWC", "HWIO", "NHWC"),
)
num_patches = patches.shape[1] * patches.shape[2]
patches_reshaped = jnp.reshape(patches, (batch_size, num_patches, -1))
hidden_states = self.linear(patches_reshaped)
return hidden_states
[docs]@register_module(
TaskType.BASE_VISION,
config=Llama4VisionConfig,
model_type="llama4_vision",
)
@register_module(
TaskType.BASE_MODULE,
config=Llama4VisionConfig,
model_type="llama4_vision",
)
class Llama4VisionModel(EasyDeLBaseModule):
def __init__(
self,
config: Llama4VisionConfig,
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.image_size = config.image_size
self.patch_size = config.patch_size
self.hidden_size = config.hidden_size
self.num_channels = config.num_channels
self.num_patches = (self.image_size // self.patch_size) ** 2 + 1
self.scale = config.hidden_size**-0.5
self.patch_embedding = Llama4UnfoldConvolution(
config=config,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
self.class_embedding = nn.Param(
self.scale
* jax.random.normal(
rngs.params(),
(self.hidden_size,),
param_dtype,
)
)
self.positional_embedding_vlm = nn.Param(
self.scale
* jax.random.normal(
rngs.params(),
(self.num_patches, self.hidden_size),
param_dtype,
)
)
self.layernorm_pre = nn.LayerNorm(
num_features=self.hidden_size,
epsilon=0.00001,
dtype=dtype,
param_dtype=param_dtype,
rngs=rngs,
)
self.layernorm_post = nn.LayerNorm(
num_features=self.hidden_size,
epsilon=0.00001,
dtype=dtype,
param_dtype=param_dtype,
rngs=rngs,
)
# encoders
self.model = Llama4VisionEncoder(
config=config,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
self.vision_adapter = Llama4VisionPixelShuffleMLP(
config=config,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
self.vision_idx = self.config.image_size // self.config.patch_size
def __call__(
self,
pixel_values: jax.Array,
attention_mask: tp.Optional[jax.Array] = None,
output_attentions: tp.Optional[bool] = None,
output_hidden_states: tp.Optional[bool] = None,
) -> BaseModelOutput:
output_attentions = (
output_attentions
if output_attentions is not None
else self.config.output_attentions
)
output_hidden_states = (
output_hidden_states
if output_hidden_states is not None
else self.config.output_hidden_states
)
batch_size_times_num_tiles = pixel_values.shape[0]
num_concurrent_media = 1
num_chunks = 1
hidden_states = self.patch_embedding(pixel_values)
_, num_patches, hidden_dim = hidden_states.shape
# Add cls token
hidden_states = hidden_states.reshape(
batch_size_times_num_tiles * num_concurrent_media * num_chunks,
num_patches,
hidden_dim,
)
class_embedding = jnp.broadcast_to(
self.class_embedding.value,
(hidden_states.shape[0], 1, hidden_states.shape[-1]),
)
hidden_states = jnp.concatenate([hidden_states, class_embedding], axis=1)
num_patches += 1
# Position embeddings
hidden_states = hidden_states.reshape(
batch_size_times_num_tiles * num_concurrent_media,
num_chunks,
num_patches,
hidden_dim,
)
hidden_states = hidden_states + self.positional_embedding_vlm
hidden_states = self.layernorm_pre(hidden_states)
hidden_states = hidden_states.reshape(batch_size_times_num_tiles, -1, hidden_dim)
output = self.model(
hidden_states,
output_hidden_states=output_hidden_states,
output_attentions=output_attentions,
frequencies=_vision_freqs(
self.vision_idx,
self.config.hidden_size,
self.config.num_attention_heads,
self.config.rope_theta,
),
)
hidden_states = output.last_hidden_state
hidden_states = self.layernorm_post(hidden_states)
hidden_states = hidden_states[:, :-1, :]
hidden_states = self.vision_adapter(hidden_states)
all_hidden_states = output.hidden_states if output_hidden_states else None
return BaseModelOutput(
last_hidden_state=hidden_states,
hidden_states=all_hidden_states,
attentions=output.attentions,
)
[docs]@register_module(
TaskType.IMAGE_TEXT_TO_TEXT,
config=Llama4Config,
model_type="llama4",
)
class Llama4ForConditionalGeneration(EasyDeLBaseModule):
"""
Llama4Vision model for conditional text generation based on image inputs.
Combines a vision tower and a language model with a multi-modal projector.
Attributes:
config (Llama4VisionConfig): Configuration object.
dtype (jnp.dtype): Data type for computation.
param_dtype (jnp.dtype): Data type for parameters.
precision (jax.lax.PrecisionLike): JAX precision level.
rngs (nn.Rngs): Random number generators.
"""
loss_type = "ForCausalLM"
def __init__(
self,
config: Llama4Config,
dtype: jnp.dtype = jnp.float32,
param_dtype: jnp.dtype = jnp.float32,
precision: jax.lax.PrecisionLike = None,
*,
rngs: nn.Rngs,
):
"""Initializes the Llama4VisionForConditionalGeneration model."""
super().__init__(
config=config,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
self.vision_model = Llama4VisionModel(
config=config.vision_config,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
self.multi_modal_projector = Llama4MultiModalProjector(
config=config,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
self.language_model = Llama4ForCausalLM(
config=config.text_config,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
self.vocab_size = config.text_config.vocab_size
self.pad_token_id = (
self.config.pad_token_id if self.config.pad_token_id is not None else -1
)
[docs] def get_image_features(self, pixel_values: chex.Array, **kwargs) -> chex.Array:
"""Extracts and projects image features from the vision tower.
Args:
pixel_values (chex.Array): Input pixel values for the images.
Returns:
chex.Array: Processed image features ready for the language model.
"""
kwargs = {k: v for k, v in kwargs.items() if v is not None}
image_outputs = self.vision_model(
pixel_values,
output_hidden_states=False,
**kwargs,
)
hidden_states = image_outputs.last_hidden_state
return hidden_states
def __call__(
self,
input_ids: chex.Array = None,
pixel_values: chex.Array = None,
attention_mask: tp.Optional[chex.Array] = None,
position_ids: tp.Optional[chex.Array] = None,
segment_ids: tp.Optional[chex.Array] = None,
mode: tp.Optional[common_types.RUNTIME_MODE_TYPES] = None, # type:ignore
past_key_values: tp.Optional[TransformerCache | PagedAttentionCache] = None,
cache_metadata: tp.Optional[TransformerMetadata | PagedAttentionMetadata] = None,
inputs_embeds: tp.Optional[chex.Array] = None,
output_attentions: tp.Optional[bool] = None,
output_hidden_states: tp.Optional[bool] = None,
**lm_kwargs,
):
if (input_ids is None) ^ (inputs_embeds is not None):
raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
output_attentions = (
output_attentions
if output_attentions is not None
else self.config.output_attentions
)
output_hidden_states = (
output_hidden_states
if output_hidden_states is not None
else self.config.output_hidden_states
)
if input_ids is not None and self.config.image_token_index >= self.vocab_size:
special_image_mask = input_ids == self.config.image_token_index
llm_input_ids = input_ids
llm_input_ids = jnp.where(special_image_mask, 0, llm_input_ids)
else:
llm_input_ids = input_ids
if inputs_embeds is None:
inputs_embeds = self.language_model.model.embed_tokens(llm_input_ids)
if pixel_values is not None:
orgshape = inputs_embeds.shape
image_features = self.get_image_features(pixel_values)
vision_flat = image_features.reshape(-1, image_features.shape[-1])
projected_vision_flat = self.multi_modal_projector(vision_flat)
final_mask = jnp.expand_dims(input_ids == self.config.image_token_index, axis=-1)
inputs_embeds_flat = inputs_embeds.reshape(-1, inputs_embeds.shape[-1])
final_mask_1d = final_mask[..., 0].reshape(-1)
num_projected_tokens = projected_vision_flat.shape[0]
image_token_indices = jnp.where(
final_mask_1d,
size=num_projected_tokens,
fill_value=-1,
)[0]
inputs_embeds_updated_flat = inputs_embeds_flat.at[image_token_indices].set(
projected_vision_flat
)
inputs_embeds = inputs_embeds_updated_flat.reshape(orgshape)
outputs = self.language_model(
attention_mask=attention_mask,
position_ids=position_ids,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
mode=mode,
past_key_values=past_key_values,
cache_metadata=cache_metadata,
inputs_embeds=inputs_embeds,
segment_ids=segment_ids,
**lm_kwargs,
)
return Llama4CausalLMOutputWithPast(
loss=None,
logits=outputs.logits,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
image_hidden_states=image_features if pixel_values is not None else None,
)
[docs] def init_cache(
self,
batch_size,
max_length,
starts=None,
shardings=None,
pad_token_id=None,
):
return self.language_model.init_cache(
batch_size,
max_length,
starts,
shardings,
pad_token_id,
)
def _get_compile_model_kwargs(
self,
batch_size: int,
input_tokens_length: int,
input_sharding: jax.sharding.PartitionSpec,
rngs: jax.random.PRNGKey,
vision_included: bool = False,
vision_batch_size: int = 1,
vision_channels: int = 3,
vision_height: tp.Optional[int] = None,
vision_width: tp.Optional[int] = None,
required_props: tp.Optional[tp.Mapping[str, tp.Dict[str, tp.Any]]] = None,
**kwargs,
):
"""Helper function to get keyword arguments for model compilation, potentially including vision inputs.
Args:
batch_size (int): Batch size for text inputs.
input_tokens_length (int): Sequence length for text inputs.
input_sharding (jax.sharding.PartitionSpec): Sharding specification for text inputs.
rngs (jax.random.PRNGKey): Random number generator key.
vision_included (bool): Whether to include dummy vision inputs. Defaults to False.
vision_batch_size (int): Batch size for vision inputs. Defaults to 1.
vision_channels (int): Number of channels for vision inputs. Defaults to 3.
vision_height (Optional[int]): Height for vision inputs (defaults to config).
vision_width (Optional[int]): Width for vision inputs (defaults to config).
required_props (Optional[Mapping[str, Dict[str, Any]]]): Required properties.
**kwargs: Additional arguments passed to the language model's compile kwargs method.
Returns:
dict: Keyword arguments for model compilation.
"""
basics = self.language_model._get_compile_model_kwargs(
batch_size=batch_size,
input_tokens_length=input_tokens_length,
input_sharding=input_sharding,
rngs=rngs,
vision_included=vision_included,
vision_batch_size=vision_batch_size,
vision_channels=vision_channels,
vision_height=vision_height,
vision_width=vision_width,
required_props=required_props,
**kwargs,
)
if vision_included:
pixel_values = jnp.ones(
(
vision_batch_size or 1,
vision_channels or 3,
self.config.vision_config.image_size,
self.config.vision_config.image_size,
),
dtype="f4",
)
basics.update({"pixel_values": pixel_values})
return basics