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from __future__ import annotations
import math
from dataclasses import dataclass
from typing import TYPE_CHECKING, Any, Optional
import torch
from torch import nn
from transformers.modeling_utils import PreTrainedModel
from transformers.utils import ModelOutput, logging
from transformers.utils.deprecation import deprecate_kwarg
from fla.layers.attn import Attention
from fla.layers.mamba import Mamba
from fla.models.mamba.modeling_mamba import MambaCache
from fla.models.samba.configuration_samba import SambaConfig
from fla.models.utils import FLAGenerationMixin
from fla.modules import FusedCrossEntropyLoss, FusedLinearCrossEntropyLoss, RMSNorm
from fla.modules import GatedMLP as SambaMLP
from fla.modules.l2warp import l2_warp
if TYPE_CHECKING:
from transformers.processing_utils import Unpack
try:
from transformers.modeling_layers import GradientCheckpointingLayer
except ImportError:
from fla.models.modeling_layers import GradientCheckpointingLayer
logger = logging.get_logger(__name__)
class SambaBlock(GradientCheckpointingLayer):
def __init__(self, config, layer_idx):
super().__init__()
self.config = config
self.layer_idx = layer_idx
self.mixer_norm = RMSNorm(hidden_size=config.hidden_size, eps=config.norm_eps)
if config.attn is not None and layer_idx in config.attn['layers']:
self.mixer = Attention(
hidden_size=config.hidden_size,
num_heads=config.attn['num_heads'],
num_kv_heads=config.attn['num_kv_heads'],
qkv_bias=config.attn['qkv_bias'],
window_size=config.attn['window_size'],
rope_theta=config.attn['rope_theta'],
max_position_embeddings=config.max_position_embeddings,
layer_idx=layer_idx,
)
else:
self.mixer = Mamba(
hidden_size=config.hidden_size,
state_size=config.state_size,
conv_kernel=config.conv_kernel,
intermediate_size=config.intermediate_size,
time_step_rank=config.time_step_rank,
use_bias=config.use_bias,
layer_idx=layer_idx,
)
self.mlp_norm = (RMSNorm if config.fuse_norm else nn.RMSNorm)(config.hidden_size, eps=config.norm_eps)
self.mlp = SambaMLP(
hidden_size=config.hidden_size,
hidden_ratio=config.hidden_ratio,
hidden_act=config.hidden_act,
fuse_swiglu=config.fuse_swiglu,
)
def forward(
self,
hidden_states: torch.Tensor,
cache_params: tuple[torch.Tensor] | None = None,
**kwargs: Unpack[dict],
) -> tuple[torch.FloatTensor, tuple[torch.FloatTensor, torch.FloatTensor] | None]:
residual = hidden_states
hidden_states = self.mixer_norm(hidden_states)
if isinstance(self.mixer, Mamba):
hidden_states = self.mixer(hidden_states, cache_params=cache_params, **kwargs)
else:
hidden_states, _, cache_params = self.mixer(hidden_states=hidden_states, past_key_values=cache_params, **kwargs)
if self.config.fuse_norm:
hidden_states, residual = self.mlp_norm(hidden_states, residual, True)
else:
hidden_states = residual + hidden_states
residual = hidden_states
hidden_states = self.mlp_norm(hidden_states)
hidden_states = self.mlp(hidden_states, **kwargs)
hidden_states = residual + hidden_states
return hidden_states
class SambaPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = SambaConfig
base_model_prefix = "backbone"
_no_split_modules = ["SambaBlock"]
supports_gradient_checkpointing = True
def _init_weights(self, module):
"""Initialize the weights."""
if isinstance(module, nn.Linear):
nn.init.normal_(module.weight, mean=0.0, std=self.config.initializer_range)
if module.bias is not None:
if not getattr(module.bias, "_no_reinit", False):
nn.init.zeros_(module.bias)
elif isinstance(module, Mamba):
module.A_log._no_weight_decay = True
module.D._no_weight_decay = True
dt_init_std = self.config.time_step_rank**-0.5 * self.config.time_step_scale
if self.config.time_step_init_scheme == "constant":
nn.init.constant_(module.dt_proj.weight, dt_init_std)
elif self.config.time_step_init_scheme == "random":
nn.init.uniform_(module.dt_proj.weight, -dt_init_std, dt_init_std)
dt = torch.exp(
torch.rand(self.config.intermediate_size)
* (math.log(self.config.time_step_max) - math.log(self.config.time_step_min))
+ math.log(self.config.time_step_min),
).clamp(min=self.config.time_step_floor)
# # Inverse of softplus: https://github.com/pytorch/pytorch/issues/72759
inv_dt = dt + torch.log(-torch.expm1(-dt))
with torch.no_grad():
module.dt_proj.bias.data = nn.Parameter(inv_dt.to(module.dt_proj.bias.device))
module.dt_proj.bias._no_reinit = True
elif isinstance(module, nn.Embedding):
nn.init.normal_(module.weight, std=self.config.initializer_range)
elif hasattr(module, 'reset_parameters'):
module.reset_parameters()
if self.config.rescale_prenorm_residual:
# Reinitialize selected weights subject to the OpenAI GPT-2 Paper Scheme:
# > A modified initialization which accounts for the accumulation on the residual path with model depth. Scale
# > the weights of residual layers at initialization by a factor of 1/√N where N is the # of residual layers.
# > -- GPT-2 :: https://openai.com/blog/better-language-models/
#
# Reference (Megatron-LM): https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/model/gpt_model.py
for name, p in module.named_parameters():
if name in ["out_proj.weight"]:
# Special Scaled Initialization --> There are 2 Layer Norms per Transformer Block
# Following Pytorch init, except scale by 1/sqrt(2 * n_layer)
# We need to reinit p since this code could be called multiple times
# Having just p *= scale would repeatedly scale it down
nn.init.kaiming_uniform_(p, a=math.sqrt(5))
with torch.no_grad():
p /= math.sqrt(self.config.num_layers)
@dataclass
class SambaOutput(ModelOutput):
"""
Class for the Samba model outputs.
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the model.
cache_params (`MambaCache`):
The state of the model at the last time step. Can be used in a forward method with the next `input_ids` to
avoid providing the old `input_ids`.
Includes both the State space model state matrices after the selective scan, and the Convolutional states
hidden_states (`tuple(torch.FloatTensor)`, *optional*,
returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (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.
"""
last_hidden_state: torch.FloatTensor | None = None
cache_params: MambaCache | None = None
hidden_states: tuple[torch.FloatTensor] | None = None
@dataclass
class SambaCausalLMOutput(ModelOutput):
"""
Base class for causal language model (or autoregressive) outputs.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Language modeling loss (for next-token prediction).
logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
cache_params (`MambaCache`):
The state of the model at the last time step. Can be used in a forward method with the next `input_ids` to
avoid providing the old `input_ids`.
Includes both the State space model state matrices after the selective scan, and the Convolutional states
hidden_states (`tuple(torch.FloatTensor)`, *optional*,
returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (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.
"""
loss: torch.FloatTensor | None = None
logits: torch.FloatTensor | None = None
cache_params: MambaCache | None = None
hidden_states: tuple[torch.FloatTensor] | None = None
class SambaModel(SambaPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.embeddings = nn.Embedding(config.vocab_size, config.hidden_size)
self.layers = nn.ModuleList([SambaBlock(config, layer_idx=idx) for idx in range(config.num_hidden_layers)])
self.gradient_checkpointing = False
self.norm_f = RMSNorm(config.hidden_size, eps=config.norm_eps)
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.embeddings
def set_input_embeddings(self, new_embeddings):
self.embeddings = new_embeddings
def forward(
self,
input_ids: torch.LongTensor | None = None,
inputs_embeds: torch.LongTensor | None = None,
cache_params: MambaCache | None = None,
use_cache: bool | None = None,
output_hidden_states: bool | None = None,
return_dict: bool | None = None,
**kwargs: Unpack[dict],
) -> tuple | SambaOutput:
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
use_cache = use_cache if use_cache is not None else (self.config.use_cache if not self.training else False)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if (input_ids is None) ^ (inputs_embeds is not None): # ^ is python for xor
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.embeddings(input_ids)
if cache_params is None and use_cache:
cache_params = MambaCache(
self.config, inputs_embeds.size(0), device=inputs_embeds.device, dtype=inputs_embeds.dtype,
)
hidden_states = inputs_embeds
all_hidden_states = () if output_hidden_states else None
for mixer_block in self.layers:
hidden_states = mixer_block(
hidden_states,
cache_params=cache_params,
**kwargs,
)
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if use_cache:
cache_params.seqlen_offset += inputs_embeds.shape[1]
hidden_states = self.norm_f(hidden_states)
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if not return_dict:
return tuple(v for v in [hidden_states, cache_params, all_hidden_states] if v is not None)
return SambaOutput(
last_hidden_state=hidden_states,
cache_params=cache_params if use_cache else None,
hidden_states=all_hidden_states,
)
class SambaForCausalLM(SambaPreTrainedModel, FLAGenerationMixin):
_tied_weights_keys = ["lm_head.weight"]
def __init__(self, config):
super().__init__(config)
self.backbone = SambaModel(config)
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
self.criterion = None
# Initialize weights and apply final processing
self.post_init()
def get_output_embeddings(self):
return self.lm_head
def set_output_embeddings(self, new_embeddings):
self.lm_head = new_embeddings
def get_input_embeddings(self):
return self.backbone.get_input_embeddings()
def set_input_embeddings(self, new_embeddings):
return self.backbone.set_input_embeddings(new_embeddings)
def _update_model_kwargs_for_generation(
self, outputs: ModelOutput, model_kwargs: dict[str, Any], **kwargs,
) -> dict[str, Any]:
model_kwargs["cache_params"] = outputs.get("cache_params", None)
return model_kwargs
@deprecate_kwarg("num_logits_to_keep", version="4.50", new_name="logits_to_keep")
def forward(
self,
input_ids: torch.LongTensor | None = None,
attention_mask: Optional[torch.Tensor] = None, # noqa
inputs_embeds: torch.FloatTensor | None = None,
cache_params: MambaCache | None = None,
labels: torch.LongTensor | None = None,
output_hidden_states: bool | None = None,
return_dict: bool | None = None,
use_cache: bool | None = None,
logits_to_keep: int | None = 0,
**kwargs: Unpack[dict],
) -> tuple | SambaCausalLMOutput:
r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
`labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100`
are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]`
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.backbone(
input_ids,
cache_params=cache_params,
inputs_embeds=inputs_embeds,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
use_cache=use_cache,
**kwargs,
)
hidden_states = outputs[0]
loss, logits = None, None
if not self.config.fuse_linear_cross_entropy or labels is None:
logits = self.lm_head(hidden_states if logits_to_keep is None else hidden_states[:, -logits_to_keep:])
if labels is not None:
if getattr(self, 'criterion', None) is None:
if self.config.fuse_linear_cross_entropy:
criterion = FusedLinearCrossEntropyLoss(use_l2warp=self.config.use_l2warp)
elif self.config.fuse_cross_entropy:
criterion = FusedCrossEntropyLoss(inplace_backward=True)
else:
criterion = nn.CrossEntropyLoss()
else:
criterion = self.criterion
labels = labels.to(hidden_states.device)
labels = torch.cat((labels[..., 1:], torch.full_like(labels[:, :1], criterion.ignore_index)), 1)
if self.config.fuse_linear_cross_entropy:
loss = criterion(hidden_states, labels, self.lm_head.weight, self.lm_head.bias)
else:
loss = criterion(logits.view(labels.numel(), -1), labels.view(-1))
loss = l2_warp(loss, logits) if self.config.use_l2warp else loss
if not return_dict:
output = (logits,) + outputs[1:]
return (loss,) + output if loss is not None else output
return SambaCausalLMOutput(
loss=loss,
logits=logits,
cache_params=outputs.cache_params,
hidden_states=outputs.hidden_states,
)
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