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Unverified Commit 603a661a authored by Mor Zusman's avatar Mor Zusman Committed by GitHub
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[Model] factoring out MambaMixer out of Jamba (#8993) 


Signed-off-by: default avatarmzusman <mor.zusmann@gmail.com>
parent fb2716d6
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Showing with 245 additions and 374 deletions
vllm/model_executor/layers/mamba/mamba_mixer.py 0 → 100644
View file @ 603a661a
import torch
from torch import nn
from torch.nn.parameter import Parameter
from vllm.attention.backends.abstract import AttentionMetadata
from vllm.distributed.parallel_state import (
get_tensor_model_parallel_rank, get_tensor_model_parallel_world_size)
from vllm.model_executor.custom_op import CustomOp
from vllm.model_executor.layers.layernorm import RMSNorm
from vllm.model_executor.layers.linear import (ColumnParallelLinear,
MergedColumnParallelLinear,
RowParallelLinear)
from vllm.model_executor.layers.mamba.ops.causal_conv1d import (
causal_conv1d_fn, causal_conv1d_update)
from vllm.model_executor.layers.mamba.ops.mamba_ssm import (
selective_scan_fn, selective_state_update)
from vllm.model_executor.models.mamba_cache import MambaCacheParams
from vllm.model_executor.utils import set_weight_attrs
# Adapted from transformers.models.mamba.modeling_mamba.MambaMixer
@CustomOp.register("mamba_mixer")
class MambaMixer(CustomOp):
"""
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,
ssm_state_size: int,
conv_kernel_size: int,
intermediate_size: int,
time_step_rank: int,
use_conv_bias: bool,
use_bias: bool,
use_rms_norm: bool,
rms_norm_eps: float = 1e-5,
activation="silu"):
super().__init__()
self.time_step_rank = time_step_rank
self.ssm_state_size = ssm_state_size
self.use_rms_norm = use_rms_norm
self.activation = activation
self.conv1d = ColumnParallelLinear(
input_size=conv_kernel_size,
output_size=intermediate_size,
bias=use_conv_bias,
)
# unsqueeze to fit conv1d weights shape into the linear weights shape.
# Can't do this in `weight_loader` since it already exists in
# `ColumnParallelLinear` and `set_weight_attrs`
# doesn't allow to override it
self.conv1d.weight.data = self.conv1d.weight.data.unsqueeze(1)
self.in_proj = MergedColumnParallelLinear(hidden_size,
[intermediate_size] * 2,
bias=use_bias)
# selective projection used to make dt, B and C input dependent
self.x_proj = RowParallelLinear(
intermediate_size,
time_step_rank + ssm_state_size * 2,
bias=False,
)
# time step projection (discretization) -
# In the forward we need to apply dt_proj without the bias,
# as the bias is added in the selective scan kernel.
self.dt_proj = ColumnParallelLinear(time_step_rank,
intermediate_size,
bias=True,
skip_bias_add=True)
def weight_loader(param: Parameter, loaded_weight: torch.Tensor):
tp_rank = get_tensor_model_parallel_rank()
tp_size = get_tensor_model_parallel_world_size()
param.data.copy_(
loaded_weight.data.split(loaded_weight.shape[0] // tp_size,
dim=0)[tp_rank])
def A_weight_loader(param: Parameter, loaded_weight: torch.Tensor):
weight_loader(param, -torch.exp(loaded_weight.float()))
tp_size = get_tensor_model_parallel_world_size()
self.A = nn.Parameter(
torch.empty(
intermediate_size // tp_size,
ssm_state_size,
dtype=torch.float32,
))
self.D = nn.Parameter(torch.ones(intermediate_size // tp_size))
set_weight_attrs(self.D, {"weight_loader": weight_loader})
set_weight_attrs(self.A, {"weight_loader": A_weight_loader})
self.out_proj = RowParallelLinear(
intermediate_size,
hidden_size,
bias=use_bias,
input_is_parallel=True,
)
self.dt_layernorm = RMSNorm(time_step_rank,
eps=rms_norm_eps) if use_rms_norm else None
self.b_layernorm = RMSNorm(ssm_state_size,
eps=rms_norm_eps) if use_rms_norm else None
self.c_layernorm = RMSNorm(ssm_state_size,
eps=rms_norm_eps) if use_rms_norm else None
def forward_native(self, hidden_states: torch.Tensor,
attn_metadata: AttentionMetadata,
conv_state: torch.Tensor, ssm_state: torch.Tensor):
pass
def forward_cuda(self, hidden_states: torch.Tensor,
attn_metadata: AttentionMetadata,
mamba_cache_params: MambaCacheParams):
# 1. Gated MLP's linear projection
projected_states = self.in_proj(hidden_states)[0].transpose(-2, -1)
hidden_states, gate = projected_states.chunk(2, dim=-2)
# 2. Convolution sequence transformation
conv_weights = self.conv1d.weight.view(self.conv1d.weight.size(0),
self.conv1d.weight.size(2))
if attn_metadata.query_start_loc is not None \
and attn_metadata.context_lens_tensor is not None:
# |---------- N-1 iteration --------|
# |---------------- N iteration ---------------------|
# |- tokenA -|......................|-- newTokens ---|
# |---------- context_len ----------|
# |-------------------- seq_len ---------------------|
# |-- query_len ---|
hidden_states = causal_conv1d_fn(
hidden_states,
conv_weights,
self.conv1d.bias,
activation=self.activation,
conv_states=mamba_cache_params.conv_state,
has_initial_state=attn_metadata.context_lens_tensor > 0,
cache_indices=mamba_cache_params.state_indices_tensor,
query_start_loc=attn_metadata.query_start_loc)
else:
hidden_states = causal_conv1d_update(
hidden_states.transpose(0, 1),
mamba_cache_params.conv_state,
conv_weights,
self.conv1d.bias,
self.activation,
conv_state_indices=mamba_cache_params.state_indices_tensor)
hidden_states = hidden_states.transpose(0, 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(-2, -1))[0]
time_step, B, C = torch.split(
ssm_parameters,
[self.time_step_rank, self.ssm_state_size, self.ssm_state_size],
dim=-1,
)
if self.use_rms_norm:
assert self.dt_layernorm is not None
assert self.b_layernorm is not None
assert self.c_layernorm is not None
time_step = self.dt_layernorm(time_step.contiguous())
B = self.b_layernorm(B.contiguous())
C = self.c_layernorm(C.contiguous())
discrete_time_step = self.dt_proj(time_step)[0].transpose(-2, -1)
# 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 attn_metadata.query_start_loc is not None \
and attn_metadata.context_lens_tensor is not None:
scan_outputs = selective_scan_fn(
hidden_states,
mamba_cache_params.ssm_state,
discrete_time_step,
self.A,
B.transpose(-2, -1),
C.transpose(-2, -1),
self.D.float(),
gate,
time_proj_bias,
delta_softplus=True,
cache_indices=mamba_cache_params.state_indices_tensor,
has_initial_state=attn_metadata.context_lens_tensor > 0,
query_start_loc=attn_metadata.query_start_loc)
else:
scan_outputs = selective_state_update(
mamba_cache_params.ssm_state,
hidden_states.transpose(0, 1),
discrete_time_step.transpose(0, 1),
self.A,
B,
C,
self.D,
gate.transpose(0, 1),
time_proj_bias,
dt_softplus=True,
state_batch_indices=mamba_cache_params.state_indices_tensor)
scan_outputs = scan_outputs.transpose(0, 1)
# 4. Final linear projection
contextualized_states = self.out_proj(scan_outputs.transpose(-2,
-1))[0]
return contextualized_states
vllm/model_executor/models/jamba.py
View file @ 603a661a
......@@ -12,26 +12,19 @@ from vllm.config import CacheConfig, LoRAConfig, SchedulerConfig
from vllm.distributed import get_tensor_model_parallel_world_size
from vllm.model_executor.layers.fused_moe import FusedMoE
from vllm.model_executor.layers.layernorm import RMSNorm
from vllm.model_executor.layers.linear import (ColumnParallelLinear,
MergedColumnParallelLinear,
QKVParallelLinear,
from vllm.model_executor.layers.linear import (QKVParallelLinear,
ReplicatedLinear,
RowParallelLinear)
from vllm.model_executor.layers.logits_processor import LogitsProcessor
from vllm.model_executor.layers.mamba.ops.causal_conv1d import (
causal_conv1d_fn, causal_conv1d_update)
from vllm.model_executor.layers.mamba.ops.mamba_ssm import (
selective_scan_fn, selective_state_update)
from vllm.model_executor.layers.mamba.mamba_mixer import MambaMixer
from vllm.model_executor.layers.quantization import QuantizationConfig
from vllm.model_executor.layers.sampler import Sampler, SamplerOutput
from vllm.model_executor.layers.vocab_parallel_embedding import (
DEFAULT_VOCAB_PADDING_SIZE, ParallelLMHead, VocabParallelEmbedding)
from vllm.model_executor.model_loader.weight_utils import (
composed_weight_loader, default_weight_loader, sharded_weight_loader)
from vllm.model_executor.model_loader.weight_utils import default_weight_loader
from vllm.model_executor.models.mamba_cache import (MambaCacheManager,
MambaCacheParams)
from vllm.model_executor.sampling_metadata import SamplingMetadata
from vllm.model_executor.utils import set_weight_attrs
from vllm.sequence import IntermediateTensors
from vllm.worker.model_runner import (_BATCH_SIZES_TO_CAPTURE,
_get_graph_batch_size)
......@@ -41,179 +34,6 @@ from .interfaces import HasInnerState, SupportsLoRA
KVCache = Tuple[torch.Tensor, torch.Tensor]
# Adapted from transformers.models.mamba.modeling_mamba.MambaMixer
class JambaMambaMixer(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, config: JambaConfig):
super().__init__()
self.config = config
self.hidden_size = config.hidden_size
self.ssm_state_size = config.mamba_d_state
self.conv_kernel_size = config.mamba_d_conv
self.intermediate_size = config.mamba_expand * config.hidden_size
self.time_step_rank = config.mamba_dt_rank
self.use_conv_bias = config.mamba_conv_bias
self.use_bias = config.mamba_proj_bias
self.conv1d = ColumnParallelLinear(
input_size=self.conv_kernel_size,
output_size=self.intermediate_size,
bias=self.use_conv_bias,
)
# unsqueeze to fit conv1d weights shape into the linear weights shape.
# Can't do this in `weight_loader` since it already exists in
# `ColumnParallelLinear` and `set_weight_attrs`
# doesn't allow to override it
self.conv1d.weight.data = self.conv1d.weight.data.unsqueeze(1)
self.in_proj = MergedColumnParallelLinear(self.hidden_size,
[self.intermediate_size] * 2,
bias=self.use_bias)
# selective projection used to make dt, B and C input dependent
self.x_proj = RowParallelLinear(
self.intermediate_size,
self.time_step_rank + self.ssm_state_size * 2,
bias=False,
)
# time step projection (discretization) -
# In the forward we need to apply dt_proj without the bias,
# as the bias is added in the selective scan kernel.
self.dt_proj = ColumnParallelLinear(self.time_step_rank,
self.intermediate_size,
bias=True,
skip_bias_add=True)
tp_size = get_tensor_model_parallel_world_size()
self.A = nn.Parameter(
torch.empty(
self.intermediate_size // tp_size,
self.ssm_state_size,
dtype=torch.float32,
))
self.D = nn.Parameter(torch.ones(self.intermediate_size // tp_size))
set_weight_attrs(self.D, {"weight_loader": sharded_weight_loader(0)})
a_weight_loader = composed_weight_loader(
sharded_weight_loader(0), lambda x: -torch.exp(x.float()))
set_weight_attrs(self.A, {"weight_loader": a_weight_loader})
self.out_proj = RowParallelLinear(
self.intermediate_size,
self.hidden_size,
bias=self.use_bias,
input_is_parallel=True,
)
self.activation = config.hidden_act
self.dt_layernorm = RMSNorm(self.time_step_rank,
eps=config.rms_norm_eps)
self.b_layernorm = RMSNorm(self.ssm_state_size,
eps=config.rms_norm_eps)
self.c_layernorm = RMSNorm(self.ssm_state_size,
eps=config.rms_norm_eps)
def forward(self, hidden_states: torch.Tensor,
attn_metadata: AttentionMetadata,
mamba_cache_params: MambaCacheParams):
# 1. Gated MLP's linear projection
projected_states = self.in_proj(hidden_states)[0].transpose(-2, -1)
hidden_states, gate = projected_states.chunk(2, dim=-2)
# 2. Convolution sequence transformation
conv_weights = self.conv1d.weight.view(self.conv1d.weight.size(0),
self.conv1d.weight.size(2))
if attn_metadata.query_start_loc is not None \
and attn_metadata.context_lens_tensor is not None:
# |---------- N-1 iteration --------|
# |---------------- N iteration ---------------------|
# |- tokenA -|......................|-- newTokens ---|
# |---------- context_len ----------|
# |-------------------- seq_len ---------------------|
# |-- query_len ---|
hidden_states = causal_conv1d_fn(
hidden_states,
conv_weights,
self.conv1d.bias,
activation=self.activation,
conv_states=mamba_cache_params.conv_state,
has_initial_state=attn_metadata.context_lens_tensor > 0,
cache_indices=mamba_cache_params.state_indices_tensor,
query_start_loc=attn_metadata.query_start_loc)
else:
hidden_states = causal_conv1d_update(
hidden_states.transpose(0, 1),
mamba_cache_params.conv_state,
conv_weights,
self.conv1d.bias,
self.activation,
conv_state_indices=mamba_cache_params.state_indices_tensor)
hidden_states = hidden_states.transpose(0, 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(-2, -1))[0]
time_step, B, C = torch.split(
ssm_parameters,
[self.time_step_rank, self.ssm_state_size, self.ssm_state_size],
dim=-1,
)
time_step = self.dt_layernorm(time_step.contiguous())
B = self.b_layernorm(B.contiguous())
C = self.c_layernorm(C.contiguous())
discrete_time_step = self.dt_proj(time_step)[0].transpose(-2, -1)
# 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 attn_metadata.query_start_loc is not None \
and attn_metadata.context_lens_tensor is not None:
scan_outputs = selective_scan_fn(
hidden_states,
mamba_cache_params.ssm_state,
discrete_time_step,
self.A,
B.transpose(-2, -1),
C.transpose(-2, -1),
self.D.float(),
gate,
time_proj_bias,
delta_softplus=True,
cache_indices=mamba_cache_params.state_indices_tensor,
has_initial_state=attn_metadata.context_lens_tensor > 0,
query_start_loc=attn_metadata.query_start_loc)
else:
scan_outputs = selective_state_update(
mamba_cache_params.ssm_state,
hidden_states.transpose(0, 1),
discrete_time_step.transpose(0, 1),
self.A,
B,
C,
self.D,
gate.transpose(0, 1),
time_proj_bias,
dt_softplus=True,
state_batch_indices=mamba_cache_params.state_indices_tensor)
scan_outputs = scan_outputs.transpose(0, 1)
# 4. Final linear projection
contextualized_states = self.out_proj(scan_outputs.transpose(-2,
-1))[0]
return contextualized_states
class JambaMoE(nn.Module):
def __init__(self,
......@@ -284,9 +104,18 @@ class JambaMambaDecoderLayer(nn.Module):
cache_config: Optional[CacheConfig] = None,
quant_config: Optional[QuantizationConfig] = None) -> None:
super().__init__()
self.layer_idx = layer_idx
self.config = config
self.mamba = JambaMambaMixer(config)
self.mamba = MambaMixer(hidden_size= config.hidden_size,
ssm_state_size = config.mamba_d_state,
conv_kernel_size = config.mamba_d_conv,
intermediate_size = config.mamba_expand *\
config.hidden_size,
time_step_rank = config.mamba_dt_rank,
use_conv_bias = config.mamba_conv_bias,
use_bias = config.mamba_proj_bias,
use_rms_norm=True,
rms_norm_eps=config.rms_norm_eps,
activation=config.hidden_act)
num_experts = config.layers_num_experts[layer_idx]
ffn_layer_class = JambaMoE if num_experts > 1 else JambaMLP
......
vllm/model_executor/models/mamba.py
View file @ 603a661a
......@@ -10,27 +10,19 @@ from vllm.attention.backends.abstract import AttentionMetadata
from vllm.config import CacheConfig, LoRAConfig, SchedulerConfig
from vllm.distributed import get_tensor_model_parallel_world_size
from vllm.model_executor.layers.layernorm import RMSNorm
from vllm.model_executor.layers.linear import (ColumnParallelLinear,
MergedColumnParallelLinear,
RowParallelLinear)
from vllm.model_executor.layers.logits_processor import LogitsProcessor
from vllm.model_executor.layers.mamba.ops.causal_conv1d import (
causal_conv1d_fn, causal_conv1d_update)
from vllm.model_executor.layers.mamba.ops.mamba_ssm import (
selective_scan_fn, selective_state_update)
from vllm.model_executor.layers.mamba.mamba_mixer import MambaMixer
from vllm.model_executor.layers.quantization.base_config import (
QuantizationConfig)
from vllm.model_executor.layers.sampler import Sampler, SamplerOutput
from vllm.model_executor.layers.vocab_parallel_embedding import (
DEFAULT_VOCAB_PADDING_SIZE, ParallelLMHead, VocabParallelEmbedding)
from vllm.model_executor.model_loader.weight_utils import (
composed_weight_loader, default_weight_loader, sharded_weight_loader)
from vllm.model_executor.model_loader.weight_utils import default_weight_loader
from vllm.model_executor.models.interfaces import (HasInnerState,
IsAttentionFree)
from vllm.model_executor.models.mamba_cache import (MambaCacheManager,
MambaCacheParams)
from vllm.model_executor.sampling_metadata import SamplingMetadata
from vllm.model_executor.utils import set_weight_attrs
from vllm.sequence import IntermediateTensors
from vllm.worker.model_runner import (_BATCH_SIZES_TO_CAPTURE,
_get_graph_batch_size)
......@@ -38,194 +30,27 @@ from vllm.worker.model_runner import (_BATCH_SIZES_TO_CAPTURE,
KVCache = Tuple[torch.Tensor, torch.Tensor]
# Adapted from transformers.models.mamba.modeling_mamba.MambaMixer
class MambaMixer(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, config: MambaConfig, layer_idx):
super().__init__()
self.config = config
self.layer_idx = layer_idx
self.hidden_size = config.hidden_size
self.ssm_state_size = config.state_size
self.conv_kernel_size = config.conv_kernel
self.intermediate_size = config.intermediate_size
self.time_step_rank = int(config.time_step_rank)
self.is_falcon_mamba = config.model_type == "falcon_mamba"
self.conv1d = ColumnParallelLinear(
input_size=self.conv_kernel_size,
output_size=self.intermediate_size,
bias=config.use_conv_bias,
)
# unsqueeze to fit conv1d weights shape into the linear weights shape.
# Can't do this in `weight_loader` since it already exists in
# `ColumnParallelLinear` and `set_weight_attrs`
# doesn't allow to override it
self.conv1d.weight.data = self.conv1d.weight.data.unsqueeze(1)
self.in_proj = MergedColumnParallelLinear(self.hidden_size,
[self.intermediate_size] * 2,
bias=config.use_bias)
# selective projection used to make dt, B and C input dependent
self.x_proj = RowParallelLinear(
self.intermediate_size,
self.time_step_rank + self.ssm_state_size * 2,
bias=False,
)
# time step projection (discretization) -
# In the forward we need to apply dt_proj without the bias,
# as the bias is added in the selective scan kernel.
self.dt_proj = ColumnParallelLinear(self.time_step_rank,
self.intermediate_size,
bias=True,
skip_bias_add=True)
tp_size = get_tensor_model_parallel_world_size()
self.A = nn.Parameter(
torch.empty(
self.intermediate_size // tp_size,
self.ssm_state_size,
dtype=torch.float32,
))
self.D = nn.Parameter(torch.ones(self.intermediate_size // tp_size))
set_weight_attrs(self.D, {"weight_loader": sharded_weight_loader(0)})
a_weight_loader = composed_weight_loader(
sharded_weight_loader(0), lambda x: -torch.exp(x.float()))
set_weight_attrs(self.A, {"weight_loader": a_weight_loader})
self.out_proj = RowParallelLinear(
self.intermediate_size,
self.hidden_size,
bias=config.use_bias,
input_is_parallel=True,
)
self.activation = config.hidden_act
if self.is_falcon_mamba:
self.dt_layernorm = RMSNorm(self.time_step_rank,
eps=config.mixer_rms_eps)
self.b_layernorm = RMSNorm(self.ssm_state_size,
eps=config.mixer_rms_eps)
self.c_layernorm = RMSNorm(self.ssm_state_size,
eps=config.mixer_rms_eps)
def forward(self, hidden_states: torch.Tensor,
attn_metadata: AttentionMetadata,
mamba_cache_params: MambaCacheParams):
# 1. Gated MLP's linear projection
projected_states = self.in_proj(hidden_states)[0].transpose(-2, -1)
hidden_states, gate = projected_states.chunk(2, dim=-2)
# 2. Convolution sequence transformation
conv_weights = self.conv1d.weight.view(self.conv1d.weight.size(0),
self.conv1d.weight.size(2))
if attn_metadata.query_start_loc is not None \
and attn_metadata.context_lens_tensor is not None:
# |---------- N-1 iteration --------|
# |---------------- N iteration ---------------------|
# |- tokenA -|......................|-- newTokens ---|
# |---------- context_len ----------|
# |-------------------- seq_len ---------------------|
# |-- query_len ---|
hidden_states = causal_conv1d_fn(
hidden_states,
conv_weights,
self.conv1d.bias,
activation=self.activation,
conv_states=mamba_cache_params.conv_state,
has_initial_state=attn_metadata.context_lens_tensor > 0,
cache_indices=mamba_cache_params.state_indices_tensor,
query_start_loc=attn_metadata.query_start_loc)
else:
hidden_states = causal_conv1d_update(
hidden_states.transpose(0, 1),
mamba_cache_params.conv_state,
conv_weights,
self.conv1d.bias,
self.activation,
conv_state_indices=mamba_cache_params.state_indices_tensor)
hidden_states = hidden_states.transpose(0, 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(-2, -1))[0]
time_step, B, C = torch.split(
ssm_parameters,
[self.time_step_rank, self.ssm_state_size, self.ssm_state_size],
dim=-1,
)
# Note that Jamba and FalconMamba normalizes B, C, and time_step here
# but Mamba doesn't.
if self.is_falcon_mamba:
time_step = self.dt_layernorm(time_step.contiguous())
B = self.b_layernorm(B.contiguous())
C = self.c_layernorm(C.contiguous())
discrete_time_step = self.dt_proj(time_step)[0].transpose(-2, -1)
# 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 attn_metadata.query_start_loc is not None \
and attn_metadata.context_lens_tensor is not None:
scan_outputs = selective_scan_fn(
hidden_states,
mamba_cache_params.ssm_state,
discrete_time_step,
self.A,
B.transpose(-2, -1),
C.transpose(-2, -1),
self.D.float(),
gate,
time_proj_bias,
delta_softplus=True,
cache_indices=mamba_cache_params.state_indices_tensor,
has_initial_state=attn_metadata.context_lens_tensor > 0,
query_start_loc=attn_metadata.query_start_loc)
else:
scan_outputs = selective_state_update(
mamba_cache_params.ssm_state,
hidden_states.transpose(0, 1),
discrete_time_step.transpose(0, 1),
self.A,
B,
C,
self.D,
gate.transpose(0, 1),
time_proj_bias,
dt_softplus=True,
state_batch_indices=mamba_cache_params.state_indices_tensor)
scan_outputs = scan_outputs.transpose(0, 1)
# 4. Final linear projection
contextualized_states = self.out_proj(scan_outputs.transpose(-2,
-1))[0]
return contextualized_states
class MambaDecoderLayer(nn.Module):
def __init__(self,
config: MambaConfig,
layer_idx: int,
cache_config: Optional[CacheConfig] = None,
quant_config: Optional[QuantizationConfig] = None) -> None:
super().__init__()
self.layer_idx = layer_idx
self.config = config
self.is_falcon_mamba = config.model_type == "falcon_mamba"
self.mixer = MambaMixer(config, layer_idx)
mixer_rms_rps = config.mixer_rms_rps if self.is_falcon_mamba else None
self.mamba = MambaMixer(hidden_size=config.hidden_size,
ssm_state_size=config.state_size,
conv_kernel_size=config.conv_kernel,
intermediate_size=config.intermediate_size,
time_step_rank=config.time_step_rank,
use_conv_bias=config.use_conv_bias,
use_bias=config.use_bias,
use_rms_norm=self.is_falcon_mamba,
rms_norm_eps=mixer_rms_rps,
activation=config.hidden_act)
self.norm = RMSNorm(config.hidden_size, eps=config.layer_norm_epsilon)
def forward(
......
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