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from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import einsum, rearrange, repeat
from torch.nn import CrossEntropyLoss
from transformers.modeling_outputs import (
BaseModelOutputWithPast,
CausalLMOutputWithPast,
SequenceClassifierOutputWithPast,
)
from transformers.modeling_utils import PreTrainedModel
from .configuration_mamba import MambaConfig
class MambaRMSNorm(nn.Module):
def __init__(self, d_model: int, eps: float = 1e-5):
super().__init__()
self.eps = eps
self.weight = nn.Parameter(torch.ones(d_model))
def forward(self, x):
output = (
x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps) * self.weight
)
return output
class Mamba(nn.Module):
def __init__(self, config: MambaConfig):
"""A single Mamba block, as described in Figure 3 in Section 3.4 in the Mamba paper [1]."""
super().__init__()
self.config = config
self.in_proj = nn.Linear(config.d_model, config.d_inner * 2, bias=config.bias)
self.conv1d = nn.Conv1d(
in_channels=config.d_inner,
out_channels=config.d_inner,
bias=config.conv_bias,
kernel_size=config.d_conv,
groups=config.d_inner,
padding=config.d_conv - 1,
)
# x_proj takes in `x` and outputs the input-specific Δ, B, C
self.x_proj = nn.Linear(
config.d_inner, config.dt_rank + config.d_state * 2, bias=False
)
# dt_proj projects Δ from dt_rank to d_in
self.dt_proj = nn.Linear(config.dt_rank, config.d_inner, bias=True)
A = repeat(torch.arange(1, config.d_state + 1), "n -> d n", d=config.d_inner)
self.A_log = nn.Parameter(torch.log(A))
self.D = nn.Parameter(torch.ones(config.d_inner))
self.out_proj = nn.Linear(config.d_inner, config.d_model, bias=config.bias)
# self.norm = MambaRMSNorm(config.d_model)
def forward(self, x):
"""Mamba block forward. This looks the same as Figure 3 in Section 3.4 in the Mamba paper [1].
Args:
x: shape (b, l, d) (See Glossary at top for definitions of b, l, d_in, n...)
Returns:
output: shape (b, l, d)
Official Implementation:
class Mamba, https://github.com/state-spaces/mamba/blob/main/mamba_ssm/modules/mamba_simple.py#L119
mamba_inner_ref(), https://github.com/state-spaces/mamba/blob/main/mamba_ssm/ops/selective_scan_interface.py#L311
"""
(b, l, d) = x.shape
# x_copy = x # There was a separate class for residual, I deleted that part and added it here.
# x = self.norm(x)
x_and_res = self.in_proj(x) # shape (b, l, 2 * d_in)
(x, res) = x_and_res.split(
split_size=[self.config.d_inner, self.config.d_inner], dim=-1
)
x = rearrange(x, "b l d_in -> b d_in l")
x = self.conv1d(x)[:, :, :l]
x = rearrange(x, "b d_in l -> b l d_in")
x = F.silu(x)
y = self.ssm(x)
y = y * F.silu(res)
# output = self.out_proj(y) + x_copy
output = self.out_proj(y)
return output
def ssm(self, x):
"""Runs the SSM. See:
- Algorithm 2 in Section 3.2 in the Mamba paper [1]
- run_SSM(A, B, C, u) in The Annotated S4 [2]
Args:
x: shape (b, l, d_in) (See Glossary at top for definitions of b, l, d_in, n...)
Returns:
output: shape (b, l, d_in)
Official Implementation:
mamba_inner_ref(), https://github.com/state-spaces/mamba/blob/main/mamba_ssm/ops/selective_scan_interface.py#L311
"""
(d_in, n) = self.A_log.shape
# Compute ∆ A B C D, the state space parameters.
# A, D are input independent (see Mamba paper [1] Section 3.5.2 "Interpretation of A" for why A isn't selective)
# ∆, B, C are input-dependent (this is a key difference between Mamba and the linear time invariant S4,
# and is why Mamba is called **selective** state spaces)
A = -torch.exp(self.A_log.float()) # shape (d_in, n)
D = self.D.float()
x_dbl = self.x_proj(x) # (b, l, dt_rank + 2*n)
(delta, B, C) = x_dbl.split(
split_size=[self.config.dt_rank, n, n], dim=-1
) # delta: (b, l, dt_rank). B, C: (b, l, n)
delta = F.softplus(self.dt_proj(delta)) # (b, l, d_in)
y = self.selective_scan(
x, delta, A, B, C, D
) # This is similar to run_SSM(A, B, C, u) in The Annotated S4 [2]
return y
def selective_scan(self, u, delta, A, B, C, D):
"""Does selective scan algorithm. See:
- Section 2 State Space Models in the Mamba paper [1]
- Algorithm 2 in Section 3.2 in the Mamba paper [1]
- run_SSM(A, B, C, u) in The Annotated S4 [2]
This is the classic discrete state space formula:
x(t + 1) = Ax(t) + Bu(t)
y(t) = Cx(t) + Du(t)
except B and C (and the step size delta, which is used for discretization) are dependent on the input x(t).
Args:
u: shape (b, l, d_in) (See Glossary at top for definitions of b, l, d_in, n...)
delta: shape (b, l, d_in)
A: shape (d_in, n)
B: shape (b, l, n)
C: shape (b, l, n)
D: shape (d_in,)
Returns:
output: shape (b, l, d_in)
Official Implementation:
selective_scan_ref(), https://github.com/state-spaces/mamba/blob/main/mamba_ssm/ops/selective_scan_interface.py#L86
Note: I refactored some parts out of `selective_scan_ref` out, so the functionality doesn't match exactly.
"""
(b, l, d_in) = u.shape
n = A.shape[1]
# Discretize continuous parameters (A, B)
# - A is discretized using zero-order hold (ZOH) discretization (see Section 2 Equation 4 in the Mamba paper [1])
# - B is discretized using a simplified Euler discretization instead of ZOH. From a discussion with authors:
# "A is the more important term and the performance doesn't change much with the simplication on B"
deltaA = torch.exp(einsum(delta, A, "b l d_in, d_in n -> b d_in l n"))
deltaB_u = einsum(delta, B, u, "b l d_in, b l n, b l d_in -> b d_in l n")
# Perform selective scan (see scan_SSM() in The Annotated S4 [2])
x = torch.zeros((b, d_in, n), device=deltaA.device)
ys = []
for i in range(l):
x = deltaA[:, :, i] * x + deltaB_u[:, :, i]
y = einsum(x, C[:, i, :], "b d_in n, b n -> b d_in")
ys.append(y)
y = torch.stack(ys, dim=1) # shape (b, l, d_in)
y = y + u * D
return y
class MambaBlock(nn.Module):
def __init__(self, config: MambaConfig, layer_idx: int = 0):
"""A single Mamba block, as described in Figure 3 in Section 3.4 in the Mamba paper [1]."""
super().__init__()
self.config = config
self.mixer = Mamba(config)
self.norm = MambaRMSNorm(config.d_model)
def forward(self, x):
return self.mixer(self.norm(x)) + x
class MambaPreTrainedModel(PreTrainedModel):
config_class = MambaConfig
base_model_prefix = "backbone"
supports_gradient_checkpointing = True
_no_split_modules = ["MambaBlock"]
def _init_weights(self, module):
std = 0.02
if isinstance(module, (nn.Linear, nn.Conv1d)):
module.weight.data.normal_(mean=0.0, std=std)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.Embedding):
module.weight.data.normal_(mean=0.0, std=std)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
class MambaModel(MambaPreTrainedModel):
def __init__(self, config: MambaConfig):
"""Full Mamba model.
Mamba model decoder consisting of *config.n_layer* layers. Each layer is a [`MambaBlock`]
Args:
config: MambaConfig
"""
super().__init__(config)
# self.config = config
self.embedding = nn.Embedding(self.config.vocab_size, self.config.d_model)
self.layers = nn.ModuleList(
[MambaBlock(self.config, layer_idx) for layer_idx in range(self.config.n_layer)]
)
self.norm_f = MambaRMSNorm(self.config.d_model)
self.gradient_checkpointing = False
self.post_init()
def forward(
self,
input_ids: torch.LongTensor = None,
output_hidden_states=False,
return_dict: Optional[bool] = None,
**kwargs,
) -> BaseModelOutputWithPast:
batch_size = input_ids.shape[0]
hidden_size = self.config.d_model
hidden_states: Tuple[
torch.Tensor[(batch_size, sequence_length, hidden_size)]
] = ()
sequence_length = input_ids.shape[1]
output_hidden_states = output_hidden_states or self.config.output_hidden_states
last_hidden_state = self.embedding(input_ids)
assert last_hidden_state.shape == (
batch_size,
sequence_length,
hidden_size,
), f"{last_hidden_state.shape} != {(batch_size, sequence_length, hidden_size)}"
hidden_states += (last_hidden_state,)
for layer in self.layers:
last_hidden_state = layer(last_hidden_state)
assert last_hidden_state.shape == (
batch_size,
sequence_length,
hidden_size,
), f"{last_hidden_state.shape} != {(batch_size, sequence_length, hidden_size)}"
hidden_states += (last_hidden_state,)
last_hidden_state = self.norm_f(last_hidden_state)
assert last_hidden_state.shape == (
batch_size,
sequence_length,
hidden_size,
), f"{last_hidden_state.shape} != {(batch_size, sequence_length, hidden_size)}"
hidden_states += (last_hidden_state,)
assert (
len(hidden_states) == self.config.n_layer + 2
), f"{len(hidden_states)} != {self.config.n_layer + 2}"
return BaseModelOutputWithPast(
hidden_states=hidden_states if output_hidden_states else None,
last_hidden_state=last_hidden_state,
)
class MambaModelForCausalLM(MambaPreTrainedModel):
_tied_weights_keys = ["lm_head.weight"]
def __init__(self, config, **kwargs):
super().__init__(
config,
**kwargs,
)
self.backbone = MambaModel(
config=self.config,
)
self.lm_head = nn.Linear(
in_features=self.config.d_model,
out_features=self.config.vocab_size,
bias=False,
)
self.post_init()
def _tie_weights(self):
self.lm_head.weight = self.backbone.embedding.weight
def forward(
self,
input_ids,
labels: Optional[torch.LongTensor] = None,
output_hidden_states=False,
**kwargs,
) -> CausalLMOutputWithPast:
batch_size = input_ids.shape[0]
output_hidden_states = output_hidden_states or self.config.output_hidden_states
sequence_length = input_ids.shape[1]
vocab_size = self.config.vocab_size
outputs = self.backbone(
input_ids=input_ids,
output_hidden_states=output_hidden_states,
)
last_hidden_state = outputs.last_hidden_state
logits: torch.FloatTensor[batch_size, sequence_length, vocab_size] = (
self.lm_head(
last_hidden_state,
)
)
if labels is not None:
shift_logits = logits[..., :-1, :].contiguous()
shift_labels = labels[..., 1:].contiguous()
loss_fct = CrossEntropyLoss()
shift_logits = shift_logits.view(-1, vocab_size)
shift_labels = shift_labels.view(-1)
shift_labels = shift_labels.to(shift_logits.device)
loss = loss_fct(shift_logits, shift_labels)
else:
loss = None
return CausalLMOutputWithPast(
hidden_states=outputs.hidden_states if output_hidden_states else None,
logits=logits,
loss=loss,
)
def prepare_inputs_for_generation(
self, input_ids, attention_mask=None, **model_kwargs
):
return {
"input_ids": input_ids,
}
# class MambaModelForSequenceClassification(MambaModelForCausalLM):
# def __init__(
# self,
# config,
# id2label={0: "NEGATIVE", 1: "POSITIVE"},
# label2id={"NEGATIVE": 0, "POSITIVE": 1},
# num_labels=2,
# **kwargs,
# ):
# super().__init__(
# config,
# **kwargs,
# )
# self.id2label = id2label
# self.label2id = label2id
# self.num_labels = num_labels # TODO: config.num_labels
# self.score = nn.Linear(
# in_features=self.config.vocab_size,
# out_features=self.num_labels,
# bias=False,
# )
# def forward(
# self,
# input_ids: Optional[torch.Tensor] = None,
# labels: Optional[torch.Tensor] = None,
# output_hidden_states=False,
# **kwargs,
# ) -> SequenceClassifierOutputWithPast:
# batch_size = input_ids.shape[0]
# hidden_size = self.config.vocab_size
# hidden_states: Tuple[
# torch.Tensor[(batch_size, sequence_length, hidden_size)]
# ] = ()
# num_labels = self.num_labels # TODO: config.num_labels
# sequence_length = input_ids.shape[1]
# vocab_size = self.config.vocab_size
# output_hidden_states = output_hidden_states or self.config.output_hidden_states
# outputs = super().forward(
# input_ids=input_ids,
# labels=None,
# output_hidden_states=output_hidden_states,
# **kwargs,
# )
# last_hidden_state = outputs.logits
# assert last_hidden_state.shape == (
# batch_size,
# sequence_length,
# hidden_size,
# ), f"{last_hidden_state.shape} != {(batch_size, sequence_length, hidden_size)}"
# hidden_states += (last_hidden_state,)
# logits: torch.FloatTensor[batch_size, num_labels] = self.score(
# last_hidden_state[:, -1, :] # TODO: Check if this makes sense
# )
# if labels is not None:
# loss_fct = CrossEntropyLoss()
# loss = loss_fct(logits, labels)
# else:
# loss = None
# return SequenceClassifierOutputWithPast(
# loss=loss,
# logits=logits,
# hidden_states=hidden_states,
# )
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