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from __future__ import annotations
import warnings
from typing import TYPE_CHECKING
import torch
import torch.nn as nn
from transformers.utils import logging
from fla.modules.activations import ACT2FN
with warnings.catch_warnings():
warnings.simplefilter('ignore')
try:
from mamba_ssm.ops.selective_scan_interface import mamba_inner_fn, selective_scan_fn
from mamba_ssm.ops.triton.selective_state_update import selective_state_update
except ImportError:
selective_state_update, selective_scan_fn, mamba_inner_fn = None, None, None
try:
from causal_conv1d import causal_conv1d_fn, causal_conv1d_update
except ImportError:
causal_conv1d_update, causal_conv1d_fn = None, None
is_fast_path_available = all((
selective_state_update,
selective_scan_fn,
mamba_inner_fn,
))
if TYPE_CHECKING:
from transformers.processing_utils import Unpack
from fla.models.mamba.modeling_mamba import MambaCache
logger = logging.get_logger(__name__)
class Mamba(nn.Module):
"""
Compute ∆, A, B, C, and D the state space parameters and compute the `contextualized_states`.
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)
"""
def __init__(
self,
hidden_size: int = 2048,
state_size: int = 16,
conv_kernel: int = 4,
use_conv_bias: bool = True,
intermediate_size: int = 2048,
time_step_rank: int = 256,
use_bias: bool = True,
hidden_act: str = "silu",
layer_idx: int = None,
backend: str = "cuda",
):
super().__init__()
self.hidden_size = hidden_size
self.ssm_state_size = state_size
self.conv_kernel_size = conv_kernel
self.use_conv_bias = use_conv_bias
self.intermediate_size = intermediate_size
self.time_step_rank = time_step_rank
self.use_bias = use_bias
self.conv1d = nn.Conv1d(
in_channels=self.intermediate_size,
out_channels=self.intermediate_size,
bias=use_conv_bias,
kernel_size=conv_kernel,
groups=self.intermediate_size,
padding=conv_kernel - 1,
)
self.activation = hidden_act
self.act = ACT2FN[hidden_act]
self.layer_idx = layer_idx
# projection of the input hidden states
self.in_proj = nn.Linear(self.hidden_size, self.intermediate_size * 2, bias=use_bias)
# selective projection used to make dt, B and C input dependant
self.x_proj = nn.Linear(self.intermediate_size, self.time_step_rank + self.ssm_state_size * 2, bias=False)
# time step projection (discretization)
self.dt_proj = nn.Linear(self.time_step_rank, self.intermediate_size, bias=True)
# S4D real initialization. These are not discretized!
# The core is to load them, compute the discrete states, then write the updated state. Keeps the memory bounded
A = torch.arange(1, self.ssm_state_size + 1, dtype=torch.float32)[None, :]
A = A.expand(self.intermediate_size, -1).contiguous()
self.A_log = nn.Parameter(torch.log(A))
self.D = nn.Parameter(torch.ones(self.intermediate_size))
self.out_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=use_bias)
if not is_fast_path_available:
logger.warning_once(
"The fast path is not available because on of "
"`(selective_state_update, selective_scan_fn, causal_conv1d_fn, causal_conv1d_update, mamba_inner_fn)`"
" is None. Falling back to the naive implementation. "
"To install follow https://github.com/state-spaces/mamba/#installation and"
" https://github.com/Dao-AILab/causal-conv1d",
)
import os
backend = os.environ.get('FLA_CONV_BACKEND', backend)
assert backend in ['cuda', 'triton'], f"Unsupported backend: {backend}"
if backend == 'cuda' and causal_conv1d_fn is None:
logger.warning_once(
"The CUDA backend is not available because `causal_conv1d` is None. "
"Falling back to the Triton backend. "
"To install follow https://github.com/Dao-AILab/causal-conv1d",
)
backend = 'triton'
if backend == 'triton':
from fla.modules.convolution import causal_conv1d as causal_conv1d_triton
from fla.modules.convolution import causal_conv1d_update as causal_conv1d_update_triton
self.causal_conv1d_fn = causal_conv1d_triton
self.causal_conv1d_update = causal_conv1d_update_triton
else:
self.causal_conv1d_fn = causal_conv1d_fn
self.causal_conv1d_update = causal_conv1d_update
self.backend = backend
def cuda_kernels_forward(
self,
hidden_states: torch.Tensor,
cache_params: MambaCache | None = None,
cache_position: torch.LongTensor | None = None,
attention_mask: torch.LongTensor | None = None,
**kwargs: Unpack[dict],
):
# 1. Gated MLP's linear projection
projected_states = self.in_proj(hidden_states).transpose(1, 2)
if self.training and cache_params is None: # Doesn't support outputting the states -> used for training
contextualized_states = mamba_inner_fn(
projected_states,
self.conv1d.weight,
self.conv1d.bias if self.use_conv_bias else None,
self.x_proj.weight,
self.dt_proj.weight,
self.out_proj.weight,
self.out_proj.bias.float() if self.use_bias else None,
-torch.exp(self.A_log.float()),
None, # input-dependent B
None, # input-dependent C
self.D.float(),
delta_bias=self.dt_proj.bias.float(),
delta_softplus=True,
)
else:
hidden_states, gate = projected_states.chunk(2, dim=1)
if attention_mask is not None:
hidden_states = hidden_states * attention_mask.unsqueeze(1)
# 2. Convolution sequence transformation
conv_weights = self.conv1d.weight.view(self.conv1d.weight.size(0), self.conv1d.weight.size(2))
if cache_params is not None and cache_position[0] > 0:
hidden_states = self.causal_conv1d_update(
hidden_states.squeeze(-1),
cache_params.conv_states[self.layer_idx],
conv_weights,
self.conv1d.bias,
self.activation,
)
hidden_states = hidden_states.unsqueeze(-1)
else:
if cache_params is not None:
conv_states = nn.functional.pad(
hidden_states, (self.conv_kernel_size - hidden_states.shape[-1], 0),
)
cache_params.update_conv_state(self.layer_idx, conv_states, cache_position)
hidden_states = self.causal_conv1d_fn(
hidden_states, conv_weights, self.conv1d.bias, activation=self.activation,
)
if attention_mask is not None:
hidden_states = hidden_states * attention_mask.unsqueeze(1)
# 3. State Space Model sequence transformation
# 3.a. input varying initialization of time_step, B and C
ssm_parameters = self.x_proj(hidden_states.transpose(1, 2))
time_step, B, C = torch.split(
ssm_parameters, [self.time_step_rank, self.ssm_state_size, self.ssm_state_size], dim=-1,
)
discrete_time_step = self.dt_proj.weight @ time_step.transpose(1, 2)
A = -torch.exp(self.A_log.float())
# 3.c perform the recurrence y ← SSM(A, B, C)(x)
time_proj_bias = self.dt_proj.bias.float() if hasattr(self.dt_proj, "bias") else None
if cache_params is not None and cache_position[0] > 0:
scan_outputs = selective_state_update(
cache_params.ssm_states[self.layer_idx],
hidden_states[..., 0],
discrete_time_step[..., 0],
A,
B[:, 0],
C[:, 0],
self.D,
gate[..., 0],
time_proj_bias,
dt_softplus=True,
).unsqueeze(-1)
else:
scan_outputs, ssm_state = selective_scan_fn(
hidden_states,
discrete_time_step,
A,
B.transpose(1, 2),
C.transpose(1, 2),
self.D.float(),
gate,
time_proj_bias,
delta_softplus=True,
return_last_state=True,
)
if ssm_state is not None and cache_params is not None:
cache_params.update_ssm_state(self.layer_idx, ssm_state)
# 4. Final linear projection
contextualized_states = self.out_proj(scan_outputs.transpose(1, 2))
return contextualized_states
def slow_forward(
self,
input_states,
cache_params: MambaCache | None = None,
cache_position: torch.LongTensor | None = None,
attention_mask: torch.LongTensor | None = None,
**kwargs: Unpack[dict],
):
batch_size, seq_len, _ = input_states.shape
dtype = input_states.dtype
# 1. Gated MLP's linear projection
# [batch, 2 * intermediate_size, seq_len]
projected_states = self.in_proj(input_states).transpose(1, 2)
hidden_states, gate = projected_states.chunk(2, dim=1)
if attention_mask is not None:
hidden_states = hidden_states * attention_mask.unsqueeze(1)
# 2. Convolution sequence transformation
if cache_params is not None:
ssm_state = cache_params.ssm_states[self.layer_idx].clone()
ssm_state = ssm_state.to(hidden_states.device)
# use `cache_position.shape[0]` to check whether we are in prefill
# stage, it's equivalent to check `cache_position[0] == 0`, which
# breaks dynamo fullgraph constraints
if cache_position.shape[0] == self.conv_kernel_size:
conv_state = nn.functional.pad(
hidden_states,
(self.conv_kernel_size - hidden_states.shape[-1], 0),
)
cache_params.update_conv_state(self.layer_idx, conv_state, cache_position)
# [batch, intermediate_size, seq_len]
hidden_states = self.act(self.conv1d(hidden_states)[..., :seq_len])
else:
conv_state = cache_params.update_conv_state(self.layer_idx, hidden_states, cache_position)
hidden_states = torch.sum(conv_state * self.conv1d.weight[:, 0, :], dim=-1)
if self.use_conv_bias:
hidden_states += self.conv1d.bias
# [batch, intermediate_size, 1] : decoding
hidden_states = self.act(hidden_states).to(dtype).unsqueeze(-1)
else:
ssm_state = torch.zeros(
(batch_size, self.intermediate_size, self.ssm_state_size),
device=hidden_states.device, dtype=dtype,
)
# [batch, intermediate_size, seq_len]
hidden_states = self.act(self.conv1d(hidden_states)[..., :seq_len])
if attention_mask is not None:
hidden_states = hidden_states * attention_mask.unsqueeze(1)
# 3. State Space Model sequence transformation
# 3.a. Selection: [batch, seq_len, self.time_step_rank + self.ssm_state_size * 2]
ssm_parameters = self.x_proj(hidden_states.transpose(1, 2))
time_step, B, C = torch.split(
ssm_parameters, [self.time_step_rank, self.ssm_state_size, self.ssm_state_size], dim=-1,
)
# [batch, seq_len, intermediate_size]
discrete_time_step = self.dt_proj(time_step)
# [batch, intermediate_size, seq_len]
discrete_time_step = nn.functional.softplus(discrete_time_step).transpose(1, 2)
# 3.b. Discretization: B and C to [batch, seq_len, intermediate_size, ssm_state_size] (SRAM)
# [intermediate_size, ssm_state_size]
A = -torch.exp(self.A_log.float())
# [batch, intermediate_size, seq_len, ssm_state_size]
discrete_A = torch.exp(A[None, :, None, :] * discrete_time_step[:, :, :, None])
# [batch, intermediate_size, seq_len, ssm_state_size]
discrete_B = discrete_time_step[:, :, :, None] * B[:, None, :, :].float()
deltaB_u = discrete_B * hidden_states[:, :, :, None].float()
# 3.c perform the recurrence y ← SSM(A, B, C)(x)
scan_outputs = []
for i in range(seq_len):
# [batch, intermediade_size, ssm_state]
ssm_state = discrete_A[:, :, i, :] * ssm_state + deltaB_u[:, :, i, :]
# [batch, intermediade_size, 1]
scan_output = torch.matmul(ssm_state.to(dtype), C[:, i, :].unsqueeze(-1))
scan_outputs.append(scan_output[:, :, 0])
# [batch, seq_len, intermediade_size]
scan_output = torch.stack(scan_outputs, dim=-1)
scan_output = scan_output + (hidden_states * self.D[None, :, None])
scan_output = (scan_output * self.act(gate))
if cache_params is not None:
cache_params.ssm_states[self.layer_idx].copy_(ssm_state)
# 4. Final linear projection
# [batch, seq_len, hidden_size]
contextualized_states = self.out_proj(scan_output.transpose(1, 2))
return contextualized_states
# fmt: on
def forward(
self,
hidden_states,
cache_params: MambaCache | None = None,
cache_position: torch.LongTensor | None = None,
attention_mask: torch.LongTensor | None = None,
**kwargs: Unpack[dict],
):
if is_fast_path_available and "cuda" in self.x_proj.weight.device.type:
return self.cuda_kernels_forward(hidden_states, cache_params, cache_position, attention_mask, **kwargs)
return self.slow_forward(hidden_states, cache_params, cache_position, attention_mask, **kwargs)
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